esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Ion exchange membranes (IEMs) are actively used in modern technologies, including water purification, concentration, electrochemical synthesis, and sensors [1; 2; 3; 4; 5]. Their use in fuel cells [6; 7; 8; 9], in energy storage and conversion systems, e.g., metal-ion batteries [10; 11; 12], reverse electrodialysis [13; 14; 15], and redox batteries [16; 17; 18; 19] has received the most attention in recent years. There are a number of other technologies in which ion-exchange membranes can also be used. For example, they can successfully compete with porous membranes in the processes of alkenes/alkanes separation [20; 21; 22; 23; 24]. Undoubtedly, ionic conductivity plays a dominant role in these applications. Even the above-mentioned alkene transfer proceeds via the a complex with silver ions or protons [25; 23; 26; 27].  
At the same time, the ion separation by membranes never occurs with 100% selectivity and is usually accompanied by an undesired transfer of molecules or opposite charged ions. Despite the fact that these processes proceed to a much less extent, they usually determine the decrease in the efficiency of electrodialysis, fuel cells and other devices based on ion-exchange membranes [16; 28; 29; 30]. That is why their understanding is so important for numerous membrane technologies.  
The selectivity of transport processes in ion-exchange membranes is determined by the structure of their pores and channels [31; 32; 33; 34]. As this structure is formed as a result of the self-organization, it is difficult to control but it is the key aspect which allows one to regulate the ion-exchange membrane selectivity. In addition, there are major differences between the morphology of homogeneous and heterogeneous membranes, which lead to a lower selectivity of the latter [33; 35; 36]. Nevertheless, lower cost of heterogeneous membranes determines their widespread use in electrodialysis plants and in a number of other devices [37; 38].  
Recently, increasing attention has been paid to the separation and processing of complex mixtures, including industrial and domestic wastes, sea water [39; 40; 41; 42; 43; 44; 45]. There is every reason to believe that their processing, along with recycling, would determine a major part of production of a number of metals and their compounds [39; 46; 47; 48; 49; 50]. For the development of such technologies the selectivity between ions with different valences of the same sign (mono- and multivalent) is no less important [51; 52; 53; 54]. The processes of their separation have recently been extensively reviewed [55; 56; 57; 58]. However, this topic cannot be ignored, and in order to avoid overlapping, in this review, we will consider it briefly, focusing on newer studies in this area.  
This review is devoted to the consideration of the selectivity of transport processes in ion-exchange membranes. The membrane structure-selectivity relationship and various methods for selectivity management are discussed. The main attention is paid to the selectivity of transport processes of ions with different charges and ions with neutral molecules.
## 5 Analysis of Examples
### Cluster Grading and Effectiveness Analysis of the Status Values of Traffic Interruptions  
#### 5.2.1 Cluster Grading  
Some hyperparameters need to be preset before clustering.  
Fuzzy index m: [PERSON] found that m is related to the convergence of the algorithm, and its value is linked to the quantity of sample data (\(n\)). m should be greater than \(n/(n-2)\), and its empirical range is \(1.1\leq m\leq 5\). [PERSON] et al. believed that the optimal value for \(m\) falls within [1.5, 2.5], and it is recommended to take a value of 2 [23], based on the experimental verification of clustering algorithm's effectiveness. The geometric characteristics of clustering vary with \(m\). Clustering exhibits Boolean characteristics when \(m\) approaches 1, with most fuzzy membership degrees being either close to 0 or to 1. The fuzzy membership degree of the cluster resembles a Gaussian function when \(m=2\). The function of fuzzy membership tends to peak as \(m\) increases [24]. Considering these insights, \(m=2\) in the work.  
The maximum number of iterations is set to 100 to ensure that the algorithm has enough opportunities to identify an appropriate clustering center.  
Iteration precision is set to \(10^{-5}\) to ensure the accuracy of clustering results  
Figure 12: Evaluation results of traffic interruptions.  
Figure 13: Evaluation results of each highway.  
