esa-sceva/satcom-chunk-collection huggingface dataset:
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 4 The K3P Algorithm
### Uneven Terrain  
When a phase shift has been triggered, the tripod in the swing phase has to land. If the surface is uneven, the limbs have to adapt to the elevation changes of the surface. In contrast to the tripod control strategy, where the three legs are commanded simultaneously, during landing, each limb is controlled individually and the three landing events are treated separately. By utilizing interoceptive information, the position of each leg is always known. K3P considers that the swing phase has ended only when all three limbs of the swinging tripod have touched the surface, and therefore, it can begin the support phase of the gait cycle (line 19 of Algorithm 1). To adapt to changes in elevation, K3P must receive information when every \(SP\) has touched the surface and updates their height with respect to its corresponding reference frame (\(E\) or \(O\)). The update process is carried out according to the displacement \(\Delta_{z}\) performed by the tripod in swing phase while landing (lines 18 and 20 of Algorithm 1). The displacement update \(\Delta_{z}\) for the \(i\)-th limb (line 17 of Algorithm 1) is expressed as the difference of the \(z\) components of two consecutive state vectors of the moving tripod.  
The swing phase ends when all limbs have landed, and at this moment, their positions with respect to \(B\) have been adapted to the elevation of the terrain below the robot. In order to perceive ground contact, we consider using inexpensive ToF distance sensors (for example, VL53L0X by ST semiconductor). Eventually, when a tripod restarts its swing phase, the limbs take off, starting from the lowest limb (line 20 of Algorithm 1). The update process \(\Delta_{z}\) is applied only to those limbs at the same elevation with respect to \(B\), making the limbs separate from the ground in the opposite order on which they landed and move at unison once all the limbs of the tripod have taken off.  
Figure 5: First two criteria that trigger a phase shift. (**a**) K3P\({}_{1}\). The linear distance between centroids at start and end positions is limited to \(l_{max}\) meters. (**b**) K3P\({}_{2}\). As the even legs turn counter-clockwise, the angle \(\varphi\) cannot be smaller than \(\varphi_{m}\).  
Using this approach, the K3P algorithm does not require prior information about the terrain texture. In the Results section, simulations obtained with predefined values of \(l_{h}\) and \(l_{g}\) are shown. However, it is possible to adapt these parameters during execution based on information obtained, for example, from visual data regarding terrain conditions, allowing a higher-level trajectory planning module to modify these parameters.  
However, it is important to highlight the limitations of the algorithm: the gait pattern generator will fail when encountering obstacles of significant height, such as large debris or stairs. Additionally, the terrain must be rigid; hence, viscous terrains cannot be considered either.
## 2 Theoretical Foundation
### Radar Data Processing  
In [11], networked radar systems receive a great deal of attention due to their higher probability of target detection, better target location and tracking performance compared to monostatic/bistatic radar systems.  
According to the complexity and difficulty of practical application, network radar systems can be divided into simple network radar systems with decentralized processing, and traditional network radar systems with centralized data and information fusion processing. In addition, multiple flight parameters are automatically calculated, such as speed with respect to the ground, length and trajectory duration.  
According to the General Direction from Civil Aviation, DGAC (2014), the processing of radar data is shown through a display that details the geolocation of the aircraft, where the radar parameters are defined during the preparation of the data and their corresponding display (position, elevation, refresh rate, and azimuth).
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 4 Experiment and Analysis
### Performance Indicators  
Considering that there were some unlabeled categories in the dataset, this study selected clustering metrics to measure the accuracy of the results. The evaluation indicators of clustering selected purity [19] and KL (Kullback-Leible) divergence [20].  
Purity is the most intuitive evaluation index to measure the clustering effect simple and transparent. Its calculation formula is similar to the accuracy rate commonly used in classification. First, the correspondence between clusters and classes needed to be assigned. The class with the most samples in the cluster was taken as the representative class of the cluster. This was calculated as follows:  
\[P=(B,C)=\frac{1}{N}{\sum_{k}{\max_{j}{\left|{b_{k}\cap{c_{j}}}\right|}}}\]  
The concept of KL divergence could be used to measure the difference between two distributions. The smaller the difference between the two distributions, the smaller the KL divergence. Its calculation formula is as follows:  
\[D(p/q)=\sum_{x}p(x)\log\frac{p(x)}{q(x)} \tag{7}\]  
In the formula, \(p\) and \(q\) represent the category distribution of the clustering results and the distribution of actual samples, respectively. The smaller the KL divergence, the more similar the distribution of clustering results was to the actual distribution.  
