esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Number of Sampling Points and Spatial-Prediction Error  
The error of the spatial prediction was related to the number of sampling points. Some studies had shown that the spatial prediction errors of soil properties decreased as the samples number increases [13; 29; 32]. In this paper, the prediction accuracy of SOM was evaluated by three indicators: mean error (ME), root-mean-square error (RMSE) and mean standard error (MSE). Although the spatial-prediction error value of the SOM was missing at 900 sampling points, the trend of the predicted error changing from 50 to 800 sampling points still showed that the sample point numbers had some effect on the prediction accuracy of SOM.  
The values of ME achieved the optimal prediction accuracy when the number of sample points was greater than or equal to 150, essentially keeping about 0, and there was no obvious trend with the changing of the sample point numbers. The values of RMSE did not have a particularly obvious trend with the increase in the number of samples. It had a certain degree of volatility when the sample numbers were greater than 400; in particular, the values of RMSE at 500 and 600 samples were significantly higher than those of in other sample sets. This showed that the single RMSE index did not well-indicate the prediction accuracy of SOM in this paper. The value of ASE remained around 4 when the sample-point numbers were greater than or equal to 150, and there was less impact on its trend with the increase in sampling-point numbers. At this time, the differences between ASE and RMSE were small, and the accuracies of spatial prediction were improved. It indicated that the accuracies of spatial prediction were stable at an acceptable level when the number of samples increased to a certain value, and the accuracies of spatial prediction were not significantly improved by the further increase in the number of samples. According to the study of [PERSON] et al., this situation may be due to the fact that when the number of sample points was small, the sample points could not be ensured to be distributed in \"key areas\" that could effectively reflect the spatial-distribution characteristics of soil properties [33]. When the sample points reached a certain number, the integrity and rationality of sample points in spatial distribution would be improved, and the spatial prediction accuracy of soil properties may have improved to a certain extent and maintained at a reasonable value.
## 5 Conclusions  
The interactions between the weak grid and VSCs can cause oscillations which severely threaten the stable operation of power grids. However, the existing studies on stability analysis cannot quantitatively describe the system stability, so the parameter optimization  
Figure 24: \(Dq\)-axis currents before and after using optimized PLL parameters.  
Figure 23: Changes before and after using selected PLL parameters.  
of VSCs lacks quantitative guidance to prevent oscillation. To solve this issue, this article proposes a quantitative method for evaluating the system stability, then the current control and PLL of VSC are optimized based on the quantitative method. Specifically, the conclusions and contributions can be summarized as follows:  
(1) By introducing the stability evaluation angle between the Nyquist curve of the weak grid-connected VSC and the negative real axis, a quantitative method for assessing the system stability is proposed which can define the stability bounds for the control parameters. Larger values of the defined angle imply a higher level of system stability.  
(2) The stability of VSC can be influenced by the current control loop, a decrease in the proportional coefficient will cause oscillations. An active damping method is used in the current loop, it can suppress oscillations without changing the original parameters so that the VSC stability boundary is extended. The optimization of the damping coefficient is given by using the proposed quantitative stability evaluation method.  
(3) By improving the anti-disturbance ability of PLL, the stability of the VSC system can be improved and oscillations can be suppressed. In combination with the anti-disturbance ability improvement, the proposed quantitative stability evaluation method can further optimize the parameters of PLL to improve system stability.  
The quantitative stability evaluation method proposed in this article has been analyzed in a single VSC grid-connected system. It provides a basis for further analysis and application in multiple VSC systems.  
