esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Edible films and coatings as a non-invasive technology play a very important role in maintaining the quality of fresh vegetables. A whey-protein-isolate-based edible coating incorporated with jojoba oil was used to protect fresh-cut root parsley by an immersion process. Samples of fresh-cut parsley uncoated (control) and coated with whey protein film-forming solution were characterized by a decrease in hardness, while parsley coated with whey protein isolate with the addition of jojoba oil showed higher values in hardness during storage. The L*, a*, and b* color parameters and hue angle mostly remained at similar levels, which indicate the maintenance of the desired color, but changes in the values were observed during storage. The research shows that the use of whey protein coatings and the storage time had a positive impact on the contents of polyphenols and flavonoids in fresh-cut parsley. Scanning electron microscopy showed a smaller number of pores in the vegetable tissue as a result of the whey protein coatings. The storage deteriorated the quality of the fresh-cut parsley surface, and the drying effect was visible. The use of protein coatings incorporated with jojoba oil modifies the quality characteristics of fresh-cut parsley, which can find application in reducing the waste of minimally processed vegetables  
Figure 1: Scanning electron micrographs of samples uncoated (control) and coated with whey protein isolate (WPI) coatings incorporated with jojoba oil (JO) of fresh-cut root parsley at 0 and 28 days of storage (magnification 100\(\times\)).  
during storage. The research shows that the technology of edible coatings based on whey protein isolate and jojoba oil is a promising technology for improving fresh-cut parsley roots; however, more research is needed, especially a sensory analysis and a study on storage stability.  
Conceptualization, S.G.; methodology, S.G.; software, S.G.; validation, S.G.; formal analysis, S.G.; investigation, S.G.; resources, S.G.; data curation, S.G.; writing--original draft preparation, S.G. and M.M.; writing--review and editing, S.G., M.M., A.C. and M.J.; visualization, S.G.; supervision, S.G.; project administration, S.G.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.  
The work was co-financed by a statutory activity subsidy from the Polish Ministry of Science and Higher Education for the Faculty of Food Sciences of Warsaw University of Life Sciences.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON] and [PERSON] for participation in the experimental part of the research.  
The authors declare no conflict of interest.
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 3 Preliminaries
### Compared Solution Methods for Imbalanced Datasets from the Literature  
The SMOTE (Synthetic Minority Oversampling Technique) approach [5] is one of the most popular methods for balancing datasets. This method creates synthetic data by analyzing existing minority data. SMOTE does not accurately reflect the distribution of the original samples in the newly created artificial samples. Therefore, when using SMOTE-based oversampling methods, there may be errors in the distribution of samples, which may affect the accuracy of the classifier. This may increase the probability and cause misclassification of samples [6].  
The SLS (safe-level SMOTE) method was proposed by [PERSON] et al. [2]. The \"safe level\" is determined using nearest neighbor minority samples, and minority data with the same weight value within this safe-level region are carefully sampled along the line. The authors proved in the study that this method obtained better results than SMOTE.  
The combination of SMOTE and ENN and SMOTE and Tomek represents hybrid resampling techniques designed to solve class imbalance in datasets. Both methods commence with the oversampling step of SMOTE, generating synthetic instances for the minority class. Following this, SMOTENN employs edited nearest neighbors to eliminate examples, while SMOTETomek utilizes Tomek links to identify and remove instances causing ambiguity at the decision boundary. In this study, SMOTEENN and SMOTETomek were utilized to compare the proposed method [19].  
In another study, [PERSON] et al. [1] proposed SIMO (Synthetic Informative Minority Oversampling) and a variation known as weighted SIMO (W-SIMO). In these algorithms, after separating the training and test data, an SVM is applied to the training data, and the decision boundaries between the classes can be determined. The aim of these methods is to reproduce only informative minority data close to the boundary region to avoid overfitting when oversampling minority data. The results of the study were verified with different imbalanced data learning approaches using the G-mean metric.  
Before briefly introducing RusAda [22], we should note that boosting [40] is an idea that focuses on misclassified instances and gives more weight to these instances. The AdaBoost algorithm [41] can be considered the first boosting algorithm. It combines boosting and a random undersampling strategy, outperforming SMOTEBoost. RusAda, a method we compared our results with, is an algorithm that incorporates the boosting procedure from AdaBoost to improve the existing RusBoost algorithm.