Among them, purity was used to intuitively measure clustering results, and KL divergence was used to observe the overall distribution of results. The combination of the two indicators could make a clearer judgment on the clustering effect.
## 5 Results and Discussion
### Review Rating Prediction  
#### 5.2.1 Results Based on Yelp IMDB Dataset  
Table 5 represents a comparative analysis of the Review Rating Prediction model for several performance metrics through altering training data. The analysis of TD-Spider TaylorChoA-GRU for the precision, recall and F-measure is listed below. In 70% of training data, the precision of the developed TD-Spider TaylorChoA-GRU is 0.897. The recall of TD-Spider TaylorChoA-GRU is 0.914 for 70% of training data and, considering 70% of training data, the F-measure of the developed model is 0.906.  
Figure 8 represents the accuracy and loss for the Review Rating Prediction model. Figure 8a represents the accuracy curve and Figure 8b represents the loss curve. The accuracy increases with the increase in the iteration and the loss decreases with the increase in the iteration.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **Data (\%)** & **DNN** & **CNN+LSTM** & **Bi-GRU** & **CNN** & **[3]** & **[34]** & **[17]** & **Proposed** \\ \hline \multirow{4}{*}{Precision} & 60\% & 0.635 & 0.685 & 0.741 & 0.845 & 0.858 & 0.865 & 0.877 & 0.887 \\  & 70\% & 0.648 & 0.703 & 0.765 & 0.860 & 0.869 & 0.885 & 0.889 & 0.897 \\  & 80\% & 0.685 & 0.745 & 0.785 & 0.885 & 0.898 & 0.919 & 0.920 & 0.925 \\  & 90\% & 0.695 & 0.765 & 0.805 & 0.901 & 0.916 & 0.931 & 0.937 & 0.943 \\ \hline \multirow{4}{*}{Recall} & 60\% & 0.654 & 0.699 & 0.754 & 0.850 & 0.869 & 0.885 & 0.889 & 0.897 \\  & 70\% & 0.675 & 0.714 & 0.785 & 0.870 & 0.887 & 0.905 & 0.908 & 0.914 \\  & 80\% & 0.695 & 0.754 & 0.804 & 0.908 & 0.916 & 0.933 & 0.938 & 0.941 \\  & 90\% & 0.724 & 0.775 & 0.825 & 0.928 & 0.948 & 0.954 & 0.959 & 0.965 \\ \hline \multirow{4}{*}{F-measure} & 60\% & 0.645 & 0.692 & 0.748 & 0.847 & 0.863 & 0.875 & 0.883 & 0.892 \\  & 70\% & 0.661 & 0.708 & 0.775 & 0.847 & 0.878 & 0.895 & 0.898 & 0.906 \\  & 80\% & 0.690 & 0.750 & 0.795 & 0.847 & 0.907 & 0.925 & 0.929 & 0.933 \\  & 90\% & 0.709 & 0.770 & 0.815 & 0.847 & 0.932 & 0.943 & 0.948 & 0.954 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of Review Rating Prediction for IMDB dataset in terms of precision, recall and F-measure.  
Figure 7: Performance analysis based on (**a**) accuracy and (**b**) loss for Sentiment Classification using social media app review dataset.
## 2 Encoding of Categorical Variables
### One-Hot Encoding  
One-hot encoding \(f_{O}\) maps the \(N\) categories \(c_{i}\) into \(N\) binary row vectors with \(N\)-dimensional size, where each binary vector has only one valid digit 1 and the rest of the positions are 0, and the positions of the valid digits are different between different vectors.  
\[CV:\begin{bmatrix}c_{1}\\ c_{2}\\ \cdots\\ c_{i}\\ \cdots\\ c_{N}\end{bmatrix}_{N\times 1}\overset{f_{O}}{\rightarrow}NV:\begin{bmatrix} \boldsymbol{n_{1}}\\ \boldsymbol{n_{2}}\\ \cdots\\ \boldsymbol{n_{i}}\\ \cdots\\ \boldsymbol{n_{N}}\end{bmatrix}_{N\times N}=\begin{bmatrix}1&0&\cdots&0& \cdots&0\\ 0&1&\cdots&0&\cdots&0\\ &&\cdots\\ 0&0&\cdots&1&\cdots&0\\ &&\cdots\\ 0&0&\cdots&0&\cdots&1\end{bmatrix}_{N\times N} \tag{6}\]  
For example, the categorical variable \"Occupation\" contains three categories \"Teacher, Firefighter, Construction Worker\", which are mapped to binary vectors [1,0,0], [0,1,0], [0,0,1] using one-hot encoding \(f_{O}\).  