Conceptualization, X.J., X.H., and P.J.; methodology, X.J.; software, H.S.; validation, X.J.; formal analysis, L.L.; investigation, P.P. and G.C.; resources, X.H., Q.S. and P.J.; data curation, X.J., X.H., and H.S.; writing--original draft preparation, X.J.; writing--review and editing, X.J., X.H. and H.S.; visualization, L.L.; supervision, P.P.; project administration, G.C.; funding acquisition, X.H. and H.S. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the science and technology project of State Grid Sichuan Electric Power Company of China (NO: B1199721009M, Mechanism Analysis and Suppression of Wide-band Oscillation for High Proportion of Power Electronic Equipment Connected to Weak Power Grid). Meanwhile, the authors are grateful to the financial support from the China Postdoctoral Science Foundation under grant 2021M693815.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of HP Treatment on Microbial Growth of Bratwurst Sausages
Nitrite did not sufficiently prevent the growth of LAB, especially for storage temperatures higher than 10 \({}^{\circ}\)C, where LAB populations of nitrite-made sausages are similar to those of control samples (Figure 2). In contrast, HP treatment induced a significant (\(p<0.05\)) reduction of LAB initial count from 2.4 to 1 log CFU/g, compared to the control and nitrite-containing samples. During storage at 0 and 5 \({}^{\circ}\)C, growth of LAB on HP-treated samples was very slow; LAB counts did not reach the level defined as the spoilage level (10\({}^{8}\) CFU/g, [29]), remaining below 3.6 log CFU/g even after 80 days at 0 \({}^{\circ}\)C and 5.9 log CFU/g after 40 days at 5 \({}^{\circ}\)C. Storage at higher temperatures (10 and 15 \({}^{\circ}\)C) allowed LAB on HP-treated samples to reach a population of approximately 8 log CFU/g after 30 days and 10 days, respectively. Control and nitrite enriched samples reached the level of 10\({}^{8}\) CFU/g after storage at 10 and 15 \({}^{\circ}\)C for approximately nine and five days, respectively. The inhibition of lactic acid bacteria, which have been reported as the dominant spoilage bacteria in vacuum-packed sausages, by high pressure treatment, in combination with storage temperature lower than 5 \({}^{\circ}\)C, leads to a significant (\(p<0.05\)) extension of shelf life of those products.
## 2 Materials and Methods
### Biochar Preparation  
To evaluate the size distribution, a sample of as-received biochar was processed in a vibratory sieve shaker (Retsch AS200 control G) equipped with a sieves tower composed by the following sieves: 710 \(\upmu\)m, 500 \(\upmu\)m, 125 \(\upmu\)m, 63 \(\upmu\)m, and 32 \(\upmu\)m. An amount of 101 g of \"as-received\" biochar was put on top of the sieve tower, shook for 5 min, and then the process stopped and the result collected.  
In a second test, to obtain a cleaner powder, biochar pellets were submerged overnight in isopropyl alcohol. IsoPropyl Alcohol (IPA) is commonly used in gasification applications to dissolve the polycyclic hydrocarbon contaminants that are carried with the gas due to incomplete gasification [30; 31; 32]. Previous studies [1] have shown that the gasification process consumes the biomass structures, leading to large charred particles with high porosity. There is the chance that portions of the material are kept in the combustion or char reduction zone for too long, leading to an excessive carbon conversion. Furthermore, due to the extreme conditions within the reactor, it is not uncommon to find small ceramic or metallic particles derived from the power plant components itself [33]. In order to select the char particles only, the char-alcohol mix was filtered with a 3 mm metallic mesh, a dimension that is slightly smaller than the pellets' diameter (4 mm), to remove all the liquid (isopropanol + undesired particles), keeping the clean biochar pellets only. The remaining isopropanol was evaporated for several hours, leaving the filtered pellets on a plate under the fume hood. Pellets were later analyzed using a scanning electron microscope (ESEM).  
The results of size distribution analysis showed the necessity to reduce the particle size through milling. A small-capacity laboratory mill (Retsch Mixer Mill MM400) was used. Each mill vessel had a volume of 50 mL. An amount of 10 g of biochar was inserted in each of the 2 stainless-steel vessels containing each of the 8 stainless-steel milling spheres with a diameter of 12 mm and an individual weight of 6.97 g.  
