esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 PSO Based on the PSO-BP Model
### Model Parameter Setting and Data Processing  
#### 4.3.1 Data Preparation and Preprocessing  
1. This study used the key significant parameters screened by the central composite response surface experiments in Table 10 as the total data (23 sets), performing stratified random sampling into training, validation, and test sets [34]. The training set was used for model parameter learning, the validation set was used for hyperparameter tuning and preventing overfitting, and the test set was used for the final generalization capability evaluation. Among these, the training set comprised 17 sets, the validation set 3 sets, and the test set 3 sets, accounting for approximately 70%, 15%, and 15% of the total data, respectively.
2. Data normalization  
As shown in Table 12, the input variables undergo feature scaling and are normalized to the \([0,1]\) range using Min-Max normalization:  
\[x_{norm}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{16}\]  
The output variable is the angle of repose value, maintaining the original unit (degree).
###### Abstract  
In this paper, we propose a method that uses the femoral and tibial elevation angles to quantitatively evaluate the symmetry of lower limb movement during the transition from a sitting position to a standing position. In kinematic analysis of the transition from sitting to standing, the angles of the three joints of the lower limb are often measured. However, due to the large number of variables, it is difficult to evaluate the symmetry of the lower limb movement by comparing data from the six joints of the left and right lower limbs. In this study, therefore, we measured the femoral and tibial elevation angles of healthy participants and rehabilitation patients and visually and numerically evaluated the symmetry and asymmetry of the movement of the left and right lower limbs. We were able to identify the kinematically major lower limbs in the transition from sitting to standing and quantify the symmetry of the movement patterns of the left and right lower limbs. Furthermore, we examined the possibility that the method could be effectively used in the rehabilitation field to evaluate the motor co-ordination that constitutes the lower limb movement pattern in the transition from the sitting to standing position, such as the gait plane rule.  
symmetry; sit-to-stand; elevation angle; coordination Article
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 4 Routing Algorithms Based on Node Path Prediction and Cache Management  
### Utility Value of the Message  
Nodes carry a large number of message copies in the cache as they transmit messages. When there is not enough room in the cache for new messages to be received, older messages with lower utility values can be discarded to make room. Messages with high diffusion and high energy consumption are classified as having low utility value based on the global dissemination of messages and the energy consumption of messages to the current node.  
The utility value of the message can be calculated by the following equation.  
\[U^{m}=1\Big{/}(\sum\limits_{i=1}^{n}node_{i}^{m}/node_{all}+\sum\limits tran^{m}/ \sum\limits_{j=1}^{buf\textit{size}}tran^{j}) \tag{17}\]  
where \(\sum\limits_{i=1}^{n}node_{i}^{m}\) is the number of nodes that the message m passes through during transmission, the larger the value of \(\sum\limits_{i=1}^{n}node_{i}^{m}\) means the higher the diffusion of the message, \(node_{all}\) is the total number of nodes in the network, \(tran^{m}\) is the number of times the current node forwards the message m, the larger the value means the greater the energy consumption of the current node, \(\sum\limits_{j=1}^{buf\textit{size}}tran^{j}\) is the number of times the current node forwards all messages in the cache and \(buf\textit{size}\) is the size of the current node cache, according to the above formula, the utility value of any message at any point can be calculated.  
To increase the node transmission success rate and reduce energy consumption, when a new message arrives, it is sorted according to the cache space utility value, and the messages with lower utility values are removed from the cache in priority to release the cache, and when the message m is moved out of the cache due to its lower utility value, no more messages m forwarded by other nodes are received.
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 4 Experimental Results and Discussion
### Alleviating Overestimation Experiments  
This study adopts the TD3 algorithm, aiming to alleviate the common problem of value function overestimation in the Actor-Critic architecture. The Actor-Critic algorithm combines value function approximation (Critic) and policy approximation (Actor), where the Critic is responsible for estimating state-value or action-value functions, and the Actor updates the policy based on these estimates. However, due to errors in function approximation, nonlinear approximations, and limited sample sizes, the Critic network in the Actor-Critic algorithm often overestimates the true value, leading to suboptimal or even unstable policies. This experiment compared the DDPG framework with the TD3 framework. We recorded their Q-values and true values across 10,000 episodes. In Figure 12, the horizontal axis represents the episodes, and the vertical axis represents the magnitude of Q-values. Figure 12a shows the Q-values and true values of DDPG. It is observed that the Q-values are consistently higher than the true values, which also proves the existence of overestimation in the AC framework. Next, we introduced the TD3 framework, and in Figure 12b, it is shown that the Q-values of TD3 overlap significantly with the true values, indicating that the TD3 framework effectively alleviates the overestimation problem.
