esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Methodology
### Crack Initiation Model
\[T\gamma_{\text{TP}}=\frac{\sigma_{y}+\sigma_{\text{UTS}}}{2\sqrt{3}}A\gamma_{ \text{TP}} \tag{3}\]  
where \(T\gamma_{\text{TP}}\) is the wear number value at the turning point or zero damage crossing point of the damage function, \(\sigma_{y}\) is the yield strength, \(\sigma_{\text{UTS}}\) is the ultimate tensile strength of the material, \(A\) is the assumed contact area and \(\gamma_{\text{TP}}\) is the creepge at the turning points. After the MBS, the RCF module evaluates the apparition of cracks. The wear number allows calculating the \(DI\) of particular points of the rails. The RCF module calculates separately both left and right rails. The assessment is performed for each rail cross-section where the MBS gives an output, at each contact point taking part in the wheel-rail interaction. These cross-sections are divided into smaller sections of around 5 mm, setting up a matrix where the rows i are the different positions along the track and the columns j the divisions of the cross-sections. To store data in the different cells, an elliptical distribution is assumed for each parameter analyzed. This is done under the assumption that the spin effect is negligible regarding the slip-adhesion ratio at each side of the contact patch. Although the interpenetration between wheel and rail could lead to another contact distribution [40], the elliptic contact assumption is coherent with the calculation of contact patches by MBS. Avoiding the recalculation of the contact patch is essential to guarantee the computational cost-effectiveness of the proposed methodology. A weighting function is proposed by  
\[Wf_{\text{cp}}(y)=\sqrt{1-\frac{\left(y-ycr\right)^{2}}{\left(\frac{wp}{2} \right)^{2}}} \tag{4}\]  
which is calculated for the analyzed contact point cp at each cross-section (Figure 3). The parameters \(wp\) and \(ycr\) define the width of the contact patches and their lateral position at centre, respectively. The \(DI\) value at each cell reads  
\[DI_{\text{i,j}}=\sum_{\text{cp}=1}^{N_{\text{c,i}}}\sum_{\text{k}=1}^{N_{\text{ d}}}\frac{DI_{\text{cp}}\cdot Wf_{\text{cp,k}}}{N_{\text{d}}} \tag{5}\]  
where k is the discretization of the analysed cells, \(N_{\text{c,i}}\) is the number of contact points on each cross-section i and \(N_{\text{d}}\) is the number of discrete points at the analyzed cells. The calculated \(DI\) at each \(\Delta t\) is then added to a previous \(Total\)\(DI\) matrix at a calculated time \(T_{\text{calc}}\). When any value of \(Total\)\(DI\) matrix is higher than one (Figure 4) the crack growth is studied. The value of each \(Total\)\(DI\) matrix cell is calculated as  
\[Total\)\(DI_{\text{i,j}}(T_{\text{calc}}+\Delta t)=Total\)\(DI_{\text{i,j}}(T_{\text{calc}})+DI_{\text{i,j}}(\Delta t) \tag{6}\]  
Figure 3: \(DI\) distribution for each division of the rail cross-section. (ycr: lateral position of the contact patch center; WP: width of the contact patch).
## 5 Experimental Results
### Software Used
#### 5.3.1 Main Node State Diagram  
According to the system requirements and the tools provided by the IEEE 802.15.4 standard, the following State Diagram for the Main Node was designed (Figure 12).  
The main node starts operating with the Boot.booted event; at that point, it begins its configuration process and starts the beacons transmission. When it receives an association request (MLME_ASSOCIATE.request), the node attends the request and sends an answer to the request transmitter node. Then, when receiving information packages from the associated nodes, it retransmits them through the serial port to a computer.
