esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Self-Healing Networks
#### 1.5.2 Identifying the Cause of Fault  
* This section suggests remedies for a cell's aberrant and dysfunctional condition. As a result, it is crucial to start by limiting the fault within a cell. The nearby cells should work together to reduce the fault's detrimental impact if it cannot be swiftly fixed. Corrective actions must be conducted to eliminate the fault when the fault's root cause is identified. The following section goes into more depth about this phase.
* Hardware and software issues may be causes of cell faults, and other potential causes include incorrect configuration, inadequate coverage, and interference. As previously stated, alarms alone cannot identify the source of an issue and additional data, such as the values of the indicators, must also be examined.
* The following categories serve as a general summary of the current methods used in this field. Here, the primary focus is on techniques based on data mining and finding significant patterns in the data to identify the cause of the fault.
* Methods based on data mining
* Statistical methods  
Data mining techniques are among the approaches adopted in the automation of network fault detection, due to the enormous and growing volume of data accessible for the evaluation of the quality of cellular networks. The absence of a database of classified instances, highlighted in the introduction section, is one of the most significant issues in this sector. Moreover, this area is separated from other areas related to identifying the cause of faults, due to the continuous nature of the performance evaluation indicators and the existence of logical faults that are not dependent on the physical parts of the equipment and are probably related to the wrong settings of the equipment. For instance, Rule-Based Systems (RBSs), which are a subset of Expert Systems employing a set of \"if-then\" rules [10], did not gain enough success in cellular communication networks because they did not perform successfully in cases with both large and unpredictable ranges. Making rules in dynamic and unpredictable contexts has its own set of issues.  
The two main divisions of data mining methods are unsupervised and supervised methods. The following sections review the methods used in these two types.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 4 Computer Procedure for Connection between BIM and 3D Concrete Printing
#### 4.1.3 Preparation of a Control Code for the Printing Process with the Robotic Arm  
This stage is performed in DynamoTM. The steps to carry out this procedure are described below:  
1. To obtain the surfaces to create the points cloud of the element to be printed. To get the points cloud that describes the construction element, it is necessary to delimit the previously selected element in RevitTM. To do it, a bounding box for the element must be created and transformed into a collection of surfaces and a solid body. Here, the thickness of each layer is specified to create a sequence of heights consistent with the total height of the element.
2. To create a points cloud that describes the contour of the figure. The sequence of heights is later converted into a sequence of surfaces, which contain the information of the contour points and the lengths of each trace. From this information, it is possible to create a points cloud that extends uniformly on each surface that describes the perimeter of the figure.
3. To create the printing path from the points cloud. Once the contour path that describes the dimensions of the construction element in two dimensions (\(X\) and \(Y\)) has been  
Figure 4: Communication RevitTM, DynamoTM, and KUKATM.  
created, it is essential to generate an additional elevation path (\(Z\)-axis) to avoid accumulations of unwanted material. This is achieved by gradual elevation, such that the last point on one surface on the \(Z\)-axis coincides with the first point on the next surface. Thus, continuity in the print path is achieved.
4. To transfer the points cloud, within the coordinate plane, to a position within reach of the robotic arm. The objective of this step is to synchronize the position of the figure in the action plane of the robotic arm. To do this, it is necessary to understand that the points cloud is within an already defined coordinate plane, so its transfer simply requires a modification of the coordinates of each point of the cloud along this plane.
5. To develop the KUKA(tm) robot arm control code. At this stage, the main objectives are to correctly select the KUKA(tm) robot model to be used and to define the control parameters of the robot arm. A node that controls the linear movement of the robot and the value of the printing speed must be established.
# Survey of Blockchain-Based Applications for IoT
## 6 Data Integrity and Trust
### Data Transparency and Traceability  
Data transparency and traceability refer to the ability to observe and track the origin and movement of data throughout their lifecycles. As a decentralized storage system, a blockchain offers a high level of tamper resistance and includes mechanisms that prevent synchronization discrepancies across the network. This inherent reliability has made blockchains particularly valuable in industries that require secure and verifiable records, and their use is already widely recognized and adopted within the financial sector [46]. The decentralized and immutable nature of blockchains ensures data transparency and traceability and provides secure, verifiable, and tamper-proof records across various industries.  
