esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Data Acquisition
#### 3.1.3 REALWORLD16 Dataset  
The HAR process starts with collecting sensor data recorded by peripheral devices. To implement this approach, the REALWORLD16 dataset [36] was chosen as the standardized public HAR dataset. The dataset includes data from acceleration, GPS, gyroscope, light, magnetic field, and sound-level sensors sampled at 50 Hz. Activities such as ascending and descending stairs, jumping, lying, standing, sitting, running, and walking were performed in real-world conditions. Fifteen subjects of diverse ages and genders--average age 31.9 \(\pm\) 12.4, height 173.1 \(\pm\) 6.9, weight 74.1 \(\pm\) 13.8; eight males and seven females--engaged in each activity for ten minutes, except for jumping, which was repeated for approximately 1.7 min.  
Wearable sensors were attached to each subject's waist, forearm, head, chin, chest, and thigh to capture signals from various body positions. A video camera was used to document every movement, aiding the subsequent analysis. A detailed summary of the distinctive features of each REALWORLD16 dataset is presented in Table 1. The REALWORLD sensor data underwent sampling using fixed-width sliding windows lasting 3 s without overlap. This process led to 150 readings per window, as the recording was conducted at 50 Hz.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 5 Opportunities for Further Research
### Hyperparameter Optimization Problem  
Hyperparameter optimization is one of the hardest problems in machine learning. For example, the selection of kernel determines the final performance of SVR-based prediction models. As for ANN-based methods, the number of hidden-layers and the number of neurons are also crucial. Although approaches like grid search can be used for solving this problem to some degree, further research works are still necessary.
## 2 Materials and Methods
### Deep Learning Models
#### 2.2.2 Xception Model  
The Xception architecture is a linear stack of deeply separable convolutional layers with residual connections, and the modules are connected by linear residuals. In this paper, the model decomposes the scalar convolution into spatial convolution and point-by-point convolution and performs 3 \(\times\) 3 convolution operations for each channel after 1 \(\times\) 1 convolution, respectively, which is finally expressed by a fully connected layer. By separating the tasks of learning spatial correlation and learning inter-channel correlation, the theoretical computation of the model is significantly reduced and only a small amount of accuracy is lost.
## 4 Decoupled Computation Offloading and Resource Allocation with Coordinate Descent (CD)
```
Input:\(\mathbf{x}^{0}=(x_{1},x_{2},\cdots,x_{m},\cdots,x_{M})\); Output: the optimal \((\mathbf{x}^{*},\beta^{*},p^{*})\); \(\mathcal{M}_{1}=\{m|x_{m}=1,\forall x_{m}\in\mathbf{x}^{0}\}\) for offloading UDs; \(\delta=1\times 10^{-6}\), \(Lower_{\lambda_{1}}=0\), \(Lower_{\lambda_{2}}=0\), \(Upper_{\lambda_{1}}\) and \(Upper_{\lambda_{2}}\) are big enough; while\(|Upper_{\lambda_{1}}-Lower_{\lambda_{1}}|\geq\delta\)do if\(U_{1}(\lambda_{1})>0\)then \(Upper_{\lambda_{1}}=\lambda_{1}\); else \(Lower_{\lambda_{1}}=\lambda_{1}\); while\(|Upper_{\lambda_{2}}-Lower_{\lambda_{2}}|\geq\delta\)do \(\lambda_{2}=\frac{Lower_{\lambda_{1}}+Upper_{\lambda_{2}}}{2}\); if\(U_{2}(\lambda_{2})>0\)then \(Upper_{\lambda_{2}}=\lambda_{2}\); else \(Lower_{\lambda_{2}}=\lambda_{2}\); for\(m\in\mathcal{M}_{1}\)do \(p_{m}=P_{m}^{max}\); return\((\alpha_{m},\beta_{m},p_{m})\);
```

