esa-sceva/satcom-chunk-collection huggingface dataset:
# DTV Essential Hidden Area Decoder for DTV Broadcasting
## 5 Conclusions  
The embedded DTV Essential Hidden Area (DEHA) holds significant potential for terrestrial, satellite, and internet HDTV and UHD broadcasting. But, DEHA is a small part of the video Bandwidth in the decoder. In conclusion, DEHA is an essential and redundant element in MPEG2 that must be present in the system, but it may go unused and be discarded.  
Figure 15: Active region PSNR of Old Town Cross.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**DEHA Utilization** & **Proposed Method** \\ \hline \multirow{7}{*}{Advantages} & Information can be transmitted by utilizing the unused space during the video encoding process. Here are a few examples: \\  & 1. & It can include copyright information to establish \\  & & ownership of the video. \\  & 2. & This is valuable for verifying the authenticity of the \\  & 3. & Additionally, the subtitle area used in existing DTV can \\  & & be utilized, serving as a means to enhance image quality \\  & & if necessary. \\ \multirow{2}{*}{limitations} & The maximum amount of data that can be inserted is fixed as \\  & 68 Bytes/frame, so only limited information can be included. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Advantages and limitations for DEHA utilization.  
Our proposed DEHA decoder has been successfully implemented to analyze and utilize this previously overlooked feature. By displaying the DEHA as a black bar or the last line copy in conventional broadcasting, we have provided a means to investigate and understand its role in HDTV processes. Furthermore, our experiments have demonstrated the practicality of embedded DEHA for program-related metadata services, enabling the transfer of technical information and details about cameras and post-production technologies. The integration of checked pattern block images has proven effective in facilitating this transfer.  
This technique can be utilized for copyright and security purposes to differentiate between the original video and copies, and it can also enhance video quality by incorporating features like subtitles. With its versatility and compatibility with established standards like ATSC, ISDB, and DVB, the embedded DEHA utilization offers a valuable tool for enhancing DTV services and ensuring synchronized video content.  
Conceptualization, T.-h.K.; Methodology, C.-h.H. and K.-h.P.; Software, T.-h.K.; Validation, T.-h.K. and C.-h.H.; Writing--original draft, T.-h.K.; Writing--review & editing, C.-h.H. and K.-h.P.; Supervision, K.-h.P.; Funding acquisition, K.-h.P. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the BK21 Four project funded by the Ministry of Education, Korea (4199990113966).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Setup
### Dataset Preprocessing  
The data preprocessing stage is mainly divided into the following three stages: wavelet denoising, normalization, and sliding-window segmentation. The sEMG signal can be disturbed by various types of noise, such as noise from electronic devices (from 0 Hz to thousands of Hz) and noise from motion artefacts. Therefore, filtering operations are needed to remove the noise information in order to preserve the original signal's characteristic information as much as possible. The same filtering method is used for the five public datasets to filter the original EMG signals. Wavelet denoising is used to filter the original EMG signals, and the third-level mother wavelet \"db7\" is selected for wavelet filtering. The filtered data are then normalized using min-max normalization to ensure that all data are distributed between \([0,1]\). The formula for min-max normalization is as follows:  
\[\overline{x}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{6}\]  
where \(x_{min}\) represents the minimum value of the signal vector (\(x\)), and \(x_{max}\) represents the maximum value of the signal vector (\(x\)).  
To achieve real-time classification of muscle activity, patterns need to be captured rapidly within as short time windows as possible [31]. This allows for a quick response without physiological awareness of latency. In order to achieve this, we decomposed sEMG signals into small segments using the sliding-window strategy with an overlapped  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database** & **Gestures** & **Subjects** & **Channels** & **Trials** & **Training** & **Testing** & **Sampling Rate** \\ \hline Ninpro & & & & & & & \\ DB4 & 53 & 10 & 12 & 6 & 1, 3, 4, 6 & 2, 5 & 2000 Hz \\ Ninarop & 53 & 10 & 16 & 6 & 1, 3, 4, 6 & 2, 5 & 200 Hz \\ DB5 & & & & & & & \\ BioPatRec & & & & & & & \\ DB2 & 26 & 17 & 8 & 3 & 1, 3 & 2 & 2000 Hz \\ BioPatRec & & & & & & & \\ DB3 & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the sEMG databases used in this paper.  
windowing scheme [32]. The sliding window is used to simulate the real-time processing and classification of continuous-stream EMG signals to realize real-time gesture recognition.  
The length of the window represents a compromise between time latency and classification accuracy. As described in [31], to satisfy the requirements of real-time control, the time latency should be less than 300 ms. Longer extended-window length leads to more controller delays, although it also increases classification accuracy [12; 33; 34]. To test the performance of the proposed algorithm in different scenes, in this study, we selected three different window sizes of 50 ms, 150 ms, and 250 ms and a stride of 25 ms for comparison with similar works. Figure 1 presents the segmentation and combination of sEMG signals. Figure 2 shows the process of segmentation.
# Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays
## 3 Numerical Experiments
### Deep Neural Network Results
The solutions provided by the RNN for these ideal radiation patterns are shown in Table 2. As mentioned before, the output from the RNN served as input signals for the antenna array to generate a radiation pattern, as illustrated in Figure 1. In Figure 5, the radiation pattern is labeled as \"Output pattern (after DL),\" and the pulse of the input data is depicted as an \"Ideal input pattern\". Due to the non-realistic nature of the input pattern, a complete match with the solution was not achievable. Nevertheless, the proposed RNN obtained an output value capable of generating a result similar to the desired beam pattern. Figure 4: Performance of the LSTM during training and validation. (**a**) MSE during the training and validation process. (**b**) MAE during the training and validation process. Figure 5: Synthesized beam patterns obtained with idealized inputs: (**a**) Arbitrary triangular pulse and (**b**) arbitrary square. As part of a test case, we synthesized radiation patterns at fixed locations where the test data do not overlap with the training data; in other words, with radiation patterns that the neural networks had never seen before. Specifically, the deep neural networks (DNNs) had not been trained using these specific positions. Figure 6 illustrates the test input patterns at 55\({}^{\circ}\) and 105\({}^{\circ}\) (shown as dashed lines). Additionally, the recurrent neural network (RNN) provided predictions for these inputs, represented by the synthesized patterns (continuous lines) at these locations. It is essential to mention that radiation patterns of the training data were generated within the range of 50\({}^{\circ}\leq\theta\leq\) 130\({}^{\circ}\), with the phase incremented by 20\({}^{\circ}\), completely differing from the validation and test data. Furthermore, Table 2 displays the complex excitation produced by the RNN for these test patterns. The final radiation synthesis result highlights the DNN's capability to effectively manage the trade-off between antenna parameters (SLL and D), thanks to the information learned during the pre-synthesis process of the beam patterns. Moreover, the proposed DNN demonstrated the ability to predict and adapt to any direction of interest, including ideal beam shapes, as illustrated in Figure 5. Specific numerical values for the sidelobe level and directivity of the output patterns generated by the RNN (both ideal and test patterns) are presented in Table 3. On average, the output beam patterns achieved an isolation level of approximately 12 dB and a directivity close to 8 dB. These results, produced by the LSTM-RNN, are closely aligned with the near-optimal outcomes (based on their fitness and according to the case study) obtained with the metaheuristic optimizer used in the training phase.
## 3 Fundamentals of GLI-Split
### GLI-Addressing
#### 3.2.1 Address Format  
Figure 3 shows the encoding of the three address types reusing the 128-bit IPv6 address format. The 64 higher-order bits are used for routing and special tasks while the 64 lower-order bits contain an  
Figure 2: GLI-nodes translate identifier addresses to local or global addresses when handing data from the transport layer to the network layer and _vice versa_.  
identifier. A similar separation is done by ILNP [22]. All GLI-addresses have a special n-bit GLI-prefix to differentiate them from other IPv6 addresses. Routing is based only on the higher-order bits and our assumption is that appropriate GLI-prefixes are announced in the IPv6 backbone. A marker (L, G) indicates whether the locator part contains a local or global locator. Global addresses have the GL followed by a GAP-bit which is used for multipath-routing, traffic engineering, and interworking (see Section 4.2.2). Identifier addresses have the locator field filled with padding zeros.
## 3 Bi-Level Optimal Model of a Distribution Network Comprising a Micro Energy Grid
### Optimal Model of the Distribution Network  
#### 3.1.1 Objective Function  
The power-supply equipment connected to the upper distribution network mainly included two waste-to-energy power stations, and the micro energy grid. The upper model used the minima of three objectives--the integrated operating cost of the distribution network, the network's active power loss, and the standard deviation of the voltage deviation in the distribution network. Because the number of objectives in the distribution-network level was large, the judgment-matrix method [34] was used to transform the multi-objective problem into a single-objective problem in this research. The weight vector was \(W=(0.5393,0.2974,0.1633)\). The independent variables of the function were the electrical power of the two waste-to-energy power stations, the integrated operating cost of the micro energy grid, the network's active power loss, and the nodal voltage. In this paper, the costs of the distribution line, transformer, and switchgear mainly considered the overall cost of periodical inspection, cleaning, and annual preventive experiments, which was set as a fixed cost and was not embodied in the objective function. The objective function was defined as follows:  
\[F_{1} =\min[w_{1}F_{2}(t)+w_{2}F_{3}(t)+w_{3}F_{4}(t)]. \tag{1}\]  
\[\left\{\begin{array}{c}F_{2}(t)=\sum\limits_{t=1}^{T}\left[C_{\text{gp1}}P_{ \text{gp1}}(t)+C_{\text{gp2}}P_{\text{gp2}}(t)+F_{5}(t)\right]\\ \hskip 14.226378 ptF_{3}(t)=\sum\limits_{t=1}^{T}P_{\text{loss}}(t)\\ \hskip 14.226378 ptF_{4}(t)=\sum\limits_{t=1}^{T}\sqrt{\frac{1}{N}\sum\limits_{ i=1}^{N}\left(\Delta U_{i}(t)\right)^{2}}=\sum\limits_{t=1}^{T}\sqrt{\frac{1}{N} \sum\limits_{i=1}^{N}\left(U_{i}(t)-U_{R}\right)^{2}}\end{array}\right., \tag{2}\]  
where \(F_{2}(t)\) is the integrated operating cost of the distribution network; \(F_{3}(t)\) is the active power loss of the distribution network; \(F_{4}(t)\) is the standard deviation of the voltage deviation in the distribution network, which was used to measure the voltage deviation in the distribution network in a dispatching cycle; \(F_{5}(t)\) is the integrated operating cost of the micro energy grid; \(C_{\text{gp}}\) is the cost of garbage power;  
Figure 3: Bi-level optimization model.  
