esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### PMSM Mathematical Model  
PMSM plays an increasingly important role in the speed regulation system because of its significant advantages in structure, efficiency, and so on compared with other types of motors. In existing literatures, all the PMSM mathematical model are based on the conventional assumption [15]. In order to improve the effectiveness of the speed controller, this paper considers the mathematical model in the \(d\)-\(q\) synchronous rotating reference. The stator's voltage equation is shown as follows.  
\[\left\{\begin{array}{l}u_{d}=Ri_{d}+\frac{d}{dt}\psi_{d}-\omega_{e}\psi_{q} \\ u_{q}=Ri_{q}+\frac{d}{dt}\psi_{q}+\omega_{e}\psi_{d}\end{array}\right. \tag{1}\]  
in which, subscript \(d\) and \(q\) refer to the \(d\)-component and \(q\)-component. \(u\), \(i\), and \(\psi\) represent the stator's voltage, stator's current, and stator's flux, respectively. Then \(\omega_{e}\) is the electrical angular velocity and \(R\) is the stator resistor.  
For surface-mounted PMSM, it can be obtained that stator inductor \(L_{s}=L_{d}=L_{q}\). The following mathematical model with the assumption that \(B=0\) is as following:  
\[\left\{\begin{array}{l}\frac{d\dot{q}_{d}}{dt}=\frac{1}{L_{s}}\!\left(\!-Ri_ {d}+p_{n}L_{s}\omega_{m}i_{q}+u_{d}\right)\\ \frac{d\dot{q}_{m}}{dt}=\frac{1}{L_{s}}\!\left(\!-Ri_{q}-p_{n}L_{s}\omega_{m}i _{d}-p_{n}\psi_{f}\omega_{m}+u_{q}\right)\\ \frac{d\omega_{m}}{dt}=\frac{1}{J}\!\left(\!-T_{L}+\frac{3p_{n}\psi_{f}}{2}i_{ q}\right)\end{array}\right. \tag{2}\]  
in which, \(\psi_{f}\) is the flux of magnet, \(p_{n}\) is the pole pairs of PMSM, \(\omega_{m}\) is the mechanical angular velocity, \(J\) is the inertia, \(B\) is the friction coefficient, and \(T_{L}\) is the load torque.  
Based on the characters of PMSM, one can get that \(\omega_{m}\), \(i_{d}\), and \(i_{q}\) are variables and bounded. Thus, the nonlinear term in (2) is bounded and the varying range is determined by the value of mentioned parameters above.  
As is known to all scholars, the controller can achieve better performance according to using rotor field orientation control (\(i_{d}=0\)) strategy. Then, the mathematical model in Equation (2) can be written as follows.  
\[\left\{\begin{array}{l}\frac{di_{q}}{dt}=\frac{1}{L_{s}}\!\left(\!-Ri_{q}-p_ {n}\psi_{f}\omega_{m}+u_{q}\right)\\ \frac{d\omega_{m}}{dt}=\frac{1}{J}\!\left(\!-T_{L}+\frac{3p_{n}\psi_{f}}{2}i_{ q}\right)\end{array}\right. \tag{3}\]  
Therefore, the complex nonlinear mathematical model function is simplified and linearized.
###### Abstract  
This study examined the clinical feasibility of a prototype device (development name: Ghost) for facilitating range of motion (RoM) recovery in the acute phase in patients with distal radius fractures (DRF). The Ghost device involves the administration of a combination of vibratory and visual stimuli. We divided the patients into the Ghost (\(n=10\)) and control group (\(n=4\); tendon vibration only) groups. The experimental interventions were administered between the day after surgery and day 7 postoperatively. Traditional hand therapy was provided to both groups once daily from day 7 until day 84 postoperatively and once a week from day 84 until the end of the intervention period. Because vibratory stimulation makes the patient focus on wrist flexion, the primary outcome was the arc of wrist flexion-extension on the injured side, which was measured on days 7, 14, 28, 42, 56, 70, and 84. Analysis of covariance was applied using a bootstrap method to evaluate changes over time and compare them between the groups. Analyses was performed after stratification by age and body mass index. Both interventions improved RoM over time in patients with DRF. Results showed that Ghost has greater efficacy for improving wrist RoM in DRF patients than vibration alone. Treatment with Ghost can result in good RoM improvement during the acute phase of DRF in young patients and those with and normal or low body mass index. Further study is needed to verify our findings and assess the extent of RoM recovery.  
