esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupled Vibration Analysis of Multi-Span Continuous Cable Structure Considering Frictional Slip
## 2 Nonlinear Coupled Vibration Model
### Main Cable Subsystem Model
#### 2.2.1 Two\(-\)Node Catenary Cable Element  
Figure 5 shows a two-node planar catenary cable element; A and B are the two nodes of the element, and the curve equation of the element is [28]  
\[\left\{\begin{array}{l}l=-\frac{F_{1}g}{EA}-\frac{F_{1}}{q}\ln\Big{(}\frac{ T_{A}+F_{2}}{I_{B}-F_{4}}\Big{)}\\ h=-\frac{F_{2}g}{EA}+\frac{qg^{2}}{2 EA}+\frac{T_{B}-T_{4}}{q}\end{array}\right. \tag{3}\]where \(q\), \(E\), \(A\) and \(s\) are the load set, modulus of elasticity, area and unstressed length of the cable, respectively. \(F_{1}\)-\(F_{4}\) are the nodal component forces of the cable. \(F_{3}=-F_{1}\); \(F_{4}=qs-F_{2}\). \(T_{A}\) and \(T_{B}\) are the cable forces at nodes \(A\) and \(B\), respectively.  
\[\left\{\begin{array}{l}T_{A}=\sqrt{F_{1}^{2}+F_{2}^{2}}\\ T_{B}=\sqrt{F_{3}^{2}+F_{4}^{2}}\end{array}\right. \tag{4}\]  
Differentiating Equation (3) results in  
\[\left(\begin{array}{c}dl\\ dh\end{array}\right)=\begin{bmatrix}f_{11}&f_{12}\\ f_{21}&f_{22}\end{bmatrix}\left(\begin{array}{c}dF_{1}\\ dF_{2}\end{array}\right)=F\left(\begin{array}{c}dF_{1}\\ dF_{2}\end{array}\right) \tag{5}\]  
where \(f_{11}=-\left[\frac{s}{EA}+\frac{1}{q}\ln\left(\frac{T_{A}+F_{2}}{T_{B}-F_{4}} \right)\right]+\frac{F_{1}^{2}}{q}\left[\frac{1}{T_{B}(T_{B}-F_{4})}-\frac{1}{ T_{A}(T_{A}+F_{2})}\right]\), \(f_{12}=f_{21}=\frac{F_{1}}{q}\left(\frac{1}{T_{B}}-\frac{1}{T_{A}}\right)\), \(f_{33}=-\frac{s}{EA}-\frac{1}{q}\left(\frac{F_{1}}{T_{B}}+\frac{F_{2}}{T_{A}}\right)\).  
The stiffness matrix of a cable element can be solved by the inverse matrix of its flexibility matrix:  
\[[K]=[F]^{-1}=\begin{bmatrix}f_{11}&f_{12}\\ f_{21}&f_{22}\end{bmatrix}^{-1} \tag{6}\]  
The nodal force vectors of the elements can also be obtained as  
\[\left\{F_{\text{int}}\right\}=\left\{F_{1}\quad F_{2}\quad F_{3}\quad F_{4} \right\}^{T} \tag{7}\]  
The tangent stiffness matrix of the element can be solved by Newton-Raphson iteration; the initial value of the iteration and the iteration procedure can be found in the literature [28].  
The damping matrix of the element is  
\[\mathbf{C}=c\left[\begin{array}{cccc}1&0&-1&0\\ 0&0&0&0\\ -1&0&1&0\\ 0&0&0&0\end{array}\right]=\begin{bmatrix}\mathbf{c}_{11}&\mathbf{c}_{12}\\ \mathbf{c}_{21}&\mathbf{c}_{22}\end{bmatrix} \tag{8}\]  
where \(c\) is the viscous damping coefficient of the element in units of force \(\times\) time \(\times\) length.
