esa-sceva/satcom-chunk-collection huggingface dataset:
# Biomechanical Analysis of Gait Compensation Strategies as a Result of Muscle Restriction
## 4 Discussion
### Compensation Strategies
#### 4.1.1 C-Restriction
In addition, we observed an increased hip extension at the end of the terminal stance phase, which suggests a weakness in the tensor fascia data that limits the hip extension before the hip starts to flex in the pre-swing in the normal gait. Moreover, the increased hip flexion observed in the initial swing suggests increased activity in the ILPS muscle, also seen in [PERSON]'s simulations, as a compensation for the GAS muscle weakness. This could be an action to compensate for the lack of the hip flexion moment that is normally generated in pre-swing and is usually enough to drive the hip passively during the initial swing phase [23]. As a result, the extra hip flexion would support the above-mentioned compensation of the knee, and contribute to the foot clearance. **Summary of C-restriction:** When the muscles at the calf area were restricted (C-restriction), the weakness of the ankle in reaching the normal dorsal flexion angle during the swing phase was compensated for by a higher knee and hip flexion, showing a priority in compensating for foot drop. The knee also compensated to protect itself from hyperextension in the terminal stance phase. The hip compensated for itself during the initial swing phase by flexing more to create the necessary moment, normally generated in the terminal stance phase, to advance the leg forward. The hip also compensated for the hyperflexion of the knee in the loading response phase by extending more. In this muscle restriction condition, the weakness of the joint was always compensated for within the kinematic chain, either by the joint itself or by others.
# A Study on Detection of Malicious Behavior Based on Host Process Data Using Machine Learning
## 2 Related Works
### Machine Learning Algorithms
#### 2.2.3 Random Forest (RF)  
RF is an ensemble technique that is a classification and regression analysis algorithm based on Decision Trees. RF generates multiple decision trees, and the most frequently selected result among the results predicted by each tree is determined as the final prediction result. To achieve this, RF performs random sampling from the given dataset to create multiple datasets. Then, a decision tree is generated by performing random sampling from each dataset. Generally, Information Gain or Gini Index, and other split criteria are used to select the optimal split at each node. The most frequently selected result among the results predicted by each decision tree is determined as the final prediction result. RF generates trees as shown in Equation (3).  
\[\text{Random Forest}(X)=\frac{1}{T}\sum\
olimits_{i=1}^{T}\text{Decision Tree}_{i}(X) \tag{3}\]In RF, \(X\) represents the input data, and \(T\) represents the number of decision trees to generate. Since each tree is created in parallel, RF can significantly reduce the computation time. Additionally, RF prevents overfitting by making each decision tree different through randomness. The split criterion at each node is determined in RF according to Equation (4).  
\[\text{split}(X)=\text{argmax}_{j\mu}\Delta I(j,t) \tag{4}\]  
In this equation, \(j\) is the index of the feature and \(t\) is the split point in that feature. \(\Delta I(j,t)\) represents the information gain obtained by that split. RF estimates the probabilities of each class as shown in Equation (5).  
\[P(y=C_{k}|X)=\frac{1}{T}\sum\
olimits_{i=1}^{T}P_{i}(y=C_{k}|X) \tag{5}\]  
The variable \(y=C_{k}\) represents the probability of the data belonging to the class \(C_{k}\), and \(P_{i}(y=C_{k}|X)\) represents the probability of belonging to the class \(C_{k}\) in the \(i\)-th decision tree. RF can be applied to both classification and regression problems and is known for its high predictive accuracy, interpretability, and low computational cost. Due to these features, RF is widely used in various fields.
# Flavoromics Analysis of Passion Fruit-Roasted Chicken
## 2 Results and Discussion
### Evolution Patterns for Precursors of Flavor Substances
#### 2.5.3 Free Fatty Acid Components and Content  
The fat in animal meat has a crucial impact on the production and processing of characteristic flavor meat. The types and contents of fatty acids in the processing of passion fruit-roasted chicken are shown in Table S6. The intramuscular fat of chicken is composed of phospholipids, triglycerides, and free fatty acids, among which free fatty acids are important nutritional factors, flavor substances, and flavor precursors. Unsaturated fatty acids are prone to lipid peroxidation due to their presence of unsaturated bonds. The resulting peroxides are degraded to produce small-molecule flavor substances such as aldehydes, ketones, alcohols, carboxylic acids, etc. Therefore, unsaturated fatty acids play an important role in the formation and improvement of the flavor of passion fruit-roasted chicken. The highest content of fatty acids was found in raw chicken, which was 5874.59 mg/100 g. With heating, the total amount of fatty acids significantly decreased (\(p<0.05\)), reaching its lowest point during the 15-30 min roasting stage and fluctuating within the range of 3375.85-3398.19 mg/100 g.  
