esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influences of Self-Introspection and Credit Evaluation on Self-Organized Flocking
## 2 Models
### Vicsek Model (VM)  
The original Vicsek model [17] is a multi-particle swarm model, where \(N\) particles are initially distributed randomly within an \(L\times L\) square with a periodic boundary. All particles have the same magnitude of velocity, and for every particle, its orientation is determined by the average orientation of all the particles in its neighborhood, which is defined by a circle with a radius \(r\) around this particle. Thus, the position of the \(i\)th particle \(\vec{x}_{i}\) is updated according to  
\[\vec{x}_{i}(t+\Delta t)=\vec{x}_{i}(t)+\Delta t\cdot\vec{v}_{i}(t),\ i=1,2, \cdots,N, \tag{1}\]where \(\Delta t>0\) represents the time step increment and \(\vec{v}_{i}=v_{0}(\cos\theta_{i},\sin\theta_{i})^{\mathrm{T}}\) is the velocity of the \(i\)th particle at time \(t\). It has a constant speed \(v_{0}>0\) and an orientation \(\theta_{i}(t)\), which is updated according to the following rule:  
\[\theta_{i}(t+\Delta t)=\arctan\frac{\sum_{j\in D_{i}(t)}v_{j}\sin(\theta_{j}(t ))}{\sum_{j\in D_{i}(t)}v_{j}\cos(\theta_{j}(t))}+\Delta\theta_{i}, \tag{2}\]  
where \(D_{i}(t)=N_{i}(t)+\{i\}\) and \(N_{i}(t)\) represents the set of neighbors of the \(i\)th particle at time \(t\); that is, \(D_{i}(t)\) refers to the set of \(i\)'s neighbors together with the \(i\)th particle itself. \(\Delta\theta_{i}\) is the noise randomly distributed in the interval \([-\eta/2,\eta/2]\), whose amplitude is determined by \(\eta>0\). In particular, in Equation (2), if \(\eta=0\), then the system is regarded as noiseless.  
In the original [PERSON] model, global consistency is used to measure the alignment degree of all particles [17]. The specific expression can be expressed as  
\[\phi=\left|\frac{\sum_{i=1}^{N}\vec{v}_{i}}{\sum_{i=1}^{N}|\vec{v}_{i}|}\right|,\ 0\leq\phi\leq 1. \tag{3}\]  
It can be seen from Equation (3) that if all the individuals are moving in the same orientation, then \(\phi\) goes to one, and if the orientations of all the individuals are randomly distributed, like at the beginning, then \(\phi\) tends toward zero. In ref. [17], [PERSON] et al., concluded that, through the rule in Equation (2), all the individuals driven by Equation (1) will move in the same orientation (i.e., \(\phi\to 1\)) after a period of time under the condition of high density (quantity divided by area) and insignificant noise. In other words, in the original [PERSON] model, the consistency of orientation is achieved through the simple rule of average orientation.
# Numerical and Experimental Analysis of Torsion Springs Using NURBS Curves
## 4 Results and Discussion  
### Experiment-Data Analysis  
The force according to the rotation angle was measured using a load cell and rotary encoder. Figure 6 shows images of the x- and y-coordinate measurements obtained according to the angle of the torsion spring, and Figure 7 shows a load graph according to the rotation angle. This graph was drawn using the average data from the results of five replicates. Analytical results indicated that the torsion-spring load was generated nonlinearly according to the rotation angle. The regression equation was derived using the measured data and was expressed as a quadratic polynomial in the graph. R-square values of the regression equations were 98% or more. This regression equation had a high adjustment. In addition, it was found that the load increased as the cross-sectional diameter (d) of the torsion spring increased, and that the load decreased as the number of spring turns (\(N\)) increased.
