esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of the Ionic Conductivity  
The ionic conductivity reflects how fast the lithium-ions migrate between the positive and negative electrodes during the operation of a lithium-ion battery and indirectly reflects the size of the diaphragm resistance. The larger the diaphragm resistance, the smaller its ionic conductivity; the slower the rate of lithium-ion transfer in the battery, and the poorer the performance of the battery exhibited [23]. Figure 5 is an impedance diagram of the commercial Celgard 2400 and PU/PAN fiber diaphragms. The intercept on the horizontal axis represents the bulk resistance of the diaphragm. The ionic conductivity of the Celgard 2400 diaphragm was only 0.82 mS/cm, as obtained by Equation (3), but the ionic conductivity of the PU/PAN fiber diaphragms was improved due to their 3D mesh structure resulting from the centrifugal spinning method. In the same size diaphragm, more electrolytes can be absorbed and provides a large number of channels for lithium-ion penetration in the diaphragm [33]. This structure improved the transport rate of lithium-ions in the fiber diaphragm.  
The ionic conductivities of 18% PU, 18% PU:PAN = 8:2, and 18% PU:PAN = 7:3 fiber septa were 1.43 mS/cm, 1.68 mS/cm, and 1.79 mS/cm, respectively. The ionic conductivity of the fiber septa increased with the increasing PAN content and was highest at 18% PU:PAN = 7:3. This was because the addition of PAN enhanced the liquid absorption and retention capacity of the diaphragm. Additionally, in the diaphragm of the hot-pressing process, the fibrous filaments did not melt and form physical cross-linking, and its body resistance did not increase. The performance of the PU-based lithium-ion battery diaphragm was better than that of the commercial diaphragm and could meet the high conductivity requirement in lithium-ion batteries.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **Porosity (\%)** & **Absorption Rate (\%)** \\ \hline Celgard & 38 & 116 \\
18\% PU:PAN = 8:2 & 83.5 & 454 \\
18\% PU:PAN = 7:3 & 83.9 & 493 \\ PU & 82.2 & 412 \\ \hline \end{tabular}
\end{table}
Table 3: Porosity and electrolyte absorption of the commercial Celgard 2400 and PU/PAN fiber diaphragms.
###### Abstract  
Hypersonic targets have complex motion states and high maneuverability. The traditional interactive multi-model (IMM) has low tracking accuracy and a slow convergence speed. Therefore, this paper proposes a strong tracking cubature Kalman filter (CKF) adaptive interactive multi-model (AIMM) based on multiple fading factors. Firstly, this paper analyzes the structure of the CKF algorithm, introduces the fading factor of the strong tracking algorithm into the covariance matrix of the time update and measurement update, and adjusts the filter gain online and in real time, which can reduce the decline infilter accuracy caused by model mismatch. Secondly, [PERSON] model, \"current\" statistical (CS) model, and Jerk model are selected in the model set of IMM and introduced singular value decomposition (SVD) decomposition to solve the problem that Cholesky decomposition cannot be performed in the CKF due to the model dimension expansion. Last, an adaptive algorithm for the Markov matrix in the IMM is proposed. The transition probability was adaptively modified by the value of the model likelihood function to enhance the proportion of matching models. The simulation results show that the proposed algorithm enhanced the proportion of matching models in the IMM and improved the tracking accuracy by 16.51% and the convergence speed by 37.5%.  
hyposronic target; cubature Kalman filter; strong tracking filtering; fading factor; adaptive interactive multiple model +
Footnote †: journal: Applied Sciences
## 6 Denoising Processing of Positioning Data Based on Particle Filter  
In order to improve the reliability of the information collected by the ZigBee-GPS/BDS multi-sensor positioning system and reduce external noise interference, the multi-sensor data after the consistency inspection and variance weighting were filtered and denoised using the basic particle-filter algorithm [25].
