esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation  
### Numerical Model  
Due to the limited field observation data, the global wind conditions and sand sedimentation changes cannot be reflected. Numerical simulation is used to supplement the relevant test data. The overall size of the calculation domain model is 40 m \(\times\) 140 m and a distance of 40 m between the flow field entrance and the embankment section is reserved to ensure the full development of sandy air current in the flow field. According to the scale of the dune measured by UAV aerial photography, the half width of the dune model is 15 m with 3 m height and the embankment is 1 m high, of which the top surface is 14 m wide and the slope ratio is 1:3, the bottom width of the subgrade is 17 m, and the width of the asphalt pavement is 5 m. The embankment cross section models of windward and leeward dunes were established respectively, and their specific calculation size is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Observation** & **Area of Sand** & **Maximum** & **Average Sand** \\
**Time** & **Accumulation/m\({}^{2}\)** & **Thickness/cm** & **Accumulation Width/cm** \\ \hline
2020-12 & 19 & 1.5 & 68 \\
2021-04 & 24 & 21 & 70 \\
2021-08 & 27 & 50 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Area of the sand sedimentation on highway surface statistical table.  
Figure 6: Change of sand-accumulation area on windward side of embankment: (**a**) winter in 2020, (**b**) spring in 2021, and (**c**) summer in 2021.  
According to the field observation data, the annual sand-driving wind speed in this region is mainly concentrated in 5-18 m/s. In order to ensure the matching between the wind speed value in simulation and the engineering site, the prevailing wind of 8 m/s and the maximum wind speed of 18 m/s are selected for numerical simulation. The grit size in the study area is 0.075-0.25 mm. The grit size in the sandy air current is set as 100 \(\upmu\)m; grit density is 2650 kg/m\({}^{-3}\), and viscosity \(\mu\)s is 0.047 Pa\(\cdot\)s [31]. The initial grit volume fraction is 3%; air density \(\rho_{S}\) is 1.225 kg/m\({}^{-3}\); viscosity \(\mu\) is 1.789 \(\times\) 10\({}^{-5}\) Pa\(\cdot\)s; and inlet wind speed adopts a typical wind speed profile [32; 33; 34]. The equation of wind speed is as follows:  
\[u(z)=\frac{u_{*}}{k}\ln\frac{z}{z_{0}} \tag{2}\]  
where \(u(z)\) is the velocity at the height \(z\); \(u\)* is the friction of wind speed; \(K\) is von Karman coefficient set to 0.4; \(z\) is height; and \(z_{0}\) is the roughness length.  
Euler two-fluid model [35] supplemented with k-\(\epsilon\) turbulent flow equation [35; 36] was adopted as the calculation model and SIMPLEC was used as the solving algorithm. The wind speed in the study area is no more than 50 m/s and grits are mainly affected by gravity. The sandy air current can be regarded as incompressible fluid [37] and the heat exchange between sandy air current can be ignored; thus, the energy equation is not considered and the continuity equation, momentum equation, and k-\(\epsilon\) turbulence model are considered in the study of wind-sand activity characteristics.
## 2 Materials and Methods
### Measurements
#### 2.3.6 Statistical Analysis  
Normality of data was assessed using the Kolmogorov-Smirnov one-sample test. Data are presented as percentages for qualitative variables, mean \(\pm\) standard deviation (SD) for parametric variables, and median and 25 th and 75 th percentiles for non-parametric variables. Independent samples t-tests or Mann-Whitney U-tests were used to assess differences between groups (RMTgroup versus CPAPgroup). Paired _t_-test or Wilcoxon signed-rank test were used to assess within groups differences before and after the 4-week intervention period (RMTgroup and CPAPgroup). Correlations between continuous variables were assessed using [PERSON]'s Rho and [PERSON]'s R correlation coefficients for non-parametric and parametric variables, respectively. A Bonferroni post hoc test was used to detect differences between means. Two-way repeated measures analysis of variance (2 measurements \(\times\) 2 groups) was used to test for differences in dependent variables and interactions. [PERSON]'s d was calculated from the mean difference between groups (M\({}_{1}\) and M\({}_{2}\)) and by the pooled SD: [PERSON]'s d = \(\frac{\text{M}^{2}-\text{M}1}{\text{SD}_{\text{pool}}^{2}}\) and \(\text{SD}_{\text{pool}}\) = \(\sqrt{\frac{\text{SD}^{2}+\text{SD}2^{2}}{2}}\). For all tests, a _p_-value of <0.05 was considered statistically significant. The IBM SPSS 21 statistical package (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses.
