esa-sceva/satcom-chunk-collection huggingface dataset:
# Verification of Control System Runtime Using an Executable Semantic Model +
## 3 Runtime Formal Model
### Data Value Interpretation  
Since the memory read functions return values in the form of memory bytes, it was necessary to introduce functions that assign numerical values to IEC 61131-3 data types, such as BOOL, BYTE, INT, LINT, REAL, and others. Additionally, functions were introduced to convert the numerical value of a given type to the byte format, as memory update functions accept a value represented in the form of bytes as a parameter. The models of functions performing the interpretation of values based on bytes and vice versa for some basic types are presented using Formula (12). Other types are interpreted analogously:  
\[\begin{split} FromBool&=BOOL\to OneByte\\ FromByte&=BYTE\to OneByte\\ FromInt&=INT\to TwoBytes\\ FromDInt&=DINT\to FourBytes\\ FromLInt&=LINT\to EightBytes\\ FromReal&=REAL\to FourBytes\\ \end{split} \tag{12}\]  
Arithmetic operations such as the addition, subtraction, and multiplication of operands of the same type may cause overflow of the range of that type. To address this issue, limited arithmetic operators are introduced: limited addition (\(\oplus\)), limited subtraction (\(\ominus\)), and limited multiplication (\(\otimes\)). Consider, for instance, the signed addition \(\oplus\) of integers. For signed integers, the operator \(\oplus\) is defined by  
\[\begin{split} a\oplus b=&\textbf{if }(a+b)>MaxRange(a)\textbf{ or }(a+b)<MinRange(a)\\ &\textbf{then }-MinRange(a)+(a+b)\textit{ mod }(-MinRange(a))\\ &\textbf{else }a+b\end{split} \tag{13}\]  
where the \(MinRange\) for INT, DINT, and LINT means \(-\)32,768, \(-\)231, and \(-\)263, respectively, and \(MaxRange\) for INT is 32,767, for DINT, \(2^{31}-1\), and for LINT, \(2^{63}-1\)24. Similar definitions may be given for other operators and types.  
Value conversions are utilized to transfer a value from one type to another (if feasible). Two instances of such conversions are depicted in Equation (14). For an unsigned value, \(ByteToWord\) inserts zero bits into the more significant byte of \(TwoBytes\), while for signed values, the byte is filled with the sign bit. Conversely, \(WordToByte\) decreases the value by eliminating the most significant bits.  
\[\begin{split}&ByteToWord=OneByte\to TwoBytes\\ & WordToByte=TwoBytes\to OneByte\end{split} \tag{14}\]
## 2 Materials and Methods
### Data Reduction and Model Learners
#### 2.1.1 Singular Values Decomposition  
The purpose of the SVD [13] is to decompose any rectangular matrix \(A\) of dimensions \(N\times T\) as the product of three matrices: \(U\), \(\Sigma\), and \(V^{\mathbb{T}}\)  
\[A=U\Sigma V^{\mathbb{T}}, \tag{2}\]  
where \(U\) is an orthogonal matrix of dimension \(N\times N\), \(\Sigma\) a diagonal matrix and \(V\) an orthogonal matrix of dimensions \(T\times T\) (here, it is assumed that all the matrices are real valued).  
Columns of \(U\) are the principal components of \(A\), also called modes. Non-zero components of \(\Sigma\), \(\sigma_{1}\geq\sigma_{2}\geq\cdots\), are called singular values. Columns of \(V\) are the principal components of size \(T\) instead of \(N\): if \(N\) is the number of degrees of freedom and \(T\) the number of time instants in the dataset, columns of \(U\) are the space principal components and columns of \(V\) are the time principal components.  
With this decomposition, one can easily approximate \(\mathbf{a_{i}}\), the \(i\)th column of \(A\), by projecting it on the principal components, to compute its reduced coordinates \((\alpha_{i1},\cdots,\alpha_{ir})\)  
\[[\alpha_{i,1}\ \cdots\ \alpha_{i,r}]=\mathbf{a_{i}^{T}}\tilde{U}, \tag{3}\]  
where \(\tilde{U}\) contains the first \(r\) columns of \(U\).  
The columns \(\mathbf{a_{i}}\) can be approximated in the reduced basis composed by the columns of \(\tilde{U}\)  
\[\mathbf{a_{i}}\approx\sum_{j=1}^{r}\alpha_{ij}\mathbf{u_{j}}. \tag{4}\]  
Thus, the SVD allows for approximating any rectangular matrix \(A\) by projecting its columns in a reduced basis consisting of \(r\) dimensions instead of \(N\), with in general \(N\gg r\). More details on this technique can be found in [14; 15; 16].
