esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Physical Exercise on Substance Use Disorder: A Comprehensive Review
## 3 Results
### Assessment and Impact of Physical Exercise on Crawings in Individuals with SUD
#### 3.3.1. Craving and Types of Physical Exercise in Intervention Programs
In summary, aerobic exercises, such as running or cycling, are the most widely used and effective exercises for reducing cravings in individuals with SUD due to their accessibility and social interaction benefits. While mind-body practices (e.g., Yoga, Tai Chi) show potential, their effectiveness is less consistent in the literature, with only high-frequency or long-duration programs achieving significant reductions. Strength training is typically employed as a complementary intervention and has less impact on craving.
# Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows
## 3 Proposed Dynamic Window Approach
### The Evaluation Function for DF_DWA  
Once the dynamic window is set, each set of speeds (linear and angular velocities) is evaluated to select the best combination in the iteration. This paper uses the speed, goal distance, goal orientation, and obstacle clearance as components of the evaluation function. The detailed content of the algorithm DF_DWA is given in Algorithm 1.  
```
1:procedure DF_DWA(goal, x_initial = [x0, y0, z0], parameter_initial, Kinematic, p_e, r0)
2:i+1, \(\lozenge\) iteration counter
3:for I = 1:5000
4: If norm(x(i)-goal)>p_e \(\lozenge\) p_e is the allowable position error
5: If norm(x(i)-obstacle(i))<= r0
6: break;
7: If (there are large obstacles or U-shaped obstacles) then
8:gend\(\leftarrow\)(Equation (9), Equation (10), Equation (14), Equation (15), Equation (16));
9:goal_r+goal && goal\(\leftarrow\)V_goal;
10: If norm(x(i)-goal)<= p_e;
11: goal\(\leftarrow\)goal_r;
12: end if
13: end if
14: EvalParameter\(\leftarrow\)-earlits([distance(x(i)-goal), distance(x(i)-obstacle(i))]; \(\lozenge\) Calculate parameters by fuzzy rule.
15:[V\({}_{min}\) (i), V\({}_{max}\) (i), \(\omega_{min}\) (i), \(\omega_{max}\) (i)]\(\leftarrow\)DynamicWindow(x(i), Kinematic);
16: f (V\({}_{0i}\)) \(\omega_{(U)}\)distgoal(t), distobstacles(t), heading(yav(t), pitch(t)), Evalparameter)\(\leftarrow\)Equation (23);
17:ind(i)\(\leftarrow\)max(f); \(\lozenge\) Find the velocity group with the highest evaluation function value
18:[u(i)]\(\leftarrow\)Kinematic(ind,1:3); \(\lozenge\) Finding the optimal speed group
19:x(i + 1)\(\leftarrow\)P(x,u); \(\lozenge\) Iteration
20:else If norm(x(i)-goal)<p_e
21: break;
22: end if
23: end
24:end Procedure
```  
**Algorithm 1** DF_DWA.
## 4 Application of the CrSA for Parameter Extraction of the Solar Module
### Discussion  
This work utilizes the CrSA method to enhance the efficiency of parameter extraction in PV models, as well as perform a comparative analysis with alternative strategies. Based on the simulation findings, we can summarize the following:  
1. In over 20 trials, the CrSA consistently produces highly accurate results when using the provided error function as the objective function.
2. The simulation results indicate that the estimated parameters yield \(V\)-\(I\) curves that accurately pass through all three crucial points, with an error of approximately \(10^{-14}\).
3. The proposed method has been compared with the recent OAs in the literature by using convergence analysis. It has been observed that the proposed approach exhibited the fastest rate of convergence on each of the PV panels.
4. The results indicate that the proposed algorithm demonstrated satisfactory performance characteristics and its practical application is highly recommended.
