esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation  
### Variogram Maps for Evaluating Dataset Coherence: Anisotropy and Isotropy of Data  
Datasets (A, B, C, D, E), as well as their combinations (AB, BD, BD, DB, F, G), were analyzed using co-kriging to achieve the main research objective. Datasets (AB, BA, BD, DB) were merged using co-kriging. The first letter denotes the initial dataset in the co-kriging. Set F contained datasets A and B as a single file, and dataset G contained datasets B and D. All datasets were analyzed for the presence of trends, and their coherence was evaluated. Trends were determined using polynomial regression. The degree of the polynomial was selected empirically by analyzing the coefficient of determination R\({}^{2}\). The results of the coherence analysis are presented in the semivariogram maps in  
An analysis of the semivariogram maps (Figure 3) revealed anisotropy of the data in datasets where vertical movements were based on leveling data. Anisotropy was determined in datasets A and B, and in merged datasets (C, AB, and BA). Dataset F was the only exception, which indicates that leveling data were influenced by an additional external factor. In paper [34] autors demonstrated that adjustments introduced to leveling data, in particular normal adjustments, could be responsible for such external effects. Dataset D (vertical movements based only on GNSS data) was isotropic, similar to datasets BD and DB, where vertical movements from both sources were used. The above implies that the even distribution of GNSS stations significantly influenced the data coherence. However, this observation was not confirmed by an analysis of dataset G or the combined adjustment of the network of vertical movements based on leveling data and GNSS data, which indicates that leveling data exerted a considerable effect on the adjustment process (set E). The weights calculated for each observation using inverse distance weighing played a more important role in this process [18]. The anisotropic direction varied across the analyzed datasets. Datasets A and AB had a northeastern orientation, whereas the remaining datasets (B, C, E, BA, G) had a northwestern to western orientation. The variation was higher around the central point of each dataset, which points to differences in the values of neighboring points (nugget effect). The above resulted from the merger of datasets, where vertical movements at the same points or proximal points were determined in two different processes (which is well illustrated in dataset F). This observation can also be attributed to a local increase in vertical movements caused by mining operations and geological features. Figure 3.
# Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
## 3 Effectiveness of ML and HPC Techniques in the COVID-19 Era
### Role of ML and HPC Techniques in the General-Purpose Applications Related to the COVID-19 Era  
Despite the specific applications in diagnostic and analytics, ML and the HPC techniques can be used for general-purpose applications. We summarize the potential general-purpose applications of ML and HPC as follows.  
* Creating awareness about COVID-19 through sentiment analysis and recommending health tips.
* Regional- and community-level analysis of electronic health records of confirmed/ suspected COVID-19 cases.
* Providing medical statistics about COVID-19 symptoms from real case data.
* Providing the locality of hospitals and clinics.
* Increasing data utility by recommending/sharing results on cell phones.
* Processing and utilization of cell phone signal data for people identification.
* Extracting and fusing multistyle data for accurate analysis.
* Combining multimodel results to erase people's anxiety on results' credibility.
* Analyzing and sharing human behavior for vulnerable links identification in the community.
* Analyzing the technology implications on society through relevant features.  
There exist multiple general applications of both HPC and ML that are assisting societies in multiple ways [13; 14]. Furthermore, both techniques can be used for measuring and analyzing the data of multiple institutions for better analysis of the COVID-19 pandemic.
## 3 Experiment
### Performance Metrics for Evaluation  
Three evaluation metrics were used to assess the accuracy of model predictions in the experiments.which are Root Mean Square Error (RMSE), Coefficient of Determination (R?), and Variance Accounted For (VAF). The formulas of them are given in the following:  
\[RMSE=\sqrt{\sum_{i}^{N}\frac{\left(y_{i}-\hat{y_{i}}\right)^{2}}{N}} \tag{8}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\left(y_{i}-\hat{y_{i}}\right)^{2}}{\sum_{i=1}^{N} \left(y_{i}-\hat{y}\right)^{2}} \tag{9}\]  
\[VAF=[1-\frac{var(\hat{y_{i}}-y_{i})}{var(y_{i})}] \tag{10}\]  
where, \(N\) represents the number of data points, \(y_{i}\) is the actual value, \(\hat{y_{i}}\) is the predicted value, and \(\hat{y}\) is the mean of the actual values. RMSE is a metric used to gauge the model's predictive errors by measuring the root mean square difference between the model's predictions and the actual observed values. Therefore, for RMSE, a lower value is deemed more favorable, indicating smaller predictive errors by the model. R?, is a metric utilized to assess the model's goodness of fit, signifying the percentage of variance in the dependent variable that the model can account for. Consequently, for R?, a higher value is preferred, signifying a superior fit of the model to the actual data. VAF is a metric used to quantify the proportion of total variance in the data that the model can explain. Thus, for VAF, a higher value is also considered more desirable, indicating the model's enhanced ability to describe the data.
