esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
## 2 Beamforming and Deconvolution
### Deconvolved Beamforming
#### 2.2.3 Fast R-L Algorithm  
A detailed description of the R-L recursive algorithm is referred to as Algorithm 1, from which it can be estimated that, per iteration, the multiplication number is \(2N^{2}+N\) (\(N\) is the number of beams), the division number is \(N\), and the addition number is \(2N(N-1)\). The time consumption of a multiplication or division is far greater than that of an addition or subtraction on the computer or digital signal processor (DSP). Therefore, the computation complexity of an algorithm mainly depends on how many times there are multiplications and divisions. As shown, the computational complexity of the R-L algorithm is \(\mathrm{O}\big{[}N^{2}\big{]}\). In other words, the amount of calculation is proportional to the square of the number of the beams. As \(N\) becomes larger, the amount of calculation increases sharply.  
```
Initialization: \(\hat{\mathbf{S}}_{0}=\mathbf{F}\) (or \(\hat{\mathbf{S}}_{0}=[1,1,\ldots,1]_{1\times N}^{T}\)), \(N\) is the number of elements of \(\hat{\mathbf{S}}_{0}\) or the number of beams. for \(k=1,2,3,\ldots\) { \(\hat{\mathbf{F}}_{k}=\mathbf{F}_{p}\otimes\hat{\mathbf{S}}_{k}\); Estimate \(\mathbf{F}\) at the iteration time \(k\) \(\hat{\mathbf{S}}_{k+1}=\hat{\mathbf{S}}_{k}\big{(}\mathbf{F}_{p}*\frac{\mathbf{F}}{\hat{\mathbf{F }}_{k}}\big{)}\); Estimate \(\mathbf{S}\) at the iteration time \(k+1\) }
```  
**Algorithm 1**R-L algorithm.  
It can be seen through the careful analysis of the R-L algorithm that the reason why it requires a huge computation is because of the convolution and correlation operations, and their computation complexity is \(\mathrm{O}\big{[}N^{2}\big{]}\). According to the convolution theorem, the spectrum of the convolution of two signals in a time domain is equal to the product of their spectrum in the frequency domain. The spectrum of the signal can be efficiently calculated utilizing the fast Fourier transform (FFT) technique, and the computation complexity of FFT is between \(\mathrm{O}[N]\) and \(\mathrm{O}\big{[}N^{2}\big{]}\). Therefore, by utilizing the convolution theorem and FFT technique, the computation complexity of the R-L algorithm can be greatly reduced. The specific implementation methods is referred to as Algorithm 2, from which we can estimate, per iteration, that the multiplication number is \(8N\log_{2}N+9N\), the division number is \(N\), and the addition number is \(8N\log_{2}N+4N\). The computation complexity is between \(\mathrm{O}[N]\) and \(\mathrm{O}\big{[}N^{2}\big{]}\)
# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 3 Problem Definition and Formulation
### Long Short-Term Memory (LSTM) Networks  
LSTM networks represent a prominent class of NN extensively employed in stock-price prediction research. LSTMs, categorized as recurrent NNs, distinguish themselves from their feed-forward counterparts by incorporating the previous output as an input for the subsequent timestamp. Unlike feed-forward NNs like MLP, which treat the first and 1000 th inputs similarly, recurrent networks process data sequentially. This characteristic makes LSTMs particularly suitable for addressing the inherent linearity of MLPs. Despite the resemblance of basic linear transformations within an LSTM to those in an MLP, the pivotal feature contributing to the widespread adoption of LSTMs is the integration of gates and states, which fundamentally alter the nature of the NN.  
To construct the LSTM network, similar to the MLP, we specify two hidden layers with the objective of minimizing the mean squared error (MSE) as the cost function. Additionally, based on the demonstrated effectiveness in prior studies [156; 157], we have chosen to combine LSTM and the Adam optimization algorithm for this LSTM NN. Moreover, con sidering the network's complexity, we set the number of hidden neurons and the maximum number of epochs to 60 and 1000, respectively, when the training sample size is 1000.
