esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations and Discussions
### Evaluation of Detection Capability
The _RIB download_ time can be calculated as \(14.94+6\approx 21\) ms (downlink transmission delay of RIB and average propagation delay of the satellite-ground link), the _RT download_ time can be calculated as \(11\times 3.46+6\approx 44\) ms (downlink transmission delay of RT and average propagation delay of the satellite-ground link), and the _RT upload_ time can be calculated as \(1.73+6\approx 8\) ms (uplink transmission delay of RT and average propagation delay of the satellite-ground link). In this case, after sending the RIB to the ground station, the PS continues to send the routing tables of all satellites in the same orbit, and the ground station uploads the correct RTs according to the ID of the affected satellites. Figure 21 shows the routing convergence time in each case. The average time under NS (NS Avg) is 254.5 ms. Compared with NS Avg and NS Max, the time cost under SN increased by 17.6% and 3.1%; the time cost under GN increased by 20% and 5.1%; and the time cost under SGN increased by 28.2% and 12.3%, respectively. In theory, the probability of high-energy protons or galactic cosmic rays causing an SEU is 16% [1; 37], and the experimental results of this article show that the probabilities of SH and GH occurrence caused by SEU are around 1% and 2%, respectively. Therefore, the probabilities of SH and GH occurrence caused by high-energy protons or galactic cosmic rays are around 0.16% and 0.32%, respectively. Therefore, the satellite network is under the NS situation most of the time. At the same time, when there are SHs and GHs caused by SEU, the additional time cost range is between 3.1% and 28.2%, which is acceptable for the satellite system as a whole.
# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
## 2 Method  
The proposed method utilizes a comprehensive approach for the prediction of GNSS deformation monitoring in hydraulic structures. It offers a novel framework for feature extraction and long-term sequence modeling, enabling accurate and proactive deformation prediction within hydraulic structures. The workflow integrates the CNN and GRU models to capture spatial-temporal patterns and dependencies, which are presented in Figure 1. The proposed method combines the feature extraction capability of CNN with the temporal modeling ability of GRU, which effectively captures spatial and temporal features in GNSS time series.  
The steps of the proposed method are as follows:  
1. Data Preprocessing: firstly, normalize the input data.
2. Convolutional Feature Extraction: CNN is employed to extract spatial features from the input data. Typically composed of convolutional layers and pooling layers, CNN performs feature extraction through convolutional operations and reduces the size of feature maps via pooling operations.
3. Modeling for GNSS time series: the convolutional features extracted by CNN are inputted into GRU for modeling. Through the update gates and reset gates of GRU, the flow of information is controlled, enabling the network to better capture long-term dependencies within sequences.
4. Prediction and Evaluation: the output of GRU is connected to the fully connected layer of CNN to accomplish specific tasks.  
The entire CNN-GRU model is trained using labeled data and optimized via a back-propagation algorithm to better fit the training data and generalize new data. The trained model is evaluated using a validation set or test set to assess its performance on unseen data. Various metrics such as accuracy, precision, and recall are utilized to evaluate the model's performance.
# DTV Essential Hidden Area Decoder for DTV Broadcasting
## 3 The Proposed DEHA Decoder for DEHA Utilization
### Metadata Transfer for DEHA Utilization  
Program-related technical metadata plays a crucial role in ensuring the compatibility and quality of television programs. These metadata are specifically tailored to suit the requirements of different cameras, post-production technologies, and editorial processes. By including both editorial and technical metadata, a consistent set of information is created to facilitate the processing, review, scheduling, and distribution of programs within the television industry. However, despite the significance of technical metadata for broadcast producers, a significant portion of these metadata is often missing in the DTV stream used for broadcasting. To address this limitation, a careful selection of relevant metadata has been made for utilization within the DEHA framework. The specific technical metadata chosen for DEHA utilization can be found in Table 2, ensuring their inclusion and utilization for enhanced broadcasting capabilities.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field Name** & **Technical Metadata** \\ \hline Producer & SVT Technology R\&D \\ Camera & ARRI AmFlex 765.65 mm \\ Exposure Time & 1/100 s \\ Post-processing & Apple Shake \\ Editing & Cut and Dissolve \\ Time of original & October 2004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical metadata for DEHA utilization.  
