esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Power ECG Processor ASIC Based on an Artificial Neural Network for Arrhythmia Detection
###### Abstract  
The early detection of arrhythmia can effectively reduce the risk of serious heart diseases and save time for treatment. Many healthcare devices have been widely used for electrocardiogram (ECG) monitoring. However, most of them can only complete simple two-classes detection and have unacceptable hardware overhead and energy consumption. For achieving accurate and low-power arrhythmia detection, a novel ECG processor application specific integrated circuit (ASIC) is proposed in this paper, which can perform the prediction of five types of cardiac arrhythmias and heart rate monitoring. To realize hardware-efficient R-peak detection, an ECG pre-processing engine based on a first derivative and moving average comparison method is proposed. Efficient arrhythmia detection is realized by the proposed low-power classification engine, which is based on a carefully designed lightweight artificial neural network (ANN) with good prediction accuracy. The hardware reuse strategy is used to implement the hardware logic of ANN, where computations are executed by only one processing unit (PU), which is controlled by a flexible finite state machine (FSM). Also, the weights of ANN are configurable to facilitate model updates. We validate the functionality of the design using real-world ECG data. The proposed ECG processor is implemented using 55 nm CMOS technology, occupying an area of 0.33 mm\({}^{2}\). This design consumes 12.88 uW at a 100 kHz clock frequency, achieving a classification accuracy of 96.69%. The comparison results with previous work indicate that our design has advantages in detection performance and power consumption, providing a good solution for low-power and low-cost ECG monitoring.  
arrythmia detection; ECG processor; low power; classification engine; artificial neural network (ANN) +
Footnote †: journal: Applied Sciences  
Arrythmia detection; ECG processor; low power; classification engine; artificial neural network (ANN)
## 4 Results
### Validation of Proposed Method  
Three hundred right-turn data points at signalized intersections were extracted from the ND-DB. For each data point, dynamic occlusions caused by oncoming right-turning vehicles and changes in traffic lights were analyzed. Right turns, where an oncoming right-turning vehicle is present without an occlusion, and right turns, where no oncoming straight-ahead vehicle is approaching the intersection when the lights changed, were excluded. Figure 6 compares the right-turn ratio with the critical factors between the NMI  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Driver’s Expectations** & **Frequency** \\ \hline \multirow{3}{*}{1} & An oncoming vehicle going straight will not enter & 25 \\  & the intersection at a red light. & 17 \\  & An oncoming vehicle going straight will not enter & 2 \\  & the intersection at a yellow light. & 1 \\  & An oncoming vehicle going straight will pass & 2 \\  & through the intersection without slowing down. & 1 \\  & An oncoming vehicle will not come straight. & 1 \\  & An oncoming vehicle will not change its direction. & 1 \\  & An oncoming vehicle will run a different path. & 1 \\  & A stopping vehicle will not enter the intersection. & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Driver’s expectations in incident data points of genotype “F2 Expectance of Certain Behaviours” (\(N=49\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Object** & **Frequency** \\ \hline
1 & Oncoming vehicle making a right turn & 40 \\
2 & Oncoming vehicle making a left turn & 6 \\
3 & Oncoming vehicle going straight & 5 \\
4 & Preceding vehicle going straight & 4 \\
5 & Preceding vehicle making a right turn & 4 \\
6 & Crossing vehicle & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Occlusion-causing objects in NMI data with the genotype “K1 Temporary Obstruction of View” (\(N=60\)).  
and ND data points. In the ND data, both factors were encountered in more than 10% of the total data points (light gray bars in Figure 6). This result indicates that these factors can be encountered daily and are common scenario components. In contrast with the ratio of ND data points, both of the factors accounted for more than 25% of the NMI data points (dark gray bars in Figure 6). Statistical test results clarified that the right-turn ratio of NMI data points was significantly higher than that of ND data points (\(p<0.001\)). These results suggest that driver errors are more likely to occur in scenarios where these two factors are present and the identified scenario components are critical.
# An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer
## 3 Design of the SERCT
### The Temperature and Pressure Decoupling Control System  
The temperature and pressure control of SERCT is divided into two types: (1) Initial environment reconstruction and (2) P-T preservation. The initial environment reconstruction establishes the target deep in situ temperature and pressure in the chamber from the normal temperature and pressure, which is a heating and pressurizing process. The P-T preservation means that after the initial environment reconstruction finishes, the system needs to reduce the fluctuations of the temperature and pressure caused by the movements of the push rod and the valves.  
