esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Open Issues for Cooperative and Precoding Schemes in VLC
### Is the Concept of Massive Mimo Applicable to VLC?  
Massive MIMO has been widely proposed for improving the achievable rate in RF communications [185]. Let us consider the channel matrix described in (22). The key idea of massive MIMO is to increase the number of transmit antennas so that the channel responses of the users are asymptotically orthogonal. That is, the channel responses among users are orthogonal, since the channel of each user comprises a large number of dimensions (transmitters) in comparison with the total number of users. As a consequence, linear precoding schemes, subject to a low complexity in comparison with DPC, obtain a performance close to the capacity. Mathematically, this condition is given by  
\[\lim_{M\rightarrow\infty}=\bar{\mathbf{H}}\bar{\mathbf{H}}^{H}=M\mathbf{I}_{K}, \tag{24}\]  
which holds if the entries of \(\bar{\mathbf{H}}\) (see (22)) are independent and identically distributed. For RF systems, this condition is naturally satisfied, assuming rich-scattering environments. Thus, simple linear precoding such as ZF provides a performance close to the capacity [185]. However, due to the deterministic behaviour of the optical channel (see (16)), the resulting channel matrix is typically ill-conditioned to exploit the benefits of massive MIMO in VLC systems. In [186], an alternative precoding based on single value decomposition is proposed to overcome the high correlation of the ill-conditioned channel matrix. The channel estimation in a VLC system, which requires FDD operating mode, is analyzed in [187] when the number of optical APs grows enough to consider the massive MIMO condition. Focusing on indoor positioning, the use of large-scale VLC systems is considered in [188]. However, there are still several issues to analyze about the implementation of massive MIMO in VLC, such as the impact of ADRs or vROA photodetectors, the capacity bounds as the number of optical APs grows or the precoding schemes that achieve rates close to the capacity.
## 3 Topology Optimization Model Considering Fatigue Characteristics  
### Multiple Load Case Model Considering Fatigue Characteristics  
In this article, the required material for the design is TC4, which has a higher yield limit compared with steel and aluminum alloy. When the bolts are tightened, the upper fixture loading of the bracket is loaded symmetrically with the four screw holes fixed at the bottom. In this situation, the stress state is relatively balanced. Therefore it is considered that the bracket has a low probability of fatigue failure during the normal loading of the fixture. As the bracket is loaded for a considerable time, the bolts using for fixing the bracket are probably loosened under the cyclic load, at which time the loading condition of the fixture will appear asymmetrically loaded in a localized way. Due to the asymmetric loading, the bracket will be deformed asymmetrically, which will eventually lead to excessive stress. Accordingly, it is assumed that the probability of fatigue under these asymmetric conditions with fixture loading is much greater than the probability of fatigue under normal loading. Therefore, this paper proposes to transform the fatigue damage problem of the bracket in the cyclic loading process into the topology optimization problem that controls the strain energy of the bracket under a variety of extreme conditions and to combine the geometric reconstruction to smooth the region with large stress values, so as to reduce the overstress phenomena caused by geometric singularities. By controlling the strain energy and stress magnitude under extreme operating conditions, the probability of fatigue failure of the bracket can be reduced even if it is subjected to cyclic loading for a long time and the service life of the bracket can be extended.  
In this paragraph, the external loads and boundary conditions for our proposed extreme operating conditions are presented in detail. First, the original bracket bolt hole designations are agreed as shown in Figure 5; the three bolt holes in the upper part of the vertical plate are assigned the designations A, B, and C; the four bolt holes in the lower bottom plate are assigned the designations a, b, c, and d in clockwise order. A total of 19 extreme working conditions in which the bolt may fail have been summarized with the fixture loaded and the specific parameters are shown in Table 2. Among these conditions, from 1 to 15 conditions are asymmetric loaded and the last four conditions are symmetric loaded. It is assumed that each condition is subject to six varieties of identical loads. When the vertical plate bolt hole is loosened, it is assumed that the screw will be in contact with the upper half of the bolt hole, the lower half of the bolt hole, the left half of the bolt hole, or the right half of the bolt hole respectively and the magnitude of these loads are 0.2 times the magnitude of the loads in the vertical direction. The six kinds of loads in the first condition are shown in Figure 6 and the other conditions can easily be constructed referring to condition 1.
# Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch
## 3 Results
### Reliability Prediction
#### 3.2.1 Reliability Prediction of Electrical Control System
\begin{table}
\begin{tabular}{c c c c c} \hline
**Serial Number** & **Name** & **Quantity** & **Failure Rate (1/h)** & **Cumulative Failure Rate (1/h)** \\ \hline
1 & Sensor & 34 & 3.858 \(\times\) 10\({}^{-6}\) & 1.312 \(\times\) 10\({}^{-4}\) \\
2 & Connector & 500 & 7.890 \(\times\) 10\({}^{-8}\) & 3.945 \(\times\) 10\({}^{-5}\) \\
3 & Digital module & 6 & 5.877 \(\times\) 10\({}^{-6}\) & 3.472 \(\times\) 10\({}^{-5}\) \\
4 & Circuit breaker & 7 & 2.894 \(\times\) 10\({}^{-8}\) & 2.026 \(\times\) 10\({}^{-5}\) \\
5 & AC contactor & 3 & 6.705 \(\times\) 10\({}^{-6}\) & 2.012 \(\times\) 10\({}^{-5}\) \\
6 & Light curtain & 2 & 5.787 \(\times\) 10\({}^{-6}\) & 1.157 \(\times\) 10\({}^{-5}\) \\
7 & Intermediate relay & 5 & 2.251 \(\times\) 10\({}^{-6}\) & 1.126 \(\times\) 10\({}^{-5}\) \\
8 & Touch screen & 1 & 9.645 \(\times\) 10\({}^{-6}\) & 9.645 \(\times\) 10\({}^{-6}\) \\
9 & PLC & 1 & 8.267 \(\times\) 10\({}^{-6}\) & 8.267 \(\times\) 10\({}^{-6}\) \\
10 & Communication module & 1 & 7.716 \(\times\) 10\({}^{-6}\) & 7.716 \(\times\) 10\({}^{-6}\) \\
11 & Switch & 1 & 6.430 \(\times\) 10\({}^{-6}\) & 6.430 \(\times\) 10\({}^{-6}\) \\
12 & Analog module & 1 & 5.877 \(\times\) 10\({}^{-6}\) & 5.578 \(\times\) 10\({}^{-6}\) \\
13 & Servo drive & 3 & 9.873 \(\times\) 10\({}^{-7}\) & 2.962 \(\times\) 10\({}^{-6}\) \\
14 & Switching power supply & 2 & 1.250 \(\times\) 10\({}^{-6}\) & 2.500 \(\times\) 10\({}^{-6}\) \\
15 & Tricolor lights & 1 & 9.690 \(\times\) 10\({}^{-7}\) & 9.690 \(\times\) 10\({}^{-7}\) \\
16 & Breakout connector & 8 & 6.693 \(\times\) 10\({}^{-8}\) & 5.522 \(\times\) 10\({}^{-7}\) \\
17 & Isolating switch & 1 & 3.041 \(\times\) 10\({}^{-7}\) & 3.041 \(\times\) 10\({}^{-7}\) \\
18 & Operating handle & 1 & 3.041 \(\times\) 10\({}^{-7}\) & 3.041 \(\times\) 10\({}^{-7}\) \\
19 & Switch & 1 & 3.041 \(\times\) 10\({}^{-7}\) & 3.041 \(\times\) 10\({}^{-7}\) \\
20 & Button & 3 & 7.180 \(\times\) 10\({}^{-8}\) & 2.154 \(\times\) 10\({}^{-7}\) \\
21 & Frequency converter & 2 & 2.355 \(\times\) 10\({}^{-8}\) & 4.079 \(\times\) 10\({}^{-8}\) \\  & Statistics & & 3.146 \(\times\) 10\({}^{-4}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Reliability prediction table.
## 2 Popular Optimization Problems in Transportation
### The Team Orienteering Problem  
One of the main differences between the TOP and the VRP is that in the former it is not mandatory to visit all customers. In other words, some nodes can be omitted during the generation of the routing plan. This is due to restrictions on the fleet size and on the maximum length that can be covered by any route. In a typical TOP, rewards are collected the first time a node is visited and therefore the objective is to maximize the total reward collected by a fixed fleet of vehicles. Vehicles depart from an origin node and have to reach a destination node. Cargo constraints are not usually considered in the basic version of the TOP. However, as in the case of the VRP, many variants can be found in the literature, e.g.: capacity constraints [25; 26; 27], maximum driving range [28], stochastic travel times [29], etc.  
