esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 LPWAN Application Requirements
### Operations, Interrelationships, and Interworkings  
The network should be able to handle heterogeneous devices. These large numbers of devices may share the same radio resources causing intra- and internetwork and technology interference,resulting in a degradation of network performance. Hence, LPWAN devices should possess the ability to connect and operate in varied LPWAN technology environments with interference tolerance, handling, and mitigation capabilities. The network should be able to enable connectivity of devices irrespective of hardware infrastructure and application programming interface (API). Seamless end-to-end interoperability needs to be supported between different network technologies. This may require standardization and gateways with adaptability protocols between various communications technologies. Full end-to-end application integration is expected.  
In summary, the LPWANs are resource-constrained networks and have the critical requirements for long battery life, extended coverage, high scalability capabilities, low device cost, and low deployment cost.
## 3 Mobility Methodology
### Hybrid of NDN with C-IoT and AI  
For hybrid NeMO with cloud operation, as shown in Figure 7, the mobile producer moves from the current location under agent router 1 (AR 1) to the newest location on AR 2. The processing of data in the mobile producer using this hybrid technology uses AI to speed up the transmission process. During this movement, all information of the data packet and the interest packet is currently updated through FIB, but in this technology, BIT reduces the functionality of FIB. AR 1 and AR 2 interact with each other on the NDN network cloud. After BIT has been updated at AR 2, it sends the latest information to MR 2 and, finally, to the mobile producer. After this operation, which is called interNeMO, the consumer obtains the data packet successfully. Table 4 explains the operation of hybrid NeMo in cloud storage.  
Figure 6: The PMSS with cloud operation.  
Figure 7: Hybrid NeMO with cloud operation.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 3 Numerical Study of Blasting Seismic Energy Generation and Attenuation under In Situ Stress
### Numerical Model and Simulation Method
#### 3.2.3 Validation of Material Models and Parameters  
Li [51] studied the RHT model parameters of different rocks through SHPB tests and comparisons between laboratory tests and numerical simulations. In the present study,  
Figure 3: Illustration of the RHT constitutive model. the RHT model parameters of marble reported in Li [51] are adopted, as listed in Table 1. The explosive constants in the JWL equation for many common explosives have been determined by dynamic experiments [52,53]. In this simulation, the JWL parameters of emulsion explosives validated by [PERSON] et al. [52] are used as follows: C\({}_{1}\) = 191.722 GPa, \(C_{2}\) = 1.316 GPa, \(R_{1}\) = 4.629, \(R_{2}\) = 0.588, \(E_{0}\) = 1.933 and \(\omega\) = 0.37. The other explosive properties used in the simulation are: density \(\rho_{\rm e}\) = 1188 kg/m\({}^{3}\), velocity of detonation \(V_{\rm D}\) = 5323 m/s, Chapman-Jouguet pressure \(P_{\rm CJ}\) = 7.90 GPa, and specific heat of explosion \(W_{\rm e}\) = 2235 kJ/kg [53]. [PERSON] et al. [54] have carried out single-hole blasting tests on marble slabs. In their tests, the marble slabs have a width of 0.2 m, a height of 0.2 m, and a thickness of 0.023 m, and the circular blastholes have a radius of 0.004 m. [PERSON] et al. [54] performed two sets of tests, one without initial stress on the slab boundaries and the other one with initial stress of 5 MPa uniaxially applied to the slab boundaries. The two sets of tests are simulated in this study to validate the selected material models and parameters. In the simulation, the sizes of the marble slabs and blastholes and the initial stress conditions remain consistent with those in the laboratory tests. The rock fracture modes obtained by the numerical modeling are shown in Figure 4. Both the testing and numerical results show that when the marble slab is free from initial stress, the blast-induced cracks spread radially around the blasthole. While the initial stress of 5 MPa is applied, the crack propagation tends to the uniaxial stress direction. In the laboratory tests, the longest crack is about 27 times the blasthole radius. In our numerical modeling, the longest crack extends to a length of 24 times the blasthole radius. The agreement between the laboratory tests and the numerical simulations on the blast-induced rock fracture mode and crack length indicates that the chosen material models and parameters are appropriate, whether in the presence or absence of initial stress.
