esa-sceva/satcom-chunk-collection huggingface dataset:
# Compaction Characteristics and Mechanical Response of Gravel-Glass-Rubber Mixtures +
## 4 Experimental Results and Discussions
### Compaction Characteristics of GGRM
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Materials** & \multicolumn{3}{c}{**Dry Density (kg/m\({}^{3}\))**} \\ \hline
**Gravel** & **Rubber** & **Glass** & **Measured \#** & **Prediction** & **Reletions** \\ \(G_{\text{R}}\) & \(R_{\text{B}}\) & \(G_{\text{L}}\) & **G\({}_{\text{L}}\)** & **Equation (4)** & **References** \\ \hline \(1\) & - & - & 1754 & 1754 & [PERSON] et al. [38] \\ \(0.9\) & \(0.1\) & - & 1621 & 1643 & \\ \(0.8\) & \(0.2\) & - & 1514 & 1533 & \\ \(0.75\) & \(0.25\) & - & 1487 & 1477 & \\ \(0.6\) & \(0.4\) & - & 1315 & 1312 & \\ \(0.55\) & \(0.45\) & - & 1248 & 1257 & \\ - & \(1\) & - & 649 & 649 & \\ \hline \(0.6\) & \(0.4\) & - & 1310 & 1310 & This study \\ \(0.45\) & \(0.4\) & \(0.15\) & 1274 & 1277 & \\ \(0.3\) & \(0.4\) & \(0.3\) & 1232 & 1243 & \\ \(0.15\) & \(0.4\) & \(0.45\) & 1223 & 1210 & \\ - & \(0.4\) & \(0.6\) & 1167 & 1176 & \\ \(0.45\) & \(0.55\) & - & 1141 & 1146 & \\ \(0.3\) & \(0.55\) & \(0.15\) & 1132 & 1112 & \\ \(0.15\) & \(0.55\) & \(0.3\) & 1095 & 1079 & \\ - & \(0.55\) & \(0.45\) & 1058 & 1045 & \\ \(1\) & - & - & 1749 & 1749 & \\ - & \(1\) & - & 652 & 652 & \\ - & - & \(1\) & 1526 & 1526 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of standard Proctor compaction tests carried out on dry specimens of GGRM. \begin{table}
\begin{tabular}{c c} \hline
**Mixture Type** & **Equation** \\ \hline Gravel–glass–rubber & \(\rho_{\text{dry(mix)}}=\rho_{\text{dry(gravel)}}\cdot G_{\text{R}}+\rho_{ \text{dry(glass)}}\cdot G_{\text{L}}+\rho_{\text{dry(rubber)}}\cdot R_{\text{B}}\) & (4) \\ Gravel–glass (\(R_{\text{B}}=0\)) & \(\rho_{\text{dry(mix)}}=\rho_{\text{dry(gravel)}}\cdot G_{\text{R}}+\rho_{ \text{dry(glass)}}\cdot G_{\text{L}}\) & (4a) \\ Gravel–rubber (\(G_{\text{L}}=0\)) & \(\rho_{\text{dry(mix)}}=\rho_{\text{dry(gravel)}}\cdot G_{\text{R}}+\rho_{ \text{dry(rubber)}}\cdot R_{\text{B}}\) & (4b) \\ Glass–rubber (\(G_{\text{R}}=0\)) & \(\rho_{\text{dry(mix)}}=\rho_{\text{dry(glass)}}\cdot G_{\text{L}}+\rho_{ \text{dry(rubber)}}\) & \(\cdot R_{\text{B}}\) & (4c) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Predictive equations for the estimation of dry density of GGRM. Figure 5: Compaction-induced glass breakage: (**a**) example of change in PDS before and after compaction for pure green glass; and (**b**) summary of glass breakage for GGRM (\(R_{\text{B}}=0.4\) and \(0.55\)).
## 4 Model to Minimize Cost Dynamically
### General Energy Consumption  
Equation (1) computes the energy consumption based on the dynamic power consumed during the execution. The potency is a relation \(\propto CV^{2}f\), defined in [PERSON] et al. [34]. Each device type \(D_{T}\) has a frequency and a commutative capacitance \(C\) associated with a core \(k\) in the processor, which depends on the chip architecture [25]. The potency is defined in Equation (2).  
A system has a total of \(A=\{1,2,3,\ldots,a\}\) tasks. Each task \(i\) is associated to a tuple \(A_{i}=(C_{c_{i}},sc_{i},d_{i},t_{i})\) composed of CPU cycles (\(C_{c}\)) needed to conclude an execution. The tuple includes the source code (\(sc\)) offloading from IoT to MEC, the input data (\(d\)), and the deadline (\(t\)) associated with the task. Offloading is an advantage in industrial applications to reduce the congestion of data transmission and save energy consumption [13].  
