esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Parametric Study
### The Three-Dimensional Prediction Surfaces  
Based on previous studies, the relationship between the seismic parameters and response can be described as a linear fit, with the fitting slope and intercept subject to a quadratic polynomial fitting based on the incident angle. Thus, taking the incident angle and seismic parameters into account, to assess the dynamic response of dams when NF motion is obliquely incident, the three-dimensional (3D) prediction surface of dynamic response can be expressed as follows:  
\[EDP=(a\theta^{2}+b\theta+c)\cdot S_{a}(T_{1})+d\theta^{2}+e\theta+f \tag{17}\]  
where \(a\), \(b\), \(c\), \(d\), \(e\), and \(f\) denote the coefficients fitted to the 3D prediction model, and \(\theta\) denotes the absolute value of the incident angle. The proposed predictive model highlights the coupled effects of the incident angle and spectral acceleration of the NF motion based on a study of the influencing factors. Figures 17 and 18 show the 3D predictive surfaces of the DVR and displacement response based on the incident angle and seismic parameters for the four seismic conditions, respectively. The fitting results show that the \(R^{2}\) values of all earthquake input cases are greater than 0.85, indicating that the proposed prediction model meets the accuracy requirements. Moreover, the 3D curve shows that with an increase in spectral acceleration, the response indicators exhibit a rapid growth trend. Additionally, the 3D fitting surface of the DVR index has a  
Figure 16: Fitting line of spectral acceleration and displacement under NF motion with varying incident angles and directions: (**a**) 15\({}^{\circ}\); (**b**) 30\({}^{\circ}\); (**c**) 45\({}^{\circ}\); (**d**) 60\({}^{\circ}\); (**e**) 75\({}^{\circ}\); (**f**) The variation trend of the fitting slope. narrow and sharp peak value under negative oblique incidence compared with that in the positive incident direction. Compared to studies relevant to SV oblique wave or vertical incidence [1; 17; 22; 24], this work systematically investigates the relationship among incident angle, ground motion intensity parameters, and structural response indices. This analysis underscores the distinct characteristics of dam seismic response under oblique wave incidence, highlighting the necessity of explicitly incorporating wave polarization and incident direction within seismic assessment frameworks. Table 2 lists the six fitted coefficients for the damage and displacement responses under different motion groups in the 3D prediction model. The most critical incident angle can be determined based on the seismological parameters and prediction surfaces, resulting in the identification of the maximum seismic response. These numerical results clarify the seismic response patterns and reveal the failure mechanisms of the dam body under oblique P wave incidence, providing solid evidence for the practical engineering of concrete dams in proximity to fault regions.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 3 System Model
### Antenna Model  
The omnidirectional antenna can receive the RF signal from all directions, so its antenna gain \(G_{r}\) is one. Directional antennas can only receive RF signal in a fixed direction, so the antenna gain \(G_{t}\) is expressed by Equation (3), where \(\theta_{t}\) is the transmission angle. The value of \(\theta_{t}\) is determined by the difference of two azimuth angles used to indicate the area covered by the RF signal.  
\[G_{t}=\frac{2\pi}{\theta_{t}} \tag{3}\]  
Because the realistic directional antenna model is complex and difficult to implement, we adopt a typical simplistic antenna model, a sector model, instead of a realistic directional antenna model, as shown in Figure 2. Given a transmission angle, \(\theta_{t}\), the dotted line represents a realistic directional antenna's beam. We can see that there are few beams behind the directional antenna. The solid line represents the beam of the sector model.
## 4 Simulation Results
### Tree Topology WSNs
#### 4.2.2 Delay Evaluation  
Figure 13 shows the average delay of each scenario and each method. In this study, the delay is defined as the time from the focused packet transmission to arrival at the coordinator. The lower the delivery ratio becomes, the more packet retransmission occurs. If the focused packet is not successfully transmitted within the active period, the packet is  
Figure 11: PDR of TMP in scenario 3.  
Figure 12: PDR of proposed method in scenario 3.  
newly transmitted at the next superframe, which causes extreme delay. In Scenarios 1 and 3, the delay of TMP increases due to the decrease in PDR.
