esa-sceva/satcom-chunk-collection huggingface dataset:
# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 3 Methods
### Swin Transformer-Encoding Block  
Although convolutional neural networks (CNNs) are good at extracting local information from text images, the depth of the network leads to loss of specific object details. To address this issue, this study incorporates the concept of the swin transformer [30] and introduces a transformer-encoding block. This block employs a window-based multi-head attention mechanism to capture extensive global contextual information while minimizing the computational power required for low-resolution mapping.  
The network model diagram of the Swin Transformer is shown in Figure 5, illustrating the following steps to extract feature information from the input image. First, the RGB image was divided into small and non-overlapping feature map modules, and feature information was extracted from these sequential elements. Next, the global modeling semantic information was extracted, and the mapping was linearly embedded. A two-layer Swin Transformer block was used to obtain semantic feature information, and finally the input dimension was reduced by patch merging. The receptive fields of patches and windows are enhanced to extract feature information from each layer.  
The structure of the Swin transformer block is shown in Figure 6 and consists of three main components: layer norm (LN), window-based multi-head self-attention (W-MSA), and shifted window multi-head self-attention (SW-MSA). This module builds windowed multi-head self-attention and multi-layer perceptrons using residual connections. It adopts local windows as basic units and focuses on windows to preserve image features and minimize computational complexity. Furthermore, feature maps are shifted in various directions based on half the window length to facilitate cross-window feature interactions and improve feature extraction.  
The complete calculation formula for the Swin transformer module is presented below:  
\[\overline{Z}^{l}=W-MSA(LN(Z^{l-1}))+Z^{l-1} \tag{3}\]  
\[Z^{l}=MLP(LN(\overline{Z}^{l}))+\overline{Z}^{l} \tag{4}\]  
Figure 5: Swin transformer module diagram.  
Figure 6: Swin transformer Block structure.  
\[\overline{Z}^{l+1}=SW-MSA(LN(Z^{l}))+Z^{l} \tag{5}\]  
\[Z^{l+1}=MLP(LN(\overline{Z}^{l+1}))+\overline{Z}^{l+1} \tag{6}\]  
where \(Z^{l}\) and \(\overline{Z}^{l}\) are the output of the MLP and self-attention in the first block, \(Z^{l-1}\) is the input image sequence, \(Z^{l+1}\) is output image feature sequences, \(LN\) is a layer normalization operation, _W-MSA_ is a windowed multi-head self-attention module, _MLP_ is a multi-layer perceptron, and _SW-MSA_ is a shifted window self-attention module.
# Improved ECG Watermarking Technique Using Curvelet Transform
## 5 Pseudo Code
### At Receiving: Reversible Blind Extraction  
1. Read watermarked 1D-ECG signals that embedded with the patient's information;
2. This watermarked ECG signals is altered into a 2D-ECG image as the procedure discussed in Section 3.1;
3. To convert time-domain image into frequency domain curvelet transform was applied on 2-D watermarked ECG image;
4. Then form this frequency domain the course level was selected. The selected scale's coefficients are classified into clusters as discussed in Section 3.4 above;
5. The selected clusters used in the embedding procedure numbered and treated as the key in the extraction process;
6. Then for each selected cluster calculate I\({}_{0}\)(q), I\({}_{1}\)(q), L\({}_{0}\)(q), L\({}_{1}\)(q) by using \(\rm{Radi}_{i1}\), \(\rm{Radi}_{i2}\) as discussed in Equations (5)-(7);
7. Then for each selected cluster extract bits of the watermark by using Equation (10). Now convert these bits into the 2-D image to get the extracted watermark image, i.e., text data of patient information in the form of an image.
# Experimental Study of 3D Concrete Printing Configurations Based on the Buildability Evaluation
## 2 Materials and Equipment  
### Mix Proportion  
The cementitious materials used in this study were ordinary portland cement (OPC 42.5 R, purchased from Foshan China) and sulfoaluminate cement (SAC, 42.5 R, purchased from Shanghai, China). The purpose of using SAC was to meet the requirements of (1) fast setting to keep the designed geometry, (2) high early strength to bear the continuous layer weight, and (3) low shrinkage to prevent cracks along the interfaces between layers and lines [23]. Table 1 shows the chemical composition of both OPC and SAC materials.  
