esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods
### Comprehensive Semantic Similarity Calculation Method
#### 3.4.1 Edge-Counting Similarity  
Ontologies employ tree-like structures to represent relationships between concepts, where the geometric arrangement of the tree encodes semantic information about the concepts. When studying semantic similarity based on the distance between two concepts, it is necessary to compare the distance between the positions of these two concepts in the ontology tree. [PERSON] and [PERSON]'s method [23] and [PERSON] and [PERSON]'s method [24] comprehensively analyze the impact of common parent nodes and the depth of the compared concepts in the semantic network on the calculation results. Furthermore, the distance between two nodes is also influenced by the relationship between them. The bridge ontology constructed in this study mainly consists of three types of relationships: part-whole, concept-instance, and synonym. Clearly, the distance between concepts influenced by synonym relationships should be smaller than that influenced by part-whole relationships. After analyzing the concepts and relationships in the bridge ontology model, different weights for the edges corresponding to different relationships are set according to the following criteria:  
\[W_{dis}(c_{i},c_{j})=\left\{\begin{array}{ll}0&\text{synonym}\\ 0.2&\text{concept}-\text{instance}\\ 0.5&\text{part}-\text{whole}\\ \end{array}\right. \tag{11}\]  
The distance with additional weighted edges \(Dis(c_{1},c_{4})\) between node \(c_{1}\) and node \(c_{4}\), where the path from \(c_{1}\) to \(c_{4}\) is \(c_{1}\) to \(c_{2}\), \(c_{2}\) to \(c_{3}\), and \(c_{3}\) to \(c_{4}\), can be represented by the following formula:  
\[Dis(c_{1},c_{4})=W_{dis}(c_{1},c_{2})+W_{dis}(c_{2},c_{3})+W_{dis}(c_{3},c_{4}) \tag{12}\]  
The formula for calculating Edge-Counting similarity used in this study is shown as Equation (13):  
\[Sim^{dis}(c_{i},c_{j})=\frac{dep(lca(c_{i},c_{j}),c_{i})+dep(lca(c_{i},c_{j}),c _{j})}{(Dis(c_{i},c_{j})+1)(max(dep(c_{i}))+max(dep(c_{j})))} \tag{13}\]  
In the formula, \(lca(c_{i},c_{j})\) represents the lowest common ancestor node between nodes \(c_{i}\) and \(c_{j}\), \(dep(lca(c_{i},c_{j}),c_{i})\) represents the depth of the lowest common ancestor node in the branch of the ontology tree where node \(c_{i}\) is located, and \(max(dep(c_{i}))\) represents the maximum depth of the branch starting from node \(c_{i}\) in the ontology tree.
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 3 The Proposed Signature Scheme
### The Proposed Scheme
#### 3.1.4 A Practical Example  
To show how the proposed model works, we present the following example: prime number \(p\) of size equal to 1 kbits and prime number \(q\) of size equal to 1 kbits; this gave us a modulus \(n=p\times q\) of size equal to 2 kbits. We chose a secret key \(k\) of size equal to 0.5 kbits and a random number \(r\) of size equal to 1 kbits. This gave us a public key \(pk\) of size equal to 2 kbits. Both sub-keys \(r_{1}\) and \(r_{2}\) are of size equal to 0.5 kbits. The message \(m\) is equal to 7 bits. After calling the \(Sig\) function, we obtain both \(s_{1}\) and \(s_{2}\) of size equal to 160 bits. After calling the \(Verf\) function, we obtain the values of \(x\) and \(y\) (both of size equal to 160 bits). Finally, we found \(x=y\), demonstrating the proposed scheme's validity and applicability.  
We note that the value of \(x\) is equal to the value of \(y\), knowing that \(y\) is calculated by using the split sender's public key \(r_{1}\) and computed \(m\), and \(x\) is calculated by using the whole sender's public key \(pk_{s}\).
# Biodegradation of Sulfamethoxazole in Milkfish (_Chanos chanos_) Pond Sediments
## 3 Results and Discussion
### Isolation and Identification of Sulfamethoxazole-Degrading Bacteria
The morphology of the four bacterial strains (curved rod shape for _Vibrio_ sp. and rod shape for _Pseudomonas_ sp. and _Marinobacter_ sp.) confirmed by scanning electron microscopy is shown in Figure 6.  
