esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Research has also explored the quality characteristics of sweet red pepper paste [24], the ripening stages of lamuyo pepper [25], capsaicin content and pepper powder adulteration [26], antioxidant activity and water content in chili powder (_Capsicum annuum_ L.) [27], vitamin C and antioxidant activity of intact red chilies [28], quality compounds in _Capsicum_ species (_Capsicum annuum_ L. and _Capsicum futescens_ L.) [29], chili powder antioxidant activity classification (_Capsicum annuum_ L.) [30], total caroten and vitamin C determination in red pepper powder [31], and the impact of skin thickness on soluble solids content in fruits [32]. Therefore, according to the research conducted in this field, it can be seen that non-destructive techniques have the ability to improve the evaluation and control of the quality of fruits and vegetables. The VIS/NIR spectroscopy, as a non-destructive method, has been applied in assessing the quality of various agricultural products, offering advantages such as high speed and accuracy. However, focus on the evaluation of different varieties of bell pepper and the specific quality attributes considered in this study has received less attention from researchers. This study addresses this gap by exploring the use of VIS/NIR spectroscopy for a rapid and accurate assessment of bell pepper quality. The findings of this research could contribute to the improvement and standardization of quality assessments for this product within the agricultural supply chain and processing industries. Therefore, the aim of this study is to investigate the feasibility of using visible/near-infrared (VIS/NIR) spectroscopy as a rapid and non-destructive method for determining the quality parameters of bell peppers. Given the importance of accurately assessing the quality of agricultural products and the need for a method that is both time-efficient and non-invasive, this study seeks to achieve this goal by developing a partial least squares (PLS) model. In this regard, the wavelengths influencing bell pepper quality were identified using soft computational techniques, and multiple modeling approaches were employed for more precise analysis. Ultimately, this research aims to offer an optimized method capable of performing quality assessments of this product with high accuracy and speed for use in the agricultural and packaging industries.
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 3 Design Considerations  
### Design of the Inductor  
The inductors for this proposed topology were designed based on the current ripple and the applied voltage across it. The differential equation for each of the inductor during the first mode of operation is stated as follows. The minimum requirement value of the  
Figure 8: Minimum inductors value requirement for the operation of CCM and DCM and Boundary condition mode (**a**) value of inductor \(L_{1}\) vs. duty cycle, (**b**) value of inductor \(L_{2}\) value vs. duty cycle for 150 W load, \(V_{in}\) 48 V, \(V_{out}\) 400 V.  
inductors was also calculated, which sets the boundary of the CCM and DCM mode of the converter characteristics.  
\[V_{L1}=L_{1}\frac{di_{L1}}{dt}=V_{in} \tag{32}\]  
\[L_{1}=\frac{V_{in}dT}{\Delta i_{L1}} \tag{33}\]  
\[V_{L2}=L_{2}\frac{di_{L2}}{dt}=V_{\text{C3}} \tag{34}\]  
\[L_{2}=\frac{V_{\text{C3}}dT}{\Delta i_{L1}}=\frac{(2-d)V_{in}dT}{(1-d)\Delta i _{L1}} \tag{35}\]  
\[\Delta i_{L1}=\frac{V_{in}dT}{L_{1}} \tag{36}\]  
\[\Delta i_{L2}=\frac{V_{\text{C3}}dT}{L_{2}}=\frac{(2-d)V_{in}dT}{(1-d)L_{2}} \tag{37}\]  
The average current through \(L_{1}\) and \(L_{2}\) are as follows  
\[I_{L1}=\frac{2I_{0}}{\left(1-d\right)^{2}}=\frac{2V_{0}}{\left(1-d\right)^{2}R} \tag{38}\]  
\[I_{L2}=\frac{2I_{0}}{\left(1-d\right)}=\frac{2V_{0}}{\left(1-d\right)R} \tag{39}\]  
\[\left(I_{L1}\right)_{min}=\frac{2V_{0}}{\left(1-d\right)^{2}R}-\frac{V_{in}dT }{2L_{1}} \tag{40}\]  
\[L_{1}\geq\frac{d(1-d)^{4}R}{8(2-d)f_{sw}} \tag{41}\]  
\[\left(I_{L2}\right)_{min}=\frac{2V_{0}}{\left(1-d\right)R}-\frac{d(2-d)V_{in} T}{2(1-d)L_{2}} \tag{42}\]  
\[L_{2}\geq\frac{d(1-d)^{2}R}{8f_{sw}} \tag{43}\]  
In Figure 8 the value of the minimum inductor vs. duty cycle was plotted from Equations (41) and (43) for the condition of the CCM, DCM, and Boundary condition mode operating mode.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 5 Experiments
### Generalization Study  
It is computationally intractable to learn models from scratch to tackle unseen VRP-PDs. Therefore, it is of great importance to evaluate the generalization ability of learning-based methods to tackle routing problems with larger scales and different distributions.  