As in the VRP case, the TOP network is composed of an origin depot, a destination depot, and a set of customers. It can be defined as a directed graph \(G=(N,E)\), where (i) \(N=\{0,1,\ldots,|C|,n\}\) is the set of nodes, including the origin depot (node 0) and the destination depot (node \(n\)); and (ii) \(E=\{(i,j)\mid i,j\in C,i<j\}\) represents the set of edges connecting the nodes. Each customer \(i\in C\) offers a reward \(u_{i}>0\) the first time it is visited. A fleet of vehicles is available at the origin depot. Each vehicle visits a selected subset of nodes in \(C\) to collect the associated rewards, and moves to the destination depot. This process is presented in Figure 2. Note that, unlike the network topology depicted in  
Figure 1: Network topology of a basic VRP.  
Figure 1, this time a subset of nodes is not visited. Typically, these are customers located far away from each other or offering low rewards.
## 4 Results and Discussion
### Discussion  
The results of our study provide some insights into user perceptions and preferences regarding obfuscation techniques for privacy protection in AR applications. Firstly, demographic factors such as age, education level, and occupation significantly influence privacy concerns. Older participants and those with higher education levels or technical backgrounds tended to exhibit more privacy concerns, highlighting the importance of tailoring privacy solutions to different user groups.  
Secondly, the user acceptance of obfuscation techniques varied depending on the AR context and the type of visual content. Blurring emerged as the most preferred technique for outdoor contexts involving people, while pixelation was favored for indoor document contexts. This suggests that a standard approach may not be suitable, and context-specific solutions are necessary to balance privacy protection and user experience.  
Thirdly, user familiarity with AR technology played a crucial role in the acceptance of obfuscation methods. Blurring was consistently preferred across different familiarity levels, indicating its versatility. However, masking and pixelation showed more variability in user satisfaction, suggesting that these techniques may be more context-dependent.  
Overall, our findings emphasize the need for a nuanced approach to implementing obfuscation techniques in AR applications. Privacy protection solutions should take into account demographic factors, contextual requirements, and the varying levels of user familiarity with AR technology.  
\begin{table}
\begin{tabular}{c c c c} \hline
**AR Level** & **Blurring** & **Masking** & **Pixelation** \\ \hline
1 & 2.33 & 3.67 & 3.33 \\
2 & 3.67 & 3.33 & 2.00 \\
3 & 3.29 & 2.94 & 3.00 \\
4 & 3.75 & 2.75 & 3.44 \\
5 & 3.60 & 2.47 & 3.13 \\ \hline Mean & 3.48 & 2.81 & 3.13 \\ \hline Std. Dev. & 1.37 & 1.33 & 1.10 \\ \hline \end{tabular}
\end{table}
Table 1: Mean satisfaction – Comparison of blurring, masking, and pixelation across different AR familiarity levels.
## 4 Methodology
### Data Preprocessing  
Policymakers and emergency services recently envisaged creative strategies to elicit the information posted on social networks in crises such as an earthquake. These data, however, are sometimes unstructured, diverse, and distributed over a great number of tweets in such a manner that they could not be utilized explicitly. Thus, transforming that chaotic information into a set of explicit and precise texts for a crisis manager is necessary. In this study, data in a text format related to post-earthquake tweets were used for damage assessment. Textual data are regarded as unstructured data that are incomprehensible to computers. Therefore, in order to prepare these data for computer analysis, they must be structured and computer-usable data. In this section, following the process shown in Figure 3, the normalization of unstructured data was performed manually after removing the missing values. At first, during the tokenization process, the textual data were subdivided into smaller linguistic units called tokens. Words, numbers, and punctuation are linguistic units known as tokens. Then, in order to integrate the text in either uppercase or lowercase letters, all the letters in the textual data were converted to lowercase letters. Next, numbers, punctuations and inappropriate letters were removed from tweets. Then, ineffective words (stop words) that do not convey significant semantic content (words like \"The\", \"On\", \"Is\", \"All\", and \"an\" in English) were deleted from the textual data. Short words with three characters or less were removed. After that, stemming was used to return words to their root form, for this purpose, the Porter algorithm, one of the most popular algorithms for performing stemming operations, was used. Finally, using n-Gram algorithms, the expressions and collocation words were removed from the text data [5].
