esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Audio Quality Degradation  
To evaluate the quality degradation of audio signals processed by the proposed method, we performed a DMOS assessment test according to ITU Telecommunication Standardization Sector (ITU-T) Recommendation P.800 [20]. The experimental conditions such as audio clips, participants, and listening room are identical to those of the experiment described in Section 4.2. Each participant listened to a pair of audio clips composed of an original and processed version by either the DS beamformer or the proposed method. Then, each was asked to rate the degree of quality degradation from five to one. Table 2 describes the scores and their meanings for DMOS assessment.  
Table 3 compares the results of DMOS assessment between the conventional DS beamformer-based method and the proposed method in three different recording environments. We conducted a statistical analysis and indicated the 95% confidence intervals (CIs) as numbers in parentheses in Table 3. As shown in the table, the proposed method provided average DMOS scores of approximately four for all environments, which implied that there were no annoying effects [32]. However, there was significant quality degradation in the conventional method, especially in the real reverberant environment. It was revealed from statistical analysis that the quality degradation of the audio signals enhanced by the proposed method was statistically less than those enhanced by the DS beamformer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Score** & **Description** \\ \hline
5 & Degradation is inaudible \\
4 & Degradation is audible but not annoying \\
3 & Degradation is slightly annoying \\
2 & Degradation is annoying \\
1 & Degradation is very annoying \\ \hline \hline \end{tabular}
\end{table}
Table 2: Score and description of degraded mean opinion score (DMOS) assessment.  
Figure 12: Comparison of the perceived azimuth depending on the direction of the source in three different environments: (**a**) anechoic; (**b**) convolutive; and (**c**) real reverberant room.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Environment** & **Method** & **DS** & **Proposed** \\ \hline Anechoic & 3.61 (0.2898) & 4.04 (0.2622) \\ Convolutive & 3.31 (0.3147) & 3.97 (0.2581) \\ Real Reverberant & 3.46 (0.2950) & 3.90 (0.2711) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of DMOS assessment results of the conventional and proposed methods for three different recording environments where the numbers in parentheses indicate 95% CIs.
## 1 Introduction
Moreover, users can freely select the analysis results from each NDE method and utilize interactive visualization tools to explore the boundary points for each damage stage, thereby enabling the evaluation of the effectiveness of each NDE modality. Finally, the effectiveness of this method is verified through case studies of damage feature data from AE and DIC in composite materials. Overall, NDEExplorer provides a powerful platform for staff to efficiently and interactively analyze NDE results and explore the correlations between different NDE methods, making it easier to understand the structural health conditions of materials and facilitate decision making in engineering applications. In summary, the main contributions of this article are as follows:  
* A visual analytics system based on NDE methods, NDEExplorer, is designed to explore and evaluate the material damage evolution process;
* The novel glyph design displays image features of material damage, facilitating the extraction of damage image information and its integration with AE features;
* Our research is valuable in illuminating the analysis of composite material damage evolution and the exploration of multimodal data relationships. The remaining sections of the paper are organized as follows: Section 2 summarizes the related work regarding the methods and visualization of NDE; Section 3 presents the system design framework, describes the process of multimodal data extraction, and constructs the system design methods; Section 4 presents case studies and user research, and it discusses the findings based on the research results; Section 5 analyzes the advantages, limitations, and future research directions of the NDE analysis, based on the visualization methods and results discussed above.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 3 Star Background Simulation
### Infrared Magnitude Calculation  
In the MSX catalog, the brightness of stars is represented by Jansky (\(Jy\)), which is denoted by \(F_{\
u}\)([PERSON], 1964).  
\[1\ Jy=10^{-26}\ W\cdot\mathrm{m}^{-2}\cdot\mathrm{Hz}^{-1}, \tag{1}\]  
where \(Jy\) is the unit of spectral flux density, \(W\) is the unit of power and \(m\) is the unit of length.  
Irradiance within a specific wavelength band is denoted as \(F_{\lambda}\) and measured in W\(\cdot\)cm\({}^{-2}\cdot\upmu\)m\({}^{-1}\), and the conversion relation is ([PERSON], 1964):  
\[F_{\lambda}=F_{\
u}\times 10^{26}\cdot c/\lambda^{2}, \tag{2}\]  
where \(c\) is the speed of light and its value is \(3\times 10^{8}\) m/s; \(\lambda\) is the wavelength.  
