esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Since the beginning of the COVID-19 pandemic, there has been enormous interest in the development of measures that would allow for the swift detection of the disease. The rapid screening and preliminary diagnosis of SARS-CoV-2 infection allow for the instant identification of possibly infected individuals and the subsequent mitigation of the disease spread. Herein, the detection of SARS-CoV-2-infected individuals was explored using noninvasive sampling and low-preparatory-work analytical instrumentation. Hand odor samples were obtained from SARS-CoV-2-positive and -negative individuals. The volatile organic compounds (VOCs) were extracted from the collected hand odor samples using solid phase microextraction (SPME) and analyzed using gas chromatography coupled with mass spectrometry (GC-MS). Sparse partial least squares discriminant analysis (sPLS-DA) was used to develop predictive models using the suspected variant sample subsets. The developed sPLS-DA models performed moderately (75.8% (\(\pm\)0.4) accuracy, 81.8% sensitivity, 69.7% specificity) at distinguishing between SARS-CoV-2-positive and negative -individuals based on the VOC signatures alone. Potential markers for distinguishing between infection statuses were preliminarily acquired using this multivariate data analysis. This work highlights the potential of using odor signatures as a diagnostic tool and sets the groundwork for the optimization of other rapid screening sensors such as e-noses or detection canines.  
SARS-CoV-2; COVID-19; odor signature; HS-SPME-GC-MS; machine learning; sPLS-DA modeling; non-invasive diagnostic tool +
Footnote †: journal: Physics Letters B
## 4 Results and Discussion
### Structural Analysis  
Examination of the first rose diagram (Figure 7B) shows that the observed geological faults extracted from the geological map are globally dispersed among main directions ENE-WSW, E-W and WNN-ESE. In return, the rose diagram relating to the magnetic lineaments shows that the latter are aligned in lightly diverse orientations, NE-SW, NW-SE and fewer ostensibly N-S. The noticeable contrast in these alignments can be associated with the underlying structural composition of the basement resulting from the magnetic interpretation. These structures are entirely hidden beneath Quaternary sediments, and consequently, they are not depicted on the geological map. Moreover, the coverage of the geophysical data exceeds the limits of the Bou Azzer inlier.  
The NE-SW direction particularly represents strike-slip faults that cause a notable offset of the ophiolitic body.  
The WNN-ESE to E-W (Figure 7C) directions are particularly evident in the inlier central zone, this is the main orientation of the Bou Azzer ophiolitic complex, these directions represent the faults inherited from the Pan-African orogen and which were reactivated in the long term until the uplift by Variscan deformation [15; 16]. It is a period marked by a generalized shortening, at the level of the central and eastern Anti-Atlas, so we are witnessing a reactivation of the Precambrian structures along the Major Anti-Atlas Accident which led to the raising of the inlier along a NW-SE axis [50].  
The structural rose diagram (Figure 7D) shows that the lineaments display an E-W, NE-SW, WNN-ESE main trend and N-S less important direction.  
The obtained results demonstrate that the studied region is prominent owing to its high structural complexity resulting from polyphase tectonics at the level of the Bou Azzer inlier.
# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration
## 4 Use Case Scenarios and Simulations
### Use Case 2: Medium-Size Network  
A medium-size network of 110 \(\times\) 110 m\({}^{2}\), with 428 nodes and 1 sink node, was fed to the NN solver, which consisted of two hidden layers (use case 1a). We used a deterministic topology, as proposed by a CVT (i.e., Voronoi) graph. This means that according to the Voronoi graph theory, we placed 428 nodes at the vertexes of hexagons in order to cover an area of 110 \(\times\) 110 m\({}^{2}\).  
According to Table 3, which gives parameters and values for the simulation, the sink node was placed at (0, 0); for case 2a, we used two hidden layers, and for case 2b, we used three hidden layers. Since we had 150 reserved points, the solver relocated 150 nodes out of 428 nodes, which means that almost 35% of the topology nodes changed positions, lowering the objective function value. According to the simulation:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Definitions** & **Values** \\ \hline Sensing radius & Sensing radius of sensor nodes & 3 m \\ pk & Packet size (in bits) & 1500 \\ initial\_energy & Initial energy at every node’s battery & 2 \\ dataset & Data file with coordinates of theoretical points & coordinates.csv \\ query\_points & Data file with coordinates of query points & query\_points.csv \\ e\_elec = e\({}_{8}\) = e\({}_{1}\) = e\({}_{r}\) & Energy dissipated at the electronic transceiver (/bit) & 50 nJ/bit \\ \(\beta\) & Transmission quality estimation & 0.1 nJ/bit/m\({}^{2}\) \\ \(\alpha\) & Signal degrading factor \(2\leq\alpha\leq 5\) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective functions and solver’s simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Type of topology & Deterministic \\ Deployment area & 20 m \(\times\) 20 m \\ Sensing area width & 20 \\ Sensing area length & 20 \\ Topology type & CVT \\ Number of initial nodes & 15 \\ Number of relocated nodes & 8 \\ Sink node coordinates & (0, 0) \\ Solver’s hidden layers & 2 (case 1a) and 3 (case 1b) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters for use case 1: small-size network.  
