GEOmedia_3_2023

Rivista bimestrale - anno XXVII - Numero - 3/2023 - Sped. in abb. postale 70% - Filiale di Roma 

LAND CARTOGRAPHY 

GIS 

CADASTRE 

GEOGRAPHIC INFORMATION 

PHOTOGRAMMETRY 

3D 

SURVEY TOPOGRAPHY 

CAD 

BIM 

EARTH OBSERVATION SPACE 

WEBGIS 

UAV 

URBAN PLANNING 

CONSTRUCTION 

LBS 

SMART CITY 

GNSS 

ENVIRONMENT 

NETWORKS 

LiDAR 

CULTURAL HERITAGE 

May/June 2023 year XXVII N°3 

Development of a 

Utilities Management 

Platform for the case 

of Quarantine and 

Lockdown – eUMaP 

GEOMATIC TECHNIQUES FOR 

UTILITIES CONSUMPTION 

JAMMING AGAINST 

GNSS RECEIVERS 

DRONES POWERED BY 

GALILEO OSNMA SERVICE

DWG

Geomatics and pandemic 

During the recent pandemic period, the world's attention has shifted towards the healthcare sector, 

with world leaders striving to avoid the collapse of their national healthcare systems; the economy 

has entered an artificial coma, while utility systems, including energy, water, telecommunications and 

waste management systems, have been asked to act immediately in response to these unprecedented 

conditions, placing extreme pressure on public utility systems. 

During crisis situations, it was also observed that in a very short period of time, most of the activity 

of the European economy shifted from industry and offices to homes via teleworking. In this 

context, the residential construction of a city that can be considered smart in the management and 

provision of public services, involves the creation of a stable and reliable network of sensors and 

actuators in homes. 

This highlights the significant role that smart buildings can play in cases of force majeure, where 

quarantine and lockdown conditions are required. Building Information Modeling (BIM) technology 

and digital twins are pioneering the way smart buildings are operated and will significantly 

contribute to the work of smart building applications. BIM provides an interactive environment 

where synergies between different skills, information and data can be combined, with real-time 

online management based on Digital Twin using BIM with enormous resource saving potential for 

the built environment. 

Although the capabilities of geomatic techniques together with BIM offer many opportunities for the 

efficient management of energy, water, waste and telecommunications networks, there are challenges 

to be addressed before these opportunities could be actually realised. 

The editorial contributions in this issue of GEOmedia fall in this context. Some were developed 

during the implementation of a project dedicated to the development of a utilities management 

platform for the case of quarantine and lockdown, eUMaP, within the EU MSCA RISE H2020 

framework, the exchange program of research and innovation personnel of the European 

Community. eUMaP is actually studying an open platform through which local authorities 

will be able to plan and manage the demand and supply of services in buildings in the event of 

quarantine or health emergency or lock down, including energy, water and waste networks and 

telecommunications. Through a partnership between universities, research institutes and companies 

involved in these fields of investigation, some appropriate analysis tools have been further studied 

and developed. 

Geomatic techniques are the basis of the findings produced by some of these studies, highlighting the 

important relationships that should be established in the analysis of any territorial situation. 

Enjoy your reading, 

Renzo Carlucci

In this 

issue... 

FOCUS 

REPORT 

COLUMNS 

42 MERCATO 

36 SPACE 

46 AGENDA 

FOCUS 

Quantifying how 

a zone is residential 

A Multi-Criteria 

Decision Making 

approach 

by Simone Guarino, Camilla 

Fioravanti, Gabriele Oliva, 

Roberto Setola, Giovanni De 

Angelis, Marcello Coradini 

6 

12 

Spatial, functional 

and temporal analysis 

of Wi-Fi hotspots during 

covid-19 curfew 

In selected EU cities 

Rome, Thessaloniki, 

Nicosia, Kaunas 

by Marius Ivaškevičius 

On the cover a map derived from 

Copernicus EMS Rapid Mapping 

Center that shows the situation 

in the area of Rome Center at the 

date of 20/07/2020 during the 

emergency for Covid-19. The 

thematic layer has been derived 

from post-event satellite image 

by means of visual interpretation. 

Map produced by e-GEOS 

released by e-GEOS (ODO). © 

European Union 

For full Copyright notice visit 

https://emergency.copernicus.eu/ 

mapping/ems/cite-copernicusems-mapping-portal 

16 

Geomatic techniques 

for utilities 

consumption analysis 

in urban areas during 

emergency periods 

by Sara Zollini, Maria 

Alicandro, Donatella 

Dominici 

4 GEOmedia n°3-2023 

GEOmedia, published bi-monthly, is the Italian magazine for 

geomatics. Since more than 20 years publishing to open a 

worldwide window to the Italian market and vice versa. 

Themes are on latest news, developments and applications in 

the complex field of earth surface sciences. 

GEOmedia faces with all activities relating to the acquisition, 

processing, querying, analysis, presentation, dissemination, 

management and use of geo-data and geo-information. The 

magazine covers subjects such as surveying, environment, 

mapping, GNSS systems, GIS, Earth Observation, Geospatial 

Data, BIM, UAV and 3D technologies.

ADV 

22 

PASSport: a sample 

of heterogeneous 

fleet of drones 

powered by Galileo 

OSNMA service 

by M. Nisi, M. Lopez 

Codevintec 48 

Epsilon 45 

Esri 20 

GISTAM 43 

Gter 34 

Planetek 21 

SmartGEOExpo 35 

Stonex 47 

Strumenti Topografici 2 

TechnologyForAll 11 

Teorema 46 

Windows opening in 

naturally ventilated 

classrooms: 

management 

strategies to balance 

energy use and 

reduction of risk 

infection transmission 

By Giulia Lamberti, 

Giacomo Salvadori 

26 

On background image, Copernicus 

Sentinel-2 image 

highlights the colours of 

autumn over the southern 

part of New York state in 

the US. 

Credit: European Union, 

Copernicus Sentinel-2 

imagery 

30 

Building Energy 

resilience: the role 

of energy management 

systems, smart devices 

and optimal energy 

control techniques 

By G.Chantzis, 

A.M.Papadopoulos 

published by 

Science & Technology Communication 

Chief Editor 

RENZO CARLUCCI, direttore@rivistageomedia.it 

Science & Technology Communication 

Editorial Board 

Vyron Antoniou, Fabrizio Bernardini, Caterina Balletti, 

Roberto Capua, Mattia Crespi, Fabio Crosilla, Donatella 

Dominici, Michele Fasolo, Marco Lisi, Flavio 

Lupia, Luigi Mundula, Beniamino Murgante, Aldo Riggio, 

Monica Sebillo, Attilio Selvini, Donato Tufillaro 

Managing Director 

FULVIO BERNARDINI, fbernardini@rivistageomedia.it 

Editorial Staff 

Gabriele Bagnulo, Valerio Carlucci, Massimo Morigi 

Gianluca Pititto, Maria Chiara Spiezia 

redazione@rivistageomedia.it 

Marketing Assistant 

TATIANA IASILLO, t.iasillo@mediageo.it 

Design 

DANIELE CARLUCCI, dcarlucci@rivistageomedia.it 

Editor 

MediaGEO soc. coop. 

Via Palestro, 95 00185 Roma 

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info@rivistageomedia.it 

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Issue closed on: 08/09/2023

FOCUS 

Quantifying how a zone is residential 

A Multi-Criteria Decision Making approach 

by Simone Guarino, Camilla Fioravanti, Gabriele Oliva, Roberto Setola, Giovanni De Angelis, Marcello Coradini 

The COVID-19 pandemic 

has greatly impacted 

education, work dynamics, 

and social interactions. 

Allocating building 

utilities effectively during 

lockdowns is a challenge. 

Strategic resource 

allocation prioritizes 

residential areas over 

commercial ones based 

on population density. 

Accurately delineating 

residential zones is 

difficult due to complex 

urban landscapes. 

Fig. 1 - Example of a pictorial questionnaire filled by an expert. 

This paper discusses 

an indicator that uses 

open-source intelligence 

and a decision-making 

framework to assess the 

likelihood of an area being 

residential. The indicator 

optimizes resource 

allocation for power, gas, 

and water distribution. 

A case study in Nicosia, 

Cyprus, demonstrates its 

effectiveness. 

Assessing the residential 

nature of an area in 

complex urban landscapes, 

especially in major cities, 

is challenging. However, 

quantifying residential likelihood 

would be valuable during 

crises or resource scarcity 

(Carlucci et al., 2021). For instance, 

in strict pandemic lockdowns 

or energy disruption 

scenarios, identifying residential 

areas becomes crucial for 

effective resource distribution 

and rationing policies. 

This article reviews the work 

undertaken by NITEL and S3 

within the EUMAP project 

(Oliva et al., 2021). In particular, 

the undertaken activities 

were aimed to develop a 

comprehensive indicator using 

open-source intelligence to assess 

residential likelihood. 

Notably, if satellite information 

is directly used and integrated 

for this purpose, managing 

and optimizing the connection 

and data transfer aspects is 

highly beneficial (Belli et al., 

2009, Abdelsalam et al., 2017, 

Abdelsalam et al., 2019). 

In this paper we consider a 

complementary approach and, 

specifically, we rely on Open- 

StreetMap data (OpenStreet- 

Map) obtained through Over- 

Pass APIs (Olbricht,2015) to 

identify indirect indicators like 

road network density, pres- 

6 GEOmedia n°3-2023

FOCUS 

ence of shops, entertainment 

venues, places of worship, and 

financial facilities. Human 

decision-makers contributed 

their expertise to determine the 

indicators’ relative importance, 

which we distill using the Incomplete 

Analytic Hierarchy 

Process technique (Bozóki et 

al. 2010, Oliva et al., 2017, 

Bozóki and Tsyganok, 2019, 

Oliva et al., 2019). These absolute 

utility values form the 

basis of a weighted sum, creating 

a holistic index for each 

zone. 

Multi-Criteria Decision 

Model for Residential Area 

ùAssessment 

In this section, we discuss the 

proposed Multi-Criteria Decision 

Model, including the 

metrics considered along with 

their weights derived from 

interactions with decisionmakers. 

The metrics serve 

as indirect measures of residential 

likelihood and are obtained 

from public information 

sourced from OpenStreetMap 

via the Overpass APIs. 

Let us consider a specific location 

j, defined by latitude latj 

and longitude lonj. We also 

define an “area of interest” 

surrounding the location, represented 

by the set of points h 

where lath ∈ [latj − ∆lat, latj + 

∆lat] and lonh ∈ [lonj −∆lon, 

lonj + ∆lon]. For this study, 

we consider ∆lat and ∆lon 

values corresponding to a one 

square kilometer bounding box 

centered on the location. The 

residential nature of an area 

in complex urban landscapes, 

especially in major cities, is 

challenging. However, quantifying 

residential likelihood 

would be valuable during 

crises or resource scarcity 

(Carlucci et al., 2021). For instance, 

in strict pandemic lockdowns 

or energy disruption 

scenarios, identifying residential 

areas becomes crucial for 

effective resource distribution 

and rationing policies. In the 

following, we list the indicators 

we considered as indirect 

measures of residential likelihood: 

1.Road Ramification: Number 

of nodes in the area of 

interest, representing the 

road network’s density (e.g., 

highways have fewer nodes, 

while densely populated 

neighbourhoods have multiple 

nodes). 

2. Road Intersections: Number 

of nodes in the area of interest 

that correspond to road 

intersections (nodes with a 

degree greater than two). 

3. Road Coverage: Total road 

length in the area of interest 

(highways generally have 

a single main road, while 

densely populated areas 

have multiple intersections). 

4. Food: Total count of foodrelated 

shops (e.g., supermarkets, 

groceries, restaurants) 

in the area of interest. 

5. Financial: Total count of 

finance-related facilities 

(e.g., ATMs, banks, currency 

exchange) in the area of interest. 

6. Education: Total count of 

education-related facilities 

(e.g., colleges, driving 

schools, kindergartens) in 

the area of interest. 

7. Healthcare: Total count of 

healthcare-related facilities 

(e.g., hospitals, clinics, dentists) 


8. Entertainment: Total count 

of entertainment-related facilities 

(e.g., art centres, cinemas, 

theatres) in the area of 

interest. 

9. Public Service: Total count 

of public service facilities 

(e.g., courthouses, fire stations, 

post offices) in the 

area of interest. 

