esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of Problem  
### Mission Scenario  
To investigate the complexities of UAV mission planning, this study develops a simulation for the expedited search operations conducted by a UAV cluster (Figure 1). Within the designated mission area, 100 target points are randomly generated, represented by green points in Figure 2. The drone swarm gathers from multiple points outside the mission area, corresponding to the red points in the picture. Once the mission directive is issued, the drone swarm, equipped with electro-optical payloads, departs from the gathering point and conducts a quick search and filming for each target. Regarding the photoelectric payload of the UAVs, the visual range at each target point manifests as a spherical cone, wherein the apex of the cone and the sphere's center align perfectly. As the UAV approaches the target from its cruising altitude, it swiftly intersects the upper circular edge of this spherical cone, transitioning to horizontal flight, enabling it to spot the target and maintain level flight. Upon reaching this upper circular boundary, the UAV alters its course towards the next target, maintaining level flight. Once this boundary is again reached, the UAV initiates an ascent, rapidly returning to cruising altitude before proceeding to the subsequent target. If the proximity to the subsequent target does not permit ascent to cruising altitude, the UAV preemptively transitions into a descent to facilitate efficient target engagement.  
In this task, the UAV swarm initiates its operation from the starting point, maintaining a consistent speed of 100 km/h and a cruising altitude of H = 3 km. As it approaches the vicinity of a target point, it transitions into a descent trajectory, eventually stabilizing at a lower altitude to facilitate level flight. Concurrently, the UAV swarm executes close\(-\)range imaging of the target point. After the shooting is completed, they ascend and re\(-\)enter cruising altitude until they approach the next target and then descend again, thus perpetuating this cyclic process throughout the mission duration.
# A Study of Correction to the Point Cloud Distortion Based on MEMS LiDAR System
## 3 Experiment
Obviously, as we can see from the Figure 8 that after using the correction method, the point cloud image has almost no distortion, which further proves the robustness of the proposed method. Therefore, the method proposed in this paper can fully meet future vision imaging requirements. Figure 6: (**a**) XZ plane contour before correction; (**b**) YZ plane contour before correction; (**c**) XZ plane contour after correction; (**d**) YZ plane contour after correction; \(\alpha\) and \(\beta\) are the deflection angle. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Before Correction/\({}^{\circ}\)** & **After Correction/\({}^{\circ}\)** & **Distortion Reduction** \\ \hline XZ plane & 15.255 & 3.901 & 74.428\% \\ YZ plane & 17.650 & 5.194 & 70.572\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of point cloud distortion before and after correction.
# An Intelligent Approach for Contact Pressure Optimization of PEM Fuel Cell Gas Diffusion Layers
###### Abstract  
The compression of the gas diffusion layer (GDL) greatly affects the electrochemical performance of proton exchange membrane fuel cells (PEMFCs) by means of both the equivalent value and distribution of contact pressure, which depends on the packing manner of the fuel cell. This work develops an intelligent approach for improving the uniformity and equivalent magnitude of contact pressure on GDLs through optimizing the clamping forces and positions on end plates. A finite element (FE) model of a full-size single fuel cell is developed and correlated against a direct measurement of pressure between the GDL and a bipolar plate. Datasets generated by FE simulations based on the optimal Latin hypercube design are used as a driving force for the training of a radial basis function neural network, so-called the agent model. Once the agent model is validated, iterations for optimization of contact pressure on GDLs are carried out without using the complicated physical model anymore. Optimal design of clamping force and position combination is achieved in terms of better contact pressure, with the designed equivalent magnitude and more uniform distribution. Results indicate the proposed agent-based intelligent optimization approach is available for the packing design of fuel cells, stacks in particular, with significantly higher efficiency.  
PEMFC; clamping; GDL; contact pressure; artificial intelligent; agent model +
Footnote †: journal: Applied Sciences
## 2 Problem Formulation  
Actual observational data indicate that photovoltaic (PV) power outputs from geographically close locations exhibit high similarity due to similar random factors, such as solar radiation intensity and weather variations. Therefore, the spatial correlation of PV power generation can be described using output spatial correlation. Specifically, output spatial correlation refers to the degree of similarity between PV power output sequences in different geographical regions. These similarities decrease as the distance between two locations increases. In the latitude direction, as latitude increases, solar radiation intensity gradually decreases, leading to higher output spatial correlation between neighboring regions. In the longitude direction, the phase difference between PV power output sequences in two locations increases with the time difference, thereby affecting the output spatial correlation.  
