esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 6 Evaluation
### Quality of Covert Channel  
In order to evaluate the quality of the proposed EM covert channel, multiple network quality metrics were considered and experimentally evaluated. Bit error rate (BER) is a measure of the percentage of bits with errors against the total number of bits transferred through the network. One factor that can affect the BER is the physical distance between the transmitter and receiver on the network. In order to evaluate how the BER varies with distance between the target Ethernet cable and the attacker's hardware, the following experiment was conducted. While keeping the data rate of the covert channel to 4 bps, and the Ethernet cable type to Cat5 UTP, the distance between the victim computer and the SDR receiver were varied from 0 m to 2 m. The EMR signal was captured at 220 MHz and demodulated to retrieve the data. Figure 5 illustrates the variation of BER with distance. At 2 m distance, the BER reaches an acceptable 25%.  
Figure 4: Cross-correlation for the two traffic patterns across the frequencies analysed.  
Figure 3: Cosine similarity (red) and Pearson correlation (blue) for the two traffic patterns across the frequencies analysed.  
The signal-to-noise ratio (SNR) is a measure of how clear the transmitted signal is against the background noise in the physical medium. The variation of SNR was calculated for the previous experiments, along with the distance between the victim computer and the attacker's hardware setup. The SNR varied from approximately 19 dB at 0 m to 4 dB at 2 m distance for Cat5 UTP cable.  
Another important aspect of the proposed covert channel is the ability to transfer data in an acceptably fast rate. To evaluate the data rates achievable through the proposed covert channel, the following experiment was conducted. From the target computer, frames were transferred at different data rates and captured in the corresponding EM traces. The captured EM traces were demodulated to calculate their BER at different data rates. The bottom of Figure 5 illustrates the variation of BER against the data rate on the network. It is evident that the BER linearly increases from 0% to approximately 45% while the data rate varies from 2 bps to 12.5 bps. According to the figure, with a Cat5 UTP cable, a data rate of 4 bps can be achieved without any bit errors from a distance of 2 m.
## 5 Model-in-the-Loop Testing
### Client Phasing  
The MC campaign results, shown in Figure 17, confirm that the GNC chain operates as intended. The controllability of the position and attitude at any point is demonstrated, achieving the Control design guidelines formulated in Section 4.2, with the Navigation system being able to provide relative-position estimation between the servicer and the client using the Narrow-Angle Camera. The results also indicate that the ROE approach for the rendezvous is working correctly, with all orbits being passively safe between transitions and corrections. The simulations also show that no trajectory from the MC campaign unintentionally enters the Approach Zone, which has a radius of 1 km, as mentioned in Section 3.2.
## 5 Conclusions
### Limitations of the Study
#### 5.1.2 Short-Term Knowledge Recall and Long-Term Retention  
Another important limitation concerns the short-term nature of the assessment. The post-test was conducted immediately after the intervention, meaning that improvements in conceptual understanding may partially reflect short-term recall rather than deep learning consolidation. While the results indicate a significant increase in learning gains, this design does not account for the sustainability of knowledge over time. Future longitudinal studies should assess whether the observed learning improvements persist over extended periods and how IVR-based learning compares to traditional instruction in terms of knowledge retention and transferability to real-world problem-solving contexts. Implementing delayed post-tests at multiple time points would provide a clearer picture of long-term learning stability.
###### Abstract  
The application of the Internet of Medical Things (IoMT) in medical systems has brought much ease in discharging healthcare services by medical practitioners. However, the security and privacy preservation of critical user data remain the reason the technology has not yet been fully maximized. Undoubtedly, a secure IoMT model that preserves individual users' privacy will enhance the wide acceptability of IoMT technology. However, existing works that have attempted to solve these privacy and insecurity problems are not space-conservative, computationally intensive, and also vulnerable to security attacks. In this paper, an IoMT-based model that conserves the privacy of the data, is less computationally intensive, and is resistant to various cryptanalysis attacks is proposed. Specifically, an efficient privacy-preserving technique where an efficient searching algorithm through encrypted data was used and a hybrid cryptography algorithm that combines the modification of the Caesar cipher with the Elliptic Curve Diffie Hellman (ECDH) and Digital Signature Algorithm (DSA) were projected to achieve user data security and privacy preservation of the patient. Furthermore, the modified algorithm can secure messages during transmission, perform key exchanges between clients and healthcare centres, and guarantee user authentication by authorized healthcare centres. The proposed IoMT model, leveraging the hybrid cryptography algorithm, was analysed and compared against different security attacks. The analysis results revealed that the model is secure, preserves the privacy of critical user information, and shows robust resistance against different cryptanalysis attacks.  
