esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study introduced a deep-learning-based method for determining the length of dry beach using satellite images. A case study was performed to verify the precision and efficiency of the proposed method. The following conclusions were drawn.  
We started by collecting satellite images of tailing ponds to create a dataset. The dataset was used to train the YOLOv5-seg model, and the indicators of the trained model showed an accurate recognition effect. The dry beach length was eventually determined by three steps: edge detection, linear equation fitting, and length calculation. The results of the case study indicate that the minimum error for calculating the length of dry beach using this method is 2.10%, the maximum error is 3.46%, and the average error is 2.70%, indicating its feasibility in dry beach monitoring. This method is less affected by external environmental factors, requires less manual participation, and has high work efficiency. It can offer new ideas for monitoring the length of dry beaches and the safety of tailing ponds.  
This study measures the dry beach's length using satellite images of tailing ponds. The proposed method can save labor and measurement equipment. But there are still some limitations of this study. Satellite data updates have periodicity and cannot meet the requirements of real-time data acquisition. To address this, it is necessary to integrate unmanned aerial vehicles (UAVs) and remote sensing to establish a comprehensive data monitoring system.  
Conceptualization, Z.D. and Q.L.; Methodology, Y.T. and Z.D.; Dataset, Z.D. and G.L.; Software, Y.T. and X.C.; Validation, Y.T. and Z.D.; Formal Analysis, X.C. and Z.D.; Writing--Original Draft Preparation, Z.D.; Writing--Review and Editing, Z.D. and S.Z.; Supervision, Q.L.; Funding acquisition, Q.L. and Z.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Fund of China (grant number 52304202), the National Key Research and Development Program of China (No. 2021 YFC3001303), Science and Technology Plan of Beijing: Beijing-Tianjin-Hebei Science and Technology Innovation Collaboration (Z231100003923001), and the Research Start-up Fund of North China University of Technology.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to company privacy.  
Authors [PERSON] and [PERSON] were employed by the company BGRIMM Technology Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Materials and Methods
### Analysis of Individual Polyphenols via High-Performance Liquid Chromatography (HPLC)  
HPLC analysis of individual polyphenols was performed using an Agilent 1200 series HPLC (Agilent Technologies, Santa Clara, CA, USA) equipped with ChemStation software, a degasser, a quaternary gradient pump, an autosampler, a column oven, and a multiwavelength detector. Separation of polyphenols was achieved using a Poroshell 120 Stable bound SB-C18 column (\(150\times 4.6\) mm, 2.7 \(\mu\)m particle size Agilent Technologies, Santa Clara, CA, USA), with a Poroshell 120-C18 guard column (\(5\times 4.6\) mm, 2.7 \(\mu\)m particle size Agilent Technologies, Santa Clara, CA, USA), operated at 30 \({}^{\circ}\)C. The mobile phase consisted of 0.1% formic acid in water (Solvent A) and 0.1% formic acid in acetonitrile (Solvent B). The multiwavelength detector was set to 280 nm. Then, 1 \(\mu\)L of each sample was directly injected after filtering through a 0.2-\(\mu\)m PTFE (Polytetrafluoroethylene) syringe filter. The flow rate was set at 0.3 mL/min. The gradient program started at 0% B and increased to 15% B over the course of 5.25 min and held at 15% B for 5.4 min. The percentage of mobile phase B was then increased to 40% over 26.85 min and kept at 40% B for 1.5 min. The mobile phase was ramped to 100% B in 1.5 min and held at 100% B for 3 min. Then, the gradient program was set to its initial conditions of 0% B within 1.5 min and followed by re-equilibration of the column for 10 min. Individual polyphenic compounds were identified by comparing the retention times of analytical reference materials. Concentrations of the individual phenolic compounds were quantified using external calibration curves generated for the analytical standards.