# A Novel Interpretable Deep Learning Model for Ozone Prediction
## 4 Discussion
### Derivative Model Discussion  
In this study, we designed a model that learns spatial information from the encoding hidden states (Figure 11). However, spatial information can be also captured during decoding. Based on the STAGRU, we moved the spatial information learning process from the encoding hidden states of past sequences of each monitoring station to the decoding hidden states of each station that are in the same prediction step (namely STAGRU-Decoder). The details of this model are shown in Figure 12. Firstly, it is clear that the spatial attention layer of the STAGRU-Decoder receives the decoding hidden states of all monitoring stations in the same prediction step. Then, all stations make predictions simultaneously during decoding. In this manner, the STAGRU-Decoder can achieve synchronous prediction for multiple monitoring stations, which reduces the model training overhead.  
We investigated the effectiveness of the STAGRU-Decoder by comparing it with the STAGRU in five cities. The results are shown in Figure 13. According to the results, the mean performance of the STAGRU-Decoder is better than the STAGRU in Beijing and Guangzhou and it performed similarly in other cities. However, the STAGRU-Decoder becomes unstable as the forecast lead time increases, after the 8 th hour specifically, according to the undertint area. We considered that the predictions made by the STAGRU-Decoder for the monitoring stations are built on the predictions of others, which causes error superposition. Thus, the stability of the STAGRU-Decoder deteriorates as forecasting continues. Furthermore, we note that the applicable scope of the spatial information learning in the STAGRU and STAGRU-Decoder is limited by the wind force, where the air pollutant is transported more widely when the wind becomes stronger.  
Figure 7: Heatmap of the relative importance of other stations concerning the CCM at each prediction step. Note, 0 means less important compared to the other stations.
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 2 Short-Circuit Fault Level Estimation for Active Distribution Networks
### The Fuzzy Logic Identifier to Distinguish Useful Disturbance  
There are some difficulties for the deterministic theory to distinguish the useful disturbances in the proposed method. On one hand, it is impossible to draw a distinct line between the disturbances on two different directions, as the slight fluctuations are always happening in the whole system. Therefore, the mere index \(Dir\) might not always be always accurate enough, especially in the special situation that the value of \(Dir\) is in a small vicinity of zero. On the other hand, the threshold is required to identify the \"non-trivial\" change of the voltage or current, as the desired information in the trivial fluctuation could be overshadowed by the errors. However, similarly, it is hard to find a once-for-all setting for the threshold, as the power girds with different load types have a diversity of characteristics.  
In this paper, a fuzzy logic identifier is illustrated to identify the disturbance in a more adaptive manner. Fuzzy logic methods have been invented for decades, to provide alternative solutions in adaptive control [29]. Two aspects need to be identified, including the disturbance direction with respect to the studied breaker, and the deviation level of the voltage or the current. The design of a fuzzy logic identifier usually includes three steps: (1) setting the rule of the problem; (2) selecting the proper linguistic terms which can describe the input and output properly and normalize the input-output variables; and (3) defining the fuzzy logics for all fuzzy variables [30].
# Wavelength Selection for Satellite Quantum Key Distribution
## 5 Results and Discussion  
The signal gain is determined by the channel efficiency, which is a multivariate equation given by Equation (1). The largest contributors are atmospheric extinction and geometric coupling efficiency. Both depend on wavelength, \(\lambda\), but the geometric coupling efficiency also depends on the initial beam waist, \(w_{0}\), propagation distance, \(z\), and the receiver aperture diameter, \(D_{r}\). Similarly, the background probability is a function of wavelength, and it is highly correlated with the receiver diameter and the spatial filter field of view.  
