esa-sceva/satcom-chunk-collection huggingface dataset:
# Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
## 3 Materials and Methods
### Methods
#### 3.3.3 Logistic Regression  
The logistic regression (LR) or logit model is commonly used for classification and prediction. The method creates probabilities between one and zero. Using a given dataset of independent variables, LR estimates the probability of the occurrence of an event. In this  
Figure 3: Number of accidents under each injury severity classification for highway crashes in Qassim Region between January 2017 and December 2019.  
study, LR predicted the probability of injury, death, and PDO with three possible outcomes: y1 = property damage; y2 = injury, and y3 = death (coded as y1 = 0; y2 = 1; y3 = 2). For LR, the modeling function described the relationship between a class's probability and the set of independent or predictor variables. For the current problem, a typical LR model equation may be expressed as:  
\[P(y=1|x)=\frac{1}{1+e^{(-z)}}=\frac{e^{(z)}}{1+e^{(z)}}\in[0,1] \tag{1}\]  
\[z=\beta\_0+\beta\_1\_x\_1+\beta\_2\_x\_2+\cdots\cdots+\beta\_n\_x\_n=x\beta \tag{2}\]  
where \(x\beta\) represents the sigmoid S-shaped function. In the event that the probability is greater than 0.5, the dataset is classified as a death, an injury, or property damage. The parameters included in LR were the number of iterations, epsilon, learning rate strategy, step size, and regularization. Moreover, the learning rate strategy and regularization were considered fixed and uniform.
## 1 Introduction
Generally, the RE and BSS belong to different investors [20], and it is difficult to popularize the operational mode of the joint system mentioned above. Therefore, it is necessary to investigate the coordination between RE and BSS in a multi-stakeholder scenario. In a deregulated electricity market, the RE and BSS in a multi-stakeholder scenario are market signal-oriented and only pay attention to their own interests. Therefore, their generation and consumption are highly subjective, and it is challenging to coordinate and manage them effectively. In recent years, game theory has been widely used in a power system [21; 22; 23; 24]. Game theory is a theory to study how to optimize the decision-making strategy of each decision-making body which has interest correlation or conflict. This theory provides a new means to solve the cooperative optimization problem among the different investment entities mentioned above. In this paper, the co-optimization of RE and BSS is studied. The innovation points are as follows. (1) Aiming at the situation that RE and BSS belong to different investment subjects, a cooperation mode is proposed. Different from the previous research, this cooperation mode no longer requires the two partners to belong to the same investment body, so this mode is more feasible in an electricity market where the investors are diversified. (2) Based on game theory, a Stackelberg model is established, in which the RE company is the leader and the BSS is the follower. The model could effectively reflect the co-existence relationship of competition and cooperation between RE and BSS. (3) In the model proposed in this paper, a unified coordinated control for the RE and BSS is no longer needed. In fact,an optimal allocation and a win-win situation could be automatically achieved in the course of the RE's and BSS's pursuit of maximizing their own interests respectively. The organization of this paper is as follows. Section 2 introduces the operation mode of RE and BSS. The proposed Stackelberg game model is described in Section 3. In Section 4 we analyze the two kinds of game equilibrium solutions and propose the corresponding algorithm. Simulation analysis is given in Section 5. Finally, the conclusions are drawn in Section 6.
# Short Cyclic Algorithm for Turbo Block Construction
### A New Algorithmic Framework for Turbo Block Construction  
This paper explains a methodology for the design of turbo block spiraling lanes, featuring several key contributions. First, the method proposes a fixed width for the first segment and a variable width for other segments, optimizing pavement utilization. Second, it addresses the question of whether to match curb or marking lines between the successive segments, supporting both techniques. Third, the methodology presented herein explains precise positioning for all of the centers, which is often lacking in national policies across the world, by using simple linear and angular parameters. At the end, all the parameters discussed are integrated into a compact cyclic algorithm, facilitating efficient geometrical calculation and a graphical output.  
