esa-sceva/satcom-chunk-collection huggingface dataset:
# A Piecewise Linear Regression Model Ensemble for Large-Scale Curve Fitting
###### Abstract  
The Linear Hinges Model (LHM) is an efficient approach to flexible and robust one-dimensional curve fitting under stringent high-noise conditions. However, it was initially designed to run in a single-core processor, accessing the whole input dataset. The surge in data volumes, coupled with the increase in parallel hardware architectures and specialised frameworks, has led to a growth in interest and a need for new algorithms able to deal with large-scale datasets and techniques to adapt traditional machine learning algorithms to this new paradigm. This paper presents several ensemble alternatives, based on model selection and combination, that allow for obtaining a continuous piecewise linear regression model from large-scale datasets using the learning algorithm of the LHM. Our empirical tests have proved that model combination outperforms model selection and that these methods can provide better results in terms of bias, variance, and execution time than the original algorithm executed over the entire dataset.  
one-dimensional piecewise regression; non-linear regression; curve fitting; ensemble model; model selection; model combination; model parallelism +
Footnote †: journal: _Algorithms_  
0000-0001-122-4011-1101-1011-1011-1011-1011-1011-1011-1011-1011-1011-1011-1011-1011-1011-10111-1011-1011-1011-1011-1011-  
In this context, regression methods are clearly not an exception. They usually require iterative calculations, and therefore, scaling them up is not straightforward. Even in the case of basic multivariate regression applying ordinary least squares (OLS), the matrix operations that must be carried out can be infeasible in terms of memory, and some efficient matrix decompositions have been proposed (e.g., see [5], chapter 5).  
Specifically, in the context of one-dimensional non-linear regression (such as polynomials, smoothers, or splines), the Linear Hinges Model (LHM), proposed in [9], is an efficient approach to one-dimensional curve fitting under high-noise conditions, that automatically estimates the number and position of the knots of a piecewise linear model. However, its iterative nature over all the training dataset makes it unsuitable for large-scale problems, and it must be adapted in order to enable their concurrent execution.  
This paper presents some ensemble alternatives, based on model selection and combination, that allow for obtaining a piecewise linear model from a large-scale dataset that did not fit in memory. First, the problem is defined in Section 2, then Sections 3 and 4 describe how to estimate the parameters of these models from small and big datasets, respectively. After that, Section 5 presents the proposed methods for large-scale datasets, and finally, Section 6 consists of several empirical analyses of those methods in different problems and our final results.
## 6 State of Art in Control and Automation in Distribution Networks
### Control and Automation at the Medium Voltage (MV) Level  
The exponential rise of DG in distribution networks, which takes place at the MV level, means the distribution system operator (DSO) task becomes more complex. DSO will need to perform tasks in a well-coordinated control process for system stability, which is the primary responsibility of a transmission system operator [110]. One paramount challenge is the voltage spikes at the DG connection point during the planning phase of the interconnections and future planning of the required operational limit of the future. The control of distributed networks is attained in three control phases: primary, secondary, and tertiary control, as depicted in Figure 6. Each control level works at different grid levels and time responses, but communication with high priority is connected between secondary and tertiary controllers [111].  
Primary Control: This control employs measured values from devices and executes decisions autonomously in seconds. This is achieved by IEDs, which control devices, such as on-load tap changers (OLTC) that are controlled by automatic voltage controllers (AVC) relays in substations, and reactive power compensation.  
Secondary control: This is where the monitoring and control of substations happens with the assistance of a substation automation unit (SAU), which keeps network data and then computes state estimations and voltage control algorithms to decide on optimal network state. This information is then sent to controllers responsible for the distribution network [111; 112].  
Figure 6: Distributed automation and control architecture [111].  
Tertiary control: This control is at the control center where the time response is from minutes to hours, it deals with network rearrangement, and energy market decisions are decided here [113].  
The primary objective of automation in electrical networks is to improve fault management to reduce power outages and costs resulting in unsupplied energy and, consequently, improve the reliability of the system [114].
