esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### In Situ Measurement Setup
Measurements were conducted in accordance with CISPR 11 [26] and CISPR 16 [27]. The loop antenna was placed 10 m from the PV system under test, in positions B and E, as shown in Figure 2. During the pre-scan, positions B and E were identified as the positions with strongest emissions. A distance of 30 m is given to the unit under test for the limit in CISPR 11, class A, group 1. The 30 m limit from CISPR 11 was increased by 10 dB to correspond with a 10 m distance, to obtain a reference value for the emission levels in this study. That reference value is further referred to as \"EMC limit\". Figure 2: Schematic overview of inverter, modules, cables, pyranometer, and antenna positions B and E on the test site. The measurement proceedings for each position always contain background measurements BG2 as well as system measurements M_sys with a loop antenna. Hereby, the loop antenna is placed in the X, Y, and Z directions for each position. For the background measurements BG2, the PV system is turned off entirely. That means that the inverter is disconnected from AC; if module optimizers are used, they are disconnected from their PV module, to de-energize the optimizer. According to CISPR 16, before conducting system measurements M_sys, the PV system must be turned on and, when possible, operate in the highest disturbance field strength. This was not practically possible during this study. Instead, we used as a criterion that the inverter needs to feed into the grid. This reflects normal operation and makes it possible to conduct measurements on many system combinations within the available time frame. For the system measurements M_sys, the PV system needs to feed into the grid as defined earlier. That means that if module optimizers are used, the PV modules are reconnected to the optimizers, and the inverter is powered by AC. After a device dependent startup sequence of 1-10 min, the inverter feeds into the grid.
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 6 Experiments and Results  
In this section, the environment and establishment of the experiment are introduced firstly. And then the results and analysis of delay, throughput and packet loss rate are given.
## 3 Results
### Interannual Variation in Aboveground Biomass  
There were obvious fluctuations in the aboveground biomass of the Xilingol grassland between the years 2005 and 2012. These biomass values ranged from 10.56-17.54 Tg. As shown in Figure 5, the lowest biomass occurred in 2007, at 10.56 Tg, approximately 26% lower than the mean annual average biomass. The highest biomass was 17.54 Tg, in 2012. There were large interannual fluctuations in the aboveground biomass of the Xilingol grassland from 2005-2009, with an initial decrease being observed prior to an increasing trend. The grassland aboveground biomass from 2009-2012 exhibited an increasing trend, with biomass peaking in 2012.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Grassland Types**} & \multirow{2}{*}{
\begin{tabular}{c} **Grassland** \\ **Area (km\({}^{2}\))** \\ \end{tabular} } & \multicolumn{8}{c}{**Aboveground Biomass Density (g-m\({}^{2}\))**} \\ \cline{3-10}  & & **2005** & **2006** & **2007** & **2008** & **2009** & **2010** & **2011** & **2012** & **Average** \\ \hline Low-land meadow & 25,981 & 109.74 & 83.87 & 62.04 & 88.41 & 79.65 & 80.92 & 84.84 & 101.02 & 86.31 \\ Improved grassland & 472 & 67.42 & 63.52 & 50.80 & 68.73 & 58.06 & 54.71 & 60.26 & 73.89 & 62.17 \\ Montane meadow & 1581 & 202.45 & 154.89 & 123.03 & 153.47 & 145.24 & 145.58 & 159.17 & 164.75 & 156.07 \\ Temperate meadow-steppe & 24,875 & 155.81 & 110.19 & 75.29 & 109.83 & 110.44 & 111.83 & 117.66 & 130.82 & 115.23 \\ Temperate steppe-desert & 5108 & 24.94 & 19.78 & 19.66 & 24.91 & 23.72 & 17.07 & 20.33 & 28.69 & 22.39 \\ Temperate steppe & 108,488 & 80.40 & 65.11 & 51.62 & 75.06 & 63.34 & 64.82 & 64.39 & 88.02 & 69.10 \\ Temperate desert-steppe & 29,598 & 30.68 & 23.36 & 23.89 & 31.03 & 27.79 & 21.17 & 24.20 & 35.71 & 27.23 \\ Temperate desert & 143 & 22.93 & 16.58 & 16.91 & 22.43 & 18.53 & 14.64 & 16.94 & 23.93 & 19.11 \\ Marsh & 330 & 126.45 & 71.32 & 55.81 & 83.17 & 75.64 & 84.50 & 84.36 & 97.95 & 84.90 \\ Total & 196,576 & 85.88 & 66.52 & 51.54 & 73.88 & 65.71 & 65.71 & 67.39 & 86.29 & 70.36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Aboveground biomass density in the different grassland types.  
