esa-sceva/satcom-chunk-collection huggingface dataset:
# FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
## 4 Experiments and Analysis
### Comparison of Communication Efficiency  
In our experimental setup, for MNIST, Fashion-MNIST, and SVHN, we established the baseline accuracy using FedAvg's performance after 100 training rounds. Table 4 displays the count of communication rounds necessary for all methods with default parameters to reach the benchmark accuracy. As seen from the table, FedRDS demands notably fewer communication iterations when compared to FedAvg and FedProx, indicating that regularization methods can offer efficient communication besides improving the generalization performance. Furthermore, in most cases, FedProx follows FedAvg throughout the training process. We used a small \(\mu\) parameter (\(\mu\) = 0.01), so the proximal term in FedProx seems to have a minor effect on the training procedure. The findings illustrated in Figure 4 indicate that FedRDS converges faster than the other two methods in the initial stage due to its higher stability with non-IID data, enabling it to reach the benchmark accuracy sooner and ensure efficient communication.  
The FedRDS algorithm has proven to be highly efficient in communication across all the datasets used in our experiments. It is worth highlighting that the algorithm demonstrated remarkable efficiency when dealing with the difficult-to-classify and semantically complex SVHN dataset, achieving a 2.78-fold enhancement. Incorporating a larger shared dataset size for CIFAR-10 proved beneficial in reducing the gap between the client and server data distributions. It facilitated faster target accuracy attainment during the initial training stages of FedRDS. Moreover, dynamically adjusting the shared dataset size as per actual requirements is possible without any additional communication costs, and this strategy only needs to be executed once during initialization.
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 4 Results
### Resulting Thickness Distribution  
The derived polynomial approximation result was used to translate the distribution of the slowness (Figure 4c) into a thickness map (Figure 6c), which clearly shows the results for the required objectives of the surveys. As can be seen from Figure 6c, the thin areas of the coal are mainly distributed around 320-550 m and the triangular region in the left side, and faults have been disclosed around the areas. Therefore, affected by the faults, the rock at the top and bottom of the coal layer extends into the coal seam under the action of tension. This leads to a reduction of coal thickness. The distribution of the fault F3 disclosed is basically consistent with the inversion result; fault F3 runs diagonally across the working face from the belt entry to the head entry, and the fault drop gradually changes from 2.0 m to 3.5 m. As the channel wave propagated within the thin coal area caused by fault F3, it had a low propagation slowness. The thinnest area of coal in this area is 0.4 m, which has a great impact on mining. It was difficult to continue mining when the mining process (from left to right) reached the position of 360 m from belt entry and 430 m from head entry, according to the mining record.  
As the survey area has been mined, coal thickness distribution results were obtained based on the GA (Figure 6b) and the SIRT (Figure 6a) respectively with the same method. This was done in order to compare the performance of the introduced hybrid approach to that of the GA and the SIRT.  
Figure 5: Fitting result between coal seam thickness (\(H\)) and slowness (\(s\)). The fitting equation is \(H\) = 0.185\(s^{2}\) + 0.124s + 0.433, where the coefficient of determination \(R^{2}\) = 0.9654 and the root mean square error \(RMSE\) = 0.087.
## 3 Results and Discussion  
### Validation of an Existing Model  
In 2022, a study trained a 2D CNN model to detect patients with an LVEF below 40% and provided a demonstration site to showcase the model [11]. In 2024, another study validated this pre-trained model using an external dataset [12]. We also validated the demo model using our dataset. The model's output is the probability of LVSD, expressed as a continuous variable between 0 and 1. Therefore, we used [PERSON]'s J index to find the optimal cut-off. The cut-off value where [PERSON]'s J is maximized represents the balance between sensitivity and specificity and is considered the optimal threshold for test performance [24]. The test results shown in Figure 11 indicate that the optimal cutoff value for the dataset used in this study is 0.858, as represented by the red line.  
The prediction results using the pre-trained model for the 100 subjects in the overall lead test were as follows: an accuracy of 0.6, a precision of 0.62, a recall of 0.2, an F1-score of 0.28, and an AUC of 0.56. These results suggest that the pre-trained model does not perform well on datasets with LBBB.  