**Algorithm 2** Bisection-Search-Based Resource Allocation

### Local Processing Part

Once the offloading decision is determined, tasks processed locally can be extracted and further expressed as:

\[\underset{f_{n}}{Maximize}: \sum_{n\in\mathcal{M}_{0}}e_{n}^{f}(T_{n}^{f}-\frac{\rho_{n}^{f} \underline{f}_{n}^{f}}{f_{n}})\] \[\text{s.t.}\quad C1:\;\;f_{n}\leq Fc_{n}\quad,\forall n\in \mathcal{M}_{0}.\] (P1)

\(C1\) represents the local processing capacity constraint. It is quite intuitive to infer from (P1) that a UD will greedily utilize all its computing resources to process the task locally. The best local computing resource allocation for UD \(n\) (\(n\in\mathcal{M}_{0}\)) is \(f_{n}=Fc_{n}\). \(\mathcal{M}_{0}\) and \(\mathcal{M}_{1}\) represent the local processing UD set and offloading UD set, respectively. Thus, given decision \(\mathbf{x}\), (P1) can be solved and calculated with \(f_{n}=Fc_{n},n\in\mathcal{M}_{0}\). The remaining problem is how to solve (P2), which is described in the next section. ### Edge Processing Part

For tasks offloaded to the edge, the BS allocates its available resources to accommodate these requests. The optimal resource allocation problem between offloading UDs can be denoted as:\[\underset{m,m,m,m,m,m,m,m}{Maximize}:H =\sum_{m\in\mathcal{M}_{1}}e_{m}^{f}(T_{m}^{f}-\frac{l_{m}^{f}}{ \alpha_{m}B_{n}\text{log}_{2}(1+\frac{p_{m}h_{m}^{2}}{\sigma^{2}})}-\frac{\rho_ {m}^{f}l_{m}^{f}}{\beta_{m}F_{e}})\] (9) s.t. \[\text{C1}:\quad\sum_{m\in\mathcal{M}_{1}}\alpha_{m}\leq 1\] \[\text{C2}:\quad\sum_{m\in\mathcal{M}_{1}}\beta_{m}\leq 1\] \[\text{C3}:\quad p_{m}<P_{m}^{max}\quad,\forall\text{m}\in \mathcal{M}_{1}.\] (P2)

**Corollary 1**. : _(P2) is a convex optimization problem on \(p_{m},\alpha_{m},\beta_{m}\quad\forall m\in\mathcal{M}_{1}\) with a given \(\mathcal{M}_{1}\)._

Proof. : Please see the detailed proof in Appendix A.
# Requirements and Hardware Limitations of High-Frame-Rate 3-D Ultrasound Imaging Systems
## 2 Materials and Methods
### Case Study: ULA-OP 256 towards 3-D High Frame Rate Imaging
#### 2.3.1 The ULA-OP 256  
The ULA-OP 256 (ULtrasound Advanced Open Platform) is a research scanner capable of managing 256 independent TX/RX channels. The system includes up to eight Front-end (FE) boards, and one master control board (MC). The latter performs signal and image processing tasks and communicates through a universal serial bus interface with the host personal computer, in which a dedicated software runs [11].  
As sketched in Figure 1, each FE board hosts the electronics to perform the TX, RX, and real-time processing of signals to/from 32 probe elements. The echo signals are digitized with 12-bit resolution, at a maximum rate of 78.125 mega samples per second (MSPS), by four ultrasound analog front end integrated circuits (AFE5807, Texas Instruments, Austin, TX, USA). The 32 acquired echo-signals are DAS beamformed (this is here called  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline
**Imaging Mode** & **N\({}_{\text{CH}}\)** & **N\({}_{\text{L}}\times\) N\({}_{\text{G}}\)** & **RD [MB]** & **R [Hz]** & **B\({}_{\text{wr}}\) [MB/s]** & **MAC/s** & **B\({}_{\text{BF}}\), B\({}_{\text{IQ}}\) [MB/s]** \\ \hline
2-D LL 128 & 128 & 123 k & 22.5 & 10.4 & 234.4 & 1.6 \(\times\) 10\({}^{8}\) & 4.9 \\
2-D PW 128 & 128 & 123 k & 0.23 & 1000 & 234.4 & 1.6 \(\times\) 10\({}^{10}\) & 468.8 \\ \hline
3-D LL 256 & 256 & 1.3 M & 480 & 0.98 & 468.8 & 3.2 \(\times\) 10\({}^{8}\) & 4.9 \\
3-D PW 256 & 256 & 1.3 M & 0.47 & 1000 & 468.8 & 3.3 \(\times\) 10\({}^{11}\) & 5000 \\
3-D PW 1024 & 1024 & 1.3 M & 1.88 & 1000 & 1875 & 1.3 \(\times\) 10\({}^{12}\) & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of requirements for 2-D and 3-D imaging.  
