esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The average performance of Modified-Alexnet after two sets of 4-fold cross validation was 97.6% in the training set, 95.6% in the validation set, 91.9% in the test set, and 96.4% in total. For MobileNet, it was 98.5% in the training set, 92.7% in the validation set, 83.8% in the test set, and 95.4% in total, and for Resnet50 it was 98.1% in the training set, 94.5% in the validation set, 83.8% in the test set, and 95.6% in total (Figure 3).  
The standard deviations of the success rate for Modified-Alexnet, MobileNet, and Resnet50 were 1.5%, 1.7%, and 2.1% in the training set; 3.5%, 7.6%, and 6.1% in the validation set; and 1.1%, 2.2%, and 4.7% in the test set, respectively.  
The screening performances of the three DCNN models tested in this study are displayed in Table 2. It can be observed that Modified-Alexnet achieved the highest performance, with an accuracy of 0.919 (95% CI 0.888 to 0.949), sensitivity of 0.852 (95% CI 0.811 to 0.893), and specificity of 0.973 (95% CI 0.956 to 0.991).  
Figure 4 shows the receiver operating characteristic (ROC) curves of all models. The area under the ROC curve (AUC) was also calculated. The Modified-Alexnet model achieved the highest AUC of 0.969, while Resnet50 achieved an AUC of 0.923 and MobileNet achieved an AUC of 0.908.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AUC (95\% CI)** & **Accuracy (95\% CI)** & **Sensitivity (95\% CI)** & **Specificity (95\% CI)** \\ \hline Modified-Alexnet & 0.969 (\(\pm\)0.019) & 0.919 (\(\pm\)0.030) & 0.852 (\(\pm\)0.041) & 0.973 (\(\pm\)0.017) \\ MobileNet & 0.908 (\(\pm\)0.032) & 0.838 (\(\pm\)0.429) & 0.761 (\(\pm\)0.051)  
In cases of successful prediction, the ROI identified by class activation mapping (CAM) was mainly focused on the maxillary and mandibular teeth, mandibular symphysis, and mandible. In cases of prediction failure, the highlights were turned elsewhere or the ROI was scattered throughout, indicating that they were not properly focused (Figure 5).
## 4 Experiment and Analysis
### Generalization Experiment of Root Cause Analysis Model for Cast Product Defects  
Tables 6 and 7 show the experimental results of KGTBRN on the benchmark data sets FB15K-237 and WN18 RR, respectively. The selected comparison models are ConvE [43], TransE [38], ConvKB [36], KGLM [41], KERMIT [42] and NBFNET [44]. It can be seen that on the FB15K-237 data set, KGTBRN achieves the highest HITS@1 and HITS@3 and is suboptimal in HITS@10. Also, KGTBRN achieves the highest HITS@3 on WN18 RR, indicating that the two-branch reasoning network can improve the overall reasoning accuracy of the KG by effectively handling different entity distribution patterns. Tables 6 and 7 further reflect the robustness and generalization capability of KGTBRN. 1-1-type triples represent only 0.94% and the 1-N-type ones only 6.32%. However, the distribution on the continuous casting data set is more uniform, so it is speculated that the uneven distribution of triples in the FB15K-237 affects the reasoning accuracy of the 1-side of KGTBRN; then, the overall reasoning results are reduced in the HITS@10 index, achieving suboptimal results. Table 9 shows examples of actual reasoning results. According to the needs of the reasoning task, KGTBRN can use different reasoning methods according to the 1-side and N-side and give the top two root causes obtained by reasoning. When researchers need to reason about the root cause based on the appearance of a certain cast product defect, they can use \"cause\" as the input relation of the root cause analysis task. When researchers need to reason about the root cause based on a certain production phenomenon, \"lead to\" can be used as the input relation. From the first row of Table 9, it can be seen that when cracks appear on the surface of the casting, KGTBRN gives the top two root causes as \"the first brittleness temperature zone moves towards low temperature\" and \"deviation in critical stress appears\". Based on these results, workers can make the corresponding adjustments to relevant continuous casting process parameters. Figure 6 illustrates the cause reasoning results for \"Crack\". KGTBRN identifies eight potential root causes for it, including \"Stress concentration\", \"Shell unevenness\", \"Critical stress drop\", etc. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Percentage of Relation Type (\%)**} \\ \cline{2-5}  & **1-1** & **1-N** & **N-1** & **N-N** \\ \hline FB15K-237 & 0.94 & 6.32 & 22.03 & 70.72 \\ Continuous cast data set & 8.9 & 21.5 & 34.3 & 35.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical results of triple distribution of FB15K-237 and continuous casting data set.
