esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Performance Analysis
### The Evaluation of Path-Planning and Charging Selection  
In general, traffic conditions are subject to change over time due to factors such as road construction, maintenance, or unexpected vehicle accidents, which may cause road congestion. Likewise, the service status of charging infrastructures can vary over time, a significant number of AEVs charging may result in long queuing times.  
We simplify the transportation network in Cologne, Germany, as illustrated in Figure 11(a). The transportation network comprises 29 road intersections, with 2 CSs located at nodes 17 and 28. Traffic and charging infrastructure data obtained by AEVs includes the number of vehicles per route, the service status of charging slots, the number of AEVs queuing, and the arrival rate of AEVs. Suppose there are charging demands at intersections 4 and 12, and their destinations are nodes 10 and 18, respectively.  
Figure 11: charged time and SoC level.  
Figure 12: Simplified transportation network and navigation path. (**a**) Illustration of a 29-intersection transportation system; (**b**) navigation path.  
The dynamic changes in the traffic conditions and service status of charging infrastructures require the navigation approach and charging selection to be adaptive. The real-time information is updated by joint push-pull mode. The optimal path for AEVs is rescheduled with real-time data according to their current locations and real-time system conditions. Due to heavy road congestion on the road segments \((23,9)\) and \((9,10)\). AEV 4 reschedules its path from 4-18-19-27-28-23-9-10 to 4-18-19-27-28-23-14-11-10. Since \(cs_{28}\) is now fully loaded, AEV 12 is navigated to charge at \(cs_{17}\). The schedule navigation results for AEV 4 and 12 proposed in this paper and the electric-vehicle routing problem with time windows and time of use prices (EVRPTW-TOU) in [29] are shown in Figure 12b. The results demonstrate that the proposed dynamic optimization can reschedule the optimal path and charging selection promptly in response to system parameter variations.
## 4 Experimental Results
### Experimental Settings  
The performance of the proposed technique is evaluated using settings in Table 3. In Settings 1-5, the selected joints are used as input, while in Settings 6-7, the RGB images are used instead for comparison. Settings 1-5 present the ablation study, where clinical data were gradually added to the initial training and testing sets composed of volunteer data only and fully replaced them all. In Setting 1, the training and testing sets include only the volunteers, and the leave-one-person-out strategy [35] is adopted. In Setting 2, the training set includes all the volunteers, while the testing set includes all the clinical data. In Setting 3, in addition to the volunteer samples, partial clinical data were used during training, and the remaining samples were used for testing. More clinical data were used for training in Setting 4. Similarly, in Setting 5, the volunteer samples were excluded for training. For Settings 3-5, equal and random selections were conducted among the three postures.  
Figure 4: Some samples of left-side lying at different levels of sideward orientation; (**a**) two pillows under the right back (without covering), (**b**) two pillows under the right back (with covering), (**c**) one pillow under the right back (without covering), (**d**) one pillow under the right back (with covering), (**e**) the head orientation is not consistent with the body posture, (**f**) the bedhead is inclined at 40 degrees.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Skeleton-Based Posture Classification**} \\ \hline  & **Training Data** & **Testing Data** \\ \hline Setting 1 & 19 volunteers & One volunteer (leave-one-person-out [35]) \\ \hline Setting 2 & 20 volunteer (3154 samples) & All clinical data (2581 samples): male (left, supine, right) = (431, 532, 562) & \\  & (left, supine, right) = (1053, 1054, 1047) & female (left, right) = (504, 552) \\ \hline Setting 3 & 1. 20 volunteer (3154 samples)(left, supine, right) = (1053, 1054, 1047) & \\ Setting 3 & 2. Partial clinical data (108 samples):male (left, supine, right) = (18, 36, 18)female (left, right) = (18, 18) & \\ \hline Setting 4 & 1. 20 volunteer (3154 samples)(left, supine, right) = (1053, 1054, 1047) & \\ Setting 4 & 2. Partial clinical data (1116 samples):male (left, supine, right) = (186, 372, 186) female (left, right) = (186, 186) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different Settings 1-7 for training and testing sets in our ablation experiments.  
To verify the effectiveness of the proposed skeleton-based technique, Settings 6 and 7 were considered, and the input for posture classification is the associated RGB images for 20 volunteers. Setting 6 is for volunteers covered without blankets, and Setting 7 is with blankets. Similar to Setting 1, the leave-one-person-out strategy is adopted. Meanwhile, Alexnet [36] was used as the classifier, a deep learning-based architecture consisting of five convolution and two dense layers.
