esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Answers to RQs
#### 5.1.3 RQ3: What Are the Current Challenges in the Management of DSPL?
Eighteen papers (21.4%) present challenges in the area of _Open variation_. Fifteen papers (17.9%) present as a result the _validation of the proposal_. Four papers (4.8%) present challenges in the area of _Explicit variation points_. Three papers (3.6%) present challenges with _binding time_ in DSPLs. Two papers (2.4%) show challenge the _Variant isolation_. One paper (1.2%) has challenges with _Non-code artifacts_. One paper (1.2%) contains challenges related to feature _Granularity_. One paper (1.2%) presents challenges in the area of _Support of defaults_ and fifteen papers (17.9%) have _No challenges mentioned_. See details in Figure 7 and the classification of each item according to the criteria previously defined in Table 12.  
Figure 7: Challenges in managing DSPLs.
## 4 Results
### Effect of Scanning Angle on Foliage Profile Estimation
Although the rate of change of the _RMSE_ was small, there was a rising trend when the flying altitude increased from 1000 m to 3000 m and a declining trend when the flying altitude increased from 3000 m to 4000 m. The _RMSE_ at the flying altitude of 3000 m was the largest, and it was the smallest at the flying altitude of 1000 m. The _Diff_ increased when the flying altitude increased from 1000 m to 3000 m, and it decreased when the flying altitude increased from 3000 m to 4000 m. Therefore, the _Diff_ at the flying altitude of 3000 m was the largest, and it was the smallest at the flying altitude of 1000. However, the overall change of the _Diff_ was small and was only from 0 to 0.3. Therefore, when the flying altitude increased from 1000 m to 4000 m, the estimation accuracy was highest at the flying altitude of 1000 m and smallest at the flying altitude of 3000 m. However, the _RMSEs_ and the _Diff_s at all flying altitudes were very close. Figure 5:0 The estimated foliage profiles at flying altitudes from 1000 m to 4000 m at 1000 m intervals (the scanning angle and pulse density are 20\({}^{\circ}\) and one pulse/m\({}^{2}\), respectively). (**a**) The estimated foliage profiles at all flying altitudes; (**b–e**) the histograms of the estimated foliage profiles at the flying altitudes of 1000 m, 2000 m, 3000 m, and 4000 m, respectively.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## Appendix A  
Firstly, obtain the point cloud slice \(D=\{p_{1},p_{2},\ldots,p_{m}\}\) at the top and bottom 10 cm of the breast height after point clouds preprocessing, and initialize the core point object \(\Omega=\varnothing\). Then, starting from any point \(p_{j}(j=1,\,2,\ldots,m)\) in point cloud slice \(D\), find the set of points \(q\) in point cloud slice (including point \(p_{j}\) itself). Satisfy that the Euclidean distance between point \(p_{j}\) and any other point \(q\) in point cloud slice \(D\) is less than or equal to \(\varepsilon\). Record the set of points \(q\) as \(N_{\varepsilon}(p_{j})\), that is, the \(\varepsilon\) neighborhood sample set of point \(p_{j}\) (Equation (A1)):  
\[N_{\varepsilon}(p_{j})=\big{\{}q\in D\big{|}dist\big{(}p_{j},q\big{)}\leq \varepsilon\big{\}},\] (A1)  
Figure 13: Partial complex tree segmentation results.  
Secondly, if there are enough points in the \(\varepsilon\) neighborhood sample set of point \(p_{j}\), and the number is greater than or equal to \(Min\)_Pts_ (Equation (A2)). Then, point \(p_{j}\) is marked as the core point and add the point \(p_{j}\) to the core point object set \(\Omega=\Omega\cup\left\{p_{j}\right\}\). Repeat the above steps for the remaining points in point cloud slice \(D\), and constantly update the core point object set \(\Omega\) until all the core points in point cloud slice \(D\) are found.  
\[\left|N_{\varepsilon}\big{(}p_{j}\big{)}\right|\geq Min\)_Pts,_ (A2)  
Thirdly, initialize the cluster number \(k=0\) and the unvisited sample set \(\tau\). Record the current visited sample set \(\tau_{old}=\tau\).  
