esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Bidirectional Reservation Method for Anti-Permeation Geomembrane Slack
## 5 Conclusions  
In this paper, the existing geomembrane relaxation unidirectional reservation method is modified on the basis of the proposed bidirectional reservation method of geomembrane relaxation. The feasibility of the proposed new geomembrane construction method was verified by a geomembrane-laying test, and the specific conclusions are as follows.  
1. Geomembrane folds are concentrated at the cross-intersection of the groove in the bidirectional reservation method, and the space at the cross-intersection can be increased by grinding the four corners to consume excess geomembrane. The corners of the cross-intersection should be polished to a 35\({}^{\circ}\) angle of inclination, and the diagonal arc length of the cross-intersection should be 1/2 of the total length of the groove.
2. The cross-Z-shaped bidirectional folding and reservation method produces a negligible number of folds due to the excessive neoprene rods. However, the required neoprene rods cannot achieve 180-degree folding, which is the largest obstacle to the effective implementation of this method. Once the reserved portion is separated, the folded layer cannot be restored to its original state when the stress disappears, and there is no slack to protect the geomembrane when it is subjected to tensile stress.
3. The bidirectional reservation method combining the Z- and groove-shaped techniques produces a negligible number of folds, and it does not require a 180\({}^{\circ}\) folding layer of neoprene rods. Only a unidirectional U-shaped groove is provided, which greatly reduces the earthwork excavation cost. The disadvantage of this method is that once the unidirectional Z-shaped folded layer is separated, it cannot be restored to its original form and instead becomes a unidirectional reservation mode after one use.
4. For safety reasons only, bidirectional notch-type allowances should be prioritized for the amount of slack in geomembrane placement.  
Conceptualization, W.W. and X.X.; methodology, W.W.; software, Z.L.; validation, W.L., W.W. and X.X.; formal analysis, R.H.; investigation, Z.L.; resources, W.L.; data curation, Z.L.; writing--original draft preparation, W.W.; writing--review and editing, W.W.; visualization, X.X.; supervision, W.W.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Natural Science Foundation of Shandong Province, China (ZR2019 MEE106).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the reviewers for their critical reviews and suggestions for improving the quality of this manuscript.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [PERSON] is an employee of Shandong Survey and Design Institute of Water Conservancy Co., Ltd., who provided methodology, situation analysis, and technical support for the work.
# MINC-NRL: An Information-Based Approach for Community Detection
## 5 Experiments
### Preliminary Experiments: Comparison on Different Network Representation Methods  
As a pre-step to hierarchical clustering, the network representation method can significantly affect the community evolution process generated. As mentioned in Section 4.2, comparative experiments are conducted to evaluate the impact of the network representation methods on the final results and find which of the network representation method is the most appropriate for our framework. The first comparison is made by replacing the network representation process of MINC-NRL (Step (1) and (2) in Section 4.3) by different network representation methods, including Deepwalk [7], Node2 Vec [20], Walklets [36], RandNE [37], BoostNE [21], GLEE [23], NetMF [38], GraRep [22], NMFADMM [39], and verified with non-overlapping community detection tasks on real-world datasets.  
The following table shows the NMI of using different network representation methods in MINC-NRL. The bold numbers emphasize the best experimental results within each data set.  
As illustrated in Table 3, random-walk-based methods perform better than the other algorithms, including Deepwalk, Node2 vec, and Walklets, and Deepwalk reaches the highest NMI on most of the datasets. Figure 6 shows the representation of Football network using Deepwalk, which is reduced to 2D by a principal component analysis (PCA). The results indicate that Deepwalk is more appropriate for MINC-NRL.
## 4 Results  
### Experimental Dataset  
The data used in the study were taken from an LPG company in Turkey, and the image data were acquired with a Basler acA2500-60 uc USB 3.0 camera manufactured by Basler AG, located in Ahrensburg, Germany, which provides 60 FPS per second imaging at 5 MP resolution. The dataset contains a total of 2360 colour images with a size of 2592 \(\times\) 2048. Since only LPG cylinders with surface defects were to be detected, the defects in each image were labelled using a single-class tool: [[https://www.makesense.ai/tool](https://www.makesense.ai/tool)]([https://www.makesense.ai/tool](https://www.makesense.ai/tool)) (accessed on 18 May 2024). The dataset was divided into training, validation, and test sets, at 70%, 15%, and 15% respectively. The test set was used to evaluate the defect detection accuracy of the trained model.  
