esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Classifier Selection and Statistical Significance  
Linear discriminant analysis was first conducted in order to statistically classify patients based on the R-R-interval derived features (mean R-R interval duration, RMSSD, and SDSD) obtained at each time-frame before SCA onset. The best accuracy was obtained at 8 min prior to SCA which exhibited an accuracy of 72.8%. On the other hand, the earliest time-frame which exhibited the best accuracy was 30 min, which produced an accuracy rate of 72.3%. The highest sensitivity was exhibited at 20 min prior with a rate of 70.2%, while the highest specificity rate was observed at 3 min prior at a rate of 83.8% (See Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Period** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline
3 h prior & 56.7\% & 49.7\% & 68.8\% \\
2 h prior & 61.5\% & 55.5\% & 67.5\% \\
1 h prior & 68.3\% & 59.5\% & 76.7\% \\
50 min prior & 67.0\% & 55.3\% & 78.7\% \\
40 min prior & 72.2\% & 68.8\% & 75.5\% \\
30 min prior & 72.3\% & 69.0\% & 75.7\% \\
20 min prior & 71.3\% & 70.2\% & 72.3\% \\
10 min prior & 70.8\% & 63.8\% & 77.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tabular summary of the accuracy, sensitivity, and specificity of the linear classifier.  
Figure 6: Time series plot of the changes in heart rate variability (HRV): the square root of the mean of differences between all adjacent R-R intervals (RMSSD) and the standard deviation of differences between all adjacent R-R intervals (SDSD) (\(n=10\)). ECG recordings were taken from patients in the SCA cohort and subjected to processing via MATLAB. The results were recorded as mean RMSSD (\(\pm\)standard deviation) and SDSD (\(\pm\)standard deviation) denoted by the error bars.  
Two support vector machine (SVM) classifiers were implemented in this study: linear SVM and a non-linear fine Gaussian SVM. For the linear SVM. The highest accuracy was obtained at two time-frames: 10 min and 8 min prior, both of which exhibited an accuracy rate of 78.9%. The earliest time-frame with reasonable accuracy was at 40 min, which exhibited a rate of 77.1%. The highest sensitivity was exhibited at 8 min prior with a rate of 83.2%, while the highest specificity rate was exhibited at 50 min prior with a rate of 79.0% (See Table 2). For the non-linear SVM, the highest accuracy was obtained at 2 min prior, which exhibited an accuracy rate of 83.9%. The earliest time-frame with reasonable accuracy was at 50 min prior, which had a rate of 80.6%. The highest sensitivity was exhibited at 2 min prior with a rate of 91.5% while the highest specificity was observed at 50 min prior with a rate of 82.5% (See Table 2).  
Across all classifiers, the time-frames that yielded the best accuracy rates were 2 min and 8 min prior to SCA. The earliest time-frames that exhibited the best accuracy rates relative to adjacent time-frames across all classifiers were 50 min and 40 min prior to SCA (See Tables 1 and 2).
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 3 Accurate Objects' Height from Focus Measurement
### Optimised Polynomial Fitting
#### 3.1.1 Experimental Set-Up  
We performed a preliminary experiment to determine the most appropriate number of points, i.e., the sample size \(SZ\), to use for evaluating the MSE objective function. For this, we used synthetic data obtained by generating a ground truth of height samples from a 2 nd order polynomial setting of coefficients \(a_{0}=3.4\), \(a_{1}=0.022\) and \(a_{2}=0.00003\) (\(\pm 2\) steps of uniform noise in the range of 0 to 2 is added to the ground truth data).  
To obtain the best polynomial function match, we ran the PSO algorithm (parameters are empirically tuned on the smallest \(SZ\) returning the minimum error ) set with \(NP=50;c_{1}=1.50;c_{2}=0.30\); \(w=0.92\); velocity components clamping within \([-0.05,0.05]\); \(I_{\text{max}}=5000\); but with increasing height samples \(SZ\), from 25 to 1000. The setup for changing \(SZ\) is shown in Figure 1. For each height sample size, 10 independent runs of the PSO were performed. This process returns the polynomials functions required to calibrate the systems. As this is a measurement system, its accuracy is determined by its maximum error. Hence, for each run, we also record the corresponding max step error and compute their average. As evident from Table 1, it could be noted that this averaged max error (which is expected to be smaller when the sample size is higher) stopped decreasing at \(SZ=250\). Hence, we use and suggest this sample size (plus 10 extra points to allow for a margin of error) for fitting the polynomials with the PSO algorithm (i.e., we work with \(SZ=260\)).
