esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### ReliefF-Based Feature Selection  
According to the ReliefF evaluation, a sequence of features sorted by correlation from largest to smallest can be obtained. It is only necessary to decide how many of the features with the lowest ranking can be deleted to perform feature selection. In this study, two types of classification were used for damage; thus, the selection of acoustic features obtained from frequency hammering was also divided into two types: feature selection based on two classifications (Table 1) and feature selection based on four classifications (Table 2). These were added together with the MFCCs of single-frequency hammering using SVM for comparison.  
Figure 10: Twelve typical acoustic analyses of four-category under three frequency hammerings (**a**) Condition 1: Healthy area; (**b**) Condition 2: Surface damage area; (**c**) Condition 3: Internal damage area; (**d**) Condition 4: Compound damage area.  
In addition, the experimental results of feature selection showed that for the damage selected in this study, feature selection was more inclined toward a specific hammering frequency. In addition, based on the number of classifications, the required hammering frequency changed after the addition of the surface, internal, and combined surface-internal damage classifications, except for the offset single hammering frequency. Different instances of damage have different degrees of sensitivity corresponding to different hammering frequencies. Exploring this degree of sensitivity can help to further classify concrete damage.
###### Abstract  
In this paper, a Modified Adaptive Selection Cuckoo Search Algorithm (MASCSA) is proposed for solving the Optimal Scheduling of Wind-Hydro-Thermal (OSWHT) systems problem. The main objective of the problem is to minimize the total fuel cost for generating the electricity of thermal power plants, where energy from hydropower plants and wind turbines is exploited absolutely. The fixed-head short-term model is taken into account, by supposing that the water head is constant during the operation time, while reservoir volume and water balance are constrained over the scheduled time period. The proposed MASCSA is compared to other implemented cuckoo search algorithms, such as the conventional Cuckoo Search Algorithm (CSA) and Snap-Drift Cuckoo Search Algorithm (SDCSA). Two large systems are used as study cases to test the real improvement of the proposed MASCSA over CSA and SDCSA. Among the two test systems, the wind-hydro-thermal system is a more complicated one, with two wind farms and four thermal power plants considering valve effects, and four hydropower plants scheduled in twenty-four one-hour intervals. The proposed MASCSA is more effective than CSA and SDCSA, since it can reach a higher success rate, better optimal solutions, and a faster convergence. The obtained results show that the proposed MASCSA is a very effective method for the hydrothermal system and wind-hydro-thermal systems.  
Cuckoo Search Algorithm (CSA); Fixed-Head Short-Term Model; Hydrothermal System; Optimal Scheduling of Wind-Hydro-Thermal System (OSWHTS)
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## Appendix A Spectral Indices
### PCA  
[PERSON] et al. [63] also selected two- and four-band subsets for the application of selective PCA to isolate target spectral signatures of Li pegmatites (Table A3). Input data were the ASTER, Landsat-5, Landsat-8, and Sentinel-2 raw bands.
# FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
## 2 Related Work
To handle computing and memory burdens, an optimized compression strategy is proposed for an efficient implementation of FPGA-based accelerators with a compressed CNN [29]. The proposed study decreases the amount of parameters of AlexNet by proposing a reverse-pruning strategy. In addition to this, a peak-pruning strategy is applied to prune low-weight connections by eliminating connections below a pre-defined threshold. The second part of the proposed idea presents an effective storage strategy to reduce the cache overhead for convolutional and fully connected layers. Performance verification to test the efficiency of the proposed strategy was conducted on a Xilinx ZCU104 platform. The outputs noticeably decreased the size of AlexNet from 243 MB to 8.7 MB. To speed up CNNs on FPGAs, two models based on BNNs have been proposed to come up with a lightweight mechanism for high accuracy and low computational complexity, power, and storage [30]. The first model, a full-BNN model, enables the integration of convolution, batch normalization, and activation function, thereby binarizing the complete process of the layers, with the exception of the last fully connected (FC) layer. The second model, a mixed-precision BNN model, permits the use of fixed-point data in the first convolutional layer, which results in a balance between complexity and accuracy. In the performance tests, a MNIST dataset on a DSP + Xilinx 325T development board (Avnet, Phoenix, AZ, USA) was implemented successfully, with low power consumption and memory utilization being achieved for complex classification tasks. StereoEngine, as an FPGA-based accelerator, has been designed to accelerate stereo vision in a fully pipelined manner [31]. The proposed stereo approach is optimized efficiently for hardware-friendly implementation by harnessing the potential of BNNs. One notable contribution of StereoEngine is its robust mechanism for learning feature descriptors to address the issue of disparity. An integral component of StereoEngine is the processing of pixels with the same reception rate as the pixels over the generation rate of the disparity data. The efficacy of StereoEngine in terms of run-time and energy was validated through experimental results using the challenging KITTI 2015 dataset. The results confirmed that images with 1024 \(\times\) 768 at 114.04 FPS consuming 3 W power can be handled.
## 1 Introduction
Moreover, this study compared our model with various ML and DL methods using advanced Natural Language Processing (NLP) techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), n-gram, AraBERT, Word2 Vec (Skip-gram), and fastText (Skip-gram). The Arb-MCNN-Bi model is evaluated across three different datasets containing reviews in various Arabic dialects and compared to state-of-the-art models based on accuracy, precision, recall, and F1-score values. Through extensive experimentation and model optimization, the effectiveness of the Arb-MCNN-Bi model is demonstrated. These concerted efforts make a significant contribution to the field of Arabic sentiment analysis (ASA), with the following key actions:  
* Proposing a novel Arb-MCNN-Bi model: this model integrates the AraBERT pre-trained language model with multichannel convolutional layers and BiGRU units, capturing both local and global dependencies within Arabic textual data. * Comprehensive Comparative Analysis: the research provides a thorough comparison of the proposed model against various traditional ML and DL algorithms, utilizing advanced NLP techniques such as TF-IDF, n-gram, Word2 Vec (Skip-gram), and fastText (Skip-gram). * The assessment of the model's performance across three diverse Arabic datasets: Arabic Customer Reviews Dataset (ACRD), Large-scale Arabic Book Reviews (LABR), and the Hotel Arabic Reviews dataset (HARD), demonstrating its robustness and generalizability.
# Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
## 6 Discussion
### The Vertical Settlement and Deformation Laws of Aeolian Sand  
Through the in situ water immersion load test, the vertical settlement deformation curves of the soil in each test pit under different loads were obtained, as shown in Figure 12 (P-s curves; \(P\) is the load and \(s\) is the settlement).  
The results indicate that 14 in situ water immersion load tests showed the three-stage deformation characteristic in the P-s curve. During 0-100 kPa, it is in a linear elastic deformation stage. In this stage, the slope of the curve (\(\Delta s/\Delta P\)) is approximately constant, and the deformation modulus is constant and large. As the settlement deformation increases, the elastic-plastic deformation stage begins after 100 kPa. The slope of this curve (\(\Delta s/\Delta P\)) gradually increases, and the deformation modulus gradually decreases. This stage shows that, as the strain and damage in the soil accumulate, the tangent modulus decreases. After stabilizing at 200 kPa, the soil is immersed in water and then enters the collapsible deformation stage. In this stage, the soil becomes saturated gradually, and the vertical settlement deformation increases sharply.  
Furthermore, although the deformation characteristics of the 14 tests were consistent, the consolidation settlement and the collapsible settlement after immersion were different. In general, the consolidation settlement was 11.779-34.354 mm, and the collapsible settlement was 6.335-34.541 mm. The deformation moduli of the 14 soil samples were also different. Some had a high initial modulus, while others decreased rapidly in the second stage due to damage accumulation. These differences may have been caused by the differences in the formation environment of different soil samples.  
