esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Teleoperation System Overview  
The proposed robotic manipulation system and the related mapping are shown in Figure 1. On the MR side (local space), a binocular HMD is tracked by the HTC Vive VR platform (HTC Corporation, Taoyuan, Taiwan) and used to display the virtual manipulation scene generated in Unity (Unity Technologies, San Francisco, CA, USA) to the user. Motion tracking devices (HTC lighthouses) are linked to the Unity computer and track the pose of the user's head, while they inspect the workpiece from different perspective angles and perform teleoperation tasks. Figure 2 shows the data communication and information exchange for intuitive motion control of the imitation-based MR-HRI of the teleoperated robotic arm-hand system.  
Figure 1: Schematic of the motion-mapping approach for the MR subspace-enhanced imitation-based HRI system of the teleoperated robotic arm-hand. (**a**) The robot working environment; (**b**) the MR scene displayed to the operator; (**c**) the user space. The MR subspace decouples the human from the robot, and the mappings for sensors and grippers are not necessarily direct and identical. The user’s eyes [E] and hand [H] and the state of the camera [C] as well as the robot arm’s grippers [G] are linked to each other through the MR subspace [S], with [T], [W], and [B] denoting reference frames of the target objects, robot wrist and robot arm base.  
Figure 2: The communication system for information exchange and motion control of the MR subspace-enhanced imitation-based HRI of the teleoperated robotic arm-hand system.
# Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy
## 2 Clustering Optimization Design Procedure
### Clustering Analysis of Driving Cycles  
There are three case studies proposed in this paper, which are optimizations at the rated operation point, under the urban dynamometer driving schedule (UDDS), and the highway fuel economy test (HWFET). To improve the optimization efficiency, the K-means clustering method is used to analyze driving cycles [24; 26], which splits the torque-speed profile obtained from the driving cycle data into different groups considering the position and density of the operating points on the plane. Euclidean distance is used to determine the similarity between samples; therefore, the cluster \(S_{i}\) and its centroid \(l_{i}\) can be determined iteratively as follows:  
\[S_{i}^{(t)}=\left\{z_{j}:\left\|z_{j}-l_{i}^{(t)}\right\|^{2}\leq\left\|z_{j}- l_{i}^{(t)}\right\|^{2}\forall i,i=1,2,3\ldots k\right\} \tag{1}\]  
\[l_{i}^{(t+1)}=\frac{1}{\left|S_{i}^{t}\right|}\sum_{x_{j}\in S_{l}^{t}}x_{j} \tag{2}\]  
The iteration repeats until the centroid of each cluster no longer changes. The cluster number \(k\) is determined based on the sum of the distances of the load points to their corresponding cluster centroid, and the cluster centroid is selected as the RP of motor optimization. In this way, the two specific driving cycles shown in Figure 2a,c can be partitioned into several clusters, as shown in Figure 2b,d. Moreover, the RPs and their energy weights are listed in Table 2. It can be found that the motor operating conditions are mostly located in the low-speed or low-torque region in UDDS, and the motor runs less in the conditions of high speed or heavy load. Meanwhile, the motor operating conditions are mostly located in the high-speed and low-torque regions of HFET. Therefore, the clusters and the number of RPs determined by the proposed clustering analysis are different for the two driving cycles, which will be used for the subsequent optimization design.  
Figure 1: Framework of the proposed system-scale optimization for driving motors targeting driving cycles.
## 2 Proposed Approach
### Model Architecture  
#### 2.3.1 Encoder  
Input features are effectively embedded by applying convolutional blocks and parameter-sharing techniques in the encoder layers. In the last decades, deep learning with convolutional neural networks (CNNs) at the forefront has dramatically upgraded the performance in several research fields (image classification, object detection, optical character recognition (OCR), etc.). CNN-based models such as AlexNet, GoogleNet, VGGNet, Xception, ResNet, ResNeXt, and DenseNet have been released [33]. Especially, its efficient representation directly extracted from raw input data based on these verified models leads to impressive  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & \(\theta\) & \(-\theta\) \\ \hline \(\phi-90^{\circ}\) & \(\mathbf{X}^{\prime}\leftarrow\mathbf{Y},\mathbf{Y}^{\prime}\leftarrow-\mathbf{X}, \mathbf{Z}^{\prime}\leftarrow\mathbf{Z}\) & \(\mathbf{X}^{\prime}\leftarrow\mathbf  
performance. In this study, to extract rich internal features, local correlation information is obtained from acoustic features through convolutional layers.  
