esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Sample Homogenization--Dry, Wet and Cryogenic Grinding
#### 2.1.1 Dry Grinding  
Many studies have demonstrated the efficiency of dry grinding procedures for different matrices, aiming at sufficient practical size reduction (e.g., passing through #20 sieve) and uniform distribution of contaminated portions via blending and mixing. For instance, using dry grinding, [PERSON] et al., 2014 [38] pulverized brown rice samples into the size of less than 1 mm and sieved samples to obtain a homogenous sample particle size. The same strategy of grinding (and sieving) was applied in red chili [39], and dry date [40] for the determination of aflatoxin B\({}_{1}\). [PERSON] et al., 2019 [41] also demonstrated the use of dry grinding for a range of cereal matrices providing satisfactory homogeneity and recoveries of detected aflat (84-106%). In another study, [PERSON] et al. [42], who performed dry grinding as per the AOAC method [34] for various spices (e.g., black pepper and red chiles) and dry fruits (e.g., dried apricots and figs). [PERSON] et al., 2008 [43] successfully used dry milling to process peanut, pistachio, wheat, maize, cornflakes, raisins and figs for the analysis of multiple mycotoxins including aflatoxin B\({}_{1}\). However, due to capacity limitations of their device, the drying milling protocol was only developed for matrices with sample sizes less than 1.5 kg.  
Numerous heavy-duty and batch-type milling and mixing devices (e.g., Hobart VCM, Waring Blendor and Polytron) are commercially available and have been tested in cereal grains, tree nuts, botanicals and feeds [44; 45; 46; 47]. Although, dry grinding has been widely used by mycotoxin labs, when sample sizes are large or food products contain high sugar or fat content, dry grinding could suffer clogging or buttering due to excessive heat generated by fast moving blades. In such situations, sample grinding results in insufficient homogenization (e.g., particle sizes are too large) [48]. Therefore, despite its laborious and time-consuming nature, wet grinding can serve as an alternative milling approach, providing better homogenization with smaller particle sizes and more uniform distribution of aflat.
## 4 Discussion
(2015) showed that for proper penetration of irrigants, removal of debris and the smear layer from the apical third region, the enlargement to #30 file size is adequate when the suitable coronal taper is achieved. A more recent study by [PERSON] et al. (2018) determined that when the apical size increased to #40, the remnant debris significantly decreased in the mesial roots of mandibular first molars [30]. In our study, we observed an important reduction of the organic remnant when using PUI during apical preparations up to #25 (1A vs. 2A groups). In addition, the amount of organic remnant in group 1A was comparable with what we observed in group 2B. In other words, using PUI instead of CI improves the #25-diameter efficiency as well as the #35-diameter with a CI protocol. This is particularly relevant when the clinician is instrumenting complex root canal morphology (e.g., mesiobucucal roots of upper molars), where it often becomes impossible to reach apical diameters of #35 or higher without transportation and/or perforation risk. Additionally, we found a cleaner area of the samples studied at 4 mm from the apex with and without PUI, compared to the area at 2 mm from the apex which had the highest content of detritus. This coincides with [PERSON] et al. (2005), who found an increase in the amount of debris towards the apical region using RaCe without PUI [31]. We believe that this may be explained by an increased flow, and therefore, greater solvent action of hypochlorite in this region, at 4 mm from the apex. It is important to note that none of the root canals from the four groups analysed in this study was completely free from hard-tissue debris, with debris readily accumulated in the isthmus of mesial root canals (Figures 2 and 3). In this regard, we consider that all rotary instrumentations must be complemented by a thorough manual instrumentation in order to achieve optimal debridement, because those systems tend to keep a centred positioned inside the canal, failing to reach both internal and external pole of the canal. This, together with activation of sodium hypochlorite during instrumentation, could more effectively eliminate the presence of organic debris within the canals, especially from areas which are difficult to access or inaccessible by mechanized instrumentation. One of the limitations of this study was the use of an ordinal scale to assess a change which is intrinsically infinitesimal, like the quantity of organic remnant found in RCS after instrumentation. This limitation can be overcome by calculating and comparing the surface occupied by debridement in the root canal of the control and experimental histological samples. A further development of this study will be to evaluate the debridement efficacy of infected root canals using EndoActivator (Dentsply Maillefer) or XP-Endo Finisher (FKG Dentaire) compared with PUI.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 1 Introduction
The experimental results show that SDRNet significantly outperforms other traditional algorithms in terms of accuracy and robustness in the channel estimation task. Meanwhile, the proposed adversarial attack method also demonstrates a higher attack success rate.
