esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Accuracy Assessment of Spatial Decomposition Results Using Twelve Regression Models
The population distribution in the study area presented an obvious spatial pattern: high in the center and low in the periphery (as seen in Figure 6). Specifically, the population density was highest in the northeast part of YueXiu district, LiWan district, and TianHe district. The cells with high population density were mainly distributed in the range of 5 km to the north of the Pearl River channel and 3 km to the south of the channel. The population in the northern part of the Pearl River channel was higher than that in the southern part of the Pearl River channel. It was demonstrated that the spatial pattern illustrated by the gridded population data was clearer than the demographic data. The relative error map of each subdistrict is illustrated in Figure 7 in order to evaluate the KNN model results at the subdistrict scale. As can be seen from Figure 7, most of the populations of the subdistricts in LiWan district, YueXiu district, and the western part of HaiZhu district were underestimated. Meanwhile, most of the populations of the subdistricts in TianHe district, the northern part of HuangPu district, and the eastern part of HaiZhu District were overestimated. Specifically, the subdistricts with an RE greater than 50% included Taihe, Zhongluotan, Yongping, Junhe, Shijing, Songzhou, Jinsha, Tongde, Tangjing, Xinshi, Sanyuanli, Tonghe, Liuhua, Fenghuang, Yuangang, Changxing, Xintang, Huangcun, Zhuji, Qianjin, Dasha, Suitong, Huazhou, Nanzhou, Pazhou, Liede, Xiancun, and Linhe. The subdistricts with an RE lower than \(-\)32% included Changzhou, HuangPu, Shayan, Nanshitou, Ruibo, Meihucun, Jianshe, Dadong, Nanhuaxi, Renmin, Zhanqian, Xicun, Nanyuan, Caihong, Changhua, Fengyuan, Duobao, Lingnan, ShiweiTang, Huadi, Dongjiao, Baihedong, and Dongsha. Based on the demographic data, it was found that the KNN algorithm generally underestimated populations in the subdistricts with higher population density, and overestimated them in the subdistricts with lower population density. Combined with the spatial pattern of the ancillary data, such as POI, night lights data, and so on, we found that in the areas where various modeling data were densely distributed, the prediction results of the KNN algorithm were mostly underestimations. On the contrary, in the area where the ancillary data were sparsely distributed, the prediction results of the KNN algorithm were mostly overestimations. A reasonable inference is that due to the fact that the differences in the socio-economic environment within the region were ignored, a single or global  
Figure 7: The relative error map of the KNN model. KNN model may have led to unreasonable estimations in the model output. According to previous studies, the zonal strategy can effectively solve the shortcomings of the global model by using secondary partition modeling [23; 63]. Therefore, the zonal strategy should be given priority when dealing with the shortcomings of the global model.
## 2 Materials and Methods
### Data Processing and Analysis
#### 2.3.1 Acoustic Analysis Procedures  
After the speech samples were collected, trained phoneticians segmented the sono-rant portion of each syllable manually using Praat (Version 6.3.10) [36]. Only the sono-rant portions were included in the analysis because they are the lexical tone-bearing units. Following the segmentation, 20 time-normalised fundamental frequency (F0) values were extracted from the sonorant portion of each token using a Praat script ProsodyPro [37]; for other options, we used the default in the script: F0 range of 75-600 Hz, and F0 sample rate of 100 Hz. To eliminate pitch range differences across genders and speakers, the F0 values, originally measured in Hz, were converted to \(T\)_values_ ranging from 0 to 5 using the following equation:  
\[T_{m}=\frac{\log F_{m}-\log min_{n}}{\log max_{n}-\log min_{n}}\times 5 \tag{1}\]  
where \(T_{m}\) and \(F_{m}\) represent the converted \(T\)_value_ and the original F0 value at the \(m^{th}\) time point, respectively, and \(max_{n}\) and \(min_{n}\) stand for the maximum value and the minimum value of all the original F0 values of the \(n^{th}\) speaker. The converted \(T\)_values_ are thus comparable across genders and speakers and can be directly compared with the tone letters in Table 1.  
To analyse the tone trajectories, we fitted generalised additive models (GAMs) with the'mgcv' package [38] in R (Version 4.5.0) [39; 40]. GAMs were chosen because the relationship between tone production and other variables may not be linear. To compare the production of each tone between the two groups, we fitted six models with \(T\)_value_ as the dependent variable, _group_ as the main predictor, and _normalised time_ as a smooth term. An interaction between _group_ and _normalised time_ was included to detect potential differences in tonal contour between the two groups. To compare the production of the six tones within each group, we fitted two models with \(T\)_value_ as the dependent variable, _tone_ as the main predictor, and _normalised time_ as a smooth term. An interaction between _tone_ and _normalised time_ was included to investigate differences in the tonal contour of the six tones. Additionally, in each model, _repetition, context_ and _syllable_ were included as controls, and random intercepts by the speaker were included to account for individual differences. A \(p\)-value of 0.05 is set as the threshold for statistical significance.