Figure 11: [PERSON]’s J cut-off graph.  
Figure 10: A lead-wise ensemble example.  
In addition to validating the pre-trained AI model, we conducted a reference analysis using a simple rule-based criterion based on QRS duration. Specifically, we compared prediction performance at various QRS duration thresholds to examine its utility as a basic clinical benchmark for LVSD prediction in patients with LBBB. To determine the optimal threshold, we also plotted [PERSON]'s J index across different cut-off values, as shown in Supplementary Figure S1. Detailed accuracy by threshold is summarized in Table S1.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 4 Experiments  
In order to validate the effectiveness of the proposed registration method, we conducted experiments using the Argoverse 2 dataset [4]. The dataset provides sequential visual images, frame-by-frame LiDAR point clouds, 3D HD vector maps, and accurate pose information for both images and LiDAR point clouds with respect to the HD maps. The utilized two sequences were collected in Miami (Figure 9a) and DTW (Figure 9b), covering a distance of 200 and 600 m, respectively. In the first sequence, the environment is situated under an overpass bridge. The presence of pillars and nearby buildings can potentially lead to misrecognition of poles and signs. The second sequence features a wider road with multiple lanes and an increased number of poles. The sequences are depicted overlapping with OpenStreetMap in Figure 7, serving as a geospatial reference for the collected data.  
The evaluation experiments are divided into five parts. Firstly, we describe the specific data preprocessing steps, including labeling poles and traffic signs from LiDAR point clouds and postprocessing on the image segmentation results. Next, we present the results of the random experiments, followed by the sequential experiments, which compare the  
Figure 8: Flowchart of EKF Pose Fusion.  
performance of the lane marking-only registration method (VisionHD) [27], an existing multi-object registration method (MOR) [10], and the proposed EOR method. Subsequently, we conduct an ablation study evaluating the contributions of SDF, SSC, and key feature types (lane markings, poles, and traffic signs) to localization performance. Finally, we assess the computation time of key modules to evaluate the efficiency of the registration method.  
To ensure a fair comparison, all methods in our experiments utilize the ICP process for pose initialization. We implemented the compared methods by ourselves, following the principles described in the literature. To ensure clear presentation and effective comparison of the results, we employ several metrics for robustness and accuracy evaluation. The robustness metric measures the probability of achieving successful registration, indicating the method's reliability. The accuracy assessment includes classic 6-DoF pose errors, which provide a measure of the registration accuracy in terms of translation (along X, Y, and Z axes), and rotation (in roll, pitch, and yaw). Additionally, we employ Intersection over Union (IoU) values [27] based on the registered HD map and segmented image. A high IoU value indicates a high registration accuracy, while a low value suggests misalignment. These metrics enable effective comparisons with other registration approaches.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 2 Piezoresistive Mechanisms
### Percolation Network  
As the distance between adjacent conductive fillers increases with strain, the tunneling effect decreases gradually. In this situation, electrons can just pass through the contacting and overlapping nanomaterials. The conductive pathways form a percolation network. Piezoresistive films are usually constructed as a double-layer structure that is composed of a conductive layer and an elastomer substrate layer. Nevertheless, the conductive layer will crack or even fall off from the substrate layer in the cyclic test of long-term stability [38]. To improve the long-term stability, percolation network methodologies based on CPCs with good performance have been developed [39; 40; 41].  
The resistivity of conductive composites depends on the content of conductive filler per unit volume in the polymer matrix. When the conductive filler has very low content, the average distance between conductive particles is large. In this case, very few conductive paths are formed; thus, the resistivity is too high for an insulator. With the increase in filler content, the average distance between conductive particles decreases, whilst the number of conductive paths increases to form a percolation network, leading to decreased resistivity of the composite. When the filler content is large enough, the conductive particles are connected to or overlap each other to form a conductive network throughout, transforming the CPC film from an insulator into a conductor. The critical volume ratio of the conductive filler at the transforming point is regarded as the percolation threshold. Figure 4a,b illustrate the development of a percolation network as strain increases in graphene flake- and silver nanowire-based CPC films, respectively.  