Fourth, select a core point object \(o\) from the core point object set \(\Omega\) randomly, and initialize the queue \(Q=\{o\}\). (1) Take the first sample \(q\) in queue \(Q\). If the number of points in the \(\varepsilon\) neighborhood of sample \(q\) is greater than or equal to \(Min\)_Pts_, then set \(\Delta=N_{\varepsilon}(q)\cap\tau\), and add the sample in \(\Delta\) to queue \(Q\), then set \(\tau=\tau-\Delta\). (2) Next, take the next sample \(q\) from the queue \(Q\), repeat process (1) in step 4 to calculate the number of points in the \(\varepsilon\) neighborhood of sample \(q\) and subsequent operations, until all samples in queue \(Q\) are calculated. When queue \(Q\) is empty, that is, all the samples in queue \(Q\) are calculated, then set \(k=k+1\) and a new cluster is formed. Record the new cluster as \(C_{k}=\tau_{old}-\tau\), update the core point object set \(\Omega=\Omega-C_{k}\) at the same time. (3) Finally, repeat the whole process of (1) and (2) in step 4 until the core point object set \(\Omega\) is empty. The final clusters \(C=\{C_{1},C_{2},\ldots,C_{k}\}\) represent the initial clustering results of trunk detection.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
###### Abstract  
The generation of realistic electrical load profiles is essential for advancing smart grid analytics, demand forecasting, and privacy-preserving data sharing. Traditional approaches often rely on large, high-resolution datasets and complex recurrent neural architectures, which can be unstable or ineffective when training data are limited. This paper proposes a data-driven framework based on a lightweight 1D Convolutional Wasserstein GAN with Gradient Penalty (Conv1D-WGAN-GP) for generating high-fidelity synthetic 24 h load profiles. The model is specifically designed to operate on small- to medium-sized datasets, where recurrent models often fail due to overfitting or training instability. The approach leverages the ability of Conv1D layers to capture localized temporal patterns while remaining compact and stable during training. We benchmark the proposed model against vanilla GAN, WGAN-GP, and Conv1D-GAN across four datasets with varying consumption patterns and sizes, including industrial, agricultural, and residential domains. Quantitative evaluations using statistical divergence measures, Real-vs-Synthetic Distinguishability Score, and visual similarity confirm that Conv1D-WGAN-GP consistently outperforms baselines, particularly in low-data scenarios. This demonstrates its robustness, generalization capability, and suitability for privacy-sensitive energy modeling applications where access to large datasets is constrained.  
synthetic load profile; generative adversarial networks (GANs); deep learning; energy consumption +
Footnote †: journal: Applied sciences
# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 2 Mask and Region-Based Convolutional Neural Network (Mask R-CNN)
### Loss Functions  
To train the layers of the Mask R-CNN described above, loss functions are designed considering the task of each output layer. The five layers in Figure 1 (classification layer and regression layer in the RPN, box-classification layer and box-regression layer in the classification stage, and the output layer of the mask branch) use loss functions for the training. In the RPN, the classification layer uses a logarithmic loss for binary classification that distinguishes objects from backgrounds, and the regression layer uses a smooth L1 loss to optimize for estimated four elements representing the bounding box locations. In the classification stage, box classification uses a log loss to classify all foreground objects and backgrounds, and the box-regression layer uses a smooth L1 loss to locate the bounding boxes as the regression layer of the RPN does. The mask branch is trained using a binary cross-entropy loss that distinguishes objects from backgrounds in the bounding box. In this study, the five loss functions are set to be simultaneously optimized during the training. [PERSON] et al. [37] and [PERSON] et al. [33] can be referred for detailed description of each loss function.
