esa-sceva/satcom-chunk-collection huggingface dataset:
# Technical-Tactical Analysis of Corner Kicks in Male Soccer: A Systematic Review
## 4 Discussion
### Practical Applications for Coaches  
Based on the findings of this systematic review, several practical recommendations can be made for coaches and performance analysts in designing and training both offensive and defensive corner kick strategies. First, it is essential to move beyond static and isolated practice routines [48]. Corner kicks should be trained within dynamic, game-representative contexts that also incorporate the transition phase [49]. Teams that integrate offensive corner execution with immediate defensive organization (and vice versa) are likely to gain a tactical advantage, especially in managing counterattacks. Furthermore, expert staff should design set-piece sessions that simulate real match conditions, including context-specific variables such as scoreline, time of match, and player fatigue [50]. Generally, corner kicks must not be rehearsed in isolation; rather, they should involve live decision making, pressure from opponents, and immediate shifts into defensive or attacking transition.  
For attacking strategy, teams should consider using 3-4 actively involved players and favor dynamic movements (e.g., \"jellyfish\" or \"love train\" formations) to increase the likelihood of creating goal-scoring opportunities. To maximize the effectiveness of offensive corner kicks, coaches should prioritize set-piece routines that involve multiple attackers actively participating in the play and aim for indirect deliveries--especially toward the far-post zone inside the goal area. Designing corner kick strategies that not only exploit high-traffic zones like the near post but also include secondary movements or flick-ons that redirect the ball to the far post are likely to disrupt the opposing defense [51; 52].  
Conversely, from a defensive point of view, a mixed marking system, with at least one player guarding the near post, may offer greater security than pure man marking or zonal marking. Coaches should also ensure numerical superiority in the penalty area and consider assigning specific roles (e.g., near-post coverage, back-space protection, and counterattack positioning) based on opponent tendencies.  
Finally, it seems that collaboration between coaches, set pieces experts, and analysts is key. Match footage and notational data should guide tactical adjustments and be translated into clear, individualized instructions for players [53; 54].
## 3 Results
### Demonstration on a Case Study
#### 3.2.3 Models for Stiffness Degradation  
Besides the original BWBN degrading model, an additional parametric model is now introduced to investigate the capability of the Gaussian model discrepancy term to cover model uncertainty. An important part of the current literature on damage indices focuses on exponential function of the dissipated energy.  
Consequently, an energy-based exponential function, according to [45], has been used in this work to replace the original BWBN stiffness degradation term \((1-\delta_{A}\varepsilon(t))\) that multiplies the initial stiffness \(k_{i}\):  
\[D_{E}(t,\varepsilon,\beta)=\exp\left(-\beta\frac{\varepsilon(t)}{\int\left(-n \ddot{u}-ma_{g}\right)dt}\right), \tag{20}\]  
where \(\beta\) is an additional parameter to infer jointly with \(k_{i}\). In order to replicate the degrading effect on the initial stiffness \(k_{i}\) through non-parametric models, probability distribution functions have been used herein. First, according to the procedure used in [46], a Weibull distribution function was adopted:  
\[D_{W}(t,R_{W},p_{1},p_{2})=\left(1-\left[R_{W}-R_{W}\,exp\left(-\frac{t}{p_{1 }}\right)^{p_{2}}\right]\right), \tag{21}\]  
where \(R,\ p_{1},\ p_{2}\) are the distribution parameters to infer jointly with \(k_{i}\). Meaning to these parameters may be given as follows: \(p_{1}\) represents the instant of time at which there is loss of stiffness; \(p_{2}\) controls its rate; and \(R_{W}\) controls the amount of damage. Secondly, a logistic distribution function was adopted:  
\[D_{L}(t,R_{L},\mu,s)=\left(1-\frac{R_{L}}{1+exp\left(-\frac{t-\mu}{s}\right)} \right). \tag{22}\]  
In this case, parameter \(\mu\) is the instant of time in which the loss of stiffness occurs, while parameter \(s\) represents its rate, and \(R_{L}\) defines the amount of degradation.
