esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Posture Control Method of Hexapod Robot for Rugged Terrain
## 5 Simulation and Experiment  
### Posture Control Simulation for Uneven Terrain  
As shown in Figure 15a, the terrain is randomly generated in a three-dimensional (3D) coordinate system by using MATLAB software, and the maximum height difference of the entire terrain is 210 mm. In addition, the hexapod robot model is established to verify whether the posture control strategy can realize the robot's traversing of rugged terrain. The target path of the robot traversing the terrain is a diagonal line through the coordinate origin, and different colors of the terrain represent different heights. The projection of the main support foot in the XY plane in each stage of walking is calculated, as shown in Figure 15b, where the dotted line is LMP projection and the solid line is RMP projection. Finally, the robot successfully traverses the terrain.  
Figure 16 shows the curves of the trunk yaw angle, pitch angle, and roll angle. Each curve either maintains a zero value or changes smoothly and slowly at different stages. This control method can make the robot switch between mild and severe rough posture control mode in a timely manner with the change of terrain. In addition, it can adapt to the changing rugged terrain and complete the walking of the complex terrain through the posture adjustment of severe rough terrain.  
Figure 17 is the foot trajectory curve that the hexapod robot adjusts automatically when encountering obstacles. The trajectory at the beginning is planned according to the established  
Figure 16: Curve of angles of posture.  
Figure 15: Posture control simulation in MATLAB: (**a**) Simulation environment of unstructured terrain walking, and (**b**) projection on the XY plane of MP.  
boundary conditions and functions. Starting from the starting point A, the foot touches the obstacle during the swing process and ends. For the initial swing trajectory, update the boundary conditions and call the support phase trajectory function. Starting from the transition point B, the foot end moves along the support phase trajectory to the end point C. It can be seen that this strategy can generate a foot trajectory that satisfies the functions of swinging and supporting motion and enables the trajectory to be adjusted autonomously in real time according to changes in terrain [30].
## 3 Results and Discussion
### Optimum Processing Condition of Laver Chip Obtained through RSM  
The predicted and actual values of brittleness and puffing ratio of the laver chip, processed with the optimum processing condition obtained through RSM, were 6.5973 N and 115.03% and 6.93 \(\pm\) 0.33 N and 116.19 \(\pm\) 0.48%, respectively. These were within a 10% error rate for both brittleness and puffing ratio. In addition, the predicted and actual values of baked potato-like and savory odors of RFIS obtained through RSM were 6.5321 and 4.6537 and 6.00 \(\pm\) 0.78 and 4.00 \(\pm\) 0.91, respectively, according to QDA testing (9-point scale). It was confirmed that the baked potato-like and savory odors of RFIS were both within a 10% error rate.  
Therefore, the optimized processing condition obtained through RSM were as follows: The major ingredients, laver (20%), surimi (21.3%), and rice flour (58.7%), were mixed together. GDL (3 g), NaHCO\({}_{3}\) (1.5 g), soybean oil (4 g), corn syrup (8 g), salt (1 g), saccharin (0.01 g), 126 mL of distilled water, and 14 mL of RFIS were added to the mixture as minor ingredients. The shaped laver chip pellet was dried at 50 \({}^{\circ}\)C (1-2 h) and then air-fried in the air-fryer (195 \({}^{\circ}\)C and 52.5 s). At this time, the RFIS was processed by reacting methionine (0.54% \(w/v\)), threonine (3.30% \(w/v\)), glycine (2.40% \(w/v\)), glutamic acid (0.90% \(w/v\)), and glucose (3% \(w/v\)) in 100 mL of distilled water at 121 \({}^{\circ}\)C for 90 min.  
The moisture and crude protein, fat, and ash contents of the laver chip processed with the optimum composition and the processing condition were 5.51 \(\pm\) 0.02 g% and 24.26 \(\pm\) 0.10 g%, 2.19 \(\pm\) 0.05 g%, and 4.30 \(\pm\) 0.07 g%, respectively. While commercial laver chips typically contain 3 to 6% protein, the high-protein laver chip developed in this study had an enhanced protein content of 24%.  