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 1 Introduction  
Optimized signal waveform with features, higher utilization rate, lower peak-to-average power ratio (PAPR), and higher main lobe energy can be applied in many significant applications, such as mobile telecommunication, satellite communication, aircraft measurement, tracking, and controlling systems [1; 2; 3]. Previous studies have provided many effective optimization methods for different scenarios. In order to higher the utilization rate in band limited channel, modulations based on orthogonal frequency division multiplexing (OFDM) and quadrature amplitude modulation (QAM) are provided, both of which suffer from high PAPR [3; 4; 5]. Nonlinear characteristics of the transmitter amplifier lead to undesirable out-of-band radiation, which restrict the use of OFDM and QAM. Moreover, modulation with high PAPR is not suitable in nonlinear channel, where the signal spectrum could also be extended [1]. Efforts have been made in many studies to reduce the out-of-band radiation. In the work [6], minimum shift keying (MSK) is proposed, which is featured with constant envelope and has less out-of-band radiation compared with quadrature phase shift keying (QPSK). Furthermore, Gaussian filtered minimum shift keying (GMSK) shows better outband reduction compared with MSK, which takes the sacrifice of the constant envelope [7]. [PERSON] et al. [8] proposed the bandwidth efficient reduction of the crest factor (BERC) method.  
In most canonical communication systems featured with constant or quasi-constant envelope, a rectangular base band signal is applied in QPSK, offset QPSK (OQPSK), and frequency-shift-keying (FSK)modulation systems; cosine signal is used in MSK and GMSK after filtering. These kinds of modulation systems show a lesser utilization rate compared with multiple carrier modulation. However, the contradiction between the utilization rate, main lode energy, and PAPR still needs further study. In recent years, there have been few studies giving mathematical optimization models based on certain criteria. In this paper, a novel method to design an optimized base band waveform is proposed based on minimum out-of-band radiation criterion and additional restrictions. In this method, we first give the target Pan-function based on the minimum out-of-band radiation criterion. Then,we construct the boundary function with the restrictions, symbol boundary condition, single-symbol signal energy condition, constant-envelope condition, and minimum cross correlation function. Hence, [PERSON]'s equation is derived. After that, the base band waveform is discreted using Fourier series. The optimization problem is transmitted to finding the coefficient of the Fourier series, which could minimize the [PERSON]'s equation. To verify the proposed method, a communication system simulation model is set up. The numerical results show that the optimized waveform presents sharper roll-off, small out-of-band radiation, quasi-constant envelope, and good bit error rate (BER) performance.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 2 Materials and Methods
### Multi-Scale Feature Fusion  
Conveyor belt images are a special image data and differ hugely from natural images. In this paper, conveyor edge detection is transformed into pixel-level classification in image segmentation [19; 20]. FCNs (Fully Convolutional Networks) [21], a classical full convolution network, replaced the last three full connection layers of VGG (Visual Geometry Group) [22] with convolution layers, which reduced parameters and kept the spatial information of the image. There are some advantages in FCNs that are very suitable for processing conveyor images, so this paper takes the FCNs as the basic architecture to study the deviation detection task of conveyor images.  