The procedure for the \"as-received\" and \"clean\" biochar was the following:  
* Set frequency of vibration to 25.0 Hz;
* Mill for 2 min;
* Cool down the vessels for 5 min to reduce the powder temperature;
* Mill for 2 more minutes (as-received) or 3 min (clean);
* Cool down the vessels for 5 min to reduce the powder temperature;
* Sieve with the 32 um sieve (as-received); wet-sieve with the 32 um sieve (clean).  
Wet sieving consists in reducing the formation of biochar micro-agglomerates, pouring isopropanol in the milled powder, and sieving it with the 32 um sieve. The bottom collection container was left under the fume hood to let isopropyl alcohol evaporate. \"As-received\" and cleaned biochar grains and sieved powders were further analyzed in a scanning electron microscope (LEO 1455 VP).
###### Abstract  
We prepare and test four types of glass antennas for X-band applications and energy harvesting. These antennas are made of three different glass metallization schemes, including conductive copper foil (CCF), conductive silver paste (CSP) and indium tin oxide (ITO) thin film. Compared with conventional microstrip patch antennas, the dielectric substrate materials of these designs are replaced with silicon-boron glass (\(\varepsilon_{r}=6\), tangent \(\delta=0.002\)). The antenna with CCF as a radiator and ground plane (case one) is compared with the antenna with ITO replacing the radiator (case two) and ground plane (case three), respectively, and the glass antenna made of CSP (case four) is also presented. In this paper, these four types of glass antennas are measured and analyzed, and a comparison of the fabrication process and performance of these antennas is demonstrated. This study could contribute to the development of human-machine interactivity (HMI) systems with glass dielectric substrates.  
glass antenna; transparent antenna; conductive copper foil (CCF); conductive silver paste (CSP); indium tin oxide (ITO)
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 4 Results  
The Bayesian neural network model's performance was computed utilizing the MSE, RMSE, and R evaluation metrics. Figure 10 demonstrates the regression model's actual and predicted data distribution. Figure 11 illustrates the model's learning curve with 1000 epochs; the best performance is at epoch 62, based on the mean squared error. Figure 12 indicates the model prediction error histogram. The proposed Bayesian neural network model was compared with the random forest regression, gradient boosting, decision tree, and k-neighbors regression models, to outline the performance of the BNN model. Table 3 presents the results of the comparison between the models, based on the evaluation metrics. The proposed model is superior to the traditional machine learning methods.  
Figure 10: Actual and predicted data distribution with the fit line: the white circles are data points, and blue, green, and red lines are the best-fit lines.  
Based on the results, considering the root-mean-square error, [PERSON]'s correlation coefficient, and the mean squared error, the proposed BNN model outperformed the four  
Figure 11: The BNN learning curve is as follows: the blue line is training, and the red line is the test, based on the mean squared error.  
Figure 12: Proposed model prediction error histogram, the blue bars correspond to training, while the red bars refer to the test error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R** & **RMSE** & **MSE** \\ \hline Bayesian neural network & 94 & 0.17 & 0.03 \\ Random forest regressor & 76 & 0.23 & 0.06 \\ Gradient boosting regressor & 74 & 0.23 & 0.06 \\ Decision tree regressor & 70 & 0.26 & 0.82 \\ k-neighbors regressor & 67 & 0.25 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model comparison based on the R, RMSE, and MSE evaluation metrics.  
traditional machine learning methods. The proposed BNN model obtained a [PERSON]'s correlation coefficient of 94%, a mean squared error of 0.03, and a root-mean-square error of 0.17%. The k-NN regression model had the lowest correlation coefficient (67%), while the random forest model provided the highest correlation coefficient (76%), among the traditional machine learning models. In summary, the proposed model outperforms traditional machine learning models, making it superior in predicting ground vibration in mining blasting operations.
## 2 Background and Related Work  
### Automatic Signature Verification  
Automatic signature verification has been developed for decades. However, compared to other biometric approaches such as fingerprints, handwritten signatures have relatively high intra-class variability (a high variability between the signatures of a specific person) as well as low inter-class variability (skilled forgeries can be very similar to the genuine one). For these reasons, although signature verification has been widely used in the field of personal authentication, it is one of the most challenging issues in biometric technology, especially when we perform signature verification based on a single known sample. It is because we don't have enough samples to exclude the high-variability factor.  