# A Critical Review of Human Jaw Biomechanical Modeling
## 1 Introduction
The current state of art provides both experimental [40], through the use of strain gauges, photoelasticity or innovative approaches like image correlation, as proposed by [PERSON] et al. [41], and numerical tests [42]. The clear advantages offered by the latter in terms of their non-invasive techniques, the simplicity ensured by high-performance software and especially their good agreement with experiments [43] make them a valid and popular alternative, as confirmed by [PERSON]'s study [44]. The flow process involves a computer tomography (CT) scan image of the jaw, solid conversion using 3D CAD tools  
Figure 4: Human jaw muscle attachment: lateral inferior pterygoid (LP) in green, posterior temporalis (PT) in red, middle temporal (MT) in violet, anterior temporalis (AT) in pale blue, medial pterygoid (MP) in ochre, superficial masseter (SM) in blue and deep masseter (DM) in orange. Figure 3: Human skull CAD files with mandible movements in their respective directions. and mesh construction [45]. Nevertheless, numerical models are usually based on exemplifying imposed hypotheses [46] such as the idea that the bone properties are intrinsically anisotropic and not homogenous [47]. Consequently, their comparison with in vivo trials is necessary to validate their results. In this regard, [PERSON] et al. [48] evaluated experimentally the biomechanical behavior of the mandible when the chin was loaded. The forces and displacements were thus extrapolated, acting as the basis for computational models. [PERSON] et al. [49] investigated, via a computational approach, the biomechanics of the jaw during three movements, middle and maximum opening and protrusion, at different bone mineral densities, finding out that the latter determined the highest deformation, mostly corresponding to the mandibular angle. [PERSON] et al. [50] proposed an experimental setup for calculating the mandible's stiffness without any constraints. In this regard, a point of distinction is the physical nature of the jaw, which can be extrapolated from human cadavers or be purely animal in origin, as with pigs or sheep. An alternative is realization using additive manufacturing, as carried out by [PERSON] et al. [51] by adopting a poly(methyl methacrylate) core and glass fiber reinforcement. Overall, to our knowledge, the review manuscripts regarding mandible biomechanics have essentially focused on the methods of reconstruction [52], dedentulous conditions [53], the stress patterns during mastication [54], or reference to peculiar zones, such as the temporomandibular joint (TMJ) [55]. [PERSON] et al. [56] proposed an overview of all the biochemical models, both physical and computational. On the contrary, our aim is to analyze and discuss from a more computational perspective the biomechanics of this biological apparatus, accenting the roles of the muscular forces and the temporomandibular joint and their interaction.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 8 Perceptual Experiment 3 (Audio-Visual)
The difference in the quality score was defined by the stronger effect of packet-losses over frame-freezing pauses (HRC2: number = 1, length = 2 s, HRC3: PLR = 1%). This is also the case for HRC12 and HRC13 (see Table 9). Equivalence in terms of bitrate and a certain predominance of packet-loss over frame-freezing is too observed for the Echo type of distortion. More specifically, for HRC5-HRC7 and HRC15-HRC16, both with fixed Echo parameters (see Table 9). For the Background Noise type of distortion, results for HRC1 and HRC6 were almost equivalent. For this case, most of the audio and video parameters were the same (see Table 9), the small difference (no statistical significance) between both test conditions was determined by the car noise type at 15 dB (HRC1) being less annoying than the office noise at 10 dB (HRC6). For the case of HRC9 and HRC10, with fixed parameters of audio (office noise with 10 dB), results were also equivalent, and the small difference was due to the coding parameters (HRC9: 2000 kbps, HRC10: 800 kbps) and the slightly stronger annoyance caused by the frame-freezing pauses over the packet losses (HRC9: PLR = 5%, HRC10: number = 3, length = 7 s). Finally, by observing the \(\text{MQS}_{\text{HRC}}\) values for the different video distortions in Figure 10b, we notice that frame-freezing distortions were less annoying to participants than packet-losses. This is in agreement with the results from Experiment 1. Figure 9: Mean Quality Score (MQS), and its respective spread of scores, for the different Hypothetical Reference Circuit (HRC) in Experiment 3. Figure 10 presents the MCS\({}_{\text{HRC}}\) values for each HRC gathered in Experiment 3. As in Experiments 1 and 2, MCS\({}_{\text{HRC}}\) values vary in a small range, fluctuating around the value '3' (\"Neutral\" Content).