The resolution of the feature image is reduced because of several convolutions and poolings. FCNs addresses this by adding links that combine the final prediction layer with lower layers with finer strides. Then, the stride 32, 16 or 8 predictions are upsampled back to the image, which corresponds to FCN-32s, FCN-16s and FCN-8s. Considering that FCN-8s, which gets finer precision, only fuses the output of the last three stages of pooling by per-pixel adding, it still loses much detailed edge information for conveyor belt images. In this paper, a multi-scale feature fusion network, which builds on the FCNs, is proposed for global and local feature extractions. As shown in Figure 2, the output of each stage after pooling is upsampled by 2, 4, 8 and 16 times the original image. In addition, by changing the fusion strategy of adding pixels-by-pixels in FCNs, the channel fusion method is used to fuse the low-level features with the rich location and detail information and the high-level features with stronger semantic information. Therefore, the network can learn abundant multi-scale features and solve the problem of detail recovery in edge detection.  
Figure 1: The overall architecture of the proposed algorithm. According to the specific task required of belt deviation detection, this paper develops an algorithm based on a multi-scale feature fusion network comprising of a feature extraction module and a deviation detection module. Then, training data are fed into the feature extraction module and parameters are updated with backpropagation. In addition, parameters of the best model are used to predict results on test data. Finally, the deviation detection module identifies and monitors the deviation fault.
## 2 Overview of the Proposed Neural Networks and Theoretical Background
### Recurrent Neural Network (RNN)  
An RNN is ideal when required to process sequential information like the translation of sentences and production of titles for an input video. An RNN utilizes past data in its internal memory to forecast future outcomes based on previously computed parameters. In contrast to a traditional NN, like feedforward neural networks, which are unable to save past data, the RNN uses past data to process sequential inputs with recurrent units [62]. Figure 1 illustrates the structure of a recurrent unit w.  
The following equation updates the state of each neuron \(H_{k}\) in the hidden layer at each time step:  
\[H_{k}=g\big{(}w_{h}\cdot\big{[}\begin{array}{cc}H_{k-1}&x_{k}\end{array} \big{]}+b_{h}\big{)} \tag{2}\]  
where g denotes the activation function such as a hyperbolic tangent function or a logistic sigmoid function; \(w_{h}\) and \(b_{h}\) are the weight matrix between a hidden layer and an input layer and a bias parameter of the recurrent neuron, respectively. Once the current state \(H_{k}\) is calculated, the output is computed using the following equation:  
\[y_{k}=g\big{(}w_{h}\cdot H_{k}+b_{y}\big{)} \tag{3}\]  
Figure 1: Structure of a recurrent neural network unit (RNN).  
where \(b_{y}\) and \(w_{h}\) are the bias and the weight matrix between a hidden layer and an input layer, respectively. Since the RNN naturally encodes historical inputs, the network cannot capture all information in the case of a long sequence. In that case, it may lose valuable information from previous time steps. As RNN processes more steps, it suffers more from the fading gradient than other neural network architectures. To solve this problem called gradient vanishing, two popular architectures are used: long short-term memory (LSTM) and gated recurrent unit (GRU). The concept of gradient vanishing refers to the phenomenon where the gradients become increasingly small as they propagate through the layers of the network during training. This can hinder the learning process, particularly in the case of RNNs, or when processing long sequences of data. TensorFlow with the Keras functional API was used in this study to build, test, and train the proposed recurrent neural networks.
## 3 Equipment and Testing  
The proposed scheme was evaluated by collecting pedestrian data in GNSS signal challenged environments. The data collection equipment setup depicted in Figure 4a was used for two scenarios. Data was collected with the NavCube, a data collection platform developed in the PLAN group of the University of Calgary [11]. The device includes four GNSS receivers, namely a NovAtel OEM628 (NovAtel Inc., Calgary, AB, Canada), an u-blox 6T (U-blox, Thalwil, Zurich, Switzerland), a SiRF IV (SiRF Technology, Inc., San Jose, CA, USA) and a Teseo II (STMicroelectronics, Geneva, Switzerland). It also includes an internal inertial sensor and the option to add other sensor sets through external cabling. All of the data is time tagged using the GPS time obtained from one of the receivers inside the device. The inertial data for the tests in this work were collected using an Analog Device's ADIS16488 sensor unit; related accelerometer and gyroscope specifications are given in Table 1.  