## 2 System Model
### Performance Metrics  
Evaluation of the proposed semantic-aware speech communication system requires metrics that assess both signal fidelity and perceptual quality. Unlike conventional transmission systems that rely primarily solely on BER or Symbol Error Rate (SER), semantic-aware  
Figure 2: Architecture of the semantic-aware digital speech communication system. (1) A semantic encoder for feature extraction and compression; (2) a conventional transmitter implementing digital communication protocols; (3) the wireless channel modeling real-world propagation effects; (4) a conventional receiver for signal recovery; and (5) a semantic decoder for speech reconstruction.  
transmission focuses on preserving the intelligibility and meaning of speech rather than exact waveform reproduction. To comprehensively evaluate system performance, the following three key metrics are employed.
# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Group**} & \multicolumn{3}{c}{**Feature Point**} & \multicolumn{2}{c}{**Match Pair**} \\ \cline{3-6}  & & **Reference** & **Be-Registered** & **Rough** & **Uniformly** \\ \hline I-RANSAC & T3 & 2103 & 2269 & 740 & 690 \\
[15] & T4 & 2344 & 2648 & 816 & 735 \\ IIR & T3 & 1686 & 1761 & 623 & 586 \\  & T4 & 1851 & 2096 & 676 & 628 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison of feature point information between two algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Group**} & \multirow{2}{*}{**ORB [6]**} & \multirow{2}{*}{**FAST [4]**} & \multirow{2}{*}{**SURF [5]**} & \multicolumn{2}{c}{**I-RANSAC**} \\  & & & & & **[12]** & \\ \hline Matching & T3 & 90.10 & 87.46 & 91.13 & 93.24 & **94.06** \\  & T4 & 87.62 & 85.37 & 88.36 & 90.07 & **92.90** \\ accuracy & Mean & 88.86 & 86.42 & 89.75 & 91.66 & **93.48** \\  & T3 & 2.39 & **2.21** & 2.33 & 2.51 & 2.42 \\  & T4 & 2.46 & **2.28** & 2.41 & 2.61 & 2.49 \\ time & Mean & 2.43 & **2.25** & 2.37 & 2.56 & 2.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A comparison of matching accuracy and matching time between different algorithms. Figure 17: The fusion images for T3: (**a**) I-RANSAC; (**b**) IIR. For the same underwater image, the image fusion of T3 (**a**) can see the stitching trace, while the fusion trace of T3 (**b**) disappears. The same method of weighted smoothing was adopted for image fusion, but the final results are different, as shown in Figures 17 and 18. Compared to the I-RANSAC algorithm, the image fusion effect by the IIR algorithm was better because the image seams disappeared in the fusion images. The reason for this is that the IIR algorithm can eliminate more mismatching points in image registration, which improves the percentage of accurate matching pairs. Therefore, the fusion effect is also improved. Therefore, the algorithm in this paper also has a good application in underwater image mosaic, which cannot only improve the accuracy and rapidity but also improve the image mosaic quality.
# Double Deep Q-Network with a Dual-Agent for Traffic Signal Control
## 1 Introduction  
Adaptive traffic signal control (ATSC), which automatically adjusts traffic signal timing according to real-time states of intersections, has been shown to be an effective way to reduce traffic congestion. For example, fuzzy logic [1], approximate dynamic programming [2], and genetic algorithm [3] are typical adaptive control methods and have all been successfully applied to current traffic signal control problem. In these models, a traffic signal control policy is established based on recorded traffic flow information or abstract human-crafted features. However, transportation systems have large randomness in reality and traffic flows change remarkably in short term. It is still difficult to possess an expansible and applicable ATSC system employing these models.  
As a machine learning framework that attempts to approximate an optimal decision-making policy, deep reinforcement learning (DRL) has shown the great potential of traffic light control in complex transportation systems. Recently, a lot of traffic signal control algorithms based on DRL have been successfully applied in the complex traffic environment. [PERSON] et al. [4] first adopted the DRL model to build ATSC systems. They used deep stacked auto-encoders (SAE) to extract vital information from real-time traffic flow and then output Q-value that is corresponding to each traffic signal phase. [PERSON], [PERSON], et al. [5] compared traffic signal control performances between two kinds of DRL algorithms: the deep policy-gradient and the value-function. The results show that the policy-gradientoutperforms the value-function in the traffic signal control. In [6], [PERSON], [PERSON], et al. implemented a DRL agent of traffic signal control in a more complex traffic environment with a four-phase signalized intersection, and demonstrated that the DRL control method has offered higher performances than the fixed timing method [7] and the longest queue first algorithm [8]. In [9], Deep Q-Network (DQN) [10] was utilized by introducing the coordination algorithm for a scalable approach to controlling large-scale traffic lights. However, most of works built the responsive adaptive traffic signal control system based on the two-phase signalized intersection, or neglected the control of left-turn phase as in [6]. Practically, optimally controlling the operation of four-phase signalized intersection is also significant for urban traffic capacity.  
