esa-sceva/satcom-chunk-collection huggingface dataset:
# Personal Information Classification on Aggregated Android Application's Permissions
## 3 Risk Modeling
### The Personal Information Scope Model  
Firstly, information usability must be defined to estimate the scope of personal information. PII, also known as significant personal information (SPI), is a vast theme. This study only focused on the PII and SPI from the privacy leaking aspects. Based on the personal data composed from well-established institutes, a detail explanation of available data types are stated here:  
**Definition 5**.: _Direct identifiers can directly identify the user's individuality (described in Section 2.1). This study stated that as PII = {S\({}_{1}\), S\({}_{2}\).... S\({}_{NPII}\)}, where N\({}_{PII}\) are the elements of PII._  
**Definition 6**.: _Quasi identifier or partial identity can jointly identify user identity (described in Section 2.1). This study termed that as PPII = {Z\({}_{1}\), Z\({}_{2}\).... Z\({}_{NPII}\)}, N\({}_{PPII}\) are the elements of PPII._  
**Definition 7**.: _Non-personal data no longer indicates a user's identity (described in Section 2.1). This study termed that as NPII = {N\({}_{1}\), N\({}_{2}\).... N\({}_{NNPII}\)}, N\({}_{NPII}\) are the elements of NPII._  
**Definition 8**.: _De-identification or anonymization refers to the process by which PII {S\({}_{1}\), S\({}_{2}\).... S\({}_{NPII}\)} and PPII {Z\({}_{1}\), Z\({}_{2}\).... Z\({}_{NPII}\)} are eliminated from the information to convert data into NPII {N\({}_{1}\), N\({}_{2}\).... N\({}_{NNPII}\)}. This study denoted the de-identification method as Did (described in Section 2.1)._  
Based on Definitions 5-8, this study stated a few more assumptions on data characteristics.  
**Assumption 4**.: _By nature, PII and PPII are not alike (PII \(\
eq\) PPII). Therefore, the study assumed that a single PPII could never create or indicate the PII._  
**Assumption 5**.: _Alternatively, a set of capable PPIIs (linked attributes) can generate or indicate a specific PII. At least, two or more PPIIs are needed to generate or leak a specific PII._  
Now, the correlation between dangerous Android permission and personal data will be discussed.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 4 Experimental Example
### Introduction of Sensor Placement and Damage Cases  
Considering the #1 and #2 T-shaped beams supporting the moving vehicle, a total of 10 displacement sensors are placed along the towing beams, that is, 5 sensors are attached to each beam; a detailed drawing is provided in Figure 12. As shown in Figure 13, a total of 22 devices are designed to simulate the damage of the transverse connection between the two T-shaped beams. Different damage extents are simulated by removing different numbers of the above devices. During the test, a total of 32 cases are implemented and 10 displacement ILs are obtained in every case, as described in Table 4.  
Figure 11: Detailed drawing of the cross-section of the three main beams of the model bridge (unit: mm).  
Figure 10: Photo of the entire experimental system of the model bridge.  
Using the electronic machine, the loading vehicle is pulled over the model bridge with a slow uniform velocity representing the quasi-static load test defined in this study. During one test, 10 sensors record 10 quasi-static displacement ILs. All 32 cases of the quasi-static load tests can be divided into 5 parts as follows:  
Part I: reference state, that is, case 1, as listed in Table 4.  
Part II: healthy state under the same loading conditions as the reference state, that is, cases 2 to 10, as listed in Table 4.  
Part III: damaged state under the same loading conditions as the reference state, that is, cases 11 to 20, as listed in Table 4.  
Part IV: damaged state under different loading conditions from the reference state, that is, cases 21 to 30, as listed in Table 4.  