## 2 Methodology  
The study was divided into three stages: conducting surveys, data pre-processing, and comparison of results by the Bayes factor (Figure 1). First, surveys were carried out in Likert and binary formats. The Likert survey was performed by an interviewer from the University of Vigo, while the binary-format survey was conducted online. A lapse of one week was left between the completion of both, in an attempt to minimize the use of short-term memory. It should be noted that the binary version focused exclusively on questions related to the subject. Both surveys included the same questions. Second, the results were transformed from the Likert format to a dichotomous scale. It was essential that the samples being compared were of a similar nature. On one hand, there is the qualitative Likert scale with scores ranging from 1 to 5, each denoting a level of qualitative agreement. On the other hand, there is the binary scale with two extremes. It would be impossible to compare both of them without a common framework. Finally, once the results of both surveys were on the same scale, they were compared to find out if the scale influenced the respondent and the results.  
Figure 1: Steps taken in the present work.
## 7 Evaluation
### Evaluation
#### 7.2.3.1 Opinion Event Detection  
The results for the event detection show that it is hard even for human annotators to detect when an opinion event happens, as we only got a moderate inter-annotator agreement (calculated as Spearman's correlation \(\rho\)) of \(0.52\). For 44 of the 72 events, both evaluators agreed that the event was correct, resulting in a precision of \(61\%\). If we count an event as correct if at least one of the annotators marks it as correct, which is reasonable given the low inter-annotator agreement, the precision increases to \(72\%\). In future, we could consider investigating the effect of adding more annotators in order to get a better consensus.  
In this work, we did not specifically check the recall of the event detection algorithm, as we had no ground truth to compare with. Creating such a baseline would require defining an explicit threshold of when an opinion change becomes significant. This decision is however inherently context-dependent: the strength of opinions and consequently also of opinion changes depends on the opinion target, the culture in which the event happens or on other events happening at the same time. It is therefore hard to find a universal minimal threshold, even though it is easy to verify major opinion events such as [PERSON]'s death (described above). As the primary application of opinion event detection is in the exploration of a document collection, we concentrated in our work on correctly identifying the most prominent events. End user applications should allow an adaptation of the event detection sensitivity to allow the user to find opinion events relevant for their specific interests and contexts.
## 2 Materials and Methods
### Bracket Retention Force  
The bracket retention force of the GaMBN-containing resins was measured using a universal testing machine (Instron Corporation, Canton, MA, USA). Eight extracted premolars were used for orthodontic testing; teeth with prosthetic treatments, preservation treatments, cracks, WSLs, caries, or enamel defects were excluded. Bonding was performed according to the protocol recommended by Transbond(tm) XT Primer, and the brackets were tested using the following method. Each tooth was cleaned using fluorine-free pumice from a prophylaxis cup, which was applied to the side of the tooth that would be attached to the bracket, washed with distilled water for 10 s, and then gently air-dried. Each tooth was then exposed to 15 s of etching and suction with 35% phosphoric acid gel (Ultra Etch, Ultradent, USA), and subsequently washed with distilled water and gently air-dried. The dried tooth surface was coated with Transbond(tm) XT Light Cure Adhesive Primer (3M, Nonrovia, CA, USA), gently air-dried for 2 s, and then a premolar bracket (Orthos AP Metal Bracket, Ormco, Glendora, CA, USA) was applied along the facial axis of the clinical crown with sample resin. The excess sample resin around the bracket was removed and the sample resin securing the bracket was photopolymerized along a mesial and distal angle for 5 s.  
Bracket-bonded teeth were stored in distilled water for 24 h prior to testing with the Instron universal testing machine. The steel rod of the machine was placed perpendicular to the bracket and the maximum load was measured at a crosshead speed of 1 mm/min. The measured load (N) value was divided by the bracket base area (12.98 mm\({}^{2}\)) and converted to bond strength (MPa). The adhesive remaining on teeth that were separated from their bracket was assessed using the adhesive remnant index (ARI): (1) All of the adhesive remained on the tooth; (2) more than 90% of the adhesive remained on the tooth; (3) 10-90% of the adhesive remained on the tooth; (4) less than 10% of the adhesive remained on the tooth; and (5) no adhesive remained on the tooth.  