Fine sands with a diameter from 210 \(\upmu\)m to 250 \(\upmu\)m were used as the aggregates. Polypropylene fiber (PFB, round interface, 98% carbon content, density 1.7 g/cm\({}^{3}\), tensile strength 4900 MPa, tensile modulus 228 GPa, fiber diameter of 7 \(\upmu\)m, length ranging from 3-6 mm) was used to increase the tensile and flexural strength of the printed outputs. High  
Figure 1: A typical concrete printing procedure lacks configurations from the printing material.  
range water-reducing agent (HRWR, polycarboxylic acid superplasticizer) and thickening water-retaining agent (HPMC, hydroxypropyl methylcellulose) were chosen to improve the printability of paste by improving the viscosity [24; 25]. A defoaming agent (DFA) decreased the air bubbles generated during the mixing and extruding processes.
# RF Energy Harvesting Wireless Communications:
## 7 Potential Applications
### Healthcare of Animals  
Studies have revealed that animals usually hide their illness and pretend to be good even when they are sick. This feature makes it difficult for veterinarians to save animal's lives once clear illness symptoms appear. Therefore, how to monitor the health status of a large number of animals in a zoo or on a farm becomes an important but challenging issue.  
The multi-tier RF-EHWC network provides a cost-effective solution, as shown in Figure 20. In Tier 1, energy harvesting biosensors are implanted into an animal's body and form an ad hoc BAN. In Tier 2, each BAN is associated with a sink node to form a local area healthcare network (LAHN). The sink node is embedded in a collar, an ear tag, or a leg band of an animal. It is responsible for forwarding the data collected by the biosensors to the nearest AP. In Tier 3, a wide area healthcare network is established by connecting all APs into Ethernet for real-time health monitoring.
## 4 Evaluation
### Settings  
**Simple experiment.** We will conduct experiments in a simulated environment; the experimental data use the real data of the 4G/LTE dataset. We compare the adaptive performance of our model with a simple experiment where throughput drops suddenly.  
Figure 4: Process of the training.  
On the server side, the system is Ubuntu 16.04 with the Apache HTTP server version 2.2.22. On the client side, the system uses Windows 10, and the CPU is Intel(R) Core(TM) i5-8400. For live connections, the server's timeout is set to 100s, and the video stored on the server is subdivided into simultaneous 2 s video clips. Additionally, there are six available bitrate versions for each clip. They are 300, 750, 1200, 1850, 2850, and 4300 kbps, respectively. The initial buffer delay is equal to 10 durations (20 s).  
**Complex experiment.** The complex environment has three edge servers, and the penalty function for the number of connections is distributed at set coordinates, from which the data of each user are calculated. We designed three allocation strategies to compare with our model:  
* Greedy strategy. At each time slot, each user is assigned to the closest agent as long as there are remaining connections on that edge server, and the streaming bitrate transmitted is based on the Pensieve model.
* Random strategy. Each user is randomly assigned to an edge server, as long as there are remaining connections on that edge server, and the streaming bitrate transmitted is based on the Pensieve model.
* Step-by-step reinforcement learning. Reinforcement learning is used to implement user assignment, and then a Pensieve model is used to select the bitrate of the video stream.  
**Implementation Details.** We have set the learning rate \(\alpha=0.0002\), the discount factor \(\gamma=0.9\), the experience pool size buffer\(\_\)size \(=\) 10,000, and the batch size per training batch\(\_\)size \(=\) 16. For the calculation of QoE in (1), we set \(bw\), \(sw\), \(tw\), and \(rw\) to be 1, 1, 1.5, and 0.01, respectively. Based on the Ornstein-Uhlenbeck process [28], a noise \(\mathcal{N}\) is introduced in order to add a bit of randomness and increase the learning coverage when selecting actions. It starts with an exploration factor \(e=1\) that decreases to a minimum of 0.01 at the end of the training, with a decay factor of 0.999, indicating that the noise has less and less influence. Although it will increase the training time, the soft update factor \(\tau=\) 0.01, which will increase the stability of the training. The parameters are shown in Table 3.