There are mainly two kinds of traffic signal control algorithms based on DRL to solve the real-time traffic signal control in a four-phase signalized intersection. One is the control model which ignored the fixed phasing sequence of traffic signal in real-life intersections [11; 12; 13; 14]. It defines each phase as an agent action selected according to the maximal Q-value, which changes randomly. Nevertheless,algorithm are proposed. Simulation results and discussion are provided in Section 5. Conclusions are given in Section 6.
## 1 Introduction
### Our Contributions  
This article explores whether there can be a convergence in network naming strategies to improve performance, particularly under mobility. The key contributions of this article are the following:  
* The summary of the properties for place specification from linguistics that will help simplify future discussions about network naming schemes. Of particular importance is the fact that place specification is a relator that requires the definition of a figure \(F\) and a ground \(G\) (Section 2).
* The creation of a questionnaire to help network architects think about what needs to be named to be found in a network and the properties that can be integrated into these names (Section 4).
* The highlighting of the improvable points in the ICN architecture's main naming strategy by an evaluation utilizing the questionnaire (Section 5).
* The modification of the ICN architecture to satisfy the questions not satisfied in the initial evaluation (Section 6).
* The value of the questionnaire is demonstrated with the modified architecture obtaining a goodput above 88% of the ideal result, a delay below 0.104 s and a network time-out Interest ratio below 0.082, regardless of node speed on the mobility framework for the single mobile push producer, single mobile consumer scenario, clearly outperforming the original architecture (Section 8).  
The rest of the article is organized as follows: The current understanding of the codification of place specification in languages used to simplify future discussions is realized in Section 2. Section 3 explains the basic components required to understand how names are utilized in an ICN for PDU forwarding. This section includes some of the mobility issues identified by researchers when using the ICN approach, as well as the MAP-Me mobility enhancement available for ICN architectures. Section 4 utilizes the linguistic understanding presented in Section 2 to create a questionnaire to evaluate network naming schemes and their forwarding strategies. The evaluation of the ICN procedures explained in Section 3 are done in Section 5. In Section 6, modifications to the naming and forwarding schemes are proposed to improve the network architecture. Section 7 presents the experimental framework and the parameters used when evaluating the modifications to ICN. Section 8 explains the evaluation of the proposals' performance. The results are discussed in Section 9. Finally, in Section 10, the conclusions are presented.
## 5 Results
### Azimuth Offset from Corner Reflectors  
The derived azimuth offset is depicted in Figure 7 using L0 data (blue points) for both satellites (S-1A: left and S-1B: right). Similar to the range results, the azimuth offsets derived from L1 products are also shown (black points). For the L0 products, a very small azimuth offset remains, with \(-11\) cm on average for S-1A and \(-14\) cm for S-1B. After compensating for these offsets, the standard deviation is a measure of the pixel localization accuracy in azimuth, which is derived from L0 products to 12 cm for both satellites.  
In contrast to these results, the analysis performed using L1 products shows a remaining bias of \(-2.3\) m for both S-1A and S-1B. Note that these results are consistent with other previous studies [14; 15; 16; 17], in which similar values were obtained. This remaining azimuth bias for L1 products corresponds to an azimuth shift of about half the pulse repetition interval. As the proposed method does not perform SAR azimuth processing, it is assumed that such an azimuth bias of L1 products is artificial and probably arises due to an incorrect azimuth shift during operational SAR data processing.  
Figure 6: Range offset derived from corner reflector responses for S-1A (**a**) and S-1B (**b**) obtained using both product types: L0 products (blue) and L1 products (black).  