This bracket retention testing was reviewed and approved by the Institutional Review Board of Pusan National University Dental Hospital (PNUDH-2018046).
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 2 Theoretical Foundation
### Support Vector Machine  
SVM is one of the most celebrated and popular classification algorithms in the field of multi-classification [39; 40; 48]. The prime purpose of SVM is to maximize the margin of between-class, meanwhile narrowing the distance between the hyperplane focuses. The training set of SVM is denoted by  
\[S=\left\{(x_{i},y_{i})_{i=1}^{n}|x_{i}\in\mathcal{R}^{N},y_{i}\in\{-1,1\},i=1,2,\ldots,n\right\},\]  
where \(x_{i}\) and \(y_{i}\) are the sample data and category, respectively. The hyperplane can separate the data correctly with the maximum interval. The solution of the optimal separating plane is described as:  
\[\left\{\begin{array}{rl}\text{min}&\frac{1}{2}\|\omega\|^{2}\\ s.t.&y_{i}(\mathbf{\omega}x_{i}+\mathbf{b})\geq 1^{\prime}\end{array}\right. \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time-Domain** & **Feature Expression** & **Frequency-Domain** & **Feature Expression** \\
**Parameter** & & **Parameter** & **Parameter** \\ \hline F1 & \(|\max(x_{i})-\min(x_{i})|\) & F6 & \(\frac{\sum_{i=1}^{N}f_{i}G(f_{i})}{\sum_{i=1}^{N}G(f_{i})}\) \\ F2 & \(\frac{\max(x_{i})}{\frac{1}{N}\sum_{i=1}^{N}|x_{i}|}\) & F7 & \(\frac{\sum_{i=1}^{N}f_{i}G(f_{i})}{\sum_{i=1}^{N}(f_{i})}\) \\ F3 & \(\frac{1}{\left(\frac{1}{N}\sum_{i=1}^{N}\sqrt{|x_{i}|}\right)^{2}}\)where \(\mathbf{b}\) is the bias vector, \(\mathbf{\omega}\) denotes the weight vector, and \(\xi_{i}\geq 0(i=1,2,\ldots,n)\) represents a relaxing factor, which ensures the accuracy of classification. We rewrite the problem (8) as the following minimization problem:  
\[\left\{\begin{aligned} &\min&\frac{1}{2}\|\omega\|^{2}+ \lambda\sum_{i=1}^{n}\xi_{i}(\xi\geq 0)\\ & s.t.&\begin{cases}y_{i}(\mathbf{\omega}x_{i}+\mathbf{b}) \geq 1-\xi_{i}&(i=1,2,\ldots,n)\\ \lambda\geq 0\end{cases}\end{aligned}\right., \tag{9}\]  
where \(\lambda\) is the penalty factor, which is used to relieve the SVM classifier dilemma between accuracy and complexity. The classical hinge loss function is applied in this research. Thus, the dual optimization problem of SVM is described as:  
\[\left\{\begin{aligned} &\max\quad L(\alpha_{i})=\sum_{i=1}^{n}\alpha_{i}- \frac{1}{2}\sum_{i=1}^{n}\sum_{j=1}^{n}\alpha_{i}x_{j}y_{i}y_{j}K\big{(}x_{i}, x_{j}\big{)}\\ & s.t.\quad\begin{cases}\sum_{i=1}^{n}\alpha_{i}y_{i}=0\\ 0\leq\alpha_{i}\leq\lambda,i=1,2,\ldots,n\end{cases}\end{aligned}\right., \tag{10}\]  
where \(K\big{(}x_{i},x_{j}\big{)}\) denotes a kernel function, \(K\big{(}x_{i},x_{j}\big{)}=\big{\langle}\Phi^{\intercal}(x_{i})\cdot\Phi(x_{ j})\big{\rangle}\), and \(\cdot\) is the inner product operation. The SVM classifier with a radial basis function (RBF) kernel, \(K\big{(}x_{i},x_{j}\big{)}=exp^{-\gamma\|\big{(}x_{i}-x_{j}\big{)}\|^{2}},\gamma>0\) is applied in this work, where \(\gamma\) is a hyper-parameter that affects the merits of the SVM. Thus, the classification is described as:  
\[S(x)=sign\Bigg{(}\sum_{i=1}^{n}\alpha_{i}y_{i}\big{\langle}\Phi^{\intercal}(x _{i})\cdot\Phi\big{(}x_{j})\big{\rangle}\Bigg{)}+b. \tag{11}\]
## 3 Results
### Experiments
#### 3.1.1 Brodatz Database  
The methodology, as in Reference [1], used four different sets of images from the same database: the first one consisted of 10 images, the second of 30 images, the third of 40 images, and the fourth of all 111 images. The classification results in previously published articles reached 91.03% in Reference [1], 89.71% in Reference [43], and 88.15% in Reference [44] for the fourth set of the Brodatz database. Weused the same partition used for training and testing as in [PERSON] et al., 2011 [1]. The Brodatz image database consists of 111 images of size \(640\times 640\) pixels. Partitioning each image into nine non overlapping sub-images of \(213\times 213\) pixels, we obtained 999 sub-images from 111 texture classes. [PERSON] et al., 2011 [1], using the \((2\times 2)\) center sub-image as training and the other to test, reached the best classification results (see Figure 2).  