10.Worship: Total count of 

worship facilities (e.g., 

churches, mosques, synagogues) 


11.Transportation: Total count 

of transportation-related facilities 

(e.g., bus stops, parking 

lots, taxis) in the area of 

interest. 

12.Shops (excluding Food): 

Total count of shops, excluding 

food-related establishments 

(e.g., clothing stores, 

hardware stores, stationery 

shops) in the area of interest. 

Calculating the weights 

We now discuss the computation 

of the metrics weights wi, 

which are essential for the holistic 

indicator used to assess 

the residential likelihood of a 

zone. Six decision-makers, including 

industry and academia 

experts with critical infrastructure 

analysis and management 

experience, were interviewed. 

To collect their opinions, a 

pictorial questionnaire was 

presented (see Figure 1), 

where experts indicated their 

preferences by drawing arrows 

and associating symbols from 

Saaty’s scale (Saaty, 1990). 

The symbols were translated 

into numerical values according 

to Saaty’s scale (Table 1). 

The experts compared pairs 

of alternatives based on their 

comfort level, resulting in a 

disconnected graph in some 

cases. However, by combining 

the opinions of multiple 

decision-makers, a connected 

graph and proper ranking 

were obtained. Table 2 shows 

the weights wi associated 

with each metric which were 

computed using the Logarithmic 

Least Squares approach 

to solve the Incomplete 

Analytic Hierarchy Process 

problem, e.g., see (Oliva et 

GEOmedia n°3-2023 7

FOCUS 

Referring to wi > 0 as the 

weight associated with the i-th 

criterion and cij as the value 

assumed by the j-th residential 

zone according to the i-th 

criterion (possibly normalized 

between zero and one), the 

holistic score for the j-th residential 

zone is given by: 

Fig. 2 - Code snippet of the proposed MATLABTM implementation. 

al., 2019). The total number 

of transportation-related facilities 

is considered the most 

important factor, contributing 

approximately 14.19% to the 

holistic metric. Conversely, 

the number of worship places 

is deemed the least important, 

contributing approximately 

1.78% to the holistic metric. 

Fig. 3 - Graphical user interface. 

Assessing the likelihood that 

a zone is residential 

Consider a set of k alternatives, 

such as zones to be 

ranked based on their likelihood 

of being residential. 

Each zone is associated with 

n metrics or indicators that 

describe its importance according 

to a specific criterion. 

Note that the scores cij are 

normalized in the interval 

[0,1] since the different metrics 

may have varying scales. 

Specifically, for each criterion 

h, we normalize the raw values 

craw using the min-max 

normalization technique (Patro 

and Sahu, 2015), a popular 

approach for normalizing features 

in machine learning applications. 

Implementation 

OpenStreetMap (OSM) is 

an open-source editable map 

database that provides geospatial 

information. The data 

is organized in features represented 

by tags, allowing users 

to access various physical 

elements such as buildings, 

forests, and more. The data 

can be retrieved through the 

OpenStreetMap Overpass API, 

which serves custom-selected 

parts of the map data. In this 

paper, MATLAB is used to 

implement a function that requests 

geo-referenced data for 

a specified geographical area 

using a bounding box. 

The provided MATLAB 

language code snippet (Figure 

2) demonstrates how the function 

retrieves the requested 

data and parses it into a Struct 

data type. The features are 

obtained based on predefined 

tags, such as clinics, theaters, 


FOCUS 

and restaurants, and the function 

returns the count of occurrences 

for each feature. Additionally, 

the code mentions 

that by selecting specific types 

like nodes and ways, it is possible 

to reconstruct the road 

network topology. Attributes 

like length can be obtained for 

each way, and the graph representation 

allows the identification 

of actual traffic intersections 

by selecting nodes with a 

degree greater than two. 

Figure 3 shows the graphical 

user interface developed in 

order to support the user in the 

selection of the locations to be 

evaluated and compared. Specifically, 

the interface is developed 

in MATLAB, using 

the App Designer interactive 

development environment. 

Case Study 

The effectiveness of the proposed 

framework is demonstrated 

through a case study 

in Nicosia, Cyprus. Five 

locations are considered, visually 

represented as stars on a 

satellite image (Figure 4). The 

latitude, longitude, and raw 

scores craw for the 12 metrics 

are provided in Table 3. 

The normalization results are 

shown in Table 4. The holistic 

indicator, obtained from the 

multi-criteria decision model, 

is presented in Table 5 for the 

five locations. 

Based on the proposed holistic 

index, Location #4 is identified 

as the most important, 

while Location #3 is deemed 

the least important. These 

findings align with expectations, 

as Location #4 corresponds 

to the city center, while 

Location #3 is situated near a 

highway in the Strovolos area. 

Notably, Location #1, despite 

its proximity to the city center, 

receives lower scores compared 

to Location #2 (also in 

a central zone) in categories 

such as Food, Healthcare, 

Transportation, and Shops due 

to its adjacency to the Alsos 

Forest. Furthermore, Location 

#5, situated in an area with 

ministries and offices, exhibits 

a relatively low holistic indicator 

value. 

Overall, the case study indicates 

that the proposed holistic 

index is effective in distinguishing 

between densely and 

sparsely populated zones. 

Conclusions 

This article discusses a holistic 

indicator for quantifying 

Tab. 2 - Table summarizing the weights 

wi associated with the 12 different metrics. 

the residential likelihood of 

a zone, utilizing open-source 

intelligence and multi-criteria 

decision-making. The effectiveness 

of this approach is 

demonstrated through a case 

study conducted in Nicosia, 

Cyprus. The proposed index 

serves as a foundation for optimizing 

resource distribution, 

such as power, gas, or water. 

Tab. 3 Table summarizing the latitude, longitude and raw scores craw of the five 

considered locations. (source (Oliva et al., 2022)). 

Tab. 1 - Saaty’s ratio scale (Saaty, 1990). 

Fig. 4 - Satellite map of the city of Nicosia, Cyprus, showing the five considered 

locations as magenta stars (source (Oliva et al., 2022). 


FOCUS 

Future work aims to enhance 

the model by incorporating additional 

features, such as road 

types (one-way or two-way), 

and exploring other types of 

infrastructures, including telecommunications 

base stations 

and electrical power cabins 

within a zone. Additionally, 

the possibility of assessing 

the residential likelihood for 

zones of varying sizes will be 

explored, along with investigating 

automatic parameter 

tuning based on specific city 

characteristics. 

Tab. 4 - Table summarizing the normalized scores cij of the five considered locations 

according to the 12 different considered metrics (source (Oliva et al., 2022)). 

Tab. 5 - Holistic index obtained for the five considered locations as a result of the 

proposed Multi-Criteria Decision Model (source (Oliva et al., 2022)). 

REFERENCES 

A. Abdelsalam, M. Luglio, C. Roseti and F. 

Zampognaro (2017) TCP connection management 

through combined use of terrestrial 

and satellite IP-based links, 40th International 

Conference on Telecommunications and 

Signal Processing (TSP), Barcelona, Spain, 

July 5-7, 

A. Abdelsalam, M. Luglio, C. Roseti, F. 

Zampognaro (2019) Analysis of bandwidth 

aggregation techniques for combined use of 

satellite and xDSL broadband links, International 

Journal of Satellite Communications 

& Networking, Volume 37, Issue 2, 1 March 

2019. 

F. Belli, M. Luglio, C. Roseti and F. Zampognaro 

(2019) An Emulation Platform for 

IP-based Satellite Networks, in proceedings 

of 27th AIAA International Communications 

Satellite Systems Conference ICSSC 

2009, June 1-4 2009. 

Bozóki, S., Fülöp, J. and Rónyai, L. (2010) 

On optimal completion of incomplete pairwise 

comparison matrices. Mathematical and 

Computer Modelling 52(1-2), 318–333 

Bozóki, S., Tsyganok, V. (2019) The (logarithmic) 

least squares optimality of the 

arith- metic (geometric) mean of weight 

vectors calculated from all spanning trees for 

incomplete additive (multiplicative) pairwise 

comparison matrices. International Journal 

of General Systems 48(4), 362–381 

Carlucci, R., Di Iorio, A., Fokaides, P., Ioannou, 

A., Luglio, M., Quadrini, M., Roseti, 

C., Zampognaro, F. (2021) Architecture 

definition for a multi-utility management 

platform. In: 2021 International Symposium 

on Networks, Computers and Communications 

(ISNCC). pp. 1–6. IEEE 

Olbricht, R.M. (2015) Data retrieval for 

small spatial regions in OpenStreetMap. In: 

OpenStreetMap in GIScience, pp. 101–122. 

Springer 

Oliva, G., Setola, R., Scala, A. (2017) Sparse 

and distributed analytic hierarchy process. 

Automatica 85, 211–220 

Oliva, G., Scala, A., Setola, R., Dell’Olmo, P. 

(2019) Opinion-based optimal group formation. 

Omega 89, 164–176 

Oliva, G and Guarino, S and Setola, R and 

De Angelis, G and Coradini, M (2022) 

“Identifying Residential Areas Based on 

Open Source Data: A Multi-Criteria Holistic 

Indicator to Optimize Resource Allocation 

During a Pandemic”, International Conference 

on Critical Information Infrastructures 

Security, pp. 180–194 

OpenStreetMap https://www.openstreetmap.org 

Patro, S., Sahu, K.K. (2015) Normalization: 

A preprocessing stage. arXiv preprint 

arXiv:1503.06462 

Saaty, T.L.: How to make a decision: the analytic 

hierarchy process. European journal of 

operational research 48(1). 

KEYWORDS 

COVID-19; Resource Allocation; Residential 

Area Identification; Multi- 

Criteria Decision-Making; · Incomplete 

Analytic Hierarchy Process 

ABSTRACT 

The COVID-19 pandemic has had an 

unprecedented impact on various aspects 

of our lives, including education, work 

dynamics, and social interactions. Dealing 

with the provision of building utilities 

in such circumstances has become a formidable 

challenge. During lockdowns, it 

becomes crucial to allocate resources strategically, 

giving priority to residential areas 

over commercial and financial districts 

based on population density. Identifying 

residential areas is of utmost importance 

not only for effective emergency response 

during natural disasters but also for ensuring 

fair distribution of electricity and 

gas when resources are scarce. However, 

accurately delineating residential zones is 

challenging due to the intricate nature of 

urban landscapes. 

This paper aims to discuss a comprehensive 

indicator that utilizes open-source intelligence 

and incorporates a multi-criteria 

decision-making framework to assess the 

likelihood of an area being residential. 

This indicator will greatly assist in optimizing 

resource allocation for power, gas, 

and water distribution. To demonstrate 

the effectiveness of the proposed approach, 

a case study conducted in Nicosia, Cyprus, 

is presented. 

AUTHOR 

Simone Guarino 

s.guarino@unicampus.it 

Università Campus 

Bio-Medico di Roma 

Camilla Fioravanti 

c.fioravanti@unicampus.it 



Gabriele Oliva 

g.oliva@unicampus.it 



Roberto Setola 

r.setola@unicampus.it 



Giovanni De Angelis 

gianni.deangelis@spacesystems.solutions 

Space Systems Solutions 

Marcello Coradini 

marcello.coradini@spacesystems.solutions 

Space Systems Solutions 


FOCUS 

Il Forum dell'Innovazione 

Tecnologie per il Territorio, Beni Culturali e Smart Cities 

Roma 

14 - 16 NOV 2023 

www.technologyforall.it 


REPORT 

Spatial, functional and 

temporal Analysis of Wi-Fi 

hotspots during covid-19 curfew 

In selected EU cities Rome, Thessaloniki, 

Nicosia, Kaunas. 

by Marius Ivaškevičius 

Behavior of people in 

extreme condition is one 

of the main target of the 

eUMaP project, funded 

under European Union’s 

Horizon 2020 project Marie 

Slodowska-Curie Actions. 

Problem and Relevance 

In order to model behavior of 

people in extreme conditions 

we need recorded data that represents 

this behavior. 

How to measure behavior of 

people? 

Exact data representing behavior 

is very sensitive and can 

be used in illegal ways even by 

accident, therefore we want approximate 

data. 

Hypothetical scenario and 

Data availability 

In modern world Internet became 

almost free and highly 

popular means of worldwide 

communication. 

Wi-Fi became one of most popular 

means to deliver internet 

to end user. 

Gatherings of people create 

temporary or more permanent 

Wi-Fi hotspots to share internet. 

Wi-Fi hotshots broadcast basic 

information on open channels. 

It becomes public information. 