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 8 Simulation Results
### Comparison of Our Proposed System with the 2D Optimal Static Traffic Light Intersection System
Meanwhile, in the traffic light system, the duration of the green time is fixed, hence the delays experienced by all lanes are equally distributed. The comparison of the average number of UAVs in the intersection on this scenario is shown in Figure 29. There is no difference using our system and the optimal traffic light system when the arrival rate is less than or equal to 40 UAVs per minute. While when the arrival rate is 50 UAVs per minute, the average number of UAVs in the intersection using the traffic light is greater by one UAV compared to our system. The maximum of average number of UAVs in the intersection is 16 UAVs for the traffic light and 14 UAVs for our system. Similar to the previous scenario, we also obtained the number of UAVs in the intersection every 1 s for the traffic light system and every 0.2 s in our system, as shown in Figure 30. The arrival rate in each simulation is 50 UAVs per minute per direction. We started obtaining the number of UAVs for 25 s after the simulation has been running for 2 min. Looking at the traffic light system, there are mostly 60 UAVs for the first 3 s, then it decreased after the traffic light turned yellow. The number of UAVs started to increase again when the light of the next phase turned green. However, the maximum number of UAVs during the next phase only reached 20. This is because the green light is assigned to phase 3, as shown in Figure 24b. On phase 3, the UAVs entering the intersection are from the left-most lanes. In our system, the maximum number of UAVs is 29, while the minimum is four UAVs. This also reflects the average number of UAVs when arrival rate is 50 UAVs, as shown in Figure 29.  
Figure 28: Average queueing delay of the UAVs using 2D optimal static traffic light and using our proposed system in 2D intersection on Scenario 2.
## 3 Methodologies
### Frequency Ratio Analysis  
The FR reflects the significance of different attribute intervals of the indicator factors in relation to landslide susceptibility. An FR greater than 1 indicates that a specific category interval influences landslide occurrence, whereas an FR less than 1 suggests no association with landslide formation [30]. In this study, the FR method was applied to categorize continuous indicator factors, and the FR for each influencing factor category was calculated using the following formula:  
\[FR=\frac{\frac{A}{B}}{\frac{C}{D}}, \tag{1}\]  
where \(A\) is the landslide area within a specific category, \(B\) is the total landslide area in the study region, \(C\) is the area occupied by that category in the study region, and \(D\) is the total area of the study region.  
Continuous factors were segmented into smaller intervals, and the frequency ratios for both continuous and categorical factors were computed using the above formula. The intervals of the continuous factors with similar frequency ratios were subsequently combined. The results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Influencing Factors** & **Classification** & **FR** & **Influencing Factors** & **Classification** & **FR** \\ \hline \multirow{8}{*}{Aspect (\({}^{\circ}\))} & Plane & 0.00 & & I & 0.00 \\  & North & 1.27 & & II\({}_{1}\) & 1.61 \\  & Northeast & 1.10 & & II\({}_{2}\) & 0.94 \\  & East & 1.26 & & II\({}_{3}\) & 0.53 \\  & Southeast & 1.02 & EGRG & III\({}_{1}\) & 3.98 \\  & South & 0.98 & & III\({}_{2}\) & 0.19 \\  & Southwest & 0.64 & & III\({}_{3}\) & 0.67 \\  & West & 0.78 & & \(\Pi_{4}\) & 1.55 \\  & West & 0.96 & & IV & 0.00 \\ \hline \multirow{8}{*}{Slope structure} & Near-horizontal & \multirow{2}{*}{0.76} & \multirow{8}{*}{Curvature} & \multirow{2}{*}{\(-\)1—0.6} & \multirow{2}{*}{1.04} \\  & layered & & & & \\ \cline{1-1}  & Downward dip & & & & \\ \cline{1-1}  & slope & & & & \\ \cline{1-1}  & Synclinal slope & 0.94 & & & \\ \cline{1-1}  & Parallel slope & 0.93 & & & \\ \cline{1-1}  & Lateral slope & 0.89 & & 0.2–0.8 & 1.14 \\ \cline{1-1}  & Anticinal slope & 1.05 & & 0.8–1 & 1.04 \\ \cline{1-1}  & Reverse slope & 1.30 & & \(>\)1 & 0.87 \\ \hline \multirow{8}{*}{Elevation (m)} & \textless{}0 & 0.00 & & Cropland & 2.31 \\  & 0–300 & 5.98 & & Forest & 0.64 \\ \cline{1-1}  & 300–600 & 2.81 & & Shrub & 0.36 \\ \cline{1-1}  & 600–900 & 1.19 & & Grassland & 0.37 \\ \cline{1-1}  & 900–2100 & 0.30 & & Water & 0.00 \\ \cline{1-1}  & \textgreater{}2100 & 0.00 & & Impervious & 11.02 \\ \hline \multirow{8}{*}{Flow width (m)} & \textless{}31 & 1.05 & & \textless{}1430 & 0.00 \\ \cl
# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
## 2 Methodology  
### Structure of the Proposed MS Minimisation Control Strategy  
Figure 2 depicts the proposed MS minimization control structure. It consists of an outer and an inner loop system. The outer loop is a path tracking system, where the vehicle tracks the desired coordinates, \(x_{ref},\ y_{ref}\) and heading, \(\varphi_{ref}\). The system applies a Stanley controller to obtain the wheel angle, \(\delta_{s}\) from the information of lateral error, \(e\) and heading error, \(\varnothing\). \(e\) is computed based on the desired coordinates, \(x_{ref},\ y_{ref}\) and the current vehicle coordinates, \(x_{c},\ y_{c}\) while \(\varnothing\) is calculated based on the desired heading, \(\varphi_{ref}\) and the current heading, \(\varphi\).  