In the literature available, no algorithms for turbo roundabout design have been proposed in this way. As national standards provide typical geometrical solutions that rely on manual design techniques, they do not offer an algorithmic approach for automating the design process. This paper introduces a cyclic algorithm specifically developed for turbo block construction and addresses an important gap in both the academic and practical domains of turbo roundabout design, by providing a structured, programmable approach. Consequently, this algorithm enhances the speed and precision of turbo roundabout design in a CAD environment and offers flexibility for modifications and improvements based on specific design criteria.
# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
## 2 Research Procedure  
The experimental phase of this study was conducted as part of the TRUSTONOMY (Building Acceptance and Trust in Autonomous Mobility) project, a European initiative aimed at enhancing the safety and acceptance of automated driving systems. All experiments were carried out using a high-class AS 1200-6 driving simulator for passenger vehicles (AutoSim, Tromso, Norway), housed at the Motor Transport Institute (ITS), which was utilised for both the training and effectiveness evaluation phases.  
This study analysed data from 61 participants. The demographic structure of the study group reflected the age distribution of drivers in Poland, with a balanced representation of genders. Participants were divided into the following two age groups: 27-39 years (31 participants) and 40-65 years (30 participants). In the study, the gender distribution was as follows: 31 women (51%) and 30 men (49%). All participants were active drivers aged between 27 and 65, without prior experience operating vehicles equipped with driver assistance systems, particularly adaptive cruise control and lane-keeping assistance. To ensure consistency and control for variations in content delivery, all participants within a specific training group were exposed to identical instructional materials and scenarios. The practical training followed a standardised script, the e-learning module was uniform for all users, and the brief manual was a single, standardised document. To control for potential age-related confounding variables, the study design incorporated several measures. The age groups were balanced across the three training conditions to mitigate systematic bias. Furthermore, all participants underwent an identical adaptation drive and pre-screening process, including the RSSQ, to ensure a uniform baseline for simulator familiarity and to account for potential pre-existing sensitivities to the simulated environment
## 3 Dynamic Expected Threat (DxT) Model Theory  
### General DxT Equation  
This section outlines the theoretical framework for constructing the Dynamic Expected Threat model (DxT). This model is designed to generate an xT map tailored to each specific game scenario. Unlike static models, DxT considers not only the ball's position but also a range of other features that influence the action being evaluated. These features, denoted as \"features\({}_{i,j}\)\", encompass a plethora of elements that contribute to the potential threat of the action. Rather than defining a fixed set of features at this stage, we present a generalized equation, which will be further refined based on the features available in our dataset. Equation (1), reformulated to incorporate these considerations, is presented as follows:  
\[\begin{array}{ll}DxT_{i,j,features_{i,j}}&=s_{i,j,features_{i,j}}\times xG_ {i,j,features_{i,j}}\\ &+m_{i,j,features_{i,j}}\\ &\times\sum\limits_{i=1}^{L}\sum\limits_{j=1}^{W}T_{i,j,features_{i,j}\to i,j,features_{i,j}}DxT_{i,j,features_{i,j}}\end{array} \tag{4}\]  
where:  
* \(features_{i,j}\) represent all features influencing the potential threat posed by the action located in \((i,j)\) grid cell.
* features\({}_{i,j}\) represent all features influencing the potential threat posed by the action located in \((i^{\prime},j^{\prime})\) grid cell.
* \(s_{i,j,features_{i,j}}\) represents the probability of a player opting to take a shot on goal from \((i,j)\) grid cell, while considering features labeled as \"\(features_{i,j}\)\".
* \(m_{i,j,features_{i,j}}\) represents the probability of a player opting to move the ball from \((i,j)\) grid cell to another grid while considering features labeled as \"features\({}_{i,j}\)\".
* \(xG_{i,j,features_{i,j}}\) represents the probability of scoring a goal from \((i,j)\) grid cell, while considering features labeled as \"features\({}_{i,j}\)\".
