esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Complete System Formation
### Electronic BLDC Commutator and VSI Switching  
Commutation in Permanent Magnet DC Motor (PMDC) is obtained by a commutator and brushes. Nevertheless, hall sensors are important components employed in BLDC motors which sense the position of a rotor as the commutation wave. Coils and permanent magnets are employed as stator and rotor respectively, in which stator's magnetic field rotates the rotor. Armature of a BLDC motor consists of a permanent magnet as a substitute of the coil which does not require brushes. Figure 6 demonstrates BLDC-driven structure with induced EMF and reference current. The electronic commutation process is used to control the VSI-employed BLDC motor in which winding current is adjusted with the help of decoder in proper sequence. In this method, symmetrical DC currents are situated in the phase voltage at 120\({}^{\circ}\). Based on the motor alignment, the hall sensors produce signals of 60\({}^{\circ}\) phase difference. The gating signal for 3-phase VSI generated by transforming hall signals using the decoder is illustrated in Figure 7. The pulse width modulated pulses are generated by comparing triangular signal with duty cycle produced through MPPT. Table 4 portrays Hall signals and Switching states of BLDC used with electronic commutation. The high-frequency PWM pulses and six fundamental signals are operated with an AND gate, which produces 6 gating pulses for VSI inverter. As the atmospheric conditions change, the duty cycle is also regulated using MPPT methods which control the VSI and finally the BLDC motor is adjusted accordingly.  
The BLDC motor is analyses mathematically as [27]:  
\[\left[\begin{array}{c}V_{ap}\\ V_{bp}\\ V_{cp}\end{array}\right]=\left[\begin{array}{ccc}R_{T}&0&0\\ 0&R_{T}&0\\ 0&0&R_{T}\end{array}\right]\left[\begin{array}{ccc}I_{ap}\\ I_{bp}\\ I_{cp}\end{array}\right]+\left[\begin{array}{ccc}L_{1}-M_{1}&0&0\\ 0&L_{1}-M_{1}&0\\ 0&0&L_{1}-M_{1}\end{array}\right]\frac{d}{dx}\left[\begin{array}{c}I_{ap}\\ I_{bp}\\ I_{cp}\end{array}\right]+\left[\begin{array}{c}E_{lu}\\ E_{bb}\\ E_{bc}\end{array}\right] \tag{16}\]  
Developed electromagnetic torque (\(T_{EM}\)) by BLDC motor can be expressed mathematically as:  
\[T_{EM}=\frac{E_{ba}\times I_{ap}+E_{bb}\times I_{bp}+E_{bc}\times I_{cp}}{ \omega_{rotor}} \tag{17}\]  
where,  
\(V_{ap}\), \(V_{bp}\), \(V_{cp}\) = Phase voltage of a 3-Phase BLDC motor  
\(I_{ap}\), \(I_{bp}\), \(I_{cp}\) = Phase Currents  
\(E_{ba}\), \(E_{bb}\), \(E_{bc}\) = Phase Back EMF of BLDC motor  
\(L_{1}\) = Each Phase self-inductance  
\(M_{1}\) = Two phase's mutual inductance \(T_{EM}\) = Developed Electromagnetic torque of BLDC motor  
\(\omega_{Rotor}\) = Rotor Speed  
Figure 6: (**a**) BLDC driven structure (**b**) induced EMF and reference current.  
Figure 7: Gating signal for 3-phase VSI.