Keywords:distal radius fractures; vibration; visualization; rehabilitation; range of motion +
Footnote †: journal:
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 5 Voting Experience
### Voting Phase  
The casting of votes takes place during the voting period. After passing the authentication stage, which is illustrated in Figure 5, the voter is forwarded to the voting webpage. The voter is presented with choices and is prompted to make a selection(s). After the voter has confirmed their selection(s), they may proceed to submit their ballot. A ballot, in encrypted form, which contains the voter's choices, is sent to the authentication server within the EA for checking and signing. The server checks whether the ballot is accurately formatted. An example inspection which is carried out examines if the ballot has the accurate election identifier tag. When the ballot is correctly formed, the authentication server responds to the front-end system with an acknowledgement. This acknowledgement contains EA's signature on the ballot. Ultimately, the signed encrypted ballot is paired with the encrypted tracking number and stored on the DLT within the front-end system.
# Electrically Driven Micro- and Nano-Scale Semiconductor Light Sources
## 9 Electrically Pumped Plasmonic Light Sources and Optical Nanocircuits  
The integration of multiple optical elements is fundamentally size-limited because the size of each optical component is diffraction-limited. In addition, the light interaction between the optical elements requires a minimum coupling length that is sometimes a few-fold larger than the wavelength of light. The heterogeneous integration, therefore, is suitable for optical circuits whose sizes are larger than a few mm\({}^{2}\) or cm\({}^{2}\), and is limited for highly dense nanoscale circuits. Indeed, an ultracompact optical nanocircuit requires subwavelength-scale photonic elements and their integration at nanoscale precision. Plasmonic integrated circuits can be a promising solution for this purpose. The key plasmonic elements such as light sources, waveguides, and couplers can be fabricated at deep subwavelength scale. The plasmonic interaction between these elements occurs in a truly nanoscale regime, allowing high density of integration in an extremely small physical volume.  
There has been considerable research on plasmonic integrated circuits [39]. However, electrical empowering is still a critical issue to be addressed. Recently, [PERSON] et al. have succeeded in electrically empowering optical nanocircuits, comprising a nanoscale GaAs ridge LED that was coupled to subwavelength-sized metal-slot waveguides [51]. Figure 13 shows a schematic of the plasmonic circuit. The SEM images exhibit cross-sectional views of a 130 nm-tall and 60 nm-wide GaAs ride LED, and a 150 nm-tall and 80 nm-wide slot waveguides, revealing truly subwavelength-scale individual circuit components. The authors demonstrated the electrical generation of SPPs, coupling them to a propagating plasmonic waveguide mode, performing 90\({}^{\circ}\) light splitting in a T-splitter, and coupling to another slot waveguide in a plasmonic directional coupler. All these circuits were fabricated on the order of a few \(\upmu\)m\({}^{2}\), which is difficult to achieve in conventional optical circuits.  
Figure 12: Heterogeneous integration of electrically driven nanobeam lasers with SOI waveguides. (**a**) Schematic illustration of an InP-based electrically driven PhC nanobeam laser heterogeneously bonded on top of an SOI waveguide. Cross-sectional schematic shows device configuration, material composition, and doping profile (inset). (**b**) SEM image of the fabricated nanobeam laser diode after \(n\)- and \(p\)-type contact metallization and before SiO\({}_{2}\) passivation [50]. Figure reproduced with permission from Springer Nature Limited.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods
### Convolutional Neural Networks  
CNNs are heavily applied whenever complex information extraction from images is required. In a rough approximation, they are stacks of convolution and maxpooling layers. They are capable of learning high- and low-level features of an image regardless of their position and orientation. A great advantage of a CNN is that the weights to be learned are shared between the layers, minimizing the needed memory storage.  
The convolutional layer works in the following way: a convolution operation is performed by sliding a small window (typically \(3\times 3\) pixels) over height and width of the image and over its color channels. The window creates a patch feature map, which is multiplied with a learned weights matrix called convolution kernel. This operation could cause image size reduction, which is handled by applying padding, which means to add border pixels in order to make the output size the same as the input. The neurons contained in the network layers are activated through an activation function; ReLU (rectified linear unit), softmax and sigmoid are typically used.  
The pooling layer has the aim to downsample the image in order to optimize the learning of features. In particular, the max pooling layer takes a portion of the image (typically a \(2\times 2\) window) and substitutes it with the maximum pixel value.  