Part V: healthy state under different loading conditions from the reference state, that is, cases 31 to 32, as listed in Table 4.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
###### Abstract
This research aims to expand our understanding of the EM emanation of devices such as laser printers, scanners, conductors, PES, and RFID devices in the context of the growing concern over security threats once reserved exclusively for the military and now for general-purpose devices. More precise computing and greater storage capabilities available in recent years have played a significant role in this transition. Attacks on sensitive data through EM leaks have become simpler and more frequent as a consequence of the above. The contribution of this study can be summarized based on several objectives, main goals, and considerations:  
* The first objective of this study is to advance the prevention of EM data leakage by providing valuable insights into the latest trends and standards to effectively mitigate the risk of unintentional EM emissions. * We provide a comprehensive analysis of side-channel attacks and various telecommunication electronics materials protected from emanating spurious (TEMPEST) specifications and settings by reviewing the available references. TEMPEST specifications include EM shielding, zoning, and masking, as well as the minimum physical distance between wires or equipment that transmit or process sensitive data. * Another goal is to provide a detailed analysis of TEMPEST specifications and configurations, emphasizing their relevance and implications in the context of unintentional EM emanations. * We also discuss countermeasures against malicious activity and techniques for protecting devices, tools, and equipment with EM shielding. * Finally, we consider TEMPEST profiling associated with various EM leakage sources. This paper is organized as follows: Section 1 is an introduction, followed by a related work. Section 3 describes the sources of electromagnetic leakage. Section 4 focuses on TEMPEST profiling. Section 5 contains a comparative analysis and discussion. Section 6 concludes the paper.
## 4 Results
### Simulation Results
#### 4.4.2 Model Accuracy Results  
Consistency is one of the main reasons to implement the values of these functions using the PySpark ML library. The implementation provides the correct computations of these values using Python code. Table 4 shows the values of the model accuracy results under different machine learning models. These simulations are computed and collected over the five-simulation environment to collect these vehicles over the network environment. Figure 14 shows the accuracy value of the model over the multiple attack densities. The weak point of the MLPC model can be observed using the below-mentioned graph. Overall, the attack densities in the production model show an F1 score. The overall model is not as effective as the densities, as shown in their results. According to the analysis in Figure 15, GBT and RF show higher accuracy values for all network densities. The proposed system outperforms the SVM and LR models with a 40% attack density over the network performance layers.
## 3 Results and Discussion
### Electrochemical Polymerization and Polymer Characterization
Hence, UV-Vis spectra of the polymer species deriving from unsubstituted and Br-substituted terthiophene confirm the hypothesis from the electrochemical data about the low efficiency of the polymerization process giving only low-molecular-weight species. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **E\({}_{\text{ox}}\) (V)** & **E\({}_{\text{rid}}\) (V)** & **HOMO (eV)** & **LUMO (eV)** & **Eg.ec (eV)** & **Eg.opt (eV)** \\ \hline Poly-**2** & 1.24 & \(-\)1.44 & \(-\)5.68 & \(-\)3.34 & 2.34 & 2.08 \\ Poly-**3** & 1.28 & \(-\)1.48 & \(-\)5.81 & \(-\)3.35 & 2.46 & 2.21 \\ Poly-**5-ET** & 0.93 & \(-\)1.68 & \(-\)5.37 & \(-\)3.63 & 1.74 & 1.89 \\ Poly-**3’-ET** & 1.05 & \(-\)1.73 & \(-\)5.47 & \(-\)3.40 & 2.07 & 2.06 \\ \hline \end{tabular}
\end{table} 
