esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Furthermore, to incorporate randomization within the optimization process, we utilize two PRNGs, namely the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister (SFMT) [34]. These PRNGs have demonstrated exceptional performance and quality, as evidenced by their top rankings in a comprehensive evaluation of various PRNGs in [20]. As mentioned, using high-quality PRNGs enhances the optimization process by reducing the probability of premature convergence of the metaheuristic algorithm and mitigating the risk of getting trapped in local optima. To evaluate the effectiveness of our proposed hybrid metaheuristic, we will evaluate its performance on the NP-hard Container Relocation Problem (CRP) [35], which is also one of the NP-complete problems. The CRP, also referred to as the Block Relocation Problem (BRP), is an engineering optimization problem that involves reshuffling containers within a container terminal stacking area to minimize the number of reshuffles required for retrieving specific containers. This paper focuses on evaluating our hybrid algorithm using a test set of restricted CRPs, as described in [36], which is considered the most relevant test set for evaluating CRP solution models. The restricted CRP, a subproblem of the CRP, specifically involves reshuffling only those containers that block the retrieval of the desired containers. We selected the restricted CRP for evaluation because of its higher complexity compared to the standard CRP. The results of our novel hybrid algorithm, which includes adaptive switches and the selected PRNGs, are compared with individual GA and ABO algorithms that use standard and only one PRNG. Moreover, the optimization results of our new algorithm were compared with the optimization method from [36], for which, to the best of our knowledge, the best optimization results were obtained by CRPs. Our new algorithm achieved the best optimization results for the tested CRPs. Moreover, our algorithm outperforms the individual algorithms from GA and ABO, proving that the implementation of such a hybrid solution is justified in scientific research. The paper is divided into five interdependent sections. Section 2 contains a literature review of hybrid nature-inspired metaheuristic approaches and an examination of various PRNGs discussed in the literature. Section 3 presents the basic terminology of the algorithms GA and ABO, which are integral parts of our hybrid metaheuristic. In addition, both the 64-bit and 32-bit versions of SFMT pseudorandom generator are described in this section. The proposed hybrid nature-inspired metaheuristic algorithm is then presented in Section 4. Section 5 presents the evaluation of our new algorithm using the most challenging test instances of restricted CRPs. Finally, Section 6 provides a conclusion.
# Review of Weed Detection Methods Based on Computer Vision
## 4 Weed Detection and Identification Methods Based on Deep Learning
### Weed Detection and Identification Methods Based on CNNs  
CNNs are increasingly used in weed detection, and methods based on deep CNNs have achieved good results in weed detection and classification. For instance, [PERSON] et al. [124], [PERSON] et al. [125], and [PERSON] et al. [21] used such methods. [PERSON] et al. [126] adopted two different CNNs to process RGB and NIR images to identify crops and weeds rapidly and accurately. A lightweight CNN was used for fast and robust vegetation segmentation, then a deeper CNN was used to classify the extracted pixels between crops and weeds. [PERSON] and [PERSON] [127] evaluated the performance of two weed detection algorithms based on UAV images, ANN and AlexNet. The experimental results showed that the accuracy of AlexNet in weed detection was more than 99%, whereas the accuracy of ANN on the same dataset was 48%. [PERSON] et al. [128] established an aerial image weed segmentation model and compared it with SegNet and U-Net. The research results showed that the data balance and better spatial semantic information made the experimental results more accurate. [PERSON] et al. [129] proposed an improved Mask RCNN model to extract early cranesbill seedlings. These weeds can be used as herbal medicines for rheumatic disease. The proposed method enabled the weeds to be completely separated from the original image to obtain complete nutrients and increase yield. [PERSON] et al. [130] proposed a semantic segmentation method for weed crop detection based on deep neural networks (DNNs). Four additional components were integrated to improve the segmentation accuracy, which provided enhanced performance for weeds of arbitrary shape in a complex environment. These methods do not rely on image preprocessing and data conversion and can independently obtain useful feature information in images. The recognition accuracy is better than that of manually designed features under traditional ML methods.  
CNN frameworks, such as AlexNet [19], ResNet [131; 132], VGG [133], Google [134], U-Net, MobileNets, and DenseNet [135], are also widely used in weed detection. These methods stand out from other conventional index-based methods. For example, [PERSON] et al. [135] measured four different plants in diverse growing places and light conditions, and their custom framework combined U-Net, MobileNets, DenseNet, and ResNet.
