esa-sceva/satcom-chunk-collection huggingface dataset:
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
## 3 Variable Voltage Regulator for LCRs to Balance the String LED Currents  
### \(Vv_{dc}\) Synthesized to Clamp Low-Dropout \(v_{DS}\) in the MOSFET  
As mentioned in Figures 3 and 4, for a LCR with constant voltage \(V_{dc}\) supply, the drain-to-source voltage \(v_{DS}\) is not only dependent on the forward voltage of the string LED, but is also subject to the voltage drop \(v_{R}\) of the source resistor \(R_{s}\). Accordingly, the \(v_{R}\) will increase, due to the increase of \(i_{DS}\); the \(v_{DS}\) may fall dramatically to enter into the triode region of the MOSFET. On the contrary, the \(v_{DS}\) will be located far away from the triode region, due to the reducing \(i_{DS}\), and the operation point will move right. Here, it will be sustained on the flat \(v_{GS}\)-characteristic curve, on which the drain current \(i_{DS}\) will remain almost constant in a wide variation of \(v_{DS}\), resulting in a \(N\)-LED luminance output which is almost the same. In order to remedy the issue due to a constant voltage \(V_{dc}\) supply, a constant low-dropout \(v_{DS}\) is initially specified as \(V_{Q}\), close to the boundary of triode region, but on the flat portion of the \(v_{GS}\)-characteristic curve. Subsequently, all drain voltages at points \(M\), \(v_{Mk}\), are only supplied by a variable dc supply \(VV_{dck}\) from a variable voltage regulator (VVR), in which the low dropout \(V_{Q}\) is involved, i.e.,  
\[\begin{array}{l}v_{Mk}=VV_{dck}-Nv_{DR}\\ =V_{Q}+v_{Rk}\end{array}, \tag{12}\]  
where the footnote \"\(k\)\" denotes the \(k\)th operation point, and:  
\[v_{Rk}=i_{DSk}R_{s}, \tag{13}\]  
Then, the output voltage \(VV_{dc}\) of VVR can be estimated as:  
\[\begin{array}{l}VV_{dck}=Nv_{Dk}+V_{Q}+i_{DSk}R_{s}\\ =Nv_{Dk}+v_{Mk}\end{array}, \tag{14}\]  
Figure 4: Bias situations for MOSFET and a single string LED during dimming process at \(T=T_{0}\), where the LCR is supplied with constant \(V_{dc}\).  
where the \(V_{Q}\) is a constant low voltage placed close to the boundary of the triode region, but in the saturation region, as shown in Figure 5a, i.e.,  
\[\begin{array}{ll}V_{Q}&=VV_{dck}-v_{k}\\ &\equiv\text{constant}\end{array}, \tag{15}\]  
where the sum of the voltage \(v_{k}\) is given by:  
\[v_{k}=Nv_{Dk}+i_{DSk}R_{s}, \tag{16}\]  
Accordingly, the \(VV_{dc}\) can then be synthesized form (14), including \(Nv_{D}\), \(i_{DS}R_{s}\), and the specified low-dropout \(V_{Q}\), as shown in Figure 5b. In Figure 5a, the supply \(v_{Mk}\) for different operation points under constant \(V_{Q}\), can be easily found in (12). Remarkably, with a variable supply \(VV_{dc}\), the LCR is able to keep the MOSFET operating at the specified \(V_{Q}\) under different \(v_{GS}\)'s, which are the corresponding dimming levels. In addition, with the low-dropout \(V_{Q}\), the MOSFET can only dissipate low conduction loss, and the string \(N\)-LED can emit luminance, proportional to the dimming levels.