Volunteers of WiGLE community 

(http://wigle.net) gather 

this data and publish it on 

their site. It is also possible to 

gain access directly to database. 

WiGLE.net 

WiGLE.net is a submissionbased 

catalog of wireless networks. 

Submissions are not 

paired with actual people; rather 

name/password identities 

which people use to associate 

their data. It's basically a "gee 

isn't this neat" engine for learning 

about the spread of wireless 

computer usage. 

WiGLE concerns itself with 

802.11a/b/g/n and cellular networks 

right now, which can be 

collected via the WiGLE WiFi 

Wardriving tool on android. 

We also have a bluetooth stumbling 

client for Android, but 

do not maintain a catalog of 

bluetooth networks. 

WiGLE consolidate location 

and information of wireless 

networks world-wide to a 

central database, and has a 

user-friendly desktop and web 


REPORT 

LAND COVER FROM COPERNICUS AND WIFI NETWORK USE FROM WIGLE IN 4 EU CITIES 

Kaunas City – Copernicus Land Cover Kaunas City Features count 246,482 

Nicosia – Copernicus Land Cover Nicosia – Features count 116,033 

Rome – Copernicus Land Cover Rome - Feature count 407,632 

Thessaloniki – Copernicus Land Cover Thessaloniki - Feature count 465.497 

Fig. 1 – Land Cover from Copernicus and WIFI network use from WIGLE in 4 EU Cities 


REPORT 

Fig. 2 - Time slice analysis in Kaunas City. 

Fig. 3 - Grid Count Scan in Kaunas City. 

applications that can map, 

query and update the database 

via the web. 

Everyone can contribute 

to this mapping sending 

wireless network traces 

(in any of listed formats, 

usually pairings of wireless 

sample, names and network 

hardware addresses 

- for uniqueness -, data/ 

SNR triples and GPS 

coordinates) or enter networks 

manually. 

For more information: 

https://wigle.net/wiki/ 

index.cgi 

Data download 

WiGLE limits requests per 

day. 

Automated using crontab. 

Hosts a web page to monitor 

progress. 

Conclusions 

Counting Wi-Fi hotspot 

by land use categories in 

multiple time slices can 

pinpoint human behavior 

during the lockdown. 

Wi-Fi hotspot change centered 

in the event of curfew 

start allows to inspect 

territories by positive or 

negative trend. 

Proposed parameter, Wi-Fi 

index, can be used to describe 

the communications 

state of the city. It reveals 

temporal trends that could 

not be detected without 

it. It represents speed of 

Wi-Fi hotspot creation 

normalized by area of explored 

territory. 

Although there are no 

common trends between 

cities, differences could 

potentially be related to 

macro parameters. This 

could be validated statistically 

by including more 

cities in the research. 

Fig. 4 - Time step scan in Kaunas City. 


REPORT 

WIFI CHANGE IN 4 EU CITIES DURING COVID 19 

Kaunas City WiFi change 

Nicosia WiFi change 

Rome WiFi change 

Thessaloniki WiFi Change 

Fig. 5 – WIFI change in 4 EU cities during COVID 19. 

KEYWORDS 

Covid19; eUMaP; WiFi 

ABSTRACT 

Behavior of people in extreme condition is one 

of the main target of the eUMaP project, funded 

under European Union’s Horizon 2020 project 

Marie Slodowska-Curie Actions. 

AUTHOR 

Marius Ivaškevičius 

Kaunas University of Technology 

Faculty of Civil Engineering and Architecture 

Department of Architecture and Urbanism 

Studentų st. 48-303 Kaunas, LT-51367, 

Lithuania 

marius.ivaskevicius@ktu.lt 

Fig. 6 - Possible Dependencies, WIFI index in 4 EU cities. 


REPORT 

Geomatic techniques for utilities 

consumption analysis in urban 

areas during emergency periods 

by Sara Zollini, Maria Alicandro, Donatella Dominici 

Understanding the effects of 

Covid-19 by studying indirect 

factors with the synergy of geomatic 

techniques, namely images from 

optical sensors mounted on aircraft, 

UAV and satellites. Proposal of a 

new methodology to highlight the 

changes between the periods pre-, 

during, and post-lockdown. 

Fig. 1 – Case study in the Ano Poli of Thessaloniki (Greece). 

To understand 

the influence 

of Covid-19 to 

utilities consumption in 

residential zones, a proposal 

of methodology to extract 

parking spots in the urban area 

of Thessaloniki is presented. 

Taking advantage of the 

synergy between different 

geomatic techniques, change 

detection analysis can lead to 

a better management of the 

territory. 

Introduction 

At the beginning of 2020, 

Europe went through an 

arduous period because 

of the contagious disease 

of Covid-19. The disease 

spread all over the world 

at incredible rate, so each 

state, with the aim of 

reducing and stopping the 

transmission of coronavirus, 

adopted extreme restrictions 

(lockdown) and measures 

that inevitably affected not 

only the economic but also 

the social and psychological 

life of the citizens. During 

this period, one of the 

problems that occurred was 

the management of building 

utilities. More specifically, 

more people started working 

remotely from their homes 

instead of the working place, 

creating numerous problems 

related to the integrity of 

everyday utilities, such as 

power outages, water shortage 

and insufficient internet 

connection. The management 

of this kind of public utilities 

mainly describes an issue of 

high complexity because they 

are not always freely available, 

or they refer to build-up areas 

and not to specific buildings. 

So, it could be of great 

advantage to study indirect 

factors, such as the occupation 

of parking spots in residential 

areas. The synergy between 

different geomatic techniques 

can accomplish this task. 

In this work, a proposal of 

methodology to extract cars 

occupation is presented. UAV 

photogrammetry, satellite and 

aerial remote sensed images are 

specifically used to achieve this 

purpose. 

Study area 

The case study is located in the 

Municipality of Thessaloniki, 

in the Ano Poli region, around 

the old Byzantine church Saint 

Nicolas Orfanos, in Greece 

(Figure 1). This is one of the 

six districts in which the city 

is divided. This urban block 


REPORT 

covers an area of 5000 m 2 and 

all residential buildings within 

the urban block have same 

typology but different age 

of construction. During the 

Great Fire in 1917, two thirds 

of the city of Thessaloniki was 

destroyed, except for the area 

of Ano Poli, which remained 

unscathed. The government 

commissioned the French 

architect Ernest Hébrard to 

design a new urban plan for 

the burned areas and for the 

future expansion of the city. 

Its architecture contrasts 

with the Byzantine style of 

Ano Poli, which was even 

declared UNESCO heritage. 

The Upper Town preserves 

Thessaloniki’s Ottoman-era 

attributes, including small 

stone-paved streets, old city 

squares, and houses densely 

built in traditional Greek and 

Ottoman fashion (Boston, 

2014). 

Data acquisition 

The main and final idea of the 

work is to analyse the changes 

happened in the periods 

before, during and after the 

lockdown. The available 

data are aerial orthoimages 

(LSO) provided by the 

Greek National Cadastre and 

acquired in 2015 with 50 

cm spatial resolution (prelockdown), 

satellite VHR 

(Very High Resolution) 

GaoJing/Superview-1 images 

acquired in 2020 with 50 cm 

and 2 m spatial resolution 

for the panchromatic 

and multispectral images 

respectively (during 

lockdown), and UAV images 

acquired in 2021 (postlockdown) 

with a GSD 

(Ground Sample Distance) 

equal to 1.26 cm/pixel. 

GaoJing/SuperView-1 

constellation is composed 

of 4 identical VHR EO 

Tab. 1: Superview-1 specs. 

satellites running along the 

same orbit and phrased 90° 

from each other. The first 

two satellites were launched 

in December 2016 and the 

second two were launched in 

January 2018. They are highresolution 

commercial satellites 

designed, developed, and 

operated by China. GaoJing/ 

SuperView-1 constellation 

is China's first commercial 

satellite constellation with 

high agility and multi-mode 

imaging capability. When 

4 GaoJing/SuperView-1 

satellites work concurrently, 

they can collect over 2 million 

square kilometres every day 

and revisiting any target on 

the Global within 1 day. The 

optical payload contains a 

pushbroom type camera with 

0.5 m GSD for a panchromatic 

imagery and 2 m GSD in four 

MS (Multispectral) bands (red, 

green, blue and NIR). The 

swath width of the generated 

image is 12 km (European 

Space Agency, 2016). The 

technical specs are reported in 

Table 1. This constellation has 

been used in wide applications 

and environments, like forest 

(Chen et al., 2023), mapping 

(Li et al., 2021), urban areas 

(Khryaschev and Ivanovsky, 

2019), soil (Prokopyeva, 

2022), landslide (Xia et al., 

2021) and so on. 

After the lockdown, on 

September 9th, 2021, 244 

UAV georeferenced images 

were acquired with a GSD 

of 1.26 cm/pixel, established 

by setting a flight altitude 

of 100 m above the take-off 

point. The used UAV was 

the DJI Matrice 300 RTK, 

with dual receivers that 

connect to permanent GNSS 

stations network via 4G 

(SmartNET Europe by Leica 

Geosystems). The used sensor 

is a full frame Zenmuse P1 of 

45Mpixels. The longitudinal 

and transverse overlap was 

70%. The UAV and sensor 

Fig. 2 – UAV and sensor specifications and flight planning for data acquisition. 


REPORT 

Fig. 3 – GaoJing/Superview-1 panchromatic and multispectral images and the proposed 

pre-processing. 

specifications, as well as the 

flight plan used to acquire the 

data are illustrated in Figure 2. 

Proposed methodology 

The aim of this work is to 

analyse any changes happened 

in the period around the 

lockdown to understand the 

utilities consumption. So, the 

proposed methodology consists 

in applying an object-based 

image analysis (OBIA) on the 

images pre-, during and postcovid 

for a change detection. 

The research started from the 

GaoJing/Superview-1 preprocessing. 

The processing 

level used for this work is the 

1B, the basic product, that 

is corrected for radiometric 

and sensor distortion but not 

geometrically corrected or 

projected to a plane using a 

Fig. 4 – Flowchart of the proposed methodology. 

map projection or datum. So, 

they need to be projected and 

resampled. For this reason, 

the first step to be performed 

is the pansharpening, a fusion 

technique used to obtain a 

new image with the spatial 

resolution of the panchromatic 

(50 cm) and the spectral 

resolution of the multispectral 

one. The NNDiffuse 

algorithm should be used 

because it works best when 

the spectral response function 

of each multispectral band 

has minimal overlap with one 

another, and the combination 

of all multispectral bands 

covers the spectral range of 

the panchromatic raster (Sun 

et al., 2014). Then, in order 

to correct the geometry and 

the spatial position of the 

image, the orthorectification 

and georeferencing have to 

be performed. At the end, a 

stack of all the layers can be 

applied and the area of interest 

is extracted by making a subset 

on the image. In Figure 3 

the original images and the 

proposed pre-processing is 

showed. 

The research continued 

with the UAV images, 

which are treated within the 

photogrammetric process. The 

well-known workflow starts 

from the acquisition phase 

and then goes through the 

photogrammetric elaboration. 

The first step is divided 

into two main phases, the 

survey planning, and the data 

acquisition. The second step 

include the Structure from 

Motion, the dense matching 

and the mesh and texture 

generation. The final products 

are the 3D model, the point 

cloud, the Digital Elevation 

Model (DEM), and the 

orthomosaic. The last is used 

to perform the OBIA. The 

OBIA consists of two steps, 

segmentation and classification 

(Teodoro and Araujo, 2016). 

In segmentation, pixels with 

similar features, like brightness, 

colour, texture are grouped 

to form vector objects called 

segments. In the classification, 

each object is assigned to 

the class that better define it 

according to its characteristics. 

The classification is generally 

supervised, so a user chooses 

how much and what classes 

should be created to train 

the decision model. This 

technique is one of the most 

advantageous for many 

reasons. Segmentation divides 

an image into objects as the 

human eyes do. By creating 

objects, the computational 

burden is less than other 

techniques, like, for example, 

the pixel-based ones. The 


REPORT 

segments, composed by many 

pixels, have additional spectral 

information compared to the 

individual pixels (like average, 

minimum and maximum 

values, variance and so on). 

They also contain spatial 

information, like mutual 

distances between objects, 

number of pixels which 

compose the object, topology, 

and so forth (Blaschke, 2010). 