## 1 Introduction
### Related Work
The use of fiducial features is not a trivial task to achieve, because their accuracy relies on the performance of the fiducial point detection algorithm [24]. Non-fiducial features usually apply Fourier or Wavelet transforms [25; 26]. Fiducial features tend to work better in databases with lower variability (i.e., databases with a low number of subjects), because it avoids the use of unnecessary data, deleting noise as the selected signal characteristics belong to specific, narrower regions. However, in databases with higher variability, non-fiducial features work better. They deal more efficiently with the higher chances of noise in a greater number of samples [27]. One of the most common techniques for ECG classification used to be Support Vector Machines (SVMs) [28] and the k-Nearest Neighbour classifier (k-NN) [7]. Although they are still applied techniques, Artificial Neural Networks (ANNs) have increased their popularity since they starting being used at the beginning of the last decade [29]. Concerning this, future approaches are expected to use ANNs and Deep Neural Networks, due to their potential to solve SVM and k-NN problems, while even improving the results of more conventional classifiers [19].
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 15 Conclusions  
The performance evaluations reveal that the design decisions of moving the perceptual weighting outside of the analysis-by-synthesis loop can, not only perform well, but also result in a substantial reduction in algorithmic complexity. The sequentially adaptive long- and short-term predictors coupled with the small look ahead of the tree coder keeps the latency low as well. The use of VAD plus CNG for multimode coding pushes the bitrate down for utterances with about 50% silence, as is relatively common. Therefore, the Multimode Tree Coder with sequentially backward adaptive short- and long-term prediction and perceptual pre- and post-weighting filters can reduce complexity and latency in those applications where the reasonable performance penalty compared to increased complexity and latency codecs can be absorbed.  
Further research that incorporates a more advanced sequentially adaptive prediction algorithm, such as recursive least squares, could provide substantial performance improvements while maintaining low latency with slightly increased complexity. Codecs built around the MMT-WP using a two-band codec structure can extend the approach to wideband speech and is an area of future research. In fact, given the combination of performance, low latency, and low complexity provided by the MMT-WP codec for narrowband speech, it is natural to consider extending the approach to wideband speech-coding. One such initial study split the wideband speech, usually 50 Hz to 7 kHz, into two bands using the quadrature mirror filters used in G.722 [45] and then coded the low band with an early version of the codec described in the current paper and coded the upper band using tree-coding with the upper band ADPCM coding of G.722 [46].  
Improved performance of the above suggested wideband codec structure could be obtained at the cost of increased complexity using a recursive least squares adaptation of a higher order short-term predictor as in [30] and in [47], particularly in the low band. Low latency would still be retained.  
A second possible approach is to filter the input 50 Hz to 7 kHz into the bands 50 to 6400 Hz and 6400-7000 Hz as in AMR-WB and code the lower band with a reoptimized version of the current MMT-WP codec. A new coding approach would need to be found for the high frequency band.  
All authors contributed equally to all aspects of this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors are grateful to [PERSON] at the University of California, Santa Barbara, for the preparation of Figures 1-5.  
The authors declare no conflict of interest.
## 5 Simplified Calculation Formula for Load-Displacement Curve  
As mentioned in Section 2.5.2, \(l\) is close to 0. The bilinear model composed of two line segments is simple in calculation and can accurately capture the characteristics of the \(F\)-\(u\) curves of the FASSI both in the elastic stage and failure stage. Therefore, the \(F\)-\(u\) curves of different cases were simplified by using the bilinear model \(F(k,F_{\text{u}},u)\), which is shown in Figure 22 and can be calculated by using Equation (3). In addition, \(F(k,F_{\text{u}},u)\) is applied when sand is employed as backfill and \(F\) is loaded as monotonic. Based on the \(F_{\text{u}}\) and \(k\) values obtained from numerical \(F\)-\(u\) curves of the cases with different \(H\) and \(L\) as listed in Table 3, \(F_{\text{u}}\) and \(k\) can be set as a function of \(H\) and \(L\), namely \(k(H,L)\) and \(F_{\text{u}}(H,L)\).  
\[F(k,F_{\text{u}}\,u)\ =\ \left\{\begin{array}{ll}k\cdot u,&0\leq x<u_{0}\\ F_{\text{u}},&x\geq u_{0}\end{array}\right. \tag{3}\]  
where \(u_{0}\) at the intersection of the two lines, which can be calculated by \(F_{\text{u}}/k\).  