One application of blockchain traceability is to enhance the protection and tracking of original intellectual achievements, such as patents and copyrights. A blockchain-based service architecture can address challenges in intellectual property management, including confirming ownership, tracing transactions, and distributing rewards among contributors. By utilizing decentralized storage, immutable records, smart contracts, and timestamping, the system enables secure registration, transparent transfer histories, and automated incentive mechanisms [47]. Simulations demonstrate blockchains' ability to detect tampering, ensure data integrity, and support efficient querying of achievement records [47].
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 3 Problem Formulation
### Dynamic Task Processing Model  
In the multi-UAV-vehicle cooperative data acquisition problem, the dynamic task processing primarily involves adjustments to the scheme due to changes in the speeds of UAVs and vehicles. In practice, factors such as terrain and weather may cause the speeds of UAVs and vehicles to fluctuate. Fluctuations in the vehicle's speed have little effect on the delineation of anchor points, the vehicle's routes, and the UAV's routes, but they can alter the vehicle's travel time, resulting in some fluctuations in the total time cost. However, when the UAV's speed fluctuates, it may cause the originally planned single-trip UAV routes to either not be completed or be completed earlier, which significantly impacts the UAV route planning and task allocation scheme within the current anchor point. Therefore, this paper focuses on the optimization and adjustment of the dynamic scheme for UAV speed changes, specifically local optimization within the anchor point.  
We monitor the current state of the multiple UAVs at fixed intervals \(\tau\), remove the executed task points, and replan and reassign the UAVs' flight routes for the remaining tasks within the anchor points. To evaluate the effectiveness of the new scheme after replanning and reallocation, we need to calculate its cost by considering the total execution time of the remaining tasks within a single anchor point \(a_{q}\) as the optimization objective. Based on the total flight time of UAV \(u_{k}\) at anchor point \(a_{q}\) in Equation (8), we compare the total flight times of all UAVs to find the UAV with the longest time, denoted as \(t_{a_{q}}^{u_{max}}\), also referred to as \(T_{a_{q}}\). The optimization objective is then expressed in Equation (18). This objective aims to find a route assignment scheme that minimizes the total time required for all UAVs to complete the mission within anchor point \(a_{q}\), thereby improving the efficiency and responsiveness of the entire system. This approach ensures that the UAV-vehicle cooperative system maintains efficient and flexible mission execution, even in the face of uncertainties such as speed fluctuations.  
\[\mathcal{T}_{a_{q}}=\min t_{a_{q}}^{u_{max}}=\min\frac{\sum_{j\in R}z(u_{k},r_{ j})x(a_{i},r_{j})l_{r_{j}}}{v_{aux}} \tag{18}\]
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 5 Example Analysis
### Time-Consuming Test and Analysis
It can be seen from Table A1 that compared with other technologies, the fault diagnosis system proposed in this paper takes into account the reliability and efficiency of fault diagnosis and fault tracking. At the same time, this system can still provide diagnosis and output results even when partial information is missing. The output results of this system are also more comprehensive, which is conducive to the self-healing and maintenance of the power grid after a fault. Therefore, this system has preferable application value in actual engineering.
## 3 Procedure to Synthesize the IKM Using Groebner Basis
### Third Step: Obtention of the Polynomial Equation System  
Once the robot's Forward Kinematics is calculated, the equations that relate the pose of the end effector with the robot's state can be extracted from the homogeneous transformation matrix obtained in the second step. In this work, for simplicity, we are only interested in the position of the end effector, which is only a part of its pose. However, all the calculations made in the developed procedure can be also extended to the orientation, to complete the whole pose.  
Continuing with the case of the PUMA robot, these equations are the ones presented in Equation (19), which correspond to the first three elements of the fourth column of Equation (1):  
\[\begin{split}& p_{x}=-\sin(q_{1})[a_{2}\cos(q_{2})+d_{4}\cos(q_{2}+q_ {3})+a_{3}\sin(q_{2}+q_{3})]-d_{2}\cos(q_{1})\\ & p_{y}=\cos(q_{1})[a_{2}\cos(q_{2})+d_{4}\cos(q_{2}+q_{3})+a_{3} \sin(q_{2}+q_{3})]-d_{2}\sin(q_{1})\\ &\qquad\qquad p_{z}=d_{1}-a_{2}\sin(q_{2})-d_{4}\sin(q_{2}+q_{3}) +a_{3}\cos(q_{2}+q_{3})\end{split} \tag{19}\]  
As it can be seen in Equation (19), the final result obtained from the robot's Forward Kinematics is a trigonometric equation system that establish the position of the end effector, and it is from this equation system that the calculation of the IKM begins.  