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 4 HAR Datasets and Experiment Settings
### Experiment Settings  
Experiments are conducted on TensorFlow's deep learning platform, and various parameters are adjusted for different datasets to achieve better results. For wireless sensor data mining action recognition, due to the different characteristics between different datasets, the initial learning rate is typically set to different constants, including 0.1, 0.01, 0.001, and 0.0001. After multiple accuracy comparisons, it was found that the optimal parameter for the UCI dataset lies between 0.001 and 0.01. Subsequently, using 0.001 as the step size, the final initial learning rate for the UCI dataset was chosen as 0.005. Similarly, we selected 0.002 as the initial learning rates for the model on the PHAD dataset.  
The sensor signal data is divided according to each action segment. The three-axis acceleration data for the action behavior of walking is shown in Figure 7a, and gyroscope data is shown in Figure 7b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activity** & **Distribution** & **Confused** \\ \hline Walking & 36,427 (24.10\%) & 86 (43.00\%) \\ Sitting & 26,815 (17.75\%) & 3 (1.50\%) \\ Standing & 31,764 (21.03\%) & 8 (4.00\%) \\ Running & 29,589 (19.58\%) & 37 (18.50\%) \\ Walking Upstairs & 13,431 (8.89\%) & 37 (18.50\%) \\ Walking Downstairs & 13,067 (8.65\%) & 29 (14.50\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The class distribution of HAR data and the proportion of mixed data.  
Figure 7: Signals of walking behavior collected by sensors on the arm. (**a**) Acceleration data, (**b**) Gyroscope data.  
Optimizers including BGD, SGD, Momentum, Adadelta, RMSprop, and Adam [34] were compared for each dataset. The Adam optimizer yielded the highest accuracy and was chosen for implementation in the model. Additionally, signal-based classification of human activity typically relies on precision and recall metrics to evaluate the performance of the methods that were used in this study to assess the OI-EleAttG-GRU method.
## 2 Basic Equations of MGT Thermoelasticity  
According to the theory of thermoelasticity, the basic system equations for homogeneous solids can be expressed as [23]:  
\[\sigma_{ij}=C_{ijkl}e_{kl}-\gamma_{ij}(T-T_{0}) \tag{1}\]  
\[\rho S=\frac{\rho C_{E}}{T_{0}}(T-T_{0})+\gamma_{ij}e_{ij} \tag{2}\]  
\[e_{ij}=\frac{1}{2}\big{(}u_{i,j}+u_{jj}\big{)} \tag{3}\]  
\[\frac{\partial S}{\partial t}+\frac{1}{\rho T_{0}}q_{i,i}=\frac{1}{T_{0}}Q, \tag{4}\]  
\[q_{i}=-K_{ij}\rho_{,j} \tag{5}\]  
where \(\sigma_{ij}\) represents the stress tensor, \(u_{i}\) denotes the displacement components, \(\theta=T-T_{0}\), \(T\) is the absolute temperature, \(T_{0}\) indicates the ambient temperature, \(e_{ij}\) symbolizes the strain components, \(C_{ijkl}\) stands for elastic parameters, \(K_{ij}\) represents components of the thermal conductivity tensor, \(\gamma_{ij}\) denote the coupling coefficients, \(C_{E}\) indicates the specific heat, \(i,j,k=1,2,3\), \(Q\) denotes the heat source, \(q_{i}\) is the heat flow, and \(\rho\) represents the substance's density.  
A parabolic equation is produced when the energy conversion Equation (4) and Fourier's law (1) are combined. This allows the waves to spread at nearly unlimited rates. This conclusion is physically unlikely, and many scientists have claimed that this model is incompatible with the idea of extremely fast thermal conductivity. For this reason, the MGT equation was employed in this investigation to propose a revised law of thermal conductivity to solve this physical discrepancy. In the proposed model, this problem is solved by producing a hyperbolic partial differential equation that represents the motion equation that is based on the displacement vector and another one that represents the thermal conductivity equation that is based on the temperature change.  
The modified MGT thermoelastic equation can be written as [25; 27]:  
\[\left(1+\tau_{0}\frac{\partial}{\partial t}\right)\left[\rho C_{E}\frac{\partial ^{2}\partial^{2}}{\partial t^{2}}+\beta_{ij}T_{0}\frac{\partial^{2}u_{m,m}}{ \partial t^{2}}-\rho\frac{\partial Q}{\partial t}\right]=\left(K_{ij}\dot{ \theta}_{J}\right)_{,i}+\left(K_{ij}^{*}\dot{\theta}_{J}\right)_{,j} \tag{6}\]  
The Moore-Gibson-Thompson model of thermoelasticity, abbreviated as MGTTE, can be derived from four separate models of classical and generalized thermoelasticity, depending on the specific conditions of a particular case and the thermal constants. Here is a list of previous thermodynamic models that can be derived:  
* When \(\tau_{0}=0\) and \(K_{ij}^{*}=0\), it is possible to use the conventional thermoelastic theory (CTE).