Figure 12: P-s curve of the in situ water immersion load test.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Single-Channel MAC Protocols
#### 4.1.2 PTMac  
This protocol is used for collision prediction and collision reduction effectively in two-way traffic as well as in four-way intersections. The protocol revolves around three steps: potential collision detection, potential collision prediction, and potential collision elimination. Potential collision is detected based on slot information; the prediction of collisions in the future is based on traffic and vehicle information conditions. The potential collision can be eliminated by rescheduling the slots (mostly tackling encounter collision). Every node broadcasts its information, including location, slot reserved, speed, and direction. The frame information (FI) contains information about its one-hop neighbor nodes and itself. The new joining node has to listen to the channel before contending for slot allocation. Potential collision detection in same and opposite directions is depicted in Figure 4.  
In potential collision detection, every node checks whether the same slot is occupied by any of its one-hop or two-hop neighbors. The intermediate nodes contain slot reservation information and detects potential collision between the two one-hop neighbors. After detecting potential collisions, the nodes that collide are allocated different slots and broadcast messages to their one-hop neighbor.  
There is no slot partitioning; thus, unbalanced traffic density has no impact on the performance. PTMAC effectively handles access collision as well as contention collision. However, network overhead and power consumption need to be studied more.  
Figure 4: Potential collision detection: (**a**) same direction; (**b**) opposite direction [70].
# Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning
## 4 Implementation of Proposed Approach
### Training Details  
The algorithms described in Section 3 are used to train the target grasping task. The episodes are divided into 600 time-steps where each of them corresponds to roughly 0.1 s of real-time. At every step of the episode, the actions to be executed in the environment are sampled from Gaussian distributions parameterized by the mean and standard deviation for each action dimension. The agents use a replay buffer that stores the transition experience of the episodes. At every update iteration, random samples of a batch size of 512 are collected from the buffer to update the network parameters. While using a PPO agent, the buffer is cleared and the samples are discarded after performing each update because this is an on-policy algorithm. Both agents have the same parameters, the same state-space is fed into both of the agents, and both agents follow a given policy where the output of each agent is a vector of four values, each corresponding to the given motors. Each agent gives half of the output needed for the system and these outputs are combined and fed into the system. The policy network, as well as the value network, are encoded by MLP--multi-layer perceptron. The policy network consists of three layers and Rectified Linear Unit (ReLU) non-linearity. The input layer, representing the state of the system is 46-dim. The hidden layer size is of 64-dim and the output layer is 8-dim. The output layer of a policy network is two-fold, producing the mean and standard deviation (parametrizing the Gaussian distribution) for each action dimension. The value network outputs only a scalar value, which specifies the corresponding value function of the state. All inputs to the policy and value networks are normalized with running estimates of the mean and variance. Both the policy and value networks are shown in Figure 5.
## 3 Results
### Allan Deviation  
In this section, we analyze the frequency stability of the estimated clocks for the different scenarios. The frequency stability is defined as the variation in the frequency between two consecutive periods. Computing an Allan variance, the frequency stability is shown in the time spectrum. The square root of the Allan variance gives the Allan deviation. With the simulation period of 10 days, it is possible to determine the Allan deviation up to about 1 day.  
Figure 8 (left) shows the Allan deviation for the estimated PHM clocks with respect to the true clocks for the L-band-only, L-band+OTWL, L-band+OISL and L-band+OTWL+OISL scenarios. The given results apply to the MEO satellite, as an example. The estimated clock from the L-band-only scenario does not reach the stability of the true clocks for the interval up to one day. On the other hand, the estimated clocks from the L-band+OISL, L-band+OTWL and L-band+OTWL+OISL scenarios are close to each other and reach the stability of the PHM for time intervals larger than about 1000 s. This indicates that the PHM is still limiting the potential of the combination of observation techniques. With a much more stable clock, the real potential of the combination of observation techniques can be investigated. The ACES clock is such a clock, which we use as an alternative clock in this work. While the L-band-only scenario mainly shows white noise, the downward slope of the L-band+OISL, L-band+OTWL and L-band+OTWL+OISL scenarios is larger for time intervals up to around 500 s. This is quantization noise, as the current International GNSS Service (IGS) clock's Receiver INdependent EXchange (RINEX) file--the file type we use to load our simulated clocks into the Bernese software--is primarily developed for GNSS, and, hence, the file accuracy is not quite sufficient for combinations of the observation techniques.  