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## 2 The PDM-Based CVQKD Scheme  
We consider a discretely modulated CVQKD protocol since it is suitable for long-distance transmission [29]. In order to increase the transmission capacity, the PDM scheme can be applied in modulation. In Figure 1a, we show the schematic diagram in the PDM-based CVQKD system. At [PERSON]'s side, the light beam from laser is encoded and modulated, resulting in PDM signals. We will elaborate on this specific process after we put forward the coding scheme. A polarization beam splitter is used for splitting the signals into two polarizations, and the two polarizations are recombined by using a polarization beam combiner after modulation. At destination, [PERSON] demultiplexes the incoming signals and measures one of them with homodyne detector. The transmission of two polarizations with PDL is shown in Figure 1b. There is a component projection on the corresponding channel, leading to the crosstalk between channels.  
Figure 1: (Color online.) **(a)** The discretely modulated PDM-based continuous-variable quantum key distribution (CVQKD) system. PBS: polarization beam splitter. PBC: polarization beam combiner. LO: local oscillation. **(b)** The transmission of two polarizations. The orthogonality of two polarizations is destroyed due to the non-negligible polarization dependent loss (PDL).  
The transmission of signals in optical fiber through a single beam is involved in the PDM-based CVQKD system, which can be described as follows:  
**Step 1:** [PERSON] randomly picks up a random variable \(x_{k}\in\{1,2,3,4\}\) and encodes a coherent state \(|\alpha_{k}\rangle\in\{|\alpha_{1}\rangle=|-r+ri\rangle,|\alpha_{2}\rangle=|r+ri \rangle,|\alpha_{3}\rangle=|-r-ri\rangle,|\alpha_{4}\rangle=|r-ri\rangle\}\), where \(r\) is a positive real number depending on [PERSON]'s signal-to-noise ratio (SNR) and \(k\) denotes the index of time slot--and sends it through a quantum channel with loss and noise interference. After perfect PDM, there will be two mutually delayed polarization signal lights from the same beam of light.  
**Step 2:** [PERSON] sends the prepared coherent states through a quantum channel to [PERSON]. [PERSON] receives quantum states from two polarizations separated by a PBS. [PERSON]'s detection process of quantum states on two channels is similar. Then, [PERSON] randomly selects the measurement basis through the decoding system and measures the components. Through the classical authentication channel, the selected measurement basis is published. [PERSON] and [PERSON] will discard the wrong measurement basis.  
**Step 3**: [PERSON] identifies and extracts effective coherent state signals and derives the phase of the coherent state including serial information. He performs the demodulating and decoding operations, and finally establishes a shared key with [PERSON] after the error correction and privacy amplification processing.
## 3 Materials and Methods  
The dual-stage supervised network CRTD proposed in this paper, designed for optical satellite images under thin cloud cover, is illustrated in Figure 3. The CRTD framework consists of two models: the Thin Cloud Removal model (TCR) and the Small Target Detection model (STD). After the images are cropped, they are fed into the TCR, which mitigates the effects of thin cloud coverage. The TCR primarily comprises a DehazeFormer (DF) block and GARM components. The DF block includes normalization, a simple linear activation function, and a spatial information aggregation mechanism. Once the image has been clarified by the TCR, it is processed by the STD for detection. The STD is an improved version of the DETR detection framework, incorporating a Compact Inverted Block (CCIB) to enhance target features. These two models are closely integrated, with the output of the TCR being directly fed into the STD for further processing.  
