esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design
### Docking Mechanism  
The principle of the docking mechanism is based on a parallel gripper that can connect or separate two parts, e.g., two robot modules or a robot module and a station. The docking mechanism consists of two separate parts, an active part at the front of one robot module and a passive part at the back of another robot module, as shown in Figure 4. In the middle of a passive part, there is a rectangular solid, where two conoid grooves on each side are designed, and there are two corresponding conoid pins on each side of the active part. When docking, the conoid pins are inserted into the grooves. Even if the pin and groove are not aligned, within an expected certain displacement distance (\(d<2\) mm, as shown in Figure 4b) and displacement orientation (\(a<6^{\circ}\)) in Figure 4c, the conoid form can guide the two parts to completely match. The passive system can work without power.  
As shown in Figure 4a, the active part can drive two opposing jaws (XI) to travel in parallel to make the pin (XIII), which is inserted into or pulled out of the grooves (IV). The closing and opening of the jaws are achieved by means of a pair of bidirectional lead screws (X) and nuts (IX). The two opposite sections of the lead screw are left-hand and right-hand thread-mated, respectively, with two lead nuts. The lead nut is installed into a sleeve inside the connecting structure, rigidly connected to a sliding block (VIII), resulting in limiting the rotation but allowing the axial movement of the nut. Meanwhile, the upper and lower sliding blocks on the frame of the active part can support the docking mechanism for a large payload. The lead screw is driven by a DC motor via gear transmission. A linear  
Figure 3: The overall design of SMMRob modules, including the motion module (default) and sensing module. In the self-assembled snake-like system, the motion module is a component of the body, whereas the sensing module is used as the head. The unit of length is mm.  
potentiometer is equipped onto the frame to measure the displacement of the nut to provide feedback of for the opening and closing of the jaws. After docking, the spring contact (XII) of the active part can touch the contact socket of the passive part to achieve CAN bus communication. The lead screw and nuts are self-locking and therefore the docking mechanism will maintain a stable connection and will not unexpectedly separate when the lead screw is at rest with the motor power turned off.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 4 Methodology
### Analysis  
We collected both quantitative and qualitative data. For the quantitative data, we used the Wilcoxon signed-ranks test to analyze the ordinal data for users' satisfaction and concerns regarding using standard email and an end-to-end encryption email. The Wilcoxon signed-ranks test is a nonparametric statistical hypothesis test [39]. Since, the sample size in our study was small, and the data were not normally distributed, we used this test over the Student's \(t\)-test. We used Statistical Package for the Social Sciences (SPSS) for quantitative analysis. For the qualitative data, we utilized an inductive approach. The data were first coded independently by two researchers in Microsoft Excel to avoid any bias and to capture all possible codes. The coding criteria used were as follows. We looked for patterns in the data to develop themes that could explain the patterns in our data. These patterns were then categorized into themes. We then discussed, refined, and updated the two sets of coded data to resolve any disagreements. Therefore, the [PERSON]'s Kappa (inter-rater agreement) test was not conducted [40].
# Using Autoregressive with Exogenous Input Models to Study Pulsatile Flows
## 4 ARX Model Results
### Test for Gaussianity and Linearity  
According to the results, our model proved to have better performance when dealing with the pressure difference than with the opening area. Hence, as a final step, we analyzed the prediction error vector \(\tilde{y}_{t}=y_{t}-\hat{y}_{t}\) obtained in \(S_{v}\) to verify its distribution (Gaussian or non-Gaussian) and its possible non-linear behavior in some sections. The test proposed by [PERSON] [49] assumes two different hypotheses: the first one is to verify if the process is Gaussian distributed or not; then, if the first hypothesis is accepted, the second one consists of verifying whether the process is linear or not. Therefore, for Gaussianity, we have a Probability of False Alarm (PFA) parameter that indicates the probability that our data have a non-zero bispectrum. Hence:  
**Hypothesis 1:**_If PFA \(\leq\) 0.05; the process is non-Gaussian, i.e., non-zero bispectrum._  
**Hypothesis 2:**_If PFA \(>\) 0.05; the process is Gaussian, i.e., zero bispectrum._  
Then, if Hypothesis 1 is true, we can test \(\tilde{y}_{t}\) to verify if it contains linear or non-linear behavior. Since this test assumes a non-zero bispectrum, it should only be considered if and only if \(\tilde{y}_{t}\) obtained a \(PFA\leq\) 0.05. Thus, \(R_{est}\) is the estimated and \(R_{th}\) is the theoretical interquartile range of a chi-squared distribution:  
**Hypothesis 3:**_If \(R_{est}>>R_{th}\) or \(R_{est}<<R_{th}\), the process is non-linear._  
**Hypothesis 4:**_If \(R_{est}\approx R_{th}\), the process is linear._  
Under these assumptions, we use the High Order Spectral Analysis (HOSA) toolbox for MATLAB\({}^{\circledR}\) to conduct the test. Results in Table 6 show that all our \(\tilde{y}_{t}\) vectors are non-Gaussian distributions and contain non-linear processes within them. Conclusively, for providing a more accurate analysis, future experiments should include a model that considers the estimation of these potential non-linear processes as well. For more details, see [49; 50].
