esa-sceva/satcom-chunk-collection huggingface dataset:
# _Bacillus subtilis_ Simultaneously Detoxified Aflatoxin B1 and Zeralenone
## 4 Discussion
subtilis_ ZJ-2019-1 may be actively secreted into the culture media, while demonstrating a remarkable temperature resistance ranging from 100 to 120 \({}^{\circ}\)C. In the previous study, it was found that the supernatants of _Microbacterium proteolyticum_ B204 exhibited a remarkable AFB1 degradation capacity of 70% after being subjected to boiling water treatment at 100 \({}^{\circ}\)C for 20 min [35]. These findings suggest that the bacterial supernatants have exceptional thermal and pressure stability. Subsequent proteinase K treatment slightly decreased the degradation rate of AFB1 and ZEN in the supernatants of _B. subtilis_ ZJ-2019-1. However, when treated with either SDS alone or a combination of proteinase K and SDS, there was a significant reduction in the ability of the supernatants of _B. subtilis_ ZJ-2019-1 to degrade AFB1 and ZEN (Figures 2 and 3), which is consistent with previous studies [57; 62]. The crude enzymes of _B. subtilis_ ZJ-2019-1 exhibited enhanced AFB1 and ZEN degradation activity at elevated temperatures, achieving degradation rates of 100% and 94.29%, respectively (Figures 5 and 6). This suggests the potential application of these bacteria in food or feed processing [57]. Furthermore, the crude enzymes of _B. subtilis_ ZJ-2019-1 demonstrated improved mycotoxin degradation within a pH range of 7.0-9.0 (Figures 5 and 6), indicating that acidic conditions reduce the activity of crude enzymes in degrading AFB1 and ZEN. Previous studies have shown that certain metal ions, including Co\({}^{2+}\), Fe\({}^{2+}\), Cu\({}^{2+}\), Mn\({}^{2+}\), and Zn\({}^{2+}\), can significantly enhance the mycotoxin degradation activity of bacteria and secreted enzymes; moreover, these ions may competitively or non-competitively promote or inhibit the activity of active enzymes in bacterial supernatants [61]. In this study, Co\({}^{2+}\), Fe\({}^{2+}\), Mn\({}^{2+}\), and Ni\({}^{2+}\) significantly enhanced the degradation rate of AFB1 and ZEN by _B. subtilis_ ZJ-2019-1, suggesting their potential role as electron carriers in enzymatic reactions involved in degradation. Conversely, Fe\({}^{3+}\) and Mg\({}^{2+}\) exhibited inhibitory effects on mycotoxin degradation by crude enzymes of _B. subtilis_ ZJ-2019-1 (Figure 6), consistent with previous findings [39; 57]. Both Fe\({}^{3+}\) and Mg\({}^{2+}\) may act as inhibitors for the enzymatic system responsible for AFB1 and ZEN degradation. Furthermore, EDTA markedly attenuated the degradation rate of AFB1 and ZEN by _B. subtilis_ ZJ-2019-1 within 72 h (Figures 5 and 6). It has been demonstrated that the presence of EDTA can significantly inhibit the AFB1-degradation activity of recombinant lactose, potentially due to conformational changes in mycotoxin-degrading enzymes, leading to their deactivation or reduced affinity for mycotoxins [57; 63]. In further studies, it is necessary to identify and evaluate the cytotoxicity of degradation products derived from AFB1 and ZEN by _B. subtilis_ ZJ-2019-1 towards animals.
## 4 Experiments
### Real vs. Complex  
To demonstrate the effectiveness of the incorporation of CVNN, we illustrate our qualitative and quantitative results in Figure 3 and Table 1, respectively. Given that finding peppers as much as possible is of the same importance under the premise of accurate classification, we report three kinds of metrics including accuracy, recall rate, and F1 score for a comprehensive evaluation of the experimental results. For fairness, RVNN as a comparison is trained under the same configuration, including the network structure described in Figure 2 and training details in Section 4.1. As can be seen in the 3 rd and 4 th columns in Figure 3, CVNN has higher accuracy than a real-valued one at the cost of sacrificing a bit of recall rate. However, we only need a non-low recall rate to determine the general position and shape of the green pepper to help the robot hands locate the stems to be cut; thus, a small part of the missing recall rate can be made up after this pepper is picked. In contrast, more accurate classification of hard negative samples helps to avoid unnecessary time wasted on the wrong location. This fact can also be proved by specific numbers in the first two rows in Table 1, where CVNN outperforms RVNN by 3.9% and 1.33% in terms of accuracy and F1 score, respectively, despite a 0.73% drop in recall rate.  