From this encoding method, it is easy to see that each vector is a unit vector and the vectors are orthogonal to each other, i.e., the vectors are equal in distance and independent of each other, which is fully consistent with the characteristics of unordered categorical variables.  
However, one-hot encoding also has some problems: the vectors are only composed of 0 and 1, and when the categorical variables are of large categories, the encoding obtained from the mapping will be sparse and of huge dimensions, which will make the computational cost large.
## 3 Technologies for Treating Landfill Leachate
### Advanced Oxidation Progresses  
#### 3.2.1 Electrooxidation  
Electrooxidation (EO) represents an advanced oxidation technology for organic pollutant degradation, operating through two distinct pathways: direct electron transfer at the electrode surface and indirect oxidation via electrogenerated reactive species. The process involves in situ generation of oxygen and chlorine-derived oxidants (e.g., \(\bullet\)OH and HClO) under controlled electrochemical conditions, typically achieved through anode polarization at optimized potentials [113]. The principal merit of this technology lies in its mineralization capability, converting organic contaminants into benign end-products (CO\({}_{2}\) and H\({}_{2}\)O) through complete oxidation, thereby preventing secondary contamination through phase transfer phenomena.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & \begin{tabular}{c} **adsorbent/** \\ **Coagulant** \\ \end{tabular} & \begin{tabular}{c} **Dosage** \\ **(g/L)** \\ \end{tabular} & **pH** & **Parameter** & \begin{tabular}{c} **Concentration** \\ **(mg/L)** \\ \end{tabular} & \begin{tabular}{c} **Removal** \\ **Rate (\%)** \\ \end{tabular} & **Ref.** \\ \hline ADS & PAC & 0.1 & 7.8 & \
\begin{tabular}{c} TOC\({}_{\text{i}}\) \\ TOC\({}_{\text{f}}\) \\ \end{tabular} &
\begin{tabular}{c} 2.5 \\ 1.6 \\ 197.5 \\ 57.7 \\ 78.4 \\ 198.0 \\ 198.0 \\ 197.5 \\ 200.0 \\ 200.0 \\ 1998.0 \\ 200.0 \\ 200.0 \\ 1900.0 \\ 200.0 \\ 200.0 \\ 191.00 \\ 200.0 \\ 200.0 \\ 2  
Electrochemical processes such as electrocoagulation (EC) have proved to be a promising option in the treatment of various types of industrial effluents, for the removal of dyes, pesticides, heavy metals, and pharmaceutical compounds [114]. [PERSON] et al. [114] innovatively utilized electrodes fabricated from steel shavings (SFEs) for EC to remove heavy metals and thermotolerant coliforms from landfill leachate, and the novel electrodes demonstrated removal efficiencies of 51-95% for heavy metals and achieved 100% elimination of thermotolerant coliforms present in the leachate. [PERSON] [113] studied the efficiency of different processes such as EC, EO, and sulphate-based advanced oxidation processes (SR-AOPs) in landfill leachate treatment, and found that the removal of COD was 60.0%, 50.0%, and 77.9%, respectively. The total removals of COD, TOC, BOD, and ammonia (NH\({}_{3}\)-N) by the sequential method were 95.6%, 90.5%, 91.6%, and 99.8%, respectively. Converting dissolved organic pollutants into separable insoluble matter is an energy-efficient strategy. [PERSON] [115] engineered a novel dual-anode electrochemical system (Fe\({}^{2+}\)/HCl) for advanced treatment of landfill leachate concentrates, employing an innovative oxidative coupling-mediated insolubilization mechanism. This configuration demonstrated exceptional performance, with the DOC removal efficiency reaching 89.8%, while simultaneously achieving superior environmental compatibility by successfully suppressing toxic byproduct generation, maintaining total organic chlorine (TOCI) concentrations below the 0.1 mg/L threshold.