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## 3 Results  
### 1 AF-Measured Yield  
The spatial pattern of crop cut yields in general followed the weather gradient across the study area (Figure 1). Higher yields occurred in the northeast and the southeastern districts, where growing season temperature (\(T_{avg}\)) was lower and precipitation was abundant (Figure 1e,f). Low yields were mostly reported in the west, where \(T_{avg}\) is high. For both districts and divisions, the standard error of the mean for aggregated yields ranged from 100 to over 500 kg/ha (Figure S3). High yield standard errors (greater than 300 kg/ha) located in the north were typically because of a small number of observations. Districts and divisions with lower standard error mostly occurred in the west and the central regions. Reported yields at division level had a higher frequency of yield standard error above 300 kg/ha than at District level, suggesting lower credibility of conclusions made at the division level.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 5 H\({}_{2}\) Generation in DGRs
### Reservoir Engineering Aspects
#### 5.2.1 Mode of Application
Some results of the study are given in Figure 4. In Figure 4a, the H\({}_{2}\) concentration at the uppermost block of the model reaches a concentration of 0.6 (mole fraction) whereas CO\({}_{2}\) is around 0.05. The CO\({}_{2}\) concentration in the lowermost block reaches approximately 0.5 whereas H\({}_{2}\) is around 0.08 in the same block; the remaining gas consists of CH\({}_{4}\). Figure 4b,c depict the profile of the gas distribution in the blocks with the highest H\({}_{2}\) and CO\({}_{2}\) concentrations, respectively. In conclusion, even in such overstated cases, the mixing occurs with other gases, making the in situ separation of the gases questionable. One aspect that could contribute to the gas separation process is the individual permeability of different gas molecules through the reservoir rock. This effect is industrially used to separate gases by means of molecular sieves, e.g., in a pressure swing adsorption. However, the pore diameter in commercial zeolite-based molecular sieves is in the range of 3 A to 10 A and is therefore significantly smaller than the pores in the reservoir. A few studies claim the use of membrane filters to separate H\({}_{2}\) from other gases in the borehole; however, such efforts are still far from industrial applicability; their capacities are significantly low and their effectiveness is susceptible to the presence of other gases such as H\({}_{2}\)S and CO [66; 67]. A systematic investigation addressing this aspect of the separation process in the reservoir is missing in the literature. Another reservoir engineering issue of H\({}_{2}\) generation in DGRs is the time needed to bring the reservoir to the pressure level sufficient to produce the generated gas. With the generation rates due to the discussed processes, the built-up pressure can take decades or even longer. Figure 4: Modeling the movement of the gases as the result of in situ H\({}_{2}\) generation by steam injection in a generic DGR model: (**a**) the development of the molar concentrations of H\({}_{2}\) and CO\({}_{2}\) in the uppermost and lowermost grid blocks, with (**b,c**) showing the vertical H\({}_{2}\) and CO\({}_{2}\) concentration distribution, respectively, after 100 years from the termination of the project.
# Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
## 2 Materials and Methods
### (Stacked) Bidirectional LSTM and (Stacked) Bidirectional GRU  
Bidirectional LSTM and bidirectional GRU networks process input data in both forward and backward directions by considering both future and past context states, offering a more holistic understanding [58; 59]. Two separate hidden layers are used in bidirectional models: one processes the input forwards, while the other processes it backward. This approach allows the model to learn dependencies between time steps in both directions.  
During the calculation, bidirectional LSTM and GRU would generate two \(\mathsf{h_{t}}\) values: \(\mathsf{(h_{t})}\) for the forward direction and \(\mathsf{(h_{t})}\) for the backward direction [60]. These two vectors would usually be added or concatenated to obtain the final hidden state. To increase their capacity and performance, bidirectional models can also be stacked, showing a stacked bidirectional LSTM network [61; 62].
## 3 UAV-Based Targeting for Emergency Material Delivery
### LSCDHead: Lightweight Shared Convolutional Detection Head  
The detection header in YOLOv8 contains two branches, localization (bounding-box regression) and classification, each of which typically has separate convolutional layers to extract features, leading to an increase in model Params and FLOPs. This design is not optimal for single-target detection tasks because both localization and classification tasks aim to identify the same class of objects and do not require independent feature learning paths. The classification branch only learns to recognize whether a target exists rather than distinguishing between different classes. Therefore, merging the two branches can reduce the Params and FLOPs and improve the model efficiency in single-target detection scenarios. Therefore, in this paper, the following measures were carried out to propose a LSCDHead:  
1. Replace BatchNorm (BN) with GroupNorm (GN): GN has been shown to improve the detection and classification performance in FCOS [30]. The GN feature channel enhances the model's adaptability to targets of different scales and sizes. Replacing BN with GN improves the detection and enhances the model's adaptability to small batches of data. In this paper, GnConv is designed to replace the BN of Conv with GN to better extract P3, P4, and P5 features.
2. Propose Shared Convolution (ShConv): based on GnConv, ShConv is proposed to capture basic features to accomplish classification or localization tasks through different network branches. The shared convolution strategy improves model efficiency and performance by allowing the model to learn once and reuse standard features on multiple tasks.