## 1 Introduction
Traditional methods like empirical mode decomposition (EMD), ensemble empirical mode decomposition (CEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) have drawbacks such as mode mixing, poor adaptability, and stability issues [21]. Additionally, traditional neural network models like Back Propagation (BP), recurrent neural network (RNN), and Radial Basis Function (RBF) algorithms have disadvantages, such as slow training speeds and susceptibility to local optima. The long short-term memory (LSTM) model can overcome these shortcomings, offering advantages, such as fast convergence speed and high computational accuracy, and it has been widely applied in regression testing and model prediction fields [22]. It is noteworthy that parameter optimization of the LSTM neural network significantly impacts prediction accuracy. Therefore, existing studies have often focused on either single data series decomposition or the initial parameter optimization of the LSTM model [23], while research combining these two aspects to study the fluctuating load of water flow is relatively scarce. In conclusion, the limited understanding of the physical mechanisms governing fluctuating flow has constrained research into hydrodynamic pressure in the discharge process of radial gates, thereby impacting studies on FIV and dynamic stability. This paper addresses these challenges by constructing a large-scale submerged radial gate model at a 1:25 length scale. Through extensive experimentation, the fluctuating pressure on the submerged radial gate plane was systematically investigated, and the distribution characteristics of time-averaged pressure and RMS of fluctuating pressure on the gate panel were analyzed. Additionally, the generation mechanism of fluctuating pressure on the gate panel was explored using the improved ICEEMDAN-HHT method. Furthermore, ICEEMDAN-SSA-LSTM was employed to predict panel fluctuation on submerged radial gate panels, offering valuable insights for engineering design and operation.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
## 5 Performance Evaluation
In Table 5 with variable SNR, it can be observed that the proposed approach achieved performance gains of 0.5, 0.8, and 0.2 in terms of MOS for estimated source \(\hat{S}_{1}\) and 0.4, 0.6, 0.12 for estimated source \(\hat{S}_{2}\) in comparison with other multistage BSS methods, respectively. For varying the RT parameter, having a fixed window length = 512, FFT = 1024, and SNR = 4 dB, the average MOS are shown in Table 6 with gains of 0.4, 0.6, and 0.2 for \(\hat{S}_{1}\) and 0.5, 0.7, 0.2 for \(\hat{S}_{2}\). The same procedure was followed to verify the second proposed model and the results are displayed in Tables 7 and 8. For varying the SNR, it can be observed in Table 7 that the proposed model achieves MOS gains of 0  
From Figure 9a,b, it is clear that the first model provides significant improvements in comparison to the second model for estimated source signals \(\hat{S}_{1}\) and \(\hat{S}_{2}\) with variable SNR. Similarly, for varying RT, it can be observed in Figure 10a,b that the first model shows considerable performance gains in comparison to the second model for \(\hat{S}_{1}\) and \(\hat{S}_{2}\). The performance of the first model (Figure 1) is better than the second model (Figure 2) for both RT and SNR because the first model suppresses the noise in the estimated source signal extracted from the noisy convolutive mixture, while in the second model, the de-noising technique suppresses noise and estimated source signals mixed in the noisy convolutive mixture. The de-noising module considers the other estimated signals in the noisy convolutive mixture as noise resulting from performance degradation.
# New Detection Method for Fungal Infection in Silver Fir Seeds
## 3 Materials and Methods
### Fungal Selection and Identification  
After incubation of the 20 Petri dishes with seeds, nine plates were selected for further investigation based on homogenous appearance, with similar fungal colonies appearing in the sets of plates. In addition to these plates, several other plates were selected for identification by subculturing and DNA sequencing. Genomic DNA was extracted from hyphae of three green and three white colonies. Then, genomic DNA was extracted using the NucleoSpin Plant II kit (Macherey-Nagel, Duren, Germany) according to the manufacturer's instructions.  