###### Abstract  
It is difficult to accurately extract the health index of non-stationary signals of rolling bearings under variable rotational speed, which also leads to greater prediction error for bearing degradation models with fixed parameters. For this reason, an angular domain unscented particle filter model with time-varying degradation parameters is proposed to deal with the remaining useful life (RUL) prediction of rolling bearings. Order analysis is first performed to transform the variable-speed signal from time domain to angular domain for extracting the health index in the angular domain representation. To track the bearing degradation state, a real-time finite element model is established to guide the parameters updating the procedure of the performance degradation model. Finally, the bearing degradation state is estimated by the unscented particle filter (UPF), and then the remaining useful life of the bearing is predicted. In this way, the time-varying degradation model is developed by considering both non-stationary feature extraction and dynamic state tracking for rolling bearings. The proposed method is verified by both benchmarks: bearing experimental data, and a bearing accelerated life experiment. Compared with state-of-the-art prognostic methods, the present model can predict the bearing remaining useful life (RUL) more accurately under variable rotational speed.  
remaining useful life prediction; rolling bearing; variable rotational speed; time-varying degradation model; unscented particle filter
## 4 Identification of Deficient Classes Within Object-Oriented Programming Languages
### Derivation of Threshold Values  
In our study, we calculated threshold values using the derivation approach proposed by [PERSON] et al. [18]. The approach takes into account a statistical distribution of software metric values [48] and derives thresholds based on the most common value of a software metric [18]. To provide repeatability and objectivity, we designed a reusable suite of software products in three selected programming languages: Java, C++ and C#. For each language we gathered 100 open source software products from the SourceForge [49] online repository, chosen systematically from different domain categories. Software metrics were collected for all 300 software products and a repository of software metrics values for each of the three selected programming languages was composed.  
The used derivation approach consider frequency of occurrence of specific software metric value [18]. By starting with an analysis of the statistical distributions of the gathered values, the appropriate way for deriving threshold values is determined. As the majority of software metrics values follow a power law distribution [18; 28; 50], we cannot derive thresholds using approaches that apply to normal distribution; in this manner, the mean and standard deviation cannot represent a reliable threshold value. When we fitted the data to the most suitable distribution, the calculation of thresholds was performed.  
For seven out of eight selected metrics, the thresholds were determined with the 90 th percentile, since they followed a power law distribution. The only exception was the metric _PercentLackOfCohesion_, which measures the lack of cohesion within a software class. As its values followed a normal statistical distribution, the threshold was derived using an arithmetic mean and standard deviation. Interestingly, this metric received a lot of attention in the literature and consequently many variations of the metric exist [51]. Within the study we used the definition where the value can be between 0 and 100, expressing a lack of cohesion within the class in terms of percentages.  
All the derived threshold values are presented in Table 1. The thresholds were determined for eight software metrics in three different programming languages. For example, the C++ classes, in which the sum of cyclomatic complexity of all methods exceed 45, express a very high risk for containing deficient source code according to the complexity aspect. Whereas in Java the threshold value for the same metric is 33 and within C# it is 36. On the other hand, a class in C# that contains more than 278 lines of source code means that there is a high risk that a class is too big in terms of the size quality aspect. Within Java, the threshold for this same metric is determined at 197 and within C++ at 235 lines of code. As indicated by the numbers in Table 1, the threshold values among programming languages differ.
## 3 Results
### Validation on an Historically Contaminated Soil  
#### 3.2.1 Environmental Availability  
For the first experiment on an historically contaminated soil, the environmental availability in vitro assay confirmed an important sequestration efficiency for NDL-PCBs 138, 153 and 180 (Figure 3). As all results concerning PCB 101 were below LOQs, including non-amended soil (SS), no sequestration potential could be derived for this specific compound. For the three most lipophilic compounds, intense reductions were obtained as all environmental values except one (PCB 138, AC2 amendment) were below LOQs. Thus, high levels of reduction were obtained and a minimum reductions factor of (i) 83 to 90% (mean) for PCB 138, (ii) 91% for PCB 153 and (iii) 27 to 44% for PCB 180 were obtained (Table 2). These results provided evidence that even in a known historic contamination context (over a decade between contamination context and sampling of the soil) such a strategy could efficiently limit the environmental availability of these pollutants.  
