esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
Below are the pseudo-codes and MATLAB(r) function files (ffa_mincon.m; factor_alpha.m; ffa_move.m; findlimits.m; escala.m; param_intrp.m) used for this research:  
**begin**  
Define number of fireflies in a subpopulation (m),  
number of variables (nv),  
and number of subpopulations (np)  
for sp = 1:np  
for i = 1:nn  
for v = 1:nnv  
Random generation of  
the design variable \(\mathbb{X}_{i,v,v}\) (CV)  
random scanning of  
the design variable \(\mathbb{X}_{i,v,v}\) (DV)  
Evaluate coordinate \(\mathbb{P}_{i,v,v}=\mathbb{X}_{i,v,v}/\mathbb{X}_{i}\)  
and for v  end for i end for sp for k = 1:k... (Main loop, Iteration k)  
Evaluate randomization parameter a(k/k...)  
and absorption coefficient \(\forall\)(k/k...)  
parfor sp = 1:np  
for i = 1:nn  
Evaluate \(\mathbb{X}_{i,v}=\mathbb{P}_{i,v}\) * \(\mathbb{X}\) (CV)  
Random rounding of the design \(\mathbb{X}_{i,v}\) (DV)  
Evaluate fitness function \(\mathbb{F}_{i,v}\)  end for i end for sp parfor sp = 1:np  
Find the current best design \(\mathbb{F}_{i,v}=\) \(\min\)([\(\mathbb{P}_{i,v}\mathbb{F}_{i,v}\mathbb{I}_{i,v}\mathbb{I}_{i,v}\mathbb{I}_{i,v} \mathbb{I}_{i,v}\mathbb{  
function [vdbest,ffbest,costobest,Gbest,NumEval,time]=...  ffa_mincon(pc,para,Gamma,Mm,perturb_sigma,venta,tip_var)  % Main function  % Input arguments:  % pc (logic) 1 == PCPPAM, 0 == PFAM  % para (vector) FA's parameters  % Gamma (vector) vector to store the gamma parameter  % Mm (vector) vector to store the m parameter  % perturb_sigma (logic) 1 == perturbs the best design  % 0 == does not perturb the best design  % venta (logic) 1 == enable graphic output  % 0 == disable graphic output  % tip_var (string) tip_var = 'discrete' or 'continuous'  %  % Output arguments:  % vdbest (vector) optimal variables in the np populations  % ffbest (escalar) optimal fitness function in np  % populations  % costobest (escalar) optimal objective (cost) function in  % the np populations  % Gbest (vector) constraints on optima of np populations  % NumEval (escalar) number evaluations objective function  % time (escalar) time spent finding design  
fich=uigetfile('*.dat','load analysis data file');  fichdat=lower(fich);  [titulo,identificador,NUD,COOR,MAT,PROP,CONECT,LOADN,LOADN_P,...  LOAND_C,LOADE,LOADE_P,LOADE_C,tipanl,nincr,estab,tol_f,tol_d,...  tol_fac,itermax,rig,_-,-,-,-,-,...  VAR,PROP_DIS,p_esp,beta,s_max,gdl,d_max,f_max,codigo,units,...  EST,FAC_EST,sec,metopt,cnnmod] =d_lecdat(fichdat);  
% Algorithm parameters  % ************************  n=para(1);  np=para(2);  MaxGeneration=para(3);  alpha=para(4);  tc=para(5);  speed=para(6);  
% Variable limits  % ************************  NVAR = size(VAR,1); % number of variables  FieldDR=zeros(2,NVAR);  for ilopo = 1:NVARFieldDR(1,ilopo)=VAR(ilopo,2);  FieldDR(2,ilopo)=VAR(ilopo,4);  end  vlb=FieldDR(1,:); % lower limit of variables  vub=FieldDR(2,:); % upper limit of variables  
if length(vlb) -=length(vub),  disp('!! : Incorrect variable limits!!