# Automatic Segmentation of Histological Images of Mouse Brains
## 1 Introduction
For instance, [PERSON] et al. developed Nutli, a pre- and post-processing toolbox for histological rodent brain section images [6]. Nutli is notable for its user-friendly interface and efficient handling of very large images, optimizing speed, memory usage, and parallel processing. Despite the amount of benefits we find in this tool, it has a lack of customization and management by the user when it comes to sectioning. [PERSON] et al. introduced an unsupervised method for histological image segmentation based no tissue cluster level graph cup, particularly useful in unsupervised learning scenarios for initial segmentation, especially in the context of cancer detection [7]. [PERSON] et al. presented a workflow for quantification and spatial analysis of features in histological images from mouse brain [8]. They start with a first step of registration of the brain image series to the Allen Mouse Brain Atlas to produce customized atlas maps adapted to match the cutting plane and proportions of the sections with QuickNII software v1 [9]. For the segmentation task, the labeling was segmented from the original images by the Random Forest Algorithm for supervised classification from the ilastik software 1.4.0 [10]. Similarly, [PERSON] et al. worked on an automated brain region segmentation for histological images with single cell resolution [11]. They proposed an hierarchical Markov random field (MRF) algorithm where a MRF is applied to the down-sampled low-resolution images and the result is used to initialize another MRF for the original high-resolution images. A fuzzy entropy criterion is used to fine-tune the boundary from the hierarchical MRF model. While these are examples of automated segmentation methods for histological studies that are likely to multiply in the future, histology-based mouse brain studies were found. [PERSON] et al. [12] developed a two-step automated method for hippocampus segmentation, showing an average segmentation accuracy of 92.25% and 92.11% on real and synthetic test sets, respectively. This method employs a unique combination of Deformable Models and Random Forrest, providing high accuracy. However, it relies on manually identified landmarks, which could introduce variability in results. More recently, [PERSON] et al. [13] using a U-Net-based architecture, efficiently segmented two subregions on histological slides: the Substantia Reticular part (SNr) and Substantia Nigra Compacta, dorsal tier (SNCD). achieving Dice coefficient of 86.78% and 79.28%, respectively. Their approach, which combines U-Net with EfficientNet as an encoder, excels in detecting specific brain regions despite limited training data. However, the model's dependence on the quality and diversity of annotated images could be limitation in broader applications. This demonstrates significant lack of model over the more than 10 years separating the methods proposed by [PERSON] et al. [12] and [PERSON] et al. [13], reflecting the little advancements in automated image analysis in the field of automated mouse histology.
# Real-Time Detection of At-Risk Movements Using Smartwatch IMU Sensors
## 4 Results
### Comparative Experimental Results of Algorithms  
First, we evaluated the performance of each model. For machine learning models, The support vector machine (SVM) methods [17], which used both linear and RBF kernels, were configured with a regularization parameter (C) of 10 and a kernel coefficient (gamma) of 0.001. For the \(k\)-nearest neighbors (\(k\)-NN) model [18], we opted for a simplistic approach by selecting only a single neighbor (\(k=1\)) and used the Manhattan distance. The random forest model [19] was robustly set up with 450 trees and a maximum depth of 20. Lastly, stochastic gradient descent (SGD) [20] was implemented with a log loss function and an elastic net regularization. The learning rate was set at 0.01 with a maximum iteration count of 3000. These models are generally recognized for their low computational costs, making them particularly suitable for embedded systems due to their lightweight nature. For neural network models, we used a one-dimensional convolutional neural network (1D CNN) from the deep learning models for our analysis. The 1D CNN is favored in time series data analysis among other deep learning models due to its relative computational efficiency.  
Table 1 shows that the SGD model shows the greatest improvement in the frequency domain, with about a 34% improvement over the time domain. Most of the models show better results in the frequency domain. This is because the frequency transform creates a sparse representation, which allows for efficient computation by removing or compressing unimportant coefficients [21]. However, the SVM model with the RBF kernel actually decreased the accuracy in the frequency domain by 28.67%. The RBF kernel is designed for nonlinear classification and works based on the similarity between data points in an infinite number of dimensions. Based on this, it can be seen that the transformed frequency domain data are more intuitive in the time domain data and are advantageous for linear data classification problems.  