Figure 4: Biodegradation of sulfamethoxazole (SMX) by the four isolated bacterial strains (A2, A12, L, and M) without fish pond sediments. (**A**) Aerobic SMX biodegradation by strains A2 and A12. (**B**) Anaerobic SMX biodegradation by strains L and M. Data from three independent experiments are presented as the mean \(\pm\) SE. Figure 5: Phylogenetic analysis of 16S rRNA sequences of the four bacterial strains (A2, A12, L, and M). The ruler indicates the scale of the branch length of the tree, which estimates the average number of nucleotide substitutions per site. Bootstrapping values at each branch point indicate times out of 1000 the same branch was observed when repeating the phylogenetic reconstruction.
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 3 Related Works
Nevertheless, they do not mention the protocols used for distributing the information from vehicles to the fusion center and only apply the CS technology from a theoretical point of view. Table 1 summarizes the different features extracted from the different proposed solutions where data gathering has been addressed, either by using compressing techniques or not, or even if they come from the WSN or the VSN domain. As the reader can observe, data gathering proposals which apply CS are mostly focused on the adoption of this technology without considering other aspects such as the distribution of the query in a region of interest, or if an underlying routing protocol is required in order to make the transmission of the sensed information to the FC more efficient. In this paper, our objective is to analyze through network simulations the impact of CS in a VSN environment considering the high mobility of the vehicles, their mobility restrictions following roads, and link breakage and variability these factors cause in the network. We have considered also the necessity of efficiently broadcast a query message to a specific RoI, as well as the transmission of the information from the nodes inside the RoI to the FC. For this purpose, unlike the related works commented above, we have defined an efficient harvesting scheme called CS-Vehicular Data Harvesting (VDH) which includes a geocast solution for an efficient query distribution in a RoI, a VANET routing protocol for efficiently receiving the information from the vehicles, and the integration of such a routing protocol with the CS technology. \begin{table}
\begin{tabular}{p{113.8 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 5 Overview of the Results  
Our final method has been tested against watermarks, scoreboards, texts, logos, color filters, and other image alterations. In addition, widespread techniques for avoiding copyright (such as color filters, re-sized images and even flipped images) have also been thoroughly checked. Our method has proved to be resistant to all these editing techniques, independently of whether one or several have been applied to a single video. Error rate is below \(4\times 10^{-8}\%\) in all our tests, which are meant to simulate all aspects of a real-world case. Two groups of datasets have been used for testing; terrorism-related retrieved videos and videos of the Football World Cup held in Russia in 2018. In both cases the algorithm have successfully identify all the sources.  
The configuration which provided the best performance was a 5 f/s extraction rate in the original videos and a 12 f/s extraction rate in the new videos, a threshold of 3 units. However, we recommend using a 3-5 f/s configuration with a threshold of 4 units if fast search results of new videos are required. An example of the obtained results can be found in Figures 4 and 5.
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 2 Materials and Methods
### Methods
#### 2.2.1 Construction of DPC Water Vapor Retrieval Lookup Table  
In this study, the radiative transfer model Second Simulation of a Satellite Signal in the Solar Spectrum vector (6 SV) [36] was used to simulate the water vapor absorption transmittance in the DPC 910 nm band under different observed geometries and water vapor content. Then, we achieved water vapor retrieval by minimizing the simulated water vapor absorption transmittance and the actual water vapor absorption transmittance. Running 6 SV in real time to calculate the water vapor absorption transmittance for PWV retrieval for each pixel is time-consuming, resulting in low retrieval efficiency. To improve the efficiency of DPC PWV retrieval, a 4-dimensional lookup table was constructed using 6 SV before PWV retrieval. These four dimensions are solar zenith angle (0-72\({}^{\circ}\), interval of 4\({}^{\circ}\)), observed zenith angle (0-72\({}^{\circ}\), interval of 4\({}^{\circ}\)), PWV (0-8 cm, interval of 0.1 cm), and DPC 910 nm band water vapor absorption transmittance.