The CPU cycles is a task property. The total execution time of a task is calculated based on total cycles \(C_{cT_{i}}\)[35] where \(i\in A\) to a CPU core \(j\in PL\) in Equation (3). Each local IoT device or MEC server can process zero or more tasks. The deadline associated represents if a task is normal (\(t=0\)) or if it is critical (\(t>0\)).  
\[E_{i,D_{T}(k)}=P_{i,D_{T}(k)}*T_{i,D_{T}(k)} \tag{1}\]  
where,  
\[P_{i,D_{T}}=C_{D_{T}(k)}*V_{D_{T}(k)}^{2}*f_{D_{T}(k)} \tag{2}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \# & **Description** & \# & **Description** \\ \hline \(A\) & The task set that will be executed. & \(k\) & An individual core. \\ \(C\) & Commutative capacitance. & \(P\) & Power consumed. \\ \(C_{c}\) & CPU cycles. & \(P_{i,mcc}\) & The power consumed in the MEC server. \\ \(C_{cT}\) & Total clock cycles. & \(PL\) & A CPU core set. \\ \(Cost_{i,mcc}\) & The total cost in MEC server. & \(pl_{i,n}\) & A core \(n\) of a mobile device \(j\). \\ \(D\) & A set of mobile IoT devices. & \(PS\) & A processor in the MEC server. \\ \(d\) & The input data. & \(r\) & Data transfer rate. \\ \(d_{i}^{\prime}\) & The return to the origin of results. & \(S\) & The number of MEC servers. \\ \(E_{i,mcc}\) & The dynamic energy consumed in & \(sc\) & Source code offloading. \\  & a MEC server by a task \(i\). & \(S_{i}\) & Local MEC server. \\ \(E\) & Energy consumption in idle time. & \(t\) & The deadline associated with the task. \\ \(f_{mcc}\) & Frequency of MEC server process. & \(T\) & Total execution time. \\ \(f\) & Processor frequency. & \(T_{i,mcc}\) & Total execution time in the MEC \\ \(H\) & A set of the available wireless channels. & \(T_{i,mcc}\) & Serial. \\ \(h_{i}\) & The wireless channel associated to & \(V_{local}\) & Voltage in the IoT device process. \\  & task \(i\). & & sor. \\ \(l\) & Mutual interference rate. & \(V_{mcc}\) & Voltage in the MEC server process. \\ \(i\) & An individual task. & \(W_{\rm C}\) & The number of wireless channels. \\ \(j\) & An individual mobile device. & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation adopted for the model description.  
\[T_{i,D_{T}}=\frac{C_{cT_{i}}}{f_{D_{T}(k)}} \tag{3}\]
## 3 Experimental Results and Analysis
### Ablation Experiment
#### 3.3.1 Self-Attention Mechanism and Spatial Attention Module  
The experimental scheme in this subsection uses only the cross-entropy loss function to calculate the loss and compares the following four Vision Transformer network structures: (a) ViT denotes the original Vision Transformer model; (b) ViT(EA) denotes the model after improving the self-attention module in (a) to an efficient self-attention module; (c) ViT(IEA) denotes the model after replacing Instance Normalization in the efficient multi-headed self-attention module with Batch Normalization; and (d) ViT(IEA&5 PA) denotes the model after introducing the spatial attention module based on the improvements made in (c).  
Figure 7: Example of blast furnace tuyere operating conditions: (**a**) Normal: The color inside the tuyere is bright, the edge is smooth, and the dark area of coal spray is clear; (**b**) Falling chunks: The tuyere appears to darken extensively and returns to brightness when the large pieces are melted; (**c**) Material block: There is a clear gray coke gyratory movement in the tuyere; (**d**) Coal break: The edge of the tuyere is smooth, the color inside the tuyere is bright, and there is no dark area of coal spray; (**e**) Hanging slag: The edge of the tuyere is not smooth, and there are dark areas with large ashiness at the hanging slag; (**f**) Blowing down: The coke gyration slows down to a pile-up while the wind gap gradually darkens.  