## 2 Background and Related Works
### Background
#### 2.2.2 Text2 Cypher and Comparison with Text2 SQL  
Text2 SQL and Text2 Cypher both translate natural language into structured queries, but differ in complexity and resource availability. While SQL queries typically involve selecting columns from tables and joining them based on keys, Cypher is designed for graph databases, which model data as nodes, relationships, and properties. This structure is inherently more complex than the tabular format of relational databases used by SQL. Translating natural language into Cypher often requires understanding and mapping multi-hop relationships and graph patterns, which is more challenging than mapping to the relatively straightforward joins and selections in SQL.  
Text2 SQL benefits from more mature, larger, and higher-quality datasets, making it easier to train and benchmark models. Text2 Cypher suffers from a scarcity of high-quality, publicly available datasets. Datasets are often developed independently, making them difficult to combine and use effectively. This lack of resources makes it more difficult to train robust models for Text2 Cypher.  
Text2 Cypher requires an understanding of graph database schema, node, and relationship types, as well as how to express complex traversals in Cypher. This demands more specialized knowledge compared to SQL, which is more widely known and used. Text2 SQL requires an understanding of table schemas and relationships, but the learning curve is generally less steep.
# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 2 Methodologies
### Indicators for Model Evaluation  
In order to facilitate a more robust evaluation of the model's prediction performance, this study employs the metrics of accuracy, AUC (area under curve), and the Kappa coefficient to assess the model's efficacy. Specifically, accuracy signifies the proportion of accurately predicted instances among all instances; AUC is defined as the area under the ROC curve enclosed with the coordinate axis, with values approaching 1 indicating higher accuracy; and the Kappa coefficient is a statistical metric employed for the performance evaluation of multiclassification models, with its value denoting the degree of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Parameters** & **Description** \\ \hline Input Layer & N/A & Concatenation of the input vector and local \\
1D Conv & in\_channels = input\_size, & 1D convolutional layer for encoding, mapping \\  & out\_channels = d\_model/2, & from input\_size to d\_model/2 \\  & kernel\_size = 3, padding = 1 & \\ Relu & N/A & Activation layer \\
1D Conv & in\_channels = d\_model/2, & 1D convolutional layer for encoding, mapping \\  & out\_channels = d\_model, & from d\_model/2 to d\_model \\  & kernel\_size = 3, padding = 1 & \\ Relu & N/A & Activation layer \\ KAN & [d\_model, d\_model/2, d\_model/2, d\_model/4] & RAN module with hidden layers of sizes \\  & & d\_model, d\_model/2 and d\_model/4 \\ Dropout & rate = 0.15 & Dropout layer \\ Output layer & d\_model/4, num\_classes & Fully connected layer \\ \hline \hline \end{tabular}
\end{table}
Table 1: TRNet model components with their parameters and descriptions.  
consistency between the actual results and the model prediction results. The formulae for these indicators are shown in Equations (17)-(20).  
\[\begin{split} Accuracy=\frac{1}{m}\sum_{i=1}^{m}\mathrm{H}(f(x_{i} )=y_{i})=\\ \frac{TP+TN}{TP+TN+FP+FN}\end{split} \tag{17}\]  
\[AUC=\int_{0}^{1}\frac{TP}{TP+FN}d\bigg{(}\frac{FP}{FP+TN}\bigg{)} \tag{18}\]  
\[P_{\epsilon}=\frac{\sum_{i=1}^{n}\left(\mathrm{L}_{i}\times\mathrm{L}_{i} \right)}{N^{2}} \tag{19}\]  
\[K=\frac{Accuracy-P_{\epsilon}}{1-P_{\epsilon}} \tag{20}\]  
where \(TP\) denotes a true case, i.e., the real label and prediction result are both positive cases; \(TN\) denotes a true negative case, i.e., the real label is a positive case and the prediction result is a negative case; \(FP\) denotes a false positive case, i.e., the real label is a positive case and the prediction result is a positive case; and \(FN\) denotes a false negative case, i.e., the real label and prediction result are both negative cases. \(\mathrm{H}(.)\) The condition discriminant returns 1 if the condition is satisfied and 0 if it is not. \(L_{i}\) denotes the number of correctly predicted samples of the class, \(i,L_{i}\) denotes the total number of samples of class \(i\), and \(N\) denotes the total number of all samples.
