esa-sceva/satcom-chunk-collection huggingface dataset:
# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
## 2 Literature Review  
### Feature Selection  
Feature selection is an important data pre-processing step in data mining and knowledge discovery from databases. It focuses on selecting representative features from a given training set, which have higher discriminative power to make classifiers better able to distinguish between different classes. Moreover, another advantage of feature selection is to reduce feature dimensionality, which lowers the computational complexity during the classifier training stage [5; 18].  
In general, feature selection algorithms are composed of four basic steps, which are a generation procedure to generate the candidate feature subset, an evaluation function to evaluate the effectiveness of the feature subset, a stopping criterion to determine when to stop the previous steps, and a validation procedure to examine whether the feature subset is valid [19].  
Existing feature selection algorithms can be divided into filter, wrapper, and embedded methods depending on how they combine the feature selection search with the construction of the classifiers. In filter methods, the relevance of features such as distance, consistency, dependency, information, and correlation are assessed,. That is, the feature relevance score is calculated, in which low-scoring features are removed. Some representative methods include relief, the Fisher score, and information gain.  
In wrapper methods, a specific classification algorithm is used to determine the quality of different subsets of features. Since the space of feature subsets can grow exponentially with the number of features, heuristic search methods are used to guide the search for an optimal subset. Therefore, wrapper methods are very computationally intensive, especially when the construction of the chosen classifier requires a high computational cost. One representative wrapper method is the genetic algorithm.  
In embedded methods, feature selection is incorporated as part of the classifier training process. That is, the feature selection method is embedded in the modeling algorithm, where the classifier is used to evaluate the quality of the selected subset of features. Embedded methods have the advantage of including interaction with the classification model, while at the same time being far less computationally intensive than wrapper methods. One representative wrapper method is the decision tree classifier.
## 2 Methods
### Hierarchical Reinforcement Learning Algorithm
#### 2.4.1 HRL Algorithm-Layer One  
To ensure efficient and effective mission execution, the first layer of HRL involves computing the most appropriate crew and robot based on mission and environment requirements. This is achieved by using a Q-learning algorithm with the goal of maximizing the task and environment fitness for each task. To calculate the sum of the task and environment fitness, the function of reward \(R_{m}^{1}\big{(}s^{1},a^{1}\big{)}\) is proposed.  
\[R_{m}^{1}=\Big{(}s^{1},a^{1}\Big{)}=\big{[}\alpha\quad\beta\big{]}\Bigg{[} \begin{matrix}TF\big{(}a_{i,j},t_{m}\big{)}\\ EF\big{(}a_{i,j},t_{m}\big{)}\end{matrix}\Bigg{]} \tag{11}\]  
The task fitness of agent \(a^{1}\) for the \(m\)th task is denoted by \(TF\big{(}a_{i,j},t_{m}\big{)}\), while \(EF\big{(}a_{i,j},t_{m}\big{)}\) represents the environment fitness of agent \(a^{1}\) for the \(m\)th task. The total number of tasks is \(M\), and \(a^{1}\) refers to the combination of the \(i\)th type of \(i\)th type of astronaut and the \(j\)th type of robot for human-robot collaboration. Coefficients \(\alpha\) and \(\beta\) have also been defined.  
\[a^{1}=h^{i}\cup r^{i},ie[1,I],je[1,J] \tag{12}\]  
The variables \(I\) and \(J\) represent the entire number of astronauts and lunar robots, respectively.  
\[s^{1}=\big{[}H\quad R\big{]} \tag{13}\]  
The variable \(s^{1}\) denotes the state, comprising of both the astronaut (\(H\)) and lunar robot (\(R\)). The agent in this context is described as:\[a^{1}=\begin{cases}h^{i}&if\ s^{1}=H\\ r^{j}&if\ s^{1}=R\end{cases} \tag{14}\]  
In Q-learning, the time difference method is used to update the formulation. The formula for updating is provided as follows:  
\[Q^{1}\left(s^{1},a^{1}\right)=Q^{1}\left(s^{1},a^{1}\right)+\varepsilon\Delta Q ^{1}\left(s^{1},a^{1}\right) \tag{15}\]  
The Q value of agent \(a^{1}\) in state \(s^{1}\) is represented by \(Q^{1}\left(s^{1},a^{1}\right)\). This value is updated by adding the increment to the total value and multiplying it by the learning rate \(\varepsilon\).  
\[\Delta Q^{1}\left(s^{1},a^{1}\right)=R^{1}_{n}\left(s^{1},a^{1}\right)+ \delta\underset{A^{1}}{\max}Q^{1}\left(\left(s^{1}\right)^{\prime},A^{1} \right)-Q^{1}\left(s^{1},a^{1}\right) \tag{16}\]  
To update the state \(\left(s^{1}\right)\), we select the highest value of \(Q^{1}\) for the next state \(\left(s^{1}\right)^{\prime}\), and then multiply it by the decay factor \(\mathcal{E}\). This is then added to the present reward and subtracted from the present value of Q.