## Materials and Methods
### Test Configuration and Preparation of Fracture Specimens  
In order to assess the size effect on the NFR of PMMA and GPPS materials, RV-notched Brazilian disk (RVNBD) and RV-notched semi-circular bend (RVSCB) specimens were employed. Figure 1 presents the schematics of RVNBD and RVSCB specimens. RVNBD is a circular disk with radius R = D/2 (D is the diameter of BD sample) and thickness t, having a central rhombic hole in which the large diagonal and notch opening angle are, respectively d = 2a and 2\(\alpha\) (a being half of the defect length). The lower and upper corners of the rhombic hole are blunted by the radius of \(\rho\), generating a RV-notch. The RVSCB is a semi-circular disk with a radius of R and thickness of t, in which a RV-notch is generated on the edge of the disk. The angle and length of the resulting RV-notch are 2\(\alpha\) and a, respectively. The loading condition in the RVSCB sample is provided by a three-point fixture in which the spam between the supports is 2S. When the direction of the applied load and the bisector line of the RV notch are the same in the RVSCB specimen, and the bottom supports are symmetric relative to this direction, this sample is subjected to pure mode I. Likewise, pure mode I loading is archived in the RVNBD sample by setting the applied load along the bisector line of the RV notch.  
Since the fracture toughness K\({}_{\text{lc}}\) of PMMA and GPPS materials must be specified for calculating the FPZ length, several cracked Brazilian disk (CBD) and semi-circular bend (SCB) specimens were prepared and tested. The CBD and SCB specimens are similar to the RVNBD and RVSCB samples, except that a sharp crack of length 2a for CBD and length a for SCB is generated in the center of the sample instead of in the rhombic hole and other configurations remain constant. Table 1 gathers the dimensions and loading conditions for all samples.  
All samples and notches were cut by a waterjet machine from PMMA and GPPS 6 mm thick sheets. The cracks in both CBD and SCB were created by jigsaw with a thickness of 0.2 mm. Then, the generated cracks were sharpened by way of a razor blade with a thickness of 40 \(\upmu\)m. Figure 2 shows a schematic of all tested specimens with various sizes.
## 3 Field Investigation and Laboratory Tests
### Laboratory Tests  
The physical and mechanical parameters of coal and rock masses were used for numerical simulation, scheme design, and rock classification. To accurately analyze the mechanism and influencing factors of the deformation and failure of the surrounding rock of the roadway, relatively intact rock samples were selected for the physical and mechanical tests.  
Roadway excavation revealed that yellow mudstone has low strength and is easily broken, making it difficult to process them into test blocks for uniaxial compressive tests. Therefore, only the uniaxial compressive strength (UCS) of sandy mudstone was tested. The samples were made into 50 mm wide and 100 mm high standard cylinders. Each test group included three rock samples. The test was completed using an electronic universal testing machine.  
Figure 2: (**a**) Flattening failure of a U-shaped steel shed top (**b**) Distortion failure of a U-shaped steel shed foot.  
The test results are presented in Table 1. The average compressive strength of sandy mudstone was 15.49 MPa, the average tensile strength was 0.72 MPa, and the average shear strength was 3.13 MPa; for the yellow mudstone, the average tensile strength was 0.42 MPa and the average shear strength was 1.98 MPa. The International Society for Rock Mechanics (ISRM) describes rocks with UCS of 0.25-25 MPa as'very weak' to 'weak' (ISRM 1981). [PERSON] [37] considered the upper limit of UCS to be 25 MPa for soft rocks. According to the rock strength classification, both sandy and yellow mudstones are soft rocks, and their unique mechanical properties are extremely detrimental to the stability of the surrounding rock of the roadway.