EXIF-orientation stripping and resizing to 640 \(\times\) 640 were performed on the images. A data augmentation technique was applied to reduce the overfitting problem and increase the generalisation capability of the model. These included contrast-limited adaptive histogram equalisation (CLAHE), blur, random brightness and contrast adjustments, gamma corrections, and colour jitter. The augmentation pipeline was realised in code with the albumentations library, together with a bounding box format for YOLO. Figure 6 gives an overview of the dataset preparation and augmentation process.  
Figure 7 shows the defect location and size distribution in the dataset images. The graphs at the bottom of Figure 7 show the location density of the defects. When the distribution of the x and y coordinates of the defects was examined, they were more dense in a certain region, and when the distribution of the bounding box dimensions (width-height) was examined, the defects were small and medium in size. The graph on the upper right of Figure 7 shows the areas where the defect bounding boxes overlap. This may indicate that the defects are repeated in similar locations.  
Figure 6: Dataset preparation and augmentation process.
## 3 Results and Discussion
### LC-QToF-MS Analysis
Among these, five were detected only in the carob beer samples: (a) myricitrin, a naturally occurring polyphenol hydroxy flavonoid, which has been reported to possess anti-inflammatory properties [54]; (b) quercetin, which is known as an antioxidant, anti-inflammatory, cardioprotective, and anti-obesity compound, also thought to be beneficial against cardiovascular diseases, cancer, diabetes, neurological diseases, obesity, allergy asthma, and atopic diseases [55]; (c) naringenin, which is a flavonoid belonging to flavanones subclass with several biological activities such as antioxidant, antitumor, antibacterial, antiviral, anti-inflammatory, antiadipogenic, and cardioprotective effects [56]; (d) taxifolin (dihydropyacetrin), which is a powerful antioxidant with antioxidant, anti-inflammatory, anti-microbial, and other pharmacological activities [57]; and (e) luteolin, which is an important natural polyphenol present in several plants that show antioxidant, anti-inflammatory, anticancer, cytoprotective, macrophage polarization, and neuroprotective effects [58; 59]. Finally, another phenolic compound identified in the carob beers was phloroglucinol. Phloroglucinol derivatives are a major class of secondary metabolites that can be classified into monomeric, dimeric, trimeric, higher phloroglucnols, and phlorotannins [60]. Phloroglucnols are known for their broad-spectrum antiviral, antibacterial, antifungal, antihelmithic, and phytotoxic activities [61]. The phenolic profile in a similar study was composed of p-coumaric acid, epigallocatechin gallate, epigallocatechin, catechin, syringic acid, quercetin glycoside, caffe acid, gallic acid, catechin gallate, myricetin 3-glycoside, and cinnamic acid in acetone extracts from carob syrups analyzed by using HPLC-DADeESI-MS technique [30]. In another study, in ripe pulp extract, ultrasound-assisted extraction of polyphenols from carob pulp revealed a phenolic profile, which was dominated by gallic acid (65%) but also catechin, naringenin, cinnamic acid, quercetin, catechol, ferutlic acid, gentisic acid, and gallic acid were detected [37]. Myricetin, catechin, epicatechin, quercetin, rutin, and syringic acid were found in the acidic acetone and acetone-water extracts of carob fruits extracted with various extraction solvents [62]. In an Egyptian study, aqueous extracts of carob pods contained a high content of gallic acid followed by catechin, protocatechic acid, cinnamic acid, and a low concentration of p-coumaric acid, rutin, gentisic acid, p-hydroxybenzoic acid, vanillic acid, and ferulic acid [63]. Aqueous decoction of carob kibbles was found to be rich in antioxidants such as gallic acid, catechin, epigallocatechin gallate, gallocatedin gallate, and epicatechin [64].