# Air Quality Assessment of a School in an Industrialized Area of Southern Italy
## 2 Materials and Methods  
### Area of Study  
The indoor and outdoor measurements were conducted in a secondary school located in Galatina (Lecce), a small city in Southern Italy (40\({}^{\circ}\)10\({}^{\prime}\)0.5\({}^{\prime\prime}\) N; 18\({}^{\circ}\)10\({}^{\prime}\)25\({}^{\prime\prime}\) E; 76 m a.s.l.) (Figure 1) with around 27,000 inhabitants. The school enrolls approximately 800 students per year, with an average of 25 students per classroom. School activities start at 8:00 and finish at 13:00. There is a 15\({}^{\prime}\) break in the middle of the day. Students sometimes have to go to another specific classroom to learn sports in the daily schedule. The area hosts a large cemetery and other small industries situated about 4-5 km southwest of the school. Measurements were made indoors in 2 classrooms (4B and 4E) and 2 bathrooms (B1, B2). In the classroom (4E), indoor measurements were made simultaneous to outdoor ones on the classroom balcony. The school planimetry, highlighting the 4B and 4E classroom positions, can be found in the Supplementary Materials as Figure S1.  
The sampling was carried out from 1 February to 12 March 2019. To study their influence on measurements, local meteorological parameters were investigated using weather data, such as temperature (\({}^{\circ}\)C), relative humidity (%), rain (mm), air pressure (hPa), wind speed (m/s), and direction, provided by the ground-based meteorological weather station (AQ\({}_{1}\) in Figure 1) located nearby the school (about 500 m) and managed by the regional environmental protection Agency of Apulia Region, Arpa Puglia ([[https://www.arpa.puglia.it](https://www.arpa.puglia.it)]([https://www.arpa.puglia.it](https://www.arpa.puglia.it)) (last access: 16 September 2021)). The sampling campaign was mainly characterized by clear skies, and only 4 rainy days occurred. Average daily temperatures ranged between 6.8 \({}^{\circ}\)C and 16.0 \({}^{\circ}\)C, with values of approximately 7.8 \({}^{\circ}\)C at 8.00 (school starting time) and 15.4 \({}^{\circ}\)C at 13.00 (school finishing time). Relative humidity varied between 42% and 84%, while wind speed showed values between 0.4 and 5.0 m/s (see Supplementary Materials Figures S2-S4). The same station (AQ\({}_{1}\)) provided measurements of air pollutants NO2, PM2.5, PM10, and CO.
## 3 Results
### Factor Screening
#### 3.1.1 Semi-Normal Probability Diagram Method  
The normal or semi-normal graph method is a graphical analysis approach proposed and refined by [PERSON] [22]. The method involves plotting the estimated effect values on a normal or semi-normal probability plot. In the plot, the points corresponding to non-significant effects should be approximately arranged in a straight line, while the points corresponding to significant effects should be far away from this straight line. This is because, assuming that the error is normal, independent, and of the same variance, the effect estimators are mutually independent. The estimator with zero effect follows the same normal distribution, with an expectation of zero. Therefore, the observed value on the (semi) normal probability plot should be located on a straight line passing through the origin. For the estimator with a non-zero effect, its expectation is non-zero, and it should deviate from the line passing through the origin.  
Based on the data in Table 1 (Appendix A), the semi-normal probability plots for SEA, \(\omega\), and In are shown in Figure 9, and the factor screening results are presented in Table 2. While the graphical method has the advantage of intuition, it suffers from subjectivity. Thus, this study incorporates a numerical analysis method to assist with factor screening.  
Figure 9: Semi-normal probability diagram of three responses: (**a**) specific energy absorption; (**b**) angular velocity; (**c**) head cover displacement.
