esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sparse Direct Algorithms
### The Importance of Scaling  
Scaling the problem is a key part of solving large sparse linear systems. In addition to reducing the residual, in the indefinite case, a good scaling can help from a purely computational standpoint by reducing the number of delayed pivots and, hence, the size of the computed factors and overall solution time. In this case, the factorization of the scaled matrix \(\overline{A}=SAS\) is computed, where \(S\) is a diagonal scaling matrix. How to find a good \(S\) is an open question. A number of options for scaling are included within HSL and these are discussed in [32; 33]. The indefinite HSL solvers in this study allow the user to specify scaling factors on the call to the factorization. In addition, HSL_MA86 and HSL_MA97 will compute scaling factors using either an iterative method based on matrix equilibration or a weighted bipartite matching. The latter may optionally combine the scaling and the ordering, and this can be beneficial in reducing delayed pivots for tough indefinite problems [30]. For very large matrices that will not fit into main memory, HSL_MA77 offers the option of scaling the matrix using an out-of-core iterative algorithm that aims to compute \(S\) so that the infinity norm or one-norm of each row and column of \(\overline{A}\) is approximately equal to one. Since each iteration involves reading the stored matrix data from disk, this is expensive and is only recommended if the user is unable to temporarily hold the matrix in main memory and call one of the HSL scaling packages.
# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 2 Methodology
### Optimal Number of Sub-Models  
For deciding the optimal number of sub-models or modes, a novel method that uses the consistency of the variable selection result is proposed. The hypothesis here is that the true model for a driver should be replicative; thus, in this study, optimality is defined as the most structural-consistent identified hybrid system model, which in fact can be interpreted as the optimum trade-off between the model robustness and model error. The step-by-step procedure of the novel method is given in Algorithm 1.  
The most important detail of Algorithm 1 can be summarized as, taking a cluster, randomly divide or partition the cluster into some number of folds. For each fold, variable selection using BIC is carried out, and the results of the variable selection are compared between the folds. The best cluster is the cluster that replicates the same variable selection results amongst the folds. A pictorial example of using consistent variable selection for deciding the optimal number of sub-models or modes is shown in Figure 2.  
```
0:\(K-1\) sets of clusters, \(C_{i=1,\ldots,s}\) for \(s=2,\ldots,K\)
0: Optimal number of sub-models, \(opt_{s}\)  Initialisation :
1: Given the Input
2:for\(p=1\) to \(P\)-number of times do
3:for\(s=2\) to \(K\)do
4:for\(i=1\) to \(s\)do
5: Randomly partition \(C_{i}\) into \(n\) folds
6:for\(q=1\) to \(n\)do
7: Perform variable selection using BIC on \(C_{i}^{q}\)
8: Store variable selection result in \(V_{i}^{q}\)
9:endfor
10: Let \(V_{i}^{q=1,\ldots,n}=V_{i}^{r=1,\ldots,n}\); and \(T_{i}^{q,r}=1\) if \(V_{i}^{q}=V_{i}^{r}\), or 0 otherwise
11: Compute \(freq_{C_{i}}=\dfrac{1}{n^{2}}\sum_{r=1}^{n}\sum_{q=1}^{n}T_{i}^{q,r}\)
12:endfor
13: Compute \(avg.freq_{s}=\dfrac{1}{s}\sum_{i=1}^{s}freq_{C_{i}}\)
14:endfor
15: Store \(temp_{opt_{s}}^{p}=\max_{s}(ave.freq_{s})\)
16:endfor
17:return\(opt_{s}=\text{mode}(temp_{opt_{s}}^{p=1,\ldots,P})\)
```  
**Algorithm 1** Deciding the optimum number of sub-models
# Adhesive Single-Lap Joint Evaluation Using Ultrasound Guided Waves
## 4 Results
### Phase Velocities and Displacements in a Bonded Lap Joint in Water  
For the bonded region, the transfer matrix method introduced in Section 2.2 was used to obtain the modal solutions of the matrix system. The real part of the solutions, which is the phase velocity of the different modes, is presented in Figure 6.  
Figure 4: Leaky attenuation of the fundamental modes S0 and A0 in an aluminum plate of 4 mm thickness immersed in water.  
Figure 5: Displacements of the fundamental modes in an aluminum plate of 4 mm thickness: (**a**) S0; (**b**) A0.  