Extended Kalman filter (EKF) was applied to estimate the pose and of the robot [1], but the performance was not ideal. For some strong nonlinear systems, this method will bring more truncation errors, which may lead to inaccurate positioning and mapping. Particle filter approaches [2; 3] were introduced because they can effectively avoid the nonlinear problem, but it also leads to the problem of increasing the amount of calculation with the increase of particle number. In 2007, [PERSON] proposed a milestone of LiDAR-SLAM method called Gmapping [2], based on improved Rao-Blackwellized particle filter (RBPF), it improves the positioning accuracy and reduces the computational complexity by improving the proposed distribution and adaptive re-sampling technique. As an effective alternative to probabilistic approaches, optimization-based methods are popular in recent years. In 2010, [PERSON] proposed such a representative method called Karto-SLAM [3], which uses sparse pose adjustment to solve the problem of matrix direct solution in nonlinear optimization. Hector SLAM [4] proposed in 2011 uses the Gauss-Newton method to solve the problem of scanning matching, this method does not need odometer information, but high precision LiDAR is required. In 2016, Google put forward a notable method called Cartographer [5], by applying the laser loop closing to both sub-maps and global map, the accumulative error is reduced.
# PpyNT/NR/NBR Composites with Excellent Microwave Absorbing Performance in X-Band
## 2 Materials and Methods
### Fabrication of PPyNT/NR/NBR Composites  
First, 100 phr (parts per hundred weights concerning rubber) rubber blend with different NR/NBR weight ratios from 10/90-90/10 was mixed using a two-roll open mill (BL-6175-AL, Baolun, China) for 60 s with a roll spanning of 1 mm and a friction ratio of 25/18. Then 3 phr ZnO, 1 phr SA and 1 phr RD were added into the mill simultaneously and blended for 300 s. After that, different weight of PPyNT (3 phr-12 phr) was introduced into the mill and blended for 120 s. Then 1.5 phr NS and 2 phr S were added into the chamber subsequently and blended for 120 s. The temperature was maintained at around 40 \({}^{\circ}\)C during the entire blending process. Following the blending process, the compounds were vulcanized under pressure at 160 \({}^{\circ}\)C for the optimum vulcanization time (t\({}_{00}\)), obtained from a moving die rheometer (RHEOMETER MDR 2000, ALPHA Technologies, Wilmington, DE, USA).
## 2 Related Work
### Handcrafted Methods  
Early endeavors in ear biometrics concentrated on employing handcrafted strategies for feature extraction, specifically aimed at delineating the anatomical structure of the ear. [PERSON] et al. [13] systematically tested and compared various extensions of the local binary patterns (LBPs) descriptor for ear recognition. They sought to improve performance by introducing a new variant called averaged local binary patterns (ALBPs) through a simple thresholding scheme. In a subsequent study, [PERSON] et al. [14] developed robust local oriented patterns (RLOPs) based on LBPs, demonstrating superior rotation invariance and noise tolerance in unconstrained ear recognition compared to the conventional LBPs and their derivatives. [PERSON] et al. [15] incorporated the Jaya algorithm to enhance the quality of ear images. They employed the speeded-up robust features (SURF) descriptor for extracting local invariant pose features. [PERSON] and [PERSON] [16] proposed a hybrid approach utilizing local and global features in the frequency domain for ear image representation. Employing the local phase quantization (LPQ) texture descriptor for local features and the Gabor-Zernike (GZ) operator for global features, a genetic algorithm was employed to optimize feature selection and enhance recognition accuracy. [PERSON] and [PERSON] [17] proposed a two-step ear recognition system. The first step involves the application of an ear segmentation approach to extract a region of interest, which is free of non-ear parts. The second step employs the binarized statistical image features (BSIFs) descriptor to extract and classify features. The results demonstrated that employing ear segmentation rather than original images improves the recognition performance. [PERSON] et al. [18] explored the transformation of two-dimensional ear images into one-dimensional representations. Their investigation focused on the one-dimensional local binary patterns (1D-LBP) descriptor and its variations as an alternative solution for feature extraction.  
While these handcrafted methods demonstrated noteworthy achievements in ear recognition, their inherent architectural constraints limit the discriminative information extractable from captured images.
