esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Literature [22] proposed a joint channel estimation and precoding (JCEP) algorithm for data detection of FTN signaling on frequency selective fading channels. With the development of AI technology, the problem of data-driven communication signal detection has received increasing attention. On the one hand, various deepearning methods have recently been used to solve the problem of the physicalayer of wireless communication, and valuable results have been obtained [23]. In addition, the signal training process of LSTM has passed the basic principle of signal processing, and theLSTM expansion model has been formally explained in the framework of approximating the IIR system with the FIR model, and it has been proven to be suitable for sufficient conditions forearming signal sequences [24]. Yet, on the other hand, as far as our research has uncovered, no or very few scholars use data-driven methods to solve FTN detection algorithms in multipath channels. The existing data-driven related work mainly stays in AWGN channel or special scene channel (similar to AWGN optical communication channel and underwater communication channel) [25; 26; 27]. Preliminary research results show that for FTN signaling with a higher compression rate, the detection algorithm based on deepearning can obtain better performance than the MAP algorithm [28]. Compared with the FTN research in the AWGN channel, the research of the FTN signaling receiving algorithm based on the multipath fading channel has more practical value. However, FTN signaling transmission in multipath channels will also bring major challenges to existing receiver algorithms. This is because the inherent ISI of FTN signaling and the non-linear interference caused by the multipath channel are aliased with each other, which makes it difficult for traditional detection algorithms to recover the correct transmitted signal. In addition, most of the existing data-driven FTN detection algorithms use traditional neural networks such as DNN and CNN as the basic structure, and there are relatively few structures that use RNN as a model. On the one hand, RNN networks are widely used in the field of signal processing by virtue of their excellent sequence processing capabilities. On the other hand, our simulation results also show that the RNN network structure is better than the traditional neural network structure for feature extraction of FTN signal interference. This article will propose a new idea for FTN detection with great potential. Compared with traditional methods with perfect mathematical arguments, neural network detection algorithms help to achieve online real-time detection by characterizing training samples offline in advance. With this method, improvements with better robustness and/or complexity can be obtained. In general, as the spectrum resources areimited and the computing power is increasing day by day, FTN and data-driven signal processing algorithms are promising development directions and are expected to play an important role in the future communication physicalayer transmission technology.
## 2 Literature Review
### Feature Selection for Stock Price Prediction  
Feature selection in stock price prediction has been crucial in unraveling the complex attributes and interconnections within financial market data. Initially, research in this domain primarily utilized basic statistical methods and straightforward economic indicators to forecast market trends and stock price fluctuations.  
Advancing this research, [PERSON] and [PERSON] (2010) developed a methodology that integrates various feature selection techniques, including Principal Component Analysis (PCA), Genetic Algorithm (GA), and Classification and Regression Trees (CARTs) [26]. This approach aimed to identify key features to enhance the accuracy of stock price predictions. They found that the combined application of PCA and GA, as well as the integration of PCA, GA, and CARTs, were particularly effective. This method distilled approximately 80% of the initial 85 features, identifying 14-17 critical features. [PERSON] et al. (2011) adopted the fractal feature selection method in conjunction with a Support Vector Machine (SVM) for daily stock price trend forecasting, demonstrating superior average prediction accuracy with a smaller subset of features [27]. [PERSON] and [PERSON] (2019) utilized 33 technical indicators along with the Boruta feature selection technique for forecasting stock prices, reducing the error rate to 12% through an ANN regression model [13]. [PERSON] et al. (2020) analyzed Chinese A-share market data using various feature selection algorithms and machine learning models, including time-sliding window cross-validation [28]. Their findings underscored the effectiveness of the Random Forest algorithm in predicting stock price trends. [PERSON] and [PERSON] (2023) introduced a novel stock price trend prediction methodology using feature selection based on the coefficient of variation and various neural network models, showing substantial improvements in performance compared to traditional feature selection techniques [29].  
Previous research has indicated a significant gap in the context of the Korean market, often focusing either on broad market indices or individual stock predictions, with a limited range of feature selection methods and types. Korean industry-specific prediction research also focuses on improving stock prediction performance. To overcome these limitations, this study aims to explore a spectrum of feature selection techniques, seeking to identify the most effective feature sets for predicting stock prices within key domestic industry classifications.
# Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers
## 5 Color Compensation  
Tone changing through the image fusion causes color distortions originating from the balance changes of RGB channels in the tone changing. The ratio of RGB signals changes, and color saturation would be reduced [13]. Tone changes should be applied to only the luminance channel, and the process sequences preserve the color information of the visible image. Converting from RGB signals to opponent color signals is necessary for constructing a uniform perceptual color space and correlated of various appearance attributes [14]. To correct this chromatic defect of tone mapping, we adopt a color compensation method that restores a ratio of color to luminance after tone mapping.  
After VIS and NIR input images are blended to generate the proposed radiance map, the proposed color compensation uses a ratio between the radiance map and the VIS input image. The color compensation must consider the fact that tone scaling has a brightness gap. Therefore, the color compensation ratio is calculated using the radiance map and the VIS input image in the luminance channel in Equation (18). The ratio represents the variation in the luminance channel between the radiance map and the VIS input image:  
\[r=\frac{\textit{Radiance map}}{L_{\textit{VIS}}}, \tag{18}\]  
where \(r\) is the surrounding brightness variation between the VIS input image and the radiance map.  
\[L_{s}=(g(x,y)*L_{\textit{VIS}}(x,y))^{\gamma}, \tag{19}\]  
where \(L_{s}\) is a surrounding image with gamma correction, and \(g(x,y)\) is a Gaussian blur function. We used a \(15\times 15\) size; the \(\delta\) (sigma) value of the Gaussian filter size is 15. \(L_{\textit{VIS}}\) is a VIS luminance channel image. Further, \(\gamma\) is the gamma value. We used the \(\gamma\) value as 0.1.  
\[r_{adpt}=r\times L_{s}+(1-L_{s}), \tag{20}\]where \(r_{adpt}\) is an adapted local brightness variation. Further, \(r\) is the surrounding brightness variation between the VIS input image and radiance map given in Equation (18), and \(L_{s}\) represents a surrounding image with gamma correction, as shown in Equation (19).  
\[d^{\prime} =(a-128.0)\times r_{adpt}+128.0, \tag{21}\] \[b^{\prime} =(b-128.0)\times r_{adpt}+128.0, \tag{22}\]  
where \(d^{\prime}\) and \(b^{\prime}\) are the compensated chrominance channels. The term \(r_{adpt}\) is introduced to eliminate color noise in dark areas. This term also increases the color gain in the bright areas and preserves the color, which is obtained from VIS input image, in the dark area. The chrominance channel is compensated by using \(r_{adpt}\) as proposed in Equations (21) and (22), where \(a\) and \(b\) are the chrominance channels in the CIELAB color space. This color compensation preserves the VIS input-image color to allow its natural application to the proposed radiance map.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 6 Open Research Issues and Challenges
### Multi-UAV-Aided WSNs  
A multi-UAV network [59] is a promising approach to reduce the data-collection time, end-to-end delay, and fault tolerance, as well as to increase communication reliability and network lifetime. UAV coordination and physical collision avoidance are emerging challenges in multi-UAV networks. In UAWSNs, the ferrying methodology can be further extended by a store-carry-forward (SCF) [59] mechanism. SCF is a suitable method for data forwarding, which can enable delay-tolerant delivery in UAWSNs.