The total thickness of the system is 8.15 mm (two 4 mm aluminum plates and 0.15 mm adhesive), so for the working frequency, four different modes coexist: A0, S0, A1, and S1. The behavior is very similar to that previously obtained for the single plate, which is justified by the fact that the system is mostly composed of aluminum with a very small amount of epoxy. The behavior difference is characterized by an increase in the number of propagation modes, which is related to the increase in thickness. The attenuation values due to leakage for 500 kHz were obtained in the same way as previously for the single plate and are presented in Table 2.  
The displacements for the mentioned modes can also be obtained using the transfer matrix method [66]. Considering the boundary conditions for all interfaces, a matrix composed of the amplitude coefficients for the three layers is obtained. As the system is immersed, there are two additional unknowns corresponding to the amplitude of the longitudinal waves at the bottom and top of the plate. By solving the matrix system, the displacement values in arbitrary units as a function of the thickness were evaluated and are presented in Figure 7. There is now a more complex pattern when compared with the single plate, which is due to the epoxy adhesive layer. Looking at the displacements at the surface of the system that are in contact with the water, the relation between the out-of-plane and in-plane values agrees with the attenuation values presented in Table 2. For example, in the S1 mode, the displacement is essentially in-plane, giving rise to extremely low attenuation values. For the A1 mode, the two types of displacements are similar, giving rise to already noticeable attenuation values. Finally, for A0 and S0 modes, the out-of-plane displacements are greater than the in-plane ones, leading to higher attenuations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **A0** & **S0** & **A1** & **S1** \\ \hline Attenuation [Np/m] & 22.7 & 37.5 & 10.5 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attenuation due to leakage to the fluid in the bonded plate region for the different propagation modes.  
Figure 6: Phase velocity dispersion curves for two bonded aluminum plates immersed in water.
## 4 System Design
### Vital Sign Processing  
Figure 8 shows the life signal processing flow.  
Figure 7: Binocular camera ranging principles.  
1. Range frequency Fourier transform (FTT): FFT is performed on the raw complex signals in an ADC to obtain the range spectrum.
2. Binocular camera ranging: a binocular camera is used to acquire the images, calibrate the target, and calculate the target depth information by using the parallax method.
3. Range bin tracking: with the centralized target depth information, the range error and radar distance resolution are combined to determine the distance range of the human targets and identify the maximal energy spectrum peak within that range to obtain the corresponding range bins.
4. Phase extraction: The phases in the target range bins are cyclically extracted at a frame periodicity of 50 ms. That is, the target phases are extracted once per frame. If the radial distance between the target and the original range bin changes, the range-bin tracking algorithm is used to calculate the current range bin, followed by cyclic phase extraction and the remittance of N frames to obtain the framewise phase variation in the target. This value can also be regarded as the relationship between the phase and time of the target.
5. Phase unwrapping: Because the phase values are in the range \([-\pi,\pi]\), phase unwrapping must be employed to obtain the actual displacement curve. Whenever the phase difference between consecutive values are outside the range of \(\pm\pi\), unwrapping is performed by subtracting \(2\pi\) from the phase.
6. Phase difference: this is performed on adjacent phases to strengthen the pulse signal by subtracting the successive phase values and removing the phase shift.
7. Bandpass filtering: the phase values (frequency values caused by phase change) are filtered according to differences in the HR and BR for the subsequent interpretation of respiration and heartbeats.
8. Spectral estimation: FFT is conducted to obtain the corresponding BR and HR in N frame frequencies based on the waveform amplitude and characteristics.
9. Decision: respiration and frequencies are acquired for a certain period, wherein the HR and BR at that time are determined according to different confidence indicators, and the relationship between the HR and BR over time is obtained.  
Table 1 shows the configuration parameters of the millimeter wave radar.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Typical Values** \\ \hline Bandwidth & 4 GHz \\ Fc (starting frequency) & 77 GHz \\ Fs (slow time axis sampling) & 20 Hz \\ Fs\_ADC (ADC sampling rate) & 2 MHz \\ N (samples per chirp) & 100 samples \\ Tc (chirp duration) & 50 us \\ \hline \end{tabular}
\end{table}
Table 1: Chirp parameters.  
Figure 8: Vital sign processing flow.