## 2 Results and Discussion
### Effect of a Protecting Needle Gauge Size  
Commercial SPME fiber assemblies are available with two different sizes of a protecting needle 24 ga and 23 ga having an internal diameter (I.D.) 310 and 340 \(\upmu\)m, respectively. A cross-section area of the 23 ga needle is 20.3% greater than that of 24 ga needle, which (according to Equation (1)) should result in the proportionally greater amount of an analyte extracted by a 23 ga SPME assembly. However, as shown above, faster extraction rates result in a faster saturation of the coating and lower recovery at longer sampling times. According to the results of COMSOL simulations, despite \(\sim\)19% greater amounts of extracted analytes compared to a 24 ga assembly, sampling with a 23 ga assembly provided similar recoveries of analytes. Such results can be explained by considering the effect of a greater space between the coating and the internal wall of the protecting needle allowing faster diffusion of analytes to the side and rear sides of the coating (Figure 5). This is consistent with recent experimental observations where straight glass GC liners were used (actual measured I.D. is \(\sim\)0.84 mm compared with the nominal 0.75 mm I.D.) instead of SPME needle for sampling with retracted fiber [20]. Thus, TWA sampling using 23 ga SPME assembly is recommended over 24 ga for achieving lower detection limits without negative impact on the accuracy. All further modeling was conducted using a 23 ga SPME device.
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 1 Introduction
However, he only explained the source of the unbalanced current and did not analyze the specific process of interference on the equipment. [PERSON] analyzed the influence of the unbalanced traction return current and the anti-interference direction of the track circuit [13]. [PERSON] et al. analyzed the characteristics of the signal current in the adjustment state and the broken rail state of the broken rail detection circuit. Based on the analysis of the distribution and characteristics of the traction return current under the AT power supply mode, the mechanism of the unbalanced traction return current interfering with the broken rail detection circuit was deeply analyzed [14]. Yet, they did not analyze the mechanism of the influence on the key components inside the choke transformer and relay. Therefore, this paper studies the problems which are not discussed by the other scholars above, and it analyzes the action process and influence of the unbalanced current of the specific type of track circuit on equipment. The conclusion of this paper can play an important role in the research of restraining the interference of the track circuit. Because of the above problems, this paper studies the mechanism of 25 Hz phase-sensitive track circuit equipment misoperation based on an unbalanced current. Section 2 studies the causes of the red ribbon problem, analyzes the mechanism and causes of the unbalanced current caused by the traction unbalanced current and the grounding wire, and discusses the interference of the unbalanced impulse current intrusion on the choke transformer coil and the binary two-bit relay wing plate and coil. In Section 3, the excitation current, secondary voltage, magnetic flux density, core loss of choke transformer, magnetic flux density and core loss of relay under different unbalanced impulse currents are simulated and the causes of misoperation are analyzed. In Section 4, through the unbalanced current simulation test, the unbalanced current test in the driving process, the ground wire test, combined with the test results, this paper analyzes and verifies the causes of the abnormal red ribbon phenomenon induced by the unbalanced current affecting the relay misoperation. It is concluded that under the interference of unbalanced current, the choke transformer will have magnetic saturation, which will affect the relay coil voltage and cause misoperation.
## 4 Results
### Major Elements  
The major element compositions of the sedimentary strata are listed in Table 1. The Wenquanhe Formation and Guxiangtun Formation yield similar chemical compositions. The sediments from the Wenquanhe and Guxiangtun Formation have SiO\({}_{2}\) contents of 69.62-77.03 wt.% (average 74.35 wt.%) and 67.32-78.17 wt.% (average 75.16 wt.%), respectively. The Al\({}_{2}\)O\({}_{3}\) content of sediments are 9.72-15.03 wt.% (average 11.67 wt.%) and 9.37-15.60 wt.% (average 11.11 wt.%), which exhibit variations with the content of clay minerals. The high SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) contents indicate a higher compositional maturity. Compared to the upper continental crust (UCC) [30], the sand compositions exhibit similar TiO\({}_{2}\) and K\({}_{2}\)O contents. Moreover, they are depleted in other major elements, such as Fe\({}_{2}\)O\({}_{3}\), MgO, MnO, CaO, and Na\({}_{2}\)O.  