According to Table S6 and Figure 4, a total of 34 free fatty acids were identified, including 13 SFAs, 9 MUFAs, and 12 PUFAs. During the processing and roasting process, the unit content of the three fatty acids was PUFA > MUFA > SFA, and the proportion of PUFA to the total fatty acids varied between 41.18% and 45.25%. Palmitoleic acid (C16:1) and oleic acid (C18:1) were the main monounsaturated fatty acids; linoleic acid (C18:2), linolenic acid (C18:3), arachidonic acid (C20:4), and docosadienoic acid (C22:2) were the primary polyunsaturated fatty acids, and their content significantly decreased after 70 min of roasting (\(p<0.05\)). Research has confirmed that polyunsaturated fatty acids such as linoleic acid can undergo pyrolysis to produce acrolein [41], while monounsaturated acids such as oleic acid can undergo pyrolysis to produce substances with carboxylic acid groups. A research group once studied the reaction model of the effect of free fatty acids on pork flavor and found that oxidized pork fat can participate in the Maillard reaction to produce aromatic compounds [42]. The alcohols and carboxylic acids produced by the degradation of unsaturated fatty acids in chicken can also self-cyclize to form meat-flavored lactone compounds [43]. They can also undergo esterification reactions with the alcohols and
# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 3 Quality Assessment Using EMD
### Local EMD Based on SIFT Features  
People usually hope that retargeted images can accurately preserve the local structural information in corresponding regions in the reference images. If we establish the pixel correspondence, we can compare the structural information in corresponding local regions between two images. Thus, implementation of this pixel correspondence between images is a key factor in quality assessment.  
Therefore, we should build a matching algorithm between pixels in the reference image and retargeted image. The SIFT descriptor is widely used in pixel matching between different size scenes [15], and it has been verified to be very effective in matching areas of corresponding dense in images [16]. As in optical flow algorithms, SIFT flow utilizes SIFT descriptors, rather than original pixels, to match image densities.  
Firstly, in the reference image, we extract all SIFT feature points and their eigenvalues; then, we get an image patch around each feature point, and transform the image patch into a histogram. Secondly, we transfer all the eigenvalues and corresponding image patch histograms through an ancillary channel. Thirdly, in the retargeted image, we also extract all SIFT feature points and their eigenvalues; then, we find all matching points by the feature matching, and we can easily get image patch histograms around each matching point. Lastly, we use above EMD to calculate these two histograms centered on feature points. In fact, we do not match these points directly; rather, we just find the corresponding SIFT feature points in the retargeted image from reference image.  
The local image quality can be expressed by all the matching image patches, so we calculate the local image quality score using EMD (LEMD) by average strategy:  
\[\text{LEMD}=\frac{1}{M}\sum_{i=1}^{M}EMD(IH_{p,i}^{ref},IH_{p,i}^{ret}), \tag{4}\]  
where \(M\) is the total number of matching points, and \(\text{EMD}(IH_{p,i}^{ref}\) and \(IH_{p,i}^{ret})\) are the \(i\)-th matching image patch normalized weighted histogram for reference and retargeted images, respectively.
## 3 Results  
### Aquaponic System Characterization  
During the experimental period, water temperature displayed the same trend among the three aquaponics tanks, following expected seasonal variations. Temperature ranged between 18.2 \({}^{\circ}\)C (autumn) and 21.4 \({}^{\circ}\)C (summer) in the tank with _H. portulacoides_, between 19.1 \({}^{\circ}\)C (autumn) and 23.1 \({}^{\circ}\)C (summer) in the tank with _S. ramossissima_, and between 18.1 \({}^{\circ}\)C (autumn) and 22.3 \({}^{\circ}\)C (summer) in the tank with _S. perennis_. Concerning pH, this parameter showed a higher variability, ranging from 7.47 (autumn) to 7.76 (summer) in the tank with _H. portulacoides_, 7.45 (autumn) to 8.73 (summer) in the tank with _S. ramossissima_ and 7.24 (autumn) to 7.57 (summer) in the tank with _S. perennis_. The average concentrationof dissolved inorganic phosphororous (DIP) and dissolved inorganic nitrogen (DIN) during the experimental period is available in Table S1 (Supplementary Material).  