* \(T_{i,j,features_{i,j}\to i^{\prime},j^{\prime},features_{i,j^{\prime}}}\) represents the probability of moving the ball from \((i,j)\) grid cell to \((i^{\prime},j^{\prime})\) grid cell, given the alteration in features from the state \"\(features_{i,j}\)\" to the state \"\(features_{i,j}\)\".
* \(DxT_{i,j,features_{i,j}}\) represents the dynamic threat of the action that takes place in \((i,j)\) grid cell, while considering features labeled as \"\(features_{i,j}\)\".
* \(DxT_{i,j^{\prime},features_{i^{\prime},j^{\prime}}}\) represents the dynamic threat of the action that takes place in \((i^{\prime},j^{\prime})\) grid cell, while considering features labeled as \"\(features_{i^{\prime},j^{\prime}}\)\".
# Calculating the Moore-Penrose Generalized Inverse on Massively Parallel Systems
## 2 Moore-Penrose Generalized Inverse  
In this section, we give a brief introduction to the Moore-Penrose pseudoinverse, a generalization of the matrix inverse.  
**Definition 1** ([37]).: _The inverse of a given regular square matrix \(A\in C^{n\times n}\) is a square matrix \(A^{-1}\) such that_  
\[AA^{-1}=I_{n},\,A^{-1}A=I_{n},\]  
_where \(I_{n}\) is the \(n\times n\) identity matrix._  
_A square matrix \(A\in C^{n\times n}\) has a unique inverse if \(\text{det}(A)\
eq 0\). If \(A\) has an inverse, we say that \(A\) is a nonsingular (regular) matrix. Otherwise, we say that \(A\) is a singular matrix._  
One of the basic and the most important applications of a matrix inverse is in solving a system of linear equations  
\[Ax=b, \tag{1}\]  
where \(A\in R^{m\times n}\) and \(b\in R^{m}\). If \(A\) is regular, the solution to the system (1) is given by  
\[x=A^{-1}b. \tag{2}\]  
However, by Definition 1, the inverse can exist only for regular square matrices (satisfying \(m=n\)). Generalized inverses are introduced to overcome these restrictions of the ordinary inverse [1]. System (1) has at least one solution if \(b\in R(A)\), where \(R(A)=\{y\mid\exists\ x\in R^{m},\,Ax=y\}\). In other words, \(b\in R(A)\) if there exists a matrix \(X\in R^{n\times m}\) such that \(x=Xb\) is a solution to the linear equations system. The matrix \(X\) can be described using the following Penrose equations [38]:  
\[\begin{array}{c}AXA=A,\,\text{(a)}\\ XAX=X,\,\text{(b)}\\ (AX)^{*}=AX,\,\text{(c)}\\ (XA)^{*}=XA.\,\text{(d)}\end{array} \tag{3}\]  
A matrix \(X\), \(X\in R^{n\times m}\) that satisfies (c) is called the Moore-Penrose generalized inverse of matrix \(A\) or the pseudoinverse of matrix \(A\) and is denoted by \(A^{\dagger}\). The authors of [38] proved that the linear equation system (3) has a unique solution for the arbitrary matrix \(A\).  
If \(A\) is a regular square matrix, its inverse, \(A^{-1}\), satisfies the equations in (3), and it is equal to pseudoinverse \(A^{\dagger}\). Sometimes, we may need to find a matrix \(X\) that satisfies only some equation from 3. For an array \(S\) of elements from [1, 2, 3, 4], the set of matrices that satisfies the equations from 3 indexed by elements from \(S\) is denoted by \(A\{S\}\) (if \(S=\{1,\,2,\,3,\,4\}\); the set \(A\{S\}\) has exactly one element, \(A\{S\}=\{A^{\dagger}\}\)).