###### Abstract  
The accurate detection and precise spatial localization of rebar intersection points are essential for advancing automation in construction tasks, such as robotic rebar tying. This paper presents a vision-based methodology that integrates RGB-D sensing, camera calibration, and coordinate transformation techniques to robustly detect and localize rebar crossing points. A structured detection framework efficiently extracts intersection coordinates from RGB-D imagery, subsequently mapping these points to a global reference frame using extrinsic camera calibration parameters. To achieve comprehensive site coverage and optimize operational efficiency, the path planning challenge is reformulated as a sequencing optimization problem of the identified intersections. We propose a greedy optimization algorithm that generates smooth, snake-like traversal paths in an efficient manner. Experimental validation confirms the effectiveness of our approach, demonstrating detection accuracy exceeding 99%, an average processing time below 125 ms per intersection point, and a maximum coordinate transformation error under 2 mm. The presented solution offers a lightweight, precise, and scalable framework, significantly facilitating the integration of vision-based methods into automated construction workflows.  
automated construction; rebar intersection detection; vision-based detection; path planning; coordinate transformation; RGB-D sensing Article  
1  
[PERSON], et al. (2020), 2020 IEEE/RSJ International Conference on Robotics and Automation (ICRA), Vol., pp. 203-204, 2010. External Links: Document Cited by: SS1.
# Characterization of Deburring by Abrasive Flow Machining for AL6061
## 3 Preliminary Testing
### Computational Fluid Dynamics (CFD) Model  
Local flow conditions near the burr edge, characterized by wall shear stress, streamline geometry, and flow speed, are essential for estimating the objective function. For this purpose, a CFD model was constructed using ANSYS 2019 R1 Fluent. The specimen for the preliminary test was modeled with 706,589 tetrahedral elements and 119,166 nodes, as shown in Figure 14. The element size ranged from 0.25 mm near the burr edge to 5 mm beyond the flow channel. The analysis employed the \(\kappa\)-\(\omega\) SST model, which offers an advantage when analyzing the wall flow. The viscosity was set to 43.8 poise and the density to 1141 km/m\({}^{3}\).  
Figure 11: Deburred edge with C0.2 and C0.3 chamfers at the flow speed of 7.8 m/s: (**a**) before deburring; (**b**) C0.2 at step 3; (**c**) C0.3 at step 6.  
Figure 12: C0.3 chamfer achieved at the final step six at various flow speeds: (**a**) 3.3 m/s; (**b**) 4.5 m/s; (**c**) 5.6 m/s; (**d**) 6.7 m/s; (**e**) 7.8 m/s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Step of Deburning Speed** & **3.3 m/s** & **4.5 m/s** & **5.6 m/s** & **6.7 m/s** & **7.8 m/s** \\ \hline Step 1 & 0.015 & 0.009 & 0.010 & 0.009 & 0.010 \\ Step 2 & 0.021 & 0.019 & 0.019 & 0.014 & 0.013 \\ Step 3 & 0.030 & 0.027 & 0.029 & 0.029 & 0.023 \\ Step 4 & 0.044 & 0.037 & 0.041 & 0.036 & 0.035 \\ Step 5 & 0.053 & 0.050 & 0.053 & 0.049 & 0.045 \\ Step 6 & 0.063 & 0.058 & 0.062 & 0.060 & 0.055 \\ \hline \end{tabular}
\end{table}
Table 5: Deburred areas measured for each of the six steps (Table 4) at different flow speeds (in mm\({}^{2}\)).  
Due to the no-slip boundary condition, streamlines could not be generated at the burr edge. To address this problem, streamlines passing through the 0.1 mm offset position from the burr edge, along the angular bisector, were chosen as shown in Figure 15a. The shear stress was estimated along the burr edge, as shown in Figure 15b.
## 5 Conclusions  
1. The parameter \(E_{1j}^{i}/E_{0j}^{i}\) has no impact on the dissipation of EPAP and EPWP during the early stage. As \(E_{1j}^{i}/E_{0j}^{i}\) increases, the coefficient of consolidation becomes larger, and the dissipation of EPWP occurs earlier and more rapidly in the later stages.