In both the time and frequency domains, the 1D CNN model shows the highest accuracy. This is because 1D CNN models learn while preserving the temporal order and structure of the input data. Notably, the accuracy in the frequency domain exceeds that in the time domain. This can be explained by the frequency domain's ability to provide vital information about periodic components and other significant features of time series data, which are less discernible in the time domain [22].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-3}  & **Time Domain** & **Frequency Domain** \\ \hline SVM (kernel: RBF) & 74.81\% & 46.14\% \\ SVM (kernel: Linear) & 69.04\% & 94.30\% \\ \(k\)-NN (\(k=1\)) & 76.10\% & 97.79\% \\ Random Forest & 76.10\% & 96.88\% \\ SGD & 58.82\% & 92.46\% \\
1D CNN & 95.64\% & 97.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of model accuracy across time and frequency domains.
# Drug-Food Interactions with a Focus on Mediterranean Diet
## 3 Mediterranean Food Products and Potential DFIs
### Drug-Med Diet Interactions
#### 3.2.1 Vegetables, Herbals, Olive Oil, Cereals, and Nuts
Garlic has shown promising data as an antidiabetic agent; thus it may enhance the pharmacologic effect of antidiabetic medicines [122]. Turnips have demonstrated synergism with antidiabetic drugs towards hypoglycemia while yams have a good quality of evidence for synergy with estrogens and thus special precautions should be made for patients under estrogen therapy [123; 124]. Naturally occurring levodopa and carbidopa have been quantified in fava beans in fair amounts, thus patients with Parkinson's under treatment should be aware of possible synergism with co-administered medications [125]. Concerning olive oil, its protective role against inflammation-related chronic noncommunicable diseases (cardiovascular, diabetes, cancer etc.) have been described thoroughly [126]. Proposed mechanisms involve the action of bioactive constituents of olive oil on interleukin-6 (IL-6) and platelet activating factor (PAF) inflammation pathways. In particular, PAF, which is a class of lipid chemical mediators with messenger functions, has gained research attention as a potential drug target due to its involvement in inflammatory diseases such as allergies, asthma, atherosclerosis, diabetes etc. [127]. Moreover, it is a point of focus as a contributing biological mechanism for anti-inflammatory action of several food products with protective roles against inflammation [31]. Several bioactive phytochemical compounds such as terpenes and constituents in olive oil with PAF action have been related to protective mechanisms against atherosclerosis [128; 129]. In addition, as of today there is no contribution of olive oil or its constituents in potential DFIs. On the other hand, for some terpenes with PAF action, i.e., cedrol, inhibiting properties against human P450 have been described in vitro and further studies are needed to clarify potential DFIs [130]. Regarding potential PD-DFIs of PAF modulators with anti-platelet medications, there are not any reports suggesting potential contribution in DFIs.
## 2 Materials and Methods
### Development of the Human Head Numerical Model  
The development of numerical models representing the human heads of a 77-year-old and a 28-year-old involved several stages, including the acquisition of medical imaging data and the progression through geometric segmentation. This comprehensive process of obtaining, discretizing, and validating finite element method (FEM) models was presented in previous studies by the authors [28,29]. The building model method can be described in short: Medical Data Acquisition and Craniometry - Computer-Aided 3D Modelling and Structure Separation - Finite Element Modelling with Experimental and Submodel Tests - Detailed Modelling of CNS Structures - Verification Tests - Bridging Veins Strain Comparison.  
It is worth underlining that the evaluation presented in this study is examined with strict cooperation between engineers and medical doctors. The medical data were collected, and specific regions of interest, crucial to understanding vascular haemorrhage pathomechanisms, were identified. Patients with average geometric parameters were investigated and chosen to ensure that the head models accommodated a broader demographic. Subsequently, head models of a 77-year-old and a 28-year-old individual were crafted using medical images derived from computed tomography and magnetic resonance imaging. To achieve this, pathology-free patient data were collected in the digital imaging and communications in medicine (DICOM) format from various medical institutions, including the Provincial Specialist Hospital in Legnica, the Lower Silesia Specialist Hospital of T. Marciniak, and the University Hospital of Wroclaw, Poland.  