# Constructed Wetlands for the Wastewater Treatment: A Review of Italian Case Studies
## 3 Discussion
 Note: BODs, biochemical oxygen demand of S days, \(\tt{B  
The pollutant removal efficiencies of the hybrid CWs in the reviewed case studies were very high compared to the other types of CWs, which are similar to research results reported elsewhere [24; 32; 45]. The removal efficiency of COD ranged from 53% to 80%, but the highest values (79-97.5%) were obtained in trials with hybrid CWs, which is in line with a recent work that achieved a COD removal of 97.56 \(\pm\) 1.6% [149]. Similarly, removal efficiency for TN of the reviewed work was between 60% and 66%; however, hybrid CWs managed to increase the performance (64-88%). This result is in agreement with the work of [150], who reported 82.71 \(\pm\) 3.92% TN removal in an anoxic-aerobic system combined with an integrated vertical-flow constructed wetland. The removal efficiency of TSS was also very high (89% to 95%), indicating the potential of CWs. This result is supported by the range of values (81.6-97.1%) for TSS removal from anaerobic reactor brewery effluent reported by [151]. Moreover, CWs were able to remove BOD successfully (75-80%), but then again the highest values (93-95%) were recorded in experiments conducted with hybrid CWs. These values are also consistent with reported BOD removal that ranged between 85% and 94% [152]. Even though the removal efficiency for TP was relatively low (10-73%), hybrid CWs gave higher values (47-94%), which is also comparable with the removal percentage (92.28 \(\pm\) 2.78%) reported by [150]. Heavy metals (Zn, Cu, Pb, Ni) and pathogens (total coliforms, faecal streptococci, _E.coli._) were also removed in some of the experiments conducted with CWs. For instance, the removal efficiency of Zinc ranged between 65% and 85%; nonetheless, the hybrid CWs resulted in high performances (70-98%). A total reduction in zinc and copper almost closer to zero was also reported [145]. The lead removal efficiency was 78-88%. The hybrid CWs again resulted in high removal potentials (70-98%). The removal efficiency for nickel was between 35 and 58%, but hybrid CWs, in the same way, gave a high value (92%). Removal efficiency for copper ranged from 46 to 80%, then again a removal potential of 99.3% was obtained with hybrid CWs.
## 4 Experiments
### Value of Multisource Heterogeneous Information  
To verify the value of fusing different sources of UGC, three pair-to-pair fusion models were designed in this experiment, namely, fusion of videos and comments (denoted as the video-review model), fusion of videos and properties (denoted as the video-property model), and fusion of comments and properties (denoted as the review-property model). Compared with the original fusion model, in these three models, only the data input changed, whereas the other parameters remained unchanged.  
In Figures 7-9, \"val_acc\", \"val_recall\", and \"val_f1\" represent the accuracy rate, recall rate, and \(F_{1}\) value of the original model for the verification set, respectively. It can be observed in Figures 7-9 that the fusion of three features is better than that of two. For the fusion of videos and comments reflected in Figure 7, the accuracy of the original model was 21% higher. Meanwhile, Figure 7a,b also reflects the overfitting phenomenon of the video-review model (Figure 8). The accuracy of the original model was approximately 10% higher. It can be observed that for commodity data, comments and commodity attribute text are equally important. In Figure 9a, the accuracy of the verification set was 16.7% higher than that of the training set. This phenomenon was caused by the addition of the dropout layer to the full connection layer, which made the accuracy of the training set lower than that of the verification set. To summarize, each heterogeneous feature of a commodity  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MCB** & **ViLT** & **Ours** \\ \hline Time & 0.0518 & 0.2842 & 0.0737 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the running time of MCB, ViLT, and the proposed algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Concatenate** & **MCB** & **ViLT** & **Ours** \\ \hline Accuracy & 0.8889 & 0.9931 & 0.8123 & 0.9998 \\ Val\_Accuracy & 0.8286 & 0.2115 & 0.8149 & 0.9982 \\ Precision & 0.9658 & 1.0000 & 0.9561 & 0.9971 \\ Val\_Precision & 0.8942 & 0.0698 & 0.9606 & 1.0000 \\ Recall & 0.8596 & 0.9724 & 0.7411 & 0.9946 \\ Val\_Recall & 0.7982 & 0.1568 & 0.7381 & 1.0000 \\ \(F_{1}\) & 0.8938 & 0.9772 & 0.6909 & 0.9997 \\ Val\_F1 & 0.8221 & 0.0793 & 0.7022 & 0.9963 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative experimental results on the Amazon dataset. Note: Val_Accuracy, Val_Precision, Val_Recall, and Val_F1 are the accuracy, accuracy, recall, and \(F_{1}\) score, respectively, for the validation set.  
plays an important role in the commodity itself. The algorithm in this study is suitable for the fusion of video and text features, but it cannot deal with plain text features.  