## 3 Materials and Methods
### System Pipeline
#### 3.2.3 Post-Processing  
After segmentation, undesirable results such as inner holes, small noise regions, and mistaken regions may appear. Given the final segmented output masks, a post-processing step was performed to improve the robustness of the segmentation model and correct these unwanted results. First, a morphological operation of hole filling was used to remove the inner holes, followed by dilation with a 3 \(\times\) 3 cross-shaped mask to grow and smooth the wound boundaries. In order to remove the small noise areas, regions with a pixel area inferior to 2000 pixels were excluded. Lastly, the final segmented images, as illustrated in Figure 6, were obtained through pixel-wise multiplication between the post-processed binary masks and the original input image.
## 4 Discussion  
The primary focus of this study was to record the motion during a topspin forehand stroke against backspin and topspin (AB vs. AT) incoming balls using the Qualisys motion system. The motion was divided into three main phases: backward swing (T\({}_{1}\)-T\({}_{2}\)), forward swing (T\({}_{2}\)-T\({}_{3}\)), and follow-through (T\({}_{3}\)-T\({}_{4}\)). Then, we used the SPM1D method to explore and compare the two similar movements and discussed them further.  
The main results showed the detailed center of gravity (COG), joint angles, and joint moments phase analysis information. In addition, the result of the racket analysis from multiple dimensions was also helpful in this study. The athletes were found to keep a lower COG for the upcoming backspin ball to meet the lower limbs' power requirements by fully pushing off the ground with the racket-side foot and extending their bilateral knee joints. Meanwhile, they would unbend their elbow slightly, increase the racket face's tilt angle at impact, and increase the range of the movement to a faster racket velocity, especially in the vertical direction.
## 3 Results and Discussion
### Phase Transition of Zinc Titanate Precursor Powders calcined at Various Temperatures for 1 h
[6] also proposed using an amorphous material prepared by the simultaneous hydrolysis of zinc acetylautonate and titanium isopropoxide for synthesis of the ZnTiO\({}_{3}\) powders. The XRD result shows the reflection peaks of the compound corresponding to Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) appeared at 600 \({}^{\circ}\)C [5] and the intensity of the reflection peaks increased rapidly up to 760 \({}^{\circ}\)C. No other compounds and free species, except for the hexagonal form of ZnTiO\({}_{3}\), are observed up to the decomposition temperature at 965 \({}^{\circ}\)C. These results suggest that the compound so far denoted as Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) is a low temperature form of ZnTiO\({}_{3}\). On the other hand, the phase of Zn\({}_{2}\)TiO\({}_{4}\) formed by the thermal decomposition of Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) in the range of 650-900 \({}^{\circ}\)C has been reported by previous studies [5, 25]. Figure 2(c) shows that for the zinc titanate precursor powders calcined at 700 \({}^{\circ}\)C for 1 h, only a small fraction of Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) decomposed and formed the ZnTiO\({}_{3}\) and rutile TiO\({}_{2}\). This result was attributed to the fact that the phase of Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) at 700 \({}^{\circ}\)C still has thermal stability. When calcined at 900 \({}^{\circ}\)C for 1 h, the Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) phase disappeared and the reflection peaks of ZnTiO\({}_{3}\) also nearly vanished, but the intensity of Zn\({}_{2}\)TiO\({}_{4}\) and rutile TiO\({}_{2}\) increased. This is because the Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) and ZnTiO\({}_{3}\) phases decomposed, leading to the formation of Zn\({}_{2}\)TiO\({}_{4}\) and rutile TiO\({}_{2}\). These reactions can be expressed as follows:  
\[\rm{Zn_{2}Ti_{3}O_{8}}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \longrightarrow \rm{Zn_{2}TiO_{4}+2 TiO_{2(r)}}\] (2) \[\rm{2 ZnTiO_{3}}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!
## 7 Integration of Knowledge-Based Engineering System  
Working principles, structure, and information flow through the developed algorithm of the KBE system (Figure 13), and can be described via the following steps:  
1. The optimization algorithm has a task to calculate the values of optimization parameters (inside defined range) which satisfy constrains (displacements and stresses), and at the same time, minimize the cost function (volume). This algorithm generates a different combination of optimization variables, which are variables for potential optimum, and which are then used by the parametrization algorithm as design parameters. In this way, the optimization algorithm changes the geometry of the external fixation device.
2. The developed algorithm for parametric 3D CAD modeling uses optimization variables as design parameters. Changing these design parameters, the design of the external fixation device can be changed. The calculation of volume for every new design of the external fixation device is automatically carried out by the optimization algorithm.
3. The FEM model is updated, and a structural analysis is carried out. Updating the CAD model leads to the automatic update of the FEM model. The mesh of the FEM model and the connection between components are updated. Numerical structural analysis is carried out again, and new results are obtained.
