esa-sceva/satcom-chunk-collection huggingface dataset:
# Prototype Design and Performance Tests of Beijing Astronaut Robot
## 3 Testing Results
### Motion Performance Test of A Single Arm  
The motion performance of a single arm is the basis for the robot to complete all the tasks. Thus, we test the motion performance of a single arm to get the properties of the robot, like the motion accuracy, and the loading capability. We test the motion performance of a single arm under both no-load and load states by sending motion instructions and obtain the actual motion of joint from the encoder feedback.  
The end effector of one arm holds the handrail while the other two are unmounted from the torso of the robot. We hope to test all joints at the same time, and all joint angles are set to the same value. Simple geometric analysis shows that when the joints of the single arm move from \(0^{\circ}\) to \(30^{\circ}\), the arm of the robot reaches a harsh condition, which can lay a foundation for the following load test. In the actual motion of the robot, we want the robot's displacement, velocity, and acceleration to change smoothly, and we can give the first and last values of the three. Thus, in the test, every joint rotates smoothly from using a fifth-order polynomial.  
Figure 7: The actual test environment of the robot.  
The robot finishes the planned motion under the no-load condition; the process is shown in Figure 8.  
In the process, the expected angles sent by the central controller and actual motion angles measured by the encoder of every joint is shown in Figure 9. The location of joints 1-4 are shown in Figure 3. It can be seen that the expected and actual angles are consistent, basically, and the maximal error is 0.03\({}^{\circ}\).  
In order to check the performance of a single arm in the normal working condition, we add a 20 kg load, which is equivalent to the other two arms on the torso, and the process of motion is shown in Figure 10. Although the mass distribution is not exactly the same as the case where all three arms of the robot are installed, they are basically similar. Compared to the actual working conditions, such a loading method results in a slightly poor mass distribution causing a higher requirement for the single arm load capacity. Therefore, the load test can reflect whether the load capacity of each joint of one arm can meet the needs. It is proved that the arm can complete the planning motion well under the 20 kg load, and the maximal error is 0.04\({}^{\circ}\).  
Figure 8: The motion of single arm under the no-load condition. (**a**) Motion start; (**b**) In the process of motion; (**c**) Motion complete.  
Figure 9: The motion of four joints under no load.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 5 Mass Storage
### Adjoint Solutions
#### 5.1.2 Goal-Oriented Error Estimation  
For goal-oriented adaptivity, we consider solving the PDE (7) by a Galerkin approximation,  
\[\text{find }x_{h}\in X_{h}\text{ such that }c(x_{h};\varphi_{h})=0\quad\forall \varphi_{h}\in\Phi_{h}, \tag{12}\]  
with suitable finite dimensional subspaces \(X_{h}\subset X,\Phi_{h}\subset\Phi,Z_{h}\subset Z\). Here the functional \(J\) measures some quantity of interest, e.g., the solution's value at a certain point, or in case of optimal control problems the objective, with the aim that  
\[|J(x_{h})-J(x)|\leq\epsilon. \tag{13}\]  
The dual weighted residual (DWR) method [84; 85] now seeks to refine the mesh used to discretize the PDE by weighting (local) residuals with information about their global influence on the goal functional \(J\)[86]. These weights are computed by the dual problem  
\[\text{find }p\in Z^{*}\text{ such that }c_{x}^{*}(p;x_{h},\varphi)=J_{x}(x_{h}, \varphi)\quad\forall\varphi\in\Phi^{*}, \tag{14}\]  
which depends on the approximate primal solution \(x_{h}\), which therefore needs to be stored.  