\"partial beamforming\" since it refers to part of the channels) by an FPGA (ARRIA V GX Family, Altera, San Jose, CA, USA). The FPGA implements six beamforming units working in parallel. A suitable combination of sequential and parallel beamforming [40] can process hundreds of thousands of lines/s. The beamformed samples are then coherently demodulated, low-pass filtered and optionally compounded by two DSPs (320C6678 family, Texas Instruments, Austin, TX, USA).  
The number, NFE, of boards installed on the ULA-OP 256 should guarantee that the number of channels, NCH, matches the number of transducer elements to be simultaneously controlled. The NFE boards are digitally interconnected with each other and with the MC board through a high-speed communication SerialRapidIO (SRIO) ring. The data produced by each FE board are sent to the MC board, which hosts a DSP (320C6678 family, Texas Instruments, Austin, TX, USA) and an FPGA (Cyclone V SoC, Altera, San Jose, CA, USA). The MC DSP adds up the demodulated partially beamformed baseband data from the NFE boards and transfers the final frame to the personal computer.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Vehicle Onboard Sensing
#### 3.3.4 Semantic Segmentation  
Detecting the road regions at the pixel level is a type of image segmentation focusing on the road instance. There has been a trend in onboard sensing to segment the entire video frame at pixel level into different object categories. This is called semantic segmentation and is considered a must for advanced robotics, especially autonomous driving [171; 172; 173; 174; 175; 176; 177; 178; 179]. Compared to other tasks, which can usually be fulfilled using different types of onboard sensors, semantic segmentation is strictly realized using visual data. Nvidia researchers [172] proposed a hierarchical multi-scale attention mechanism for semantic segmentation based on the observation that certain failure modes in the segmentation can be resolved in a different scale. The design of their attention was hierarchical so that memory usage was four times more efficient in the training process. The proposed method ranked top on two segmentation benchmark datasets. Semantic segmentation is relatively computationally expensive; thus, working towards the goal of real-time segmentation is a challenge [171; 174]. [PERSON] et al. [171] targeted proposing a general framework for real-time segmentation and ran 15 fps on Nvidia Jetson TX2. Labeling at the pixel level is time-consuming and is another challenge for semantic segmentation. There are some benchmark datasets available for algorithm testing, such as Cityscapes [180]. Efficient labeling for semantic segmentation and unsupervised/semi-supervised learning for semantics segmentation are interesting topics worth exploring [175; 176; 177].