## 2 Inlet/Outlet Independent Regulation and Coordinated Control
### Flow-Rate Regulation Mechanism of Inlet/Outlet Independent Metering
the excellent dynamic performance of the electro-hydraulic control system, how to increase the energy efficiency of system has become an important research topic. 2. With the relatively large amount of attention devoted to valve-controlled cylinder systems, references are comparatively scarce on independent and coordinated control of pump/valves for pump-controlled motor system. However, the study on inlet/outlet independent metering control for pump-controlled motor system also has important practical value in engineering application. 3. For the inlet/outlet independent metering control of electro-hydraulic system, while adopting different control strategies and structural parameters of the valve, the control performance and energy-saving efficiency are affected to a certain extent, which has been the focus of intensive research. 4. As new hydraulic components, the digital programmable valve and novel flow rate amplifier valve with digital pilot are applied to the independent metering control system, which promotes the modern intelligent control for electro-hydraulic system. However, most of the digital valves currently studied are only as high-speed on-off valves. Because of the limitation with their structure, it can only be applied to pilot control and small flow rate control applications. 5. The key issue of the study on inlet/outlet independent metering control system is to coordinate control variables of double electro-hydraulic control elements, which usually includes the flow-rate and the pressure regulation. Therefore, considering the system under different working modes, independent and coordinated regulation mechanism based on motion-force hierarchical control architecture is always an important research topic. 6. The function exchange of motor and pump under sustained negative load seriously affects the dynamic stability performance for motion control system. Aiming at the problem, development of speed smoothness control strategy with motion-force hierarchical structure have been an urgent problem to be solved. 7. With sustained negative load in engineering applications, the study of regulation mechanism for suppressing negative load and integrated energy management scheme are of great practical value for electro-hydraulic motion control system.
# Low-Complexity Convolutional Neural Network for Channel Estimation
###### Abstract  
This paper presents a deep learning algorithm for channel estimation in 5G New Radio (NR). The classical approach that uses neural networks for channel estimation requires more than one stage to obtain the full channel matrix. First, the channel has to be constructed by the received reference signal, and then, the precision is improved. In contrast, to reduce the computational cost, the proposed neural network method generates the channel matrix from the information captured from a few subcarriers along the slot. This information is extrapolated by applying the Least Square technique only on the Demodulation Reference Signal (DMRS). The received DMRS placed in the grid can be seen as a 2D low-resolution image and it is processed to generate the full channel matrix. To reduce complexity in the hardware implementation, the convolutional neural network (CNN) structure is selected. This solution is analyzed comparing the Mean Square Error (MSE) and the computational cost with other deep learning-based channel estimation, as well as the traditional channel estimation methods. It is demonstrated that the proposed neural network delivers substantial complexity savings and favorable error performance. It reduces the computational cost by an order of magnitude, and it has a maximum error discrepancy of 0.018 at 5 dB compared to Minimum Mean Square Error (MMSE) channel estimation.  
chann estimation; computational cost; convolutional neural network; MIMO system; MSE analysis +
Footnote †: journal: Electronics  
1
## 3 Estrogen Receptors
### Estrogen Receptor Structure  
ER\(\alpha\) and ER\(\beta\) are members of class I nuclear receptors. They are ligand-activated receptors and can dimerize into homo- or heterodimers. Even though ER\(\alpha\) and ER\(\beta\) are encoded by two different genes located in two different chromosomal loci, 6q25.1-25.2 and 14q23.2-q23.3, respectively, they have similar overall structure and share a high degree of amino acids homology. [PERSON] et al. (2013) nicely describe the structure of the estrogen receptors. There are six domains in their structure, named A to F (see Figure 2).  
The A and B domain, at the N-terminal, encodes for the ligand-independent Activation Function-1 (AF-1). The C domain encodes for the DNA-binding domain and it is also important for dimerization. The D domain contains the nuclear localization signal (NLS) and an area of bending allowing the C domain to come close to the E and F domains which contain the ligand-binding domain, along with the ligand-dependent Activation Factor-2 (AF-2). When estrogen binds to the ligand-binding domain, there is a conformational change that brings the AF-2 domain, in the C-terminal, close to helices 3, 4, and 5 located in the N-terminal of the receptor. This new conformation allows for the recruitment of cofactors that will ultimately regulate gene expression [28]. The main difference between the two receptors is in the amino-terminal. In the ER\(\alpha\), this area encodes for the AF-1, that when it comes into proximity with the AF-2 sequence, it recruits transcriptional factors leading to activation of targeted genes. In the ER\(\beta\), this area may encode for a repressor domain that exerts the antagonistic effects seen in the different systems [25]. The ligand-binding domain is 60% conserved between the two estrogen receptors, but the NH2-domain, including the transcriptional activation domain AF-1, is less than 25% conserved.