In addition, image objects 

take into account many 

features (like shape, texture, 

relationship with other objects) 

which are not present in single 

pixels. At last, segmentation 

reduces the spectral variability 

between the classes (Alicandro 

et al., 2023; Zollini et 

al., 2020). The proposed 

methodology is illustrated in 

Figure 4. 

First results 

The first results come from 

the photogrammetric process. 

The obtained orthomosaic 

has a final RMS of 0.05 m 

and a DEM with 5.6 cm/pixel 

resolution (Figure 5). 

On the orthomosaic, the 

OBIA was applied, but it still 

requires further investigation, 

as the classifier presented 

difficulty in distinguishing 

cars from the road and the 

cemetery. What can be done, 

is to isolate the roads from 

the rest of the image and 

apply the OBIA only in that 

part of the image. As far as 

the orthomaps acquired in 

2015 is concerned, as well as 

the Superview images, 50 cm 

resolution could not be enough 

for car detection. According 

to the literature, there are two 

deep learning-based models 

for vehicle counting from 

optical satellite images coming 

from the Pleiades sensor at 

50-cm spatial resolution. Both 

segmentation (Tiramisu) and 

detection (YOLO, You Only 

Look Once) architectures were 

investigated in Froidevaux et 

al., 2020, but a deeper study 

of the state of art will be 

performed. Once the roads 

are isolated, a subset of the 

image can be performed, and 

the surrounding noises can 

be removed. The DEM was, 

instead, used to orthorectify 

and georeferencing the satellite 

images with the image-toimage 

technique. 30 GCP 

were used and a final RMS of 

0.768 was achieved. The next 

step would be applying the 

OBIA also to the pre-processed 

Superview-1 and compare 

eventual changes. Finally, the 

same procedure will be applied 

to the aerial images provided 

by the Greek National 

Cadastre acquired in 2015 and 

the final change detection will 

be performed. 

Conclusions and future 

developments 

To conclude, this paper aimed 

to propose a methodology to 

indirectly study the effects 

of Covid-19 on the increase 

of utilities consumption. 

The input data refer to the 

periods pre-, during and 

post-lockdown and make 

use of different geomatic 

techniques, such as satellite 

remote sensing and UAV 

Fig. 5 - Orthomosaic and DEM of Thessaloniki Ano Poli. 

photogrammetry. A change 

detection can be performed by 

analysing the input images at 

first with the OBIA, but then, 

other algorithms within the 

machine learning world will 

be also tested and compared. 

The road extraction leads to 

understand the occupation 

of parking spots and, so, the 

probable presence inside the 

houses during the working 

hours. Consequently, the real 

consumptions can be used to 

validate the results. Moreover, 

they can be integrated inside 

BIM and GIS and could be 

very helpful for those in charge 

of the territory management. 

This can lead to a less 

expensive and time-consuming 

analysis for future cities 

development. 

Acknowledgment 

The authors would like to 

thank professors Konstantinos 

Tokmakidis and Panagiotis 

Tokmakidis of Aristotle 

University of Thessaloniki for 

their precious contribution on 

UAV data collection. Many 

thanks also to Vasiliki (Betty) 

Charalampopoulou and the 

staff of GSH of Athens, who 

hosted the authors during the 

secondments and provided the 

satellite imagery within the 

eUMaP project. 


REPORT 

BIBLIOGRAPHY 

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S. (2023). Enhanced Algorithms to Extract Decay Forms of Concrete 

Infrastructures from Uav Photogrammetric Data. The International Archives 

of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 

48, 9-15. 

Blaschke, T. (2010). Object based image analysis for remote sensing. 

ISPRS J. Photogramm. Remote Sens. 65, 2-16. https://doi.org/10.1016/j. 

isprsjprs.2009.06.004 

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gaojing#Gaojing-Superview.html.1 (Accessed 9-11-23). 

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S., ... & Huynh, T. L. (2020, September). Vehicle detection and counting 

from VHR satellite images: Efforts and open issues. In IGARSS 2020-2020 

IEEE International Geoscience and Remote Sensing Symposium (pp. 256- 

259). IEEE. 

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segmentation and deep neural network. E3S Web Conf. 135, 01064. https:// 

doi.org/10.1051/e3sconf/201913501064 

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Prokopyeva, K.O. (2022). The Use of Multi-Temporal High-Resolution 

Satellite Images to Soil Salinity Assessment of the Solonetzic Complex 

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S2079096122040163 

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pan-sharpening algorithm for spectral images. Opt. Eng. 53, 013107. https:// 

doi.org/10.1117/1.OE.53.1.013107 

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image analysis techniques available in open source software (Spring and 

Orfeo Toolbox/Monteverdi) considering very high spatial resolution data. J. 

Appl. Remote Sens. 10, 016011. https://doi.org/10.1117/1.JRS.10.016011 

Xia, W., Chen, J., Liu, J., Ma, C., Liu, W. (2021). Landslide Extraction 

from High-Resolution Remote Sensing Imagery Using Fully Convolutional 

Spectral–Topographic Fusion Network. Remote Sens. 13, 5116. https://doi. 

org/10.3390/rs13245116 

Zollini, S., Alicandro, M., Dominici, D., Quaresima, R., Giallonardo, 

M. (2020). UAV Photogrammetry for Concrete Bridge Inspection Using 

Object-Based Image Analysis (OBIA). Remote Sens. 12, 3180. https://doi. 

org/10.3390/rs12193180 

KEYWORDS 

UAV photogrammetry; remote sensing; Superview-1; change 

detection; Covid-19 

ABSTRACT 

This paper has the main purpose of proposing a methodology to 

understand the occupation of parking spots by using the synergy 

of different geomatic techniques. Aerial, satellites, and UAV data 

are studied through the OBIA to analyse, by change detection, the 

main differences pre-, during and post-lockdown due to Covid-19. 

The first results are really promising and pave the ground for a future 

automation of the proposed procedure. The results can be also 

integrated in BIM and GIS to help the management of utilities 

consumption in emergency periods, and they create a dataset to 

enhance and increase consumption efficiency in residential areas. 

AUTHOR 

PhD Eng. Sara Zollini 

sara.zollini@univaq.it 

PhD Eng. Maria Alicandro 

maria.alicandro@univaq.it 

Prof. Donatella Dominici 

donatella.dominici@univaq.it 

DICEAA, Department of Civil, Construction-Architectural 

and Environmental Engineering, University of L’Aquila, 

Via G. Gronchi 18, 67100, L’Aquila, Italy 


REPORT 


REPORT 

PASSport: a sample of 

heterogeneous fleet of drones 

powered by Galileo OSNMA service 

by A. R. Martín, I. Armengol, M. López, H. Llorca, M. Nisi, M. Lopez 

Ports and National Authorities 

around the world assuming its role 

as critical infrastructure have the 

commitment to establish, update 

and maintain a security plan. The 

issue of security in maritime ports is 

a well-known complex problem due 

to the particular characteristics of 

these facilities. They consist of large 

wide areas with many entry points, 

usually very transited and operating 

24 hours per day, every day. 

These features lead to 

certain vulnerabilities 

and threats whose 

risks may be mitigated by 

implementing innovative 

surveillance actions. Besides 

these widely known dangers, 

over the past two decades, 

new risks have emerged with 

the development of new 

technologies, such as jamming 

and spoofing of GNSS signals 

that in practice represents the 

Denial-of-Service (DDoS). 

These might lead to failures 

or disruptions in the daily 

operations of the port, 

degrading its services and/or 

infrastructures. In particular, 

signal jamming consists of 

interfering GNSS receivers 

with higher-power signals 

on the GNSS frequency 

bands at user level, or with 

unintentional interferences 

due to space weather or other 

nearby radiating equipment. 

Jamming techniques can be 

followed by spoofing attacks, 

whose goal is to deceive 

receivers with GNSS-like 

signals that contain wrong 

observable data. 

These problems have been 

identified by the European 

Commission and the need 

of improving security and 

safety in port areas has been 

portrayed in the directive 

2005/65/CE. As part of the 

important search of solutions, 

PASSport (Operational 

Platform managing a fleet 

of semi-autonomous drones 

exploiting GNSS High 

Accuracy and Authentication 

to improve Security & Safety 

in port areas) is an EUSPA 

funded project that responds 

to the needs expressed by port 

authorities, harbour master and 

border control authorities by 

extending situational awareness 

to improve safety and security 

in port areas. 

The surveiilance solution 

The proposed surveillance 

solution of the project 

combines both aerial and 

underwater drones with a 

network of RFI monitoring 

stations. The use of this 

fleet of drones is intended 

to provide innovation and 

operational support to the 

recognition, management and 

analysis of safety and security 

aspects of daily operations, 

with particular attention to 

pollution monitoring, support 

to e-navigation, protection 


REPORT 

of critical infrastructures 

and against non-cooperative 

small craft approaching port 

areas, and underwater threats 

monitoring. Particularly, the 

drones combine state of the 

art technologies to collect on 

field data in real time, which 

allows surveillance with an 

extended situational awareness 

by covering larger areas. So 

far, operational surveillance 

activities to guarantee security 

and safety are dealing with 

static sensors, and the data 

collected cannot automatically 

trigger dedicated operational 

procedures. With PASSport 

vision, this limitation is 

overcome by proposing a 

holistic surveillance solution. 

The solution will be connected 

with already deployed 

operational platforms and 

exploit the innovation brought 

by drones assisted with 

E-GNSS technology. 

Drone fleet and GNSS 

hybridisation 

The above-mentioned drone 

fleet integrates, among other 

sensors, the use of GNSS 

receivers for a secure, safe and 

accurate guidance, navigation, 

and control (GNC). GNSS 

technologies are widely used 

for many purposes in drone 

navigation systems, as they 

are integrated in most, if not 

all, conventional autopilots. 

However, accuracy and 

security of this technology can 

be compromised in certain 

demanding areas, such as 

port infrastructures due to 

multipath, or if subjected 

to certain interferences, 

either intentional or not, 

and this is the reason why 

hybridisation with other 

sensors is usually contemplated 

in a risk assessment. In any 

case, even with hybridised 

configurations, a diminished 

GNSS performance may lead 

to a potential degradation of 

the drone navigation system. 

Open Service – Navigation 

Message Authentication 

(OSNMA) 

Taking this into consideration, 

the integration and 

exploitation of new GNSS 

services oriented to improving 

accuracy and security is 

not only justified but also 

necessary. 

In terms of accuracy, a PPP 

algorithm in post-processing 

is considered, as it is a widely 

mature positioning technique. 

This positioning method 

uses single or dual-frequency 

code and carrier phase 

measurements for centimetric 

accuracy applications. On 

the other hand, in terms of 

security, navigation with 

Galileo’s newcomer Open 

Service – Navigation Message 

Authentication (OSNMA) 

is used. OSNMA is a data 

authentication function 

for Galileo that provides 

receivers with the assurance 

that the received Galileo 

navigation message is coming 

from the system itself and 

has not been modified. The 

use of this service in the 

PASSport solution helps in 

the avoidance of some of 

the aforementioned threats 

in port context. In terms of 

safety an integrity (as IBPL) 

approach is considered to 

provide protection levels (PLs). 

The described capabilities in 

terms of accuracy, integrity, 

and security will be introduced 

in an evolution of GMV’s 

GNSS receiver MAGIC 

User Terminal, which will be 

embarked onboard the aerial 

drones. For the monitoring 

backbone, GMV’s srx-10i 

(also known as DINTEL) will 

provide a cost-effective, dualband, 

simultaneous monitoring 

of GNSS bands. Monitoring 

stations will augment onground 

the decision-making 

process of port area operators 

by providing alert mechanisms 

and automated report 

functionalities on the presence 

of RFI threads. 

The purpose of this paper, 

in this context, is to assess 

the functionalities of PPP, 

OSNMA and RFI monitoring 

in terms of robustness against 

spoofing attacks and accuracy 

of positioning obtained with 

authenticated navigation 

messages compared to nonauthenticated 

navigation 

results. These functionalities 

are validated with results 

obtained from different flight 

campaigns using real Signal-in- 

Space (SiS). These campaigns 

are performed in different 

European ports such as 

Kolobrzeg, Valencia, Le Havre, 

Hamburg and Ravenna. 

KEYWORDS 

Ports; OSNMA; GNSS; Galileo; 

Drone fleet 

ABSTRACT 

PASSport is an Operational Platform managing a 

fleet of semi-autonomous drones exploiting GNSS 

High Accuracy and Authentication to improve Security 

& Safety in port areas. EUSPA funded the 

project that responds to the needs expressed by 

port authorities, harbour master and border control 

authorities by extending situational awareness 

to improve safety and security in port areas. 