## 3 Design of the Compound Controller
### Designs of the Tracking Differentiator  
The TD in traditional ADRC is usually discrete as shown in Equation (7):  
\[\left\{\begin{array}{l}x_{1}(k+1)=x_{1}(k)+hx_{2}(k)\\ x_{2}(k+1)=x_{2}(k)+h\Big{[}-r^{2}[x_{1}(k)-y_{d}(k)]-2 rx_{2}(k)\Big{\}}\end{array} \right., \tag{7}\]  
where \(y_{d}\) is input signal, \(x_{1}\) is the transition process of input signal, and \(x_{2}\) is its differential signal. \(h\) is an integral step that is designed to set the corresponding step size according to the engineering requirements and the controller capability, \(r\) is the adjustable speed factor that adjusts the speed of excessive processing according to the actual process needs. Thus, we introduce a continuous form of TD as shown in Equation (8).  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=-rsign\Big{[}x_{1}-y_{d}(t)+\frac{x_{2}|x_{2}|}{2r}\Big{]}\end{array} \right., \tag{8}\]  
The function of TD is shown in Figure 4. When the input signal of the system has internal disturbance noise or change uncertainties, TD provides the transition process through the input signal to realize the smooth processing of the signal, thus ensuring the continuity of the input of the controller and reducing the negative impact of the disturbance.  
As Figure 4 shows we can get a smoother curve for the square wave signal with noise interference after TD, and differential signals are also obtained which can be transmitted to the controller for control.
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 1 Introduction
### Related Work
[26] explores the influence of different urban intersection designs on V2V communications. Employing a 2D geometric path loss model that accounts for blockages by buildings, the study assesses communication efficacy through metrics like packet drop and delivery rates, reception rates, and message longevity, thereby navigating the balance between minimizing interference and optimizing V2V transmission efficiency. However, existing research evaluating V2V communication performance often overlooks the vehicles' altitude, leading to potential inaccuracies in the analysis of system performance. This oversight renders 1D and 2D models less effective, particularly in scenarios characterized by high vehicle density. Such models fall short of capturing the complexities of real-world environments, where factors like elevation angles, vehicle dimensions, and height variations among transmitters, receivers, and obstacles play a critical role. Consequently, there is a clear need for a more sophisticated and realistic 3D modeling approach to V2V communications. To our knowledge, the literature has yet to address the incorporation of 3D modeling in V2V communication systems comprehensively. This gap underscores the novelty and necessity of our proposed investigation. To illustrate our distinction from previous research, we present a comprehensive comparison of methodologies and tools used in related studies in Table 1, focusing on stochastic geometry-based approaches.
## 4 Case Studies
### Case 1: A Fault on Bus 2-3  
In case 1, a fault on the branch between bus 2 and bus 3 is considered. To isolate the fault from the main source, the switches on bus 2 and bus 3 are opened, resulting in severe outages on most of the loads in the system. Initial populations are randomly generated by the proposed algorithm except for a variable for the faulty branch, and the variable holds a value of '0' representing the open branch.  
Table 5 shows the simulation result for case 1. The best SR plan derived by the proposed algorithm divides the isolated part of a distribution system into five islands. According to the anti-parallel constraint, each island has one DG as a power source for feeding the loads in the island. Since the total capacity of DGs is less than the total amount of loads in the outage area, islands are created to restore the maximum possible loads considering the importance of each load. As indicated in Table 5, loads of 850.19 kW that are 86.7% of the total capacity of DGs are restored by the intentional islanding scheme, and three controllable loads at buses 17, 18, and 63 are disconnected to restore more important loads.  
Figure 9 depicts the best cost of the objective function according to the iteration for case 1, and the best cost converges to a value of 6.6216 to minimize the objective function as the iteration of the GA-related process repeats. The graphical representation of the islanding partitioning result for case 1 is provided in Figure 10. As shown in the figure, most of the important loads, such as class 1 and 2 loads, are restored by the proposed algorithm although the class 1 load at bus 58 is still in the outage area due to the lack of capacity of DGs. Figure 11 illustrates the voltage profiles of each island created by the proposed algorithm for case 1. The maximum and minimum voltages are calculated as 1.010 p.u. corresponding to each DG bus and 0.963 p.u. at bus 12, respectively. The voltage magnitude of all the buses in the islands is within the allowable range, 0.95 p.u.-1.05 p.u., as the populations with voltages outside the acceptable range are eliminated due to the penalty value in the network constraint check. The algorithm takes about 8 s to derive the optimal solution, and it can be considered acceptable to provide a distribution network operator with service restoration solutions.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 2 Related Works  
### Image Restoration  
Image restoration aims to reproduce clean counterparts from images damaged by various types of degradation, and this field has been extensively studied for a long time.  
