esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DL Models
### Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM)  
Both RNN and LSTM models are designed for sequential data processing, such as speech [35], [36], text [37], and handwriting recognition [38]. These models attempt to recognize a pattern in the sequence. Previously, [PERSON] et al. [39] proposed LSTM model to analyze brain EEG for brain-computer interface (BCI) systems. Their model achieved a low misclassification rate of 3.09% and 2.07% for two publicly available BCI datasets. A recent study by [PERSON] et al. [40] demonstrated the effectiveness of the LSTM and RNN in analyzing bio signals. The LSTM-based deep RNN model, proposed in their study, achieved exemplary performance: 100% accuracy, precision, and sensitivity in personal authentication based on ECG signals. This makes them suitable for classifying bio signals like PSG recordings, which have distinct patterns in different sleep stages.  
It needs to be noted that very early versions of RNNs were incapable of learning long-term dependencies, because these RNNs were unable to form connections between old and new data when  
Figure 2: Basic Convolutional Neural Network (CNN) model architecture for polysomnogram (PSG) recording analysis. The CNN output model is represented by five red boxes indicating five prediction classes for example W (Wakefulness), S1–S3 (Non-REM sleep Stages 1–3) and REM (rapid eye movement) sleep.  
a large information gap existed between them [41]. This resulted in a phenomenon known as vanishing gradient problem, where the error signals vanished during backpropagation, which eventually led to a model breakdown. [PERSON] and [PERSON] [42] developed LSTM to solve the vanishing gradient problem, but [PERSON] et al. [43] showed that LSTM was still not able to efficiently learn sequences that were very long or continuous. The reason for this failure was that the internal values of the memory cell in LSTM models had grown out of bound from the continuous input stream, despite the fact that the LSTM model was programmed to reset itself when faced with this kind of problem. As a remedy, \"forget gates\" were introduced to LSTMs, which remove data that were no longer relevant in memory cells, thereby forgetting and resetting information in memory cells at appropriate times [43]. Useful information, on the other hand, was continuously backpropagated, thus, allowing these models to memorize relevant information and recognize patterns in long-term dependencies. The architecture of the LSTM model is shown in Figure 3.  
However, high computational complexity is the downside of RNN and LSTM, and they need a large memory bandwidth to train [44; 45]. As such, hardware designers often experience difficulty when dealing with RNN or LSTM models, because these models occupy a large amount of cloud space, which is not scalable. Therefore, reducing the computational complexity of RNN and LSTM models must be considered for real-time or mobile applications.
## 3 System Model
### Link Quality
#### 3.2.2 The Effect of the Doppler Shift  
In a UANET, the Doppler shift is a significant phenomenon in which the received signal frequency is not the same as the transmitted signal frequency when there has been relative motion between the transmitter and the receiver. The magnitude of the Doppler shift is proportional to the magnitude of the relative velocity. If the difference between the received and the transmitted signal frequencies is high, it significantly influences the communication quality.  
When node \(i\) communicated with its neighboring node \(j\), the movement of receiving node \(j\) led to a distance difference \(\Delta l\) of the received signal, and the value of the phase difference variation of the received signal could then be expressed as the following:  
\[\Delta\varphi=\frac{2\pi\Delta l}{\lambda}=\frac{2\pi v_{j}\Delta t}{\lambda}\cos\theta \tag{2}\]  
where \(\theta\) is the angle between the direction of the radio wave incidence and the direction of movement of node \(j\); \(v_{j}\) is the speed of movement; and \(\lambda=c/f\) is the wavelength of the transmission signal. Based on this, the value of the frequency change, i.e., the Doppler shift \(\Delta f_{i,j}\) could be obtained as follows:  
\[\Delta f_{i,j}=\frac{\Delta\varphi}{2\pi\cdot\Delta t}=\frac{v_{j}}{\lambda} \cdot\cos\theta \tag{3}\]  
Based on Equation (3), the Doppler shift was not only related to the relative movement speeds of the transmitter and receiver but also the angle between the direction of the radio wave incidence and the direction of the movement. If node \(j\) traveled toward node \(i\), the \(\Delta f_{i,j}\) was positive, i.e., the received signal frequency increased. If node \(j\) traveled away from node \(i\), the \(\Delta f_{i,j}\) was negative. The received frequency decreased.  