AUTHOR 

A. R. Martín, 

I. Armengol, 

M. López, H. 

Llorca, GMV; 

M. Nisi, 

marco.nisi@grupposistematica.it 

SISTEMATICA S.p.A; 

M. Lopez, EUSPA 

NOTE 

More information available on paper presented 

at ION GNSS+ 2023 

Session B2: Marine Applications, and Search and 

Rescue 

https://www.ion.org/gnss/sessions.cfm?sessionID=1566 


REPORT 

Windows opening in naturally ventilated 

classrooms: management strategies to 

balance energy use and reduction of risk 

infection transmission 

By Giulia Lamberti, Giacomo Salvadori 

Ensuring proper ventilation to reduce infection risks 

indoors has become increasingly important, especially 

during the COVID-19 pandemic. For naturally ventilated 

buildings, generic guidelines, such as "open windows as 

much as possible," pose challenges in effectively managing 

ventilation rates. This work addresses this complexity by 

focusing on classrooms at diverse educational stages to 

quantify the management of naturally ventilated spaces. 

The study presents a method to determine window opening 

time and frequency, considering window characteristics, 

indoor and outdoor conditions, and room occupancy. 

Results reveal that opening time correlates with room 

surface and occupancy but diminishes with larger window 

areas and favourable discharge coefficients based on 

window types. Additionally, in windless conditions, opening 

time decreases as the indoor-outdoor temperature 

difference increases. 

This research emphasizes the urgent need for more 

efficient guidelines for naturally ventilated environments, 

ensuring healthy indoor conditions not only during the 

pandemic but also in the post-pandemic era. Implementing 

these findings will promote safer and healthier indoor 

spaces for educational settings and beyond. 

Since people are spending 

an increasing amount 

of time indoors, Indoor 

Environmental Quality (IEQ) 

has become a very important 

issue to improve the health 

and well-being of occupants 

(Bluyssen, 2020). During the 

COVID-19 pandemic, the 

necessity to ensure healthy 

environments has become 

even more evident, since IEQ 

can have direct effects on 

occupants’ safety (Awada et 

al., 2021) and environmental 

quality has a direct effect on 

infection control (Azuma et 

al., 2020). 

The airborne transmission of 

COVID-19 has been widely 

recognised by researchers as 

one of the three modalities, 

together with large respiratory 

droplets (falling in a range 

of 1-2 m) and direct contact 

with contaminated surfaces 

(Morawska et al., 2020; 

Noorimotlagh et al., 2021). 

This evidence has been 

confirmed by several cases 

of COVID-19 spread due 

to environmental factors, 

such as the restaurant in 

Guangzhou, China (Lu et al., 

2020) where the direction 

of the airflow from the air 

conditioners was consistent 

with the disease transmission. 

Li et al. (Li et al., 2020) 

also brought evidence that 

the aerosol transmission of 

COVID-19 was related to 

poor ventilation of buildings. 


REPORT 

Further cases confirming this 

infection pathway are the call 

centre in Seoul, South Korea 

(Park et al., 2020), which 

showed that the exposure time 

is a determinant in increasing 

the risk, or the case of the 

choir in the Skagit Valley, USA 

(Hamner et al., 2020), which 

highlighted the influence of 

activity on COVID-19 spread. 

In particular, increasing the 

ventilation can reduce the 

infection risk indoors (Dai and 

Zhao, 2020; Lipinski et al., 

2020; Morawska et al., 2020) 

and environmental parameters 

should be also used to prevent 

the pandemic (Yao et al., 2020). 

For this reason, several 

guidelines have been provided 

to manage buildings during 

this period. Generally, the 

indications for buildings 

provided by HVAC systems are 

much more detailed than the 

ones for naturally ventilated 

environments. In general, it was 

required to maintain ventilation 

systems as usual, keeping 

them on two hours before 

and after the room occupancy 

(ECDC, 2020). If possible, it 

was also required to avoid air 

recirculation and to increase 

the airflow in the space. Except 

for Norway, which suggests 

ACH=7l/s pers, for everyday 

environments (e.g. offices, 

schools, etc) the national and 

institutional guidelines do not 

provide an exact value of air 

changes required during the 

pandemic period, but they 

recommend at least maintaining 

usual the required airflow for a 

longer period. 

For naturally ventilated 

buildings, the situation is 

even more complex as it is not 

possible to monitor directly the 

airflow passing through doors 

and windows. Indications are 

usually generic and specify that 

windows should be opened 

Tab. 1 - Classrooms’ characteristics in Italy according to different educational stages (DM 

18-12-1975, 1975). 

“regularly” and in some cases, 

they are in contrast with each 

other (e.g. Italy recommends 

keeping doors closed, while the 

Netherlands maintains windows 

and doors open). Moreover, 

guidelines provide approximate 

opening times such as 10 to 15 

minutes once or more times 

per day, which is a very generic 

indication that does not take 

into account parameters such 

as the room characteristics or 

occupancy. Only Germany, 

Norway, and REHVA suggest 

the use of CO2 sensors as 

an indicator of potential 

SARS-CoV-2 presence, which 

has been demonstrated as a 

useful tool to assess infection 

probability (Peng and Jimenez, 

2020; Fantozzi et al., 2022). 

This paper, therefore, aims to 

evaluate window openings to 

ensure healthy conditions and 

reduce the risk of infection, 

taking school buildings as a 

reference. 

Methodology 

Characteristics of the classrooms 

Classrooms’ characteristics may 

vary according to the school 

stage considered. Standards 

report the typical features that 

classrooms at various school 

stages present, as reported in 

Table 1. 

For calculation purposes, the 

dimensions of the classrooms 

were assumed equal to 45 m 2 

for kindergartens, primary and 

middle schools, and 60 m2 for 

high schools. 

Required ventilation rate for 

reducing the infection risk 

Since in the guidelines, the 

required ventilation rates were 

very variable between countries, 

room management generally 

Tab. 2 - Characteristics and ventilation rates for different educational stages according to 

the Italian standard (UNI 10339, 1995). 


REPORT 

referred to existing standards. 

As this paper considers Italian 

classrooms, the required 

ventilation rates (Ǭreq, m 3 /s) 

provided by UNI 10339 (UNI 

10339, 1995) were assumed 

as reference values. Values 

reported for different types of 

classrooms are reported in Table 

2. 

Determination of the natural 

ventilation rate 

Natural ventilation is a 

function of different factors, 

such as the position and flow 

characteristics of the opening, 

the surface mean pressure 

coefficient distribution for the 

wind direction considered, and 

the internal and external air 

temperature (BS 5925, 1991). 

In the case of educational 

buildings, which are in most 

cases naturally ventilated, 

classrooms present openings 

on one side only. Since the 

aim of this work is to provide 

a simplified methodology 

that can be used by buildings 

managers to reduce the 

infection probability in 

classrooms, the effect of air 

infiltration and wind were 

neglected as a precaution. 

The first is because the air 

infiltration is a function of the 

buildings’ characteristics and 

cannot be generalized to every 

educational building, while the 

second is because the wind is 

variable and depends on the 

localization of the building 

and on weather conditions, 

which cannot be controlled in 

advance. 

Moreover, for precautionary 

reasons, the aim is to evaluate 

the most critical conditions 

that are represented by the 

absence of wind. The natural 

ventilation rate from window 

(ǬW, m3/s) was calculated 

for spaces with one opening 

on one wall only and due to 

temperature difference (BS 

5925, 1991): 

(1) 

where: 

C d 

is the discharge coefficient for 

the opening (n.d.), 

A is the equivalent area of the 

opening (m 2 ), 

ΔT is the indoor-outdoor 

temperature difference (K), 

g is the gravity acceleration (m/s 2 ), 

H is the vertical difference between 

the top and bottom edges of a 

rectangular opening (H), 

TM is the average of inside and 

outside temperatures (K). 

The discharge coefficient Cd 

depends on the pressure drop 

encountered by the air flow as 

it crosses the opening and it 

can quantify, other parameters 

being equal, the airflow 

efficiency of an opening. Table 

3 shows some typical discharge 

coefficients for windows under 

buoyancy-driven ventilation, 

which were obtained from a 

single width-to-height opening 

ratio (Brandan and Espinosa, 

2018). 

Calculation of the minimum 

time of window opening 

Knowing the Ǭreq value for 

ensuring adequate ventilation 

(from Table 2), and the ǬW 

value (from Equation 1, which 

requires knowledge of the 

environmental conditions, and 

the geometrical characteristics 

of the windows), it is possible 

to calculate the ratio 

R = Ǭreq / ǬW 

(2) 

If R >1, the ventilation 

flow rate Q is not sufficient 

to guarantee the required 

conditions and more indepth 

analyses should be 

conducted (e.g. evaluation 

of the contribution of the 

wind action). If R ≤1, fixing 

a reference time interval ∆τ 

(s), R can be used to suggest 

the minimum duration of 

the windows opening (τmin), 

according to the following 

equation 

τmin = R∙∆τ 

(3) 

In particular, ∆τ=3600 s (one 

hour) can be usually assumed 

as a reference time interval for 

educational buildings, as it is 

the minimum duration of a 

single lecture. 

Equation 3 gives the minimum 

window opening duration but 

Tab. 3 - Typical discharge coefficients for windows under buoyancy-driven ventilation (Brandan and Espinosa, 2018). 


REPORT 

not its frequency. The windows 

opening could be continuous, 

for a time τmin, or it could be 

repeated (series of openings and 

subsequent closures, so that the 

sum of the times in which the 

windows are open is equal to 

τmin). 

A useful criterion for deciding 

the frequency of windows 

openings is to create cycles of 

opening and subsequent closing 

based on the complete exchange 

of the air volume. That is, 

the windows are opened and, 

once the volume of air in the 

classroom has been completely 

replaced, they are closed. It is 

then possible to calculate the 

number of times (n) that the 

window should be kept open 

in the reference time interval, 

and the duration of each single 

opening (τop), according to the 

following equations: 

τop = V/ǬW ; n = τmin/τop 

(4) 

where V is the room volume 

(m3). Obviously, the strategy 

described by equations 4 can be 

applied only if n>1. 

Results and discussion 

Effect of occupancy 

The number of people in a 

room affects the infection 

probability (Fantozzi et al., 

2022). To study its impact on 

window opening time, typical 

characteristics of naturally 

ventilated classrooms were 

considered. Four windows, 

each 2.0 m x 2.2 m, with a 

total area of 10.6 m2 and 

a discharge coefficient of 

0.13 were assumed, to meet 

hygienic standards. The indoor 

temperature was set at 20°C 

for comfort, while the outdoor 

temperature was 10°C for 

comparison. 

Figure 1 shows the increase 

in window opening time 

Fig. 1 - Windows’ opening time to ensure the required ventilation rate as a function 

of the number of people. Legend: opening time for kindergartens (red line), elementary 

schools (blue line), middle schools (green line), and high schools (yellow line). 

based on the number of people 

and the educational stage. 

Ventilation rates, determined 

by standards (UNI 10339, 

1995), vary for different stages. 

Kindergarten requires 11-18 

minutes of opening per hour, 

elementary school 14-21 

minutes, middle school 16-27 

minutes, and high school 19-32 

minutes. 

The opening time significantly 

rises with the number of 

occupants, and for high 

schools, it can reach half of 

the class hour. This increase 

has implications for energy 

consumption and thermal 

discomfort, particularly for 

students close to windows 

experiencing cold air currents. 

Effect of window area and type 

Figure 2 demonstrates how 

window area and type directly 

influence the natural ventilation 

rate (ǬW) and subsequently 

the required opening time. 

To evaluate this effect across 

different educational stages, 

25 students in a room with an 

indoor temperature of 20°C 

and an outdoor temperature of 

10°C were considered. Standard 

windows, 1.2 m in length and 

2.2 m in height were used. 

For the window area (Figure 2, 

left), having only one window is 

insufficient to meet ventilation 

requirements, resulting in 

opening times exceeding 60 

minutes. Increasing the window 

area reduces the required 

opening time, reaching a 

minimum of 6 minutes for 

kindergartens. However, 

very large window areas are 

impractical for classrooms. 

For different window types 

Fig. 2 - Windows’ opening time to ensure the required ventilation rate as a function of the window 

area (left) and discharge coefficient (right). Legend: opening time for kindergartens (red line), elementary 

schools (blue line), middle schools (green line), and high schools (yellow line) 


REPORT 

Fig. 3 - Windows’ opening time to ensure the required ventilation rate as a function of ΔT. 