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 6 Conclusions  
In this paper, we introduced a new model for clustering Arabic text. The model is consolidated by SOM and the GWO algorithm. The purpose of applying GWO is feeding SOM with new weights during the initiation of the network. In our experiment, the model was tested on two datasets, MSA and NADA. To obtain an equitable clustering evaluation, several experiments were executed on the same datasets. These included using SOM independently, with optimization, and also using different clustering techniques (i.e., K-Means). After evaluating all the results, we concluded that our introduced model resulted in a significant improvement in clustering accuracy and training time. We hope these results advance the broader Arabic Natural Language Processing (ANLP) field by giving researchers insights on how to improve the results of the clustering they might apply in their research. In the future, we plan to evaluate optimization techniques other than GWO and measure their impact for clustering Arabic texts. We also plan to gather new Arabic datasets encompassing a wider range of topics and subtopics. These additions will allow us to conduct more comprehensive evaluations, ultimately enriching the field of Arabic Natural Language Processing (ANLP).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Representation** & **Weights Initialization (GWO Optimization)** & **Training** & **Total Time** \\ \hline \multirow{2}{*}{MSA} & CV & 02:23 & 04:37 & 07:00 \\  & TF-IDF & 01:38 & 03:10 & 04:48 \\ \hline \multirow{2}{*}{NADA} & CV & 05:00 & 13:00 & 18:00 \\  & TF-IDF & 04:53 & 12:08 & 17:01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Breakdown of the GWO-optimized SOM running time (in minutes).  
**Author Contributions:** Conceptualization, M.B.A. and S.L.-M.-S.; methodology, M.B.A.; software, M.B.A.; validation, M.B.A., S.L.-M.-S. and A.A.; formal analysis, M.B.A.; investigation, M.B.A.; resources, M.B.A.; data curation, M.B.A.; writing--original draft preparation, M.B.A.; writing--review and editing, A.A.; visualization, M.B.A.; supervision, S.L.-M.-S.; project administration, S.L.-M.-S.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Participants  
A total of 20 native American English speakers with normal hearing thresholds were recruited from the University of Iowa student population (mean age: 23.2 years, SD: 1.33 years, 6 males: 30%). All the participants had 20 dB HL or lower thresholds at any tested frequency, measured in octaves from 250 to 8000 Hz. After consenting to participate in the trial, participants were randomized to either the Experimental or Placebo Group (i.e., single-blinded design). All participants underwent four consecutive weeks of training lasting one hour per week, as well as pre- and post-training speech-in-noise (SiN) tests at their first and last visits. The work was finished in compliance with the International Medical Association's Code of Ethics, and written informed consent was acquired (Declaration of Helsinki). The Institutional Review Board at the University of Iowa examined and approved all study methods [11].  
A power analysis based on the effect size reported by earlier perceptual training studies, such as [9], can support the sample size. These estimates indicated that 10 people per group would be needed for the current study, assuming a significance level of 0.05 and a power of 0.80. We chose a 4-week training period, as did [9], in order to (1) ensure overnight consolidation, which has been claimed to be essential for perceptual training [33; 34; 35] and (2) prevent learning and memory of speech stimuli that were used in pre- and post-training tests [36; 37].
## Appendix C MAP Variability among Different Patients and the Safety Intervals  
The boxplot in Figure 11 illustrates the distribution of MAP values for each patient, highlighting the variability in MAP measurements.  
The observed differences in MAP values among different patients can be attributed to several factors, including individual physiological differences, varying responses to anesthesia, and underlying medical conditions. Some patients exhibit wider variability in their MAP measurements, as shown by the larger distribution. Based on this variability, it is evident that forecasting the MAP every 5 s during surgery is crucial. The situation where the MAP exceeds the safety limits (65-75 mmHg), highlighted in green in the figure, underscores the need for real-time forecasting of the MAP. This paper marks the initial step toward a real-time, online forecasting procedure using AI tools. Future work will focus on developing an algorithm for forecasting MAP and detecting when it exceeds the safety limits, thereby significantly reducing the risks associated with these exceedances.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 5 Reconfiguration Tactics
### KubeletConfiguration Manifest  
To reconfigure Kubelets with a changed configuration manifest in a vendor-agnostic manner, we take the approach of privileged Daemonsets [37; 45; 46]. A Daemonset is a workload resource that deploys a pod on every node in a cluster. As the name suggests, a Daemonset is typically used to deploy daemons on nodes, such as resource monitoring and logging tools. We will take advantage of this feature of Daemonset to deploy a privileged pod on each node. This pod can run in the same IPC and network Linux namespaces as the host machine by setting the hostIPC and hostNetwork fields to true in the pod manifest. Moreover, the pod can use nsenter to execute the scripts that are installed in a mounted host directory. In other words, the pod looks like any other normal process on the operating system, with access to the file system and host network stack, etc.  