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
## 2 Acquisition Scheme
### Acquisition Procedure
 \tag{3}\]  
The distance between the three satellites is almost equal, so we also have:  
\[\theta_{BCp}=\theta_{BCp1}+\theta_{BCp2}\approx\theta_{ACp}+\theta_{ABp} \tag{4}\]  
So, the uncertainty cone of SC will be changed from a circular area to a narrow bar-like area according to the information of the actual direction of SB and SC. It will greatly reduce the establishment time of the third link. SB can use its T2 to scan the uncertainty cone of SC which is shown in Figure 6 as the orange bar-like area. When SC receives the laser signal from SB, it will control its T1 to point to SB and turn its laser. When SB finishes scanning  
Figure 6: Schematic diagram of the third two-way laser link. Where the initial LOSB2C denotes the initial line of sight of the T2 of SB towards SC, segment AC\({}^{\prime}\) denotes the projection of the actual direction between SA and SC on the circular plane area of the initial uncertainty cone of SC, while segments AO and AB\({}^{\prime}\) denote the projection of the initial LOSA2C and the actual direction between SA and SC, respectively, \(\varphi_{1}\) denotes the angle between AC\({}^{\prime}\) and AO, and \(\varphi_{2}\) denotes the angle between AO and AB\({}^{\prime}\). and turns off its laser, it will find the laser signal from SC and control its T2 to point to SC then turn on its laser. Finally, the Constellation laser links are fully established; namely, the acquisition phase is completed.
## 1 Introduction
The classification accuracy on the dataset reaches 98.9%. To sum up, researchers have made a lot of improvements in the adaptation of STDP algorithm and SNN model in their respective sub-fields, but in the face of constraints such as resource limitations and computing power bottlenecks that exist widely in practical application scenarios. There is still room for optimization. [PERSON] et al. [18] constructed an SNN model, which included 28 \(\times\) 28 neurons in the input layer, processing layer neurons and corresponding inhibitory layer neurons in second layer, synapses are regulated by the STDP mechanism. It is found that the accuracy of this model on MNIST dataset can reach 95%. Though this model achieved better classification performance, it occupied more hardware resources and led to higher computing power consumption [19; 20; 21]. Software algorithms are the core of neuromorphic computing, and hardware devices are software dependent [22; 23]. As the carrier of operation, the two are inseparable. The one-sided pursuit of software performance is mostly the overdraft of hardware computing power [24; 25; 26; 27]. Especially with the rise of heterogeneous computing software platforms, software and hardware co-design has gradually become one of the commanding heights of technological competition [28; 29; 30]. Accordingly, this paper proposes a spiking neural network model for lateral suppression of STDP with adaptive threshold. The conversion from grayscale image to pulse sequence is completed by convolution normalization and first pulse time coding. The network self-classification is realized by combining the classical pulse time-dependent plasticity algorithm (STDP) and lateral suppression algorithm. The introduction of adaptive thresholds ensures the sparsity of pulse delivery and the specificity of learning features, effectively suppresses the occurrence of over-fitting, and facilitates the migration of software algorithms to the bottom layer of the hardware platform, which can be a high-efficiency, low-power, small-sized and intelligent provide reference for the realization of edge computing solutions of hardware terminals.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Seismic Monitoring Applications
#### 3.5.2 Wireless Sensor Node for Seismic Monitoring  
The node proposed by [30] was developed for the seismic monitoring of buildings. Pertaining to the processor and memory specifications, the node was developed around the launch pad CC3200 ARM(r) Cortex(r)-M4 Core-based microcontroller (at 80 MHz) by Texas Instruments. It contains up to 256 kB RAM and external serial flash bootloader, and peripheral drivers in ROM. The Wi-Fi incorporated in the CC3200 chip is used for communication among the sensor nodes. Furthermore, the node accommodates a three-component seismic sensor (Mark-I-4C3D) having a natural frequency of 1 Hz. The node can be powered by 5 V that can be obtained from batteries or a micro-USB charger. Concerning power consumption, the node draws 70 mA and 80 mA of current in the standby and sampling modes, respectively.
## 3 Proposed Architecture
### Loss Function for Training the SNN  
The loss function used during the training phase was the Large Margin Cotangent Loss (LMCoT) [44]. The primary motivation behind LMCot is to address the limitation of the cosine function used in existing methods, such as ArcFace [45]. The cosine function returns values between \([-1,1]\), which limits its ability to accurately reflect the angle between  
Figure 4: Similarity learning with CNN-ViT sub network and Large Margin Cotangent Loss (LMCot) [44].  
vectors. In contrast, the cotangent function has an unrestricted range of values, making it more suitable for measuring angles.  
