esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sliding Mode Controller  
### Controller Design  
In our design, the objective of the controller is to adhere to a predefined trajectory. Figure 2 shows the proposed controller. Given the non-holonomic constraints inherent to this motion problem, the appropriate selection of the sliding surface is imperative to ensure that deviations in both position and orientation asymptotically approach zero within a finite time frame. We utilize the control-oriented model delineated in Equation (1) and adhere to the notations depicted in Figure 1. Hence, the following sliding surface is then selected as  
\[\gamma(t)=\varepsilon_{d}+k_{1}\varepsilon_{d}. \tag{4}\]  
where \(k_{1}\) is a positive constant that is suitably large, and the lateral error \((\varepsilon_{d})\) is given by  
\[\varepsilon_{d}=-\sin(\theta_{d})(x_{2}-x_{d})+\cos(\theta_{d})(y_{2}-y_{d}). \tag{5}\]  
where \(x_{2}\) and \(y_{2}\) are the coordinates of \(P_{2}\), shown in Figure 1. The parameters \(x_{d},y_{d}\), and \(\theta_{d}\) denote the desired position and orientation, as delineated by a trajectory planner. The time derivative of \(\gamma(t)\) is given by  
\[\dot{\gamma}(t)=\varepsilon_{d}+k_{1}\varepsilon_{d}. \tag{6}\]  
Utilizing [PERSON]'s reaching law methodology as elucidated in [38], the encompassing generic form of the reaching law can be expressed as  
\[\dot{\gamma}(t)=-Q\ \text{sgn}(\gamma). \tag{7}\]  
Here, \(Q\) belongs to the set \(\in\mathbf{R}_{>0}\). The desired steering input is given by merging the kinematics Equation (1) into (6) and (7):  
\[\begin{split}\varphi_{d}^{SMC}&=\tan^{-1}(-\frac{ \tan(\psi)}{l}+\frac{k_{1}L_{2}\sin(\varepsilon_{\theta})}{vl\cos(\psi)\cos( \varepsilon_{\theta})}\\ &\quad+\frac{L_{2}\ Q\ \text{sgn}(s)}{v^{2}l\cos(\psi)cos( \varepsilon_{\theta})}).\end{split} \tag{8}\]  
It should be noted that the computed steering input command incorporates a heading angle error term, thereby coupling the lateral distance and heading angle errors. Moreover, the potential singularity occurring at \(|\psi|=\frac{\pi}{2}\) is mitigated by the use of the Control Steering Geometry (CSG). The objective is to adhere to a feasible trajectory such that \(|\varepsilon_{\theta}|<\frac{\pi}{2}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Unit** \\ \hline \(\theta_{1}\) & Tractor’s heading angle & rad \\ \(\dot{x}_{1}\) & Tractor’s velocity in the x-axis & m/s \\ \(\dot{y}_{1}\) & Tractor’s velocity in the y-axis & m/s \\ \(\dot{\theta}_{1}\) & Tractor’s heading angular rate & rad/s \\ \(\varphi\) & Tractor’s steering angle & rad \\ \(v\) & Speed of the tractor system & m/s \\ \(L_{1}\) & Tractor’s wheelbase length & m \\ \(L_{2}\) & Length between the tractor’s hitch and the trailer’s rear axle & m \\ \(l\) & Length between the tractor’s hitch and the tractor’s rear axle & m \\ \(\dot{\psi}\) & Articulated angle rate & rad/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of model parameters and symbols.
# A New Design of Codebook for Hybrid Precoding in Millimeter-Wave Massive MIMO Systems
## 4 Simulation Results
### Comparison and Analysis of Computational Complexity  
This paper mainly compares the performance of four algorithms. The proposed codebook design and OMP-based codebook [4] algorithms mainly include three steps. The first step is analog codebook design. The proposed codebook algorithm uses the CoSaMP algorithm to update iteratively, while the OMP-based codebook [4] algorithm uses the OMP algorithm to update iteratively. In the second step, both of them use dictionary learning algorithm for digital codebook design. In the third step, both of them use the Lloyd clustering algorithm for codebook space reduction. According to the conclusion of reference [21], the computational complexity of the CoSaMP algorithm is \(\mathcal{O}(N_{\text{tr}}N_{\text{t}}N_{\text{s}})\), while that of the OMP algorithm is \(\mathcal{O}(N_{\text{tr}}N_{\text{t}}N_{\text{s}}^{2})\). Therefore, we can conclude that the computational complexity of proposed codebook algorithm is lower than that of the OMP-based codebook [4]. The hybrid precoding with quantification [1] algorithm is also based on the OMP algorithm for analog codebook design, but their digital codebook design directly uses Grassmannian codebook, and does not use some clustering algorithms to  
Figure 4: Spectral efficiency varying with the number of RF chains in different precoding schemes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Spectral Efficiency (bps/Hz)**} \\ \cline{2-4}  & \(N_{\text{t}}\)**RF = 6** & \(N_{\text{t}}\)**RF = 8** & \(N_{\text{t}}\)**RF = 12** \\ \hline Hybrid Precoding with quantization [1] & 13.85 & 13.12 & 11.86 \\ \hline OMP-based Codebook [4] & 14.24 & 13.73 & 13.02 \\ \hline Proposed Codebook & 15.10 & 14.43 & 13.84 \\ \hline Optimal unconstrained precoding & 16.50 & 16.50 & 16.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spectral efficiency of different algorithms with the number of RF chains.  