## 4 LLC Converter Design Steps
### The DC Gain Requirements  
According to the application requirements, the LLC resonant converter should be designed in such a way that would meet all load conditions. Therefore, the maximum and minimum required DC gain, \(M_{max}\) and \(M_{min}\) can be calculated based on the maximum and minimum input voltage, \(V_{in\_max}\), \(V_{in\_min}\), respectively, by using the following equations [4]:  
\[M_{max}=\frac{nV_{o\_max}}{V_{in\_min}/2} \tag{2}\]  
\[M_{min}=\frac{nV_{o\_min}}{V_{in\_max}/2} \tag{3}\]  
where \(V_{o\_max}\) and \(V_{o\_min}\) are the maximum and minimum allowed output voltage, respectively.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 4 Results and Discussion
### Discussion
#### 4.6.3 Innovation and Further Research  
This study developed an EWT-PSO-SVM model to predict runoff in karst basins. In previous studies, hybrid models were also used to predict runoff, but most of them used EMD and EEMD to preprocess the runoff series. The feasibility of EWT has been effectively verified in many fields [19], but it has rarely been applied in runoff forecasting fields in karst basins. In this paper, EWT was introduced into the decomposition of runoff series to enhance the precision of runoff forecasting in the Chengbi River Karst Basin. The \(M_{R}\) of the EWT-PSO-SVM model reached 0.68 indicating that the developed model has the higher forecasting accuracy when compared with PSO-SVM model. Moreover, the developed model is significantly optimized in the forecasting of large flow events compared to the other two models, which means that applying the developed model can better assist in the optimal allocation of water resources. Meanwhile, this paper also investigated the predictive capability of the three models under the monthly structure. The \(M_{R}\) of the EWT-PSO-SVM (M) model reached 0.67, which shows that the developed model outperforms the highest forecasting performance under the new data structure input. The comprehensive performance of the developed model always outperforms the other two models with different data structure inputs, which further indicates that the developed model has a robust performance superiority.  
Given the phenomenon that the EWT-PSO-SVM model has higher prediction accuracy under large flow events but inadequate accuracy under small flow events, future research can consider dividing the runoff series according to the size of the annual water inflow and predicting them separately. Moreover, the selection of model plays a critical role in determining the precision of forecasting results, and different types of models have different forecasting performances. This study attempted to construct an EWT-PSO-SVM model to predict runoff to verify the hybrid performance of EWT, PSO, and SVM, and obtained a good forecasting result. However, due to the complexity of runoff series in the karst basin, there is still a large research space for runoff forecasting. Therefore, it is valuable to further investigate the hybrid performance of EWT and other artificial intelligence or machine learning methods, such as ANN, LSTM, and Random Forest.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 4 Annotation Scheme
### Therapist Utterance Attributes
#### 4.1.2 Question  
Therapists use asking to develop an understanding of the client and their problems. Therefore, we include Question as a therapist behaviour and define any question as open or closed in accordance with mainstream MI coding conventions. An open question allows a wide range of possible answers and may seek information, invite the client's perspective, or encourage self-exploration, while a closed question implies a short answer such as Yes/No, a specific fact, a number, etc. [3]. Some examples are given in Table 5.
# Triaxial Compression Performance Research of Steel Slag Concrete on the Unified Strength Theory
###### Abstract  
To better explore the mechanical properties of steel slag concrete (SSC) under triaxial compression, true triaxial tests were performed on SSC with three replacement ratios (30%, 70%, 100%) by a servo-controlled setup (TAWZ-5000/3000). Through the test, failure modes, peak stress, and corresponding strain of SSC are obtained. Results show that the failure modes of SSC are plate-splitting and slant-shear. Compared with the corresponding uniaxial strength, the triaxial compressive strength of SSC is significantly improved and is influenced by the stress ratio and the replacement ratio. Finally, based on unified strength theory, the strength failure criterion formula of SSC with different replacement rates under triaxial compression is given.  
steel slag concrete; triaxial strengths; mechanical properties; stress ratio +
Footnote †: journal: Medical  
industry. Presently, the world is advancing sustainable development strategies, and it is of great strategic significance to use waste steel slag.  