Legend: opening time for kindergartens (red line), elementary schools (blue line), middle schools 

(green line), and high schools (yellow line). 

(Figure 2, right), increasing 

the discharge coefficient 

(Cd) significantly reduces the 

opening time, highlighting its 

importance on the ventilation 

rate. Although precise values of 

Cd can be obtained for specific 

cases, using recommended 

values in this study provides 

valuable information for 

building practitioners. 

Effect of temperature difference 

Figure 3 illustrates the 

investigation of temperature 

difference's impact on opening 

time. The ventilation rate 

was calculated for spaces with 

one opening on one wall, and 

the temperature difference 

plays a crucial role, especially 

in the absence of wind. The 

study assumed 25 people in 

the room, the same window 

characteristics as in the previous 

case (four windows 1.2 x 2.2 

m, Cd=0.13), and varied the 

outdoor temperature from 0°C 

to 19°C. 

The relationship between 

window opening time and 

temperature difference (ΔT) is 

shown as a curve in Figure 3: as 

ΔT increases, the opening time 

decreases. However, for low 

ΔT, the opening time exceeds 

one hour for middle and 

high schools, indicating that 

even keeping windows open 

throughout the lecture won't 

meet the required ventilation 

rate. 

The difference between indoor 

and outdoor temperatures is 

influenced by the climate zone, 

season, and indoor comfort 

requirements. Setting outdoor 

temperatures is not possible, 

and indoor conditions must 

remain within an acceptable 

range for comfort. Studies 

on adapting to the thermal 

environment during the 

pandemic period are needed 

to understand occupants' 

needs. Higher ΔT reduces 

opening time but may lead 

to discomfort and increased 

energy consumption, especially 

with high indoor-outdoor 

temperature differences. 

Conclusions 

During the COVID-19 

pandemic, the need to maintain 

indoor health was crucial. 

However, guidelines for 

naturally ventilated buildings 

lack precision in controlling 

ventilation rates. This study 

offers a method to determine 

window opening time and 

frequency based on window 

characteristics, indoor and 

outdoor conditions, and room 

occupancy. Different ventilation 

rates, aligned with national 

and international standards for 

building types or educational 

stages, were considered to 

reduce infection probability. 

The results show that opening 

time increases with room 

surface and occupancy, while it 

decreases with larger window 

areas and favourable discharge 

coefficients based on window 

types. In the absence of wind, 

opening time decreases as the 

indoor-outdoor temperature 

difference increases. 

Balancing increased ventilation 

rates with energy consumption 

control and ensuring comfort 

remains a crucial area 

for further investigation. 

Nonetheless, this study provides 

a useful tool to enhance 

indoor environmental quality, 

promoting healthier buildings 

not only during but also postpandemic 

periods, guiding 

building practitioners towards 

improved awareness of indoor 

health. 


REPORT 

REFERENCES 

Awada, M. et al. (2021) ‘Ten questions 

concerning occupant health in 

buildings during normal operations 

and extreme events including the 

COVID-19 pandemic’, Building 

and Environment, 188, p. 107480. 

Available at: https://doi.org/10.1016/j. 

buildenv.2020.107480. 

Azuma, K. et al. (2020) ‘Environmental 

factors involved in SARS-CoV-2 

transmission: effect and role of indoor 

environmental quality in the strategy 

for COVID-19 infection control’, 

Environmental Health and Preventive 

Medicine, 25(1), p. 66. Available at: 

https://doi.org/10.1186/s12199-020- 

00904-2. 

Bluyssen, P.M. (2020) ‘Towards an 

integrated analysis of the indoor 

environmental factors and its effects 

on occupants’, Intelligent Buildings 

International, 12(3), pp. 199–207. 

Available at: https://doi.org/10.1080/17 

508975.2019.1599318. 

Brandan, M.A.M. and Espinosa, F.A.D. 

(2018) ‘Modelling natural ventilation in 

early and late design stages: developing 

the right simulation workflow with 

the right inputs’, in. 2018 Building 

Performance Analysis Conference and 

SimBuild co-organized by ASHRAE and 

IBPSA-USA, Chicago, USA, pp. 242– 

249. Available at: https://www.ashrae. 

org/File%20Library/Conferences/ 

Specialty%20Conferences/2018%20 

Building%20Performance%20 

Analysis%20Conference%20and%20 

SimBuild/Papers/C034.pdf. 

BS 5925 (1991) Code of practice for 

ventilation principles and designing for 

natural ventilation. 

Dai, H. and Zhao, B. (2020) 

‘Association of the infection probability 

of COVID-19 with ventilation rates in 

confined spaces’, Building Simulation, 

13(6), pp. 1321–1327. Available at: 

https://doi.org/10.1007/s12273-020- 

0703-5. 

DM 18-12-1975 (1975) ‘Norme 

tecniche aggiornate relative all’edilizia 

scolastica, ivi compresi gli indici 

di funzionalità didattica, edilizia 

ed urbanistica, da osservarsi nella 

esecuzione di opere di edilizia 

scolastica’. 

ECDC (2020) ‘Heating, ventilation 

and air-conditioning systems in the 

context of COVID-19: first update’. 

Available at: https://www.ecdc.europa. 

eu/sites/default/files/documents/ 

Heating-ventilation-air-conditioning- 

systems-in-the-context-of-COVID-19- 

first-update.pdf. 

Fantozzi, F. et al. (2022) ‘Monitoring 

CO2 concentration to control the 

infection probability due to airborne 

transmission in naturally ventilated 

university classrooms’, Architectural 

Science Review, pp. 1–13. Available at: 

https://doi.org/10.1080/00038628.202 

2.2080637. 

Hamner, L. et al. (2020) ‘High 

SARS-CoV-2 Attack Rate Following 

Exposure at a Choir Practice — Skagit 

County, Washington, March 2020’, 

Morbidity and Mortality Weekly Report, 

69(19), pp. 606–610. Available at: 

http://dx.doi.org/10.15585/mmwr. 

mm6919e6external icon. 

Li, Y. et al. (2020) ‘Evidence for 

probable aerosol transmission 

of SARS-CoV-2 in a poorly 

ventilated restaurant’, medRxiv, p. 

2020.04.16.20067728. Available at: 

https://doi.org/10.1101/2020.04.16.2 

0067728. 

Lipinski, T. et al. (2020) ‘Review of 

ventilation strategies to reduce the 

risk of disease transmission in high 

occupancy buildings’, International 

Journal of Thermofluids, 7–8, p. 

100045. Available at: https://doi. 

org/10.1016/j.ijft.2020.100045. 

Lu, J. et al. (2020) ‘COVID-19 

Outbreak Associated with Air 

Conditioning in Restaurant, 

Guangzhou, China, 2020’, Emerging 

Infectious Disease journal, 26(7), 

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airborne transmission of COVID-19 

indoors be minimised?’, Environment 

International, 142, p. 105832. 


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systematic review of possible airborne 

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(SARS-CoV-2) in the indoor air 

environment’, Environmental Research, 

193, p. 110612. Available at: https:// 

doi.org/10.1016/j.envres.2020.110612. 

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Disease Outbreak in Call Center, 

South Korea’, Emerging Infectious 

Disease journal, 26(8), p. 1666. 

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‘Exhaled CO2 as COVID-19 infection 

risk proxy for different indoor 

environments and activities’, medRxiv, 

p. 2020.09.09.20191676. Available at: 

https://doi.org/10.1101/2020.09.09.2 

0191676. 

UNI 10339 (1995) Impianti aeraulici 

al fini di benessere. Generalità, 

classificazione e requisiti. Regole per la 

richiesta d’offerta, l’offerta, l’ordine e la 

fornitura. 

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transmission and control of 

SARS-Cov-2’, Science of The Total 

Environment, 731, p. 139178. 


scitotenv.2020.139178. 

KEYWORDS 

Indoor Air Quality; Natural ventilation; 

School buildings; CO- 

VID-19 

ABSTRACT 

The study presents a method to 

determine window opening time 

and frequency, considering window 

characteristics, indoor and outdoor 

conditions, and room occupancy. 

Results reveal that opening time 

correlates with room surface and 

occupancy but diminishes with larger 

window areas and favourable discharge 

coefficients based on window types. 

Additionally, in windless conditions, 

opening time decreases as the indooroutdoor 

temperature difference 

increases. 

AUTHOR 

Giulia Lamberti 

giulia.lamberti@phd.unipi.it 

Giacomo Salvadori 

giacomo.salvadori@unipi.it 

University of Pisa, School of Engineering, 

Largo Lucio Lazzarino 1, 56122 

Pisa, Italy 


REPORT 

Building Energy resilience: the role of 

energy management systems, smart devices 

and optimal energy control techniques 

By G.Chantzis, A.M.Papadopoulos 

Energy Management Systems 

The first step on the road to 

energy resilience is the selection 

of the appropriate energy 

management system. There 

are two main types of energy 

management systems the Centralized 

Energy Management 

systems and the Decentralized 

Energy Management systems. 

The Centralized Energy Management 

systems present advantages 

considering economic and 

political aspects. They are also 

found to have better response 

during lockdown in urban areas 

and in islanding modes. Still, 

they come with low levels of 

flexibility and usually require 

high levels of organization to 

operate. On the other hand, the 

Decentralized Energy Management 

systems are characterized 

by increased flexibility and enhanced 

environmental management 

capabilities. They are also 

considered to respond better in 

areas with frequent extreme weather 

events. When permissible 

constraints allow, a centralized 

energy system is chosen to better 

deal with crisis scenarios, 

such as pandemic conditions. 

[1] 

Fig. 1 - Features and technologies of flexible and resilient buildings [1]. 

This contribution 

describes the creation 

of a support system for 

the operational activities 

of detailed perimeter of 

wooded areas attacked by 

insects/pathogens and/or 

forest fires, by means of 

a SAPR (Airborne System 

with Remote Piloting) with 

definition of the trajectory 

in automation via real-time 

recognition assisted by 

an airborne sensor and 

satellite system. 

Flexible and resilient built environment 

Resilient buildings characteristics 

Resilient buildings have some 

special characteristics. They 

have installed HVAC systems 

that adapt to the needs of users 

and indoor and ambient conditions. 

They are capable of 

exploiting the streams of data 

flowing through them in real 

time. They are efficient, smart 

and flexible. 

A two-way communication 

between the grid and building- 

HVAC systems-occupants is 

established. They are ready for 

disruptions to power and water 

services (energy efficiency, energy 

and water harvesting). They 

manage use of services with the 

target of energy consumption, 

cost and CO2 emissions reduction, 

while at the same time 

maintaining comfort conditions. 

On the road to smart, flexible 

and resilient buildings there is 

a need of implementing smart 

metering, IOT and building 

automation and control technologies. 

Metering technologies 

will help harvesting historical 

weather, energy consumption 

and RES production data. Then 

those data are fed to the core 

IT modules that undertake to 


REPORT 

utilize the historical data and to 

produce demand, production 

and weather forecasts. At the 

same time by utilizing data of 

energy prices or carbon dioxide 

intensity they manage to produce 

the optimal control strategy 

which will be applied to the building 

energy systems through 

the Building Automation Control 

system. 

Smart metering 

As we mentioned above, a vital 

step on the road to smart and 

resilient buildings is the collection 

of historical data. The data 

is collected by smart meters that 

are installed in the buildings 

and their systems. 

Energy metering solutions 

To receive data about energy 

consumption we use energy meters. 

There are DIN Rail energy 

meters can be installed in the 

electrical panels of buildings 

and can measure various parameters 

such as voltage, current, 

power and energy. Some meters 

such as pulse meters require also 

the installation of a pulse reader 

which is responsible for translating 

and sending the measured 

data via Wi-Fi or other communication 

protocols. Some meters 

of this type have the ability to 

measure multiple loads and 

multiple parameters. The communication 

and data sending 

methods vary (WIFI, Ethernet, 

GSM/GPRS, etc.). In addition, 

there are plug energy meters 

which can only measure plug 

loads and are non-configurable. 

Finally, there are central smart 

meters which most utilities 

plan to install in new but also 

existing electrical installations. 

Those meters are capable of 

measuring multiple electrical 

properties, support most communication 

protocols and have 

configurable parameters. 

Fig. 2 - Energy efficiency – Resilience Nexus [2]. 