reduce codebook space, so hybrid precoding with quantification [1] algorithm is lower than the proposed codebook and OMP-based codebook [4] algorithm in terms of computational complexity. The optimal unconstrained precoding algorithm is directly based on SVD decomposition of channel \(\mathbf{H}\) to get \(\mathbf{F}_{\text{opt}}\). Compared with the first three algorithms, its computational complexity can be ignored, but this algorithm is not realizable in reality. It is only used for performance comparison and is regarded as an ideal benchmark.
## 3 Results
### Health Characteristics
\begin{table}
\begin{tabular}{l l c c} \hline \hline  & & **HID** & **HD-CP** \\  & & **(n = 41)** & **(n = 80)** \\ \hline \multirow{4}{*}{Mental health, n (\%)} & Poor & 6 (14.6) & 0 (0.0) \\ \cline{2-4}  & Fair & 18 (43.9) & 13 (16.3) \\ \cline{2-4}  & Good & 10 (24.4) & 27 (33.8) \\ \cline{2-4}  & Very Good & 5 (12.2) & 24 (30.0) \\ \cline{2-4}  & Excellent & 2 (4.9) & 16 (20.0) \\ \hline \multirow{4}{*}{Emotional health, n (\%)} & Poor & 7 (17.1) & 3 (3.8) \\ \cline{2-4}  & Fair & 12 (29.3) & 26 (32.5) \\ \cline{2-4}  & Good & 15 (36.6) & 25 (31.3) \\ \cline{2-4}  & Very Good & 5 (12.2) & 20 (25.0) \\ \cline{2-4}  & Excellent & 2 (4.9) & 6 (7.5) \\ \hline \multirow{4}{*}{Financial health, n (\%)} & Poor & 14 (34.1) & 11 (13.8) \\ \cline{2-4}  & Fair & 14 (34.1) & 14 (17.5) \\ \cline{2-4}  & Good & 8 (19.5) & 28 (35.0) \\ \cline{2-4}  & Very Good & 5 (12.2) & 17 (21.3) \\ \cline{2-4}  & Excellent & 0 (0.0) & 10 (12.5) \\ \hline \multirow{4}{*}{Social health, n (\%)} & Poor & 2 (4.9) & 5 (6.3) \\ \cline{2-4}  & Fair & 16 (39.0) & 18 (22.5) \\ \cline{2-4}  & Good & 13 (31.7) & 21 (26.3) \\ \cline{2-4}  & Very Good & 8 (19.5) & 21 (26.3) \\ \cline{2-4}  & Excellent & 2 (4.9) & 15 (18.8) \\ \hline \multicolumn{4}{c}{HD Symptomology} \\ \hline \multirow{4}{*}{Symptoms experienced in the past 12 months, n (\%)} & Difficulty walking or & 21 (51.2) & NA \\ \cline{2-4}  & Salimfiness in the body & 14 (34.1) & NA \\ \cline{2-4}  & Having trouble grasping small & & \\ \cline{2-4}  & items such as using a pen, hand & 24 (58.5) & NA \\ \cline{2-4}  & Sewing, etc. & & \\ \cline{2-4}  & Difficulty speaking & 20 (48.8) & NA \\ \cline{2-4}  & Difficulty swallowing & 22 (53.7) & NA \\ \cline{2-4}  & Involuntary movements or & & \\ \cline{2-4}  & spasms in the body & 26 (63.4) & NA \\ \cline{2-4}  & Muscle contractions that change & & \\ \cline{2-4}  & the shape of the body or make it & 16 (39) & NA \\ \cline{2-4}  & difficult to stand up straight & & \\ \hline Slowed eye movement & 15 (36.6) & NA \\ \cline{2-4}  & Difficulty remembering & 29 (70.7) & NA \\ \hline Difficulty with planning, & & \\ \cline{2-4}  & problem solving and/or & 29 (70.7) & NA \\ \cline{2-4}  & decision marking & & \\ \hline Feeling very sad or depressed & 21 (51.2) & NA \\ \hline Easily becoming angry & & & \\ \cline{2-4}  & or annoyed & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Physical, emotional, and social health were reported as poor/fair in nearly half of IHD (46.3%, 46.4% and 43.9%, respectively) and in roughly one-third of HD-CP (21.3%, 36.6%, and 28.8%, respectively). Additionally, more than half of IHD perceived their overall mental and financial health as poor/fair (58.5% and 68.2%), while 16.3% and 31.3% of HD-CP reported their own mental and financial health as poor/fair.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 3 Propose Scheme
### Virtual Multi-Hop Data Delivery for Connection-Independent Unicast Applications  
Most unicast applications in vehicular networks, such as software upgrading, car sensor data collection and so on, require a reliable end-to-end data delivery. TCP is a widely used end-to-end acknowledgment-based data delivery approach. In TCP, the sender node adapts the sending rate, namely congestion window size, by using a congestion avoidance algorithm according to the acknowledgment packets successfully received. The throughput of a TCP connection is affected significantly by the end-to-end packet loss probability. As a result, TCP has a performance degradation problem in a lossy multi-hop networks where a high end-to-end packet loss ratio restricts the increase of the congestion window size at the sender node.  