Doppler shifts degraded the performance of the received signal. If the Doppler shift \(\Delta f_{i,j}\) was equal to the symbol rate, the signal power would exceed the band limit of the filter. To solve this problem, a simple solution was to widen the filter bandwidth of the receiving node, so the SNR loss caused by the Doppler frequency shift was calculated as follows:\[df_{i,j}=10\cdot\log\left(\frac{2\cdot\Delta f_{i,j}+R_{d}}{R_{d}}\right)=10\cdot \log\left(\frac{2\cdot\Delta f_{i,j}}{R_{d}}+1\right) \tag{4}\]  
where \(R_{d}\) is the symbol rate.  
The result of Equation (4) is in dB, so we converted it to a formal absolute value as follows:  
\[DF_{i,j}=10^{\frac{df_{i,j}}{10}}=\frac{2\cdot\Delta f_{i,j}}{R_{d}}+1 \tag{5}\]
# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 2 Methodology
### Image Conversion Stage
#### 2.1.2 Conversion from Time-Series into RP Images  
RP was a time-time signal processing method that could be used to show the periodicity of trajectory in the phase space and reveal the internal structure of the time-series. It consisted of two-time axes, a black dot, and a white dot. The black dot indicated that recursion occurred in the state corresponding to the horizontal axis and vertical axis, and the white dot indicated that recursion did not occur. The key to constructing the RP image was to reconstruct the phase space, which needed to select the appropriate delay coefficient, embedding dimension, and threshold for reconstructing the time-domain information in the original phase space and promoting the signals to a higher dimension.  
The transformation of the RP was divided into three steps, which were described as follows.  
Step 1. For time-series \(\mathbf{z}\), the sampling interval was determined to be \(\tau\). The appropriate embedding dimension \(k\) was determined through relevant theoretical calculation, and the time-series \(\mathbf{s}\) was reconstructed. The reconstructed power system is  
\[s_{i}=\left(z_{i},z_{i+\tau},\;\ldots,z_{i+(k-1)\tau}\right) \tag{4}\]  
\[\mathbf{s}=\left(s_{1},s_{2},\;\ldots,\;s_{e}\right) \tag{5}\]  
Figure 2: The effect of converting six UCR datasets into the grayscale image.  
The length \(e\) of time-series \(\mathbf{s}\) is  
\[e=c-(k-1)\tau \tag{6}\]  
where \(c\) is the length of time-series \(z\).  
Step 2. The calculation of the distance between point \(i\) and point \(j\) in the reconstructed phase space is  
\[D_{i,j}=\big{|}\big{|}s_{i}-s_{j}\big{|}\big{|}\forall i,j\in\{1,\ \ldots,e\} \tag{7}\]  
where \(||\cdot||\) represents the L2 norm.  
Step 3. The calculation of the recursive value is  
\[\mathbf{R}_{ij}=\theta\big{(}\varepsilon-D_{ij}\big{)} \tag{8}\]  
\[\forall i,j\in\{1,\ \ldots,e\}\]  
where, \(\mathbf{R}_{ij}\) is a square matrix with the size of \(e\times e\), \(e\) is equal to the vector numbers of \(z\), \(\varepsilon\) is the threshold, and \(\theta(\cdot)\) represents the Heaviside function. The calculation of \(\theta(\cdot)\) is  
\[\theta(f)=\left\{\begin{array}{ll}0&\qquad f<0\\ 1&\qquad f\geq 0\end{array}\right. \tag{9}\]  
The effect of converting samples from different datasets into RP images is shown in Figure 3.  
The RP images clearly show the changes of different datasets through the arrangement of black and white blocks. For example, the SyntheticControl dataset generally presents a drift trend. The RP images of the UMD appear in large black areas and show a mutation mode, which is caused by the rapid change of data.
# Multi-View Visual Relationship Detection with Estimated Depth Map
## 2 Related Work
### Depth Map Embedding  
To introduce depth information into original flat RGB images, the first step is monocular depth estimation. [PERSON] et al. [18] conducted depth estimation, surface normal estimation and semantic labeling simultaneously in a proposed single multiscale CNN. This depth estimation network was composed of three modules: (1) A full-image view module. The job of this module was to predict the coarse visual features of an entire input image for the full-image field of view; (2) A predictions module. This module produced predictions at mid-level resolution utilizing the full-image information output by the coarse network; and (3) A higher resolution module. This final module refined the predictions to a higher resolution which were regarded as the output of the overall network.  