Status values undergo fuzzy processing using FCM based on data calculated by the entropy weight-TOPSIS method to yield a fuzzy membership matrix.  
The relationship matrix \(U\) is first initialized using the interruption number and the traffic interruption status value; the distance between the samples \(d_{ij}\) is calculated.  
Then, its fuzzy affiliation matrix is obtained after 100 iterations by updating the affiliation matrix \(U\) and the cluster center matrix \(V\), using Equations (14) and (15). A portion of this matrix, specifically about the attributes of traffic interruption severity, is presented because the following discussion involves the clustering effects of varying number of clusters. This matrix shows the membership degree of each traffic interruption to levels 1-4. The sum of the membership degrees for each traffic interruption equals 1, which signifies the likelihood of the event belonging to different levels (Table 5).  
The above table shows the grading results for varying number of clusters. The categorization of status values becomes more refined with the higher number of clusters, which facilitates a more precise evaluation of the severity of traffic interruptions.
###### Abstract  
The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite, GNSS receivers are susceptible to large-ranging errors, which are particularly prevalent in urban areas where precise positioning is indispensable. The deployment of the high-spatial-density Fifth-Generation (5G) network makes it possible to provide a broad area with high-precision positioning service. Obviously, it promoting the deep integration of the GNSS system and the 5G mobile communication network and establishing a Highly Dependable Spatio-temporal Network (HDSN) have become an inevitable trend. The existing algorithm for the fusion of multiple signals has difficulty settling problems such as the fast fluctuation of available signal sources and the poor stability of multi-scale multi-type signal estimation in GNSS-5G hybrid networks. Here, we propose a Square Root Unscented Stable Filter (SRUSF) for GNSS and 5G joint positioning with a compact coupled filter group architecture in a highly dependable spatio-temporal network. A stabilized coefficient is added to guarantee positive covariance of the estimation error. The possibility of divergence of filtering results due to the variation in signal sources and the incomplete agreement between the system model and the actual situation are reduced. The simulation results show that the proposed SRUSF method substantially enhances the positioning accuracy and reliability compared with the other five joint estimation methods for multiple signals. This work will enable the terminal of mass users to provide timing and positioning services with unprecedented accuracy and dependability under the GNSS and 5G-based spatio-temporal network's architecture.  
highly dependable spatio-temporal network; GNSS; 5G network; hybrid positioning; estimation method +
Footnote †: journal: remote sensing
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 1 Introduction
Well calibrated SAR data products with accurately measured radar brightness are required for a number of applications and compose the basis for the quality of higher level products. The measured radar brightness can be exploited for various scientific applications and used to classify or even quantify the observed target area, e.g., ice areas [16], forests or other kinds of vegetation [17], soil moisture of wetland and grassland [18], as well as ocean currents or wind speeds [19]. By using a radar constellation it is important that each sensor produces comparable results, in particular in terms of measuring backscatters. In an ideal case, the same results should be achieved for the same observation area under constant conditions for each instrument so that the users do not have to care about the sensor used for acquiring the scene. This comparability should be valid over the whole backscattering range from targets with a very high signal-to-noise ratio (SNR) down to noise level. The current study evaluates the intercomparability of S-1A and S-1B in terms of detected radar brightness and investigates reasons for found discrepancies. First, RCSs derived from point targets are evaluated and compared between S-1A and S-1B over a two-year observation period. Similar to SAR system calibration, the absolute radiometric accuracy and stability for both SAR instruments is derived from these measurements. These reference targets are appropriated for calibration purposes due to their high signal-to-clutter ratio and a negligible impact of system noise. Based on these results, the radar brightness of distributed targets are evaluated for different backscatter regimes: starting from medium backscatter found in Amazon rainforest areas over lower values in ice areas of Greenland down to near noise backscatters which are expected for calm water. For these different target types the radiometric stability and the comparability between S-1A and S-1B are analyzed. For targets with low SNR (below about 10 dB), the impact of system noise is no more negligible and has to be taken into account. The accuracy of determined low radar brightness depends on the accuracy of the derived noise but is also limited by the noise level itself. Therefore, the noise level of S-1A and S-1B is also compared and the impact of noise on low backscatter is analyzed. Finally,the transmit power of both SAR instruments is compared by using transponder (ground receiver) recordings. The used data and methods are introduced in Section 2, the results are presented in Section 3 and discussed in Section 4, and Section 5 concludes the paper.
