esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Method
### Principal Component Regression (PCR) Method
#### 2.2.3 Principal Component Analysis (PCA)  
PCA commences with the covariance matrix of the standardized dataset, which quantifies linear relationships between variables (e.g., UCS, stabilizer dosage, curing time) within the multivariate soil stabilization data. Eigen decomposition of this matrix yields orthogonal principal components (PCs), ranked by explained variance, with the first PC capturing maximal data variance and subsequent PCs sequentially maximizing residual variance under orthogonality constraints. Eigenvector loadings derived from the covariance matrix define each PC's composition, revealing how input variables (e.g., physicochemical properties, amendment proportions) contribute to latent variance patterns [27]. By projecting standardized data onto these PCs, PCA reduces dimensionality while preserving covariance-driven relationships, enabling the distillation of complex stabilization mechanisms--such as hydration kinetics or particle bonding--into interpretable components [28]. This process facilitates regression modeling by linking latent variables to geotechnical performance metrics, ensuring robust mechanistic insights. The covariance matrix thus anchors PCA as both a variance-exploration tool and a bridge to predictive analytics. The covariance matrix captures relationships between soil properties of the standardized data as in Equation (2).  
\[\text{Cov}(X)=\frac{1}{n-1}\sum_{i=1}^{n}{(x_{i}-\overline{x})(x_{i}-\overline{x })^{T}} \tag{2}\]  
where  
\(X=\text{matrix of standardized data}\)  
\(n=\text{number of samples}\)  
\(\overline{x}=\text{mean vector of X}\)  
\(x_{i}=1,2,3,\ldots\)
## 6 Experimental Setup  
The hardware setup used in this work is composed of three devices and a personal computer: a data acquisition module from National Instruments, model NI USB 6218 connected through a USB interface with the personal computer. This module has an analog-to-digital converter with 16 bits. Although it can operate at different sampling rates, the value used throughout this work was 100 kHz. It also has many input ranges, but here the bipolar \(\pm 1\) V range was used. Two general purpose function generators were used, one, from Stanford Research, model DS360, was used here to produce a sinusoidal signal, which is applied to channel ai0 of the data acquisition module. The other function generator is an Agilent 33220A general purpose function generator which was configured to produce a rectangular signal that is input into the acquisition module to determine the sampling instants (clock signal). This is connected to the data acquisition module inputs PFI3 and GND (Figure 7). The rectangular signal produced is phase-modulated by an internally generated signal, which is random noise with a normal distribution, and whose peak-to-peak value can be adjusted by the user.  
A block diagram of the test setup can be observed in Figure 7. There, a personal computer, the data acquisition module, the two function generators, and the connections between the different modules with an indication of the terminal names and pin numbers are shown.  
The personal computer is able to control the two function generators. In the case of the function generator producing the clock signal, a USB interface is used. In the case of the other function generator, which produced the sinusoidal stimulus signal, it does not have a USB interface. It only has a general purpose interface bus. It is thus connected to the personal computer using an interface module from Keysi  
Figure 7: Block diagram of the test setup where one can observe the personal computer, the data acquisition module, and the two general purpose function generators: one of them (the right one) creates a rectangular signal that controls the instants that the input voltage of the data acquisition module is sampled and which introduces the phase noise, whose effect one wants to study; the other one (the left one) creates a sinusoidal signal that goes to the input of the data acquisition module and creates the sinusoid, whose data points are going to be fit to the sinusoidal model. Those two function generators are controlled by a personal computer using a USB interface or a GPIB interface through a proper USB/GPIB interface module.  
The hardware setup shown here can create a sinusoidal voltage signal, which is sampled and converted from analog to digital by a data acquisition module that times the sample using a rectangular voltage signal produced by a second function generator. The data points acquired are sent to the personal computer, where software developed using the National Instruments LabVIEW programming language is running, as described in the next section.