Internet of Medical Things; healthcare data; elliptic curve cryptography; privacy preservation; Caesar cipher; security +
Footnote †: journal:  
0023 by the authors. Liconse 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/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 3 Computation and Comparison
### Improvement of Computational Efficiency of Parallel Approach  
In order to verify the characteristics of the parallel approach in terms of efficiency, the parallel approach is compared with the non-parallel method. The efficiency improvement of the approach in many aspects is further analyzed, such as multi-system, number of threads, and number of processes. In addition, the computational time changing with the number of stations is analyzed, which reflects the advantage of the parallel approach in the improvement of efficiency and provides a reference for the selection of the number of stations in the 1-h update frequency ultra-rapid POD.
## 2 Materials and Methods
### Goodness-of-Fit Measurement of Mixture Modeling  
Deciding on the number of components is one of the most significant issues in fitting heterogeneous distributions with mixture models [25]. In mixture models, the number of components can be defined with a variety of criteria. The **fitmix** package utilizes four approaches to assess the performance of fitted models. Given a collection of mixture models for the RLS (lifetime or survival data), the measurements of four goodness-of-fits can be listed as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log.likelihood) statistics.
# Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
## 3 Proposed Algorithm  
Our algorithm is specifically designed to address the capture of data and its transmission in linear infrastructures. Although many infrastructures are not strictly linear (e.g., a pipeline or a road may have branches), they can be separated in linear segments. Therefore, we make the following assumptions:  
* We consider a linear structure, without branches, with \(n\) nodes and two border nodes or base stations, \(v_{0}\) and \(v_{n+1}\), which are responsible for sending information to the central monitoring using long-range communication protocols.
* Each node works with the IEEE 802.15.4 standard in beaconless mode (CSMA/CA).
* Each node has a coverage area of 50 m (a reasonable distance for an IEEE 802.15.4 network in the 2.4 GHz band [43]), without obstacles and transmission conditions in the line of sight, and is associated with four nodes, two on the left and two on the right.
* The nodes are deployed with a separation of 25 m. Therefore, a node has at least 4 nodes in range.
* The participating nodes use a predefined PAN identifier to prevent the connection to other nodes that are not part of the linear infrastructure.
* Each node has an identifier assigned sequentially and automatically using processes at the link level, as indicated in [44].
* The bits of the header identified for future use are used to implement the control messages required by the proposed algorithm.  
We propose using LWSN topologies as the one shown in Figure 1 which consists of \(n+2\) nodes, sequentially identified from 0 to \(n+1\). The sequential assignment of the identifiers is an essential feature on this research approach and solved as detailed in [44].  
Nodes \(v_{0}\) and \(v_{n+1}\) are connected to the control center and do not have any limitation on energy or communication technologies, while nodes \(v_{1}\) to \(v_{n}\) are nodes with sensors that capture information and also retransmit the frames of the neighboring nodes. Sensor nodes generate traffic when an event happens (e.g., the rupture of oil pipelines, unauthorized entry into borders, etc.). The information generated by node \(v_{i}\) is transmitted to the neighboring nodes, creating two traffic streams simultaneously, \(P_{l}\) (left direction) and \(P_{r}\) (right direction), so it can reach the control center through \(v_{0}\) and \(v_{n+1}\). This feature can be used to introduce redundancy in the network, but the operation with both flows is similar, so we are going to study only the \(P_{r}\) flow. The nodes are in fixed locations, and therefore, the topology does not change in time. Modifications to the topology will only be made when a node or several nodes fail, or a link failure occurs. This feature allows the use of simpler routing algorithms and facilitates the location of nodes.