Based on Figure 2, the focus of this study is the usage of fuzzy-PID controller in the inner loop with a dedicated purpose to compute an additional corrective wheel angle, \(\delta_{c}\) to diminish the lateral acceleration, \(a_{y}\). The scaling factors, \(K_{e}\), \(K_{d}\), \(\beta\), \(\alpha\) of the Fuzzy-PID are optimized by using particle swarm optimization (PSO) to acquire the optimal values. The corrective wheel angle, \(\delta_{c}\) is computed by the fuzzy-PID controller. The inputs for the fuzzy-PID are head roll error, \(e_{H}\) and derivative head roll error, \(e_{H}\). The responses of estimated driver's and passenger's head roll angles, \(\theta_{HD}\), \(\theta_{HP}\) represent desired and actual responses. The difference between these responses is calculated to compute head roll error, \(e_{H}\).  
Both head roll responses are estimated by RBFNN modeling method. The input of the RBFNN prediction model is the lateral acceleration, \(a_{y}\). An experiment is carried out to provide data for the  
Figure 2: The proposed control structure.  
modeling process. Lastly, the wheel angle generated by the path tracking, \(\delta_{s}\) and the corrective wheel angle produced by Fuzzy-PID controller, \(\delta_{c}\) are totaled and become the final wheel angle input, \(\delta\).
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 4 Experiment
### Experimental Results
#### 4.3.3 Impact of Neural Network Architecture  
For specific tasks, there is a significant variation in high classification accuracy achieved by different network depths and neuron counts [43]. In the combined detection method (with CCA), using a neural network as the classifier required exploring the number of hidden layers and the number of neurons in each layer to determine the optimal network structure. Table 5 presents the relationship between the hidden layer structure and classification metrics (sensitivity, specificity, accuracy). From this table, it can be observed that using a two-layer hidden layer structure yielded significantly higher specificity and accuracy compared with a single-layer hidden layer structure. Furthermore, the classification performance reached its optimum when the number of neurons in the hidden layers was set to 128 and 64, respectively.  
Figure 6: The effect of the reduced dimensionality on the detection performance. (**a**) The effect of the reduced dimensionality on the sensitivity. (**b**) The effect of the reduced dimensionality on the specificity. (**c**) The effect of the reduced dimensionality on the accuracy.
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 3 Results and Discussion
### Comparison of Drought Index Calculation Results Based on Satellite Data and Ground Data  
For the forecast and warning operation period from 2017 to 2019, a meteorological drought index (SPI), VHI, and hydrological drought index (WBDI) calculated from satellite data were spatially illustrated, compared, and analyzed with the drought index for each type of drought calculated from ground data (Figure 7). In the case of meteorological drought indices, since the same SPI was applied, the spatiotemporal drought patterns of the drought index calculated using ground and satellite data appeared to be similar. However, the agricultural and hydrological drought indexes exhibited significantly different spatiotemporal changes, such as the depth and time of drought. In the case of satellites-based indices, VHI, an agricultural drought index, was calculated by applying vegetation-related indices, whereas WBDI, a hydrological drought index, was calculated and applied through the basin-wise water balance. Hence, there is a significant difference between these and the drought indices calculated based on ground data. In the case of satellite data, there is a  
Figure 4: Accuracy distribution of the ground data-based drought index.  