Figure 5: Variation in the aboveground biomass of the Xilingol grassland from 2005–2012.  
The aboveground biomass of different grassland types appeared to fluctuate from 2005-2012 (Figure 6). Large variations occurred in the temperate meadow-steppe and the typical grassland, which presented higher yields; whereas there was little change in the temperate desert-steppe and temperate steppe-desert, which showed lower yields. From 2005-2012, the overall grassland production in the montane meadow and the temperate meadow-steppe decreased, whereas it remained steady after showing fluctuations in the lowland and the temperate meadow and showed little change in the temperate desert-steppe and the temperate steppe-desert.
## 1 Introduction
Critically, no study to date has directly compared the effects of two distinct lower-body glyometric protocols--Plyometric Stair Jump (PSJ) and Plyometric Reaction Box Jump (PRJ)--on boxing-specific performance variables, such as punch impact force and upper-body strength. This constitutes a meaningful research gap, especially considering the sport-specific neuromuscular coordination required for transferring lower-limb force into effective upper-body striking. Moreover, many prior investigations have either generalized pyometric effects across diverse athletic populations or failed to isolate boxing-specific training adaptations. As such, the problem lies not only in the scarcity of comparative studies within boxing, but also in the lack of a targeted investigation into how different modalities of glyometric training affect the force transmission along the kinetic chain and impact punching efficacy. This limits the translation of existing findings into boxing-specific training strategies. The present study addresses these limitations by employing a randomized design with a homogenous sample of national-level male boxers and implementing two differentiated training interventions over a controlled 8-week preparatory period. By aligning biomechanical, physiological, and performance-based outcome measures--including straight punch impact force, cardiovascular fitness (Yo-Yo IR2, VO2 max), and upper-body strength (bench press 1 RM and bar velocity)--this study creates a comprehensive evaluation framework for glyometric efficacy in boxing. Furthermore, this research applies load-specific and velocity-sensitive assessments, addressing another common shortcoming in earlier work that relied heavily on general strength or endurance measures without considering load-velocity profiles relevant to explosive performance. In sum, this study is distinguished by the following: its exclusive focus on boxing, rather than extrapolating from general athletic samples; its direct comparison of two glyometric modalities, addressing an explicit gap in the literature; its multifactorial approach that integrates physiological, biomechanical, and performance variables; and its aim to produce practical, evidence-based guidelines for strength and conditioning coaches aiming to enhance punch performance through modality-specific glyometric training. By situating glyometric interventions within the specific movement and energy demands of boxing, this study makes a novel contribution to both the academic literature and applied training practices in combat sports.
## 3 Results  
### CS Calculation and Validation  
The diagnoses issued by the specialist were compared with the total CS value per scan for each of the density score calculation methods (Original, Gaussian, MinMode), and the average error was calculated between each of the comparisons, obtaining the following errors: (a) OsiriX vs. Original = 3.8%, (b) OsiriX vs. Gaussian= 4.7%, (c) Osirix vs. MinMode = 1.9%. Another characteristic that was compared was the risk of cardiovascular event corresponding to each of the studies according to the classification given by the CS. Of the 30 studies carried out with the OsiriX software, 7 belonged to the Moderate risk group, 10 to the Moderate-High group, and 13 to the High risk group. The Original function did not correctly classify 1 study as belonging to Moderate-High risk, while the functions with additional processing (Gaussian and MinMode) correctly classified all studies regarding to the results obtained with OsiriX. Figure 6 shows the results of the total CS of each one of the studies where the difference in the results of CS with each of the proposed methods is observed and the limits of the classification by risk of cardiovascular event are also observed.  
