esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Protocol  
Participants were asked not to use caffeine-containing products, cigarettes, or alcohol 72 h before the study and not to eat or drink anything other than water 12 h before the study. Measurements were carried out at normal room temperature (20 \(\pm\) 2 \({}^{\circ}\)C) in a partially isolated dim-light environment against external factors. The participants were told to rest for 20 min before the baseline measurements. Disposable Ag-AgCl electrodes were placed, and 5-min electrocardiogram signals were recorded from the participants in resting and supine posture. For this procedure, an ECG100C unit and BIOPAC Acknowledge acquisition software version 3.9 (Biopac System Inc., Santa Barbara, CA, USA) with a sampling frequency of 200 Hz were used. After electrocardiogram recording, systolic and diastolic blood pressure were measured manually by using a stethoscope (Littman Classic II, Saint Paul, MN, USA). Measurements were performed before consumption and 30 and 60 min after consumption. The caffeine group consumed 200 mg of caffeine + 0 mg of taurine; the taurine group consumed 0 mg of caffeine + 2500 mg of taurine; the caffeine + taurine group consumed 200 mg of caffeine + 2500 mg of taurine; and the control group consumed the same amount of water within 5 min. The doses of caffeine and taurine consumed were approximately the same as in a 20 oz energy drink. Caffe and taurine were given in capsule form with water. The subjects were not informed about the ingredients in the capsule.
# Lessons Learned from Investigating Robotics-Based, Human-like Testing of an Upper-Body Exoskeleton
## 3 Methods and Results
### Approach C: Specialized Human Arm Dummy
#### 3.3.2 Implementation  
For the experiments, a 3D-printed human arm dummy is created from a 3D scan of a real human upper limb. The used arm model and robotic setup can be seen in Figure 9. For the conducted experiments, a rigid arm dummy is used for the prototype, and effects caused by soft tissue effects are neglected. Additionally, a rigid adapter is designed to attach the arm dummy to an end effector of the robot.  
Similar to the previous approaches, identifying an optimal configuration of the robot and exoskeleton to execute the desired pose trajectory is challenging due to workspace constraints and physical dimensions. While changing the robotic hardware might address some issues related to a small workspace and singularity avoidance, improvements in collision avoidance may be limited. Nonetheless, a viable setup was identified for the Panda robot. More details about the experimental setup can be found in Appendix A.  
Figure 8: The concept of a human arm dummy lying inside the arm shell fastened by the exoskeleton’s standard fixation system.  
To achieve the human-like movement of the arm dummy, motion data were recorded from various users wearing the exoskeleton. Users donned a motion capture suit with X-base markers based on the Plug-in Gait full-body model from the Vicon motion capture system. Additionally, users wore the upper body exoskeleton, and to align the user's arm posture with the dummy arm, an elbow angle stabilizer was employed. Users were instructed to execute a straight lifting and lowering movement of the right arm. The recorded data were post-processed and utilized to train a DMP-based motion representation.  
Nevertheless, applying these learned motions directly to the robot is not straightforward. Given that the robot-arm dummy-exoskeleton forms a closed-loop system, generated motions must adhere to the constraints imposed by the coupling. Failure to do so may result in additional constraint forces affecting the torque profiles. Specifically, the intricate shoulder motion of a human arm and the corresponding relative motion between the human arm and the exoskeleton must be accurately captured in the motion planning to satisfy all the constraints. In our experiments employing user-recorded data, we were not able to avoid configurations of the human arm dummy that introduced significant constraint forces. However, achieving constraint-consistent motion generation in this setting poses a complex optimization problem, requiring compromising the reproduction of recorded and learned motion data with satisfying constraints introduced by the coupling. Given these challenges, we opted to record a hand-guided motion of an arm lift from the robotic setting directly and trained a corresponding DMP, following a procedure similar to the one used in the \"Direct Attachment\" approach. As in the previous experiment, the DMPs function as trajectory generators for our controller.
# A Novel Hierarchical Multi-Stable Cylindrical Structure with Superior Energy Trapping
## 1 Introduction
The outline of this paper is as follows: Firstly, a novel hierarchical multi-stable cylindrical structure (HMCS) based on cosine curved beams is designed. The designed new structure consists of two traditional multi-stable cylindrical structures (TMCSs) combined by nesting. Secondly, the effects of the snap-through behavior and geometrical parameters of the HMCS structure on the energy trapping properties and bi-stable properties are investigated. Then, a multi-stable metamaterial with a tailorable snap-through sequence is obtained by adjusting the thickness of the cosine beam. Finally, conclusions are given.