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 2 Problem Formulation
### Active Control Implementation  
In active vibration control, the control algorithm needs to calculate the control signal based on the system's response. This section presents the implementation of the high-dimensional high-frequency vibration control problem, as shown in Figure 2. An acceleration sensor is installed near each of the four electromagnetic actuators to collect the acceleration signals at the respective positions. The four control signals are computed in real time by the controller running a trained reinforcement learning neural network, based on signals from the four acceleration sensors located near the actuators. The signal conditioner is used to filter and amplify the signals from the four acceleration sensors, which are then converted into digital signals via an AD module and fed into the controller for processing. The control signals are amplified by a driver into control current signals after being processed via a DA module to drive the actuators. The electromagnetic actuator consists of a permanent magnet armature and a coil, with the permanent magnet armature positioned in the middle of the coil. When current flows through the coil, the permanent magnet armature moves up and down under the influence of the coil's magnetic field, generating actuating force. The driver is a power amplifier that amplifies the control signal calculated by the controller into current to drive the electromagnetic actuator to operate.  
Since the vibration control system is often applied in complex vibration environments, where theoretical models cannot accurately describe the input-output response of the system, real experimental data are used for the research in this paper. In the following sections, the vibration control system is modeled as a simulator constructed from these experimental data. Based on the simulator, a neural network vibration controller is designed using reinforcement learning methods to solve the high-dimensional high-frequency vibration control problem.
## 3 AIS Sensor Data Acquisition and Preprocessing
### AIS Sensor Data Cleaning  
AIS sensor data unavoidably contains data anomalies, missing data and other issues that necessitate cleaning of the data after trajectory extraction. After trajectory extraction, the original cluttered AIS data are processed into trajectory data sets. In each trajectory data set, if the acquisition time of two data points is quite different, this indicates that data in the sequence are missing and must be interpolated. According to the experimental error results of linear interpolation, Lagrange interpolation, median interpolation and mean interpolation discussed in reference [42], the Lagrange interpolation method has the lowest interpolation error for missing data. Therefore, the Lagrange interpolation method was used to interpolate missing values of ship latitude, longitude, heading and speed.
## 2 Methods for Removing Rain Streaks Based on Single-Frame Images
### Single-Frame Image-Based Methods for Removing Rain Streaks Based on Image Decomposition  
In earlier works, [PERSON] et al. adopted the idea of layer separation to separate the components of the high-frequency part, representing rain through dictionary learning to obtain an image without streaks. Dictionary learning and sparse coding consumed most of execution time and increased computational complexity [6]. [PERSON] et al. improved this to propose a discriminative sparse coding method based on an analysis of the components of the image's morphology. The aim is to learn the dictionary using the rainy image and that without streaks. Discriminant sparse coding is then used to separate the background image to remove the rain streaks. Discriminant sparse coding separated the background image to remove the rain streaks. The ambiguities between the low-pass frequency components of the image layer and the rain layer cannot be solved [7]. [PERSON] et al. [8] proposed a unidirectional global sparse model that used the alternating direction method of multipliers to remove rain streaks and optimize the complexity of the algorithm. However, when the number of rain streaks in the image was high, the effect of removal was not satisfactory. The method proposed by [PERSON] et al. [9] used the layer separation idea based on the Gaussian mixture model, which could model the background and rain streaks separately for adapting to multiple directions and scales of rain streaks.
## 5 Discussion  
In this study, we conducted an in-depth exploration of Federated Learning (FL) methodology across heterogeneous nodes, offering critical insights into its application within the context of 6G technology. Our investigation revolved around a hardware-software integrated FL model, ingeniously leveraging a combination of Raspberry Pi devices and virtual machines as FL clients, each equipped with unique datasets sourced from the CIFAR10 dataset, a widely accepted benchmark in image classification. The experiments were meticulously designed to mirror real-world scenarios, addressing multifaceted challenges including varying computation resources, uneven dataset distributions, and the heating issues inherent in wireless devices.  
A pivotal aspect of our research involved examining the impact of cooling mechanisms on training accuracy, elucidated in detail in Section 4.1. The insights garnered from Figure 5 underscore the significance of cooling devices in expediting model convergence, highlighting their practical relevance, especially in resource-constrained environments. Additionally, we delved into the complexities of heterogeneous client compensation, meticulously examining asymmetric data distribution scenarios, both with and without random selection, compared to a Centralized Benchmark, as delineated in Section 4.2 (Figures 6-8). These analyses illuminated the nuanced dynamics of FL performance, emphasizing the intricate balance required in distributing training data among disparate nodes. Furthermore, our study probed the issue of overfitting in FL, a critical concern often encountered in decentral  
Figure 19: TL with 2 clients.  