## 3 Materials and Methods
### Usability Testing
#### 3.3.2 Data Collection  
Various data collection methods were used to collect both quantitative and qualitative data. These methods included pre- and post-questionnaires and direct observation during the test sessions.  
* Pre-questionnaire: A pre-questionnaire was used to collect data on the participants' usage of the Noor, Faris, and iEN systems (see Appendix B). Participants were asked about the tasks they  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & & **Selected Pages** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Noor System} & \(\bullet\) Logging in \\  & & \(\bullet\) Home page \\  & & \(\bullet\) Skills \\  & & \(\bullet\) Entering grades \\  & & \(\bullet\) Learning resources \\  & & \(\bullet\) Borrowing books request \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Faris System} & \(\bullet\) Logging in \\  & & \(\bullet\) Home page \\  & & \(\bullet\) Requesting a vacation \\  & & \(\bullet\) Requesting a salary certificate \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{iEN System} & \(\bullet\) Logging in \\  & & \(\bullet\) Home page \\ \cline{1-1}  & & \(\bullet\) Downloading course content \\ \cline{1-1}  & & \(\bullet\) Playing interactive content \\ \cline{1-1}  & & \(\bullet\) Viewing course plans \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected pages of the three systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Grade** & **Level of Blindness** & **Years of Experience** & **Computer Usage Rate** \\ \hline P1 & Elementary & Completely & 6 & Occasionally \\ P2 & Secondary & Partially & 7 & Occasionally \\ P3 & Secondary & Completely & 18 & Occasionally \\ P4 & Elementary & Completely & 15 & Very frequently \\ \hline \hline \end{tabular}
\end{table}
Table 3: Profiles of participants.  
usually performed on the three systems and their usage rate. The pre-questionnaire also included questions about the hardware and software tools they used to access the three systems. We also asked the participants to rate the systems from different perspectives.
* Usability testing sessions: During the usability testing sessions, several types of data were collected while observing the participants performing a total of ten developed task scenarios; three each for Noor and Faris and four for iEN. These data included quantitative data, such as the time to complete the task and the task completion status. Qualitative data, which included difficulties encountered by the participants when performing the tasks and the feedback from the participants, was also recorded through written notes.
* Post-questionnaire: After performing the tasks for each system, users were asked to fill in a post-questionnaire in order to assess their satisfaction with the tested system. A separate form for each system was created (see Appendix C). Preferences information was gathered using seven five-point Likert scale questions. Furthermore, five open-ended questions were created to collect some qualitative data regarding the participants' opinions and feelings toward the tested systems.
# Artificial-Hand Technology--Current State of Knowledge in Designing and Forecasting Changes
## 4 Actuating System
### Underactuation and Shape-Adaptive Motion  
Considering robotic-hand parts always leads to the need for the reconciling of two objectives: number of actuators and control strategy. There are many schemes for reducing control complexity where precise kinematic calculations were presented [171; 172; 173], and robot trajectory was based on captured human movements [174]. One of the possible and already adopted methods is underactuation. Generally speaking, a hand is underactuated when it has a smaller number of actuators than the number of degrees of freedom. Since some of the joints are passively controlled, a shape-adaptive grip occurs, which was observed in human adhesion, too [134; 175; 176].  
In [84; 112; 139], four-bar mechanism variations were used to distribute drive to distal phalanges. While their first had 16 DOF, actuated by a record one actuator through a complex gear system in the palm, the last was able to perform even some precision grasps. Contrary to this, both [81] and [106] used a single tendon attached to distal phalanges to control a 9- and 10-DOF hand, respectively, with three actuators. Moreover, [PERSON] and [PERSON] showed that their device could perform the 10 most necessary grasps in daily activities. The same solution was applied in [40; 126], where a special tendon-routing technique provided following rotation by distal digits when previous ones were blocked by an object. It was also used in the X hand, of which the tendon system was additionally equipped with springs and that passed the [PERSON] test [119].  
It should be noted that all passive pneumatic hands meet the requirements of shape adaptation because of uncontrolled finger flexion that is affected by unfolding an object in a finger chain or finger-palm grip [104; 114].