Figure 2: The whole pipeline of our proposed complex model. “cl” and “ck” represent the abbreviation for the complex linear layer and complex ReLU activation function, respectively, and the number on each complex linear layer denotes its number of complex neural nodes.
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
###### Abstract  
Dynamic resource provisioning is made more accessible with cloud computing. Monitoring a running service is critical, and modifications are performed when specific criteria are exceeded. It is a standard practice to add or delete resources in such situations. We investigate the method to ensure the Quality of Service (QoS), estimate the required resources, and modify allotted resources depending on workload, serialization, and parallelism due to resources. This article focuses on cloud QoS violation remediation using resource planning and scaling. A Resource Quantified Scaling for QoS Violation (ReSQoV) model is proposed based on the Universal Scalability Law (USL), which provides cloud service capacity for specific workloads and generates a capacity model. ReSQoV considers the system overheads while allocating resources to maintain the agreed QoS. As the QoS violation detection decision is Probably Violation and Definitely Violation, the remedial action is triggered, and required resources are added to the virtual machine as vertical scaling. The scenarios emulate QoS parameters and their respective resource utilization for ReSQoV compared to policy-based resource allocation. The results show that after USlbased Quantified resource allocation, QoS is regained, and validation of the ReSQoV is performed through the statistical test ANOVA that shows the significant difference before and after implementation.  
cloud computing; SaaS; resource allocation; QoS; scalability; USL Article  
ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services. _Future Internet_**2022**, _14_, 131. [[https://doi.org/10.3390/614050131](https://doi.org/10.3390/614050131)]([https://doi.org/10.3390/614050131](https://doi.org/10.3390/614050131))
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 2 Methodology Model
### Method of Prediction
#### 2.3.2 Time Series Forecasting to Supervised Learning  
The main steps to train a deep learning model for wind power prediction are as follows: Firstly, data cleaning is performed on the SCADA data of wind turbines, and multi-dimensional feature variables are normalized. Then, the normalized data is transformed into a supervised learning dataset, divided into a training set and a test set, and transformed into a standard matrix format specified by the model. Then, we define the model input and output parameters and nodes. Finally, the data of the training set is used to train the model to get the best threshold and weight parameters, and the test set is used to test the prediction accuracy of the model. The framework of the above supervised learning process is shown in Figure 4.  
Figure 4: Schematic diagram of data processing flow of supervised learning task.  
Figure 3: Schematic diagram of logical architecture of LSTM network unit.
# Stability Analysis of Isolated Roof in Overlapping Goal Based on Strength Reduction
## 5 Discussion
### Criteria for Judging the Critical Equilibrium State of the Isolated Roof
Figure 11c shows the evolution of the plastic zone of the roof. When the reduction factor is increased to 4.0, the failure rate of units is only 21.3%, but the roof has been penetrated by the plastic zone, which is the critical equilibrium state identified by the plastic zone penetration criterion, but far from reaching the calculation of the non-convergence criterion. If the strength continues to be reduced, the calculation process will not converge until the failure rate reaches 84.3% (Figure 12). At this point, the roof has been covered with failure units, and the roof must have been damaged, rather than being considered to be in critical equilibrium (Figure 13). Therefore, the non-convergence criterion is also not suitable for determining the critical equilibrium state. Based on the above analysis, the plastic zone penetration criterion is relatively objective and accurate in determining the critical equilibrium state of the roof. Nevertheless, for the numerical simulation study of rock mechanics, scholars still have different understanding of the critical equilibrium state, and it is still controversial whether the criterion of plastic zone penetration is the best criterion. From the perspective of numerical simulation analysis, the plastic zone refers to the area where the rock mass undergoes irreversible deformation after being subjected to forces exceeding its strength limit. The larger the plastic zone, the more significant the rock mass failure. But this is not enough to directly indicate that when the plastic zone penetrates the roof, it is in a critical equilibrium state. In fact, when the plastic zone extends to a specific range, the roof may shift from a stable state to instability. So far, there is still no clear understanding of the extent of this plastic zone and the relationship between the plastic zone and the failure of the interlayer roof. To address this issue, further research will be carried out in order to propose a more appropriate criterion. Figure 11: Mechanical response of the roof during strength reduction. (**a**) Z-displacement of the roof; (**b**) Z-displacement contour of the roof; (**c**) Plastic zone of the roof.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 3 Proposed Method
### Keypoint Cloud Extraction
#### 3.3.1 Sobel Operator  
The Sobel operator, a 3 \(\times\) 3 kernel, consists of two sub-operators for horizontal and vertical directions. It detects edges by calculating the brightness differential in both directions at the target pixel. Let (x, y) denote the coordinates of a pixel in an image, and \(f\)\((x,y)\) represent the grayscale value at that pixel. The mathematical expressions for the horizontal (\(G_{x}\)) and vertical (\(G_{y}\)) components are shown in Equations (3) and (4), and their matrix forms are illustrated in Equations (5) and (6).  
