esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Harmonic Suppression Method at DC Link of Series-Connected 18-Pulse Rectifier
## 6 Conclusions  
To suppress the harmonics of input voltage, this paper proposed a multipulse rectifier which combines multiple output transformer (nine outputs and above) and the hybrid harmonic suppression method. The proposed rectifier not only can improve the output voltage level, but also improve the input power quality. Through injecting two sets of six-times three-step voltages with a phase difference of 20 degree at DC link, the (18\(k\)\(\pm\) 1)th and (36\(k\)\(\pm\) 1)th harmonics of input voltages are significantly suppressed. When using harmonic reduction method, the experimental THD of the input voltages is reduced from 6.8 to 2.4%, and the experimental THD of the input currents is reduced from 5.8 to 2.7%, and the output voltage and output current remain constant. Under some experimental conditions, the capacity of the hybrid harmonic reduction is only about 1% of load power. The proposed converter is qualified for the interface between the AC generator and the DC bus of the aircraft electrical system.  
Conceptualization, Q.L.; methodology, Q.L. and X.Y.; software, F.M.; formal analysis, Q.L.; investigation, X.H.; data curation, G.W.; writing--original draft preparation, C.G.; writing--review and editing, Q.L.; visualization, Q.L. and X.Y.; supervision, F.M.; project administration, X.H.; funding acquisition, F.M. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China, 51777042.  
Not applicable.  
Data Availability Statement: Data available on request from the authors.  
The authors declare no conflict of interest.  
Figure 18: The input and output of the rectifier when the load changes. (**a**) Simulation result. (**b**) Experimental result.
# Overview of an Experimental Program for Development of Yield Surfaces Tracing Method
###### Abstract  
This paper develops an experimental technique to evaluate the initial yield surfaces of metallic materials, as well as to study their evolution during plastic flow. The experimental tracing of yield surfaces is necessary for deriving and calibrating more robust phenomenological models of directional distortional hardening. Such models can be used to characterize the behavior of structures experiencing complicated and demanding loading modes, such as multiaxial ratcheting. The experimental technique developed in this work uses thin-walled tubular specimens, along with a servo-hydraulic machine, under various modes of tension/compression and torque. Identification of the onset of plastic flow is based on a small proof equivalent plastic strain evaluated from the outputs of a contact biaxial extensometer firmly attached to a specimen surface. This allows for evaluation of both the initial yield surface, as well as theevolved yield surface after a plastic prestrain. Throughout a test, continuous and fully automatized evaluation of elastic moduli and proof plastic strain is assured through algorithms written in C# language. The current technique is shown to provide promising results to effectively capture the yield surfaces of conventional metallic materials.  
experimental mechanics; phenomenological plasticity theory; yield surface; directional distortional hardening +
Footnote †: journal: Applied Sciences
## 2 Study Area and Data
### Research Data  
Building extraction based on deep learning is a computer vision semantic segmentation task, that is, dividing real objects into buildings and non-buildings [32]. We obtained Google remote sensing images of some rural areas in the above research areas in 2020 with a resolution of 0.29 m from Google Earth and used the open-source LabelMe software version 5.2.1 based on PyQt for image annotation (buildings in red, non-buildings in black) to generate annotated images corresponding to the original images [33]. To adapt to the training process of deep learning, each village orthophoto was uniformly cropped into 256 \(\times\) 256 pixels to generate cropped images. Data augmentation methods can effectively improve the generalization capability of models and solve overfitting problems [34]. Therefore, this study adopts data augmentation methods to expand rural building sample data. The main methods include applying clockwise rotation of 90 degrees, 180 degrees, 270 degrees, horizontal flipping and vertical flipping to the cropped images obtained in the previous step to generate augmented images after data enhancement. The enhanced dataset contains a total of 13,428 images and annotations, of which 10,742 were randomly selected for model training and 2686 for testing. Figure 2 shows a set of original images, annotated images and augmented images after data enhancement.
