esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Processing
### Pre-Processing
#### 4.2.1 Screening Outliers
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Threshold** & **0.1** & **0.08** & **0.06** & **0.04** \\ \hline Outliers & 34 & 59 & 110 & 211 \\ Remaining & 303 & 278 & 227 & 126 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relation between the largest allowable cost function value (Threshold), the number of data points identified as outliers, and the number of data points remaining available for further analysis in the time series of Figure 3.  
Figure 3: Time series plots showing how a threshold value of the cost function distribution (in this instance 0.1) flags obvious and less apparent cases of problematic values in Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the time series at the reference pixel of the Skukuza flux tower and for MISR Paths 168 and 169.  
function threshold for all pixel locations in the FAPAR record was 0.099, which resulted in about 10.7% of the values in the image time series being discarded. Figure 5 summarizes the number of valid FAPAR values available per month, for each year in the record, averaged over the target area around the Skukuza flux tower. It can be seen that remote sensing observations are most often lacking during the wet season (December to March), due to the prevailing cloudiness. The longest continuous period without any observations in this particular case can last up to four months, as happened between October 2009 and January 2010, and between December 2011 and March 2012, inclusive. This situation would be even worse close to the Equator, both because of the prevailing cloudiness and because of the minimal overlap between consecutive MISR Paths. Contrariwise, at higher latitudes, in both hemispheres, a larger overlap between Paths provides more frequent opportunities to observe the surface. Figure 4: Histogram of FAPAR values for the area surrounding Skukuza and for Paths 168 and 169 (a) before screening outliers and (b) after discarding outliers using a threshold value of the cost function (0.099). Figure 5: Distribution of the availability of the number of valid FAPAR values as a function of month (along the vertical axis) and year (along the horizontal axis), averaged over the target area around the Skukuza flux tower.
## 2 3D LiDAR Fundamentals
### Pulse Repetition Frequency  
A laser pulse is a portion of the beam that has an energy level above a certain threshold. The time it takes to produce a laser pulse is called the pulse duration (pulse width). Pulse duration is the time interval during the first transition when the intensity of the pulse reaches a specified level of its final amplitude, and the time the pulse intensity falls, on the last transition, to the same level. The interval between two times of maximum effect (or minimum effect) of a signal characteristic that varies regularly with time is called the jitter period. The small jitter interval has a significant impact on increasing the range accuracy.  
The number of pulses must be increased to a high repeating frequency of the measurement. This is possible by increasing the speed of electronic signal processing circuits but increases the cost of the measuring instrument. The number of pulses that are transmitted per second is called the pulse repetition frequency (PRF) of the LiDAR system. The time between the beginning of one pulse and the start of the next pulse is called pulse repetition time (PRT), and the relationship between PRT and PRF is expressed as PRT = 1/PRF. If the receiving time ends before the next emitting pulse, the result is a dead time. Since the signal cannot be received during the dead time, there is an indefinite time interval that affects the size of selectable details.
## 2 BIT* Algorithm Procedure
### A Performance-Enhanced Variant of the BIT* Algorithm
#### 3.2.2 Thread-Parallel Algorithm and Its Optimization  
Employing key point selection for sub-map generation, multi-threaded parallel computation can minimize path planning time and memory footprint. Therefore, precise and rapid generation of key point pairs is necessary. The calculation formula is as follows:  
\[\left\{\begin{array}{l}x_{i}=x_{1}+\frac{i\times\Delta x}{n}\\ y_{i}=y_{1}+\frac{i\times\Delta x}{n}\end{array}\right., \tag{6}\]where \((x1,y1)\) represents the coordinates of the starting point, \(\Delta x\) and \(\Delta y\) denote the changes in the horizontal and vertical distances from the starting point to the endpoint, respectively; \(i\) represents the segment number; and \(n\) represents the total number of segments. Utilizing Equation (6), the key points within the map can be precisely computed, and these key points serve as the starting and ending points for the sub-maps. Subsequently, leveraging these key points, sub-maps are generated using the coordinates of the bottom-left and top-right corners of the global map.  