Subsequently, the Pearson's correlation coefficient (r) was calculated for each of the comparisons made, the OsiriX vs. Original comparison obtained r = 0.9996, the OsiriX vs. Gaussian comparison obtained the lowest coefficient with r = 0.9995, and the comparison of OsiriX vs. MinMode r = 0.9997, this being the one with the best correlation of the three.  
According to the results obtained in the comparisons of the image processing and CS calculation methods, it is possible to observe that the MinMode technique is the one that most closely resembles the results obtained with the reference commercial software, so this proposal is the one selected to be used in the web application. Figure 7 shows an example on how the results were compared and validated using the same slice from a CTCS scan that had the same pixel area selected. Figure 7a shows the image that was analyzed with Osirix, and the image in Figure 7b was analyzed with the proposed web application.  
Figure 6: Total CS and risk category classification of the performed studies.
## 2 Related Work
In addition, deep learning models perform far better than traditional machine learning algorithms as the amount of learning data increases. The deep learning method has achieved very good performance in many domains including image classification and speech recognition [11; 12]. Recently, network traffic classification using deep learning has begun to be studied. [PERSON] et al. [27] used a CNN model to classify malware traffic and general traffic. First, if a five-tuple (source IP/port, destination IP/port, protocol) is the same among the packets. One flow is defined as one dataset. The constructed dataset is used to train the CNN model to classify malware traffic and general traffic. The accuracy to classify malware traffic and general traffic was not low. [PERSON] et al. [28] performed network traffic classification using a deep learning model that combines CNN and LSTM. The packet data, extracted from the header information and payload data in the packet using the DPI tool, was used as the learning data. The extracted learning data was used as input data for the combined model of CNN and LSTM. With datasets including the short length of packets in a flow, they showed that the combination of CNN and LSTM models perform better than the individual CNN and LSTM models. However, these previous works include packet header information in their traffic dataset, while we use a payload-based dataset excluding packet header information in this paper. In addition, we use the multi-layer LSTM model with deeper layering architecture. In Reference [29], we already used payload-based datasets excluding the IP and TCP/UDP headers, and developed CNN and ResNet models for traffic classification. The results of network traffic classification show that CNN and models have the F1-score values of 0.95625 and 0.96875, respectively, and ResNet model is superior to the CNN model. In this study, we extend the previous work and develop a new multi-layer LSTM model for network traffic classification when the datasets consist solely of the payload of the packets. In addition, this study shows that the multi-layer LSTM model performs better than the CNN and LSTM model (proposed by Reference [28]). In addition, we propose a new SDN-based network architecture that can generate flow rules according to QoS of various applications by utilizing the network traffic classification result of the learned deep learning model.
## 3 Experimental Results
### Shear Behavior
#### 3.4.2 Effect of the Material Properties on the Shear Behavior  
The ultimate loads of the shear strength of the RC beams from the experiment are shown in Figure 20 along with their material properties, including (1) the concrete compressive strength from the cylinder specimen (100 mm in diameter and 200 mm in height), (2) the mortar compressive strength from the cylinder specimen (50 mm in diameter and 100 mm in height), and (3) the static and (4) dynamic elastic moduli of the concrete. The total maximum shear strength of the large beams, NB60, HB60, MB60, and LB60, was 93, 102, 110, and 99 kN, respectively. The total maximum shear strengths of all of the large beams, using the recycled aggregate, were higher than that of the NB60 natural aggregate concrete beam, without any attenuation in the observed shear strength. In addition, the shear strength of MB60 was the highest among the group and 7.8% higher than that of HB60. This high shear strength could be due to the 4% and 4.9% higher compressive strengths of the concrete and mortar, respectively, compared to those of HB60. The concrete compressive strength of HB60 was 32 MPa, 6.2% higher than that of NB60 at 30.2 MPa, even though the compressive strength of the NB60 mortar was slightly higher. The natural aggregate had a relatively smooth surface and round shape,  
Figure 19: Load-deflection relationship for the small RC beams.  
Figure 17: Failure mode of the small RC beams. (**a**) Beam NS60; (**b**) beam HS60; (**c**) beam MS60; and (**d**) beam LS60.  