## 3 Methodology
### Use Case  
Organizations are obliged by the GDPR to record their processing activities of personal data. Each record of a processing activity must include specific fields, such as the purpose of processing, the legal basis, deletion rules, or categories into which the processed data can be classified. In this study, we focused on data category generation--such as names,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Release Date** & **Training Data** & **License** & **Model Size** & **Publisher** \\ \hline \multirow{2}{*}{Qwen2-7B [31]} & \multirow{2}{*}{May 2024} & Public web data in 27 languages & Apache 2.0 & 7 billion & \multirow{2}{*}{Alibaba} \\  & & & & & parameters & \\ \hline \multirow{2}{*}{Meta-Llama-3-8B [32]} & \multirow{2}{*}{April 2024} & \multirow{2}{*}{Offline dataset} & Llama3 & \multirow{2}{*}{8 billion} & \multirow{2}{*}{Meta} \\  & & & & & \\ \hline \multirow{2}{*}{zephyr-7b-beta [33]} & \multirow{2}{*}{October 2023} & Mix of publicly & \multirow{2}{*}{MIT} & \multirow{2}{*}{7 billion} & \multirow{2}{*}{HuggingFaceH4} \\  & & available, synthetic & & & \\ \multirow{2}{*}{octa\_mini\_v7\_7b [35]} & \multirow{2}{*}{May 2024} & Public web data & \multirow{2}{*}{Apache 2.0} & \multirow{2}{*}{7 billion} & \multirow{2}{*}{[PERSON]} \\  & & and various SFT & & & \\ \hline GPT-4 [20] & March 2023 & Proprietary data & Proprietary & \textgreater{}1 trillion & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Metadata of the selected Large Language Models.  
email addresses, health data, and financial information--as a representative task in GDPR documentation. Accurate generation of data categories is essential for GDPR compliance, as it defines the scope of data processing. These categories also affect adherence to other compliance principles, such as purpose limitation and data minimization.  
To standardize our evaluation, each LLM was instructed to generate exactly ten data categories for the processing activity _operation of a webshop_. This number was chosen based on the average number of categories observed in our dataset of GDPR compliance records. Ten data categories struck a balance between smaller processing activities with limited contexts and larger, more extensive operations with broader contexts. Note that this ensured that the task reflected real-world scenarios while maintaining a manageable scope for evaluation.  
Figure 4 illustrates our setup, presenting an example of the desired output quality. That example served as the benchmark for evaluating the model performance. By focusing on that task, we aimed to evaluate the potential for partially automating the creation of GDPR documentation and ultimately explore whether LLMs could contribute to reliable, scalable compliance solutions.
## 4 Results and Discussion
### Localization Results Using Random Forest
The OOB error effectively measures the proportion of misclassified samples among the OOB samples for a given decision tree. For localization of impact, the OOB error is the ratio of accurately located impacts to the total number of impacts. The random forest-predicted impact locations are rounded and then compared to the actual impact locations. To enable a meaningful comparison between the predicted impact locations and their actual counterparts, the random forest model's predictions are rounded to the nearest integer values. The OOB error analysis revealed that the model attained the minimum OOB error (18.19%) when approximately 100 decision trees were incorporated. Consequently, a random forest model comprising 100 trees was selected for the remainder of this study. This optimization process ensures that the model achieves a balance between complexity and generalizability, minimizing the risk of overfitting while maximizing predictive performance. Upon optimizing the number of decision trees, the AE dataset, consisting of 15 features, was incorporated into the random forest model as input. The dataset was partitioned using a 70/30 training/testing ratio to ensure a robust evaluation of the performance. The results demonstrated a high level of accuracy, indicating that the model is well suited for the given task. Figure 8 illustrates a comparison of the actual impact locations with their corresponding predicted impact locations derived from the model. The R\({}^{2}\) value, equal to 0.98616, signifies a good fit between the predictions and the actual labels. This suggests that the model is adept at accurately capturing the underlying relationships between the predictor variables and the response variable. Figure 9 presents a comparative analysis of the true impact locations and their respective model-generated predictions. Upon closer examination, it is evident that the discrepancies between the predicted values and the actual labels are minor, signifying a high degree of accuracy in the performance. To quantify these errors more rigorously and evaluate the reliability, the RMSE was calculated. In this case, the random forest model boasts a notably low RMSE value of 0.6778, which further substantiates its accuracy and precision. The findings of R\({}^{2}\) and RMSE suggest that the predictions align closely with the  
Figure 8: Random forest result—actual impact locations versus predicted impact locations. actual impact locations, making the random forest model a dependable tool for locating the impact location within the confines of this study.