In reality, concrete is not only subjected to uniaxial forces but may also be subjected to forces in multiple directions. Concrete in applying engineering practice is mostly under complex stress states [18], such as the shear wall, beam-column connection, arch dams, nuclear reactor pressure container, and shell structure [19]. Moreover, many experts and scholars prove that the behavior of concrete under triaxial compression is not the same as uniaxial compression [18; 19; 20; 21]. Therefore, it is not comprehensive to only consider uniaxial strength theory in actual engineering. Due to the constraint of lateral stresses, concrete under triaxial compression has advantages in ductility, bearing capacity, and peak strain [22]. The results show that concrete's multiaxial mechanical behaviors are closely related to the type of concrete [23; 24; 25; 26]. Many studies have focused on concrete's uniaxial mechanical behaviors; however, few studies have investigated the multiaxial mechanical behaviors of concrete, especially steel slag coarse aggregate (SSCA). It would appear necessary to research the triaxial mechanical properties of SSC.  
Based on the background, we have investigated the triaxial mechanical properties of SSC. The failure mode, strength, peak strain, and stress-strain curve of SSC are analyzed. Draw on the unified strength theory [PERSON] [27] put forward, a modified strength failure criterion for SSC is given. This paper's experimental data and theoretical models can provide a reference for SSC, which has great significance for reusing waste steel slag and environmental protection.
## 3 Path Loss Models
### UMi Path Loss Models
#### 3.1.2 3 GPP Model  
In this model, the distance \((R_{3D})\) between transmitter and receiver (T-R) is estimated based on antenna heights and \(R\), which is given by \(\sqrt{(R)^{2}+(h_{1}-h_{2})^{2}}\)[19]. Where \(R\) is the actual distance between \(T\) and \(R\), \(h_{1}\) and \(h_{2}\) are the actual BS and user equipment antenna heights, respectively. In a 3 GPP LOS scenario, \(PL\) is estimated based on break point distance \((d_{BP})\), i.e., if \(R<d_{BP}\) then \(PL_{1}\) otherwise, \(PL_{2}\) is used to estimate the \(PL\) using the large-scale CI model [38; 39; 40]. Break point distance is estimated as [41]  
\[d_{BP}=4h_{11}h_{21}f\times 10^{9}/c \tag{1}\] \[h_{11}=h_{1}-1.0\ \text{m}\] \[h_{21}=h_{2}-1.0\ \text{m}\]  
where \(h_{11}\) and \(h_{21}\) are effective antenna heights and \(c\) is the velocity of free space 3 \(\times 10^{8}\) m/s.  
Large scale ABG model is used to estimate the PL of UMi-NLOS scenario [19].
## 3 Experiment and Result
### Robotic Arm Trajectory Planning Experiment in the Real World  
An improved FMT* trajectory planning algorithm was effectively implemented for the AUBO-I5 robotic arm. As for the hardware, a personal computer running served as the primary controller for the robotic arm, while the AUBO-I5 robotic arm and its associated control cabinet acted as the execution mechanism. On the software side, a C++ program utilizing the Moveit interface was developed to incorporate the enhanced FMT* algorithm, with visualization performed through RVIZ. The program was developed and executed within the ROS Melodic environment, specifically on Ubuntu 18.04.6 LTS, and was written adhering to the C++11 standard.  
As shown in Figure 13, obstacles were created in the working environment of the robotic arm, and the initial and target poses of the robotic arm were set. Then, the improved FMT* algorithm was used to plan a collision-free trajectory from the start point to the  
Figure 11: Comparison of trajectory planning results of the robotic arm with and without the application of the singular point avoidance algorithm.  
Figure 12: Variation of joint angles and angular velocities of the robotic arm in simulation experiment.  
endpoint. Finally, the robotic arm's working environment and trajectory planning were visualized using RVIZ. Since the algorithm in this study also has singularity avoidance capabilities, the collision-free trajectory planner can avoid the singular points of the robotic arm, thus improving the stability of the robotic arm.  
The generated robotic arm trajectory is converted into specific commands using Moveit and sent to the robotic arm control cabinet, thereby driving the robotic arm to follow the predetermined path. The actual motion of the robotic arm is illustrated in Figure 14.