In training, each training sub-image with a size of \(213\times 213\) pixels was subdivided into a number n of images. Features were extracted from each subdivided image. The parameter n changed from 2 to 7 in the first three experiments, and from 2 to 9 in the last one. Once the feature vector was computed, each vector was assigned to a texture class using an SVM as a classifier.
# Line-of-Sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification
## 3 Multi-Link LOS Pointing Model  
### The Principle of LOS Pointing  
As shown in Figure 1, when the space optical terminal is used in dynamic applications, it is necessary to compensate for changes in the position and attitude of the optical terminal in real time to achieve multi-link optical pointing. After a series of coordinate conversion and transfer processes, we used the multi-link LOS initial pointing model, established based on the point-to-point LOS initial pointing model, to obtain the azimuth and elevation angles required to rotate the LOS of each mirror relative to its initial zero position [9]. Applying the coordinate transformation matrix requires the clarification of the number of coordinate systems, the order of the coordinate systems, and the positive and negative standards for rotation angles. It should be noted that the same standard must be used for each coordinate system.
## 3 Results  
### Platform Performance  
The communication delay and positioning on the real-time platform must be evaluated as accurately as possible because they are directly related to the accuracy of the maneuverability model. After the functional testing of the platform, the measurement environment depended on the control ground station, and the UAV debugging mode was set up. For the wireless transmission delay, the half delay time of the wireless data sent from the ground station to the UAV and then returned from the UAV to the ground station was defined as the wireless signal transmission delay. The sets of MAVLink heartbeat packets were sent to the UAV and returned, and the average transmission delay value was only 3.0\(\sim\)3.5 ms from the statistical data in the test environment of different transmission rates.  
The measurement of positioning accuracy requires selecting nine positions and acquiring the corresponding coordinate values many times with the UWB positioning subsystem of four base stations. An optical flow sensor and barometer were used to ensure the redundancy and stability of the system. If obvious fluctuations or errors appear in the UWB positioning values, the data obtained from other positioning methods were used to modify and supplement. The positioning accuracy of the static UAV mode was obtained through multiple tests at the detection point, and the RMS errors of the results were analyzed. To further obtain the positioning accuracy of the UAV in dynamic mode, the UAV was controlled to hover in the position under the remote control state. Figure 4 shows the data collected with the UAV hovering in the position (3.000, 3.000, 1.000). The position fluctuation is within 15 cm. Influenced by the drift of the UAV while hovering, the coordinate values in Figure 4a will also drift in the horizontal direction to some extent. In the height direction, the results show that the error is still within 15 cm.  
