esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Threats and Cryptographic Protocols for Medical Wearables
## 5 Discussion
Ascon is highly appropriate for wearable health-monitoring lightweight systems where several communications protocols are involved, it can be efficiently used for these systems where side-channel resistance is essential. Moreover, Ascon is a simple algorithm, as it only uses bitwise Boolean functions. The security provided by all Ascon family members protects the plaintext confidentiality and the plaintext, the associated data and the public message number integrity. The use of the same underlying permutation for authenticated encryption and hashing reduces the area requirements for hardware implementations and restrict the code base and thus, the workload is reduced. Conceptualization, A.Q.-D. and J.J.B.P. ; methodology, L.H.-A. and A.Q.-D.; software, F.K.B. ; validation, L.H.-A., J.J.B.P. and A.Q.-D.; investigation, L.H.-A. and F.K.B. ; resources, L.H.-A. and J.J.B.P. ; writing--original draft preparation, A.Q.-D. and F.K.B. ; writing--review and editing, L.H.-A. and J.J.B.P. ; visualization, J.J.B.P. ; supervision, A.Q.-D.; funding acquisition, A.Q.-D. All authors have read and agreed to the published version of the manuscript. This work was supported by the Spanish State Research Agency (AEI) of the Ministry of Science and Innovation (MICINN), project P2 QProMeTe (PID2020-112586 RB-I00/AEI/10.13039/501100011033), co-funded by the European Regional Development Fund (ERDF, EU). Not applicable. [PERSON] would like to thank CSIC Project 202050E304 (CASDiM). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} WHMS & Wearable health-monitoring systems \\ ECG & Electrocardiogram \\ RHMS & Remote health monitoring systems \\ MHMS & Mobile health monitoring systems \\ ROI & regions of interest \\ BAN & Body area network \\ BLE & Bluetooth Low Energy \\ RF & RadioFrequency \\ MITM & Man-in-the-middle \\ TLS & Transport Layer Security \\ DoS & Denial-of-service \\ AI & Artificial Intelligence \\ LWC & Lightweight cryptography \\ ABM & Agent-based model \\ AEAD & Authenticated encryption with associated data \\ CAESAR & Competition for Authenticated Encryption: Security, Applicability, and Robustness \\ \end{tabular}
## 4 Simulations and Results Analysis
### Resilience Probability (\(P_{r}\))
#### 4.4.1 Behavior Analysis of \(P_{r}\)  
To show the behavior of \(P_{R}(x)\), the key management resilience probability as a function of the amount \(x\) of captured nodes, we use the q-composite scheme [22], because it is a widely-known key management mechanism, on which several others are based.  
Figure 4 presents how \(P_{R}\) varies with \(x\), for different values of \(q\) (the minimum amount of common keys necessary for establishing a secure link between any two nodes). Notice that the number of keys stored by each node is \(k=200\), and the connection probability between a node and its neighbors is \(p=0.33\). The key pool size \(|K|\) is computed from the values of \(p\), \(k\) and \(q\), according to Equations (9) and (10).  
In Figure 4, we can see that up to about \(50\) captured nodes, the resilience probability remains close to one, for all values of \(q\). However, for values of \(x\) greater than \(50\), \(P_{R}\) decreases more significantly, besides being smaller for greater values of \(q\). This is because when \(q\) is small, the nodes will share a smaller amount of common keys, which makes a node capture reveal little information about the other  
Figure 3: Accuracy of the key strength probability (\(P_{F}\)) _vs._ time \(t\), for different network sizes, for a test rate \(f=394,875,793,722\) keys/s, and \(s=64\) bits.  
keys in the network. On the other hand, as \(q\) increases, a node capture has a far greater impact on the security of the network, causing more keys to be revealed. When the number of captured nodes is large, the negative impact on the security of the network is even more evident. Thus, it is possible to conclude that the q-composite scheme is not able to maintain the network as very secure when the number of captured nodes is very large.
