esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an Open Remote Electrocardiogram (ECG) Service
## 4 A Prototype for a Remote ECG Service
### Patient Side HTTP User-Agent and WiFi Interface  
We used a ESP12e board (Espressif Systems, Shangai, China) to implement the two functions. We plugged the board, which provides a 32-bit MCU with a WiFi interface (ESP8266 ex), into a _ProtoboardArduino shield stacked on top of the other two boards (see Figure 5). We connected the power and serial lines using jumpers from Arduino pins to the ESP12e ones on the breadboard.  
Using the Arduino IDE, we wrote a UA that opens a WebSocket connection with the server using _Links2004_ WebSocket library functions. When the WebSocket is opened, the UA starts sending frames containing the data received on the serial line. Each frame is encoded in JSON as an array of objects, one for each data frame received from Arduino, with a timestamp and room for six floating point values (only the first one being finally visualized). The snippet in Listing 2 shows the loop that prepares the buffer and delivers it through the WebSocket.
```
while(true){ JSONBuffer[0]='[';//Openanarray ptr=1; for(int=0;n<N;n++){ digitalWrite(PROBEPIN2,HIGH);//probe if(fillBuffer()!=packetLength)continue; digitalWrite(PROBEPIN2,LOW); ptr=formatJSON(ptr); } JSONBuffer[ptr-1]=']';//closethearray digitalWrite(PROBEPIN1,HIGH);//probe webSocket.sendTXT(JSONBuffer); digitalWrite(PROBEPIN1,LOW); }
```  
Listing 2: The endless loop delivering a frame to the server. Each frame contains N samples. Probe pins allow to inspect operation. The complete sketch is available from the repository [24].  
The JSON array has a variable length in the interval \([740,750]\) bytes. The frame is sent to the server through the WebSocket as a unique TCP segment. Since the length of a single IP packet would exceed the standard length--notably, 576 octets--each frame is divided into two IP packets that sum up from 849 to 857 octets: in theory, the generated Internet traffic is of 21.2 KB/s.  
Note that, given a packet size around 850 octets, the 14 octets difference between the length of CoAP and HTTP headers anticipated in Section 3 is negligible (<2%).  
Figure 5: The stack of three Arduino-compliant boards: below the Arduino, the Olimex acquisition board in the middle, the protoboard hosting the ESP12e on the top
# Efficient Parallel FDTD Method Based on Non-Uniform Conformal Mesh
## 1 Introduction
The present study integrates non-uniform conformal grids, MPI parallel techniques, and CPML absorption boundaries into the traditional FDTD algorithm. A series of computational experiments were conducted on a supercomputing platform to verify the accuracy and efficiency of the algorithm.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 5 System Model
### Performance Analysis  
An essential step in determining a predictive model's efficacy is performance analysis. In this paper, mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as metrics for performance analysis.  
Performance analysis workflow:  
* Model Training: Train three deep learning models using the training dataset.
* Hyperparameter Tuning: Fine-tune different hyperparameters, which are mentioned in Section 5.3, based on performance metrics (MAE, MAPE) calculated on the validation dataset.
* Model Evaluation: Evaluate the prediction performance of the completed trained model using the testing dataset.
* Performance Metrics Calculation:
* Use the trained model to make predictions for energy demand on the testing dataset.
* Calculate MAE and MAPE metrics using the actual demand value of the test dataset and predicted value from a trained model using testing data.
* These metrics give us information about the precision and accuracy of the predictions made by our final model.
## 4 Discussion
### Effects of Vegetation and Climate Change on ET in the HLS
For example, the soil types in the northern and central regions are relatively loose, which can better store and utilize PRE, promote vegetation growth, and increase ET, while the soil in the southern region may be denser, which limits the infiltration and storage of water, resulting in a lower contribution of PRE to ET [47]. For Et, VPD is the main influencing factor, especially in the central and southern regions. The contribution of TMP and PRE to Et showed significant spatial heterogeneity. TMP had a significant negative contribution to Et in the central and southern regions, while PRE had a positive contribution to Et in the northern and central regions. The contribution of RAD to Et was more dispersed, showing a mixed distribution of positive and negative contributions. This phenomenon may be related to vegetation type and coverage, and the response of vegetation to climate factors is different in different regions [6]. For example, dense woodlands may have a stronger response to precipitation and radiation, while loose grasslands are more sensitive to temperature changes. The dominant influence area of Ei, TMP, and RAD increased significantly, while the dominant influence area of PRE was smaller. The contribution of TMP to Ei is positive in the north and part of the central region, while the southern region shows a negative contribution. The contribution of RAD to Ei is small, ranging from \(-0.5\) to \(0.5\) mm/a in most areas. The contribution of VPD to Ei is negative in most regions, especially in the southern region. This may be due to the fact that Ei is mainly affected by leaf area index and precipitation, while TMP and RAD have a greater impact on these factors [48]. Under conditions of high temperature and radiation, leaf water evaporation increased, leading to a rise in Ei. For Es, the area primarily influenced by TMP expanded, while the areas influenced by RAD and VPD contracted. TMP's contribution to Es was more pronounced in the northern and central regions. The contribution of RAD to Es was small in most regions, about \(-0.5\) to \(0.5\) mm/a. The contribution of VPD to Es was mostly negative in the study area, especially in the southern region, showing a significant negative contribution. This may be closely related to soil type and water status, and the response of soil in different regions to climatic factors is different [49]. For example, sandy soil has a weak water retention capacity, and soil moisture is easily lost under high-temperature and high-VPD conditions, resulting in a decrease in Es.