## 4 Conclusions  
In the proposed auralization model, emission sounds of accelerating passenger cars are artificially generated based on spectral modeling synthesis. Whereas the sound of tires is synthesized as stationary noise, which is time-dependently shaped in third octave bands, the realistic synthesis of sounds of propulsion requires more subtlety.  
It is synthesized as the superposition of a noise component and tones. Frequency-dependent periodic short-term modulations are applied to the noise component in order to create a rattling sound eliciting a roughness sensation. The tones are related to the engine orders. It was found that a large number of engine orders are needed (50...100) to convincingly represent different engine speeds and loads. Moreover auralizations revealed that the order phases have to be included as synthesizer parameters. In conclusion, the presented emission synthesizer gives complete control over the signal characteristics, but is computationally much more demanding than a synthesizer based on granular synthesis [5] with limited flexibility. However, a hybrid approach could profit from both advantages, _i.e._, by pre-creating relevant signal grains using spectral modeling synthesis and usage for granular synthesis in real-time applications.  
Analysis of the propagation filtering algorithms yielded two main insights. Aliasing, arising from the simulation of the Doppler effect, can be reduced by incorporating a band-limited resampling strategy, such as the windowed sinc interpolation. Furthermore, due to the low source height, a significantly higher number of filter taps is needed to correctly simulate the ground effect in relevant situations, as compared to elevated sources, such as airplanes or wind turbines [46].  
We conclude that with the presented synthesizer structure, audio signals from vehicle pass-bys can be represented in a compact and elegant manner. To give the reader an impression of the subjective quality of the proposed model, auralizations of two examples are published as supplementary data (see the videos in the Supplementary File and Section 2.3 for details).  
This work was supported by the Swiss National Science Foundation (SNSF) (# 200021-149618). In addition to the four anonymous reviewers, we would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for their help in the measurement campaigns. The first author also thanks [PERSON] for his valuable explanations on automotive engineering.  
**Author Contributions:** [PERSON] drafted the main manuscript. [PERSON] conducted the measurements on the chassis dynamometer and assisted in the development of the driving dynamics simulation. [PERSON] supervised the research and helped in the preparation of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 4 Simulation and Result Analysis
### Experimental Results
#### 4.2.1 Test Performance on MAE, RMSE, MAXE, and Ratio-1  
Test results before and after transfer learning on the four evaluation metrics are shown in Table 3.  
We can observe from the result in Table 3 that the performance of the model after transfer learning has been greatly improved. The average angle error MAE drops from 13.77\({}^{\circ}\) to 0.716\({}^{\circ}\), the root mean square angle error RMSE decreased from 34.72\({}^{\circ}\) to 0.912\({}^{\circ}\), the maximum angular error MAXE decreased from 179.827\({}^{\circ}\) to 3.775\({}^{\circ}\), and the ratio of absolute error less than 1\({}^{\circ}\) Ratio-1 increased from 0.043 to 0.737. What can be inferred from the test result can be found below:  
1. Transfer learning enables the model to learn the error knowledge in the actual environment with a small number of actual signal samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Type** & **Kernel Size** & **Input** & **Output** \\ \hline
1 & Conv & \(1\times 1\) & \(5\times 5\times 1\) & \(5\times 5\times 8\) \\
2 & Conv & \(3\times 3\) & \(5\times 5\times 8\) & \(2\times 2\times 16\) \\
3 & Conv & \(1\times 1\) & \(2\times 2\times 16\) & \(2\times 2\times 32\) \\
4 & Conv & \(1\times 1\) & \(2\times 2\times 32\) & \(2\times 2\times 64\) \\
5 & Conv & \(1\times 1\) & \(2\times 2\times 64\) & \(2\times 2\times 64\) \\ \hline
6 & Fc & & 256 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The structure and parameters of a deep convolutional neural network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Evaluation Metrics** & **RMSE (*)** & **MAXE (*)** & **Ratio-1** \\ \hline Before transfer learning & 13.770 & 34.720 & 179.827 & 0.043 \\ After transfer learning & **0.716** & **0.912** & **3.775** & **0.737** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results before and after transfer learning.  