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 3 Method
### RHPNet Architecture  
HRNet [4] is a highly representative high-resolution multi-scale feature fusion network, as shown in Figure 1. The network consists of four stages, each of which incorporates output features from different scales. The output of the final stage is directly fused through an information interaction unit, resulting in the generation of the final heatmap of keypoint locations on the human body. The output features from different stages represent semantic information at different levels. Shallow-level features contain information on crucial details, as is essential for the task of human pose estimation. However, the method for directly aggregating features of the final stage does not incorporate the fusion of shallow-level features. Instead, in order to fully leverage the detailed information contained in shallow-level features, this study proposes an RFSP module and integrates it with HRNet. This approach leads to an improved high-resolution multi-scale feature fusion network called Running Human Posture Network (RHPNet) that effectively enhances the localization accuracy of occluded joints and edge joints in the model.  
RHPNet is a pose estimation network for multi-person instances, and its architecture and processing flow are illustrated in Figure 2. Firstly, the input image is fed into a backbone network consisting of an improved HRNet (a residual connection was added to HRNet). Subsequently, refined hybrid features are generated by fusing the shallow features with the deep features generated by the backbone network. Finally, the hybrid features and shallow features are passed through the RFSP module for processing, resulting in K heatmaps, with each heatmap corresponding to a keypoint.  
RHPNet integrates multi-scale methods [4; 16] and leverages spatial pyramid pooling [17] to enhance the model's performance in multi-scale feature extraction and fusion. Furthermore, its modularized design makes it easy to implement and train.
## 2 Literature Overview
[27] focus on processing raw speech data with convolutional layers to extract discriminant features for speaker recognition. A novel architecture called SincNet was proposed in Ref. [28]; this architecture is based on the convolutional layer of the same name. This layer convolves bandpass filters represented in time domain as sinc functions with raw audio frames. The obtained results exhibited EER values of 0.85 and 0.96 using the TIMIT and LibriSpeech databases, respectively. [PERSON] et al. [29] concatenated features extracted from a SincNet layer with embeddings generated by a pre-trained X-vectors system. These new features are fed to a set of fully connected layers, achieving an EER of 3.56 in the VoxCeleb1 database. An interesting study on the effect that different softmax based loss functions can have on the performance of a SincNet model can be found in Ref. [30]. The authors present a detailed comparison on TIMIT and LibriSpeech datasets. [PERSON] et al. [9], trained a CNN, a Deep Neural Network (DNN) and two Time-delay Deep Neural Networks (TDNN) with a multitask setting for speaker verification and anti-spoofing. The first three models were trained with Crossentropy loss function and one TDNN with a modified Triplet loss function employing different cepstral speech features. Their results compared the performance of all models on both tasks employing the ASVspoof 2017 and ASVspoof 2019 databases. The models trained with Crossentropy yielded EER values below 7.5% for speaker verification, less than 14% for anti-spoofing, and a joint verification of less than 12.7%, while the TDNN with Triplet loss yielded values below 6%, 12.3%, and 10.6% respectively. A detailed overview of Deep Learning on speaker recognition is found in Ref. [31]. [PERSON] et al. [32] present a comprehensive survey on audiovisual biometric recognition and attack detection. Refs. [6; 7; 33] are comprehensive reviews on recently published multimodal biometric systems. Table 1 summarizes the relevant work in chronological order about multimodal biometric systems whose content was considered important to our research. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Author** & **Modalities** & **Features** & **Classifier** & **Fusion method** & \ \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  \\ \hline \hline \multirow{7}{*}{[PERSON] et al., 2017} & \multirow{7}{*}{Its and face,} & \multirow{7}{*}{Fusion method} & Fully & \multicolumn{2}{c}{CSAL-Biector} \\  & & & & 13.925, & Verification \\  & & & & & ND-246, 405, & \\  & & & & & 1358, & \\  & & & & & 1358, & \\  & & & & & & 1358, & \\  & & & & & & & \\
# Comparative Study of Physics Engines for Robot Simulation with Mechanical Interaction
## 2 Physics Engine Review
### MuJoCo  
Multi-Joint dynamics with Contact (MuJoCo) is a commercial physics engine that requires a license. The collision detection algorithm in MuJoCo uses a general-purpose convex collider implemented by libccd [24], which is an open-source collision detection library, so it supports primitive-shape and convex mesh objects, but not nonconvex mesh objects. Unlike other physics engines, MuJoCo does not approximate the friction cone, but converts the contact problem into a convex optimization problem by relaxing the nonpenetration constraint between colliding objects [6; 25]. To solve this convex optimization problem, MuJoCo provides the conjugate gradient method, Newton's method, and PGS, which used to be the default. Similar to other physics engines, MuJoCo also reduces the contact points to reduce the computational burden by keeping only the first 16 contact points. In MuJoCo, two integrators are currently available, which are the semi-implicit Euler method and the fourth-order Runge-Kutta method. Additionally, it uses the generalized coordinate representation.