Figure 3: Environmental available concentrations of PCB 138, PCB 153 and PCB 180 present in St Cyprien soil. Values correspond to the mean \(\pm\) SE (n = 3) concentrations of (**A**) PCB 138, (**B**) PCB 153 and (**C**) PCB 180 obtained through the environmental availability assay. As all concentrationsobtained for PCB 101 were below LOQ, data were not analyzed. Group mean values with superscript asterisks are statistically different from non-amended St Cyprien soil (SS) (0.05: * > 0.01: ** > 0.001: ***) using a variance analysis and a Dunnett post-hoc test. Groups mean concentrations with different superscript letters (a) are statistically different (\(p<0.05\)) from each other using a complementary variance analysis followed by a [PERSON] post-hoc test on previously found significant groups using Dunnett's. #: Values below LOQs were replaced by the corresponding LOQ, superscript numbers indicate the number of replaced values.
# Study on Rock Damage Mechanism for Lateral Blasting under High In Situ Stresses
## 3 Results and Analysis
### Influence of Biaxial Static Load on the Damage Distribution
In the XZ plane, when \(\lambda=0\), the damage zone near the free surface is induced by the coupling effect of the reflected tensile stress wave and the X-direction compressive stress field. When \(\lambda=0.4\), the combined effect is weakened by the addition of the X-direction tensile stress field induced by P2. Afterwards, with the increase of \(\lambda\), the P2 becomes the dominant factor affecting the crater shape and the damage mechanism begins to change. When \(\lambda=1.6\) and \(2.0\), two damage zones appear on the left side and the right side along the Z direction, which are formed by the coupling effect of the X-direction tensile component of the reflected wave and the X-direction tensile stress field. It can be seen that when \(\lambda\geq 0.4\), the variation characteristics of crater shape at each plane are similar to these in the uniaxial loading cases of P1. For case II, as shown in Figure 11c, the crater volume \(V\) reduces first before \(\lambda\) increases to \(0.4\) and then increases with \(\lambda\) increases. The turning point of \(V\) is mainly caused by the change of the dominant damage mechanism, as described in the above analysis. It can be found that \(\lambda=1\) is a demarcation point. The crater volume \(V\) is greater for case I (the borehole is placed on the right side of the rectangular cavity) when \(\lambda<1\), but greater for case II (the borehole is placed on the top side of the rectangular cavity) when \(\lambda>1\). There is a common feature that the \(V\) is greater when the borehole is placed on the side of the max static load. Taking the demarcation point of \(\lambda=1\) as the reference point, the increase of \(V\) with the increase of the static load on the side of the borehole is greater than that with the reduction of the static load on the other side. The result indicates that the crater volume is more sensitive to the variation of static load on the same side than the other side, which is consistent with the uniaxial load numerical result in Section 3.1 that the effect of P1 on the increase of \(V\) is greater than that of P2.
# A Deep Attention Model for Environmental Sound Classification from Multi-Feature Data
## 3 Time-Frequency Attention Mechanism Model Based on Multi-Feature Parameters
### Feature Extraction  
It is difficult to capture the useful information from original one-dimensional audio data due to a large amount of noise. In order to solve this problem, the popular method is mapping the audio data into Log-Mel spectrogram or MFCC spectrogram. Log-Mel spectrogram is designed by imitating the auditory system of the human ear. It takes the short-time Fourier transform of the sound signal and maps its frequency to the Mel frequency. However, Log-Mel spectrogram does not contain the phase information, which is an important feature of the sound signal. Therefore, it will lead to incomplete sound features and is not the optimal solution using Log-Mel spectrogram as the input of the sound recognition system. For further improving the recognition ability, the phase spectrogram of the sound is used as additional information and combines the Log-Mel spectrogram and time-frequency spectrogram as the input of neural network.  
In order to obtain Log-Mel spectrogram and absolute phase spectrogram of the sound, the sound signal is subjected to short-time Fourier transform and the steps are as follows: first, the input audio signal x(n) is divided into multiple short-time parts, length of each part is 43 ms, and the part shift is 21 ms. Then, each short-time part is multiplied by the Hamming Window to improve the continuity. Finally, a 1024-point Fast Fourier Trans  
Figure 1: Overall design scheme of environmental sound recognition.  
form is performed on each part, and a feature map containing time, frequency, amplitude, and phase is obtained, where magnitude and phase are combined in complex numbers. Complex features cannot be directly fed into the neural network. Therefore, this paper separates the complex-valued spectrogram D into its magnitude (S) and phase (P) components. The matrix S and P are the time-spectrogram and the phase-spectrogram of the signal, respectively. The matrix S is subjected to a Mel-filter and logarithmic operation to obtain a Log-Mel spectrogram. Figure 2 shows three spectrograms of saw wood sound.