## 2 Materials and Methods
### Simulator
#### 2.1.3 Network Speed  
In a simulation, every 90 s (considering steps of 1 s to acquire data), we compute the network speed, density, and the flow of each link to build the network's MFD. The speed on link x at time t is calculated using Equation (1),  
\[\text{INST\_speed}_{\text{t}}^{\text{x}}=\frac{1}{\text{N}_{\text{t}}^{\text{i} }}\sum_{\text{i=1}}^{\text{i=N}_{\text{t}}^{\text{x}}}\text{veh\_speed}_{ \text{i}}^{\text{t}} \tag{1}\]  
where \(\text{N}_{\text{t}}^{\text{x}}\) is the number of vehicles on link x at time t and veh_speed\({}_{\text{i}}^{\text{t}}\) is the speed of the i vehicle at time t. The representative speed of link x at period T is named PER_speed\({}_{\text{T}}^{\text{x}}\) and is calculated using Equation (2) (expressed as pseudocode),  
\[\text{PER\_speed}_{\text{T}}^{\text{x}}=0;\text{k}=0\] \[\text{For t}=\text{t}_{1}\text{to t}=\text{t}_{2}:\] \[\text{if N}_{\text{t}}^{\text{x}}>0:\] \[\text{PER\_speed}_{\text{T}}^{\text{x}}=\text{PER\_speed}_{\text{ T}}^{\text{x}}+\text{INST\_speed}_{\text{t}}^{\text{x}}\] \[\text{k}=\text{k}+1\] \[\text{PER\_speed}_{\text{T}}^{\text{x}}=\text{PER\_speed}_{\text {T}}^{\text{x}}/\text{k} \tag{2}\]  
where k is the number of INST_speed\({}_{\text{t}}^{\text{x}}\) measurements in the period T (ranging from t\({}_{1}\) to t\({}_{2}\)) for which \(\text{N}_{\text{t}}^{\text{x}}>0\). When a simulation starts, the time is initiated at 0 s. The first period (T = 1) considers the period of time in the range [1 s, 90 s], the second period (T = 2) considers the period of time in the range [91 s, 180 s], and so on (units in seconds). Any period considers 90 steps of 1 s. Each 1 s (a time step) is attempted to calculate INST_speed\({}_{\text{t}}^{\text{x}}\), and therefore the maximum value of k can be 90. Finally, the network's speed at period T is calculated using Equation (3),  
\[\text{NET\_speed}_{\text{T}}=\frac{1}{\text{M}}\sum_{\text{x}=1}^{\text{x}= \text{M}}\text{PER\_speed}_{\text{T}}^{\text{x}} \tag{3}\]where M is the number of links considered for the analysis. The network speed, i.e., \(\text{NET\_speed}_{\text{T}}\), is the representative speed of the network at period T.
## 3 System Models
### PMBM Recursion  
The recursion of PMBM consists of the prediction and updating step.  
Prediction Step: Given a posterior PMBM density of \(p_{k}(X_{k})\) at timestep k, as shown in Equation (9) with the parameters of \(D_{k}(X_{k}^{u})\), \(\left\{w_{k^{\prime}}^{j}\left\{r_{k}^{j,i},p_{k}^{j,i}(X_{k}^{j,i})\right\}_{i \in\mathbb{I}^{j}_{k}}\right\}_{j\in\mathbb{J}_{k}}\) and the standard dynamic model, the predicted density is the PMBM density expressed in Equation (10).  
\[p_{k+1}(X_{k+1})=\sum\limits_{X^{u}\cup X^{d}=\mathbf{X}}e^{-\int D_{k+1}(X^{ u})dX}\prod\limits_{X\in\mathbf{X}^{u}}D_{k+1}(X^{u})\sum\limits_{j\in \mathbb{J}_{k+1}}w_{k+1}^{j}\sum\limits_{\cup i\in\mathbb{I}_{k+1}^{i}}\sum \limits_{x\in\mathbb{J}_{k+1}^{i}}r_{k+1}^{j,i}p_{k+1}^{j,i}(X^{ji}). \tag{10}\]  
\[D_{k+1}(X^{u})=D_{k+1}^{b}(X)+\int D_{k}(X^{u})p_{s}f_{k+1|k}(X)\mathrm{d}X. \tag{11}\]  
\[r_{k+1}^{j,i}=r_{k}^{j,i}\int p_{s}p_{k}^{j,i}(X)\mathrm{d}X. \tag{12}\]  
\[p_{k+1}^{j,i}(X^{j,i})=\frac{\int p_{k}^{j,i}(X)p_{s}f_{k+1|k}(X)\mathrm{d}X}{ \int p_{s}p_{k}^{j,i}(X)\mathrm{d}X}. \tag{13}\]  
\[w_{k+1}^{j}=\sum\limits_{i\in\mathbb{I}}w_{k}^{j,i}. \tag{14}\]  
Updating Step: Given a prior PMBM density of \(p_{k+1}(X_{k+1})\) at timestep \(k+1\), as shown in Equation (10) with the parameters of \(D_{k+1}(X_{k+1}^{u})\), \(\left\{w_{k+1}^{j,i},\left\{r_{k+1}^{j,i},p_{k+1}^{j,i}(X_{k+1}^{j,i})\right\}_ {i\in\mathbb{I}_{k+1}^{i}}\right\}_{j\in\mathbb{J}_{k+1}}\) and a set of measurements \(\mathbf{M}\), the updated density is the PMBM density expressed in Equation (15).  