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 4 Experiments and Results
### Quality Measures  
We used two evaluation indicators to evaluate our method, including the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Metric (SSIM). These are common indicators for image quality evaluation. PSNR is generally used for engineering projects between maximum signal and background noise. It is based on the error between the corresponding pixels, expressed as:  
\[PSNR=10\times\log_{10}\left\{\frac{L^{2}}{MSE}\right\} \tag{8}\]\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\left[y(i,j)-x(i,j)\right]^{2} \tag{9}\]  
where \(x\) represents the generated image, \(y\) represents the really clear image and the image size is \(m\times n\). MSE represents the mean square error between \(x\) and \(y\) and \(L\) is the dynamic range of the pixel values. The higher the PSNR value, the better the generated image. SSIM is used to evaluate the similarity of two images by using the mean value as the brightness estimation, the standard deviation as the contrast estimation and the covariance as the structure similarity measurement. The SSIM expressed as:  
\[SSIM(x,y)=\frac{\Big{(}2\mu_{x}\mu_{y}+c_{1}\Big{)}\Big{(}2\sigma_{xy}+c_{2} \Big{)}}{\Big{(}\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\Big{)}\Big{(}\sigma_{x}^{2}+ \sigma_{y}^{2}+c_{2}\Big{)}} \tag{10}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the averages of \(x\) and \(y\), \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances of \(x\) and \(y\), \(\sigma_{xy}\) is the covariance of \(x\) and \(y\), \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\) are the constants used to maintain stability and \(k_{1}=0.01\) and \(k_{2}=0.03\) are the default values. The value range of SSIM is 0 to 1. The closer the SSIM value is to 1, the more similar the two images are.
# Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm
## 7 Conclusions  
Machine learning algorithms are extensively being in used for the prediction of numerous diseases, e.g., diabetic retinopathy, COVID-19. The prediction/diagnosis is achieved with the aid of a classifier. In this work, a new classifier based on DT and tabu search is presented to optimize the performance and accuracy at the same time. Half a dozen supervised machine learning (supervised ML) techniques (including TSODT) were exploited to diagnose the accustomed COVID-19 illness victimization using the clinical information of the patients. The experimental results reveal that the proposed classifier algorithm provides a higher performance for the medical applications with an accuracy of 98%. Additionally, the execution time required for the training is 55.6 ms. Consequently, the proposed classifier will help the relevant community solve different medical, social and statistical problems with an elevated accuracy and performance parameters.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Classifier**} & **NB** & **LR** & **DT** & **SVM** & **RF** & **KNN** & **TSODT** \\ \hline NB & 0 & 14 & 2 & 5 & 4 & 9 & 18 \\ \hline LR & 3 & 0 & 9 & 7.5 & 10 & 7.1 & 11 \\ \hline DT & 5.6 & 9.3 & 0 & 8 & 6 & 3.6 & 9.6 \\ \hline SVM & 6.9 & 11 & 5 & 0 & 8.5 & 9.2 & 15 \\ \hline RF & 5.8 & 10.7 & 10 & 5.5 & 0 & 9.5 & 4.3 \\ \hline KNN & 14 & 17 & 9 & 8 & 5.9 & 0 & 10.3 \\ \hline
**TSODT** & 8.6 & 15 & 20 & 4 & 10 & 8.1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results using post hoc analysis.  
Figure 9: Performance validation (based on AUROC) of our proposed TSODT classifier.  
**Author Contributions:** Conceptualization, H.T. and M.R.; methodology, M.A.H.; software, M.A.H. and Z.U.A.; validation, M.A.H., Z.U.A. and M.R.; formal analysis, M.R.; investigation, S.S.A.; resources, S.S.A. and M.H.S.; data curation, M.H.S.; writing--original draft preparation, M.A.H.; writing--review and editing, S.S.A. and M.R.; visualization, Z.U.A.; supervision, S.S.A.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the King Abdulaziz City for Science and Technology (KACST) under Grant 14-ELE1049-10, and in part by the Science and Technology Unit (STU), Mecca, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We are thankful for the support of King Abdul-Aziz City for Science and Technology (KACST) and Science and Technology Unit (STU), MAKKAH, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 4 Skyband and Dominating Query Processing
### Data Map and Ranking  
We first vertically split the dataset into \(m\) partitions, if the number of attributes in a dataset is \(m\). Therefore, if the number of data subsets was \(n\), then the total number of partitions was equal to \(n\times m\) (e.g., \(\{s_{1,1},\cdots,s_{1,m},\cdots,s_{n,1},\cdots,s_{n,m}\}\)). For simplicity, we denote \(\{s_{1,1},s_{2,1},\cdots,s_{n,1}\}\), \(\{s_{1,2},s_{2,2},\cdots,s_{n,2}\}\), and \(\{s_{1,m},s_{2,m},\cdots,s_{n,m}\}\) as \(\{S_{1}\}\), \(\{S_{2}\}\), \(\cdots\), and \(\{S_{m}\}\), respectively. In the example, \(DS_{1}\) had two attributes \(a_{1}\) and \(a_{2}\). We split \(DS_{1}\) into two partitions called \(s_{1,1}\) and \(s_{1,2}\). Since we had two partitions, we needed at least two map workers to complete the computation.  