Fig. 3 - The roadmap to smart, flexible and resilient buildings [3]. 

Fig. 4 - Energy metering solutions. 

Indoor conditions & Weather 

metering 

There are mainly two types of 

sensors for indoor environment, 

temperature sensors with probe 

(a) and indoor air quality sensors 

(b) which are capable of measuring 

multiple parameters (temperature, 

humidity, CO2, etc.). 

To harvest ambient weather 

data, smart weather stations are 

used (c). Measured parameters 

include wind direction and 

speed, outdoor temperature and 

humidity, rainfall, solar and UV 

radiation. 


REPORT 

Fig. 5 - Indoor conditions and weather metering solutions. 

The sensors support sending 

the collected data, at a configurable 

frequency, through 

LORA, Bluetooth, Wi-Fi, etc. 

Special attention should be given 

while selecting the number 

and position of smart sensors 

to ensure proper HVAC operation, 

and indoor comfort. 

Control techniques 

There are two main types of 

control Rule-Based Control 

and Model Predictive control. 

Rule-Based Control is a heuristic 

technique that monitors the 

status of a parameter, for example 

temperature, and sets value 

limitations for it. The controlled 

system responds changing 

its function according to a predefined 

strategy [4], [5]. 

Model Predictive Control is 

a more complex method. It 

requires modelling a building 

and forecasting its energy behavior. 

The most efficient energy 

management strategy, results 

from solving an optimization 

problem [4], [5]. 

The existence of controllers installed 

in the energy system that 

we wish to control is required. 

The output of the control strategy 

is used as input of the controller. 

The final control is done 

through temperature or power 

regulation or by changing the 

operating profile of the system. 

Resilient building example 

The conservatory in Thessaloniki 

dates to the 1980’s. It was 

refurbished for climate adaptation, 

to deal mainly with increasing 

cooling demands in the 

summer and heating demands 

in the winter. The installed PVs 

contribute to further reduction 

of electricity consumption and 

CO2 emissions. 

Energy efficiency measures include: 

• External thermal insulation 

for heating loads reduction. 

• Installation of ventilated facades 

to further reduced energy 

consumption for heating, but 

mainly to reduce cooling loads. 

• Phase change materials in 

ventilated façade to further 

reduce electricity consumption 

for cooling. 

The contribution of photovoltaics 

was significant, covering 

up to 10% of total electricity 

consumption of the building. 

Temperature and air quality 

sensors are installed to monitor 

thermal comfort conditions. 

Sensors in the façade cavity 

Fig. 6 - The conservatory in Thessaloniki. 

Tab. 1 - Control types, sensors and measured data implementation. 


Fig. 7 - Installation of temperature sensor probes inside the ventilated façade cavity. 

enable monitoring the thermal 

behavior of the facades and 

automation and control of the 

installed mechanical ventilation 

in the cavity. 

Figure 7 depicts the installation 

of sensors inside the 

façade cavity. The measured 

temperatures in each layer of 

the ventilated façade are shown 

in figure 8. The effect of the 

ventilated façade is evident for 

both the gypsum board and the 

PCM section. In the gypsum 

board section it is observed that 

for an external temperature of 

32°C the temperature inside 

the cavity reaches 30°C and the 

indoor temperature is kept at 

27°C. We notice that a significant 

improvement is achieved 

in the indoor conditions on a 

typical summer day, especially 

if we compare to the previous 

situation, where for an outdoor 

temperature of 30.5°C, the indoor 

temperature reaches 29°C. 

The PCM seems to further improve 

the situation since for an 

ambient temperature of 33°C 

the indoor temperature is kept 

at 26°C. Another measure to 

further improve the function of 

the ventilated façades is the installation 

of mechanical ventilation 

in some air cavities. Thus, 

some zones of natural and some 

zones of mechanical ventilation 

Fig. 8 - Temperature distribution of ventilated façade sections. 

Fig. 9 - Mechanical ventilation automation and control system. 

are created. The mechanical 

ventilation is controlled by automation 

and control systems 

illustrated in figure 9. The automation 

utilizes the measured 

temperature at various points 

of the façade and differentially 

controls the operation of the 

installed crossflow fans. 

In the case of ventilated facades, 

the short-term comparison of 

the results between natural and 

mechanical ventilation from the 

recorded temperature difference 

is relatively small. However mechanical 

ventilation was found 

to provide better temperature 

stabilization. 

The resilient building example 

is part of the project Intelligent

REPORT 

Facades for Nearly Zero 

Energy Buildings, co-financed 

by the European Union 

and Greek national funds 

through the action Competitiveness, 

Entrepreneurship 

and Innovation, under the 

call RESEARCH–CREA- 

TE-INNOVATE (project 

code:Τ1EDK-02045) [6]. 

Project team of PEDL- 

AUTh: 

• Prof. Agis M. Papadopoulos 

• Dr. Effrosyni Giama, Mechanical 

Engineer, M.Sc. 

• Dr. Panagiota Antoniadou, 

Civil Engineer, M.Sc. 

• Dr. Elli Kyriaki, Mechanical 

Engineer 

• Maria Symeonidou, Mechanical 

Engineer, M.Sc. 

• Georgios Chantzis, Mechanical 

Engineer 

REFERENCES 

[1] A. Zafeiriou, G. Chantzis, and A. Papadopoulos, “COVID-19 Challenges, 

Opportunities - A Valuable Lesson for the Future Sustainable Development of 

Energy Management,” Chem. Eng. Trans., vol. 94, pp. 1201–1206, 2022, doi: 

10.3303/CET2294200. 

[2] “U.S. Department of Energy.” https://www.energy.gov/ (accessed Aug. 31, 

2023). 

[3] “Smart Energy Management — Core.” https://www.core-innovation.com/ 

smart-energy-management (accessed Sep. 01, 2023). 

[4] T. Q. Péan, J. Salom, and R. Costa-Castelló, “Review of control strategies for 

improving the energy flexibility provided by heat pump systems in buildings,” 

J. Process Control, pp. 35–49, 2019, doi: 10.1016/j.jprocont.2018.03.006. 

[5] J. Clauß, C. Finck, P. Vogler-Finck, and P. Beagon, “Control strategies for 

building energy systems to unlock demand side flexibility-A review.” 

[6] “IF-ZEB – Intelligent Facades for Nearly Zero Energy Buildings.” https:// 

ifzeb.gr/ (accessed Sep. 01, 2023). 

KEYWORDS 

Energy resilience; management systems; smart device; optimal energy 

control techniques 

ABSTRACT 

This contribution describes the creation of a support system for the operational 

activities of detailed perimeter of wooded areas attacked by insects/pathogens 

and/or forest fires, by means of a SAPR (Airborne System with Remote Piloting) 

with definition of the trajectory in automation via real-time recognition 

assisted by an airborne sensor and satellite system. 

AUTHOR 

A.M.Papadopoulos 

G.Chantzis 

chantzis@meng.auth.gr 

Process Equipment Design Laboratory, Dept. of Mechanical Engineering, 

Aristotle University of Thessaloniki 

GR-54124 Thessaloniki 


SPACE 

Jamming against GNSS receivers: 

attacks and mitigation techniques 

by Marco Lisi 

Jamming interference to GNSS 

receivers is a growing threat as 

more systems and devices rely on 

GNSS for Positioning, Navigation, 

and timing (PNT). The European 

GNSS Agency (GSA today EAASA) 

estimated there were 6.4 billion 

GNSS-enabled devices in use 

worldwide in 2019, and forecasts 

this will rise to 9.5 billion by 2029 — 

equivalent to 1.1 devices for every 

person in the world. 

Figure 1: jamming of GPS signals detected by Hawkeye 360 satellite in Ukraine 

Many of GNSS 

receivers are used in 

safety-critical and 

liability-critical systems. In 

the US, 13 of the 16 sectors of 

critical national infrastructure 

rely on GNSS, according to 

the Department of Homeland 

Security. The more the world 

relies on GNSS, the greater 

the threat presented by RF 

interference — whether 

intentional GNSS frequency 

jamming by malicious 

or mischievous actors or 

unintentional interference from 

radio transmissions in bands 

close to the GNSS frequencies. 

The impact of jamming is 

being felt worldwide. In the 

maritime sector, jamming 

traced to the Syrian conflict 

has been disrupting shipping 

in the Eastern Mediterranean 

since 2018. In aviation, the 

number of reports of suspected 

GPS signal jamming made 

to NASA’s Aviation Safety 

Reporting System (ASRS) has 

been steadily rising. In road 

transport and logistics, illegal 

jammers are widely used to 

disrupt employer telematics, as 

well as for criminal activities. 

The war in Ukraine has 

shown in all its evidence that 

security concerns must be 

extended to all space assets, 

since cyberattacks, often 

combined with physical 

ones, target all digital 

infrastructures, on ground and 

in space, well knowing their 

interdependencies. 

As far as satellite positioning 

systems are concerned, 

European aviation authorities 

reported a sudden increase 

of interferences against GPS 

signals in places as far away as 

Finland, the Mediterranean 

and Iraq since Russia invaded 

Ukraine, forcing aircraft to 

reroute or change destination 

(Fig. 1). 

Definition and impact 

of GNSS jamming 

GNSS (Global Navigation 

Satellite System) jamming refers 

to intentional interference 

with the signals of satellite 

navigation systems like GPS 

(Global Positioning System) 

or Galileo. These jamming 

techniques aim to disrupt or 

disable the operation of GNSS 

receivers, preventing accurate 

positioning, navigation, and 

timing information. 

GNSS jamming can have 

various effects depending 

on the severity and duration 

of the interference. Some 

common effects include 


SPACE 

degraded accuracy in 

positioning, navigation, and 

timing information, loss of 

signal lock or signal dropouts, 

incorrect positioning or velocity 

estimates, and increased 

vulnerability to spoofing 

attacks. These effects can impact 

critical infrastructure, such as 

aviation, maritime navigation, 

transportation systems, and 

military operations. 

The effectiveness of jamming 

signals can vary depending on 

factors such as the power level 

of the jammer, the proximity 

to the targeted GNSS receivers, 

and the specific techniques 

employed. 

Figure 2, however, gives an 

immediate feeling of how 

destructive jamming can be. 

A very simple and inexpensive 

jammer transmitting a tiny 10 

mW signal can cause the loss of 

the GPS L1 signal in receivers 

in a radius of 1 km and prevent 

its acquisition in a radius of 10 

km. 

GNSS Jamming Techniques 

Before addressing the various 

GNSS jamming techniques 

most adopted, it is worth 

looking at the present allocation 

of the RF spectrum to the main 

GNSS systems (fig.3) 

As state-of-the-art receivers, 

even for commercial and 

consumer applications (e.g., 

smartphones), operates in dual 

frequency mode (e.g.: L1 and 

L2 for GPS) and even in multifrequency, 

multi-constellation 

mode, it is evident that to 

be effective a GNSS jammer 

should operate over a quite large 

spectrum, generating multiple 

interfering signals or, otherwise, 

very broadband signals. 

Depending on the 

sophistication of the jammer 

and its target receiver, various 

jamming techniques are 

adopted: 

Figure 2: Susceptibility to Interference/Jamming 

• Continuous Wave (CW) 

Jamming: this technique 

involves transmitting a 

continuous, high-power radio 

signal on the frequency band 

used by GNSS satellites. The 

jammer emits a powerful 

and persistent signal that 

can overpower the relatively 

weak GNSS signals, making 

it difficult for receivers to 

accurately acquire and track 

satellite signals; 

• Narrowband Jamming: in this 

technique, jammers transmit 

signals that occupy a narrow 

frequency band within the 

GNSS frequency range. The 

jammer's signal may have 

similar characteristics to the 

GNSS signals, making it harder 

for receivers to distinguish 

between the genuine satellite 

Figure 3: GNSS signals spectrum allocation 

signals and the jamming signals; 

• Pulsed Jamming, involving 

the transmission of 

intermittent bursts of jamming 

signals. These bursts can be 

synchronized with the GNSS 

signal structure, mimicking the 

normal GNSS transmissions; 

• Broadband Jamming: 

broadband jammers transmit a 

wide range of frequencies that 

encompass the GNSS frequency 

bands. These jammers can 

interfere with multiple GNSS 

systems simultaneously, such as 

GPS, GLONASS, Galileo, and 

BeiDou, increasing the chances 

of disrupting GNSS-based 

positioning and navigation. 