###### Abstract  
The centroid and moment of inertia directly affect the dynamic characteristics of aircraft, and accurate mass characteristic measurements are crucial to adequately control aircraft attitude. This paper proposes a new measurement method for determining aircraft mass characteristics that can improve measurement precision when used in regard to pico-satellites. The measurement system is designed according to the principles of three-point measure and constant torque. The feasibility of the test method of this study for determining the mass characteristics is proved by using a dynamic simulation and an experimental analysis method. Through a number of standard workpiece tests, the deviation of the measurement system and effective compensation methods of the mass characteristics are obtained. The measurement system and compensation methods are applied to measure the mass characteristics of a pico-satellite, which can be detected by only one time clamping. The measurement system proposed herein can effectively improve measurement precision, as it was found that the accuracy of the centroid and the moment of inertia of the pico-satellite are less than 1 mm and 1.5 \(\times\) 10\({}^{-2}\) kgm\({}^{2}\), respectively. The proposed measurement system also has many advantages, such as a simple operation, high efficiency, small volume, and low cost.  
pico-satellite; mass characteristics; centroid; moment of inertia 2018 8, 104; 10.3390/app8010104
## 4 MMS-DSR Mathematical Formalization
\]  
Each high-level feature \(x_{l,t}\) is computed using the following equation:  
\[x_{l,t}=f_{l}(w_{t}\otimes S_{t}+b_{t})\]  
The term \(w_{t}\) denotes the filter with \(K-L+1\) coefficients, while \(S_{t}\) represents the \(t\)-th column of the matrix \(S\), and \(b_{t}\) is the bias term. The activation function \(f_{l}\) applies a non-linear transformation, and \(\otimes\) indicates the convolution operation. After extracting high-level spatial features, these vectors are selected as input for the LSTM network. The input to the LSTM is expressed as follows:  
\[F=[F_{0}\;F_{1}\;\cdots\;F_{N-1}]\]  
The high-level spatial feature map for each LSTM network \(n\), denoted \(F_{n}\), is formulated as follows:  
\[F_{n}= \begin{bmatrix}x_{1,t+n-N}&x_{1,t+n-N+1}&\cdots&x_{1,t+n-N+M-1}\\ x_{2,t+n-N}&x_{2,t+n-N+1}&\cdots&x_{2,t+n-N+M-1}\\ \vdots&\vdots&\ddots&\vdots\\ x_{L,t+n-N}&x_{L,t+n-N+1}&\cdots&x_{L,t+n-N+M-1}.\end{bmatrix} \]  
In this formulation, \(M\) indicates the adjustable input window size. The spatio-temporal features generated by the LSTM are denoted as follows:  
\[H=[H_{0}\;H_{1}\;\cdots\;H_{N-1}]\]  
Each \(H_{n}\) is a matrix of size \(K\times T\), with \(T\) representing the adjustable output window size, subject to the condition \(M+T\leq N\). The iterative process for generating spatio-temporal features is defined by the following equations:  
\[f_{n} =\sigma\Big{(}W_{f,F}\cdot\text{vec}(F_{n})+W_{f,H}\cdot\text{ vec}(H_{n-1})+b_{f}\Big{)}\] \[i_{n} =\sigma(W_{i,F}\cdot\text{vec}(F_{n})+W_{i,H}\cdot\text{vec}(H_{ n-1})+b_{i})\] \[c_{n} =f_{n}\odot c_{n-1}+i_{n}\odot\tanh(W_{c,F}\cdot\text{vec}(F_{n} )+W_{c,H}\cdot\text{vec}(H_{n-1})+b_{c})\] \[o_{n} =\sigma(W_{0,F}\cdot\text{vec}(F_{n})+W_{0,H}\cdot\text{vec}(H_{ n-1})+b_{o})\] \[H_{n} =o_{n}\odot\tanh(c_{n})\]  
where \(W_{s,F}\) and \(W_{s,H}\) are the weighting matrices associated with the current input high-level spatial feature matrix \(F_{n}\) and the previous spatio-temporal feature matrix \(H_{n-1}\), respectively. The vectorization operation, denoted \(\text{vec}(\cdot)\), accounts for the differing sizes of \(F_{n}\) and \(H_{n-1}\). The functions \(\sigma\) and \(\tanh\) represent the sigmoid and hyperbolic tangent functions, which are applied element-wise.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 1 Introduction
The model test provides the pull-out bearing capacity of this new connector, and an ABAQUS finite-element model is constructed and validated with experimental data. Subsequently, 54 parametric models are formulated to analyze the key factors influencing performance. The study concludes with the proposition of a calculation method for the pull-out bearing capacity of the redesigned connector.
