esa-sceva/satcom-chunk-collection huggingface dataset:
# Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks
## 2 System Model  
Let us consider a downlink MU-MIMO system where a BS transmits simultaneously to \(N_{u}\) users. The BS is equipped with \(N_{tx}\) antennas and each user has \(N_{rx}\) antennas, as illustrated in Figure 1. We assume that the signal can be represented as \(\mathbf{s}=\left[{s_{0}}^{T},\ldots,{s_{N_{u}-1}}^{T}\right]^{T}\), where \(\mathbf{s}_{k}\in\mathbb{C}^{N_{s}\times 1}\) contains the information transmitted to user \(k\) and \(N_{s}\leq N_{tx}/N_{u}\).  
Considering that GSM is being used, only \(N_{a}\) positions of \(\mathbf{s}_{k}\) are nonzero. These correspond to active indexes, which carry M-QAM modulated symbols. The signal vector of each user can be written as  
\[\mathbf{s}_{k}=\left[\ldots,0,s_{k^{\prime}}^{0},0,\ldots,0,s_{k}^{N_{u}-1},0,\ldots\right]^{T} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Conventional MU-MIMO** & \multicolumn{2}{c}{**GSM MU-MIMO**} \\ \hline Precoder & \(\bullet\) & Block Diagonalization & \(\bullet\) & Block Diagonalization \\  & & & \(\bullet\) & Single-user GSM detector capable of \\ Detector & \(\bullet\) & conventional single user MIMO & & operating in undetermined scenarios \\  & \(\bullet\) & proposed ADMM-based receiver & & (sMMP, OB-MMSE,... ) \\  & & & \(\bullet\) & proposed ADMM-based receiver \\ Information Mapping & \(\bullet\) & Modulated Symbols & \(\bullet\) & Modulated Symbols \\  & & & (Virtual) Antenna Indices \\ Possibilities for & \(\bullet\) & Increase modulation order & \(\bullet\) & Increase modulation order \\   improving Spectral & \(\bullet\) & Increase number of transmit & \(\bullet\) & Increase number of transmit \\ Efficiency & & antennas \(\rightarrow\) additional transmit & & antennas \(\rightarrow\) no additional transmit \\  & & power needed & & power needed \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the reference conventional multi-user multiple input multiple output (MU-MIMO) and the proposed generalized spatial modulations multi-user multiple input multiple output (GSM MU-MIMO).  
Figure 1: Transmitter and receiver scheme.  
where \(s_{k}^{j}\in\mathcal{A}(j=0,\ldots,N_{a}-1)\) with \(\mathcal{A}\) denoting an \(M\)-QAM complex valued constellation set. According to this model, the information will be divided in such a way that part of the data are used to select an active index (AI) from a total \(N_{comb}=2^{\lfloor\log_{2}\binom{N_{a}}{N_{a}}\rfloor}\) AI combinations (AICs) available per user. The remaining data are mapped onto \(N_{a}\) complex-valued \(M\)-QAM symbols. The resulting SE is then  
\[N_{bts}=N_{u}\big{\lfloor}\log_{2}\binom{N_{a}}{N_{a}}\big{\rfloor}+N_{u}N_{a} \log_{2}(M) \tag{2}\]  
bits per channel use (bpcu).