The geometric models were detailed and constructed using open-source 3D Slicer software ([[https://www.slicer.org/](https://www.slicer.org/)]([https://www.slicer.org/](https://www.slicer.org/))). In particular, there have been no automated segmentation algorithms available for highly deformable tissues with irregular geometry, such as brain tissue. Consequently, the segmentation of brain structures required manual intervention. The process involved separating the left and right hemispheres of the brain, executed image by image. Much effort was expended to mimic the complicated external geometry of the brain, encompassing its detailed gyri and sulci.  
Subsequently, the resulting 3D object was exported to the STL format and processed further digitally through computer-aided engineering (CAE) class software CATIA V5 R21. Finally, the geometry was translated into a discrete model (CAE) utilising the LS-DYNA R13 code (Figure 6). The material data for the individual head structures were determined based on the literature [30].  
Figure 6: (**a**) 77-year-old numerical head model; (**b**) 28-year-old numerical head model  
A detailed description of the numerical modelling, as well as the material model comparison, is available in the authors' previous study regarding a 28-year-old head model [29].
## 4 Performance Assessment
### Power Utilization  
In Figure 10, power utilization is decreased by estimating the power allocation for the number of users and resources. Scheduling is performed for the queued devices, which deploy the current state and derive the neuron layers. The hidden layer is used to derive access to resources without congestion, and it is represented as \(\left(\frac{i\epsilon/M}{\delta+s_{u}}\right)+\left(b^{\prime}\times p_{i}\right)\). The analysis is performed to determine probabilistic balancing. Here, the network and device are used concurrently, and service sharing, and resource access are determined. Probabilistic balancing is conducted for service sharing and examines the power allocation, and it is computed as \(\frac{X+s_{u}}{s_{u}}\). Here, the prior state matches the current state and provides the power management for the allocated resources. In this case, service sharing and resource access are provided concurrently for efficient processing. In this computation step, the current state determines the prediction with the prior state and provides reliable sharing. Sharing is achieved for the number of resources and determines the training service by performing hidden layers. If the utilization increases, the failure and outage of this proposed work will decrease. This utilization concerns users and scheduling intervals and shows less processing than the existing three methods.  
The power allocation and latency observed for different users and sharing intervals are tabulated in Table 4.  
Figure 10: Power utilization comparisons.  
Figure 9: Outage comparisons.  
and user density. The proposed plan delivers a 14.9% high-service distribution for various users, 18.29% less delay, 13.34% less failure, 5.54% less downtime, and 18.68% less power consumption. However, the limitation of this proposed method is that it is not appropriate for large environments and increases in computing time. Future studies will explore large-scale and resource-constraint IoT scenarios with real data to meet user requests.  
Conceptualization, M.A. and H.M.; methodology, M.A. and H.M.; software, M.A. and H.M.; validation, Z.A. and H.M.; formal analysis, M.A. and Z.A.; resources, Z.A. and H.M.; data curation, Z.A.; writing--original draft preparation, M.A. and H.M.; writing--review and editing, M.A. and Z.A.; visualization, Z.A. and H.M.; funding acquisition, Z.A. and H.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1F1A1055408).  
Data Availability StatementData is contained within the article.  
The authors express their sincere appreciation to the Researcher Supporting Project Number (RSPD2024R1113) King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflicts of interest.  
References
## 2 Experimental and Computational Numerical Methods
### Experimental Setup
#### 2.1.2 Image Acquisition and Post-Processing
(**c**) Identification of droplets to calculate pixel sizes. (**d**) Calculation of droplets’ diameters. Lastly, the term \"\(d_{32}\) diameter\" refers to the Sauter mean diameter (SMD), which characterizes the average size of particles in a sample. It is specifically defined as the diameter of a sphere that has the same volume-to-surface area ratio as the droplet of interest [34]. \[d_{32}=\frac{\sum n_{i}d_{i}^{3}}{\sum n_{i}d_{i}^{2}} \tag{7}\]  
In these definitions, \(n_{i}\) represents the count of droplets falling in the focused camera frame \(i\), whereas \(d_{i}\) denotes the corresponding representative water droplet diameter in the same frame. The summations encompass all particles, with \(N\) representing the total count of droplets.
