esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
## 5 Evaluation and Result Analysis
Similarly, the DM has an input size of \(4\times 32\) and an output of size \(4\times 128\). In the RCL module, the data feature size was always kept at \(4\times 128\). In the TDNN operation, the data feature size was also \(4\times 128\). Since the input feature size is \(4\times 128\) and there is eight input channels, the number of convolution channels of \(1\times 1\) is 32; the number of convolution channels of \(3\times 3\) is 64; the number of convolution channels of \(4\times 4\) is 64; the convolution kernel parameters are \(4\times 128\times 8+1\times 1\times 32+3\times 3\times 64+4\times 4 \times 64=5728\). Table 2 shows the specific parameter settings of the proposed model in the simulation phase, which mainly contains the parameters of the complex-CGAN, RCL module, and TDNN.
# Wireless Sensor Networks for Big Data Systems
## 3 Technical Approaches to WSN-Based Big Data Systems
### Data System
#### 3.4.5 Data Analysis  
Prior to analyzing big data, it is necessary to define system architecture. To meet this requirement, [PERSON] et al. [20] propose a new framework that consists of Hadoop over MapReduce and Hive data warehouse. To perform MapReduce jobs, an SQL-like language, HiveQL, is used to execute queries. Based on these techniques, a data warehouse model for WSN is proposed to analyze all gathered data and detect abnormal behavior.  
As a result of data analysis, fast detection and identification of the location of errors in sensor datasets should be provided. To achieve this goal, [PERSON] et al. [50] developed a novel data error detection approach that exploits the full computational potential of the cloud platform and the network features of WSN. In the proposed scheme, a set of sensor data error types are classified and defined over cluster based WSN. In particular, the error detection over a scale-free network topology and most of the detection operations can be conducted in limited temporal or spatial data blocks instead of the complete big data set. Another error detection, as well as cleaning big data sensor error issues, are addressed in [51]. In this study, the performance is improved by employing a scale-free network topology and classification of the error types at the same time. Moreover, extensible sensor stream processing (ESP) can be used for pervasive applications to build sensor data cleaning infrastructures.  
As one of the important applications for WSN, intruder detection becomes pervasive with an accurate measurement of the image in terms of width and resolution. To operate it practically, a three-layered big data analytic architecture [52] is designed to analyze the data generated during the construction of the barrier and the detection of the intruder using the camera sensors. In addition, a cloud layer is implemented to study the behavior of the intruder with a minimum number of camera sensors. As indicated in the previous case, data analysis becomes more beneficial in wireless multimedia sensor networks, compared with typical WSN. Thus, a big data model based on a graph database model that deals with data generated by wireless multimedia sensor networks is proposed in [53]. With the help of a simulator to generate synthetic data, and store and query big data in the proposed model, the authors present the evaluation results for well-known graph-based NoSQL databases.
# LVRT and Stability Enhancement of Grid-Tied Wind Farm Using DFIG-Based Wind Turbine
## 5 Simulation Results  
PSCAD/EMTDC software was used to perform the simulation analysis on the system depicted in Figure 1. Moreover, to analyze the simulation performance, the most severe triple-line-to-ground (3 LG) fault was appraised to be a network disruption near the infinite bus with a simulation run time of 10 s that is illustrated in Figure 1. The fault arose at 0.1 s and the total fault time span was 0.1 s. At 0.2 s, the circuit breakers (CBs) on the faulted line were tripped to detach it from the power grid. The fault was assumed to have been cleared, and the CBs were recclosed at 1.0 s. Since the transient stability analysis period was so short, it was assumed that the wind speed did not vary significantly during this interval. As a consequence, the wind speed utilized to individual WT remained constant.  
To justify the feasibility of the proposed PI-controlled RSC controller of DFIG, simulations were conducted for two cases:  
Case 1: Conventional cascaded scheme of RSC of DFIG presented in [33].  
Case 2: Proposed RSC controller of DFIG illustrated in Section 4.  