* The framework for Lord and Shulman (LS) may be produced if the value of \(K_{ij}^{*}\) is equal to zero.
* The idea of Green-Naghdi (GN-II) may be derived if \(\tau_{0}=0\) and \(K_{ij}=0\)
* The model of Green-Naghdi (GN-II) of type III (GN-III) through the usage of \(\tau_{0}=0\).
## 3 Simulation Tests and Analysis
### Reconstruction Error Analysis
#### 3.3.2 Effect of Sample Rate  
The proposed method provides a new way of using limited measurement data to get temperature information. These measurement data play a very important role in the reconstruction process, so the number of sensors has an effect on the reconstruction accuracy to a certain extent.  
In this simulation test, the number of nodes on the object plane was approximately 4000. All of these nodes can be seen as a measuring point. Considering the measurement cost and reconstruction accuracy, a certain number of sensors is needed.  
As mentioned above, methods based on POD require sensor numbers equal to or larger than the total number of basis vectors used. With eight POD basis vectors used for reconstruction, a 0.1% sample rate (four sensors) and a 0.2% sample rate (eight sensors) were not suitable for this problem.  
For a general conclusion, the averages of the reconstruction errors from 1000 runs under different noise levels were compared, as the sample rate increased from 0.3% to 1.2%. These results are presented in Figure 4.  
As shown in Figure 4, in all conditions, it is obvious that the reconstruction error decreased as the sample number increased from 0.3% to 1.2%. The greater the number of sensors used, the better the reconstruction solution. Another remarkable outcome is that using a high sample rate resulted in a decrease in the effect of noise. This indicates that, even with measurement noise, using the proposed method and moderate measurement data can yield a good reconstruction solution.  
Reconstruction errors with a 0.3% sample rate (12 sensors) for different test cases are shown in Table 4.  
As shown in Table 4, when the sample rate was 0.3% (12 sensors), the reconstruction errors were all less than 3%, which is an acceptable result. Considering the reconstruction accuracy and measurement cost, in this simulation test, a sample rate of 0.3% (12 sensors) was used in following analysis.  
Figure 4: Comparison of reconstruction errors between different sample rates with different noise levels (original measurement data, SNR = 40 dB, SNR = 45 dB, SNR = 50 dB). (**a**) Comparison in test case 1. (**b**) Comparison in test case 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Case Number** & **Original Data** & **SNR = 40 dB** & **SNR = 45 dB** & **SNR = 50 dB** \\ \hline
1 & 0.35 & 0.84 & 0.56 & 0.43 \\
2 & 0.42 & 0.87 & 0.60 & 0.48 \\
3 & 0.53 & 0.94 & 0.68 & 0.68 \\
4 & 0.59 & 0.97 & 0.74 & 0.64 \\
5 & 0.66 & 1.01 & 0.79 & 0.71 \\
6 & 0.51 & 0.92 & 0.67 & 0.57 \\ special test case & 0.60 & 0.98 & 0.73 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reconstruction errors (%) in seven test cases with eight POD basis vectors.
# Acoustic Emission Analysis of Fracture and Size Effect in Cementitious Mortars
## 2 Experimental Program and AE Analysis Methods
### Materials and Test Specimens  
Mortar cylinders with varying sample sizes were made in accordance with EN 196-1 [25]. The cement mortar is prepared with a Portland cement (CEM II/B (S-L) 32.5 N) and river sand with a maximum aggregate size of 4 mm and 0/2 grain size curve distribution according to EN 13139 [26]. The sand:water:cement ratios were 3:0.5:1 by weight. According to EN 196-1 [25], three prisms (40 mm \(\times\) 40 mm \(\times\) 160 mm) were prepared from the same batch. After casting, the prisms were covered by a glass plate and kept in a room at \((20\pm 1)\)\({}^{\circ}\)C. The cylinders were stored in a curing room controlled at \((20\pm 1)\)\({}^{\circ}\)C and \((95\pm 3)\)% relative humidity. The molds were removed after 24 h and all the specimens (prisms and cylinders) were placed in the curing room for 28 days. At the age of 28 days, the prisms were tested according to EN 196-1 and resulted in a mean compressive strength of 20.65 MPa with a standard deviation of 0.71 MPa and a mean flexural strength of 3.56 MPa with a standard deviation of 0.33 MPa.