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 2 FBG Temperature Sensing Principle
### Heat Transfer Model
#### 2.1.1 Single-Point Model  
For a discrete IGBT module with direct on-chip sensor placement, a simplified static thermal resistance model can be used to estimate heat transfer across the chip-to-sensor interface. In this case of the IGBT-FBG sensor interface, we neglect the coupling effect between the switches; therefore, we can estimate the junction temperature with the thermal resistance network provided that the sensor is held very close to the IGBT's chip and assuming that the measurement is completed in an enclosure with an ambient temperature \(T_{a}\). The general expression for \(T_{j}\) is provided by  
\[T_{j}\ =\ \left[P_{di}R_{th(j-a)}\right]+T_{a} \tag{4}\]  
where \(P_{di}\) is the allowable dissipated power across the device; \(R_{th(j-a)}\) is the junction-to-ambient thermal resistance.  
In this study, the sensor is placed between the IGBT's baseplate and the heatsink. Thus, we can modify (4) to capture the heat loss across the various layers of the module.  
Figure 1: Schematic of a three-phase inverter.  
As shown in Figure 2, the MOSFETs and IGBTs used in high-power applications typically have an additional diode in parallel to provide a reverse current path for inductive loads. The corresponding junction-to-case thermal resistance for such a module \(R_{eq(j-c)}\) can be expressed as an equivalent sum of the anti-parallel diode and IGBT as  
\[R_{eq(j-c)}\ =\ \frac{\left(R_{th(j-c)\ I}\ R_{th(j-c)D}\right)}{\left(R_{th(j- c)I}+R_{th(j-c)D}\right)} \tag{5}\]  
where \(R_{th(j-c)\ I}\) and \(R_{th(j-c)D}\) are the thermal resistance for anti-parallel diode and IGBT, respectively. Typically, \(R_{eq(j-c)}\) and \(R_{th(c-h)}\) are specified by the manufacturer. Generally, \(R_{th(c-h)}\) can be merely estimated as a function of thermal conductivity of the used thermal pad through the following relation.  
\[R_{th(c-h)}\ =\ \frac{T}{kA} \tag{6}\]  
where \(T\) is the thickness of the interface layer between the module and the heatsink, \(A\) represents the area occupied by thermal paste, and \(k\) is the specified thermal conductivity of the material. The equivalent thermal resistance for junction-to-ambient \(R_{th(j-a)}\) is a sum of \(R_{eq(j-h)}\) and \(R_{eq(j-a)}\), which are the thermal resistance for junction-to-heatsink and junction-to-ambient, provided as  
\[R_{eq(j-h)}\ =\ R_{eq(j-c)}\ +R_{eq(c-h)} \tag{7}\]  
\[R_{th(j-a)}\ =\ R_{eq(j-h)}+R_{th(h-a)} \tag{8}\]  
The estimated junction temperature for a point measurement with reference to the measured temperature of FBG \(T_{FBG}\) attached to the IGBT baseplate is provided as  
\[T_{j}=\Big{(}R_{eq(j-c)}\ +R_{th(c-h)}\ +R_{th(h-a)}\Big{)}P_{di}+T_{FBG} \tag{9}\]  
where \(R_{th(c-h)}\) is the thermal resistance of the device's casing-to-heatsink. Thus, the relationship for the thermal resistance heatsink-to-ambient can be expressed as  
\[R_{th(h-a)}=\frac{T_{j}-T_{FBG}}{P_{di}}-\left(R_{th(j-c)}+R_{th(c-h)}\right) \tag{10}\]
# Mobility Prediction of Mobile Wireless Nodes
## 1 Introduction and Motivation
We used four types of mobility models, i.e., RSSGM, Gauss-Markov, random direction, and random walk. To analyze mobility models' ability in all cases, we alternately paired the number of centroid nodes (5, 10, 15, and 20) with the number of access points (5, 10, 15, and 20) to cover sixteen diverse scenarios. Then, we compared the accuracy and performance of all types. Finally, we applied the better mobility model's results through a different count of steps to study the classification behavior if the user has to move more or fewer steps than average. Comparison results of this work depend on the prediction accuracy of user movement. Prediction models based on random mobility models vary in terms of prediction accuracy, starting from 39% and not exceeding 81%. On the contrary, Gauss-Markov's accuracy reached 89% using the LDA classifier. Finally, the RSSGM model was shown to have a very high prediction accuracy in all cases, hitting 98% with the decision tree classifier through the average movement. This result indicates that RSSGM is sufficient for the classification prediction of repeated visits to particular locations. The remainder of this paper is organized as follows. A brief theoretical background on categories of mobility models and several types of machine learning classification algorithms is introduced in Sections 2 and 3. In Section 4, the related literature is reviewed. The study methods used are described in Section 5. Evaluation and analysis are presented in Section 6, and a discussion is presented in Section 7. Lastly, concluding observations and suggested future research guidelines are discussed in Section 8.