\[O=\Psi_{\text{STD}}(\Psi_{\text{TCR}}(I)) \tag{1}\]  
In this context, \(I\) and \(O\) represent the multi-frame images and the final detection results, respectively, while \(\Psi_{\text{STD}}\) and \(\Psi_{\text{TCR}}\) correspond to the computational processes of the STD and TCR models. It is important to note that we have proposed an RoI-Loss method to achieve unified training for CRTD. In the following sections, we will provide a detailed explanation of each component of the proposed method.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 4 Test Feeder Characteristics and Problem Parametrization
### Second Test Feeder  
The electrical configuration of the 69-bus grid is depicted in Figure 2(b). This system's main characteristic is that it operates with a monopolar DC voltage of 12.66 kV between the positive pole and the return wire. All the branch and load parameters for this test feeder are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Node \(i\)** & **Node \(j\)** & \(R_{ij}\) **(\(\Omega\))** & \(P_{j}\) **(kW)** & **Node \(i\)** & **Node \(j\)** & \(R_{ij}\) **(\(\Omega\))** & \(P_{j}\) **(kW)** \\ \hline
1 & 2 & 0.0922 & 100 & 17 & 18 & 0.7320 & 90 \\
2 & 3 & 0.4930 & 90 & 2 & 19 & 0.1640 & 90 \\
3 & 4 & 0.3660 & 120 & 19 & 20 & 1.5042 & 90 \\
4 & 5 & 0.3811 & 60 & 20 & 21 & 0.4095 & 90 \\
5 & 6 & 0.8190 & 60 & 21 & 22 & 0.7089 & 90 \\
6 & 7 & 0.1872 & 200 & 3 & 23 & 0.4512 & 90 \\
7 & 8 & 1.7114 & 200 & 23 & 24 & 0.8980 & 420 \\
8 & 9 & 1.0300 & 60 & 24 & 25 & 0.8960 & 420 \\
9 & 10 & 1.0400 & 60 & 6 & 26 & 0.2030 & 60 \\
10 & 11 & 0.1966 & 45 & 26 & 27 & 0.2842 & 60 \\
11 & 12 & 0.3744 & 60 & 27 & 28 & 1.0590 & 60 \\
12 & 13 & 1.4680 & 60 & 28 & 29 & 0.8042 & 120 \\
13 & 14 & 0.5416 & 120 & 29 & 30 & 0.5075 & 200 \\
14 & 15 & 0.5910 & 60 & 30 & 31 & 0.9744 & 150 \\
15 & 16 & 0.7463 & 60 & 31 & 32 & 0.3105 & 210 \\
16 & 17 & 1.2860 & 60 & 32 & 33 & 0.3410 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: First test feeder, composed of 33 nodes.  
Figure 3: Test feeder topologies: (**a**) 33-bus grid and (**b**) 69-bus grid.
## 5 Numerical Experiments
### Sensitivity Analysis
#### 5.5.2 Effect of the Distribution of Depots and Empty Containers  
It is worth pointing out that while the number of depots and empty containers is vital for a cost-effective container dragage system, their distribution is equally important and should also be considered. In the preceding sections, depots are randomly placed on a Euclidean plane. Additionally, multiple depots can be uniformly distributed within a specified area, or strategically placed in several key areas, depending on the transportation demand. These three distribution types are referred to as random, uniform, and focused distribution, respectively. Likewise, empty containers can be allocated across multiple depots using the same three distribution strategies. Specifically, the focused distribution of empty containers involves allocating more containers to depots situated within or near key areas. In this section, three combinations of distribution strategies--R/R, U/U, and F/F--are investigated with the results presented in Table 9. For example, F/F distribution means that both the distribution of depots and empty containers are focused within a defined area.  
Figure 6: Effect of the number of depots and the number of empty containers.  
The results clearly indicate that as the number of empty container-related requests increases, the cost savings become greater. The average cost saving of the selected six instances under the U/U and F/F distribution from the R/R distribution is, respectively, 3.02% and 8.46%, demonstrating that both the U/U and F/F distribution outperforms the R/R distribution. This finding suggests that, under random request distribution, uniformly distributed depots and containers help maintain a relatively balanced service area and empty container transportation volume for each depot, reducing situations of excessively long transport distances and preventing the over-concentration or over-dispersion of empty container transportation. In contrast, focused distribution of depots and empty containers helps reduce the transport distance of large quantities of empty containers between depots and request vertices, as well as the distance for empty trucks departing from or returning to the depots, thereby more effectively reducing total transport costs.
# Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
## 2 Materials and Methods
### Classification Performance Measures  
In order to assess the performance of our heartbeat classification approach and allow the comparison with other methods found in the literature, the sensitivity (_Se_), positive predictivity (+_P_), F1-score (_F_1),  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rank** & **Feature** \\ \hline
1 & \(QRSw2\) (normalized) \\
2 & \(QRSw4\) (normalized) \\
3 & \(RR_{0}/avgRR\) \\
4 & \(RR_{+1}/RR_{0}\) \\
5 & \(QRSw2\) \\
6 & Fitting coefficient 1 of HBF with degree 4 (\(HBF4\)c1) \\
7 & \(HBF4\)d\(c2\) \\
8 & \(QRSw4\) \\
9 & \(HBF4\)d\(c0\) \\
10 & \(RR_{-1}/RR_{0}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ten most informative features ranked according to the mutual information criterion [28] relative to the NB, SVEB, and VEB classes.  
Figure 1: Illustration of the temporal characteristics and amplitude differences extracted from the cardiac cycle in a normal electrocardiogram (ECG) and the fiducial points identified in order to obtain them.  
and overall accuracy (_Acc_) of the classification outcome were calculated. The sensitivity, also found in the literature as recall, refers to the relative number of correct detections of a particular class of heartbeats. The positive predictive value, also called precision, refers to the relative number of true positive heartbeats in all detected heartbeats of that class. The F1-score is the harmonic average of the precision and recall, and here is used to assess the classification performance on each class and compare it with state-of-the-art methods using a single value. The overall accuracy refers to the relative number of correctly classified heartbeats considering all classes. Here, the _Acc_ is used to guide feature selection and model development and as a general indicator of performance. All these performance indicators are calculated from the classification confusion matrix. Performance measures from the training set are calculated with \"leave-one-patient-out\" cross-validation.
## 2 Statement of the Problem in the Frequency Domain
The structure is surrounded by an external inviscid acoustic fluid (gas or liquid) that is detailed in Appendix B. The fluid occupies the infinite three-dimensional domain \(\Omega_{E}\) whose boundary \(\partial\Omega_{E}\) is \(\Gamma_{E}\). The inward unit normal to \(\partial\Gamma_{E}\) is \(\mathbf{n}^{S}\), defined above (see Figure 1). In \(\Omega_{E}\), the pressure field is denoted by \(p_{E}(\mathbf{x},\omega)\). There is no Dirichlet boundary condition on \(\Gamma_{E}\). An acoustic source density \(Q_{E}(\mathbf{x},\omega)\) is given in \(\Omega_{E}\). This acoustic source density induces a pressure field \(p_{\text{given}}(\omega)\) on \(\Gamma_{E}\) (defined in Appendix B). For the sake of brevity, the case of an incident plane wave is not considered here, and the reader is referred to [41] for this case. External Inviscid Acoustic Fluid Equations in the Frequency Domain and Acoustic Impedance Boundary Operator  
Let \(\rho_{E}\) and \(c_{E}\) be the constant mass density and the constant speed of sound of the external acoustic fluid at equilibrium. Let \(k=\omega/c_{E}\) be the wave number at frequency \(\omega\). The pressure is the solution of the classical exterior Neumann problem related to the Helmholtz equation with a source term [2; 3; 4; 41],  