Therefore, it was possible to process the laver chip with a very high-protein content, gluten-free, and low calorie (371.56 kcal) by applying air-frying and reaction flavor technologies.
## 4 Discussion  
Based on the experimental results, a new prototype was proposed, developed, and applied for CO gas detection. The prototype device consisted of a chemical sensor and a DC electronic circuit. The chemical sensor was made using the nickel antimonate oxide. The oxide was synthetized by means of an alternative microwave-assisted wet-chemistry process, using nickel nitrate, antimony chloride, ethylenediamine, and ethyl alcohol. The precursor material was dried at 200 \({}^{\circ}\)C and later calcined at 800 \({}^{\circ}\)C in static air. The crystalline phase of the calcined material was analyzed by X-ray diffraction, finding the cell parameters a = b = 4.641 A and c = 9.219 A, and spatial group P4/mm  
Figure 11: CO detection device built and its electronic elements.  
Figure 12: Signals measured with our CO detection device (operating temperature to 50 ppm and T = 300 \({}^{\circ}\)C).  
(136). Their sensitivity tests were developed applying static tests (Direct current, DC), an excellent electrical response was obtained. For its signal adaptation a DC electronic circuit was built which was based on a Wheatstone bridge, a DC source, a differential amplifier, and an operational amplifier comparator. That electronic circuit was implemented on a PBC of size 15 cm \(\times\) 15 cm.  
The CO detection device has good characteristics: low cost, high sensitivity, good performance, fast response, selective sensitivity (through trimmer potentiometers), adaptability, CO concentration detected into the interval of 1 to 300 ppm, operating temperatures 100, 200, and 300 \({}^{\circ}\)C, optimal operating temperature at 300 \({}^{\circ}\)C, its dimension (15 cm \(\times\) 15 cm), AC supply voltage (120 V) and DC output voltage (11.2 V). However, such characteristics can be improved using better technology for its implementation.  
In reference [42], the authors proposed a CO\({}_{2}\) detection system based on CoSb\({}_{2}\)O\({}_{6}\) oxide. For its signal adaptation, the impedance response was analyzed. From the analysis, an electronic circuit was proposed. Its implementation had high complexity because the signal analysis was done on the s-plane, and the electronic circuit was based on the impedance. In this work, another chemical sensor was applied for the CO gas detection but its signal adaptation was made based on the direct current, simplifying the analysis and its implementation. Therefore, comparing both proposals, our proposal reduced complexity, analysis, cost, materials, electronic components and optimized the performance.  
The CO detection device finds practical safety applications where CO detection is desirable. Some examples are boiler safety system, locked chimneys, inverted fireplace effect, corroded ventilation pipes and more. Therefore, our future work is in the following direction: CO detector applications, the detector device would be optimized through digital electronics and the nickel antimonate (NiSb\({}_{2}\)O\({}_{6}\)) oxide would be applied for the propane (C\({}_{3}\)H\({}_{8}\)) gas detection.
## 1 Introduction
Most prior studies either examine dynamic activities (e.g., gait or jumping) or apply static optimization to single postures, with few investigating how the two approaches differ under truly isometric conditions. One relevant study compared static optimization and a dynamic simulation (computed muscle control) during an athletic maneuver, finding generally similar muscle force patterns but significant differences for multi-joint (bairticular) muscles [17]. This suggests that dynamic models may capture certain intermuscular coordination complexities that static analyses miss, even when joint motion is minimal. More broadly, advanced simulations have proven valuable for revealing biomechanical insights beyond what is possible through experiments. For instance, large-scale models illuminate how muscles and the nervous system interact to produce coordinated movements [18]. Dynamic analyses have also shown how bairticular muscles redistribute energy during activities like jumping and pedaling [19]--phenomena that cannot be directly measured in vivo. In light of this context, the present study aims to bridge the noted gap by comparing muscle activity predictions from static and dynamic models during an isometric external hip rotation task. By investigating a large sample (33 participants) in a single standardized task, this study aims to offer a novel contribution: understanding how static vs. dynamic modeling assumptions influence predicted muscle activity during isometric exercise. The findings are expected to inform model selection and highlight potential pitfalls when applying musculoskeletal models to rehabilitation exercise analysis. A custom-designed device was used to conduct controlled isometric rotations of the lower limb while measuring the generated moments and ground reaction forces. This provided a consistent dataset to drive both a static musculoskeletal model and a dynamic simulation in the AnyBody Modeling System. By analyzing the activation levels of key hip and knee stabilizer muscles at peak effort, we seek to determine how the static and dynamic approaches diverge in their predictions. Through this comparison, our goal is to highlight the unique contributions and limitations of each modeling method in the context of isometric exercise, thereby informing their appropriate application in biomechanical research and practical settings (such as ergonomic design, sports training, and rehabilitation).