## 4 Results and Discussion
### Comparison of Computational Cost  
The computational costs of the optimization are quantified while using the wall time of High Performance Computer (HPC) that was running to more systematically compare the effectiveness of the methods. The computation is conducted using two 64-bit Intel Xeon Cores and 2 Gigabytes memory running on SUSE Linux operating system, and the codes are compiled with the Intel compiler. Figure 7 shows the running time of 20 \(\times\) 30, 40 \(\times\) 60, and the coordinate method for a different number of wind turbines. From the results, it is found that the 40 \(\times\) 60 grid method consumes much more (almost two- or three-fold) computational time than the other two methods. In comparison, the increase of computational time with the coordinate method is faster than that with the 20 \(\times\) 30 grid method. When there are less wind turbines, the 20 \(\times\) 30 grid method consumes slightly more time than the coordinate method. When the number of wind turbines further increases, the coordinate method consumes more time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of Turbines** & **40** & **41** & **42** & **43** & **44** & **45** & **46** \\ \hline Relative power increase using (40 \(\times\) 60 grid) & 1.13\% & 1.4\% & 1.19\% & 1.16\% & 1.53\% & 1.42\% & 1.49\% \\ Relative power increase using coordinate method & 1.47\% & 1.69\% & 1.58\% & 1.6\% & 1.87\% & 1.84\% & 1.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Total wind farm power output increase of different number of wind turbines using refined 40 \(\times\) 60 grid method and coordinate method with respect to the coarse 20 \(\times\) 30 grid density.  
Figure 5: Wind farm efficiency results for different design methods.  
Figure 6: Total wind farm power for different design methods.
# Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks
## 4 Queuing Model and Feedback Strategy  
In this section, with consideration of sporadic wireless data generation and a limited buffer, we adopted the feedback queuing model to capture the characteristic of the retransmission mechanism. This paper adopted the feedback priority strategy and gave precedence to the feedback data. This was suitable to model the behavior of data transmission under the SW-ARQ protocol. Then, the SW-ARQ protocol avoided the reorder operation in the receiver buffer. Modeling SW-ARQ by using feedback queuing theory provided fundamental research of further dynamic modeling for the complex ARQ protocol. In the derivation, the embedded Markov chain was used to obtain the key parameters of D2D underlay cellular networks. The general queuing model with feedback is described in detail in this section.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 6 Reception Results
### Reception from L-Band Antenna System for GEO Satellites
#### 6.1.3 GEO-KOMPSAT-2A (GK-2A)  
The GEO-KOMPSAT-2A (GK-2A) satellite, or Geostationary-Korea Multi-Purpose SATellite 2A, is a meteorological satellite from South Korea operating in geostationary orbit [28]. The ground station in this research successfully received and decoded LRIT  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Satellite & Electro-L N3/N4 \\ Mode & XRTI \\ SNR & \(\sim\) 10 dB SNR \\ Az/Ele of antenna & N3: 242.6\({}^{\circ}\), 57.5\({}^{\circ}\) \\ N4: 96.3\({}^{\circ}\), 16\({}^{\circ}\) \\ Receiving antenna & 1.9 m dish antenna \\ Decoding software & Set/Dump \\ Spectrum & 3-channel visible light \\ Duration of receiving & 2-channel infrared \\ Image size & 2784 \(\times\) 2784 \\ Spatial resolution & 4.5 km /px \\ Receiving time & Electro-L N3 09.42 UTC, \\ Electro-L N4 0642 UTC \\ Electro-L N3: 76.7 E \\ Electro-L N4: 165.8 E \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters for the reception of Electro-L.  