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
###### Abstract  
Steel bridges are widely used in bridge engineering. In the structural design of steel bridge deck pavement systems, engineers focus on obtaining mechanical properties by calculating design parameters and are keen to establish a quick and accurate solution method. Because of the complex knowledge system involved in the numerical calculation method, it is difficult for the general engineering designer to master it. Researchers have started using artificial intelligence algorithms to solve problems in civil engineering. This study developed an XGBoost-based mechanical performance prediction model for steel bridge deck pavement systems. First, numerical simulation tests are conducted at unfavorable load locations using a finite element model to establish a dataset. Then, an XGBoost model is built using this dataset, and its parameters are optimized and compared with traditional machine learning models. Finally, an explanatory analysis of the model is performed using SHAP, an interpretable machine learning framework. The results indicate that the developed XGBoost model accurately predicts the mechanical properties of steel bridge deck pavement systems.  
steel bridge deck pavement; artificial intelligence; ensemble learning; mechanical property prediction; feature importance
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 3 Proposed Method
### Keypoint Cloud Extraction
#### 3.3.2 RS Algorithm for Edge Keypoint Extraction  
The specific process of the target edge point cloud keypoint extraction algorithm is illustrated in Figure 2 and consists of the following steps:  
1. Pixel-Level Segmentation: Process the RGB image using the PIDNet semantic segmentation algorithm to achieve pixel-level segmentation of the target object.
2. 2D Edge Detection: Apply the Sobel edge detection operator to extract 2D edges of the target object and refine them using the segmentation results. This ensures the detected edges accurately correspond to the target object.
3. 3D Transformation: Combine the camera intrinsic matrix and depth map to obtain depth information. Using Equation (2), transform the 2D edge keypoints into 3D point cloud edge keypoints, thereby completing the extraction of edge keypoints from the point cloud.  
This three-step process effectively extracts edge keypoints from the point cloud, ensuring accuracy and relevance to the target object.
## 2 Background  
### Infrared (IR) Pedestrian Detection  
In the past decades, IR pedestrian detection has attracted much interest from many researchers. Hotspot + SVM [23] acquiesced that a pedestrian's body looks brighter than the background. It generated pedestrian candidates by searching hotspot regions, then a support vector machine (SVM) was applied to conduct the detection stage. On the basis of hotspot features, [PERSON] et al. [24] added analysis of face and shoulder parts in candidate hotspot regions and exploited a random forest classifier to classify the candidates. In [25], a dual-threshold segmentation method was developed to generate regions of interest (ROIs) via detecting hotspot regions. Then the Haar-like and histogram-of-oriented-gradients (HOG) features were extracted for classification. However, the aforementioned hotspot-based methods only show reasonable performance when the thermal energy difference between pedestrians and background is distinguishable. It generates considerable missing instances during hot weather or when the pedestrians wear well-insulated clothing. In [26], a stereo system formed by combining two IR cameras was introduced, in which hotspot detection, edge detection, and disparity calculation were adopted to produce candidate regions. Then, the morphology and thermal features of the pedestrian's head were used to validate candidate regions. In [27], a feature-based region growth with a high-intensity seed method is proposed for segmenting pedestrians, in which vertical deviation-based morphological closing is combined to compensate for distortion caused by clothing. [PERSON] et al. [28] first proposed a contour precision saliency map for detecting ROI. Then the shape distribution histogram feature was acquired by calculating the distances between the random points on the thinned contour map in the ROI. Finally, a modified sparse representation classification (MSRC) method was developed to detect IR pedestrians. OCS-LBP + CaRF [21] exploited the oriented center-symmetric local binary pattern (OCS-LBP) features to describe the IR pedestrians, and proposed a cascade random forest (CaRF) to classify pedestrians. [PERSON] et al. [29] proposed a local steering kernel (LSK) to reduce intrinsic noise in IR images and combined an image similarity kernel with SVM to train the LSK tensor. Recently, [PERSON] et al. [30] applied a CNN, named 'you only look once version 2' (YOLOv2) [20] to IR pedestrian detection. They proposed a handcrafted adaptive Boolean-map-based saliency (ABMS) kernel to infuse with YOLOv2. [PERSON] et al. [31] presented an automatic region proposal network by designing a new loss function and adding a segmentation task, for IR pedestrian detection. This method does not consider the factors that IR images are susceptible to environmental changes, and thus the results are not ideal. In the latest work [32], a CNN-based IR person detector based on residual network (ResNet) [33] and atrous spatial pyramid pooling (SPP-net) [34] was proposed. The developed IR person detector was evaluated by using infrared closed-circuit television (CCTV) images.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 2 Related Work
### Statistical-Driven Approach  
Statistical methods have been adopted for predicting the future amount of computing resources or future prices.  