Figure 9: Decoded block image by horizontal and vertical motion and mQuant in MPEG CBR encoding.  
An Example Metadata for DEHA Utilization  
Blocks consisting of black, white, and gray pixels have a single DC coefficient in the transformed frequency domain. When these blocks are employed in practical DTV data services, the video quality achieved using the proposed method will remain consistent with conventional quality standards. To facilitate the utilization of DEHA, the proposed program-related metadata transfer method employs 240 black and white blocks, as depicted in Figure 10. By leveraging these blocks, the proposed method ensures efficient utilization of DEHA while maintaining the desired video quality standards.  
Similar to a chess board, the metadata are merged at the bottom of an active video within the video frame memory and subsequently compressed into the video stream. Upon decoding, the compressed DEHA data is transferred to the decoder. However, it should be noted that conventional HDTV receivers do not utilize or display these data for DTV services. In contrast, the proposed decoder is capable of displaying the metadata on the screen as a Picture-in-Picture (PIP) image accompanied by text. This functionality allows for the presentation of metadata resembling a chess board or QR code, particularly in the case of black and white blocks. Given that each frame of the DEHA contains 30 characters (8 bits per character) and a total of 240 blocks, the proposed decoder enables the display of metadata in a visually structured manner.
### Outline  
In this context, two control strategies are investigated: a threshold-based method and a decentralized PID controller. The threshold-based approach relies on simple, rule-driven actuations based on predefined conditions. In other words, when a preselected threshold is reached by the transported material, the device actuates according to implemented laws. In contrast, the PID controller introduces a feedback system that computes the input variable using the dynamic equations of the transported material. These equationsincorporate the specific conceptual model and adjustments to traditional formulations to account for the device's novel design.  
This paper also addresses the challenges posed by the nonlinearities inherent in underactuated systems, as well as the physical simplifications of the surface design. Through experimental validation and simulation, the proposed control methodologies are evaluated in terms of handling capability and qualitative accuracy. In particular, the first method was experimentally validated using the system prototype setup applied to typical handling operations, while the second method was validated in a simulation environment. More specifically, this second evaluation was carried out using two potential industrial applications as case studies, one of which is associated with a real-world problem identified in collaboration with the industrial partner _Dumarey Flowmotion Technology._ The promising results highlight the potential of this method to enhance the functionality and adaptability of the system without compromising its cost-effectiveness or energy efficiency. The remainder of this paper is structured as follows: Section 2 describes the system concept and digital model, Section 3 outlines the control methods, Section 4 discusses the results of testing and control evaluations, and Section 5 concludes with a summary of the paper and key insights.
