esa-sceva/satcom-chunk-collection huggingface dataset:
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 5 Repair of Point Cloud
### Point Cloud Repairing of ISS + ICP Algorithm  
The internal shape descriptor algorithm of intrinsic shape signatures (ISS) is the representation of three-dimensional geometry of a target shape. Its principle is to establish a local coordinate system for each key point in the point cloud data, provide a weight radius with each key point as the center, and calculate the covariance matrix of all covered points and key points. The relationship between the eigenvalues of the covariance matrix is used to describe the characteristic degree of the point. After the ISS algorithm detects the feature points of the target point cloud and matches the descriptors, the target point cloud and the source point cloud already have a good initial pose, and then the ICP algorithm is used for fine registration between the source point cloud and the target point cloud, so as to meet the requirements of point cloud repair. The RGB-D roadway point cloud and 3D laser scanning point cloud were repaired by the ISS + ICP algorithm; the RMSE value was 13.8524 mm and the repair results are shown in Figure 15a,b.  
Figure 14: (**a**) Point cloud patching diagram of FFTH + ICP algorithm. (**a1**) Initial pose; (**a2**) Coarse registration; (**a3**) Fine registration side view; (**a4**) Fine registration top view. (**b**) Fine registration verification diagram.
# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 3 Methods  
### Overall Architecture  
The river segmentation of remote sensing images in cold and arid areas is dichotomized [25]. In this paper, we explore a river extraction method for remote sensing images in cold and arid areas. The overall network structure is shown in Figure 3. The river attention U-shaped network (RAU-Net++) proposed in this paper uses the VGG16 structure as the backbone network, and in order to make the network pay more attention to the information of the fine river, an end-to-end encoder-CBAM-decoder network is designed by introducing the CBAM at the end of the encoder. Since the original U-Net only upsampled the end of the encoder, in order to optimize the problem of insufficient upsampling of the encoder, each convolutional layer of the encoder was up-sampled, and dense connections were introduced for each convolutional layer to learn the river features of different depths, and they were integrated by means of feature overlapping, which accomplished the deep interaction of different feature levels.  
In order to further optimize the dense connections, we introduce a residual attention feature fusion module (RAFF) at dense jump connections, which contains bottleneck attention-weighted branches, maximum pooling branches, and residual branches, which reduces the loss of information in each dense connection, and enables more edge detail features to be combined with the semantic information of the river channel, RAFF module  
Figure 3: The network structure of the method in this article.  
Figure 2: U-Net++ network structure.  
optimizes the extraction of river edge detail information in remote sensing images, as well as to reduce the misdetection and omission of the river channel and its tributaries. Finally, the pixel-level optimization of river channel extraction in medium-resolution remote sensing images is carried out by combining the weighted loss function.
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 5 Analysis of Examples  
### Parameter Configuration  
In this paper, the test system consists of 16 thermal power units and four wind farms. The basic data are listed in Tables 1 and 2 in Appendix. The relationship between the wind farm power and the wind speed is as follows:  
\[P(v)=\left\{\begin{array}{l}0,v<v_{\text{ci}}\\ \frac{v_{\text{r}}-v_{\text{ci}}}{v_{\text{r}}-v_{\text{ci}}}P_{\text{r}},v_{ \text{ci}}\leq v<v_{\text{r}}\\ P_{\text{r}},v_{\text{r}}\leq v<v_{\text{out}}\\ 0,v\geq v_{\text{out}}\end{array}\right., \tag{34}\]  
where \(\text{P}_{\text{r}}\) is the rated power of a wind farm; \(v_{\text{ci}}\), \(v_{\text{r}}\), and \(v_{\text{out}}\) are the cut-in speed, rated speed, and cut-out speed, which are 3, 12, and 20 m/s, respectively for all wind farms. Load forecast deviation obeys the normal distribution N(0, 20\({}^{2}\)). The wind power prediction error factor is 0.02. The loss value for outage is 500 USD/MW-h. In QPSO, the number of swarms is 40, and the maximum number of iterations is 500, the parameter \(\varepsilon\)= \(1\times 10^{-4}\).