Based on [18], for the first time, [PERSON] et al. [7] combined RGB information with estimated depth maps in a visual relationship detection task. The RDBN framework [7] not only mapped generated depth maps into depth appearance feature vectors which revealed contour features of detected objects, but also utilized fuzzy strategy to construct depth spatial feature vectors, which revealed depth location relationships between detected objects. In an existing RGB-D dataset, ref. [8] trained a mapping function of RGB images to the corresponding estimated depth maps. Based on the trained depth estimation model, ref. [8] obtained estimated depth maps of a VG dataset [30]. In contrast to [7,8] did not consider the negative effects of inaccurate information in the estimated depth maps.  
In this paper, we estimated corresponding depth maps in a VRD dataset [1] with a monocular depth estimation model [18]. Then, multi-view images were generated using a specific strategy for RGB-D information. Finally, multi-view images were mapped into feature vectors which were regarded as the input to a visual relationship classifier.
## 2 Methods
### Model Selection  
The final model selection was based on the highest average DSC score achieved during validation. Additionally, ensembles of the three different nnU-Net models (2D, 3D-fullres, 3D cascade) were evaluated to explore whether combining multiple models could improve performance. The 3D full-resolution configuration was selected because it outperformed the 2D and 3D cascade configuration in terms of DSC score during the validation. Henceforth,when referring to the nnU-Net model, we refer to the trained 3D full-resolution model. The median inference time for the 3D full-resolution model was 146 s per patient.
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 6 Analysis of Simulation Results
### Sinusoidal Characteristic Simulation Analysis  
The input was a 12\({}^{\circ}\) sinusoidal signal at 2 Hz to simulate the on-load condition. Four different control strategies were applied to the control system. The sampling time was set to 0.01 s. The maximum amplitude error and the maximum phase error of the system under different control strategies were compared. Figure 11 shows the simulation results.  
Table 5 shows the simulation data of frequency response under loading. The sinusoidal signal at 2 Hz and 12\({}^{\circ}\) was input under loading. The system under the genetic fuzzy PID control was 32.30% less than the fuzzy PID control system, 80.01% less than the pure fuzzy control system, and 60.03% less than the classical PID control system in terms of the maximum amplitude error. The system under the fuzzy PID control optimized by the GA was 33.43% less than the fuzzy PID, 86.90% less than the pure fuzzy control system, and 67.29% less than the classical PID in terms of the maximum phase error.
###### Abstract  
Lithium-ion batteries are increasingly used in mobile applications where mechanical vibrations and shocks are a constant companion. There is evidence both in the academic and industrial communities to suggest that the electrical performance and mechanical properties of the lithium-ion cells of an electric vehicle (EV) are affected by the road-induced vibration. However, only a few studies related to the effects of vibration on the degradation of electrical performance of lithium-ion batteries have been approached. Therefore, this paper aimed to investigate the effects of vibration on the DC resistance, 1C capacity and consistency of NCR18650 BE lithium-ion cells. Based on mathematical statistics, the method changes of the DC resistance and the capacity of the cells both before and after the test were analyzed with a large sample size. The results identified that a significant increase in DC resistance was observed as a result of vibration at the 95% confidence level, while typically a reduction in 1C capacity was also noted. In addition, based on a multi-feature quantity, a clustering algorithm was adopted to analyze the effect of vibration on cell consistency; the results show that the cell consistency had deteriorated after the vibration test.  