The effect of compression on error estimation is illustrated in Figure 9. For simplicity we use a linear-quadratic elliptic optimal control problem here (Example 3b in [87]), with the objective as goal functional. Extension to time-dependent problems using the method of time layers (Rothe method) for time discretization is straightforward. Meshes were generated using weights, according to [PERSON] [87], for estimating the error in the reduced functional \(J(y(u_{h}),u_{h})-J(\tilde{y},\tilde{u})\), as well as due to [PERSON] et al. [85] for the all-at-once error \(J(y_{h},u_{h})-J(\tilde{y},\tilde{u})\). The compression tolerance for TCUG was chosen such that the error estimation is barely influenced, resulting in only slightly different meshes. For the final refinement step, i.e., on the finest mesh, compression resulted in data reduction by a factor of 32. Thus, at this factor the impact of lossy compression is barely noticeable in the estimated error as well as in the resulting mesh, and is much smaller than, e.g., the influence of the chosen error concept for mesh refinement. Numerical experiments using various compression techniques can be found in [88]. Instead of the ad-hoc choice of compression tolerances, a thorough analysis of the  
Figure 8: Adaptively chosen quantization tolerances \(\delta\) and corresponding compression factors for the monodomain example (9), (10) using BFGS. The relatively small adaptive compression factors are due to using worst case error estimates in (11), as well as a fixed maximum mesh size.  
influence of compression error on the error estimators is desirable; this is, however, left for future work.
# System Design and Experimental Study of a Four-Roll Bending Machine
## 5 Experimental Validation
### Experimental Validation of Rolling Accuracy  
To evaluate whether the rolling accuracy of the developed four-roll bending machine meets the requirements, this study conducted a rolling accuracy experiment. Based on the roller gap of the machine, aluminum profiles with specifications of 20 mm \(\times\) 20 mm  
Figure 18: (**a**) Four-Roll Bending Machine Schematic; (**b**) Test Profiles for Machine Error Experiment; (**c**) Test Profiles for Rolling Accuracy Experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group Number** & **Serial Number** & **Side Roll Displacement** & **Rolling Radius (mm)** & **Maximum Error (\%)** \\ \hline \multirow{4}{*}{1} & 1 & & 5620.04 & \multirow{4}{*}{0.72} \\  & 2 & 10 & 5612.56 & & 0.72 \\  & 3 & & 5579.39 & \multirow{4}{*}{0.96} \\  & 4 & & 3977.93 & \\ \multirow{4}{*}{2} & 5 & 15 & 3983.51 & & 0.96 \\  & 6 & & 3944.99 & \multirow{4}{*}{0.81} \\  & 7 & & 2373.44 & \\ \multirow{4}{*}{3} & 8 & 20 & 2354.32 & & 0.81 \\  & 9 & & 2370.29 & & \\ \hline \end{tabular}
\end{table}
Table 9: Analysis of Experimental Results for Machine Error.  
\(\times\) 1500 mm were selected as the experimental material. To ensure the representativeness of the experimental results, five roll bending tests were conducted using preset radii of 1500 mm, 2000 mm, 2500 mm, 3000 mm, and 3500 mm, with each radius corresponding to one test. The experimental results, as shown in Table 10, include the measured actual bending radii and corresponding error values. The results indicate that the developed rolling machine achieves an average rolling error of 0.798% with an error range controlled within 0.84%, satisfying the precision requirements for profile rolling and aligning with the expected objectives. The test profiles used in this experiment are illustrated in Figure 18c. The roll bending accuracy error is defined by formula (24). To further evaluate the accuracy, comparisons were made between this machine and traditional roll bending machines, with relevant data presented in Table 11. The comparison indicates that the average error of traditional roll bending machines reaches 3.676%, which is relatively large and fails to meet the bending requirements for high-precision components. Therefore, the roll bending machine developed in this study demonstrates high accuracy and can satisfy the bending demands of high-precision components.  
\[Error=\frac{\Delta Radius}{PresetRadius}\times 100\%. \tag{24}\]
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 3 Materials and Methods
### Performing NMS Later
#### 3.1.2 ID Match  
The performance of multi-target tracking largely depends on the accuracy of the detection frame. When the detection frame and the trajectory are ID matched, there are great differences in the resolution and confidence of the detected target frame. Tests on a large number of datasets show that boxes with lower confidence account for a large proportion. Moreover, it is difficult to match the boxes with low confidence, and it is easy to have disordered matching or unsuccessful matching. In the method of this paper, the detection boxes with higher scores are first matched, and then the boxes that are not successfully matched in the first match are matched with the boxes with low scores together. After the two matches are completed, the high-scoring box that has not been successfully matched is regarded as the new target of the frame \(t\) as a new track. A low-scoring box that still does not match is considered poor quality detection and is removed.
