esa-sceva/satcom-chunk-collection huggingface dataset:
# Alpine Skiing Robot Using a Passive Turn with Variable Mechanism
## 7 Conclusions  
For this study, a passive turn ski robot was developed to improve the competitive ability of alpine skiers. Subsequently, experiments were conducted to ascertain whether mechanisms for varying the COG height, edge angle and ski deflection could be added to the passive turn type ski robot to control turns solely by these changes. The results of the respective investigations are summarized below.  
1. A simple passive-turn type ski robot was fabricated to assess effects of differences of the robot construction and COG height on turning. The use of a hinge to connect the lumbar and leg allows the robot to repeat adduction and abductor movements of the hip joint using gravity exerted during sliding and to turning the hip joints continuously. In addition, when comparing the sliding trajectories of robots having different COG heights, the higher the COG is, the later the timing of switching to the opposite side turn becomes. For that reason, the cycle of the turn becomes larger.
2. To clarify the importance of the timing of changing the COG height in the turn by noting the influence on the turn caused by the COG height difference, a mechanism for varying the COG height was added to the passive turn ski robot. For turns in which the COG height was manipulated during running, the radius of curvature was almost identical. Only the cycle of the turn changed. Results show that the timing of raising and lowering the COG height changed the switching point of the turn, which is the movement of the COG in the left and right directions.
3. To clarify the importance of the edge angle in the turn by noting the influence on the turn by the difference in the edge angle, which is one factor affecting the turn, a mechanism for varying the edge angle was added to the passive turn type ski robot. Results show that the radius of curvature of the turn was changed during turning in which the edge angle was manipulated during sliding. Moreover, the depth of the arc of the turn was altered by the timing of the edge angle increase or decrease.
4. A mechanism for varying the ski deflection was added to the passive turn ski robot to clarify the importance of the ski deflection during turning by elucidating the influence on turning of the ski deflection the skier exerts during turning. Results demonstrated that the turn can be performed with a small curvature by deflecting the ski and show that the turn trajectory was altered by the plate deflection during sliding.
5. No consideration of friction exists in this study. Therefore, we would like to defer to future work our planned comparison of the running experiment results and a theoretical model incorporating the contact area of the snow in an alpine ski model.  
[PERSON] wrote the paper, performed the experiments and analyzed the data; [PERSON] conceived the method and helped to modify the paper. All authors have read and approved the final manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON] for technical assistance with the experiments.  
The authors declare no conflict of interest.
# Development of Proportional Pressure Control Valve for Hydraulic Braking Actuator of Automobile ABS
## 2 Analysis of Proportional Solenoid Force
### Permeance of Metal Zone Calculation  
As mentioned above, when the proportional electromagnet is energized, it forms a closed magnetic field and the simplified magnetic flux (\(\Phi\)) path of the metal zone is shown in Figure 9. Areas with different average \((N1)\) will lead to different magnetic field intensity \((H)\). Furthermore, the magnetomotive force \((F_{sum})\) and magnetic flux density \((B)\) exist within the metal.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Permeance** & **Mean Path Area** & **Mean Path Length (\(l_{i}\))** & **Equation (\(P_{Ai}\))** \\ \hline \(P_{A1}\) & \(CFHG\) & \(x+x_{0}\) & \(uA_{1}/l_{1}\) \\ \(P_{A2}\) & \(DLMN\) & \(x\sin\alpha\) & \(uA_{2}/l_{2}\) \\ \(P_{A3}\) & \(ABCD\) & \(\sqrt{\Omega_{1}\mathrm{D}\cdot\min(\overline{\Omega_{1}\mathrm{C}},\overline{ \Omega_{1}\mathrm{X}})}\) & \(4u_{0}R_{31}P_{32}\) \\ \(P_{A4}\) & \(ADN\) & — & \(3.315u_{0}(R_{m1}+(\overline{\Omega_{1}D}\cdot\cos\alpha)/2)\) \\ \(P_{A5}\) & \(MPQR\) & \(\sqrt{\Omega_{2}M\cdot\min(\overline{\Omega_{2}R},\overline{\Omega_{2}Y})}\) & \(4u_{0}R_{51}P_{52}\) \\ \(P_{A6}\) & \(LMP\) & — & \(3.315u_{0}[R_{02}+(\overline{\Omega_{2}M}\cos\alpha)/2]\) \\ \(P_{A7}\) & — & \(g\) & \(\frac{2\pi\mathrm{tub}}{g}(R_{\mathrm{M2}}+\frac{g}{g})\cdot\min(L_{S3},L_{P7})\) \\ \(P_{A8}\) & — & — & \(3.315u_{0}(R_{\mathrm{M2}}+g/2)\cdot K_{8}\) \\ \(P_{A9}\) & — & \(\sqrt{g\cdot R_{80}}\) & \(4u_{0}(R_{B2}-l_{9})\ln\frac{R_{8}}{g}\) \\ \hline \end{tabular}
\end{table}
Table 2: Permeance of air gaps in the proportional electromagnet.  