## Appendix A An Example of Gridding the Gravity Observation Data
### An Overview of Kriging Interpolation  
Kriging interpolation is an interpolation method based on the variation function and its model. Besides considering the correlation between the position of the interpolation points and the position of the known data points, it also reflects the spatial correlation of the variables. To calculate, we must first know the total number of data sample points \(\mathsf{n}\), the spatial distance \(\mathsf{h}\) from all surrounding sample points to the point to be interpolated, the lag distance, and the spatial attribute \(Z(x)\) of each sample point. Suppose some observation points \(x_{1},x_{2},\ldots,x_{n}\) are on the space area. \(Z(x_{n})\) is the corresponding observation value, then the observation value \(Z^{*}(x_{0})\) of the local variable at the unknown point to be interpolated \(x_{0}\) is obtained by Equation (A1).  
\[Z^{*}(x_{0})=\sum_{i=1}^{n}\lambda_{i}Z(x_{i}).\] (A1)  
Among them, \(\lambda_{i}\) is the Kriging weights coefficient.  
Conditional unbiasedness, minimum estimated variance, and the Lagrange multiplier are used to obtain the Kriging equations composed of variation function. The variation function is defined as Equation (A2), denoted as \(\gamma(h)\) :  
\[\gamma(h)=\frac{1}{2n}\sum_{i=1}^{n}[Z(x_{i})-Z(x_{i}+h)]^{2}.\] (A2)  
Among them, \(Z(x_{i})\) and \(Z(x_{i}+h)\) are the true values of the local variable \(Z(x_{n})\) at the distances \(x_{i}\) and \(x_{i}+h\). To obtain the variation function at any distance and facilitate subsequent data interpolation or prediction, it is necessary to perform fitting of \(\gamma(h)\).
# Efficient Layered Parallel Architecture and Application for Large Matrix LDPC Decoder
###### Abstract  
For a 50G passive optical network (PON) low-density parity-check (LDPC) decoder, decoding performance and area efficiency must be balanced. This paper adopts a layered decoder method to improve the area efficiency of the decoder. By parallel processing of three submatrices and storage reuse of node information, optimizing the matrix partitioning and processing order of the 50G-PON standard, the throughput of 1235 bps was reached under 100 MHz circuit frequency in field-programmable gate array (FPGA) implementation, and 9.864 Gbps was achieved based on Taiwan semiconductor manufacturing company (TSMC) 65 nm synthesis with 800 MHz circuit frequency in an area of 2.61 mm\({}^{2}\) by proposing a mechanism of spare decision storage to avoid errors caused by quantization overflow of the decoder and using full verification to terminate decoding in advance to improve performance. Finally, at an input bit error rate (BER) of \(2.3\times 10^{-2}\) (signal-to-noise ratio (SNR) of about 3.72 dB), the output BER was lower than \(10^{-12}\), and the throughput area rate (TAR) also increased by 2 to 4 times compared with other papers. In conclusion, an area-efficient LDPC decoder without sacrificing decoding performance is made.  