Major element compositions are often employed in the classification of sedimentary rocks to distinguish mature from immature rocks [31]. For example, the SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) ratio [32] reflects the relative abundance of quartz vs. clay or feldspar, while the Fe\({}_{2}\)O\({}_{3}\)/K\({}_{2}\)O ratio allows for better identification of arkoses from litharenite [29]. The log(SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\)) vs. log(Na\({}_{2}\)O/K\({}_{2}\)O), and log(SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\)) vs. log(Fe\({}_{2}\)O\({}_{3}\)/K\({}_{2}\)O) diagrams show that most of the sand samples from the Songhujiang River fall in the litharenite and arkoses field (Figure 4). The composition of the sand is arkoses.
# Colored 3D Path Extraction Based on Depth-RGB Sensor for Welding Robot Trajectory Generation
## 5 Conclusions  
To improve the efficiency of programming welding robots, this study proposed a color point cloud segmentation system to extract 3D paths. The major conclusions are generalized as follows:  
1. A welding robot sensor based on stereo vision and RGB sensor was implemented in this paper that could finish the 3D color reconstruction task of welding workpiece, with a reconstruction standard deviation less than 1 mm, which is a parameter comparable to that shown by [PERSON] [33] for similar devices.
2. In order to achieve quick and robust weld 3D path extraction, a color segmentation based on color point cloud reconstruction was performed, with thresholds in HSV color space and an interpolation of the segmented points. The trajectory extraction results show errors close to or below 1.1 mm for V-type butt joint and below 0.6 mm for a straight butt joint, comparable with other stereo vision studies; for example, [PERSON] et al. [20] show that the measurement resolution is less than 0.7 mm for V-type butt joint, and in contrast, [PERSON] et al. [23] show a pose accuracy RMSE of 0.8 mm for a cylinder butt joint using a RealSense D415 sensor.
3. In addition to the above, the adaptability of the proposed trajectory extraction system, due to being a global capture system, shows results that encourage experimentation in V-type welding as one of the more studied in the literature, but also in other types of welding that would give a differential over most of the proposals found in the literature.  
In the future, we aim to improve and complete our work. Firstly, we plan to conduct experiments on different test pieces and demonstrate that the proposed method is also suitable for different weld beads. In addition, we seek to analyze and extract the trajectory without applying a color marker, looking for the shadows or shines that are generated in the welding region. Finally, the measurement precision needs to be improved with a quality test of the proposed method against a laser sensor.  
Conceptualization, J.B.R.-S., E.C.-U. and A.G.-E.; methodology, J.B.R.-S., E.C.-U., J.A.E.C., R.L.S. and A.G.-E.; software, J.B.R.-S., J.A.E.C. and R.L.S.; validation J.B.R.-S.; formal analysis, J.B.R.-S., E.C.-U., J.A.E.C., R.L.S. and A.G.-E.; investigation, J.B.R.-S.; resources, J.B.R.-S. and J.A.E.C.; data curation, J.B.R.-S.; writing--original draft preparation, J.B.R.-S.; writing--review and editing, J.B.R.-S., E.C.-U., J.A.E.C., R.L.S. and A.G.-E.; visualization, J.B.R.-S.; supervision, E.C.-U. and A.G.-E.; project administration, J.B.R.-S. and A.G.-E.; funding acquisition, A.G.-E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 11: Target straight butt joint trajectory vs. computed trajectory.
# One-Dimensional Modeling of the Pressure Loss in Concrete Pumping and Experimental Verification
## 3 Concrete Pumping One-Dimensional Flow Model Construction
### Calculation of the Equivalent Diameter of Nonspherical Particles  
The variation in the flow velocity within the lubrication layer is mainly concentrated within the flow layer around the coarse aggregate particles, and the friction loss in which mechanical energy is converted into heat energy is also mainly concentrated in this region, that is, in the contact area between the lubrication layer and the coarse aggregate particles. Additionally, the size of the coarse aggregate particles affects the friction loss in pumping. The hydraulic radius is used in hydrodynamics to construct a connection between them, and the hydraulic radius \(R_{1}\) is  
\[R_{1}=\
icefrac{{V_{a}}}{{\rho}} \tag{20}\]  
Here, \(V_{a}\) is the volume of coarse aggregate particles, and \(S_{a}\) is the surface area of coarse aggregate particles. Coarse aggregate particles have complex shapes and different sizes, and the particle volume and surface area are difficult to directly measure. In this paper, a number of coarse aggregate particles are randomly selected, and the volume of the coarse aggregate particles \(V_{c}\) is measured via the drainage method.  
\[V_{c}=\sum_{i}^{k}V_{i} \tag{21}\]  
Here, \(k\) is the number of coarse aggregate particles selected, and \(V_{i}\) is the drainage volume of the \(i\)-th coarse aggregate particle.  