# Production of Biogas and Biomethane as Renewable Energy Sources: A Review
## 3 Results and Discussions
### Purification and Improvement of biogas
The positive aspect of this technology is that methane produced through hydrogenation is easy to transport, fully compliant with current natural gas networks and suitable for regions with low natural gas availability. Membrane separation uses semi-permeable membranes to separate the different components of biogas, based on their actual size. Membrane technology is a viable alternative to the traditional biogas upgrading system based on adsorption, presenting competitive efficiency. The operation of this technique is based on the properties of selective permeability of the membranes that allow the separation of the various components of biogas. Commercially successful membranes, such as polyimide and cellulose acetate membranes, are used [142]. Membrane fiber materials used in the gas separation technique can be classified into three groups: inorganic membranes, polymer membranes and membranes in a combined form. Drawbacks related to the use of membranes include their high cost and their degradation over time, including damage caused by vibration of colloidal solids [142; 147].
## 3 Results Analysis and Discussion
### Water Stability Test Results
#### 3.2.2 Freeze-Thaw Splitting Test Results  
The results of the indirect tensile strength from the normal temperature group (RT1), freezing and thawing group (RT2), and tensile strength ratio (TSR) are presented in Table 7 and Figure 9. Results showed that the water stability performance of asphalt mixes modified with lignin fiber and diatomite were increased by 2.6% and 6.05%, respectively, compared to the control mix. Meanwhile, the TSR of the composite asphalt mix modified with of diatomite and lignin fiber (DLFMAM) was better than the other three types of mixes, where the TSR of DLFMAM was improved about 7.5% as opposed to the values of control mix.  
In summary, the results of water stability tests indicated that the addition of lignin fiber and diatomite improved the water damage resistance of asphalt mixes. Nonetheless, the asphalt mix modified with diatomite is superior to that modified with lignin fiber. In addition, the composite asphalt mix of diatomite and lignin fiber (DLFMAM) is greater than the asphalt mix which is only modified with lignin fiber or diatomite.  
These results can be analyzed as moisture susceptibility is related to the combination of asphalt film and aggregate, as well as the additives type. After adding diatomite and lignin fiber, the parameter values of the modified asphalt mix was higher than that of unmodified asphalt mix, leading to the increase of the cohesive force between the asphalt mastic and the aggregate. Finally, it will lead to an improvement in the shear resistance and stability of mixes. In addition, the RMS and TSR of asphalt mixes modified with lignin fiber are lower than that modified with diatomite. This is likely due to the air voids; the asphalt mixes modified with lignin fiber will be harder to compact, and the void of mixes would also increase.  
In addition, Figures 8 and 9 show that the Residual Marshall Stability ratio (RMS) is higher than the tensile strength ratio (TSR). The reason is that the specimens for the Marshall Immersion test were compacted about 75 times for each side, which leads to reduce the porosity of mixes to 3-5%. As a result, it became difficult to penetrate water through the voids between aggregate and asphalt film.  
Figure 8: Results of the Immersion Marshall Stability test.  
On the contrary, freeze-thaw splitting test specimens were only compacted 50 times, so the porosity of mixes increased to 7%.
# Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters
## 5 Conclusions  
This study analyzed the engine operating conditions curve of a corn kernel harvester during operation. The relationship curves of the engine speed and feed rate, the threshing cylinder speed and feed rate, and the engine speed and threshing cylinder speed were obtained from field trials.  
The ternary quadratic regression of the orthogonal center-of-rotation combination optimization test method was used to produce an operating quality contour plot, from which a better combination of operating parameters could be found. A mathematical regression model was fitted using DPS V9.0.1 software to relate the influencing factors to the operating efficiency and operating quality. The effects of the feed rate, cylinder speed, and concave clearance on the corn kernel loss rate, impurity rate, engine speed variation rate, and cylinder speed variation rate were investigated.  