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 5 Results and Discussion
### Reference Channels  
To obtain additional references for those extreme cases, we simulated two hypothetical scenarios. Scenario 1 presents the maximum number of PUs detected, and Scenario 2 detects noise signals only (no PUs detected). By analyzing these two extreme scenarios, we identified valuable references useful to find E-TVWS. Results from simulations are presented in Table 5. When these results are compared with the average of the tabulated values from Table 2, we can observe that there is no significant variation in terms of noise signal \(W(p)\). For instance, \(\overline{W}\) varies in 0.12%, \(\tau_{\overline{W}}\) varies in \(-3.27\%\), I varies  
Figure 15: PDFs of sensed parameters \(W(p)\) and \(X(p)\) showing calculated parameters \(\overline{W}\), \(\overline{X}\), and \(T\) as well as auxiliary parameters \(I\) and \(Z_{1}X\) for points P1 (**a**) to P9 (**i**).  
in \(-0.65\%\), and T varies in \(0.22\%\). Those parameters related with composite signal \(X(p)\) showed considerable variation, and it can be considered when scrutinizing the elements to obtain E-TVWS.  
In Scenario 1 with 32 PUs detected, the \(P_{d}\) corresponded to \(83.89\%\), whereas in Scenario 2, with no PUs detected, the \(P_{d}\) corresponded to \(3.70\%\). Then, \(P_{d}\) is the main parameter to be considered. Figure 16 shows the extreme cases analyzed; in panel (a) are plotted ROC curves for Scenarios 1 and 2, and panel (b) shows the extreme ROC curves for Scenarios 1 and 2 and the \(P_{(TVWS)}\) for points P1 to P10, with green arrows showing limits for the extreme cases. It is easy to observe that, when the Pd increases (more PUs may be detected), the \(P_{TVWS}\) decrease.
## 5 Conclusions and Discussion About Advantages and Limitations of the Proposed Method
While exact analysis can detect all uncovered transitions, it is exponentially time-bounded, making it impractical for rapid verification, especially when the system's complexity increases. Another limitation is the need for the use a Petri net as a modelling tool. This can be considered either as an advantage (for designers familiar with this technique) or a limitation (for people unfamiliar with Petri nets). Nevertheless, in our consideration we assumed that Petri nets are relatively easy to use. Moreover, the growing popularity of Petri net-based tools and algorithms among the designers of concurrent systems, cyber-physical systems, and (especially) manufacturing systems (as well as in related companies) encouraged us to follow this direction. Plans include further development of the proposed algorithm. The results obtained are very promising; thus, we are considering enhancing the technique. In particular, a detailed analysis of the improperly analysed benchmarks will be conducted. Although there are only two such benchmarks, this examination may help us understand why the proposed method is not able to find uncovered transitions. To overcome this limitation and further improve the algorithm, future research is being pursued to identify additional conditions under which a transition can be confirmed as uncovered. One promising direction is the reordering of columns in the matrix during the RREF computation. By exploring alternative column orders, we aim to improve the algorithm's ability to detect more or even all of the uncovered transitions. Furthermore, it is planned to combine the proposed method with place invariant analysis. It seems that such a conjunction may benefit the synergy of the analysis of both place and transitions in the Petri net-based system. Conceptualization, M.W. and R.W. ; methodology, R.W. and M.W. ; software, M.M., R.W., M.W. and D.K. ; validation, R.W. and M.W. ; formal analysis, R.W., M.W., Z.L. and A.O. ; investigation, M.W., R.W. and M.M. ; resources, M.W., R.W. and M.M. ; data curation, M.M. ; writing--original draft preparation, M.W. and M.M. ; writing--review and editing, R.W., Z.L., A.O. and M.W. ; visualization, M.M. ; supervision, R.W., M.W. and Z.L. ; project administration, M.W. ; funding acquisition, R.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: Dataset available on request from the authors. The authors declare no conflicts of interest.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 3 Review of Related Literature
### Filter Feature Selection Methods  
In the following, the most recent filter FS studies from the three categories (distance, information, and dependency) are presented.  