To account for this parameter interdependence, the secret key rate \(R\) is analysed as a function of \(\lambda\), \(D_{r}\) and \(w_{0}\). The methodology exploits the fact that \(w_{0}\) has more of an effect on the receiver aperture and maximum key rate than it does on wavelength selection. \(w_{0}\) is set to a constant value and \(R\) is solved for a range of \(\lambda\) and \(D_{r}\) values. For each wavelength region, the highest performing \(\lambda\) is selected and used to solve \(R\) for a range of \(D_{r}\) and \(w_{0}\) values. The resulting 3D arrays are plotted, and the optimal \(D_{r},w_{0}\) is found for the chosen wavelengths, enabling a best-case comparison of each transmission window. The simulation parameters used in this work are listed as follows: optical receiver efficiency, \(\eta_{opt}=0.45\), detector efficiency, \(\eta_{det}=0.8\), signal-state mean photon number, \(\mu=0.7\), decoy-state mean photon number, \(\
u=0.1\), detector dark counts, \(f_{dark}=10\) Hz, temporal filter width, \(\Delta t=1\) ns, spectral filter width, \(\Delta\lambda=1\) nm, background error rate, \(e_{0}=0.5\), polarization cross-talk error, \(e_{d}=0.01\), error correction efficiency, \(f_{cc}=1.22\), and propagation distance, \(z=500\) km. Note that the values chosen for detector efficiency, \(\eta_{det}=0.8\), and detector dark counts or dark count rate (DCR), \(f_{dark}=10\) Hz, are based on recent advances in SNSPD technology. Current single-photon detectors with \(\eta_{det}>0.5\) typically operate at wavelengths shorter than 2 \(\upmu\)m. For example, at a wavelength of 1550 nm, state-of-the-art SNSPD detector efficiencies/DCR of 90%/10 cps (NbN), 93%/1000 cps (WSi), and 87%/100 cps (MoSi) have been demonstrated to work [64]. NIST have taken steps to develop SNSPDs based on tungsten silicide (WSi) by demonstrating saturated internal detection efficiency up to 10 \(\upmu\)m [65]. By optimizing absorption in the nanowiresand coupling to active areas of detectors, it may be possible to achieve mid-infrared high-detector-efficiency SNSPDs.
## 3 Case Study: Entire Frame
### Evolution of Velocity Uncertainty  
The velocity uncertainty \(\sigma_{v}\) can be estimated by  
\[\sigma_{v}=\frac{2\sqrt{3}\sigma_{e}}{N^{0.5}T} \tag{4}\]  
where \(\sigma_{e},N,\) and \(T\) are the uncertainty at each epoch, the number of image data, and the observation time period length, respectively, under the assumption that the measurement errors are uncorrelated ([PERSON], 1963). It is noteworthy that, if network gaps are present, \(T\) is not equivalent to the time length of the observation period but, rather, the timespan of the connected network. Therefore, the presence of a gap can significantly degrade the accuracy of the velocity estimation (see Section 3.3).  
We calculate InSAR LOS velocities starting at the first Sentinel-1 acquisition (i.e., 25 November, 2014) to each subsequent acquisition epoch; GNSS LOS velocities for the corresponding periods; and the STD of the difference between these estimates. We also calculate the STD of the InSAR LOS velocity at each subsequent acquisition epoch using the percentile bootstrap method ([PERSON], 1963; [PERSON], 1963).  
The temporal evolution of the InSAR-GNSS and InSAR-bootstrap STDs are largely consistent with the theoretical velocity uncertainty model calculated using Equation (4) with \(\sigma_{e}\) of 9.0 mm or 7.5 mm (calculated in Section 3.2 by comparison with GNSS), respectively, and a sampling interval (\(dt=T/(N-1)\)) of 24 days for \(\sim\)2.3 years (i.e., until February 2017; Figure 9). An excursion in InSAR-GNSS STDs around 1.5-2.0 years (i.e., May to November 2016) might be caused by residual noise in the InSAR time series (Figure 6c, Figures S5 and S6). The STD of InSAR-GNSS converges to \(\sim\)2 mm/yr, not 0 mm/yr, presumably because of a contribution from colored noise components in InSAR and/or GNSS ([PERSON], 1963) or discrepancies in the location of the measurement points between InSAR (i.e., coherent sum of the scatters in each resolution cell) and pointwise GNSS.  
Figure 8: (**a**) LOS velocity without the GACOS correction. Symbols are the same as Figure 6a. (**b**) Difference of the LOS velocity with and without the GACOS correction (i.e., Figures 6a, 7a and 8a). Note that the color scale is different from Figures 6a and 8a.  
Our prediction of the velocity uncertainty is useful for estimating how small displacements can be detected given a certain amount of data or how long a time period is required to achieve a certain velocity precision. For example, if the sampling interval is 70 (corresponding to past Envisat acquisitions every two cycles), 24, 12, or 6 (current best-case with Sentinel-1A/B) days, it would take 3.1, 2.2, 1.8, or 1.4 years to achieve a velocity STD of 2 mm/yr, respectively.