Figure 5: Comparison of the correlation analysis results by the drought index for each method.  
time delay in providing data, and it is difficult to calculate indices that can replace actual drought forecast operating data.  
Although monitoring drought using satellite data offers many advantages (e.g., monitoring ungagged basins, utilizing various hydrological factors, etc.), there are several drawbacks as well, such as the difficulty of real-time monitoring, the need for extensive data processing to derive desired hydrological factors, and large deviations from ground observation data. To this end, a plan for integrating drought forecast and warning based on a drought index using ground data was proposed in this study.
# Variable Guide Vane Scheduling Method Based on the Kinematic Model and Dual Schedule Curves
## 3 Analytical Model of the VGV Link Mechanism
### Model for the Links from Actuator to Unison Ring
\begin{array}{cccc}r_{11}&r_{12}&r_{13}&x_{ 1}\\ r_{21}&r_{22}&r_{23}&y_{1}\\ r_{31}&r_{32}&r_{33}&0\\ 0&0&0&1\end{array} \right]=\left[ \begin{array}{cccc}c\theta_{1}&-s\theta_{1 }&0&L_{x}c\theta_{1}+L_{x}\\ s\theta_{1}&c\theta_{1}&0&L_{x}s\theta_{1}\\ 0&0&1&0\\ 0&0&0&1\end{array} \right] \tag{5}\]  
The position terms of the Equation (5) matrix are summarized as Equations (6) and (7) to obtain \(\theta_{0}\) and \(\theta_{1}\). \[\theta_{1}\ =\ \cos^{-1}\left(\frac{x_{1}^{2}+y_{1}^{2}-L_{x}^{2}-L_{x}^{2}}{2 L_{x}L_{e}}\right) \tag{6}\]  
\[\theta_{0}\ =\ \sin^{-1}\left(\frac{y_{1}L_{e}\cos\theta_{1}+L_{x}y_{1}-x_{1} L_{e}\sin\theta_{1}}{x_{1}^{2}
# Plasma Medicine Technologies
### Bio-Sterilization and Decontamination  
Nonthermal biocompatible plasma is a promising technology for the control of environmental pollution. Plasma-generated reactive species can be used to degrade organic and inorganic pollutants. [PERSON] et al. reviewed various investigations into the use of nonthermal atmospheric pressure plasma technology to control organic wastewater purification. This study provided key outlines of the operational conditions, standards, parameters, and boundaries, and analyzed the continuing advancements related to the use of cold plasma in wastewater pollution control [9]. This review summarized previous investigations into the applications of plasma in water purification. Plasma production methods and their effectiveness have been investigated extensively. These investigations suggested that the results of interactions between the plasma and waste liquid could be utilized as an innovative water management technology that could eradicate pollutants in drinking water. Moreover, it is suggested that more thorough investigations with complex analytical methods are needed to elucidate the mechanisms implicated in plasma interactions with hazardous substances in water.  
Several fungal species that release high amounts of pathogenic spores into the air are indoor air and food contaminants. Plasma can also be used to control fungal spores efficiently. A study by [PERSON] et al. elucidated the death mechanism of fungal spores using a nonthermal biocompatible air gas plasma jet, simply called soft plasma jet [10]. They investigated the effect of plasma on the viability of spores and indicated that changes in cell wall rigidity and an induction of DNA damage were mainly affected by plasma-treated _C. pruinosa_ spores. This study elucidated the effects of plasma-induced reactive species as the main stress source for _fungal_ spores. Thus, plasma devices with a biosterilization function could be used for sanitary purposes in the medical manufacturing and food industries.  
This research received no external funding.  
Authors are supported by National Research Foundation under the Ministry of Science and ICT (NRF-2016K1A4A3914113) of the Korean Government, European Social Fund (ESF/14-BM-A55-0006-18) and German Federal Ministry of Education and Research (03Z22 DN11, 03Z22D1i, 16 GW0344k, 03 COV06A), The Flanders Research Foundation (12S9221N, G044420N) and a Research Grant from Kwangwoon University in 2021.  
The authors declare no conflict of interest.