esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results
### Experiments and Discussion
#### 5.4.4 Ablation Experiments  
The ablation experiments utilized the YOLOv8n model as the baseline and assessed the impact of incorporating the deformable CSP bottleneck (DCB) module, aggregated triplet attention (ATA), meter data augmentation (MDA), and network pruning (NP). The results, presented in Table 5, reveal that each of the proposed modules in this study contributed to enhanced performance when incorporated into the network model.  
In order to assess the effectiveness and reliability of meter data augmentation, we incorporated it into various models and evaluated it using the meter reading recognition dataset to compare the performances. The findings of our experiments are presented in Table 6. It is evident that meter data augmentation enhances the precision of distinct models, thus demonstrating its positive impact on meter reading.  
\begin{table}
\begin{tabular}{l c c} \hline
**Model** & **mAP@50:95\%** & **Parameters/M** \\ \hline YOLOv8s & 96.01 & 11.16 \\ \hline YOLOv8n & 96.47 & 3.15 \\ YOLOv8n-MobileNetV3 & 92.76 & 2.25 \\ YOLOv8n-ShuffleNetV2 & 93.27 & 1.86 \\ YOLOv8n-RepVGG & 95.38 & 2.89 \\ \hline DAM-YOLO & 97.95 & 3.46 \\ DAMP-YOLO & 95.72 & 1.94 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of the area detection performance of lightweight networks for the meter reading area detection dataset.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Model** & **DCB** & **ATA** & **MDA** & **Prune** & **mAP@50:95** & **Parameters/M** & **
\begin{tabular}{c} **Inference/ms** \\ **(Jetson TX1)** \\ \end{tabular} \\ \hline YOLOv8n & & & & & 87.92 & 3.15 & 76.0 \\ D-YOLOv8n & ✓ & & & & 88.36 & 3.46 & 126.2 \\ A-YOLOv8n & & ✓ & & & 88.21 & 3.15 & 84.4 \\ M-YOLOv8n & & & ✓ & & 88.46 & 3.15 & 76.0 \\ DA-YOLOv8n & ✓ & ✓ & & & 88.60 & 3.46 & 136.5 \\ DAM-YOLOv8n & ✓ & ✓ & ✓ & & 88.77 & 3.46 & 136.5 \\ DAMP-YOLOv8n & ✓ & ✓ & ✓ & & 88.82 & 2.40 & 129.6 \\ \hline \end{tabular}
\end{table}
Table 5: The ablation experiments for the meter reading recognition dataset. DCB stands for deformable CSP bottleneck; ATA represents aggregated triplet attention module; MDA represents meter data augmentation; Prune denotes the prune channels for network pruning.
## 3 Results and Discussion
### Results of MLP-Based Models
Table 4 indicates the values of coefficient of correlation and RMSE for all the developed models for Pb, and this table suggests that that Model 4 with the structure 2-10-1 is the best performing of all the developed models for the training and validation stages, and shows comparable results in the testing stage with CC values in training (0.8562), testing (0.3706) and validation (0.7114), compared to RMSE values obtained in training (0.0231), testing (0.1071) and validation (0.0126). The model MLP-2-10-1 is the best for predicting Pb content, and the 2 signifies the number of input combinations (pH and phosphorus) and 10 is the number of neurons in a single hidden layer. From the inferences obtained using MLP models, we can say that metals concentration may nearly be appraised with these neural network models and it is comparatively easy to assess variables; it can be promising to identify metals that are detrimental to the feasibility of soils, both rapidly and economically [44]. Scatter plots revealed that inferences obtained through MLP-based  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} & \multicolumn{2}{c}{**Validation**} \\ \cline{2-9}  & **Run** & **CC** & **RMSE** & **CC** & **RMSE** & **CC** & **RMSE** \\ \hline MLP 2-4-1 & 25 & 0.9407 & 0.0557 & 0.7420 & 0.0878 & 0.6635 & 0.1440 \\ MLP 2-10-1 & 25 & 0.9488 & 0.0519 & 0.7366 & 0.0891 & 0.8626 & 0.0943 \\ MLP 2-7-1  
models of neural networks are acceptable for Cu, Co and Pb. Indeed, the determined models do not guarantee very high conformity amid assessments and amounts. However, this provides a useful method for assessing the eminence of soils [45]. [PERSON] [46], in their studies, applied a neural network approach based on MLP and multiple linear regression for predicting the concentration of Cu, Pb and Cr and inferred that NN models based on MLP are best predictors of the content of these metals with coefficients of determinations were 0.98 for Cu and 0.99 for Cr and Pb, respectively. [PERSON] et al. [47] used various machine learning techniques in estimating the content of PTEs, for example, nickel, and concluded that our MLP-based NNs models better predict the content of PTEs in contrast with other models. [PERSON] et al. [15], while working on potentially toxic elements (Fe, Mn, Cu and Zn) in Neyshabur plain, Iran, applied different models such as NN-based MLP, M5 model tree (M5) and bagging approach (BM5P). They concluded that MLP models are the best predictors of Fe and Cu, while BM5P and M5P are appropriate models for predicting the Zn and Mn.