# A Low Phase Noise Dual-Loop Dual-Output Frequency Synthesizer in SiGe BiCMOS
## 4 Conclusions  
In this article, the development of dual-loop dual-output frequency synthesizer for IEEE802.11 aj (45 GHz) is presented. By utilizing dual-loop topology, the Output1 frequency is fixed for easy design of IF filter and IF transceiver, and the Output2 frequency is variable for channel selection. In addition, the power hungry high-speed prescaler (or MMD) is replaced with a mixer in the second loop, to improve both in-band phase noise and DC power consumption. Experimental prototype has been designed and fabricated in 0.13 \(\upmu\)m SiGe BiCMOS process. The measured phase noise are \(-\)79.3 dBc/Hz@10 kHz and \(-\)129.1 dBc/Hz@10 MHz at 12.96 GHz for Output1 and \(-\)76.6 dBc/Hz@10 kHz and \(-\)117.2 dBc/Hz@10 MHz at 32.535 GHz for Output2. The low-reference spur of \(-\)69.2 dBc and low-power level spurious tones at the outputs are also observed during the measurement. With the properly designed super-heterodyne transceiver, the proposed dual-loop dual-output frequency synthesizer is well suited for the IEEE802.11 aj (45 GHz) standard.  
Conceptualization, Z.C.; formal analysis, Z.C. and D.H.; circuit design, Z.C.; validation, J.C. and P.Y.; review and editing, Z.C. and J.C. All authors have read and agreed to the published version of the manuscript.  
This research and APC were funded by National Natural Science Foundation of China, grant number 62171128.  
Not applicable.  
The authors declare no conflict of interest.
## 2 State-of-the-Art and Proposed Vector Modulator  
As shown in Figure 2a, vector modulators create phase variations by combining in-phase (\(vil\)) and quadrature (\(viQ\)) versions of the input signal with different weights. The phase of the output signal can be controlled by varying the in-phase and quadrature gains, \(gml\) and \(gmQ\). However, changing these gains also affects the output amplitude, so the gains must be carefully chosen to maintain a constant output amplitude. This architecture only allows for 90\({}^{\circ}\) phase changes. To achieve 360\({}^{\circ}\) phase shifts, two additional inverting amplifiers are typically added, as shown in Figure 2b. In each quadrant, only one \(l\) and one \(Q\) amplifier are active. To avoid using inverting and non-inverting amplifiers, the architecture with crossed outputs shown in Figure 2c is often used [3; 4; 5].  
The architectures in Figure 2a-c use single-ended signals. However, it is generally preferred to work with differential signals to improve the common-mode rejection ratio. Figure 3a shows a commonly used differential VSPS based on a double Gilbert-cell structure. First, a balun converts the single-ended input signal to differential mode and then a differential quadrature generator generates the in-phase and quadrature differential versions of the input signal. Two Gilbert cell-type variable gain amplifiers (VGAs) are then used to create the final vector by adding the current-domain signals at the output nodes with appropriate gains to give the desired phase. This architecture requires at least four digital to analog converters (DACs), one for each VGA that is active at any given time [6; 7]. However, for each quadrant, only two different gains need to be controlled, so by switching the DAC that controls each gain, only two DACs are needed. This technique is commonly used to reduce the number of DACs needed [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31].  
To reduce the number of VGAs and, therefore, the area and consumption, some authors have proposed the architecture shown in Figure 3b [32; 33; 34]. The particularity of this scheme is that it uses an adder element at the output instead of a subtractor. With this architecture, the four quadrants are covered, but the amplitude of the output signal is half compared to the architecture in Figure 3a. If an output subtractor is used instead of an adder, it would not be possible to cover all four quadrants. In this work, we propose a modification of this scheme, which consists of using a subtractor at the output and inserting the switches shown in Figure 3c. In this way, if we allow the four VGAs to be active at the same time, the four quadrants are covered and, in addition, the output amplitude would be the same as in Figure 3a.  
Figure 1: Phased array block diagram.
# Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times
## 4 Heuristic Algorithms
### Simulated Annealing Algorithm  
Simulated annealing (SA) is one of the most popular metaheuristic algorithms for combinatorial optimization problems including operations scheduling. In addition, SA showed good performance in a recent paper related to a two-machine flowshop with urgent jobs [1]. This study also adopts SA for the solution methodology. Generally, in flow shop scheduling problems, SA attempts to improve a current schedule (\(S\)) by making a small change in \(S\). To accomplish this, _insertion_, which moves a randomly selected job to a randomly selected position, is used to generate a neighborhood solution (\(S^{\prime}\)). If \(S^{\prime}\) is better than \(S\), \(S^{\prime}\) replaces \(S\). On the other hand, if \(S^{\prime}\) is worse than \(S\), \(S\) is replaced with \(S^{\prime}\) with a probability \(\exp(-\Delta/T)\). The initial temperature is obtained by Equation (19), and the temperature is gradually decreased by multiplying a cooling ratio \(\gamma\) if \(S\) cannot be improved for \(E\) iterations (\(E\) is called an _epoch length_.). The algorithm is terminated when the maximum computation time is elapsed. The whole procedure is given in Figure 3.
## 4 Materials and Methods
### Methodology
#### 4.2.3 Implementation Details  
The codebase was developed in Python 3.10, utilizing TensorFlow 2.15 and Keras 3.0 for neural network implementation. Data preprocessing and augmentation pipelines relied on OpenCV 4.8.0, Numpy 1.24.0, and TensorFlow's ImageDataGenerator 2.15 for dynamic resizing, normalization, and data augmentation (e.g., rotation, zoom, brightness adjustment). Geological image-specific preprocessing included adaptive input scaling based on seismic reflection thickness (64 \(\times\) 64 to 256 \(\times\) 256 pixels) and cross-modal feature fusion with domain knowledge embedding. Computations were accelerated on an NVIDIA GeForce RTX 3060 Laptop GPU (6 GB) 576.80, enabling efficient training of lightweight architectures with mixed-precision optimization.
# Hardware Mechanism for Energy Saving in WiFi Access Points
## 1 Introduction
### Main Contribution  
The main idea of our mechanism is to show that it is possible to save energy in any kind of WiFi AP without using extra software and using a standard battery and an extra electronic circuit that control the energy supply for the AP: the battery or any other standard voltage supply (electrical, PSE or solar panel). The battery and electronic control circuit are externally docked to any WiFi AP. Our mechanism continuously tries to shorten the charge period of the battery and to enlarge the discharge period of the battery. When the battery is being charged, the AP will use the standard electrical supply. After that period of time the battery will be discharged supplying electric energy for the AP. That cycle is continuously chained (change and discharge cycles).  
The main contributions of our paper are:  
* A formal model that shows that if the discharge period is larger than the charge period then our Mechanism will achieve energy saving. That parametric model is useful to adapt the implementation of our Mechanism to any WiFi AP taking into account its particular conditions.
* The implementation of our electronic control circuit for controlling the supply of a WiFi AP from a PSE supply or our external battery. The implementation of the control circuit is guided by our formal model. We present a general schematic of an easy to implement electronic circuit based on ordinary components. The need of that hardware is to control the charge and discharge periods in a simple way complementing other existing software or firmware techniques.
* The realization of a number of experimental tests for verifying the formal model and the implementation of our hardware mechanism. Those experimental tests reveal that it is possible to save energy with our mechanism. The projection of results for a number of hours shows that a considerable amount of energy could be saved.  
The structure of the rest of the paper is the following: Section 2 presents the main idea of our novel mechanism, a formal model showing when energy saving could be possible and the hardware implementation of the Mechanism. In Section 3 we present the experimental tests of our mechanism. Section 4 discusses how experimental results verify our formal model and project results for energy saving in a greater amount of time and WiFi APs. Finally, we sum up some conclusions.
## 2 Class-D PA for Polar-Mode Architectures
### Class-D PA Demonstrator Measurements  
The power measurement results obtained from this amplifier are shown in Figure 6, and good agreement was found between the simulated and measured results. Figure 6 shows the measured output power (\(P_{OUT}\)) and drain efficiency (\(\eta_{D}\)) of the amplifier versus frequency at different power supply voltages (\(V_{DD}\)) and driven by a \(P_{IN}\) = 30 dBm input sinusoidal signal, exhibiting a very flat response within its operating bandwidth. For the same \(P_{IN}\), the static \(V_{DD}\)-AM characteristic of the class-D PA is also shown, plotting the amplifier output voltage (\(V_{EQUIT}\)) as a function of the supply voltage (\(V_{DD}\)), where good linearity is expected when the class-D RF PA is used as an AM high-level modulator. It is important to mention that, in these measurements, the static power supply signal referred to as \(V_{DD}\) corresponds to the dynamic modulating signal referred to as \(v_{E}\) in the other sections.  