Conventional image restoration approaches restrict the clean latent space based on various assumptions and handcrafted preprocessing techniques [1,2]. Recently, data-driven methods designed with CNNs have shown substantial improvements in restoration capacity compared to conventional algorithms [3, 4, 5, 20, 21]. These CNN-based methods primarily utilize encoder-decoder frameworks, particularly U-shaped architectures, which can yet effectively aggregate hierarchical representations. Furthermore, advanced techniques have been introduced to enhance performance, including dilated convolutions [4, 22], multi-stage frameworks [3, 23], contrastive learning [20, 24], dense connections [25, 26], and attention mechanisms [3, 21]. Recently, newly proposed methods have included the design of transformer-based and MLP-mixer-based models to capture long-range dependencies, demonstrating remarkable performance in several image restoration tasks [7, 9, 23, 27]. While these approaches have been developed to perform restoration only in the spatial domain, we can selectively aggregate features in both the spatial and frequency domains for image restoration.
# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
## 4 Simulation and Verification
### Comparison of Routing Time Delay  
Figure 6 shows the average service delay of several algorithms for a single node in typical communication environments and routing task objectives in each sub-network. With different routing application times, the average delay of the cross-domain network based on this algorithm is 0.46 s, while other algorithms are above 0.5 s, reducing the average service delay by 8%. It can be seen that when the number of routing applications is exceeded, the average maximum communication delay in the ORPT is smaller than the algorithm in this paper. This is because the routing blocking delay reduces the routing efficiency improvement caused by different network strategies when there are more applications.  
Figure 5: Comparison of link load for different route application times.  
Figure 6: Maximum service time delay for different routing application times.
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 4 Experiment and Result
### 3D Detection  
For the final 3D detection results, this paper uses two metrics to measure the precision of 3D localization and 3D bounding box detection. For 3D localization, this paper projects the three-dimensional boxes onto the ground plane to obtain a bird's eye view of bounding boxes. This paper calculates the Average Precision of Bird's Eye View (AP\({}_{\text{BEV}}\)) bounding boxes. For 3D bounding box detection, this paper uses the Average Precision (AP\({}_{\text{3D}}\)) metric to evaluate the complete 3D bounding boxes. In the context of 3D object detection, AP\({}_{\text{3D}}\) evaluates the precision of fully 3D bounding boxes by considering their location, size, and orientation in three-dimensional space, providing a comprehensive measure of detection accuracy. Complementing this, AP\({}_{\text{BEV}}\) assesses detection performance by projecting 3D boxes onto the ground plane, focusing on the accuracy of object localization from a bird's-eye perspective, which is critical for tasks like autonomous driving.  
When evaluating with AP\({}_{\text{3D}}\) and AP\({}_{\text{BEV}}\), we set an IoU threshold of 0.7 for the car class. On the test set, as shown in Table 1, the network in this paper basically achieves the optimal effect compared with the state-of-the-art networks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**AP\({}_{\text{3D}}\) (\%)**} & \multicolumn{3}{c}{**AP\({}_{\text{BEV}}\) (\%)**} \\ \cline{2-7}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline F-PointNet & 81.20 & 70.39 & 62.19 & 88.70 & 84.00 & 75.33 \\ F-ConvNet & 85.88 & 76.51 & 68.08 & 89.69 & 83.08 & 74.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with state-of-the-art methods on the KITTI test set for car 3D detection. The best scores are highlighted in bold.  
Figure 6: Detection result diagram. The system represents predicted cars with green frames and true values GroundTruth with red frames. The first score reflects the confidence score of the 3D prediction box, while the second score represents the Intersection over Union (IoU) between the 3D predicted box and the true value.
## 2 Materials and Methods
### Measures and Procedures  
After a 10 min warm up (easy cycling at 100 W, preferred cadence), each participant completed six testing trials using three different workloads and three different cadences. Three workloads (LOW, MID, HIGH) were set based on the participant's body mass and were set to 2, 2.5 and 3 W/kg, respectively. These three conditions were performed at a preferred cadence. The three cadence conditions (C75, C85 and C95) were performedat a constant workload of 2 W/kg but with a dedicated cadence of 75, 85 and 95 rpm, respectively. After gradually reaching the required workload and cadence trial lasted 2 min. There was at least 5 min between the trials to avoid fatigue. Trials were performed in a random order and cadence was controlled using a metronome. The three workloads and cadences were selected based on a prior verbal interview with each participant and their performance data.  
Each participant brought his/her own road bicycle to the lab, which was mounted on the cycling ergotrainer (RacerMate, Computrainer, Seattle, WA, USA). Calibration of the ergotrainer was performed after the warmup according to the manufacturer's instructions to ensure the most accurate measurement, rear tire pressure was checked prior each test and ambient temperature was controlled continuously. One force pedal (Forped, Cycling Science Ltd., Kranj, Slovenia) was mounted on the left side, which recorded vertical (FV), anterior-posterior (FAP) and medial-lateral (FML) forces at a sampling rate of 1000 Hz. The force pedal was designed for Shimano SPD-SL pedal cleats and this type of the pedal was mounted on the other side. Those participants who did not use a Shimano system, cleats were changed on their shoes matching the position and orientation based on the pedal axis (marks on cleats). The force pedal and the normal pedal had the same axis height and lateral stack.  