# Analysis of Operating Safety of Tractor-Trailer under Crosswind in Cold Mountainous Areas
## 4 Validation of the Simulation Results on the Driving Simulator
### Analysis of Experimental Results  
The results of measured lateral acceleration under different wind speeds and different driving speed conditions are shown in the Figures 18 and 19, which show that at a speed of 30 km/h, the maximum lateral acceleration of the truck was around 0.15 g, and the steering wheel angle was within a small range, so the truck could still drive safely on ice and snow roads. When the speed reached 40 km/h, the maximum lateral acceleration of the truck reached 0.33 when the wind speed was 74 km/h, which was already beyond the safe lateral acceleration threshold, and the truck then slid sideways to a certain extent, and the driver made the truck return to the right track by frequently adjusting the steering wheel.  
Figure 17: The scenario of the driving simulator experiments.  
Figure 18: Lateral acceleration of the truck at driving speeds of **(a)** 30 km/h and **(b)** 40 km/h.  
By comparing the simulation results and experimental results in Figure 20, it is not difficult to find that the lateral acceleration results of the semi-trailer are similar under different running speeds and wind speeds. In actual driving, the vehicle is more sensitive to crosswind. Considering the influence of driver factors, the change of steering wheel angle will change the magnitude of lateral acceleration. On the one hand, the experimental results verify the influence trend of wind speed and vehicle speed changes on driving stability in the simulation. On the other hand, it also illuminates the changing trend of lateral acceleration. The results are in line with the above conclusions on the recommended speed and safe lateral acceleration.  
As can be seen from Figure 21, under the same speed conditions, with the wind speed increased, the vehicle's driving stability decreased continuously; similarly, under the same wind speed conditions, vehicle's driving stability changed drastically with the speed change. When the speed exceeded the suggestion speed, the lateral displacement changed amplitude and changed frequently, the vehicle could have been involved in an accident if the driver had not constantly adjusted the direction.  
Figure 19: Steering FL wheel angle of the truck at driving speeds of (**a**) 30 km/h and (**b**) 40 km/h.  
Figure 20: Lateral acceleration of experimental and simulation results under different wind speeds and driving speeds. (**a**) Crosswind speeds of 62 km/h, 68 km/h, and 74 km/h at a driving speed of 30 km/h. (**b**) Crosswind speeds of 62 km/h, 68 km/h, and 74 km/h at a driving speed of 40 km/h.
## 4 The System Framework
### Data Storage Module  
This module offers the flexibility of transferring and storing data within a local database, which can subsequently be shared and refreshed on cloud-based document-sharing platforms like ProjectWise or transmitted to cloud-based databases such as Microsoft Access or MySQL. It can also be integrated into BIM collaboration platforms like Speckle that connect with data visualization tools, such as Microsoft Power BI. For this study, a specialized Excel file was created to store all of the information and results produced by the Data Extraction and Analysis Module. This database is segmented into two primary sections: statistics and a list of information. The statistics section includes critical data such as the number of failing elements, the total number of components, and the quality score of each item based on the project's QC checklist. The second section provides a detailed list of the names and unique IDs of failing elements within the BIM model. This is particularly useful for engineers and BIM modelers to identify and correct these elements in subsequent versions of the model, thereby enhancing the overall quality and accuracy of the BIM models.
# Method for Measuring Absolute Optical Properties of Turbid Samples in a Standard Cuvette
## 3 Results
### Development of Fit for Absolute Optical Properties
#### 3.2.3 Fit Initial Guess  
We finish our development of the fit for \(\mu_{a}\) and \(\mu_{s}^{\prime}\) with a demonstration of exact retrieval when the same inverse and forward models are used for \(\widetilde{T}\) without noise (Equation (10)). In doing so we also investigate the effect of different initial guesses on \(\mu_{a}\) and \(\mu_{s}^{\prime}\) to show that convergence is not dependent on this initial guess (The result is not dependent of initial guess given that the initial guess is of reasonable optical properties). For this, the fit was implemented by using the MathWorks MATrix LABoratory [Natick, MA USA] (MATLAB) function fmincon to minimize \(\chi^{2}\) (Equation (28); \(\kappa=1.2\times 10^{-3}\)) function. For fmincon, the algorithm interior-point was used and the minimum constraints on \(\mu_{a}\) and \(\mu_{s}^{\prime}\) set to [0,0], respectively, with all other bound types unconstrained.  