## 5 Results
### Complexity of the Model  
Table 4 shows a comparison of the complexity of the models trained on different input features, in terms of trainable and non-trainable parameters. Similarly, Figure 11 compares the complexity of models trained on different input features in terms of trainable and non-trainable parameters. It is evident that the reduction in input space (i.e., 1D input size) requires lesser trainable parameters and, hence, it reduces the complexity of the model by minimizing model training time.  
Figure 11: Trainable parameters for different cases of multi-emotion classification.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Window** & **Step** & **Overlap between** & **Test** \\
**Size** & **Size** & **Consecutive Samples (\%)** & **Accuracy (\%)** \\ \hline
200 & 100 & 50 & 52.95 \\
400 & 100 & 75 & 62.44 \\
**256** & **16** & **93.75** & **95** \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of different segmentation window sizes and step sizes and their effect on test accuracies. A window size of 256 and a step size of 16 produce the best test accuracies.  
Figure 10: The Accuracy curve (**a**) and Loss curves (**b**) computed with the magnitude of 3D accelerations as input signal using multi-emotion classes (six emotions). The training and testing curves show a good fit, ruling out over-fitting.  
Table 5 and Figure 12 present the computation time required by the proposed model for the training and inference under different sizes of input signals, i.e., 1D (\(\overline{mag_{a}^{w}}\)), 3D (\(a_{x}^{w},a_{y}^{w},a_{z}^{w}\)), and 6D (\(a_{x}^{w},a_{y}^{w},a_{z}^{w},\omega_{x}^{w},\omega_{y}^{w},\omega_{z}^{w}\)). With a 1D input size (\(\overline{mag_{a}^{w}}\)), the model was trained in 147,615 ms, whereas the inference took only 67 ms. When the input size was 3D (\(a_{x}^{w},a_{y}^{w},a_{z}^{w}\)), the model took 269,682 ms for training and 229 ms for inference. With a 6D input size (\(a_{x}^{w},a_{y}^{w},a_{z}^{w},\omega_{x}^{w},\omega_{y}^{w},\omega_{z}^{w}\)), the training time of the model was 286,227 ms and inference time was 385 ms. In sum, the training and inference time required by the proposed 1D input size is faster than 3D and 6D by a factor of about 3.4 and 6.4, respectively. With these findings, it can be concluded that the 1D input size, being efficient and lightweight, is appropriate for the real-time classification of human emotions using wearable inertial sensor data. The experiment was conducted on a single Google Colab instance, equipped with a Tesla T4 GPU, two CPU cores, and 12.68 GB of RAM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cases** & **Trainable** & **Non-Trainable** \\  & **Parameters** & **Parameters** \\ \hline \(a_{x}^{w},a_{y}^{w},a_{z}^{w},\omega_{x}^{w},\omega_{y}^{w},\omega_{z}^{w}\) (6D) & 758,160 & 896 \\ \(a_{x}^{w},a_{y}^{w},a_{z}^{w}\) (3D) & 756,624 & 896 \\ \(\omega_{x}^{w},\omega_{y}^{w},\omega_{z}^{w}\) (3D) & 756,624 & 896 \\
## 4 Analysis of Simulation Results  
### Simulations on the Effectiveness of the Pollution Control Measures  
The emissions recorded in January, April, July, and October 2013 were simulated to analyze the seasonal characteristics of air pollution in Taiwan. Based on these results, we then estimated the annual mean of air pollutant concentrations in Taiwan. The simulation included all of Taiwan and East Asia, and the performance of the simulation conformed to the air quality simulation standards established by the Taiwan EPA [50]. By varying the control matrix, the RSM system can be used to adjust various pollutant concentrations simultaneously to provide insight into the effects of pollution control measures on air quality. The performance of the RSM model was validated using the method developed by [PERSON] et al. [27], i.e., by computing the coefficient of determination (R\({}^{2}\)) between the RSM and CMAQ models. We applied RSM to the training samples and used four additional CMAQ cases to fit and validate the RSM [14; 51]. In this study, the R\({}^{2}\) always ranged between 0.998 and 0.99, while mean bias and mean normal bias ranged between 0.03-0.02 and 0.01-0.04, respectively. Therefore, the results of the RSM simulation are exceptionally reliable (Figure 2). Here, we estimate the changes in PM\({}_{2.5}\) concentration that result from each pollution control measure and their corresponding health benefits with the assumption that the meteorological conditions of the target year are equal to those in 2013. The average PM\({}_{2.5}\) concentration in Taiwan is 30.5 \(\mu\)g/m\({}^{3}\). Figure 3 illustrates that the largest reductions in PM\({}_{2.5}\) concentration are due to the TAPCAP that occurred in central and southern Taiwan. Between the various pollution control measures, reductions in the number of diesel trucks resulted in the largest difference in PM\({}_{2.5}\) concentration (2 \(\mu\)g/m\({}^{3}\) or 6.5%) (Table 3). The next most significant measures are the port area air quality regulations and power sector regulations, which reduced the average PM\({}_{2.5}\) concentration of Taiwan by 0.5 \(\mu\)g/m\({}^{3}\) and 0.4 \(\mu\)g/m\({}^{3}\)  