To address the issue of traditional path planning algorithms failing in high-density obstacle environments, this research proposes a parallel optimization framework based on dynamic sub-map expansion. In the rasterized map processing stage, we establish a sub-map feasibility judgment model: Set the sub-map area as \(\big{(}S=\{c_{ij}\mid i\in[x_{min},x_{max}],j\in[y_{min},y_{max}]\}\big{)}\) represents the grid value at coordinates \((i,j)\) (0 indicates free space, 1 indicates an obstacle). Define the path feasibility criterion as \((\prod_{c\in S}c\equiv 0)\), i.e., when there exists at least one obstacle-free path, the product of all grid values traversed by all paths is zero. This mathematical model can formally verify sub-map connectivity. When the criterion is not satisfied, a dynamic expansion mechanism is triggered--expanding the search boundary layer by layer with the current sub-map as the center, according to an exponentially increasing radius \(\Big{(}R_{k}=2^{k-1}\delta\Big{)}((\delta)\) is the initial resolution, \((k\in N^{+}))\) until the \((\prod c=0)\) condition is met or the system maximum expansion threshold is reached.  
We offload the path planning task to the GPU to leverage its parallel processing capabilities. By optimizing the algorithm with CUDA, we divide the map into sub-regions for parallel processing, effectively reducing complexity and enabling rapid path planning. Subsequently, we summarize the results to derive a global optimal solution. The path planning process is illustrated in Figure 2.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 4 Experiment
### Results and Analysis
#### 4.4.2 TF-DD-CNN Model Target Recognition Experiment  
Experiment 2 aimed to validate the efficacy of the network model that was constructed. Experiment 1 provided the recognition results of joint features (TF-DD) by the VGG model, ResNet model, and GoogleNet model; so, Figure 21 only displays the training loss function of the TF-DD-CNN model, while the model's recognition accuracy outcomes are shown in Table 5. Figure 22 shows the prediction accuracy of the VGG, ResNet, GoogleNet, and TF-DD-CNN models.  
Figure 21 shows the curve of the loss function changing with the number of training iterations. The loss function of the model reaches its lowest level after 15 training iterations, and the training efficiency is higher than the three models in Section 4.4.1. This efficient training performance benefits from the simple model structure, which reduces the computational burden during the model training process. From Table 5, it can be seen that in the recognition results of the training set, the multi-input recognition model can achieve a recognition accuracy of 94%, and it can also achieve an accuracy of 92% on the test set. Figure 22 intuitively displays the accuracy distribution of the multi-input recognition model with joint features. Compared to the other three recognition models, this model can improve the recognition accuracy of ship targets by 15% to 20%, significantly improving the  
Figure 21: Loss function of the TF-DD-CNN model.  
Figure 22: Distribution of the prediction accuracy of the four models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Acc (\%)** & **Pre (\%)** & **Rec (\%)** & **F1 (\%)** \\ \hline Training sets & 93.56 & 93.64 & 93.57 & 93.60 \\ Testing sets & 92.23 & 92.34 & 92.23 & 92.28 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Recognition results of the TF-DD-CNN model.  
recognition accuracy. The experimental results not only verify that the multi-input target recognition model can effectively recognize ship targets but also further confirm that the model has high accuracy in target recognition.
## 2 Materials and Methods
### Signal Separation Metrics
#### 2.6.3 Synergy Effect Metric  
As a result of combining entropy features during signal separation, \(A_{\text{RKF}}\) classification becomes more accurate than if each feature were used individually. Using the formula below, the synergistic effect coefficient \(K_{syn}\) was estimated.  
\[Ksyn=\frac{1-MAX(A_{RKF}[\text{Entropy1}],A_{RKF}[\text{Entropy2}])}{1.001-A_{ RKF}[\text{Entropy1},\text{Entropy2}]} \tag{9}\]  
where, \(A_{\text{RKF}}[\text{Entropy1}]\) is the classification accuracy using one entropy feature Entropy1, and \(A_{\text{RKF}}[\text{Entropy1},\text{Entropy2}]\) is the classification accuracy using two entropy features. \(MAX\) is the maximum value selection function.