Figure 3: Flow chart of the RNN-LSTM maneuverability model.
## 2 Materials and Methods
### Stability of Periodic Orbits  
The periodic orbits of the system displayed in Equation (6) correspond to the fixed points of the Poincare map given by the relations shown in Equations (26)-(28). First,the fixed points of the Poincare application are presented for the case where there is no saturation of the duty cycle.  
Fixed points are those at which \(\mathbf{x}_{3}((n+1)T)=\mathbf{x}(nT)\). This last equality can be used for Equation (26), determining sufficient conditions for the existence of \(1T\) periodic orbits:  
\[\mathbf{x}(nT) =\bigg{[}\mathbf{I}_{2}-\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)} \phi_{2}(T-d_{n})\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)}\bigg{]}^{-1}.\] \[\Bigg{[}\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)}\phi_{2}(T-d_{n}) \psi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)} \tag{29}\] \[+\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)}\psi_{2}(T-d_{n})+\psi_{1 }\bigg{(}\frac{d_{n}}{2}\bigg{)}\Bigg{]}.\]  
With this methodology, the existence of \(1T\)-periodic orbits is restricted to the invertibility of the matrix \(\mathbf{A}=\mathbf{I}_{2}-\phi_{1}\Big{(}\frac{d_{n}}{2}\Big{)}\phi_{2}(T-d_ {n})\phi_{1}\Big{(}\frac{d_{n}}{2}\Big{)}\).  
From a functional analysis, if the spectral radius of the matrix is less than \(1\), then the matrix \(\mathbf{A}\) is invertible. Given an initial condition, this last matrix is a function of the duty cycle, and the latter is a function of \(\gamma\).  
\[\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)}\phi_{2}(T-d_{n})\phi_{1}\bigg{(}\frac{ d_{n}}{2}\bigg{)}. \tag{30}\]  
Fixing the period at \(T=0.18s\) and varying \(\gamma\) in the interval \((0.3,0.5)\), the spectral radius of the matrix \(\phi_{1}\Big{(}\frac{d_{n}}{2}\Big{)}\phi_{2}(T-d_{n})\phi_{1}\Big{(}\frac{d_{ n}}{2}\Big{)}\) is less than \(1\), and it follows that the matrix \(\mathbf{A}\) is invertible. When the duty cycle is saturated, the following conditions for the existence of orbits are obtained:  
\[\text{if}\quad d_{n}=0,\mathbf{x}(nT)=\big{[}\mathbf{I}_{2}-\phi_{2}(T) \big{]}^{-1}\psi_{2}(T), \tag{31}\]  
\[\text{if}\quad d_{n}=T,\mathbf{x}(nT)=\big{[}\mathbf{I}_{2}-\phi_{1}(T) \big{]}^{-1}\psi_{1}(T). \tag{32}\]  
As the spectral radius of the matrix \(\phi_{1}(T)\) is \(1\), Equation (12) is not applicable to determine orbits of period and in the case where the cycle saturates at \(T\).
## 2 Methodology
### Fuzzy Control System
#### 2.2.3 Defuzzification  
The final step of the fuzzy control system involves defuzzification, a process in which the fuzzy output, known as the fuzzy set, is transformed into a crisp value. This step is essential in fuzzy logic systems to convert the qualitative results derived from inference rules into a usable, quantitative output. Therefore, the values obtained through defuzzification represent numerical equivalents of linguistic expressions that contain the system's final assessment.  
In general, this phase involves two key steps:  
* selecting the defuzzification method;
* normalizing the final score.  