# A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks +
#### Related Works
In contrast to exact and heuristic approaches, our matheuristic has the merit of running fast, while at the same time exploiting the precious information that a mathematically stronger model of the problem can provide. In particular, such information is obtained from suitably strengthened linear relaxations and can be used to operate a better setting of the values of the decision variables (fixing) within the genetic algorithm and the ILP improvement heuristics. Furthermore, by exploiting linear relaxations, we can also compute an _optimality gap_ indicating how far the best feasible solution found is far from the optimum and thus providing an indication of how good are produced feasible solutions. To our best knowledge, there is just one work that tried to combine a metaheuristic with an exact improvement heuristic for the design of DVB networks, namely [31], which proposes an improved ant colony algorithm. However, in contrast to our present work, it is important to highlight that [31] considers an approximated model where service coverage is provided by one single transmitter (differently from the present work, where we consider the combined effect of multiple serving transmitters as detailed in the DVB-T technical specifications) and was not developed with the aim of sorting out numerical instabilities that characterize wireless network design.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 3 Results
### Final Proposed Positioning Method  
In order to obtain a final positioning method, the combination from the previous results that yielded the best overall performance was chosen. For the LS and WLS, this corresponds to the case without threshold filtering and with a Hatch Filter with \(\gamma=0.010\); In the case of the EKF and UKF, it is the case with a \(2\sigma\) threshold and a Hatch Filter with \(\gamma=0.010\). The error metrics in these conditions are presented in Table 3 and the mean error for the whole survey is given in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Error Metric** & **LS** & **WLS** & **EKF** & **UKF** \\ \hline Mean (m) & 1.386 & 1.300 & 0.694 & 0.584 \\ \hline RMS (m) & 2.655 & 2.597 & 1.536 & 1.314 \\ \hline DRMS (m) & 1.589 & 1.557 & 0.740 & 0.676 \\ \hline MMSE (m) & 2.264 & 2.249 & 1.370 & 1.177 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error metrics for 24 h position survey of the reference station antenna using the finalized position methods.  
Figure 8: Effect of a \(2\sigma\) threshold on the mean error over time for the reference station antenna with no Hatch Filter.  
Figure 9: Effect of a \(2\sigma\) threshold on the mean error over time for the reference station antenna with Hatch Filter and \(\gamma=0.010\).  
While using a Hatch Filter for the LS and WLS increases the mean error slightly, it also reduces the noise in the position estimates significantly, allowing for much more precise, even if slightly less accurate, position estimates; for this reason, it was opted to be included in the final proposed method.
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
## 3 CRC Computational Process  
To fully understand the proposed algorithm, we will briefly revisit the serial CRC generation process, lookup table based method, matrix transformation method, and introduce the proposed hybrid LUT-MT algorithm.
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 2 Materials and Methods
### Methodology  
#### 2.3.1 Data Pre-Processing  
Data pre-processing is an essential step to guarantee the integrity and dependability of the data utilized in modeling [46]. This entails the following:  
1. Data cleaning:
2. Handling missing values: Methods include replacing missing values with the mean, using regression to estimate missing values, or removing incomplete cases. The mean is commonly employed to replace missing values in numerical data when the data distribution is symmetrical and devoid of outliers. This approach is straightforward and maintains the average of the data. This method uses multiple imputation to generate several datasets with imputed values, which are subsequently merged to provide the final outcome. It takes into consideration the lack of certainty in the estimated values and is beneficial when there are more intricate connections between variables.
3. Correcting inconsistencies and outliers: This involves identifying and resolving errors or outliers using data profiling and statistical methods. Outliers are identified as data points that fall far from the \"cloud\" of other points in a dataset and can significantly affect the results of a regression analysis. This study employs Huber regression to address outliers, as it is a robust technique that mitigates the impact of outliers by utilizing a combination of squared and absolute residuals. It exhibits greater robustness to outliers compared to ordinary least squares (OLS) regression.
4. Normalize data:  
Ensures all variables are on the same scale, typically between 0 and 1, to improve algorithm accuracy. The formula used is as follows:  
\[X_{norm}=\frac{X_{0}-X_{min}}{X_{max}-X_{min}} \tag{1}\]  
Figure 2: The average monthly precipitation and CWBL for eleven meteorological stations in the Mekong Delta from 1985 to 2022.  
where X norm is the normalized value, and X\({}_{0}\), X\({}_{\text{min}}\), and X\({}_{\text{max}}\) are the real value, minimum value, and maximum value of the same variable, respectively.
3. Split data: In machine learning, the data will be divided into two sets: a training set and a testing set. The training set will consist of 80% of the data, equivalent to 4012 records. The remaining 20% of the data, consisting of 1003 records, will be used to evaluate the model. In deep learning, the data are divided into three partitions: training, validation, and testing. The training set consists of 70% of the data, while the validation and testing sets each contain 15% of the data.