Figure 8 (right) shows the Allan deviation of the estimated clocks when using the ACES clocks in the simulation. For the L-band-only scenario, no improvement in frequency stability can be achieved compared to the use of the PHM clocks. In the L-band+OISL scenario, the frequency stability of the estimated clocks further improves but does not reach the frequency stability of the true clocks. The L-band+OTWL scenario behaves similarly to the L-band+OISL scenario up to intervals of 5000 s but achieves a minimal better frequency stability. For larger intervals, the L-band+OTWL scenario can gain from the long-term stability of the ACES clock. The L-band+OTWL+OISL scenario now achieves a far higher frequency stability compared to the estimated clocks from the L-band+OISL and L-band+OTWL scenarios. The estimated clocks even reach the stability of the true ACES clocks for intervals larger than about 20,000 s. This means that the L-band+OTWL+OISL scenario can completely benefit from the long-term stability of the ACES clock. On the other hand, there might still be potential for further improvements in the long-term stability when using even more stable clocks.
## 3 Results and Discussion
### Microstructure Analysis  
To accurately analyze the influencing mechanism of early damage on the physiome-chanical properties of coal-grout rock, we scanned sample fracture surfaces using SEM. Examples of the microstructures are shown in Figure 10.  
Coal is a typical heterogeneous medium with numerous structural defects, such as micropores and cracks. As shown in Figure 10, microscale groove-like structural fractures and black coal particles are present on the surface of the coal, while the slurry penetrates the sheet structure. At 0% early damage, the slurry did not fully penetrate into the microcrack, and the crack expanded along the original microcrack of the coal body under pressure  
Figure 10: SEM images of disturbed samples under different degrees of damage.  
Figure 9: Statistical characteristics of internal cracks in samples under different early damage.  
stress. For 20% early damage, the crack width in the raw coal was 24 \(\upmu\)m. The slurry enters the fissure along the crack on the surface of raw coal, and the hydration reaction produces C-S-H gel; thus, the cracks on the surface of the coal are filled. When 40% early damage occurred, early interference pressure stress promoted slurry penetration expansion, more C-S-H gel filled the cracks, the microcrack width decreased to approximately 20 \(\upmu\)m, sampling density improved, and the rupture surface integrity recovered, and the resistance of the rock body to deformation was enhanced. When 60% of the early disturbance occurred, the electron microscope scanning results revealed cracks inside the slurry. Because the strength of cement slurry is much greater than that of raw coal, fracturing in the rock body under pressure after curing should occur at the coal-slurry interface or inside the coal body. The reason for the occurrence of internal cracks in the slurry is that during the early disturbance process, the slurry did not fully solidify, and its strength was low. The cracks ultimately led to the strength degradation of the coal-slurry rock body after being subjected to significant early disturbance. An internal crack in the grout was caused because the slurry had not yet completely solidified during early interference. This results in low strength and the formation of cracks, which ultimately enhances the early damage in the intensity of the coal-cement grout.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 2 Related Works
### RSA Public-Key Cryptography
#### 2.1.2 Encryption and Decryption  
Assume that the user \(U_{2}\) wants to send a message \(m\) to the user \(U_{1}\). \(U_{2}\) uses \(U_{1}\)'s public key \((e_{1},\ n_{1})\) to encrypt \(m\) to generate the ciphertext \(C=m^{e_{1}}\ \mathrm{mod}\ n_{1}\), and sends it to \(U_{1}\). When receiving \(C\), \(U_{1}\) can use \(U_{1}\)'s private key \(d_{1}\) to recover the original message by computing \(m=C^{d_{1}}\ \mathrm{mod}\ n_{1}\).