## 2 Materials and Methods  
A block diagram of the proposed method is shown in Figure 1. It consists of five major stages: (i) the pre-processing stage, which includes denoising and R-peak detection; (ii) atrial activity analysis, which comprises three transformations of the atrial activity extracted from the ECG signal; (iii) heart rhythm analysis; (iv) the gathering of data for the feature vectors; and (v) the classification stage, which includes training (to create a classification model) and testing. The implemented feature estimation methods are all inherently finite or possess stopping criteria to avoid latency or runtime errors. With a data window width of 30 beats [8], the window is marked with a pre-trained classifier. On this basis, the beginning of an episode of atrial fibrillation can be detected with a latency of 30 heartbeats.  
The initial pre-processing step aims to reduce the wandering baseline in the ECG signal. Additionally, since raw ECG signals are frequently contaminated by various types of noise, such as muscle noise and 50/60 Hz power line interference, Savitzky-Golay filtering is used. Subsequently, R-peak detection is performed, for which different algorithms can be used [48]. The detected R-peaks are the basis for calculating the RR interval. RR\({}_{i}\) variations between heartbeats reflect both the vagal and sympathetic modulation of the heart sinus node and are commonly used to perform heart rate variability (HRV) analysis. Knowing RR\({}_{i}\), the instantaneous heart rate values HR\({}_{i}\) (bpm) are computed. The results of the first stage of the presented method are illustrated in Figure 2. As mentioned in ref. [8], 30 beats are considered to guarantee stable feature extraction, especially for dynamic complexity description factors [49]. The part of the ECG signal that contains these successive QRS complexes is analyzed by the variance of the MODWT, the energy of the corresponding components of the DQ-TQWT, and ensemble EMD transformations.
## 1 Introduction  
The power MOSFET (metal oxide semiconductor field effect transistor) has been widely used in the analog and digital circuits. It is one of the most vital research fields to reduce the on-state specific resistance (\(R_{\text{SP}}\)) for a certain breakdown voltage (BV). However, the \(R_{\text{SP}}\) in conventional MOSFET (MOSFET) is limited by the 1D Silicon limit where the \(R_{\text{SP}}\) and the BV are oppositely affected by the doping concentration. In order to improve the tradeoff between \(R_{\text{SP}}\) and BV, the two-dimensional charge coupling method has been proposed [1]. There are two kinds of power MOSFET structures, one is the power Superjunction (SJ) MOSFET [2; 3; 4]. The SJ MOSFET can decrease the \(R_{\text{SP}}\), however,it is complicated to obtain high quality p-pillar and n-pillar in the production process. The second approach, called the resurf stepped oxide (RSO) U-shape MOSFET (UMOSFET), which use the electrode embedded within oxide coated deep trenches as a part of the drift region [5; 6; 7; 8; 9; 10], but the input and gate transfer capacitances of this structure is relatively large. To solve this problem, the split-gate RSO (SGRSO) UMOSFET [11; 12; 13; 14; 15; 16; 17; 18; 19; 20] has been proposed. However, some performances of SGRSO are less ideal than those of SJ MOS: when the BV is higher than 200 V, the phenomenon is more pronounced [21].  
In recent years, several devices have been reported to improve the tradeoff between \(R_{\rm SP}\) and BV on the same concept. For instance, the split-gate UMOSFET with the p-pillar [14], the double split-gate [16], linearly graded doping profile [22] or the slope side oxygen [23], etc. However, these new structures generally require extra specific process steps which means more costs and time. More seriously, some structures are difficult to achieve by controlling the process.  
The split-gate resurf stepped oxide with high doped epitaxial layer (HDSGRSO) UMOSFET has been proposed. The epitaxial layer of HDGRSO UMOSFET has been divided into three parts: the upper epitaxial layer, the lower epitaxial layer and the middle epitaxial layer with higher doping concentration. The Technology Computer Aided Design (TCAD) simulation results show that the Reduced SURFade Field (RESURF) active has been enhanced due to the high doped epitaxial layer, which can modulate the electric field distribution and reduce the internal high electric field. Therefore, the HDGRSO UMOSFET has a higher BV, a lower \(R_{\rm SP}\) and a better FOM. Furthermore, there is no extra special process required in this advanced fabrication procedure, which is relatively cost-effective and achievable.