The responses to reactive power from DFIG and SCIG are indicated in Figures 9 and 10. It could be observed that during the transition phase, the DFIG provided SCIG with an adequate percentage of reactive power in Case 2. Therefore, the voltage at the connection point recovered more rapidly to the rated condition in Case 2, and the system became stable, which was presented in Figure 11. The reactive power in Case 2 rose significantly as soon as the terminal voltage decayed at 0.1 s (approximately), and it supplied around 12 MVAR until the terminal voltage reached the normal condition, as indicated in Figures 9 and 11. When the terminal voltage resumed to 1.0 pu, the reactive power also reached its pre-fault condition.  
Figure 9: Reactive power response of DFIG.  
However, in Case 1, as described in Figures 9 and 10, the DFIG could not supply SCIG with a sufficient amount of reactive power. As a consequence, the terminal voltage, in this case, could not regain the rated value. Hence, the grid code requirement was not fulfilled in Case 1, and the system was unstable. Figure 12 depicts the rotor speed response of the SCIG, which revealed that in Case 2, the response was stable. However, in Case 1, it was unstable. This was because the capacitor bank, which was attached to the terminal of SCIG, was not enough to inject sufficient reactive power at the transient state. Thus, the developed electromagnetic torque decreased, and the rotor speed increased, as indicated in Case 1. However, in Case 2, the proposed method of DFIG helped to inject reactive power from the DFIG and made the rotor speed stable.  
Figure 11: Voltage performance at the connection point.  
Figure 10: Reactive power response of SCIG.  
Depending on the above observations, it was possible to conclude that the proposed DFIG controller could effectively stabilize the grid-tied WF during transient periods. Furthermore, it ensured LVRT capability.
# Advances in the Neural Network Quantization: A Comprehensive Review
## 3 Quantization Techniques
### Qat  
Quantization-aware training (QAT) is a technique in deep learning that integrates the quantization process into the training phase of a neural network model. Unlike post-training quantization (PTQ), which applies quantization after the model has been trained, QAT allows the model to learn and adapt to the quantization effects during training. The steps of QAT are shown in Figure 5.  
Figure 3: The activation distributions of three representative convolutional layers of the YOLOv3-tiny model. These distributions are asymmetric. The horizontal axis represents the activation value, and the vertical axis represents the activation density. (**a**) The activation distribution of layer 1. (**b**) The activation distribution of layer 23. (**c**) The activation distribution of layer 3.  
Figure 4: The steps of PTQ.  
Figure 5: The steps of QAT.  
QAT is a powerful technique for preparing neural network models for deployment on quantization-sensitive hardware. By training the model to be aware of quantization, it can better adapt and maintain accuracy even with reduced precision. However, most QAT methods use the straight-through gradient estimation (STE) technique, which causes a significant gradient error. To solve this problem, researchers have proposed alternative approaches. [PERSON] [44] explores the impact of activation value trimming on quantization performance. [PERSON] [45] adopts a differentiable tanh function to gradually approximate the quantization function. [PERSON] [25] proposes tailoring the weight range prior to quantization. [PERSON] [46] uses the gradient updating process and weight scale adjustment during training to improve quantization performance. Sharpness-aware quantization (SAQ) [47] provides a unified view of both quantization and sharpness-aware minimization (SAM) by treating them as introducing quantization noises and adversarial perturbations to the model weights. [PERSON] [48] proposes a solution by training the low-precision network with a full-precision auxiliary module and constructs a mix-precision network by augmenting the original low-precision network with the full precision auxiliary module. It is worth noting that although QAT methods can deliver better quantitative performance, they often require large training datasets and significant computational resources, with training times exceeding 100 GPU hours [49].
## 2 Modeling and Parameter Adjustment
### Boundary Conditions and Initial Conditions  
According to the PyroSim manual [34], the simulation domain boundaries were set as open boundaries, as these are essential for ventilation control in fire simulations without restricting airflow movement. Furthermore, defining open boundaries within the domain allows free air circulation between the interior and exterior, thereby simulating passive openings such as windows or doors. The Mesh Boundary Vents settings in PyroSim were configured with an exterior temperature of 0.0 \({}^{\circ}\)C and a differential pressure between the exterior and interior of 0.0 Pa, in order to define the external environmental conditions and regulate airflow, ensuring the correct interaction between the indoor and outdoor environments during the fire simulation.  