Figure 7 plots the spectral purity of the amplified signal when the amplifier is operating at its maximum output power and for the edges of its operating band.  
The measured harmonic rejection obtained by this amplifier, which uses no harmonic low-pass filter, is better than 35 dBc at 2\(f_{O}\) harmonics within its operating bandwidth, and the rejection of all other harmonic content is better than 27 dBc. These results show a good balance of the push-pull structure, and, although it should be noted that the amplifier would need a harmonic rejection filter to meet the requirements of almost any commu  
Figure 6: Power measurements of the class-D PA prototype (\(I_{DQ}\) = 2 A and \(P_{IN}\) = 1 W): (a) output envelope voltage and drain efficiency as a function of supply voltage and (b) output power flatness and drain efficiency performance over the operating frequency band.  
Figure 7: Measured harmonic rejection of the class-D PA at 100 W output power (\(V_{DD}\) = 28 V, \(I_{DQ}\) = 2 A, and \(P_{IN}\) = 1 W): (a) 118 MHz CW carrier and (b) 138 MHz CW carrier.  
nications service in this band, these measurements also serve to demonstrate that more than 99% of the RF power handled by the class-D RF PA is converted at its fundamental frequency component. Regarding the harmonics rejection required for a real transmitter, it can be said that, in view of the results obtained, it would be easy to meet harmonic rejection requirements ranging from 50 dBc to 60 dBc with a simple low-pass filter.
# Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties
## 2 Problem Formulation
### MPC Control Structure
#### 2.2.6 Establishing the Conclusive Nonlinear MPC Closed-Loop System via Lyapunov Methodology  
At this stage, the cost function defined in Equation (12) is minimized using the predicted state variables obtained from the Lyapunov function. The control signals \(u_{k}^{*}(t)\) are then determined through this minimization process. The resulting control signal corresponds to the Lyapunov-based MPC law. In order to determine the optimal control law for nonlinear predictors based on Lyapunov, the Hamiltonian matrix is defined as the following relation:  
\[H(x.u.\lambda)=\left(\overset{\sim}{x}^{T}Q_{c}\overset{\sim}{x}+{u_{k}}^{T}R _{c}\;u_{k}\right)+\lambda^{T}(Ax+Bu) \tag{34}\]  
where \(\lambda\) is the Lagrangian coefficient. The following equation is obtained by applying [PERSON]'s maximum principle to the Hamiltonian function.  
\[0=\frac{\partial H}{\partial u}=R_{c}\;u_{k}+B^{T}\lambda \tag{35}\]  
Equation (23) presents the [PERSON]'s MPC law as follows:  
\[u_{NLMPC}=-{R_{c}}^{-1}(t)\;B^{T}(t)\;\lambda \tag{36}\]  
Using the LQR theory, the Lagrangian coefficient is calculated as follows.  
\[\lambda=P(t) \tag{37}\]  
In which the P matrix is the same matrix obtained from solving the Riccati equation in Equation (17). Therefore:  
\[u_{k}^{*}(t)=u_{LNMPC}=-R^{-1}(t)\;B^{T}(t)\;P(t)\;x \tag{38}\]  
Then, using of this control law and the state variables of the DFIG system, the final closed loop system is determined according to the following equation.  
\[\overset{\cdot}{X}(t)=f\left(\overset{\sim}{X}(t).\;u_{k}^{*}(t)\right) \tag{39}\]
# Novel Designated Ownership Transfer with Grouping Proof
## 3 Security Analysis
### Denial of Proof  
Suppose a malicious attacker intercepts all previously generated grouping proof messages transmitted between all communicating parties and attempts to generate a bogus proof by using fake information, causing the verifier to reject and discard the proofs, resulting in denial of proof.  