## 3 Experiment
### Experiment Setup
We define the uniform delay for cross-network protocol conversion as 50 ms, which includes the time for parsing message headers and performing protocol conversion. By introducing the cross-layer protocol encapsulation method, we effectively solve the protocol conversion problem between different heterogeneous networks and enhance the efficiency and reliability of data transmission. To better evaluate the security and reliability of the MOMTA-HN algorithm under attack conditions, we designed a series of experiments to randomly attack different nodes in the network. In heterogeneous networks, attacks tend to be random and continuous, with nodes interacting and the network crashing at an accelerating rate. We used the transmission success rate as a metric to measure the performance of the algorithm. To ensure the fairness and statistical significance of the experimental results, we performed 1000 independent tests for each round of experiments. The transmission success rate is calculated using the following formula:  
\[P_{success}=\frac{N_{success}}{N_{total}} \tag{8}\]  
where \(P_{success}\) is the success rate, \(N_{success}\) is the number of successful transmissions, and \(N_{total}\) is the total number of trials. For multipath transmission algorithms, the transmission success rate must be calculated by its complement. Suppose there are \(m\) paths in the network and the attack occurs randomly at any node on any path. After \(N\) attempts, the event of successful transmission on the \(i\)-th path is denoted as \(S_{i}\), and the event of successful multipath transmission \(S\) is the case where at least one of all paths is successfully transmitted. If the success rate of each path independently is \(P_{1},P_{2},\ldots,P_{m}\), then the multipath transmission success rate \(P_{multi}\) can be calculated by the following formula:  
\[P_{multi}=1-\prod_{i=1}^{m}\left(1-P_{i}\right) \tag{9}\]  
Figure 8: Heterogeneous network composition of ChinaNet network topology. where, \(1-P_{i}\) denotes the probability that the transmission of the \(i\)-th path fails, and thus \(\prod_{i=1}^{m}\left(1-P_{i}\right)\) denotes the probability that all paths fail, while its complement \(1-\prod_{i=1}^{m}\left(1-P_{i}\right)\) is the probability that at least one path succeeds. In our experiments, we assume the existence of \(m\) independent transmission paths and evaluate the success rate of each path independently. Assuming that the number of successful transmissions of the \(i\)-th path in \(N\) experiments is \(NS_{i}\), the success rate \(P_{i}\) of the \(i\)-th path is  
\[P_{i}=\frac{NS_{i}}{N} \tag{10}\]  
This allows us to systematically evaluate the performance of the MOMTA-HN algorithm in heterogeneous networks, particularly its robustness against malicious attacks. The experimental results demonstrate the advantages of the algorithm in complex network environments, as shown below.
# An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming
## 3 Method and Implementation  
This paper proposes MGPRepair, a multi-objective genetic programming-based method for automatic software defect repair. It is designed to address the inefficiencies and test case overfitting issues inherent in traditional methods that rely solely on single-objective validation and selection of offspring patches. First, MGPRepair introduces a new fitness function that incorporates code similarity and positional information. This approach allows for a more accurate assessment of patch effectiveness and helps avoid the local optima problems often seen in traditional fitness functions when dealing with high computational complexity. Additionally, MGPRepair integrates the multi-objective optimization algorithm NSGA-II [19], which considers both the ability of offspring patches to pass test cases and the impact of patch size on quality. This dual consideration helps reduce the dependency on test cases during the repair process. By selecting a high-quality initial population prior to the genetic search process, MGPRepair accelerates the evolutionary process and reduces defect repair time. Figure 1 illustrates the technical framework introduced in this chapter.  
As shown in Figure 1, the proposed method is mainly divided into three stages. The first stage is defect localization. It starts by inputting a test case set and the source code with defects. By using mature defect localization methods (such as Ochiai [25; 26]), the possible locations of defects are detected, the suspicion values of each suspicious location are calculated, and the locations are sorted according to the suspicion values. The second stage is the patch generation process. First, based on the location of the defect, a large number of candidate repair statements are extracted from the source code. Lightweight context analysis is used to filter out repair materials unrelated to the defect location. Then, the repair materials are further filtered and classified according to established rules. A higher-quality initial population is generated based on the defect location and corresponding repair materials. The multi-objective genetic algorithm NSGA-II is then employed to more quickly find high-quality offspring that pass more test cases and have smaller patch sizes. During the verification of offspring patches, a test case filtering technique is added, which can significantly reduce the number of test cases and accelerate the search speed of the algorithm. The third stage is patch validation. The generated candidate patches are validated through a complete test case set, and the validated patches undergo overfitting detection. If a patch passes the overfitting detection, it is considered a credible patch. Finally, manual verification tests determine whether this patch can truly be used as a correct patch for the defective program.