The following ambient conditions were set for the interior of the building in PyroSim:  
* Ambient temperature: 20.0 \({}^{\circ}\)C;
* Ambient pressure: 1.01325 \(\times\) 10\({}^{5}\) Pa;
* Ambient oxygen mass fraction: 0.232378 kg/kg;
* Ambient carbon dioxide mass fraction: 5.95 \(\times\) 10\({}^{-4}\) kg/kg;
* Relative humidity: 40.0%;
* Maximum visibility: 30.0 m;
* Visibility factor: 3.0;
* Gravity: gravitational acceleration of \(-\)9.81 m/s\({}^{2}\) along the \(Z\)-axis.  
To realistically simulate outdoor environmental conditions, PyroSim 2024.2.1120 applied the Monin-Obukhov similarity theory to model stratified flows in the atmospheric boundary layer. This approach ensures horizontal homogeneity and accuracy in modeling thermal and turbulent interactions, which is crucial for complex scenarios such as pollutant dispersion and urban microclimates [41].  
In this study, the scaling length \(L\) was set to 350 m, with a roughness height \(Z_{0}\) of 0.03 m. The reference height was established at 2 m, and the potential temperature scaling was set to 0 K. The initial horizontal wind speed was 1.0 m/s, directed at 180\({}^{\circ}\) (south). Default settings were applied for the vertical variation in wind speed with height (\(Z\)-axis), while the temperature profile included ground level at 0 m and a lapse rate of 0.0 K/m. These conditions are essential for simulating wind behavior over an open, thermally stable area, applied here in the CFD analysis of airflow patterns.  
Boundary conditions in this study were carefully defined to realistically replicate typical indoor and outdoor environments influencing fire and smoke dynamics in high-rise buildings. Indoor parameters--including ambient temperature, pressure, and gas concentrations (oxygen and carbon dioxide)--were established based on standardized conditions reported in the fire safety literature [10]. Outdoor boundary conditions, such as wind speed, wind direction, and atmospheric stability, were modeled using the Monin-Obukhov similarity theory, which enables the accurate simulation of stratified atmospheric flows and turbulence. To enable natural airflow exchange between the building interior and exterior, open boundary conditions were applied, which is critical for accurately capturing ventilation-driven smoke movement and fire behavior.
## 2 Materials and Methods
### Select the Best Vegetation Index as a Proxy for GPP  
Despite the usefulness of VIs in other habitats as proxies of both LUE, fAPAR [19; 21; 22; 23], and GPP [16; 36; 37], these methods need to be adapted for tidal marshes. To create the best MODIS GPP model, it was important to test a variety of vegetation indexes, including the Normalized Difference Vegetation Index (NDVI) [38], the Soil Adjusted Vegetation Index SAVI [39], the Normalized Difference Moisture Index (NDMI) [40], the Visible Atmospherically Resistant Index (VARI) [41], the Enhanced Vegetation Index (EVI) [35], and the Wide Dynamic Range Vegetation Index (WDRVI) [42]. Equations for these indices are  
\[NDVI=\frac{\rho_{NIR}-\rho_{R}}{\rho_{NIR}+\rho_{R}} \tag{5}\]  
where \(\rho\) is spectral reflectance at the wavelength indicated by the subscript, NIR is near-infrared as indicated previously and \(R\) is the red wavelength.  
\[SAVI=\frac{\rho_{NIR}-\rho_{R}}{\rho_{NIR}+\rho_{R}+L}(1+L) \tag{6}\]  
where \(L\) is a constant and \(L\) = 0.5 is the default value and works well in most situations.  
\[DMI=\frac{\rho_{0.86\upmu\text{m}}-\rho_{1.24\upmu\text{m}}}{\rho_{0.86\upmu \text{m}}+\rho_{1.24\upmu\text{m}}} \tag{7}\]  
where the MODIS bands involved are 2 (near infrared at 0.858 \(\upmu\)m) and 5 (1.24 \(\upmu\)m).  