Table 4: Voltammetric data in CH\({}_{3}\)CN/TEAPF\({}_{6}\) 0.1 M and UV-Vis data of the polymer films. Figure 5: Cyclic voltammetry characterization of poly\(-\)**2** (a)**, poly\(-\)**3** (b)** and poly\(-\)**5\(-\)ET** (c)** in CH\({}_{3}\)CN/TEAPF\({}_{6}\) 0.1 solution. On the other side, the spectra of poly-**5-ET** and poly-**3\({}^{\prime}\)-ET**[22] do not evidence the presence of unpolymerized monomer, showing a broad absorption band over 400 nm. Eg,opt values were calculated from the onset wavelength. They are in good qualitative agreement with the corresponding values from the electrochemical investigation (Table 4). For all polymers, both electrochemical and spectroscopic Eg values are lower than the relevant monomers. Furthermore, the lowering of the Eg value from poly-**2** (unsubstituted poly-terthiophene) to poly-**3\({}^{\prime}\)-ET** (ethynyl substituent on the central ring) looks smaller than to poly-**5-ET** (ethynyl on a peripheral ring). Such a difference suggests some comments on the effect of the ethynyl fragment depending on the position (central or external ring, respectively) where it is anchored on the relevant terthiophene monomer. A plausible reason for this behavior can be found according to the proposed coupling route for poly-**3\({}^{\prime}\)-ET** and for poly-**5-ET** in Figure 7. In particular, an unsaturated function on the central heterocycle ring (**3\({}^{\prime}\)-ET**) seems to be scarcely influencing the growing mechanism of the polymer chain. This feature can be reasonably attributed to \(\alpha\)-\(\alpha\) couplings between external thiophene rings of two different terthiophene units, not directly involving the ethynyl substituent on C-3\({}^{\prime}\), which appears outside of the main chain. On the other hand, the growth of a poly-**5-ET** chain can also involve homo-couplings between terminal ethynylfunctions, as well as hetero-couplings between a free \(\alpha\) position on a terminal ring and the ethynyl fragment on a second one. The presence of the unsaturated portion inside the polymer chain facilitates a more extended electron density delocalization along the polymer chain, causing the observed higher lowering of Eg values in poly-**5-ET**.
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 3 Visibility-Based Collision Warning System Design
### Rear-End Collision Warning Model  
As shown in Figure 4, the rear-end collision warning model is based on BPNN. It consists of 3 layers including input layer, hidden layer, and output layer. There are three input neurons in the input layer, including the headway distance (\(h\)) between SV and LV, the LV velocity (\(V_{LV}\)), and the SV velocity (\(V_{SV}\)). The number of nodes in the hidden layer is twice as much as that in the input layer. We adopt the Levenberg-Marquardt method to train the BPNN and select hyperbolic tangent sigmoid transfer function for neuron activation. First, we calculated TTC as defined in Equation (1) with real-time data, and transformed it into a collision warning level using a Forward Collision Probability Index (FCPI) [29]. Then, we trained the network to make the output represent the collision warning level, which is a ratio between 0 and 1. Finally, we predicted the collision warning levels for all time slots in the prediction horizon. The final collision warning level was computed by using the maximum function over all predicted warning levels in the prediction horizon.  
FCPI used a Z-shaped membership function to estimate collision probability as shown in Equation (5):  
\[f(x,a,b)=\left\{\begin{array}{rl}1,~{}\text{if}~{}x\leq a\\ 1-2\left(\frac{x-a}{b-a}\right)^{2},~{}\text{if}~{}a\leq x\leq\frac{a+b}{2}\\ 2\left(\frac{x-b}{b-a}\right)^{2},~{}\text{if}~{}\frac{a+b}{2}\leq x\leq b\\ 0,~{}\text{if}~{}x\geq b\end{array}\right. \tag{5}\]  
where the parameters \(a\) and \(b\) are set as 0.5 and 2.5, respectively, according to the three warning levels of TTC: \"overriding (TTC < 0.5 s)\", \"Immunent warning (0.5 s \(\leq\) TTC < 1.5 s)\", and \"cautionary warning (1.5 s \(\leq\) TTC < 2.5 s)\" [30]. If TTC is less than 0.5 s, the probability of collision is 100%. If TTC is larger than 2.5 s, the probability of collision is 0%. The probability of collision will increase quickly when TTC is between 0.5 s and 1.5 s, and the probability of collision will increase slowly when the TTC is between 1.5 s and 2.5 s. It is suggested that the collision warning level threshold be set as 0.5, at which the system should warn a driver to slow down [6]. Training data are collected from the Next Generation Simulation (NGSIM) US Route 101 Dataset [31].