## 4 Comparison
### Comparison of Charging Algorithms
Any additional request beyond its capacity may result in drone failure and severe congestion at providing service, which may lead to longer waiting times in CSs. This further increases the flight time of drones and reduces the average delivery time of goods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Main Idea** & **Advantages** & **Limitations** \\ \hline \multirow{6}{*}{CBPTCS [11]} & & The restriction of placing CSs & The limited battery resources of CSs are not considered in the optimization process. \\  & & location based on the & Additionally, CBPTCS relocates a CS based on the numerical computation, whereas an analytical solution is required for faster computation. \\ \hline \multirow{6}{*}{DDC [17]} & Maximizing the delivery area through optimal clustering of delivery locations and minimizing the number of required CSs. & Through optimal \(k\)-means clustering of delivery locations, it jointly optimizes the CSs locations to extend the drones flight time and coverage area. & The locations of restricted areas to place CSs is ignored. Additionally, the coverage area can be improved by minimizing the gap and overlap between two neighboring cluster centroids. \\ \hline \multirow{6}{*}{ODCS [44]} & & & This method only considers the CSs location adjustment according to the fixed threshold flight time of drones. However, drone flight times may vary according to their trajectories, and environmental influences such as wind. Thus, drone energy consumption model is required to obtain realistic results in the simulation environment. \\ \hline \multirow{6}{*}{CDNC [51]} & Minimize the congestion at CSs to provide smooth charging service to drones and minimize the drones waiting time at CSs using [PERSON]’s shortest path algorithm. & This approach utilizes the global knowledge to generate more stable optimal path for drones, which reduces congestion at CSs and waiting time for charging. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of charging algorithms in drone routing in terms of main idea, advantages, and limitations.
## 2 Materials and Methods
### Access Cavity Preparation  
The teeth were separated into three different groups (_n_ = 50) according to the access cavity design: TEC, CEC, and TAC. Access cavity preparation was performed by the same operator for all teeth. The preparation of access cavities was performed using an 856-diameter diamond bur (Komet Italia S.R.L, Milan, Italy) with a high-speed handpiece under water cooling. In the TEC group, the pulp chamber's roof and all the pulp horns were completely removed, and a direct access to the root canals was created ensuring coronally divergent axial walls with no undercut. In the CEC group, teeth were prepared by undertaking partial unroofing of the pulp chamber and preserving the pulp horns. Access cavity walls were created with a slight convergence toward the occlusal surface until the canal orifices could be detected. In the TAC group, a single access to the mesial canals was formed in the buccal-lingual direction, and another access was created in order to reach the distal canal orifice. The roof of the pulp chamber between the mesial and distal cavities was preserved.  
After access cavity preparation, apical patency was determined using #10 K-file (Dentsply Maillefer). The #10 K-file was inserted into the canal until it could be observed from the apical foramen. Root canals which were wider than a size #20 K-file at the apical foramen were excluded. The working length was calculated as 1 mm shorter in comparison to the length in question, and the canals were instrumented up to a #20 K-file. Between each file, 2 mL of 5.25% sodium hypochlorite (NaOCI) was used for the irrigation of the root canals. The root canals were then filled with 17% ethylenediaminettetraacetic acid (EDTA) for one minute and were rinsed with 5.25% NaOCI, followed by sterile distilled water to remove the smear layer.  
The apical part of each root was sealed with composite resin, and the radicular surface was varnished with nail polish for the purpose of preventing bacterial leakage. Cotton pellets were placed into the canal orifice and subsequently sealed with a temporary filling. To facilitate both handling and identification, each tooth was placed vertically into an Eppendorf tube (Jencons Scientific Ltd., England) with acrylic resin. After the acrylic resin was set, the removal of the temporary filling and pellet was carried out. The tubes were placed in autoclave sachets, and their sterilization in an autoclave was conducted at a temperature of 121 \({}^{\circ}\)C for a period of 15 min. Sterilization was ensured using an indicator (4A Medical, Emulating Indicator, Class 6, Turkey) present in the sachets.