# A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory
## 1 Introduction
Furthermore, the inherently meshless nature of PINNs eliminates the tedious process of mesh generation, effectively transforming the complexity of the physical problem into a focused network optimization task. The efficacy of this approach is demonstrated in relevant applications, particularly for PMSM modeling. [PERSON] et al. [22] successfully applied PINNs for parameter identification in PMSMs. Their work constructed a high-fidelity motor model capable of capturing critical nonlinear effects like magnetic saturation and cross-coupling. This was achieved by utilizing polynomial fitting to characterize dq-axis flux-current relationships while embedding the fundamental motor voltage equations into the PINN's loss function. This physics-constrained framework enabled robust parameter optimization, subsequently validated through both simulation and experiment. The principle of embedding physical knowledge into learning frameworks is also evident in other power electronics domains, as seen in [PERSON] et al. 's [23] work on optimizing DAB converters using a physics-informed deep transfer reinforcement learning method. However, as demonstrated by [PERSON], [PERSON] et al. [24], soft constraints indirectly impose boundary conditions or physical laws through penalty terms in the loss function. They cannot guarantee strict satisfaction of boundary conditions, their weighting relies on subjective tuning (prone to imbalance between competing constraints during optimization), are susceptible to local minima in high-dimensional or nonlinear problems, lack strict theoretical convergence guarantees, and exhibit insufficient robustness to noisy data and complex geometric scenarios. This paper is organized as follows: Section 2 derives the mathematical model of a PMSM in both ABC and dq0 reference frames based on differential inductance and conducts angular frequency-domain analysis. Section 3 presents the methodology for constructing physical constraints specific to PMSMs and details the corresponding PINN architecture. Section 4 establishes a PINN surrogate model using a finite element model of a 4-pole 48 slot interior PMSM, validating the accuracy of its torque calculations through comparative analysis with alternative approaches.
## 3 Establish an EFLP Model and Solution Method
### Model Building  
Considering the different categories \(t\) of DG and the different demands of emergency materials required for emergency rescue, an EFLP model with standby coverage under an uncertain RDGT scenario is proposed.  
Objective function:  
\[D(X)=\sum_{i=1}^{n}\sum_{j=1}^{m}\beta_{i}X_{i,j}d(i,j) \tag{1}\]Convert to:  
\[\sum_{i=1}^{n}\sum_{j=1}^{m}\beta_{i}X_{i,j}d(i,j)\leq Y \tag{2}\]  
The objective function indicates the shortest distance from the emergency facility site \(j\) to the emergency demand point \(i\) to provide emergency rescue services after an outbreak involving different hazardous materials, i.e., the shortest emergency rescue time.  
The constraint on the emergency facility site \(j\) of the service area:  
\[X_{i,j}d(i,j)\leq\min\{d_{i},d_{j}\}, \tag{3}\]  
The facility point provides its own range of services, and the demand point has its own range of needs; only when both parties are satisfied can a service relationship be established.  
Therefore, the supply relationship constraint is as follows:  
\[\sum_{i=1}^{n}\beta_{i}X_{i,j}\leq C_{j} \tag{4}\]  
The emergency service capacity \(C_{j}\) provided by the RDGT emergency facility site \(j\) to meet the demand \(\beta_{i}\) for various types \(t\) of DG in the event of an accident, that is, the demand cannot exceed the emergency facility point capacity after the occurrence of an emergency event at the demand point.  
Relationship between demand \(\beta_{i}\) and quantity of hazardous materials:  
\[\beta_{i}=\sum_{n=1}^{T}a_{i,t}y_{i,j} \tag{5}\]  
Constraints on the number of times the emergency demand point \(i\) is covered:  
\[1\leq\sum_{j=1}^{m}X_{i,j}\leq k \tag{6}\]  
Constraints on the number of facility site \(j\) services:  
\[\sum_{i=1}^{n}X_{i,j}\leq h \tag{7}\]  
A facility site \(j\) serves up to \(h\) demand points. Once the service relationship is established, the capacity of the facility point must be able to serve all the demand points for which the service relationship is established at the same time.  
Constraints on decision variables:  
\[X_{i,j}\in\{0,1\} \tag{8}\]  
The decision variables are 0-1 variables.