Figure 2: Simulation of PM\({}_{2.5}\) pollution in 2013 by the advance response surface model (RSM) system and Community Multi-scale Air Quality model (CMAQ). respectively. This result is consistent with the findings of [PERSON] et al. (2020), which indicated that the control of road traffic emissions is highly effective in reducing PM\({}_{2.5}\) concentration and that it is necessary to simultaneously control multiple sources of PM\({}_{2.5}\) to maximize reductions in PM\({}_{2.5}\) pollution. Figure 3: PM\({}_{2.5}\) reduction concentration and the expected reductions in the number of deaths of TAPCAP implement. (**a**) PM\({}_{2.5}\) reduction concentration of TAPCAP (ug/m\({}^{3}\)). (**b**) Estimated PM\({}_{2.5}\) related health impact (number of preventable deaths) due to (**a**).
# Malware Detection on Byte Streams of Hangul Word Processor Files
## 2 Background  
### Malware Detection on Stream Data  
Malware is a program designed to harm or disturb computer systems. It usually steals important information of the computer user or request ransom money for maliciously encrypted data. Malware can be divided into two categories: executables and non-executables. The danger of malicious executables (e.g., exe files) is widely known by the public, and there are many security programs (e.g., Norton, Kaspersky) to detect malicious actions of the executables. On the other hand, the non-executables (e.g., docx, HWP files) often bypass some existing security programs and there is a high risk of false positives. Moreover, the non-executables tend to be considered as being insignificant by many users, which makes them more dangerous.  
There have been studies to detect the malicious actions of non-executables. [PERSON] and [PERSON] [5] proposed a format-free hierarchical document structure, namely Hidost, to represent structural paths of non-executable documents, and train machine learning models with features about the obtained paths. [PERSON] et al. [6] defined features using PDFiD Python script that verifies objects in PDF files. Because the features might be vulnerable to a simple trick such as a gradient-descent attack, they also introduced two feature selection steps: (1) applying a threshold for each feature, and (2) reducing the number of non-critical features. [PERSON] and [PERSON] [7] defined features obtainable from document metadata, such as the length of title and image sizes. [PERSON] et al. [8] proposed a feature extractor for PDF documents, namely FEPDF, that generates features about a document header, objects, cross-reference, and trailer. All of these studies mainly put much effort into finding effective features based on their knowledge about the target file. It is expensive to design high-quality features, and different feature definitions will be required for different types of documents.  
In this paper, we aim at detecting malicious actions within Hangul Word Processor (HWP) file that is a widely used non-executable format in South Korea. Due to the political and military situation between South and North Korea, one of main targets of malicious HWP files is the governmental institutions of South Korea. As far as we know, this is the first study that detects malicious actions of the HWP files by analyzing byte sequences. We design a convolutional neural network that determines whether a given byte sequence contains malicious actions or not. This is based on an assumption that the byte sequences of malicious actions contain particular patterns. Our proposed network is intended to automatically extracts effective features about the patterns. Although the proposed network does not require intensive feature definition, it is still important to examine and understand the structure of the target format (i.e., HWP). The reason is that the better features can be generated only if input data is wisely defined by domain experts, and better understanding of the file structure might help to design better networks.