The statistics of the derived pixel localization accuracy with mean value and standard deviation are summarized in Table 3 separately for range and azimuth offset. Similar to Figures 6 and 7, the results from L1 products are listed for a comparison. While the standard deviation is similar in range for both methods with 12 cm for S-1A and 7 cm for S-1B, a reduced variation is found for the azimuth offset with the proposed method using L0 data. The relative pixel accuracy in azimuth is determined for both S-1A and S-1B by 12 cm, which is about half the value found for L1 products where we estimated 25 cm for S-1A and 22 cm for S-1B.
###### Abstract  
The use of wireless sensor networks (WSNs) in critical applications such as environmental monitoring, smart agriculture, and industrial automation has created significant security concerns, particularly due to the broadcasting nature of wireless communication. The absence of physical-layer authentication mechanisms exposes these networks to threats like spoofing, compromising data authenticity. This paper introduces a multi-attribute physical layer authentication (PLA) scheme to enhance WSN security by using physical attributes such as received signal strength indicator (RSSI), battery level (BL), and altitude. The LoRaWAN join procedure, a key risk due to plain text transmission without encryption during initial communication, is addressed in this study. To evaluate the proposed approach, a partially synthesized dataset was developed. Real-world RSSI values were sourced from the LoRa at the Edge Dataset, while BL and altitude columns were added to simulate realistic sensor behavior in a forest fire detection scenario. Machine learning (ML) models, including Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN), were compared with deep learning (DL) models, such as Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). The results showed that RF achieved the highest accuracy among machine learning models, while MLP and CNN delivered competitive performance with higher resource demands.  
wireless sensor networks; physical-layer authentication; deep learning; machine learning; spoofing; multi-attribute; altitude; radio frequency fingerprinting; battery level; RSSI; LoRaWAN
# A Survey of Secure Time Synchronization
## 3 Current Time Synchronization Methods
### Lightweight Time Synchronization (LTS) Protocol
The reference node is the root node of the spanning tree and is responsible for starting the synchronization of the network. The depth of the spanning tree is communicated back to the root, because it affects the time to synchronize the whole network, and therefore, this information can be used for deciding the time of resynchronization. A critical issue with this is the communication cost because a pairwise synchronization costs three packets and synchronizing the whole network will cost the number nodes x 3 packets which increases the energy consumption used to construct the spanning tree [51]. In the distributed approach, a spanning tree structure is not used and each node can decide the time of its own synchronization [25]. When any node such as Node 'A' needs to be synchronized, it sends a synchronization request to its nearest reference node, as shown in Figure 8[51]. All the nodes must be synchronized along the path from the reference node to Node 'A' which saves the unnecessary efforts of frequent synchronization. Since all the nodes from the reference node to the initiator node need to be synchronized, a collective synchronization request can be generated to reduce the number of resources used [25].
## 3 DTN Structure and Implementation Issues
### Issues and Performance Objectives  
_Issue 1_: Suppressing the number of bundle copies  
Increasing the number of bundle copies, as is done by _Epidemic Routing_, raises the delivery rate, but increases the chances of network congestion. When congestion occurs, safety confirmation information cannot be sent. Therefore, it is necessary to suppress the number of flooding attempts.  
_Issue 2_: Reducing battery consumption of victim terminals  
Following a disaster, batteries of victim terminals cannot be recharged. Therefore, it is necessary to minimize battery consumption for sending bundles that contain safety confirmation information so that the period in which mobile terminals stay usable can be extended.  
_Issue 3_: Reducing storage usage of carrier terminals  
A large-scale disaster affects a large number of people. The greater the number of victims, the greater the number of bundles that each carrier terminal receives, and the larger its required storage capacity. It is necessary to handle safety confirmation information efficiently so that the required storage capacity can be minimized.  
To achieve the above, we have set the performance objectives shown in Figure 3 for the protocol used in the DTN. Specific items of objectives are the delivery rate, the number of bundles generated, battery consumption and storage usage. For the purpose of comparison, Figure 3 also shows cases for _Epidemic Routing_ and _Spray and Wait_.  
Figure 3: Performance objectives for the protocol used to collect safety confirmation information.  
Figure 2: Protocol stack structure for communication in a DTN.