## 2 Materials and Methods
### Methods
In particular, the inclusion criteria were both genders, age below 65 years, self-declaration of no basic pathologies and no pregnancy, and particularly no symptoms with respect to the salivary glands and oral mucosa. Exclusion criteria were pregnant women, participants who complain of dry mouth or dry eyes, persons with oral lesions or other contact sensitivity, subjects suffering from autoimmune diseases, and individuals with acute or chronic use of medications known to cause oral dryness. The study was approved by the Ethical Committee of Dental Clinic, Faculty of Medicine, University of Novi Sad, and all participants gave their informed consent. The salivary samples were collected before chewing a gum with xylitol, and during chewing a gum (Orbit, Wrigley Jr. Company, Peoria, IL, USA) at specific times: after 1, 5, 10, 30 min. Salivary pH, dissolved O\({}_{2}\), and CO\({}_{2}\) were determined at baseline and after 1, 5, 10, and 30 min. The spitting method [24] was used for sample collection, and the measurements were done immediately after collecting the saliva in order to prevent O\({}_{2}\) and CO\({}_{2}\) exchange between sample and environment. The spitting method implied the accumulation of saliva in the floor of the mouth and spitting into the preweighed and graduated test tube every 30-60 s [25]. The samples were collected by spitting the saliva into the test tube, collecting the sample in a syringe, and then filling the chip with the syringe. When it comes to statistical analyses, the data were presented in the form of mean values and standard deviation. To evaluate how far data were from normality, the Shapiro-Wilk test was used. In cases when data were with disturbed, distribution normality, the significance of the difference between the examined research groups, was tested by the Wilcoxon signed-rank test, while for the data that exhibited normal distribution one way ANOVA and paired samples T-tests were used. The level of statistical significance was set at 5% (\(p<0.05\)). For all statistical calculations Jamovi software (version 0.9.2.8) was used.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 2 Recent Research Direction for D2D Communications
### Neighbor Discovery and Selection (NDS)  
NDS is a crucial design aspect in D2D networks, in which a device should discover its neighbor devices and select the best one for constructing the D2D link. There are two approaches of D2D ND, namely network-centric (NC) and device-centric (DC). In NC ND, the cellular network itself will discover the nearby devices, while in DC, the devices themselves will do the job. Both NC and DC have their advantages and disadvantages.  
Figure 2: Recent D2D research directions.  
However, in dense user scenario NC ND performs better than DC ND, and vice versa. Quick ND is preferred due to the limited battery capacity of the devices in addition to reducing the consumed overhead. In mmWave D2D, the problem of ND turns to be more significant due to the use of BT, which consumes high energy and overhead. To overcome this problem, the authors in [6] used out-of-band ND assistance, in which the wide coverage unlicensed \(\mu\)W band is used to assist the discovery of the mmWave devices and hence reducing the overhead and energy consumption.
## 3 Methodology
### Audio Clips and VGGVox  
In [1], the VGGVox model was first introduced as a modified VGG-M CNN architecture [30] aimed at speaker recognition through examination of spectral representations of audio clips. Given its extensive training with over 100,000 utterances by 7000+ distinct speakers, the observed excellent performance has been recognized as a state-of-the-art standard in recent research. Thus, the model is ideal for extrapolating speaker specific cues and discern multiple distinct speakers in an active speaker detection scenario.  
In terms of actual audio clips to be analyzed, they should be required to undergo minimal preprocessing only while still being able to progress through a machine learning model. In the past, many techniques have been proposed to obtain audio representations which achieve this goal such as the known Mel-frequency cepstral coefficients [31]. Despite their success, such techniques are noted to introduce increased overhead and require a manual tuning which somewhat invalidates the posterior model's positive performance. Therefore for each raw audio clip in our approach, a sliding Hamming window of width 25 ms was applied with a step of 10 ms in order to generate a respective narrowband spectrogram, as well as means and variances being normalized at each frequency bin of the spectrum. These operations were the single ones performed on the audio data, following VGGVox's preprocessing stage.
# Black Truffle Aroma Evaluation: SPME-GC-MS vs. Sensory Experts
## 2 Materials and Methods
### Volatile Organic Compounds Analysis by SPME-GC-MS  
The methodological approach was carried out according to [PERSON] et al. (2023) [22]. Briefly, a solid-phase microextraction (SPME) was used to extract the aromatic compounds. For this, a fused silica fiber coated with a 50/30 \(\upmu\)m layer of DVB/Car/PDMS from Supelco (Barcelona, Spain) was chosen. The samples (2 g of truffle) were placed in a 20 mL glass vial closed with a cap with septum PTFE/sil. After this, the vial was conditioned at 50 \({}^{\circ}\)C for 10 min. The fiber was then exposed to the headspace of the vial for 20 min. Analyses were carried out by duplicate.  
The volatile organic compounds (VOCs) profile of the different samples was analyzed via GC-MS using a gas chromatograph Agilent 6890 series coupled with a mass selective spectrometer detector 5973N series (Agilent Technologies, Santa Clara, CA, USA). This instrument was equipped with a capillary column HP-5 MS (Agilent Technologies, Santa Clara, CA, USA) of 30 m, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness and a flow of 1 mL/min with helium as the carrier gas. The injector temperature was 250 \({}^{\circ}\)C. The SPME fiber was injected and desorbed during the first 5 min of the running. The oven temperature was 45 \({}^{\circ}\)C, which was held for 2 min, 45-246 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and finally to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held for 4 min. The MS used the electron impact mode with an ionization potential of 70 eV and an ion source temperature of 230 \({}^{\circ}\)C. The interface temperature was 250 \({}^{\circ}\)C. The MS scanning was recorded in full-scan mode (35-350 \(m/z\)). MSD ChemStation (Agilent G1701 DA) software was used for controlling the GC-MS system.  