The previous equation system is completed with the corresponding trigonometric identities of all the robot's rotational degrees of freedom (DoFs), identities of the form \(\sin(q_{i})^{2}+\cos(q_{i})^{2}=1\). Then, all the trigonometric expressions are expanded, and variable substitutions of the form \(\sin(q_{i})=s_{i}\) and \(\cos(q_{i})=c_{i}\) are applied. This step's objective is to obtain a polynomial equation system where the variables are either a pair sine-cosine of a rotational DoF (\(s_{i}\) and \(c_{i}\)), or directly the position value of a prismatic DoF (\(q_{j}\)).  
For the case of the PUMA robot, this step's output is the trigonometric equation system presented in Equation (20). In this equation system, \(p_{x}\), \(p_{y}\), and \(p_{z}\) are the three components of the position vector of the PUMA's wrist (\(\mathbf{\bar{p}}\)), and they are input parameters. The six variables of Equation (20) that should be solved are \(s_{1}\), \(c_{1}\), \(s_{2}\), \(c_{2}\), \(s_{3}\), and \(c_{3}\).  
\[\begin{split}-a_{2}s_{1}c_{2}-d_{4}s_{1}c_{2}c_{3}+d_{4}s_{1}s_{2 }s_{3}-a_{3}s_{1}s_{2}c_{3}-a_{3}s_{1}c_{2}s_{3}-d_{2}c_{1}-p_{x}=0\\ a_{2}c_{1}c_{2}+d_{4}c_{1}c_{2}c_{3}-d_{4}c_{1}s_{2}s_{3}+a_{3}c_{1}s_{ 2}c_{3}+a_{3}c_{1}c_{2}s_{3}-d_{2}s_{1}-p_{y}=0\\ d_{1}-a_{2}s_{2}-d_{4}s_{2}c_{3}-d_{4}c_{2}s_{3}+a_{3}c_{2}c_{3}-a_{3 }s_{2}s_{3}-p_{z}=0\\ s_{1}^{2}+c_{1}^{2}-1=0\\ s_{2}^{2}+c_{2}^{2}-1=0\\ s_{3}^{2}+c_{3}^{2}-1=0\end{split} \tag{20}\]
## 2 Materials and Methods
### Experiments
#### 2.2.1 Experimental Setup
The depth of the coupling agent is calculated according to Equation (21), and the position of the interference component in the Y direction is adjusted according to the arriving time of the echo reflected from the coupling agent-component interface on the digital oscilloscope. Then, the interference component is fine-tuned in the Y direction until the signal reflected at the interface is maximized. \[\left\{ \begin{array}{c}a=F_{C}-h_{h}-\frac{mq}{R\zeta\sin\varepsilon-\zeta} \\ m=R_{0}+a-F_{C}\\ \xi=\sin\varepsilon-\frac{F_{C}-\alpha}{R}\sin\varepsilon\\ \zeta=\left(\sqrt{1-\xi^{2}}\sin\varepsilon-\zeta\cos\varepsilon\right)\left( k\xi\sqrt{1-\xi^{2}}-\xi\sqrt{1-\left(k\xi\right)^{2}}\right)\end{array} \right. \tag{21}\]  
where a is the depth of the couplant; \(F_{C}\) is the focal length in the coupling agent; \(h_{h}\) is the thickness of the cylinder; \(m\) is the distance from the original focus \(M\) to the cylinder axis; \(k=C_{s}/C_{c}\) is the refractive index (\(C_{c}\) is the wave velocity in the coupling agent; \(C_{s}\) is the wave speed in a solid); \(R_{0}\) is the radius of the cylinder; \(\varepsilon\) is half the beam angle. Figure 5: Interference component stress-distribution measuring device. Figure 6: Scanning scheme of stress distribution on mating surface. Figure 7: The relative position of the transducer to the component.
## 4 Experimental Results and Analysis
### Energy Dissipation  
The energy dissipation capacity, a critical factor in assessing seismic performance, refers to the ability of a structure to absorb seismic energy. The higher this capacity is, the more it can reduce damage caused by earthquakes. This capacity is calculated  
Figure 16: Strain of longitudinal reinforcement. (**a**) NE-10, (**b**) E130-10, (**c**) NE-5, (**d**) E65-5, and (**e**) E130-5.  
from the area under the load-displacement curve obtained from cyclic load tests. The cumulative energy dissipation for each specimen at different drift ratios is summarized in Table 5. Figure 17 illustrates the normalized cumulative energy dissipation capacity at the conclusion of the experiments, using the specimen exhibiting the highest cumulative energy dissipation as a benchmark. In Figure 17a, the data are normalized to those of the NE-10 specimen to investigate the effect of sliding slot length on the cumulative energy dissipation capacity. Conversely, Figure 17b employs NE-5 as the normalization reference to evaluate the influence of eccentric distance on the cumulative energy dissipation capacity.  