The performance of a CNN is represented by values of loss and accuracy, which are computed during training, validation and test processes. It is desirable to obtain similar accuracies in all these phases to avoid overfitting and generalize network performances against never seen data. There are different ways to overcome this issue. These include L2 weight regularization [24], batch normalization [25], data augmentation [26] and dropout [27].
# Joint Communication-Motion Planning in Networked Robotic Systems
## 2 Conventional Relay-Assisted Robot System  
In order to present a comprehensive description of the algorithm design for both this section and Section 3, we give a cognitive-relay-assisted robot system scheme based on the OODA loop in Figure 2. Based on the basic theory of the OODA loop, the interaction between the robot and external environment can be divided into four parts: observe, orient, decide and act. Specific to the algorithm solution in this section: the first part in this scheme corresponds to resource sensing, that is, sensing the external wireless communication environment, which is based on the Rayleigh fading channel. We also consider the impact of path loss and other factors on communication performance. The second part corresponds to performance evaluation, which is divided into communication energy consumption evaluation and motion energy consumption evaluation. The third part corresponds to the JCMP, which jointly optimizes the position of the relay robot, transmit powers of the relay robot and transmit powers of the sensing robot in order to minimize the overall energy consumption of the system. The fourth part corresponds to task execution, which is divided into task execution in communication domain (i.e., power optimization) and task execution in geographic domain (i.e., location optimization). After the actions of these four parts are executed, they will have an effect on the environment and the next round of OODA loop starts.  
In this section, we propose a relay-assisted robot surveillance prototype, and illustrate the benefit of JCMP. We consider a single-sensing-robot surveillance system depicted in Figure 1. We may still follow the OODA procedure in [14] to design the cognitive-relay-assisted robot surveillance example, with the emphasis on the JCMP procedure for minimizing the energy consumption.  
The sensing robot is responsible for collecting monitored data with fixed sampling rate \(\eta\) (bit/s) along a predefined route. After operating data collection for a period of \(T\), the robots stop and send the monitored data to the base station through the relay robot. In this example, time division multiple access (TDMA) and decode-and-forward (DaF) relaying are adopted, where a transmission period is divided into two time slots (TSs). In the first TS, the sensing robot transmits the monitored data to the relay robot, while in the second TS, the relay decodes the data, re-encodes and forwards to the base station using same modulation and coding. We define a step, which comprises the data collection period \(T\) plus the transmission period of 2 TSs. The surveillance tasks may endure multiple steps, and our objective is to minimize the total energy consumption which may contain two main parts: the communication and motion energy consumption.  
Let us follow the OODA procedure in [14] to design the relay-assisted robot surveillance example, with an emphasis on the JCMP module.
# Changed Detection Based on Patch Robust Principal Component Analysis
## 2 Preprocessing
### Linear Interpolation  
For statistical background modeling, to obtain a stable background model, sufficient sampling frames are required [19]. If the sequence itself contains only a few frames of images, it is usually difficult to generate a stable background model, as the intensity abruptness in the sequence will significantly distract the model. In order to obtain more background frames to produce a stable background model, linear interpolation is used in two different intensity levels of the image pair to expand the short sequence into a long sequence with slowly changing intensity [11].  
For two images, \(I_{1}\) and \(I_{2}\), supposing the reference image \(I_{1}\) is taken in the early stage, which can be regarded as the background image, then the current image \(I_{2}\) is used to compare with the background to discriminate whether there was a change. Since \(I_{1}\) is turned into \(\tilde{I}_{1}\) and \(\tilde{I}_{21}\) after taking the illumination correction and normalization [10], the low-rank background model is based on \(I_{1}\), so linear interpolation is performed between \(\tilde{I}_{1}\) and \(\hat{I}_{12}\). The formula is presented in (3):  
\[\tilde{I}_{1k}=\frac{k}{p_{1}}\tilde{I}_{12}+(1-\frac{k}{p_{1}})\tilde{I}_{1},\quad k=1,2,\cdots,p_{1}, \tag{3}\]  
where \(\tilde{I}_{1k}\) is the \(k\)-th interpolated frame \(k=1,2,\cdots,p_{1}\), and \(p_{1}\) is the number of interpolated frames. Generally, the larger the \(p_{1}\), the smaller the intensity abruptness between the two successive frames is, and the more stable the background model is.  