## 2 Materials and Methods
### Fuzzy Logic Model (FLM)  
The third main component of the monitoring system is the FLM. In this subsection, we will present the structure of the FLM, including its inputs, which are pH, soil temperature, electric conductivity, soil humidity, and NPK nutrients, the output of the FLM (soil quality) and sub-outputs (physicochemical quality and macronutrient concentration), its rules, and how it was implemented for integration into the web platform.  
A Mamdani-type fuzzy inference system (FIS) was chosen for the FLM due to its ability to incorporate expert knowledge [25; 31]. This is particularly useful when dealing with the value range classifications of linguistic variables, such as low temperature, optimal temperature, etc. It also allows for the creation of custom outputs, such as medium-quality, medium-high-quality, or high-quality. Mamdani-type FISs are widely used in various domains, including performance assessment, the prediction of variables, and classification [26; 31; 39].  
To effectively assess SQ, it was determined that the FLM should consider the influence of seven key soil parameters: pH, temperature, humidity, electric conductivity, nitrogen, phosphorus, and potassium. These parameters collectively provide valuable insights into the physical, chemical, and nutrient characteristics of the soil [10; 40]. Recognizing that SQ encompasses multiple aspects, the FLM aims to decompose it into three primary outputs: physicochemical quality, macronutrient concentration, and overall SQ.  
Physicochemical quality (PQ) captures the overall status of the soil's physical and chemical properties, while macronutrient concentration (MC) focuses on the availability and balance of essential macronutrients [41; 42], which are considered to be implemented  
Figure 6: Expected JSON for the API’s endpoint for adding measurements, it supports different node types and showcases different node addresses.  
as outputs of the FIS. Figure 7 provides a comprehensive overview of the fuzzy models developed to address each output.
## 3 Results and Discussion
### Amino Acids Profile  
Quantitative and qualitative determination of amino acid are shown in Table 1. The total amino acids composition represents both protein constituents, free amino acids and/or amino acid salts. Protein quality is valued based on 8 essential amino acids, namely, methionine, leucine, isoleucine, lysine, phenylalanine, threonine, tryptophan and valine [31]. The essential amino acids score (EEAS) \"determines the effectiveness with which absorbed dietary nitrogen can meet the indispensable amino acids requirement at a safe level of protein intake\" [34]. The quantity of each essential amino acid in each microalgae is compared with the level of the amino acids required for pre-school children (2-5 years old) [34]. Additionally, according to the report of the joint FAO/WHO expert consultation (1989) this scoring pattern is robust enough to be used for all ages and adults, except for infants [46]. Sample for each amino acid determination was analysed in triplicate and reported as a mean \(\pm\) SD value using one-way Anova. Means that do not share a superscript letter are significantly different based on Tukey Simultaneous 95% Cis. \(\dagger\): The pattern requirement (g/100 g protein) for pre-school children (2-5 years) recommended by the Food and Agriculture Organisation (FAO), World Health Organisation (WHO), and United Nations University (UNU). The results exhibited no significant difference between the total sum of the essential amino acids per g protein of _C. reinhardtii_ and _Spirulina_ (458.4 and 449.8 mg/g, respectively), however, it was a bit higher in _Chlorella_ (468.9 mg/g). For their EAAS i.e., _C. reinhardtii_ (1.49) _Chlorella_ (1.55) and _Spirulina_ (1.48) the three species can be labelled as a source of \"high-quality\" protein and their EAA composition meets the FAO requirements of pre-school children. However, this conclusion should be further supported by in-vitro and in-vivo digestibility trials of various microalgae species and be compared with another protein source. The essential amino acid with the lowest score represents the \"most limiting amino acid\", hence, the limiting amino acids in the three species was lysine. This is in good agreement with the general
# Study of the Failure Mechanism of Soft Rock Mining Roadways Based on Limit Analysis Theory
## 2 Analysis of the Deformation and Failure of a Soft Rock Mining Roadway
### Limit Analysis Theory
#### 2.1.2 Basic Assumptions  
When limit analysis theory is used to analyze and calculate the failure of soft rock mining roadways, the following assumptions need to be made first. The axis of the roadway is infinitely long and the research can be simplified into a two-dimensional plane strain problem. The rock surrounding the roadway is a Mohr-Coulomb material with a certain cohesion \(c\) and internal friction angle \(\varphi\). The rock mass is an ideal elastic-plastic material and obeys the associated flow rule. The slider is a rigid block, and the volume remains unchanged during the failure process; that is, the volume strain is 0, so the energy dissipation is only generated on the velocity discontinuity line. The energy dissipation rate inside the rock surrounding the roadway is neglected. The shape of the roadway section is an equivalent rectangular section.