1. The initial topology leads to a network lifetime of 1646 rounds.
2. The final topology, as proposed by the solver with (2) hidden layers, leads to a network lifetime of 1710 rounds, which is an increase of 3.89%.
3. The final topology, as proposed by the solver with (3) hidden layers, leads to a network lifetime of 1716 rounds, which is an increase of 4.25%.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
###### Abstract
However, most trust-based methods allow malicious nodes to attack trust, such as forgery of trust [19]; that is, these techniques may lack security performance compared to cryptography-based methods. Motivated by this background, we propose a cryptography-based routing protocol that achieves both communication efficiency and security performance. In our proposed protocol, while digital signatures validate the route information created by each node, the intermediate nodes in the route save previously received packets from the destination node. Thus, they can prove that there is a valid route to the destination by disclosing these packets to other nodes. Thus, intermediate nodes are encouraged to begin constructing a route by exchanging route information with a source node while ensuring safety and security. The protocol is based on an ID-based signature scheme. The ID-based signature allows nodes to sign packets based on their secret keys and verify their integrities [20]. Unlike public key-based signatures, this feature removes the requirement for public key infrastructure (PKI), helping to prevent impersonation attacks. The major contributions of this study are as follows:  
* We propose a routing scheme that can reduce the number of hops in the routing phase, as with non-secured AODV, while maintaining security performance. * We analyze the practical overhead routing performance, which comprises the sum of the computation and communication times. Emulation analyses based on an IoT device reveal that the proposed method can improve overhead performance compared with related protocols, even in a small-scale network. Note that this work is an extended version of our earlier work in [21]. In the present work, we compare the performance of our proposed approach with a novel related method proposed in [11], providing implementation results with a Raspberry Pi. These results reveal that the protocol proposed in this paper improves the routing efficiency. The remainder of this paper is organized as follows. In Section 2, we summarize related works. Section 3 defines the system model, and we present the proposed routing protocol in Section 4. A comparison of different methods is presented in Section 5, and the performances of the proposed and the comparison methods are evaluated in Section 6. Finally, we conclude the paper in Section 7.
## 6 Various Criteria for Assessing the Power Quality and Efficiency of the Studied System
### Power Quality
#### 6.1.5 Floating-Capacitor (FC) Voltage  
Compared to other DTLI configurations, whether with isolated or shared continuous buses, the structure based on a continuous bus and an FC has the notable advantage of minimizing the circulation of ZSVs (ZSCs), which is virtually nonexistent, and does not require additional isolation circuits. However, this approach is not without significant challenges. The main obstacle it faces lies in FC voltage management, which, due to its floating nature [119], generates undesirable voltage steps at the converter output.  
As a result of the voltage control approach applied to both converters, the voltage across the load becomes unpredictable. These unwanted voltage steps have the potential to significantly degrade output performance, thereby reducing the attractiveness of this configuration [120]. For example, in the case where the voltage ratio is \(1:0.5\), Table 5 presents the 37 space vectors that will charge or discharge the FC and generate a voltage variation. These vectors can be classified into three types: Type 1 for vectors with redundancy states that cause the phase current to act on both the positive and negative polarities of the FC,  
Figure 21: DTLI with a nested rectifier–inverter with OEWM phase connection for the \(V_{11}V_{j1}\) state reported in [117].  
Type 2 for vectors that have only one instance of current flow, and Type 3 for vectors that have no effect on the voltage variation in the FC.
* Maintaining FC voltage  
The authors in [121; 125] addressed the issue of FC voltage regulation in a DTLI while maintaining multilevel operation. Several approaches were examined:  
1. Coupled Modulation [121; 123; 124; 125; 128; 159]: This method treats the DTLI as a single unit for modulation, enabling analysis of the switching states' impact on the capacitor voltage in steady-state operation. However, there is a risk of under-utilizing the capacitor voltage, which can reduce the output level.