The LMCot loss function is defined as follows:  
\[L=-\frac{1}{N}\sum_{i=1}^{N}\text{log}\frac{e^{s(\text{cot}(\theta yi+m))}}{e^{ s(\text{cot}(\theta yi+m))}+\sum\limits_{j=1,j\
eq ji}^{n}e^{s\cot\theta ji}}, \tag{2}\]  
where \(L\) represents the LMCot loss, \(N\) is the number of samples, \(s\) is a scale parameter, \(m\) is the margin, \(\theta yi\) is the angle between the weight and feature vector of the ground truth class, and \(\theta ji\) is the angle between the weight and feature vector of class \(j\).  
To calculate the cotangent values, the LMCot loss function utilizes the \(l_{2}\)-normalized feature vectors and weights. The loss function penalizes the difference between the cotangent of the ground truth angle \(\theta yi\) and the cotangent of the angles \(\theta ji\) for other classes. This encourages the model to optimize the decision boundary to improve classification accuracy.  
In order to facilitate the comparison of a new DFU image, a reference point is essential. This reference point is referred to as the \"class anchor\". The forthcoming section will explain the concept of class anchors and outline the methodology for their calculation.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 3 Strategies for Optimizing RF MEMS Switch Performance
### Excellent RF Performance  
In communication systems and test instruments, the superior RF performance of RF MEMS switches is reflected in two key indicators: low insertion loss and high isolation. These two factors are critical metrics for evaluating the performance of devices and systems based on RF MEMS switches [137]. High insertion loss can lead to significant RF signal loss, while low isolation may result in signal leakage, both severely affecting the overall performance of the system. Therefore, ensuring RF MEMS switches have a low insertion loss and high isolation is of paramount importance.
# Polar Code BP Decoding Optimization for Green 6G Satellite Communication: A Geometry Perspective
## 1 Introduction
Under this framework, this paper proposes two early termination algorithms for polar code BP decoding based on information geometry, which improve the convergence speed of polar code BP decoding without affecting the BER. These algorithms conduct a geometric analysis of the changes in soft information during the Belief Propagation decoding iterative process, effectively reducing energy consumption while significantly improving convergence speed. This not only underscores their broad application potential in green 6G satellite communications but also introduces a novel approach suitable for all resource-constrained scenarios in green communications, ranging from large-scale Internet of Things (IoT) deployments equipped with battery-limited sensors to unmanned aerial vehicle (UAV) communication networks. The innovations of this paper are as follows:To mathematically analyze the changes in LLR values during the BP decoding process of polar codes, this paper utilizes mathematical tools--information geometry and modern differential geometry--to analyze the BP decoding algorithms of polar codes, establish a geometric framework for polar code BP decoding algorithms, and propose a theoretical explanation for polar code BP decoding algorithms. It maps information nodes to statistical manifolds, equates the probability information contained in nodes to points on manifolds, and represents the iterative process as finding convergence points on manifolds, providing an intuitive representation of the iterative process of polar code BP decoding algorithms. Under this framework, information geometry and modern differential geometry can be utilized to improve polar code BP decoding algorithms, providing new insights for research on polar code BP decoding algorithms. 2. Addressing the issue of high iteration counts in BP decoding algorithms, this paper studies early termination algorithms for polar code BP decoding within the geometric framework. By calculating metrics, distance, and divergence in statistical manifolds, it optimizes existing algorithms and proposes early termination algorithms for polar code BP decoding based on Riemannian distance and divergence. These algorithms improve the convergence speed of polar code BP decoding without affecting the BER. The remainder of this paper is organized as follows. Section 2 reviews the BP decoding process of polar codes. Section 3 introduces the BP decoding process of polar codes from an information geometry perspective. Section 4 proposes two early termination algorithms for polar code BP decoding based on information geometry. Section 5 presents simulation analysis. Section 6 concludes the paper and discusses future directions.The main symbols used in this paper are listed in Table 1.