## 2 Related Work
After the feature extraction and dimensionality reduction of bearing vibration signals, bearing reliability evaluation can be carried out through reliability modeling. Logistic regression (LR) [21] is a commonly used mathematical modeling method for evaluating bearing reliability. [PERSON] et al. [22] used a logistic regression model to establish a bearing reliability model, effectively characterizing the reliability features of the bearing. Based on reliability assessment, advanced machine learning methods can also be used to predict the performance state of the bearing, with the predicted bearing state serving as the input for the reliability evaluation model, enabling dynamic prediction of the bearing's operational reliability. Relevance Vector Machine (RVM) [23] is a reliable machine learning method commonly used for regression and classification tasks. It shares many similarities with Support Vector Machine (SVM) [24], as both use kernel functions to map low-dimensional nonlinear problems to high-dimensional linear ones. In comparison, RVM overcomes the limitations of SVM when there are insufficient training data, while maintaining high accuracy. [PERSON] et al. [25] proposed an improved RVM model called Weight Tracking Relevance Vector Machine (WTRVM), which performs excellently in predicting the degradation state of rolling bearings. In addition, the research team designed an adaptive sequential optimal feature selection method, which effectively avoids overfitting in the prediction process by selecting the optimal features. [PERSON] et al. [26] proposed an enhanced RVM model combined with polynomial regression for predicting the degradation state of bearings. Experimental results showed that this method significantly improved prediction accuracy. [PERSON] et al. [27] proposed a hybrid bearing state prediction method combining the Grey Model (GM), Complete Ensemble Empirical Mode Decomposition (CEEMD), and RVM, which greatly improved prediction accuracy under long-term operational conditions of bearings.
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 4 Test Environment, Scenario, and Results
### Test Environment  
The MSP-EXP430 FR5994 (Texas Instruments Inc., Dallas, TX, USA) was chosen for the test environment, and is shown in Figure 2. The microcontroller in the kit contains 256 KB of ultra-low-energy consumption FRAM nonvolatile memory and operates at a clock frequency of 16 MHz. It supports a wide voltage range from 1.8 V to 3.6 V, making it suitable for low-power applications. The system is built on a 16-bit RISC architecture, capable of running at a 16 MHz system clock with 8 MHz FRAM access speed. In addition to 256 KB of FRAM, it includes 8 KB of SRAM, ensuring efficient memory usage. For analog signal processing, the microcontroller features a 16-channel 12-bit ADC and a 16-channel analog comparator, enhancing its capability for sensor-based applications. It is equipped with six 16-bit timers, each with seven capture/compare registers, allowing precise timing operations. The device also supports 6-channel direct memory access, enabling efficient data transfer without CPU intervention. Algorithms were coded in CCS (Code Composer Studio) and compiled using C code before being transferred to the MSP430 FR5994 device (Texas Instruments Inc., Dallas, TX, USA). The EnergyTrace software (version 10) was used to measure the algorithms' energy, power, and current [35]. The energy consumption of all encryption algorithms developed in the test environment was measured with the EnergyTrace software. The EnergyTrace software was used to measure the algorithms' energy, power, and current. EnergyTrace technology is an energy-based code analysis tool that monitors the application's energy profile and assists in optimizing for ultra-low energy consumption.  
The MSP-EXP430 FR5994 microcontroller was selected due to its suitability for low-power embedded system research, particularly in cryptographic and IoT-focused applications. It features an ultra-low-power FRAM architecture, built-in EnergyTrace technology for accurate energy profiling, and native AES hardware acceleration, making it ideal for real  
Figure 2: MSP-EXP430 FR5994 LaunchPad development kit.  
time cryptographic benchmarking. Additionally, it is a cost-effective and widely accessible platform, making it a practical choice for both academic research and industrial prototyping. As a Texas Instruments product, it is also commonly used in industrial applications, ensuring that the findings of this study are relevant to real-world IoT deployments.