Multi-task learning techniques have been successfully applied to various tasks such as natural language processing (NLP), automatic speech recognition (ASR), and computer vision [34]. Predicting the class and direction of a sound event are different tasks. However, they are closely related to each other, since they originate from the same sound. No matter how noisy the environment is, if someone focuses on a specific target sound, he can catch the direction of the sound source. Likewise, if he concentrates on a particular direction, it becomes easier to tell what sound is coming from that direction. Therefore, SELD is an appropriate task for multi-task learning. In the SELD, a no-parameter sharing scheme where features for SED and DOAE are not shared in any embedding stage cannot exploit the association between the SED and DOAE features. All of the parameters are shared between two subtasks in a hard parameter-sharing scheme. It can reduce the overfitting phenomenon to a specific subtask, but it can cause performance degradation because it cannot discard the feature that is only necessary for just one subtask [35]. Soft parameter sharing has a separate set of parameters per subtask and only shares partial instances, which are a crucial part of the learning. It is expected that only the information useful for each subtask will be complemented [13].  
Although we use additional spectral information and chromagram concatenated with the log-mel spectrogram as the spectral feature, the embedding scheme of the EINV2 corresponding to four convolutional blocks with batch normalization (BN), ReLU activation, average pooling, and soft parameter sharing [13] is adopted for the encoder part since it is an encoding scheme that sufficiently reflects the characteristics of SELD discussed above.
## 3 Results and Discussion
### Sensory Evaluation  
A professional panel was conducted to evaluate the sensory attributes of these passion fruit wine samples, and the aromatic profiles of each passion fruit wine was described using the terminologies in the aroma kit. The aromatic features of these passion fruit wine samples were assessed using the geometric mean (GM%). It has been accepted that the aromatic feature with a GM% higher than 20% could be considered as a distinguished aroma [25]. In the present study, five aromatic characteristics with a GM% above 20% were selected for further analysis, including passion fruit, mango, green apple, lemon and floral aromas (Figure 2). These different strain-fermented passion fruits possessed different aromatic characteristics (Table S3). For instance, the CY-fermented passion fruit wine exhibited the strongest note on the mango, green apple and floral aromas, whereas the BV passion fruit wine had passion fruit and lemon scents as the featured flavors. It has been reported that 3-mercaptoethanol and 3-mercaptoethanolacete played an essential role in the contribution of the passion fruit aroma [44]. In the current study, the CY and ES passion fruit wine possessed the highest level of 3-mercaptoethanolanol and 3-mercaptoethanolacete, respectively, among these wine samples. However, the CY passion fruit wine was rated as having the least passion fruit note by the panelists. The mango aromatic feature in these passion fruit wines was mainly attributed to ethyl caprylate, ethyl caprate, \(\beta\)-myrcene and ocimene [36]. The esters mainly strengthened the fruity character in the passion fruit wines, whereas the floral aroma in these wines was mainly from alcohol volatiles, including linalool, nerol, limonene, \(\alpha\)-terpenol and citronello. Phenyl ethyl alcohol and phenethyl acetate have a rose-like flavor. They formed in the alcoholic fermentation in yeasts and were produced from L-phenylalanine through the Ehrlich pathway. In our study, we found that phenyl ethyl alcohol and phenethyl acetate mainly contributed to a floral aromatic feature in passion fruit wine. It is worth noting that the overall aroma perception of these passion fruit wine samples judged by the panelists resulted from the flavor contribution of each major volatile and the contribution from the synergistic effects of multiple volatile compounds in these wines.