\[p_{k+1}(X_{k+1}|M_{k})=\sum\limits_{X^{u}\cup X^{d}=\mathbf{X}}p_{k}^{u}(X_{k }^{u})\sum\limits_{j\in\mathbb{J}_{k+1}}\sum\limits_{A\in\mathcal{A}^{j}}w_{A,k}^{j}p_{A,k}^{j}(X_{k}^{d}), \tag{15}\]  
\[p_{k}^{u}(X_{k}^{u})=e^{-\int D_{k}(X^{u})dX}\prod\  
\[r^{j}_{C,k}=\begin{cases}\frac{\int D^{\mu}_{k+1}(X)l_{C_{C}}dX}{\kappa_{C}+\int D ^{\mu}_{k+1}(X)l_{C_{C}}dX}&\text{if }C\cap\mathbb{I}\mathbb{I}=\varnothing,\,|C_{C}|=1\\ 1&\text{if }C\cap\mathbb{I}\mathbb{I}=\varnothing,\,|C_{C}|>1\\ \frac{\int D^{\mu}_{k+1}(X)l_{C_{C}}dX}{1-J^{\mu}_{k+1}+J^{\mu}_{k+1}\cap \int q_{D}p^{\mu}_{k+1}(X)dX}&\text{if }C\cap\mathbb{I}\
eq\varnothing,\,C_{C}= \varnothing\\ 1&\text{if }C\cap\mathbb{I}=\varnothing,C_{C}\
eq\varnothing\end{cases}, \tag{21}\]  
\[p^{j}_{C,k}(X^{C})=\begin{cases}\frac{D^{\mu}_{k+1}(X)l_{C_{C}}}{\int D^{\mu}_{ k+1}(X)l_{C_{C}}dX}&\text{if }C\cap\mathbb{I}=\varnothing\\ \frac{q_{D}(X)p^{\mu}_{k+1}(X)}{\int q_{D}(X)p^{\mu}_{k+1}(X)dX}&\text{if }C \cap\mathbb{I}\
eq\varnothing,C_{C}=\varnothing\\ \frac{l_{C_{C}}(X)p^{\mu}_{k+1}(X)}{\int l_{C_{C}}p^{\mu}_{k+1}(X)dX}&\text{ if }C\cap\mathbb{I}\
eq\varnothing,C_{C}\
eq\varnothing\end{cases}, \tag{22}\]  
where \(q_{D}\) is the probability of missed detection, and \(\mathcal{L}_{C}\) is the likelihood function of data association \(C\). \(\mathcal{A}^{j}\) represents all data associations \(A\) for the predicted global hypothesis of the \(j\)th MB, and \(C\in A\) represents an assignment of each measurement cell \(MC\) in \(\mathbf{M}\) to an existing target, either to a new target or to a clutter.
## 2 Materials and Methods  
In the literature, a number of DLAs are proposed to detect the abnormalities in medical images using conventional and customized DLAs [17; 39; 40; 41]. Development of a new DLA from the scratch is complex and requires complex work to build, train, test and validate the architecture for a chosen problem. Hence, most of the earlier works adapt the proven DLAs existing in the literature to solve a disease detection problem. Furthermore, selecting and implementing a particular architecture requires prior knowledge about its structure, complexity in implementation, initial tuning and validation procedures [17; 39; 40; 41; 42].  
