esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Summary and Conclusions  
Based on the CSGR dam of Xijiang, this research studies the constitutive model-bearing capacity of the dam body and the influence of hydraulic fracturing on structural safety and shows that the obtained bearing capacity of the dam body conforms to the actual situation. Meanwhile, the safety factor of the weathered material on the CSGR dam obtained is of theoretical and practical significance to ensure safe construction of the dam, which can be used as a reference for similar projects. The main conclusions are summarized as follows:  
1. The improved [PERSON]'s model can be used as a constitutive model in the structural calculation of CSGR with weathered material, and the dam stress may enter the nonlinear elastic stage.
2. The CSGR with weathered material features larger displacement of the dam body and slightly changed distribution of the stress contour, despite increased maximum principal stress on the downstream surface and intensified fluctuation. It is recommended to increase the material strength and structural thickness of the stepped protective layer on the downstream surface.
3. On the upstream surface, the influence of CSGR with weathered material on the seepage prevention and protection function can be ignored. However, the shear stress on the joint surface between C15 rich-mix CSGR and internal CSGR increases significantly and should be closely monitored during construction.
4. With the overload safety factor being 2.4, the comprehensive overload capacity of the CSGR dam with weathered material is stronger than that of the RCC gravity dam, while the overload safety factor of the CSGR dam without weathered material is 26.2% higher than that of CSGR dam with weathered material. The safety margin of the dam is 2.75.
5. According to results on the influence of cracks with different depths and heights, hydraulic fracturing will significantly undermine the safety of the dam body and ensure greater safety. Suggestions on the treatment of dam cracks are given with the COD.  
Conceptualization, L.Z., J.J. and C.Z.; Data curation, L.Z. and Y.W.; Formal analysis, L.Z.; Funding acquisition, J.J. and C.Z.; Investigation, L.Z., J.J. and Y.W.; Methodology, L.Z.; Project administration, J.J.; Resources, J.J. and C.Z.; Software, L.Z.; Supervision, J.J. and C.Z.; Validation, L.Z.; Visualization, L.Z., J.J. and Y.W.; Writing--original draft, L.Z.; Writing--review & editing, L.Z., J.J. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research is financially supported by the National Key Research and Development Plan of China under Grant No. 2018 YFC0406800.  
The authors declare no conflict of interest.  
Figure 19: COD of dam body at different crack heights.
## 4 Data Storage and Processing: Automated Data Processing and Storage
### Irrigation Decision Making with Machine Learning Models
[39] compared various ML algorithms, including support vector machines (SVMs) and random forests, for estimating actual crop evapotranspiration under non-standard conditions in vineyards. Their study found that ML approaches outperformed traditional methods, with the best-performing models achieving R\({}^{2}\) values exceeding 0.89, demonstrating the superior accuracy of ML in capturing complex relationships between environmental variables and crop water requirements. To address the dynamic nature of agricultural environments, online learning algorithms enable the continuous update and improvement of ML models based on incoming real-time data. These algorithms are particularly valuable in irrigation management, where conditions can change rapidly due to weather patterns, soil moisture fluctuations, and crop growth stages. For example, [PERSON] and [PERSON] [97] developed a hybrid model combining deep learning, nonlinear system identification, and ensemble tree intelligence algorithms for pan-evaporation estimation. Their approach, which incorporated principles of online learning, showed significant improvements in prediction accuracy compared to single models, with reductions in root mean square error (RMSE) ranging from 32.505% to 62.615% during the validation period. As the complexity of ML models in irrigation management increases, the need for advanced computing resources becomes more pronounced. Many state-of-the-art models utilize GPUs or TPUs to significantly enhance performance by leveraging specialized hardware designed for parallel processing. This hardware acceleration is particularly crucial for complex deep learning models used in irrigation management, which often involve processing large amounts of spatial and temporal data. For instance, [PERSON] et al. [98] compared the performance of convolutional long short-term memory (ConvLSTM) and convolutional neural network-long short-term memory (CNN-LSTM) models for predicting water stress in arboriculture using a time series of remote sensing images. These GPU-accelerated models demonstrated high accuracy in forecasting water stress, with the CNN-LSTM model excelling for longer sequences of input data.