Figure 3: Current density distribution of graphene rubber composite: (**a**) ignoring tunnel effect; (**b**) considering tunnel effect; (**c**) conductive paths formed in graphene rubber composites [37] (Copyright 2020, Elsevier Ltd.).  
Figure 2: (**a**) SEM image of possible tunneling effect between CNTs in nanocomposites; (**b**) tunnel resistance modeling of resistance network [35] (Copyright 2008, Elsevier Ltd.).
# Computational Investigations and Control of Shock Interference +
## 1 Introduction
### Technical Approach
#### 1.2.1 Computational Methods and Mesh  
Both LES and RANS represent two distinct approaches for turbulence modeling and provide different fidelity in resolving the energetics of turbulence. While LES resolves turbulent energy transport and requires modeling of turbulence dissipation only, the computational cost associated with it is multiple orders of magnitude higher than the RANS approach. Therefore, it is significant that RANS should be compared with LES to determine its ability to capture certain flow phenomena related to shock interference and interaction with the boundary layer. Such a comparison provides guidance to the vehicle designers on computational cost versus the value proposition of using either method.  
A standard computational investigation includes verification and validation (V&V) steps. Verification is performed to quantify the accuracy of the numerical methods utilized in the calculations, and validation is performed to assess the applicability and validity of the closure models that are utilized in the calculations. In other words, code verification establishes if the CFD code is solving the equations correctly, and model validation is utilized to establish if the CFD code is solving the correct equations. Both code verification and model validation have been performed, and the results are presented in the following sections. Three geometries have been considered in this paper:  
1. Verification: A one-dimensional shock-tube problem, also known as [PERSON]'s shock-tube problem [21] has been chosen for verification as the exact solution for this problem is known.
2. Validation: A backpressured isolator from NASA Langley Research Center's Isolator Dynamics Research Lab [22] has been utilized for model validation since the time-averaged measured wall pressures have been available.
3. Demonstration and Investigation: Once the computational code and model have been verified and validated, a modified shock-tube (a shock-tube with square cross-section and open ends) has been utilized to investigate shock interference and interaction with boundary-layer phenomena. This shock-tube was modified to generate the effects of air entering an isolator from the inlet, and a pseudo-shock-train was generated due to the combustor backpressure and thermal choking.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel
### Simplified Channel Models for Code Design
#### 3.3.1 Gaussian Interference Model  
Consider the interference vector \(\mathbf{z}^{(r)}\) in (2). Let its \(i\)th element \(z_{i}^{(r)}\) be non-zero, i.e., the \(i\)th packet symbol is subject to interference. Then, \(z_{i}^{(r)}\) is a sample of a complex Gaussian distribution \(\mathcal{CN}(0,2\sigma_{i}^{2})\), where \(2\sigma_{i}^{2}\) is the interference power. This assumption is accurate already in the case of a few interferers having different phase, frequency and time offset. When only one interferer affects  
Figure 2: Collision of multiple users (**left**) and the abstracted surrogate model with constant interference power over \(\alpha n_{s}\) symbols (**right**).  
a packet, the assumption becomes inaccurate. Nevertheless, we will illustrate that it is a reasonable model for the LDPC code design.  
Under the assumption of Gaussian interference, the interference plus noise also follows a complex Gaussian distribution \(\mathcal{CN}(0,2\sigma^{2})\), with:  
\[\sigma^{2}=\sigma_{n}^{2}+\sigma_{t}^{2}.\]  
Let us define \(\mathsf{C}\left(\sigma^{2}\right)\) as the QPSK constraint AWGN channel capacity for a given \(\sigma^{2}\). The corresponding outage capacity \(\mathsf{C_{o}}\) for our surrogate channel model in Section 3.3 under the Gaussian interference assumption becomes:  
\[\mathsf{C_{o}}(\alpha,\sigma_{n}^{2},\sigma^{2})=(1-\alpha)\,\mathsf{C}\left( \frac{1}{2\sigma_{n}^{2}}\right)+\alpha\,\mathsf{C}\left(\frac{1}{2\sigma^{2} }\right), \tag{3}\]  
for a fixed \(\alpha\) and \(\sigma_{n}^{2}\), we can invert (3) and write \(\sigma^{2}\) (or likewise \(\sigma_{t}^{2}\)) as a function of \(\mathsf{C_{o}}\). Then, for a fixed rate, error-free transmission is possible if the interference power \(\sigma_{t}^{2}<\sigma_{t,o}^{2}\) where \(\sigma_{t,o}^{2}\) is referred to as the Shannon limit.  