## 1 Introduction
The complexity of the algorithm was related to the data collection, which was similar to our approach, but the main difference is that here image processing is used later. High-resolution aerial imagery captured by drones can assist in identifying any structural issues, equipment malfunctions, or damage to the towers, enabling timely maintenance and preventive measures [21]. Additionally, UAVs equipped with specialized cameras and sensors can aid in wildlife and environmental impact assessments. The ability to observe wildlife habitats and ecosystems from an aerial viewpoint can provide valuable data on how electromagnetic radiation affects local fauna and flora, leading to more informed decisions on tower placement and environmental conservation [23; 24]. With the deployment of 5G and future wireless technologies, monitoring the electromagnetic radiation from these new systems becomes even more critical. UAVs can play a crucial role in assessing the specific characteristics of 5G radiation emissions and their potential effects on human health and the environment. Their agility and flexibility make them ideal for adapting to the evolving needs of monitoring advanced telecommunication infrastructure. Using UAVs for telecommunication tower monitoring also comes with its challenges. These include ensuring compliance with aviation regulations, acquiring proper permits for UAV operations, and addressing privacy concerns related to aerial surveillance. Data security during transmission and storage is also a paramount consideration when dealing with sensitive information gathered during monitoring [25, 26]. The integration of UAVs into the monitoring of telecommunication towers and base stations offers a compelling solution to improving data collection, enhancing accessibility to remote sites, and providing valuable insights into health, safety, and environmental impacts. As technology continues to advance, the synergy between UAVs and telecommunication tower monitoring will likely become an indispensable part of ensuring efficient and responsible telecommunication infrastructure management. This paper is structured as follows: Section 1 serves as an introduction and presents the background context. Section 2 provides a problem formulation and brief overview of the equipment used and the experiment setup. Section 3 presents our experiment results and the measured data. Finally, Section 4 presents the discussion, Section 5 future work and Section 6 conclusions.
## 3 Distributed Mission Scheduling Model
### ATSA for Dynamic Scheduling
#### 3.4.3 Dynamic Scheduling Evaluation  
The goal of dynamic scheduling is to complete emergency tasks with high revenue while maintaining the integrity of initial plan. Therefore, several indicators are defined to evaluate the performance of algorithm, including the completion rate (\(CR\)), priority execution rate (\(PR\)), initial plan change rate due to newly arrived targets (\(IR\)), and emergency execution rate (\(ER\)). The evaluation function is defined as follows:  
\[\begin{array}{c}f=\frac{CR+PR+(1-IR)+ER}{4}\\ CR=n_{2}/n^{\prime}\\ PR=\sum\limits_{i=1}^{n_{1}}p_{i}/\sum\limits_{i=1}^{n^{\prime}}p_{i}\\ IR=d_{3}/n_{1}\\ ER=d_{4}/(d_{1}+d_{2})\end{array} \tag{16}\]  
where \(n^{\prime}\) is the total number of targets, \(n_{1}\) is the number of targets executed in the initial plan, \(n_{2}\) is the number of targets observed after dynamic scheduling, d\({}_{1}\) is the number of newly arrived targets, \(d_{2}\) is the number of targets in the initial plan that fail to execute due to emergency, \(d_{3}\) is the number of targets of the initial plan that change due to emergency, and \(d_{4}\) is the total number of emergency targets that execute after dynamic scheduling.
## 7 Conclusions  
Through systematic investigation of damage-fracture evolution in heterogeneous sandstone, this study reveals self-organized criticality and establishes predictive frameworks for failure. Four principal conclusions emerge:  
1. Yield-phase critical transitions: Three phenomena exhibit abrupt changes during sandstone yielding: macromechanical responses, AE signals, and crack networks. The entire yield phase constitutes a self-organization process bounded by two critical thresholds. The initiation threshold (first critical point) denotes microdamage coalescence into microcracks, triggering volumetric expansion. The termination threshold (second critical point) marks microcrack reorganization into oriented macroracks preceding peak strength.
2. Power-law damage progression: AE-pore evolution adheres to power-law dynamics throughout fracturing. The self-organization phase exhibits power-law exponents (\(r\approx 0.84\)-1.15) matching complete fracture processes (\(r\approx 0.87\)-1.20) but exceeding initial phase values (\(r\approx 0.71\)-0.85). Exponent magnitude correlates with three damage characteristics: nonlinearity intensity, damage degree, and spatial localization. Escalating \(r\)-values during self-organization provide quantifiable early warning of impending failure.
3. Predictive damage signatures: Stage III damage patterns exhibit 85-90% similarity with terminal failure surfaces both spatially and temporally. These self-organization signatures enable reliable prediction of three failure parameters: fracture geometry, instability chronology, and damage progression trends.
4. Multistage fracture mechanics: Crack evolution progresses through three mechanistic phases: nucleation dominance at first criticality, propagation dominance at second criticality, and coalescence dominance at terminal failure. The developed conceptual model deciphers self-organized critical phase transitions through integrated analysis of AE-crack response patterns.  