Therefore, we can let such privileged pods run a script that overrides the Kubelet configuration file in the host file system, modifying the desired configuration fields. For example, the aforementioned CPU Manager policy feature could be enabled by setting the cpu-manager-policy field to true. In addition, by setting the kube-reserved and system-reserved fields of the Kubelet configuration file, we reserve a certain amount of CPU resources for Kubernetes and system components such as Kubelet, container runtime, etc. to prevent node crashes when pods with dedicated cores take all compute resources. Next, the script deletes all Kubelet state files and restarts the Kubelet service with systemctl. After the Kubelet restarts, it will load the modified configuration file, thus having the desired configurations.  
It is worth noting that Kubelet restarts do not modify the cgroup settings of existing running Pods, so the changed configuration settings related to reserving Kubernetes and system resources will not be applied to them. We have to restart these pods if we want them to be subject to the new configurations. Existing pods will also not stop running during a restart of the Kubelet. However, if pods define a readiness or liveness probe [47], these pods might be unsteady from the clients' point of view, and kube-proxy would not forward traffic to them because the Kubelet is stopped and no probing can be performed. The Kubelet restart process is fast, however, and pods that are not ready can usually be restored to the Ready state in less than ten seconds, so pods will not be migrated to another node during this time.
## 5 Discussion
Integrating and managing these data efficiently remains a challenge. * **Cost of Implementation:** The upfront costs associated with implementing DTs systems in livestock farming can be prohibitive, especially for small-scale farmers. Finding cost-effective solutions and demonstrating a return on investment is crucial to encourage wider adoption. * **Technical Expertise:** Operating and maintaining DTs systems demands technical expertise in areas such as data analytics, IoT, and AI. Many farmers and practitioners may lack the necessary skills, highlighting the need for training and support. * **Scale and Scope:** DTs are often applied to specific aspects of livestock production, such as health monitoring or environmental control. Expanding their scope to cover all aspects of the production cycle, from breeding to processing, is a complex task. * **Regulatory and Compliance Challenges:** Compliance with existing regulations and the development of new regulatory frameworks for DTs applications in livestock farming is a significant challenge. Ensuring that these systems adhere to animal welfare and environmental regulations is critical. * **Resource Constraints:** In some regions, limited access to reliable electricity and internet connectivity can impede the implementation of DTs technologies. Addressing these resource constraints is crucial for widespread adoption. * **Data Ownership and Privacy:** Determining the ownership of data generated by DTs systems and protecting the privacy of sensitive information, such as animal health records, is a complex issue that requires clear guidelines and regulations. * **Validation and Verification:** Ensuring the accuracy and reliability of DTs models is essential, particularly when making critical decisions in livestock farming. Developing robust methods for validation and verification is an ongoing challenge. * **Environmental Impact:** While DTs can contribute to sustainability, they also require energy for data processing and maintenance. Assessing and mitigating the environmental impact of digital twin technologies is an open issue. Consequently, while the DTs technology holds great potential for improving livestock production systems, these open issues and challenges need to be addressed to facilitate their effective implementation. Collaboration among researchers, industry stakeholders, and regulatory bodies is essential to create a framework that ensures the ethical, sustainable, and efficient use of digital twins in livestock farming. Overcoming these challenges will pave the way for a more productive and sustainable future for the livestock industry.