The effects of the improved self-attention mechanism and the spatial attention module's introduction on the model's recognition accuracy were explored, and the experimental results are shown in Table 2. According to the data in the table, ViT(EA), ViT(IEA), and ViT(IEA&SPA) each increase classification accuracy over ViT by 0.06%, 0.59%, and 0.78%, respectively. Among the four Vision Transformer network architectures, the classification accuracy of ViT(IEA&SPA) reaches the highest value of 97.52%.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 3 Surface Correlation-based Fingerprinting Method
### Generation of Reference Trajectory
#### 3.4.1 Generation of Reference Trajectory in Initial Phase  
Since there is no location information in the initial phase of localization, the current link should be determined using only PCI information and RSS measurement from a terminal device. At first, the proposed fingerprinting method selects candidate links using only PCI information, and then generates reference trajectories using the selected links. A reference trajectory is fixed by correlation the measured RSS pattern and the RSS patterns on the generated reference trajectories. In other words, SSD is the criterion not only for determining the similarity between the RSS patterns, but also for selecting the actual trajectory where the current pedestrian is located among the generated multiple reference trajectories. The detail of this process is given in Figure 6.  
**Step 1**: Generate reference trajectories as follows:  
* Find and select \(n\) candidate links including measured PCI from the fingerprinting database.
* For each of the selected candidate links, generate bi-directional \(2n\) reference.
* If the estimated walking distance \(D\) is larger than the shortest reference trajectory \(L\) before checking _Minimum_\(\rho<\beta\), generate new reference trajectories by combining links connected with the current reference trajectory. Only the newly generated reference trajectories matching with estimated  
Figure 6: Flow chart for the generation of reference trajectory at the initial phase.  
turn event (left, right, or straight) from the Pedestrian Dead-Reckoning (PDR) are used for the correlation process.  
**Step 2**: Calculate the SSD on each reference trajectory using Equation (4).  
**Step 3**: When the pedestrian moves over a certain distance (\(\alpha\)) and the SSD value is stable (_Minimum_\(\rho<\beta\)), the reference trajectory with the minimum SSD is fixed and the location is estimated on the fixed reference trajectory.
## 3 Proposed Methodology
### Data Preprocessing
#### 3.3.3 Data Segmentation  
Due to the volume of signal information obtained by wearable sensors, it is impractical to incorporate all of the data into the HAR system at once. As a result, sliding window segmentation should be performed before feeding data into the model. The sliding window approach is widely employed in HAR for the detection of periodic actions (e.g., running, walking) and static actions (e.g., standing, sitting, and lying) [47]. The unprocessed sensor data are divided into fixed-length windows. A fraction of the neighboring windows overlaps to maximize the quantity of training data samples and prevent the loss of the transition from one activity to the next. Figure 4 illustrates the windowing procedure.  
The sample data that is segmented by a sliding window with size \(N\) is a size of \(K\times N\). The sample \(W_{t}\) is denoted as:  
\[W_{t}=\left[a_{t_{1}}^{1},a_{t_{2}}^{2},\ldots,a_{t_{N}}^{K}\right]\in\mathbb{ R}^{K\times N} \tag{2}\]  
where column vector \(a_{t}^{k}=a_{t_{1}}^{k},a_{t_{2}}^{k},\ldots,a_{t_{N}}^{k}\) is the signal data of sensor \(k\) at window time \(t\), \(T\) is the transpose operator, \(K\) is the number of sensors and \(N\) is the length of the sliding window. To make use of window correlations and perform the training procedure, the window data is divided into the sequencing of windows:  
\[S=\left\{\left(W_{1},y_{1}\right),\left(W_{2},y_{2}\right),\ldots,\left(W_{T},y_{T}\right)\right\} \tag{3}\]  
where \(T\) denotes the window sequence's period, and \(y_{t}\) represents the window's matching action label of \(W\). The most often occurring sample action will be used as the window's label for windows with several activity classes.  
Figure 3: The number of activity samples in the MobiAct v2.0 dataset.
## 6 A Feasibility Analysis of the Optimization Method for the Finished State
### Optimization Results
Figure 14: Bending moment evoked by the dead load in the (**a**) main girder and (**b**) pylon. Figure 15: Normal stress of selected structures under dead load. (**a**) Top flange of main girder; (**b**) side span of pylon; (**c**) bottom flange of main girder; (**d**) main span of pylon. The cable forces under the finished dead load condition are shown in Figure 16. The optimized cable forces increased from the location near the pylon to the transition pier on both sides and were in the range of 4154-1816 kN. The maximum cable force was located in cable J22 on the mid-span side; the minimum cable force was located in cable A1 near the pylon; and the maximum cable force distribution parameter was 153 kN. The multiplier path following optimization yielded positive vertical support reactions at both the transition and auxiliary piers, indicating an adequate pressure reserve. The vertical support reactions ranged from 4153 to 9762 kN. The maximum vertical reaction was observed at the auxiliary piers, while the minimum was observed at the transition piers. The above analysis demonstrates that the multiplier path following method, applied to optimizing the finished state of the Xiangshan Port highway bridge, yielded convergence of the objective function within 30 iterations. At this stage, the optimized profile of the main girder is smooth, with a uniform stress distribution along the top and bottom flanges; the top of the pylon exhibits a predetermined lateral offset towards the side span, with a fully compressed cross-section; the cable forces increase uniformly with the cable length; and positive vertical support reactions are observed at both the transition and auxiliary piers, indicating sufficient pressure reserves. This suggests the reasonableness of the constraint range and the feasibility and efficiency of the optimization method for the finished state.
# Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm
## 2 Materials and Methods
### The Improvement Strategy of PSO
#### 2.4.2 S-curve Type Inertia Weight Nonlinear Decreasing Method  
\(\omega\) is a vital parameter in PSO. In the early iteration, it generally has a larger value with a strong global optimization ability to accelerate the population's evolution toward the dominant population [38]. When the particles approach the optimal solution in the later iteration, it can seek local optima by using smaller values \(\omega\) and performing fine searches to improve convergence accuracy. Thus, we propose an S-curve type inertia weight nonlinear decreasing method based on the tanh function [39]. The inertia weight updating formula is as follows, and Figure 7 shows the inertia weight curve:  
\[\omega=\omega_{\min}+(\omega_{\max}-\omega_{\min})(1+\tanh(2-4 iter/iter_{\max }))/2, \tag{24}\]  
where \(\omega_{\min}\) is the minimum inertia weight value; \(\omega_{\max}\) is the max inertia weight value; tanh() is hyperbolic tangent function; and \(iter_{\max}\) is the max iterations.  
Compared to Linear decreasing, the S-curve decreasing in the early and late iteration of the search is slower change. Maintaining a larger \(\omega\) value in the early iteration is beneficial for global optimization, whereas maintaining a smaller \(\omega\) value in the later iteration is beneficial for local optimization.
## 2 Materials and Methods
### Data Collection  
The dataset for the East Sea comprises CTD measurements collected by survey vessels of the National Institute of Fisheries Science (NIFS) from 1991 to 2021 and stored in the KODC. Each region of the Korean seas is divided into specific lines and stations, where the East Sea is categorized into 8 lines and 60 stations. CTD measurements were conducted every two months, in order to reflect both seasonal and long-term changes [31].  
Although data collection began in 1991, issues such as unclear station information and data discontinuities occurred in the 1990s. Additionally, as summarized in Table 1, external factors such as typhoons, vessel repairs, and logistical challenges occasionally rendered certain data unusable. To ensure consistency in analysis, this study focused on data collected between 2000 and 2021. The KODC dataset provides vertical profiles at 12 standard depth points (0, 10, 20, 30, 50, 75, 100, 125, 200, 250, 300, and 500 m).  
To calculate sound absorption at each station, coefficients were interpolated for these standard depths in order to align with the specific depths of each survey station. This interpolation process was performed under the assumption that temperature and salinity remain constant below 500 m [20]. The interpolation was conducted using the Akima interpolation method, which ensures smooth and accurate results for environmental datasets [32; 33]. When compared with raw CTD data at every 1 m interval, the interpolated results from the standard depths showed minimal differences, validating this interpolation data from the standard depths. These differences are presented in the Supplementary Materials (Figure S5), highlighting the consistency between the interpolated and raw datasets.  
For seasonal analysis, one representative month from each season was selected to evaluate the spatiotemporal variations in absorption parameters. As shown in Figure 2b,  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Day** & **Missing Point** & **Reason** \\ \hline Apr. 2011 & 102, 208, 209 All line & Equipment error \\ Feb. 2017 & 102–00 Point & Unobserved \\ Apr. 2018 & 209–00 Point & Unobserved \\ Dec. 2018 & 208–02 Point & Unobserved \\ Dec. 2019 & 102, 208 All line & Unobserved \\ \hline \hline \end{tabular}
\end{table}
Table 1: Missing CTD data for the East Sea during 2011–2021.  
specific coastal areas with available MBES seafloor data were selected for further analysis. Three stations were chosen, based on the availability of existing MBES datasets: Site A on Line 102, Site B on Line 209, and Site C on Line 208. At these sites, the absorption parameters derived from KODC data were compared with the site-specific SVP measured during MBES surveys, in order to identify deviations and analyze the impacts of the spatiotemporal variability of the water column on backscatter intensity.