With the study of electricity demand in the Baneshwor substation and its relation between the previous day's load of the same hour and temperature and an environmental variable affecting the load, this paper presents an ANN model which forecasts load for the next 24 h. The major findings based on the electricity data for STLF are explained as follows:  
* An impact study on the temperature is performed, which leads to increases in electricity demand during hot and cold temperatures for a tropical region like Nepal. * The quantitative analysis among variables such as the impact of time series variables such as holidays, working days, the time of day, day of the week, and day of the month is discussed in detail. * The development of two different forecasting models for weekends and weekdays, which leads to achieving better prediction capability among the existing literature of the Nepalese dataset. * A comparison of developed models with conventional time series models is conducted for weekdays and weekends, which shows that the FF-ANN holds the upper hand when forecasting load for both days, with a major improvement in weekend forecasting. Furthermore, this paper is organized as follows. Section 2 discusses the literature review and past work of load forecasting. Section 3 discusses the data used for thorough analysis of load relation with the lag load and temperature. Section 4 presents the methods implemented for the forecasting background and parameter selection. The results from forecasting using different methods are discussed in Section 5, and Section 6 is the summarization of this paper's findings.
## 3 Results
### Time Series Analysis  
From the time series analysis, we obtained information regarding the temporal trend of backscatter values, such as (1) the minimum date associated with low roughness values, (2) the maximum date associated with high roughness values, and (3) the coefficient of variation--hereafter called \"CoV\" (the ratio between standard deviation and the mean). In particular, the latter one provided information on the variation in land cover classes over time.  
The analysis of CoV calculated for each crop has been used as proxy to identify the stability of the signal strictly correlated with the stability of the crop over time. As is shown in Figure 11, this proxy assumes higher values for the crops that show a higher variability within the year. The tree crops class is an exception, showing a lower CoV value because it is more stable over time.  
Figure 11: Average and standard deviation of the coefficient of variation (CoV) for each class of crop.  
Figure 10: Temporal profiles of BSI values for each class of crop. The trend lines show the behavior of tree crops (green line) compared to maize (yellow line).
## 3 Results and Discussion
### Thermal Properties Analysis  
Figure 4 shows the heating and freezing curves from DSC measurements of the coconut oil + _n_-hexadecane Mixed SSPCMs. Table 4 represents the phase transition temperature and latent heat capacity of coconut oil + _n_-hexadecane Mixed SSPCMs and Table 5 represents those of coconut oil/xGnP and _n_-hexadecane/xGnP SSPCMs. As shown in Figure 4, the phase transition of the Mixed SSPCMs during heating and melting occurred at the lower temperature of the each melting and freezing point of the coconut oil and _n_-hexadecane. The Mixed SSPCMs display a tendency that the more the Mixed SSPCM contains a high rate of _n_-hexadecane, the greater the melting and freezing points. The melting temperatures at peak points of the Mixed SSPCMs were 12.53 \({}^{\circ}\)C, 14.51 \({}^{\circ}\)C, and 16.92 \({}^{\circ}\)C, while the freezing temperatures at peak points of them were 5.55 \({}^{\circ}\)C, 6.54 \({}^{\circ}\)C, and 10.31 \({}^{\circ}\)C.  
Figure 3: Fourier transform infrared spectroscopy (FT-IR) absorption spectra of Mixed SSPCMs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bonding** & **Wave Number Range (cm\({}^{-1}\))** \\ \hline -CH\({}_{3}\) & 2919–2920 \\ -CH\({}_{2}\) & 2850–2851 \\ C=O & 1735–1744 \\ C–O & 1149–1160 \\ \hline \hline \end{tabular}
\end{table}
Table 3: FT-IR absorption spectra peaks of Mixed SSPCMs.  