## 6 Discussion
### Comparison between SU and Alteration SI for Mapping Hydrothermal Alteration  
In order to reveal the advantage of using the SU method to map hydrothermal alteration over the use of an alteration SI such as NHASI (that detects Al-OH bearing minerals), we compared the two corresponding maps, namely, the hydroxyl-bearing alteration abundance map and the NHASI map, with the reference map (Figure 6g). Although the NHASI maps from the three sensors (Figure 6a-c) captured the general pattern, they did not always successfully depict the different degrees of alteration or the correct spatial extent of the alteration field. This argument is also supported quantitatively through the OAs given in Table 3 (maximum OA ~50%). On the other hand, the corresponding hydroxyl-bearing alteration abundance maps issued from SU (Figure 6d-f) not only successfully mapped the alteration field (maximum OA ~60% in Table 3) but also showed that different abundance values (low, intermediate, and high) seemed to highlight the different degrees of alteration (weak, middle, and strong, respectively). Characteristic examples are the distinctive NE-oriented linear high-alteration abundance pattern west of Stefanos crater that seemed to follow the main NE-SW striking fault system within the caldera and the very characteristic halo that was created by mud and altered rocks, in the inner part of Stefanos crater and in the area of Megalos and Mikros Polybotes from the explosions of 1887. The advantage of SU over NHASI can be explained by the fact that the NHASI approach utilizes scalar information, relating only a few entries of the spectral signature of a pixel, while SU utilizes the entire spectral signature.  
Although, SU systematically outperforms NHASI for all three sensors, the most significant differences between the two methods were observed in ASTER and Sentinel-2 MSI with the SU systematically providing the best results, meaning that the different alteration classes of the reference map were best represented by the three hydroxyl-bearing alteration abundance categories.  
Focusing now to the more detailed information given by the _CFs_, one can observe that, more or less, all six results exhibited a difficulty in discriminating between the \"middle\" and the \"strong\"  
Figure 6: Top row: (**a**) alteration SI (NHASI) map, (**b**) hydroxyl-bearing alteration abundance map calculated from ASTER dataset; second row: (**c**) alteration SI (NHASI) map, (**d**) hydroxyl-bearing alteration abundance map calculated from Landsat-8/OLI dataset; third row: (**e**) alteration SI (NHASI) map, (**f**) hydroxyl-bearing alteration abundance map calculated from Sentinel-2 MSI dataset. Bottom row (**g**): reference map (in red: strong alteration >50%, in orange: 25–50% middle alteration, and in yellow: 0–25% weak alteration) (Source: [68]).  
alteration, as they are defined in the reference map. However, this problem was more serious in the NHASI maps than in the SU maps.
## 6 Problems, Challenges, and the Future of Phage Therapy
### Cost-Benefit Analysis and Geographic Prioritization  
The implementation of mycobacteriophage therapy requires the careful consideration of cost-benefit ratios. While initial development, diagnostic, and production costs are high, especially due to personalized phage preparations, the potential to effectively treat MDR-TB and other difficult infections could significantly reduce long-term healthcare expenses by shortening the treatment duration, lowering hospital stays, and reducing the use of expensive and toxic antibiotics.  
Currently, regions with the highest burden of drug-resistant tuberculosis, such as parts of Sub-Saharan Africa, Southeast Asia, and Eastern Europe, represent priority locations where this therapy could have the greatest impact. However, limited healthcare infrastructure and diagnostic capacities in many of these areas present additional challenges to deploying such advanced therapies, emphasizing the need for investment in local diagnostic and phage production capabilities.
# Current Status and Future Directions for Combined Tillage and Sowing Technology and Equipment
## 5 Analysis of Equipment and Technology Characteristics
### Development Trend of Key Technologies
Through summarizing and inducing the characteristics of different crops and different equipment, research can flexibly integrate relevant production equipment to ensure high-quality and high-efficiency operations in more scenarios. Research explores the application of modern information technology and other means, organically integrating new technologies with production equipment to realize the real-time monitoring and control of the whole process of the operation of combined tillage and sowing machines, ensuring that the equipment can obtain a high-quality seedbed and also achieve precision sowing, and comprehensively improving the quality and efficiency of agricultural production.
## 2 Materials and Methods
### Multispectral Image Analysis  
Multispectral images represent data within a specific wavelength range in the electromagnetic spectrum. Images detected by multispectral sensors are extracted in red, green, blue, or RGB form, from which additional information that is not visually detectable can be obtained. Sentinel-2 images are widely used to monitor land vegetation and forests as well as for crop and environmental management, making them extremely useful for disaster management.
## 4 Orbit-Tracking and -Determination Algorithm Simulation
### Simulation Results of Orbit-Determination Precision
The accumulated observation data are updated on a rolling basis, achieving a position forecast accuracy of 80 m within 24 h, which is on par with GNSS receivers and represents a leading level domestically. Figure 8: Data for 1 h daily observation, a 2 day period and 3 time slots per minute; 72 h orbit error-prediction chart. Figure 7: Data for 1 h daily observations, a 2 day period and 4 time slots per minute; 72 h orbit error-prediction chart.