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
###### Abstract  
Recently, many researchers have been interested in the application of chaos in cryptography. Specifically, numerous research works have been focusing on chaotic image encryption. A comprehensive survey can highlight existing trends and shed light on less-studied topics in the area of chaotic image encryption. In addition to such a survey, this paper studies the main challenges in this field, establishes an ecosystem for chaotic image encryption, and develops a future roadmap for further research in this area.  
image encryption; chaos; chaotic encryption; chaotic image encryption; trend analysis; future roadmap +
Footnote †: journal: Applied systems
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 4 Challenges and Open Research Issues
### Fronthaul Capacity  
The fronthaul link between a BBU and RRHs substantially impacts the performance of a C-RAN. This link needs to have a high capacity and low latency. The fronthaul capacity must be considered while allocating BBU resources to the RRHs through RRH clustering during the design of the clustering algorithm. The mechanisms have to be designed such that the load on the fronthaul links does not exceed the capacity of the links. Fronthaul-aware approaches must be considered during the formulation of the optimization problem, for designing energy-efficient solutions while maintaining the QoS requirements. Moreover, other architectures such as H-CRAN and F-RAN, which are evolved from C-RAN, can be considered for future research as they can be utilized to solve the high load problem in fronthaul link. The authors in [70] studied the delivery phase of an F-RAN for an arbitrary pre-fetching, with the goal of maximizing the delivery rate while satisfying fronthaul capacity and per-RRH power constraints. They took into account two basic fronthauling modes, namely hard transfer and soft transfer, and also a hybrid mode. In the soft transfer mode, C-RAN principle is followed for the fronthaul links for transferring quantized baseband signals, whereas, in the other mode, the requested files that are not in the local caches are transmitted by the fronthaul links. Another attractive technology is narrowband Internet of things that requires moderate fronthaul capacity as it can operate in a narrow bandwidth and can be further investigated for implementation in C-RAN environment [71].
## 5 Conclusions  
Improving the accuracy of energy consumption prediction for large public buildings using the CEEMDAN-MIC-FCBF-BiLSTM method in this study contributes to more precise energy management, increased energy efficiency, and reduced carbon emissions. The research focuses on a large public building in Shaanxi Province, employing time-related and meteorological features for prediction. Initially, the CEEMDAN method decomposes energy consumption data into multiple IMF components and residuals. Subsequently, the MIC-FCBF combined feature selection method is applied to each IMF component and residual for feature selection, obtaining input features for the prediction model. Finally, the input features and corresponding IMF components and residuals are fed into the BiLSTM model for prediction, and the predicted results are aggregated to derive the energy consumption forecast for the building. Comparative analysis with five benchmark methods using six evaluation metrics demonstrates the superior predictive performance of the proposed method. This study primarily focuses on hourly energy consumption prediction for the entire building and suggests future research to refine predictions by considering the internal structure of large public buildings and implementing energy consumption zoning for enhanced accuracy. This study primarily focuses on improving the hourly energy consumption prediction accuracy of large public buildings through signal decomposition and feature screening. However, it is acknowledged that the weights and hyperparameters of deep learning models also play a crucial role in influencing prediction accuracy. In future research, we intend to enhance the accuracy of energy consumption predictions by employing swarm intelligence algorithms for optimizing both the model weights and hyperparameters. This approach aims to further elevate the energy consumption prediction precision for large public buildings.  
Conceptualization, Z.T. and D.C.; data curation, Z.T. and L.Z.; formal analysis, D.C. and L.Z.; methodology, Z.T.; writing--original draft, Z.T.; writing--review and editing, D.C. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation Program (51209167; 12002251), the Open Fund Project of Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering (YT202004), the Scientific Research Program of Shaanxi Provincial Education Department (22 JC043), the Xi'an Science, Technology Project (23 GXFW0045), and the Shaanxi Provincial Natural Science Foundation General Project (2024 JC-YBMS-286).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Discussion
Conservative treatments such as pelvic floor exercises, behavior modifications, pelvic floor stimulation, and biofeedback to relieve urinary incontinence can be administered to high-risk patients at an early stage, and unnecessary aggressive surgical correction can be avoided for patients who can expect to recover from PPI. In the current study, when a traditional LR model was used, the diagnostic performance of predictive PPI early-recovery yielded low to moderate accuracy, with an AUC of 0.63 in an internal validation group and 0.59 in an external validation group. Machine learning has been increasingly employed for precise diagnosis and prediction of outcomes [43]. This approach offers the advantage of analyzing clinical practice data with numerous confounding factors and associated factors that are closely and intricately related to each other. By using machine learning algorithms, the AUC significantly improved to 0.73 with KNN, 0.73 with DT, 0.72 with SVM, and 0.81 with RF in an interval validation group and to 0.60 with KNN, 0.61 with DT, 0.65 with SVM, and 0.61 with RF in an external validation group, although the difference was only statistically significant with SVM. These results suggest that a machine learning approach exhibited superior, or at least comparable, performance compared with conventional statistical approaches in terms of predicting early recovery from PPI. In addition, these models showed good calibration, suggesting that these data are useful in determining early recovery from PPI within 3 months after RP. This study had several limitations. First, it was vulnerable to selection bias due to the nature of its retrospective design. Other factors that can be expected to affect continence status after RP, such as the presence of preoperative lower urinary tract symptoms, were not evaluated. Second, this study was performed in a single institution with a small number of patients. We found significant improvements in diagnostic performance using machine learning algorithms in an internal validation group, but not in an external validation group, with the exception of the SVM. This may be due to the small number of events, warranting further study with a larger number of patients. Third, we used patient-reported daily pad usage to assess incontinence, which is a relatively subjective metric. However, a recent prospective multicenter study by [PERSON] et al. confirmed that the number of pads can accurately measure a patient's post-prostatectomy incontinence [44]. Finally, as there were three surgeons in our institution during the study period, inevitable technical differences between surgeons may have biased the results.