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 4 Mobile Radiation Detection Systems
### Challenges and Research
However, difficulties may appear when synchronization of mobile systems is necessary with high accuracy (e.g., fast detectors) [12]. * Alternatives to Helium-3 neutron detectors due to the worldwide shortage [217; 219]. * Use of multiple UAVs for the detection and localization of source(s), for example, by using plastic scintillators (poor energy resolution but cheaper)--the source identification could be done afterwards using a NaI system or other inorganic scintillation detector [89]. Other studies suggest the use of energy windowing to distinguish SNM from NORM [231] or by using deconvolution methods of the spectrum acquired by plastic scintillators [12]. These scintillators can also be loaded with heavy elements (e.g., bismuth) to increase the photopeak efficiency; however, this decrease the light yield [232]. * Detection materials improvement for mobile applications--research is necessary to improve the performance of the detection systems available and find new ones. An example is the fast-growing development of halide perovskite radiation detectors (e.g., halide lead perovskite). These semiconductors present some advantages as their low cost, room temperature operation, high stopping power, defect-tolerance, and high energy resolution compared to NaI(Tl) scintillator, which makes then a promising candidate in X-ray imaging and gamma-ray spectroscopy. Research is still necessary to improve their characteristics, e.g., sensitivity, dark current density, resistivity, and environmental stability (to heat and moisture) [233]. Other challenges are related to radiation hardened materials, as well as the effects of magnetic field on the detection systems. According to IAEA [27], the areas related to detection technology that should be considered for future research are: detection probability and range, source identification, and mobility.
# Assessment of the Nutritional Value and Antioxidant Properties of Plant-Based Yogurt from Chickpeas
## 2 Materials and Methods
### Antioxidant Activity Determination with DPPH and FRAP Methods  
The radical scavenging activity (DPPH\(\bullet\)) was determined as follows: 5 g of freeze-dried CP yogurt and 20 mL of methanol (70%) were shaken for 1 h and filtered. Next, 0.05 mL of the filtrate was mixed with 1.95 mL of the DPPH methanolic solution and 0.50 mL of methanol (70%) and incubated at 24 \({}^{\circ}\)C for 0.5 h. The absorbance was measured every 5 min. The antioxidant activity (%) was calculated using the following formula: AA% = [(AControl \(-\) AExtract)/AControl] \(\times\) 100 [22]. The ferric-reducing antioxidant power (FRAP) was determined using the same filtrate as used for the DPPH determination, 2.5 mL of 10 mmol/L of TPTZ, 2.5 mL of 20 mol/L of ferric chloride, and 25 mL of acetate buffer (pH 3.6, 300 mmol/L). The freeze-dried CP yogurts were mixed with 4 mL of the FRAP reagent, and the absorbance was measured at 593 nm. A calibration curve for both tests was prepared using Trolox equivalents (\(\mu\)mol TE/g) [21; 22].