## 5 Summary and Conclusions  
This manuscript has presented the problems associated to the presence of RFI in SMOS synthetic aperture microwave imagery at L-band (1.4 GHz), as well as its polarization and time dependence. Two algorithms have been conceived, implemented and presented to operate directly with L1b SMOS data either in dual-polarization or in full-polarimetric modes. In the second one, the third and fourth Stokes parameters are also used to detect potential RFI contaminated pixels, and object removal and exemplar-based algorithms are finally used to interpolate the pixel values over land that have been eliminated because of the RFI, and over the ocean corrupted pixels are replaced by their corresponding mean ones. Statistical analysis of more than 13,000 snap-shots has shown that on average there are 3 or 4 RFI sources per snap-shot (including only those larger than 150 K, Figure 26a).  
Even though the imaging technique is totally different, the proposed RFI detection and mitigation (based on masking, object removal and exemplar-based region filling) for the SMOS full-polarimetric mode, could also be applied in a straightforward way to the upcoming imagery from NASA/CONAE SAC-D/AQUARIUS and NASA/JPL SMAP missions.
## 3 Results and Discussion
### Tradeoffs of Multi-Objective Optimization and Its Value Distribution
The TDHP variation ranges from 39.3% to 42.1%, with a maximum difference value of 2.8%. The LCC is distributed at 161.7-291.8 CNY/m\({}^{2}\), with a maximum difference of 130.1 CNY/m\({}^{2}\), while the range for the LCCO\({}_{2}\) is \(-\)7.0-62.2 kgCO\({}_{2}\)/m\({}^{2}\), with a gap of 69.2 kgCO\({}_{2}\)/m\({}^{2}\). From the statistical results, the distribution range of LCC and LCCO\({}_{2}\) in Yulin is wider, and the carbon emissions also appear negative because of the carbon reduction effect of photovoltaic power generation. However, the distribution of EC and TDHP shows little difference from that in Xi'an. In terms of the sensitivity of objectives to the scheme, the response degree of the Pareto solutions to the four objectives is different. LCC has the largest numerical distribution interval, followed by LCCO\({}_{2}\) and EC, while TDHP has the smallest distribution span. This indicates that the adjustment of the design variables has a significant impact on the first three objective functions, while the effect on TDHP is relatively small. Focusing on the influence of each objective on the comprehensive evaluation for design schemes, entropy  
Figure 5: The relationships between the bi-objective Pareto solutions at Xi’an and Yulin: (**a**) Xi’an; (**b**) Yulin. values were applied to judge the dispersion degree of each indicator. The smaller the information entropy value, the greater the dispersion degree of the indicator, indicating a greater influence of the indicator on the comprehensive evaluation. The orders of the four objectives were calculated for the two cities as follows: for Xi'an, LCC > EC \(\approx\) LCCO2 > TDHP; for Yulin, LCC > LCCO2 > EC = TDHP. This shows that the influence of each objective on the comprehensive evaluation also varies in different regions.