However, in our protocol, each piece of proof computes a verification code \(V_{j,i}^{q}\). The reader can use the tag verification code \(TA^{q}\) provided by the verifier to check whether this piece of proof belongs to this delivery. To generate a valid proof, the hacker must obtain the secret \(Kt_{i}^{q}\), which is shared only between the tags and the verifier. The secret \(Kt_{i}^{q}\) are not transmitted during protocol execution, and, therefore, the attacker will have no way to acquire them, avoiding the occurrence of denial of proof.
## 3 Development of PT Signal Processing
### Differential Absolute Contrast (DAC)  
Differential absolute contrast (DAC) is a based on a one-dimensional solution of a Fourier diffusion equation (Dirac delta function) for a pulsed heatwave [51; 52]. By applying DAC, it is possible to obtain thermal response energy data at each point on the surface by modeling the time-dependent profile of the specimen surface temperature [53; 54]. Therefore, advanced as a Source Distribution Image (SDI) solution, it is possible to properly establish a sound reference area based on the assumption that all pixels exhibit a similar enumeration to the sound defect reference area in the initial thermogram.  
Assuming that the heat transfer of the specimen under inspection can be described with sufficient accuracy using the semi-finite body model, the equation for controlling the temperature contrast of the specimen surface with time after the energy impulse \(Q(\delta(t))\) is applied is as follows [55]:  
\[T_{semi-infinite\ body}(z=0,t)=\frac{Q}{e\sqrt{\pi t}} \tag{18}\]  
where \(z\) is the distance from the specimen object surface, \(t\) is time,\(e\) is effusivity, and \(Q\) is input energy. Considering the thermal image acquired at time \(t^{\prime}\) before the first defect is visually detected on the surface, the temperature \(\Delta T_{s[i,j]}(t^{\prime})\) of the sound area is as follows:  
\[\Delta T_{sound[i,j]}\left(t^{\prime}\right)=\Delta T_{[i,j]}\left(t^{\prime} \right)=\frac{Q_{[i,j]}}{e_{[i,j]}.\sqrt{\pi t^{\prime}}} \tag{19}\]  
Assuming that the energy incident on the specimen fluctuates, the value of local \(Q_{[i,j]}/e_{[i,j]}\) is as follows:  
\[\frac{Q_{[i,j]}}{e_{[i,j]}}=\sqrt{\pi t^{\prime}}.\Delta T_{[i,j]}\left(t^{ \prime}\right) \tag{20}\]  
Therefore, the \(DAC\) can be calculated as the \(Q_{[i,j]}/e_{[i,j]}\) between the thermal response solutions for semi-infinites describing Equation (21).  
\[DAC_{[i,j]}=\frac{Q_{[i,j]}}{e_{[i,j]}.\sqrt{\pi t}}=\sqrt{\frac{t^{\prime}}{ t}}.\Delta T_{[i,j]}\left(t^{\prime}\right) \tag{21}\]  
The \(DAC\) scales for every pixel in the thermal map processing and provides a result for the minimization of non-uniform heating of the surface.
## 2 Materials and Methods
### Methods
#### 2.3.4 Partial Correlation Analysis  
When NDVI was influenced by several factors simultaneously, we used partial correlation analysis to eliminate the influence of other factors and analyze the correlation between only one of the factors and NDVI. The formula for partial correlation analysis is as follows:  
\[r_{xy,z_{1}z_{2}...z_{g}}=\frac{r_{xy,z_{1}z_{2}...z_{g-1}}-r_{xzg _{2}z_{1}z_{2}...z_{g-1}}r_{yzg_{2}z_{1}z_{2}...z_{g-1}}}{\sqrt{\left(1-r_{xzg _{2},z_{1}z_{2}...z_{g-1}}^{2}\right)\left(1-r_{yzg_{2}z_{1}z_{2}...z_{g-1}}^{2 }\right)}} \tag{3}\]  
where \(r_{xy,z_{1}z_{2}...z_{g}}\) is the partial correlation coefficient of the x and y variables when they are controlled for \(z_{1}z_{2}\). \(z_{g}\).  
In this study, we eliminated the effects of SPI and STI on NDVI, which express the severity of drought and high temperatures in Inner Mongolia, respectively, and only the correlation between NDVI and SDHI for the severity of compound dry and hot events in Inner Mongolia was quantified.  
Based on the partial correlation coefficient between NDVI and SDHI, we used the t-test method to perform a significance test at a 0.05 level to determine whether the combined results were statistically significant.