In [19] short- and long-term CPU usage is predicted based on seasonal ARIMA (SARIMA) and long short-term memory (LSTM) models based on Azure data. The hedonic price index was adopted in [20] in pricing policy based on the Amazon dataset, highlighting the importance of non-functional features, which resulted in estimated prices being lower. In [21], various algorithms are applied to Amazon's spot instance prices, with SARIMAdelivering the highest prediction accuracy. In [22] the authors tested linear autoregressive (AR), the ARIMA model, exponential smoothing (ETS), and generalized autoregressive conditional heteroskedasticity (GARCH) for predicting the upcoming graphics-processing unit (GPU) spot instance price. Based on the results, the GARCH model delivered better results for dynamic short-term and middle-term GPU instance pricing prediction, whereas for variance-based middle-term and long-term GPU instance pricing prediction, the AR model provided the best results. In addition, the ETS model produced superior results for long-term GPU spot pricing prediction based on smoothness, and the ARIMA model offered the best results for long-term GPU spot price prediction based on variations. In [23], a hybrid of ARIMA and fuzzy regression, known as fuzzy autoregressive predicted traffic in cloud computing, was used. In addition, the proposed model determined models with higher accuracy. Based on the results, the root mean square error and coefficient of determination indicated that the proposed model was a suitable model for the prediction.
## 3 Results and Discussion  
### Model Validation  
To validate the flow solver, the present simulations were compared to the experimental data of [PERSON] et al. [28; 29]. They corresponds to the ice formation process around two cylinders with fixed temperatures. Figure 4 displays the temporal evolution of the solid volume ratio, As/A\({}_{\rm C}\). This is defined as the ratio of the solidified area to the total cross-sectional area of the cylinders. It can be clearly seen that the numerical results are in fairly good agreement with the experimental results, with a maximum deviation of 8.13% at the maximum flow time.  
Figure 4: Comparison between the present simulations and the experimental results of [PERSON] et al. [27; 28]. The solid volume ratio (As/A\({}_{\rm C}\)) is the ratio of the solidified area to the total cross-sectional area of the cylinders.  
Figure 3: Comparison between the inlet and outlet temperatures of the coolant fluid at Re = 2000 and T\({}_{\rm inlet}\) = 263.15 K.
## 2 Experimental Investigations
### Experimental Results and Discussion  
#### 2.3.1 Characteristics of the Catalytic Infrared Radiation Panel  
The surface temperature of the radiant panel was mainly controlled by the intake gas volume, and the relationship between the surface temperature and gas flow is shown in Figure 6a. The surface temperature of the radiant panel reached a maximum of 475 \({}^{\circ}\)C when the intake gas flow was 0.3 m\({}^{3}\)/h. When the gas flow was increased above 0.3 m\({}^{3}\)/h, the surface temperature decreased due to the excessive gas flow. At excessively high flow rates, the gas did not have enough time to react with and diffuse into the environment around the surface of the radiant panel. Moreover, the oxygen on the surface of the radiant panel was swept away, resulting in insufficient oxygen and an incomplete gas reaction.  
Figure 6: (**a**) Relationship between the surface temperature of the radiant panel and the gas intake. (**b**) The catalytic infrared radiation intensity of the radiant panel.  
Infrared radiation (IR) can be divided into three categories according to the wavelength: near-infrared (NIR), mid-infrared (MIR), and far-infrared (FIR) radiation. A wavelength range of 0.75-2 \(\upmu\)m is generally defined as NIR, 2-4 \(\upmu\)m as MIR, and 4-1000 \(\upmu\)m as FIR [31]. The catalytic infrared radiation intensity curve obtained in this study is shown in Figure 6b. The measured wavelengths were in the range of 2-25 \(\upmu\)m. Thus, this radiation was both MIR and FIR.