Figure 2: Chemical formation of triglyceride by esterification of glycerol with fatty acids.  
The difference between melting and freezing points of the Mixed SSPCMs is because of supercooling of PCMs.  
The noticeable result in this research is the latent heat capacity of Mixed SSPCMs. The latent heat capacities of the Mixed SSPCMs were 89.06, 104.30, and 124.50 J/g during melting, and those of during freezing were 92.82, 106.20, and 123.40 J/g. Although these values are lower than pure PCMs because 3-dimentional network structure of xGnP disturbs the heat flow of PCMs, these are the high values in consideration of the latent heat capacities of coconut oil/xGnP and _n_-hexadecane/xGnP SSPCMs that contain single PCM. The latent heat capacities of the Mixed SSPCMs also appear to follow the law that the more the Mixed SSPCM contains a high rate of _n_-hexadecane, the greater the latent heat capacity.
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Results  
#### 6.3.1 Network Lifetime  
A network lifetime performance comparison of GAFOR with existing schemes such as DEF [2], CCEF [3], and CCEF with re-clustering (CCEF-RC) using FND is shown in Figure 10. The reasons for choosing DEF and CCEF schemes to compare the performances of the proposed technique can be explained as follows. The DEF addresses false report injection attacks in WSNs and adopts multipath routing to deal with the topology changes of the networks. Because of its faster false reports' dropping rate with a low memory requirement, the DEF is still regarded as the benchmark en-route filtering strategy against false report injection attacks. Like DEF, the CCEF also drops fabricated reports en-route, but it does not require symmetric key sharing. In CCEF, the source node sets up a secret association with the BS for each session. Because of this stronger security protection, CCEF is also widely considered a representative en-route filtering scheme. In addition to DEF and CCEF, we have also considered CCEF with fixed time-instant-based re-clustering for a fair comparison. The x-coordinate represents network size in terms of the number of sensor nodes in a squared sensor field of fixed area. The \(y\)-coordinate is the number of communication rounds. The margin of improvement varies with network size and \(FTR\). The performance of CCEF and CCEF-RC is similar since re-clustering is performed after a certain number of nodes are depleted. In terms of performance, GAFOR outperforms existing schemes.  
In the case of performance metric PND, proposed schemes also perform best among compared schemes in all three setups with different \(FTR\) as shown in Figure 11. In the case of PND, CCEF-RC performs better than CCEF due to re-clustering. A margin of improvement is observed for network sizes of 400 and 1000 nodes compared to 700 nodes. Although GAFOR performs better in all cases, the margin of improvement decreases with an increase in \(FRT\). GAFOR shows 2.71 to 2.92 fold improvement using FND and 2.29 to 2.34 times using PND. A summary of network lifetime performance using FND and PND is shown in Table 4.  
Figure 11: Network lifetime (PND).  
Figure 10: Network lifetime (FTR 50, 70, and 90% at first node depleted (FND)).
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 3 Characterizations/Properties of DLC Coatings
### Chemical Resistance of DLC Coatings  
The chemical inertness of diamond makes it attractive as a protective coating against chemical formations [86]. In this study, 2\" diameter discs of 6061-T6 and 5053 Al, 1/4\" thick, were metallographically polished to a 0.5 \(\upmu\)m finish by utilizing a silica slurry and cleaned with isopropyl alcohol. After the deposition of DLC layers, the samples were tested for analyzing chemical attacks or formation on the substrate surface by using acid drops (Figure 3). The principal tests were produced by applying one layer of 1 \(\upmu\)m to 2 \(\upmu\)m thickness of DLC deposited on the Al substrates [87]. Further investigation was carried out for enhancing the substrate surface and deploying the SEM test examination to identify the stress cracking on the substrate surface. Al- 6061-T6 contains insolvable (FeCr)SiAli\({}_{2}\) incorporations and excess Mg:Si particles that are harder than the matrix.  
Different effects were pursued to eliminate imperfections in the DLC coatings; including the utilization of thick coatings (\(\leq\)2 \(\upmu\)m), different layered coatings, and silicon between interlayers. None of the coatings was completely free from the inclusion of undesirable particles. The thicker coating was moderately resistant to chemical formation compared to thinner coatings; since they expanded the ideal opportunity for acid permeation through the layer [87] (see Figure 3). For instance, with the thinner coatings, the response between the Al substrate and the acid was immediate. Utilizing various layered coatings (comprising both thin and thick layers, 0.5 \(\upmu\)m and 1.5 \(\upmu\)m, respectively), the protection from chemical reaction was expanded up to 20 min. At the same time, coatings with a Si layer between the DLC and Al substrate gave the best protection from permeation, delaying chemical reaction with the substrate up to 60 min. Moreover, while considering the permeation time, the effect of time on the permeation process was not completely removed by using the Si interlayer due to the inclusion particles of coating defects.