The error calibration method we proposed based on transfer learning can effectively improve the DOA estimation performance of the model in practical application.
# Large-Scale Grid Integration of Renewable Energy Resources with a Double Synchronous Controller
## 8 Conclusions  
A double synchronous controller (DSC) was projected for the interfaced converters between renewable energy resources (RERs) and the power grid. The proposed controller is based on the emulated features of SGs, i.e., inertia, virtual mechanical power reference, and virtual mechanical power error, to guarantee the stable operation of the power grid under high penetration of RERs. Using a more comprehensive relationship between the interfaced converter and SGs dynamic models along with more detailed features of SGs have been major contributions of the proposed DSC in comparison with other similar existing methods. Other contributions have been to provide more decoupling features for both d and q components that are completely involved with SGs characteristics as well as robustness against the unknown intrinsic property of parameters. The operation of the proposed DSC has been compared with a non-synchronous controller (NSC) to confirm the high performance of the DSC in the control of the interfaced converters in the power system. Moreover, a comprehensive stability analysis of the DSC power sharing capability along with its flexible regulation of both grid frequency and voltage magnitude has been executed. Obtained results confirm that the proposed DSC can behave as a power electronics-based synchronous generator for the integration of large-scale RERs into the power grid.  
Conceptualization, M.M., E.P.; Methodology, M.M.; Software, M.M.; Validation, M.M., E.P.; Formal Analysis, M.M., E.P.; Investigation, M.M.; Resources, E.P., H.S., F.B., M.R.A.C. and J.P.S.C.; Writing--Original Draft Preparation M.M.; Writing--Review & Editing, E.P., H.S., F.B., M.R.A.C. and J.P.S.C.; Visualization, H.S.; Supervision, E.P., M.R.A.C. and J.P.S.C.  
J.P.S.C. acknowledges the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under POCI-01-0145-FEDER-029803 (02/SAICT/2017).  
The authors declare no conflict of interest.  
Figure 14: Parameters variations effects on DSC and NSC: (**a**) grid frequency, and (**b**) grid voltage magnitude.
## 4 Experimental Setup
### Performance Metrics and Evaluation Standards  
To comprehensively evaluate the performance of AI models on the Manchu music dataset, this study employs a variety of performance metrics and evaluation standards. These metrics cover aspects such as model accuracy, efficiency, and generation quality, ensuring a holistic assessment of model performance. The following are the detailed performance metrics and evaluation standards.  
The performance metrics for classification models are used to evaluate the overall effectiveness of these models in tasks such as music genre and emotion classification, as shown in Formula (1) as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]  
Analyze the model's classification performance across different classes in detail using the Confusion Matrix. Identify common error patterns.  
Performance metrics for regression models include Mean Squared Error (\(MSE\)), Root Mean Squared Error (\(RMSE\)), and Mean Absolute Error (\(MAE\)). MSE is used to evaluate the prediction accuracy of regression models, such as those predicting audio features. RMSE is used to assess the prediction accuracy of regression models and facilitates the comparison with actual values. MAE evaluates the prediction error of regression models, reflecting the model's average prediction bias, as shown in Formulas (5)-(7) as follows:  
\[MSE=\frac{1}{n}{\sum_{i=1}^{n}}{(y_{i}-\hat{y_{i}})}^{2} \tag{5}\]  
\[RMSE=\sqrt{MSE} \tag{6}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}\ |y_{i}-\hat{y_{i}}| \tag{7}\]  
Performance metrics for generative models include the following:  
Generation Quality Score: This metric assesses the quality of generated music through both subjective and objective methods.  
Frechet Audio Distance (\(FAD\)): FAD is used to evaluate the distance between generated and real audio in feature space, as shown in Formula (8).  
\[FAD=\left\|{\mu_{r}-\mu_{g}}\right\|^{2}+Tr\left({\sum_{r}+{\sum_{g}}-2\left( {\sum_{r}\sum_{g}}\right)^{\frac{1}{2}}}\right) \tag{8}\]  
Efficiency Metrics:Training Time: This metric is used to evaluate the efficiency of model training, particularly significant for large-scale datasets and complex models.  