In astronomy, we commonly use equivalent magnitude to represent the brightness of celestial bodies. Therefore, we converted the brightness of stars to equivalent magnitude for storage and calculation ([PERSON], 1964). Infrared magnitudes are established with reference to apparent magnitudes. The infrared magnitude of the requested star \(s1\) is represented as \(m_{1}\). The irradiance of the zero-magnitude star and the star are represented as \(E_{0}\) and \(E_{1}\), respectively. We can then express the relationship between these values using Equation (3):  
\[m_{1}=-2.5\lg(E_{1}/E_{0}), \tag{3}\]  
By combing in Equations (2) and (3), it is possible to directly convert from the catalog \(Jy\) to the corresponding infrared magnitude. This conversion provides a straightforward method for determining the brightness of stars in infrared wavelengths:  
\[m_{1}=-2.5\lg\left(Jy_{s1}/Jy_{ Vega}\right), \tag{4}\]  
The relevant information from this catalog is presented in Table 1, including the number of stars with the same brightness in different infrared bands, as shown in Figure 5. Owing to the difference in the wavelength bands, there is a significant difference in the radiant fluxes corresponding to zero magnitude stars in band A and band C. However, the difference between band A and band C is not significant in terms of the number of stars of different magnitudes by calculation. Stars observed in the infrared band at each magnitude are one order of magnitude higher than those in visible light. Therefore, the influence of stars in the background should not be ignored.  
\begin{table}
\begin{tabular}{c c c c} \hline Band Name & Wavelength/\(\upmu\)m & Waveband/\(\upmu\)m & Zero Magnitude Flux/\(Jy\) \\ \hline A & 8.28 & 6.8–10.8 & 58.49 \\ B1 & 4.29 & 4.22–4.36 & 194.6 \\ B2 & 4.35 & 4.24–4.45 & 188.8 \\ C & 12.13 & 11.1–13.2 & 26.51 \\ D & 14.65 & 13.5–15.9 & 18.29 \\ E & 21.34 & 18.2–25.1 & 8.80 \\ \hline \end{tabular}
\end{table}
Table 1: The flux of zero-magnitude stars in MSX catalog.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 2 Overview of the AC-LSTM Framework  
As shown in Figure 1, the framework of our approach consists of three major parts: model input, feature extraction, and aggregation and prediction. Since PM\({}_{2.5}\) concentration is extremely affected by spatiotemporal features, recent air pollutant concentration, meteorological data, and the PM\({}_{2.5}\) concentrations of all adjacent stations are stacked to construct an input tensor for the one-dimensional CNN layer. In this way, the spatiotemporal features are extracted by the CNN layer. Then, the spatiotemporal correlation is learned by the LSTM layer. Because of the different effects of past states of different times on the PM\({}_{2.5}\) concentration, the attention-based layer can weigh the feature states at past different hours. Finally, the aggregation and prediction of the proposed model is achieved.  
How the model predicts the PM\({}_{2.5}\) concentrations of the next 24 h is described in Figure 2. As shown in Figure 2, \(X_{t}\) represents the input data of the model at time \(t\) (e.g., air quality data, meteorological data in Figure 1), \(Y_{t+1}\) represents the predicted value of the PM\({}_{2.5}\) concentration at time \(t+1\), and \(k\) represents the time lag. We group the air quality data within a particular time lag to formulate different inputs (shown in the broken rectangle) for multiscale predictors, which are used to train separate models corresponding to different time intervals. The time lag of the model input indicates how many hours the input data are in the past. Each blue arrow shown in Figure 2 represents a different predictor. Afterward, a separate model is trained for each hour over the next 3 h. With respect to the next 7-24 h, it is divided into three time intervals, i.e., 4-6, 7-12, and 13-24 h, where separate models are trained to predict the mean PM\({}_{2.5}\) concentration during each time interval.
## 3 Materials and Methods
### Lstm  
The LSTM model offers an effective solution to the vanishing gradient problem often encountered in RNNs [64]. As a specialized form of RNN, LSTM networks are capable of retaining relevant information over extended sequences, making them well-suited for modeling time series data. LSTMs utilize memory cells to store information and selectively update or forget it as needed. Figure 9 illustrates the architecture of a basic LSTM model [65].  
LSTM units are structured with three distinct gates: the input gate \(i_{t}\), the forget gate \(f_{t}\), and the output gate \(o_{t}\), each of which receives a value between 0 and 1. The input gate governs the integration of selected features into the memory. The forget gate discards information deemed unnecessary, whereas the output gate decides the value to be produced by the unit. These gates operate based on both the previous hidden state \((h_{t}-1)\) and the current input \((x_{t})\), collectively influencing the unit's output. These formulas are defined in Equations (9)-(11) [66].  
\[f_{t}=\sigma\Big{(}W_{fx}x_{t}+W_{fh}h_{t-1}+b_{f}\Big{)} \tag{9}\]  
Figure 8: Basic architecture of the RNN model.  