## 2 Derivation of the Azimuth and the Elevation Error under Frequency Mismatch
### Partial Derivative of the Cost Function with Respect to Elevation  
The partial derivative of the cost function with respect to \(\psi\) is written as:  
\[\frac{\partial J(\theta,\psi)}{\partial\psi}=\sum\limits_{m=0}^{M-1}\left[ \begin{array}{c}\sin\left(\frac{2\pi r}{\lambda}\cos(\theta-\frac{2\pi m}{M })\cos(\psi)-\frac{2\pi r}{Mx}\cos(\theta^{(0)}-\frac{2\pi m}{M})\cos(\psi^{(0 )})\right)\\ \cos(\theta-\frac{2\pi m}{M})\sin(\psi)\end{array} \right]. \tag{17}\]  
The second necessary condition for \(\theta=\theta^{(0)}+\Delta\theta,\psi=\psi^{(0)}+\Delta\psi\) to be a critical point of the cost function, \(J(\theta,\psi)\), is that the partial derivative of the cost function, \(J(\theta,\psi)\), in (2) with respect to the elevation, \(\psi\), should be identically zero at \(\theta=\theta^{(0)}+\Delta\theta,\psi=\psi^{(0)}+\Delta\psi\):  
\[\begin{array}{c}\frac{2J(\theta,\psi)}{\partial\psi}\left| \begin{array}{c} \theta=\theta^{(0)}+\Delta\theta\\ \psi=\psi^{(0)}+\Delta\psi\end{array} =\sum\limits_{m=0}^{M-1}\left[ \begin{array} []{c}\sin\left(\frac{2\pi r}{\lambda}\cos(\theta^{(0)}+\Delta\theta-\frac{2 \pi m}{M})\cos(\psi^{(0)}+\Delta\psi)-\frac{2\pi m}{M}\right)\cos(\psi^{(0)}) \\ \frac{2\pi r}{\lambda}\cos(\theta^{(0)}+\Delta\theta-\frac{2\pi m}{M})\sin(\psi^ {(0)}+\Delta\psi)\end{array} \right]=0. \tag{18}\]  
Due to \(\sin(\phi)\approx\phi\) for \(\phi\approx 0\), (18) can be written as:  
\[\sum\limits_{m=0}^{M-1}\left[ \begin{array}{c}\left(\frac{2\pi r}{\lambda} \cos(\theta^{(0)}+\Delta\theta-\frac{2\pi m}{M})\cos(\psi^{(0)}+\Delta\psi)- \frac{2\pi r}{\lambda x}\cos(\theta^{(0)}-\frac{2\pi m}{M})\cos(\psi^{(0)}) \right)\\ \frac{2\pi r}{\lambda}\cos(\theta^{(0)}+\Delta\theta-\frac{2\pi m}{M})\sin( \psi^{(0)}+\Delta\psi)\end{array} \right]=0. \tag{19}\]Dividing both sides of (19) by \(\frac{(2\pi r)^{2}}{\lambda}\) yields:  
\[\sum\limits_{m=0}^{M-1}\left[ \begin{array}{c}\left(\frac{1}{\lambda}\cos(a_{m} +\Delta\theta)\cos(\psi^{(0)}+\Delta\psi)-\frac{1}{\lambda_{RF}}\cos(a_{m}) \cos(\psi^{(0)})\right)\\ \cdot\cos(a_{m}+\Delta\theta)\sin(\psi^{(0)}+\Delta\psi)\end{array} \right]=0. \tag{20}\]  
By the distributive law, (20) is written as:  
\[\sum\limits_{m=0}^{M-1}\left( \begin{array}{c}\frac{1}{\lambda}\cos(a_{m}+ \Delta\theta)\cos(\psi^{(0)}+\Delta\psi)\cdot\cos(a_{m}+\Delta\theta)\sin(\psi ^{(0)}+\Delta\psi)\\ -\frac{1}{\lambda_{RF}}\cos(a_{m})\cos(\psi^{(0)})\cdot\cos(a_{m}+\Delta \theta)\sin(\psi^{(0)}+\Delta\psi)\end{array} \right)=0. \tag{21}\]  
From the double angle formula for the sine function, (21) can be written as:  
\[\sin(\psi^{(0)}+\Delta\psi)\sum\limits_{m=0}^{M-1}\left( \begin{array}{c} \frac{1}{\lambda}\left(\frac{1+\cos(2a_{m}+2\Delta\theta)}{2}\right)\cos( \psi^{(0)}+\Delta\psi)\\ -\frac{1}{\lambda_{RF}}\cos(a_{m})\cos(\psi^{(0)})\cdot\left(\cos\left(a_{m} \right)\cos\left(\Delta\theta\right)-\sin\left(a_{m}\right)\sin\left(\Delta \theta\right)\right)\end{array} \right)=0.