In the head tank the average concentration of DIP was 0.32 \(\pm\) 0.11 mg L\({}^{-1}\) (in spring DIP = 0.29 mg L\({}^{-1}\), in summer DIP = 0.21 mg L\({}^{-1}\) and autumn DIP = 0.50 mg L\({}^{-1}\)), while the average concentration of DIN (DIN = NO\({}_{x}\)-N + NH\({}_{4}\)-N) was 8.9 \(\pm\) 1.3 mg L\({}^{-1}\) (in spring DIN = 9.5 mg L\({}^{-1}\), in summer DIN = 10 mg L\({}^{-1}\) and autumn DIN = 6.8 mg L\({}^{-1}\)). The average concentration of dissolved inorganic nutrients (DIP +DIN) in both the inlets and outlets of each halophytes aquaonics tank increased over time, indicating that the concentration of dissolved inorganic nutrients from the RAS system increased during the experimental period. The removal capacity of DIP displayed by each halophyte species was negligible, as in average DIP concentrations were higher in the outlet of the aquaonics tank than in the inlet. Concerning DIN, the removal capacity of each halophyte species was higher in the summer (circa 90%). In detail, DIN removal capacity in spring, summer, and autumn, was 13%, 91%, and 51% in the tank with _H. portulacoides_, 12%, 89%, and 21% in the tank with _S. ramossisima_, and 52%, 98%, and 60% in the tank with _S. perennis_, respectively.
# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 2 Materials and Methods
### Dynamic Adaptive Prompt Fusion Module  
As shown in Figure 1, we assume that there are \(N\) encoder layers, \(\mathcal{M}_{1},\cdots,\mathcal{M}_{N}\), in the ViT backbone and the corresponding \(N\) multi-modal prompt learning modules \(\mathcal{P}_{1},\cdots,\mathcal{P}_{N}\). The prompt learning process is  
\[\left.\begin{array}{l}\mathbf{C}^{0}=Concat(\mathbf{X}_{RGB}^{0},\mathbf{Z }_{RGB}^{0})\\ \mathbf{C}_{AUX}^{0}=Concat(\mathbf{X}_{AUX}^{0},\mathbf{Z}_{AUX}^{0})\\ \mathbf{C}_{AUX}^{i}=\mathcal{P}_{i}(\mathbf{C}^{i-1},\mathbf{C}_{AUX}^{i-1} )\\ \mathbf{C}^{i}=\mathcal{M}_{i}(\mathbf{C}^{i-1}+\mathbf{C}_{AUX}^{i})\end{array} \right\}, \tag{7}\]  
where Concat represents the concatenation operation of the tokens \(\mathbf{C}^{i},\mathbf{C}_{AUX}^{i}\in\mathbb{R}^{L\times d}\), and \(L=L_{x}+L_{z}\). \(\mathcal{P}_{i}\) is the \(i\)-th dynamic adaptive prompt fusion module, which mainly includes three core components: (1) dynamic channel scaling convolution for modality-specific weighting, (2) low-rank cross-modal attention for multi-modal interaction, and (3) grouped linear mapping for efficient output mapping, as shown in Figure 2.  
Figure 2: The framework of dynamic adaptive prompt fusion.  
1. Dynamic Channel Scaling Convolution For the modal feature \(\mathbf{C}\), the statistical value \(\mathbf{s}\) of the feature is calculated by \[\mathbf{s}=\frac{1}{N}\sum_{i=1}^{N}\mathbf{C}[i,:]\in\mathbb{R}^{d},\] (8) and the dynamic scaling factor \(\boldsymbol{\alpha}\) is calculated by \[\boldsymbol{\alpha}=Softmax(\mathbf{W}_{2}\cdot GELU(\mathbf{W_{1}s})),\] (9) where \(\mathbf{W_{1}}\in\mathbb{R}^{r\times d}\) and \(\mathbf{W_{2}}\in\mathbb{R}^{K\times r}\) are learnable parameters, and \(K\) is the number of dynamic convolution kernels.  