In this paper, we propose a data-driven framework aimed at leveraging spatiotemporal correlations and periodic characteristics for short-term photovoltaic (PV) power prediction. This framework, considering the information from neighboring sites, is depicted in Figure 1 and primarily consists of the following steps:  
1. Distributed PV power data are collected to form a spatiotemporal dataset.
2. PV power time series are detrended to exclude the impacts of the diurnal cycle.
3. Detrended solar data from multiple sites are fused to form the input for data-driven forecasting models.
4. Data-driven forecasting models are developed based on the fused data.  
This framework illustrates how spatiotemporal datasets are fused using historical data from neighboring power stations. The historical PV power of station \(p\) at time \(t\) can be represented as \(f_{l}^{p}\). The historical data of station \(p\) from time \(t-n\) to \(t\) are described as \(X_{l}^{p}=\left[f_{l-n}^{p},f_{l-(n-1)}^{p},\cdots,f_{l}^{p}\right]^{T}\). Then, we combine the historical PV power from its neighboring stations to construct a spatiotemporal PV power matrix, as follows:  
\[X_{t}^{s}=\left[\begin{array}{c}X_{l-n}^{s}\\ X_{l-(n-1)}^{s}\\ \vdots\\ X_{t}^{s}\end{array}\right]^{T}=\begin{bmatrix}f_{l-n}^{1}&f_{l-(n-1)}^{1}& \cdots&f_{l}^{1}\\ f_{l-n}^{2}&f_{l-(n-1)}^{2}&\cdots&f_{l}^{2}\\ \vdots&\vdots&\ddots&\vdots\\ f_{l-n}^{m}&f_{l-(n-1)}^{m}&\cdots&f_{l}^{m}\end{bmatrix} \tag{1}\]  
The overall timeline is defined as the union of historical \(T_{h}\) and future timesteps \(T_{f}\), and \(T_{h}\cup T_{h}=\{t_{1},t_{2},\cdots,t_{h}\}\cup\left\{t_{h+1},t_{h+f}\right\}\).  
The objective is to forecast the PV generation \(Y_{T_{f}}^{\mathcal{P}}\) based on the provided numerical weather prediction (NWP) \(X_{NWP}\) and fused data \(\mathbf{X}_{t}^{\text{s}}\). This task can be framed as an optimization problem, where the aim is to determine the sequence conditional on the future \(f\) timesteps.  
\[F(X_{NWP};\mathbf{X}_{t}^{\text{s}})\to Y_{T_{f}}^{\mathcal{P}}\]  
where \(\rightarrow\) represents the complex nonlinear mapping.
## 6 Properties of Concrete Containing MoT
### Compressive Strength  
The compressive strength of C40 and C60 grade concrete with MoT as the replacement of natural sand were tested respectively by [PERSON] [13] and [PERSON] et al. [23] as shown in Figure 6. The mixture proportions of concrete prepared by [PERSON] [13] were: cement: 550 kg/m\({}^{3}\); coarse aggregate: 1387 kg/m\({}^{3}\); fine aggregate: 463 kg/m\({}^{3}\); water binder ratio: 0.26. Type I Portland cement 42.5 R and FM2.3 local river sand were used whilst the water content of fine and coarse aggregate is 0.41% and 1.59% respectively. The mixture proportions of concrete prepared by [PERSON] [23] were: cement: 435 kg/m\({}^{3}\); coarse aggregate: 1113 kg/m\({}^{3}\); fine aggregate: 682 kg/m\({}^{3}\); water binder ratio: 0.39. Type I Portland cement 42.5 R and FM3.3 local river sand were used whilst the water content of fine and coarse aggregate is 0.2% and 1.4% respectively. As the replacement of natural sand, the replacement ratio of MoT has subtle influence on the compressive strength of concrete. It is feasible to use MoT as fine aggregate in the preparation of structural concrete, in term of compressive strength.  