Using this optimization setup, the fit was run with the \(\mu_{a,true}=0.010\,\mathrm{mm}^{-1}\) and the \(\mu_{s,true}^{\prime}=1.0\,\mathrm{mm}^{-1}\) using 4 different initial _guess_ses:* \(\mu_{a\_guess}=0.005\,\mathrm{mm}^{-1}\) & \(\mu^{\prime}_{s\_guess}=0.5\,\mathrm{mm}^{-1}\).
* \(\mu_{a\_guess}=0.005\,\mathrm{mm}^{-1}\) & \(\mu^{\prime}_{s\_guess}=2.0\,\mathrm{mm}^{-1}\).
* \(\mu_{a\_guess}=0.020\,\mathrm{mm}^{-1}\) & \(\mu^{\prime}_{s\_guess}=0.5\,\mathrm{mm}^{-1}\).
* \(\mu_{a\_guess}=0.020\,\mathrm{mm}^{-1}\) & \(\mu^{\prime}_{s\_guess}=2.0\,\mathrm{mm}^{-1}\).  
The results from these fits and the fit trajectory (shown as dotted lines with circles) are shown in Figure 7. In all cases the fit converged to the _true_ optical properties regardless of start point. A second observation that can be made from Figure 7 is what trajectory the fit follows during convergence. Acknowledging that this is highly dependent on algorithm choice, we still note that the fit spent most of its time traversing in the \(\mu_{a}\) direction, converging close to the correct \(\mu^{\prime}_{s}\) comparatively fast. This is a consequence of the shape of cost space, having a longer trough in the \(\mu_{a}\) direction than the \(\mu^{\prime}_{s}\).
## 2 Related Work  
### Object Detection in SAR Images  
In the field of ship detection, particularly in SAR images, numerous studies have focused on improving detection accuracy and processing speed, yet challenges such as han dling complex backgrounds and low recall rates remain. [PERSON] et al. proposed an algorithm based on Faster R-CNN for ship detection in SAR images, achieving good results [14], but the speed of their method is not fast enough for real-time processing. To improve processing speed, [PERSON] et al. [15] enhanced YOLOv5 for ship detection by incorporating the EIoI loss function. However, their method is not generalized and struggles with processing images containing complex backgrounds. [PERSON] et al. [16] improved detection precision by adding the REPPConv module to YOLOv7, but they did not address the challenge of improving recall. [PERSON] et al. [17] and [PERSON] et al. [18] both used YOLOv8 for ship detection in SAR images, making progress in the mAP metric. [PERSON] employed depth-wise convolution, while [PERSON] utilized a deformable convolutional network and WIoU. However, both methods can only detect a single category, and the datasets they used are relatively simple. [PERSON] et al. [19] introduced a novel dynamic graph-level neural network (DGLNN) to perform the task of SAR image change detection which achieved a high score on precision. However, their work is hardly adaptive to real-time object detection.
## 4 Experimental Results  
Muzzle blast DoA detection is a widely used application in outdoor environments. In our experimental study, we determined the direction of arrival of generated explosion sounds. To achieve this, we created an experimental field as shown in Figure 14a. Each point in the experimental field was marked with a Topcon GR-5 GNSS device with an accuracy of \(\pm 2\) cm. At the zero point of our experimental site, the microphone assembly designed in the geometry shown in Figure 6 (2) and in Figure 14b was placed with its origin 190 cm above the ground. Low-noise, high-sensitivity EM272 condenser electret omnidirectional microphones were used to capture explosion sounds at long distances. The distance between microphones on the same axis was set to 1 m. The ZOOM H8 sound recorder was used to record six microphones synchronously. Explosions were recorded at 10 different distances from 50 m to 500 m in 50 m increments and at angles of 0, \(-5\), \(-10\), \(-15\), \(-30\), \(-45\), \(-60\), \(-75\), and \(-90\). \begin{table}
\begin{tabular}{c c c c} \hline
**Error Edit** & **Far Field** & **Parsayan and Ahadi** & **Proposed Closed Form** \\ \hline
**No** & 0.98 & 0.98 & 0.92 \\
**\(\mathbf{N(0,3)}\)** & 5.33 & 4.75 & 5.04 \\ \hline \end{tabular}
\end{table} 
Table 3: Average error values of elevation angle estimation between 0 and 30 degrees in the case of \(d_{m}=0.5\) m, \(d_{k}=100\) m, \(Fs=44.1\) kHz, \(\theta=45\). To keep the environmental conditions constant, the microphone array was rotated at the desired angles. In this way, 90 separate locations were created and two explosions were created from each location, for a total of 180 explosions. The explosion sounds were generated using a sound gun at a height of 1.5 m from the ground. The TDoA times between microphones for each explosion and \(\hat{\theta}\) the angles calculated from these values using the GCC-Phat algorithm are given in Table 4 and Table 7. The \(\tau_{x}\), \(\tau_{y}\), and \(\tau_{z}\) values are expressed in units of sample. In our experiment, one sample time is equal to 22.68 \(\upmu\)s. The azimuth angle is calculated by Equation (30) according to the values in Table 4 and the unit of azimuth angle is degrees. \begin{table}
The average error values of the angle values obtained experimentally and calculated with Equation (32) are given in Table 5, and the error graph is shown in Figure 15. When Table 4 is examined, no clear separation is seen in the amount of error according to the angle values.