Code design for the case of the Gaussian interference model becomes equivalent to code design for an AWGN channel where codeword bits are subjected to one of two possible noise levels. The design of LDPC photograph codes for this channel is well studied, and given that the interference is modelled as Gaussian noise, various approximations to density evolution, such as EXIT charts, are well verified in this scenario.
# A Novel Ropes-Driven Wideband Piezoelectric Vibration Energy Harvester
## 5 Conclusions  
In conclusion, we proposed a novel ropes-driven wideband piezoelectric vibration energy harvester that makes full use of the advantages of the frequency upconversion mechanism and multimodal harvesting mechanism. This device offers two obvious advantages: firstly, wider or even unlimited bandwidth can be achieved by adding LFDBs connected with ropes; secondly, the output power will not deteriorate with the increasing number of LFDBs, which was demonstrated theoretically and experimentally. Moreover, the output and bandwidth characteristics are affected by multiple parameters such as input acceleration, rope margin, and stiffness of driving beams. The experimental results proved that a wider operating bandwidth with smooth output can be realized by adjusting these parameters. Besides these parameters, there are still other parameters need to be investigated further, such as piezoelectric material, the fixed form, and material of ropes and damping, and the configuration of the device is also worthy of study for meeting the development of wireless sensors. Meanwhile, besides paying attention to the output and bandwidth behavior, the energy-harvesting efficiency of the VEH system is also a significant characteristic, thus, the energy transfer mechanism of the proposed wideband VEH system needs to be studied further. This primary study shows a promising scheme for guiding future development of wideband piezoelectric energy harvesters, and we believe that it will open up more potential applications in variable environments.  
This research was supported by the National Science Foundation of Fujian Province of China (No. 2016J01259), the Fundamental Research Funds for the Central Universities, Xiamen University (No. 20720150091), and the Specialized Research Fund for the Doctoral Program of Higher Education, state Education Ministry, China.  
Figure 9: Adjustable performance by changing (**a**) acceleration, (**b**) rope margin, and (**c**) rope margin and LFDBs’ stiffness, simultaneously.  
**Author Contributions:** [PERSON] presented the idea of a novel ropes-driven wideband piezoelectric vibration energy harvester; [PERSON] conducted the theoretical analysis, experimental study and data processing and drafted the manuscript under the supervision of [PERSON] and [PERSON]; [PERSON] and [PERSON] made contributions on the experiment measurement; [PERSON] and [PERSON] provided technical assistance in the course of this study; all authors contributed to the writing of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Imaging Attitude Control
### Coordinate Frames
#### 3.1.1 World (W) Frame  
This frame has its origin \(O_{W}\) at the local horizontal plane. The \(Z_{W}\) axis lies along the local geodetic vertical and is positive upward. The \(X_{W}\) axis points toward true north along the local horizontal plane. The \(Y_{W}\) axis forms an the orthogonal right-hand (RH) set.