Peak identification of the VOCs was achieved via a comparison of the mass spectra with mass spectral data from the Wiley275 and NIST MS Search Program 2.0 libraries and via a comparison of previously reported retention indexes (RIs) with those calculated using an n-alkane series (C6-C20) (purity 99%) (C6, C7 Merck, C8-C20 Supelco, Merck KGaA, Darmstadt, Germany) under the same analysis conditions. Truffle composition in percentage was calculated according to the area of the peaks in total ion chromatogram (TIC) mode.
# HBIM: Background, Current Trends, and Future Prospects
## 4 Data Collection and Modeling  
The HBIM methodology, primarily based on BIM technology, can be considered an adaptation of the BIM approach to address the challenges presented by the unique characteristics of historical and heritage buildings, which often have more complex geometries than modern constructions. The peculiarities of these structures, along with the need for their preservation and structural safety assessment, make data collection one of the most critical aspects of the HBIM methodology. It is essential that the final model is both reliable and accurate yet simplified enough to avoid unnecessary increases in computational cost [48]. Also, the collected data should fit the specific requirements and applications of the BIM model. After all geometrical information is collected, it is crucial to supplement this information with non-graphical data. In terms of interoperability, this non-graphical information enables HBIM models to be exchanged between different software platforms. However, the format used must ensure both interoperability and compatibility between various systems and software [49]. Nevertheless, without an accurate and reliable geometrical model, these HBIM models will have limited practical value for exchange. As a result, methods for data collection and modeling are continuously being refined to improve accuracy and reduce computational cost [50] while simultaneously advancing interoperability through universal data formats.
###### Abstract  
To determine the optimization of aging time for improved antioxidant activity and bacteriostatic capacity of garlic during its aging, garlic produced in Yunlin region, Taiwan, was employed as the test material in an analysis of the allicin content, total phenol content, antioxidant activity, and bacteriostatic capacity of fresh and aged garlic extracts. Allicin content of the aging garlic decreased to a minor level, whereas total phenol content increased to 16.96 mg gallic acid equivalent (GAE)/mL after 35 days of the aging process. The results of antioxidant testing demonstrated favorable positive correlations among IC\({}_{50}\) of DPPH scavenging capacity, Trolox equivalent antioxidant activity, and superoxide dismutase activity for both the fresh and aged garlic extracts. The analytical results showed that aging of garlic at 70 \({}^{\circ}\)C and 85% relative humidity for 40 days substantially increased the quantity of phenolics, DPPH scavenging capacity, Trolox equivalent antioxidant activity, and superoxide dismutase activity and enhanced the antioxidant activity. The extracts exhibited higher bacteriostatic capacity against Gram-positive bacteria such as _Bacillus subtilis_ and _Staphylococcus aureus_ than against Gram-negative bacteria such as _Escherichia coli_ and _Pseudomonas aeruginosa_. In conclusion, black garlic aged under the optimum conditions exhibited favorable antioxidant activity and bacteriostatic ability.  
black garlic; allicin; total phenol content; antioxidant activity; bacteriostatic capacity  
10 February 2021  
3 March 2021  
Subscribed8 March 2021  
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**Copyright:** 0 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 2 Literature Review and Applications in Disaster Management
### Applications
#### 2.2.1 Monitoring  
The monitoring of environment through UAVs can help to forecast disaster, prepare for it beforehand, and prevent it. In [9], UAVs are deployed in an area where a disaster can occur to collect aerial imagery and sensor data. An image stitching technology is proposed to detect disaster by analyzing the mapped sensor data on the stitched image. In [8], a mountain fire is monitored in real-time specifically for transmission lines using UAVs. The power consumption of UAVs is optimized by reducing the required number of waypoints to take images of the disaster-hit area and a rapid image processing method is proposed for image stitching. In [11], UAVs are used for real-time data collection from sensors and to forward them to the cloud for further processing and analysis in disaster scenarios.
## 2 Background
### How Self-Sovereign Identity Addresses These Issues  
* By using decentralized networks like blockchains, SSI reduces its dependency on a single authority. Decentralization improves security and resilience to disruptions and attacks.