L pacs: 03.67.Lx, 03.67.Lx, 03.67.Lx
## 3 Star Tracker Observation Model
\right] \tag{8}\]  
Then, after the observation vector under the geocentric observation point is rotated, it can be transformed into the observation vector under the satellite observation point. The rotation matrix is the rotation transformation matrix from the geocentric coordinate system to the star tracker coordinate system. Obtained by the reversibility of the rotation matrix  
\[\left[ \begin{array}{c}\cos\delta_{earth}\cos\alpha_{earth}\\ \cos\delta_{earth}\sin\alpha_{earth}\\ \sin\delta_{earth}\end{array} \right]\ =\ A_{1}{}^{-1}\left[ \begin{array}{c}\cos \delta_{star}\cos\alpha_{star}\\ \cos\delta_{star}\sin\alpha_{star}\\ \sin\delta_{star}\end{array} \right] \tag{9}\]  
It can be concluded from the above equation that any satellite observation vector of the debris can be rotated to obtain the observation vector of the debris based on the center of the earth, and this property is defined as the invariability of the geocentric observation vector. With the observation model of the star tracker, the rectascension and declination of the observed debris can be obtained, and it can be obtained according to Formulas (4) and (5)  
\[\alpha_{star}\ =\ \arctan\frac{y_{deb}-y_{sat}}{x_{deb}-x_{sat}} \tag{10}\]  
\[\delta_{star}\ =\ \arctan\frac{z_{deb}-z_{sat}}{\sqrt{\left(x_{deb}-x_{ sat}\right)^{2}+\left(y_{deb}-y_{sat}\right)^{2}}} \tag{11}\]  
Then the observation equation is derived:  
\[\alpha_{d}\ =\ \arctan\frac{y_{deb}-y_{sat}}{x_{deb}-x_{sat}}+\
u_{\alpha} \tag{12}\]  
\[\delta_{d}\ =\ \arctan\frac{z_{deb}-z_{sat}}{\sqrt{\left(x_{deb}-x_{sat} \right)^{2}+\left(y_{deb}-y_{sat}\right)^{2}}}+\
u_{\delta} \tag{13}\]  
The instantaneous direction of the moving target in the space (retascension, declination), brightness, velocity, and motion trajectory can be obtained by a single star tracker. From the joint calculation of the instantaneous pointing and the motion trajectory, the orbital inclination of the target can be calculated. Because of the high pointing accuracy of the star tracker, the more precise orbit inclination calculated with its data will be used as the basis for the main target recognition and classification index. Because the brightness information is closely related to the observation angle and the angle of the sun, even if certain space debris can simultaneously be observed by two star trackers, its brightness information will also vary greatly due to the influence of the observation angle and the sun angle [26]. Therefore, it is only a reference value and can be used to evaluate the target size information after the completion of orbit determination. The speed is the relative speed, which is the projection of the space debris velocity vector on the satellite optical observation platform detector. Because of the different angles of the two satellite observation positions, the relative velocity will vary greatly and is not significant for the classification and recognition by the two satellite observations.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 4 Methodology Used in DRFTSA
### Proposed Deep Reinforcement Fault-Tolerant Task Scheduling Algorithm (Dft TSA)  
The Algorithm 1 and Figure 3 is the working flow of the proposed deep reinforcement fault-tolerant task scheduling algorithm. Initially, required parameters are probability with which a task is to be scheduled randomly, learning frequency, replay memory, and batch of replay memory values. Then, the Q-function is initialized and it is set to 0. After this step, task and VM priorities are calculated. After checking priorities of tasks and VMs, the scheduler needs to identify the resource manager for availability of resources and then, if they are available, schedule tasks according to priorities considered and check the reward in the next step. If it is positive, it is checked whether the parameters are minimized or not and then the state is updated. This is repeated for all batches of tasks until they are completed.  
Figure 3: Working flow of proposed DRFTSA.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 5 Results and Analysis
### Scenario 1: Fault Resistance Effect  
The FL errors are classified for each fault type. At the same time, the errors of each fault type are presented in five different ranges of fault resistance with 20 \(\Omega\) intervals. Average and maximum errors are also identified in Figure 12a,b.  
The results obtained using the information of IEDs show a maximum average error of 0.16% and a maximum error of \(-0.46\%\). It is worth noticing here that both errors occurred for the single-phase to ground fault. These errors correspond to 90 m and 270 m, considering a feeder with 58 km of length. On the other hand, when the DER electrical model is used, the method's performance decreases, obtaining a maximum average error of 0.55% and a maximum error of 1.04%. These errors were also obtained for the single-phase to ground fault. As expected, the lowest location errors are found with the model that uses the information of the IEDs.  
Figure 12: Percentage errors as a function of the fault resistance for the proposed method: (**a**) using the Intelligent Electronic Devices (IEDs) information, (**b**) using DER electrical model instead of IEDs information.