The region of the fungal internal transcribed spacer (ITS) was amplified with primers ITS1 (5'-TCCGTAGGGTGAACCTGCGG-3'), and ITS4 (5'-TCCCTCCGCTTATTGAATGC-3') [52]. PCR was performed using the TaqManNova- RED kit (BLIRT, Gdansk, Poland). The 20 \(\mu\)L PCR mix consisted of 10 \(\mu\)L 2X TaqMan- RED mix, 2 \(\mu\)L 5 \(\mu\)M of each primer, 2 \(\mu\)L DNA extracts, and 2 \(\mu\)L H\({}_{2}\)O. Cycling was performed using a Veriti 96-well thermal cycler (ThermoFisher Scientific, Waltham, MA, USA) as follows: an initial denaturation step at 95 \({}^{\circ}\)C for 3 min, followed by 30 cycles (95 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C for 30 s, and elongation at 72 \({}^{\circ}\)C for 30 s), and a final extension step at 75 \({}^{\circ}\)C for 5 min. Excess dNTPs and unincorporated primers were removed from the PCR product using CleanPCR (BLIRT, Gdansk, Poland). DNA was eluted in 40 \(\mu\)L H\({}_{2}\)O. The amplified products were sequenced using the Sanger sequencing method in our laboratory.  
Sequencing PCR reactions were performed using 1 \(\mu\)L BigDye Terminator v. 3.1 Ready Reaction Mix (ThermoFisher Scientific), 2 \(\mu\)L BigDye Sequencing Buffer (ThermoFisher Scientific), 1 \(\mu\)L (5 \(\mu\)M) ITS1 or ITS4 primer, and H\({}_{2}\)O to bring the total volume to 10 \(\mu\)L. The thermal profile for the sequencing reactions consisted of initial denaturation step of 96 \({}^{\circ}\)C for 1 min, followed by 25 cycles of 96 \({}^{\circ}\)C for 10 s, 50 \({}^{\circ}\)C for 5 s, and 60 \({}^{\circ}\)C for 105 s. The rDNA region was sequenced using an ABI 3500 xL genetic analyzer (ThermoFisher Scientific). For the sequences obtained a 98% alignment threshold over at least 440 base pairs was applied for species identification. Assignment of the obtained sequences to species was performed using the BOLDSYSTEMS identification engine [53; 54].
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 3 Statistical Model for the DAP  
Consider an ad hoc mesh network with data that must be relayed to a destination by a given deadline. In the sequel, we derive the process of calculating the DAP for each node in the network based on known routing tables for a given destination node. We begin with Section 3.1, providing detailed information about the probabilistic model of the delays of individual links and paths followed by packets. We then derive the DAP of a node in Section 3.2 using link delays and the E2E delay statistics of its neighboring nodes.
## Appendix B Substituting Equation (23) into the Equation (24), the Lagrange multiplier must satisfy  
\[\mathbf{b}^{\mathsf{T}}\mathbf{W}\mathbf{A}\left(\mathbf{A}^{\mathsf{T}} \mathbf{W}\mathbf{A}+\lambda\mathbf{S}\right)^{-1}\mathbf{S}\left(\mathbf{A} ^{\mathsf{T}}\mathbf{W}\mathbf{A}+\lambda\mathbf{S}\right)^{-1}\mathbf{A}^{ \mathsf{T}}\mathbf{W}\mathbf{b}=0. \tag{104}\]  
Using eigenvalue decomposition, the matrix \(\mathbf{A}^{\mathsf{T}}\mathbf{W}\mathbf{A}\) can be diagonalized as follows  
\[\mathbf{A}^{\mathsf{T}}\mathbf{W}\mathbf{A}=\mathbf{\Sigma}\mathbf{D} \mathbf{\Sigma}^{-1}, \tag{105}\]  
where \(\mathbf{D}=\text{diag}\{\zeta_{1},\zeta_{2},\zeta_{3}\}\) and \(\zeta_{i},\,i=1,2,3\), are the eigenvalues of the matrix \(\mathbf{A}^{\mathsf{T}}\mathbf{W}\mathbf{A}\), and \(\mathbf{\Sigma}\) is the matrix composed of the eigenvectors. Substituting Equation (105) into \(\left(\mathbf{A}^{\mathsf{T}}\mathbf{W}\mathbf{A}+\lambda\mathbf{S}\right)^{-1}\), yields the following equation  
\[\left(\mathbf{A}^{\mathsf{T}}\mathbf{W}\mathbf{A}+\lambda\mathbf{S}\right)^{ -1}=\mathbf{S}\mathbf{\Sigma}(\mathbf{D}+\lambda\mathbf{I})^{-1}\mathbf{ \Sigma}^{-1}. \tag{106}\]  
Substituting this expression into Equation (104), which after some manipulation, can be expressed in the form of \(\lambda\) as  
\[\boldsymbol{\alpha}^{\mathsf{T}}(\mathbf{D}+\lambda\mathbf{I})^{-2} \boldsymbol{\beta}=0, \tag{107}\]  
where \(\boldsymbol{\alpha}=\mathbf{\Sigma}^{\mathsf{T}}\mathbf{S}\mathbf{A}^{\mathsf{T }}\mathbf{W}\mathbf{b}=[\alpha_{1}\,\,\alpha_{2}\,\,\alpha_{3}]^{\mathsf{T}}\) and \(\boldsymbol{\beta}=[\beta_{1}\,\,\beta_{2}\,\,\beta_{3}]^{\mathsf{T}}\). As a result of Equation (107), we have Equation (26), which is a polynomial equation of the fourth degree.