The structure of the temperature and pressure control system is shown in Figure 2a. The loop of water includes: the working chamber, the pressure relief valve, the water tank, the preheating chamber, and the booster valve. We use a two-layer control structure for temperature and pressure control: in the inner layer, the central controller obtains the data of the temperature sensors and the pressure sensor located in the working chamber, plans the decoupling control targets of temperature and pressure based on the sensor data and transmits the targets to the temperature controller and the pressure controller, respectively. In the outer layer, the temperature and pressure are independently controlled. The decoupling control flow of temperature and pressure is shown in Figure 2b. The pressure control is accomplished through the pressure valves. The temperature control is much more complicated because SERCT is a complex nonlinear large time-delay system. In order to increase the speed of temperature regulation, a preheating chamber is designed to preheat the water to be as close to the target temperature as possible before it is replenished into the working chambers. In this way, when the target temperature is below 100 \({}^{\circ}\)C, the preheated water replenished into the working chambers causes little temperature change or fluctuations. When the target temperature is higher than 100 \({}^{\circ}\)C, by preheating the water, the temperature difference between the replenished water and the water in the working chambers is reduced, and thus, the time for temperature adjustment can be greatly shortened.  
Figure 2: The temperature and pressure decoupling control system; (**a**) the structure of the temperature and pressure decoupling control system; (**b**) the decoupling control flow of temperature and pressure.
###### Abstract  
Strip filling mining in a goaf is of great significance for solving the 'three under' cools-pressure and mining-area ecological environment problems in Central and Eastern China, but the disturbance characteristics of filling parameters on overlying rock are not clear at present. Taking the geological conditions of the CT3010 working face in Mahuangliang coal mine and the short-wall interval strip filling as a background, the strength parameters (cohesion and friction angle), deformation parameters (elastic modulus and Poisson's ratio), and structural parameters (strip width and spacing) of the filling body were selected as experimental factors, and the maximum settlement of the direct root and the ground surface was taken as the evaluation index. The influence degree of each factor was quantitatively characterized via a variance analysis and an F test, and the main control factors of the strip filling overburden settlement were proposed. The roof and surface displacement, the stress evolution law of the filling body, and the shape change of the surrounding rock plastic zone under different levels of main control factors in the entire process of mining filling coupling were analyzed in detail. The results showed that the cohesion of the backfill had a highly significant impact on the direct roof settlement, the strip spacing and the friction angle of backfill had a significant impact on it, the cohesion of the backfill and the strip spacing had a certain impact on the surface settlement, and the two had a cross-coupling effect. In the process of mining and filling, the stress evolution of the filling body was extremely complex, and it finally presented a saddle shape that was high on both sides and low in the middle; the majority of the strata and the filling body primarily exhibited shear damage, with a small amount of tensile failure zones appearing only in the direct roof and mid-section of the filling body. The above conclusions have a certain guiding significance for the optimal design of strip filling in a goaf.  
strip filling; overlying rock disturbance; parameter influence; controlling factors; stress evolution; plastic zone morphology +
Footnote †: journal: Applied sciences
# Research on Real-Time Control Strategy for HVAC Systems in University Libraries
## 3 Experimental Cases
### Project Overview  
The system model was established and applied in the HVAC system of a university library building in Hubei Province. As shown in Figure 14 and Table 1, the system consists of two chillers, three chilled water pumps, three cooling water pumps, and two cooling towers. The chilled water pumps are connected in parallel, while the cooling water pumps are connected in series with the cooling towers and chillers. The water flow data and temperature data of each part are collected by the corresponding sensors. Some of the cooling water pumps and chilled water pumps are kept off standby when the HVAC cooling system supply meets the demand. The standby cooling water pumps are connected to the first freezer and are arranged in a series configuration with the cooling tower and the freezer.
