esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, we developed a flexible array probe and semi-auto scanner based on novel concepts to enhance the efficiency of non-destructive inspection of complex curvature-shaped specimens that are coupled by welding. In the FPAPr design, the element was placed at one edge of the FPAPr, to minimize the contact area with the target object and maximize the access to the weld zone. Before the fabrication, simulations were conducted to examine the applicability of the FPAPr on specimens with curvature shapes. The simulations were based on models that reflected actual conditions that the designed FPAPr would be applied. It was concluded that adequate FPAPr contact was obtained on the specimens with curvatures when an appropriate focal law was employed, which led to the implication that quantitative assessment of flaws is possible.  
The detectability of the FPAPr and PA probe were compared through experiments to verify the FPAPr performance. Artificial flaws were inserted to fabricate specimens with curvature-shaped weld zones, which were inspected by the semi-automatic scanner. The conventional PA probe could partially detect the flaws, and it could classify and characterize the flaws (45% of all specimens). Contrarily, the FPAPr accurately measured the flaw positions in all specimens and successfully classified and characterized the flaws; therefore, all inspection devices were functionally efficient. In conclusion, the FPAPr outperformed the PA probe in terms of flaw detection sensitivity and resolution. Specifically, outstanding results were obtained from the T/R type FPAPr. The use of the semi-automatic scanner was advantageous because artificial factors could be avoided while obtaining inspection results, and the raw data were consistently obtained.  
In this study, the reliability of flaw detectability was enhanced by implementing a series of design and fabrication processes, which could secure quality and integrity with the efficient use of RT replacement technology for industrial fields. Furthermore, these inspection devices could serve as more effective tools for complex inspections in industrial sites such as nuclear power, aviation industry, thermal power generation, and plant industry. In this study, the FPAPr and scanner applications were limited to certain curvature pipe shapes used in industrial fields, but future research shall expand the inspection range to various array designs and further improvements.
# TSCH Multiflow Scheduling with QoS Guarantees:
## 3 Results
### End-to-End Delay and Packet Delivery Ratio
Unlike the delay, which is different for each of the schedulers, the packet delivery ratio is close to 100% in most cases. This is because SDN WISE-TSCH and AMUS have configured the number of timeslots needed to send all the traffic they generate, which prevents buffer overflow. Orchestra has a PDR of 100% because it uses larger capacity buffers, which avoids packet loss since they are stored even if this means increasing the end-to-end delay. However, MSF is the only one that presents significant losses in the proposed scenario due to the use of shared slots that produce collisions. Only in cases of low saturation does MSF manage to increase the PDR close to 100%.
## 5 Model Application
### Data Description and Preparation
The distribution of travel speed however, shows slight differences among the different links. We took link 82 for the sparse data link. Thus, link 82 and adjacent links including link 76, link 77, link 81 and link 88, were the research objects. To remove noisy data, statistical historical data were preprocessed. At the same time, historical data on workdays (from Monday to Friday, except holidays) were filtered as experimental data. Consequently, we calculated expected speed and the standard deviation for the speed of the adjacent links for every 30 min period from preprocessed historical data according to the weekly cycle and traffic flow direction as depicted in Figure 2. The features were calculated according to Section 3 and shown in Table 9. The travel time of adjacent links is the key point when predicting target link travel time as this value reflects the localized state of traffic overall. Finally, we extracted 2078 features as input for the neural network and 2078 features as output corresponding to input features. Of all the extracted features, a portion of these features was taken as training data for the ANN model. Another different portion of these features was taken as test data to verify the validity of the model. Partial meteorological information was selected to research their influence on link travel time prediction. As for the meteorological information, we only considered the influence of temperature and rainfall on traffic based on our previous analysis discussed in Section 3.4. We defined degree of rainfall into four ranks according to historical rainfall: rainfall, drizzle, downpour and thunder, corresponding to the digital values 1, 2, 3 and 4, respectively. The input and output information of our model are depicted in Table 9. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(wd_{i}\) & \(wh_{i}\) & \(E_{i}(v)\) & \(D_{i}(v)\) & \(\Delta R_{i}(degr)\) & \(\Delta R_{i}(leng)\) & _temp_ & _rain_ & \(\Delta R_{i}(t)\) \\ \(F_{1}\) & \(F_{2}\) & \(F_{3}\) & \(F_{4}\) & \(F_{5}\) & \(F_{6}\) & \(F_{7}\) & \(F_{8}\) & \(F_{9}\) \\
**1** & 0.1 & 5.32 & 1.97 & 1 & 0.7984 & 25.25 & 1 & 0.7374 \\
**2** & 0.1 & 5.58 & 1.79 & 1 & 0.7984 & 28.78 & 1 & 0.714 \\
**5** & 3.3 & 2.55 & 0.96 & 0.8 & 1.0282 & 26.36 & 2 & 0.4355 \\
**3** & 3.4 & 5.02 & 1.02 & 1.3333 & 1.0529 & 28.34 & 1 & 2.0021 \\ \hline \end{tabular}
\end{table} 
Table 9: Example of the training and testing dataset (inputs and output of artificial neural network (ANN) model). Figure 8: Distribution of speeds among the observations of link 77 on Monday and Wednesday. Figure 7: Distribution of speeds among the observations of link 82 on Monday and Wednesday.