Figure 17: Normalized cumulative energy dissipation capacity: (**a**) effect of sliding slot length and (**b**) effect of eccentric distance.  
For specimens featuring a 5 mm sliding slot length, the cumulative energy dissipations for NE-5, E65-5, and E130-5 were 9143.1 kN\(\cdot\)mm, 8754.0 kN\(\cdot\)mm, and 8456.6 kN\(\cdot\)mm, respectively. In the case of a 10 mm sliding slot length, NE-10 and E130-10 exhibited cumulative energy dissipations of 9236.1 kN\(\cdot\)mm and 8092.6 kN\(\cdot\)mm, respectively. The difference in cumulative energy dissipation attributable to the sliding slot length was approximately 1% for specimens without eccentricity and around 4% for those with a 130 mm eccentricity. However, a notable reduction of 8% in energy dissipation was observed for the 5 mm sliding slot length with increasing eccentricity, and a 12% reduction was noted for the 10 mm sliding slot length. These findings suggest a discernible difference in performance against torsion based on the sliding slot length. The augmentation of eccentricity induces greater torsion on the column, resulting in numerous shear cracks, diminishing stiffness, and reducing energy dissipation. The point at which the steel link commences resistance against the load varies with the sliding slot length, and the degree of damage to the reinforced concrete column before the steel link starts resisting the load also differs. This discrepancy leads to a more substantial reduction in cumulative energy dissipation for specimens with a delayed onset of resistance. Consequently, it is advisable to select the appropriate sliding slot length in accordance with the design intent when employing the RSL system as a structural fuse.
## 2 Data Description and Pre-Processing
To ensure fair and consistent comparison among the three parameters (capacity, internal resistance, and RUL), we apply a min-max normalization scheme. This transformation maps each parameter to a uniform range of 0 to 1, thereby preventing any single parameter with larger numeric values from dominating the clustering algorithms. The min-max normalization for a given parameter \(x\) can be expressed as follows [22]:  
\[x_{norm}=\frac{x-x_{min}}{x_{\max}-x_{min}} \tag{4}\]  
where \(x_{min}\) and \(x_{max}\) are the minimum and maximum observed values of \(x\) in the dataset, respectively. By performing this step, the capacity, internal resistance, and RUL metrics all lie on a comparable scale, which is beneficial for methods such as SVC, K-means, and the GMM.
## 2 Background
### ADMM (Alternating Direction Method of Multipliers)  
The ADMM is an efficient method for solving optimization problems with constraints and regularizations, similarly to basis pursuit. ADMM allows solving the same problem as basis pursuit in a distributed and more efficient way, by decomposing the problem into more manageable sub-problems. The ADMM variant of basis pursuit usually involves the following formulation [7; 20]:  
\[\min_{\beta;z}\frac{1}{2}\left\|y-X\beta\right\|_{2}^{2}\;+\lambda\left\|z \right\|_{1}\;\;\text{{subject to }}\beta=z\]  
where \(\lambda\) is a regularization parameter and \(z\) is an auxiliary variable introduced to separate the L1 penalty (sparseness) from the squared error term \(\left\|y-X\beta\right\|_{2}^{2}\). The ADMM is applied to solve this problem in an iterative manner, dividing the problem into three steps that are solved iteratively:  
Step 1: updating \(\beta\):  
\[\beta^{(t+1)}=\underset{\beta}{\text{argmin}}\frac{1}{2}\;\left|\;y-X\beta\; \right|_{2}^{2}+\frac{\rho}{2}\;\left|\;\left|\;\beta-z^{(t)}+zu^{(t)}\;\right| _{2}^{2},\]  
which is a step of simple linear regression.  
Step 2: updating z:  
\[z^{(t+1)}=\underset{z}{\text{argmin}}\;\lambda\;\left\|z\right\|_{1}+\frac{ \rho}{2}\left\|\beta^{(t+1)}-z+u^{(t)}\right\|_{2}^{2},\]  
which can be solved by soft-thresholding:  
\[z^{(t+1)}=\text{{soft thresholding}}\left(\beta^{(t+1)}+u^{(t)},\frac{\lambda}{ \rho}\right).\]  
Step 3: updating the dual variable u:  
\[u^{(t+1)}=u^{(t)}+\beta^{(t+1)}-z^{(t+1)}\]  
The advantages of the ADMM algorithm derive from the fact that it can be applied efficiently to large problems due to its decomposition of the problems that it is applied to The ADMM converges faster in many practical cases than classical methods. The update steps \(\beta\) and z can be parallelized to speed up the calculation.  