## 3 Proposed Scheme
### Scheme Overview and Assumptions  
The proposed DRDT scheme guarantees reliable energy-efficient data transmission _via_ the extension layer that operates under the network layer. The basic idea behind the proposed scheme is the cooperative  
Figure 1: Packet reception rate of considered link loss model at different distance.  
retransmission of neighbor nodes that overhear the data packet transmitted to the receiver node, so as to increase the data transmission success rate and reduce the number of retransmissions. In Figure 2, every neighbor node of a receiver that overheard the data packet becomes a candidate helper node. Each decides if it should promote itself as a helper node using waiting time in a distributed manner. At this point, the helper node is selected by considering PRR to the receiver node, so that the number of retransmissions can be reduced. The selected helper node retransmits the data packet on behalf of the sender node when the receiver node failed to receive the data packet due to low PRR (Figure 2b). The helper node can effectively reduce the number of retransmissions since it uses a better PRR link than the sender node.  
DRDT functions based on the following basic and general wireless sensor network environments.  
* The network has relatively low congestion.
* Fixed sensor nodes with constant radio ranges are randomly distributed.
* Each sensor node knows itself and its geographical location information _via_ GPS or the distribution-based localization algorithm.
* Each node knows the location information of the neighbor nodes and the PRR value between itself and the neighbor nodes by transmitting a \"hello\" packet at regular intervals.
* The network uses a timeout-based ACK model to offer reliability.
* It is assumed that insignificant computing power is required for mathematical operations.
## 4 Experimental Evaluation
### Train-Test Split Approach  
The results of the train-test split approach are summarized in Table 5, which compares the performance of the models evaluated in this research with previous works using the same dataset and models, with the best results of the experiments highlighted in bold. The models were trained on 80% of the dataset using the best hyperparameters determined by Optuna and evaluated on the remaining 20%. Logistic Regression achieved an accuracy of 97%, with an F1-score of 97%, demonstrating strong generalization capabilities across all classes. AdaBoost achieved 98% accuracy, with challenges reflected in lower recall (81%) and precision (80%), likely due to the imbalanced nature of the dataset. RF, ET, XGBoost, and DNN demonstrated exceptional performance, achieving perfect scores (100%) across all metrics, highlighting their effectiveness in accurately detecting both majority and minority classes.
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 6 Performance Evaluation  
In this section, based on the system model described in Section 3, the performances of LMRSS are evaluated via numerical simulation in terms of network lifetime and time complexity. Moreover, a general WBAN model specified in IEEE 802.15.6 is also employed to comprehensively evaluate the performance of the proposed scheme. In addition to the evaluation, we validate the feasibility of implementation in a real WBAN system for the proposed scheme at the end of this section.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Error Range of \(\Omega_{1}\)** & **Error Range of \(\Omega_{2}\)** & **Error Range of \(\Omega_{3}\)** & **Error Range of \(\Omega_{4}\)** \\  & (mm) & (mm) & (mm) & (mm) \\ \hline Uncompensated & [9.2737, 10.6342] & [9.4061, 10.3392] & [3.4253, 5.6238] & [4.455, 7.997] \\ Method in Ref. [28] & [0.0723, 0.9507] & [0.1288, 0.8524] & [0.1216, 0.5048] & [0.1194, 0.9593] \\ Proposed method with corresponding \({}^{\text{W}}\)T\({}_{\text{T}}\) & [0.0623, 0.2099] & [0.011, 0.2348] & [0.0058, 0.1952] & [0.031, 0.2178] \\ Proposed method with \({}^{\text{W}}\)T\({}_{\text{T}}\) of the adjacent region & [0.2114, 0.7061] & [0.0542, 0.5798] & [0.0162, 0.3565] & [0.0602, 0.6618] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical result of the experiment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Error without** & **Error after** & **Orientation** & **Secondary Encoder** & **Number of** \\  & **Compensation (mm)** & **Compensation (mm)** & **Variation (\({}^{\circ}\))** & **Needed (Y/N)** & **Sampling Points** \\ \hline Electroimpact [3] & Not available & 0.18 & No restriction & Y & 600 \\ Cao [30] & 8.473 & 0.334 & [\(-20^{\circ}\), \(20^{\circ}\)] & N & 750 \\ This paper & 10.6342 & 0.2348 & [\(-110^{\circ}\), \(110^{\circ}\)] & N & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with other compensation methods considering orientation variation. The evidence from this experiment suggests that the calibration uncertainties of the TCP frame are the main factors affecting the positioning accuracy of the robot in the drilling  
Figure 11: Spatial correlation analysis of residual error. (**a**) Positioning error of \(\Omega_{1}\) vs. distance; (**b**) Positioning error of \(\Omega_{1}\) vs. angle deviation; (**c**) Positioning error of \(\Omega_{2}\) vs. distance and angle deviation. task of large orientation variation. Additionally, calibrating \({}^{\text{W}}\,T_{\text{T}}\) in different regions can make better use of the error similarity of the robot to reduce the error. What's more, it can also be identified that the method in Ref. [28] cannot eliminate the calibration error of the TCP frame. Although increasing the number of sampling positions with different orientations can improve the accuracy to a certain extent, the number of sampling points will increase dramatically. Therefore, by the regionalized calibration method proposed in this paper, the positioning accuracy of robots in \(\Omega_{1}\), \(\Omega_{2}\), \(\Omega_{3}\) and \(\Omega_{4}\) can fully meet the requirements of the drilling task, and the sampling time is also controlled within a minimum range, which provides a novel compensation method very suitable for robotic drilling task under large orientation variation.