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 5 Results and Discussion  
### Vehicle Detection  
We trained the model in Equation (1) on the labeled samples. Equation (3) shows the resulting logistic regression model. The output is a likelihood value between 0 and 1, with the threshold at 0.5:  
\[\begin{array}{ll}\hat{y}&=\sigma(w_{2}\mathbf{\hat{a}}^{2}+w_{1}\mathbf{ \hat{a}}+b)\quad\text{with}\quad\sigma(x)=\frac{1}{1+e^{-x}}\\ \hat{y}&=\sigma(0.082\cdot\mathbf{\hat{a}}^{2}+1.246\cdot\mathbf{\hat{a}}-5.0 56)\end{array} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Pearson** & **Spearman** \\ \hline No split & 0.774 & 0.819 \\ \hline Without vehicle & 0.904 & 0.908 \\ \hline With vehicle & 0.921 & 0.889 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation coefficient comparison.  
We evaluated the significance of the estimated parameters \(w_{2}\), \(w_{1}\), and \(b\) by calculating the corresponding \(p\)-values, which were 0.987, \(5.377\cdot 10^{-3}\), and \(6.563\cdot 10^{-12}\), respectively. The low parameter value and large \(p\)-value for the second-order term show that it does not contribute to the function, and the model effectively fits a first-order logistic regression in performing the classification.  
We evaluated the vehicle detection model on the test set, as described in Section 4. The results of the sample-based comparison can be found in Tables 2 and 3. For the event-based evaluation, the results are 0 misses and 0 false positives. Because of the perfect event-based metric, the sample-based errors can be explained by a potential misalignment between the collected ground-truth vehicle windows and the predicted windows. A vehicle is, on average, inside a station for 51.3 s based on the ground-truth data. A single sample mismatch due to human inconsistency during data collection translates to a 20% error for that vehicle event in the sample error rate. As a sample period of 10 s is used, an average vehicle event takes five samples. A comparison between the prediction (black line) and the collected ground-truth data (green spans) is shown in Figure 5.
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 3 Experimental Design  
### Dangerous Object Dataset  
Currently, there are few publicly available large-scale dangerous objects datasets, so this study constructed a custom dataset for the experiments. Given the limited occurrence and types of dangerous objects typically found under vehicles, this paper constructed dataset I and dataset II. Dataset I was constructed by collecting a wide variety of dangerous object images from online sources, while dataset II consists of images specifically collected from under-vehicle scenarios.  
In accordance with national laws and regulations, and considering the security inspection requirements for major public venues such as stations, conference centers, and airports, where dangerous objects are strictly controlled, dataset I classifies dangerous objects into four major categories: knives, guns, bottles, and packages. A total of 1600 images were obtained from online sources, with each category containing a wide range of different samples that generally cover the types of dangerous objects that could be found under vehicles. The knives category includes a variety of knives differing in type, size, and length; the guns category encompasses various types of firearms, including those commonly seen in news reports and on the internet; the bottles category includes bottles of various shapes and types, including distorted, deformed, compressed, and stretched bottles to improve the model's ability to recognize bottles as dangerous objects in under-vehicle scenarios; and the packages category includes dangerous objects packed in bags, woven sacks, and cardboard boxes, simulating packages typically seen in express deliveries. This classification and dataset construction aim to provide a comprehensive representation of potential under-vehicle hazards, thereby enhancing the effectiveness of the detection system.  
Figure 4: Structure diagram of PConv.  
Dataset II was constructed using images of dangerous objects captured by an under-vehicle scanning device. The categories of dangerous objects in dataset II are divided into four major classes, consistent with the classification in dataset I: knives, guns, bottles, and packages. The dangerous objects used include various purchased knife and gun replicas, as well as different types of water bottles and express packages. The vehicle undercarriages used for capturing these images were sourced from six different vehicle models. In this study, dangerous objects were randomly placed under the vehicles for the photoshoots, with up to four types of dangerous objects placed under each vehicle. Due to the time-consuming nature of the data-collection process, a total of 865 images of dangerous objects under vehicles were obtained.