[PERSON] et al. [24] reported a test on the concrete mixed with ground super-fine MoT powder as a replacement of cement binder. The mixture proportions of concrete prepared by [PERSON] [24] were: cement: 380 kg/m\({}^{3}\); coarse aggregate: 1030 kg/m\({}^{3}\); fine aggregate: 760 kg/m\({}^{3}\); water binder ratio: 0.38. Type I Portland cement 42.5 R and FM2.7 local river sand were used.  
It can be seen from Figure 6 that with the increase of the amount of MoT, the compressive strength of concrete increases first and then decreases. Since the activity of MoT itself is much lower than that of cement. When the content of ground MoT increases to a certain limit, the cement content of cementitious material in unit volume decreases, which weakens the bonding performance between cementitious material and aggregate interface and causes the decrease of concrete strength.
# Improved Adaptive Successive Cancellation List Decoding of Polar Codes
## 3 Improved Segmented CRC-Based Adaptive SCL Algorithm  
The SCAD-SCL proposed in [21] adds some segmented CRC based on the AD-SCL algorithm, which further reduces the complexity of AD-SCL, but it still has high computation complexity. In our work, we propose an improved algorithm SAD-SCL based on the existing CRC adaptive successive cancellation list algorithm.  
The SAD-SCL algorithm introduces segmented CRC for SCAD-SCL and uses a new method to reduce the redundancy calculation of the AD-SCL algorithm. Before each update, the value \(L\) is re-decoded, SAD-SCL first uses the existing LLR information to determine the range of the burst error bit, and then only re-decodes the information sequence of this range. Moreover, when SAD-SCL cannot obtain the correct decoding value in the first part of the information sequence, it uses SC decoding to complete the decoding in the second part of the information sequence, and the decoding performance of SAD-SCL is almost the same as that of second part of the AD-SCL algorithm. This paper introduces two improvements to SAD-SCL in Sections 3.1 and 3.2. The specific decoding process for SAD-SCL is given in Section 3.2.
# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 2 Materials and Methods
### Point Cloud Semantic Segmentation Model  
#### 2.3.1 Overall Network Architecture  
This study performs point cloud semantic segmentation based on the improved RandLA-Net, adhering to the encoder-decoder framework, as shown in Figure 2. In the encoding layers, successive random downsampling (RS) layers are applied to save computational resources and input into an improved feature aggregation module (FA*) to gradually increase the feature dimensions of each point and enrich key feature information. In the decoding layers, nearest neighbor interpolation is used to upsample (US) point cloud feature matrices, adopting the nearest neighborhood point features as features for each point. That is, upsampling operations recover higher-resolution feature matrices from lower-resolution ones. Skip link concatenation is used to cascade the upsampling results with the features obtained during the encoding stage. The results are then input into the MLP (multilayer perceptron), thus merging the features before and after sampling and restoring the dimensions of the input point cloud. Finally, through three shared fully connected (FC) layers and a dropout layer, the predicted semantic labels for the point cloud are output.  
The original input point cloud is denoted as \(P=\left\{p_{1},\cdots,p_{i},\cdots,p_{N}\right\}\in R^{N\times(3+d)}\) (i= 1, \(\cdots\), N). The features of the input point cloud include (1) positional features \(S=\left\{s_{1},\ \cdots,s_{i},\ \cdots,\ s_{N}\right\}\in R^{N\times 3}\) and (2) other features \(F=\left\{f_{1},\ \cdots,\ f_{i},\ \cdots,\ f_{N}\right\}\in R^{N\times d}\) such as color and intensity. In these expressions, P represents the input point cloud, N is the number of input points, and \(3+d\) is the feature dimension of the input point cloud.  
The feature dimensions in Figure 2 are explained below:* (N, 3+d): The input original point clouds with N points, each having 3 + d feature dimensions.
* (N/4, 32): After random sampling (RS), the number of points is reduced to N/4, with each point having 32 feature dimensions.