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 2 Materials and Methods
### Validation  
As the coefficients CoVe-Pol models and CoHo-Pol models are obtained by training the first group of data sets, additional verification tests are performed using the part of the collected data reserved for model testing. We test the CoVe-Pol model for the NRCS values and wind speed. Thus, we substitute the variables (NRCSs simulated, wind speed observed by buoys, radar incidence angles and wind directions) into the CoVe-Pol model, and we compare the simulated NRCS values with the wind speeds observed by the buoys. To provide additional testing of the RH model, CoHo-Pol, the retrieved wind speeds are obtained by substituting \(\sigma_{RH}^{0}\) and \(\theta\) as given by the reserved data sets into the RH model.
## 2 Materials and Methods
### Model Development Based on Full Spectra  
In this work, partial least squares regression (PLSR) models were established for correlating the hyperspectral absorbance spectra data with the contamination levels of peanut or walnut in the whole wheat flour. PLSR is considered one of the most reliable and robust multivariate statistical analysis methods for modeling, so long as the input variables are numerous and highly correlated. PLS regression transforms raw predictors (wavelengths) to a reduced number of new variables called latent variables (LVs), which are statistically independent and carry useful information that is relevant, with reference values leading to better predictive capability [24]. The optimal number of LVs for the PLSR model was determined on the basis of the rule of the lowest prediction error in cross-validation carried out on the data of the calibration set. The evaluation of the PLSR model performance was examined by determination coefficient as well as root mean square error of calibration (R\({}_{\mathrm{c}}\)\({}^{2}\), RMSEC), cross-validation (R\({}_{\mathrm{cv}}\)\({}^{2}\), RMSECV) and prediction (R\({}_{\mathrm{p}}\)\({}^{2}\), RMSEP). The best model should have higher values of R\({}_{\mathrm{c}}\)\({}^{2}\), R\({}_{\mathrm{cv}}\)\({}^{2}\), and R\({}_{\mathrm{p}}\)\({}^{2}\), and lower values of RMSEC, RMSECV, and RMSEP.
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 2 QKD Networks with QKP
### Point-To-Point QKD System  
The four-state protocol originally proposed by [PERSON] and [PERSON] in 1984, called BB84 [19], is marked as the beginning of the development of QKD technology and most widely implemented in the world. As shown in Figure 1, in the BB84 protocol, Z base is composed of |H> and |V> (Z = |H>\(<\)H| +|V>\(<\)V|) and X base is composed of |+> and |-> (X = |+>\(<\)+| +|->\(<\)-|). Therefore, if the Z-base is used to measure |H> and |V>, a definitive result will be obtained, while the measures |+> and |-> will lead to uncertain results. Based on the above principles, the main part of BB84 protocol is described in the following three steps.
## 6 Description of Tests and Their Procedure
### Modeling the Data Distribution Density by Probabilistic Principal Component Analysis  
In this study, data distribution density was modeled using Probabilistic Principal Component Analysis (PPCA) using full covariance matrices. The test was carried out for seven values of the intervals equal to 0.1, 0.2, 0.3, 0.5, 0.6, 0.8 and 1. The data applied in the study were represented by arithmetic mean values derived from distorted images of musical notation symbols presented in grayscale. The number of center points was equal to the number of musical symbols and equal to 33. The center values were determined as the arithmetic mean values of all images of a given symbol, e.g., all average values of the distorted images of the entire note of the C key. The model with one-dimensional PPCA components was subjected to a learning process using a probabilistic expectation-maximization algorithm (Expectation-Maximization). The presented process consisted of 30 epochs. The obtained test results are presented below. Figure 10 indicates the arrangement of the input data (blue color). Figures 11 and 12 show the view of the density function (black) and the standard deviation for all terms along each component of the model (red).  