Figure 21: Sand surface deformation distribution at different \(x\) in cases with a 100 cm \(H\) and different \(L\): (**a**) \(v_{\text{s}}\) (push); (**b**) \(v_{\text{s}}\) (pull); (**c**) \(u_{\text{s}}\) (push); and (**d**) \(u_{\text{s}}\) (pull).  
The influence of \(H\) and \(L\) on \(k\) and \(F_{\rm u}\) are illustrated in Figures 16 and 17. It can be observed that the influence of \(H\) and \(L\) on \(k\) and \(F_{\rm u}\) are nonlinear. Consequently, three-dimensional nonlinear surface fitting was employed, which is in the form of a binary quadratic polynomial, namely \(Z=Z_{0}+\mathrm{ax}+\mathrm{by}+\mathrm{cx}^{2}+\mathrm{dy}^{2}+\mathrm{fxy}\). The three-dimensional surface fitting results of the functions \(k(H,L)\) and \(F_{\rm u}(H,L)\) under push and pull displacements are shown in Figure 23.  
Figure 23: Three-dimensional nonlinear surface fitting diagrams: (**a**) \(k_{\rm push}\) (\(H,L\)); (**b**) \(k_{\rm pull}\) (\(H,L\)); (**c**) \(F_{\rm u,push}\) (\(H,L\)); and (**d**) \(F_{\rm u,pull}\) (\(H,L\)).  
Figure 22: Bilinear model \(F(k,F_{\rm u},u)\).  
The fitting formula are Formulas (4)-(7), with R-squared values larger than 0.95, indicating good fitting results.  
\[k_{\text{push}}\left(H,L\right)=203.88+39.09L+63.38H-0.03L^{2}+0.58H^{2}+0.13 LH \tag{4}\]  
\[F_{\text{u,push}}\left(H,L\right)=-9.21+0.10L-0.21H+4.15-5L^{2}+0.00544H^{2}+0.0 00784 LH \tag{5}\]  
\[k_{\text{pull}}(H,L)=9665.04-39.44L-87.92H+0.23L^{2}+2.90H^{2}+0.29 LH \tag{6}\]  
\[F_{\text{u,pull}}(H,L)=35.66-0.26L-0.21H+0.000166L^{2}-0.0015H^{2}+0.000069 LH \tag{7}\]  
The calculated \(k\) and \(F_{\text{u}}\) obtained from the simplified calculation formula were compared with the experimental and numerical values obtained from the tests and FEM to verify the accuracy of the simplified calculation formula, as shown in Figure 24.  
It can be observed that the error of most points is within 10%. It can be concluded that the simplified calculation formula is accurate enough to meet engineering application requirements.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 3 System Model and Problem Statement
### Complexity Analysis  
In Algorithm 1, finding the shortest paths among edge servers and the nearest base station for all mobile users requires \(O(|v|^{3}+|U||V|)\). Then, deploying one VNF instance for each VNF in SFC for all user requests requires \(O(|U||L_{i}||V|)\). Then, deploying parallel VNF instances to satisfy delay and reliability requirements for all user requests requires \(O(|U||L_{i}||V|+|U||L_{i}||V||n_{i}|^{2})\). Next, deploying BVNF instances to satisfy the reliability requirements for all user requests requires \(O(|U||L_{i}||V|)\). Thus, the time complexity of the throughput maximization algorithm is \(O(|v|^{3}+|U||L_{i}||V||n_{i}|^{2})\).
## 2 Related Work
### Performance  
From the point of view of performance, a set of metrics are defined at the following levels [25]: monitor--where the performance of the whole system can be watched; analyzer--where one can make performance predictions based on the monitored parameters; planner--where the optimization decision is announced, like virtual machine (VM) placement or migration; and executor--where the orchestration actions are performed. The performance metrics at the monitor level are directly measured and provided by the hardware or the hypervisor. At the analyzer level, the metrics are based on a series of observations that can be analyzed by cognitive engines; a set of such workload parameters are presented in [26]. The metrics used at the planning and execution levels are used to measure the performance of the decision and execution systems of the cloud infrastructure. Another set of performance metrics is composed of three basic metrics for quantifying performance isolation [27]: metrics based on QoS impact, significant points, and integral metrics.  