## 6 Comparative Analysis with National Standards
### Temperature Self-Equilibrating Stress Comparison  
To comprehensively analyze the influence of temperature effects on structural integrity, the European code categorizes the arbitrary temperature action on bridge sections into four components: effective temperature \(T_{e}\), vertical equivalent linear temperature difference \(T_{h}\), and residual temperature distribution \(T_{r}\). Among these components, both the vertical equivalent linear temperature difference \(T_{v}\) and residual temperature distribution \(T_{r}\) significantly impact concrete structures. Therefore, a comparison was made between the obtained vertical distribution of \(T_{r}\) by decomposing reference [34] and the above specifications, as shown in Figure 19. It is evident that both the proposed cooling model and the specifications exhibit consistent trends in terms of residual temperature distribution patterns. In all cases, negative values are observed at the top and bottom of the box girder, while positive values are found in its middle section. This indicates that compressive stresses are generated near the top and bottom of the box girder, whereas tensile stresses occur in its web plate. The residual temperature at the top surface of the cooling model presented in this paper is \(-5.36\)\({}^{\circ}\)C, slightly higher than AASHTO's specified value of \(-4.95\)\({}^{\circ}\)C and BS5400's specified value of \(-5.05\)\({}^{\circ}\)C but still lower than JTGD60-2015's specified value of \(-8.15\)\({}^{\circ}\)C. In conclusion,  
Figure 18: Comparison of negative temperature gradient mode.  
there is only a marginal difference between the proposed formula and BS5400, as indicated by a standard deviation of 0.387; nevertheless, it exhibits substantial divergence from JTGD60-2015. This inconsistency can be attributed to the amplified temperature differential at the upper section under JTGD60-2015's cooling mode and its subsequent influence on fluctuations in beam elevation.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 8 Future Research Direction
### Advancements in Machine Learning Models  
Future machine learning research for intrusion detection systems (IDSs) will have to prioritize performance and explainability. Though classic models like Decision Trees (DTs) and Random Forest (RF) demonstrated outstanding accuracy, more advanced techniques can further improve detection. Researchers need to explore ways that explain models in a better manner, providing clear insight into their decision-making to increase trust and usability in practical applications. In addition, future work should investigate federated learning as a privacy-preserving alternative such that IDS models can be learned on distributed IoT nodes without centralizing sensitive data. For better generalization, domain adaptation techniques must also be brought in so that models operate reasonably well on various datasets and real-world environments.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 4 Discussion
### Regulation on Harmonic Distortion  
In Section 3.2, we found that THD and harmonic power remains well below the Norwegian regulatory requirements of \(\leq\)8% of the RMS voltage on the phase [73]. However, there is considerable variation across nodes, timescales, and seasons. Methodologies utilizing harmonics observations must therefore account for these. Variations can be accounted for implicitly (left to be dealt with by the model) or explicitly (by encoding into auxiliary features). Implicit processing requires sufficiently complex models (e.g., deep neural networks) while explicit encoding requires engineering of suitable features (e.g., information on time and season, node location, as well as other pertinent node metadata).
## 2 Background  
The Northern Apennines (Italy) consists of stacked tectonic units derived from an accretionary wedge formed during the Cenozoic closure of the Ligurian Tethys ocean [23]. The uppermost, ophiolite-bearing tectonic unit is known as the Ligurian Domain and it is traditionally divided into Internal and External Ligurian Domains (Figure 1). The former represents the oceanic lithosphere close to the rifting zone, and the latter is interpreted as a transitional zone towards the Adria continental margin. The ocean floor metamorphic overprint does not exceed sub-greenschist facies conditions. The Vigonzano mine belongs to the External Ligurian Domain, characterized by an ophiolite-bearing sedimentary melange overlain by a thick Upper Cretaceous sedimentary succession.  
The Cu mineralization of the Northern Apennines are classified as Cyprus-type volcanogenic massive sulfide deposits [24] and they occur as (i) massive sulfide lenses in serpentinite breccia and (ii) stockwork-style mineralization hosted by serpentinite, gabbro, and pillow basalt. The exploitation of copper sulfide from the Northern Apennines ophiolites has been known since the Bronze Age [25], even though the mining activity peaked between 1850 and 1970 [26].  