## 4 Experiment
### Results and Discussions  
#### 4.4.1 Comparison 1  
To verify the effectiveness of the data augmentation strategy, the comparison experiments between the Wiener-LSTM model with data augmentation and without data augmentation are taken. The predicted RUL by 6 training samples without data augmentation on test bearing1_3 and bearing1_4 are shown in Figure 9. The blue line is the real RUL from time 1000-0 s. The orange line represents the outcome that the Wiener-LSTM model anticipated. In the training process, only six training samples are used to train the Wiener-LSTM model such as bearing1_1, bearing1_2, bearing2_1, bearing2_2, bearing3_1, bearing3_2. The result of the test bearing1_3 is shown in Figure 9a. The result of the test bearing1_4 is shown in Figure 9b. The predicted RUL are far from the real RUL in the Figure 9 both in test bearing1_3 and test bearing1_4. Thus, enough training samples are necessary for the training process of the Wiener-LSTM model.  
Figure 8: The loss function curve of the Wiener–LSTM model training process. Three stages can be divided among the epoch of the loss function, which include the rapid the decline stage, and the slow decline stage, convenience stage.  
Figure 9: The comparison between Wiener–LSTM model with data augmentation and without data augmentation. **(a)** The predicted results on test bearing1_3. **(b)** The predicted results on test bearing1_4. The blue line is the real RUL from 1000–0 s. The red line is the predicted result by the Wiener–LSTM model without data augmentation. The orange line is the predicted result by the Wiener–LSTM model with data augmentation.
## 3 Numerical Modeling
### Mesh  
Four different meshes with cubic cells were utilized in the simulations. Mesh 1 serves as the reference, while the other three are sequential refinements in the region of interest, focusing on the jet and considering a pool depth of 0.8 m. Figure 4 shows a cross-section by the axis illustrating the different meshes. All meshes maintain a constant cell size of 50.0 mm in the air far-field. Below the water surface, a cell size of 12.0 mm was applied in regions distant from the zone of interest. Near the nozzle, cell sizes range from 12.0 mm in Mesh 1 to 1.5 mm in Mesh 4.  
**Mesh 1** features a uniform cell size of 12.0 mm throughout all water domains, represented by the black line in Figure 4, corresponding to 6 cells across the jet diameter (D6), resulting in 5.0 million cells.  
**Mesh 2** introduces a refinement zone near the jet axis, indicated by the green line, with cells measuring 6.0 mm. This refinement increases the resolution to 12 cells across the jet diameter (D12), bringing the total to 6.7 million cells.  
**Mesh 3** incorporates three levels of refinement, resulting in 24 cells across the jet diameter (D24). The smallest cells, shown by the red line, measure 3.0 mm, with 19.0 million cells.  
**Mesh 4**, the most detailed configuration, features 36 cells across the jet diameter (D36) and a cell size of 1.5 mm near the jet (indicated by the orange line). This setup leads to 58.6 million cells across the entire model of the experimental facility.  
At this stage, a symmetry case was also tested. Three symmetry meshes were used: half of the experimental facility, a quarter, and an edge. The analyses of the flow characteristics lead to differences in the pressure at the plunge pool bottom and in the air concentration values along the pool depth. Due to that, this type of domain was discarded.
# Object Detection-Based Video Compression
## 5 Conclusions  
In this paper, we proposed a method for incorporating video compression with an object-detection algorithm. The QP was adjusted in a CU-unit, and the CUs containing objects provide better video quality in the detected-object areas than the background based on a lower QP. By comparison, the CUs without the detected-object areas had low subjective quality based on a higher QP. The averages of \(\Delta\)Y-PSNR, \(\Delta\)Cb-PSNR, and \(\Delta\)Cr-PSNR were better for the areas where objects were detected. The average \(\Delta\)PSNR in the AI configuration was better than that in the RA configuration because the quality of the reference pictures to be used for inter predictions was worse. The overall BD-rate performance in the AI and RA configurations was poor due to the PSNR drop in the background areas. However, the subjective image quality improved when the proposed object detection-based coding was applied. Therefore, the detected objects showed better picture quality. As the objects were not precisely detected in the boundaries of objects, it was not easy to clearly divide and encode the object boundaries using the block-based VVC standard. Although the proposed object detection-based video coding increased the BD-rate, as shown in Table 5, it has many application fields, such as traffic monitoring, density detection and prediction, traffic flow prediction, and resource allocation, in which it is necessary to display only a specific object with high quality without background areas, display all detected objects without background areas, recognize a specific object, obtain security information, etc. Our proposed method is provided with executable code and two batch files in [27]. This consists of two batch files that process the encoder and decoder.