Figure 18: Load-deflection relationship for the large RC beams.  
which weakened the bond between the cement matrix and the aggregate. For the small beams, the beam width was kept constant at 150 mm, and the effective depth decreased. The influence of the recycled aggregate could be clearly observed in the small beams, even though the concrete compressive strengths of HS60, MS60, and LS60 were 12.4%, 12.6%, and 5.7% higher than that of NS60, respectively.
## 1 Introduction
### Motivation and Contribution of the Study  
The increasing size and complexity of real-world data have made feature selection a critical step in data mining and machine learning processes. As the number of features grows, the search space required to identify the optimal subset expands exponentially; this situation increases computational complexity and adversely affects model performance. Therefore, developing efficient and scalable feature selection methods is of great importance.  
Although many metaheuristic algorithms have been proposed for feature selection, most of these algorithms face challenges such as premature convergence, imbalance between exploration and exploitation, or insufficient adaptation to binary search spaces. The WSO, originally developed for continuous problem domains, has strong exploration capabilities and, inspired by the predatory behavior of sharks, provides a promising foundation for binary optimization problems.  
In this study, to overcome the aforementioned limitations and leverage WSO's potential in binary problem domains, a binary version of WSO (bWSO) specifically adapted for feature selection tasks is proposed. The contributions of this study to the literature can be summarized as follows:  
* A novel logarithmic transfer function offering a flexible and adaptive mechanism for converting continuous values into binary decisions is proposed.
* The original WSO algorithm is adapted to binary problem solving by incorporating the proposed logarithmic transfer function along with eight other commonly used transfer functions, resulting in the bWSO algorithm.
* The performance of the bWSO-log algorithm is evaluated on nineteen benchmark datasets, and it is observed to outperform other well-known metaheuristic algorithms in the context of feature selection.
* In terms of classification accuracy, the algorithm is comprehensively compared with Artificial Algae Algorithm (AAA) [11], Bat Optimization Algorithm (BAT) [14], Firefly Algorithm (FA) [15], Grey Wolf Optimizer (GWO) [16], Moth Flame Optimizer (MFO) [17], Multi-Verse Optimizer (MVO) [18], Particle Swarm Optimization (PSO) [19], and Whale Optimization Algorithm (WOA) [20]
* A simple yet effective feature selection framework combining WSO's powerful search mechanism with adaptive discretization techniques is presented.  
The remaining sections of the paper are organized as follows: Section 2 provides a brief overview of related work. Section 3 introduces the original WSO algorithm. The details of the proposed bWSO approach are presented in Section 4. Section 5 describes the experimental setup. Section 6 presents the results along with a discussion, while Section 7 offers conclusions and suggestions for future work.
## 3 Results
### Measurement of ADE Effect on MDA and NO Contents  
One significant biomarker of oxidative stress is malondialdehydedehyde (MDA). Malondialdehydedehyde (MDA) is a lipid peroxidation byproduct found in the lung and serum and is used to estimate lipid peroxidation. Our data showed that the oxidative stress markers, including MDA (43.8 nmol/g) and NO levels (42.3 mmol/L), were considerably elevated in the B(a)P-treated group when compared to the normal control group (MDA 24.9 nmol/g, and NO 24.2 mmol/L) (\(p<0.05\)). However, ADE showed in ADE-treated groups, MDA (35.6 nmol/g), and NO level (36.1 mmol/L) was meaningfully decreased when compared to the B(a)P-treated group only (\(p<0.05\)) (Figure 2). The results confirm the inhibition potential of ADE against membrane damage.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 3 Proposed EPBTL-RDHEI
### Data Extraction and Image Recovery  
To extract secret data, we first classify the pixels into groups according to the labels. Then, extract embedded data from the embeddable group. Count the number of non-recordable pixels and the special pixel and remove their corresponding auxiliary information from the head of the data stream. Finally, decrypt the secret data with the data-hiding key.  
To recover image, the parameters and the version code are decoded from the special pixel first. Then, pixels are classified into groups according to the labels. Extract the embedded data from the embeddable group and retrieve the auxiliary information to recover the special pixel and the non-recordable pixels. The reference pixels are recovered with the image encryption key. Finally, the embeddable pixels and the self-recordable pixels are recovered according to the recorded prediction errors. Thus, the original image can be completely restored.