This work initially considers the existing DLAs discussed in [20] to detect the brain tumors from the considered MRI database. This work employs the transfer learning concept to train, test and validate the adopted DLAs using the SoftMax classifier. The DLA which offered the enhanced classification accuracy is then selected and its performance is further enhanced using the proposed method. The initial experimental outcome of this research is confirmed: the VGG19 offers better classification accuracy compared to the alternatives, and hence conventional and customized VGG19 are then considered in this research to attain the better tumor detection accuracy.  
After selecting the VGG19 to solve the considered image examination problem, its performance enhancement is tried using the following approaches: (i) replacing the SoftMax classifier with DT, KNN, SVM-Linear and SVM-RBF classifiers, and (ii) enhancing the outcome of VGG19 using a new feature vector obtained by fusing the handcrafted and deep features. The earlier research work confirms that if the feature vector is enhanced, then the pre-trained DLA will offer better detection accuracy compared to the conventional DLA [1; 29; 30]. Figure 1 depicts the customized VGG19 proposed and implemented in this research. The pre-trained DLA will provide a one-dimensional deep-feature vector, and the hand crafted texture features are attained using counterlet transform (COT) (38 features), curvelet transform (CUT) (121 features) and discrete wavelet transform (DWT) (40 features) [43]. The existing deep and handcrafted features are sorted and serially combined based on principal component analysis (PCA), and these features are then used to train, test and validate the classifier unit, which separates the given image into normal/tumor classes [1].
## 4 Empirical Results
### Key Results
First, VMD effectively decomposes the original signals into distinct IMFs, allowing the model to extract meaningful patterns while reducing noise. Previous studies have demonstrated that VMD improves predictive accuracy across various domains, including water quality forecasting [15] and wind speed prediction [16], by enhancing pattern recognition and filtering out noise. In the financial domain, decomposition-based approaches, such as VMD, have been shown to significantly enhance forecasting performance by isolating meaningful components from volatile market data [17]. These findings further validate the effectiveness of VMD in financial time-series modeling, where capturing complex volatility patterns is essential. Furthermore, VMD is particularly beneficial in financial markets, where price movements often exhibit nonstationary and highly volatile characteristics. By decomposing raw VIX data into multiple IMFs, the model can distinguish fundamental trends from short-term fluctuations, allowing for more stable and interpretable forecasting results. The effectiveness of this approach aligns with prior studies, demonstrating that decomposition-based forecasting methods significantly improve accuracy in volatile financial environments. Multi-input structures such as VMD LSTM 1 (2,1) and VMD LSTM 2 (2,1) leverage both S&P 500 and VIX features, further enhancing predictive performance. However, despite these improvements, the absence of Attention in these models limits their ability to selectively focus on critical time steps, leading to suboptimal performance compared to the proposed approach. Second, the Attention mechanism enables the model to prioritize critical parts of the data, capturing key patterns and temporal dependencies more effectively. Studies have shown that incorporating Attention into LSTM improves stock price prediction by allowing the model to focus on the most informative time steps [12]. Attention networks have been found to enhance long-term trend forecasting by selectively emphasizing key sequential dependencies [11]. Additionally, Attention mechanisms have been demonstrated to help capture complex financial patterns, mitigating short-term noise while preserving essential information [21]. Similarly, an Attention-based LSTM model has been shown to dynamically adjust its focus, leading to more accurate and stable predictions [5]. This selective attention leads to a significant reduction in prediction errors compared to models without it. By dynamically adjusting its focus on relevant time steps, the model mitigates the impact of short-term noise and better captures long-term volatility trends. By leveraging both VMD and Attention, the proposed model achieves a more accurate and robust performance in forecasting financial time series. These results provide strong empirical evidence that combining VMD with an Attention-based LSTM framework significantly enhances VIX prediction accuracy, especially in volatile market conditions.