## 4 Results  
The following figure (Figure 16) compares the design of the traditional approach (Figure 3), adopted as the baseline, with the results of the TO (Figure 5) and GD (Figure 8) approaches for the design of an aluminum rocker. In particular, the comparison was performed with the same material and FoS achieved by the design solution obtained through the traditional approach (Figure 16a). The design solution achieved by adopting the TO tool (Figure 16b) showed a very similar mass, in fact, a very small reduction of 2.2% was observed. On the contrary, a significant improvement was observed in terms of stiffness with a 46% reduction in maximum displacement. Given the same FoS, i.e., 2.4, the GD tool generates a solution, depicted in Figure 16c, that features a significant mass reduction of 38% and reduction in maximum displacement of 42% with respect to the solution obtained through the traditional approach (Figure 16a). While, Figure 16d depicts the best solution, generated by the GD tool, in terms of FoS. This solution, in fact, is characterized by a 35% mass reduction, a 30% reduction in maximum displacement, and a 25% increase in the minimum FoS.  
By comparing the results depicted in Figure 15, it is therefore evident that both TO and GD approaches allow to improve the design of the rocker obtained through the conventional approach.  
The next figure (Figure 17) shows the results of the rocker redesigned in titanium by adopting the TO (Figure 7) and GD (Figure 9) tools. In particular, the design obtained through the GD approach (Figure 17b) allows to achieve a 33% weight reduction with respect to the design obtained with the adoption of the TO tool (Figure 17a).  
Figure 16: Aluminum rocker designed through a traditional approach (**a**), with TO (**b**) and GD (**c**,**d**) tools.  
Figure 15: Titanium brake pedal designed by adopting GD tools.  
The following figure (Figure 18) depicts the results obtained for the redesign of the brake pedal through the adoption of TO and GD tools. From the comparison with the component designed through the conventional approach (Figure 18a), it emerges that the TO approach allows to generate a solution (Figure 18b) that improves performance, specifically a 42% reduction in maximum displacement and a 9% increase in the minimum FoS. The GD approach allows to further improve the solution obtained by using the TO. In particular, the best solution is the one made of aluminum (Figure 18c) for which there is an 8.5% reduction in mass, a 58% reduction in the maximum displacement, and a 100% increase in the safety factor. Concerning the best solution in titanium (Figure 18d), realized through the GD approach, it is worse than the aluminum version (Figure 18c) but still has some improvements compared to the one obtained through the traditional approach (Figure 18a) as it features a 8.5% mass reduction, 36% displacement reduction, and of 33% increase of the SoF.
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
###### Abstract  
Soil salinization is the main reason for declining soil quality and a reduction in agricultural productivity. We derive the spatial distribution of soil moisture from the temperature vegetation dryness index (TVDI) of Landsat TM-8 OLI images to analyze the effect of spatial heterogeneity of soil moisture on the retrieval accuracy of soil salinity. We establish five soil salinity inversion models for different soil moisture levels (drought levels) based on the canopy response salinity index (CRSI), normalized difference vegetation index (NDVI), and automatic water extraction index (AWEI) derived from Landsat TM-8 OLI images. The inversion accuracy of soil salinity is assessed using 42 field samples. The results show that the average accuracies of the five inversion models are higher than that of the traditional soil salinity inversion model of the entire study area. The proposed model underestimates soil salinity in high-moisture areas and overestimates it in drought areas. Therefore, inversion models of soil salinization should consider spatial differences in soil moisture to improve the inversion accuracy.  