\[\mathbf{\
abla}^{2}p_{E}+k^{2}\,p_{E}=-i\omega\,Q_{E}\quad\text{in}\quad\Omega_{E}\,, \tag{1}\]  
\[\frac{\partial p_{E}}{\partial\mathbf{n}^{S}}=\omega^{2}\,\rho_{E}\,\mathbf{u }\cdot\mathbf{n}^{S}\quad\text{on}\quad\Gamma_{E}\,, \tag{2}\]  
\[|\,p_{E}\,|=O(\frac{1}{R})\quad,\quad\left|\,\frac{\partial p_{E}}{\partial R }+i\,k\,p_{E}\,\right|=O(\frac{1}{R^{2}})\,, \tag{3}\]  
with \(R=\|\mathbf{x}\|\to+\infty\), where \(\partial/\partial R\) is the derivative in the radial direction and where \(\mathbf{u}\cdot\mathbf{n}^{S}\) is the normal displacement field on \(\Gamma_{E}\) induced by the deformation of the structure. Equation (3) corresponds to the outward Sommerfeld radiation condition at infinity. In Appendix B, it is shown that the value \(p_{E}|_{\Gamma_{E}}\) of the pressure field \(p_{E}\) on the external fluid-structure interface \(\Gamma_{E}\) is related to the given external pressure field \(p_{\text{given}}|_{\Gamma_{E}}\) on \(\Gamma_{E}\) and to the normal displacement field \(\mathbf{u}(\omega)\cdot\mathbf{n}^{S}\) on the external fluid-structure interface \(\Gamma_{E}\) by the equation  
\[p_{E}|_{\Gamma_{E}}(\omega)=p_{\text{given}}|_{\Gamma_{E}}(\omega)+i\omega\, \mathbf{Z}_{\Gamma_{E}}(\omega)\{\mathbf{u}(\omega)\cdot\mathbf{n}^{S}\}\,, \tag{4}\]  
Figure 1: Geometry of the vibroacoustic system. in which \(\mathbf{Z}_{\Gamma_{E}}(\omega)\) is the acoustic impedance boundary operator. It can be proven that for all given real \(\omega\) and for all given \(\mathbf{u}\cdot\mathbf{n}^{S}\) sufficiently regular on \(\Gamma_{E}\), the boundary value problem defined by Equations (1)-(3) (e.g., [97; 98]) admits a unique solution.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 4 Overview of Energy Consumption in WSNs
### WSN Energy Dissipation Model  
Consider the simplified WSN energy dissipation model illustrated in Figure 3[47]. As shown in the figure, the transmitting SN consumes energy to drive its radio subsystem which includes the radio electronics, and power amplifier. The receiving SN also dissipates energy to drive its radio electronics. The distance \(d\) between the transmitting SN and receiving SN usually computed in the form of a distance metric specifies the channel model used by the power amplifier. If the distance \(d\) is greater than a set threshold \(d_{0}\), the multipath model is assumed. Otherwise, the free space channel model is used for \(d<d_{0}\). Therefore, the required energy for a SN to transmit a \(q\) bit message is defined as [48]:  
\[E_{T}(q,d)=\left\{\begin{array}{ll}qE_{elec}+qE_{f}d^{2},&d<d_{0}\\ qE_{elec}+qE_{m}d^{4},&d\geq d_{0},\end{array}\right. \tag{4}\]  
where \(E_{f}\) and \(E_{m}\) are the free space and multipath power loss respectively, \(E_{elec}\) is the energy dissipated to drive the radio electronics, and \(d_{0}\) is the transmission distance threshold expressed as:  
\[d_{0}=\sqrt{\frac{E_{f}}{E_{m}}}. \tag{5}\]  
On the other hand, the energy consumed by the receiving SN is defined as:  
\[E_{R}(q)=qE_{elec}. \tag{6}\]  
These equations defined in (4)-(6) are used by developers of energy-efficient and energy-balanced routing protocols to theoretically verify their proposed algorithm because of their simplicity. However, in practice, we emphasise that the radio wave propagation varies rapidly and can be difficult to model using Equations (4)-(6).