Based on the volume of aggregate particles, the average density is  
\[\rho_{c}=\frac{m_{C}}{\left/{V_{C}}\right.} \tag{22}\]  
For aggregate particles with complex shapes, a HandySCAN noncontact 3D laser scanner and the reverse modeling software Geomagic Design X(v.19.0.2) are used for geometric reconstruction, as shown in Figure 3. By using the geometric surface area statistics function of the 3D CAD 2023 software, the total surface area \(S_{a}\) of the selected aggregate particles is calculated.  
\[S_{a}=\sum_{i}^{k}S_{i} \tag{23}\]  
Here, \(S_{i}\) is the surface area of the \(i\)-th aggregate particle counted by the 3D software.  
Let the hydraulic radius of nonspherical particles be equal to that of spherical particles; then, the equivalent diameter \(K\) of nonspherical particles is calculated as follows:  
\[K=\frac{6V_{a}}{\left/{S_{a}}\right.} \tag{24}\]
## 3 Results and Discussion
### Decision Trees  
A second simple and easy to interpret algorithm is the DT. Despite not so accurate comparing to other decision tree methods (e.g., random forests), gradient boosting is still a good alternative for regression and classification problems. This algorithm splits the predictor space into several regions and makes predictions based on the mean of the training observations of the corresponding region [29]. Starting from the root node, a decision tree is constructed via recursive partitioning. Each node is then split into two child nodes, based on the observation parameter that results in the maximum information gain. This process, known as recursive binary splitting, continues until the leaves are pure (i.e., samples at each node belong to the same class). The two basic steps of the DT process include the following [29]:  
* Predictor space division into \(J\) distinct and non-overlapping regions, \(R_{1}\), \(R_{2}\),... \(R_{J}\). The criterion to determine the optimal split point is to minimize the RSS given by, \[\sum_{j=1}^{J}\sum_{i\in R_{j}}\left(y_{i}-y_{R_{j}}\right)^{2}\] (6) where \(y_{R_{j}}\) is the mean response for the training observations in the \(j\)-th box.
* For every observation falling into a certain region, the prediction emerges from the response mean value based on the training observations that belong to the same region.  
Figure 4: Performance metrics plot for KNN algorithm against different number of neighbors.  
Figure 5 shows the first three levels of the decision tree to predict RSSI. The root node assigns observations where T \(\leq\) 299.65 to the left branch with a corresponding mean RSSI value of 423.452. The right branch (i.e., false outcome) leads to a child node that is further split based on the solar flux value and a corresponding mean RSSI value of 375.092, as shown in more detail in Figure 6. That is, for any training observation that includes a solar flux parameter value less than 4.9, the left branch will be selected and the corresponding values for the dew point parameter will then be examined.  
The process described above can be followed until different depth levels are reached. However, there is a critical number of levels that improve the fit to the training data but decreases the fit to the test data; in other words, an overfitting effect comes into play. This issue is shown in Figure 7, where up to a tree depth of nine, the training and testing R\({}^{2}\) values coincide. For larger tree depths, the testing R\({}^{2}\) values tend to converge to a value of 0.9 and no more improvement can occur. For this model, a tree depth of 20 is selected, as it provides the optimum performance metrics values for the testing subset. Larger tree depth values can result in a decrease of model performance.  
Figure 5: Decision Tree model for RSSI prediction. Each node depicts the split selection parameter, the resulted mean squared error (MSE) of the split, the number of samples included to the specific region and the mean RSSI value based on the samples of the region.  
Figure 6: Resulting child node for RSSI.