## 5 Simulated Jamming Results Analysis
### High jamming Scenario  
The performance of the GNSS receiver was further assessed under high jamming conditions to assess the contribution of using multi-frequency, multi-constellation GNSS signals to improve positioning performance during jamming.
## 1 Introduction
Accurate house thermal models [19] and air conditioning models have a significant impact on the conditioning effect. In terms of air conditioning, constant frequency air conditioning has been gradually replaced by inverter air conditioning (IAC). The performance of IAC is related to the indoor/outdoor temperature and the compressor speed, and its steady-state model can be used to study the coupled dynamic characteristics of the room and IAC. Research and experiments on steady-state models are abundant [20; 21; 22], but their computation time is unacceptable for scheduling problems. In [16; 23], the performance map based on the steady-state model was obtained for the direct control of IAC. This paper proposes a day-ahead group coordinated preheating method based on demand response model for large-scale electric heating load, which is carried out in a framework composed of a central controller and several regulation groups. Under this framework, users can reduce electricity bills through the proposed demand response model, in which personalized settings such as travel schedules and user temperature demand curves can be fully considered. The central controller updates the marginal cost curve after each round of regulation and transmits it to the next group, and the interaction avoids new peaks. In each round of adjustment, households in the regulation group solve their respective optimization problems in parallel, ensuring computational efficiency. In addition, a room equivalent thermal parameter (ETP) model and an IAC model are established to form a single household demand response model. In order to quickly obtain the performance parameters of IAC under given conditions, this paper develops a performance map based on the steady-state model of IAC, which can be applied to the direct control of the compressor frequency to determine the power consumption. And the mapping can be easily transformed into piecewise linear constraints and added to the optimization problem. This article is organized as follows. In Section 3, a detailed ETP model of residence and the performance map based on the IAC model are established. In Section 4, a single household demand response model is given, and the grouping coordinated preheating framework is proposed. In Section 5, the performance of the proposed methods are presented through numerical simulation. Finally, the main conclusions are discussed in Section 6.
# Instance Segmentation and Number Counting of Grape Berry Images Based on Deep Learning
## 2 Materials and Methods
### Improved Algorithm
#### 2.4.2 Soft-NMS  
The main problem of the NMS algorithm is that the adjacent candidate boxes with high overlap are directly removed. Therefore, when there are real targets in the highly overlapping adjacent candidate frames, they are difficult to retain, resulting in a decrease in the model detection accuracy, which in turn affects the subsequent segmentation accuracy, resulting in the missing detection problem. To solve this problem, an improved algorithm of NMS, Soft-NMS [28], was adopted, and the pseudocode of the algorithm is shown in Algorithm 1, where f(_iou_(_M_, _bi_)) is a weighting function, including a linear weighting function and a Gaussian weighting function, which attenuates adjacent candidate frames with a high degree of overlap with the detection frame M; the higher the degree of overlap, the greater the attenuation amplitude. In this paper, the idea of linear weighting is used to improve the problem of the missed detection of overlapping grapes. The formula of linear weighting is as shown in Formula (2). When the degree of overlap _iou_(_M_, _bi_) is greater than the threshold \(N_{t}\), it is different from NMS, which directly removes adjacent candidate boxes. Soft-NMS retains the candidate frame, assigns a smaller score value \(s_{i}\), and sets the score threshold to remove the candidate frames whose score is lower than the threshold.  
\[s_{i}=\begin{cases}s_{i},iou(M,b_{i})<N_{t}\\ s_{i}(1-iou(M,b_{i})),iou(M,b_{i})\geq N_{t}\end{cases} \tag{2}\]  
```
Input: candidate box set: B = {\(b_{1}\),..., \(b_{N}\)}; detection score set \(S\) = {\(s_{1}\),..., \(s_{N}\)}; _IoU_ threshold \(N_{t}\) Output: The candidate boxes set D and detection score set \(S\) processed by the algorithm
1: D = {}
2: while B \(\
eq\) empty do
3: m = argmax \(S\)
4: M = \(bm\)
5: D = D \(\cup\) Mi, B = B - \(M\)
6: for bi in B do
7:\(s_{i}=s_{i}\) f(_iou_(_M_, _bi_))
8: end
9: end
10: return D, \(S\)
```  
**Algorithm 1**Soft-NMS