## 4 The Proposed Mechanisms
### The ReCIF Mechanism
The reason for computing \(C_{n}\) from a discarded interest packet is to maintain the protocol awareness upon new network changes. Figure 3 presents two scenarios where a central node \(B\) will decide if it forwards or discards a received packet. The blue or grey nodes are the central nodes and are running the ReCIF-soft mechanism. Let us assume that the second scenario initiates after the first scenario. For the first scenario, we assume that \(C_{n}(t)=5\), due to prior calculations. Thus, when the packet arrives at node \(B\), after being forwarded four times, its \(c_{i}\) is 4. The node then compares \(c_{i}\) and \(C_{n}(t)\) and then accepts the forwarding of the packet, because the packet was forwarded less than five times. After this, \(B\) calculates the new value for \(C_{n}(t)\) which is \((4*0.1+5*0.9)=4.9\). Thereafter, for the second scenario, the value of \(C_{n}(t)\) is 4.9. The packet received by \(B\) in the second scenario has been forwarded seven times before, which is higher than \(C_{n}(t)\). Therefore, the packet will be discarded, and the new \(C_{n}(t)\) will be recalculated to 5.11. We also evaluated ReCIF-soft with the threshold \(G=0\) (Algorithm 4). In this scenario, all nodes forward interest based on the criterion adopted by ReCIF-soft, regardless of its cent  
Figure 3: An example of ReCIF-soft in action. behind this strategy is to avoid the broadcast storm, even on nodes located in low-density regions of the network. In some experimental traces, we observed that nodes with a low centrality degree also contribute to increasing the amount of interest forwarded by nodes with a high degree of centrality. ```
Data: (\(c_{i}\), \(pck.interest\).) begin
1 ifthen
2 ReceivedPcksN == P
3 ifthen
4 ReceivedPcksN == P
5 ifthen
6 ReceivedPcksN == P
7 end if
8\(C_{n}(t)\leftarrow\left((1-\alpha)*c_{i}+\alpha*C_{j}(t-1)\right)\); if\(c_{i}<=C_{n}(t)\)then
9 Forward(pck.interest) ;
10 else
11 Discard(pck.interest);
12
13 end if
14
15 end for
```  
**Algorithm 4**ReCIF-soft G=0 Mechanism.
# Spinal Cord Segmentation in Ultrasound Medical Imagery
## 4 Experimental Results
### Experiments on Dataset-A
#### 4.2.2 Modifications Applied to FC-DenseNet103  
ASPP DenseBlock  
In order to capture the multi-scales features inside the data, we replaced the DenseNet block in FC-DenseNet103 illustrated in Figure 6 by the ASPP-DenseNet block illustrated in Figure 9. We noted that the modification improves the convergence of the algorithm without improving the IoU.  
Figure 15 shows the IoU change after every epoch of the training.  
Figure 14: Samples from Dataset-A (patient 06 to patient 10), the ground truth and the predicted segmentation mask.  
Table 4 shows the metrics measured before and after applying the ASPP block on FC-DenseNet103 network.  
The ASPP is a widely used concept in semantic segmentation. Some studies show an increase of efficiency when integrating it with FC-DenseNet in some tasks like breast tumor segmentation [41]. However, It does not help in improving the results in spinal cord segmentation. This is probably because the size of the spinal cord in the images has small variance and does not have multi-scale patterns to capture contrarily to the breast tumors [41], which differs largely in size.  
Depthwise Separable Convolution  
In order to reduce the size and the computations needed to train FC-DenseNet, we changed all the convolutional layers in the network by Depthwise Separable Convolutions (DSC). As expected, the operations reduce the size of the parameters without affecting the measured metrics. Only a slight decrease in convergence is noted as shown in Figure 16.  