[PERSON] et al. describe the performance and scalability metrics for software that resides in the cloud [28]. The platforms used for testing are Amazon and Microsoft Azure. The comparison is made to two cloud software with the same purpose on different platforms and two different cloud offerings on the same platform. Two metrics are described: scalability and elasticity. Besides performance, cost measurements also have a role in the final metric computation. Scalability is defined by the addition of another instance if needed at runtime; it represents the ability to manage the changes when required. The first conclusion presented in [28] is that the required volume of service scales up linearly when the demand grows. The deficiency is met if the demand is larger than the ability to scale in time. First, one calculates the ratio for the ideal instances based on the demand, then one measures the response time of the service for a corresponding demand level. The ideal time for response is approximated. The testing is done by comparing the scalability of VM's provision in AWS and Microsoft Azure. The scaling is made when the CPU utilization passes 80%. The demand that is done on both VMs that run a web app is done 20 times, in 640 tests on which one calculates the response time for scalability. The result is quite surprising, as the average response times vary on both cloud platforms. In multiple scenarios, one of the cloud platforms came on top in terms of volume scaling performance.
# Feature Extraction and Recognition of Chinese Mitten Crab Carapace Based on Improved MobileNetV2
## 3 Experiment and Analysis
### Experimental Configuration  
The experiments in this paper were conducted on a Windows 10 system using the PyTorch 1.9.1 deep learning framework, Python 3.8 version, NVIDIA RTX 3080 Ti graphics card (NVIDIA, Santa Clara, CA, USA) with 12 GB of VRAM, and an AMD Ryzen 7 5800X3D 8-Core processor (AMD, Santa Clara, CA, USA) equipped with CUDA 11.1 for high-performance GPU computing.  
During the training phase, each RGB crab carapace image in the dataset was resized to 112 \(\times\) 112 pixels, and the pixel values were normalized to the range of [\(-\)1, 1]. This normalization helps to maintain a consistent distribution in the training data, accelerates model convergence, and prevents the problem of gradient vanishing to some extent. All feature embedding dimensions were set to 128. The overall experimental process used SGD as the training optimizer, with the batch size set to 128, and the model was trained for 5000 iterations.  
In the testing phase, ten-fold cross-validation (10-fold cross-validation) was used to evaluate the performance of the algorithm. With a similarity threshold set at 0.90, ten different cross-validation tests were repeated, and the results of these ten tests were averaged. This method allows us to more thoroughly explore the model's generalization ability, thereby ensuring the robustness and reliability of our findings.
###### Abstract  
As the commercialisation of Intelligent Connected Vehicles (ICVs) accelerates, Vehicle-to-Everything (V2X)-based general testing and assessment systems have emerged at the forefront of the research. Current field testing schemes mostly follow the norms of traditional vehicle tests. In contrast, Original Equipment Manufacturers (OEMs) have increasingly focused on the potential influence of V2X communication performance on the application response characteristics. Our previous work resulted in a C-V2X (Cellular-V2X) large-scale testing system (LSTS) for communication performance testing. However, when addressing the need to combine application and communication, the system software faces confronts heightened technical challenges. This paper proposes a layered software architecture for the automated C-V2X LSTS, which is tailored to combined scenarios. This architecture integrates scenario encapsulation technology with a large-scale node array deployment strategy, enabling communication and application testing under diversified scenarios. The experimental results demonstrate the scalability of the system, and a case study of Forward Collision Warning (FCW) validates the effectiveness and reliability of the system.  
C-V2X testing system; software architecture; application; communication testing; large-scale testing Article
## 3 Model Parameters and Boundary Conditions
### Finite Element Model and Material Properties  
This study chose a specific section of a high groundwater level for the numerical simulation analysis of the infiltration-stress coupling. A three-dimensional finite element numerical simulation model based on the drawings of the typical section design was established. Figure 4 depicts the canal's general details and the distribution of structural materials in each portion. Part of the model parameters are as follows: the canal bottom width is 13.5 m; the digging depth is 46 m; an 8 cm-thick C25 concrete slab is used as the lining plate, under which there is a composite geomembrane and then a 25 cm-thick coarse sand cushion, with the foundation at the bottommost; the designed water level is 8 m; the increased water level is 8.77 m; and the underground water level is 41.28 m.  