## 3 Study Areas and Data Process
### Data Process
#### 3.3.2 Risk Assessment Experiment
The entropy weight method is used to calculate the weight of the data, and finally, the factors combined with the weight value grading are superimposed onto the raster data, and the risk evaluation results for Shanghai city are obtained. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**First-Grade Index** & **Second-Grade Index** & **Secondary Index Weight** & **Comprehensive Weight** \\ \hline \multirow{4}{*}{Hazard} & Land use type & 0.2381 & 0.1071 \\  & Drainage density & 0.1919 & 0.0854 \\ \cline{1-1}  & Subsidence & 0.3426 & 0.2188 \\ \cline{1-1}  & Vertical velocity & 0.2274 & 0.1424 \\ \hline \multirow{4}{*}{Vulnerability} & Population density & 0.2203 & 0.0651 \\ \cline{1-1}  & GDP per unit & 0.2059 & 0.0781 \\ \cline{1-1}  & Road density & 0.1273 & 0.092 \\ \cline{1-1}  & Building density & 0.1582 & 0.1302 \\ \cline{1-1}  & Green space density & 0.1301 & 0.0379 \\ \cline{1-1}  & Ground elevation & 0.1582 & 0.043 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Entropy weight of risk assessment index. Figure 6: Distribution of land subsidence vulnerability factors in Shanghai city: (**a**) is the population density; (**b**) shows the GDP; (**c**) describes road density with unit of km\({}^{2}\); (**d**) shows the building density of administrative areas; (**e**) marks the digital elevation model; and (**f**) represents the green space density.
## 2 Tension-Shear Coupling Model
### Input Energy of Shear Force  
As shown in Figure 3, when a fabric RVE undergoes shear deformation, the node \(P\) moves to \(P^{\prime}\) with displacement of \(S_{node}\). The yarn angle \(\alpha\) decreases with the increase of shear angle \(\varphi\), where a relation exists: \(\alpha=\pi/2-\varphi\). Then, the incremental input energy is given by  
\[dW_{node}=F_{node}dS_{node}. \tag{4}\]  
In order to characterize the shear behavior of the RVE, it is necessary to express \(F_{node}\) and \(dS_{node}\) as functions of the shear stress and shear angle. Firstly, with Equation (2), the following equation is given as:  
\[F_{node}=2 cos\Big{(}\frac{\pi}{4}-\frac{\varphi}{2}\Big{)}F_{sl}. \tag{5}\]  
Furthermore, for the purpose of normalization, the shear stress \(\tau_{sh}\) instead of the shear force \(F_{sl}\) is employed. Then, we have  
\[F_{sl}=\ 2\tau_{sl}L_{R}t, \tag{6}\]  
where \(L_{R}\) is the side length of the RVE, and \(t\) is the yarn thickness. Secondly, \(dS_{node}\) is the differentiation of the node displacement \(S_{node}\), whose relationship is given by  
\[dS_{node}=\frac{d(S_{node})}{d\varphi}d\varphi. \tag{7}\]  
From Figure 3, \(S_{node}\) is given by  
\[S_{node}=2L_{R}\cos\Big{(}\frac{\pi}{4}-\frac{\varphi}{2}\Big{)}-2L_{R}cos \frac{\pi}{4}. \tag{8}\]  
Then, \(dS_{node}\) is given by  
\[dS_{node}=\frac{d(S_{node})}{d\varphi}d\varphi=L_{R}sin\Big{(}\frac{\pi}{4}- \frac{\varphi}{2}\Big{)}d\varphi. \tag{9}\]  
Finally, substituting Equations (5), (6), and (9) into Equation (4), the input energy is expressed in terms of the shear stress and the geometric parameters as follows:  
\[dW_{node}=4 tl_{R}^{2}\tau_{sl}\cos\Big{(}\frac{\pi}{4}-\frac{\varphi}{2}\Big{)} sin\Big{(}\frac{\pi}{4}-\frac{\varphi}{2}\Big{)}d\varphi=2 tl_{R}^{2}\tau_{sl}sin \Big{(}\frac{\pi}{2}-\varphi\Big{)}d\varphi=2 tl_{R}^{2}\tau_{sl}cos\varphi d\varphi. \tag{10}\]
# Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder
## 2 Background and Method
An illustration of a control system with feedforward with fixed gain is shown in Figure 3.  