The authors of [16] proposed a new filter FS technique based on the dependency concept using Discriminative Correlation Filter (DCF) for visual object tracking. Many benchmark datasets were used to measure the effectiveness of the proposed method, along with the Deep Neural Network. The comparison results confirmed the dominance of the proposed FS over the existing techniques. The study [18] used the concept of distance. The authors suggested a new filter FS using an artificial immune system. They minimized the intra-class distance and maximized the inter-class distance in the defined regions. Then, a comparison study was successfully performed with a global and local FS algorithms using UCI and synthetic datasets. The obtained results were encouraging. Another study mainly based on the information concept [17]. It consists of analyzing several filter FS methods. Twenty-two filter FS were presented such as oneR, Variance, permutation, Info gain, Gain ratio, and others. The authors compared their performance in terms of predictive accuracy and run time. They applied the k-NN, the Logistic Ridge Regression, and SVM classifiers to determine the accuracy of each filter FS method. They concluded that there is no best filter FS method for all the datasets.
## 1 Introduction
The feedback information can be extracted in a few different ways, i.e., either by directly sensing the local temperature of the PIC [25], by monitoring the optical power at the drop port [26; 27; 28; 29; 30] or, less conveniently, tapping it from the through port [31], or even analyzing the BER [32] resulting from the instantaneous alignment of the OTF. While the latter metric reflects the transmitter's performance most relevantly in telecommunication terms, it requires a complex, high-speed monitoring and control system. There is yet another potential approach to extracting the feedback information that relies on detecting the phase response of the MRR's OTF. Unfortunately, such an approach requires precise interferometric measurements that need to be implemented at the scale of a PIC. With the feedback signal sorted, a variety of mechanisms for tuning the OTF accordingly has been proposed and demonstrated. Some are employing integrated resistive heaters [26; 27; 33], then others indirectly control the temperature by adjusting the bias current of the diode junction [31; 34] (applicable to carrier injection micro-ring modulators) and then there are also some solutions that rely on the EO effect [35; 36]. Our solution would fall into the second category, while tackling the challenge from a different angle. Since only the relative alignment of the carrier wavelength and the MRR's OTF are important for maintaining the maximum SBSR, we propose tuning the carrier wavelength, e.g., of a heat-pump-controlled laser, instead. Unlike some other approaches monitoring the MRR's temperature, we extract the feedback signal by continuously measuring the optical power at the drop port, which directly reflects the state of the wavelength's (mis)alignment. An advantage is that this approach does not rely on complex photonic structures, enabling the manipulation of the MRR's characteristics. Rather, it employs a basic add/drop MRR with no additional features and a common distributed feedback (DFB) laser, an inherently necessary device, with heat-pump-enabled wavelength tuning. To the best of our knowledge, no research has been conducted to address this challenge, respectively. The rest of this paper is organized as follows. In Section 2, a theoretical background of the dispersion-induced power penalty (PP), an MRR-enabled mitigation approach, the MRR's temperature dependence and a proposed solution to the latter are presented. A potential solution, i.e., a closed-loop control (CLC) system, is synthesized, designed and the corresponding prototype manufactured to accommodate the experimental evaluation followed in Section 3. There, the evaluation metrics are declared, the experimental setup(s) are described, and the acquired results are presented. In Section 4, we evaluate the significance of the results and their implications. Finally, in Section 5, we conclude the paper and give possible future directions for research and practical applications.
# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 2 System Models
### C-V2X Mode 4  
In C-V2X Mode 4, vehicles exhibit autonomous functionality by operating independently of cellular infrastructure. This autonomy extends to resource allocation decisions facilitated by the SB-SPS method introduced in Release 14 [20]. The SPS mechanism enables vehicles to autonomously reserve specific resources for a variable number of consecutive packet transmissions before relinquishing them. The duration of this resource reservation, known as the holding period, is determined by the packet transmission frequency (\(\lambda\)) and is expressed as a random value within a predefined range. For example, when \(\lambda\) is set to 10 Hz, 20 Hz, or 50 Hz, the corresponding holding periods are randomly selected from the ranges of 5-15, 10-30, and 25-75 transmission intervals, respectively [20]. In vehicular resource allocation, a sophisticated mechanism is employed when additional resources are required. The vehicle initiates this process by randomly selecting a Reselection Counter from its available inventory. Subsequently, after each transmission event, this counter decrees one unit until it reaches zero. At this juncture, new resources are selected and reserved with a probability of \(\frac{1}{P_{K}}\), where \(P_{K}\) is constrained within the range of 0-0.8. Notably, the vehicle's  
Figure 1: Simulation scenario diagram.  