\[\begin{array}{l}G_{x}=f_{x}(x,y)=f(x+1,y-1)+2f(x+1,y)+f(x+1,y+1)\\ \qquad\qquad-f(x-1,y-1)-2f(x-1,y)  
\[G_{x}=f_{x}(x,y)=\begin{bmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{bmatrix}*\textbf{F}, \tag{5}\]  
\[G_{y}=f_{y}(x,y)=\begin{bmatrix}-1&-2&-1\\ 0&0&0\\ 1&2&1\end{bmatrix}*\textbf{F}, \tag{6}\]  
In Equations (5) and (6), the symbol \(*\) denotes the convolution operation. The matrices in these equations represent gradient templates used to compute the horizontal and vertical components of the image gradient. Here, **F** is the matrix of grayscale values from the neighborhood of the pixel point (\(x,y\)).  
In this paper, we opted for the Sobel operator for edge detection due to its high computational efficiency and ease of implementation, particularly effective in images with distinct edges and clear textures, aligning well with our application scenario. While we considered other edge detection algorithms such as the Prewitt operator, Canny operator, and LoG (Laplacian of Gaussian) operator, these often yield fewer keypoints despite higher precision in certain aspects. Experiments with Sobel operators of different scales (e.g., \(5\times 5\), \(7\times 7\)) revealed that larger scales, though more robust to noise, further reduce the number of keypoints.  
Our pose estimation algorithm relies heavily on the quantity of keypoints, as a sufficient number provides richer information for subsequent pose estimation, enhancing measurement accuracy and reliability. Thus, our keypoint extraction algorithm aims to ensure an adequate number of feature points rather than solely pursuing extraction precision. We chose the Sobel operator and optimized its performance by adjusting parameters and integrating strategies like differential curvature calculation to better fit our pose estimation framework.
# Fuzzy Adaptive Dynamic Surface Control with Constant Gain for Non-Affine Pure-Feedback Systems
## 4 Simulation Results
### Simulation Example 1
Figure 4: State variable \(x_{2}\) of the presented control scheme. Figure 3: Comparison of the tracking error of this paper (red solid line) with existing control method (solid line marked with diamond shape). Figure 2: System tracking comparison of the proposed controller (red dotted solid line) with existing control method (solid line marked with diamond shape). **Remark 8**. : _A significant performance improvement is observed when comparing these two control strategies. The developed control scheme exhibits superior tracking accuracy over the conventional existing fuzzy control approach. Figure 3 demonstrates the relative tracking errors for the proposed and existing fuzzy controller. The maximum and minimum tracking errors of the proposed controller are 0.06339 and \(-\)0.06792, respectively, so its corresponding RTE index is calculated as 0.06339 + 0.06792 = 0.131. Similarly, the RTE index of the existing fuzzy controller is calculated as 0.09857 + 0.1199 = 0.2185. By contrast, the proposed controller achieves a 39.91% reduction in RTE, calculated as \(\left|0.1313-0.2185\right|/0.2185\). This enhancement confirms the effectiveness of the new control strategy._  
Figure 5 depicts the estimation of the adaptive constant parameter \(\theta\). The final control signals are depicted in Figure 6. The state trajectory \(x_{2}\) is plotted in Figure 4. At the beginning of the control process, the conventional controller generates a negative control signal with an amplitude exceeding 50. In contrast, the developed controller produces a  
Figure 5: Adaptive constant estimation of the presented control scheme. Figure 6: Comparison of the actual control input \(u\) of this paper (red solid line) with the existing control method (solid line marked with diamond shape). significantly reduced signal amplitude of \(-5.451\). As a result, the overshoot is effectively suppressed. This improvement contributes to enhanced actuator durability.