## 3 Results
### Land Use
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Inside and Outside**} & \multicolumn{1}{c}{**Type**} & \multicolumn{3}{c}{**Area/ha**} & \multicolumn{3}{c}{**Change Rate (K)/\%**} \\  & & **2000** & **2010** & **2020** & **2000–2010** & **2010–2020** & **2000–2020** \\ \hline \multirow{8}{*}{Inside} & Cropland & 25,723.60 & 25,720.10 & 29,793.00 & \(-0.0014\) & 1.5835 & 1.5820 \\  & Forest land & 19,330.00 & 19,324.30 & 17,881.80 & \(-0.0029\) & \(-0.7465\) & \(-0.7492\) \\  & Grassland & 26,524.90 & 26,539.20 & 27,133.90 & 0.0054 & 0.2241 & 0.2296 \\  & Wetland & 127,938.00 & 127,936.00 & 122,823.00 & \(-0.0002\) & \(-0.3997\) & \(-0.3998\) \\  & Water & 7774.32 & 7774.40 & 10,315.90 & 0.0001 & 3.2691 & 3.2692 \\  & Impervious & 1732.97 & 1731.23 & 2200.61 & \(-0.0100\) & 2.7113 & 2.6985 \\  & Barren & 4507.66 & 4506.79 & 3384.49 & \(-0.0019\) & \(-2.4902\) & \(-2.4917\) \\  & Cropland & 124,200.00 & 124,191.00 & 124,245.00 & \(-0.0007\) & 0.0043 & 0.0036 \\  & Forest land & 20,653.00 & 20,649.60 & 16,738.40 & \(-0.0016\) & \(-1.8941\) & \(-1.8954\) \\  & Grassland & 61,684.80 & 61,687.50 & 63,247.20 & 0.0004 & 0.2528 & 0.2533 \\  & Wetland & 17,125.80 & 17,127.40 & 16,480.00 & 0.0009 & \(-0.3780\) & \(-0.3771\) \\  & Water & 15,110.80 & 15,111.30 & 16,120.50 & 0.0003 & 0.6678 & 0.6682 \\  & Impervious & 6769.50 & 6768.12 & 10,619.10 & \(-0.0020\) & 5.6899 & 5.6867 \\  & Barren & 9638.28 & 9646.68 & 7726.32 & 0.0087 & \(-1.9907\) & \(-1.9837\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The change of land use in ZINNR from 2000 to 2020.  
area of croplands underwent the most significant change, 138.28 ha (\(K=0.2532\%\)). The most substantial change was observed in grasslands (\(K=15.8699\%\)). In total, 761.04 ha and 1582.36 ha of land inside and outside of MdfNNR underwent changes, accounting for approximately 3.58% and 6.85% of the total area inside and outside of the NNR, respectively (Figure 4c,d). Among these, the largest transfer inside the MdfNNR was observed in forest lands, with an area of 392.26 ha and a ratio of 2.11% of the total forest lands area, primarily converted into croplands (382.24 ha). The area of grasslands transferred was the least (5.96 ha), with a ratio of 88.54% of the total grasslands area, all of which were converted into forest lands. Outside the MdfNNR, the area of forest lands transfer was the largest (740.58 ha), with a ratio of 4.33%, which was mainly converted into croplands (690.47 ha). The grasslands contributed the smallest area change of 9.25 ha, but the largest conversion ratio of 91.03% of its total area, mainly transformed into forest lands (8.13 ha).
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 1 Introduction
In the regions with relatively more in situ data, e.g., this study, we can make full use of more in situ data to further improve the estimate accuracy. In addition, as a relatively independent catchment unit, the sub-basin can clearly reflect the hydrological processes within and between units. Therefore, studying TWSC at the sub-basin scale can better reflect the TWSC variation pattern. Hence, in this study, we took the Ganjiang River Basin (GRB) as a case, divided the GRB into several sub-basins, and applied multi-source data, i.e., topography, hydrology, meteorology, and remote sensing, as the data sources. Based on the water balance equation, ET and runoff were modeled by two state-of-the-art models, and we estimated the TWSC at the sub-basin scale (i.e., \(\sim\)600 km\({}^{2}\)). Then, we verified the accuracy of this method through GRACE data, groundwater level data, and soil moisture data. We also analyzed the temporal and spatial variations in precipitation, ET, runoff, and TWSC in the GRB during the study period. Our study is based on the water balance and focuses more on the hydrological mechanism compared with the common methods. It is essential to understand the distribution of water resources, flood prevention, and disaster reduction at local scales (county levels), and provide scientific and accurate data support for water resource surveys in small-scale basins.