During this generation, deep learning has been one of the biggest breakthroughs in the artificial intelligence (AI) research area. Deep learning improves the deficiencies of traditional artificial neural networks by extending the depth, scale, and complexity of the network layers. This makes deep learning become a superb way to solve most kinds of pattern recognition problems, such as signature verification. Although deep learning-based signature verification methods have achieved great breakthrough in recent years, most of them still require several (more than one) genuine reference signature samples for training the networks [1; 2; 3; 4].  
Comprehensive surveys and state of the art reviews of the recent literatures can be found in the works of [1; 4]. Among numerous papers, a growing number of researchers have realized that \"learning with small datasets\" will be a key factor for the success of signature verification in real practical scenarios. Authors such as [PERSON] et al. [6] try to train the model with a small number of signature samples through a one-class support vector machine (OC-SVM) classifier. [PERSON] et al. [7] propose a solution based on a meta-learning approach.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 1 Introduction
Most studies related to malware detection have either (i) taken the perspective of a general-purpose intrusion detection system (IDS) that includes the existing signature-based and the latest machine learning-based approaches as it is impossible to apply a universal model for all types of devices owing to the heterogeneity of IIoT devices; (ii) focused on a centralized malware detection method rather than a distributed edge architecture in terms of the deployment method, which has excellent scalability and detection time; or (iii) proposed malware detection methods that do not deeply consider the viability of devices with limited resources. Thus, malware detection approaches using edge computing-based deep learning are still rare. As most IIoT devices are difficult to install on expensive hardware owing to the price-performance tradeoff, it is difficult to implement deep learning models directly on IIoT terminals [18]. Deep learning models have many parameters to adjust for effective data training and require much space and high computational resources as they have to be trained and refined repeatedly for many epochs. However, in distributed IIoT devices with limited resources, such as smart factories, the complexity and high computational overhead of deep learning become a problem. As this is the most time-consuming and laborious part of the neural network training process, it must be performed by offloading it to another device with good computational power. This problem can be solved using edge computing [19] to offload some or all of the deep learning calculation tasks to an edge or cloud server environment equipped with high-performance hardware for distributed processing [20]. This paper provides the following major contributions:  
1. An edge computing and malware detection system is proposed for the distributed IIoT environments of smart factories. 2. A convolutional neural network (CNN) model using image visualization techniques is incorporated for malware classification. 3. The proposed malware detection system is validated on the Malimg dataset. 4. A detection accuracy of 98.93% is achieved for 25 different malware types in experiments conducted. The remainder of this paper is organized as follows. Section 2 reviews the general cybersecurity threats and types of cyberattacks that can occur in smart factories, smart factory cyberattack structures, and recent malware-based attack cases targeting the manufacturing industry presented in previous studies. Section 3 introduces the proposed approach by describing the use of edge computing and deep learning for smart factory IIoT cybersecurity, as well as edge computing and deep learning-based malware detection systems as countermeasures (solutions) to cyberattacks, their main functions, and deep learning (CNN) for malware detection. Section 4 outlines the experiments conducted to verify the efficacy of the proposed CNN-based malware classification system. Section 5 presents and analyzes the results obtained. Section 6 presents concluding remarks.