## 3 Recognition Methods and Their Implementation
### Image Feature Extraction Algorithm  
The BRIEF algorithm generates binary descriptors by comparing local pixel pairs and has lower computational complexity. This means that during the process of image processing, BRIEF can quickly extract feature points and generate their descriptors. The region growing algorithm relies on certain feature points as seeds or reference points, and the efficiency of BRIEF helps improve the speed of preprocessing for region growth. In regression analysis, the dependent variable used is the threshold size of regional growth, which is obtained by artificial experience. The independent variable is the input image's features.  
In the process of crack detection, common factors affecting the detection include water stains, dirt, uneven lighting, and so on. They are reflected on the grayscale map by changes in grayscale mean and variance. So in this study, the mean and variance of the grayscaleimage are used as two features. In addition to these two simple mathematical features, the study needed to consider the grayscale relationships of random point pairs in the local image neighborhood. So SURF [14], FAST [15], BRIEF [16], and other feature recognition algorithms were discussed. These common image feature analysis algorithms have been studied and compared before [17]. SURF has high computational complexity and low operation efficiency; FAST has no directionality and shows scale invariance; the BRIEF (Binary Robust Independent Basic Features) method can retain direction invariance while calculating quickly, and uses binary sequences to describe features. The value of each binary sequence depends on the comparison between the intensity values of two points in the image partition centered on the currently described feature. If the strength value of the first point of a given point pair is higher than the strength value of the second point, the bit corresponding to the point pair is assigned, otherwise the bit is reset. BRIEF is faster and consumes less CPU memory than other methods. However, due to its sensitivity to noise, the algorithm needs to smooth the image before calculation.  
The calculation steps of BRIEF are as follows.  
First, select Nd pairs of pixels in the S \(\times\) S area around the feature point; these pixels shall be selected randomly. Then take the grayscale value of each pair of pixels and compare the size:  
\[\tau(p;x,y):=\left\{\begin{array}{c}1\;\text{if}\;p(x)<p(y)\\ 0\;\text{otherwise}\end{array}\right.. \tag{4}\]  
Then each bit of the descriptor, which is composed of X and y pixels, can be calculated and the \(\tau\) Function gives the following:  
\[f_{n_{d}}(p):=\sum_{1\leq i\leq n_{d}}2^{i-1}\tau(p;x_{i},y_{i}) \tag{5}\]  
In this study, the BRIEF method is used as the third image feature, together with the gray mean and variance as independent variables, to input the regression function.
###### Abstract  
The integration of unmanned aerial vehicles (UAVs) into vehicular networks offers numerous advantages in enhancing communication and coverage performance. With the ability to move flexibly in three-dimensional space, UAVs can effectively bridge the communication gap between intelligent connected vehicles (ICVs) and infrastructure. However, the rapid movement of UAVs and ICVs poses significant challenges to the stability and reliability of communication links. Motivated by these challenges, integrated UAV-ICV networks can be viewed as vehicular delay-tolerant networks (VDTNs), where data delivery is accomplished through the \"store-carry-forward\" transmission mechanism. Since VDTNs exhibit social attributes, this paper first investigates the opportunistic transmission problem in the presence of selfish nodes. Then, by enabling node cooperation, this paper proposes an opportunistic transmission scheme for integrated UAV-ICV networks. To address the issue of node selfishness in practical scenarios, the proposed scheme classifies the degree of cooperation and analyzes the encounter probability between nodes. Based on this, information is initially flooded, and the UAV is selected for data distribution by jointly considering the node centrality, energy consumption, and cache size. Finally, simulation results demonstrate that the proposed scheme can effectively improve the delivery ratio and reduce the average delivery delay compared to state-of-the-art schemes.  
