esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Central Data Processing System
#### 2.3.7 The Tropospheric Profiling Module
We consider all valid ground-based measurements within \(\pm 1\) h of TROPOMI overpass time and calculate the corresponding daily median values. Next, we select only those days in the monthly mean that have a valid TROPOMI pixel over the measurement site as well as a valid ground-based measurement. Hence, the ground-based flagging also impacts the median values of the satellite data in the annual variation. It is clearly visible that including the extra-error flag removes days with higher concentration and hence introduces a bias in the median. Details on the TROPOMI processing version are given in Section 3.4. For the OEM-based retrieval code (MMF), the standard a priori profiles correspond to decreasing exponential profiles with a scale height of 1 km, and the default integrated column up to 4 km is 9 \(\times\) 10\({}^{15}\) molec/cm\({}^{2}\) for NO\({}_{2}\) and 8 \(\times\) 10\({}^{15}\) molec/cm\({}^{2}\) for HCHO. If  
Figure 9: Annual variation of the tropospheric NO\({}_{2}\) column for ground-based MAX-DOAS and TROPOMI over Xianghe, using only coincident measurements. Black curves show the ground-based median, red curves show TROPOMI median, dashed lines show the filtering with processor version 1.0, solid lines with filtering of processor version 1.1. For details, see text. one year of data are available at a station, suitable adjustments for each station are made for the integrated a priori value based on the mean from the geometric approximation of at least one year of data. Likewise, the covariance matrix for MMF is constructed from the a priori profiles (50% on the diagonal, 200 m correlation length for calculating the off-diagonal elements). Since MAPA uses pre-calculated LUTs in combination with a parameterisation approach, no a priori values are needed. However, since, e.g., the surface albedo is currently not a parameter, stations located in areas for which the surface albedo greatly deviates from the standard value of 0.06 (that was used for the LUT creation) should be handled with caution. Additional LUTs for different conditions are currently prepared by MPIC to overcome this limitation. Apart from the profiling of trace gases and aerosols, the tropospheric processing module currently implements the broken clouds algorithm of [PERSON] et al. (2018) and the effective distance calculation developed in the framework of the EU FP7 QA4 ECV project ([PERSON] et al., 2018). However, the broken cloud flag is currently not used since the implementation will soon change to the algorithm described in Section 2.2.3.
## 4 Results and Experiments
### Results
#### Multi-Class Classification  
The performance metrics for various models in multi-class classification on the NF-UNSW-NB15-v2 and CICIDS2017 datasets, utilizing data resampling techniques and class weights, are summarized in Table 33. On the NF-UNSW-NB15-v2 dataset, the CNN model achieved an accuracy of 98.36%, with precision at 98.66%, recall at 98.36%, and F-score at 98.46%. The auto encoder showed slightly lower performance, with an accuracy of 95.57%, precision of 96.54%, recall of 95.57%, and an F-score of 95.77%. The DNN model attained an accuracy of 97.65%, with precision at 98.09%, recall at 97.65%, and F-score at 97.77%. The Transformer-CNN model stood out with the highest performance, achieving an accuracy of 99.02%, precision of 99.30%, recall of 99.02%, and F-score of 99.13%. On the CICIDS2017 dataset, the CNN model achieved an accuracy of 99.05%, with precision at 99.12%, recall at 99.05%, and F-score at 99.07%. The Auto Encoder performed similarly, with an accuracy of 99.09%, precision of 99.12%, recall of 99.09%, and F-score of 99.09%. The DNN model reported an accuracy of 99.11%, with precision at 99.20%, recall at 99.11%, and F-score at 99.14%. The Transformer-CNN model once again outperformed the others, achieving the highest accuracy of 99.13%, precision of 99.22%, recall of 99.13%, and F-score of 99.16%. These results emphasize the strong performance of each model in multi-class classification tasks across both datasets, showcasing their reliability and robustness in real-world applications. Notably, the Transformer-CNN model demonstrated the highest effectiveness, standing out as the most proficient model for multi-class classification on both datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Metric** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline NF-UNSW-NB15-v2 & CNN & 99.69\% & 99.69\% & 99.69\% & 99.69\% \\ \cline{2-6}  & Auto Encoder & 99.66\% & 99.66\% & 99.66\% & 99.66\% \\ \cline{2-6}  & DNN & 99.68\% & 99.68\% & 99.68\% & 99.68\% \\ \cline{2-6}  & Transformer-CNN & 99.71\% & 99.71\% & 99.71\% & 99.71\% \\ \hline CICIDS2017 & CNN & 99.86\% & 99.86\% & 99.86\% & 99.86\% \\ \cline{2-6}  & Auto Encoder & 99.73\% & 99.73\% & 99.73\% & 99.73\% \\ \cline{2-6}  & DNN & 99.88\% & 99.88\% & 99.88\% & 99.88\% \\ \cline{2-6}  & Transformer-CNN & 99.93\% & 99.93\% & 99.93\% & 99.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 32: Performance metrics in binary classification using data resampling and class weights.
## 5 Platform Preparation for the Field Test
### Preparation of the Gateway for the Field Test  
To reduce the cost, the gateway node prototype for laboratory tests (described in Section 4.3) is implemented using resources that are typical for a WSN sensor node. The prototype can be used for the field test, although it is not optimized for low energy, as we will show below.  