Keywords:lithium-ion battery; vibration load; electrical degradation; mathematical statistic; consistency analysis +
Footnote †: journal: Applied Sciences
# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 1 Introduction
The thermal sensor detects the thermal radiation from an object, which allows the detection and tracking of pedestrians and cyclists in low-light conditions. The accuracy of the classification, detection and pose estimation are based on the quality of the sensor data information. The focus of this paper is to provide an overview of the current pedestrian and cyclist detection and intent estimation techniques and compare the existing techniques. Building upon the vast existing literature in the field of computer vision and object detection, pedestrian and cyclist detection will be explored and discussed. The detection stage allows for identification and location of such objects in images and video frames [19], therefore making it a vital part of autonomous vehicles [20; 21; 22; 23]. Detection results are then used for tracking and pose estimation of the pedestrians/cyclists. As DL techniques for VRU detection and intent estimation will be the primary focus, this will not encompass tracking techniques. The purpose of this survey is to provide comprehensive review of the recent studies undertaken in both pedestrian and cyclist detection and pose estimation based on state-of-the-art sensor fusion and DL techniques. There is limited work focused on cyclists detection compared to pedestrian detection. There is also limited work on using multispectral data for VRU detection. Using sensor fusion techniques with DL can lead to improved results based on previous state-of-the-art methods. Therefore, it is critical to find an optimal fusion technique to improve the detection accuracy of the system. Once detected, pose estimation techniques can be applied to the VRUs. The organisation of the paper is as follows: Section 2 will highlight the challenges and importance of detection and intent estimation for autonomous vehicles. Sections 3 and 4 will provide a brief history of object detection techniques and its typical detection pipeline. Section 5 will explore the state-of-art techniques based on DL currently used in pedestrian and cyclist detection. Section 6 discusses the architectures of the DL-based detectors for pedestrians and cyclists. Section 7 outlines the datasets used for pedestrian and cyclist detection. Section 8 will discuss DL-based sensor fusion approaches for an improving detection. Section 9 introduces the latest DL approaches that are used for pose estimation and intent estimation. Concluding remarks and future works will be presented in Section 10. Figure 1: A flowchart of detection and intent estimation system.
## 1 Introduction
The actual failure cases have highlighted that the weld seam (especially at the connecting fin) poses a high risk for cracking and failure of water-cooled walls. Moreover, the post-welding residual stress is often a crucial factor triggering structural failure. Therefore, it is of great significance to explore the evolution of post-welding residual stress under different welding parameters and structural sizes. Such research provides guidance for obtaining appropriate welding  
Figure 1: Failure photograph displaying the flue-side wall of a thermal power plant. parameters and structural dimensions, which could ensure the safety of the flue-side wall during service. At present, the methods for detecting residual stress are mainly divided into two categories: destructive and nondestructive testing. Among them, destructive testing methods include the blind hole, indentation, and cutting methods [27; 28; 29; 30] and nondestructive testing methods include ultrasonic, magnetic, X-ray diffraction, and neutron diffraction methods [31; 32; 33; 34; 35; 36]. The measurement of residual stress in both destructive and nondestructive testing has its disadvantages, as shown in Table 1. Most of the existing test instruments need to frequently change samples, rotate sample angles, manually calculate data, and formulate calibration curves, which are often limited by various actual engineering conditions. Thus, the finite element simulation method is widely used for predicting residual welding stresses. In this study, the finite element method was employed to establish a one-to-one model according to the size of the actual flue-side wall. Then, the effect of fin size on the stress and deformation of the welding structure was explored. Our findings offer a theoretical basis for optimizing the welding structure and ensuring the safe operation of the flue-side wall used in USC-TPPs.
## 4 Findings
### Smart Environments
#### 4.3.4 Smart Traveling  
Due to the massive data generated and the vast scale of IoT networks, data fusing and privacy are still significant challenges. Thus, an inter-cloud data fusing and privacy-protected platform based on JointCloud is proposed to address the analytic activities and data mining of IoT [124]. The authors discussed two main platforms implemented on single clouds: Baidu and Amazon AWS. Then, they presented their design based on JointCloud Computing (JCC) because it is more suitable for constructing complex applications. This framework can be broken down into three tiers. The first tier is made up of a variety of sensors that are linked to several clouds. In the second tier, JointCloud Collaboration Environment (JCCE) links clouds together. Services are located in the third tier, based on the JCC, and each user is provided with an application and personalized service. This platform offers enhanced security because data are stored in a private cloud. In addition, it eliminates privacy disclosure and prejudice because, in JCCE, trades are automatically executed.
# Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
## 2 Materials and Methods
### DRIVE Dataset  
In this work, we have chosen the DRIVE (Digital Retinal Images for Vessel Extraction) dataset as a reference to present the results obtained by the different models [22]. It is, together with STARE [23] and CHASE [24], an image dataset frequently used in the study of ocular diabetic retinopathy segmentation and as a benchmark for medical image segmentation models.  