GNSS Jamming Devices 

GNSS jammers come in 

various forms, ranging from 


SPACE 

Figure 4: Commercial GNSS (GPS) Jammers 

small portable devices to more 

powerful and sophisticated 

equipment. Portable jammers 

can have a limited range and 

may be used for personal 

privacy reasons or illicit 

activities. Higher-power 

jammers can cover larger areas 

and have a more significant 

impact on GNSS signals. 

There is a huge selection of 

commercial jammers available 

on the Internet for less than 

$100 (fig.4). 

What used to be expensive 

military technology a few 

decades ago is nowadays easily 

available, rather cheap, offthe-shelf 

products. Studies of 

many of these jammers have 

been performed, and they were 

categorized into the following 

three categories: 

a. Jammers that are designed to 

plug into a 12 Volt car cigarette 

lighter socket power supply 

outlet. This category of jammers 

usually has rather low transmit 

power(below 100 mW) and 

the possibility to connect an 

external antenna. An example of 

a jammer from this category is 

shown in Figure 5. 

b. Jammers that have an 

internal battery and an external 

antenna connected via an 

SMA connector. Some of these 

jammers transmit at both the 

L1 and L2 frequency bands and 

additional frequency bands for 

other types of communication 

(e.g. WiFi and GSM). The 

transmit power is up to 1 W. 

An example of a jammer in this 

category is shown in Figure 6. 

c. Jammers disguised as 

harmless electronic devices, 

such as cell phones. The 

jammers in this group have 

internal batteries but no 

possibility of connecting an 

external antenna. All jammers 

in this group transmit power 

in L1, L2, and additional 

frequency bands, with up to 

100 mW power. 

Commercially available 

jammers often transmit chirplike 

signals (i.e. frequency 

modulated continuous waves 

(FMCWs)) and operate across 

the band 1565-1585 MHz. 

The jammer changes frequency 

rapidly over time and 

sweeps across the frequency, 

overpowering the GNSS signal. 

The use of frequency sweeping 

means that a narrowband 

jammer can be used to 

overpower a frequency range 

which would otherwise have 

required a broadband jammer. 

In conclusion, a recent and 

more sophisticated approach to 

jamming, named systematic or 

systemic jamming, is presented. 

A systematic GNSS jammer 

refers to a jamming device 

designed and deployed with 

Figure 5: car GNSS jammer (10 US$ on eBay) 

Figure 6: dual frequency, medium power jammer 


SPACE 

a methodical or organized 

approach to disrupt Global 

Navigation Satellite System 

(GNSS) signals intentionally. 

The concept of systematic 

jamming is the following: 

a simple jammer might 

be equipped with some 

information of the GNSS 

signals and can use this to 

perform more sophisticated 

jamming. For example, it is 

suggested that the jammer may 

be equipped with a simple 

low-cost commercial GNSS 

receiver, such that it would then 

have access to accurate position 

and time, and to satellite 

ephemerides. 

With some very basic 

integration of this information, 

it might be possible to trigger 

short and sparse bursts of 

interference at specific times, 

such as to deny GNSS to a 

nearby receiver, and to do so 

with a very low average power. 

In this manner, a receiver might 

be unable to: reliably detect that 

a jamming attack was ongoing; 

to effectively mitigate the 

jamming attack or to identify or 

localize the jamming source. 

Figure 7 shows a possible block 

diagram of a systemic GNSS 

receiver. 

Just for completeness, Figure 

8 shows some very high-power 

military jammers, like those 

utilized in these days in the 

Ukrainian war. 

Depending on the type and 

sophistication of the jammer 

equipment used, the effects of 

jamming on the GNSS receiver 

can be of basically three types: 

Figure 7: Systematic GNSS jammer 

precision (DOP) value (often 

lower-elevation signals are 

affected first); 

• Complete loss of tracking of 

GNSS signals and saturation of 

the receiver front end, meaning 

the receiver will need to reacquire 

the signals. 

GNSS Anti-Jamming 

Techniques 

To enhance the resilience 

of GNSS systems against 

jamming, researchers and 

engineers are developing antijamming 

techniques. These 

techniques include advanced 

signal processing algorithms, 

anti-spoofing techniques, secure 

signal authentication methods, 

and the use of more robust and 

interference-resistant receiver 

designs. 

Interferences that are sparse in 

the time or frequency domain 

are straightforward to mitigate: 

pulsed jammers can simply be 

blanked in the time domain, 

and stationary continuous 

wave (CW) jammers can 

be efficiently mitigated by 

applying a notch-filter. 

FMCW jammers (commonly 

perceived and referred to as 

chirps) are more difficult to 

mitigate, due to the constant 

transmission (i.e., no time 

domain blanking possible) 

and changing frequency (i.e., 

notch filter must be adaptive 

if used). This leads to the high 

complexity of the interference 

mitigation implementation, 

with often only limited 

mitigation success. 

GNSS anti-jamming techniques 

can be broadly classified into 

pre-correlation and postcorrelation 

methods based on 

when they are applied in the 

signal processing chain of a 

GNSS receiver (figure 9). 

• No effect at all, if the jammer 

is out of range, or its centre 

frequency is not aligned with 

the target GNSS frequency; 

• Degradation of GNSS signals; 

as the carrier-to-noise (C/ 

N0) ratio of received signals 

drops, affecting the dilution of 

Figure 8: Very High-Power, Military Vehicle-Mounted GPS Jammers 


SPACE 

Figure 9: Categorization of anti-jam techniques 

Figure 10: Principle of Spatial Nulling and Beamforming 

Figure 11: GPS Controlled Reception Pattern Antennas (CRPAs) with Anti-Jamming Capabilities 

These techniques aim to 

mitigate the effects of jamming 

signals before or after the 

correlation process, which is the 

fundamental step in extracting 

timing and positioning 

information from the received 

GNSS signals. 

Pre-correlation techniques 

can be implemented either 

at Rf or at IF/baseband, after 

analog-to-digital conversion. 

Post-correlation techniques 

necessarily operate at baseband. 

Both pre-correlation at 

baseband and post-correlation 

level mitigation techniques 

require access to raw GNSS 

signals and unless you are a 

GNSS receiver developer this is 

almost impossible. 

Among the Pre-correlation 

techniques it is worth 

mentioning the Automatic 

Gain Control Technique and 

the Antenna Nulling and 

Beamforming. 

The first is a very simple frontend 

level technique, operating 

at RF, based on the Automatic 

Gain Control (AGC)(either 

built into the external LNA 

or derived from the following 

GNSS receiver, if available), 

where the gain of a receiving 

GNSS antenna is automatically 

adjusted to prevent stronger 

jamming signals to reach 

the receiver. This technique 

provides some protection 

against jamming but not the 

continued operation under 

persistent jamming. 

The Antenna Nulling method 

involves using an array of 

antennas to create nulls in the 

direction of jamming sources, 

reducing the strength of the 

interfering signals before they 

enter the correlation process. 

These antennas are named 

Adaptive Antenna Arrays. 

By adjusting the phase and 

amplitude of the antenna 

elements, the receiver can create 


SPACE 

Figure 12: GPS Anti-Jamming Receiver (U-blox) 

Figure 13: effect of active notch filtering 

nulls in the direction of the 

jammer, reducing its impact 

on the received GNSS signals 

(Figure 10). 

In their practical and 

commercial implementation, 

nulling anti-jamming antennas 

are called Controlled Radiation 

Pattern Antennas (CRPA). 

CRPAs are widely used with 

digital beamforming techniques 

that quickly detect the jamming 

direction and nulls the 

reception in that direction to 

provide resilient anti-jamming 

capability to GNSS receivers. 

Figure 11 shows some 

commercially available CRPAs 

and their anti-jamming 

capabilities. 

Another pre-correlation 

technique widely adopted is 

based on active notch filtering. 

Notch filters can be configured 

in the receiver firmware or by 

the user to filter out signals in 

narrow frequency bands that are 

susceptible to interference. 

Figure 12 shows an 

implementation of active 

notch filtering in the form 

of digital filters for the “I” 

and “Q” components of the 

signal, before correlation and 

processing. 

Figure 13 shows the effect of 

digital notch filtering on the 

GNSS signal (GPS L2 in this 

case). 

KEYWORDS 

GNSS; jamming; anti-jamming 

ABSTRACT 

Attacks and mitigation techniques for the jamming 

against GNSS receivers are described according 

to the fact that many of GNSS receivers are 

used in safety-critical and liability-critical systems. 

AUTHOR 

Marco Lisi 

ingmarcolisi@gmail.com 

Member of the Italian Space Agency Board 

of Directors 


MERCATO 

CONCLUSION OF REMOT PROJECT 

Over the last decades global navigation satellite systems 

(GNSS) seem to have infiltrated the everyday life 

of millions of people all over the world and stopped 

being something unusual. The satellite receivers can be 

found in most smartphones, smart watches and even 

key-holders. However, new GNSS applications are still 

being developed, and some of them touch scientific 

fields not directly related to navigation and geodesy. 

One of such fields is human motion science, a branch 

of biomechanics studying kinematics of human movement. 

There have been major advances in this sphere 

with the availability of modern equipment such as photometric 

motion capture systems. However, a precise 

and accurate recording using such systems needs a very 

strictly defined, highly deterministic environment. 

This can be provided in a laboratory, but what about 

studying motion of athletes who perform long-distance 

runs such as marathon or even a few kilometers? Even 

а relatively small stadium cannot be fully covered with 

a photometric system. But for sports scientists it is extremely 

relevant to study how the movement parameters 

of a runner (such as length, width and frequency 

of his steps) change over long periods of time. Is there 

a way to do it in the lab? Of course, you can put the 

subject on a treadmill or make him run in circles, but 

this will be quite different from an actual outdoor training. 

The only way to study this difference is to find a 

way to study track the movement of an athlete in a real 

out-of-the-lab environment. 

This summer REMOT (Real Environment MOtion 

Tracker), a project dedicated specifically to this problem, 

met its conclusion. Financed by EUSPA, it was 

conducted by two companies: Stonex, responsible for 

the hardware and firmware part, and Gter, responsible 

for the software and processing part. 

The main idea of the project was to use recording devices 

consisting of a GNSS receiver integrated with 

an IMU (inertial measurement unit). IMU provides a 

more frequent data but is highly subject to noise, drift, 

and error accumulation related to double integration. 

GNSS part generally has a lower frequency and precision, 

but it does not accumulate error. Hence, the 

combination of these two sensors allows to receive a 

more accurate solution. 

The sensor configuration includes only three devices: 

one on the head and two on the feet. That is a minimum 

number to get a precise estimation of step parameters. 

In principle, two sensors on the feet would be 

enough, but the satellite data they receive tend to be 

noisier, and in such cases the receiver on the head can 

be used to get a more precise solution using a movingbase 

approach (the head receiver is treated like a moving 

base station). 

The sensors are worn by the subject using a headband 

and shoelace clamps, so that they are firmly fixed to 

the corresponding body part. Oscillations and impacts 

can lead to higher levels of noise in the IMU, problems 

with receiving GNSS data and general incorrectness of 

the mathematical model which leads to a wrong output. 

Also it is preferred to choose areas with clear sky 

for recording: trees and tall buildings forming so-called 

"urban canyons" obstruct the satellite signals. 

After the recording has been completed, the data processing 

stage begins. First, the GNSS data is being processed 

to obtain a positioning solution. The approach 

used by default is PPK (post-processing kinematics) 

which uses the corrections from a fixed base station 

and allows to obtain centimeter precision solutions. 

Then the IMU processing is performed. While GNSS 

receivers have a frequency of 10 Hz, the IMUs work at 

100 Hz frequency, allowing to get a more continuous 

positioning sequence. The processing is done with the 

aid of a Kalman filter, which updates the IMU positioning 

with already processed GNSS data and resets the 

velocity to zero when a static phase is detected. This 

way the error accumulation is reduced. 

Once the positioning solution is obtained, the second 

processing stage starts to extract the step parameters 

from the recorded data. Using basic geometric models, 

the step length, width and heading are computed. The 

tests have been performed in different locations on several 

subjects with different walking speed. 

The results have been presented to a EUSPA commission 

which deemed the test results quite impressive and 

declared the project to be successfully concluded. 

Nonetheless, the work is far from being finished. 

Despite the system being working in general, there is 

still a huge field for improvements: from more advanced 

hardware to more sophisticated processing techniques 

and software architectures. The project has been 

noted by movement scientists who are already making 

plans about research possible with this equipment, and 

this is a key and motivation for further development. 


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