# Ontology Modeling Using Fractal and Fuzzy Concepts to Optimize Metadata Management
## 2 Literature Review
### Ontology in Metadata Management  
Metadata, often described as \"data about data,\" provides information about the attributes, structure, relationships, and context of data. Metadata management is a critical component for efficient data utilization in modern industrial environments, particularly in continuous process industries, where large volumes of data are generated and need to be integrated from diverse sources [27]. Ontology plays a pivotal role in managing and utilizing metadata effectively, serving as a foundational tool that supports the organization, standardization, retrieval, and integration of data.  
Ontology enhances the consistency and coherence of metadata by defining and structuring it explicitly [28]. This minimizes semantic discrepancies when integrating data from different systems. For instance, in continuous process industries, process and equipment data are often stored in disparate formats and structures. Ontology facilitates their unified representation and management through a shared conceptual model, ensuring seamless data integration and interoperability. This enables real-time data exchange and analysis across multiple systems, which is critical for industrial environments.  
Furthermore, ontology supports automated retrieval and analysis of metadata [29]. By leveraging the relationships and rules defined within an ontology, users can infer new information that is not explicitly stored. For example, if equipment involved in a specific process is operating under an overload condition, an ontology-based reasoning engine is capable of analyzing the situation and generating a warning indicating the need for maintenance. Such automated capabilities significantly contribute to decision-making processes beyond traditional metadata management.  
Ontology is also advantageous in regard to handling data uncertainty and ambiguity [30]. While traditional metadata management methods focus on structured data, ontology integrates flexible approaches like fuzzy logic to manage uncertain data effectively. For instance, in continuous process industries, variables such as \"optimal temperature\" may be defined as a range rather than a fixed value. Ontology represents this using fuzzy relationships, offering a more realistic and comprehensive data model.  
The proposed FFOM framework further enhances these roles of ontology [31]. By employing a hierarchical fractal structure, FFOM improves the organization and management efficiency of large-scale data. Simultaneously, it integrates fuzzy logic to address uncertainties in metadata, enabling effective data integration and real-time decision making in continuous process industries. This approach not only redefines the possibilities of metadata management, but also significantly enhances data usability and reliability.
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 5 Defense Strategy
### Modifying Data
#### 5.1.4 Data Compression  
[PERSON] et al. [43] found that JPG compression method can improve a large number of network model recognition accuracy declined caused by FGSM attack disturbance. [PERSON] et al. [44] used a similar JPEG compression method to study a defense method against FGSM and DeepFool attacks. However, these image compression technologies still cannot serve as an effective defense against more powerful attacks, such as Carlini & Wagner attacks [24]. Similarly, the Display Compression Technology (DCT) compression method [44] used in the fight against universal disturbance attacks [87] has also been proved to be insufficient. The biggest limitation of these defense methods based on data compression is that a large amount of compression will lead to a decrease in the accuracy of original image classification, while a small amount of compression is often not enough to remove the impact of disturbance.