Defining the position error \(e\) as the position reference \(x_{ref}\) minus the measured position \(x\), the control output for this control system is given in Equation (7)  
\[E =Y-\dot{\theta}^{T}\cdot X\]0  
where  
\(u=\) controller output;  
\(k_{p}=\) proportional gain;  
\(e=\) position error;  
\(k_{ff}=\) feedforward gain;  
\(v_{ref}=\) velocity reference. Extending the traditional feedforward controller into an adaptive feedforward controller is done by replacing the fixed feedforward gain with the MIT-rule. An illustration of the adaptive feedforward scheme is shown in \[E =Y-\dot{\theta}^{T}\cdot X\]1  
The MIT-rule adaptive feedforward controller uses the position error, the velocity reference, and the constant \(\gamma\) to update the feedforward gain. The update law and the control output for this adaptive control system is then given in Equations (8) and (9). \[E =Y-\dot{\theta}^{T}\cdot X\]2 \[E =Y-\dot{\theta}^{T}\cdot X\]3  
\[E =Y-\dot{\theta}^{T}\cdot X\]4 MIT-rule adaptive feedforward. \[E =Y-\dot{\theta}^{T}\cdot X\]5 Feedforward with fixed gain. where  
\(\gamma\) = adaptation gain;  
\(z_{ff}\) = feedforward gain. Extending this controller to use sign-sign is then straightforward. An illustration of the sign-sign adaptive feedforward scheme is shown in \[E =Y-\dot{\theta}^{T}\cdot X\]6  
The update law and the control output for this adaptive control system is shown in Equations (10) and (11). \[E =Y-\dot{\theta}^{T}\cdot X\]7 \[E =Y-\dot{\theta}^{T}\cdot X\]8  
It should be noted that the sign function can produce unnecessary chattering when the input is oscillating around zero, due to the inherent discontinuity. Therefore the sign function has been replaced with the \(\tanh\) function, shown in Equation (12). \[E =Y-\dot{\theta}^{T}\cdot X\]9  
This gives a smooth output when the input is oscillating around zero. Increasing the parameter \(k\) gives a sharper rise and a closer approximation to \(\text{sign}(e)\). Another advantage of using \(\tanh\) is that the adaptation stops when the position error is zero. The parameter \(k\) has been set to \(k=100\) m\({}^{-1}\) and \(k=100\) s \(\cdot\) m\({}^{-1}\) for the position error and velocity reference, respectively.
# Assessing the Role of Program Suspend Operation in 3D NAND Flash Based Solid State Drives
## 4 Exploring the Program Suspend in 3D NAND Flash
### Total Program Time  
Suspending the program operation straightforwardly increases the overall program time depending on the number of performed suspensions. Naively, this relationship is assumed to be linear since the higher the suspension number is, the higher the time to complete the program. However, the results previously shown concerning the \(t_{PGM}\) variability in 3D NAND Flash wordlines and their impact on the number of suspend issued makes this relationship more complex.  