3. Use scale layer: Considering the possible lack of size optimization due to shared convolution, this paper introduced a scale adjustment layer to dynamically adjust the scale of feature maps for targets of different sizes. This enhances the model's ability to adapt to targets of different sizes while reducing Params.  
Figure 6: GhoHGNetv2 internal module structure. (**a**) HGStem module structure; (**b**) GhoHGBlock module structure (shortcut = True); (**c**) GhoHGBlock module structure (shortcut = False).  
Combining the above improvements, the redesigned detection head in this paper achieved fewer Params and FLOPs, minimizing the loss of mAP as much as possible. The structure of GnConv and ShConv is shown in Figure 7.
## 2 Materials and Methods
### Model Evaluation Index  
In this experiment, the modeling effect is evaluated by the coefficient of determination (\(R^{2}\)), the root mean square error (RMSE), and the residual predictive deviation (\(RPD\)). The coefficient of determination \(R^{2}\) reflects the level of intimacy between variables, the RMSE reflects the accuracy of the model, and the \(RPD\) reflects the prediction ability of the model. The higher the \(RPD\), the lower the RMSE, and the closer the \(R^{2}\) is to 1, the better the performance of the prediction model. In this paper, \(R_{c}^{2}\) and \(R_{p}^{2}\) represent the coefficient of determination of the calibration set and the prediction set, respectively, and \(RMSE_{c}\) and \(RMSE_{p}\) represent the root mean square error of the calibration set and prediction set, respectively. Besides this, the \(RPD\) has been suggested to be at least 3 for agriculture applications; where 2 < \(RPD\) < 3 indicates a model with a good prediction ability; 1.4 < \(RPD\) < 2 is an intermediate model needing some improvement; and \(RPD\) <1.4 indicates that the model has a poor prediction ability [36].
## 2 Materials and Methods
### Head Rotation Estimation  
The success of the experiment majorly depends on the accurate estimation of head rotation. EMG has been applied in previous cases to infer rotation estimation methods. In this paper, we focus on two methods: an equation model and an ML approach. In literature, real-time EMG control recommends a maximum latency of 300 ms [46]. In our method, raw EMG was recorded by DAQ with a window size of 30 and 50 ms for the equation and ML model, respectively, corresponding to 30-100 samples of the EMG sequence at a sampling rate of 2000 Hz. This value was found to satisfy the real-time system requirements.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 5 Experiments and Results
### Case Study 2: Experiments with AIDA, Using Erceg-SUI Propagation Model
\begin{table}
\begin{tabular}{c|l|l|l l l} \hline \hline \multicolumn{1}{c|}{**Region**} & \multicolumn{1}{c|}{**Metric**} & \multicolumn{1}{c|}{**AIDA (TD Approach)**} & \multicolumn{1}{c|}{**Category A Terrain**} & \multicolumn{1}{c}{**Category B Terrain**} & \multicolumn{1}{c}{**Category C Terrain**} \\ \hline \multirow{9}{*}{A} & No. of iterations & 3 & 3 & 3 & 3 \\  & No. of selected CPs & 249 & 435 & 420 & 403 \\ \cline{2-6}  & No. of unconnected SMs & 3 & 30 & 17 & 11 \\ \cline{2-6}  & Percentage of connected SMs & 99.998\% & 99.980\% & 99.989\% & 99.993\% \\  & Average LRP (dBm) & \(-\)73.43 & \(-\)78.07 & \(-\)76.91 & \(-\)75.97 \\ \cline{2-6}  & Processing time & 15 h 47 min 44 s & 21 h 25 min 45 s & 20 h 53 min 23 s & 20 h 51 min 25 s \\ \cline{2-6}  & AIDA Gain (\(G_{AIDA}\)) & & 40.562\% & \\ \hline \multirow{9}{*}{B} & No. of iterations & 3 & 3 & 3 \\  & No. of selected CPs & 44 & 74 & 76 & 75 \\ \cline{2-6}  & No. of unconnected SMs & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Percentage of connected SMs & 100\% & 100\% & 100\% & 100\% \\ \cline{2-6}  & Average LRP (dBm) & \(-\)79.14 & \(-\)79.01 & \(-\)78.23 & \(-\)77.63 \\ \cline{2-6}  & Processing time & 3 h 52 min 7 s & 5 h 39 min 50 s & 5 h 38 min 30 s & 5 h 36 min 8 s \\ \cline{2-6}  & AIDA gain (\(G_{AIDA}\)) & & 41.326\% & \\ \hline \multirow{9}{*}{C} & No. of iterations & 2 & 2 & 2 & 2 \\ \cline{2-6}  & No. of selected CPs & 169 & 298 & 304 & 292 \\ \cline{2-6}  & No. of unconnected SMs & 0 & 64 & 53 & 39 \\ \cline{2-6}  & Percentage of connected SMs & 100\% & 99.704\% & 99.754\% & 99.819\% \\ \cline{2-6}  & Average LRP (dBm) & \(-\)72.86 & \(-\)78.24 & \(-\)77.10 & \(-\)76.06 \\ \cline{2-6}  & Processing time & 0 h 55 min 4 s & 1 h 15 min 17 s & 1 h 15 min 9 s & 1 h 15 min 40 s \\ \cline{2-6}  & AIDA gain (\(G_{AIDA}\)) & & 43.273\% & \\ \hline \multirow{9}{*}{D} & No. of iterations & 2 & 2 & 2 & 2 \\ \cline{2-6}  & No. of selected CPs & 48 & 78 & 85 & 84 \\ \cline{1-1} \cline{2-6}  & No. of unconnected SMs & 4 & 16 & 18 & 12 \\ \cline{1-1} \cline{2-6}  & Percentage of connected SMs & 99.934\% & 99.738\% & 99.705\% & 99.803\% \\ \cline{1-1} \cline{2-6}  & Average LRP (dBm) & \(-\)75.58 & \(-\)81.90 & \(-\)80.49 & \(-\)80.05 \\ \cline{1-1} \cline{2-6}  & Processing time & 0 h 14 min 19 s & 0 h 18 min 38 s & 0 h 19 min 22 s & 0 h 19 min 38 s \\ \cline{1-1} \cline{2-6}  & AIDA gain (\(G_{AIDA}\)) & & 41.616\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: AIDA (TD approach, with its proposed PL model) results compared to the results of AIDA using the Erceg-SUI path loss model. Figure 10: Number of CPs selected by the AIDA method using its original path loss model (AIDA/proposed PL model) compared to the values selected by the AIDA method using the Erceg-SUI path loss model for the terrain Category A (AIDA/SUI-A), Category B (AIDA/SUI–B), and Category C (AIDA/SUI–C). The chart compares the performance of AIDA for the Regions A, B, C, and D, used in the experiments.
## 3 The Proposed Fuzzy Adaptive Filter Strategy
### The Fuzzy Adaptive System Based on Unscented Kalman Filter  
The degree of divergence (DOD) parameters for identifying the degree of change in vehicle dynamics needs to be defined. Examples for possible approaches are given as follows. The innovation information at the present epoch is employed for timely reflect the change in vehicle dynamics. The DOD parameter \(\zeta\) can be defined as the trace of innovation covariance matrix at present epoch (_i.e._, the window size is one) divided by the number of satellites employed for navigation processing:  
\[\zeta=\frac{1}{m}\sum\limits_{i=1}^{m}\mathbf{v}_{i}\mathbf{v}_{i}^{T} \tag{30}\]  
Figure 2: A fuzzy system.  
where \(\textbf{v}_{k}=[v_{0}\,v_{2}...v_{m}]^{T}\), \(m\) is the number of measurements (number of satellites). Furthermore, the averaged magnitude of innovation at the present epoch can also be used:  
\[\zeta=\frac{1}{m}\sum\limits_{i=1}^{m}\left|v_{i}\right| \tag{31}\]  
In the FLAS, the DOD parameters are employed as the inputs for the fuzzy inference engines. By monitoring the DOD parameters, the FLAS is able to on-line determine better lower and upper bounds of the process noise covariance according to the innovation information, and therefore improves the estimation performance. The flow chart of the FLAS-UKF algorithm is shown in Figure 3.  
Figure 3: The flow chart of the FLAS-UKF.