The Vigonzano mine ore geology consists of a stockwork mineralization hosted in a 100 m serpentinite block. The sulfide assemblage is mostly made of chalcopyrite and minor pyrite and pyrrhotite, whereas quartz and calcite are the main gangue minerals [9]. According to petrography, fluid inclusion microthermometry, and trace element composition of quartz veins in the stockwork zone [27], the estimated minimum temperature of formation of the mineralized quartz veins is 260-330 \({}^{\circ}\)C. The high temperatures are related to seafloor and sub-seafloor hydrothermal activity close to the hydrothermal centers [26, 28].  
The Vigonzano mine was one of the biggest copper sulfide deposits of the Northern Apennines district and was active between the years 1948 and 1970. The main orebody was about 300,000 t, with an estimated Cu-grade 2-4 wt % [29]. The detailed description of the local geology and of the stockwork is reported elsewhere [26, 30]. Today, the mining site facilities are covered by mining waste dumps extending about 5000 m\({}^{2}\). The slopes of the stockpile are steep and shaped by the surface water runoff (Figure 2). The mining waste materials are heterogeneous in grain size, with gravel size rock fragments embedded in an ochaceous silt-clay matrix (Figure 2). The <2 \(\upmu\)m fraction of the stockpile was previously investigated, and its mineralogy and geochemistry was assessed [31]. The ochaceous matrix is mostly composed of smectite, chlorite, talc, and variable amounts of goethite, which derives from the oxidation of sulfides. The concentration of Zn; Ni; and, to a minor extent, Cu, is controlled by the cation adsorption ability of matrix smectite [31].  
Figure 1: Geological sketch of the Northern Apennine between Genova and Bologna, and location of the study area. Modified from [26].
# Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels
## 3 Design Optimization
### Metamodeling  
Using the optimal design program called PIAnO, the correlations between the input and output variables were mathematically approximated [25]. Six metamodels were established for each output variable based on the DoE results. The ensemble of decision trees (EDT) is a regression model capable of dealing with large amounts of data [23]. The Kriging model, which is an interpolation model, passes exactly through the experimental points and can form complex models with a limited amount of data [16]. It is also possible to analyze prediction uncertainty using this model [26]. The multi-layer perception (MLP) model, comprising hidden and output layers, minimizes the error function using weights and biases [27]. The polynomial regression (PR) model, which uses the regression equation, can reduce errors and improve prediction performance [28]. Finally, the radial basis function (RBF) model can approximate the underlying model using the training dataset, and it can also generate a regression (Reg.) or an interpolation (Int.) model [29].  
Optimal designs by approximation functions using metamodels suffer from the problem of low accuracy. In this study, this limitation was compensated by applying six metamodel techniques to the objective function and constraints. In addition, the RMSE test was conducted to evaluate the prediction performance of the metamodels. The value of the RMSE test was obtained using Equation (7), and the results of the RMSE test corresponding to each output variable for all six metamodels are summarized in Table 3[30]. For each  
Figure 5: Sensitivity analysis using the screening technique.  
output variable, the metamodel with the lowest RMSE test value was selected. As can be seen from Table 3, the Kriging metamodel is the most suitable for the torque ripple variable, and the PR (Forward Step.) is the most suitable for both the average torque and efficiency variables.  
\[RMSE=\frac{1}{n_{test}}[y(x_{i})-\hat{y}(x_{i})]^{2} \tag{7}\]  
where \(n_{test}\) is the number of experimental points used to evaluate the metamodel, and \(y(x_{i})\) and \(\hat{y}(x_{i})\) are the values of the actual function and approximate function, respectively.
# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 3 Materials and Methods
### Databases
#### 3.1.2 Savee  
SAVEE is a speech emotion database that consists of recordings from four male actors in seven different emotion classes. The database comprises of a total of 480 British English utterances, which is quite different from the North American accent used in the RAVDESS database. These vocal utterances were processed and labeled in a standard media laboratory with high quality audio-visual equipment. The recorded vocal utterances were classified into seven classes of angry, disgust, fear, happy, neutral, sad and surprise emotional expressions. The neutral emotion constituted 120 audio files, while all the other remaining emotions comprised of 60 audio files each as illustrated in Figure 2.