Figure 16: Images from Electro-L N3 after the software process.  
Figure 17: Image from Electro-L N4 after the software process.  
Figure 15: Raw images from Electro-L N3.  
(Low-Rate Information Transmission) data, which include low-resolution images at a frequency of 1692.14 MHz, as shown in Figure 18. However, HRIT (High-Rate Information Transmission) signals could not be received due to the insufficient sampling rate of the RTL-SDR used in this study.  
The LRIT data received from GK-2A included only IR105 (10.4 \(\upmu\)m) and were enhanced using the infrared color enhancement technique. This process replaces a range of grayscale values in infrared imagery with a color gradient to facilitate easier interpretation from a single channel. In addition to satellite images, GK-2A also transmits other meteorological data, as exemplified in Figure 19b. The parameters in Table 8 indicate that the signals received from GK-2A had excellent strength, with an SNR of 20 dB.  
Figure 19: Images form GK-2A: (**a**) Raw image download, and (**b**) Meteorological data of surface pressure analysis map.  
Figure 18: Images from GK-2A after the SatDump process.
###### Abstract  
Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model (\(\varepsilon/\mathit{SIF_{\text{yield}}}\)), the one-leaf light-use efficiency (SL-LUE) model (\(\varepsilon_{\text{max}}\)), and the two-leaf LUE (TL-LUE) model (\(\varepsilon_{\text{msu}}\) and \(\varepsilon_{\text{msh}}\)). The three models were parameterized based on a maize-wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis-Hasting algorithm. The results showed that the values of the \(\varepsilon/\mathit{SIF_{\text{yield}}}\) and \(\varepsilon_{\text{max}}\) were similarly robust and considerably more stable than \(\varepsilon_{\text{msu}}\) and \(\varepsilon_{\text{msh}}\) for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation (\(CV\)) of \(\varepsilon/\mathit{SIF_{\text{yield}}}\) was relatively small (15.0%) and was obviously lower than \(\varepsilon_{\text{max}}\) (\(CV=27.0\%\)), \(\varepsilon_{\text{msu}}\) (\(CV=43.2\%\)), and \(\varepsilon_{\text{msh}}\) (\(CV=53.1\%\)). Furthermore, the SIF model's performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types.  
light use efficiency; solar-induced chlorophyll fluorescence; eddy covariance; photosynthesis; gross primary productivity
## 1 Introduction
However, to the best of our knowledge, no studies have simultaneously optimized the extraction conditions for PCB using ethanol or conducted large-scale economic evaluations through simulations. PCB extraction can be significantly influenced by factors such as solvent type, extraction temperature, extraction time, and solvent-to-solute ratio [12]. The traditional approach to optimizing extraction conditions typically involves the one-variable-at-a-time method, in which one independent variable is altered while the others remain constant. However, this unidimensional optimization model often requires extensive experiments and considerable time and may be ineffective for evaluating the interactive effects among process variables [21]. Response surface methodology (RSM) was proposed as a statistical experimental design technique to overcome these limitations. It is a mathematical and statistical approach for modeling and analyzing the conditions influenced by multiple variables [21]. Among the RSM techniques, central composite design (CCD), first developed by [PERSON] and [PERSON] [22], allows for the placement of experimental points at five levels for each factor [23]. The application of RSM in science and engineering has been shown to significantly reduce the number of experiments, cost, and time required; thus, it is often employed for the efficient extraction of valuable compounds derived from microalgae [24]. In the manufacturing of expensive materials such as PCB, optimizing processes can significantly reduce industrial losses. In industrial systems, competitive advantage is achieved through a process called optimization, which minimizes waste and ensures the effective use of time, cost, and materials. It is necessary to refine the optimization of conditions using reliable statistical techniques such as RSM rather than the traditional optimization approach of selecting a few single factors. Furthermore, the feasibility and economic feasibility of this strategy in industrial terms should be evaluated through simulation. Therefore, this study aimed to evaluate the effects of various extraction parameters (C-PC to ethanol ratio, extraction time, and temperature) on the extraction efficiency of PCB from C-PC using ethanol, and to determine the optimal extraction conditions through CCD. Furthermore, this is the first study to perform a techno-economic analysis required for industrial applications based on process simulation under optimized conditions.