## 2 Materials and Methods
### Determination of the Essential Amino Acids and Proteinogenic (Total) Amino Acids  
The mushroom samples were freeze-dried using a Christ Alpha 2-4 lyophilizer (Martin Christ GmbH, Osterode am Harz, Germany). For the determination of proteinogenic (total) amino acid content, 0.1 g of powdered oyster mushroom sample was weighed accurately and placed into hydrolysis tubes (KUTESZ, Budapest, Hungary). Ten milliliters of 6 M hydrochloric acid was added to the samples, which were then bubbled with nitrogen for 30 s. The hydrolysis tubes were sealed with Teflon-lined caps and hydrolyzed at 110 \({}^{\circ}\)C for 24 h in a block thermostat (FALC Instruments, Treviglio, Italy). After cooling, the samples were rinsed with distilled water and placed into 25 mL volumetric flasks. The mixture was neutralized by adding 10 mL of 4 M NaOH solution, and the flasks were filled with distilled water to a total volume of 25 mL. The solutions were filtered first through pleated Whatman 1001-090 filter paper (Merck KGaA, Darmstadt, Germany) and then through a 0.22 \(\upmu\)m syringe filter (FilterBio\({}^{\circledR}\) CA Syringe Filter Nantong City, Jiangsu P.R China). The homogenized samples were transferred to 1.5 mL Eppendorf tubes and stored in a deep freezer until amino acid analysis. The analysis of amino acids was carried out using an AAA 400 Automatic Amino Acid Analyzer (Ingos Ltd., Praha 4--Komofany, Czech Republic). The device operates on the principle of ion-exchange column chromatography, with post-column derivatization using ninhydrin. Detection was performed at 570 nm, with an additional measurement at 440 nm for proline, using a flow-through cuvette detector. Each sample was measured in duplicate.  
Figure 2: Images of the fermented oyster mushrooms (50 g) (Fresh—fresh; Blanch—blanched; Steam—steamed; Oven—oven pretreated; MW—microwave pretreated; HHP—HHP pretreated; UV—UV light pretreated).
# The LHAASO PeVatron Bright Sky: What We Learned
## 2 PeVatron Candidate Source Typologies
### Gamma-Ray Binaries and Microquasars  
The first interest in \(\gamma\)-ray binary systems (i.e., microquasars) such as galactic CR accelerators was born from their similarities to AGN, which are likely the main component responsible for extra-galactic CRs [46]. We know that microquasars accelerate electrons to VHE [120; 121]: their environment is radiation-dominated, making the Klein-Nishina losses ineffective in preventing acceleration, and \(\gamma-\gamma\) absorption is transparent for 100 TeV \(\gamma\)-rays. However, they are much less abundant with respect to the other kinds of candidate PeVatrons, such as SNRs or PWNe. Consequently, even if they could accelerate protons, they cannot explain either the CR energy density or the anisotropy and composition in our galaxy.  
This does not mean that some microquasars cannot be hadronic accelerators. So far, several \(\gamma\)-ray binaries system have been detected at VHE with no hint of a cutoff above 10 TeV: PSR B1259-63/LS2883 [122], LS 5039 [106; 123], V4641 Sagittarii [124] and SS 433 [125; 126; 127; 128]. In particular, SS443 was detected at E\(>10\) TeV with a jet that exceeded 10-times the required injection power, \(L_{kin}=10^{39}\) erg/s [5; 121]. The microquasar V4641, a black hole binary with ultrarelativistic jets, introduced a new class of \(\gamma\)-ray sources of great interest for future instruments. In general, the detected emission seems to be dependent on the orbital phase, confirming that it was likely produced at binary system scale and that there is some active acceleration mechanism. One of the open issues concerns if this is the same mechanism seen in the Crab Nebula or if we need to add a special mechanism of action for binary systems, as was suggested by a recent X-ray analysis of the SS 433 [128].  
No microquasars are found among the 12 PeVatron candidates of LHAASO, but this class of sources represents an important target for LHAASO and the future EAS and IACTs instruments.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis
### Experiments on CUT-2 Datasets
#### 4.2.2 Result Analysis of CUT-2 Datasets
Figure 6: Average diagnosis accuracy of fault and number of features selected at three motor speeds: (**a**) average diagnosis accuracy and number of features selected at the speed of 2000 rpm; (**b**) average diagnosis accuracy and number of features selected at the speed of 2500 rpm; (**c**) average diagnosis accuracy and number of features selected at the speed of 3000 rpm.