The main purpose of sensor data collection applications is to send the data to the cloud. Therefore, in most cases, a real-time end-to-end feedback is not required. To solve the performance degradation problem of TCP in multi-hop networks, we use a multi-hop data delivery virtualization approach [24] to conduct multi-hop data transmissions through multiple one-hop TCP sessions. The virtualization approach works on the top of transport layer. As shown in Figure 8, a multi-hop data transmission from vehicle 1 to vehicle N is conducted by multiple one-hop data transmissions where each hop is managed by a TCP session, which ensures the reliability and fairness while facilitating an efficient use of wireless resources. Each forwarder node is responsible for transmitting data segments to the next hop which is closer to the destination. Since each one-hop communication is conducted based on TCP, the congestion window size at each TCP sender node is not affected by the number of hops between the source node and the destination node.  
Figure 7: Context-aware edge selection.
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 3 Methodology  
### Network Architecture  
The proposed overall network architecture for 3D object detection, termed MPVF, is illustrated in Figure 1. It primarily consists of the IRFP module, the PVWF module, and the detection network. The IRFP module includes two components: the IR module and the FP module, which are responsible for extracting high-level semantic features and multi-scale features from images, respectively. The PVWF module is designed to fuse local and global features, while the detection network integrates the 2D detection network RetinaNet [36] and the 3D detection network VoxelNet [33] based on the outputs from the IRFP and PVWF modules, along with a projection consistency correction operation for the 3D candidate boxes and 2D detection boxes.  
Initially, the point cloud data is voxelized to extract point features. Simultaneously, a more expressive feature extraction module, IRFP, is designed, wherein the IR module efficiently extracts features from RGB images. Using the calibration data from the KITTI dataset [16], the LiDAR point cloud data are projected onto the RGB images at both pointwise and voxelwise levels, ensuring precise alignment between the camera and LiDAR. The features are then obtained through feature indexing and the Region of Interest align (RoIalign) layer [37], which respectively yield pointwise and voxelwise image features. During the intermediate feature extraction phase of the IR module, the FP module is constructed to capture fine-grained features at varying resolutions, thereby enhancing the model's performance in complex scenes. Subsequently, the FP module is embedded into RetinaNet for detection. Next, the PVWF module is designed to fuse local features extracted by the PWF module and global features obtained from the VWF module. By utilizing Stacked Voxel Feature Encoding (Stacked VFE) [33], pointwise and voxelwise image features are integrated into the point features, facilitating both pointwise and voxelwise fusion. A concatenation operation is employed to merge the pointwise fused features and voxelwise fused features, enhancing the interaction between multi-modal local and global features and thereby improving the model's detection accuracy, especially in small object detection and complex scenes. Finally, the fused features extracted by the PVWF module are passed to the 3D Region Proposal Network (3D RPN) [38] to generate 3D candidate boxes. Calibration data are then used to project these candidate boxes onto the 2D detection images for consistency correction, optimizing the 3D candidate boxes and ultimately achieving the 3D detection task.