* (N/16, 128): Further reduction in the number of points to N/16 with 128 feature dimensions after additional sampling and feature aggregation (FA*).
* (N/64, 256): Points are reduced to N/64 with 256 feature dimensions.
* (N/256, 512): Points are reduced to N/256 with 512 feature dimensions at the deepest encoding layer.
* (N/64, 256), (N/16, 128), (N/4, 32), (N, 3+d): The upsampling (US) layers increase the number of points back to their original count, restoring them to their respective feature dimensions during the decoding process.
## 4 Conclusions  
The effects of chloride ions and carbonation on the electrical resistivity of concrete are different. The purpose of this study is to examine the effect of chloride concentration on the surface electrical resistivity measurement of carbonated and non-carbonated concrete. The interference effect was examined within the experimental scope of the study, and the results are summarized as follows:  
Figure 15: Ratio of the electrical resistivity of carbonated concrete with chloride of 1.0–2.0% and the electrical resistivity of non-carbonated concrete without chloride.  
1. Chloride concentration leads to reduced resistivity of the concrete. The electrical resistivity of the concrete kept rising over time and all concrete showed the value 12 K\(\Omega\)-cm or more, except the specimen with 2.0% chloride ions and a _w/c_ ratio of 0.55. As a result of viewing the electrical resistivity rate, the value of the specimen with 2% chloride ions was 65-71% of the specimen without chloride ions.
2. For the correlation between chloride ion content and electrical resistivity in the specimen without chloride ions, the correlation between chloride concentration and electrical resistivity exceeded the determination coefficient of 0.70, indicating the obvious effect of chloride ions on electrical resistivity.
3. The rate of change in the electrical resistivity of concrete with chloride was low in the early ages. However, the electrical resistivity showed a more stable trend with no change in the long-term age. These influences are generated by the adsorption of chloride ions and the micro-structure development of concrete due to on-going hydration of the cement. The micro-structure of the hardened cement paste has a greater influence on the electrical resistivity than chloride adsorption.
4. The ratio of the electrical resistivity of concrete with chloride to that of concrete without chlorides decreased as the chloride concentration increased. The electrical resistivity of the concrete was significantly influenced by chloride concentration, even when the concentration was low.
5. The electrical resistivity of concrete under combined deterioration tended to increase in proportion to the chloride concentration and carbonation depth. In the environment of a combined deterioration, the carbonation depth of 5 to 8 mm was a meaningful criterion for determining whether carbonation or chloride ions has a large effect on the electrical resistivity of concrete.  
[PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Korean Ministry of Education (grant No.2020R1A6C1020263).  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Initial Evaluation (Training and Testing) of Machine Learning Models  
The experimental data and results obtained from the abovementioned series of experiments (140 tests) are split into two groups for initial training and testing of ML models using a 70-30% random split of data: training dataset (98 tests) and testing dataset (42 tests). After the initial training of ML models on the training dataset, the models are tested on previously unseen test dataset. Figure 7 presents the comparisons of ML model predictions with experimental results for AAR during the initial testing phase of the models for the KNN and SVR models along with the baseline MLR model. Note that the hyperparameters of the ML models are kept constant at their optimum values (described in Section 4.4): k = 3 in the KNN model, C = 1.0 and \(\epsilon\) = 0.1 in the SVR model. As seen in Figure 7, both the KNN and SVR models (MAPE = 0.17 and 0.16, respectively) outperform the baseline MLR model (MAPE = 0.21) in terms of accuracy of predictions.  