# Thermally Induced Displacements and Rotations of Pillars for Precise Geodetic Measurements
###### Abstract  
In this paper, we analyze the displacements of a geodetic reference pillar due to thermal loading, which typically occurs when the sunlit side of the pillar heats up more than the shaded side. This temperature differential induces bending of the pillar, resulting in the horizontal displacement of the screw used for forced centering of the instrument. Measuring displacement in the field is challenging, as it is difficult to thermally isolate the displacement sensor mount from the environment, whereas measuring rotations is much easier. Under controlled laboratory conditions, we measured the inclination of the plate with the forced-centering screw and simultaneously recorded the displacements near the top of a test pillar during a heating and cooling cycle totaling 10 h. During the heating phase, the heated side of the pillar recorded a temperature rise of 25.4 K, which led to a lateral displacement at the top of the pillar of approximately 1 mm. We found excellent agreement between the displacements calculated from the inclination and the directly measured displacements. The deviation between the calculated and measured displacements was less than 0.1 mm, which confirms the precision of the indirect method. Our results demonstrate that using an isolated inclinometer and converting the measured inclination values into displacements provides a representative characterization of the behavior of a pillar for precise geodetic measurements.  
geodetic reference pillar; thermal loading; bending; displacements; rotations; measurements 2020
## 2 Materials and Methods
### Animals and Experimental Design  
Twenty-eight adult male albino Wistar rats, each aged 12 weeks old and weighing 220-260 g, were obtained from the Pasteur Institute in Algiers. These rats were individually housed in plastic cages with bedding, and they had continuous access to a basal diet and tap water. The animals were kept under fixed housing conditions (12 h light/dark cycles),temperature (20-25 \({}^{\circ}\)C), and humidity 40%. After a two-week adaptation period, the rats were divided into four equal groups, each consisting of seven rats.  
The first group, serving as the control group (C), received distilled water by gavage. The volume of SiNPs injected intraperitoneally into the rats was dependent on the weight of the rats. Rats in the second group were subjected to an intraperitoneal injection of 50 mg/kg of SiNPs (represents 1/5 of the median lethal dose LD50 = 262.45 mg/kg) [33; 34]. The third group received 50 mg/kg of SiNPs [34] intraperitoneally and were simultaneously treated with gavage with 2 mL/kg of oleaster oil (SiNPs + oleaster oil). The last group received 2 mL/kg of oleaster oil [35].  
Following five weeks of treatment, under anesthesia, the rats were euthanized and dissected. The liver, kidneys, and heart were removed, cleaned in isotonic saline, and allowed to dry. Fragments of each tissue sample were preserved for the assay of various parameters, including malondialdehydedehyde (MDA), superoxide dismutase (SOD), reduced glutathione (GSH), glutathione S-transferase (GST), and catalase (CAT).  
For histological analysis, a second tissue fragment from each of the three organs was fixed in 10% neutral buffered formalin.
# Machine Learning in Detecting COVID-19 Misinformation on Twitter
## 5 Conclusions  
In an attempt to answer the first research question, a literature review was conducted to investigate existing ML models to detect healthcare misinformation on SNS. As demonstrated in previous sections, there exists a research gap in this area. The performance of previous ML models was unsatisfactory. To bridge the gap, this study proposed a framework for detecting COVID-19 misinformation spread through social media platforms, especially on Twitter. In an attempt to answer the second research question of whether the proposed ML models outperform existing ones, three ML models were implemented. The performance of the proposed models presented in this paper was evaluated using various metrics, namely, precision, recall, F measure, and PR-AUC. The models were simulated  
Figure 5: PU_AUC comparison: proposed models vs. models in the literature [78].  