###### Abstract  
Wheat straw (WS) is considered a favourable substrate for biogas production. However, due to its rigid structure and high carbon to nitrogen (C/N ratio), its biodegradability during anaerobic digestion (AD) is usually low. In the present study, the effect of steam explosion pre-treatment on WS, combined with C/N adjustment with inorganic nitrogen, on biogas production was evaluated. Additionally, co-digestion of WS with protein-rich agri-industrial by-products (dried distillers' grains with solubles (DDGS) and rapeseed meal (RM)) was assessed. Steam explosion enhanced biogas production from WS, whereas the addition of NH\({}_{4}\)Cl was beneficial (\(p<0.05\)) for the digestion of steam-exploded wheat straw (SE). Furthermore, mono-digestion of the four different substrates seemed to be efficient in both inoculum to substrate ratios (I/S) tested (3.5 and 1.75 (_w/w_)). Finally, during co-digestion of WS and SE with DDGS and RM, an increase in the cumulative methane production was noted when higher amounts of DDGS and RM were co-digested. This study demonstrated that DDGS and RM can be used as an AD supplement to stimulate gas production and improve wheat straw biodegradability, while their addition at 10% on an AD system operating with WS can enhance gas yields at levels similar to those achieved by steam-exploded straw.  
Keywords:anaerobic digestion; co-digestion; steam explosion; C/N; wheat straw; rapeseed meal; DDGS; biogas +
Footnote †: journal: Applied Sciences
## 3 A Single Traversal DP Algorithm  
A dynamic programming based ASP solver, such as DynASP2 [11], splits the input program \(P\) into \"bag-programs\" based on the structure of a given nice tree decomposition for \(P\) and evaluates \(P\) in parts, thereby storing the results in tables for each TD node. More precisely, the algorithm works in the following steps:  
1. Construct a graph representation \(G(P)\) of the given input program \(P\).
2. Compute a TD \(\mathcal{T}\) of the graph \(G(P)\) by means of some heuristic, thereby decomposing \(G(P)\) into several smaller parts and fixing an ordering in which \(P\) will be evaluated.  
Figure 1: Semi-Incidence graph of the program given in Example 1 (**upper**) and a tree decomposition of this graph (**lower**). The dashed lines indicate that where nodes have been omitted, which would be required to make the tree decomposition nice.  
3. For every node \(t\in T\) in the tree decomposition \(\mathcal{T}=((T,E,n),\chi)\) (in a bottom-up traversal), run an algorithm \(\mathbb{A}\), which we call _table algorithm_, and compute a \"table\" \(\mathbb{A}\)-Tabs[\(t\)], which is a set of tuples (or _rows_ for short). Intuitively, algorithm \(\mathbb{A}\) transforms tables of child nodes of \(t\) to the current node, and solves a \"local problem\" using bag-program \(P_{t}\). The algorithm thereby computes (i) sets of atoms called (local) _witness sets_ and (ii) for each local witness set \(M\) subsets of \(M\) called _counter-witness sets_[11], and directly follows the definition of answer sets being (i) models of \(P\) and (ii) subset minimal with respect to \(P^{M}\).
4. For root \(n\) interpret the table \(\mathbb{A}\)-Tabs[\(n\)] (and tables of children, if necessary) and print the solution to the considered ASP problem.  
```
In: Table algorithm \(\mathbb{A}\), nice TD \(\mathcal{T}=(T,\chi)\) with \(T=(N,\cdot,n)\) of \(G(P)\) according to \(\mathbb{A}\). Out: \(\mathbb{A}\)-Tabs: maps each TD node \(t\in T\) to some computed table \(\tau_{t}\). for iterate \(t\) in post-order(\(T\),\(n\))do  Child-Tabs\({}_{t}:=\{\mathbb{A}\text{-Tabs}[t^{\prime}]\mid t^{\prime}\text{ is a child of }t\text{ in }T\}\) \(\mathbb{A}\text{-Tabs}[t]\leftarrow\mathbb{A}(t,\chi(t),P_{t},\text{at}\text{ -Tabs}_{t})\)
```  
**Algorithm 1**Algorithm 2: Algorithm \(\mathbb{DP}_{\mathbb{A}}(\mathcal{T})\) for Dynamic Programming on TD \(\mathcal{T}\) for ASP [11].