Apart from the linear interpolation between the reference frames, it is also required for the current frame. For the current frame \(I_{2}\), the linear interpolation between \(\tilde{I}_{2}\) and \(\hat{I}_{21}\) is shown in the formula (4):  
\[\tilde{I}_{2k}=\frac{k}{p_{2}}\tilde{I}_{21}+(1-\frac{k}{p_{2}})\tilde{I}_{2},\quad k=1,2,\cdots,p_{2} \tag{4}\]  
where \(p_{2}\) is the number of interpolated frames of \(I_{2}\).  
After the linear interpolation of the reference image and the current image at their different brightness levels, the influence of illumination variations on the change regions is further reduced. Then, it can be easily filtered out as a low-frequency component by the detection algorithm [10]. Linear interpolation makes the comparison of the two image pairs with different intensity levels extend to the comparison of multiple different intensity levels, which enabled the abruptness between different intensity levels to decompose to the small global illumination variations of two successive frames and be filtered as the small disturbed errors of the background model by low-rank decomposition.
# Four-Channel Polarimetric-Spectral Intensity Modulation Imager
## 3 Effectiveness Validation for the FCPS Scheme
### System Matrix Calibration  
Calibrating the system matrix \(P\) is the key to realizing data demodulation in the FCPS scheme. By the system parameter of the FCPS scheme, the system matrix \(P\) can be obtained, but there are installation angle errors which will cause the system matrix \(P\) to vary. Therefore, it is necessary to calibrate the demodulation matrix to obtain high-precision demodulation data.  
In the calibration process, it is assumed that the angle of the phase retarder, the thickness of the phase retarder, and the angle of the line analyzer are not known. Therefore, when the polarized light passes through the phase retarder and line polarizer, the modulated beams can be expressed as follows:  
\[\begin{array}{rl}I(\lambda)&=\frac{1}{2}\xi_{0}(\lambda)\\ &+\left\{\begin{array}{ll}\left[\cos(2\beta)[\cos^{2}(2\alpha_{2})+\cos( \varphi_{2}(\lambda))\sin^{2}(2\alpha_{2})]+\sin(2\beta)[(1-\cos(\varphi_{2}( \lambda))\sin(2\alpha_{2})\cos(2\alpha_{2})]\  
where \(\beta\) is the direction of the linear polarizer, \(\alpha_{1}\) and \(\alpha_{2}\) are the fast axis direction of the phase retarders, \(\varphi_{1}\) and \(\varphi_{2}\) are the phase delay generated by the phase retarders. Equation (15) can be rewritten as  
\[I=E(\lambda)S_{0}(\lambda)+B(\lambda)S_{1}(\lambda)+C(\lambda)S_{2}(\lambda)+D( \lambda)S_{3}(\lambda) \tag{16}\]  
where  
\[E(\lambda)=\frac{1}{2} \tag{17}\]  
\[\begin{array  
wavelength is composed of the \(B\), \(C\), and \(D\) of different linear polaroid angles and the same wavelength, as shown in Equation (30):  
\[A(\lambda)=\begin{bmatrix}\frac{1}{2}&B_{1}(\lambda)&C_{1}(\lambda)&D_{1}( \lambda)\\ \frac{1}{2}&B_{2}(\lambda)&C_{2}(\lambda)&D_{2}(\lambda)\\ \frac{2}{2}&B_{3}(\lambda)&C_{3}(\lambda)&D_{3}(\lambda)\\ \frac{1}{2}&B_{4}(\lambda)&C_{4}(\lambda)&D_{4}(\lambda)\end{bmatrix} \tag{30}\]  
where the subscripts of \(B\), \(C\), and \(D\) represent different channels.
## 2 Projection Pursuit Regression Model  
Projection pursuit regression (PPR) is a data analysis technique designed for multivariate data, aimed at uncovering the data's structural features by reducing the dimensionality of the data. In this regression method, the data undergo preprocessing initially, followed by determination of the optimal projection direction, which is achieved by maximizing certain statistics of the projection data. The regression model is then constructed on this projection direction to facilitate the transformation of high-dimensional data into low-dimensional data. This approach eliminates the need for artificial assumptions, allowing for a more objective representation of the essential characteristics of the data. It is particularly adept at elucidating the dependency relationships between independent and dependent variables in the multi-parameter, nonlinear, and linear systems characteristic of high-dimensional data [13; 14].  