However, it is limited to static loading and does not fully cover complex engineering conditions. Real-world scenarios involve diverse stress states and multi-field coupling, which increase prediction complexity. Future research should use real-time CT and AE monitoring on varied rock samples under multi-field conditions. This will help understand rock deformation and failure mechanisms, check the applicability of the self-organized critical law, and explore predictability. It will provide a basis for studying rock damage evolution and failure prediction, supporting the accurate assessment and prevention of rock disasters.  
H.Y.: Conceptualization, Methodology, Investigation, Validation, Formal analysis, Software, Writing--Original draft preparation, Data curation. Y.S.: Conceptualization, Investigation, Supervision, Resources, Writing--Review and editing, Funding acquisition. J.R.: Methodology, Investigation. Y.C.: Methodology. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China (Grant Nos. 42277182, 42377187, 12072259, and 11972283).  
Not applicable.
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 5 Conclusions and Future Work  
In this research, a novel and simple reputation-based scheme was proposed that utilized the consumption-to-contribution ratio of each node in the network to detect and isolate the selfish nodes in MANETs. The proposed scheme was compared with the benchmark scheme, namely, OCEAN. The simulation results showed that the proposed reputation-based scheme outperformed the benchmark scheme in terms of packet delivery fraction, normalized routing load, and good throughput in the presence of malicious and selfish nodes. However, OCEAN performed well in terms of delay as compared to the proposed scheme. Furthermore, it was found that the proposed algorithm enhanced the detection accuracy of selfish nodes as compared to OCEAN. The proposed scheme suffered from a bad or hard location problem, which degraded the detection accuracy in the presence of selfish nodes.  
Future research directions include comparing the proposed scheme with some of the most recent reputation-based schemes, and incorporating machine learning, including deep reinforcement learning, to solve hard or bad location problems and enhance detection accuracy. In addition, investigating the congestion effects on the proposed scheme and reputation rebuilding mechanisms will be explored, which can lead to a node that was perceived as selfish and isolated from the network being reevaluated and reintegrated into it.  
Figure 10: Scalability of the proposed scheme with respect to fixed speed, fixed selfish population, and varying number of mobile nodes.  
**Author Contributions:** Conceptualization, M.F. ([PERSON], [EMAIL_ADDRESS]), J.G., and S.A.; methodology, M.F. ([PERSON], [EMAIL_ADDRESS]), G.M. and J.G.; software, M.F. ([PERSON], [EMAIL_ADDRESS]), J.G., and G.M.; validation, A.K., and S.A.; formal analysis, M.F. ([PERSON], [EMAIL_ADDRESS]) and J.G.; investigation, M.F. ([PERSON], [EMAIL_ADDRESS]); resources, G.M.; data curation, M.F.; writing--original draft preparation, M.F. ([PERSON], [EMAIL_ADDRESS]) and J.G.; writing--review and editing, S.A. and J.G.; visualization, A.K.; supervi-sion, A.K.; administration, J.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the \"Regional Innovation Strategy (RIS)\" through the NRF and funded by the Ministry of Education (MOE) (2021 RIS-001).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Energy Detection Model
In the case of the known noise variance \(\sigma_{n_{i}}^{2}\) and no uncertainty in noise variance (NU), \(\tau_{i}(n)\) can be approximated using Gaussian distribution alongside a central limit theorem, where the hypotheses \(H_{o}\) and \(H_{1}\) (from relation (1)) become [45]:  
\[\tau_{i}(n)\mid H_{0}\ \sim\mathcal{N}\bigg{(}\sigma_{n_{i}}^{2},\ \frac{2}{N}\sigma_{n_{i}}^{4}\bigg{)} \tag{5}\]  
\[\tau_{i}(n)\mid H_{1}\ \sim\mathcal{N}\bigg{(}P+\sigma_{n_{i}}^{2},\ \frac{2}{N}(P+\sigma_{n_{i}}^{2})^{2}\bigg{)} \tag{6}\]  