Two types of features are separately learned using depth-wise separable convolution through  
\[\mathbf{X}=Reshape(\mathbf{C})\in\mathbb{R}^{d\times H\times W}\] (10) \[\mathbf{M}=DWConv(\mathbf{X})\odot\sum_{k=1}^{K}\boldsymbol{ \alpha}[k]\cdot\mathbf{C}_{k}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \
# Stability Analysis of Soil Flow Protector and Design Method for Estimating Optimal Length
## 2 Scaled Model Experiment
### Experimental Setup
#### 2.2.2 Plane Strain Soil Tank  
The box structure applied in this study was a continuous structure connected to the road. We conducted the experiments using a plane strain soil tank (PSST), as shown in Figure 3, to apply the plane strain conditions. We simplified the behavior in two dimensions [15,16] to compare and analyze the results. The interior of the PSST had a length of 1700 mm, a height of 760 mm, and a width of 410 mm. We prepared a 30-mm-thick wall made of modeling clay with transparent acrylic and suppressed the deformation using steel reinforcements. Furthermore, we installed a vibration motor at the bottom of the PSST and a spring to easily transmit the generated vibration. The vibration load was applied to simulate the ground settlement behavior. This differs from actual ground settlement conditions; however, the purpose of this study was to analyze the effect of ground settlement reduction with an SFP installation and to calculate the optimum length in the event of a ground settlement. Therefore, we forcibly generated a ground settlement by applying a vibration load for 1440 s.
# High-Pressure Technologies for the Recovery of Bioactive Molecules from Agro-Industrial Waste
## 3 Supercritical Fluid Extraction
### SFE of Bioactive Compounds from Waste
#### 3.2.1 Lipids
By using SFE, they observed that higher yields were obtained at a higher pressure, lower temperature, and using hexane as a modifier (rather than water or ethanol). All the yields obtained using SFE were higher than those of the other extraction methods using organic solvents except the Soxhlet extraction. Considering the fatty acid compositions, the authors observed that linoleic, palmitic, oleic, and stearic acids were among the most present; in the case of SFE, different operating conditions led to significant differences among several fatty acids, whereas no significant differences were observed in the FA compositions of the extracted oils using organic solvents. FA, above all linoleic and palmitic acids, were extracted from coffee silverskin (the thin layer directly in contact with the coffee bean, which is the only residue from the operation of roasting) by [PERSON] et al. [163], using both SFE and Soxhlet extraction. From these authors' findings, it is possible to observe that yields are similar considering the conventional extraction and SFE at the best operating conditions; however, it should be highlighted that when using SFE, the extraction time was drastically reduced and the use of hydrocarbon or chlorinated solvents was avoided. Undoubtedly, among the food waste containing fatty acids, the fishing industry also has to be considered. In fact, during processing, up to 50% of the body weight of the fish may remain as waste. The oil content of fish viscera is an important source of polyunsaturated fatty acids that have been recovered through SFE from the viscera of Indian mackerel [164] (_Rastrelliger kanagurtta_) or of yellowtail fish [165] (_Serola quinqueraliata_). When viscera were taken from Indian mackerel, the highest recovery was obtained using ethanol as a cosolvent and a pressure of 350 bar and temperature of 60 \({}^{\circ}\)C, with a yield close to the one obtained with Soxhlet extraction [164]. Considering the extraction from yellowtail fish, Soxhlet extraction with ethanol has a higher yield than SFE (56% vs. 41%), but omega-3 fatty acids extracted by SFE (at 300 bar and 40 \({}^{\circ}\)C) have a better oxidative quality than conventionally extracted oils [165]. Special consideration has to be made for carotenoids, since several applications of SFE can be found for the recovery of these natural bioactives.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 2 Traditional Methods of Modulation Recognition  
Early signal modulation identification work mainly relies on experienced experts to carry out manual operations. The final judgment result is more subjective, and the accuracy rate is difficult to guarantee. In this case, automatic modulation identification technology came into being and became the keyway to solving the above problems.