The DRIVE dataset consists of 40 high-resolution images of \(584\times 565\) pixels (see Table 1) whose blood vessels have been labeled by eye medical experts. Figure 1 shows one of the images and its corresponding label, which is actually a binary mask. More specifically, this dataset is intended to assist specialists in the diagnosis of diabetic retinopathy, where it is necessary to isolate the blood vessels from the retinal fundus in order to proceed with the subsequent inspection. Therefore, the goal to be achieved with this dataset involves a binary semantic segmentation task, in which each pixel must be classified according to whether or not it belongs to a blood vessel.  
Out of the 40 images that make up the dataset, 20 are distributed without labels and used as test partitions. In order to obtain the test metrics, the results of these images have to be submitted to the DRIVE platform [27]. Then, the dice-coefficient is calculated for every image in the test set and results can be seen in a leaderboard sorted by highest average dice-coefficient. The other 20 labeled images are split between a training (16 images) and a validation (4 images) set. In addition to this, as the amount of images available is very small, we used different data augmentation techniques that we describe in Section 2.1.1 in order to enhance the training set.
## 4 Results and Discussion
### Bump Road Excitation
Figure 12 shows the first peak value of the closed-loop transfer function \(T_{\tilde{z}_{b}/\tilde{z}_{g}}\) with the parameter-dependent controller and the reliable robust controller versus the uncertain parameters \(\lambda_{1}\) and \(\lambda_{2}\) in existence of 0%, 40%, and 80% actuator thrust loss. One can observe that the peak value of \(T_{\tilde{z}_{b}/\tilde{z}_{g}}\) goes up with an increase of actuator thrust loss. This indicates that, with the extent of failure increasing, the effect of active suspension to isolate the disturbance from the ground to the sprung mass becomes worse, deteriorating the ride comfort. In addition, one can observe that, along with the changes of the uncertain parameters \(\lambda_{1}\) and \(\lambda_{2}\), the reliable robust controller yields smaller closed-loop peak value than the parameter-dependent controller, indicating that the reliable robust controller considering actuator fault-tolerant can achieve better the ride comfort of EV than the one without fault-tolerant. Figure 13 shows the first peak value of the closed-loop transfer function \(T_{(z_{\tau}-z_{g})/\dot{z}_{g}}\) with the parameter-dependent controller and the reliable robust controller versus the uncertain parameters \(\lambda_{1}\) and \(\lambda_{2}\) in the existence of 0%, 40%, and 80% actuator thrust loss. Clearly, under different actuator thrust loss, the reliable robust controller can obtain outstanding performances in terms of isolating the disturbance from the ground to the IWM, compared with the parameter-dependent controller. In addition, one can observed that the peak value of \(T_{(z_{\tau}-z_{g})/\dot{z}_{g}}\) goes up with an increase of \(\lambda_{1}\), indicating that with the increase of sprung mass, the effect of active suspension to isolate the disturbance from the ground to the IWM becomes worse. That is to say, the increase of the sprung mass intensifies coupling effect in IWM. Figure 14 shows the frequency responses of the open and closed-loop systems by using the parameter-dependent controller and the reliable robust controller in the existence of 0% and 40% actuator thrust loss. Similar to Figure 11a,b, one can observe that compared with passive suspension and the parameter-dependent controller, the reliable robust controller can achieve better vehicle and motor performance, when actuator thrust loss occurs. Figures 15 and 16 illustrate the time-domain responses of passive suspension and active suspension with the parameter-dependent controller and the reliable robust controller in the existence of 0% and 40% actuator thrust loss. Similar to Figure 13, those figures reveal the advantage of the reliable robust controller in improving the ride comfort and inhibiting the coupling effect in IWM. Figure 14: Frequency responses for the open (passive mode) and closed loop (active mode) systems with actuator thrust loss: (**a**) transfer function \(T_{\hat{z}_{y}/\hat{z}_{g}}\) under 0% actuator loss; (**b**) transfer function \(T_{\hat{z}_{y}/\hat{z}_{g}}\) under 40% actuator loss; (**c**) transfer function \(T_