Based on the results obtained during the tests of the density function modeling, it was found that the method achieves good results in determining the location and density of data in individual clusters, showing that the values vary considerably between clusters. In Figure 11, a model of the data density distribution within the data coming from two different clusters can be seen. The results of representing the symbols of musical notation  
Figure 10: Scatter plot of the input data [39].  
Figure 9: A graph showing a set of input, output and approximation of the decision threshold between two categories [39].  
this way is not satisfactory. The possibility of a further improvement in data density modeling cannot be excluded.
## 1 Introduction  
Since the past decade, lithium-ion batteries (LiBs) have become the preferred technology in the field of rechargeable batteries. This technology is dominant from small-scale products such as mobile phones to large scale such as electric vehicles (EVs) [1]. The trend toward the electrification of private and public transport is seen as necessary to achieve a reduction in carbon dioxide emissions [2]. The performance of LiBs is significantly dependent on factors such as capacity, self-discharge, and internal resistance [3; 4]. Out of all these factors, internal resistance is one of the crucial factors to keep an eye on because it helps to identify and compare different types of cells, calculate energy efficiency, and determine the cooling system along with the power estimation [5]. Moreover, it can be said that the internal resistance is also a signal for battery ageing and, therefore, impacts the performance of the battery i.e., improvement in the battery can be done if the current internal resistance values are identified, and this consequently leads to the safety of the battery (decomposition of the active materials leads to increase in internal resistance and temperature within the battery) [6].  
The research conducted by [PERSON] et al. compared different internal-resistance-measurement methods such as current step methods, alternating current (AC) methods, and energy-loss methods. These measurements resulted in similar results for the current step method without change of charge and two energy-loss methods (Watt hour counting and calorimetry) for 60% state of charge (SoC) at 25 \({}^{\circ}\)C. The calorimeter measurements showed that with specific measurement cycles, the dissipated heat of a cell during a charging/discharging cycle can be used to calculate the internal resistance [7]. Several researchers used calorimetric measurements to determine the heat generation of Lithium-ion cells and batteries [8; 9]. Calorimeter systems for calorimetric measurements with different approaches were researched by [PERSON] et al. [10]. The outcome of this research showed that calorimetric measurements play an important role in understanding the thermal effects in lithium-ion cells.  
Therefore, the goal of this experimental investigation is to measure the internal resistance (R\({}_{i}\)) of lithium-ion cells with different cell chemistries, i.e., Lithium-ion Manganese Oxide (LMO), Lithium Iron Phosphate (LFP), Nickel Manganese Cobalt (NMC), and Lithium Titanium-Oxide (LTO), at different temperatures (from 0 \({}^{\circ}\)C to 40 \({}^{\circ}\)C) and C-rates (from 1 C to 3 C), along with the comparison of the results obtained from a variety of measurement methods, i.e., the current step method, direct-energy-loss method, and the calorimeter-measurement method. Please note that for the better comparison, all three methods were applied with a single measurement cycle simultaneously.  
This article contains detailed information about the measurement setup and the measuring process. The data analysis is shown, and the key findings are presented and described (Figure 1).
# Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments
## 3 Structure of Genetic Algorithm  
In a GA, a high-quality candidate solution is represented by a collection of genes called chromosome. A chromosome's potential is given by its fitness function. A population consists of a set of selected chromosomes, and the population is subjected to generations (or iterations). Finally, crossover and mutation operators are performed with defined probabilities to improve the solutions. A GA has several advantages over the traditional optimization methods. It may quickly arrive at a good solution set. As worse cases are eliminated, they will never affect the generated solution. GAs are one of the best methods to solve a problem about which little is known. This is because a genetic algorithm works by its own rules. This is a very useful strategy for a GA to solve highly complex problems in nature. In a GA, it is necessary for the problem solver to choose the appropriate coding method. If the solutions are coded in different combinations, then the GA will start its searching operation using its operators known as selection, crossover, and mutation, respectively. As is known, a suitable type of crossover technique for a particular problem can improve the GA's performance [47]. All these methods are probabilistic in nature. The proper stopping criteria will be given as input for the GA to stop its searching process. This is done purely based on the experience of the problem-solver. Based on the stopping criteria, the GA will stop running and give the solution that it finds at that point of time. The solution of the problem has to be represented in the GA as a genome (or chromosome). The genetic algorithm then creates a population of solutions and applies genetic operators such as mutation, crossover, and selection to evolve the solutions in order to find the best one(s). The crucial aspects of using GAs are: the definition of the objective function, the definition and implementation of the genetic representation, the definition and implementation of the genetic operators [48].  
GAs are frequently adopted to find out how to make the machine clusters form cells in accordance with the rules of production flow analysis. In cell formation, a GA works well and finds a good solution, as given in the abovementioned literature studies. Itgives high-quality solutions even if the problem has a high level of complexity. There are few other traditional metaheuristics approaches that performing good searches in such a situation [49]. The following representation is used in a typical cell-formation problem solved using GA. This representation is popularly known as real coding (see Figure 1).  
Depending on the number of cells to be accommodated in the layout, the number of genes on a chromosome increases. In the above example there are three cells and hence there are only 1, 2, and 3 values present in the chromosome of 8 genes (8 machines).
## 2 Methodology
### Measuring Performance and Dynamic Time Warping  
To measure the performance of the algorithms and compound parameters, different metrics can be used. However, it is convenient to represent the results graphically to avoid being deceived [70].  
In the situation studied, the goal is to match the overall \"shape\" of the penetrometric profile. At the 4 th ISMLG, [PERSON] et al. proposed the use of dynamic time warping (DTW) to determine similarity between pairs of cone penetration tests (CPT) [71].  
DTW [72] is an algorithm designed for the comparison of time series. This algorithm tries all possible combinations to shift one time series until it matches another (Figure 4). The minimum displacement distance gives us an idea of the similarity between both series.  
In this paper, DTW is used to compare the expected and predicted penetrometric profiles for each drilled inclusion. To limit the possibilities that DTW considers, a window of 5 is used. Otherwise, the algorithm could try paths with no geotechnical meaning (Figure 5).  
Figure 4: Comparison between two series (in blue) using DTW with a window of 5. The yellow lines represent the path to convert one series into the other.  
Long series can have longer distances than short ones, even in cases where the fit between series is better. To consider this, the obtained distance is divided by the length of the series. This gives a metric, d, with a similar meaning to MAE but considering the resemblance in shape of the two series.  
The methodology followed is summarised in Figure 6.  
Figure 5: Comparison between two series using DTW with no window applied.  
Figure 6: Scheme of the methodology followed.
## 7 Discussion
### Implications for Future Research and Practice
* **Scalability challenges:** Address scalability issues to ensure the model performs satisfactorily across various supply chain networks, managing coordination and data effectively. * **Integration of real-time data analytics:** Incorporate real-time data analytics to enhance the model's responsiveness to market changes by continuously updating probabilistic and fuzzy parameters [20]. * **Algorithm speed improvements:** Enhance the speed of genetic algorithms to expand their use among businesses with limited infrastructure, making advanced tools accessible to more companies. * **Incorporation of social responsibility and sustainability goals:** Align the model with global standards by integrating social responsibility and sustainability considerations for a holistic approach to supply chain management [36]. * **Use in other complex systems:** Validate the UDCGP-HGSA technique in other complex systems, such as healthcare logistics and disaster response networks, to assess its accuracy and reliability [19]. * **Advanced machine learning techniques:** Use advanced machine learning techniques to refine the model, providing reliable and scalable solutions in uncertain environments [40; 41].