## 4 Numerical Simulations and Discussion
### Performance Comparison  
This section focuses on two performance indicators of the proposed method and the reference methods, one is the root mean square error (RMSE) curve, the other is the computational resource requirements.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
###### Abstract  
This article puts forward a low-complexity filtering algorithm to achieve a low-complexity filtering chip design for real-time location tracking. In order to meet the need for low-complexity and real-time, the positioning tracking algorithm based on Kalman Filter (KF) is proposed. The KF itself has the functions of tracking, predicting, etc., which can correct the positioning into more accurate results. However, in the calculation of KF algorithms, each iteration often requires tedious and complex calculations of Kalman Gain (KG). Both software and hardware are very resource-intensive. Therefore, use the feature of KG in alpha-beta (\(\alpha\)-\(\beta\)) filtering algorithm which can gradually balance in each iteration. Proposed a filtering algorithm that is based on low-complexity, low cost, and high efficiency. This algorithm uses DKF (Difference Kalman Filter) and PKF (Percentage Kalman Filter) depending on different environments. In other words, DKF and PKF are the algorithms which are generated based on different judging conditions. This algorithm can not only significantly reduce the time and the complexity of computing, but also greatly shorten the circuit area of the original algorithm. This algorithm has a large number of matrix operation. In the hardware calculation process, it solves matrix problems about hardware and then developed chip design. Coefficients are used by a multiple of 2 for operation. Use shifters instead of multipliers and dividers, significantly reducing complexity and circuit area. At the same time, deal with the problem of a floating-point number, achieve circuit function verification on the FPGA, and finally tape-out. The design uses the TSMC 0.18\(\mu\)m CMOS cell library provided by the TSRI, uses EDA to implement VLSI with the Design Vision of SYNOPSYS, the operating frequency of the circuit is 83.33 MHz, the value of gate counts is 22.84 K, the power consumption is 3.86 mW, and chip area is 582.63 \(\mu\)m \(\times\) 580.23 \(\mu\)m.  
alpha-beta (\(\alpha\)-\(\beta\)) filter; chip; FPGA; Kalman filter; Kalman gain; location tracking; PKF and DKF filter +
Footnote †: journal: Electronics Electronics
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 2 Experimental Method
### Signal Preprocessing  
All of the sEMG signals and videos were processed offline. Each of the videos was segmented into a single gait cycle and then further divided into five gait phases (pre-stance, mid-stance, terminal stance, pre-wing, terminal swing) by identifying the demarcation point of the heel strike and toe off of both feet by an experienced observer [25]. The segmented video was then used as a reference for the five gait phases that were labeled on the corresponding sEMG signal [26].  
The overlapped windowing technique [7] was employed to segment the labeled sEMG signal for feature extraction in this paper (see Section 2.5), and we set 20 ms windows for feature extraction, with one window being overlapped by 10 ms, i.e., half of the last window.
# Hydrophobic Fluorinated Porous Organic Frameworks for Enhanced Adsorption of Nerve Agents
## Materials and Methods
### Characterization of C-1 and C-1-F  
The powder X-ray diffraction (XRD) measurements were performed on a SmartLab instrument with Cu K\(\alpha\) (\(\lambda\) = 1.5418 A) radiation at 40 kV and 30 mA over an angular range of 20 = 4-40\({}^{\circ}\) at a scan rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\). The scanning electron microscopy (SEM) images were recorded on a JSM 6700 and a Hitachi SUS000 scanning electron microscope at 3 kV and 5 \(\upmu\)A. The sample (1 mg) was dispersed in 2 mL ethanol using ultrasound, and the dispersed droplets were placed on a clean silicon wafer. After ethanol evaporation, the wafer-supported sample was kept at 60 \({}^{\circ}\)C for 12 h and then tested by SEM. The Fourier transform infrared (FT-IR) spectra were recorded on a Nicolet Impact 410 FT-IR spectrometer. The N\({}_{2}\) adsorption isotherms and the pore size distribution were determined using a Micromeritis ASAP 2010M analyzer. Water vapor adsorption isotherms were measured at 298 K using a Beishide vacuum steam adsorption apparatus. Thermogravimetric analysis (TGA) was performed on a Mettler Toledo TGA/DSC 3+ analyzer at a heating rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) under air flow.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 4 Discussion
### Ground Fissures Interpretation and Zone of Potential Failure  
The ground fissures that appeared in February 2011 near the Quetta Valley are forming a conjugate set, and the maximum principal stress axis is pointing north. These Coulomb-type conjugate fissures show that the orientation is controlled by the regional maximum principal stress field according to the Coulomb failure criterion in this region [49].  