Figure 4: Finite element model of a typical section of the deep excavated canal.  
Figure 3: Three-dimensional finite element model of a typical section of the deep excavated canal.  
Three monitoring points, A, B, and C, were selected in the deep excavated canal to monitor the canal's displacement and settlement. The clay materials are mainly used for the foundation and slope of the deep excavated canal section of the SNWDP. The anti-seepage system is mainly achieved by a concrete lining board, geomembrane, coarse sand cushion, and polysulfide sealant, as shown in Figure 5. During the actual operation of the SNWDP, the canal's infiltrated surface is subject to the continuous change of the permeability coefficient. Therefore, in this study, the characteristics of the water section were constantly assumed to ensure the continuity and accuracy of the results.
## 2 Improved Wavelet Threshold Denoising Method
### Method Improvement via Optimizing Wavelet Parameters
#### 2.4.2 Threshold Selection Rules  
As mentioned above, the second step of denoising is to set an appropriate threshold to filter the detail component obtained by the wavelet decomposition. The detail includes the informative signal with large coefficients and the worthless white noise signals with small coefficients. By setting the coefficients lower than the threshold to zero, the meaningless noise can be eliminated while the effective signal can be reserved.  
Threshold setting method is mainly based on an unbiased risk estimation threshold, a minimax threshold, a fixed threshold and a heuristic threshold. The fixed threshold is helpful for improving the computing efficiency of denoising. Considering that the median value of health monitoring data does not change extensively, the fixed threshold [22] is appropriate. It is calculated as in Equation (5), where \(N\) is the signal length.  
\[T=\sqrt{2\log(N)} \tag{5}\]  
The threshold function reflects the tactics for dealing with the detail coefficients above and below the threshold. The hard threshold function is adopted here, considering its superiority in retaining the edge features of the signal without adding deviation. The function is shown in Equation (6):  
\[s(\omega)=\begin{cases}\omega,&|\omega|\geq T\\ 0,&|\omega|<T\end{cases} \tag{6}\]  
where \(s(\omega)\) is the input signal, and \(T\) is the threshold.
# Research on Dynamic Reserve and Energy Arbitrage of Energy Storage System
## 4 Description and Introduction of Simulation Environment
### ESS's Response in Trip Contingency
#### 4.4.1 ESS1(Frequency Regulation)  
ESS1 is used for frequency regulation. When the frequency exceeds the deadband (59.85-60.12 Hz), the ESS will start to discharge power. The rising time from 0 to full output is 167 ms. The response is shown in Figure 4.  
Using the calibrated generator parameters set in Section 4.3, as also use in the paper [29], the ESS1 response can be replicated using the PSS\({}^{\text{\textregistered}}\)E, as shown in Figure 5. Figure 5 shows the data measured during an N-1 contingency when the load is 36.3 MW on 13 December 2019. At this time, the ESS1 and ESS2 has not been completed. Figure 6 shows the frequency measurement and simulation results for this N-1 contingency. A good match exists of \(F_{nadir}\) between calibrated simulation and measurement.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Units** & **Dynamic Models** & **Excitation System Model** & **Governor Model** \\ \hline Plant1 \#1–4 & GENSAL & ESACSB & DEGOV \\ Plant1 \#5–8 & GENSAL & IEEEX1 & DEGOV1 \\ Plant1 \#9–10 & GNSAE & AC7B & DEGOV1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Diesel Generator model in PSS\({}^{\text{\textregistered}}\)E.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Units** & **Segment 1** & **Segment 2** & **Segment 3** \\ \hline Plant1 \#1–4 & 2.526x + 13.617 & 2.690x + 12.763 & 2.853x + 11.709 \\ Plant1 \#5–8 & 2.471x + 11.564 & 2.634x + 10.699 & 2.796x + 9.635 \\ Plant1 \#9–10 & 2.576x + 9.576 & 2.781x + 8.107 & 2.986x + 6.296 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Diesel Generator Linear Fuel Cost.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Units** & **Ramping Rate (kW/s)** \\ \hline Plant 1 \#1–4 & 15 \\ Plant 1 \#5–8 & 15 \\ Plant 1 \#9–10 & 420 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ramping rate of the generator set.