## 5 Denoising Analysis of the Actual PD Signal  
The engineering scenario for detecting PD signals at a 500 kV high-voltage shunt reactor is shown in Figure 13. Its sensor is an ultrahigh-frequency sensor. In the detection mode, the signal is modulated, and the sampling frequency is 400 MHz.  
By using the proposed method, the SVD-VMD algorithm, AEEMD algorithm, and Wavelet Multilevel Soft Thresholding algorithm, the measured signals were denoised as shown in Figure 14.  
In the provided figure, the denoising result by the method improved by this paper demonstrates superior noise reduction with a notably smooth signal profile, suggesting effective noise suppression. The SVD-VMD denoising technique demonstrates limited effectiveness, as evident from the residual noise and the lack of enhancement in the useful signal. The AEEMD method shows pronounced peaks in the denoised signal, which could either denote preserved signal characteristics or insufficient noise reduction. The Wavelet method, while successful in removing a significant portion of noise and preserving signal characteristics, still leaves residual noise spikes in the time domain, which could potentially impact the accuracy of feature extraction and subsequent signal analysis.  
Figure 12: The comparison results in different input SNR.  
Figure 14: Comparison of denoising results for actual PD signals using four methods.  
Figure 13: Actual PD signal.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 3 Results
### Routing Overhead  
The routing overhead results from the OLSR and AODV protocols are shown in Figure 8. The results show that OLSR has a higher routing overhead than the default AODV protocol. The AODV protocol overhead routing has an average value of 41, while the OLSR protocol has a value of 774. On the other hand, regarding the variation in RREQ_RETRIES and MAX_RREQ_TIMEOUT values in the AODV protocol, decreasing RREQ_RETRIES to 2 (AODV 2) reduces the routing overhead by 10.8%, while increasing RREQ_RETRIES to 4 (AODV 3) increases the routing overhead by 9.75%. Regarding the decrease and increase in the value, MAX_RREQ_TIMEOUT does not indicate changes in the value of routing overhead.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 2 Problem Formulation
### Phase Control of Estimated Complex SAR Data  
The interpolation procedure is required by the GBP algorithm, which is described in Section 2.3. To ensure that the data is backprojected correctly, a phase control under interpolation is necessary.  
Let \(\tau_{\rm p}\) denote the two range traveling time from the aperture position \((\xi,0)\) to the image position \((\xi_{\rm p},\rho_{\rm p})\) given by  
\[\tau_{\rm p}=\frac{2}{\text{c}_{0}}\sqrt{(\xi-\xi_{\rm p})^{2}+\rho_{\rm p}^{2 }}. \tag{4}\]  
In modern radar systems, the raw data \(g(\xi,\tau)\) is sampled signal in time domain. To get the corresponding complex value at the range time \(\tau_{\rm p}\), surrounding samples of \(g\) given as a function of range time \(\tau_{i}\) for fixed azimuth \(\xi\) have to be used for reconstruction, which are of the form  
\[g(\xi,\tau_{i})\approx\sum_{j=1}^{K}A_{\rm tj}\,\text{sinc}[\pi(f_{\rm max}-f_ {\rm min})(\tau_{i}-\tau_{\rm tj})]\text{e}^{\text{j}2\pi f_{\rm c}(\tau_{i}- \tau_{\rm tj})}. \tag{5}\]  
Here, \(i=0,\ldots,N\), where the number of samples \(N\) depends on the one of the interpolation methods that will be presented in Section 3. Note that the information about the range distance between the SAR system and the target is contained in the phase of complex SAR data, and thus phase control under the estimation of \(g(\xi,\tau_{\rm p})\) is required. To achieve this, the following compensation procedure is introduced  
\[\tilde{g}(\xi,\tau_{i})=g(\xi,\tau_{i})\text{e}^{\text{j}2\pi f_{\rm c}(\tau_ {\rm p}-\tau_{i})}\approx\sum_{j=1}^{K}A_{\rm tj}\,\text{sinc}[\pi(f_{\rm max} -f_{\rm min})(\tau_{i}-\tau_{\rm tj})]\text{e}^{\text{j}2\pi f_{\rm c}(\tau_{ \rm p}-\tau_{\rm tj})}, \tag{6}\]  
which is only used under the local interpolation procedure to estimate \(g(\xi,\tau_{\rm p})\). Here, the proposed procedure assigns the information about the range time difference between the pixel in the defined image plane and the target to the samples to be used in the interpolation procedure. Correspondingly, the interpolated parameter \(g(\xi,\tau_{\rm p})\) will contain the assigned phase information. Interpolation algorithms that include the proposed phase-control procedure (6) will be described in Section 3.