## 2 System Description and Assumptions  
Figure 1a,b depict the schematic diagrams of the proposed sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system (supercritical CO\({}_{2}\) recompression Brayton cycle/LiBr-H\({}_{2}\)O absorption heat pump system) and a sCO\({}_{2}\)/DH system (supercritical CO\({}_{2}\) recompression Brayton cycle/direct heating system), respectively. In the sCO\({}_{2}\)/DH system, a conventional direct heating system is applied to recover the waste heat in the cooler of the sCO\({}_{2}\) cycle. The waste heat is supplied to heat users through a direct heat exchanger (DHE). Thus, the sCO\({}_{2}\)/DH system consists of a turbine, a main compressor (MC), a recompression compressor (RC), a high-temperature recuperator (HTR), a low-temperature recuperator (LTR), a cooler, and a DHE, as shown in Figure 1b.  
The following Figure 1 shows the schematic diagrams of combined heat and power systems: (a) the sCO\({}_{2}\)/AHP system; (b) the sCO\({}_{2}\)/DH system.  
Differently in the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system, a part of the waste heat in the cooler is transferred to a LiBr-H\({}_{2}\)O based AHP, so that the heat amount can be amplified to produce more heat for users, especially when the waste heat of the sCO\({}_{2}\) cycle is insufficient to satisfy the demand. The AHP includes a generator, an absorber, an evaporator, a condenser, a pump, and a solution heat exchanger (SHE), as shown in Figure 1a.  
As both LiBr-H\({}_{2}\)O solution and ammonia-water solution are frequently utilized as working fluids, a sCO\({}_{2}\)/ammonia-water AHP system is established in which an ammonia-water AHP is coupled with the sCO\({}_{2}\) cycle, so as to conduct a comparative study with the proposed sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system. As the structure and main components are the same for both combined systems, Figure 1a also describes the sCO\({}_{2}\)/ammonia-water AHP system.
# Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
## 5 Numerical and Simulation Results
Secondly, we turn to cooperative scenarios. In Figure 6, we plot the global probability of misdetection (\(Q_{m}\)) as a function of the number of SVUs for hard fusion based on counting rule. The simulation parameters are \(L=50\), \(\eta_{0}=-8\) dB, \(\gamma_{0}=5.5\) dB, \(T=N/2\) and \(Q_{f}=0.1\). As expected,  
Figure 4: The local probability of misdetection as a function of \(F_{d}T_{s}\). Figure 3: The local probability of miss detection as a function of the number of samples for various SNR. the global misdetection probability decreases along with the increase of the number of SVUs. When \(N\) is extremely big, the global mis-detection probability approaches 0. For hard fusion, Figure 6 clearly demonstrates that the derived probability of misdetection based on Lindberg-Feller approximation is relatively poorer for a smaller number of SVUs. However, along with an increasing number of SVUs, the approximation asymptotically grows closer and closer to the exact sensing performance. In Figure 7, we present the ROC curves for different channel models. The simulation parameters are \(L=50\), \(N=40\), \(\eta_{0}=-8\) dB and \(T=N/2\). Note that, in the case of \(\mathcal{M}_{1}\) and \(\mathcal{M}_{3}\), we obtain the average misdetection probability for local sensing, allowing for a much smoother global probability curve than the case of \(\mathcal{M}_{2}\). In Figure 8, we present the average \(Q_{m}\) curves as a function of the average SNR of reporting channels. The simulation parameters are \(L=50\), \(N=40\), \(\eta_{0}=-8\) dB, \(F_{d}T_{s}=0.01\), \(T=N/2\) and \(Q_{f}=0.1\). In the condition of poor reporting SNR, large bit error occurs, which severely degrades the detection performance. We can clearly see that cooperative sensing performance improves quickly with the increase of the average SNR of reporting channel from \(-8\) dB to \(8\) dB. Note that the sensing performance in AWGN channel is always better than in Rayleigh channel. In Figure 9, we plot the global probability of error detection (\(Q_{e}\)) as a function of the local false-alarm probability. The simulation parameters are \(L=50\), \(N=40\), \(\eta_{0}=-8\) dB, \(\gamma_{0}=5.5\) dB, \(F_{d}T_{s}=0.01\) and \(T=N/2\). We can clearly see that, under certain conditions, there is an minimum probability of error detection, and that can be achieved when the local probabilities of false-alarm and mis-detection are equal ( \(P_{f}=P_{m}\) ). In Figure 10, we plot the minimum achievable probability of error detection (\(Q_{e}\)) as a function of the number of SVUs. Figure 5: The local probability of misdetection for various average SNR of sensing channels. Figure 6: The global probability of misdetection as a function of the number of SVUs. Figure 7: ROC curves for different channel model. Figure 8: The global probability of misdetection as a function of average SNR of reporting channels. Figure 9: The global probability of error detection as a function of local probability of false alarm.