2. The influence of the viscosity coefficient \(\eta\) on EPAP and EPWP is very small. However, a larger viscosity coefficient \(\eta\) slows the rate of soil deformation, leading to slower consolidation.
3. The permeability coefficients of the air and water phases both change during the consolidation process. The permeability coefficient of the air phase returns to the initial value after the completion of consolidation, while the permeability coefficient of the water phase is lower than the initial value.
4. Considering the variations of the permeability coefficient of the water phase and air phase, the dissipations of EPAP and EPWP will accelerate. Via comprehensive analysis, it can be found that \(k_{\text{a}}\) is the greater influencing factor in the early stage of the consolidation and \(k_{\text{w}}\) is more important in the later stage.  
Writing--original draft and writing--review and editing: S.D.; methodology, software, funding acquisition, and writing--review and editing: L.J.; resources, formal analysis, writing--review and editing, and funding acquisition: A.Q.; data curation and writing--review and editing: Y.L. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Changes in (**a**) EPAP, (**b**) EPWP, and (**c**) U\({}_{\text{S}}\) with time at different permeability coefficients.  
**Funding:** This research was funded by the Natural Science Foundation of China (42072292), Quzhou University Research Start-up Funding (005225003).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Raw Material Purchasing Optimization Using Column Generation
## 3 Method
### Model
#### 3.2.3 LP Relaxation Solution  
Suppose that for each item \(i\in\Omega\), a subset of feasible schedules \(\Omega_{i^{\prime}}\) is already found and the RMP has a feasible solution \(\theta\), and let \(\mathbf{U}\), \(\mathbf{V}\) be the associated dual solutions and that the unrestricted dual variables \(u_{i}\left(i\in\Omega\right)\) are associated with the partitioning constraints and the negative dual variables \(v_{kt}\) (\(k=1,\ldots,K;t=1,\ldots,T\)) are associated with the \(k^{\text{th}}\) resource constraint. From linear programming duality we know that \(\theta\) is the optimal solution for LP if and only if for any \(i\in\Omega\) and any \(j\in\Omega_{i}\), the reduced cost \(\beta_{ij}\) is non-negative, i.e.,  
\[\beta_{ij}=b_{ij}\ -\sum_{t=1}^{T}\sum_{k=1}^{K}v_{kt}r_{ki}x_{ijt}\ -u_{i}\geq 0,\ \text{for all}\ i\in\Omega,\ j\in\Omega_{i} \tag{13}\]  
Testing the optimality of \(\theta\) for LP can thus be done by solving the following pricing problem:  
\[\beta_{i}=\underset{j}{\text{minarg}}\ \sum_{t=1}^{T}\ ((p_{it}-\sum_{k=1}^{K}v_{ kt}r_{ki})x_{ijt}+s_{it}Y_{ijt}+h_{it}I_{ijt})\ -u_{i},\ \text{for all}\ i\in\Omega \tag{14}\]  
If \(\beta\geq 0\) for all \(i\), then \(\theta\) is optimal for the LP relaxation, otherwise the \(i,j\) define a new entering basis column (clearly \(\beta_{ij}<0\)), Let \(\Omega_{i^{\prime}}=\Omega_{i^{\prime}}\cup\{j\}\), resolving the RMP.
# The Biocompatibility of Wireless Power Charging System on Human Neural Cells
## 3 Results
### The Effect of EMF on Cell Morphology and Viability  
To evaluate changes in cell morphology and density, we analyzed neural cells by light microscopy immediately after EMF exposure as well as after 44 h of regeneration (Figure 10). We did not observe any changes in cell morphology or affection of cell adherence, neither any dead cells presence.  
Normal healthy astrocytes (HA) retained their elongated shape, the cells were mitotically active even after 44 h of regeneration (2.1 \(\times\) 10\({}^{5}\) cells in control; 2.0 \(\times\) 10\({}^{5}\) cells in exposed sample). Similar observations were found in samples with cancer cells. T98G and SH-SY5Y did not have their proliferative activity negatively affected after 44 h of regeneration compared to the control group of cells (5.5 \(\times\) 10\({}^{5}\) cells in control; 5.3 \(\times\) 10\({}^{5}\) cells in exposed sample).  