Basis pursuit is useful in feature selection to eliminate redundant features and reduce the size of a model. The ADMM enables an efficient and scalable implementation of basis pursuit, especially for large and complex problems where classical methods may be inefficient. This method is often used in the context of sparse signal recovery or data modeling in machine learning, where the feature size is large and sparseness is desired.  
Basis pursuit and its accelerated method using the alternating direction method of multipliers (ADMM) are two approaches used for optimization problems in feature selection, but they differ significantly in terms of their formulation and computational efficiency.
# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
### Current Status of Research
Below is a table comparing different construction material price forecasting methods, as shown in Table 1.  
As shown in the table above, which compares the state of the art in research and research methods, traditional statistical techniques like ARIMA and GM (1,1) are suitable for short-term, linear, or small-sample data but struggle with nonlinear problems. On the other hand, neural networks and machine learning methods excel at capturing complex nonlinear patterns but are sensitive to noise and prone to overfitting, making them dependent on robust datasets and optimization techniques. Hybrid methods combine multiple techniques to enhance accuracy and robustness, e.g., LSTM combined with preprocessing or optimization algorithms. Furthermore, optimization algorithms (e.g., SSA, PSO) and signal decomposition techniques (e.g., VMD) improve model performance by refining parameter tuning and reducing noise.
## 3 Comprehensive Data Workflow
### Data Processing
#### 3.3.3 Data Preprocessing  
Tokenization: For Arabic texts, tokenization rules are applied to segment the text into tokens, while accounting for changes introduced by prefixes and suffixes. This process presents a significant challenge due to the morphological complexity of Arabic and the presence of clitits [22].  
Stemming: This is a technique that involves shortening words to their root form. It plays a crucial role in Arabic linguistics, contributing to both text analysis as well as other research tasks.  
Lemmatization: This is an essential process in Arabic that involves identifying the root form or dictionary form of words. This procedure accounts for morphological changes in lemmas, considering factors such as root, pattern, and other linguistic features [21].
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 3 Evaluation Methodology
### Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)  
BRISQUE [27] is an NR image/video quality metric that uses a pre-trained model for outputting scores. The model is trained on a set of images with various degradations, such as noise and compression artefacts. A smaller score indicates better perceptual quality.
# Removal of Volatile Phenols From Wine Using Crosslinked Cyclodextrin Polymers
## 2 Results and Discussion
### Determination of CD Polymer Adsorption Capacity in Model Wine
The Freundlich isotherm can be regarded as a special case of the Langmuir isotherm at intermediate adsorbate concentrations, where the adsorption capacity curve starts to plateau [39], and can be expressed as:  
\[\rm log\ q_{e}=\rm log\ K_{F}+1/n\ log\ C_{e} \tag{3}\]  
where \(\rm K_{F}\) is the maximum adsorption capacity of the system, and \(\rm 1/n\) is a natural value normally smaller than 1, which describes the extent of curving of the plotted \(\rm log\ q_{e}\) against \(\rm log\ C_{e}\), which relates to the 'adsorption intensity' or mobility of adsorbate at the adsorbent surface. The Freundlich isotherm fitted the data well in the current study. The \(\rm K_{F}\) and \(\rm 1/n\) for the Freundlich isotherm are also shown in Table 1. The \(\rm K_{F}\) value is similar to the \(\rm q_{m}\) predicted by the Langmuir isotherm, with the value ranging from 22.4 mg/g (for \(\rm\beta\)-CD-HDI adsorbing 4-ethylguaiacol) to 33.1 mg/g (for \(\rm\beta\)-CD-HDI adsorbing 4-ethylphenol). The \(\rm 1/n\) value suggests homogeneity of the adsorption process across the experimental concentrations when it is close to 1, whereas a value close to 0 suggests heterogeneity. In the present study, the \(\rm 1/n\) value ranged from 0.347 (for \(\rm\gamma\)-CD-HDI adsorbing 4-ethylphenol) to 0.762 (for \(\rm\gamma\)-CD-HDI adsorbing 4-methylguaiacol).