###### Abstract  
Clarifying the flow characteristics in the settling tank is the foundation for the design optimization and efficiency improvement of sediment removal. However, few research works have been undertaken on the precise observation of the flow movement in the tank. For this purpose, research into an inclined-tube settling tank for the water-sediment separation of the Yellow River was carried out. An acoustic Doppler velocimeter (ADV) was utilized to measure the velocity distribution in the tank. The dimensionless turbulence intensity and dimensionless Reynolds shear stress distribution were obtained subsequently. It was found that the velocity distribution in the stable settlement area (SSA) and the settlement area with inclined tubes (ISA) was uniform. The turbulence intensity and Reynolds shear stress in the tank were beneficial to the formation of the flocs and the subsequent acceleration of the sediment settling. The flow pattern could be significantly improved by the flow adjustment board and the inclined tubes. However, the inlet, the baffle and the overflow weir in the tank had negative effects on the flow pattern. The results enhanced our understanding of the flow characteristics in the inclined-tube settling tank and indicated a direction for optimizing the tank structure and improving the settling efficiency.  
inclined-tube settling tank; flow characteristics; turbulence intensity; Reynolds shear stress; sustainable utilization +
Footnote †: journal: Applied Sciences
# Review on Rail Damage Detection Technologies for High-Speed Trains
## 3 Non-Destructive Testing Methods for High-Speed Train Rails
### New Eddy Current Testing Methods
Finally, infrared images are obtained through an infrared thermal imaging instrument to achieve defect detection. The process of the induction heating of the test piece can be roughly divided into three parts: when a high-frequency excitation current is applied to the induction coil, induced eddy currents will be generated on the surface of the test piece; after inducing eddy currents on the surface of the specimen, a large amount of Joule heat will be generated; Joule heating in defective areas can be achieved through thermal conduction and other methods to heat non-defective areas. For surface open cracks with a length \(\geq\) 0.5 mm and a depth \(\geq\) 0.1 mm, the detection rate can reach 90-95%. When a high-frequency alternating current with an excitation frequency of f is applied to the excitation coil, an induced current will be generated on the surface of the test piece according to the principle of electromagnetic induction. At this point, the physical relationship between magnetic and electric fields can be clearly explained through [PERSON]'s equations, so it is necessary to focus on introducing [PERSON]'s equations here. The differential form of [PERSON]'s equations is as follows:  
\[ \begin{split}\
abla\cdot\overline{D}=\rho\\ \
abla\cdot\overline{B}=0\\ \
abla\cdot\overline{E}=-\frac{\theta\overline{B}}{\theta\overline {T}}\\ \
abla\cdot\overline{H}=\overline{J}+\frac{\theta\overline{D}}{ \theta\overline{T}}\end{split}  \tag{5}\]  
Among them, \(B\) is the magnetic flux, \(H\) is the magnetic field strength, \(E\) is the electric field strength, \(J\) is the current density, and \(\rho\) is the charge density.
###### Abstract
Furthermore, IoT sensors deployed in smart farming environments often have limited computational and energy resources; therefore, lightweight authentication protocols must be designed to guarantee security while minimizing computational and communication overheads. In 2024, [PERSON] et al. [15] proposed a privacy-centric authentication protocol for smart farm monitoring with AI and IoT. Their scheme achieves simplicity and low overheads because it employs only XOR operations and hash functions. They asserted that their scheme resists adversarial attacks, including man-in-the-middle (MITM), privileged insider, replay, and impersonation attacks. However, their scheme does not defend against ephemeral secret leakage (ESL), stolen mobile device, sensor capture, insider, and impersonation attacks. In the authentication phase of this scheme, an authentication request message is transmitted directly to the IoT sensor by the user. The sensor cannot have the ability to verify whether the user is legitimate. Hence, if an attacker repeatedly transmits authentication requests to the sensor, it may result in a denial-of-service (DoS) attack or exhaust the sensor capabilities. Furthermore, their scheme does not guarantee the untraceability of the user. Therefore, we propose a secure and privacy-preserving authentication scheme to address the vulnerabilities of the scheme by [PERSON] et al. The proposed scheme employs XOR operations and hash functions and adopts a physical unclonable function (PUF) to resist sensor capture attacks. The proposed scheme effectively withstands various adversarial attacks, including insider, stolen mobile device, and stolen verifier attacks. After authentication in the proposed scheme, the user can access the sensor data and AI results via the shared session key.
# RoPT: Route-Planning Model with Transformer
## 5 Experiments
### Qualitative Study  
To better illustrate the correspondence between the extracted node latent representations and the real road network, t-SNE is employed to perform dimensionality reduction on node features extracted with RoPT and Neuro on the Chengdu dataset. We fix the t-SNE random seed at 20150101 while retaining scikit-learn's TSNE default parameters for all other hyper-parameters. The detailed results are shown in Figure 3 where each point corresponds to a node in the road network. The position of the center point corresponds to the mean longitude and latitude of the nodes in the trajectories. The city was then divided into four parts based on the horizontal and vertical axes. The nodes in the northeast, southeast, northwest, and southwest parts are marked in pink, blue, yellow, and green.  