Figure 9: Basic architecture of the LSTM model.  
\[f_{t}=\sigma\Big{(}W_{fx}x_{t}+W_{fh}h_{t-1}+b_{f}\Big{)} \tag{10}\]  
\[o_{t}=\sigma(W_{ox}x_{t}+W_{oh}h_{t-1}+b_{o}) \tag{11}\]  
The weight matrices \(W_{fx}\), \(W_{fh}\), \(W_{ix}\), \(W_{ih}\), \(W_{ox}\), \(W_{oh}\), \(W_{cx}\), and \(W_{Ch}\) correspond to the weight parameters related to the current input \(x_{t}\) and the previous hidden state \(h_{t-1}\) for their respective gates. Bias parameters for the forget, input, output, and cell gates are expressed as \(b_{f}\), \(b_{i}\), \(b_{o}\) respectively. The notation \(\sigma\) designates the sigmoid activation function. The input gate \((i_{t})\), forget gate \((f_{t})\), the previous cell state \((C_{t-1})\), and the candidate cell state \(\left(\widetilde{C}_{t}\right)\) all contribute to calculating the updated cell state \((C_{t})\). After establishing the updated cell state, the output state \((h_{t})\) can be calculated as detailed in Equations (12)-(14).  
\[\widetilde{C}_{t}=tanh(W_{Cx}x_{t}+W_{Ch}h_{t-1}+b_{C}) \tag{12}\]  
\[C_{t}=f_{t}*xC_{t-1}+i_{t}*\widetilde{C}_{t} \tag{13}\]  
\[h_{t}=o_{t}*\tanh(S_{t}) \tag{14}\]
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 4 Methodology
### PQC-TLS Using NTRUs
#### 4.2.1 Notations  
The NTRU cryptosystem depends on three integer parameters \((N,p,q)\) and four sets \(\mathcal{L}_{f}\), \(\mathcal{L}_{g}\), \(\mathcal{L}_{\phi}\), \(\mathcal{L}_{m}\) of polynomials of degree \(N-1\) with integer coefficients. Note that \(p\) and \(q\) need not be prime, but \(p\) and \(q\) are relatively prime, and \(q\) is considerably larger than \(p\). We work in the ring \(R\)=\(\mathbb{Z}[X]/(X^{N}-1)\), and element \(F\in R\) will be written as a polynomial or vector,  
\[F=\int_{i=0}^{N-1}F_{i}x^{i}=[F_{0},F_{1},\ldots,F_{N-1}] \tag{1}\]  
We use the symbol to denote multiplication in R. This'star multiplication' is given explicitly as a cyclic convolution product,  
\[F\vartriangleleft G=H\text{ {with }}H_{k}=\int_{i=0}^{k}F_{i}G_{k-i}+\int_{i=k+1 }^{N-1}F_{i}G_{N+k-i}=\int_{i+j\equiv k\text{ {(mod }N)}}F_{i}G_{j} \tag{2}\]  
where a multiplication modulo \(q\) involves reducing the coefficients module \(q\).  
Now, we will discuss the key generation process. First, two polynomials \(f\in\mathcal{L}_{f}\) and \(g\in\mathcal{L}_{g}\) are chosen. The polynomial \(f\) must satisfy the additional requirement of having inverses modulo \(q\) and modulo \(p\), which we will denote by \(F_{q}\) and \(F_{p}\), respectively. Second, the quantity is computed as follows:  
\[h\equiv F_{q}\vartriangleleft g\text{ {(mod }q)} \tag{3}\]  
Then, the public key is the polynomial \(h\) and private key is the polynomial \(f\).  
The encryption process consists of two steps. First, a message \(m\) is selected from the set of plaintexts \(\mathcal{L}_{m}\).  
Next, a polynomial \(\phi\in\mathcal{L}_{\phi}\) is randomly chosen, and the public key \(h\) is used to compute  
\[e\equiv p\phi\vartriangleleft h+m\text{ {(mod }q)} \tag{4}\]  
After the computation, \(e\) becomes the encrypted message, also known as the ciphertext.  