Figure 3: Representative routing results of some exemplary instances for the improved VRP-PD. For the 100-node case, the proposed method achieves an average traversal length of 9.16 (**a**) compared to 11.68 by POMO (**c**). When scaled to 150 nodes, the proposed method maintains competitive performance with an average route length of 11.68 (**b**), while POMO reaches 11.81 (**d**). These results demonstrate the scalability and solution quality of the proposed approach across different problem sizes.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(N\) = 20} & \multicolumn{3}{c}{\(N\) = 50} & \multicolumn{3}{c}{\(N\) = 100} & \multicolumn{3}{c}{\(N\) = 150} \\ \cline{2-13}  & **Length** & **Gap** & **Time (s)** & **Length** & **Gap** & **Time (s)** & **Length** & **Gap** & **Time (s)** & **Length** & **Gap** & **Time (s)** \\ \hline CPLEX [31] & 4.45 & 0.00\% & 6.51 & 7.15 & 0.00\% & 3600 & 10.41 & 0.00\% & 3600 & 17.74 & 89.12\% & 3600 \\ LKH [25] & 5.77 & 29.66\% & 0.07 & 9.72 & 35.94\% & 345.06 & 14.17 & 36.12\% & 3600 & 18.02 & 37.98\% & 3600 \\ \hline GA [32] & 6.87 & 54.38\% & 19.27 & 76.58 & 971.05\% & 282.75 & 62.28 & 498.27\% & 3600 & 152.72 & 1069.37\% & 3600 \\ SA [33] & 7.86 & 76.63\% & 17.34 & 17.12 & 139.44\% & 254.46 & 33.66 & 223.34\% & 3600 & 43.13 & 230.25\% & 3600 \\ ACOQ [34] & 7.62 & 71.24\% & 15.03 & 16.44 & 29.93\% & 220.55 & 32.09 & 208.26\% & 3600 & 41.10 & 214.70\% & 3600 \\ ALNS [35] & 7.34 & 64.94\% & 0.22 & 15.60 & 118.18\% & 1.30 & 30.12 & 189.84\% & 1.90 & 38.55 & 19.18\% & 3.71 \\ \hline AM (Greedy) [24] & 10.38 & 133.28\% & 0.02 & 23.46 & 228.11\% & 0.07 & 53.96 & 418.35\% & 0.09 & 86.76 & 564.22\% & 0.13 \\ AM Sampling [24] & 5.01 & 12.58\% & 31.11 & 8.86 & 23.92\% & 69.88 & 48.90 & 369.74\% & 103.07 & 73.86 & 465.54\% & 193.33 \\ POMO [25] & 4.82 & 8.31\% & 0.10 & 7.95 & 11.19\% & 0.17 & 11.85 & 13.83\% & 0.29 & 15.07 & 15.39\% & 0.45 \\ FER [26] & 6.60 & 48.31\% & 17.68 & 12.85 & 79.72\% & 29.85 & 24.11 & 131.60\% & 63.55 & 30.35 & 132.39\% & 111.24 \\ GDRL & 4.68 & 5.17\% & 0.23 & 7.56 & 5.73\% & 0.85 & 11.36 & 9.15\% & 4.27 & 13.06 & 0.00\% & 12.23 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results on improved VRP-PDs.  
To be more specific, we apply the model trained on VRP-PD with 100 nodes to tackle two types of problems: (1) larger-scale uniform-distribution problems with 200, 250, and 300 nodes; (2) same-scale problems with different node distributions; (3) real-world benchmark datasets with varying node numbers and distributions.
## 3 Results  
Table 1 shows results of the qualitative evaluation of the pancreas segmentation of Deep U-net + RICAP + mixup and the manually annotated labels. The mean visual scores of manually annotated labels were 4.951 and 4.902 for the two radiologists, and those of automatic segmentation results were 4.439 and 4.268. The mean score of automatic segmentation results demonstrates that the accuracy of the automatic segmentation was good; more than 92.6% (76/82) and 87.8% (74/82) of the cases were scored as 4 or above. Notably, Table 1 shows that the manually annotated labels were scored as 4 (good, but not perfect) in four and eight cases by the two radiologists. Weighted kappa values between the two radiologists were 0.465 (moderate agreement) for the manually annotated labels and 0.723 (substantial agreement) for the automatic segmentation results.  