## 3 Results
### Prediction of Properties of Torrefied Biomass
#### 3.4.2 HHV Prediction  
The results of the HHV prediction of wheat straw at different torrefaction temperatures (250, 275, 300, and 325 \({}^{\circ}\)C) are presented in Figure 11. The HHV is obtained from the elemental composition using the empirical Equation (10). The torrefaction treatment shows a dependence between temperature and time in the HHV analysis from raw material to the torrefied biomass. Global parameters are shown in Figure 11a, where the results range from 18.905 to 23.191 MJ\(\cdot\)kg\({}^{-1}\). Regarding individual parameters, in Figure 11b, the HHV results are between 19.724 and 23.615 MJ\(\cdot\)kg\({}^{-1}\). In both cases, the interval from 0 to 17 min  
Figure 10: Prediction curves of the dynamics of the elemental composition percentage for individual parameters: (**a**) carbon, (**b**) hydrogen, (**c**) oxygen, and (**d**) nitrogen.  
corresponds to the time in which the heating rate (20 \({}^{\circ}\)C/min) reaches the torrefaction temperature. Subsequently, the HHV began to increase due to changes in the elemental composition of the wheat straw as the temperature increased. The results show that the carbon content increases while the oxygen content decreases.  
Table 6 shows the values obtained from experimental data for raw biomass (18.535 MJ\(\cdot\)kg\({}^{-1}\)) and torrefied biomass at the different study temperatures. In addition, the predicted HHVs are shown using global and individual parameter analysis. The relative errors between the experimental data and those obtained with the global and individual parameters were estimated to validate the prediction results. The results showed that the maximum relative error occurs at 275 \({}^{\circ}\)C in the individual parameters, with a value of 7.389. In contrast, the minimum relative error corresponds to the global parameters, with a value of 0.415 at 300 \({}^{\circ}\)C. The average relative error for the global parameters is 1.350, and for the individual parameters, it is 3.683. This is because the predictions for the global parameters show lower errors in elemental composition analysis.  
It was found that by determining the kinetic parameters at a torrefaction temperature higher than the maximum peak observed in the derivative thermogravimetric (DTG) curve (275 \({}^{\circ}\)C), it is possible to predict the behavior of the process within the 250-325 \({}^{\circ}\)C range with an R-squared value corresponding to an error lower than 3%. This approach significantly reduces the number of required experiments from twelve to only four by relying on a single isothermal condition for parameter estimation.
# Comparative Study of Minimally Invasive Microwave Ablation Applicators
## 3 Results
### Quantitative Comparison of Ablation Performance Across Applicators Under Standardized Conditions  
The final focus of the results is to provide a quantitative comparison of all designs. To ensure consistency, each applicator design is initially positioned within healthy tissue  
Figure 8: Temperature distribution with a deposited power of 40 W of monopole antenna (**a**), capacitive capped monopole antenna (**b**), multi-slot coaxial antenna (**c**), dual-slot antenna (**d**), triaxial antenna (**e**), choke dipole antenna (**f**), balun-free base-fed monopole (**g**), floating sleeve dipole antenna (FSD) (**h**), balun-free helical dipole antenna (**i**), and helical open waveguide structure (**j**) in the two-compartment model (healthy tissue and tumor). The blue, purple, red, and yellow colors indicate temperature contours of 38 \({}^{\circ}\)C, 42 \({}^{\circ}\)C, 46 \({}^{\circ}\)C, and 50 \({}^{\circ}\)C, respectively. (homogeneous model--HM) and subsequently within healthy tissue with tumor (two-compartment model--TCM) at the same insertion depth, with all configurations set to receive an identical power level (same deposited power equal to 40 W). Comparison criteria include maximum temperature, ablation zone dimensions (diameter and length), aspect ratio, and dimensions penetration of the 38 \({}^{\circ}\)C zone into healthy tissue. The outcomes of this comparison are summarized in Table 3. It is observed that the maximum temperature is too high for the applicators; Figure 1 gj presented the best energy concentration inside the tumor. However, this can be reduced by lowering the microwave source power without any compromise in the ablation zone shape. Besides that, this maximum temperature value may be fictitiously caused by the high values of the considered point SAR. In turn, excessive SAR values are related to field singularities at metallic edges. In any case, this issue needs further examination through careful research. Table 4 illustrates the performance metrics of applicators after power reduction to achieve safe maximum temperature. We consider values close to 100 \({}^{\circ}\)C as the maximum temperature, where the water starts to boil and evaporate. Although water evaporation may seem to involve major complications, there is no clear relevance in the standards, e.g., Ref. [51], denoting a maximum allowable temperature. Notably, the deposited power was reduced as shown in Table 4 to 10-30 W to maintain a maximum temperature close to 100 \({}^{\circ}\)C. The highest reduction was observed in the most focusing applicator of helical open waveguide. The question is whether, in most cases where the deposited energy is restricted within the tumor, the entire tumor is uniformly covered/ablated. To examine this option, the corresponding temperature distribution is depicted in Figure 9.