* Users are in total control of the information about themselves. They choose what details they want to share and to whom. To improve privacy, SSI employs zero-knowledge proofs and selective disclosure.
* By doing away with the need for intermediaries like CAs, SSI reduces the cost of issuing and managing certificates. Identity verification becomes easier to use and more efficient.
* SSI frameworks are designed to work with various networks and systems. This enables users to interact with different services and platforms more seamlessly.
* Revocation and identity credential recovery mechanisms can be implemented more effectively by SSI systems. Even if a user loses their device or login credentials, they can still retrieve their identity using decentralized recovery mechanisms.
* Systems for SSI based on blockchain technology can effectively manage a large volume of identity verifications. Numerous identity-management-related procedures can be automated through the use of smart contracts and decentralized applications (DApps).  
Self-Sovereign Identity solves many of the drawbacks of conventional certificate-based systems and provides a more user-centric approach to digital identity. Through the utilization of blockchain technology and a focus on user control and privacy, SSI offers a digital identity management framework that is more efficient, secure, and adaptable. This is especially helpful in the ever-changing digital landscape, where seamless and secure identity verification is essential in places like the Metaverse.
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 3 Results and Discussion
### Gas Sensor Measurements
#### 3.2.3 Sensor Response in CO\({}_{2}\) Atmosphere  
In a pure-CO\({}_{2}\) atmosphere the resistance R is greater than R\({}_{0}\), revealing a p-type behavior. In this condition the sensor is able to detect diacetyl and shows a total recovery in short time.  
In a CO\({}_{2}\) atmosphere R resulted smaller than both in air and N\({}_{2}\) (\(\sim\)30 k\(\Omega\)). Indeed, according to [PERSON] et al., when SnO\({}_{2}\) works at 240 \({}^{\circ}\)C in high CO\({}_{2}\) concentrations and less than 14% relative humidity conditions, CO\({}_{2}\) acts as an electron donor, as a weak reducing agent [14]. Such a response-type transition is also observed for other materials,  
Figure 3: Sensor measurements acquired in different atmospheres: (**a**) transient sensor response of 0.4 mg/L diacetyl in aqueous solution in air, N\({}_{2}\) and CO\({}_{2}\) environments; (**b**) schematic illustration of the sensing mechanism of SnO\({}_{2}\) powder to diacetyl in carrier and analyte exposure.  
and it is due to the inversion of the conduction type of major carriers, which limits the dynamic range of the sensor at high concentration [44]. In the pure-CO\({}_{2}\) atmosphere the sensor response to 0.4 mg/L diacetyl exposure is 1.13, with response and recovery times of 5.5 min and 7.8 min, respectively. Recovery time results are shorter than in air and in N\({}_{2}\) atmosphere cases.
# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 5 Bayesian Attack Graph for the Smart Grid Cyber Domain
### Tool Design  
Figure 5: input variables: (1) network and SG functions represent the SG network and functions such as DR, billing system, MDM, OMS, etc., (2) the network and SGA comprise the network architecture of the system that supports all the functions; (3) the list of clients, server and vendor side applications; (4) the vulnerability report to compute the likelihood of a particular system compromise. Using input variables, the tool generates BBN and displays on the dashboard. The generated BBN are:  
* Function Bayesian Network (FBN): FBN represents the causal interconnection between different functions of the SG (see Figure 6, top level). The nodes in FBN describe the functions of the SG, and edges represent the information flow from one function to another.  
Figure 4: Smart grid architecture: test network. PQ, Power Quality.  
* Network Bayesian Network (NBN): NBN represents various network components supporting a particular SG function (see Figure 6, middle level). The system engineers can select a function at the FBN level on the dashboard, view its network components and analyze function status and information flow.
* Vulnerability Bayesian Network (VBN): The system engineers select a particular network component from the NBN and view the list of its vulnerabilities (see Figure 6, bottom level). The PoC corresponding to the component is computed using the Common Vulnerability Scanning Score (CVSS) score [39]. Figure 7 describes the set of vulnerabilities associated with system components in the test network.  
Figure 5: Bayesian Attack Graph for Smart Grid tool design. BBN, Bayesian Belief Network.  
Figure 6: Function (top), network (middle) and vulnerability (bottom) Bayesian network. CVSS, Common Vulnerability Scanning Score.