\[VARI=\frac{\rho_{G}-\rho_{R}}{\rho_{G}+\rho_{R}} \tag{8}\]  
\[EVI=G\frac{\rho_{NIR}-\rho_{R}}{\rho_{NIR}+C_{1}\times\rho_{R}-C_{2}\times \rho_{B}+L} \tag{9}\]  
Figure 4: A flowchart describing the entire processing tree.  
where \(L=1\), \(C_{1}=6\), \(C_{2}=7.5\), and \(G\) (gain factor) = 2.5.  
\[WDRVI=\frac{\alpha\beta_{NIR}-\rho_{R}}{\alpha\rho_{NIR}+\rho_{R}} \tag{10}\]  
where \(\alpha\) is between 0 and 1 and is a weighting parameter for NIR reflectance.  
To select the best VI for estimating flux GPP, we divided the data into training (2015) and validation (2014) data in order to build the model and then estimate its performance on the most novel data available for our target pixel, e.g., that from the new year. For training data, we developed simple linear regression models where each candidate VI (NDVI, SAVI, NDMI, VARI, EVI, WDRVI) was used, in turn, to predict flux GPP data. We also estimated Root Mean Square Error (RMSE) of the model prediction as  
\[RMSE=\sqrt{\frac{\sum(\hat{y_{i}}-y_{i})^{2}}{n}} \tag{11}\]  
where \(\hat{y_{i}}\) was the predicted GPP for \(y_{i}\), the observed GPP value from the flux 8 day sum, and \(n\) was the sample size. For validation, we used the best model from the training regressions as indicated by R\({}^{2}\) to predict GPP. We then compared that to the \"true\" 8 day flux GPP sums to get RMSE for the de novo data without refitting the model. This RMSE for validation data provides an estimate of how well our training model works to predict new observations at our study site. Finally, for the validation GPP prediction, we compared this to the RMSE of a simple linear regression where the MODIS product MOD17A2 was the predictor of the flux GPP.
# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 5 Result and Discussion  
### Experiment Process  
In this paper, the lithium battery pack used is composed of four cells. First, the battery pack consisting of four lithium batteries was fully charged at a constant voltage of 4.2 V using a power supply. This state is defined as 100% SOC. It has a stabilization period of 1 h after charge completion. Subsequently, the state of 0% SOC, which indicates discharge completion at a constant current of 1300 mA using an electronic load, was defined as one cycle. The voltage and temperature variable data were obtained using a battery controller. The previous charging, stabilization period, and discharge were repeated to collect data for a total of five cycles. Then, using the collected data, each cell's SOC was estimated using 4 MNN and LSTM. Python, TensorFlow, and Keras package were used for program writing and learning.  
The experimental environment for the simulation of SOC estimation of the lithium battery pack is as shown in Figure 7. Figure 7.-1 and Figure 7.-2 show the power supply and electronic Load, respectively, which are used to charge and discharge the battery. Figure 7.-3 shows four lithium batteries. The experiment was conducted in the state of putting them in the safety chamber in preparation for explosion and safety accidents. The temperature control function can be used to receive high temperature variable data. Figure 7.-4 shows the battery controller, which consists of a voltage sensor, current sensor, temperature sensor, and microcontroller. Real-time SOC estimation was performed by adding an eight-channel relay module to the battery controller to achieve real-time data measurement. Subsequently, the measured data was utilized, which was sent to the PC shown in Figure 7.-5. Moreover, the SOC of each cell was estimated using the MNN and LSTM. The SOC estimation procedure for each cell of the lithium battery pack was as follows:  
1. Each lithium battery pack consisting of 4 cells was fully charged with a constant voltage of 4.2 V. This state is defined as 100% SOC.
2. After charging was complete, the pack was left to rest for 1 h.
3. Then, the lithium battery pack was completely discharged with a constant current of 1300 mA. This state is defined as 0% SOC.