## 3 Methods
### Experiment Settings and Accuracy Assessment  
In addition to the proposed approach, RF [65], one of the most commonly used and effective methods for mapping land cover and crop types using RS data, was selected to identify vineyards under the same circumstances to serve as a control group, because such applications often exploit multi-phased images while RF is not able to capture temporal information contained in multi-phased images. The original time series of three VIs were directly concatenated as one vector, and then the fitted RF classifier was applied to perform binary classification. The RF algorithm is implemented with the assistance of the Scikit-learn library, which is a machine learning library in Python. Within the fitted RF model, 50 trees were used, the maximum depth of each tree is automatically determined by the algorithm, and the maximum number of the features considered to be used when splitting each node during the construction of a tree is set to the square root of the number of the total features (i.e., 10 in this research), other parameters were set to default values. The input of RF is a vector of 99 elements (33 timestamps \(\times\) 3 indices). Note that the same training and testing datasets listed in Table 1 were used to train the RF classifier and test its performance. Finally, confusion matrices that include the producer's accuracy (Pro.'s Acc.), user's accuracy (Usr.'s Acc.), and overall accuracy (OA) were used to assess the identification performance.
# Using Multiple Robots to Increase Suggestion Persuasiveness in Public Space
## 4 Results  
During the nine days of the experiment, as indicated in Table 1, 2258 participants passed through the corridor and had the chance to use the hand sanitizer after passing by (or not) the robots. Figure 9 and Table 2 illustrate the proportion of participants that used the hand sanitizer in each scenario and the respective \(\chi^{2}\) test \(p\)-values.  
The \(\chi^{2}\) test focusing on the data for the \"Going In\" route revealed a significant difference in the distribution (\(\chi^{2}(2)=81.73\), \(p=1.78\times 10^{-18}\)). We conducted a post hoc test on \(Z\) statistics with Holm correction to identify whether there were differences in the probability of using hand sanitizer among the various configurations. We found significant differences between zero robots and one robot (\(Z=7.489\), \(p=6.97\times 10^{-14}\)) and zero robots and three robots (\(Z=8.297\), \(p\simeq 0\)), while we did not find between and one robot and three robots (\(Z=1.327\), \(p=0.18442\)).  
The same analysis was conducted for the \"Going Out\" route. The \(\chi^{2}(x)\) test revealed a significant difference in the distribution (\(\chi^{2}(2)=71.97\), \(p=2.35\times 10^{-16}\)). In the post hoc test with Holm correction, we found significant differences between the zero robot and one robot configurations (\(Z=6.457\), \(p=2.12\times 10^{-10}\)), as well as between zero robot and three robot configurations (\(Z=8.582\), \(p=\simeq 0\)). However, we did not find a significant difference between one robot and three robot configurations (\(Z=2.414\), \(p=0.01580\)).  
Between each day, the hand sanitizer bottles were weighed and the weights were recorded. The total usage of sanitizer in grams and the average per day per hand sanitizer dispenser are listed in Table 3, where \"Bottle 1\" was the one near the entrance and \"Bottle 2\" was the one near the corridor.  
Figure 9: Proportion of participants who used hand sanitizers in the experiment grouped by route taken and configuration of the experiment. * \(\chi^{2}\) test \(p\)-value \(<0.05\) after Holm correction
# Monthly Precipitation over Northern Middle Atlas, Eastern Morocco: Homogenization and Trends
## 2 Materials and Methods
### Observed Climate Data Sources and Station Selection  
The climatic data used in this work are monthly rainfall series recorded in 22 observation stations. With the exception of the Taza station, which belongs to the National Directorate of Meteorology (DMN), the other stations belong to the hydraulic basin agencies of Moulouya (ABHM) and Sebou (ABHS). These administrations are responsible for collecting all possible climatic and hydrological data in their respective basins. The records collected do not all cover the same time period (Figure 2). Since the spatial coverage of  
Figure 1: Geographical location of the northern Middle Atlas.  
The first step was to fill in the missing data. After filling, data were checked both for outliers and/or change points of non-climate origin, and heterogeneity in the data sets.  