Ground subsidence due to sediment compaction following fluid withdrawal is a widely known effect. The remaining problem is the correlation of active faults with ground fissures, a correlation which has led some investigators to interpret the latter, at least partly, as tectonic effects; and, in particular, as evidence of aseismic creep. [PERSON] et al. [50] showed that, in Greece, fissures can be controlled by bedrock faulting and differential compaction in areas of ongoing water-level decline and subsidence. Equations (5) and (8) (Section 2.5) demonstrates that the gradient of subsidence increases exponentially due to the gradient of sediment thickness. This was used to identify potential risk zone for fissures in Figure 7.  
The risk zone is defined based on the blind fault 1 mapped from the seismic profile (see Figure 5). The InSAR results show fault creep and local uplift (1.8 mm/year) near fault 1 and other pre-existing thrust faults in the Murdar Mountain Range (Figure 6A). Unlike other blind faults mapped from the seismic profiles, fault 1 is active beneath the alluvium, and the location of fault 1 may be a main control of alluvial thickness at that location (Figure 7). This is why we consider the risk zone to be close to the Murdar Mountain Range and not near the Chiltan Mountain Range or in other basins. In Figure 7, the location for fissures is parallel to faults in the carbonates and, as such, is considered a high-risk location.  
In other words, three factors control the occurrence of fissures in the Quetta Valley: (1) the regional stress field; (2) sediment compaction; and (3) active bedrock faulting. Ground fissures that appeared in February 2011 were most likely caused by the regional stress field and sediment compaction, and were thus not related to the earthquakes.
# Development of a CFD-Based Wind Turbine Rotor Optimization Tool in Considering Wake Effects
## 2 Numerical Methods
### Actuator Disc  
As the disc is assumed permeable, the mass conservation equation remains unchanged. In Equation (2), an external force \(f_{AD}\) is added that represents aerodynamic loads of wind turbine blades. To avoid the numerical singularity, each volume force needs to be smoothly re-distributed. A practical way of smearing the force is to use the 1D Gaussian function such that the actual forces in the disc is redistributed along one direction. In the current model, each force element is smeared locally in the blade normal direction with a distance \(d\) away from the disc using the convolution and the smeared force \(f^{\prime}\) is computed as [14; 47]  
\[f^{\prime}=f_{AD}\otimes\eta^{1D} \tag{15}\]  
\[\eta^{1D}(d)=1/\left(\varepsilon\sqrt{\pi}\right)\exp\left[-\left(d/\varepsilon \right)^{2}\right] \tag{16}\]  
The parameter use in the smearing function is \(\varepsilon=3\Delta z\) where \(\Delta z\) is the reference grid size in the axis direction. Based on the hypothesis of blade elements, the body forces of rotor blades can be achieved through airfoil data and the flow field information.  
The actuator disc solves the entire axisymmetric flow field and the induced velocity is naturally included into the formulation i.e., the relative velocity \(V_{rel}\) and flow angle \(\phi\) are determined from  
\[\phi=\tan^{-1}\left(\frac{V_{0}-W_{z}}{\Omega r+W_{\theta}}\right),\ V_{rel}^{2 }=\left(V_{0}-W_{z}\right)^{2}+\left(\Omega r+W_{\theta}\right)^{2} \tag{17}\]  
As shown in Figure 2, the \(z\)-axis and \(\theta\)-axis represent the rotor normal and rotational directions, respectively. The relative velocity \(V_{rel}\) seen by the blade element is a combination of axial velocity and rotational velocity, as shown in Equation (17). The lift force \(L\) and the drag force \(D\) are perpendicular and parallel to \(V_{rel}\), respectively, as sketched in Figure 2. The local angle of attack is given by \(\alpha=\phi-\gamma\), where \(\gamma\) is the local pitch and twist angle, the axial velocity \((V_{0}-W_{z})\), where \(V_{0}\) is the inflow wind speed and \(W_{z}\) is the axial induced velocity, the tangential velocity \((\Omega r+W_{\theta})\), where \(\Omega\) is the rotational  
Figure 1: Comparisons of the normalized turbulence kinetic energy.  