## 2 Materials and Methods
### SEM Investigation  
SEM analysis aimed to analyze the presence of the smear layer on the internal surface of each root. The samples were fixed for 4 h in 2.5% glutaraldehyde in 0.1 M cacodylate buffer at pH 7.4, according to our previous study [35]. Then, they were crown-apically split using a low-speed diamond saw (Buehler Isomet Low Speed Saw) and smoothened with a lapping machine (Buehler Metaserv, Buehler, Lake Bluff, IL, USA) using 600- and 800-grit silicon carbide abrasive papers under constant deionized water irrigation in order to obtain a superficial flat surface which was used to lightly groove the three thirds of the root (coronal, middle and apical regions) to standardize SEM analysis [36]. A _Z4_ gutta-percha calibrated cone (Zarc4 Endo, Shenzhen Denco Medical Co., Ltd, Xiawei Industrial Zone, Shenzhen, China) was placed inside each sample to avoid canal contamination during the root section [34]. Finally, specimens were gold sputter-coated, air-dried and placed on an aluminium holder for SEM observation. Scanning electron micrographs were taken by a field emission electron microscope Zeiss Supra 40 operating at 30 kV and with a working distance of 12 mm, using magnifications of 2000X [37]. Blindly, two independent expert operators assessed the presence or absence of smear layer and debris on the coronal, middle and apical thirds of the root canal surface. The evaluation system applied follows the [PERSON] et al. criteria [38] for results validation, as reported:  
* Score 1: Absence of smear layer, orifices of dentinal tubules uncovered;
* Score 2: A small quantity of smear layer, some dentinal tubules open;
* Score 3: Uniform smear layer covering the root canal walls, scarce dental tubules uncovered;
* Score 4: Whole root canal wall was hidden by a uniform smear layer; no dentinal tubules uncovered;
* Score 5: Substantial, uniform smear layer covering the whole root canal walls.  
Figure 1: A schematic diagram of the experimental setup.  
Finally, the scores obtained were used for statistical analysis to evaluate the removal of the smear layer for each third of the root of the four groups tested.
## 3 Proposed Methodology
### Consensus Mechanism in OSCMS
#### 3.5.3 Reaching a Consensus on Blocks  
The delegates (miners) in the current epoch should not only take turns verifying transactions and producing blocks, but also verify the content and order of all blocks to reach a consensus. For each blockchain, the module for reaching a consensus can be described as follows:  
Step 1: The miner with the current time window collects some transactions in this blockchain network and verifies them. The current block is produced by this miner according to the previous block. If no block is produced in this time window, the miner will be skipped and the next will try to produce it.  
Step 2: The current miner broadcasts the signed block to the network, which can be received by all normal nodes.  
Step 3: Miners must verify these transactions. Other nodes can choose whether to verify them. In addition, each node needs to verify the validity of the block structure and whether the producer matches the time window. If the verification passes, the current blockchain maintained by this node will temporarily accept the block. Otherwise, the block is rejected.  
Step 4: The above steps are repeated. If a miner approves all the previous blocks, it will produce a block pointing to the last one. After \(d\) blocks, the earliest block will be witnessed and approved by more than two-thirds of the miners. At this time, the majority of miners have reached a consensus on it, which is irreversible. In fact, this process only requires more than \((2/3)d\) blocks. For the convenience of description, \(d\) is still used later. How the blocks in DPoS are irreversible is analyzed in white paper [21].
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 2 Diabetic Retinopathy
### Role of OCTA in Diabetic Retinopathy  
Diabetic retinopathy profoundly affects retinal vasculature and OCTA allows depth-resolved, high-resolution imaging that has enabled the improved identification of many detailed pathological features in the superficial, intermediate, and deep vascular plexuses [12; 24]. Features that can be identified on OCTA include capillary dropout, vessel tortuosity, vascular remodeling, choriocapillaris abnormalities, areas of non-perfusion, intraretinal microvascular abnormalities, and disc, retinal, and iris neovascularization [25; 26].