## 2 Problem Description
### Problem of On-Board Real-Time Inference  
Reinforcement learning-based routing algorithms are effective for dynamic LEO. However, computing resource issues in the on-board environment and the heavy computation time of dueling DQNs are critical to the real-time performance of the algorithm. Reinforcement learning-based routing algorithms must provide optimal routing paths in response to the dynamic situation of the satellite network, which is updated periodically. The goal of this paper is to provide optimal routing paths while responding quickly to various factors such as bandwidth, latency, error rate, and satellite orbits.  
However, the CNN-based training and inference process of reinforcement learning-based routing algorithms consumes an excessive amount of time, reducing real-time performance, and ultimately fails to find the optimal routing path. To solve this real-time problem, this paper proposes a heterogeneous device co-design-based inference parallelization method. In this paper, we perform heterogeneous device co-design using PYNO-Z2, which has both a processing system (PS) that acts as a CPU and a programmable logic (PL) that has the advantages of a hardware FPGA. The dueling DQN-based LEO routing algorithm is basically performed on the PS, and the time-consuming inference process is accelerated in parallel on the PL. The proposed method can reduce the execution time of reinforcement learning and achieve real-time performance.
## 6 Discussion  
This study presented a pose estimation system designed for monitoring construction safety. We conducted a real-world evaluation of our system in a road construction setting, where trucks and loaders cause NLOS conditions and obstructions. We compared the performance of our proposed approach with that of two previous methods for pose estimation. The implementation and testing of our proposed solution was done using the DecaWave DW1000 radio chip on the RadinoL4 platform. The connection to the server and anchors was established using Raspberry Pi devices. Most of our evaluation scenarios were designed to highlight situations where ViPER struggled to perform sufficiently. We observed that the presence of heavy construction equipment leads to obstruction of the signals. This makes location/pose unavailable in ViPER due to generating incomplete inputs. This lack of availability is not acceptable in real-time monitoring systems because it could result in a significant number of missed alarms if the status of all entities is not continuously tracked. In ViPER+, we redesigned the input correction method to accept TDOA inputs with missing anchors to eliminate the incomplete input limitation in ViPER.  
ViPER+ has a limitation on the maximum number of tags that the system can track simultaneously. To prevent packet corruption, only one tag can broadcast a message at a time in our design. For this reason, the maximum number of tags that could be monitored in ViPER+ is limited to 40 tags at this time. One way to increase the number of tags is reconfiguring UWB radios to operate in different channels. DW1000 supports seven channels for configuration. Four of these channels can operate simultaneously without jeopardizing the communication on the other three channels. In addition, pulse repetition frequency (PRF) is another parameter in UWB radio configuration. Similar to channel, PRFs can also create isolated UWB links enabling concurrent communication. DW1000 supports PRF 16 and 64. With four different channels and two different PRFs, we can scale the maximum number of tags eight times (320 tags) for safety applications requiring more tags.  
The distance between the entities and the anchors is another limitation of our approach. The assumption in ViPER+ is that all the entities are in an area known as the tracking zone that is surrounded by anchors. When an entity is not within the tracking zone, there is no guarantee that the error rate and update rate requirement for that entity will be addressed by the system. Future studies on this subject will concentrate on the development and testing of an alerting system for the interaction between the server and the entities. In addition, the system needs to alert users about their situations and aid them in avoiding unsafe situations.
## 2 Geometry of Dimer Antenna and Modified Drude Model  
All dimensional parameters of the SCS dimer are depicted in Figure 1. The SCS dimer structure was mainly comprised of two SCS nanoparticles, separated by distance \"\(d\)\" and embedded in free space. Each individual particle had a passive silica core with a radius \(R_{1}\) of 37 nm and an Ag shell with a thickness of 12 nm around the silica core. The total diameter of each particle was 98 nm. The spatial directions \(X\), \(Y\) and \(Z\) were specified by a local coordinate system (\(u\), \(v\), \(w\)), as shown in Figure 1. An electromagnetic planewave propagated along the \(Z\)-axis excited the SCS dimer antenna, while its electric and magnetic components were perpendicular to the direction of wave propagation. The center of the left-handed particle lay at \(X=Y=Z=0\), while the other, similar particle on the right hand was always portable by the distance \(g\) along the \(X\)-axis.  