# Methods of Analyzing Microsized Plastics in the Environment
## 5 Emerging Technologies
### Vis-NIR Measurement  
Vis-NIR spectroscopy measures the amount of light reflected from the surface of the sample within the wavelength range of 350-2500 nm to determine the reflectance for each wavelength. This analysis method can be used to quantify microplastics because it relates to the chemical composition of the sample [80]. There is an open vis-NIR spectral data library for various plastics commonly found in the environment, which can be used to identify microplastics such as low-density polyethylene (LDPE), PET, and PVC [31]. However, organic particles can still be mistakenly identified as plastic because it is a visual identification method; therefore, it is necessary to rely on human judgment.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 2 Unmanned Aerial and Surface Vehicle Networks: Applications and Advancement in Their Optimisation
### UAV and USV Network Enhancements
We based our solution on the cooperation between UAVs and USVs as in [12], but our implementation was dedicated to exploiting the connection between more UAVs and USVs to ensure a communication chain, not the physical landing or the transportation of UAVs by USVs, as proposed by [12]. The other approach relates to the cooperation between UAVs and USVs from the point of view of the communication network. [PERSON] et al. [11] proposed a UAV-UVS architecture that was tested from the point of view of the network capacity. In contrast to [11], our architecture only employed one gateway for all UVs. [PERSON] et al. proposed the use of two different gateways, one dedicated to UAVs and one dedicated to UVSs, thus increasing the complexity of the network. Moreover, their viewpoint was concentrated on the routing protocols and network capacity, not on propagation aspects. Nevertheless, most of the initiatives found in the literature are focused on path planning algorithms, routing algorithms that consider end-to-end delay and packet error ratios, but few formation algorithms approach the FANET or SANET networks from a path loss perspective. The current paper fills this important gap by considering the impact of the propagation environment on the capability of a network to be deployed on demand. Moreover, it proposes the usage of USVs as access points in environments such as the Danube Delta or those at the shore of the rivers and lakes.
###### Abstract  
Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset of channel measurements performed to analyze outdoor-to-indoor propagation characteristics in the _mid-band_ spectrum identified for the operation of 5 th Generation (5G) cellular systems. Previous efforts to analyze outdoor-to-indoor propagation characteristics in this band were made by using measurements collected on dedicated, mostly single-link setups. Hence, measurements performed on deployed and operational 5G networks still lack in the literature. To fill this gap, this paper presents a dataset of measurements performed over commercial 5G networks. In particular, the dataset includes measurements of channel theory provides from two 5G networks in Band n78, i.e., 3.3-3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy by using the Rohde & Schwarz (R&S) TSMA6 network scanner during several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics toward the definition and refinement of empirical channel propagation models.  
radio channel measurements; outdoor-to-indoor propagation; 5G mid-band; dataset 2022
## 8 Computation from a Saved State  
While _Incremental\({}^{+}\)_ often makes it possible to efficiently incorporate new observations, it has two potential drawbacks:  
* If the observations receive require backtracking to a much earlier time step, the resulting computation may be slow. In our experiments, we observed instances in which the algorithm took about 20 s for computing the states corresponding to a timeline that contained observations for a certain collection of time steps, while the algorithm was able to complete the computation in about 8 s when provided all relevant time steps at the beginning of the computation. * _Incremental\({}^{+}\)_ stores the information about the search space in memory. Thus, the use of Incremental+ may not be feasible on cloud platforms where service instances are frequently terminated (possibly without warning) and restarted. Thus, in this section we discuss a different approach, in which we build once again upon Algorithm _Incremental\({}^{h}\)_ to make it possible to efficiently resume the computation from a saved state. This approach relies on a slight modification of Algorithm _Incremental\({}^{h}\)_, called Algorithm _Incremental\({}^{c}\)_, which takes an additional input argument \(s\), which is a set of facts encoding a previously saved state of the domain. The facts include not only information about which fluents literals are true in that state, but also (a) information about the amount of time left for durative fluents and (b) facts of the form \(time(t)\) for any future time step that needs to be considered in the evaluation of the behavior of durative fluents. The algorithm also assumes that the time step of \(s\) is included in input argument \(p\). Step 2 of _Incremental\({}^{h}\)_ is modified as follows:  