# A Microservices-Based Control Plane for Time-Sensitive Networking
Such TSN control plane (CP) implementations consume significant computational resources and increase the time invested to calculate schedules, which consist of determining the length and position of time slots assigned to different GCLs at the outgoing ports of the TSN switches along network paths. The concept of microservices is a promising approach to deal with the scalability issues of monolithic TSN CPs. Microservices are an architectural model that divides a monolithic application into different components, each one with a specific functionality [7]. Since these components are smaller than the whole monolithic application, it is easier to add or remove microservice instances [8], enabling resource allocation to highly demanded tasks more efficiently. In the literature, some works use microservices for SDN controllers: in [9], the authors create a cloud-native SDN controller based on the concept of microservices in the context of transport, while the work in [10] explores the use of microservices for a CP for open optical networks. In this paper, we describe \(\mu\)TSN-CP [11], a microservice-based SDN CP architecture for TSN capable of retrieving network topology and stream requirements, calculating valid schedules, and configuring switches accordingly by optimizing the associated resource usage. Considering the CP's atomic functionalities, \(\mu\)TSN-CP decomposes the CNC and CUC elements into microservices. Moreover, we implement a prototype of the \(\mu\)TSN-CP elements and analyze its performance on a hybrid cloud running on Amazon Web Services (AWSs) that use Docker and local instances. We focus on testing the ILP, which we provide as an example of a high-demand microservice in terms of computation, in order to analyze the qualitative characteristics that improve the computational resource usage (i.e., CPU and RAM) and the time spent calculating schedules. The remainder of the paper is structured as follows. Section 2 describes related work. Section 3 describes the \(\mu\)TSN-CP architecture. Section 4 presents some experiments done to show the overall functionality of our platform, including a discussion of the quantitative results. Section 5 provides a qualitative discussion of the advantages of using the microservices architecture. The paper ends with the conclusions and the description of lines of future inquiry.
# Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems
## 5 Results
### Case Study  
A case study has been carried out for the procurement process supported by the Microsoft Dynamics NAV ERP system. The database shares most of its elements with the running example from Section 3 and can be seen as a broader form of the running example. The data was provided by a Serbian wholesale furniture company operating in the Western Balkan region. We used NAV 2017 because the company had implemented this version of the system.  
The general process behavior is given as follows. First, a new purchase order with line items is created. The purchase order needs to be approved before it can be released to a vendor. After the approval, the goods are received, and the receipt document attached to the material is inserted into the ERP system. Also, the corresponding invoice is received and registered in the system. When the invoice is posted, the details are entered into a ledger and the invoice is released for payment. Before the invoice can be paid, it is matched to one or more purchase orders. Typically, the management expects that the corresponding invoice is not paid without the registered goods receipt document. In practice, different variations of this process exist which can be explained by the fact that ERP configuration settings are not set very strict to enable the flexibility necessary in the company. Note that the purchase orders can be linked to a purchase quote or to a contract document.  
Obviously, the process described above has one-to-many and many-to-many relations in the data. For instance, one invoice can be matched to multiple purchase orders and multiple invoices can be issued for a particular purchase order. Because of the existence of multiple data objects, it is difficult to choose a unique case or process instance notion for  
Figure 5: DSML purchase model for event log extraction.  
the whole process. The goal is to avoid the data convergence and data divergence problems (as explained in Section 3) by choosing proper multiple case notions or artifacts.
## 5 Results
### Fractal Dimension Features  
The results of the fractal dimensions (Katz, Petrosian, and Higuchi) method indicate 11,418 total features. Subject No. 1 performed well across all classifiers, with LinearSVM at 84.96% and GSVM at 84.24%. SVM with a polynomial kernel reached 83.50%, which is also considered vital. Subject No. 2's results are moderate: LinearSVM and GSVM both achieved 69.62%, while the CART score lagged behind at 45.55%. Subject No. 3 presented strong results, with LinearSVM and GSVM both achieving an accuracy of 90.37%. Subject No. 7 also showed strong results, with LinearSVM reaching 92.96% accuracy and GSVM reaching 91.48%. Subject No. 6 demonstrated the lowest overall performance, with all classifiers scoring around 50%, and SVM with a polynomial kernel was the highest (50.62%). Subject No. 9 performed well, with LinearSVM and GSVM reaching, respectively, scores of 87.31% and 86.87%.  