intelligent connected vehicles (ICVs); selfish nodes; store-carry-forward; unmanned aerial vehicles (UAVs); vehicular delay-tolerant networks (VDTNs) +
Footnote †: journal:  
Article
# Stepped Spillways and Energy Dissipation: A Non-Uniform Step Length Approach
## 1 Introduction
He found an increase of 10% in the energy dissipation compared to uniform steps. On the other hand, [PERSON] and [PERSON] [10; 24] investigated the effect of uniform and non-uniform step heights on the energy dissipation performance, and showed that there was no significant difference between the two configurations. [PERSON] et al. [25] studied the energy dissipation due to non-uniform-height steps in curved spillways. Their results show that, compared to the smooth spillway, the water flow from the concave bank to the convex bank increased the energy dissipation by up to 66%. In addition to these experimental studies, flow on stepped spillways have been modeled numerically with a high degree of accuracy using computational fluid dynamics (CFD). In numerical studies of stepped spillways, the flow over the stepped spillway is investigated by the volume of fluid (VOF) method, with turbulence closures of the k-\(\varepsilon\) family [26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. For instance, [PERSON] et al. [36] have used VOF and the realizable k-\(\varepsilon\) model to determine the inception point of air entrainment in various stepped spillways, including flat steps, pooled steps, and round steps. Their results indicated that as the step height decreases, the air entrainment decreases, and with respect to aeration, the round stepped spillway was the optimum design. The above mentioned experimental and numerical studies have investigated the energy dissipation of uniform stepped spillways for different slopes, numbers of steps, and discharges. In this study, the effect of non-uniform step length on the energy dissipation was investigated. To evaluate the performance of non-uniform step lengths on energy dissipation, the height and the number of steps in all models were fixed and equal to a constant number. Numerical analysis was performed by the InterFOAM solver in the OpenFOAM package, using the VOF method and the realizable k-\(\varepsilon\) model. The main aims of the presented study were:  
1. To investigate the effect of uniform step length on the energy dissipation for different slopes. 2. To investigate the effect of non-uniform step length on the energy dissipation for four different cases:
3. Longer steps in the beginning, with the step length decreasing progressively toward the end (convex configuration). * Shorter steps in the beginning, with the step length increasing progressively toward the end (concave configuration). * Random step length distribution
* Semi-uniform step length distribution
* Investigation of the optimum step length configuration with respect to energy dissipation.
# Time Series Feature Selection Method Based on Mutual Information
## 4 Result Analysis
### Comparative Analysis of the Selected Features  
In order to verify the effect of the selected features, the correlation between the nine sensor signals with the target variable is visualized, and the F-test scores between sensor  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Number** & **Value of Mutual Information** \\ \hline
2 & 0.570 \\
7 & 0.697 \\
8 & 0.597 \\
9 & 1.000 \\
11 & 0.951 \\
12 & 0.705 \\
13 & 0.682 \\
17 & 0.485 \\
20 & 0.506 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Value of mutual information between the sensor signal and the target variable.  
Figure 6: Mutual information histogram of sensor signals with the target variable (figure drawn by matplotlib).  
signals and target variables are given, as shown in Figure 7. The F-test score [49] is used to extract the linear relationship between the nine sensor signals with the target variable, which can be mutually verified with feature selection based on mutual information to a certain extent.  
As can be seen from Figure 6, the features selected based on the F-test are basically consistent with those selected based on mutual information. For example, for sensor signal s9, both give scores of 1 and for sensor signal s11, the scores given by both are 0.98 and 0.96, respectively. Obviously, this indicates a strong linear relationship between s9 and s11 with the target variable. However, the difference is that feature extraction based on mutual information can better analyze and extract the nonlinear relationship between features and target variables. For example, when s13, s17, and s20 do not show an obvious linear relationship with the target variables, the value of mutual information is higher than the F-test score. Based on the above analysis, the five time series characteristics that can best represent the state of the system are obtained, and their value of mutual information is s9, s11, s12, s7 and s13 from high to low.