###### Abstract  
The replenishment of aquifers depends mainly on precipitation rates, which is of vital importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in the Sinai Peninsula is a region that experiences constant population growth. This study compares the performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1\({}^{\circ}\) and 0.25\({}^{\circ}\) spatial resolution TMPA (Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement) data were retrieved and analyzed, employing appropriate statistical metrics. The best-performing data set was determined as the data source capable to most accurately bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events and more rare heavy-intensity events. With light-intensity events, the corresponding satellite-based data sets differ the least and correlate more, while the greatest differences and weakest correlations are noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior performance than TMPA in all rainfall intensities.  
precipitation; TRMM; GPM; stressed aquifers; arid areas; Sinai 1
## 2 State of the Art
### Derivation of the Problem  
In previous work, numerous methods can be used to check and improve data quality. The methods originate from different disciplines (such as database theory, measurement, and automation technology, machine tool development, or production engineering) and are usually tailored to delimited problems such as Statistical Process Control (SPC) [46] or Advanced Process Control (APC) [47]. In addition, as already shown, a very large chapter on the use of enterprise master data opens up under the heading of data quality. To differentiate, it should be noted once again that the considerations presented here focus on signal data, which usually represent time-varying data, e.g., measurement signals while relevant context information can also be included. To ensure the quality of the analysis, some process models (like SEMMA and CRISP-DM [28], ASUM-DM [30], or DMME [33; 34]), which treat the methodological process of machine learning holistically, are known.  
There is a lack of a holistic approach focused on data quality assurance, with which the relevant measures and algorithms, mostly known to be applied separately in individual special areas, are brought together. In addition, there is the requirement that all areas of influence on the quality of data and the analysis results are mapped holistically with the specifics of the relevant engineering applications.
# Incorporating Setup Effects into the Reliability Analysis of Driven Piles
## 4 Reliability Analysis  
### The Effect of FOS on Reliability Index  
Firstly, the effect of FOS on the reliability indices of driven piles considering setup effects is studied. The bias factors (\(\lambda\)) and coefficients of variation (COV) of loads and resistances of driven piles are summarized in Table 2, and the value of 1.0 for the increase factor \(M_{\rm setup}\) were also used. Meanwhile, based on the analysis results on the effect of \(\rho\) on reliability index, the value of \(\rho=3.69\) (65 m span length) was accepted for study [34; 35]. Reliability analysis was performed for the FOSs ranging from 1.0 to 5.0. The correlation between \(R_{0}\) and \(R_{\rm setup}\) was not taken into account in this part. Finally, Figure 1 shows the reliability indices corresponding to the factor of safety of driven piles in clay and sand.  
The variations in the reliability index with FOS, shown in Figure 1, obviously illustrate that the reliability indices of driven piles increase as FOS increases. This indicates the significant influence of FOS on reliability evaluation results of driven piles. In addition, the rate of increase in the reliability index corresponding to FOS slowly decreases with increasing FOS; a value of 3.0 for FOS is a key point in the transition zone of increase rate in Figure 1. Therefore, the value FOS = 3.0 can be used in later studies.
# Research on Basic Properties of Polymers for Fused Deposition Modelling Technology
## 2 Materials and Methods
### Characteristics of Printed Materials
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Manufacturer**} & \multirow{2}{*}{**Density (g/cm\({}^{3}\))**} & \multicolumn{2}{l}{**Tensile Strength**} & \multirow{2}{*}{**Hardness (HS)**} & \multirow{2}{*}{**Volumetric**} \\  & & & & **(g/cm\({}^{3}\))** & & **Declared by Product** & & **Shrinkage (\%)** \\ \hline
1 & PETG Carbon & Kimya & 1.32 & 92.9 & 78.8 & 0.2–1.0 \\
2 & FiberFlex 40D & Fiberlogy & 1.27 & 26.5 & 40.0 & 0.5–1.0 \\
3 & PETG & Sunlu & 1.23 & 28.3–101.0 & 71.4 & 0.2–1.0 \\
4 & HIPS & Devil Design & 1.05 & 37.0 & 75.0 & 0.2–1.0 \\
5 & TPU & F3D Filament & 1.20 & 28.0–96.0 & 85.0 & 0.5–1.0 \\
6 & PA+15 CF & ROSA 3D & 1.07 & 125.0 & 78.0 & 0.1–0.5 \\
7 & PETG & 3 DActive & 1.26 & 84.6 & 70.0 & 0.2–1.0 \\
8 & PLA & 3 DActive & 1.24 & 45.0 & 87.0 & 0.3–0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Materials used in the study [29; 30; 31; 32; 33; 34; 35]. and the printing surface. Inadequate bed adhesion can lead to warping, curling, or layer misalignment, compromising the quality and dimensional accuracy of the specimens. For each type of filament, the bed temperatures were fine-tuned to achieve uniform adhesion and prevent lifting at the edges of the prints, a common issue in materials like PETG and flexible filaments. The adjustment process revealed that most filaments performed well within the temperature ranges recommended by their manufacturers. However, exceptions were noted for FiberFlex 40D and PETG. For these materials, the manufacturer did not provide precise guidelines for optimal extrusion temperatures, necessitating an experimental approach by the researchers. Through systematic testing and evaluation, the researchers established custom temperature settings that produced defect-free prints with consistent mechanical properties. The selected temperatures used to produce the test samples are presented in the table below (Table 2).