soil salinity; soil moisture; vegetation indices; image processing +
Footnote †: 2021: 11, 11145. [[https://doi.org/10.3390/app11231145](https://doi.org/10.3390/app11231145)]([https://doi.org/10.3390/app11231145](https://doi.org/10.3390/app11231145))
# Remote Sensing for International Peace and Security: Its Role and Implications
## 3 Results
### Remote Sensing for Refugee Relief Operations
These images acquired before and after the incident provide clear evidence of forest disturbance due to the development of the refugee campsite (Figure 5a,b). Satellite data can be useful to provide information about environmental degradation due to international conflicts [47]. Knowing the size of refugee camps can help international agencies to plan and manage relief operations by providing logistic support, financial aids, and medical supports for these camps. Figure 4: Satellite-detected shelters at the Al Zaatari refugee camp, Jordan (**a**) 3 January 2013 (WorldView-2 image), and (**b**) 4 May 2013 (WorldView-1 image) (Source: UNITAR/UNOSAT).
## 6 Analysis of Dynamic Effects of the Earth Fissure
### Range of Influence  
From the amplification factor fitting curve, the range of distances corresponding to the amplification factor of 1.5 was divided into three regions for the influence of the dynamic response of earth fissure, as shown in Table 2. In future seismic fortification work, the seismic fortification levels of buildings and structures can be adjusted according to the amplification factors in different areas.  
When the amplification factor is greater than 1.5, it is in an area where the dynamic response of the site is amplified most intensely. All three methods have varying degrees of influence range, in which Fourier and response spectra are relatively close, the hanging side is about 5.0-7.1 m, and the heading side is 3.8-5.9 m. The area of Arias intensity is relatively large, with the hanging side at 0-13.3 m and the heading side at 0-9.2 m, and when the amplification factor is between 1.5 and 1, it is in an area where the dynamic response of the site is significantly amplified, and the buildings and structures in this area should also increase their seismic protection level accordingly. Furthermore, when the amplification factor is attenuated to 1, the site can be considered as no longer being affected by the amplification effect of the earth fissure, and therefore a zone of influence of the dynamic response can be delineated accordingly. The Fourier method yielded a hanging and heading side of approximately 20 m, while the response spectrum was about 18 m, and the Arias intensity showed an influence zone of 23.9 m for the hanging side and 17.3 m for the heading side.  
As a result, the influence range of the same method was closer to both fissure sides, but the hanging side influence range was always greater than that of the heading. Therefore, from the perspective of practical engineering for seismic protection, new buildings should try to avoid the influence area. If it cannot be avoided, the seismic protection intensity of the building should be increased as much as possible according to the corresponding amplification factor. Moreover, the dynamic amplification effect of the hanging side has a wider and more extensive scope of influence than that of the heading side, in particular for the seismic fortification level of the hanging sides of the earth fissure sites.
## 5 Discussion
### Study Limitations  
After summarizing the findings and contributions of this study, it is important to note some threats to the validity of the conducted research, inherent bias factors, and mitigation strategies that were used to counterbalance those threats.  
With respect to construct validity, i.e., the extent to which the research procedure accurately assesses the subject matter, the threat of mismatching the source material and the aim of the literature review was mitigated by implementing a thorough selection process (Figure 1).  
Regarding internal validity, i.e., establishing true causal relationships and ensuring that the reported outcomes were not a result of an unmeasured factor, any resulting threats were alleviated by the fact that this review does not focus on establishing statistical causal relationships but merely identifies key factors in the adoption of AI applications in MIS.  
Pertaining to conclusion validity, i.e., the inherent bias of the researcher when interpreting the extracted information, the following mitigation techniques were employed: a full audit trail for the selection process from the original number of studies to the selected primary studies is provided, a strict selection procedure is followed, and conclusions drawn from the analysis of the selected 60 primary studies have been agreed upon by both authors.  
Although strategies were employed to mitigate the threats to the validity of this literature review, we acknowledge that the manner of journal and conference source selection as well as the applied exclusion and inclusion criteria might limit the scope of the performed literature review.