## 3 Application
### Foliar Level  
#### 3.1.1 Diurnal Changes  
A total of 33 articles published from 1992 to 2015 analyzed the relationships between foliar-level PRI and actual photochemical efficiency (\(\Delta\)Fv/Fm\({}^{\prime}\)), NPQ, the chlorophyll/carotenoid ratio, EPS, DEPS, RUE or net CO\({}_{2}\) uptake (Figure 1). The correlations between PRI and \(\Delta\)Fv/Fm\({}^{\prime}\) (n = 57, n is the number of the correlations reported in the literature) and RUE (n = 28) were among the most relevant. Most of the relationships were focused on broadleaved and herbaceous/crop plants. The correlations for broadleaved and coniferous plants were stronger than those for herbaceous/crop plants, except for the correlations between PRI and EPS (or DEPS). \(R^{2}\) for all variables other than the chlorophyll/carotenoid ratio also showed high differentiation for herbaceous/crop plants (Figure 1). PRI for broadleaved species represented medians of 80%, 76% and 78% of the variability of \(\Delta\)Fv/Fm\({}^{\prime}\), RUE and net CO\({}_{2}\) uptake, respectively (Figure 1a,d,f).  
The \(R^{2}\) of PRI with other physiological variables is presented in Figure 2. All studies focused on broadleaved and herbaceous/crop plants, for which PRI explained averages of 68% or 67%, respectively,  
Figure 1: Boxplots of the coefficients of determination of the relationships between PRI and physiological variables at the foliar level and diurnal timescale: **(a)** Actual photochemical efficiency (\(\Delta\)F/Fm\({}^{\prime}\)); **(b)** Epoxidation or de-epoxidation state of xanthophylls (EPS or DEPS); **(c)** Non-photochemical quenching (NPQ); **(d)** Radiation-use efficiency (RUE); **(e)** Carotenoid/chlorophyll (Car/Chl) or chlorophyll/carotenoid (Chl/Car) ratio; and **(f)** Net CO\({}_{2}\) uptake. Central lines represent medians, boxes represent 50% of the data, whiskers represent minima and maxima and circles represent outliers. The numbers of correlations reported in the literature are shown in brackets.  
of the variation of emissions of volatile organic compounds (monoterpenes and isoprenes). PRI was significantly correlated with foliar nitrogen content and seed yield (\(R^{2}\) = 0.29, \(p\) < 0.0001 and \(R^{2}\) = 0.30, \(p\) < 0.0001, respectively) [115] for soybean leaves exposed to elevated ozone. PRI was also significantly correlated in six broadleaved species with water potential under water stress (0.60 < \(R^{2}\) < 0.95) [30] and with RWC (\(R^{2}\) = 0.32) under water and temperature stress [93].
# System Design and Experimental Study of a Four-Roll Bending Machine
## 3 Design  
### Roller System Layout  
This study compared various layout configurations for a four-roll bending machine and ultimately selected a symmetrical layout. This configuration strictly adheres to the principle of equal bending moments in four-point bending, employing an upper roller as the fixed primary drive roller, a vertically adjustable lower roller, and synchronized side rollers moving along symmetrical trajectories. This design ensures uniform force distribution and symmetrical stress patterns in the workpiece, establishing the foundation for high-precision roll bending. Integrated with a photoelectric encoder real-time feedback system, the symmetrical layout enables precise control over displacement consistency between the side rollers, effectively compensating for springback and further enhancing process accuracy. Additionally, its modular design features high transmission efficiency  
Figure 4: Process Flow: (**a**) Clamping and Centering; (**b**) Pre-bending; (**c**) Roll Bending; (**d**) Unloading.  
and low failure rates, particularly avoiding accelerated equipment wear caused by uneven loading on single-side rollers in asymmetric layouts when processing ultra-thick plates. While asymmetric layouts theoretically allow complex curvature control, practical implementation reveals significantly increased workpiece distortion risks due to uneven force distribution, along with challenges in maintaining displacement consistency and excessive demands on CNC system responsiveness with extended system reaction times. Therefore, after comprehensive evaluation of technical principles, process accuracy, and equipment reliability, this research confirms the adoption of the symmetrical layout scheme. Based on specified technical parameters, the machine base (as illustrated in Figure 5) was designed with a side roller center distance of 408 mm, vertical roller center distance of 172 mm, and side roller inclination angle of 25\({}^{\circ}\), incorporating dedicated mounting positions for all four rollers and guiding mechanisms.