Plasmonic resonance and near-field enhancement properties of the core-shell nanoparticle system can be tuned greatly by varying the ratio \(R_{1}/R_{2}\)[21]. To investigate the optical response of a nano-sized dimer with a metal shell and a dielectric core, size effects need to be taken into account in the Drude model because the optical response of nano-dimensional materials is intensely different from their bulk counterpart. The Drude model modified with size effects is presented in [21]. The dielectric response of any material can be written in terms of the Drude response and the interband transition, as follows:  
\[\varepsilon(R,\omega)=\varepsilon_{Drude}(R,\omega)+\chi_{Interband}(\omega) \tag{1}\]  
where \(R\) is the thickness of the surrounding shell. The Drude model can be expressed as  
\[\varepsilon_{Drude}(R,\omega)1=\frac{\omega_{p}^{2}}{\Gamma(R)^{2}+\omega^{2} }+j\frac{\Gamma(R)^{2}\omega_{p}^{2}}{\omega(\Gamma(R)^{2}+\omega^{2})}. \tag{2}\]  
This modified Drude model incorporates size effects in the form of size-dependent damping frequency. This is due to the fact that, in nano-dimensional materials (\(\leq\)100 nm) or materials smaller than the mean free path of electrons, the damping frequency \(\Gamma\)' has an inverse relation to the size of the material (\(R\)) as given in Equation (3).  
\[\Gamma(R)=\Gamma_{\infty}+\frac{AV_{F}}{R}. \tag{3}\]  
\(A\) is constant at \(\sim\)1, and \(V_{F}\) is the Fermi velocity. The values of all Drude parameters and the Fermi velocity were used to compute the dielectric dispersion in a 12 nm silver shell, where \(m^{*}/m=0.96\), \(N=5.85\times 10^{28}\) m\({}^{-3}\), \(V_{F}=1.39\times 10^{6}\) m/s, and \(\omega_{p}=1.39269\times 10^{13}\) s\({}^{-1}\), as given in [21]. It should be noted that this model incorporates size effects but excludes quantum tunneling or charge transfer between SCS nanoparticles at small gaps. The inclusion of size effects transforms the Drude model from a classical to a semi-classical model.
## 2 Principles and Methods  
### Photoacoustic Image Reconstruction  
After irradiating biological tissues with a pulse laser, laser-induced pressure wavefields are formed, where the conversion of light energy, heat energy and mechanical energy occurs, producing PA signals. When PA signal propagates through a homogeneous medium, the acoustic pressure distribution can be expressed as [19],  
\[p_{d}\left(\mathbf{r}_{0},t\right)=\frac{\partial}{\partial t}\left[\frac{t}{4 \pi}\iint_{|\mathbf{r}_{0}-\mathbf{r}|=ct}p_{0}(\mathbf{r})d\Omega\right] \tag{1}\]  
where \(c\) is the SOS and \(d\Omega\) is the solid-angle of the transducer at position \(\mathbf{r}_{0}\). \(p_{0}(\mathbf{r})\) represents the initial acoustic pressure, reflecting the spatially variant absorbed optical energy density distribution with the object.  
For simplicity, we assume a linear operator \(A\) to represent the forward model. Then, Equation (1) can be rewritten as  
\[p=Au+n \tag{2}\]  
where \(n\) denotes error or noise. The key point for reconstruction is to recover PA image \(u\) from the measurement \(p\).  
Direct PA reconstruction algorithms based on an a priori model, such as the filtered back-projection algorithm, do not perform well in the case of data incompleteness and lossy medium. Being different from the analytical method, the iterative algorithm generates a high-quality PA image with imperfect data or high noise involved, where a loss function \(\Phi(u)\) is minimized to compute the adjoint solution of Equation (2) [65],\[\operatorname*{argmin}_{u}\Phi(u)=\operatorname*{argmin}_{u}\left\{\frac{1}{2}\|p-Au \|_{2}^{2}+\lambda R(u)\right\} \tag{3}\]  
where \(\frac{1}{2}\|p-Au\|_{2}^{2}\) represents the data fit, \(\lambda\) is a regularization parameter, \(R(u)\) denotes the regularizing term that adds constraints to the loss function to search maximum optimum close to the inverse solution. Unfortunately, choosing a suitable regularization term \(R(u)\) requires prior knowledge about the structures in the image, making it not applicable to generate typical PA images of more involved structure like bone, accompanied by a formidable computational burden.