Table 5 shows the metrics measured before and after applying the Depthwise Separable Convolutions (DSC) on FC-DenseNet103. On the other hand, Table 6 shows the reduction of size after the application of DSC.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **IoU** & **Dice Score** & **Accuracy** & **Recall** & **Precision** \\ \hline before & 0.946 & 0.989 & 0.989 & 0.989 & 0.989 \\ with ASPP & 0.943 & 0.988 & 0.988 & 0.988 & 0.988 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Metrics measured before and after applying the ASPP block on FC-DenseNet103 network.  
Figure 15: IoU after every epoch of the training before and after adding the ASPP block.  
Set of Training Configurations  
To more fine-tune the FC-DenseNet network, we concluded some configurations to use during the training of the network to improve the network efficiency:  
* Data augmentation: we used vertical flip and horizontal flip transformation, brightness change of degree 20% and rotation transformation in the range of 20 degree.
* Weighted cross-entropy: We gave the weight 0.95 for the weight of the class spinal cord.
* ADAM optimizer: the best learning rate to use is 0.0001.
# Towards Incidence Management in 5G Based on Situal Awareness
## 5 Analysis and Decision-Making
### Analysis
#### 5.1.5 Prediction  
Prediction facilitates the anticipation of future complications, such as congestion of certain network regions, inclusion of large amounts of new assets or spreading of cyberattacks. The general scheme for forecasting SA studies variations on their contents by modeling the sequence of observations as time series, as it is shown in Figure 4. Each observation includes information about every SA feature, such as the network map, risk levels or recent incidents monitored over a period of time. Because of this, the prediction module is not only capable of anticipating particular situations, but also the whole future SA, thus providing general and particular overviews about everything that occurs in the system. Note that the observations are delimited by a fixed period of time. A common alternative to this fixed value is to consider more specific features, such as workload or number of situations reported. Both cases imply advantages and disadvantages, but the delimitation by time periods poses a more intuitive method. In order to avoid overloading of storage systems, after a reasonable period, the oldest observations are discarded to make way for the new ones. In this way, a sliding window of size N that gathers the observations is applied. This boundedness is important for ensuring that the implemented algorithms are computable, avoiding the case N \(\rightarrow\infty\) (i.e., the worst case), which leads to holding an infinite amount of information.
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 5 Experimental Design and Performance Evaluation  
### Experimental Design  
This subsection covers the experimental design to evaluate the performance of the proposed coded FTN signal. The simulations were performed using a DVB-S2X system with the following configuration:  
LDPC code parameters: an LDPC code length of 64,800 bits with a code rate of 2/3 was used.  
Baseband modulation: two modulation schemes were considered, namely, Quadrature Phase Shift Keying (QPSK) and 16-Quadrature Amplitude Modulation (16-QAM).  
Pulse shaping: Root Raised Cosine (RRC) pulses with a roll-off factor of 0.35 were applied, as specified in the DVB-S2X standard [31].  
Symbol interval parameter (\(\boldsymbol{\tau}_{\text{code}}\)): for all three of the coded FTN signal techniques, the \(\tau\) values were randomly generated in the range of 0.5 to 1, with an interval of 0.05.  
The average \(\tau\) value (E[\(\boldsymbol{\tau}_{\text{code}}\)]) in the symbol-wise coded FTN signal was set to 0.75. Block lengths (B) of 3, 5, and 10 symbols were evaluated in the block-wise coded FTN signal, and the average \(\tau\) value was also set to 0.75.  
For the variable-length block-wise coded FTN signal, the block length was dynamically determined based on the \(\tau\) value using Equation (7).  
Comparison Benchmarks: the performance was compared with a regular FTN signal system [31] with a fixed \(\tau\) value of 0.75. The performance of a conventional DVB-S2X system without an FTN signal was also included for reference.  
Eavesdropping Mode: to evaluate the security, an eavesdropping mode was simulated, where the receiver attempted to decode the coded FTN signal using a regular FTN receiver with \(\tau=0.75\).  
Table 1 describes the parameters for the experimental design. The parameters this paper chose are the commonly used parameters in DVB-S2X systems.