Figure 8 shows the results of the characterization performed on the overall program time in the presence of suspensions (\(t_{PGM,s}\)) achieved through different _Delay1_ and _Delay2_ combinations. In general, the number of ISPP iterations for each cell to achieve a desired threshold voltage level is the same with or without suspension (the program dynamics of the cells is not altered by the suspension). Every time we suspend, we have to wait for the \(t_{EPS}\) time; therefore, the higher the number of suspensions issued is (occurring when the delay timings are low), the larger the overhead on \(t_{PGM,s}\) imposed by that metric.  
If both delay timings approach 100% of the \(t_{PGM}\), the \(t_{PGM,s}\) is more than twice as expected (\(t_{EPS}\) is also considered as part of the programming operation since it is time required to enter the suspension mode). However, this result was not achieved for all combinations. We observed that, if _Delay1_ is relatively small (below 20% of \(t_{PGM}\)) and _Delay2_ is higher than 70% of \(t_{PGM}\), we suffer from an increased \(t_{PGM,s}\) due to the early described \(t_{EPS}\) overhead and to an additional combination of factors.  
We speculate that, if _Delay1_ is small (in the range below 20% of \(t_{PGM}\)), the memory cells within the wordline we are programming are still in an unstable region of the ISPP algorithm where the relationship between the voltage applied for programming and the threshold voltage of the cells is not linear. For high _Delay2_ values, an early retention loss of the cells may trigger additional ISPP iterations to reach a target threshold voltage. Both contributions can play a role in the overall \(t_{PGM,s}\) increase.  
Figure 7: **(a)** The average of the \(t_{EPS}\) measurements performed on 50 randomly sampled wordlines in a 3D NAND Flash block. **(b)** The worst \(t_{EPS}\) in each measure.
# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 5 Experiments
### Evaluation Metrics
#### 5.2.1 Performance Evaluation  
The evaluation of the intelligent Q&A module and intelligent judgment focuses on consistency evaluation. This is measured by comparing the system's responses to standard answers to assess the reliability and accuracy of the system's responses.  
* Consistency Analysis: The consistency of the intelligent Q&A and intelligent judgment is evaluated by comparing the results of standard answers with those provided by the system. The evaluation metrics include the BLEU score and the BERT score. The BLEU score measures the level of lexical overlap between the generated text and the reference text, ranging from 0 to 1, with higher scores indicating higher accuracy of the generated text. The BERT score assesses the semantic similarity between the generated text and the reference text using the BERT model, also ranging from 0 to 1, where scores closer to 1 indicate better text quality.  
The performance evaluation of the report generation module includes two aspects: overall quality and prediction accuracy. The overall quality evaluation examines the report's performance across multiple dimensions, including response latency, content accuracy, and richness. This comprehensive evaluation method aims to thoroughly assess the module's performance, ensuring its effectiveness and reliability in practical applications. The prediction accuracy evaluation focuses on the predictions provided in the report about future trends or events. By comparing actual results with predicted content, the system's forecasting capability is measured, offering scientific and accurate decision-making support for mine safety management.  
* Overall Quality Analysis: The evaluation metrics include Token Usage, Waiting time, Grammar Errors, and Diversity Score. Grammar Errors are detected by introducing the language_tool_python library. Language_tool_python is an open-source grammar-checking tool that supports multiple languages and allows for custom rules. It takes the system-generated report as input and records the number and types of detected grammar errors to assess the grammatical accuracy of the text. The score ranges from 0 to 1, where 0 indicates no grammar errors, and an ideal score should be less than 0.05, indicating a high level of grammatical accuracy in the text. The Diversity Score quantifies the text's diversity to evaluate the innovation and richness of the system's generated content, with a score range from 0 to 1, where 0 represents no diversity in the content, and 1 represents very high diversity.
* Alarm Prediction Accuracy Analysis: The evaluation metrics include Accuracy, Recall, F1 Score, and Coverage of Prediction Results. Accuracy measures the correctness of predictions, Recall reflects the comprehensiveness and coverage of the predicted results, and the F1 Score provides a balanced evaluation of prediction accuracy and recall capabilities.
# Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm
## 4 Conclusions and Future Work  
The query optimization problem for large-scale distributed databases is an NP-hard problem. The complexity of query optimization increases with the increase in the number of data shardings and data nodes involved in the query. In this paper, we solve the distributed database query optimization problem by combining the artificial bee colony algorithm and genetic manipulation operators (e.g., crossover and mutation). We then introduced a dynamic perturbation factor so that the control parameters of each individual in the population change dynamically according to the iteration process and the fitness values of the whole population. With the increase in the number of algorithmic iterations, the whole population keeps converging; then, the use of smaller perturbation coefficients at the later stage of the iteration can improve the optimization effect. Finally, by experimentally comparing the quality of the query execution plans generated under six different dimensional queries, it is proved that the Top-k query execution plan generated by the DYABC-GO algorithm not only has a lower query cost, but also has a higher algorithmic convergence speed, which in turn improves the efficiency of the distributed query optimization.  
Although the method proposed in this paper has improved the effectiveness of query optimization, the increase in the complexity of the algorithm has led to an increase in the computational volume of the algorithm, making the running time of the algorithm increase. Therefore, in subsequent work, the optimization algorithm can be executed by parallel computing or heterogeneous computing to improve computational efficiency [38; 39]; however, the distribution and integration of tasks in the optimization algorithm need further research. At the same time, in the case of databases deployed in a local area network (LAN), the query optimization also needs to consider local resource scheduling overhead so that the query optimization for a LAN distributed database can be taken as a multi-objective optimization [40; 41].  
Conceptualization, Y.D. and Z.D.; methodology, Y.D. and Z.C.; software, Y.D.; validation, Y.D.; formal analysis, Y.D.; investigation, Y.D.; resources, Y.D.; data curation, Y.D.; writing--original draft preparation, Y.D.; writing--review and editing, Z.D.; visualization, Y.D.; supervision, Z.D.; project administration, Z.D.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R&D Program of China under Grant 2022 YFF0 503900, the Key R&D Program of Shandong Province under Grant 2021 CXGC010104 and the National Natural Science of Foundation of China under Grant 62072016.  
Not applicable.  
Figure 19: Comparing three algorithms in terms of time metrics.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 2 Food Spectral Print Approaches
### Nuclear Magnetic Resonance (NMR)
NMR spectroscopy is a powerful, non-destructive tool that provides detailed metabolic profiles essential for wine authentication. Its ability to analyze complex mixtures with high precision and robust classification models makes it an indispensable technique for verifying wine authenticity in global markets. Despite its advantages, the use of this approach may be limited by economic and operational factors. The high cost of acquiring, maintaining,and operating NMR equipment, especially those with high magnetic fields, represents a significant barrier, especially in institutions with limited resources [19].
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation
### Dataset
#### 5.1.2 Testing Dataset  
The test dataset is divided into two different parts. The first part is the simulated test dataset, which is similar to the training dataset but differs in terms of SNR, \(RT_{60}\) and the source localizations. In total, 265 different DOA positions are selected randomly. In addition to the four rooms already mentioned in the training set, there are two additional rooms: Room 5 (4 m \(\times\) 6 m \(\times\) 3 m) and Room 6 (9 m \(\times\) 7 m \(\times\) 3 m). The rule of selecting the position of the SMA is the same as that for the training dataset. For better analysis of the relationship between the results and the parameters, \(RT_{60}\) and SNR are no longer selected randomly. \(RT_{60}\) varies from 0.2 s to 1 s in a step size of 0.2 s. The SNR changes from 0 dB to 30 dB with a step of 5 dB.. Similarly, two speech recordings are selected for the convolution with SMIR. However, this time, the recordings are selected from the Librispeech test corpus [40], not the Librispeech dev corpus. The second is the real test dataset. The LOCATA dataset [41] is  
Figure 6: The process for obtaining the speech with position information and reverberation.  
used as a real dataset. This is the IEEE-AASP challenge on sound source localization and tracking. From this, task 1 is chosen to localize the direction of the single static loudspeaker. The recordings are conducted in a room of (7.1 m \(\times\) 9.8 m \(\times\) 3 m) with \(RT_{60}=0.55\) s. Table 2 indicates the parameters for the simulated datasets.  
The parameters mentioned above are very important for generating the dataset. Different room sizes are used to enrich the information on scene and are beneficial for the algorithm to apply to more rooms. The white Gaussian noise is used as the interference to verify the robustness of the algorithm to interference. SNR and \(RT_{60}\) are utilized to enrich the complexity of the environments. Moreover, during the testing stage, these two parameters can help to show the relationship between the results and the conditions, which is effective for verifying the feasibility of the algorithm.