With respect to the first step, several defuzzification methods exist in the literature. Among them, the fuzzy mean (FM) method, highlighted by [50], is one of the most applied due to its simplicity and effectiveness. The FM method makes use of the following formula (Equation (4)):  
\[\text{FM}=\frac{\sum_{k=1}^{n}a_{k}X_{k}}{\sum_{k=1}^{n}a_{k}} \tag{4}\]  
where:  
* n is the number of fuzzy conclusions;  
\begin{table}
\begin{tabular}{c|c c c c} \hline \hline  & Very Low & Low & Average & High & Very High \\ \hline Very low & Very low & Very low & Low & Low & Average \\ Low & Very low & Low & Low & Average & High \\ Average & Low & Low & Average & High & High \\ High & Low & Average & High & High & Very high \\ Very high & Average & High & High & Very high & Very high \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy inference rule matrix used to determine the output linguistic values.  
* a\({}_{\text{k}}\) is the truth value (i.e., degree of membership) of the k-th conclusion obtained from the inference phase;
* \(X_{k}\) is the numeric value associated with the \(k\)-th conclusion. For trapezoidal fuzzy numbers, this is typically the average of the two values where the membership function reaches its maximum. Table 3 shows the fuzzy interval used to determine the \(X_{k}\) value.  
However, it is important to note that the crisp value and its range depend on the type of fuzzy numbers and linguistic scales adopted. Therefore, as a second step, a final normalization is required to express the output within a standardized range (typically between 0 and 1, with higher values indicating better performance). This is done using the following normalization formula (Equation (5)):  
\[\text{Normalized value}=\frac{(\text{x}-\text{min})}{(\text{max}-\text{min})} \tag{5}\]  
where:  
* x is the crisp sustainability index before normalization;
* min and max are the minimum and maximum values in the possible range of variation, which correspond to the extreme cases where the company achieves the lowest or highest scores across all indicators.
# Platform Economy and Techno-Regulation--Experimenting with Reputation and Nudge
## 4 Implementing a Techno-Regulation Strategy for Digital Labour Platforms
### The Techno-Regulatory Model: Nudging by Means of Information and Reputation  
The development of the _Reputation/Tranparency Module_ has been preceded by the definition of a regulatory model: a comprehensive definition of the normative goals and of the mechanism devised to accomplish them. Our regulatory model revolves around _\"information nudge\"_[46; 52; 53], that is, affecting individual and social behaviors by gathering, processing, and sharing different kinds of information. In more detail, the idea is to put together an _information asset_ encompassing: (i) _raw data_ (e.g., the amount of smart contracts or transactions for each DLP, spatial distribution of DLP); (ii) _knowledge extracted_ by applying computational heuristics to raw data (e.g., insights about DLPs behavioral patterns like unfair workers rounding or other misconducts); (iii) _reputational data_ (e.g., structured feedback provided by crowd workers, public administrations or unions about DLPs activity). In order to get an effective regulatory impact on the digital labor market, our model relies on two main factors--the number and variety of individuals reached by information and the intrinsic qualities of data conveyed by the _Reputation/Tranparency Module_. With regard to the first point, the goal is to reach different categories of entities:  
* _Crowd workers_: they can get an idea about the fairness of DLPs' conduct, and thus make more informed decisions when applying for gigs.
* _Public authorities_: administrations (e.g., labor inspectorates) can rely on trustable information highlighting DLP misconduct from an administrative and legal standpoint (e.g., breaches of labor or even criminal law).
* _DLPs_: they find all the feedback on their activity in one place, having the possibility of observing the evaluations reserved for competitors. This increases the regulatory effect of the system because a bad reputation affects the possibility of finding crowd workers; on the \"judgment\" of public institutions; on public opinion.
* _General public_: DLP's customers, people potentially interested in gigs, unions can draw relevant information from DLPs ratings. The quality of the information asset will be pursued, focusing on the following criteria:
* _Volume of data gathered_: e.g., number of reviewed DLPs and of reviewers; data deriving from the user interaction with the module.
* _Quality of data_: veracity and heterogeneity (e.g., the number of unique revewers) of reviews; extraction of actionable knowledge from raw data using computational heuristics (e.g., sentiment analysis [54] on reviews).
* _Usability_: adoption of technical solutions (e.g., information visualization techniques [55; 56]) allowing to ease the interaction with data.