speed and \(r\) is the radius of wind turbine and \(W_{\theta}\) is the tangential induced velocity. Lift and drag forces per spanwise length are found from tabulated airfoil data and the velocity triangles shown in Figure 2.  
\[L=\frac{1}{2}\rho V_{rel}^{2}cBC_{l},\ D=\frac{1}{2}\rho V_{rel}^{2}cBC_{d} \tag{18}\]  
Therefore, projecting the force in the axial and tangential directions to the rotor gives the aerodynamic force components  
\[F_{z}=L\cos\phi+D\sin\phi,\ F_{\theta}=L\sin\phi-D\cos\phi \tag{19}\]  
The forces are calculated in every time-step such that the momentum equation produces a new velocity field which updates the flow angle of attack as shown in Figure 2.
## 4 Discussion
### Challenges
#### 4.1.5 Traditional Monitoring Methods  
The existing water monitoring systems and devices suffer from various shortages. Current traditional systems are big and costly to keep up in monitoring [15]. Traditional monitoring systems require a lot of human effort in water quality monitoring, which consumes a lot of time and labor costs [17; 37; 40; 50; 57; 65; 66; 68]. In the traditional water quality prediction model, some factors cannot be considered such as biology, chemistry, physics, hydraulics, and meteorology factors [32]. Some traditional monitoring systems lack analysis and processing of the collected data [7]. Also, existing water quality monitoring systems are dismissing data analysis and the water quality data resource attributes and focus on water quality data monitoring [36]. Due to transmission failures and power failures sometimes, some data loss and dirty data may occur, which affects the quality of data monitoring and analysis [36; 57].  
The existing communication technologies (e.g., 3G, Bluetooth, WiFi, and Zigbee) suffer from short communication ranges and high power consumption [16; 18]. Transferring the data between the sensors and core network using satellite-based communication is considered slow and very costly [45]. Currently, water quality parameters are collected using fixed location sensors, which reduces the accuracy of the measured data [16; 45]. Usually, measuring the water quality is accomplished based on a single spot with no spatial coverage [2; 50]. Besides that, the manual lab-based water quality monitoring approach suffers from low sampling frequency [2; 47]. Some sensor nodes utilize radio frequency identification (RFID), which makes it challenging to main and cannot be used in applications with restricted access [47]. For the water distribution network, the wireless sensor networks have a limited number of sampled locations at a certain time and the equipment operating is costly [31]. The use of radio frequency energy harvesting in monitoring systems is insufficient due to the nondeterministic and uncontrollable, which makes these systems difficult to be reliable [34]. Employing the model of a simultaneous wireless information power transfer is practically costly and the interference may suffer from problems [34]. Water quality sensor nodes in water quality monitoring systems include various resource constraints such as limited processing capabilities, limited energy, limited data storage memory, lack of communication capabilities, and energy limitation [18]. With the lack of real-time values, the accuracy of data is reduced due to the continuous change of water parameters, and hence, farmers are unable to be warned of the changes in water parameters, which makes them fail to make any preventive actions [30; 50; 51; 52; 53; 65].
# Pixel Circuit Designs for Active Matrix Displays
## 3 OLED Pixel Circuits
### OLED Novel Pixel Circuits  
#### 3.2.1 Ltps Tft OLED Pixel Circuits  
LTPS TFT OLED pixel circuits are well developed, and their pixel compensation circuits can address IR drop variations in addition to \(V_{\text{TH}}\) shifts in some cases [76]. As display resolution improves, pixel circuits must achieve the same or better compensation effect with fewer devices. [PERSON] et al. of Nanhua University presented a 5T1C pixel circuit based on LTPS TFT [77], as shown in Figure 7a. The circuit can extract \(V_{\text{TH}}\) and mobility by forming a mirror structure of T1 and T2. The circuit generates an adaptive voltage at the TFT gate during the programming phase, compensating for the \(V_{\text{TH}}\) shift and mobility change. The circuit uses one less capacitor than the 5T2C pixel circuit of [PERSON]'s team [68], achieving a better area effect. Moreover, T5 is controlled by \(V_{\text{EM}}\) to turn on only in the emission phase, so this circuit prevents image flickering. To address the high leakage current problem of LTPS TFTs, some researchers have proposed using the high potential to suppress and successfully reduce storage drive voltage fluctuation [78].