Table 2 shows the results of the quantitative evaluation of pancreas segmentation. Mean and standard deviation of DSC, JI, SE, and SP are calculated from the validation cases of 4-fold cross validation for the Pancreas-CT dataset. Mean DSC of the deep U-net (0.703-0.789) was better than the mean DSC of the baseline U-net (0.686-0.748) across all data augmentation methods. Because mean SP was 1.00 in all the combinations, non-pancreas lesions were not segmented by the models. Therefore, mean DSC was mainly affected by mean SE (segmentation accuracy only for pancreas lesion) as shown in Table 2. Table 2 also shows the usefulness of data augmentation. In both, the baseline U-net and deep U-net, the model combined with any of the three types of data augmentation performed better than the model with no data augmentation. In addition, mixup and RICAP were more useful than the  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Radiologist** & **Target** & **Number of Score 1** & **Number of Score 2** & **Number of Score 3** & **Number of Score 4** & **Number of Score 5** \\ \hline Radiologist 1 & manually annotated label & 0 & 0 & 0 & 4 & 78 \\ Radiologist 1 & automatic segmentation & 0 & 3 & 3 & 31 & 45 \\ Radiologist 2 & manually annotated label & 0 & 0 & 0 & 8 & 74 \\ Radiologist 2 & automatic segmentation & 0 & 2 & 6 & 42 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of qualitative evaluation of automatic pancreas segmentation and manually annotated labels.  
conventional method; the best mean DSC was obtained using the combination of mixup and RICAP. The best mean DSC was obtained using the deep U-net with RICAP and mixup.  
Table 2 of Appendix B shows the results of the [PERSON] signed rank test. After the Bonferroni correction, the DSC differences between Deep U-net + RICAP + mixup and the other six models were statistically significant.  
Representative images of pancreas segmentation are shown in Figures 2 and 3. In the case of Figure 2, the manually annotated label was scored as 4 by the two radiologists because the main pancreas duct and its surrounding tissue were excluded from the label.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 5 Evaluation and Analysis
### Performance of Multipath Transmission  
In this section, we use the performance of multipath transmission to indirectly evaluate MDSM in improving multipath diversity. High multipath diversity helps improve the tolerance capability of multiple paths, so that multipath transmission should perform better with higher multipath diversity.  
Figure 7: The CDF (cumulative distribution function) of multipath stretch calculated from different multipath generating methods. (**a**) shows the CDF of multipath stretch in bearing networks generated with BRITE with scale-free feature in Figure 5; (**b**) shows the CDF of multipath stretch in practically deployed bearing networks from Internet Service Providers in Figure 6.  
The evaluation scenario adopts the bearing networks practically deployed as in Figure 6b. We still use the transmission scenario in the above Section 5.2. The quantity of end-to-end concurrent multiple paths is up to 3. We collect the sending rates for each origin end host and the variation of sending rate is depicted in Figure 8a. Background traffic with different degrees are injected into underlay bearing networks to act as the accidental occurrence of link congestion and failure. With the reference to evaluation configuration in work [27; 28; 29; 30], the evaluation scenario is configured as the total time being 32 s, the beginning time for congestion traffic or link failure follows a Weibull distribution, the duration interval congestion traffic varies between five seconds and ten seconds following a Pareto truncated distribution, and the failure links are chosen with a Power-Law based distribution. Figure 8b depicts the total injected traffic bandwidth acting as congestion. We compare the aggregating rates of different multipath generating methods, and also consider the default single path (DSP for short). Figure 8c shows the aggregating rate and it reveals that generally multipath transmission achieves better performance than single path, especially encountering link congestion and traffic burst. This is due to the fact that multipath transmission weakens congestion or failure influence of an individual path on the overall transmission and aggregate abundant transmission resources. Furthermore, MDSM achieves better performance most of the time in comparison with others; the main reason is that it directly characterizes multipath diversity and exploits multiple paths, and higher multipath diversity brings better tolerance capability.  
Figure 8: Evaluation of multipath transmission performance with injected congestion traffic and link failure. (**a**) depicts the sending rate from origin hosts; (**b**) depicts the total bandwidth of traffic injected into bearing networks as congestion; (**c**) depicts the aggregating rates from destination host using different transmission methods.