Figure 4: (**a**) Data collection equipment setup. (**b**) Block diagram of equipment setup.  
A reference solution was also obtained for each experimental scenario in order to evaluate the performance of the proposed scheme. The reference system consisted of a NovAtel OEMV3 receiver as a base station at a pre-surveyed location. The rover part consisted of a NovAtel SPAN-SE receiver and a tactical grade LCI IMU. The reference solution was obtained as a tightly-coupled GNSS/INS solution computed using NovAtel's Waypoint Inertial Explorer post-processing software. The accuracy of the reference trajectory was better than 0.2 m.  
As shown in the block diagram of Figure 4b, GNSS data collected using NovAtel's high performance GPS-702-GG antenna was split to feed the OEM6 (OEM628) receiver inside the NavCube and the SPAN-SE receiver. A small ANN-MS-0-005 patch antenna was used to collect GNSS data for the high sensitivity u-blox6 (u-blox 6T) receiver inside the NavCube. Inertial data was collected using the ADIS16488 external sensor unit, mounted besides the patch antenna on top of the backpack shown in Figure 4a. Pedestrian data was collected in two scenarios in order to thoroughly assess the proposed algorithms. The data collection environments are discussed in the following sub-sections.
## 2 Computational Model
The area-weighted-average of pressure at the end wall, known as the pressure antinode, was monitored until a steady state oscillatory flow was obtained. This is defined as a state when pressure, velocity, and temperature do not change much from cycle to cycle. By way of example, Figure 4 shows the history of oscillating pressure and velocity monitored for location \"m\" as defined in Figure 2. A steady oscillatory flow condition is achieved after six flow cycles. However, as will be discussed later, results presented in this paper are obtained after 70 flow cycles, when both the flow and thermal oscillatory flow conditions have reached a steady oscillatory state. The solutions are also monitored so that they converge in every time step at every flow cycle (through the selection of the time step size). When the steady oscillatory state condition is reached, the model is validated and used for analysis. In this study, the development of flow within one oscillatory cycle is investigated according to the time frame defined in Figure 2:0 The cycle is subdivided into twenty phases starting from the first phase, \(\phi 1\). The relationship between pressure, velocity, and gas displacement, for the 20 phases of a flow cycle is illustrated. Phase \(\phi 1\) is set for the maximum value of the oscillating pressure at the location of the pressure antinode (rigid wall of the resonator). The period of one cycle is the inverse of the operating frequency--13.1 Hz. Figure 2:1 Oscillating pressure and velocity monitored for identification of the steady oscillatory condition. Figure 2:2 Pressure, velocity, and displacement over 20 phases of a flow cycle. The model was solved using the SPECTRE High Performance Computing facility at the University of Leicester (4 login nodes for solving one model, with 28 cores of 56 threads and 256 GB memory per one login node). With this computing facility, the 2D unsteady calculation for one cycle takes about 30 min. Therefore the solution for 490 cycles (as discussed later) takes about 9.5 days.
## 2 Related Work
### Overview of Imbalanced Data Handling Methods  
The class imbalance problem is prevalent across many domains, including fault detection, medical diagnosis, financial fraud detection, and natural language processing. When the number of minority class samples is significantly lower than that of the majority class, classification models tend to be biased toward the majority class, leading to poor recognition of critical minority instances and severely degrading overall performance and generalization capability.  
To address this issue, researchers have proposed various sample reconstruction strategies, among which undersampling and oversampling are the most common. Undersampling aims to achieve class balance by strategically reducing the number of majority class samples. [PERSON] et al. developed several undersampling algorithms based on clustering and nearest neighbors to improve the classification of minority instances [19]. However, this approach may risk losing valuable information, making it less suitable for high-dimensional and complex tasks.  