Finally, we will review the method for decrypting encrypted messages. First, precompute the polynomial \(F_{p}\) from private key \(f\). Next, obtain the coefficients of \(a\) as follows:  
\[a\equiv f\vartriangleleft e\text{ {(mod }q)} \tag{5}\]  
where \(a\) belongs to the interval from \(-q/2\) to \(q/2\). After computing the following process, the final output becomes the recovered message.  
\[F_{p}\vartriangleleft a\text{ {(mod }p)} \tag{6}\]
## 2 Materials and Methods
### Estimation of Incremental Lifetime Cancer Risk (ILCR)  
Exposure concentration to PAHs via inhalation of PM10 for each receptor can be estimated as a time-weighted average concentration from the annual mean pPAHs concentrations represented by BaP-TEQ concentration. The carcinogenicity of PAHs is characterized by the inhalation unit risk (IUR) or cancer slope factor (CSF) of BaP. Idealized residents and workers were selected for the long-term exposure assessment. The resident group was subdivided by age to reflect the difference in receptor exposure parameters, i.e., body weight (BW), inhalation rate (IR), exposure duration (ED), exposure time (ET), and exposure frequency (EF). EC is used to calculate the lifetime average daily dose (LADD) for receptors at the roadside, urban background and industrial sites over a lifespan of 70 years (AT). The incremental lifetime cancer risk (ILCR) to PM10-bound exposure is the product of LADD [26] and the cancer slope factor of BaP. The daily dose was not considered when calculating the risk characterized by IUR.  
BaP is determined to cause cancer by a mutagenic mode of action and likely to represent a higher risk during early-life exposure. Thus, age-dependent adjustment factors (ADAF) are applied to both CSF and IUR [26; 27]. The age specific adjustment is recommended for three time periods, as follows:  
* ADAF = 10 during 0 to 2 years of life;
* ADAF = 3 during 2 to 16 years of life; and
* ADAF = 1 from 16 to 70 years.  
Using the lifetime exposure of 70 years, ADAFs were applied on the BaP inhalation slope factor value of 3.9 (mg kg\({}^{-1}\) day\({}^{-1}\))\({}^{-1}\) and IUR value of 0.0011 (\(\mu\)g m\({}^{-3}\))\({}^{-1}\)[28]. Carcinogenic risks can be estimate according to the following equations:  
\[\text{ILCR}=\text{LADD}\times\text{CSF}_{\text{Adj-70}} \tag{3}\]  
\[\text{Risk}=\text{EC}\times\text{IUR}_{\text{Adj-70}} \tag{4}\]
## 3 Results and Discussion
The detailed parameter comparison between the proposed model and the eight common signal modulation recognition network models shows that the parameter number of the proposed model is nearly 50% less than that of the CGDNN model with the least parameters, and nearly 98% less than that of the DENSNET model with the most parameters. This model has greater average and maximum accuracy than the other eight models that are currently in use. In order to attain the maximum recognition accuracy rate, the experiment expanded the number of training groups. Figure 10 shows that fewer groups are already able to attain the current acceptable recognition accuracy. The reduction of parameters can reduce training costs and improve training efficiency for future research. Other performance parameters are listed in Table 2.  
Figure 3:0 Recognition accuracy contrasted with other mainstream methods. Figure 3:1 Recognition accuracy of ten modulation signals under different SNR. All experiments were conducted in the same experimental environment [22]. The MCNET, IC-AMCNET and DAE models do not use traditional RNN to extract time-domain feature of signals. CGDNET and MCLDNN use more convolutions to extract feature leads to need more convolution parameters. RESNET and DENSENET have complex parameters that require a huge training time. Our proposed scheme considers the impact of signal offset while maintaining minimal parameters and saving training resources.
## 7 Q-Learning Network Architecture  
Basically, in data transmission, the frame transmitted includes data and pilot symbols. It is supposed that the implemented channel model is stationary throughout one frame transmission of data and pilot signals and the channel parameters are varying from one frame to another. The basic architecture of the channel prediction scenario based on the developed \(Q\)-learning procedure employed in our examined network is illustrated in Figure 3, which primarily consists of several stages [17; 43].  
In the first stage, initial channel parameters will be created based on a distinct Rayleigh channel models. In the second stage, we initialize the \(\mathbf{Q}\) table and initialize the reward matrix \(\mathbf{R}\) with zero values. The signal-to-interference plus noise ratio (SINR), and the minimum required rate \(R_{t}\), can be calculated for every user device in the MISO-NOMA cell with the aid of the availability of the network information such as the initial assigned power percentage for each user terminal, and the entire power transmitted from BS \(P_{T}\). Primarily, the Q-values can be adjusted based on the difference between the assigned target rate \(R_{T}\) and the initial generated user rate for each device. In the third stage, the best action will be explored and implemented by the \(\mathbf{Q}\) agent, and then updating the values for the \(Q\)-table that represent the observation action pair \(Q(s,a)\). Furthermore, the values for the reward matrix \(\mathbf{R}\) will be dynamically assigned according to the actions executed by the Q-agent.  