By setting the boundary conditions for the target quality indicators (a crushing rate less than 5%, an impurity rate less than 2%, a corn kernel loss rate less than 5%, and the threshold value for the engine speed and cylinder speed variation rate of 10%), a better combination of operating parameters was obtained by this model, which was a feed rate of 12 kg/s, a forward speed of 5 km/h, a threshing cylinder speed of 360 r/min, and a concave clearance of 30 mm, which was consistent with the combination of operating parameters obtained from the contour plot of the operating quality indicators. The harvester was tested in the field with this combination of operating parameters and obtained a good operating quality, with an average crushing rate of 3.91%, an average impurity rate of 1.71%, and an average corn kernel loss rate of 3.1%. The harvester operating with the combination of parameters derived from this model was able to achieve excellent operating quality and operational efficiency.  
Conceptualization, D.Z. and B.W.; validation, C.X., Y.X. and P.H.; writing--original draft, C.X.; writing--review and editing, D.Z. and B.W.; supervision, J.Z.; H.Y. and Q.Z. All authors have read and agreed to the published version of the manuscript. This research was supported by the Provincial School Joint Construction Project of Jilin Province, China (Grant no..XXGJXX2017-6); the Key Projects of Science and Technology Development Plan of Jilin Province, China (Grant no. 20170204015 NY); and the National Key Research and Development Program of China (Grant no. 2016 YFD070190103).  
Figure 10: Parametric verification quality data statistics graph.
## 3 Fast Handover Algorithm
### Moving Speed of HST  
The sampling points of the global positioning system (GPS) and the BeiDou Navigation Satellite System (BDS) are the main data sources for the calculation of the speed of HST. The source of original data needs to undergo the transformation and offset correction of coordinates. Furthermore, abnormal sampling points (above 10 m off the track curve) should be eliminated. The main fields of the calculation of the train's speed are shown in Table 1. Instantaneous speed \(V_{\text{ins}}^{j}\) (\(j=1,2,\ldots,M\)) is based on Equation (5). Where, latitude and longitude are (\(\theta_{j}\), \(\varphi_{j}\)), \(R\) is as the radius of the earth, and \(\Delta T\) is for the interval between two sampling points. The train D2706 runs from Urumqi to Lanzhou.  
\[V_{\text{ins}}^{j} = R\times(\arccos(\sin\varphi_{j}\sin]\varphi_{(j+1)}+\cos\varphi_{ j}\cos\varphi_{(j+1)}\cos(\theta_{j}-\theta_{(j+1)}))\times \tag{5}\] \[\pi/180^{\circ})/(\Delta T/3600)\]  
In the scenario of HSRs with a speed of 350 km/h and station spacing of 2.5 km, it takes about 26 s for the train to pass through the coverage area of a single station. Based on this, the train's speed \(v\) is calculated once within 26 effective cycles of data return, and the equation of calculation is  
\[v = \frac{V_{\text{ins}}^{j}+V_{\text{ins}}^{(j+1)}+\ldots+V_{\text{ins}}^{( j+25)}}{26} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Train** & **Data Return Time** & **Longitude** & **Latitude** & **Instantaneous Speed (km/h)** \\ \hline \multirow{8}{*}{D2706} & 9 December 2020 17:06:41 & 97.286705 & 40.104435 & 209.46 \\  & 9 December 2020 17:06:42 & 97.287155 & 40.103947 & 239.06 \\  & 9 December 2020 17:06:43 & 97.287613 & 40.103508 & 224.84 \\  & 9 December 2020 17:06:44 & 97.288086 & 40.103127 & 210.33 \\ \cline{1-1}  & 9 December 2020 17:06:45 & 97.288544 & 40.102764 & 201.95 \\ \cline{1-1}  & 9 December 2020 17:06:46 & 97.288933 & 40.102238 & 241.93 \\ \cline{1-1}  & 9 December 2020 17:06:47 & 97.289375 & 40.101746 & 238.98 \\ \cline{1-1}  & 9 December 2020 17:06:48 & 97.289810 & 40.101322 & 215.76 \\ \cline{1-1}  & 9 December 2020 17:06:48 & 97.290268 & 40.100941 & 207.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Running speed of HST.  
Figure 10: Signaling process of the fast handover algorithm.