Although several climate-data quality-control techniques are available, there is no single approach recommended and generally accepted by all researchers [21; 39]. The Climatol tool (version 3.1.1) ([[https://www.climatol.eu/](https://www.climatol.eu/)]([https://www.climatol.eu/](https://www.climatol.eu/)), accessed on 15 June 2022), chosen here, is frequently used in the climate literature for data homogenization, filling in missing data and correcting outliers [16; 18; 24].
## 3 Results
### Wind-Driven Rain (WDR) and Other Weather Data
#### 3.3.1 Wind-Driven Rain (WDR) and Dominant Winds
The results regarding the dominant wind direction over the long period from 2000 to 2020 are depicted in Figure 11, which shows the wind rose with the frequency of wind directions. According to the data, the prevailing wind direction is from the North, indicating that winds from this direction were most frequent during the analysed period. This consistent wind pattern plays a significant role in the distribution and impact of wind-driven rain (WDR) on the different sides of the St. Simeon Church, influencing both biological colonization and weathering processes. Figure 9: Wind-driven rain (WDR) loads [kg/(m\({}^{2}\cdot\)h)] for each exposure in the single month as derived by the weather stations in Kayseri and Güsheir. Moreover, as reported also in Table 2, in August the values are 0.
## 5 Conclusions  
In this paper, we present a new model for predicting price fluctuations of futures commodities using an improved meta-heuristic algorithm TCM-ABC to train LSTM neural networks and apply it to predict the daily closing prices of gold and natural gas, comparing the obtained prediction results with those of classical neural networks and other meta-heuristic trainers such as ARIMA, Transformer, GA- LSTM, etc. The results show that the TCM-ABC-LSTM model outperforms the other models. The evaluation metrics reveal that the TCM-ABC-LSTM network model achieved the lowest MAE, MSE, and RMSE errors of \(6.19\times 10^{-3}\), \(6.7\times 10^{-5}\), and \(7.7\times 10^{-3}\), respectively. So far, the TCM-ABC-LSTM model is considered as a promising technique for high-precision commodity price prediction. We will explore more theoretical results in the future and expect that, in practice, investors can obtain higher excess returns through predictions of TCM-ABC-LSTM.  
The gold and natural gas data used in this study are classical data from futures markets, and our proposed model performs very well on these datasets; we believe that the superior performance of TCM-ABC-LSTM is mainly attributed to the innovative improvement of the artificial bee colony algorithm and its application to LSTM networks. Compared with the traditional LSTM model, the proposed TCM-ABC-LSTM model has better generalization ability because it can adaptively find the optimal hyperparameters. However, there are some limitations in this study, such as that this paper only predicts the closing price of the commodity on the following day alone, but, in fact, the longer the prediction time, the more insight into the future trend of the commodity for further analysis. These will be discussed in our future research. In the future, additional research will be carried out on the following points:  
1. Extending the application of the TCM-ABC-LSTM model to diverse time series prediction tasks encompassing domains such as electricity consumption, wind power generation, and stock market dynamics.
2. Exploring alternative optimization algorithms to serve as hyperparameter selectors for LSTM models in the quest for better hybrid models.
3. Applying long-term sequence prediction using the proposed model.  
Conceptualization, L.H. and Y.X.; methodology, L.H. and Y.X.; software, Y.X.; validation, L.H., Y.X. and J.L.; formal analysis, L.H. and Y.X.; investigation, L.H. and Y.X.; resources, L.H. and Y.X.; data curation, L.H. and Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, L.H. and Y.X.; visualization, L.H. and Y.X.; supervision, L.H.; project administration, L.H. and Y.X.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The following abbreviations are used in this manuscript:  
ABC  
Artificial bee colony  
ARIMA  
Autoregressive integrated moving average  
FNN  
Feed-forward neural network  
GA-LSTM  
Genetic algorithm long short-term memory  
LSTM  
Long short-term memory
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
By expanding the reinforcement learning action decision space through high-dimensional state perception capability, the method achieves dynamic adaptation to complex environments. Compared to earlier studies [10], this experiment optimized the action space design and conducted comparative experiments with the classic greedy strategy under the same benchmark environment. Meanwhile, we systematically analyzed the impact of key hyperparameters on performance, revealing the correlation between hyperparameter configurations and convergence efficiency. Experimental simulations demonstrate that this approach significantly enhances network deployment efficiency and the rationality of emergency communication resource allocation in post-disaster dynamic environments, outperforming traditional modeling planning methods in robustness and adaptability. The research further validates the feasibility of applying DRL to solve network deployment problems in emergency communication fields, providing a novel technical pathway for communication restoration during disaster relief processes that balances real-time responsiveness with optimization performance.