## 3 Results and Discussion
Figure 11: _Cont._  
Figure 10: Real internal air temperature and CFD analysis with different sky temperatures for the InsCB module. Figure 9: Average error (%) between the real data and CFD simulations for different sky temperatures for the InsCB Module (for August month). The magnitude of the error between the CFD results and the real data largely depends on the accuracy of the sky temperature and how close it is in value to the accurate sky temperature where 5 degrees difference with the right sky temperature will result in almost double the error over one month (for 5 \({}^{\circ}\)C higher result in 10.2%, 10.4%, 9.4%, 9.3% error and for 5\({}^{\circ}\)C lower result in 13.3%, 15.1%, 14.7%, 13.9% error for the CB, InsCB, InsBV and InsRBV, respectively). Note: three sky temperature values were only presented in Figure 11 (i.e., high sky temperature of 6 \({}^{\circ}\)C, right sky temperate with least error compared to the real data and low sky temperature of \(-\)4 \({}^{\circ}\)C). The monthly sky temperature varies throughout the year from 1 \({}^{\circ}\)C in January to \(-\)7 \({}^{\circ}\)C in July and the difference for each month between the modules varies from 2 \({}^{\circ}\)C to 3 \({}^{\circ}\)C as shown in Figure 7:0  
The monthly sky temperature was determined in two methods; i.e., numerically and using CFD simulation for each module where the average simulated sky temperatures are close to the calculated sky temperatures (see Table 1) with a maximum difference of 1.1 \({}^{\circ}\)C. The average monthly sky temperatures for the temperate climate in Newcastle, Australia, for all the modules are presented in Figure 7:1  
Figure 7:2 Internal air temperature for different sky temperatures (**a**) CB, (**b**) InsCB, (**c**) InsBV and (**d**) InsRBV. Figure 7:3 Maximum and minimum sky temperatures for different months using CFD analysis. Table 2 shows the error between CFD results and real data (for 12 months' simulation) using sky temperatures higher and lower by 2 \({}^{\circ}\)C, 5 \({}^{\circ}\)C and 10 \({}^{\circ}\)C. Using the right sky temperature value for each module CB, InsCB, InsBV and InsRBV will result in 6.5%, 7.1%, 6.2% and 6.4% error, correspondingly, compared with the real data. These errors mainly refer to the simulation error. Using higher sky temperatures by +10 \({}^{\circ}\)C will significantly increase the error to 16.5%, 17.5%, 17.1% and 16.8% and lower sky temperature by +10 \({}^{\circ}\)C will also increase the error to 19.3%, 22.6%, 21.9% and 19.1% for CB, InsCB, InsBV and InsRBV modules, respectively. Using the right sky temperature will minimize the differences between simulated results and the real data. For example using the right sky temperature for February (1 \({}^{\circ}\)C, 1 \({}^{\circ}\)C, \(-2 {}^{\circ}\)C and 0 \({}^{\circ}\)C for the CB, InsCB, InsBV and InsRBV modules, respectively) will result in 5.4%, 6.1%, 5.9% and 6.1% error, correspondingly, compared with the real data. These errors mainly refer to the simulation error. On the other hand, using other sky temperature values will significantly increase the error.
## 4 Results and Discussion
### Factors Influencing the Behavior of the Products
#### 4.2.3 The Effect of Nano MgO on the Expansion of the Produced Cement  
In the present study, the ability of cement paste to expand was examined, as it is a particularly important parameter of its basic characteristics. The present experimental study used numerous samples for a significant period of time up to 400 days, making a systematic study of the raw materials which showed that a specific content of nano MgO and fly ash in powder as specific additives can be decisively active in the controlled shrinkage and expansion of the cement. This achieves the desired degree of volume dimension, while compensating for the shrinkage effects, avoiding the volume shrinkage of the cement. The present raw materials, as they do not present metallic elements, are not feasible and will affect any future Fe reinforceents. Its dilating action seems to begin in the first 8 days and ends depending on the ratio of nano MgO/fly ash from 8 to 400 days. Samples containing low percentage of MgO immediately complete their expansion, while mixtures with an increased percentage in nano MgO continue to evolve 400 days later. The expansion of the cement paste has been remarkably improved due to the nano MgO addition; nevertheless, the accurate limits of the MgO content added have not yet been defined. The expansion performance increases as the nano MgO content increases, following the extension of the curing age. Firstly, the expansion of the cement paste containing nano MgO oxide was significantly higher than that of the cement paste without MgO oxide. This may have happened because the nano MgO particles were small, and Mg(OH)\({}_{2}\) crystals were quickly formed due to hydration, and as a result, this gives higher expansion to the cement paste. Furthermore, Mg(OH)\({}_{2}\) crystals grow around the pore rims and near the MgO particles, and the increased volume of Mg(OH)\({}_{2}\) crystals squeezes the surrounding cement paste and causes expansion [6; 30; 35; 36]. The expansion of the tested cement paste samples are presented in Figure 10. As can be observed from the diagram (Figure 10), as the nano MgO rises, the expansion increases. The expansion of the sample without nano MgO was 364 \(\upmu\)m/m after autoclave curing, while the expansion amounts were 1060, 1700, 2500, 3300, and 4000 \(\upmu\)m/m when nano MgO was 4, 5, 6, 7, and 8%, respectively, after autoclave treatment. Several scientists have obtained similar results [6; 35]. It is worth mentioning that in the expansion test, a clear discrimination among the three groups is observed, characterized by clear limits in the used mixtures of nano MgO/fly ash. In the present systematic study of the effect of additives on the behavior of the produced cement, representative samples from the two groups were studied, giving the most satisfactory results in order to identify the effect of raw materials on the final behavior of cement.