# Performance Analysis of an Improved Gravity Anchor Bold Expanded Foundation
## 4 Response of the Foundation
### Foundation Stress Analysis  
#### 4.2.1 Stress Analysis of the Rebar  
There are many kinds of steel bars in the foundation which mainly bear the tensile stress, and the collaborative concrete bears part of the pressure. Through the finite element calculation and analysis, the main force transmission characteristics of the foundation can be obtained. Through calculation and analysis, the stress distribution of the steel bar can be obtained in Figure 13. It can be seen that most of the steel bars are far from reaching the yield stress under extreme load conditions. Only the inner side of the radial reinforcement at the bottom plate cantilever has a large stress. The maximum value reaches 51.22 MPa. This part of the steel bar is mainly subjected to bending moment. The vertical reinforcement not only bears the compressive stress together with the concrete but also bears the tangential shear force and the bending moment generated by the horizontal load.  
Figure 14 shows the relationship between the stress of the radial steel bar at the cantilever of the bottom plate and the loading grade. It can be seen that the steel bar stress increases with the increase in the loading level. It is mainly divided into three stages. In the first stage, when the external load is small, the stress of the steel bar increases slowly. With the increase in the external load in the second stage, especially when the increase in bending moment is close to the extreme load, the stress growth rate is significantly enlarged. In the third stage, because the bending moment is no longer increased, the growth rate of steel bar stress is slowed down again. The stress gradually increases to the maximum value of 51.22 MPa. This shows that the steel bar here is mainly subjected to bending moment and is related to the stress state of the concrete. The steel bars are far from reaching the yield stress. There is still a lot of room for optimization of the en  
Figure 12: (**a**) Settlement change curves at the bottom of the foundation and (**b**) inclination curves of the flange and subsoil.  
Figure 13: Stress distribution cloud of radial reinforcement.  
bars. Therefore, we can reduce the use of steel bars on the foundation slab and the outside of the column according to the needs of the actual project.
## 4 Experimental Results
### Ensemble Model Evaluation
#### 4.3.3 The SAVEE Dataset  
The ensemble model was trained over 200 epochs using 20 batches combined with the AdaMAX optimizer and sparse categorical cross-entropy loss. The dataset was split according to the 75/25 ratio, as discussed above. The model's average testing accuracy, precision, recall, and F1 score for all emotions in this dataset were determined to be 96.5%, 95.3%, 95.6%, and 95.2%, respectively (see Table 5). Figure 11 visualizes the loss and accuracy curves for this dataset, and the confusion matrix is included in Figure 12.
## 1 Introduction
The multi-layered soils will interfere with the wave propagations induced by the dynamic train loads, leading to a different vibration response, particularly when the soils are saturated. Furthermore, the pile-reinforced ground improvement is widely adopted in soft soil regions with high-speed railways since it can significantly reduce both total and differential settlement of soft soils, and the piles will also bring different vibration responses [33; 34]. To the author's knowledge based on our critical review of open literatures, there are no previous studies relating to 3D multi-layered saturated ground vibrations induced by train-track dynamic interactions and the influence of piles on saturated ground vibration responses. Considering both that saturated soils and wheel-rail contact are easier to be defined in the software LS-DYNA [35; 36; 37], a novel 3D coupled train-track-soil interaction model is developed in LS-DYNA based on the multi-body simulation (MBS) principle and FEM theory. This model treats the soil as a saturated medium and the wheel-rail dynamic contact induced by track irregularity is simulated based on the Hertz contact theory. The model has been validated against its results with experimental results. The one-layered soil model and the multi-layered soil models with natural and pile-reinforced grounds are developed to investigate the dynamic responses of saturated soils. This study could bring an insightful and better understanding of the saturated ground vibration responses of high-speed railway with slab tracks in practice.
## 2 Investigated Battery Module
### Experimental Setup
#### 2.1.2 Temperature Data Acquisitions Device  
In order to monitor and record the temperature of cells in the battery module during testing, a 16-channel Applied temperature data acquisition device in Figure 7 was employed.  
Figure 5: Experimental setup.  