The lowest classification average success rate was found with CART, 59.04%. Next was SVM with a polynomial kernel (75.31%), and GSVM was slightly better (78.11%), and the highest score was obtained with LinearSVM (79.04%). The results of applying fractal dimension methods for feature extraction are presented in Table 6.
# BTCAM: Attention-Based BiLSTM for Imbalanced Classification of Political Inquiry Messages
## 3 Methodology
### Framework Overview of BTCAM
#### 3.2.4 Dual-Gated Convolution Mechanism Layer  
If each word in the input text is considered equally relevant to each word in the output, the semantic vector cannot accurately depict the entire information sequence. Therefore, we propose dual-gated convolutional layers to extract important information. Dual-gated convolution layers include a convolutional block attention gate and information fusion gate. The convolutional attention mechanism gate extracts important information by SCBAM and controls the contribution ratio of different information to the prediction results, while the information fusion gate retains the important semantics of the original input through shortcut connection and max pooling operations.  
(1) Attention mechanism gate.  
The convolutional block attention mechanism employs channel and spatial attention modules sequentially to highlight significant features along the channel and spatial axes, producing positive results in computer vision tasks. Unlike image data, each channel in text data represents the relationship features of adjacent words in the text. For the reasons stated above, we propose a convolutional block attention mechanism adapted to text (named SCBAM), as shown in Figure 6. Max-pooling on the channel axis is used to first capture keyword context information, and the spatial attention mechanism is then applied to gather more details about the keywords. The implementation details of SCBAM are as follows:  
\[M_{c}(F)=\sigma(\text{MLP}(\text{MaxPool}(F))) \tag{11}\]  
\[M_{s}(F)=\sigma\Big{(}f^{t\times 2u}([\text{AvgPool}(F);\text{MaxPool}(F)]) \Big{)} \tag{12}\]  
\[I^{\prime}=M_{c}(F)\otimes F \tag{13}\]\[I^{\prime\prime}=M_{S}\big{(}I^{\prime}\big{)}\otimes I^{\prime} \tag{14}\]  
where \(\sigma\) denotes the sigmoid function and \(f^{t*2u}\) represents a convolution operation with a filter size of \(l\times 2u\). \(\otimes\) denotes element-wise multiplication.  
(2) Information fusion gate.  
To prevent the loss of important information, we use the shortcut join combination X and H to represent the fused information. Then, the maximum pool is used to reduce the risk of overfitting and improve the generalization ability of the model.  
\[P=MaxPool(X\oplus H) \tag{15}\]  
where \(\oplus\) denotes the concatenation operation.  
Finally, the attention mechanism gate and the information fusion are connected.  
\[G=I^{\prime\prime}\oplus P \tag{16}\]  
where \(\oplus\) denotes the concatenation operation.
## 3 Experimental Results and Discussion
### Experimental Result
#### 3.2.5 Comparative Analysis
As shown in Figure 3, a local area in the oil path is capable of generating a magnetic field of the order of 100 mT, which produces a stronger induced voltage signal. For a large-diameter lubricating oil circuit, it is very difficult to achieve full flow monitoring of wear debris. More is to drill a bypass on the oil circuit, and then conduct on-line sampling and monitoring of the lubricating oil. This way changes the structure of the original lubricating oil circuit and changes the flow state of the lubricating oil, which may adversely affect the reliability and lubrication of the equipment. The sensor proposed in this paper generates a local high gradient magnetic field in a local area within the oil path. When the particle flows through the local area, a corresponding induced voltage is generated to achieve particle detection. In other words, the sensor only performs on-line sampling and detection on some of the particles flowing through the oil path, but the sensor does not need to set a bypass and does not change the flow state of the lubricating oil.