Figure 8: Definition of size for the proportional electromagnet.  
The permeability coefficient of metal is a non-constant, so there is a nonlinear correspondence between its H and B. When the magnetic flux density is higher than a specific value, it will lead to magnetic saturation, so the magnetic flux density may not increase even though the magnetic field is strengthened. The magnetic field intensity could be obtained by the correspondence with magnetic flux density through the material B-H curves, and the results are shown in Table 3.  
When it is energized, the proportional electromagnet forms a closed magnetic circuit and the inside paths are connected in series. Therefore, the magnetic flux is the same and the magnetomotive force of each metal zone is obtained by magnetic flux multiplied by the reciprocal of permeance. The magnetic flux density of metal material is obtained by calculating the magnetic flux and then corresponding to the magnetic field intensity to allocate the magnetomotive force of metal and air gaps. The equivalent permeance of the metal zone within the proportional electromagnet is shown in Equation (6).  
\[P_{steel}=R_{steel}^{-1}=(\sum_{i=1}^{6}R_{Si})^{-1}=(\sum_{i=1}^{6}P_{Si}^{-1}) ^{-1} \tag{6}\]
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.23 The Indicator to Measure Using Construction and Demolition Waste (CDW)  
The indicator recommends reusing construction and demolition waste on the site. This is developed by SBTool\({}^{\text{PT}}\) Urban, SNTool, BREEAM C, and LEED ND, and addressed by Levels, ISO 37120, and SDGs. Therefore, this indicator is recognized as a critical environmental concern. For this indicator, the SBTool\({}^{\text{PT}}\) Urban Guide establishes two calculation parameters: the Percentage of Recycled CDW Incorporated, and the CDW Valorization Index. The first parameter, the ratio between the weight of CDW incorporated into materials and the total CDW weight is calculated. For this, it is necessary to create a materials database, and then it is necessary to characterize the CDW incorporated into the materials. This stage is applicable to CIM, and the ratio is obtained using Dynamo. The second parameter is calculated using a verification checklist, but most of the criteria on the list cannot be assessed using the CIM model. As a result, the parameter is considered to be \"NO\" in terms of its applicability to CIM.
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 3 Improved Spatiotemporal Multi-Channel Network (IQGMCL)
### LSTM and Bi-LSTM  
#### 3.2.1 Lstm  
A recurrent neural network (RNN) is a kind of neural network with a good processing effect on temporal data, but it has a long-term dependence problem. That is, with the increase in input sequence length, the model cannot use the earlier data information in the sequence [26]. To solve this problem, [PERSON] et al. proposed an LSTM network in 1997.  
LSTM network adds input gate \(i_{t}\), forgetting gate \(f_{t}\), and output gate \(o_{t}\) three logical units to control the output of memory units. The input gate controls the current input state in the memory units. The forgetting door screens and preserves the processing results of the previous memory units. The output gate controls the output state of the memory units.  
The LSTM network calculation process is as follows:  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{1}\]  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)} \tag{2}\]  
\[o_{t}=\sigma(W_{0}x_{t}+U_{0}h_{t-1}+b_{0}) \tag{3}\]  
\[\widetilde{c}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{4}\]  
\[c_{t}=i_{t}\cdot\widetilde{c}_{t}+f_{t}\cdot c_{t-1} \tag{5}\]  
\[h_{t}=o_{t}\cdot\tanh(c_{t}) \tag{6}\]  
Formula: \(i_{t}\) is determined by input \(x_{t}\), output \(h_{t-1}\) of the hidden layer at the previous moment and activation function \(\sigma\). Input gate \(i_{t}\) is a vector composed of real numbers  
Figure 2: IQCLNet structure.  
between 0 and 1. \(W_{i}\), \(U_{i}\), \(b_{i}\), \(W_{f}\), \(U_{f}\), \(b_{f}\), \(W_{0}\), \(U_{0}\), and \(b_{0}\) are door training parameters; tanh is the activation function.