# Comparison of NeQuick G and Klobuchar Model Performances at Single-Frequency User Level +
## 2 Data and Methods
### TEC Availability  
To obtain the TEC availability, the STEC parameter is considered, instead of the VTEC one. The reason for this choice is to be in line with the NeQuick G requirements specification, described below. Based on the NeQuick G specification, described in Section 2, this section introduces the TEC availability parameter which assesses the capability to correctly estimate the ionospheric delay affecting the Signal in Space (SiS) measurement. The TEC availability is defined as the number of epochs (for a single station) where the STEC error is under the following Galileo NeQuick G requirements [3]:  
* For an STEC \(\leq\) 66.7 [TECU]: Threshold = 20 TECU;
* For an STEC \(>\) 66.7 [TECU]: Threshold = 30% of STEC.  
From the GRC network of stations, the STEC is obtained for a grid of points using the navigation model and averaging the contribution of the different satellites; while the reference STEC is obtained with the tomographic ionosphere model (TOMION) software V1.6.4. [6] developed at the Universitat Politecnica de Catalunya (UPC). The STEC error is therefore defined as the difference between the STEC as computed from the navigation model and the reference STEC as computed using the TOMION software. To perform a fair and comprehensive performance assessment between the NeQuick G and Klobuchar ionospheric models, the Galileo SF ionospheric algorithm specification is used as a common benchmark for both models.
# An Electronically Adjustable Floating Memcapacitor Emulator Circuit Using CDBA
## 1 Introduction
However, it can be seen that these emulators differ in the number of their active and passive elements, whether they are floating or grounded, experimentally verified, contain a memristor or are electronically controllable. Therefore, the design studies on memcapacitor emulator circuits continue with increasing interest. In general, the work presented in this article includes the following innovations and contributions:  
* In our work, the CDBA element, which has important advantages such as wide dynamic range, high slope speed, differential structure at the input port and which has not been used in the design of memcapacitor emulators in the literature to the best of our knowledge, was used. * The memcapacitor emulator analyzed both in simulation and experimentally has the advantages of not having ground restriction, not having a memristor in its structure, being electronically adjustable and being able to operate in decreasing and increasing modes. * The designed memcapacitor emulator successfully passed the tests of non-volatility, frequency-dependent variable characteristics, series-parallel connection characteristics and Fourier analysis. * The proposed memcapacitor emulator circuit was implemented in a second-order active low-pass filter circuit and was found to be more efficient in both filtering performance and power consumption compared to the standard capacitor. * The proposed memcapacitor emulator also successfully completed the learning and data storage tasks in the amoeba learning circuit. The rest of the paper is organized as follows. Section 2 presents the proposed memcapacitor emulator circuit and its analytical model. The performance analysis is given in Section 3 to demonstrate the characteristics of the emulator. In Section 4, the applications of memcapacitor emulator-based active filter circuit and amoeba learning circuits are analyzed. Some conclusions are given in Section 5.
## 3 Results and Mechanism Analysis
### Uniformity Analysis of Remolded Specimens
#### 3.1.3 Analysis of Sample Uniformity via the Range Method  
Based on the stress distribution, the uniformity of soil samples in the middle and bottom was better than that in the upper part of the sample. Samples with different overall heights under the same designed dry density and the same moisture content were prepared. The upper soil of each sample was then removed, and samples of 6 cm in the middle and lower parts were taken for testing (that is, the sample was stratified by 0.5 cm, and divided into 12 layers from top to bottom). The maximum difference of the layered dry density (i.e., the range) of each part was measured, as reported in Table 2, and the range of variation is presented in Figure 6. The results demonstrate that with the increase in the overall height of the sample, the range of the dry density of the stratified sample gradually decreased, and the uniformity of the sample increased with the increase in the number of layers. By changing the overall height of the sample, the lower part of the sample can be taken, which can effectively avoid the sampling of the denser part and leave a more uniform soil sample. Under the effect of compaction, loess particles are cemented and a skeleton is formed, and the cohesion and internal friction angle reach stable values. At this time, the pores between particles are not simply compressed, but it is more likely that the skeleton changes and soil particles slide, which increases the density and uniformity of the soil [15].  