# Conditional Generative Adversarial Networks for Domain Transfer: A Survey
## 4 The Applications of CGAN
### Image Generation from Text Descriptions  
Using CGANs to generate images according to text description was first proposed by [PERSON] et al. [113]. It first encodes the text description as a vector representation and then uses the representation as a condition to train CGAN. In order to make the generated image more in line with the user's needs, [PERSON] et al. added the position constraint of the object in the image to the CGAN condition [114]. In detail, besides the text description, the expected coordinates of some objects will also be fed into the CGAN. Afterward, [PERSON] et al. proposed StackGAN [16] and StackGAN++ [43], which have been detailed in this paper earlier. In addition, HDGAN [115] adopts a multi-stage strategy similar to StackGAN++.  
The previous research in this area only used global sentence embedding as a condition to train CGAN, and the granularity was quite rough. AttnGAN [116] introduced the attention [117] module and added word-level conditions, making the generated image closely related to the description. MirrorGAN [44] borrows the idea of cycle consistency to re-convert the generated image into text and supervise the distance between the re-converted text and the original text during the training process. DMGAN [45] uses a dynamic memory module to screen out which text contents are more important to the generation of images and is dedicated to improving the resolution of relevant parts of images. SD-GAN [60] uses two networks that work together to make the generated images consistent in various descriptions.  
Similar to the task of image style transfer, some researchers also proposed some effective and efficient network architectures [58; 59; 60; 118; 119; 120; 58; 121; 122].
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 5 Reward Design
### Multi-Agent  
Multi-agent approaches can distinguish between individual agent rewards, _local_ rewards, and rewards shared by all agents, _global_ rewards. In [172; 206; 287], an additional complement is given to all agents when completing the schedule [172; 287] or all routes [206]. During the episode, a smaller reward can be given based on the percentage of completed tasks [287] or upon job completion [172].  
Some multi-agent approaches return different rewards for each agent. Ref. [164] minimizes routing costs using three different cooperative agents. While one agent rewards utilization and daily rewards, the others share the reward function, including extra penalty costs. In [78], one agent balances the load among factories, while another wants to minimize factory makespan. The former is the difference between maximum and minimum factory total processing time, while the latter reflects the makespan increment with each step.  
Specific multi-agent rewards allow for coordination and competition between agents, creating scalable solutions for decentralized systems. However, the complexity increases greatly with the number of agents, and considering global and local rewards requires careful balancing.
## 5 Conclusions  
The main findings of this paper are:  
1. The Krini, Agia Eleoussa monastery and Pititsa landslides are active landslides whose motion was measured by InSAR (C-band) time series analysis for the period 2016-2021.
2. We processed LiCSAR interferograms using the SBAS tool and we obtained average displacement maps. The results indicate slow ground motions toward the east and downward (subsidence).
3. The maximum displacement rate of each landslide is located at about the center of each landslide.
4. Our results point that there is a correlation between rainfall and landslide motion. For the Krini landslide, we found the mean time lag to be 13.5 days between the maximum rainfall and the maximum of LOS displacement (descending orbit data).
5. The displacement rates of the Krini active landslide increase after a period of rainfall. Two of the three time periods examined showed an increase in the displacement rate by about 40% when the total rainfall was quite similar (~700 mm). The period September 2017-April 2018 showed an increase in the displacement rate by about 550%. This result was accompanied by a large amount of total rainfall (~1000 mm).
6. Our findings suggest that the amount of total rainfall could control the amount of increase of the displacement rate of an active landslide.
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 1 Introduction
In this study, a speech enhancement algorithm was proposed that considers the environmental noise in which hearing aids operate and improve the speech quality using deep learning techniques. In order to accurately assess the type of environmental noise, the noise classification results with convolutional neural networks (CNNs) were used to accurately recognize the type of environmental noise [23], and a noise reduction algorithm using deep neural networks (DNNs) was applied to improve the speech enhancement.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 2 Related Work
To summarize, most of the current data transmission strategies are geared towards predictable data transfer tasks or do not fully consider the impact of energy harvesting on network performance. For practical application in time-varying satellite networks, the data transmission scheme should consider optimization of the data acquisition rate, link allocation, inter-satellite transmission rate, and energy harvesting. Although reinforcement learning can cope with the dynamics and complexity of satellite systems, such methods require extensive training to learn optimization strategies. In this paper, we work on an online optimal control method for energy-efficient data transmission that does not require the prior knowledge.