## 4 Experiments
### Dehazing  
Most dehazing models are quite large in size and need a large number of FLOPs. We compared several dehazing methods with the BPPNet's reduced version with and without LGA (refer to the ablation study in Table 7). The quantitative results presented in Table 2 show that the model with LGA performed the best in terms of SSIM. It also performed better than all the other methods in terms of PSNR. The significant reduction in computational cost of models with LGA was already established in Table 7. Therefore, it is evident that LGA also supports computationally light-weight and high-performance models for dehazing. The qualitative results of dehazing shown in Figure 6 also illustrate the superiority of image restoration achieved using the LGA-incorporated reduced BPPNet model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Performance (\(\uparrow\))**} & \multicolumn{2}{c}{**Efficiency (\(\downarrow\))**} \\ \cline{2-5}
**MODEL** & **mIoU** & **Accuracy** & **Parameters** & **FLOPS** \\  & **(\%)** & **(\%)** & **(\(\times 10^{6}\))** & **(\(\times 10^{9}\))** \\ \hline \hline \multicolumn{5}{c}{Efficient/real-time small architectures} \\ \hline FPENet [27] & 10.1 & 70.3 & 0.5 & 0.8 \\ ESPNet-v2 [28] & 12.3 & 73.0 & 3.5 & 0.8 \\ ENet [29] & 23.4 & 78.2 & 0.4 & 2.1 \\ DABNet [30] & 15.3 & 77.4 & 0.8 & 5.2 \\ LEDNet [5] & 30.3 & 72.9 & 1.1 & 19.6 \\ KCNet [31] & 23.4 & 78.2 & 7.8 & 10.6 \\ MobileNet-v2 [32] & 17.6 & 77.6 & 3.3 & 29.3 \\ ESNet [33] & 43.6 & 45.5 & 1.7 & 27.3 \\ \hline \hline \multicolumn{5}{c}{LGA incorporation into small architectures} \\ \hline Shuffle [15] with LGA & 44.5 & 78.7 & 0.4 & 3.5 \\ Squeeze [34] with LGA & 44.6 & 79.6 & 1.1 & 13.5 \\ \hline \multicolumn{5}{c}{Large architectures} \\ \hline ViT [13] & 29.6 & 67.8 & 171.6 & 176.7 \\ PSPNet (Res34) [10] & 43.2 & 82.8 & 21.5 & 19.3 \\ PSPNet (Res50) [10] & 43.2 & 83.2 & 24.4 & 24.0 \\ DeepLab [6] & 59.1 & 89.6 & 39.6 & 328.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segmentation results: quantitative comparison of various models.  
Figure 5: Examples of segmentation results of previous methods. Eleven classes of objects are color-coded.
## 2 Description of SASCHCS  
The schematic of SASCHCS is demonstrated in Figure 1. Such a hybrid system consists of three subsystems: the thermally-driven subsystem, absorption subsystem and compression subsystem. The collector and the storage tank are the evacuated tube collector (ETC) and stratified tank, respectively. The absorption subsystem is a single effect LiBr/H\({}_{2}\)O chiller. The compression subsystem is a vapor compression chiller and its refrigerant is R410A. In the case of available solar energy, absorption subsystems provide the subcooling power of compression subsystem to save compressor work. In addition, the cooling load is solely covered by compression subsystem when the solar heat is insufficient to drive the absorption subsystem. The conversion of cooling output in the compression subsystem through the subcooling power mainly depends on the flow rate of cooling water and chilled water as well as the compressor speed [22]. It was exhibited that the increase of cooling capacity to the subcooling power is 0.9-1.2 for the fixed superheating by experiment [23].  
The collector pump is turned on at 8:00 a.m. and works with constant flow rate mode. It is switched off to avoid the crystallization of absorption subsystem as the top layer temperature of storage tanks exceeds 100 \({}^{\circ}\)C. Subsequently, the collector pump restarts when the top layer temperature of storage tanks goes down to 95 \({}^{\circ}\)C. In addition, it is stopped as the collector outlet temperature is less than the bottom layer temperature of storage tanks.  
The activation temperature of generator pump relies on the flow rate of generator hot water. It equals to 70 \({}^{\circ}\)C if the flow rate of generator hot water is lower than 3 m\({}^{3}\)/h. In particular, the activation temperature of generator pump is 75 \({}^{\circ}\)C when the flow rate of generator hot water is 1 m\({}^{3}\)/h. Moreover, it equals to 65 \({}^{\circ}\)C for other cases. It is noted that such adjustments of activation temperature are to not only guarantee the operation of absorption chillers but also enhance the chiller performance [24]. The  
Figure 1: Schematic of solar absorption-subcooled compression hybrid cooling system (SASCHCS).  
generator pump operates with constant flow rate mode during the entire period. Furthermore, it is turned off if the outlet temperature of generator hot water is less than 55 \({}^{\circ}\)C.