# Multi-View Network Representation Learning Algorithm Research
## 2 Related Work
Since the research foundation of DeepWalk, many researchers have proposed a variety of network representation learning algorithms based on joint learning model, such as, TriDNR [6], Content-Enhanced Network Embedding (CENE) [20], Graph Convolutional Network (GCN) [21], Discriminative Deep Random Walk (DDRW) [22], Planetoid [23], PTE [24], Modularized Nonnegative Matrix Factorization (M-NMF) [25], Accelerated Attributed Network Embedding (AANE) [26], COSINE [27] and Community Embedding (comE) [28] and so forth. Specifically, TriDNR [6] and CENE [20] incorporate the text contents into the procedure of network representation learning by treating the content information as a special kind of node. M-NMF [25] is a modularized nonnegative matrix factorization approach, which incorporates the community structures into network representations. Both COSINE [27] and comE [28] also incorporate the community information into network representations. AANE [26] is an accelerated attributed network embedding approach, which enables the joint learning framework to be conducted by decomposing the complicated learning and training into many sub-problems. In addition, based on the fact of matrix decomposition of DeepWalk [1], [PERSON] et al. [29] proposes the Text-Associated Deep Walk (TADW) algorithm which ensembles text contents into matrix factorization algorithm. Based on the TADW [29] algorithm, the Max-Margin Deep Walk (MMDW) [30] algorithm adopts maximum margin algorithm to optimize the learnt representation vectors. There exist some community preserving [31] and heterogeneous [32] network representation algorithms.
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 3 Design of Smart Collision Avoidance Control System for USVs
### Trajectory Tracking Procedure of the Controlled USV  
As shown in Figure 4, design of a smart collision avoidance control system in the third phase will be divided into two sub-sections: 1. USV model and nonlinear \(H_{2}\) controller, and 2. Power allocation for actuators: Z-type twin propulsors, respectively.
## 3 Optimization with Consideration of the Reliability Criteria
### Objective Functions and Constraints for Market Settlement with Reliability Criteria  
As stated above, the AOs not violating the reliability constraints were eligible for incentives due to the energy discharged. Thus, the objective functions for the coordination of PV-BESS and WPG-BESS can be re-formulated as Equations (20) and (22).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Resource Type** & **Installation Site** & **SoC Limit** \\ \hline BESS Coordinated with the Renewables & Inside the Structure & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reliability constraints amended for the coordinated BESS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Resource Type** & **Reliability Criteria** \\ \hline PV—BESS & MW Output Metered at PCC \(<\) 70\% of the \\  & Nominal Capacity of the Renewables \\ \hline WPG—BESS & Additional Periods Eligible for the Settlement with \\ (Only in Island Jeju) & the Multipliers for the Energy Discharged from the BESS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reliability constraints amended for the coordinated renewables.  
1.1. Mathematical Model of the Revenue Stream with Reliability Constraints, and Therefore Incentives, from a Resource Portfolio with a WPG and BESS  
The objective function for profit maximization of the portfolio with the WPG and the BESS can be modified as follows:  
\[M_{t}^{W-B}=RWB_{t}^{SMP}+REC_{1}^{w}\cdot X^{w}+RWB^{REC_{5}}-P_{wb,t}^{loss} \cdot c_{wb},\quad\forall\;wb,t \tag{22}\]  
As presented in Equations (23) and (24), the revenue streams from the SMP and the REC with the multiplier, represented by \(RWB_{t}^{SMP}\) and \(RWB^{REC_{5}}\), respectively, include an additional incentive for the energy discharged from the BESS, denoted by \(P_{wb,t}^{B2C}\), considering the factor, \(\tau^{o}\), and noting that the BESS is installed outside.  
\[RWB_{t}^{SMP}=\left(PCC_{t}^{w}+\sum_{wb}P_{wb,t}^{B2C}\cdot\tau^{o}\right) \cdot SMP_{t},\quad\forall\;t \tag{23}\]  
\[RWB^{REC_{5}}=\omega^{w}\cdot\left(REC_{5}^{w}+\sum_{wb}P_{wb,t}^{B2C}\cdot \tau^{o}\right)\cdot X^{w} \tag{24}\]
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 3 Results and Discussion
### Experimental Comparison Models  
This study selects WDCNN, DRCNN, DilatedNN, and Vision Transformer as four commonly used deep learning fault diagnosis models for experimental comparison. WDCNN is a commonly used fault diagnosis method for rolling bearings based on a one-dimensional convolutional neural network, characterized by a larger kernel size in the first layer. DilatedNN is a fault diagnosis method for rolling bearings based on a one-dimensional dilated convolutional neural network, utilizing the model structure proposed in reference [25]. The difference between the Vision Transformer model and the MultiDilatedFormer model proposed in this paper is that it has no multi-head dilated convolution module, skip connection, or patch feature fusion layer, and the remaining structures and parameters are the same, which is used to prove the effectiveness of the added module designed in this study.