sidelink control information (SCI) encapsulates the current value of the reselection counter (RC) and the interval between packet transmissions. This critical information is leveraged by other vehicles in the network to facilitate their resource reservations, enabling them to identify available resources and significantly reduce the likelihood of packet collisions [21]. Therefore, the C-V2X Mode 4 systems demonstrate the enhanced communication efficiency and reliability by implementing the SPS method and Reselection Counter mechanism. This sophisticated approach enables vehicles to allocate resources autonomously, significantly reducing the likelihood of transmission collisions. Consequently, the performance capabilities of C-V2X Mode 4 are substantiated, fostering increased confidence in its potential for widespread adoption in intelligent transportation systems.
## 3 Results
### Xai Application
#### 3.3.1 GlobalExplanation  
To identify the most influential features and their impact on predictions, a Morris sensitivity analysis was performed using the InterpretML library [21]. The choice for this method was due to its computational efficiency, as it perturbs one feature at a time. However, it does not consider non-linearities when estimating output, which it is addressed later in local explanations for individual datapoints. Since the NN produces 36 outputs, feature importance is sorted based on the average importance. Figures 4 and 5 illustrate this for each detector/range combination. We applied this analysis to NNs trained on data from the 2019 and 2014 periods, corresponding to solar minima and solar maxima, respectively.  
In both figures, Fermi geographical longitude and the McIlwain L-parameter emerge as the most significant features. However, there is a notable difference in Figure 5, where the Earth's occultation of detectors exhibits high significance. The discrepancy between the results in Figures 4 and 5 can be attributed to the variance in the underlying data distribution and characteristics between the two periods. For instance, during periods of high solar activity, such as 2014, the obscuration of a detector's Field of View (FoV) by the Earth could lead to fewer count rates: the Earth blocks the line of sight between the detector and the source of radiation. Conversely, in 2019, with significantly less solar noise (e.g., fewer solar flares), the importance of these features becomes negligible. This suggests that  
\begin{table}
\begin{tabular}{l l|l l l l l l} \hline \hline \multicolumn{1}{c}{**Exp \#**} & \multicolumn{1}{c}{**Total Months**} & \multicolumn{1}{c}{**Train Period**} & \multicolumn{1}{c}{**Test Period**} & \multicolumn{1}{c}{**MAE Train**} & \multicolumn{1}{c}{**MAE Test**} & \multicolumn{1}{c}{**MAE Train**} & \multicolumn{1}{c}{**MeAE Test**} \\  & & & & **(Counts/s)** & \multicolumn{1}{c}{**(Counts/s)**} & \multicolumn{1}{c}{**(Counts/s)**} & \multicolumn{1}{c}{**(Counts/s)**} \\ \hline
6 & (13 + 5) + 6 & Jan19–Jul20 & Jul20–Jan21 & 4.296 & 6.684 & 3.335 & 4.532 \\
7 & (16 + 2) + 2 & Jan19–Jul20 & Nov20–Jan21 & 4.888 & 7.518 & 3.744 & 4.543 \\
8 & (18 + 2) + 2 & Jan19–Aug20 & Nov20–Jan21 & 4.464 & 7.844 & 3.403 & 4.790 \\
9 & (20 + 2) + 2 & Jan19–Nov20 & Nov20–Jan21 & 4.256 & 7.790 & 3.321 & 4.681 \\ \hline
10 & (13 + 5) + 6 & Jan19–Jan20 & and & Jan20–Jul20 & 4.390 & 6.980 & 3.365 & 4.626 \\
11 & (16 + 2) + 2 & Jan19–Jan20 & and & Jan20–Mar20 & 4.536 & 7.105 & 3.418 & 4.724 \\
12 & (18 + 2) + 2 & Jan19–Jan20 & and & Jan20–Mar20 & 4.787 & 6.667 & 3.631 & 4.778 \\
13 & (20 + 2) + 2 & Jan19–Jan20 & and & Jan20–Mar20 & 4.910 & 6.609 & 3.707 & 4.915 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._training a neural network for each period could yield benefits, as the model could leverage different features to reveal distinct patterns in the data.  
Further exploration of the detector flags indicating the Earth's FoV occultation is undertaken through an example of local analysis in the following section.