## Appendix A Statistical Indicators of Accuracy  
The accuracy of different models is measured in terms of two common statistical indicators very often used in solar radiation modeling, i.e., normalized root mean square error (\(nRMSE\)) and normalized mean bias error (\(nMBE\)):  
\[nRMSE=\frac{\left[M\sum\limits_{i=1}^{M}\left(c_{i}-m_{i}\right)^{2}\right]^{ 1/2}}{\sum\limits_{i=1}^{M}m_{i}} \tag{1}\]  
\[nMBE=\frac{\sum\limits_{i=1}^{M}\left(c_{i}-m_{i}\right)}{\sum\limits_{i=1}^{ M}m_{i}} \tag{2}\]  
where \(c\) and \(m\) refer to the estimated and measured values of a physical quantity, respectively, while \(M\) denotes the number of measurements.
###### Abstract  
In the present study, the performance of four biomass-derived physically activated biochars for dynamic CO\({}_{2}\) capture was assessed. Biochars were first produced from vine shoots and wheat straw pellets through slow pyrolysis (at pressures of 0.1 and 0.5 MPa) and then activated with CO\({}_{2}\) (at 0.1 MPa and 800 \({}^{\circ}\)C) up to different degrees of burn-off. Cyclic adsorption-desorption measurements were conducted under both dry and humid conditions using a packed-bed of adsorbent at relatively short residence times of the gas phase (12-13 s). The adsorbent prepared from the vine shoots-derived biochar obtained by atmospheric pyrolysis, which showed the most hierarchical pore size distribution, exhibited a good and stable performance under dry conditions and at an adsorption temperature of 50 \({}^{\circ}\)C, due to the enhanced CO\({}_{2}\) adsorption and desorption rates. However, the presence of relatively high concentrations of water vapor in the feeding gas clearly interfered with the CO\({}_{2}\) adsorption mechanism, leading to significantly shorter breakthrough times. In this case, the highest percentages of a used bed were achieved by one of the other activated biochars tested, which was prepared from the wheat straw-derived biochar obtained by pressurized pyrolysis.  
Keywords:postcombustion CO\({}_{2}\) capture; biomass-based adsorbents; cyclic breakthrough measurements; selectivity CO\({}_{2}\)/N\({}_{2}\); humid conditions; hierarchical porosity +
Footnote †: journal: Applied Physics Letters
# Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk
## 2 Problem Formulation  
The VPP's architecture studied in this paper is shown in Figure 1. The VPP control center has a link to the utilities/independent system operator (ISO), integrating wind power, solar power, and energy storage systems (ESS), and sells power to aggregated loads in order to obtain profit. The ISO selling power to the VPP determines whether or not to perform demand response (DR), which involves money rebate to the VPP. If DR is performed, the VPP will curtail the aggregated loads and return part of the rebate to the aggregated load. In the uncertain environment with wind and solar, VPPs seek to maximize the risk return of power dispatch through power trading and DR.