2. FC Voltage Regulation with Reference to Motor Power Factor [121]: This method involves feedback from the motor power factor angle for regulation, providing an active yet complex solution based on motor speed.
3. Decoupled Modulation [65; 122; 160; 161; 162; 163]: In this approach, the FC is treated as a reactive power capacitor, and active power regulation is applied to the FC. However, it may lead to undesirable voltage steps that affect output performance.
4. Step Modulation for the Master Inverter and High-Frequency PWM for the Slave [164]: In this scheme, the master inverter operates at the fundamental frequency, while the slave inverter uses high-frequency modulation. The FC voltage is regulated by adjusting the trigger angle of the fundamental voltage. While this approach reduces switching losses, it may not be suitable for all situations and is relatively complex.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 7 Multi-Agent Control and Consensus Algorithm
### Basic Information on Consensus Algorithm  
Graph \(\mathcal{G}\) is a pair \((\mathcal{V},\mathcal{E})\), where \(\mathcal{V}=1,\ldots,n\) is a set of nodes (agents), \(\mathcal{E}\in\mathcal{V}\times\mathcal{V}\) is a set of edges in which each edge is represented by an ordered pair of different nodes. Edge \((i,j)\) shows that node \(i\) is a neighboring to node \(j\), and node \(j\) can receive information from node \(i\). A graph is called _undirected_ if for each \((i,j)\in\mathcal{E},(j,i)\in\mathcal{E}\). The path from node \(i_{1}\) to node \(i_{l}\) is a sequence of ordered edges of the form \((i_{k},i_{k+1})\), \(k=1,\ldots,l-1\). An undirected graph is connected if for any \(i\in\mathcal{V}\), there are paths to all other nodes.  
Let graph \(\mathcal{G}\) contain \(n\) nodes. The compatibility matrix \(\mathcal{A}=\left[a_{ij}\right]\in\mathbb{R}^{n\times n}\) is defined as \(a_{ii}=0,a_{ij}=1\) if \((j,i)\in\mathcal{E}\), and 0 otherwise. The Laplace matrix \(\mathcal{L}=\left[l_{ij}\right]\in\mathbb{R}^{n\times n}\) is defined as \(l_{ii}=\sum_{j=1}^{N}a_{ij}\) and \(l_{ij}=-a_{ij},i\
eq j\).  
Let us assume that communication graph \(\mathcal{G}\) is undirected and connected (some examples of undirected connected graphs are depicted in Figure 3). Assume that \(\xi_{i}\in\mathbb{R}\) and \(\zeta_{i}\in\mathbb{R}\) contain information about the state of the \(i\)-th agent. For information states with second-order dynamics, the following fundamental second-order consensus algorithm was proposed by [PERSON] and [PERSON] in [39]:  
\[\begin{split}\dot{\xi}_{i}&=\zeta_{i}\\ \dot{\xi}_{i}&=u_{i}\end{split} \tag{33}\]  
where \(u_{i}\in\mathbb{R}\) has the following form:  
\[u_{i}=-\sum_{j=1}^{n}a_{ij}\big{(}\gamma_{0}\big{(}\xi_{i}-\xi_{j}\big{)}+ \gamma_{1}\big{(}\zeta_{i}-\xi_{j}\big{)}\big{)}, \tag{34}\]  
where \(\gamma_{0}>0,\gamma_{1}>0\).  
For consensus algorithms (33) and (34), it is assumed that consensus is achieved asymptotically among several agents if for any \(\xi_{i}(0)\) and \(\zeta_{i}(0)\) it is valid that \(\left\|\xi_{i}(t)-\xi_{j}(t)\right\|\to 0\) and \(\left\|\zeta_{i}(t)-\zeta_{j}(t)\right\|\to 0\), for all \(i\
eq j\) as \(t\to\infty\). In the case when \(\dot{\xi}_{i}\) and \(\dot{\zeta}_{i}\) represent the moving agent position and velocity, expression (34) defines its acceleration.  