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 3 Optimum Composition Ratios
### Dispersant and LS Agent  
#### 3.4.1 Dispersant  
We chose three dispersant materials (A, B, C). According to Figure 5, A was a white powder and commonly used as an asphaltene precipitation dispersant. B was a paraffin-like solid, used as a leveling agent with good dispersion and emulsification. C was a white ultrafine powder, used in the rubber industry with good lubricity and light stability.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Samples** & \begin{tabular}{c} **Rock** \\ **Asphalt/\%** \\ \end{tabular} & \begin{tabular}{c} **Reducing** \\ **Agent/\%** \\ \end{tabular} & **Penetrant/\%** & **Solvent Oil/\%** & **Plate/g** & \
\begin{tabular}{c} **Plate \& Wet** \\ **Material/g** \\ \end{tabular} &
\begin{tabular}{c} **Plate \& Dry** \\ **Material/g** \\ \end{tabular} & **POR/\%** \\ \hline
1 & 30 & 20 & 20 & 30 & 37.3 & 47.4 & 44.7 & 73.3 \\
2 & 30 & 15 & 20 & 35 & 48.5 & 55.4 & 53.2 & 68.1 \\
3 & 30 & 10 & 20 & 40 & 52.6 & 61.8 & 58.5 & 64.1 \\
4 & 25 & 25 & 20 & 30 & 51.9 & 57.6 & 55.9 & 71.9 \\
5 & 20 & 30 & 20 & 30 & 51.5 & 61.8 & 58.5 & 70.1 \\ Rhinophalt & \(\backslash\) & \(\backslash\) & \(\backslash\) & 37.1 & 47.4 & 41.9 & 47.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _POR_ test results in 5 proportions.  
Figure 4: Storage stability contrast test. (**a**) Storage stability samples; (**b**) Viscosity contrast results.  
The petroleum industry shows that a controlled dispersant dosage within 1% can significantly improve the stability of asphaltene in crude oil [23]; therefore, A, B, C were added into a pre-maintenance agent prepared at the optimum ratio. The adding amounts were 0.2, 0.4, 0.6, 0.8, and 1% of the mass of the pre-maintenance agent prepared according to the optimum ratio. The samples were sealed in glass bottles, and their consolidation condition was observed and recorded in cool, dark environment. The results are shown in Table 5.  
From Table 5, the consolidation time without any adding dispersant was 45 days. After adding A and B, the consolidation time gradually reduced with the increasing dispersant. The main reason was that adding the dispersant increases the proportion of small and medium substances, breaks the original stable state, and affects stability. The consolidation time of C was longer than A and B, and higher than 45 days in all dosages. The sample with 0.4% was not consolidated after 85 days. The storage stability of C was significantly better than A and B. From the molecular structure, A and B were short single-chain structures. C contained a positively charged and highly dispersed inorganic nuclei and two linear long hydrocarbon chains. The charged end can be wrapped in the asphaltene structure, preventing asphaltene flocculation and extending material storage time. In summary, we selected C as the dispersant agent; the dosage was 0.4% of pre-maintenance agent mass.
# Analysis and Design of Functional Device for Vehicular Cloud Computing
## 5 Design of Relay-Assisted Scheme for Vehicular Application
### Optimal Path Selection Algorithm
```
1:Geographical location coordinate acquisition: \((x_{S},y_{S})\to S\), \((x_{D},y_{D})\to S\);
2:\(S\) uploads computing information: \(S\to VC\);
3:for\(S\)do
4:\(d_{SD}=\sqrt{(y_{D}-y_{S})^{2}+(x_{D}-x_{S})^{2}}\);
5:endfor
6:if\(d_{SD}\geq R_{max}\)then
7: Collecting the maximum number of vehicles \(R_{i},i\in\{1,\ldots,N\}\) with the range of radius \(R_{max}\) from \(S\) to \(D\);
8:for\(i=1\) to \(N\)do
9: Upload the first hop link channel information: \(h_{S,R_{i}}\to VC\) ;
10: Upload the second hop link channel information: \(h_{R_{i},D}\to VC\) ;
11: VC: \(\gamma_{1,i}=h_{S,R_{i}}^{2}/N_{0}\) and \(\gamma_{2,i}=h_{R_{i},D}^{2}/N_{0}\);
12: Download computation results \(\gamma_{AF,i}=(\frac{1}{\gamma_{1,i}}+\frac{1}{\gamma_{2,i}})^{-1}\): \(VC\to S\);
13:endfor
14:\(U=sort(\gamma_{AF})\);
15:\(Q=fliplr(U)\);
16:\(n^{*}=Q(1)\). 17:\(n^{*}\) is denoted as an optimal relay-assisted vehicle. 18:else
19:\(S\) communicates directly with \(D\). 20:endif
```  
**Algorithm 1** The optimal path relay selection based on the vehicular cloud (VC).