Figure 5: Stress distribution diagram: (**a**) the stress acting on the sample is averagely distributed; (**b**) schematic diagram of the internal stress of the specimen.  
It can also be seen from Figure 6 that with the increase in the overall height of the sample, the differences in the range of the dry densities of the stratified samples with different designed dry densities gradually become stable. This can effectively explain the finding that with the increase in the overall height of the sample, the external force required by the soil sample increased, while the consumption of the top soil sample decreased after using a certain amount of force. Moreover, the force transferred to the bottom increased, resulting in more uniform stress on the whole sample and a more uniform stress distribution, which made the soil sample more uniform. This may also be due to the changes in the relative positions of water and air in the soil structure formed by remolded soil and matrix suction. With the increase in height, the more uniform the sample, the more uniform the particle arrangement, the closer the skeleton particles, the more diverse the structural units, and the more stable the porosity and permeability.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 3 Experimental Setup
### Database for Training and Evaluation
#### 3.1.1 A&d Fecgdb  
This database has been considered as gold standard and used as a benchmarking tool due to its inclusion of direct fetal ECG [28]. It contains five multi-lead fetal electrocardiogram (FECG) recordings obtained from five women at different gestational ages between 38 and 41 weeks. Each recording contains four differential signals acquired from the maternal abdomen and a reference direct fetal ECG recorded from the fetal head. These recordings were acquired by the Department of Obstetrics of the Medical University of Silesia using the KOMPOREL Fetal ECG Acquisition and Analysis System (ITAM Institute, Zabrze, Poland) and stored in EDF format. Each recording lasts five minutes and has a sampling rate of 1 kHz and a resolution of 16 bits, numbered as r01, r04, r07, r08, and r10. The provided annotation of the fetal R-wave locations was first automatically determined by the online analysis of the KOMPOREL system and then manually verified by a professional cardiologists. It can be accessed via [[https://www.physionet.org/content/adfecgdb/1.0.0/](https://www.physionet.org/content/adfecgdb/1.0.0/)]([https://www.physionet.org/content/adfecgdb/1.0.0/](https://www.physionet.org/content/adfecgdb/1.0.0/)) [accessed on 28 July 2025].  
To evaluate the noise robustness of our method, we generated calibrated datasets with controlled signal-to-noise ratios (SNRs) from the original A&D database (denoted as AD_origin). Since the native SNR of AD_origin is unspecified due to inherent noise contamination, we applied additive white Gaussian noise (AWGN) with SNR of 0 dB, 5 dB, and 10 dB to AD_origin to create three standardized dataset: AD_0, AD_5, and AD_10. Noise was added to the original signal \(x(t)\) to create the noisy signal \(y(t)\), as given by Equation (1). The noise \(n(t)\) was generated according to a normal distribution with a mean of zero and a standard deviation determined by the desired signal-to-noise ratio (SNR) in decibels, where SNR is calculated as given by Equation (2).  
\[y(t)=x(t)+n(t) \tag{1}\]  
\[\text{SNR}_{\text{dB}}=10\cdot\text{log}_{10}\bigg{(}\frac{\text{Signal Average Power}}{\text{Noise Average Power}}\bigg{)} \tag{2}\]
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 4 Functions of the Fog Server in Details
### Data Query  
Figure 8 shows the sequence of data query.  
1. A client sends a \"data query\" request to the fog server. To process the received request, the client has to pass the authentication process first (authentication steps are detailed in Section 4.1).
2. The query tracker in the accessing module caches the newest data and sends a request to the database to retrieve all the newest data every second.
3. Data are maintained in the accessing module. Consequently, when receiving a \"data query\" request, the response will be returned immediately without passing this through the message broker to the database.