Active LED markers were positioned on the greater trochanter, lateral condyle, lateral malleolus and two on the force pedal. After volume and orientation calibration following the manufacturer's guidelines for best accuracy and reliability [12], kinematics were recorded with an active kinematics system at a sampling rate of 250 Hz (3D investigator, NDI, Waterloo, ON, Canada). Pedal forces and kinematics data were synchronized through a digital pulse sent from the kinematics system and matched synchronically in the post analysis.
## 2 Impervious Surface Extraction Method
### Impervious Surface Extraction  
Using the Impervious Surface Index and the bare soil index to establish impervious surface and bare soil areas of interest, impervious surfaces and bare soil are easily confused, which seriously affects the accuracy of impervious surface extraction, whereas using the Impervious Surface Index in the impervious surface aggregation area can establish impervious surface samples with high separability. Therefore, the Normalized Vegetation Index (NDVI), Normalized Difference Water Body Index (MNDWI), and Arid Water Body Index (IBAI) were used to establish the vegetation, water body, and bare soil areas of interest. Impervious surfaces are concentrated in the built-up area, and the Enhanced Impervious Surface Index (ENDISI) is used to establish impervious surface interest zones in the built-up area to enhance the separability of the sample pool, and, finally, to realize the extraction of impervious surfaces by using the maximum likelihood classification method.  
The indices are shown in Equation (4):  
\[\left\{\begin{array}{c}NDVI=\frac{B_{2}-B_{4}}{B_{5}+B_{4}}\\ MNDWI=\frac{B_{2}-B_{4}}{B_{5}+B_{6}}\\ IBAI=\frac{(B_{3}+B_{6})-(B_{2}+B_{3}+B_{4})}{(B_{5}+B_{6}+B_{7}+B_{4})}\\ ENDISI=\frac{(B_{2}+B_{7})-(B_{4}+B_{5}+B_{7})/3}{(B_{2}+B_{7})/2+(B_{4}+B_{5}+ B_{7})/3}\end{array}\right. \tag{4}\]  
where \(B2\), \(B3\), \(B4\), \(B5\), \(B6\), and \(B7\) represent the blue, green, red, near-infrared, short-wave infrared1, and short-wave infrared2 of Landsat8 images, respectively. The use of correlation indices to establish areas of interest is more rapid than manual sample selection and allows for the establishment of denser and larger samples. Extracting vegetation using the NDVI index in areas with moderately sparse vegetation and the presence of bare cropland can distinguish soils well. The MNDWI index using green and mid-infrared waves can distinguish buildings and water bodies well. The IBAI index can distinguish bare cropland from impervious surfaces, selecting the ENDISI index to establish the impervious surface area of interest can effectively eliminate the interference of sandy bare land in arid zones, and the use of the appropriate index to establish the area of interest can effectively improve the separability of samples, thus improving the accuracy of impervious surface extraction.
# The Effect of Preprocessing on Arabic Document Categorization
## 6 Conclusions  
In this study, we investigated the impact of preprocessing tasks on the accuracy of Arabic document categorization by using three different classification algorithms. Three preprocessing tasks were used, namely, normalization, stop word removal, and stemming.  
The examination was conducted using all possible combinations of the preprocessing tasks considering different aspects such as accuracy and dimension reduction. The results obtained from the experiments reveal that appropriate combinations of preprocessing tasks showed a significant improvement in classification accuracy, whereas inappropriate combinations may degrade the classification accuracy.  
Findings of this study showed that the normalization task helped to improve the performance and provided a slight improvement in the classification accuracy regardless of feature size and classification algorithms. Stop word removal has negative impact when combined with other preprocessing tasks in most cases. Stemming techniques provided a significant improvement in classification accuracy when applied alone or combined with other preprocessing tasks in most cases. The results also clearly showed the superiority of the SVM over the NB and KNN algorithms for all experiments.  
As a result, the significant impact of preprocessing techniques on the classification accuracy is as important as the impact of feature extraction, feature selection, and classification techniques, especially with a highly inflected language such as the Arabic language. Therefore, for document categorization problems using any classification technique and in any feature size, researchers should consider investigating all possible combinations of the preprocessing tasks instead of applying them simultaneously or applying them individually. Otherwise, classification performance may significantly differ.  
Future research will consider introducing a new hybrid algorithm for the Arabic stemming technique, which attempts to overcome the weaknesses of state-of-the-art stemming techniques in order to improve the accuracy of DC system.  
[PERSON] proposed the idea of this research work, analyzed the experimental results, and wrote the manuscript. [PERSON] led and facilitated the further discussion and the revision. [PERSON] and [PERSON] have been involved in discussing and helping to shape the idea, and drafting the manuscript. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.