## 3 Results
### Evaluation of Landcover Heterogeneity  
The Landsat data provide a more spatially consistent comparison of surface reflectance against field data of LAI. The results (Figure S6) show that there are significant correlations between NDVI/EVI and LAI across sites, but the effect sizes are small (\(R^{2}\) = 0.1). Thus, the relationships between remotely-sensed NDVI/EVI at the fine resolution and field-measured LAI are less significant than those between LAI products at the coarse resolution and field-measured LAI. The forest cover proportion at the pixel level varied between 38%-100% (Table S3). The landcover data show that LAI retrievals were degraded by the heterogeneity of landcover within product pixels. There was a clear pattern of LAI bias increasing with reductions in the proportions of forest cover at the pixel scale (Figure S7).
# Selection Criterion of Reanalysis Methods for Plane Truss Optimization
## 4 Construction of Selection Criterion
### Method Comparison Study
#### 4.1.2 Increased Number of DOFs  
Under the condition of increased DOFs, there are three candidate methods, namely, the Guyan + CA method, the Guyan + Epsilon method, and the Guyan + PCG method. The Guyan reduction [19] is an independent method that reduces the size of the stiffness matrix. The stiffness matrix processed by this method can be solved using those reanalysis methods under unchanged DOFs. Following the comparison between the CA method and the PCG method in the previous section, the Guyan + CA method can be eliminated first. This section focuses on comparing the Guyan + Epsilon method and Guyan + PCG method, and it compares the \(\ln(Fr)\) and \(Mr\) values obtained using the complete analysis and the two methods. The results are shown below.  
As can be seen from Figure 6, when the number of iteration steps is small, the \(\ln(Fr)\) value of the Guyan + PCG method is significantly larger than that of the corresponding Guyan + Epsilon method, and this phenomenon occurs for various DOFs. Considering that the iteration steps of the PCG method are generally smaller than the number of basis vectors in the Epsilon method under similar accuracy, the difference between \(\ln(Fr)\) values will further widen in this case. Therefore, it can be considered that under the condition of increased DOFs, the Guyan + PCG method has better computational efficiency. As for the \(Mr\) values, both methods are smaller than those obtained by complete analysis. However, as the number of iteration steps or basis vectors increases, the storage space of the Guyan + Epsilon method gradually becomes larger.  
The same examples as in Section 4.1.1 were used for validation, but with only one modification type, i.e., adding a node to a non-boundary member in each of the three examples. Other conditions remained the same. The results are shown in Table 4.  
Figure 6: _Cont._  
According to Table 4, it can be seen that under the condition of increased DOFs, the number of basis vectors or iteration steps in both methods remains consistent with that of size modification under unchanged DOFs. Additionally, the computational time of the Guyan + Ppsilon method is longer than that of the Guyan + PCG method. For lower DOFs, the computational time of complete analysis is shorter than that of both methods. These results support the conclusions presented earlier. Therefore, the selection criterion for increased DOFs is summarized as follows: when the structure undergoes a topology modification that increases DOFs, the Guyan + PCG method should be used for solving. However, when the DOFs of the structure are below 50, the complete analysis should be adopted.
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
###### Abstract  
Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF- and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.  
Keywords:image segmentation; active contour model; level set; signed pressure force function +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Model Validation
#### 3.1.2 CO\({}_{2}\) Distribution Inside the Greenhouse  
The greenhouse model validation was conducted according to the measurement data of [PERSON], et al. [26], who studied influential positions of CO\({}_{2}\) supply in tomato plants inside the same greenhouse as the present study. In the present study, the simulation results were compared with the measurement data of CO\({}_{2}\) concentration in the condition 20 min after 1 h of CO\({}_{2}\) being supplied at the middle canopy (1.2 m above the ground, see Figure 10).  