Figure 6: Comparison between proposed models with respect to all performance evaluation metrics.  
using the CoAD misinformation dataset, a healthcare misinformation corpus. In order to avoid a biasing effect, the sampling method was also employed in this work. Our proposed misinformation-detection models more accurately and effectively classified COVID-19-related misinformation available on Twitter. The proposed models are well-suited for misinformation detection in both balanced and imbalanced corpora.  
This research has important practical implications. Misinformation is a significant problem on social media, especially when it is health-related. Trusted sources of health information could be a matter of life and death, as in the case for COVID-19. Therefore, this works intends to introduce misinformation-detection models with increased accuracy compared to that of others proposed in the literature [78]. Social media platforms could consider our approach to improve shared online content.  
Several limitations exist in the current research. First, the provided evidence is restricted to one dataset and needs to be tested on other datasets related to different areas. Next, it is important to test the generalizability of our results by using misinformation from other social media platforms. Lastly, our model was purely algorithmic, with no evidence of external validity. Future research should consider addressing the limitations of this study. This work is a starting point to further improve misinformation-detection algorithms on social media networks.  
Conceptualization, M.N.A.; methodology, M.N.A.; software, M.N.A.; validation, M.N.A. and Z.M.A.; formal analysis, M.N.A. and Z.M.A.; investigation, M.N.A. and Z.M.A.; resources, M.N.A. and Z.M.A.; data curation, M.N.A.; writing--original draft preparation, M.N.A. and Z.M.A.; writing--review and editing, Z.M.A. and M.N.A.; visualization, M.N.A.; supervision, M.N.A. and Z.M.A.; project administration, M.N.A. and Z.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
The mechanical properties of polyamide 6 glass fiber (PA6/GF) long-fiber-reinforced thermoplastics (LFT) composites were characterized by studying the process conditions in terms of manufacturing methods (direct extrusion and pulltrusion) and material characteristics (void content and fiber volume fraction). The LFT composites prepared through the pulltrusion process have higher mechanical properties than those prepared via the direct extrusion process. The PA6/GF composite prepared via pulltrusion had the tensile and flexural strengths of 233 MPa and 338 MPa, respectively. The impact strength measured using the Izod method was 296 J/m, which is 64% higher than that of the composite fabricated via the direct process. The optical microscope images showed that the glass fiber length of the pulltruded composites is longer than the fiber length of the direct composites, leading to higher mechanical properties of the LFT composites prepared through the pulltrusion process. Moreover, the interfacial shear strength between the resin and the fiber, measured via single fiber pullout tests, can account for the higher fiber reinforcing efficiency. If the void content of a composite is sufficiently small to not be detrimental to the composites, the mechanical properties are observed to be proportional to the fiber volume fraction of the composites.  
Keywords:glass fiber; polyamide 6; fiber-reinforced composite; thermoplastics; mechanical properties +
Footnote †: journal: Applied
# Target Detection of Passive Bistatic Radar under the Condition of Impure Reference Signal
## 3 Proposed Target Detection Method under the Condition of Impure Reference Signal
### Target Detection Method Based on Impure Reference Signal
(2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] A Joint Low-Rank and Sparse Method for Reference Signal Purification in DTMB-Based Passive Bistatic Radar. _Sensors_**2021**, _21_, 3607. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2011) [PERSON]; [PERSON] Reconstruction of the reference signal in DVB-T-based passive radar. _Int. J. Electron. Telecommun._**2011**, _57_, 42-48. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Performance of the least squares filter for passive radar interference cancellation applications. _IET Radar Sonar Navig._**2017**, _11_, 1208-1215. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] Direction of arrival estimation in passive radar based on deep neural network. _IET Signal Process._**2021**, _15_, 612-621. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] An inter-subband processing algorithm for complex clutter suppression in passive bistatic radar. _Remote Sens._**2021**, _13_, 4954. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] Multistage least squares algorithms for clutter suppression in airborne passive radar based on subband operation. _IEEE Trans. Aerosp. Electron. Syst._**2022**, _59_, 1893-1909. [CrossRef]