## 3 Implementation Methodology
### Performance Evaluation  
According to [PERSON] et al. [19], the most used metrics are RMSE (root mean squared error) and MAE (mean average error), calculated based on the difference between the prediction result and the true value, while another metric, \(R^{2}\) (\(R\)-squared) is essential to explain the strength of the relationship between predictive models and target variables [20]. These three metrics provide a baseline for comparative analysis across different parameter settings for each model and across different methods. However, performance validation leads to a bias when the data set is split, trained, and tested only one time. This also means the result drawn from the testing dataset may no longer be valid after the testing subset is changed. To overcome this problem, each model is re-built 20 times using different random subsets of training and testing samples. The splitting proportion remains the same (80:20).  
All metrics report only a single value from the average performance of 20 identical models validated into 20 different subsets of testing instances.
# Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
## 2 Materials and Methods
### Strategies for \(\epsilon\) Determination
#### 2.3.3 Gradient Descent Method  
A similar method is the gradient descent algorithm [17]. It uses a dynamic step size variable based on the derivative (or gradient in multi-dimensional cases) of the search space at the current positions. The main idea is based on the fact that the derivative of the fitness function at a local minimum is zero. This is because a local minimum is a point where the slope of the function changes direction, and the derivative at that point becomes zero. This leads to the assumption that positions with large derivatives (large slopes of the fitness function) are far from the global optimum; therefore, it is worth using large step sizes from here. On the contrary, small derivatives lead to small step sizes.  
This method can be very well applicable to several problems, but it is not as widely usable as the traditional HC method; the fitness function must be differentiable and have some mathematical properties to work with gradient descent. Due to these limitations, this is considered a separate method and will not be discussed in this article.
## 2 Results
### Identification and Classification of Differentially Accumulation Metabolites (DAMs)
Each sample was represented by a column, and each metabolite was represented by a row. Each column corresponds to the abundance of a specific metabolite, indicated by a distinct color. The up-regulated and down-regulated metabolites were indicated by different shades of red and green, respectively. dien-2-ol along with heterocyclic compounds such as 5-Ethenyl-4-methylthiazolo. In the comparison between leaf callus and root, 6,10-dimethylundeca-5,9-dien-2-ol exhibited the most significant up-regulation among metabolites, whereas Isopropyl methoxy pyrazine showed the most significant down-regulation. In the comparison between leaf callus and seed, an aromatic compound [(S)-1-((S)-1-Isopropyl-but-3-enyloxy)-ethyl]-benzene was highly up-regulated as a metabolite of great significance, while trans-2-Nonen-1-ol displayed a highly significant down-regulation. Among seed and root comparisons, 1-ethenyl-cyclohexanol emerged as the most significantly up-regulated metabolite, whereas (+)-\(\beta\)-Cedrene demonstrated the most significant down-regulation. Furthermore, in the stem vs. leaf comparison analysis, the results indicated that 2,2\({}^{\prime}\)-isopropylatebis(5-methylfuran) experienced substantial up-regulation while octane, 5-ethyl-2-methyl-, underwent considerable down-regulation. In addition, the leaf callus exhibited a higher abundance of up-regulated metabolites and a lower abundance of down-regulated metabolites compared to other tissue sites. To identify the prevailing patterns in the distribution of differential metabolites across various tissue sites, we conducted K-means cluster analysis (Figure 4, Table S2). The findings revealed that these trends could be categorized into 10 distinct subclasses. Specifically, subclass 1 to subclass 10 encompassed clusters of 31, 12, 7, 41, 89, 8, 41, 20, and 50 metabolites, respectively. The most volatile components (89), primarily terpenoids, including 11 sesquiterpenes, were found in subclass 5. The data indicated that terpenoids were more commonly detected in leaf callus, followed by seeds, and less frequently observed in the husk. In addition, the predominant constituents in subclasses 1, 2, and 3 were esters, terpenoids, and esters, heterocyclic compounds, respectively. Hydrocarbons emerged as the major compounds in subclasses 4, 6, and 9. Esters dominated subclass 7, whereas terpenoids prevailed in subclasses 8 and 10. The accumulation of subclasses 1, 2, 4, 5, 9, and 10 was significantly observed in callus tissue. Subclass 3 exhibited significant accumulation in seeds. Subclass 6 showed significant accumulation in stems, while subclasses 1, 8, and 10 were found to accumulate significantly in roots. Leaves demonstrated a significant accumulation of subclasses 2, 7, and 9.