## 3 Results
### Microbiome Analysis
#### 3.4.2 Rarefaction Curve Analysis  
A total of 509,199 and 656,392 16S rDNA V3-V4 sequencing reads were yielded for the pre-and post-treated G1 samples, respectively. After a series of quality control processing (where they were filtered, de-noised, and merged), 443,046 (87.00%) and 570,048 (86.84%) high-quality non-chimera reads were obtained for the pre-and post-treated G1 samples, respectively, and were clustered into OTUs. On average, each pre-and post-treated sample had 69 and 232 OTUs, respectively (Table S1) (Supplementary File S2) (Figure 6a). Similarly, a total of 756,857 and 863,350 16S rDNA V3-V4 sequencing reads were yielded for the pre-and post-treated G2 samples. After a series of quality control processing (where they were filtered, de-noised, and merged), 687,603 (90.84%) and 756,006 (87.57%) high-quality non-chimera reads were obtained and were clustered into OTUs. On average, each pre- and post-treated G2 samples were clustered into OTUs. The post-treated G2 samples were clustered into OTUs, and  
post-treated sample had 96 and 189 OTUs, respectively (Table S2) (Supplementary File S2) (Figure 6b). The rarefaction analysis suggested that the sequencing depth can recover most of the community's diversity.  
Figure 6: Refraction analysis for the G1 and G2 samples. The rarefaction curves of observed OTUs and Pathway diversity were computed for G1 (**a**,**c**) and G2 (**b**,**d**). Shannon H-index alpha Diversity was measured and compared for the G1 (**e**) and G2 (**f**) samples. The inter and intragroup species differences were compared using PCoA for G1 (**g**) and G2 (**h**) samples.
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 4 Experiment and Result  
This section verifies through simulation and real-world tasks the effectiveness of the trajectory generation method proposed in this paper. Section 4.1 verifies the trajectory generation method proposed in this paper based on simulation by comparing it with Fast-Planner [17], a classic motion planning method. Then, the actual flight test is used to verify the effectiveness of the proposed method in the real world.
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 4 Accuracy Enhancing Methods
### Optical and Mechanical Aspects  
The relevant errors generated from optical and mechanical parts of the experiment system include the mechanical vibration and the stability of moving table, the quality of optics, and the laser wavelength stability.
## Abbreviations  
The following abbreviations are used in this manuscript:  
4G Fourth Generation  
5G Fifth Generation  
5G NR 5G New Radio  
6G Sixth Generation  
A2S Air-to-Sea  
AI Artificial Intelligence  
AR Augmented Reality  
AUV Autonomous Underwater Vehicle  
BS Base Station  
DRL Deep Reinforcement Learning  
eMBB Enhanced Mobile Broadband  
FL Federated Learning  
HAP High-Altitude Platform  
IRS Intelligent Reflecting Surface  
ISAC Integrated Sensing and Communication  
ISM Industrial, Scientific, and Medical  
LEO Low Earth Orbit  
LoS Line of Sight  
LTE Long-Term Evolution (4G)  
MANET Maritime Ad Hoc Networks  
MCN Maritime Communication Network  
MEC Mobile Edge Computing  
mIAB Maritime Integrated Access and Backhaul  
MIMO Multiple-Input-Multiple-Output  
mMTC Massive Machine Type Communications  
mmWave Millimeter Wave  
ML Machine Learning  
NLoS Non-Line-of-Sight  
ANN Artificial Neural Network  
NOMA Non-Orthogonal Multiple Access  
PPO Proximal Policy Optimization  
QoS Quality of Service  
RIS Reconfigurable Intelligent Surfaces  
RL Reinforcement Learning  
S2S Ship-to-Ship  
S2 Sh Ship-to-Shore  
Sat-BS Satellite Base Station  
SemiSL Semi-Supervised Learning  
SL Supervised Learning  
UAV Unmanned Aerial Vehicle  
UHF Ultra-High Frequency  
UL Unsupervised Learning  
URLLC Ultra Reliable Low Latency Communications  
VHF Very High Frequency  
VR Virtual Reality  
XR Extended Reality
# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 3 Materials and Methods
### PBI-EdgeNeXt
#### 3.3.2 Positional Encoding  
Convolutional encoders, leveraging their distinctive receptive field convolution operations, offer a wealth of local feature information to the model. Ref. [26] examines the degree to which CNNs implicitly encode absolute position information in their learned representations, and demonstrates that deep CNN may implicitly learn to encode this information. In complex visual tasks requiring scene comprehension and multi-object interaction analysis, such positional information is essential [27]. However, acquiring crucial positional information is typically lacking during the initial stages of convolutional neural network training. If the CNN fails to learn how to encode position information, it may know what it is looking at but not where it is positioned in the image [26]. This typically occurs more frequently during the initial stages of network training, and it not only impedes the model's convergence but also potentially impacts its ultimate performance.  