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 2 Background
### Kernelized Correlation Filter  
KCF [10] has shown promising performance for SOT in SVs [36; 37; 38; 39]; we exploit and improve it to address object rotation and tracking drift for accurate semantic representations. This section introduces the KCF [10] framework based on the training and detection processes. It applies dense cyclic samples to explore the structural information of an object. A circulant matrix is used to model specific structures, and the correlation is transformed into element-wise products by a fast Fourier transform (FFT).  
Let an \(M\times N\) patch \(x\) denote a base sample that is centered on an object and is more than twice the size of the object. All cyclic shifts \(\{x_{m,n}\},\ (m,n)\in\{0,\ \ldots,M-1\}\times\{0,\ldots,N-1\}\) are considered dense sampling over the base sample. They are labeled by a Gaussian function \(y\) so that \(y(m,n)\) is the label of \(x_{m,n}\).  
In the training process, the solution \(\omega\) is obtained by minimizing the ridge regression error [10], as follows:  
\[\min_{w}\sum_{m,n}\left|\left\langle\varphi(x_{m,n}),\omega\right\rangle-y(m,n )\right|^{2}+\lambda\parallel\omega\parallel^{2} \tag{1}\]  
where \(\varphi\) is the Hilbert space mapping induced by kernel \(\kappa\). The inner product is defined as \(\varphi(x)\), \(\varphi(g)=\kappa(x,g)\). A constant \(\lambda\geq 0\) is a regularization term that avoids overfitting. After a powerful nonlinear regression using the kernel trick, the solution \(\omega\) is  
\[\omega=\alpha(m,n)\varphi(x_{m,n}). \tag{2}\]  
The discrete Fourier transform (DFT) of a vector is denoted by a hat (\(\cdot\)). The kernel matrix is a circulant matrix in commonly used kernel functions [10]. Thus, the dual-space coefficient \(\alpha\) is  
\[\hat{\alpha}=\frac{\hat{y}}{\hat{k}^{xx}+\lambda}, \tag{3}\]  
where \(k^{xx}=\kappa(x_{m,n},x)\). A Gaussian kernel is employed to compute the kernel correlation \(k^{xx}\) with element-wise products in the frequency domain. For a patch with \(C\) feature channels, the base sample is \(x=[x_{1},x_{2},\ldots,x_{C}]\). Therefore, we have  
\[k^{xx^{\prime}}=\exp\left(-\frac{1}{\sigma^{2}}\Big{(}\parallel x\parallel^{2 }+\parallel x^{\prime}\parallel^{2}-2\mathcal{F}^{-1}\Big{(}\sum_{i=1}^{C} \hat{x}_{i}\odot\hat{x}_{i}^{\prime*}\Big{)}\Big{)}\right) \tag{4}\]  
where \(\mathcal{F}^{-1}\) is the inverse Fourier transform (IFT), \(\odot\) denotes element-wise products, \(*\) denotes the complex conjugate, and \(i\) is the index of feature channels.  
In the detection process, patch \(z\) in a new frame equal to the size of \(x\) is cropped out in the center of the object in the previous frame. The response map \(f(z)\) is solved by:  
\[f(z)=\Big{(}\mathcal{F}^{-1}\Big{(}\hat{k}^{xz}\odot\hat{\alpha}\Big{)}\Big{)}. \tag{5}\]  
The object position is then obtained by determining the maximum value of \(f(z)\). To adapt to changes in the object, the two coefficients \(x\) and \(\alpha\) are updated [10].
# Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control
## 1 Introduction
Choosing a suitable method for PID tuning can also help improve the quality of control [36; 37]. The main goal of this study is to improve the autonomous terrain-following and navigation performance of a quadrotor system. We address the detailed mathematical modeling of the quadrotor's kinematics and dynamics of the quadrotor. The autonomous controller comprises of an integrated control for the quadrotor's position, attitude and height control with the multi-sensor based control loops for terrain-following depicted in Figure 1. The control methodologies are conducted in MATLAB Simulink and the experiments on a quadrotor test-bed where interference such as wind conditions and sensor noise are incorporated in both the simulation and real-world experiments. A quadcopter measures its altitude relatively to the ground by using a range-finder. However, when the surface of the ground is fluctuating, a traditional PID control algorithm cannot meet the requirements of accurate terrain-following. Work in this innovation is the adaptive control function using the integration of a Global Navigation Satellite System (GNSS) (GPS) and a BeiDou Navigation Satellite System (BDS) [38], an inertial navigation system (INS) [39], a barometer [40] and a Lidar-lite sensor [41], especially the combination of an extended Kalman filter (EKF) and the cascade PID controller. In this paper, a dynamic model of the quadcopter is built and then linearized for simplification. The cascade PID is modeled and simulated in Simulink based on a tuning method according to the transfer function. Finally, an experiment is performed based on an open-source flight controller Pixhawk. Both the simulation and the real flight show improvements with this controller. The rest of this paper is organized as follows. In II, dynamic models of the quadrotor are described. In III, we design the integration of the position and height control based on the combination of an extended Kalman filter and a cascade PID. IV verifies the reliability of the proposed models by MATLAB Simulink and real-world quadrotor flying avoiding a box above the ground. Conclusions are given in V.
## 4 Studies
### Study B  
This study examined the effectiveness of using electroencephalography (EEG) frequency attributes to detect the markers of game addiction and attempted to design a system for alerting the user to their potential game addiction based on EEG results. In the first part of the study, a questionnaire-based assessment was performed to categorize the subjects into normal or problematic game players. Then, electroencephalographic data were recorded during mobile game playing. The EEG data were then analyzed to distinguish between addicted and nonaddicted game players. The finding showed that the EEG signal parameterization from the occipital region was the best option for detecting game addiction. The second part of the study involved the design of a wearable mobile addiction sensing system. This system consisted of a wearable headset to detect occipital region signals and a smart device to receive EEG signals from the wearable headset. The signals received were then analyzed to make an alert or recommendation based on the results of the EEG analysis.  
Figure 7: The distribution of studies by addiction domain.
# _Ku_-Band CMOS Power Amplifier with Three-Stack Power Stage to Enhance Output Power and Efficiency
## 6 Conclusions  
In this paper, we proposed a structure of a power amplifier to ensure high output power and to improve efficiency by designing a driver stage in a common-source structure and a power stage in a stack structure. In order to derive the proposed structure, the output power and output impedance characteristics of the stack structure were compared with the common-source structure and analyzed. To verify the feasibility of the proposed structure, the _Ku_-band power amplifier was designed using the 65-nm RF CMOS process. At an operating frequency of 15 GHz, saturation output power and maximum power-added efficiency were confirmed to be 22.1 dBm and 17.2%, respectively.  
Conceptualization, J.Y., H.J. and C.P.; methodology, J.L. and S.J.; investigation, J.L., H.J. and C.K.; supervision, writing--original draft, C.P.; writing--review and editing, J.Y. and C.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1A2C1013666) and in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2021R1A4A1032580).  
Not applicable.  
The data can be obtained from the authors on request.  
The authors declare no conflict of interest.
## 6 Results and Discussions
### Effect of the Structural Damping  
Figure 11 shows the comparative analysis on the effect of the structural damping. The impact velocity is constantly set as 6 m/s and five different structural damping factors (i.e., Vr = 0, 0.005, 0.02, 0.1 and 0.5) are considered. It can be seen that the structural damping has negligible effect on the stress before the flow separation, and it reduces vibration stress after the flow separation. Referring to the  
Figure 9: Comparative analysis on the effect of the added mass. (**a**) S1; (**b**) S3; (**c**) S5; (**d**) S7.
## 3 Test Results and Analysis
### Influence Analysis of Confining Pressure  
Stress-strain curves of specimens under different impact velocities and confining pressures are shown in Figure 7 and the crushing morphology (impact velocity: 11.3 m/s) are shown in Figure 8.  