4. During discharge, the voltage and current were measured through the battery controller.
5. Steps (a) to (d) were repeated 5 times with data collected during each repetition. (*In case of high temperature measurements, the charging, resting, and discharging steps were performed at high temperature.)
6. The measured data was sent to a PC.
7. The data was learned using the MNN model and LSTM method.
8. The actual SOC estimation result and the learned SOC estimation result were compared.
9. The mean absolute error was used to calculate the error rate.  
Figure 7: Experimental environment for the SOC estimation simulation.
## 1 Introduction
### Durability of SFRC  
In terms of SFRC durability, [PERSON] et al. [6] investigated that the addition of fiber to concrete has no effect on its resistance to carbonation. The highest average residual strength and toughness was related to the addition of hooked-end steel fibers as compared with polypropylene and polyvinyl alcohol fibers. [PERSON] and [PERSON] [7] showed that use of steel, glass, and polypropylene fibers reduces the concrete chloride ion permeability by about 42%, 36% and 40%, respectively, and chloride ion permeability of specimens containing steel fibers is less than polypropylene and glass fibers. [PERSON] et al. [8] found that until 180 days, by addition of steel fibers to concrete, \(f_{r}\) in alternate wetting and drying cycles increases 8% to 18%. The \(f_{r}\) of specimens, which were cured in salt solution, was increased 8-15%. [PERSON] et al. [9] found that after chloride ions exposure, steel fibers on the surface of SFRC specimens are corroed up to a specified depth (3 mm), and there is no evidence of corroded fibers at a higher depth. They investigated that fiber length has not important effect on the durability properties of specimens, but a higher volume of fiber improves the durability properties.
# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 3 Dataset and Evaluation Metrics
### Model Evaluation Metrics  
In this paper, the \(SNR\) and \(r\) (Correlation Coefficient) were used to quantitatively evaluate the effect of noise reduction. The \(SNR\) and \(r\) are calculated in Formulas (6) and (7):  
\[SNR=10\log_{10}\frac{\sigma_{s}}{\sigma_{n}} \tag{6}\]  
\[r(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}} \tag{7}\]  
In Formula (6), \(/sig_{s}\) represents the energy of the clean signal, and \(/sig_{n}\) represents the energy of the noise; in Formula (7), \(X\) is the signal before denoising, \(Y\) is the signal after denoising, \(Cov(X,Y)\) represents the covariance between the signal before and after denoising, \(Var[X]\) represents the variance of the signal before denoising, and \(Var[Y]\) represents the variance of the signal after the denoising.  
\(SNR\) represents the ratio of signal to noise in the noisy signal, and the larger the \(SNR\) is, the lower the noise content is; \(r\) indicates the degree of correlation between the signal after denoising and the original signal, and its value is between 0 and 1. The closer the value is to 1, the more similar the signal waveform is to the original signal, and the better the denoising effect is.
###### Abstract  
We present a Mirau-type coherence scanning interferometer (CSI) with an oscillating reference mirror and an integrated interferometric distance sensor (IDS) sharing the optical path with the CSI. The IDS works simultaneously with the CSI and measures the distance changes during the depth scanning process with high temporal resolution. The additional information acquired by the IDS is used to correct the CSI data disturbed by unwanted distance changes due to environmental vibrations subsequent to the measurement. Due to the fixed reference mirror in commercial Mirau objectives, a Mirau attachment (MA) comprising an oscillating reference mirror is designed and built. Compared to our previous systems based on the Michelson and the Linnik interferometer, the MA represents a novel solution that completes the range of possible applications. Due to its advantages, the Mirau setup is the preferred and most frequently used interferometer type in industry. Therefore, the industrial use is ensured by this development. We investigate the functioning of the system and the capability of the vibration compensation by several measurements on various surface topographies.  
coherence scanning interferometry; in-process application; Mirau interferometer; vibration compensation; interferometric distance sensor; optical path length modulation; oscillating reference mirror +
Footnote †: journal: Applied Sciences  
Article  
Miu-Based CSI with Oscillating Reference Mirror for Vibration Compensation in In-Process Applications