4. Using structural analysis and considering: - values of cost function (volume of updated CAD model) and - stress and deformation constrains at control places generated combinations of optimization parameters (design parameters) are evaluated. Algorithm saves the best achieved result of the cost function in one iteration. Each iteration algorithm tries to improve the solution from the previous iteration.
5. Steps from 1 to 4 are repeated until the convergence of results is achieved, or one of the constrains stop the optimization process.
## 3 Data Clustering Algorithm
### Data Sets Preprocessing
#### 3.4.1 First Data Set  
To perform spectral clustering to group large data set, it should go through two steps [38]:  
The first step is data reduction, which is carried out mostly using k-means to cluster the given data set. From each cluster, some data are picked normally. They are the ones near the center of the cluster. Thus, each cluster is represented by one set [39,40]. Then, the spectral clustering can be applied to construct the similarity matrix and to classify the reduced size data into final classes.  
[PERSON] et al. [15] did the data reduction to a database of 28,244 audiograms using vector quantization of size 60. Then the authors in this paper, applied the spectral clustering to these 60 audiograms. This data can be found in Table 1. in [PERSON] work [15]. In fact, it was furtherly reduced by eliminating five audiograms that represent individuals with normal hearing. These levels are removed since the model is built for patients who experience hearing loss to assist in configuring or designing hearing aids. The audiograms were measured in standard audiometry booths at eight test frequencies. Air conduction thresholds are measured at 250 Hz, 500 Hz, 1000 Hz, 1500 Hz, 2000 Hz, 3000 Hz, 4000 Hz, 6000 Hz, and 8000 Hz.
# A Rapid Method for Information Extraction from Borehole Log Images
## 4 Corner-Based Cell Segmentation
### Corner Mark Acquisition  
After the table line is extracted, it is divided into n horizontal lines and m vertical lines by angle, and each pair of perpendicular table lines produces an intersection, representing the table line by two endpoints:  
\[\left\{\begin{array}{l}rowLine:\left[(x_{r1},y_{r}),(x_{r2},y_{r})\right] \\ colLine:\left[(x_{c},y_{c1}),(x_{c},y_{c2})\right]\end{array}\right. \tag{3}\]  
where \(x_{r1}<x_{r2}\), \(y_{c1}<y_{c2}\). Every set of segments has a unique intersection (\(xc,yr\)) and \(x_{c}\) represents the abscissa of the point on the table vertical line, which represents the distance to the left boundary of the rectangular image. \(y_{r}\) represents the ordinates of the point on the table horizontal line, which represents the distance from the top boundary on the rectangular image.  
The relationship between the intersection and the corresponding segment group can assign a corner attribute to each intersection. If the intersection is the left endpoint of the table horizontal line and is the top endpoint of the table vertical line, the corner attribute of this point is No.1, the intersection is named No.1 corner, and the relationship function is as follows:  
\[Sign_{point}=\left\{\begin{array}{l l}No.1\;Corner&(x_{r1}\leq x_{c}<x_{r2},y_ {c1}\leq y_{r}<y_{c2})\\ No.2\;Corner&(x_{r1}<x_{c}\leq x_{r2},y_{c1}\leq y_{r}<y_{c2})\\ No.3\;Corner&(x_{r1}\leq x_{c}<x_{r2},y_{c1}<y_{r}\leq y_{c2})\\ No.4\;Corner&(x_{r1}<x_{c}\leq x_{r2},y_{c1}<y_{r}\leq y_{c2})\\ Lose\;efficiency&\left(x_{c}<x_{r1}|x_{c}>x_{r2}|y_{r}<y_{c1}|y_{r}>y_{c2}\right) \end{array}\right. \tag{4}\]  
Intersection point can have multiple corner attributes. For example, the point inside a table has a maximum probability of being a compound corner with four corner attributes, and a point at the table boundary may be a compound corner with two corner attributes. The expression of a corner mark is to add an array with a length of four; the value of the array is 0 or 1.  
\[Sign:\left\{x,y,type[0,0,0,0]\right\} \tag{5}\]  
where \((x,y)\) stores the coordinates of the corner and the type stores the type of the corner. If there is a corner mark s = {point (50, 100), type [1, 0, 1, 0]}, it represents a compound corner mark located at (50, 100) with two corner attributes of No.1 and No.3.  
Figure 5: The borehole log table line obtained via the Hough transformation. (**a**) Borehole log; (**b**) table line.