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 4 Discussion
### Influence of Clay Content, Water Content, and Cementation on Shear Strength Parameters  
While previous sections mentioned above focused on how variations in water content and cementation degree influence the shear strength parameters of fault gouge, it is equally important to consider the role of mineralogical composition--particularly clay content--in controlling these relationships. Clay minerals, due to their strong water absorption and plasticity, can significantly affect how fault gouge responds to changes in water content and cementation. Therefore, this section incorporates clay content as an additional variable and investigates its coupled influence with water content and cementation degree on cohesion and internal friction angle, based on a synthesis of previous studies [28; 31; 41] and this study's results.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### Existing ML-Based approaches
#### 8.2.2 Deep Learning  
DL requires a larger amount of data samples; therefore, more processing time and power are required than with classical ML techniques, which is not favorable in resource  
Figure 6: Illustration of generalized ML conceptual methodology.  
constrained WSN contexts. DL models are more suitable for classification and prediction tasks in IoT applications that generate unstructured data, such as images, audio, and video.  
DL techniques such as recurrent neural networks (RNN), deep belief networks, and Convolutional Neural Network (CNN) are largely used for security preservation and attack detection, due to their fundamental constraints when applied to WSNs. The computational complexity associated with their training, inference, and adaptation makes their use in sensor devices impractical. Several studies have been conducted on using DL techniques, such as [72], where the authors used autoencoder neural networks with a single hidden layer of neurons for lower complexity, which suits resource-constrained WSN contexts. The authors of [75] proposed a hybrid DL model using CNN and long short-term memory (LSTM) for blackhole and grayhole attack detection. The same techniques, CNN and LSTM, were used by the authors of [76] to detect DoS attacks. The authors of [77] investigated the performance of different DL techniques, including Deep Neural Network (DNN), CNN, RNN, and CNN, in combination with RNN for a single detection layer against DoS attacks. The authors of [78] proposed a DL model using a restricted Boltzmann machine with different numbers of hidden layers. The authors of [79] proposed a DL using CNN for the detection of DoS, UR2, R2L, and probe attacks. They proposed a hybrid algorithm consisting of the whale optimization technique and artificial bee colony optimization technique. Overall, both ML and DL techniques are promising for efficient IDSs in WSNs and IoT thanks to their ability to process high-dimensional data, extract useful features from network traffic payloads, and determine complex nonlinear relationships between inputs and outputs to enable informed and intelligent decisions on the part of networks.
## 2 Quantum Key Distribution Network
### QKD Network Based on Partially Trusted Relays  
The assumption of fully trusted repeaters can be relaxed in more than three ways. First, MDI-QKD can be used, and its security is higher than QKD network with trusted relay nodes. MDI-QKD extends the quantum channel, so the nodes of both sides of communication still need to be trusted. Second, quantum repeaters can be used as an alternative. Quantum entanglement of photons can be used to store and forward the information of quantum states, achieving the purpose of communication through different quantum links. The space distance that the technology can transmit is greatly affected by the fidelity of entanglement swapping, but practical deployment has not yet been realized [31]. Third, it depends on multipath transmission and threshold encryption to reduce the risk of eavesdropping. In this paper, we only consider the case of untrusted relays in the network following the MDI-QKD protocol.  
As shown in Figure 2, the optical network model based on partially trusted relay includes four layers from top to bottom, namely the application layer, control layer, QKD layer and data layer. The data layer can also be named the optical layer. Similar to the ordinary QKDN, the application layer and control layer can also control and manage key services in practical application. In the QKD layer, a total of three types of QKD nodes are deployed, including QKD backbone nodes, trusted relays and untrusted relays. The optical layer is composed of the optical backbone network, and there are optical backbone nodes interconnected through optical links. Keys can be exchanged between the QKD layer and data layer.  
Figure 1: QKD network based on trusted relays.  
Figure 2: QKD network based on partially trusted relays.