## 2 Related Works  
At present, the detection of TV is mostly focused on biochemical and staining detection methods, whereas detection based on images or videos is used less. In our previous works, we proposed two TV detection methods based on a traditional background difference model.  
In the work [1], we proposed an improved Kalman background reconstruction algorithm to detect TV automatically. The first frame of a video is used to build the background and the additional top-hat transformation can eliminate the phenomena of tailing and ghosting. By introducing time information and neighborhood judgment into the background updating mechanism, this method caned with the problems of falsely detected static areas and missing motion areas. The algorithm results show that this method can effectively suppress the noise caused by illumination mutation, lens shift, and focal length variation, providing strong adaptability and good robustness.  
When the movement speed of the moving target is slow or the movement frequency is low, the performance of this Kalman background reconstruction algorithm can decline, resulting in a high omission ratio. In order to address the above limitations, we proposed an improved VIBE background reconstruction algorithm [2]. The background model adopts three main update strategies: the memoryless update, the time subsampling of the model and the update of the spatial domain. In order to simplify the judgment, the foreground image is extracted by the frame difference method. Similarly, time information is introduced to eliminate false alarms from impurities or formed elements. This improved method can effectively suppress false alarms caused by formed elements and missed detections caused by the background model updating during the movement.  
TV is defocused due to its motion characteristics and in most cases it appears as flat shadow regions with little difference between adjacent frames. To detect moving shadow regions, it is necessary to reduce the judgment threshold of the foreground, but this can result in false alarms where some backgrounds are detected as TV. The above two methods mainly focus on the recognition of clear TV but fail for a defocused conditions. Therefore,  
Figure 1: One frame in the video has clear formed elements. The trichomonas vaginalis (TV) and some backgrounds are marked with red and green rectangles, respectively. (**a**–**c**) show the enlarged TV and (**d**) shows the enlarged background.  
a detection method based on deep learning is proposed in this paper to solve the above problems.
## 2 The Proposed Model
### Fourier Fitting  
After the obtainment of interpolated points, a Fourier model is adopted to realize the explicit expression of interpolation data, because of its high efficiency, fast speed, and sufficient fidelity [39]. The trigonometric Fourier series form can be constructed by  
\[f(x)=a_{0}+\sum\limits_{i=1}^{m}(a_{i}\cos(i\omega x)+b_{i}\sin(i\omega x)), \tag{37}\]  
where \(a_{0}\), \(a_{i}\), \(b_{i}\) and \(\omega\) represent the coefficient of the Fourier function; \(x\) and \(f(x)\) are the axial force and bending moment of the interpolation point, respectively; and \(m\) is the number of terms.  
Equation (37) can be expressed as \(AX=Y\), with  
\[\left\{\begin{array}{l}X=\left[a_{0},a_{1},b_{1},a_{2},b_{2},\cdots,a_{m},b_ {m}\right]\\ Y=\left[f(x_{1}),f(x_{2}),\cdots,f(x_{n})\right]\end{array}\right., \tag{38}\]  
and  
\[A=\left[\begin{array}{cccccc}\cos(wx_{1})&\sin(wx_{1})&\cos(2 wx_{1})&\sin(2 wx_{1})&\cdots&\cos(mwx_{1})&\sin(mwx_{1})\\ \cos(wx_{2})&\sin(wx_{2})&\cos(2 wx_{2})&\sin(2 wx_{2})&\cdots&\cos(mwx_{2})& \sin(mwx_{2})\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \cos(wx_{n})&\sin(wx_{n})&\cos(2 wx_{n})&\sin(2 wx_{n})&\cdots&\cos(mwx_{n})& \sin(mwx_{n})\end{array}\right], \tag{39}\]  
As \(m\leq[n/2]\) in Equation (39), the least squares solution is used in this paper. The least squares solution can be obtained by  
\[X=\left(A^{\mathsf{T}}A\right)^{-1}A^{\mathsf{T}}Y. \tag{40}\]  
In order to evaluate the accuracy and stability of the fitting model, the adjusted coefficients of determination are utilized in this study [40], which are given as:  
\[SSE=\sum\limits_{i=1}^{n}w_{i}(y_{i}-\hat{y}_{i})^{2}, \tag{41}\]  
\[SST=\sum\limits_{i=1}^{n}w_{i}(y_{i}-\overline{y_{i}})^{2}, \tag{42}\]  
\[R_{adj}^{2}=1-\frac{SSE/(n-p-1)}{SST/(n-1)}, \tag{43}\]  
where \(SST\) is the residual sum of squares of \(y\), which reflects the fluctuation of \(y\); \(SSE\) is the sum of square error of \(y\), which reflects the effect of the error on response fluctuation; \(y_{i}\) is the bending moment at the interpolation knot, \(\overline{y_{i}}\) is the mean value, \(\overline{y_{i}}=\frac{1}{n}\sum\limits_{i=1}^{n}y_{i}\); \(\hat{y}_{i}\) is the evaluation value of the bending moment at the interpolation knot; and \(p\) is the number of non-constant terms in the Fourier fitting function. The closer the modified coefficient of determination \(R_{adj}^{2}\) is to 1, the higher the accuracy of the response surface model achieves.  