## 3 Results
### Seasonal Characteristics of Validation Statistics  
We examined the accuracy of daily GPP predictions according to seasons: spring (March, April, and May), summer (June, July, and August), autumn (September, October, and November), and winter (December, January, and February). Figure 8 shows the CC between the observed and the predicted GPP by the four AI models. The CC of spring and autumn was quite high (around 0.9), while that of summer was relatively low (approximately 0.7 to 0.8). The accuracy of winter was worse than other seasons: the CC for SVM, RF, and ANN was around 0 to 0.3, while the CC for DNN was about 0.45 (Figure 8). The CC values in the cold months from November to March were also low. Nevertheless, the DNN model showed an overall concentrated scatterplot along the 1:1 line compared to the other three models that had a tendency of underestimation in spring and summer and a tendency of overestimation in autumn (Figures 9 and 10).  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Year**} & **MBE (1)** & **MAE (2)** & **RMSE (0)** & **CC** & \multirow{2}{*}{**Model**} & **Year** & **MBE** & **MAE** & **RMSE** & **CC** \\  & **
# Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine
## 4 Introduction of Datasets  
The experiment was carried out using the methane detection system shown in Figure 2. Based on the infrared spectrum absorption characterization of methane gas, the long optical distance differential absorption method for methane detection was studied. The system is mainly composed of light source, filter system, double-chamber, signal collector, and a processing part.  
The light source uses a super light emitting diode (SLED). The power spectrum of the SLED was obtained using the steady-state spectrograph (AQ6317C, YOKOGAWA, Tokyo, Japan), as shown in Figure 3. The filter system uses slits, a collimator, a grating, a focus lens, and plane mirrors to obtain the necessary experimental monochrome. The chamber consists of two parts: reference chamber I and test chamber II. The length of chambers is 0.9 m. Reference chamber I is filled with nitrogen, and the test chamber II. Is filled with the target gas (methane). As shown in Figure 2, the effective optical path can be extended to 2.7 m because the light was reflected twice in chamber. Light from the light source is scattered in the air inlet of the chamber, therefore, a graduated refractive index (GRIN) rod lens is placed at the inlet and outlet of the chamber. The pigtail of the GRIN rod lens is fused with transmission optical fiber.  
Figure 2: Structural diagram of methane gas detection system. 1—super light emitting diode light source; 2—condensing mirror; 3, 9—slit; 4, 8, 11—plane mirror; 5—collimator; 6—grating; 7—focus lens; 10—beam splitter; 12, 16, 18, 22—gradient index lens; 13, 14, 19, 20—spherical mirror; 15, 21—air inlet; 17, 23—air outlet.  
Nitrogen was used as the diluting gas to create concentration standards of methane gas. Concentrations of methane at 2000 ppm, 3000 ppm, 4000 ppm, 5000 ppm, 6000 ppm, 7000 ppm, 8000 ppm, 9000 ppm, 10,000 ppm, 11,000 ppm, 12,000 ppm, 13,000 ppm, 14,000 ppm, 15,000 ppm, 16,000 ppm, 17,000 ppm, 18,000 ppm, 19,000 ppm, and 20,000 ppm were prepared. For each concentration of gas, we made three repeated measurements. The measurement results are shown in Table 1. Table 1 reveals that the maximum measuring error was 0.045, and the average error was 0.0075. The four-concentration absorption spectra of methane are shown in Figure 4. The linear relationship between optical power and methane concentration is shown in Figure 5. The linear correlation coefficient is 0.9888, and the linear equation is \(y=-0.2344\ x-41.41\).  
From the experimental results, we can see that the methane detection system shown in Figure 2 can be used to detect methane. The data sets in this paper were obtained from the above detection system.  
Figure 4: Spectra of four concentrations of methane gas.  
Figure 3: The power spectrum of SLED.
# Additive Manufacturing of PH 13-8 Mo Family: A Review
## 2 PBF-LB of PH 13-8 Mo Family
### Material Properties
#### 2.3.3 Corrosion Properties  
In relation to the corrosion properties, [PERSON] et al. [43] studied the corrosion behavior of CX built by PBF-LB and found an anisotropic corrosion response. The side of the as-built sample exhibited higher corrosion resistance compared to the top side of the sample due to differences in the microstructure, such as the level of dislocation density, lath size, grain boundary density, and residual stresses. It was observed that melt pool boundaries and pores in the microstructure are more susceptible to pitting corrosion.  
[PERSON] et al. [68] studied the influence of process parameters (laser power and scan speed) on the corrosion properties and microstructure of the as-built PBF-LB/CX. The sample (P = 170 W and V = 0.9 m\(\cdot\)s\({}^{-1}\)) that exhibited the best corrosion resistance was the one with the highest relative density and lowest average grain size. The influence of laser power (P) on corrosion resistance was found to be less significant than the speed (V).