## 4 Experiments and Result Analyses  
### Datasets  
In this study, we build a dataset of automobile wheel hub defect images to evaluate the performance of the proposed network. In addition, to verify whether the network can accurately detect defects from different products, three other public datasets are also used, namely, the magnetic tile [27], fabric [33], and weld defect datasets [31]. In order to make the network converge faster, the images of the input data set are cropped, denoised and normalized before the network training, so that the images have the same gray scale and resolution.
# Sensing Hydrogen Seeps in the Subsurface for Natural Hydrogen Exploration
###### Abstract  
The recent detection of natural hydrogen seeps in sedimentary basin settings has triggered significant interest in the exploration of this promising resource. If large economical resources exist and can be extracted from the sub-surface, this would provide an opportunity for natural hydrogen to contribute to the non-carbon-based energy mix. The detection and exploration of hydrogen gas in the sub-surface is a significant challenge that requires costly drilling, sophisticated instrumentation, and reliable analytical/sampling methods. Here, we propose the application of a commercial-based sensor that can be used to detect and monitor low levels of hydrogen gas emissions from geological environments. The sensitivity, selectivity (K \(>\) 1000), and stability (\(<\)1 ppm/day) of the sensor was evaluated under various conditions to determine its suitability for geological field monitoring. Calibration tests showed that the hydrogen readings from the sensor were within \(\pm\)20% of the expected values. We propose that chemical sensing is a simple and feasible method for understanding natural hydrogen seeps that emanate from geological systems and formations. However, we recommend using this sensor as part of a complete geological survey that incorporates an understanding of the geology along with complementary techniques that provide information on the rock properties.  
hydrogen; sensor; seeps; environmental monitoring; soil gas; geological survey +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
In this paper, the fluorinated silica materials were realized via an acid-catalyzed sol-gel process using different silane precursors (TEOS, MTES, and/or HDTMES) and fluorinated solution. The transparent and antireflective fluorinated hybrid coatings were achieved by the deposition of these materials onto glass substrates. The presented results indicated the relationship between the silane precursors (utilized in the synthesis of hybrid silica materials) and the final properties of the created coatings. The chemical structure, thermal behavior, morphology, wettability, and surface properties of the resulted materials were evaluated by FTIR, UV-Vis, TEM, TGA, AFM, ellipsometry analysis, and water contact angle measurements. The FTIR spectra showed that organo-silicate networks were formed, which included siloxane groups, alkyl chains, and fluoroalkyl chains derived from the silane precursors and from the fluorinated solution. TEM images demonstrated that the fluorinated silica materials had been cross-linked by the covalently bonded Si-O-Si structures. Optical analysis revealed that the fluorinated hybrid coatings presented lower reflectance (\(\sim\)9.7%, at 550 nm) than the uncoated glass substrate, as well as good transparency, with a transmittance of \(\sim\)90% in the visible range. Water contact angle analysis indicated that the fluorinated hybrid coatings developed hydrophobic properties (\(\sim\)110\({}^{\circ}\)). Although there are fluorinated reactive silanes that are commercially available and can be utilized with the sol-gel method, they are, in a great measure, limited to linear per-fluorinated carbon chains, which diminishes the chemical variation and the scope of the fluorinated materials. The rational structure engineering and the mechanism study are the essential scientific issues to be discussed for the future development of fluorinated materials, which will present excellent performance in large-scale applications (e.g., electronics, energy storage devices, and protective coatings).  
Conceptualization, V.P. and V.R.; methodology, V.P. and V.R.; formal analysis, F.M.R., A.R., A.N.F., R.S., M.A., H.S. and C.-A.N.; investigation, F.M.R., A.R. and S.C.; data curation, A.N.F., R.S., M.A., H.S. and C.-A.N.; writing--original draft preparation, V.P. and S.C.; writing--review and editing, V.P. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the INCDCP ICECHIM Bucharest 2019-2022 Core Program PN. 19.23-Chem-Ergent, Project No.19.23.03.01.  
Not applicable.  