Inference Time: This metric assesses the real-time performance and computational efficiency of the model, which is especially important for real-time music applications and online services.  
Other Evaluation Criteria:  
Dataset Coverage: This criterion evaluates the representativeness of the dataset and the generalization ability of the model, ensuring that the model can handle a diverse range of music types.  
User Satisfaction: This metric assesses the model's performance in real-world applications and user experience, gathered through surveys and user feedback.
## 1 Introduction
### DL Application in Quality Control of AM
#### 1.3.2 DL for Quality Inspection  
Quality monitoring has been utilized for identifying or detecting and classifying defects of AM workpieces. In [74], a DL approach is used for identifying small process shifts. Recent literature demonstrates a desire for quality monitoring systems in AM processes through the use of DL techniques. Many of the methods recently published are focused on real-time defect detection via acoustic emission and some have begun to introduce Convolution Neural Networks (CNN) and many variations of the network to predict part defects [28; 33; 36; 37; 75; 76]. Although there have been investigations focused on evaluation of the printing parameters of an AM building process. In [77], [PERSON] and [PERSON] showcase an ML framework that analyzes the sensitivity and uncertainty in Selective Laser Melting (SLM) build process, which allows the optimization of printing parameters.  
In [33], researchers utilize acoustic emission for in situ quality monitoring that combined with machine learning. In the study, acoustic features are extracted and classified as 'poor','medium', or 'high' porosity using a spectral CNN (SCNN). Researchers report a confidence interval of 83-89% for porosity classification. [PERSON], et al. investigate the use of K-means clustering algorithm for classification of different process conditions during in situ AM quality monitoring [36]. Classification through the use of K-means clustering ranged from 70 to 90%, depending on the process condition. In [76], [PERSON], et al. establish a method for detecting part defects with a single short detector network (SSD). [PERSON], et al. investigate quality monitoring during a laser-based DED manufacturing process utilizing a CNN [78]. During the study, [PERSON] et al. train a Deep CNN to observe defects through imaging and showcase how the predictive model detects defects with up to 94.71% accuracy. [PERSON] et al. present a deep learning-based quality identification method that image-based detection with low-quality (noisy and blurred images) data as inputs [79]. In the investigation, authors display a model capable of functioning with the semi-supervised, low-quality dataset. Some inputs are labeled and some are non-labeled to mimic real-world imagery. This approach separates itself from other approaches seen in the literature by exploring supervised and unsupervised data for in situ quality monitoring.  
This study investigates the layer-by-layer classification technique using a DL method that treats the real-time data as a time series dataset. Predictions are made based on a data-driven technique focused mainly on the parameters within each layer that are highly responsible for layer quality. We have developed a DL method for detecting good and bad layers based on post-process XCT porosity analysis resulting in classifying layers as good or bad. This study aims to provide insight into the effects and influences of in situ sensed or measured features and AM parameters on bad layers and how they render a part low-quality during the DED process.
# Improving Decodability of Polar Codes by Adding Noise
## 3 Conventional Schemes
### Perturbation Decoding Principle  
The perturbation of the received signal can make each received signal have multiple decoding outputs, which can improve the reliability of the transmission process and avoid retransmission of erroneously decoded information [34]. The perturbation of the received signal can be interpreted as adding independent random noise to improve the performance of suboptimal decoders.  
As shown in Figure 1, for each valid codeword \(c(c\in\{c_{1},c_{2},\ldots,c_{S}\})\), an error correction region \(a(a\in\{a_{1},a_{2},\ldots,a_{S}\})\) in the decoding space is specified, where S is the total number of valid codewords. When the received signal \(y^{N}\) falls into the error correction region, the decoder succeeds in decoding, where N is the code length. However, if the received signal \(y^{N}\) falls outside the error correction region, the decoder fails in decoding. At this point, adding a random noise \(n\) to the received signal may convert the perturbed signal \(y^{N}+n\) into the error correction region, resulting in a successful decoding.