As can be seen from Figure 7, stress-strain curves presented the following characteristics:  
(1) The rising section of the stress-strain curve with confining pressure was basically a straight line. Under the same impact velocity, the dynamic elastic modulus of specimens with confining pressure were relatively close, and their value were about 20 GPa\(\sim\)150 GPa, which increased with the impact velocity. The higher the impact velocity, the more obvious the strengthening effect of confining pressure.  
(2) When the strain rate was about 27-42 s\({}^{-1}\), the stress-strain curves of specimens with confining pressure showed type of \(\pi\) and had a yield platform. The corresponding strain of the peak stress platform was about 3-7 \(\times\) 10\({}^{-3}\), which were greater than those without confining pressure, indicating that when the strain rate was less than 42 s\({}^{-1}\), the ductility of basalt fiber concrete was enhanced under confining pressure.  
(3) With the increase of strain rate, the variation amplitude of the peak stress also increases. Figure 7a shows that the peak stress increases from 54.1 MPa to 85.8 MPa, increasing by 58.6% with confining pressure increases from 0 MPa to 15 MPa at impact velocity of 5.4 m/s. Figure 7b shows that the peak stress increases from 58.1 MPa to 136.6 MPa, increasing by 135.1% at impact velocity of 8.8 m/s. Figure 7c shows that the peak stress increases from 63.7 MPa to 162.1 MPa, increasing by 154.5% at impact velocity of 11.3 m/s.  
Figure 8 shows that when the impact velocity was 11.3 m/s, the specimen without confining pressure was broken, while the specimens with confining pressure remained intact, but the edge part peeled off. When the confining pressure was 5 MPa, the edge peeled off obviously. When the confining pressure was 5 MPa, a penetrating crack appeared in the center of the specimen, but it was not broken into pieces under the action of the confining pressure device. When the confining pressure was 10 MPa and 15 MPa, cracks appeared on the surface but did not penetrate the whole specimen. It also can be  
Figure 8: The crushing morphology of specimens under different confining pressures. (impact velocity: 11.3 m/s).  
Figure 7: Stress–strain curves of specimens under different impact velocities and confining pressures. **(a)** Impact velocity: 5.4 m/s; **(b)** impact velocity: 8.8 m/s; and **(c)** impact velocity: 11.3 m/s.  
seen that the peak stress of specimens measured are not the maximum because the specimens were not completely broken under 10 MPa and 15 MPa.
###### Abstract  
Traditional disease detection methods typically depend on visual assessments conducted by human experts, which are time-consuming and subjective. Thus, there is an urgent demand for automated and efficient approaches to accurately detect and classify tea diseases. This study presents an enhanced Real-Time Detection Transformer (RT-DETR), tailored for the accurate and efficient identification of tea diseases in natural environments. The proposed method integrates three novel components: Faster-LTNet, CG Attention Module, and RMT Spatial Prior Block, to significantly improve computational efficiency, feature representation, and detection capabilities. Faster-LTNet employs partial convolution and hierarchical design to optimize computational resources, while the CG Attention Module enhances multi-head self-attention by introducing grouped feature inputs and cascading operations to reduce redundancy and increase attention diversity. The RMT Spatial Prior Block integrates a Manhattan distance-based spatial decay matrix and linear decomposition strategy to improve global and local context modeling, reducing attention complexity. The enhanced RT-DETR model achieves a detection precision of 89.20% and a processing speed of 346.40 FPS. While the precision improves, the FPS value also increases by 109, which is superior to the traditional model in terms of precision and real-time processing. Additionally, compared to the baseline model, the FLOPs are reduced by 50%, and the overall model size and parameter size are decreased by approximately 50%. These findings indicate that the proposed algorithm is well-suited for efficient, real-time, and lightweight agricultural disease detection.  