Subsequently, we analyzed the impact of EMF on cell viability and basal energy metabolism using the MTT assay (Figure 11). In normal astrocytes (HA), we observed a slightly increased metabolic activity up to 12%, depending to location in EMF. Moreover, there were no significant differences in cell proliferation between exposed and control samples. Cancer cells (T98G as well as SH-SY5Y) were exposed along healthy cells. Therefore, we could compare the obtained results between different cell types. There was an increase in cell viability in cancer cells (6-10% in T98G, 2-9% in SH-SY5Y), but the increase was not statistically significant. These results may suggest that exposure of neural cells to 87 kHz, 1.2 mT for 30 min could not trigger any cytotoxic responses and is likely not harmful to cells.  
Figure 9: Experimental set-up of the wireless power transfer system (**left**) and detail of location of biological samples within the coils coupling system (**right**).  
Figure 11: Effect of EMF on cell viability of HA, T98G and SH-SY5Y analyzed by MTT assay. Cells were exposed to 87 kHz for 30 min and then regenerated for another 44 h at standard cultivation conditions. The control group represents 100% viability. Data are mean \(\pm\) SD. Data are representative of three independent experiments with 12 replicates each.  
Figure 10: Cell morphology of human neural cells (HA, T98G, SH-SY5Y) analyzed by light microscopy. Cells before EMF exposure (first row), cells directly after exposure (87 kHz EMF for 30 min; second row) and cells after 44 h of regeneration in standard conditions (third row). Representative photomicrographs were taken at phase contrast at magnification \(100\times\).
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## Case Study: Spatial Interpolation of Meteorological Data in Thailand
### Accuracy Assessment  
For model validation, we used the k-fold cross-validation technique, in which the data were partitioned into 10 folds of approximately equal size. Each fold was selected once for testing the model induced from the remaining nine subsets. The process was repeated 10 times until every fold was used as the testing dataset. The overall model performance was subsequently evaluated by averaging the 10 errors.  
Our work compared three main kriging models: OK, moving window kriging with a fixed window size, and adaptive moving window kriging using the K-means clustering algorithm (AMWKK). In addition, two different criteria for selecting a window size for the moving window kriging were carried out, namely, the fixed prescribed number (30 positions) (MWK) and the K-means clustering algorithm (MWKK). To assess the estimation ability of each model, we computed distinct quantitative performance metrics, including the mean absolute percentage error (MAPE), root mean square error (RMSE), percentage average estimation error (PAEE), and normalized mean squared error (NMSE). They are formulated as follows:  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\Bigl{|}\frac{Z(x_{i})-\hat{Z}(x_{i})}{Z( x_{i})}\Bigr{|}\times 100, \tag{11}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(Z(x_{i})-\hat{Z}(x_{i}))^{2}}, \tag{12}\]  
\[\text{PAEE}=\frac{1}{n\hat{Z}}\sum_{i=1}^{n}\bigl{|}Z(x_{i})-\hat{Z}(x_{i}) \bigr{|}, \tag{13}\]  
\[\text{NMSE}=\frac{1}{ns^{2}}\sum_{i=1}^{n}\bigl{(}Z(x_{i})-\hat{Z}(x_{i}) \bigr{)}^{2}, \tag{14}\]  
where \(n\) is the number of observations, and \(Z(x_{i})\) and \(\hat{Z}(x_{i})\) represent the actual measured data and the estimated value at position \(x_{i}\), respectively. \(\hat{Z}\) is the average and \(s^{2}\) is the variance of the actual measured data.  
A relative improvement (R\({}_{\text{I}}\)) regarding RMSE values of moving window kriging variants over the ordinary kriging model was also exploited as another measure for accuracy evaluation, and is defined as  
\[\text{R}_{\text{I}}=\frac{\text{RMSE}_{\text{OK}}-\text{RMSE}}{\text{RMSE }_{\text{OK}}}\times 100\%. \tag{15}\]
## 3 Results and Discussion
### Optimisation of Target Variables
Thermal expansion of these spindles will lead to increased errors and, therefore, changed values of the nozzle feed rate;
* The accuracy of the PLC temperature setting is \(\pm\)1 \({}^{\circ}\)C. These deviations can also contribute to scatter in the measurement results;
* The inner diameter of the fibre nozzle is larger than the thickness of the roving. The result is an increased play of the roving inside the fibre nozzle, and this play can lead to the uncontrolled placement of the roving in part;
* Other influences, such as the position of the specimens in the powder bed, the powder's ageing condition and the rovings' delivery condition, can lead to a deviation in the results.