## 6 Conclusions  
In this paper, we introduced a universal modulation scheme based on the CPM family for multi-band combined navigation. Two specific CPM signals, namely B\(M\)2 RC(8) and QM2 RC(10), are proposed as modulation solutions in the S and C bands, respectively. Theoretical analysis and simulation results show that the proposed modulation schemes can not only meet well the requirement of compatibility in the S and C bands and reduce the user terminal complexity in the multi-band combined navigation mode, but also offer better performance in the aspects of tracking accuracy, multi-path mitigation and anti-jamming compared to potential candidates such as BPSK, BOC, MSK, MSK-BOC, _etc._ Besides, the modulation schemes also provide new ideas and feasibility demonstration for signal design of multi-band combined navigation satellite systems.
# Study on the Influence of Groundwater Variation on the Bearing Capacity of Sandy Shallow Foundation
## 2 Experimental Tests
### Numerical Analysis Procedures  
The two-dimensional model is similar in size to the indoor model box test, which aims to verify the reliability of the model by comparing the numerical simulation results with  
Figure 6: Simplified diagram of the problem.  
the test results. Finally, it can predict the settlement of the foundation with the model and give an early warning for the foundation's safety. The specific steps of model establishment are as follows:  
1. Establish a two-dimensional model. In order to improve the convergence of calculation, a symmetric structure was adopted with a length of 0.6 m and a width of 0.45 m. The soil was divided into two layers: gravel in the lower layer and sandy soil in the upper layer.
2. Input the material parameters of the soil which are shown in Table 1.
3. Set the boundary conditions of the model. The left side of the model is sliding support. The right side is a symmetric boundary. The lower side is a fixed constraint, and the right end of the upper side is the applied boundary load.
4. Add soil weight and pore water pressure. Set soil plasticity and enable Mohr-Coulomb strength criteria.
5. Add the global control equation. Use the parameter solver to control the gradual increase in load and define the integral function to record the vertical displacement of the loading midpoint.
6. Divide the finite element mesh. Use relatively dense mesh in the contact surface of two layers of soil and the fluctuation surface of the water level.
7. Calculate and output the stress and deformation and plastic development of the model and the p-s curves at the midpoint of loading.
# Estimation of Saprolite Thickness Needed to Remove _E. coli_ from Wastewater
## 2 Materials and Methods
### Sampling Sites  
Saprolite data were obtained from a previous study that characterized saprolites at 15 different sites in the Piedmont and Mountain regions of N.C. for on-site wastewater management [17]. Due to the lack of a complete data set at some locations, we chose 12 of the sites for this study (Figure 1). The sites were selected to represent a wide range of soil and saprolite conditions that could potentially be used for wastewater disposal. Table 1 presents the location and general soil information for each of the 12 sites. Common rock types from which the saprolite formed are also shown in Table 1. All these saprolites developed from either igneous or metamorphic rock and represent the most common types of saprolite found in N.C. Saprolites from sedimentary rock are not widespread in N.C. and have not been used in OSWMS in the state.  
Figure 1: Map of North Carolina (N.C.) showing locations of sites sampled across the Mountain and Piedmont physiographic regions. Star indicates Raleigh, the capital city of N.C.  
Sites numbered 1-12 were in natural areas, and pits were excavated to describe the soil profile, classify the soil, and collect saprolite samples for characterization. Pits were dug by machine where possible, and by hand otherwise. Pit dimensions varied from site to site but were deep enough to expose the saprolite. Major soil horizons were identified on one wall of the pit to determine the depth to the C horizons, which consisted of saprolite. Disturbed samples were then collected from each horizon identified in the field and stored in plastic bags. Intact (undisturbed) \"cores\" were collected from the soil surface down to a depth well within the saprolite in close proximity to the observation pit at each site. Core samples were collected with a Giddings hydraulic soil probe (Giddings Equipment Co., Fort Collins, CO, USA) using a 3-inch diameter (7.6 cm) soil sampling tube equipped with a 6.5 or 6.6 cm diameter, quick relief cutting head. All cores were wrapped in plastic and transported to the laboratory in long semi-circular trays to maintain an \"undisturbed\" state as much as possible. Sites labeled Wake and Knightdale in Table 1 were located on developed homesites that had septic systems. Pits could not be dug at these locations, and so disturbed and undisturbed samples were collected by hand near the septic drainfields at both sites.