\(P_{\text{gp}}(t)\) is the electrical power of garbage power; \(P_{\text{loss}}(t)\) is the active power loss of the distribution network; \(\Delta U_{i}(t)\) is the voltage deviation in the distribution network; \(U_{i}(t)\) is the voltage of node \(i\); \(U_{\text{R}}\) is the rated value of the nodal voltage; \(N\) is the number of nodes in the distribution network; and \(T\) is the dispatching cycle.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 3 Study Protocol
### Research Questions  
This SLR was intended to answer the following research questions:  
* RQ1: Are there specific definitions of sustainability of e-solutions in the context of healthcare?  
Objective 1: Analyze the sustainability definitions found in the literature for e-solutions in healthcare and provide a comprehensive definition.
* RQ2: What are the existing dimensions and elements of sustainability for e-solutions in the healthcare context?  
Objective 2: Analyze the dimensions and aspects of sustainability found in the literature for e-solutions in healthcare.  
* RQ3: Are there specific metrics, measures, and assessment methods/models of evaluating sustainability for e-solutions in the context of healthcare?  
Objective 3: Analyze the assessment methods of sustainability models found in the literature for e-solutions in healthcare.
## 4 Simulation and Analysis  
### Simulation Conditions  
Simulations were conducted to show the error overboundings for UAVs under the IMU faults condition using both the full-state EKF (Appendix C) and the error-state EKF. In the simulations, the GNSS measurement noise and the IMU gyroscope and accelerometer measurement noise were modeled as Gaussian white noise, and the corresponding noise parameters were also used in the error overboundings.  
The specific UAV trajectory can be seen in Figure 3, configured as an integration of two 45\({}^{\circ}\) turns and one vertical climb. The simulation time was 7 min.  
The IMU sensors were set as consumer-grade inertial sensors with the noise parameters listed in Table 1[24]. In the simulation, a 24-satellite GPS constellation was used, and the mask angle was 7.5\({}^{\circ}\). The GNSS measurement noise was introduced into the pseudo-ranges as white Gaussian noise with a mean of zero and a variance of 30 m\({}^{2}\). The data update frequency was 5 Hz for the GNSS receiver and 100 Hz for the IMU sensors. The integrated Kalman filter update was only performed when the GNSS signal was updated, otherwise only the IMU output was used. The output frequency was 100 Hz, which was the same as the IMU update frequency.  
All simulations were performed using MATLAB R2018a on a Windows 10 PC with 12 GB of RAM and an Intel Core i7-6700 3.4 GHz processor.
## 2 Basic Principles
### Preprocessing
#### 2.4.3 Normalization  
As the amplitudes of the target signatures depend substantially on the distance between the sensor and the target, normalization of the data is required in order to maintain consistent statistical properties, e.g., uniform SNR, which are required for the training of ML models.