###### Abstract  
Support vector machines (SVMs) are a well-known classifier due to their superior classification performance. They are defined by a hyperplane, which separates two classes with the largest margin. In the computation of the hyperplane, however, it is necessary to solve a quadratic programming problem. The storage cost of a quadratic programming problem grows with the square of the number of training sample points, and the time complexity is proportional to the cube of the number in general. Thus, it is worth studying how to reduce the training time of SVMs without compromising the performance to prepare for sustainability in large-scale SVM problems. In this paper, we proposed a novel data reduction method for reducing the training time by combining decision trees and relative support distance. We applied a new concept, relative support distance, to select good support vector candidates in each partition generated by the decision trees. The selected support vector candidates improved the training speed for large-scale SVM problems. In experiments, we demonstrated that our approach significantly reduced the training time while maintaining good classification performance in comparison with existing approaches.  
Keywords:support vector machine; decision tree; large-scale dataset; relative support distance; support vector candidates +
Footnote †: journal: Applied Sciences
## 3 Description of the Method
### Particle Swarm Optimization
One generation means updating all positions of particles, computation of the fitness function, final evaluation, and update of their local \(l_{ij}\) and global \(G_{best}\) best. The setting of PSO control parameters was found while using empirical testing and are summarized in Table 1. The algorithm will keep good performance and enough particle divergence, preventing from getting stuck in local optima. The choice of parameter values is similar to research from [PERSON] [25; 31; 32]. There are a lot of proposed techniques for the improvement of PSO performance. They target different aspects of the algorithm: configuration parameters, learning strategies (for updating of positions), swarm diversity, etc. In [33], the authors propose a new method for interactive learning. Their approach is effective against getting stuck in a local minimum, especially for multimodal problems. Some papers combine PSO with other evolutionary algorithms and create hybrid PSO. The authors in [34] combine conventional PSO with an estimation of distribution (EDA) algorithm. EDA enhanced the performance of the hybrid PSO algorithm when compared with other PSO variants on the IEEE Congress on Evolutionary Computation 2003 (CEC2003) testing framework. However, their solutions bring higher computational requirements, as it adds more complex PSO behaviour and generates new particles during computation. [PERSON] et al. [35] proposed two-swarm learning PSO (TSLPSO), which is a PSO variant with subpopulations. One uses the dimensional learning strategy for fast convergence to the global best \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter(s)** & **Value** \\ \hline Max. number of PSO iterations & 250 \\ \hline Particles in swarm & 30 \\ \hline Number of dimensions & 30 \\ \hline Inertia weight (\(w\)) & 0.810 \\ \hline Search space boundary & [\(-6.28\), \(6.28\)] \\ \hline Velocity boundary (\(\Delta_{1},\Delta_{5}\)) & [\(-0.1\), \(0.1\)] \\ \hline Velocity boundary (\(\Delta_{2},\Delta_{3},\Delta_{4}\)) & [\(-0.2\), \(0.2\)] \\ \hline Local weight (\(c_{1}\)) & 0.495 \\ \hline Global weight (\(c_{2}\)) & 0.724 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PSO parameters involved in all runs. position, while the second subpopulation relies on a comprehensive learning strategy and maintains high particle diversity in the search space. Although this approach gives significant improvements in performance on various test functions, our real problem brings TSLPSO high overhead from an increased number of particle fitness evaluations. We were strictly aiming for a fast convergence to the \(G_{best}\) value, while keeping a low number of fitness function evaluations. In our preliminary research, there was not a signification boost in performance while adopting any of the aforementioned PSO techniques.