\[G\gets ground(base\cup timedep(0)\cup s\cup p\cup O) \tag{22}\]  
that is, \(s\) is included in the initial grounding process. The entire list of steps of Algorithm _Incremental\({}^{c}\)_ is not given due to the simplicity of the modifications. Rather than always starting its computation from scratch, _Incremental\({}^{c}\)_ is capable of beginning its computation from a previously computed state of the domain. The algorithm builds upon the observation that, even in the presence of durative fluents, every state of a trajectory depends solely on the state that precedes it (as well as any actions that may occur in it). Therefore, executing _Incremental\({}^{c}\)_ on a previously saved state does not hinder the correctness of its computation. The soundness and completeness of _Incremental\({}^{c}\)_ with regard to _Incremental\({}^{h}\)_ is ensured by the following property:  
**Lemma 1**. : _Let \(t_{s}\) be the time step of saved state \(s\). The value of m at step \(8\) of Incremental\({}^{c}\) (refer to the same-numbered step of Algorithm 3) is guaranteed to be greater than \(t_{s}\)._  
_Incremental\({}^{c}\)_ is leveraged by Algorithm 5, shown below.
## 4 Proposed Protocol
### Packet Forwarding  
When a sensor node has a data packet to send, it chooses the best forwarder among its neighbors by looking into the routing table. The best forwarder has the lowest pressure level. The sender node inserts the ID of the best forwarder into the data packet and forwards it. All its neighbors receive it. Every neighbor matches its own ID with the ID of the best forwarder in the data packet. The intended forwarder node accepts the packet for further transmission towards the sink. All the rest of the neighbors simply discard it because of a mismatch of the IDs. This process continues until the packet reaches the surface sink or drops if the link is not free within the maximum back-off time. If a sender node has two or more forwarder nodes with the same pressure levels, the response time is taken into account to select the best forwarder. In such a case, the best forwarder has the lowest pressure level and the shortest response time.  
During packet forwarding, the propagation delays defined by Equations (12)-(14) are calculated for multi-hop, one-hop and direct (from the sender to the sink) communications, respectively. For multi-hop communications, \(n\) should be greater than or equal to 2, as at least two forwarders should forward a packet. In the calculation of the response time, the direct communications from the broadcaster node to its neighbors are considered. The processing delay is an inherent parameter of the sensor design. It is a measure of the time difference between the reception of a hello packet to the initiation of the response to the original broadcaster. Possessing the same characteristics, it is assumed that the processing delay is the same for all nodes.  
Algorithm 1 shows the selection of the best relay. A sender node \(i\) chooses the best relay node \(j\) among its set of neighbors \(N_{i}\) with the lowest pressure level \(min(p_{j})\) or with the lowest pressure level and shortest response time \(min(p_{j},\tau_{w})\).  
Figure 2: Adaptive neighbor determination. (**a**) Node A has no neighbor within its transmission range. (**b**) Node A increases its transmission range to include one or more neighbors.  
```
\(BR\leftarrow\) the best relay \(p_{j}\leftarrow\) pressure level of a forwarder node \(j\) \(R\leftarrow\) transmission range of a sensor node \(E\leftarrow\) energy of a sensor node \(N_{i}\leftarrow\) number of neighbors of a source node \(i\) \(N_{j}\leftarrow\) number of neighbors of a relay node \(j\) \(M\leftarrow\) total number of nodes in the network for\(i=1:1:M\)do if\(E_{i}>0\) & \(E_{j}>0\) & \(j\in N_{i}\)then \(BR_{i}=argmin_{j\in N_{i}}(p_{j})\) \(OR\)\(argmin_{j\in N_{i}}(p_{j},\tau_{w})\) else all nodes are dead endif endfor
```  
**Algorithm 1** The Best Relay Selection