## 3 Results and Discussion
### Chemical Composition of Indoor Particulate Matters
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Species** & \multicolumn{4}{c}{**Indoor (No. of Sample = 25)**} & \multicolumn{4}{c}{**Outdoor (No. of Sample = 8)**} \\ \cline{2-9}
**(\(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\))** & **Mean (\%)** & **SD** & **Min** & **Max** & **Mean (\%)** & **SD** & **Min** & **Max** \\ \hline Al & 0.007 (0.2) & 0.036 & 0.000 & 0.186 & 0.085 (1.9) & 0.150 & 0.000 & 0.405 \\ Ca & 0.498 (15.0) & 0.324 & 0.197 & 1.574 & 0.389 (8.7) & 0.149 & 0.212 & 0.641 \\ Fe & 0.246 (7.4) & 0.100 & 0.095 & 0.545 & 0.411 (9.2) & 0.152 & 0.257 & 0.705 \\ K & 0.335 (10.1) & 0.113 & 0.114 & 0.543 & 0.482 (10.8) & 0.167 & 0.249 & 0.692 \\ Mg & 0.124 (3.7) & 0.079 & 0.054 & 0.429 & 0.124 (2.8) & 0.061 & 0.070 & 0.269 \\ Na & 0.347 (10.4) & 0.238 & 0.110 & 1.287 & 0.331 (7.4) & 0.078 & 0.230 & 0.450 \\ Si & 0.541 (16.3) & 0.290 & 0.205 & 1.465 & 0.957 (21.4) & 0.720 & 0.346 & 2.390 \\ As & 0.002 (0.1) & 0.002 & 0.000 & 0.007 & 0.004 (0.1) & 0.002 & 0.000 & 0.007 \\ Ba & 0.009 (0.3) & 0.003 & 0.004 & 0.015 & 0.012 (0.3) & 0.003 & 0.008 & 0.017 \\ Cr & 0.002 (0.1) & 0.001 & 0.000 & 0.004 & 0.002 (0.0) & 0.001 & 0.001 & 0.003 \\ Cu & 0.003 (0.1) & 0.001 & 0.001 & 0.005 & 0.003 (0.1) & 0.001 & 0.002 & 0.004 \\ Mn & 0.011 (0.3) & 0.005 & 0.005 & 0.026 & 0.019 (0.4) & 0.004 & 0.013 & 0.024 \\ Pb & 0.013 (0.4) & 0.006 & 0.003 & 0.023 & 0.021 (0.5) & 0.007 & 0.013 & 0.037 \\ Ti & 0.022 (0.7) & 0.008 & 0.008 & 0.038 & 0.032 (0.7) & 0.016 & 0.016 & 0.060 \\ Zn & 0.038 (1.1) & 0.013 & 0.016 & 0.068 & 0.052 (1.2) & 0.014 & 0.036 & 0.076 \\ Br & 0.004 (0.1) & 0.002 & 0.000 & 0.009 & 0.005 (0.1) & 0.003 & 0.002 & 0.011 \\ Cl & 0.193 (5.8) & 0.242 & 0.062 & 1.265 & 0.126 (2.8) & 0.084 & 0.048 & 0.333 \\ Sum & 0.926 (27.9) & 0.328 & 0.296 & 1.417 & 1.423 (31.8) & 0.916 & 0.726 & 3.805 \\  & 3.310 (100) & & & & 4.478 (100) & & & \\ \hline \hline \end{tabular} In brackets, percentages of the total concentrations.  
\end{table} 
Table 2: Average amount of inorganic elements found in PM\({}_{2.5}\).