The data are not publicly available due to their containing information that could compromise the privacy of research participants.  
The authors declare no conflict of interest.
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
## 2 Resistive Self-Mixing
The differential dependencies derived from Equation (5) can be represented using high-resolution DC measurements. To demonstrate this, we measured the behavior of the DC drain current as a function of the drain-source voltage and the gate-source voltage in steps of \(0.5\,\mathrm{mV}\). Figure 2 shows the measurement result and the resulting differentials. The measurement was smoothed with an average filter to reduce noise. Otherwise, the minimal noise between the measured values will cause artifacts when forming the second derivative. The graphs show contour lines rather than individual measurement points to maintain clarity. Figure 2a shows the drain current measurement as a function of the gate-source and drain-source voltage. The gate-source voltage was varied between zero and \(-3.5\,\mathrm{V}\), and the drain-source voltage between plus and minus \(100\,\mathrm{mV}\) to capture the component's behavior at \(0\,\mathrm{V}\) drain-source voltage. Even if the representation is somewhat unusual, it shows the behavior of a normally on component. We see the resistive control effect of the gate-source voltage up to the pinch-off at approximately \(-2.9\,\mathrm{V}\) and a linear dependence on the drain-source voltage. The graphs reported in Figure 2b-d represent the terms from Equation (5), where the colored frames correspond to the color of the equation's terms. Figure 2b displays the measured differential output conductance \(g_{ds}\), which primarily depends on the gate-source voltage. The graph in Figure 2c shows the differential conductance \(g_{ds}\) differentiated concerning the drain-source voltage. As expected from the above discussion, the result is almost constant. In Figure 2d, the differential conductance is differentiated with respect to the gate-source voltage. The maxima show the gate-source voltage at which the largest differential change in the output conductance occurs. If the other term is neglected, this should be the gate-source voltage at which the resistive mixing causes the maximum detection current. We will review this consideration again in the Results Section and show the respective measured results. From the result in Equation (5) and the graphical consideration, it can be deduced that the sensitivity of the THz detector structure depends on the properties of the HEMT and the antenna structure. The HEMT should have strong channel control, high charge carrier mobility, and low contact resistance. At the same time, the matching between the antenna structure and the HEMT should produce the highest possible gate-source voltage \(\Delta{u_{gs}}\). Based on this discussion, the THz detectors presented in the paper can be viewed as a voltage-controlled current source, with \(\Delta{u_{gs}}\) as the control voltage.
## 3 Research Method
### Model Development
#### 3.2.1 Monitoring Scenario 1: Identification of Disturbance Boundaries
Step 4: Spatial analysis  
After manual verification, the unsupervised classification imagery may still contain two types of \"noise\". The first type is \"noise\" within the identified SDAs, where some internal regions of the SDAs are incorrectly classified as other land types. The second type involves regions initially classified as SDAs, which are sparsely distributed within vegetated areas, resulting in numerous speckles (noise). These types of \"noise\" can be removed using spatial filtering. Subsequently, GIS-based buffer analysis and overlay analysis techniques can be employed to further reduce misclassification. This step ensures the accurate representation of real-world attributes, thereby achieving precise imagery classification, ultimately yielding accurate disturbance boundaries of the SDAs.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 4 Results and Analysis
### Delay (s)  
Figure 5a-f demonstrates the performance of a network in terms of end-to-end delay by varying the number of UAVs, speed, and area dimensions. The \(X\)-axis represents the simulation time and the \(Y\)-axis indicates the delay in seconds. When the number of UAVs increased, the end-to-end delay decreased. This is due to the increased probability of packets being routed instead of being apprehended in suspension buffer. When comparing the delay for each of these protocols, DSR had the highest delay in all scenarios except when the area was smaller (i.e., 1 km \(\times\) 1 km). This was because when a route request (RREQ) was sent, the destination replied to all RREQs that it delivered, which made it slower when determining the least congested route. AODV also exhibited a higher delay due to its reactive nature, where routes were assigned on-demand. OLSR had average performance in terms of delay in all scenarios, due to its proactive characteristics. ZRP had the lowest delay because of the use of the border-casting mechanism.  
Figure 4: Throughput simulation results (**a**) 30 nodes with 25 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**b**) 30 nodes with 60 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**c**) 100 nodes with 25 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**d**) 100 nodes with 25 m/s and 1 km \(\times\) 1 km speed and area, respectively; speed and area, respectively; (**e**) 100 nodes with 60 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**f**) 100 nodes with 60 m/s and 1 km \(\times\) 1 km speed and area, respectively.  