In the fourth stage, the state action value function \(\mathbf{Q}(\mathbf{s},\mathbf{a})\) that represent the values for the Q-table will be modified according to a Q-learning procedure with the aid of the following parameters, the discount factor \(\mathbf{\gamma}\), the assigned immediate reward matrix \(\mathbf{R}\), and the learning rate \(\mathbf{\kappa}\). Throughout the learning phase, the generated state action values \(Q(s,a)\) will be sampled to calculate the new channel rate and at the same time update the Q-table until the optimum rate or the terminal state is achieved.
## 2 Data Description
### The Best Track, Global Reanalysis, and AHI Data  
The International Best Track Archive for Climate Stewardship (IBTrACS) v04r01 is a global TC dataset maintained by NOAA's National Climate Data Center [40]. It provides  
Figure 1: Global distribution of the local observation time of sea surface wind observations at descending nodes (colored shading) of (**a**) MetOp-B ASCAT and (**b**) HY-2B HSCAT scatterometers on 2 August 2023.  
TC center position (\(\upl_{\rm c}\), \(\varphi_{\rm c}\)), 10-m maximum wind speed (\(\rm V_{max}\)), radius of maximum wind speed (\(\rm R_{max}\)), central pressure (\(\rm P_{c}\)), outermost closed isobar radius (\(\rm R_{out}\)) and pressure (\(\rm P_{out}\)), and radii of 34-kt winds in each quadrant.  
We employ ERA5 [41] from ECMWF, providing hourly global atmospheric reanalysis on 37 pressure levels (100 hPa-1 hPa) and 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) horizontal resolution.  
Japan's Himawari-9, launched in December 2022, replaced Himawari-8 on 13 December 2022 at 140.7\({}^{\circ}\)E. The Advanced Himawari Imager (AHI) performs a full disk scan every 10 min with 16 channels. We use Channel 13 (\(\sim\)10.45 \(\rm\mu\)m, \(\sim\)2-km resolution), which is commonly used to observe cloud-top brightness temperatures. These AHI data can help locate TC centers [24], complementing the scatterometer-based surface wind centers.
## 3 Case Study: Measurement Campaign Goal and Setup
### Reference Point Targets  
Within this study, the RADARSAT-2 system was considered radiometrically uncalibrated because of the mismatch between the system's specified relative radiometric calibration (\(<\)1 dB) [19] and the target uncertainty of about 0.2 dB. Therefore, reference point targets have been placed in the imaged scenes for manual, day-to-day calibration.  
In total 15 trihedral corner reflectors were used as the reference point targets to derive the transponder ERCS. The comparatively large number was deemed necessary in order to profit from averaging during data analysis. Reflectors with two different inner leg lengths were deployed, resulting in two distinct values for their ERCS.  
The RADARSAT-2 system operates at a center frequency of 5.405 GHz with a small relative bandwidth of 2%. Under this precondition, a corner's ERCS is, with sufficient accuracy, equal to a corner's RCS at the center frequency [15]. The peak RCS of an ideal corner reflector can be estimated with the physical optics approximation  
\[\varsigma=\frac{4\pi}{3}\frac{a^{4}}{\lambda^{2}} \tag{3}\]  
where \(a\) is the corner's inner leg length and \(\lambda\) is the wavelength [20]. The resulting values are listed in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Overpass Time** & **Orbit Direction** & **Beam Mode** \\ \hline
7 April 2013 17:11:09 & ascending & U17W2 \\
8 April 2013 05:20:16 & descending & U16W2 \\
14 April 2013 17:06:59 & ascending & U11W2 \\
15 April 2013 05:16:06 & descending & U22W2 \\
18 April 2013 05:28:36 & descending & U5W2 \\
21 April 2013 17:02:49 & ascending & U5W2 \\
24 April 2013 17:15:19 & ascending & U22W2 \\
25 April 2013 05:24:26 & descending & U10W2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of approximate acquisition times (in UTC).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Size** & **Peak RCS** & **Number of Targets** \\ \hline
1.5 m & 38.38 dBm\({}^{2}\) & 9 \\
3.0 m & 50.43 dBm\({}^{2}\) & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Corner reflectors with two different inner-leg lengths were used during the campaign.