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 5 Performance Evaluation
### Simulation Setup
Therefore, the service popularity distribution in the MEC system changes in time and space. To configure the service request of each user in the MEC system, we use the records in \(\mathcal{D}\). For instance, let us consider the \(a\)-th record in \(\mathcal{D}\), which says that a movie \(b\in P_{j}\) is rated at time \(c\). If a MECS \(m\) is the nearest MECS of a user \(u=a\) mod\(U\) and \(k\) is assigned to \(P_{j}\) in the MECS \(m\), we set that the user \(u\) requests the MECS \(m\) for a service \(k\) at time \(c\). Since there are average \(U\) ratings during six hours in \(\mathcal{D}\), we set the duration of a time slot \(\tau\) to 6 h so that the average number of service requests by each user in the MEC system is one during a time slot. We set the window size \(w_{a}=12\) for both our method and the LSTM method. By using the datasets \(\mathcal{D}\) and \(\mathcal{T}\), we measure \(\Omega(t)=\{\
u_{1}(t),\ldots,\
u_{m}(t)\}\) at the end of each time slot. We shuffle all \(\Omega(t)\)s and randomly select 90% of them as a training dataset and use the rest as a validation dataset. We use the last 10% of the total \(\Omega(t)\)s as a test dataset. We summarize the parameters used for the simulation study in Table 5.
## 3 Theoretical Analysis and Model Construction  
### Mathematical Model of Electromagnetic Wave CT Reconstruction  
In the tomographic inversion and reconstruction calculation processes of coal mine working faces using electromagnetic tomography technology, the target detection area is discretized and divided into hypothetical \(B\) grids, as shown in Figure 2. Each grid is called a pixel, and \(x_{j}\) represents the true absorption coefficient of the \(j\)th grid, \(j=1\), \(2\), \(3\ldots B\).  
Assuming that the \(i\)th ray passes through the detection area from the transmitting end to the receiving end, the intercept of the ray in the \(j\)th grid is \(d_{ij}\). If the number of rays is set to \(A\), the attenuation \(y_{i}\) of the electromagnetic wave on the \(i\)th ray path can be expressed as follows:  
\[y_{i}=\sum_{j=1}^{B}d_{ij}x_{j} \tag{1}\]  
The electromagnetic waves in the detection process were generated at multiple emission points. Further, each emission point corresponds to multiple receiving points to collect data concerning the generated electromagnetic waves. Therefore, Equation (1) can be further extended to the following:  
\[\begin{bmatrix}d_{11}&d_{12}&\dots&d_{1B}\\ d_{21}&d_{22}&\dots&d_{2B}\\ \vdots&\vdots&\vdots&\vdots\\ d_{A1}&d_{A2}&\dots&d_{AB}\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ \vdots\\ x_{B}\end{bmatrix}=\begin{bmatrix}y_{1}\\ y_{2}\\ \vdots\\ y_{A}\end{bmatrix} \tag{2}\]  
that is  
\[[\mathrm{D}][\mathrm{X}]=[\mathrm{Y}] \tag{3}\]  
In the above formula, \([\mathrm{D}]\) refers to the \(A\times B\) coefficient matrix and represents the intercept of each ray passing through each grid, where the number of rays is \(A\) and the number of grids is \(B\).  
\([\mathrm{Y}]\) is the (\(A\times 1\)) constant matrix, and the constant is related to the measured field strength under each observation method. The attenuation amount \(y_{i}\) can be calculated from the field-measured data as follows:  
\[H_{i}=H_{0}-y_{i}-\ln r_{i} \tag{4}\]  
In the above formula, \(H_{0}\) is the original field strength emitted at the signal source, \(H_{i}\) represents electromagnetic wave field strength data for the \(i\)th ray, and \(r_{i}\) represents the length of the \(i\)th ray.  
\([\mathrm{X}]\) is the (\(B\times 1\)) geomagnetic wave absorption unknown coefficient matrix, representing the electromagnetic wave absorption coefficient of each pixel, which is the target  
Figure 2: The schematic diagram of mesh discretization for electromagnetic wave tomography.  
value to be reconstructed by inversion. Specifically, the absorption coefficient of each pixel needs to be obtained, and the image generated by numerical fitting will directly show the geological environment in the working face.  
Due to the limitation of the underground enclosed space conditions, the number of rays \(A\) that can be collected around the working face is far less than the number of grids \(B\). Therefore, the solution \([\mathbf{X}]\) of the matrix Equation (2) under incomplete and highly sparse projection conditions is the core of this work.