## 6 Optimization and Simulation Results
### Energy Yield Analysis
These results are attributed to the differences in the average solar radiation rate, as shown in Figure 6. While, the maximum energy contribution of the WTG occurred in February; and the minimum energy contribution occurred in September. The optimal number of batteries, which was found by the HOMER simulation for the system is 64 battery. The battery annual energy-in is 5162 kWh, while the annual energy-out is 4409 kWh, where the roundtrip efficiency was 85%. Batteries can supply LTE-macro BS load autonomy for 95.9 h (3 days and 23.9 h), which is computed based on Equation (5), (number of the batteries is 64 \(\times\) nominal voltage of a single battery 6 V \(\times\) nominal capacity of a single battery 360 Ah \(\times\) DOD 0.7 \(\times\) 24 h) divided by (daily average LTE-macro BS load 24.22 kWh). However, one battery can supply LTE-macro BS load autonomy 1.5 h. The battery expected life is 10 years, which is computed based on Equation (6). In addition, the seasonal statistics of the maximum and minimum states of charge (SOCs) for the battery are given in Figure 16 and reveal that the highest energy contribution of the battery bank occurred at the end of July because the minimum energy contribution of the hybrid power system occurred in July, as shown in Figure 15.  
The inverter annual energy-in is 1005 kWh, while the annual energy-out is 955 kWh, based on the daily AC load needs 2.62 kWh \(\times\) 365 days/year, with 95% efficiency and 8759 h/year operation (operating hours 24 h \(\times\) 365 days/year). Figure 17 shown the monthly statistics of the output power for the inverter, which indicate that the inverter is operating normally. Figure 16: Monthly statistics of the SOC for the battery. Figure 15: Monthly power contribution of various sources at an average solar irradiation 4.0 kW/m\({}^{2}\)/day and average wind speed 4.0 m/s.
# Kinetics of Ions in Post-Lithium Batteries
## 5 Tuning the Ionic Diffusion
Finally, for Na\({}_{3}\)V(PO\({}_{4}\))\({}_{2}\) atomistic simulations revealed that the Na-V anti-site defect is the lowest energy intrinsic defect process [94]. Therefore, at high temperatures, there will be some Na ions on the V site and V ions on the Na site [94]. The high energy of the Na Frenkel mechanism will result in very few Na vacancies that are necessary for Na\({}^{+}\) diffusion. The most efficient strategy to form these Na vacancies is by doping with the tetravalent dopant Ge\({}^{4+}\) on the V site [94]. Notably, Ge doping on the P site increases the Na interstitial concentration that is required for high capacity [94]. Considering MgV\({}_{2}\)O\({}_{4}\), [PERSON] et al. [45] showed that doping with cobalt at the vanadium site can lead to the increase of Mg interstitials and O vacancies. In the fundamental study by [PERSON] et al. [19] it was determined that the control (tuning) of the non-stoichiometry in grain boundaries is an efficient way to enhance the diffusion properties. In particular, [PERSON] et al. [19] showed that the defects induced by strain in the La\({}_{1-x}\)Sr\({}_{x}\)MnO\({}_{3+\delta}\) (LSM) grain boundaries amplify the oxygen diffusion. In essence, with the use of designed interfaces and/or the formation of nanocomposites with appropriate architectures it is possible to tune the material properties of systems for energy applications without the need to use very expensive and scarce materials such as Li or rare-earths. The kinetics of Na ions (and to a lesser degree Mg) and the continuous and systematic investigation of new novel oxides for energy storage applications are very promising and are expected to compete with Li [138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148]. These ions also have considerable advantages as compared to Li. In particular, Na and Mg are far more abundant, more environmentally friendly, sustainable, and economical as compared to Li. Therefore, should the engineering requirements are met they will make a significant contribution to energy storage from renewable energy and nuclear energy production sites.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results
### Data Transmission Networks
Regarding common network access control standards used, [PERSON] et al. [38] focused on solutions for low-power consumption and stable communications through the use of standards such as IEEE 802.15.4 between nodes to ensure low-speed and IEEE 802.11b/g network structures for connection between clusters to ensure high data transmission rates. [PERSON] et al. [37] used IEEE 802.11 ad standards to simulate a seismic network with an efficient data transfer scheme for seismic acquisition with gigabit rate requirements to subsurface imaging with high quality and superior depth. The main objective was to reduce the energy consumption in a seismic network with a high number of hops, sacrificing latency.