RT-DETR; tea disease detection; lightweight; attention mechanism +
Footnote †: journal: Applied sciences
## 3 The VSWT-PMSG Control System
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Component** & **Parameter** & **Symbol** & **Value** \\ \hline \multirow{8}{*}{**Wind**} & Blade Radius & \(R\) & 40 m \\ \cline{2-4}  & Rated Wind Speed & \(V_{w}\) & 12 m/s \\ \cline{2-4}  & Rated rotation speed & _or_ & 2.43 rad/s \\ \cline{2-4}  & Maximum Power Coefficient & \(Cp_{opt}\) & 0.48 \\ \cline{2-4}  & Optimum Tip Speed Ratio & \(\lambda_{opt}\) & 8.1 \\ \cline{2-4}  & Air density & \(\rho\) & 1.225 kg/m\({}^{3}\) \\ \cline{2-4}  & Inertia & \(J_{wt}\) & 10,137,000 kg\(\cdot\)m\({}^{2}\) \\ \hline \multirow{8}{*}{**PMSG**} & Rated Voltage & \(V\) & 1.0 kV \\ \cline{2-4}  & Rated Frequency & _fe_ & 20 Hz \\ \cline{2-4}  & Magnetic Flux & \(\psi_{m}\) & 1.4 pu \\ \cline{2-4}  & Stator Winding Resistance & \(R_{s}\) & 0.01 pu \\ \cline{2-4}  & The d-axis inductance & \(L_{d}\) & 0.95 pu \\ \cline{2-4}  & The q-axis inductance & \(Lq\) & 0.75 pu \\ \cline{2-4}  & Pole pairs & \(p\) & 52 \\ \hline \multirow{4}{*}{**AC/DC/AC**} & SSC frequency Switching & _fs_ & 1 kHz \\ \cline{2-4}  & GSC frequency Switching & _fs_ & 5 kHz \\ \cline{2-4}  & \multirow{2}{*}{Grid Frequency} & _fg_ & 50 Hz \\ \cline{2-4}  & DC Link capacitor & _Cdc_ & 25000 μF \\ \cline{2-4}  & DC Link voltage & _Vdc_ & 1.75 kV \\ \hline \multirow{4}{*}{**LC Filter**} & Inverter side inductance & _Li_ & 0.6 pu \\ \cline{2-4}  & Inverter side parasitic resistance & _Ri_ & 0.005 pu \\ \cline{2-4}  & Filter capacitor & _Cf_ & 0.05 pu \\ \cline{2-4}  & Damping Resistance & _Rd_ & 0.3 pu \\ \hline \multirow{4}{*}{**Step Up**} & Transformer inductance & _Lg_ & 0.04 pu \\ \cline{2-4}  & Transformer resistance & _Rg_ & 0.016 pu \\ \cline{2-4}  & Low voltage & \(V_{TL}\) & 1.0 kV \\ \cline{2-4}  & High voltage & \(V_{TH}\) & 6.6 kV \\ \hline \end{tabular}
\end{table} 
Table 1: System parameters.
## 3 Materials and Methods
### Data Transmission
#### 3.5.4 Packet Analyzer (Wireshark)  
Wireshark is an open-source packet analyzer used for data interception over computer networks. The software allows the graphical representation of data packets and supports many internet protocols, according to the internet protocol suite (TCP/IP model) [38]. Table 7 depicts a brief list of supported protocols used for IoT applications. We study the packet round-trip time (RTT) and throughput in this work.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 3 Potential Solutions
### Other Engaging Solutions  
We have thoroughly reviewed the implications in assisting rural networks by employing UAVs; moreover, other engaging solutions can enable broad coverage in the countryside and in shedding light on its connectivity. On the onset of the first decade of 2000s, SCPs appeared to be a prominent answer for fixed and mobile applications. These devices remarkably outpaced the unprofitable gap since they have arisen as a cost-effective solution for urban, suburban, and rural areas [21].  
Aside from dedicated area coverage independence, the authors in [21] pointed out that Sky Station platforms may provide higher capacity--by higher frequency reuse--than other wireless systems, the possibility of grant enhanced roaming, and the possibility of choosing their stationary point. Another seamless option for rural connectivity includes satellites, namely LEO configuration. The following subsections will deepen the strategies mentioned earlier, and UAVs also fall into this category.