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
###### Abstract
The inclusion of LNS in the model can also improve the heterogeneous fleet VRP with draft limits, which contains a large number of objectives and has high robustness [30]. The LNS framework is used to search for efficient solutions that effectively find high-quality non-dominated solutions [31]. In general, LNS has a powerful local search capability to find the optimal solution within a certain range. It is also because of this that the LNS algorithm is prone to fall into local optimality. SA was first introduced by [PERSON] et al. [32]. The SA method is based on the analogy of cooling liquid metals to form crystals, called annealing. At high temperatures, liquid molecules have higher energy levels, making it easier for them to move towards other molecules. When the temperature is lowered, the molecules arrange themselves to find configurations with lower energy levels. By slowly reducing the temperature, the molecules can self-regulate and reach a stationary or stable state with the lowest energy level. SA is widely used in solving VRP problems. An improved SA algorithm with a crossover operator, called ISA-CO, is proposed to solve the capacitated vehicle routing problem (CVRP) [33]. Incorporating SA in the search process yields better initial solutions. To account for multiple cross-docks and heterogeneous fleets in distribution systems, an adaptive neighborhood simulated annealing algorithm is proposed, which implements an adaptive mechanism to select neighborhood moves to improve the solution [34]. The SA algorithm has a global exploration ability that can compensate for the local optimality issue in LNS. Considering the powerful search capability of LNS, it can solve the shortcoming of the BSO algorithm in finding new solutions with random values and speed up the convergence speed. SA, on the other hand, can guarantee the global exploration and avoid falling into local optimum. We establish a hybrid BSO algorithm based on the LNS and SA, and the ERRP solution model is then solved using the HBSO algorithm. All abbreviations in this article are shown in Abbreviations Part. The main innovations of this study are as follows:  
1. An emergency relief routing model is established, in which the dynamic distress of patients (the deprivation cost) is introduced. Patients in critical conditions can now obtain priority assistance. 2. The LNS and SA algorithms' concepts are included in the BSO algorithm, accelerating the convergence and enhancing the algorithm's ability to avoid falling into the local optimum trap. 3. The process of using random steps to find new solutions is changed to enhance the search capability in the BSO algorithm. This paper is organized as follows. Section 2 describes the related work of ERRP. Section 3 introduces the description and model for the ERRP. Section 4 presents the main elements of the HBSO algorithm. Section 5 briefly describes the solution strategy of the HBSO algorithm to the ERRP. Section 6 contains the experiments, corresponding results, and analysis. Section 7 concludes the paper.
## 3 Experimental Results and Analysis
### Improved CCS-YOLOv8 Model
#### 3.1.2 Changes in Performance  
To evaluate the performance increase for the livestock target recognition task, we used a set of test images as examples and performed target identification on them using the YOLOv8n baseline model and the upgraded CCS-YOLOv8 model. Due to the presence of densely aggregated livestock targets in the example images, directly displaying the  
Figure 7: Convergence curve of loss function of CCS-YOLOv8.  
classification labels and confidence values would lead to visual clutter and affect the comparison. To evaluate the detection performance of the two models more objectively, we present the detection results with only the target detection boxes retained while omitting the confidence value and category label information, as shown in Figure 8.  
In group A images, the YOLOv8n baseline model misdetects two of the sheep as four for the densely distributed sheep in the overhead view, while the CCS-YOLOv8 model correctly recognizes these sheep targets. Compared to YOLOv8n, CCS-YOLOv8 demonstrates better detection performance when detecting densely distributed sheep targets with occlusion.  
In group B images, the YOLOv8n model suffers from the problems of missed detection of lamb targets and repeated detection of adult sheep when dealing with situations where adult sheep and lambs are in the same frame, while the CCS-YOLOv8 model effectively avoids such errors. This result indicates that, compared to the baseline model, CCS-YOLOv8 has a stronger ability to detect lamb targets and significantly reduces the risk of missed and repeated detections.  
In group C images, in the oblique side view, the CCS-YOLOv8 model accurately detects a small yak in the distance, while the YOLOv8n model misses the detection. This confirms that the CCS-YOLOv8 model demonstrates better performance in detecting small targets.