## 3 Data and Methodology
### Laboratory Testing of Rock Masses
#### 3.4.3 XRD Analysis  
The fundamental principle of quantitative XRD analysis is based on X-rays irradiating a crystal and subsequently being scattered by electrons. This scattering process results in the emission of electromagnetic waves with the same frequency as the incident wave. These emitted waves are then processed to quantitatively analyze the mineral content [29]. In contrast to typical chemical and spectral analysis methods, XRD has the capability to measure the complete mineral composition of rocks rather than just the elemental composition. This method offers the advantages of simplicity in operation, cost-effectiveness in detection, and time efficiency.  
The experimental instrument used was a Rigaku Ultima IV XRD from Rigaku, Tokyo, Japan. To minimize experimental errors, three samples from each lithology were chosen for testing, resulting in a total of 12 samples.
## 6 Optimization of TMDI and Equivalent TMD
### Performance under Variable Frequency Sinusoidal Excitations  
The performance of both TMD and TMDI is compared at various mass ratio scenarios using VF sinusoidal excitations. The optimal device properties are obtained by running simulations in MATLAB for a wide range of frequency and damping ratios under VF excitations (0.05 to 4 Hz).  
Figure 11a shows the optimization of TMD parameters, and Figure 11b shows the optimization of TMDI parameters under variable frequency sinusoidal excitation. The optimum parameters of both devices for various mass ratios are listed in Table 3. Figure 11 shows both devices' most favorable frequency and damping ratios. It is observed that those parameters are different for each device to minimize the acceleration of the building. Similar to white noise excitations, under VF excitations for a 2% mass ratio, the optimal frequency ratio for TMD is smaller than 1(0.988). It is greater than 1(1.019) for TMDI. However, the optimal damping ratio is slightly higher in the case of TMD compared to TMDI. The performance of both devices increases as the mass ratio increases, as listed in Table 3. In both devices, the optimal frequency and damping ratio decrease as the mass ratio increases. Figure 12 shows the time history of displacement and acceleration of the top floor under VF excitations, and Figure 13 shows their corresponding response spectra. Optimized equivalent TMD reduced the STD displacement by 50%, whereas optimized TMDI reduced the same responses by only 43%. Similarly, peak displacement at the top floor is reduced by 17.9% using TMD, but TMDI reduces the peak displacement by 9.8%. This finding shows that optimized equivalent TMD has better control performance than TMDI when the physical mass of the inerter is considered.  
Figure 10: Effect of inertance and mass ratios on the acceleration response under filtered white noise excitations.  
Figure 11: Optimization for minimizing RMS displacement at the top floor, under variable frequency excitation: (**a**) TMD and (**b**) TMDI.  
Figure 12: The response of the 42 nd floor under VF sinewave loadings: (**a**) displacement time history and (**b**) acceleration time history.
###### Abstract  
Among the grid service applications of high-voltage direct current (HVDC) systems, frequency-power droop control for islanded networks is one of the most widely used schemes. In this paper, a new frequency-power droop coefficient determination method for a mixed line-commutated converter (LCC) and voltage-sourced converter (VSC)-based multi-infeed HVDC (MIDC) system is proposed. The proposed method is designed for the minimization of power loss. An interior-point method is used as an optimization algorithm to implement the proposed scheduling method, and the droop coefficients of the HVDCs are determined graphically using the Monte Carlo sampling method. Two test systems--the modified Institute of Electrical and Electronics Engineers (IEEE) 14-bus system and an actual Jeju Island network in Korea--were utilized for MATLAB simulation case studies, to demonstrate that the proposed method is effective for reducing power system loss during frequency control.  
g rider service of HVDC; frequency droop control; multi-infeed HVDC system; LCC HVDC; VSC HVDC; loss minimization Article
## 2 Materials and Methods
### Technical Specifications of the Examined IMMSs
#### 2.2.2 Leica Pegasus Backpack  
The system [PERSON] and [PERSON] (2017) was tested on 21 September 2017.  