## 3 Tracking Loops Error Characterization  
The precision of the receiver observations is affected by a set of factors. The most important one that limits the accuracy of a GNSS receiver is the tracking loop noise, including dynamic stress noise, thermal noise, Allan deviation phase noise and vibration-induced phase noise. As stated in [20], the thermal noise and dynamic stress noise mainly affect the correlations between received signals and replica signals, whereas the Allan deviation phase noise and vibration-induced phase noise have an effect on numerically controlled oscillators (NCOs). Since the noise sources of the DLL and PLL are identical and the PLL is more sensitive to noises, the performance analysis of the tracking loop focuses on the PLL in this paper.
## 1 Introduction  
Black tea, recognized as the world's most consumed tea category, is globally prized for its distinctive organoleptic properties [1, 2]. Yingde City and Renhua County--situated within Shaoguan Prefecture in northern Guangdong--have been identified as Guangdong's earliest tea cultivation zones, possessing a profound tea-cultural heritage [3, 4]. This region's unique terroir, characterized by Danxia landforms, karst topography, and subtropical climate [5, 6], is acknowledged for fostering exceptional tea germplasm diversity [7, 8].  
Although tea quality is theoretically influenced by multiple factors like cultivar, tree age, processing, nutrition, and post-harvest management, cultivar is established as a primary determinant of sensory characteristics. While GC-MS has been extensively employed to characterize aroma-active compounds like terpen and esters contributing to floral/honey notes [9, 10, 11, 12], and HPLC-UV routinely quantifies non-volatile taste components like catechins, amino acids, and gallic acid contributing sensory attributes like briskness [13, 14], existing research has predominantly focused on single-cultivar process optimization or static compound-class analyses. Consequently, mechanistic gaps persist in understanding key sensory differentiators among northern Guangdong black tea (NGBT) cultivars.  
To address these gaps, five representative NGBT cultivars were selected for investigation: Yinghong No. 9 (YHJ), a clonal large-leaf variety systematically bred from Yunnan large-leaf germplasm in 1961 with significant commercial presence [15]; Yingde Black Tea (QTZ), a historically important open-pollinated landrace [16]; Danxia No. 1 (DXY) and Danxia No. 2 (DXE), high-aroma cultivars developed from wild variants of Renhua white-haired tea [17; 18]; and Jinshahong tea (JSH), an emerging premium selection derived from Renhua Danxia tea populations [19]. These cultivars are cultivated under comparable climatic conditions, latitudes, longitudes, and annual precipitation. While cultivar-mediated metabolite regulation is documented to shape flavor profiles [20], characteristic compounds distinguishing these NGBT cultivars--especially emerging types like JSH--remain inadequately defined, hindering targeted utilization.  
This study was therefore designed to integrate GC-MS, HPLC-UV, and sensory evaluation for systematic elucidation of volatile/non-volatile compositional differences among JSH, DX, DXE, QTZ, and YHJ, establishing predictive \"chemical composition-sensory attribute\" correlation models. Findings are expected to provide scientific foundations for NGBT germplasm evaluation while supporting breeding programs and processing optimization for high-aroma black teas, addressing critical knowledge gaps in multi-cultivar flavor chemistry.
## 5 Data Analysis
### Building Regression Models Using the Available Database  
Various factors can affect pavement performance. The factors considered in the present study were roadway class, present pavement condition, pavement age, traffic level, and maintenance type. They are briefly discussed below.  
* Roadway Class  
Riyadh Municipality classifies the roadways into two classes, main and secondary streets. Roadway class can affect the maintenance policy and criteria to a high extent, for example, the critical serviceability for main streets may be higher. In this project, a pavement performance model was developed for main streets only.
* Pavement Age  
The deterioration of paved roads is partly related to pavement age. Over time, the hardening of asphalt increases, thus making it more brittle and susceptible to cracking. In the pavement performance model development, pavement age was measured from the date of construction or from the date of the last major maintenance.  
* Traffic Level  
Pavement deterioration is highly affected by traffic volume and vehicle types. Riyadh Municipality categorizes highways based on the traffic levels into two levels: high and low.  