Figure 3 illustrates the \"data map and ranking\" procedure. Figure shows that objects \(O_{1,2}\) and \(O_{2,1}\) have rank \"1\" for attribute \(a_{1}\) and \(a_{2}\), respectively. Therefore, \(O_{1,2}\) had the smallest \(a_{1}\) value and \(O_{2,1}\) has the smallest \(a_{2}\) value.  
Recall that in the Hadoop framework in which we have implemented our system, each map worker operates on a non-overlapping partition of the input file independently and the worker emits key-value pair lists in parallel according to a user-defined \"map function\". In proposed algorithm, each map worker produces \((Val,OID)\) pairs, where \(Val\) is the numeric value of each object in the attribute domain and \(OID\) is the corresponding object \(ID\). Next, the reduce workers begin their processing job. They receive \((Val,OID)\) pairs as inputs and produce \((OID,Rank)\) pairs for each object, where \(Rank\) is the ascending order sorted rank value of each object in the attribute domain. To calculate the rank value for each key-value pair of \(S_{l}(l=1,\cdots,m)\), the corresponding reduce worker sorts its attribute in ascending order. The reduce worker, then, replaces the values with their corresponding ascending rank value.
# Integration of Deep Learning and Collaborative Robot for Assembly Tasks
## 3 Implementation
### Voice Recognition System  
In the voice recognition system, a neural network was implemented to classify the audio input from a microphone. The audio recognition process comprises three stages: audio recording, preprocessing, and classification. For audio recording, an nRF52840 SoC Nordic semiconductor(r) (Trondheim, Norway) processor coupled with an omnidirectional digital microphone MP34 DT06 JTR from STMICROELECTRONICS(r) (Geneve, Switzerland) was utilized. The audio amplitude of the audio wave is captured in real-time, allowing for the collection of these amplitudes over time to reconstruct and analyze the audio wave at a later time.  
The audio wave capture initiates when the amplitude exceeds a predetermined, experimentally set threshold. This ensures that the recording begins when the microphone detects a high level of sound activity. Each audio sample has a duration of 2 s and contains 3327 amplitude registers. As mentioned in Section 2.2, 2D spectrogram representations of audio signals are widely used for audio classification. Therefore, prior to analyzing the audio samples collected, the audio data were processed by a Python3 script that generated a 2D spectrogram.  
The spectrogram is generated by applying a Hamming window to the audio segments. A fast Fourier transform with a size of 256 was used, along with a 50% overlap between segments, representing the magnitude of spectral coefficients. This provides a visual representation of the spectral energy of the audio signal over time, enabling classification in the voice recognition system. Once the audio is represented as a 2D spectrogram, the voice recognition system utilizes a CNN to classify the audio into 1 of the 11 different classes recognized as commands. The neural network consists of four convolutional layers, each followed by a max pooling layer and a final dense layer with SoftMax activation to classify audio into the specified commands, as illustrated in Figure 6.  
The neural network was trained with a dataset of 300 audio samples per class, recorded by 10 different individuals. An additional noise class was added to recognize when no command was spoken. The dataset was distributed as follows: 80% for training, 10% for validation, and 10% for testing. To simplify the problem, two different models were trained, one to recognize numbers and the other one to recognize actions. Both models included the command 'cancel' to be able to use this command regardless of the model. The whole process was integrated into a Python3 script running an ROS node, which receives the audio waveform as an input and outputs the class predicted by a neural network and the confidence rate of the prediction made. This node also oversees switching between the two models (numbers and actions models) used along the robot routines.  
Figure 6: CNN for voice command structure and sizes.