## 4 Conclusions  
We proposed two speech-enhancement methods with two-stage processing based on DNNs for reducing distortion of speech observed with an LDV, i.e., a method using two-stage processing with amplitude-spectrum reconstruction and phase-spectrum estimation DNNs (STFT-based method), and a method with two DNNs of noise suppression and high-frequency component reconstruction (waveform-based method). Since an LDV uses a laser to measure vibrations caused by sound waves, various types of deterioration are mixed in due to the frequency response of the irradiated object. Due to these various types of deterioration, there is a problem that the accuracy of the conventional speech  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PESQ Score** & **LSD [dB]** & **STOI Score** \\ \hline (b) & 1.76 \(\pm\) 0.40 & 1.62 \(\pm\) 0.10 & 0.85 \(\pm\) 0.04 \\ (c) & 2.25 \(\pm\) 0.35 & 2.17 \(\pm\) 0.30 & 0.87 \(\pm\) 0.04 \\ (d) & 2.25 \(\pm\) 0.30 & 1.09 \(\pm\) 0.08 & 0.93 \(\pm\) 0.02 \\ (e) & 2.35 \(\pm\) 0.30 & 1.11 \(\pm\) 0.08 & 0.94 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Objective evaluation result of each method.  
Figure 15: Examples of clean, observed, and enhanced speech spectrograms: (**a–e**) are clean speech, observed speech, speech enhanced with the conventional method, speech enhanced with proposed STFT-based method, and speech enhanced with the proposed waveform-based method, respectively.  
enhancement method for LDV is unsatisfactory. Objective evaluation experiments showed that the proposed methods improved enhancement accuracy by comparing them with the conventional method in terms of sound quality, degree of deterioration, and intelligibility. The methods solve the problem of sound-quality degradation when using LDVs to acquire speech. These methods can be applied to recording sounds from areas where people cannot enter, such as disaster sites, or to recording target speech in noisy environments, such as recording the sound of a stage at a concert.  
We plan to study differences in enhancement accuracy due to the loss function in the proposed STFT-based method. We also plan to investigate a speech enhancement method that takes into account the deterioration of the observed speech due to changes in the material of the measurement object and an increase in the distance between the irradiated object and LDV.  
[PERSON] and [PERSON] conceived the proposed method. [PERSON] developed the method and conducted the experiments. [PERSON] wrote this manuscript and modified the figures and expressions of the equations. [PERSON] is an administrator of this study. [PERSON] and [PERSON] supervised this research. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported in part by the Ritsumeikan Advanced Research Academy (RARA), Ritsumeikan Global Innovation Research Organization (R-GIRO), and by JSPS KAKENHI Grant Nos. JP19H04142, JP21H03488, JP21H04427 and JP21K18372.  
Not applicable.  
The authors declare no conflict of interest.