As shown in Figure 2(a), the points in these four colors are relatively clustered, where green points are mainly located on the margins of the upper part, yellow points are mostly in the middle, pink points are concentrated in the southwest, and blue points, though more dispersed, can be observed to cluster roughly into two groups in the northeastern and southwestern parts. In contrast, in Figure 2(b), the points of different colors are more scattered. The pink points clustered in the north are interspersed with many yellow points, and the blue points clustered in the west are also mixed with a large number of green, yellow, and some red points. Notably, the eastern yellow clusters and southern green clusters both demonstrate pronounced heterogeneity through admixture with other colored points. To  
Figure 3: Effect diagram of Chengdu node feature based on RoPT and Neuro. Here, nodes with same color are located in same region, and red triangle markers indicate an example of continuous road segments.  
Figure 2: The influence of hyper-parameters on the accuracy of route prediction by RoPT model (Chengdu), where (**a**) shows the influence of \(\lambda\) value on Recall, and (**b**) shows the influence of GCN layer number on Recall.  
further demonstrate the effectiveness of the feature extraction, several consecutive segments of a road are chosen and highlighted in Figure 3a,b. Here, red triangles correspond to several continuous segments on the northern part of the Chengdu Ring Road, which are also circled in the figure. As in Figure 3a, with the latent representations learned with RoPT, these segments are clustered closely on the right side of the figure. Conversely, Neuro's representations result in the significant dispersion of these segments. Especially, the segment located in the leftmost circle in Figure 3b is far away from the other segments. This phenomenon demonstrates that RoPT is more effective in capturing the correlations between nodes in the road network.
# pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework
## 5 Experiments
### Performance Evaluation of pFedBASC
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**MNIST**} & \multicolumn{2}{c}{**FMNIST**} & \multicolumn{2}{c}{**CIFAR-10**} & \multicolumn{2}{c}{**CIFAR-100**} \\ \cline{2-9}  & **non-iid** & **iid** & **non-iid** & **iid** & **non-iid** & **iid** & **non-iid** & **iid** \\ \hline Central & \multicolumn{2}{c}{90.3\%} & \multicolumn{2}{c}{85.38\%} & \multicolumn{2}{c}{76.99\%} & \multicolumn{2}{c}{49.48\%} \\ \hline FedAvg & 89.43\% & 90.02\% & 83.84\% & 81.56\% & 63.44\% & 56.24\% & 30.72\% & 30.62\% \\ \hline Local & 86.39\% & 84.79\% & 78.72\% & 69.92\% & 51.75\% & 27.59\% & 18.21\% & 6.33\% \\ \hline FedProx & 89.75\% & **90.09\%** & 84.68\% & 82.04\% & 63.26\% & 55.13\% & 28.28\% & 30.94\% \\ \hline FedDyn & 89.44\% & 89.98\% & 84.76\% & 82.18\% & 61.33\% & 56.05\% & 26.43\% & 31.13\% \\ \hline FedGen & 88.78\% & 89.1\% & 83.54\% & 80.86\% & 60.85\% & 56.76\% & 23.92\% & 14.57\% \\ \hline FedBN & **90.09\%** & 90.07\% & 82.98\% & 81.55\% & 62.91\% & 30.59\% & 32.13\% & 29.14\% \\ \hline FedBabu & 89.9\% & 89.25\% & **85.21\%** & 82.59\% & 66.79\% & 58.52\% & 39.74\% & 33.3\% \\ \hline FedPer & 89.55\% & 89.62\% & 83.86\% & 81.61\% & 65.76\% & 57.04\% & 28.3\% & 15.53\% \\ \hline FedRep & 88.75\% & 89.11\% & 83.45\% & 78.8\% & 68.19\% & 59.57\% & 26.1\% & 11.2\% \\ \hline pFedSim & 89.58\% & 89.88\% & 83.78\% & 82.4\% & 64.05\% & 59.48\% & 39.72\% & 33.75\% \\ \hline pFedLa & 89.59\% & 89.14\% & 83.96\% & 80.49\% & 60.26\% & 51.76\% & 26.73\% & 23.73\% \\ \hline pFedBASC & 88.45\% & 89.45\% & 84.54\% & **83.19\%** & **69.8\%** & **70.99\%** & **43.6\%** & **41.31\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experimental results of pFedBASC performance on iid and non-iid datasets across four datasets.