## 4 Design of DDNB
### DDNC (Dynamic Decentralized Network Consensus)  
To implement the service layer as an overlay network on top of a permissioned blockchain layer, the authenticity of all service nodes must be verified first. As anyone can participate in service layer, a verification process to ensure SNs' reliability and trustworthiness is essential. SN should act as intended by the application business logic, and every SN in a single domain acts like a clone of itself. This means that all SNs must always output a consistent value given some input. Based on this fact, a simple execution and comparison is carried out between SNs to determine whether they behave identically. This verification process is named DDNC (Dynamic Decentralized Network Consensus).  
In DDNC, if a new node wishes to participate in the service layer, it will be verified by preexisting nodes (verifier nodes) as shown in Figure 3. Candidate node sends a request for the list of SNs to seed node, then asks for 'add node' permission to all SNs in the list. Each verifier node examines whether the candidate node behaves identically or not via the 'Service Node Verification Protocol' in Figure 4, and adds it to the host list if the candidate node behaves equally to a verified node. Otherwise, it will be purged. This 'peer-review' process runs not only when 'add node' is requested, but also periodically to make sure that all nodes continue to act as intended. Therefore, service layer can detect abnormal SNs and remove them from the network automatically and autonomously at runtime. With this verification process, SNs are assured and confirm the share of same domain and mutual trust. Furthermore, it gives service providers the advantage of being able to scale out their SNs flexibly while reducing unexpected risks.  
Figure 2: Dynamic Decentralized Certification Protocol (DDCP).
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 6 Discussion and Recommendations
### Recommendations for Optimal Grounding Strategies  
Best practices for grounding design differ significantly across voltage levels; the specific needs of the installation dictate these differences. For low-voltage systems (e.g., 400 volts), normally, a simple solid grounding system or TN-C-S system is used; these systems assuredly protect in very much lower fault-risk environments. Medium- and high-voltage systems (e.g., 10 kV and above) utilize resistance grounding or multiple electrodes to cope with hefty fault currents and diminish ground potential rise (GPR), thus providing better protection and functional stability. Optimizing grounding systems in the present involves the loading of intelligent monitoring technologies-real-time resistance measure and automatic fault detection systems that bring in the smart, responsive, and high-efficiency maintenance system. Consequently, these technologies facilitate continuous monitoring and adjustment of grounding systems with minimal maintenance, which in turn boosts performance, permits lower maintenance costs, and makes them responsive. With all this, further protection can also be allotted to those grounding systems to optimize performance with due consideration to safety, reliability, and cost across different voltage levels and installation types.
# Fiducial Lower Confidence Limit of Reliability for a Power Distribution System
###### Abstract  
Reliability performance, especially the lower confidence limit of reliability, plays an important role in system risk and safety assessment. A good estimator of the lower confidence limit of system reliability can help engineers to make the right decisions. Based on the lifetime of the key component in a typical satellite intelligent power distribution system, the generalized fiducial method is adopted to estimate the lower confidence limit of the system reliability in this paper. First, the generalized pivotal quantity and the lower confidence limit of reliability for the key component are derived for the lifetimes of the exponential-type and Weibull-type components. Simulations show that the sample median is more appropriate than the sample mean when the lower confidence limit of reliability is estimated. Moreover, the lower confidence limit of reliability is obtained for the typical satellite intelligent power distribution system through the pseudo-lifetime data of the metallic oxide semiconductor field effect transistor. The lower confidence limit of reliability for this power distribution system at 15 years is 0.998, which meets the factory's reliability requirement. Finally, through the comparison, a hot standby subsystem can be substituted with a cold standby subsystem to increase the lower confidence limit of the system reliability.  