Figure 5: _Cont._
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 3 Our Methods
### Architecture
#### 3.1.3 Segmentation Depth Module  
We implemented the SegDepth module in addition to the heatmap module. The encoder in the SegDepth module has the same structure as the encoder in the heatmap module (see Figure 2).  
The architecture of xR-EgoPose [8] provided inspiration for the design of the decoder of the SegDepth module. xR-EgoPose [8] extracted the 3D pose features using the joint features of the 2D heatmap. Similarly, our SegDepth module extracts the depth features from a segmented image using 2D features from a segmented image (see the top right images in Figure 3). Since the depth map visually consists of three regions: body, background, and vignetting regions, our proposed segmentation is designed to have the three regions (see the top middle, and bottom left images of Figure 4). Our SegDepth module regresses the depth map along with the segmented 2D image, thus inducing the module to focus on the body region when extracting depth features. Additionally, the embedded features from the regression of the 2D segmentation help enhance the depth regression by leveraging 2D information.  
The decoder of the SegDepth module utilizes 4 \(\times\) 4 deconvolution layers for the regression of the segmented image and depth map (see Figure 3). This is possible because the segmented image and depth map have the same optical shape and structure, allowing the network to learn from each feature, thereby enhancing each other with the information obtained during the estimation process from a distorted image. By implementing the decoder in this manner, we mitigate weight bias that sometimes occurs during training and can reduce the network size while maintaining the performance of the architecture. After processing the deconvolution block, two layers regress the segmented image and depth map individually.  
Figure 3: The decoder takes ResNet and conv features as inputs. The deconv and conv blocks are repeated twice with ReLU, BN, and dropout steps. The SegDepth module decodes the depth and segmented images using the deconv layer. The heatmap module increases the heatmap size by bilinear interpolation.
## 3 Results and Discussion  
### Chemical Composition of Soil Samples  
Figure 3 shows box-and-whisker plots, illustrating the concentrations of different stable elements in the soil samples. The data indicate that SiO\({}_{2}\) is the principal compound, followed by these other major elements: Al, Ca, Fe, K, P, and Mg. Cu, Mn, S, and Ti were the predominant elements with higher percentages. The concentrations of the major elements, namely O, Si, Al, Ca, Fe, K, P, and Mg, were equivalent to those typically found in soil samples [32]. Cu had an average concentration of 1600 mg kg\({}^{-1}\), which is significantly higher than the European average value of 35.3 mg kg\({}^{-1}\) in unimpacted soils [33] and 496.3 mg kg\({}^{-1}\) in soils contaminated by pesticides [34]. However, the levels found were consistent with those in other areas affected by Cu mining, where a value of 1600 mg kg\({}^{-1}\) was also obtained [35]. Sulphur also showed values well above the average in soils of 24.6 mg kg\({}^{-1}\). On the other hand, the As value was consistent with that obtained in other studies on soils affected by U mining, whose average value of 233 mg kg\({}^{-1}\) is equivalent to the one obtained in this work of 303 mg kg\({}^{-1}\)[36]. The elements identified were consistent with the mineral species characteristic of a copper mine tailings site. On the one hand, Cu is associated with chalcopyrite (CuFeS\({}_{2}\)), bornte (Cu\({}_{1.8}\)Fe\({}_{1.2}\)S\({}_{4}\)), and chalcocite (Cu\({}_{2}\)S) [37]. Mn is a part of pyrolusite (MnO\({}_{2}\)); Fe is found in hematite (Fe\({}_{2}\)O\({}_{3}\)), pyrite (FeS\({}_{2}\)), and magnetite (Fe\({}_{3}\)O\({}_{4}\)); and Ti is found in rutile or ilmenite (FeTiO\({}_{3}\)) [38]. The trace elements found were also consistent with those typically encountered in such tailings [39]: (i) As as arsenopyrite (FeAsS), (ii) Zn as sphalerite (ZnS), (iii) Ba as barite (BaSO\({}_{4}\)) and witherite (BaCO\({}_{3}\)), and Cr due to the presence of chromite (FeCr\({}_{2}\)O\({}_{4}\)).  
Figure 3: _Cont._