* [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON]; [PERSON] A multistage processing algorithm for disturbance removal and target detection in passive bistatic radar. _IEEE Trans. Aerosp. Electron. Syst._**2009**, _45_, 698-722. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Sliding extensive cancellation algorithm for disturbance removal in passive radar. _IEEE Trans. Aerosp. Electron. Syst._**2016**, _52_, 1309-1326. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Coherent integration loss due to pulses loss and phase modulation in passive bistatic radar. _Digit. Signal Process._**2013**, _23_, 1265-1276. [CrossRef]

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# Screen-Based Sports Simulation Using Acoustic Source Localization +
###### Abstract  
In this paper, we introduce a novel acoustic source localization in a three-dimensional (3D) space, based on a direction estimation technique. Assuming an acoustic source at a distance from adjacent microphones, its waves spread in a planar form called a planar wavefront. In our system, the directions and steering angles between the acoustic source and the microphone array are estimated based on a planar wavefront model using a delay and sum beamforming (DSBF) system and an array of two-dimensional (2D) microelectromechanical system (MEMS) microphones. The proposed system is designed with parallel processing hardware for real-time performance and implemented using a cost-effective field programmable gate array (FPGA) and a micro control unit (MCU). As shown in the experimental results, the localization errors of the proposed system were less than 3 cm when an impulsive acoustic source was generated over 1 m away from the microphone array, which is comparable to a position-based system with reduced computational complexity. On the basis of the high accuracy and real-time performance of localizing an impulsive acoustic source, such as striking a ball, the proposed system can be applied to screen-based sports simulation.  
acoustic source localization; impulsive sound; delay and sum beamforming; microelectromechanical system; screen-based sports simulation +
Footnote †: journal: Accepted: 5 June 2019; Accepted: 23 July 2019; Published: 24 July 2019
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 8 Behavioral Anomaly and Change Detection
### Point Anomaly Detection
#### 8.1.2 Isolation Forest-Based Anomaly Detection  
We also conduct point anomaly detection using Isolation Forest as a baseline. Specifically, given the daily sleep profile representation, we apply the Isolation Forest using the specific user's data only. The first two weeks of sensor readings after the AAL system deployment are considered to be the initial learning phase, after which we run the Isolation Forest algorithm daily on data from the past \(N\) days and the current day (test day) to compute anomaly scores. As in Section 8.1.1, we forget data that is older than \(N\) days for the model to adapt to new behavior patterns. A test day is considered to be abnormal if its anomaly score is larger than \(M_{IS}+\alpha\times ST_{IS}\), where \(M_{IS}\) and \(ST_{IS}\) are the mean and standard deviation of the isolation scores of the reference data (past \(N\) days). Figure 14 (bottom) depicts the detected abnormal days (\(\alpha=2\), \(N=60\)) as red dots.
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 5 Results and Analysis
### Executed Data of Trusted Nodes  
Performance of TETES is analyzed by computing the total amount of data for all those offloaded tasks that are executed successfully for varying capacity of fog node and for varying number of offloaded tasks as shown in Figures 8 and 9 respectively. The performance of TETES is analyzed in terms of executed data and is compared with STF, LTF, and FCFS.  
Results shown in Figure 8 verify that the data executed by TETES is more than the other three schemes. The results further show that with the increase in processing capacity of the fog node, the amount of data executed by all algorithms increases. However, offloaded task data, that are executed by TETES is significantly large as compared to the other three schemes. This is due to the fact, that TETES not only eliminate the malicious nodes tasks but also efficiently executes the offloaded tasks by applying the 0/1 knapsack algorithm.  
Figure 9 shows the executed amount of data against varying numbers of offloaded task requests received by the fog node. The results show that the amount of data executed by the proposed TETES is more than the other three schemes. The results further show that with the increase in the number of offloaded tasks, TETES executes more data. However, the amount of data executed by the other three schemes remains almost the same with the increased number of nodes and does not show an increasing trend as seen for TETES. This is due to the optimal selection of offloaded tasks from the offloaded tasks' range within the execution capacity of the fog node.  
Figure 8: Total Amount of Processed Data for Varying Capacity of Fog Nodes.