In contrast, oversampling generates synthetic minority samples to achieve balance without discarding original data, and is therefore more widely adopted. The most rep resentative technique is SMOTE (Synthetic Minority Oversampling Technique), which interpolates between existing minority samples to create new ones, effectively enhancing the model's ability to learn from minority data. Based on SMOTE, researchers have proposed improved variants such as Borderline-SMOTE and ADASYN, which place greater emphasis on boundary samples and adaptive sample generation, respectively [20,21].  
In recent years, with the advancement of deep learning, generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have been introduced into imbalanced learning. [PERSON] et al. combined GAN and VAE to generate high-quality minority class samples, offering a more diverse and realistic data distribution than traditional interpolation-based methods [22]. Other approaches--such as attention mechanisms to enhance focus on critical features, multi-branch network designs, transfer learning, and self-supervised learning--have also been explored to mitigate data imbalance [23].  
In the context of power battery fault detection, the minority class typically represents fault data, which are much less abundant than normal operation data. Considering the rarity and difficulty of acquiring fault samples, this study adopts a GAN-based oversampling strategy to balance class distribution while preserving feature integrity, thereby providing a more reliable foundation for classifier training.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 2 Methodology  
This review focuses on the image-based 3D reconstruction in the field of construction progress monitoring, hoping to obtain a comprehensive technical path, which provides technical selection reference for researchers. To achieve this goal, the following work was carried out in this study.  
1. Literature search and screening: This study searched the relevant research results since 2008 from Google Scholar, and the key words included image, photography, video, depth image, computer vision, three-dimensional reconstruction, construction progress monitoring, construction progress tracking, etc. Then, articles related to this topic were selected, and the papers indexed by Web of Science were focused on. Finally, a total of 66 articles were selected, as shown in Table 1.
2. Method classification: The knowledge and methods used in these papers are divided and classified into these six aspects: knowledge representation, image collection, and 3D point cloud generation, image-to-BIM alignment, point cloud segmentation, point cloud semantic recognition, and progress reasoning.
3. Methods comparative analysis: The methods of each aspect were classified and summarized, and the advantages and limitations of various methods were analyzed.
## 3 Design of the DAB Converter Prototype  
This paper employs a digital signal processor (DSP) to process the feedback signal from feedback circuits and control the mode switching, as shown in Figure 9.  
Figure 9: Block diagram of the proposed dual-mode control for the DAB converter.  
The DSP program updates its input regularly by sampling the output voltage and current via the ADC module. These values serve to calculate the output power \(P_{O}\). The DSP program compares the \(P_{O}\) value with a threshold value (200 W for forward conversion or 100 W for backward conversion) for mode switching. Based on the comparison result, the mode selector function will choose the suitable operation mode. A duty-cycle value or phase-shift value is calculated and sent to the PWM module, depending on the mode selected. There are two cases: when the energy is transferred from the \(V_{Dchus}\) to \(V_{bat}\), \(V_{bat}\) would be an output voltage. In this case, the voltage value from feedback circuit B and the current value from current sensor B will be used to calculate output power \(P_{O}\). On the other hand, when energy is transferred from \(V_{bat}\) to the \(V_{Dchus}\), the voltage value from feedback circuit A and the current value from current sensor A will be used to calculate output power \(P_{O}\).  
The DAB converter prototype was designed in accordance with the design parameters in Table 2.  
The rated power is 1 kW. The voltage of the high-voltage side is 400 V, and that of the low-voltage side is 50 V. For the high-voltage side of the converter, MOSFET SCT3080 ALGC11, produced by ROHM Semiconductor (Kyoto, Japan), was selected for the power switch. On the other hand, MOSFET IRFP4321 PBF, produced by Infineon (Neubiberg, Germany), was chosen for the power switches on the low-voltage side.  
The flow chart diagram of the design process for energy storage components is given in Figure 10.