Figure 10: CO\({}_{2}\) concentration of measurement (north \(\blacklozenge\) and south \(\blacktriangle\)) and simulation (north and south \(\blacklozenge\)) data according to the position from the ground.  
Figure 9: Measured and simulated data of CO\({}_{2}\) concentration inside the chamber.  
The CO\({}_{2}\) concentrations were measured at two sides of 3.4 m from the north and south wall with 0.6, 1.2, 1.8, 2.4, and 4.2 m above the ground. The initial simulation value of CO\({}_{2}\) concentration was assumed to be constant in every mesh inside the greenhouse, whereas the actual condition has various CO\({}_{2}\) concentrations. In contrast, model simulation accuracy was also evaluated by comparing the percentage errors of each measured and simulated point (Table 4).  
According to the results of the CO\({}_{2}\) distribution using the CFD model within the greenhouse, the MAPE of north and south were 4.95% and 2.04%, respectively, whereas the RMSE of north and south were 25.33 and 10.57 ppm, respectively, compared with the measured values. However, the simulation results in this study may be reasonable to predict the CO\({}_{2}\) distribution considering CO\({}_{2}\) absorption through the process of photosynthesis of the plant inside the greenhouse (Figure 11).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Height (m)**} & \multicolumn{2}{c}{**Measured (ppm)**} & \multicolumn{2}{c}{**Simulation (ppm)**} & \multicolumn{2}{c}{**Percentage Error (\%)**} \\ \cline{2-7}  & **North** & **South** & **North** & **South** & **North** & **South** \\ \hline
**4.2** & 391 & 418 & 435 & 435 & 11.15 & 3.96 \\
**2.4** & 411 & 439 & 433 & 432 & 5.28 & 1.53 \\
**1.8** & 418 & 431 & 432 & 432 & 3.34 & 0.14 \\
**1.2** & 432 & 443 & 432 & 432 & 0.03 & 2.55 \\ \hline
**MAPE (\%)** & 4.95 & 2.04 & & & & \\ \hline
**RMSE (ppm)** & 25.33 & 10.57 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of CO\({}_{2}\) concentration between measured and simulated data for model validation in greenhouse.  
Figure 11: CO\({}_{2}\) distribution inside the greenhouse considering CO\({}_{2}\) absorption through photosynthesis by plants (image taken at cross-section 3.4 m from south wall).
# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 3 Experimental Results
### Dataset Experiment and Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PSNR**} & \multicolumn{3}{c}{**SSIM**} \\ \cline{2-7}  & \(\times\)**2** & \(\times\)**4** & \(\times\)**8** & \(\times\)**2** & \(\times\)**4** & \(\times\)**8** \\ \hline Bilinear & 23.7236 & 20.4894 & 18.5695 & 0.8270 & 0.7490 & 0.6563 \\ Bicubic & 23.9589 & 20.6875 & 18.8796 & 0.7958 & 0.6942 & 0.5709 \\ POCS & 25.4371 & 23.1215 & 19.3532 & 0.8621 & 0.7872 & 0.7016 \\ POCS [17] & 26.7464 & 24.1632 & 20.6725 & 0.8823 & 0.8057 & 0.7225 \\ POCS with LK & 26.6824 & 23.9758 & 20.6346 & 0.8723 & 0.7974 & 0.7183 \\ POCS with Gradient & 26.1728 & 23.5432 & 20.1325 & 0.8711 & 0.7925 & 0.7097 \\ Proposed & 27.1419 & 25.2231 & 21.1322 & 0.8985 & 0.8154 & 0.7348 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the PSNR and SSIM of the car range profiles under different magnifications. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**AG**} & \multicolumn{3}{c}{**ES**} & \multicolumn{3}{c}{**Time/ms**} \\ \cline{2-7}  & \(\times\)**2** & \(\times\)**4** & \(\times\)**8** & \(\times\)**2** & \(\times\)**4** & \(\times\)**8** & \(\times\)**2** & \(\times\)**4** & \(\times\)8** \\ \hline Bilinear & 1.27 & 1.21 & 1.01 & 13.84 & 13.45 & 12.07 & 14.3 & 15.7 & 16.9 \\ Bicubic & 1.64 & 1.29 & 1.08 & 16.27 & 16.06 & 15.17 & 14.8 & 16.1 & 17.4 \\ POCS & 2.01 & 1.36 & 1.12 & 19.02 & 18.79 & 17.16 & 36.7 & 43.2 & 51.2 \\ POCS [17] & 2.27 & 1.47 & 1.16 & 19.58 & 19.17 & 17.63 & 44.8 & 49.7 & 55.1 \\ POCS with LK & 2.35 & 1.49 & 1.20 & 19.49 & 18.98 & 17.54 & 47.3 & 52.9 & 59.4 \\ POCS with Gradient & 2.13 & 1.41 & 1.14 & 20.08 & 19.59 & 17.97 & 43.4 & 47.8 & 53.8 \\ Proposed & 2.38 & 1.52 & 1.21 & 20.35 & 19.70 & 18.08 & 49.6 & 55.4 & 61.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the evaluation indicators of the car range profiles under different magnifications.