# Earthquake Risk Probability Evaluation for Najin Lhasa in Southern Tibet
## 2 Methods
As the conditional probability of the magnitude, it can reflect the inhomogeneity of the spatial distribution of earthquake intensity in the seismic zone, and it is normalized for the specified magnitude \(f_{i,m_{j}}\) in the entire seismic zone, namely:  
\[\sum_{i=1}^{N_{S}}f_{i,m_{i}}=1 \tag{4}\]  
In the formula, \(N_{S}\) is the total number of potential source areas in the seismic zone; \(f_{i,m_{j}}\) can be determined by comprehensive assessment by statistical methods. \(\Delta m\) is the step size of the magnitude bins, and \(m_{i}\) is defined as the central magnitude of the \(j\)th bin in several bins from the starting magnitude \(m_{0}\) to the magnitude upper limit \(m_{uzz}\) of the potential source area. The seismicity within the potential source region is consistent. It is assumed that there is a total of \(N_{s}\) potential hypocenter areas \(\{S_{1},S_{2},\cdots\cdots S_{N_{S}}\}\) in the seismic zone. According to the piecewise Poisson distribution model and the full probability formula, the annual transcendence probability of the earthquake occurring inside the seismic zone affecting the ground motion parameter value \(A\) at the site exceeding the given value \(a\) is:  
\[P_{k}(A\geq a)=1-exp\{-\frac{2v_{0}}{\beta}\cdot\sum_{j=1}^{N_{m}}\sum_{i=1}^ {N_{s}}P(A\geq a|E)\cdot f(\theta)\cdot\frac{f_{i,mj}}{A(S_{i})}\cdot f\Big{(} m_{j}\Big{)}\cdot Sh(\frac{1}{2}\beta\Delta m)dxyd\theta\} \tag{5}\]  
\(A(S_{i})\) is the area of the \(i\)th potential source area within the seismic zone, and \(P(A\geq a|E)\) is the ground motion at the site larger than \(a\) when a specific seismic event occurs in the \(i\)th potential source zone within the seismic zone, with the epicenter of \((x,y)\), a magnitude of \(m_{j}\pm\frac{1}{2}\Delta m\), and the rupture direction is determined. \(f(\theta)\) is the probability density function of the rupture direction. Assume that there are \(N_{z}\) seismic zones that contribute to the seismic risk of the site. If the \(k\)th seismic zone has a contributing probability of \(P_{k}\) (\(A>a\)) for the seismic motion at the site, then the total seismic motion annual transcendence probability of the site can be expressed as:  
\[P(A\geq a)=1-\prod_{k=1}^{N_{z}}(1-P_{k}(A\geq a)) \tag{6}\] \begin{table}
\begin{tabular}{c c c c c c} \hline
**Earthquake Statistical Region** & **Maximum Magnitude Limit** & **Threshold Magnitude** & **Background Earthquake** & \(b\)**Value** & \(\mathbf{R_{M\geq 4}}\) \\ \hline Central Tibet earthquake & 8.5 & 4.0 & 6.5 & 0.81 & 25 \\ statistical region & 8.5 & 4.0 & 6.5 & 0.85 & 83 \\ Himalayan earthquake & 8.5 & 4.0 & 6.5 & 0.85 & 83 \\ statistical region & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Activity parameters of the earthquake statistical area.
## 6 Profound Impact Potential on Decision-Making Processes
### China's Advancements in Meteorological Satellite Constellations  
China has made significant strides in disaster monitoring and weather forecasting through its Fengyun (FY) satellite series and the complementary Tianmu-1 and Yunyao-1 [43] constellations. The FY-4 series, in particular, has reduced typhoon recognition times from 15 min to just 5 min and has substantially improved track prediction accuracy.  
These advancements have strengthened global numerical weather prediction (NWP) capabilities, enhanced severe weather monitoring, and improved the detection of environmental changes and ecological hazards. Furthermore, China's meteorological satellites actively support international collaboration, providing data to over 115 countries, reinforcing China's position as a leader in meteorological innovation and ecological sustainability [44].
## 3 Results and Discussion  
### Effect of Foam Content on Density  
Figure 3 illustrates the changes in density of both types of cement mixes with increasing foam content. As expected, density reduced with increased foam volume. Change in density as a function of foam volume can be considered linear for blended mix within the range considered in this study. However, the GP cement mix's density varies non-linearly as foam volume increases. Beyond 50% foam volume, the reduction in density is higher. As the foam amount increases, the possibilities of various bubble networks increase, which in turn can make some networks unstable, affecting the density.  
Even though the blended binder mix attained lower densities compared to its GP cement counterpart, it exhibited a more stable void network. For instance, GP mixes containing for than 50% foam volume witnessed a concave-shaped top surface with an approximate depth of 3-4 mm, as shown in Figure 4. This occurred after the foam concrete hardened and possibly due to the collapse of some void/bubble network within the samples. As the density decreases in foam concrete, stabilisation of the bubble network is difficult to achieve, resulting in dimensional instability [38]. However, this effect was negligible  
Figure 3: Change in density of foam concrete with added foam content.  
Figure 2: Test setup for setting time and compressive strength.  
in the blended mix samples containing more than 50% foam volume. Lastly, this figure also compares the difference in fresh and hardened densities of both the mixes and the difference between these two was found to be negligible.