# Feasibility Pump Algorithm for Sparse Representation under Gaussian Noise
## 3 Results
Figure 2: Relative errors for Sparse Quadratic Feasibility Pump (SQFP) for matrix conditioning of 10,000. Figure 4: Mean errors for all algorithms, dictionary size \(80\times 200\), condition number 100. Figure 5: Mean errors for all algorithms, dictionary size \(80\times 200\), condition number 1000. Figure 6: Mean errors for all algorithms, dictionary size \(80\times 200\), condition number 10,000. To evaluate the quality of support recovery, we note that, out of the 640 tests, false negatives appear in 254 (39%) cases for OMP, in 225 (35%) cases for SQFP and in 281 (44%) cases for FISTA. While SQFP needs the longest running time, it recovers the support more precisely than the two other methods. FISTA shows a false positive in 609 (95%) cases. False negatives indicate when an atom is missing from the support of the true solution. False positives appear when the support of the computed solution contains atoms outside the true support. The second experiment is made with the same parameters as before, but now the dictionary size is \(80\times 400\), the condition number is 1000 and \(K\in\{9,11,13,15\}\). The overcompleteness factor is larger than in the first experiment, hence the properties of the dictionary are less favorable to sparse recovery. Also, higher sparsity levels are considered, hence the problem becomes more difficult. The mean errors are shown in Figure 8. Now the results of SQFP are even clearly better than those of OMP and FISTA, confirming its ability to work well in more difficult conditions.
# Haptic-Enabled Hand Rehabilitation in Stroke Patients: A Scoping Review
## 3 Results  
A total of 3136 results were generated from the literature search. After removing duplicates and screening titles and abstracts, full-text articles for 516 articles were retrieved. Those reported on the use of technology to support hand therapy intervention targeting the motor and functional recovery, without necessarily mentioning technology types in the title and/or abstract. Of those, 42 peer-reviewed articles involved the use of haptic technology in particular to support the hand therapy intervention and were included in the study. Articles were published between 2001 and 2018 with an average of 2.6 (\(\pm\)1.8) articles per year (Figure 2). Of the articles, 61% were published in the USA (26 articles). Three articles were published in Sweden, two in Taiwan, China and Italy, and one article in Australia, Canada, Netherlands, Russia and the United Kingdom.  
Figure 1: Study selection process [50].  
Figure 2: Frequency of publication per year.  
Figure 3 represents the characteristics of the studies reported in the 42 included articles. Of the articles, 43% (\(n=18\)) were feasibility studies followed by non-randomized trials (21.4%), longitudinal studies (19%) and randomized controlled trials (17%) (Figure 3a). Most of the studies (64%) were carried out by occupational therapists or physiotherapists (Figure 3b). Many studies have not reported explicitly the profession of the person who is responsible for the therapy and the evaluations. The studies involved an average of 9.9 participants (\(\pm\)9.51), ranging from 1 to 40 participants. Figure 3c shows that the number of participants tends to be low in most studies with some outliers studies, including higher numbers such as 30 or 40 participants. The interventions included about 14.8 sessions (\(\pm\)8.8) ranging between 8 and 35 sessions. Figure 3d shows that the number of therapeutic sessions tends to be low in most studies.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Experimental Setup--Modeling the Fixed-Wing UAV  
A reduced-order kinematic guidance model for fixed-wing UAVs, as detailed by [PERSON] and [PERSON] in [38], was employed. This model was adapted based on its MATLAB (2022b) implementation described in [39]. The controlled parameters include airspeed (\(v_{a}^{c}\)), flight-path angle (\(\gamma^{c}\)), and roll angle (\(\phi^{c}\)), each regulated via proportional controllers. Additionally, a PD controller is utilized for managing the roll angle (\(\phi\)). The adaptation introduces extra constraints influenced by altitude and course angle.  