The PPR model is represented by Equation (1) [13; 14; 15; 16; 17; 18; 19]:  
\[F(x)=E(y_{i}|x_{1},x_{2},\ldots,x_{p})=\overline{y_{i}}+\sum_{j=1}^{M}\beta_{ j}j(\sum_{k=1}^{p}\alpha_{jk}x_{k}) \tag{1}\]  
where \(x_{k}\) denotes the \(k\)th independent variable, \(y_{i}\) represents the \(i\)th dependent variable, \(F(x)\) is the regression function of \(y_{i}\) based on \(P\) independent variables, \(\overline{y_{i}}\) is the mean of the \(n\)th observation of the \(i\)th dependent variable, \(l_{j}\) represents the \(j\)th ridge function that satisfies \(El_{j}=0\) and \(El_{i}^{2}=1\), \(M\) expresses the upper limit number of the explored ridge functions, \(\beta_{j}\) denotes the weight coefficient of the \(j\)th ridge function, and \(\alpha_{jk}\) is the \(k\)th component in the \(j\)th projection direction that satisfies the equation \(\sum_{j=1}^{p}\alpha_{jk}^{2}=1\).  
The constraint conditions are represented by Equation (2) and must satisfy the minimization criterion [13; 14; 15; 16; 17; 18; 19].  
\[L_{2}=\sum_{i=1}^{Q}W_{i}E\left[(y_{i}-\overline{y_{i}})-\sum_{j=1}^{MII}\beta _{j}l_{j}(\sum_{K=1}^{p}\alpha_{jk}x_{k})\right]^{2}=min \tag{2}\]  
where \(MU\) denotes the number of optimal ridge functions, \(W_{i}\) is the dependent variable weight coefficient, and the remaining parameters are the same as those in Equation (1).  
In the regression model, the coefficient of each independent variable can be interpreted as the contribution weight of the variable in the projection direction. In the aforementioned formula, \(\beta_{j}\) represents the relative contribution weight required, which enables us to discern the relative significance of each independent variable to the change in the dependent variable. These weights can assist us in identifying the crucial factors within the individual independent variables and facilitate further data analysis and decision-making.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 2 Related Works
[PERSON] et al. [40] proposed an IDR Net algorithm for extracting ice thickness on transmission lines, designed a multi-scale icing feature extraction module, and improved the traditional upsampling module. However, its detection performance was poor, and the model parameters were large. [PERSON] et al. [41] utilized multi-stage image processing techniques and fused multi-scale adaptive Retinex to detect the ice thickness on transmission lines, but it was greatly affected by background and had poor scalability. Table 1 shows the advantages and disadvantages of traditional methods and deep learning methods. At present, most methods for detecting the thickness of ice on ice-covered transmission lines still use traditional edge detection algorithms, which can only detect ice in scenes with relatively simple backgrounds and have poor detection performance. At present, deep learning methods for detecting ice cover thickness are promising for future development. However, current algorithms have not been specifically designed for the characteristics of ice-covered transmission lines, nor have they considered details such as ice-covered edges. At present, there is relatively little research on using deep learning edge detection algorithms for ice cover detection. Although various edge detection algorithms have been applied to the detection of ice-covered transmission lines, there is currently a lack of deep learning edge detection algorithms specifically designed for the characteristics of ice-covered transmission lines. Ice cover has unique and complex characteristics, such as irregular shapes of ice edges, blurred boundaries between the ice layer and the background, and unstable features caused by changes in lighting conditions and different weather conditions. The existing algorithms have obvious limitations in processing these complex features, making it difficult to accurately distinguish between ice layers and backgrounds, and thus they are unable to accurately detect ice thickness. Taking traditional algorithms as an example, although the Canny operator can recognize simple edge information, its detection accuracy is less than 40% when facing ice-covered edges in complex backgrounds; although the ratio operator has the ability to detect multi-scale and multi-directional edge information, it is extremely sensitive to noise. In terms of deep learning algorithms, the IBP algorithm model training process is complex, parameter optimization is difficult, and it is not precise enough when dealing with details of ice-covered edges; the Mask RCNN network can only identify the approximate range of ice thickness and cannot accurately determine a specific numerical value. All of these highlight the necessity of developing new models. Therefore, we designed a neural network model suitable for edge detection of ice-covered transmission lines using edge detection methods, considering two aspects: network structure and loss function.