In this paper, the PCHIP and Fourier series fitting model were carried out in MATLAB 2011a.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 4 Methodology
### LogEDL
#### 4.5.4 Anomaly Detection  
After training, we can deploy LogEDL for detecting anomalous log sequences. The idea behind applying LogEDL for log anomaly detection is that \"there are differences in the context between normal system logs and anomalous system logs\". In other words, when a language model trained solely on normal log data encounters the context of anomalous logs during testing, it is expected to exhibit higher uncertainty.  
In this study, the defined uncertainty refers to the logit values produced by the model when predicting the [MASK] token, indicating the network's inability to make a judgment on this contextual pattern. Furthermore, when the uncertainty is high, it suggests that the corresponding context is difficult to find in normal contexts, thus identifying it as an anomaly.  
Therefore, we can derive the anomaly score of a log sequence based on the predicted logits for the [MASK] token. To achieve this, given a test log sequence, we first randomly replace a certain proportion of log keys with the [MASK] token, similar to the training process, and use the randomly masked log sequence as input to LogEDL. The uncertainty of the \(t\)th log key refers to the degree of unknowns represented by \(u_{t}\) when the log key at the [MASK] position is considered as the answer. For each [MASK] token, we compute \(u_{t}\), which reflects the uncertainty at this masked position. For \(N\)[MASK] tokens, we select the \(top-n\ u_{t}\) values and compute the final anomaly score to detect anomalies. The formula for calculating the anomaly score of a test log sequence is shown in Equation (6).  
\[\text{abnormal }=\frac{1}{n}\sum_{t\in Top-n\ indices}u_{t} \tag{6}\]
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 2 Materials and Methods
### Search Strategy and Study Selection  
The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines [49; 50] served as the basis for this study. Our search of the literature identified potential studies that investigated the effects of robotic training combined with tDCS protocols on motor functions of upper and lower extremities post-stroke. We searched the literature from 1 September 2022 to 15 November 2022, and the publication date of the potential articles was not limited. Keyword inputs based on Boolean logic for searching included: (stroke OR brain infarction OR cerebrovascular disease) AND (tDCS OR transcranial direct current stimulation) AND (robot OR robotic OR robot-assisted). We used constant keyword inputs and the same search procedure for three search engines: (1) PubMed, (2) Web of Science, and (3) Cochrane Database of Systematic Reviews. We considered all types of publications (e.g., journal articles, conference abstracts, dissertations) for this meta-analysis.  
Initially, we identified 233 potential studies with 66 articles from PubMed, 86 from Web of Science, and 81 from the Cochrane Database of Systematic Reviews. After removing 127 duplicate studies, we closely read 106 articles. Based on the PICO criteria, we excluded 86 studies (i.e., 30 review articles, 4 case study articles, 5 studies with no sham control group, and 47 studies irrelevant to the current topic). Thus, 20 studies qualified for inclusion in our meta-analysis [37; 45; 46; 47; 51; 52; 53; 54; 55; 56]. The specific study identification procedures are shown in our PRISMA flow chart (Figure 1).  
\begin{table}
\begin{tabular}{p{85.4 pt} p{284.5 pt}} \hline \hline \multicolumn{1}{c}{**Contents**} \\ \hline P (Population) & Patients with stroke without restriction on age and demographic variables \\ I (Intervention) & Combined protocols including tDCS and robotic training \\ C (Comparator) & Patients with stroke with robotic training combined with either sham tDCS or no tDCS condition \\ O (Outcome) & Quantitative changes in either upper or lower limb motor functions \\ \hline \end{tabular}
\end{table}
Table 1: PICO criteria for inclusion and exclusion.