# Human Height Estimation by Color Deep Learning and Depth 3D Conversion
## 2 Related Works  
### Object Detection from Color Information by Convolutional Neural Network  
Object detection problems in color image can generally be classified into four categories: classification, localization, detection and object segmentation. First, the classification determines an object category for single object in the image. Second, the localization finds the boundary box for single object in the image. Third, the detection finds the boundary boxes and determines object categories for multiple objects. Finally, the object segmentation finds pixels where each object is. CNN can solve whole categories of object detection problems. CNN replaces the weights of the neural networks with kernels which are rectangular filters. Generally, object detection through CNN are classified as 1-stage and 2-stage methods [27]. The 1-stage method performs both the location and the classification at once. The 2-stage method performs the classification after the location. The 1-stage method is faster than the 2-stage method but is less accurate. R-CNN [20] is a first proposed method for the detection through CNN. R-CNN applies a selective search algorithm to find the boundary box with a high probability where an object exists. The selective search algorithm is the method of constructing the boundary box by connecting adjacent pixels with similar texture, color and intensity. The object is classified through SVM (support vector machine). The feature map of the boundary boxes is extracted through AlexNet. R-CNN has disadvantages that the object detection is seriously slow and SVM should be trained separately from CNN. Fast R-CNN [21] has higher performance than R-CNN. Fast R-CNN applies a RoIPool algorithm and introduces a softmax classifier instead of SVM, so the feature map extraction and the classification are integrated into one neural network. faster R-CNN [22] replaces the selective search algorithm into a region proposal network (RPN) so whole processes of object detection are performed in one CNN. YOLO [23; 24] is the 1-stage method for object detection. YOLO defines the object detection problem as a regression problem. YOLO divides an input image into the grid cells of a certain size. The boundary boxes and the reliability of the object are predicted for each cell at same time. YOLO detects the objects more quickly than the 2-stage methods but is less accurate. SSD [25] allows the various sizes of the grid cells in order to increase the accuracy of object detection. mask R-CNN [26] is proposed for the object segmentation unlike other R-CNNs.
###### Abstract  
Soil salinization is a significant threat to agricultural production, making accurate salinity prediction essential. This study addresses key challenges in the Yellow River Delta (YRD) soil salinity inversion, including (1) determining which Landsat 8 OLI level performs better, (2) identifying the most suitable month for salinity inversion, and (3) improving model performance and identifying important variables in modeling. Thus Landsat 8 OLI images (Level-1 and Level-2) for 12 months were collected, then images having less than 10% cloud cover were selected and processed to extract spectral values. A total of 86 sampled points were processed to measure soil salinity. Using Pearson correlation and expert insights, January 15 and August 26 were identified as suitable dates for inversion. Then, seven original bands, 29 spectral indicators, and 39 derived variables which created through six mathematical transformations, were used to construct the following three models: partial least squares regression (PLSR), random forest (RF), and backpropagation neural network (BPNN). The results showed the following: (1) The Level-1 data, after FLAASH atmospheric correction, outperforms Level-2 data. (2) January is optimal for salinity inversion. (3) Among the three models, RF outperformed the others, achieving test set R\({}^{2}\) = 0.55, RMSE = 3.4, suggesting that the combination of spectral indicators and mathematically transformed variables can effectively enhance model accuracy for predicting soil salinity in the YRD. Furthermore, SWIR1, SWIR2, CLEX, second-order difference of SWIR1, and first-order difference of SWIR2 along with NIR played a key role in modeling.  
Keywords:soil salinity inversion; remote sensing; Landsat 8 OLI; machine learning models; Yellow River Delta; spectral indicator +
Footnote †: journal: Applied sciences