Taking into account the reasons mentioned above, for the image classification task with a complex background, we introduce the positional encoding (PE) module in stages 3 and 4 of EdgeNeXt (see Figure 3). This addition provides extra spatial information for the model, enhancing its ability to comprehend the relative and absolute positional relationships of each part in the image [27].  
Our positional encoding employs sine and cosine functions at varying frequencies:  
\[PE(posi,2i)=sin(posi/10000^{2i/d_{model}}) \tag{1}\]  
\[PE(posi,2i+1)=cos(posi/10000^{2i/d_{model}}) \tag{2}\]  
where _posi_ stands for location, and \(i\) stands for dimension. By enhancing the capability of the model to express positions within its feature maps, it accelerates convergence during training and performs better after deployment when handling tasks requiring precise positional information.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Communications Model  
Connectivity is required for ships to communicate with one of the four manned Vessel Traffic Service (VTS) stations in the area: Elsam Traffic (node 0), a mobile VTS moored at Mostyn Quay in the River Dee (N53.32\({}^{\circ}\), W3.268\({}^{\circ}\)) for the simulated period; coastguard radio stations at Shaefell (node 1), South Stack (node 2), and Crosby (node 3). As fixed stations with land-based, wired connections, these nodes provide connections to the internet and other communication systems, as well as coastguard services. In reality, only the Crosby station is manned while the others are radio relays for the local manned coastguard services. Data traffic in this model is constant bit rate over User Datagram Protocol (UDP). This fosters connectivity testing whilst reducing the simulation overheads that could arise in more complex communications.  
A majority of MANET research uses 802.11 wireless Ethernet as its MAC layer communications built into ns-2. The physical layer of 802.11, operating at 2.4 GHz, has a free space range of only a few hundred metres, which is insufficient to connect this network. All ships in the model carry marine VHF radio, and this is used for AIS data transmission and voice communication. Therefore, this radio model has been used at the physical layer. A number of runs have been performed using different ranges to determine whether it is possible to construct a MANET using VHF radio in this scenario. The antenna heights for the VTS significantly affect the radio range. Marine VHF radio operates in the 160 MHz band and has line of sight range of approximately 5 km if the antenna is 2 m high. Raising the antenna can increase this range significantly, with a mast height of 70 m being required for a 30-km  
Figure 11: Location of nodes at start of a sample run including vessel traffic service (VTS). The four fixed nodes are numbered (0–3), labelled, and shown in black. The remaining nodes are shown in grey with their node numbers.  
range. The Elsam Traffic ship-based VTS and the Crosby coastguard station are both based at sea level with antenna attached to adjacent masts, so their mast heights will only give a range of about 10 km. The South Stack mast is on top of a hill at 130 m, giving a range of approximately 40 km, and Snaefell is 600 m high, giving about 87 km of range. Ship antennae locations vary considerably but tend to be mounted as high as possible on the ship for maximum range. It is assumed that the ships' antennae are at least 10 m above sea level giving about a 10-km range.
# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 3 Methods  
### ETAFHrNet Architecture  
This paper presents a novel network architecture, termed the Efficient Transformer-Enhanced and Adaptive Fusion High-Resolution Network (ETAFHrNet). The overall architecture is illustrated in Figure 2. Building upon the HRNet framework, the proposed model preserves HRNet's strength in maintaining multi-resolution feature representations, while integrating two key innovations: the Efficient Hybrid Attention Transformer (EHAT) and the Cross-Scale Hybrid Attention Module (CSHAM). These components are specifically designed to enhance the network's capacity for crack representation by improving global context modeling and multi-scale feature fusion.  
The architecture comprises three primary components. First, the HRNet backbone extracts multi-resolution features through parallel branches, maintaining high-resolution feature flow while generating rich semantic representations. Second, the EHAT module is embedded into the high-resolution branch to perform lightweight long-range dependency modeling. This is achieved through a combination of adaptive channel dimensionality reduction, axial positional encoding, and local window attention-mechanisms that are particularly effective in enhancing the perception of linear and directional crack structures. Third, following feature alignment via cross-resolution upsampling, the CSHAM module conducts cross-scale adaptive fusion and directional enhancement of multi-level features. This ensures that the segmentation head receives a comprehensive, high-resolution feature representation, enabling the generation of accurate prediction maps aligned with the input resolution.  
To overcome the limitations of conventional HRNet in pavement crack detection--specifically, its limited global semantic modeling and rigid feature fusion--this study introduces two architectural innovations. First, the EHAT module improves linear feature representation by integrating axial positional encoding and local window attention. Its hybrid MLP structure combines the local inductive bias of convolutional operations with the global modeling capacity of Transformers, making it highly effective in capturing crack features across multiple orientations and scales, particularly in complex or subtle crack scenarios. Second, the CSHAM module applies a cross-scale attention mechanism to adaptively weight multi-resolution features and leverages axial attention to enhance directional feature expression. This design alleviates the information loss often caused by static fusion in conventional HRNet and performs robustly in scenes where slender, intersecting, and multi-scale cracks coexist.The following subsections provide a detailed explanation of the proposed EHAT and CSHAM modules.
# Effect of Bond-Slip on Dynamic Response of FRP-Confined RC Columns with Non-Linear Damping
## 5 Conclusions  
In order to further improve the FRP composite material application in RC structures in seismic design, this paper designed a new train of thought. By introducing zero-length units for reinforcement bond-slip and utilizing regression loss coefficient to redefine the nonlinear damping ratio, we established a nonlinear damping model for FRP-C RC columns considering bond-slip of steel bars, which was successfully used in dynamic response analysis. The conclusions of this study are as follows:  
1. By comparing with the experimental results, the proposed model of FRP-C RC column considering bond-slippage is proved to be reasonable for hysteretic dissipation energy analysis. Additionally, the relative errors of simulation and test results are less than 10%, except for the case of AR-1 specimen with a lateral displacement rate at 0.005, the relative error is 12.05%.
2. By calculating the hysteretic behavior of the proposed model under horizontal reciprocating loads, the unit energy dissipation regression formula considering steel bars' bond-slip is established, as the Equations (17) and (18).
3. Based on the complex damping theory, the loss factor expression considering steel bars' bond slip is established, and the damping ratio is redefined.
4. By calculating the time history responses of FRP-C RC circular and square columns under unidirectional and bidirectional harmonic loads, it can be seen that the column top maximum displacement of the proposed damping model is almost 5%-7% larger than that of the [PERSON]'s damping model and 15%-21% larger than that of the constant damping model.  
Conceptualization, Y.W. and K.G.; methodology, Y.W. and K.G.; validation, K.G. and Q.G.; investigation, K.G.; resources, K.G. and Q.G.; writing--original draft preparation, K.G. and Q.G.; writing--review and editing, K.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.