## 6 Simulation Results and Discussion
### Simulation Parameters
#### 6.1.5 Packet E2E Delay  
Packets E2E delay is shown in Figure 20. The GEDAR has high packet E2E delay due to its DA strategy. In addition, continuous DA in GEDAR results in high EC. This dissipation leads the network to originate a new void hole in that region. Then, GEDAR performs some more DAs to avoid the void hole in that region. This adjustment results in some more E2E delay. In addition, LMPC uses a layered multi path routing approach. This approach generates multiple copies of data and transmits over multiple paths using a binary tree approach. This strategy helps the network for minimum packet loss. However, LMPC protocol takes higher packet delay than LETR because of the collisions of multiple copies of DPs originating from cross nodes. However, this E2E delay is less than GEDAR and higher than LETR. The reason for less E2E delay of LETR is that LETR prioritizes the transmission range adjustment over DA and has no multiple copies of data.  
Meanwhile, both protocols well performed in minimizing the packets E2E delay than benchmark proactive routing protocols (i.e., Hydrocast, Co-Hydrocast, Improved Hydrocast and Co-Improved Hydrocast). EP-VIR-Three provides an efficient routing path with no collision and interference (using 3-hop neighbor's information and CF value). This routing path includes the forwarders node having a minimum number of neighbors with minimum hop counts from the source node to the sink node, while, in BF-SPR-Three, the packets E2E delay is less than LMPC, GEDAR, EP-VIR-Three, Improved Hydrocast and Co-Improved Hydrocast routing protocols because of shortest and efficient route selection using 3-hop neighbor's information and binary tree generation (the shortest path is originated through Bellman-Ford algorithm.)
# Adaptive Multi-Level Search for Global Optimization:
## 5 Benchmark Problem
### Welded Beam
Both ASIM and MAM demonstrate efficiency in finding the optimal design owing to their accuracy approximations and adaptive trust region strategy at local level exploitation. On average, hundreds of evaluations are required to determine an optimum. It is noted that enhancement of global exploration for the optimal solution by the PSO process in the ASIM framework could be demonstrated by a standard deviation of zero (\(1.1\times 10^{-7}\)) for statistical results, which is approximately four orders of magnitude smaller than the value by MAM (0.0031358). Furthermore, by comparison with the NFEs (200,000) obtained by co-evolutionary PSO [22], the accurate surrogates built by ASIM framework indeed assist each particle in finding the local refinement position and speed up the converged global optimum. In conclusion, ASIM needs less computational cost for a global optimum with improved accuracy and great robustness. Figure 4: Schematic of the welded beam structure with indication of design variables. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(\mathbf{x_{1}(h)}\) & \(\mathbf{x_{2}(I)}\) & \(\mathbf{x_{3}(t)}\) & \(\mathbf{x_{4}(b)}\) & **Cost** \\ \hline GA-based [39] & 0.205986 & 3.471328 & 9.020224 & 0.20648 & 1.728226 \\ CPSO [22] & 0.202369 & 3.544214 & 9.04821 & 0.205723 & 1.728024 \\ ES-based [40] & 0.199742 & 3.612060 & 9.037500 & 0.206082 & 1.737300 \\ CSS [41] & 0.20582 & 3.468109 & 9.038024 & 0.205723 & 1.724866 \\ CBO [42] & 0.205722 & 3.47041 & 9.037276 & 0.205735 & 1.724663 \\ MAM & 0.2057296 & 3.4704893 & 9.0366242 & 0.2057297 & 1.724852 \\ ASIM & 0.2057296 & 3.4704887 & 9.0366239 & 0.2057296 & 1.724852 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of present optimized designs with the literature for welded beams.