Figure 20: TL with 3 clients.  
ized learning paradigms. Through Figure 9, we identified the challenge and subsequently addressed it by incorporating random selection strategies, showcased in Figures 10-12, thereby mitigating overfitting risks while optimizing model generalization. A comprehensive exploration into the scalability of FL was presented in Section 4.3, analyzing the impact of increasing the number of users on the system's performance (Figures 13-15). This analysis provided valuable insights into FL's scalability potential, crucial for its adoption in large-scale, dynamic environments. The effectiveness of pretraining in enhancing accuracy rates was explored in Section 4.4, revealing significant improvements showcased in Figures 16-18. Pretraining emerged as a powerful technique, elevating the model's performance and showcasing its potential for optimizing FL outcomes. Finally, in Section 4.5, we delved into Transfer Learning's potential, evaluating its impact with varying numbers of clients (Figures 19 and 20). The results underscored Transfer Learning's capacity to enhance FL model performance, particularly when faced with diverse client configurations.
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 4 Precise Three-Impulse Transfer Trajectory Determination
### Perilune to the Boundary of SOI
Thus, the state of the probe at the boundary of the SOI of the Moon that satisfies the given constraints at the perilune is obtained as \(X_{\text{SOI}}=[\tau_{\text{SOI}},\upsilon_{\text{SOI}}]\). Using this state as the initial value, perform a high-precision integration on the trajectory in the SOI of the Moon, and the perilune state \(\mathbf{X}_{M}=[\mathbf{r}_{M},\mathbf{v}_{M}]\) is obtained. When the probe applies the departure impulse from the lunar circular orbit with altitude \(r_{pm}\) to insert the high-precision Moon-to-Earth transfer trajectory, the required velocity increment is  
\[\Delta\mathbf{v}_{M}=\left(\|\mathbf{v}_{M}\|-\sqrt{\mu_{M}/\left(R_{M}+r_{pm}\right)} \right)\cdot\mathbf{v}_{M}/\|\mathbf{v}_{M}\| \tag{28}\]  
where \(R_{M}\) is the radius of the Moon. The steps for precise transfer trajectory design in the SOI of the Moon are shown as module M4 in Figure 3.
# Endoscopy at Bedside in Isolated Patients with Severe COVID-19: Our Approach during the Pandemic
## 3 Our Experience  
Our institute is a tertiary referral center for severe COVID-19 patients thanks to our innovative instrumentation, including an extracorporeal membrane oxygenation (ECMO) system. During the outbreak of SARS-CoV 2, we admitted 95 patients with severe COVID-19 at our ICU-dedicated department (ICU-COVID area). We apply a multidisciplinary approach to these patients when they need an invasive procedure, which is usually performed in a dedicated room with radiological instrumentation and lead-shielded walls, such as operating or endoscopic theaters. When possible, it was decided to perform a less traumatic and invasive procedure at bedside in order to avoid patient transportation, so reducing, as much as possible, any unnecessary insults and risk of contamination out of the COVID-department. Specific personal protective equipment (PPE) for COVID-19, including masks and complete medical overalls, was assigned to those who took part in the endoscopic procedures at bedside. Furthermore, they had the chance to get dressed and undressed in separated and dedicated rooms in order not to spread the virus by contamination.  
Among the 25 patients needing endoscopy--and for some, the procedures were performed more than once--we performed a total of 61 procedures at bedside in the ICU-COVID area. Fifty of them were gastroscopies (EGDS), performed mostly after clinical suspicion of trachea-esophageal fistula (40%) and gastrointestinal (GI) bleeding (30%). One case of EGDS showed a gastric perforation, which was treated with endoscopic sutures. Colonoscopies were performed mainly for GI bleeding (85.71%). We also performed more advanced and technically complex endoscopic procedures at bedside: three (4.81% of total procedures) endoscopic retrograde cholangiocarcinoma-rectography (ERCP), all of them for complications of biliary lithiasis. One patient developed a walled-off pancreatic necrosis (WOPN), which needed an endoscopic ultrasound (EUS)-guided drainage through apposition of a luminal apposing metal stent (LAMS). Patients and procedures characteristics are summarized in Table 1. Our findings reflect similar results from the literature, even if other approaches were not well described. Nevertheless, we observed that our procedures at bedside were safe, and patients' mortality was mainly due to SARS-CoV2 infection and its systemic complications.