Figure 6: (**a**) Built battery pack from fiberboard, (**b**) Placement of thermocouples to the cells. (**c**) Protective wool placed on top of the battery cells.  
The T-DAQ used in the experimental setup was powered by an ARM microprocessor capable of measuring temperatures from a variety of thermocouple types (T, J, K, E, etc.) at three sampling rates (fast, medium and slow) and had a resolution of 0.1 \({}^{\circ}\)C. It allowed for multiple recording of temperature values from 16 channels simultaneously which it stored onto a USB stick or directly onto a computer via a USB-serial connection.  
K-Type thermocouples sensors with lower and upper limits of 0 \({}^{\circ}\)C and 200 \({}^{\circ}\)C temperature ranges well within the limits of the expected temperature rise of the cells in the battery module to be tested were employed during the experiment to measure temperature profiles of selected cells.  
The positions of cells to be monitored was systematically selected based on the assumption that the temperature of each selected cell would represent the local temperature of other cells in its surrounding. Factors such as the number of channels the T-DAQ is limited to impacted the decision to monitor only 16 cells out of 100 cells in the battery module, as well as the preexisting compact nature of the battery module (see Figure 8 below).  
The following measurements were taken to ensure proper contact between the sensor and the cells:  
* In order to ensure proper cell-sensor connection and improve conduction between the two entities, a thermal paste compound was applied around the sensor and the battery body in contact after which strips of strong adhesive tape were used to secure the sensor to the body of the cell (see Figure 9).
* Silicone glue was applied to hold the protruding sensor wire at the attachment point on top of the cell terminal to provide for extra attachment strength (see Figure 9).  
Figure 8: Thermocouple position selection and attachment.  
Figure 7: Temperature data acquisition device (T-DAQ).  
Finally, the majority of the sensor wire length was kept folded in a separate partitioned section inside the MDF box to prevent accidental tension that might threaten or sever the connection between the sensor-cell attachment.
## 6 Regulator Structure Optimization Analysis
### Analysis of Optimization Results
The above diagnostic analysis, regression analysis, and ANOVA can obtain response surface equations with good fit and high predictive ability, and the response surface's fitting coefficients are substituted into the objective optimization function (Equation (9)). Thus, the optimization function for the gas pressure regulator can be obtained. \[Y=204.94-5.02x_{1}-3.20x_{2}+19.90x_{3}+2.18x_{1}x_{2}+7.48x_{1}x_{3}+13.87x_{2 }x_{3}-10.68x_{1}^{2}-17.62x_{2}^{2}-13.03x_{3}^{2} \tag{10}\]  
The extreme value of the response surface is the optimal parameter. The optimal parameters of the gas regulator can be obtained according to the surface and optimization function analyses. The sleeve wall thickness is 7.25 mm, the valve hole diameter is 25 mm, the outlet valve seat wall thickness is 31.05 mm, and the equivalent stress is 135.62 MPa. All the above parameters are within the constraints specified in Table 2. A comparison of the equivalent stress in Figure 9 shows that at the 20% opening, the equivalent stress after optimization is significantly lower than the equivalent stress before optimization, and this optimization scheme has high feasibility. Through this method, the pressure use requirements of the regulator can be met while reducing the equivalent stress and improving the reliability of the structure through the optimization of the key structural dimensions of the regulator. Figure 10: Perturbation diagram (A: sleeve wall thickness; B: valve hole diameter; C: outlet valve seat wall thickness). Figure 11: Response surface and contour map. (**a**) Relationship among valve hole diameter, outlet valve seat wall thickness, and equivalent stress, (**b**) Contour map between outlet valve seat wall thickness and valve hole diameter, (**c**) Relationship among sleeve wall thickness, outlet valve seat wall thickness, and equivalent stress, (**d**) Contour map between outlet valve seat wall thickness and sleeve wall thickness, (**e**) Relationship among sleeve wall thickness, valve hole diameter, and equivalent stress, (**f**) Contour map between valve hole diameter and sleeve wall thickness.