Denoting \(\xi=[\xi_{1},\ldots,\xi_{n}]^{\mathsf{T}}\) and \(\zeta=[\zeta_{1},\ldots,\zeta_{n}]^{\mathsf{T}}\), one can write the system model in the following vector form  
\[\left[\begin{array}{c}\xi\\ \zeta\end{array}\right]=\Gamma\left[\begin{array}{c}\xi\\ \zeta\end{array}\right] \tag{35}\]  
where  
\[\Gamma=\left[\begin{array}{cc}0_{n\times n}&I_{n}\\ -\gamma_{0}\mathcal{L}&-\gamma_{1}\mathcal{L}\end{array}\right] \tag{36}\]  
In the vector form, the control law is as  
\[U_{c}=-[\gamma_{0}\mathcal{L}\xi+\gamma_{1}\mathcal{L}\zeta] \tag{37}\]  
where \(U_{c}\in\mathbb{R}^{n}\).
# Interconnected Government Services: An Approach toward Smart Government
## 2 Related Work
### Overview of Smart Government
This concept should stimulate economic growth, make public administration more efficient and economical, provide better-quality services for citizens, ensure transparency, and reduce the scope for corruption. So, the benefits are (1) political and social, (2) economic, and (3) operational and technical. They are related to citizens' better awareness and capability to use e-government easier, faster, and more effectively. The effects of open-government initiatives are considered in [19]. The authors have introduced the metrics and processes for measuring the success of such efforts [20]. Smart government should enable free access, and unrestricted information flow within the public administration and between some of its authority bodies and citizens, as stated in [21]. According to professionals, data openness and open administration are of great importance. According to [22], the implementation of the e-government aims to make the government-to-citizen (G2C), government-to-business (G2B), and government-to-government (G2G) interactions more efficient and transparent. Two factors significantly influence the qualities of these interactions: (1) governmental policy, which makes the data available, and (2) the use of information technology. Many governments are trying to reduce costs and stimulate organizational innovation. Consequently, the authors of [23] have proposed the idea of a third-party service to accomplish this. However, the authors of [24] report that this is not easy in practice as there are privacy-related problems with open data disclosure. Therefore, a nuanced approach is recommended, avoiding releasing data for its own sake. Besides that, a legal framework for data release might be helpful. This approach is necessary for data-release strategies because of all of the implications of the service's implementation. For this reason, it is necessary to consider how data are stored, accessed, and formatted, as well as the financial, legal, and other aspects. Smart government is based on the foundation of smart governance, suggesting that both concepts are closely related [21]. Public administrations demand more intelligent technologies to connect with citizens and understand their demands. The authors in [25; 26] emphasize that much-needed information exists on social media, where members of diverse groups share different interests, post statuses, and review and comment on various topics. Generally speaking, moving towards a smart government relies on improvements in organizational, technical, safety, and other interoperability aspects [27]. In summary, the professionals have only developed rudimentarily smart-governance and smart-government concepts so far [28].
## 2 Results and Discussion
### Optimization of MSPE Conditions  
#### 2.3.1 Effect of pH Value  
The effect of the solution pH (2.0-10.0) on the adsorption of OFL by the MMIPs was investigated using 20.0 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) OFL solutions, as shown in Figure 7a. The OFL adsorption capacity was highly dependent on pH. The q values of \(S\)-(\(-\))-OFL and \(R\)-(\(+\))-OFL gradually increased as the pH increased from 2.0 to 5.0. Moreover, pH values of 5.0 and 6.0 were optimal for OFL adsorption. As the solution pH increased from 6.0 to 10.0, the q values sharply decreased. A t test of the q values of \(S\)-(\(-\))-OFL  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Analyytes**} & \multicolumn{4}{c}{**Freundlich Model**} & \multicolumn{2}{c}{**Langmuir Model**} \\ \cline{2-7}  & **n** & **K\({}_{\mathbf{F}}\)** & **R\({}^{2}\)** & **q\({}_{\mathbf{m}}\) (mg\(\cdot\)g\({}^{-1}\))** & **K\({}_{\mathbf{L}}\) (L\(\cdot\)mg\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline \(S\)-(\(-\))-OFL & 1.279 & 0.833 & 0.9926 & 106.383 & 0.004 & 0.9507 \\ \(R\)-(\(+\))-OFL & 1.274 & 0.824 & 0.9921 & 107.527 & 0.004 & 0.9523 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters from the isotherm models for the adsorption of OFL onto MMIPs.  
Figure 6: Adsorption isotherms for adsorption of OFL onto MMIPs.  
and \(R\)-(+)-OFL showed that there was no significant difference (\(p>0.05\)) between the adsorption of S-(\(-\))-OFL and \(R\)-(+)-OFL on the MMIPs using _rac_-OFL as the template.  