## Materials and Methods
### Voltage Stability Margin Calculation  
The VSM can be computed using the TE circuit. The nose curve is drawn in theory using the TE voltages and impedances. To compare the operation and the nose point, the VSM should be obtained. The power systems are reduced to the TE form as shown in Figure 7. To simplify the equations for the nose curve, it is assumed that \(\overline{E_{Th}}=E\triangle 0\) and \(\overline{V_{L}}=V\triangle 0\), and the resistance of the transmission line is ignored. Subsequently, the following equations are obtained.  
\[\overline{V}=\overline{E}-jX\overline{I} \tag{18}\]  
The complex power is absorbed by the load and decomposes into the following:  
\[S=P+jQ=\ \overline{V}\overline{I}^{\prime}=\frac{j}{X}\Big{(}EVcos\theta+ jEVsin\theta-V^{2}\Big{)} \tag{19}\]  
\[P=-\frac{EV}{X}sin\theta \tag{20}\]  
\[Q=-\frac{V^{2}}{X}+\frac{EV}{X}cos\theta. \tag{21}\]  
Eliminating \(\theta\) from Equations (20) and (21) yields  
\[\Big{(}V^{2}\Big{)}^{2}+\Big{(}2 QX-E^{2}\Big{)}V^{2}+X^{2}\Big{(}P^{2}+Q^{2} \Big{)}=0. \tag{22}\]  
Figure 7: Thevenin equivalent circuit of the power systems.  
This equation is a second-order equation with respect to \(V^{2}\). Changing this equation to a function composed of \(V/E\) and \(PX/E^{2}\) using the quadratic equation yields  
\[\frac{V}{E}=\sqrt{\frac{1}{2}-\left(\frac{PX}{E^{2}}\right)tan\varnothing\pm \sqrt{\frac{1}{4}-\left(\frac{PX}{E^{2}}\right)^{2}-\left(\frac{PX}{E^{2}} \right)tan\varnothing}} \tag{23}\]  
where \(tan\varnothing=Q/P\). Using Equation (23), Figure 8 is constructed in the \((P,V)\) space. The curves shown in Figure 8 are typically known as PV curves or nose curves.  
As shown in Figure 8, the upper part of the curves corresponds to the solution with the plus sign in Equation (22) or the higher voltage solution. Meanwhile, the lower part corresponds to the solution with the minus sign, which is the low-voltage solution. These curves can be applied to the Thevenin impedances as every coefficient is calculated from the phasor measurement data. The curve can be drawn in real time using the TE circuit. Subsequently, the VSM can be computed by comparing the operation point and nose point, as shown in Figure 9.  
As shown in Figure 9, the VSM value is correct when the loads are only of the constant power type. However, the power system loads are nonlinear. To obtain a more exact VSM, the load modeling parameters should be applied in this curve.  
Figure 8: Typical PV curve with the \(\tan\Phi\).  
Figure 9: Voltage stability margin with PV curve.