# Impact of Subsurface Drainage System Design on Nitrate Loss and Crop Production
## 4 Conclusions  
Subsurface drainage has demonstrated its capacity to enhance the productivity of poorly drained soils; however, it concurrently increases nutrient leaching through the soil profile. Our 4-year investigation indicated that tile drainage configuration significantly influences drain flow and nutrient losses. Comparisons between narrowly spaced and widely spaced tile-drained plots revealed significant variations in drain flow, with tile drains removing an average of 41.6% of rainwater across the six plots. We found that plots with a drain spacing of 24.4 m (CS-2 and CS-4) and a drain spacing of 18.3 m (CS-5 and CS-6) showed 40% and 55% lower drain flows, respectively, compared to plots with a drain spacing of 12.2 m (CS-1 and CS-3). This suggests that the narrower drain spacing in the plots has a significant influence on drain flow. Seasonal tile flow showed that the non-growing season accounted for approximately three times more flow than the crop-growing season across all plots. During the growing season, drainage flow ranged between 32 mm and 201 mm (removing an average of 25.7% of rainwater), while in the non-growing season it ranged from 131 mm to 468 mm (removing an average of 55.7% of rainwater). On average, for each plot, total tile flow over the 4 years during the non-growing season was 94% (CS-2) to 305% (CS-6) higher than during the crop-growing season. The mean NO\({}_{3}\)-N concentration in drainage water was higher than 10 mg L\({}^{-1}\) in most plots, except for CS-1 and CS-2. Our field records showed that plots with wider drain spacing retained more NO\({}_{3}\)-N than those with narrower spacing and deeper drains, except for CS-4. The average annual NO\({}_{3}\)-N losses from all plots over 4 years were around 50 kg N ha\({}^{-1}\). Cumulative 4-year NO\({}_{3}\)-N losses from plots CS-2 and CS-4 (24.4 m drain spacing) and CS-5 and CS-6 (18.3 m drain spacing) were 27% and 37% lower, respectively, compared to CS-1 and CS-3 (12.2 m drain spacing). Plot CS-4 showed NO\({}_{3}\)-N losses that were 77%, 16%, and 99% higher compared to CS-2, CS-5, and CS-6, respectively, despite four plots having the same drainage coefficient properties. Our observations indicated that tile flow results are highly influenced by physical variables such as drainage design, drainage coefficient, soil types, landscape position, and land slope. However, when considering water quality from agricultural fields, the mechanism is influenced not only by physical variables but also by biochemical effects in the soil. Therefore, it is evident that NO\({}_{3}\)-N losses are influenced by various factors beyond drainage design alone. Seasonal NO\({}_{3}\)-N losses showed that the non-growing season accounted for approximately twice the NO\({}_{3}\)-N
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## Abbreviations  
The following abbreviations are used in this paper.  
3 GPPThird-Generation Partnership Project  
5G Fifth Generation  
6G Sixth Generation  
CMOS Complementary Metal Oxide Semiconductor  
FPGA Field-Programmable Gate Array  
IC Integrated Circuit  
IoT Internet of Things  
ISM Industrial Scientific and Medical  
M2M Machine to Machine  
mGbps Multi-Gigabits per Second  
MIMO Multiple-Input-Multiple-Output  
ML Machine Learning  
mmWave Millimeter Wave  
NR New Radio  
P2P Point-to-Point  
P2 MP Point-to-Multipoint  
PN Positive Intrinsic Negative  
SNR Signal-to-Noise Ratio  
SiP System-in-Package  
SoC System-on-Chip  
URLLC Ultra-Reliable and Low-Latency Communication  
WiGig Wireless Gigabit