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 2 Formulation of Analytical Model
### Dataset Generation  
The displacement data of each node was collected under environmental excitation. The number of displacement data that each node collects is 19,950, and the acquisition frequency is 100 Hz. The data were transformed into the interlayer displacement. The interlayer displacement data of 12 nodes (1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12 and 13) were selected as the dataset. The number of interlayer displacement data that each sample contains is 50. There is no overlap between each sample. Thus, the number of each type of sample is 399. The generation process of each type of sample is shown in Figure 3. In this paper, the displacement of each node was used as the input. Therefore, the detection of building displacements is a very important topic. For large infrastructures, it is not practicable to use ordinary laser displacement sensors to monitor the displacement. In order to improve the feasibility and convenience of detection, machine vision methods can be used to obtain the displacement data of each node. For instance, the displacement detection method based on optical flow and unscented Kalman filter can accomplish the displacement of large infrastructures with low noise levels [41].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & \(\mathbf{H\times B\times t_{1}\times t_{2}}\) **(mm)** & **Materials** \\ \hline Columns (No. 1–4) & 500 \(\times\) 400 \(\times\) 12 \(\times\) 16 & \\ Columns (No. 5–12) & 350 \(\times\) 350 \(\times\) 12 \(\times\) 16 & Q235 \\ Beams (1–12) & 350 \(\times\) 350 \(\times\) 12 \(\times\) 16 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of beams and columns.  
Figure 2: The model and the number of beams, columns, and joints.
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 4 Detection Algorithm and Its Applicability  
Object detection algorithms allow a computer system to \"see\" its environs by detecting instances of objects belonging to certain classes in digital visual images or videos. Moreover, these algorithms are classified into two main types: one-stage and two-stage detection algorithms. The former combines all the processes in a single go by predicting bounding boxes around an object making them faster and structurally simple, such as SSD, YOLO, and RetinaNet, while the latter divides the task into two stages, by firstly making a region proposal of objects using deep features and lastly classifying objects with their respective bounding box regression for each object, such as Faster-RCNN, Mask-RCNN, and granulated-RCNN. This makes the two-staged object detection algorithm slower, and structurally complex with better accuracy. However, object detection algorithms have been applied across various spectrums with success, such as autonomous driving, medical feature detection in healthcare, pedestrian detection, theft detection, performance assessment in sports, farm automation, and plant disease detection [49; 50; 51; 52; 53; 54; 55].
###### Abstract  
The use of motion-analysis devices that can measure the progress of rehabilitation exercises for nerve paralysis is increasing because of the need to confirm the effectiveness of treatment for sports injuries. This study developed a new motion-analysis device that can be easily handled compared with the existing VICON motion-analysis device. Motion analysis of the human body (specifically, hip flexion, knee flexion, and trunk rotation) performed simultaneously with the new device and the existing VICON device was compared. Five healthy young men voluntarily participated in this study. Various joint angles were captured using a marker-less multi-view image-based motion-analysis system and a VICON motion capture system with markers during lower-extremity work. Intra-class correlation coefficient (ICC) analysis was used to examine simultaneous- and angular-limit validity and the intra-joint reliability of multi-point image-based motion-analysis systems. Simultaneous validity analysis showed that the highest ICCs for hip flexion, knee flexion, and trunk rotation were 0.924-0.998, 0.842-0.989 or higher, and 0.795-0.962, respectively. We confirmed that this new marker-less motion-analysis system has high accuracy and reliability in measuring joint kinematics in the lower extremities during rehabilitation and in monitoring the performance of athletes in training facilities.  
4
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 3 Results and Discussion
### Effects of MGWP on Microstructure Properties of Mortar  
The microstructure of MGWP-containing mortar was analyzed using Scanning Electron Microscopy (SEM), Fourier transform infrared (FTIR), thermogravimetric analysis (TGA), and differential thermal analysis (DTA). The tests were conducted on mortar samples containing 10% and 30% MGWP. The results of these tests were compared to those of the control mix. The findings of each test are outlined below.