## 2 Materials and Methods
### sEMG Recording  
The sEMG recording was performed by connecting the microelectrode array to the electrophysiology system comprising a signal processor and an amplifier (TDT RZ5 and PZ2, Tucker Davis Technology Inc., Alachua, FL, USA). The recorded sEMG on the skin of the arm was extracted from the system and processed using MATLAB. To be specific, the isometric contraction of flexor carpi ulnaris muscle was recorded through a differential sEMG measurement. For the reproducible recording, the forearm was located at 120\({}^{\circ}\) with respect to the arm, and isometric contraction was achieved by manual resistance with 50% of the MVC level. To reduce the electrode-skin interface impedance, the skin had been cleaned with water before placing the microelectrode [6]. Additionally, we located a surface microelectrode on the muscle belly of flexor carpi ulnaris along the muscle fibers to avoid a reduction in signal [16; 17]. For the data processing, we used a high-pass filter (HPF) and a low-pass filter (LPF). To reduce the impact of motion artifacts, we set the HPF cutoff frequency as 5 Hz [18]. The most meaningful sEMG signal is in the frequency range of 0-500 Hz; therefore, the LPF cutoff frequency was set to 500 Hz [19].
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 2 Related Works and Background
### Weight-Based Enhancements to DV-Hop  
In this section, we provide a comprehensive explanation of the localization process in the DV-Hop approach. Additionally, we introduce two localization algorithms that we previously proposed in [39; 40; 41], denoted W-DV-Hop and HW-DV-Hop. These localization algorithms are designed with the intent of significantly improving the accuracy when estimating the sensor positions.
# Hardware-Aware Mobile Building Block Evaluation for Computer Vision
## 4 Hardware-Aware Mobile Building Blocks Evaluation
### Setup  
We evaluate building blocks for mobile vision models based on the _RegNet_ design space [20]. This is defined by four parameters: depth \(d\), initial width \(w_{0}\), slope \(w_{a}\) and quantization \(w_{m}\). Instead of making the width of every block in the neural network a hyperparameter, the RegNet design space parameterizes the block widths as \(u_{j}=w_{0}+w_{a}\cdot j\) for \(0\leq j<d\). This block width is additionally quantized through the hyperparameter \(w_{m}\), such that the network only increases width at each of the four stages, where each stage consists of a sequence of identical blocks (see [20] for more details).  
For all evaluations, we sampled 130 models (as described in Section 3.2) from the design spaces created by the evaluated building block and trained each model for 10 epochs. The models were trained using SGD and a cosine learning rate schedule with initial learning rate 0.05, a weight decay of \(10^{-4}\) and a batch size of 128 on an RTX 3090.  