## 3 Results and Discussion
Of the 16 variables considered for the sensitivity analysis, binder dosage (\(D_{\text{kg/m3}}=12.8\%\) or \(7.6\%\)), cement content (\(a_{w}=5.1\%\) or \(5.5\%\)), soil properties (\(w_{o}=11.3\%\) or \(10.4\%\), \(\%clay=10.7\%\) or \(6.8\%\)), fibre content (\(T_{fibre}=16.9\%\) or \(8.2\%\)), ratio between water and cement contents (\(w_{o}/a_{w}=10.8\%\) or \(5.1\%\)), and age of cure (\(t=5.2\%\) or \(7.5\%\)) are the most important variables that contributed to the predictions for ANN and SVM, respectively. This is consistent with previous studies [17; 21; 22; 24; 38], which reveal the most important parameters controlling \(\text{E}_{\text{u}.50}\) in soil-cement mixtures. Moreover, \(\text{E}_{\text{u}.50}\) is related to the compressive strength. As indicated in [4; 9; 16], the input variables for UCS prediction are similar to those for \(\text{E}_{\text{u}.50}\), indicating the accuracy of the prediction models, especially for ANN. In the case of RF, \(a_{w}\) and \(w_{o}\) showed an exaggerated relative importance of 22.0%. These are influential parameters for the development of the mechanical properties of soil-cement mixtures, especially in relation to the \(\text{E}_{\text{u}.50}\). Figure 4: The relationship between experimental and predicted elastic modulus values of soil-cement mixtures for the ML models adopted.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 3 Low Complexity Detection Algorithm
### Complexity  
The ML detection complexity \(\eta\) for the ML criterion in (6) was measured in terms of complex operations (CO). One arithmetic operation was considered as one CO; also, one comparison was considered as one CO. The lattice in the system had \(2^{m_{QSM}}\) points. Subtraction, obtaining the square, and finding the minimum in (6) resulted in \(2^{m_{QSM}+2}\) CO. Considering \(n_{R}\) receive antennas, the complexity for ML in (6) can be approximated by:  
\[\eta\approx 2^{m_{QSM}+2}n_{R}. \tag{11}\]  
Table 2 shows a comparison of the complexity for two MIMO configurations. In Table 2, the QSM scheme with ML detector is considered as the reference with 100% of complexity for an SNR of 9 dB. The last column shows the complexity of the proposed Near-ML algorithm where it is observed how this detector outperformed significantly the QSM ML detector in terms of complexity.  
Figure 2: Performance comparison for an \(2\times 2\) and \(8\times 8\) configuration at \(m_{QSM}=4\) and \(m_{QSM}=8\) bpcu respectively.  
The results showed that the proposed Near-ML algorithm performed very near to the optimal one with the advantage of a reduction in detection complexity of 88%.
## 2 Materials and Methods
### Preliminary Optimization Parameters for Fiber-Constrained Flexible Actuators  
#### 2.2.1 Preliminary Selection of the Fiber-Constrained Annular Flexible Actuator  
It has been shown that the fiber spacing affects the radial expansion and bending angle of flexible actuators with semi-circular cross-sections. In order to analyze the effect of fiber spacing on the deformation of the rectangular flexible actuator, the strip flexible actuator equivalent to the annular flexible actuator chamber was established, and the fiber spacing was defined as 5 mm, 4.5 mm, 4 mm, 3.5 mm, 3 mm, 2.5 mm, 2 mm, 1.5 mm, 1 mm and 0.5 mm.
## 5 Conclusions  
The strength of the received GPS signal is lower for urban or indoor environments because of multipath or sheltered effects from the neighboring buildings or trees. This might result in degraded navigation accuracy or complete loss of receiver tracking. This paper proposed a signal existence verification (SEV) process for low received power GPS signal detection based on the time-frequency analysis, which could generate a spectrogram with small error. As shown in the results of this paper, the SEV process can successfully verify the presence of one satellite signal even if the acquired SNR of that specific satellite is below the threshold. The proposed SEV process first removes the C/A code effect from the original signal according to the acquired code delay information in the acquisition process. The spectrogram of the remaining signal is subsequently generated to observe the characteristic of a sinusoid wave at the corresponding Doppler frequency. The experimental results  
\begin{table}
\begin{tabular}{|c|c|c|} \hline \begin{tabular}{c} **Sampling** \\ **frequency** \\ **(MHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Total samples** \\ \end{tabular} &
\begin{tabular}{c} **Consumption** \\ **time/bin** \\ **(Second)** \\ \end{tabular} \\ \hline
16.368 & 16,368 & 1.02 \\ \cline{2-3}  & 32,736 & 3.16 \\ \cline{2-3}  & 49,104 & 5.20 \\ \hline
8.184 & 8,184 & 0.32 \\ \cline{2-3}  & 16,368 & 1.17 \\ \cline{2-3}  & 24,551 & 1.37 \\ \hline
4.092 & 4,092 & 0.16 \\ \cline{2-3}  & 8,184 & 0.31 \\ \cline{2-3}  & 12,275 & 0.46 \\ \hline \end{tabular}
\end{table}
Table 5: The computation cost of the SEV process.  
also demonstrated the effectiveness of the proposed SEV process in the low received power GPS signals. Furthermore, the proposed approach is capable of detecting and verifying the satellite signal with low received power even if the estimated code delay information has an offset of half a chip from the correct one.