## 5 Sensitivity of the Material Properties of the FSPM  
In this section, parametric studies are performed to study the sensitivity of the dynamic response of the FSPM to the material properties, including the permeability coefficient, elastic modulus of the solid skeleton and porosity. A new calculation model, shown in Figure 13, is used for the study. The width and height of the calculating domain are 100 m and 50 m, respectively. The top surface of the calculation model is set as the free and drainage boundary, and the left, right and bottom boundaries of the calculation model are all set as the transmitting artificial boundary. The four-node rectangular element is used for the finite element discretization of the calculation domain, From the associated consideration of the computational accuracy and effort, a finite element mesh size of 5 m by 5 m is adopted for the numerical study in this section. The dynamic response of node A (50, 45) and B (50, 20) in the finite element model are investigated.  
The strong motion record from the Ninghe earthquake is used as seismic excitation, input perpendicularly from the bottom boundary of the finite element model as the compressive wave. The duration of the Ninghe earthquake record is 19.2 s; the displacement time history of the strong motion record is shown in Figure 14. The time step \(\Delta t\) used for the calculation is 0.0001 s.  
Figure 12: Solid skeleton velocity time history of the free surface in the horizontal direction (shear wave input).
## 2 Materials and Methods
### Methods  
#### 2.2.1 Concrete Compressive Strength Development Over Time Model  
The explicit formulation of CCSDOT model is expressed in Equation (1) below:  
\[\begin{array}{ll}S_{t}&=S_{\text{C}}\Big{(}q_{C}^{i_{\text{C}}}+ss_{SL}q_{SL}^{ i_{\text{SL}}}+ss_{FA}q_{FA}^{i_{FA}}+s_{LF}q_{LF}^{i_{LF}}\Big{)}\\ &\times\Big{[}1+\Big{(}\lambda_{C}q_{C}^{i_{\text{C}}}+\lambda_{SL}q_{SL}^{i_{ \text{SL}}}+\lambda_{FA}q_{FA}^{i_{FA}}+\lambda_{LF}q_{LF}^{i_{LF}}\Big{)}ln \frac{t}{28}\Big{]}\end{array} \tag{1}\]  
where \(S_{t}\) is the concrete compressive strength after \(t\) days; \(S_{\text{C}}\) is the 28-day compressive strength of concrete mixed with cement; \(q_{C}\), \(q_{SL}\), \(q_{FA}\), and \(q_{LF}\) are the percentages of each cementitious material's content by weight; \(i_{C}\), \(i_{SL}\), \(i_{FA}\) and \(i_{LF}\) denote the impact index of cement, slag, fly ash, and limestone flour, respectively; \(ss_{SL}\), \(ss_{FA}\) and \(ss_{LF}\) are the 28-day compressive strength contribution coefficient of slag, fly ash, and limestone flour; \(\lambda_{C}\), \(\lambda_{SL}\), \(\lambda_{FA}\) and \(\lambda_{LF}\) indicate the factor of the compressive strength in the later period (more than 28 days) of cement, slag, fly ash, and limestone flour respectively.  
Besides the CCSDOT model, this study also adopts the modified sequence quadratic method, with the monitoring technology combining constraint condition and objective function, and temporary expansion of the feasible domain method, to obtain the optimum solution of Equation (1). Interested readers can refer to [PERSON] [49]. [PERSON] only applied the CCSDOT model to the green concrete. In this study, the CCSDOT model is applied in 18 practical engineering and lab reports, covering roller compacted concrete, ordinary concrete, etc. In brief, the overview of the model and methodology is shown in Figure 2 [49].  
Figure 2: Overview of the model and methodology. SQP: sequence quadratic programming; CCSDOT: concrete compressive strength development over time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{5}{c}{**Compressive Strength (MPa)**} \\ \cline{2-6}  & **7 days** & **28 days** & **90 days** & **180 days** & **360 days** \\ \hline
1 & 18.5 & 32 & 43.2 & 45.2 & 46.5 \\
2 & 24.1 & 31.7 & 43.2 & 46.0 & 47.2 \\
3 & 22.5 & 32.7 & 44.5 & 46.7 & 47.9 \\
4 & 17.9 & 29 & 40.6 & 42.8 & 44.4 \\
5 & 15.6 & 26.4 & 37.2 & 39.5 & 43.4 \\
6 & 16.1 & 27.2 & 38.3 & 40.0 & 44.2 \\
7 & 11.2 & 22 & 31 & 38.8 & 42.6 \\
8 & 9.6 & 16.9 & 27.8 & 33.3 & 37.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compressive strength of the Suofengying three-graded roller compacted concrete.