To assess the performance of ED, a signal level that will be used as a decision threshold (\(\lambda_{i}\)) must be defined and comparison with the energy test statistic signal level (\(\tau_{i}\)) generated at the location of \(i\)-th SU must be performed. The decision threshold is determined based on the noise variance information obtained from the previous block, as in (Figure 3) [11,45,48]. The detection rule is defined by:  
\[\tau_{i}(n)>\lambda_{i}(n),\text{PU present} \tag{7}\]  
\[\tau_{i}(n)<\lambda_{i}(n),\text{ PU absent} \tag{8}\]  
where the signal level (\(\tau_{i}(n)\)) of the test statistic is obtained by calculating the received signal energy according to (2). This is obtained for a set of \(n\) samples and compared with the threshold \(\lambda_{i}(n)\), which can be dynamically adapted and thus differs at the moment when a number of samples \(n\) is used for ED. Basically, the performance of the ED as an SS technique is done by examining the Neyman-Pearson hypothesis. To assess the Neyman-Pearson hypothesis, the comparison between the decision threshold and log-likelihood ratio of the received signal will be [11]:  
\[H_{0}:\ log\Bigg{(}\frac{P\Big{(}y_{0},\ y_{1,\ \ldots},y_{(N-1)}\Big{|}H_{1} \Big{)}}{P\Big{(}y_{0},\ y_{1,\ \ldots},y_{(N-1)}\Big{|}H_{0}\Big{)}}\Bigg{)}<\lambda_{i},\text{ PU absent} \tag{9}\]  
\[H_{1}:\ log\Bigg{(}\frac{P\Big{(}y_{0},\ y_{1,\ \ldots},y_{(N-1)}\Big{|}H_{1} \Big{)}}{P\Big{(}y_{0},\ y_{1,\ \ldots},y_{(N-1)}\Big{|}H_{0}\Big{)}}\Bigg{)}>\lambda_{i},\text{ PU present} \tag{10}\]  
where \(P(y|H_{0})\) and \(P(y|H_{1})\) indicate the probability density functions (PDF) of the hypothesis \(H_{1}\) and alternative null hypothesis \(H_{0}\), respectively. The hypothesis \(H_{0}\) in relation (9) is valid if the received signal energy is lower than the set threshold (\(\lambda_{i}\)), and this confirms the existence of a spectrum hole. Based on the relation (10), another hypothesis \(H_{1}\) is confirmed if the received signal energy is larger than the threshold value (\(\lambda_{i}\)), which leads to the cognition that the PU actively transmits [11; 24; 45]. The threshold \(\lambda_{i}\) can be fixed or dynamically adjusted where threshold levels can be selected from the pool of values in a specific range, which is derived and explained in Section 5.
# Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach
## 2 Computational Methods
### Implementation Details  
In recent times, faster execution of an algorithm is mainly achieved not by increasing the performance per processing unit, but by enlarging the number of executioners [38; 39; 40]. In a highly parallelisable problem such as parallel-beam tomography [41], the typical approach consists of exploiting the inherent data parallelism [40], meaning that the whole computation can be divided into many smaller problems which can be solved concurrently [38]. Not all computational imaging techniques are so blessed. Massively parallel accelerators such as GPUs are currently extensively used, thanks to a plethora of GPU-ready available algorithms (e.g., [42; 43; 44]) that in turn are implemented in several reconstruction frameworks such as [7; 45; 42].  
From the analysis of the alignment algorithm [12], we detected three main areas for the global acceleration (Figure 2): (1) a tomography reconstruction and projection component is required to gather the volume estimate and the synthetic projections; (2) a motion estimation procedure is employed to check for the registration parameters; and (3) a warp interpolator is used to generate the new registered dataset. While for the two latter modules we also studied a CPU-only parallel solution (Figure 2), for the CT component, it was already clear that a GPU implementation would have been the only viable option.  
In this work, we deconstructed the algorithm by implementing each step with a highly parallelised version of each computational block; this is crucial to utilise all the computational resources at hand. The result is a modular structure that can be used to solve not only the tomography alignment problem as a whole, but each module can be used separately in any image-processing pipeline (e.g., Hough transform for pattern matching and computer vision applications, image registration, etc.).