The type of M&R performed affects the pavement performance. Four maintenance strategies were considered in this project: no maintenance, basic routine maintenance (low, normal, and high), overlay, and reconstruction.
## 3 Classification and Synthesis of Bimetallic Nanoparticles
### Other Synthetic Methods  
Most of the synthetic methods discussed in the previous sections occur in the liquid-phase. However, gas-phase synthetic methods have also been widely used to prepared both single-metal and multi-component nanoparticles and are reviewed in the literature [138]. Currently, green chemistry has received considerable attention, with new promising synthetic routes being proposed for the formation of nanoparticles that are environmentally friendly [139; 140; 141]. Some examples of the synthetic strategies being employed for the synthesis of bimetallic nanoparticles include the use of plant extracts [89; 142], enzymes [143], and microorganisms [144] as reducing agents and capping agents. In addition to being non-hazardous, these reducing agents can be used at room temperature and atmospheric pressure, which not only saves energy, but also minimizes pollution that can accompany certain reaction pathways [44].  
The use of bimetallic nanoparticles as catalysts has a long history but offers an ever-growing presence in the literature [145]. For example, [PERSON] et al. utilized a soft-templating technique to synthesize mesoporous bimetallic RhCu nanospheres [100]. In this example, the bimetallic nanoparticles were grown in the presence of a polymeric micelle, which was later removed to yield the mesoporous structure. Furthermore, bimetallic nanoparticles have also been directly fabricated on catalytic templates such as graphene, metal oxide structures, and biomolecules for use as electrodes in electrochemical reactions [75; 90; 91; 146].  
Often, a combination of multiple synthetic strategies is employed for the fabrication of bimetallic nanoparticles. By combining more than one method, one can capitalize on the advantages each method offers. For example, ultrasound-assisted electrochemistry combines a sonochemical method with electrochemistry. This technique was utilized to synthesize core-shell nanoparticles by [PERSON] et al. for the generation of Cu@Ag nanoparticles [101]. Similarly, [PERSON] and co-workers combined galvanic replacement and diffusion through a protective porous silica shell to synthesize SiO\({}_{2}\)-coated hollow AgAu nanoparticles for use as stable plasmonic agents for NIR activation [1].
## 4 Result and Discussion  
### Fault Model Validation Result  
The fault model proposed in the study was validated by comparing the simulation result with field data acquired from the fault recorder. Current waveform generated from the simulation was validated by evaluating its root mean square error (RMSE) for each cases. The equation of the RMS is defined as:  
\[RMSE=\sqrt{\left(x-x_{i}\right)^{2}} \tag{31}\]  
where \(x\) is the actual waveform while \(x_{i}\) is the generated waveform. Detail validation result is described in the following subsection.  
Figure 13a illustrates the generated simulation and actual waveform due to lightning strike. The generated waveform of 180-degree inception angle and 330 MVAR load were chosen for comparison with the actual fault occurs within 20 km from the substation. The RMSE value of the generated waveform current is 0.0342. Figure 13b illustrates the generated simulation and actual waveform due to insulator degrading. The RMSE generated waveform of 180-degree inception angle and 330 MVAR load was evaluated with actual fault occurs within 60 km from the substation, which gives the value of 0.0498. Furthermore, Figure 13c illustrates the generated simulation and actual waveform due to the tree encroachment. The actual waveform was taken on 30 th December 2019 during the fault occurrence. The generated waveform chosen at 30-degree inception angle and 330 MVAR load was compared with the actual fault that occured within 10 km from the substation. The RMSE value of generated waveform current and is 0.0696. Meanwhile, Figure 13d illustrates the generated simulation and actual waveform due to crane encroachment. The generated waveform with fault triggered at 30-degree inception angle and 330 MVAR load is evaluated based on actual fault occurrences within 30 km from the substation. The RMSE value of generated waveform current is 0.0498. Therefore, the RMSE value of all simulated fault causes are less than 0.1 which is considered small and indicated the correctness of the models.