Figure 8 presents the initial testing results of three DTR-based ensemble ML models (RFR, ABR and GBR) along with their MAPE values. As with the previous three models, the hyperparameters for all of these models are also kept at their optimum values: maximum depth of tree = 6 and number of trees in the ensemble = 100 for all three ensemble models. A single DTR model results in a MAPE of 0.17 during the initial testing phase (not shown in the figure). However, as can be seen from Figure 8, when 100 trees are combined together, all three DTR-based ensemble models (MAPE = 0.14 to 0.15) outperform other models presented in Figure 7 in terms of accuracy of predictions. In terms of consistency among different models, the MAPE of all five nonlinear, nonparametric ML models vary between 0.14 and 0.17 and the MAE values vary between 0.08 and 0.11. This shows excellent  
consistency among the ML models developed for the problem considered in this study. For comparison, the MAPE and MAE resulting from the statistics-based simple liner regression model (SLR) presented in Figure 3 are 0.23 and 0.15, respectively. It should be noted that the SLR model uses the entire dataset for fitting a linear relationship and uses the same dataset for calculating the MAPE and MAE values. Despite that, it is interesting to note that the testing errors of the MLR model (MAPE = 0.21 and MAE = 0.12) are still slightly better than those of the statistics-based (non-ML) SLR model.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
###### Abstract  
Network Intrusion Detection Systems (NIDS) often suffer from severe class imbalance, where minority attack types are underrepresented, leading to degraded detection performance. To address this challenge, we propose a novel augmentation framework that integrates Soft Nearest Neighbor Loss (SNNL) into Generative Adversarial Networks (GANs), including WGAN, CWGAN, and WGAN-GP. Unlike traditional oversampling methods (e.g., SMOTE, ADASYN), our approach improves feature-space alignment between real and synthetic samples, enhancing classifier generalization on rare classes. Experiments on NSL-KDD, CSE-CIC-IDS2017, and CSE-CIC-IDS2018 show that SNNL-augmented GANs consistently improve minority-class F1-scores without degrading overall accuracy or majority-class performance. UMAP visualizations confirm that SNNL produces more compact and class-consistent sample distributions. We also evaluate the computational overhead, finding the added cost moderate. These results demonstrate the effectiveness and practicality of SNNL as a general enhancement for GAN-based data augmentation in imbalanced NIDS tasks.  
network intrusion detection; WGAN; minority-class synthesis; soft nearest neighbor loss; data augmentation +
Footnote †: journal: Future Internet
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 5 Conclusions  
This paper has presented a shared key generation architecture using the ECDH protocol of ECC over \(GF(2^{233})\) with the consideration of high-speed and low-area at the same time. A 2-stage pipelining and a Karatsuba multiplier are incorporated to achieve high speed. The employed pipelining has reduced the critical path and improved clock frequency. Similarly, the utilization of the Karatsuba multiplier has decreased clock cycles. Towards the low-area goal, singular adder and multiplier units are included for arithmetic operations. These operations are addition, multiplication, squaring (providing similar inputs to the multiplier) and inversion (using the Itoh-Tsujii algorithm). This strategy has ultimately helped us to reduce the hardware resources. Two FSM-based dedicated controllers are employed for efficient control functionalities. The implementation results after place-and-route are provided on Virtex-7, Spartan-7, Artix-7 and Kintex-7 FPGA devices. Over \(GF(2^{233})\), the utilized FPGA slices are 5102 (Virtex-7), 5634 (Spartan-7), 5957 (Artix-7) and 6102 (Kintex-7). The computational time for one shared key generation is 31.08 (Virtex-7), 31.68 (Spartan-7), 31.28 (Artix-7) and 32.51 (Kintex-7). The proposed ECDH architecture outperforms others on Virtex-7 FPGA in terms of \(\frac{throughput}{area}\) (\(FPGA\ slices\)) (the achieved value is 6.30). Moreover, it has been shown that the proposed architecture is 302.7 and 132.88 times faster in terms of latency as compared to state-of-the-art ECDH designs of [35; 38], respectively. The achieved results and performance comparison statistics reveal the suitability of the proposed architecture in high-speed with low-area key agreement applications.  
Conceptualization, M.R. and A.A.; data extraction, S.Z.K. and Z.M.; results compilation, M.R. and S.Z.K.; validation, M.R. and Z.M.; writing--original draft preparation, S.Z.K. and H.K.; critical review, M.R. and I.B.; draft optimization, S.Z.K. and H.K.; supervision, M.R.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
We are thankful for the support of the Deanship of Scientific Research at King Khalid University, Abha, Saudi Arabia for funding this work under grant number R.G.P.2/132/42.  
Not applicable.  
The authors declare no conflict of interest.