## 3 Constitutive Model and Simulation Results with MATLAB
### Parameter Determination and Ratchet Simulation of Several Typical Kinematic Hardening Models  
The initial yield stress \(\sigma_{0}\) of the material was 120 MPa, and the elastic modulus was the slope of the elastic section. By fitting the linear stress-strain curve, the slope was the elastic modulus \(E=194\) GPa. The material parameters \(\gamma_{i}\) and \(r_{i}\) are determined by the following formula through the uniaxial tension curve.  
\[\mathrm{r}_{i}=\left(\frac{\sigma_{i}-\sigma_{i-1}}{\varepsilon_{i}^{p}- \varepsilon_{i-1}^{p}}-\frac{\sigma_{i+1}-\sigma_{i}}{\varepsilon_{i+1}^{p}- \varepsilon_{i}^{p}}\right)\varepsilon_{i}^{p},i\
eq 1,\gamma_{i}=\frac{1}{ \varepsilon_{i}^{p}}. \tag{9}\]  
Finally, \(r_{i}\) is obtained by  
\[\sum_{i=1}^{M}r_{i}+\sigma_{0}=\sigma_{max}. \tag{10}\]  
where \(\sigma_{i}\) and \(\varepsilon_{i}^{p}\) are the stress and plastic strain values corresponding to the endpoints of each section. \(\sigma_{0}\) is the stress value when the plastic strain value on the curve is 0. The number of components that are needed for evaluation of the accuracy of the backstress are set as \(M=8\).  
The parameters of the models are shown in Table 3.  
With the incremental loading method in MATLAB, the ratcheting strains were simulated with the above five models for the uniaxial ratcheting experiments discussed in Section 2.2.2. The simulation effects of these models on ratcheting strain of S30408 stainless steel are shown in Figure 5. The simulation of ratcheting strain by these models had low prediction at the early stage of the cycle and over prediction at the later stage of the cycle. The yield platform stress of the material was 580 MPa. Therefore, when simulating the ratcheting with a maximum stress of 580 MPa, the ratcheting of the material could no longer be accurately obtained through the stress control experimental method. Thus, the ratcheting of the mean stress of 290 MPa and the amplitude stress of 290 MPa will not be discussed further.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Parameter** \\ \hline OW-II & \(r^{(1-8)}\) 80, 56, 69, 72, 80, 61, 31, 15 & \(\gamma^{(1-8)}\) 8000, 1500, 350, 180, 150, 72, 65, 12 \(m_{i}=7\) \\ AKO & \(r^{(1-8)}\) 90, 72, 78, 60, 61, 58, 34, 25 & \(\gamma^{(1-8)}\) 8000, 1500, 350, 180, 150, 72, 65, 12 \(\mu_{i}=0.2\) \\ AKO IV & \(r^{(1-8)}\) 80, 56, 69, 72, 80, 61, 31, 15 & \(\gamma^{(1-8)}\) 8000, 1500, 350, 180, 150, 72, 65, 12 \\  & \(\eta_{01}=0.1\), \(\omega_{1}=5\), \(\eta_{\infty}=0.05\), \(\chi=0\) & \(\eta_{02}=0.04\), \(\omega_{2}=3\), \(\eta_{\infty}=0.03\) \\ OWII-AF & \(r^{(1-8)}\) 80, 56, 69, 72, 80, 61, 31, 15 & \(\gamma^{(1-8)}\) 8000, 1500, 350, 180, 150, 72, 65, 12 \\  & \(\mu_{i}=0.04\), \(m_{i}=7\) \\ M OWII-AF & \(r^{(1-8)}\) 80, 56, 69, 72, 80, 61, 31, 15 \(\gamma^{(1-8)}\) 8000, 1500, 350, 180, 150, 72, 65, \\  & \(12\)\(m_{i}=7\)\(\eta_{0}=0.04\), \(\omega=8\), \(\eta_{\infty}=0.01\), \(\phi_{\infty}=0.4\), \(\omega_{\phi}=1\)\(\chi=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter determination of different models.