It is a wearable system and it combines two linear Velodine VLP-16 scanners (one vertical and one horizontal), plus five cameras for data texturing, a GNSS/INS integrated by Novatel SPAN technology, batteries, and the control unit [PERSON] and Singh (2017). During the data acquisition, a rugged tablet shows the videos from the cameras, profiles from the two linear scanners, and a diagnostic tool with information about the GNSS and INS sensors.  
GNSS initialization requires a few minutes (of course in an open outdoor environment): after that, it is necessary to walk a few more minutes in order to calibrate the INS. When the acquisition path is entirely outdoor, at the end, a short GNSS static acquisition is required: the path is then open, but nevertheless constrained on two points with fixed (by GNSS) positions. Instead, when the path is indoors, the indications are to start and close it by walking for a little while outdoors. If this starting/closing procedure is not possible, a suitable platform can be used to re-collocate the IMMS in exactly the same point, that is, to guarantee the correspondence of the start and end points, namely a \"forced\" closed-loop. A short stop (about half a minute) is required before and after steps or ramps. The 3D point model is based on scans acquired by the rear scanner, while profiles scanned by the upper scanner support the SLAM algorithm.  
As reported in Section 2.1, data post-processing is done in two main steps: firstly, GNSS and INS data are integrated to compute the IMMS trajectory (position and rotation of the SPAN platform) as the starting solution, and later to compute the optimized solution by considering the 3D profiles from both linear scanners. For the first step, Inertial Explorer software (by Novatel) is used, then the data are processed by Leica Pegasus AutoP software. The positioning solution is improved thanks to the contribution of the images, by means of an approach similar to that in [PERSON] and [PERSON] (2017); [PERSON] and [PERSON] (2017). Furthermore, images from the camera are linked to 3D data for explorations and used for point cloud texturing.
# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
###### Abstract  
The study focuses on Alzheimer's and dementia detection using machine learning, acknowledging their impact on cognitive health beyond normal aging. Data markers, rather than biomarkers, are preferred for diagnosis, allowing machine learning to play a role. The objective is to design and test a model for early dementia detection using lifestyle data from the National Health and Ageing Trends Study (NHATS). This could aid in flagging high-risk individuals and understanding aging-related parameter changes. Using NHATS data from 5000 individuals aged 60+, encompassing 1288 parameters over a decade, the study shortlists parameters relevant to dementia. Artificial neural networks and random forest techniques are employed to build a model that identifies key dementia-related parameters. Temporal analysis reveals features that exhibit declining social interactions, quality of life, and increased depression as individuals age. Results show the random forest model achieving an accuracy of 80% for dementia risk prediction, with precision, recall, and F1-score values of 0.76, 1, and 0.86, respectively. Temporal analysis offers insights into aging trends and elderly citizens' lifestyles, using daily activities as parameters. The study concludes that NHATS data analysed using machine learning techniques aids in understanding aging trends and that machine learning models based on identified parameters can non-intrusively assist in clinical dementia diagnosis and trend-based detection.  
d 20231310630 11 September 202322 September 202324 September 2  
to diagnose dementia. The first part of this research revolves around the identification of important lifestyle parameters that influence and have a major impact on the onset of dementia and its progression.
## 5 IETF ROLL Working Group
### Implementation and Evaluation
#### 5.2.2 Using the Protocol  
RPL is designed to be widely applicable; therefore many configuration options are available. Based on experience, _Recommendations for Efficient Implementation of RPL_ (draft-gnawali-roll-rpl-recommendations-04) [58] presents different design choices and configuration parameters that envision an efficient RPL implementation and operation. In _Performance Evaluation of the Routing Protocol for Low-Power and Lossy Networks (RPL)_ (RFC 6687) [59], an overview and evaluation is given of how the protocol can handle two different use cases (a small outdoor deployment of sensor nodes for building automation and a large-scale smart meter network) to meet the desired requirements.  
It is worth noting that a difference in design choices and configuration parameters can lead to interoperability problems. In [60] the interoperability between ContikiRPL and TinyRPL is investigated. The paper shows that, despite having two good performing independent implementations, a combination can lead to large scale differences in behavior when combining them in a mixed set up. It is also illustrated that subtle difference in lower layers can affect the system performance in unexpected ways.