The dependence of OFL adsorption on pH can be explained from the perspective of surface chemistry and the ionization state of OFL in the aqueous phase. The primary driving force for binding between OFL and the MMIPs is hydrogen bonding, which is strongly related to the solution pH. In the present work, the ionization states of OFL (pK\({}_{a1}\) = 5.77, pK\({}_{a2}\) = 8.44) [28] and the amino groups on MMIPs are significantly affected by the solution pH. When the pH is low (pH < 5.0), the amino groups on both the surface of the MMIPs and OFL are protonated and in an ionic state. There is electrostatic repulsion between the MMIPs and OFL and the formation of hydrogen bonds is difficult, leading to poor OFL adsorption efficiency. When the pH is 5.0-6.0, the amino groups on OFL and the MMIPs and the carboxyl groups on OFL are all in the molecular state and hydrogen bonds (-O-H\(\cdots\)N, -C=O\(\cdots\)H, and -N-H\(\cdots\)N) can easily form; therefore, the highest adsorption efficiencies were achieved at pH values of 5.0 and 6.0. At higher solution pH values (pH > 6.0), the carboxyl groups on OFL are gradually deprotonated to an ionic state, resulting in a decline in the adsorption amounts. Thus, pH = 5.0 was selected for all subsequent adsorption experiments.
## 2 Experiments and Numerical Models  
### Experimental Methods  
The experimental setup, mainly composed of a wind tunnel, is illustrated in Figure 1a. The test cabin, with an internal size of 2.0 m (L) \(\times\) 1.6 m (W) \(\times\) 1.3 m (H), was made of 3 mm-thick steel plates. The ambient air temperature was 28 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C. A 0.08 m (L) \(\times\) 0.08 m (W) \(\times\) 0.03 m (H) square steel pan containing 100 mL N-heptane was placed at the longitudinal center line of the tunnel. There was a baffle of 1 m in length at the leading edge of the square plate, and the baffle height varied from 2 to 10 cm in the experiments.  
The crosswind speeds in the experiments ranged from 0 to 5 m/s. Before the fire experiments, a hot wire anemometer probe (\(\pm\)0.01 m/s accuracy) was installed 0.1 m upstream of the baffle to record the wind velocity in real time, as shown in Figure 1b. From Figure 1b, the ratio of the standard deviation of the ventilation velocity \(\delta\)u to the average ventilation velocity u\({}_{\text{means}}\) characterizes the wind stability in this case.  
As summarized in Table 1, a total of 30 scenarios were tested in this research under a series of crosswind velocities and 5 different baffle heights. All of the scenarios were repeated twice to avoid random errors.  
Figure 1: (a) Diagram of the experimental section and (b) instantaneous horizontal wind speeds.
## 6 Simulations and Discussion  
### Experimental Settings  
The scenarios are generated with input parameters such as network size, speed, transmission range, and random waiting time for clusterhead contention in order to evaluate different aspects of performance such as cluster stability, overhead consumption, network behaviors and so on.  
Consider a sensor moving within the bounded region \(A=[0,\ell]^{2}\) according to the moving pattern in [26], where \(\ell\) is the side length of a square. The movement of mobile nodes is randomly generated and continuous within the whole simulation period. At some random times \(0\leq T_{1}<T_{2}<\cdots\) a new speed and a new direction are randomly selected at time \(T_{j}\), which initializes the _j_th movement of a sensor. Let \(\tau_{j}=T_{j+1}-T_{j}\), \(j\geq 0\), be the duration of the _j_th movement. During the interval \([T_{j},T_{j+1})\) the sensor moves at constant speed \(v_{j}\in S\), where \(S=[v_{\min},v_{\max}]\) with \(0\leq v_{\min}<v_{\max}\).  
The sensor mobility is obtained by sampling from a uniform distribution with maximum value \(v_{\max}=v+\triangle v\) and minimum value \(v_{\min}=v-\triangle v\), where \(v\) is a given mean node speed and \(\triangle v=1\)In this work, the performance of the proposed scheme is simulated using Matlab and the experimental results are the time average over a period of 200 seconds; the duration of the _j_th movement of a sensor is \(\tau_{j}=1\) time unit (for \(\forall j\geq 1\)); the transmission range \(R_{\mathcal{M}}\) is determined by (9); the side length of a square is \(\ell=100\) m.