Figure 6: Comparison of the effects of the loss function. The results for (**a**) accuracy, (**b**) recall, and (**c**) \(F_{1}\) score.  
Figure 7: Experimental results of the video–review model: (**a**) accuracy, (**b**) recall, and (**c**) \(F_{1}\) score.
# Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints
## 1 Introduction
With a large grid division, the storage of environmental information is small, and the planning time is short, but the resolution is low; in the opposite situation, the resolution is high, but the planning time is long. For a robot motion control with a complex external environment and multiple constraints, it is difficult to describe the target state and the global environment directly and quickly. Therefore, some scholars have proposed a better task planner, the hierarchical task network (HTN) [8; 9]. The HTN planner decomposes the original task into several atomic tasks that can be executed directly. [PERSON] et al. [10] proposed a task planner based on an HTN planning framework for multi-robot cooperative tasks. [PERSON] et al. [11] proposed a task and motion planning method based on symbolic search, which solves decision-making problems such as grasping position, placement position and moving path under geometric constraints. However, the geometric constraint solver used in the algorithm requires a long preprocessing time, reducing the planning efficiency. The on-board computer used in the space manipulator cannot bear such a large amount of calculation. The above algorithms are aimed at joint space planning and are not applicable when there are strict requirements for end posture. In addition, they only provide the action sequence and do not reasonably plan the action details, so they cannot be directly used in the robot control system. To study the disassembly and planning methods for complex tasks of a space manipulator, it is important to improve the autonomy of the space manipulator. This study used an improved HTN theory to design a manipulator task planning algorithm to improve the task execution efficiency. HTN planning adopts the idea of task decomposition to disassemble complex non-original tasks layer by layer into original tasks that can be directly executed by actuators. HTN abstracts the target state of the task into the target task. The task has a complex to simple layered structure, and the lowest layer is the original task that can no longer be decomposed. HTN consists of the \"operator set\" of the original task and the \"method set\" of the disassembly of the composite task. Given the target task and the current environment state, the planner uses the methods in the planning domain to simplify the task and provide a feasible basic action sequence. The rest of the paper is organized as follows: In Section 2, HTN fundamentals are introduced. In Section 3, we present the general idea of the research. In Section 4, the mission profile of the space manipulator is analyzed. The task intermediate point planning is modeled in Section 5. The mission planning simulation is described in Section 6, and conclusions are summarized in Section 7.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
###### Abstract  
As the demand for 3D information continues to grow in various fields, technologies are rapidly being used to acquire such information. Laser-based estimation and multi-view images are popular methods for sensing 3D information, while deep learning techniques are also being developed. However, the former method requires precise sensing equipment or large observation systems, while the latter relies on substantial prior information in the form of extensive learning datasets. Given these limitations, our research aims to develop a method that is independent of learning and makes it possible to capture a wide range of 3D information using a compact device. This paper introduces a novel approach for estimating the 3D information of an observed scene utilizing a monocular image based on a catadioptric imaging system employing an omnidirectional camera and a spherical mirror. By employing a curved mirror, it is possible to capture a large area in a single observation. At the same time, using an omnidirectional camera enables the creation of a simplified imaging system. The proposed method focuses on a spherical or spherical cap-shaped mirror in the scene. It estimates the mirror's position from the captured images, allowing for the estimation of the scene with great flexibility. Simulation evaluations are conducted to validate the characteristics and effectiveness of our proposed method.  
3D estimation; catadioptric imaging system; omnidirectional camera; spherical mirror; epipolar geometry