reliability assessment; lower confidence limit; power distribution system; fiducial method; Monte Carlo simulation +
Footnote †: journal: Physics Letters B
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### RFI Power Affecting the Pixel after SAR Processing  
Based on Section 2.1, both \(P_{s}\) and \(P_{th}\) are easy to estimate, but \(P_{RFI}\) deserves further study. According to (6), the gain obtained by RFI in SAR processing and the receiving aperture area are crucial to \(P_{RFI}\). Both the two elements are different for GEO-to-LEO RFI and LEO-to-GEO RFI. Thus, we will discuss the power of GEO-to-LEO RFI and LEO-to-GEO RFI, respectively.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 4 Results and Discussion
### Discussion of xLSTM Results
#### 4.2.2 Seasonal Wind Speed Distribution Prediction Performances  
The comparative analysis of wind velocity distributions between SCADA measurements and xLSTM predictions yields significant insights across all four seasons, as shown in Table 6. The winter distribution exhibits predominantly unimodal characteristics with high prediction accuracy. The primary mode appears at 7.8 m/s in SCADA measurements versus 7.5 m/s in xLSTM predictions, demonstrating a 3.8% deviation. Central tendency measures show strong agreement, with mean wind speeds of 8.2 m/s and 8.1 m/s for SCADA and xLSTM, respectively. The distribution shape parameters indicate similar patterns between measured and predicted data, with coefficients of variation (0.58 for SCADA measurements, 0.57 for xLSTM results) and skewness values (0.67 for SCADA measurements, 0.64 for xLSTM results) showing minimal discrepancy. The exceptional model performance is further validated by an \(R^{2}\) value of 0.987 and a low MAPE of 4.3% in probability density estimation. Summer distributions reveal strong alignment in central tendency metrics, with mean and median wind speeds differing by only 2.7% and 1.5%, respectively. The unimodal structure (bimodality coefficient: 0.47 for SCADA, 0.45 for xLSTM) shows consistent representation between measured and predicted distributions, with primary mode probability densities closely matched (0.099 for SCADA measurements, 0.102 for xLSTM results). Distribution shape characteristics show remarkable consistency in dispersion patterns (standard deviation: 4.3 m/s for SCADA measurements, 4.2 m/s for xLSTM results) and coefficient of variation (0.59 for both). Performance metrics confirm this strong alignment with an \(R^{2}\) value of 0.978 and an MAPE of 5.2%, positioning summer as the second-best predicted season after winter. Autumn presents a moderately bimodal structure (bimodality coefficient: 0.53 for SCADA measurements, 0.51 for xLSTM results) with the most significant modal shift among all seasons (primary mode: 3.5 m/s for SCADA measurements, 4.8 m/s for xLSTM results). Despite this modal displacement, other central tendency measures show closer agreement (mean: 6.4 m/s for SCADA measurements, 6.5 m/s for xLSTM results). Distribution shape parameters reveal similar kurtosis values (2.65 for SCADA measurements, 2.72 for xLSTM results) but slight differences in standard deviation (4.3 m/s for SCADA measurements, 4.5 m/s for xLSTM results). The probability mass in the low-speed range (<5 m/s) shows a 7.9% overestimation (0.38 for SCADA measurements, 0.41 for xLSTM results). Performance metrics indicate moderate predictive accuracy with an \(R^{2}\) value of 0.971 and an MAPE of 7.1%, placing autumn third in prediction quality among the four seasons.
## 2 Design of POSS-Containing MQL Oil
### Synthesis of POSS Ester Compound  
Figure 2 shows the process of preparing the octa-chloro-POSS, used as a basic structure for producing the three kinds of POSS ester compounds designed in this study, as described in previous studies [21; 23]. After reacting triethoxysilane with HCl and H\({}_{2}\)O for 48 h, di-_n_-butyltin dilaurate was added to react again for 48 h at room temperature, producing octa-chloro-POSS with a yield of 35%. These synthesized octa-chloro-POSSs were reacted with potassium octanoate using 5 mol% potassium iodide (KI) as a catalyst and 5 mL of dimethyl formamide (DMF) as a solvent for 8 h at 10 \({}^{\circ}\)C to produce POSS C8, in which an organic substituent (R) is linked to eight carbon atoms. Figure 3a displays a schematic diagram highlighting the process of preparing POSS-C8. Figure 3b depicts the process of designing POSS-C2, wherein octa-chloro-POSS is used as the structural frame, as in the case of POSS-C8. The sodium acetate was reacted with the octa-chloro-POSS using 5 mol% KI as a catalyst and 5 mL of DMF as a solvent for 48 h at 100 \({}^{\circ}\)C, to produce POSS-C2, in which an organic substituent (R) is linked to two carbon atoms.  
Figure 2: Synthesis of POSS ester compounds [22; 23]: (**a**) POSS-C8 (1). (**b**) POSS-C2 (2). (**c**) POSS-C1 CF\({}_{3}\) (**3**).  
POSS-C1 CF\({}_{3}\) was designed by adding potassium trifluoroacetate, a popular additive for conventional cutting oil, to strengthen the heat resistance. The identification of each compound by \({}^{1}\)H-NMR is shown in Figure 3, and TGA analysis has already been performed for POSS ester [23].