All evaluated networks were trained on the Visual Wake Words dataset [27]. This is specifically designed for vision models in embedded applications. It represents the vision use-case of identifying whether a person is present in an image or not. The dataset was derived from the publicly available COCO dataset and provides a realistic benchmark for tiny vision models.  
Figure 4: Structure of the MBConv building block, consisting of a depthwise separable convolution with an inverse bottleneck and squeeze-and-excitation unit.
# A Joint Strategy for Fair and Efficient Energy Usage in WLANs in the Presence of Capture Effect
## 5 PHY Capture Induced Unfairness in WLAN and Proposed Solutions Based on Joint Strategy
### Contention Window Adjustment Technique (CWAD)  
According to this strategy, node(s) suffering an unsuccessful transmission due to capture failure refrain from increasing the size of their CW, whereas in case of transmission failure due to collision, the tagged node increases the size of its \(CW\) according to BEB. The idea behind choosing the same CW when a transmission failed due to capture failure is to compensate the node for spending energy in contention and overhearing transmissions in the preceding round.  
Note that transmission failure due to capture is different than collision; the former happens when transmission of the tagged node is failed due to the successful capture of another transmission whereas the latter happens when all the simultaneously arriving packets in the same slot are destroyed. The mechanism behind recognition of whether a transmission is failed due to capture failure is the overhearing of the ACK frame transmitted from the AP to the sender node whose packet has successfully captured the channel. In contrast, during collision no ACK frame is received or overheard during the ACK time out.  
Hence, if a node after it transmits a data frame overhears an ACK which is destined for another node in the network declares a capture failure and randomly chooses a backoff counter without increasing its retransmission counter. This strategy gives the node suffered from capture failure the opportunity to conserve its energy by not staying for longer duration in contention. However, a node declares collision if it neither overhears nor receives an ACK frame after it transmits a data frame and would therefore increases its backoff stage for the next transmission attempt.
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 2 Background
### GNSS Multipath Effect
\begin{array}{l}B_{1}=-\left(1+\frac{2}{\alpha-1}\right)n_{1}\lambda_{1}+ \left(\frac{2}{\alpha-1}\right)n_{2}\lambda_{2}\\ B_{2}=-\left(\frac{2\alpha}{\alpha-1}\right)n_{1}\lambda_{1}+\left(\frac{2 \alpha}{\alpha-1}-1\right)n_{2}\lambda_{2}\end{array} \]  
To obtain the multipath error variations, we subtract the mean multipath value over time from the multipath combinations:  
\[MP_{1}=mp_{1}-mp_{1t}\text{ and }MP_{2}=mp_{2}-mp_{2},\]  
where \(mp_{1t}\) and \(mp_{2t}\) are the time-averaged multipath values:  
\[mp_{1t}=\frac{1}{t}\underset{t}{\sum}mp_{1}\text{ and }mp_{2t}=\frac{1}{t} \underset{t}{\sum}mp_{2}.\]  
While these fundamental principles describe the multipath phenomenon, effectively detecting and analyzing these effects in real-time requires advanced computational approaches. Deep learning, particularly recurrent neural networks with LSTM cells, offers promising solutions for processing such temporal data.
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 3 Results  
### Characterization of Moldy Maize Kernels  
#### 3.1.1 Greyscale Slice Image Analysis  
The imaging principle of X-ray tomography technology is based on the fact that the density and structure of each tissue in the scanned object differ, resulting in varying X-ray absorption capabilities. This led to the formation of grayscale images with different shades, which could be observed and analyzed to draw conclusions about the structure of each seed grain. Taking maize sample 8 as an example, Figure 5 below shows 2D sliced grayscale images of maize sample 8 in the xy and yz planes at four time points from 0 d to 15 d. When observing the xy-plane slice images, it is evident that the brightness of the embryo part gradually decreased and the grey value diminished as the molding time progressed. This decrease was notably lower than the grey value of the endosperm. Figure 5 shows sectional images of maize kernels in the yz plane, revealing a significant presence of mold spores at the embryo and endosperm pores of the kernel at 10 d, as well as mold originating from the tip of the maize kernel.