# Sensor Micro and Nanoparticles for Microfluidic Application
## 2 Construction Principles of Sensor Particles
### pH Sensing
The integrated pH sensor microstructures for time-domain dual lifetime referencing (t-DLR) measurements in microfluidic channels were presented by [PERSON] et al. [52] and applied in miniaturized electrophoretic procedures (\(\upmu\)FFIEF). For preparation of a luminescent sensor, pH indicator 5(6)-carboxyfluorescein N-hydroxy-succinimide ester (CF) was covalently bonded to the pHEMA copolymer and dissolved in ethanol. The mixture was added to aqueous solution that contained Ru(dpp)\({}_{3}\) embedded PAN reference nanoparticles and the polymer ink was formed. The polymer ink was inkjet-printed on a glass slide rapidly with a low sample consumption. Then, the glass slide was integrated into a microfluidic chip. The sensor was used in order to separate and determine three different proteins (\(\upbeta\)-lactoglobulin A, conalbumin and myoglobin), which were labelled with red fluorescent dye P503 (Py1). The pH sensor worked in the pH range from 4 to 8 and showed fast response times. The microfluidic chip demonstrated very high stability during measurements under flow conditions with flow rate of 50 \(\upmu\)L/min. In the t-DLR scheme, a signal, which was proportional to the pH value, was estimated from the ratio of the two images, which were taken in different time moments. The first image was made with 5 \(\upmu\)s exposure time under LED irradiation. It provided information about the luminescence decay times of pH indicator and reference dye. After illumination was stopped and delay of 1 \(\upmu\)s, another image was taken and collected the information only about the emission decay of reference dye. Due to the fact that lifetime-based ratiometric pH sensing was independent of background noises, it provided more accurate data. However, the pH sensor calibration was valid for a limited time due to the decreased intensity caused by applied constant LED illumination for 30 min.
# Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence
## 3 Methods  
### Model Definition  
In this paper, we propose a cross-modal fake news detection method based on multi-level fusion without evidence (CM-MLF). As shown in Figure 1, the model is mainly composed of four parts: feature extraction, feature alignment, feature fusion, and feature detection.  
For each input multimodal news \(x=[T,~{}P]\in D\), where \(T,~{}P\), and \(D\) denote text, data, and dataset separately. Firstly, in the feature extraction module, for the text T of a given news, high quality semantic features are extracted from the news text using the large-scale pre-trained language model BERT, which serve as the base text features \(e^{t}\) input to the subsequent module. And for a given news image \(P\), a large-scale pre-trained deep learning architecture Resnet is utilized to extract high-quality semantic features from the news image as the base image features \(e^{v}\) input to the subsequent module. Secondly, in the feature alignment module, the extracted text features \(e^{t}\) and image features \(e^{v}\) are cross-modal aligned by comparative learning, and finally the aligned unimodal representations \(m^{t}\) and \(m^{v}\) are obtained. Next, in the feature fusion module, this part is mainly divided into two stages. In the first stage, the cross-modal aligned text features \(m^{t}\) and image features \(m^{v}\) are input into the multi-head attention mechanism for cross-modal feature fusion to obtain cross-modal primary fusion features \(m^{final}\). In the second stage, KL disambiguation scores are introduced to assist in the design of dynamically learnable attention weights that adaptively adjust the contribution of aligned text features \(m^{t}\), aligned image features \(m^{v}\) and primary fusion features \(m^{final}\). These three features are fused again and the resulting features are used as detection features \(\widetilde{X}\). Finally, in the feature detection module, the detection features \(\widetilde{X}\) are used as the input information for the false news detection classifier, and the final binary classification result for information detection is obtained.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 6 Simulation Analysis
### Simulation Comparison  
#### 6.4.1 Formation-Switching Method  
The formation-switching control method proposed in this paper mainly dealt with the communication abnormalities of the UAVs during the flight. Therefore, the classic consistency control method [31], which could handle changes in the communication topology, was selected to compare to illustrate the effectiveness of the algorithm.  
We selected six UAVs for simulation, and used the simulation scenario in Section 6.3.2 to set the consistency control step to meet the minimum algorithm stability requirement, which was 0.2 s. The simulation of the classical consistency control method is shown in Figure 21, and the method proposed in this paper is shown in Figure 22.  
Figure 19: Formation switch under abnormal communication.  
Figure 20: Formation change process under abnormal communication.  
The algorithm performance during the simulation process was recorded, as shown in Table 6. Simulation comparison.  
The simulation results showed that when the step length was 0.5 s, in the case of abnormal communication, the classic consistent formation control method needed to communicate with neighboring UAVs 200 times before the formation could be changed from an inverted V to a V-shape. The route-based formation-switching method proposed in this paper only needed two communications to obtain the required formation waypoints, which greatly reduced the pressure on the airborne communication system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** &
\begin{tabular}{c} **Classical Consistency** \\ **Control Method** \\ \end{tabular} & **Proposed Method** \\ \hline Number of communication requests & 200 times & 2 times \\ Average solution time per & 125 ms & 53 ms \\ communication & 1521 ms & 131 ms \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation comparison.  
Figure 21: Formation switching of the classical consistency control method.  
Figure 22: Formation switching.  
In addition, the classic consensus algorithm obtained the coordinated route of the UAV by controlling the deflection angle of the track. The average solution time per communication took a longer time, and required high control system performance, which could not guarantee the real-time performance of online formation flying. The solution proposed in this paper was time-consuming, and only required the UAV to perform direct flight and turning maneuvers. It was easy to control during the flight and easy to implement in engineering. However, the disadvantage was that the planned path distance was relatively long.