APP generates a sequence of waypoints for the aircraft to follow. Given the stochastic nature of real-world flight conditions, the responsibility for precise waypoint tracking is delegated to the low-level controller, which lies beyond the scope of APP. Nonetheless, simulating this behavior is essential for validating the paths generated by the APP prior to deployment. The waypoint-following algorithm proposed by [PERSON] et al. [40] was utilized to control the virtual UAV.
## 3 Results and Discussion
### Metagene Analysis  
The encoding matrix \(H\) was used to cluster the HMCLs and to extract the metagene expression profiles, whereas the basis matrix \(W\) contains the metagenes, represented as sets of genes with different weights. We extracted five different metagenes. Figure 7 depicts the top most 20 genes involved in each metagene. The genes have been ordered and visualized, according to their influence (expressed in percentage) on each metagene. For a broader analysis, twenty genes are reported even though we observed the highest weights for the first five/six genes. These graphs give more insights to the domain experts that are helpful to drive the biological analysis of the HMCLs.  
As an example, analyzing Figure 5 we know that the HMCL KMM1 is mostly composed of the expression of metagene 1, partly by metagene 2, and minimally by metagene 4. Figure 7 specifies the genes that contribute to each metagene definition. The genes PTGES2, HSD17B4, LTA4H, ACAA, CBRI,  
Figure 6: Metagene expression profile through the HMCLs.  
and PPARD are the heaviest in the definition of metagene 1, so the expression of these genes highly affects the HMCL KMM1; the genes MGST2, GGT1, SCP2, FPR2, PTGES3, PLAG10, and CYP2W1 from metagene 2 only partly influence KMM1, and finally, the genes CYSLTR1, CBR1, CYP2J2, EHHADH, CYP1A2, and ACOX3 from metagene 4 slightly condition KMM1. It is possible to conduct a similar analysis on the other cell lines, looking for a relationship among the genes and the cellular functions they are involved in.  
The taxonomy in [24] can give more insight into the role of the genes in each metagene. For example, the genes in the first metagene mostly belong to the cyclooxygenaseases and related enzymes involved in eicosanoid biosynthesis in humans, enzymes involved in eicosanoid catabolism in human, eicosanoid receptors, and transcription factors in humans. Similar analyses could be conducted on the other metagenes.  
Figure 7: Composition of the metagenes MG1, MG2, MG3, MG4, and MG5 in terms of the original genes.
# An Improved SPEA2 Algorithm with Local Search for Multi-Objective Investment Decision-Making
## 2 Methodology
### Evaluation Criteria  
In order to evaluate the convergence performance of the algorithm, a closer evaluation method for convergence index is used in this work [43]. This method evaluates the approaching degree of the algorithm by calculating the minimum distance from the solution set to the reference set or the Pareto optimal solution set. The smaller the distance, the higher the approaching degree of the solution set. When evaluating the convergence performance of a multi-objective evolutionary algorithm, this method needs to determine the reference set \(P^{*}\), which selects the known Pareto optimal solution set or the non-dominant set of past generations. \(P^{*}=\text{Nondominated}(\text{U}_{\text{t=0}}^{\text{T}}\text{NDSet}^{t})\), where \(\text{NDSet}^{t}\) is the non-dominant set of \(t\)th generation. Because the Pareto optimal solution set is generally difficult to determine, the reference set \(P^{*}\) usually takes the non dominated set of past generations. Firstly, calculate the shortest distance from \(i\) to \(P^{*}\) for each individual in the current non dominated set:  
\[Pd_{i}=\text{min}_{j=1}^{|P^{*}|}\sqrt{\sum_{k=1}^{m}\frac{f_{k}(i)-f_{k}(j)}{ f_{k}^{\text{max}}-f_{k}^{\text{min}}}}, \tag{3}\]where \(f_{k}^{\max}\) and \(f_{k}^{\min}\) are the maximum and minimum values of the \(k\)th target in reference set \(P^{*}\) respectively. Calculate the average of \(Pd_{i}\):  
\[C(P^{t})=\sum_{i=1}^{\text{NDSet}^{t}}\frac{Pd_{i}}{\text{NDSet}^{t}}. \tag{4}\]  
In order to satisfy \(C(P^{t})\in[0,1]\), normalization processing is used as:  
\[\bar{C}(P^{t})=C(P^{t})/C(P^{0}), \tag{5}\]  
where \(\bar{C}(P^{t})\in[0,1]\). The smaller the value of \(\bar{C}(P^{t})\), the closer the solution set to the Pareto optimal boundary.