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 3 Development of the Smart Bolt  
### A Smart Bolt and Finite Element Analysis  
In general, Fiber Bragg Grating (FBG) sensors, strain gauges, and piezoelectric sensors have been studied to monitor the joint bolt [20]. In this study, a form of inserting strain gauges in bolts was conceived as a method for monitoring the joint bolt. A 2-mm-diameter hole was drilled to a depth of 35 mm from the center of the head of an M16 80-mm high-tension bolt installed in the valve in the studied water supply pipe, as shown in Figure 2. An internal strain gauge (BTM-6C for steel; Tokyo Sokki Kenkyujo, Tokyo, Japan) was inserted into the hole and injected with epoxy. An acceleration sensor (3055D4; Dytran Instruments Inc., Chatsworth, CA, USA) was fixed to the smart bolt head. The specifications of sensors in the smart bolt are as follows (Table 1).  
Figure 2: M16 80-mm smart bolt.  
Finite element analysis (FEA) is an analytical method generally used to evaluate a specific object under conditions that simulate the applied environment and is used in various fields [21; 22]. In order to confirm the applicability of the smart bolt, FEA was conducted with ANSYS 2019 R2 to verify the integrity of the bolt depending on the presence or absence of a bolt hole for inserting a strain gauge. The physical properties of the bolt (SM45C carbon steel) are E = 250 Gpa, \(\
u\) = 0.29, \(\sigma\) = 490 Mpa, and \(\sigma\)u = 686 MPa. The bonding condition between the jig and the bolt is bounded, and the boundary condition of the top of the jig is fixed. After the finite element modeling, as shown in Figure 3, the von Mises equivalent stress was examined by moving the lower jig coupled with the bolt 0.1 mm downward.  
Figure 3a shows the boundary condition of the FEA, Figure 3b is the FEA result of the high-strength M16 bolt without the hole, and Figure 3c is the FEA result of the M16 bolt with the hole. Regardless of the presence or absence of a hole, the maximum equivalent stress of the two models was lower than the yield tensile strength. The location of the maximum equivalent stress and the distribution of the equivalent stress inside the bolt were similar. However, in the case of the bolt with a hole, the equivalent stress was about 30% greater than that without a hole. This is thought to be because of an increase in sensitivity in the analysis due to the presence of a large number of micro-sized grids.
## 2 Signal and Channel Model
### Geometrical Relation between the RIS Controller and the Connected Vehicle Position  
Let \(\mathbf{b}=(b_{x},b_{y},b_{z})\), \(\mathbf{r}=(r_{x},r_{y},r_{z})\), and \(\mathbf{u}=(u_{x},u_{y},u_{z})\) are the position of the transmitting vehicle, the RIS controller and the user vehicle, respectively in the Cartesian coordinate as shown in Figure 1, and \(\mathbf{g}\) is the vector of \(\mathbf{G}\) channel. The \(\mathbf{g}\) is given by  
\[\mathbf{g}=\text{vec}(\mathbf{G})=\mathbf{a}_{R}(\theta_{t}^{az},\
u_{t}^{el}), \tag{13}\]  
where \(\theta^{az}\) denotes the azimuth of the angle of departure (AoD) and \(\
and  
\[\
u_{t}^{el}=\arcsin\Bigg{\{}\frac{u_{z}-r_{z}}{||\mathbf{u}-\mathbf{r}||_{2}} \Bigg{\}}. \tag{15}\]  
Similarly, we can make a positional relationship between the transmitting vehicle and the RIS controller as  
\[\theta_{r}^{zz}=\arcsin\Bigg{\{}\frac{r_{x}-b_{x}}{\sqrt{(r_{x}-b_{x})^{2}+(r_ {y}-b_{y})^{2}}}\Bigg{\}}, \tag{16}\]  
and  
\[\
u_{r}^{el}=\arcsin\Bigg{\{}\frac{r_{z}-b_{z}}{||\mathbf{r}-\mathbf{b}||_{2} }\Bigg{\}}. \tag{17}\]  
Using (13), the passive beamforming gain \(\mu_{l,m}\) is computed as  
\[\boldsymbol{\mu}=\mathbf{g}^{H}\Theta\mathbf{g}=[\mathbf{g}\odot\mathbf{g}^{ *}]^{H}\boldsymbol{\vartheta}, \tag{18}\]  
where \(\Theta=\text{diag}\{\boldsymbol{\vartheta}\}\) and the operator \((\cdot)^{*}\) denotes the conjugate, and \(\odot\) denotes the element-wise product. The optimal phase-shift \(\boldsymbol{\vartheta}^{opt}\) is given by  
\[\boldsymbol{\vartheta}^{opt}=\underset{\boldsymbol{\vartheta}}{\arg\max}\,|[ \mathbf{g}\odot\mathbf{g}^{*}]^{H}\boldsymbol{\vartheta}| \tag{19}\]