The resources of the microcontroller are sufficient for the gateway tasks. However, more SRAM would allow it to aggregate additional field messages, reducing the frequency of connections to the server and the energy consumption.  
The transceiver for in-field communication is used to receive messages from sensor nodes and to communicate with peer gateways for health checks and to increase the availability of service, e.g.:  
* in case of failure of the GPRS link of a gateway:
* it can send its field messages to peers, so that they forward them to the server;
* send self-diagnostic data to the server through peers to provide the remote operators the necessary information for corrective actions;
* failures of the field receiver would prevent the gateway from receiving hello messages from peers; thus, the gateway would eventually declare all peers \"missing\"; however, since the peers would regularly contact the server without reporting errors, this condition can be detected and appropriately flagged on the server;
* failures of the field transmitter would prevent the peers from receiving the gateway hellos, which they will report to the server; the faulty gateway itself would not report any anomaly.  
Gateways require much more energy than sensor nodes due to the continuous LPL of the sensor node channel (to support the random MAC) and energy consumption of the GPRS modem. Although the latter is turned off most of the time, the average energy consumption of the gateway is still too high to allow its long-term operation from a reasonably-sized primary cell. Thus, we installed solar panels on the gateway for energy harvesting, since the test field receives abundant sunlight.  
Considering the above, the gateway was embedded in a birdhouse, as shown in Figure 9a.  
Figure 8: Sensor node package hosting the PCB, a half-AA battery and the normal mode helix antenna (NMHA) antenna. The aperture on the left improves the NTC thermal contact (**a**); fixed on trees with screws (**b**).  
The solar panels for energy harvesting were placed on the roof, and the internal space of the birdhouse was divided horizontally into two compartments using a wooden separator. In the lower one were placed the gateway, the rechargeable battery and the GPRS modem in a sealed plastic package. The antenna of the GPRS modem was left inside the plastic package, because the test field had good cellular coverage. The antenna for in-field UHF communications, a \(\lambda\) /4 whip, was kept inside the birdhouse for aesthetic reasons. As expected, this position reduced the RF sensitivity, as we will discuss below.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 3 Imaging in BAV
### Cardiac MRI (CMR)  
Cardiac MRI (CMR) is considered the gold standard for comprehensive assessment of BAV and aortopathy. CMR provides high-resolution images of the aortic valve and the entire aorta without the use of ionizing radiation. It allows for detailed evaluation of valve morphology, aortic dimensions, and functional assessment of the left ventricle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Authors** & **Number of Participants** & **Study Design** & **Sequencing Technology** & **Main Findings** \\ \hline IncRNA & [33] & 29 BAV patients, 7 controls & Case Control Study & RT-qPCR for targeted expression analysis & TUG1 and MIAT IncRNAs \\  & [33] & 29 BAV patients, 7 controls & Case Control Study & RT-qPCR for targeted expression analysis & aortic tissue, indicating potential roles in pathogenesis of BAV \\  & & & & & \\  & & & & & \\ \hline ROBO4 & [14] & Familial study, two families & Family Study & Whole Exome Sequencing (WES) & Pathogenic ROBO4 variants associated with BAV, AVS, and thoracic aortic aneurysms. \\ \hline  & [15] & Familial study & Family Study & Whole Exome Sequencing (WES) & Pathogenic variants in ROBO1 found in familial BAV. \\ \hline  & [29] & Experimental study with 304 BAV patients & Experimental Study & Whole Exome Sequencing (WES) & Extracellular Matrix \\  & & & & & Disorganization Caused by ADAMTS16 Deficiency Leads to \\  & & & & & Bicuspid Aortic Valve With Raphe \\  & & & & & Formation. \\ \hline FBN1 & [30] & 10 BAV patients without Marfan syndrome & Genetic Study & Targeted sequencing & Common and rare variants identified in non-syndromic BAV cases, also associated with aortic \\  & & & & & \\  & & & & & root aneurysms without features of \\ \hline MMP-2 & [31] & 30 BAV patients, 28 TAV patients & Comparative Cohort Study & PCR, Western Blot, ELISA, and immunohistochemistry-chromistry & Increased MMP-2 levels and decreased miR-29A in BAV patients, indicating a role in extracellular matrix destruction and aneurysm development. \\ \hline ACTA2 & [32] & 20 patients with BAV and TAA & Genetic Study & Targeted sequencing (NGS panel) & ACTA2 mutations were not found to play a significant role in BAV \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Additionally, CMR can measure blood flow dynamics, which are crucial for understanding the hemodynamic impact of BAV [16].  
4D Flow MRI: 4D Flow MRI is a novel technique that captures three-dimensional blood flow within the heart and great vessels over time. This comprehensive visualization of blood flow patterns allows for detailed analysis of hemodynamics and wall shear stress (WSS), providing valuable insights into the pathophysiology of BAV and associated aortopathy [41].
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 2 Methodology
### Dynamic Data Filtering
#### 2.7.1 Normalisation  
The intention of normalisation is to bring the measurement data to a relative frame of reference to reduce the absolute differences due to time and space. The effect is a compression of the data-density distribution. Considering the spatial and temporal dependency of the measurement values \(\alpha\) and \(u_{r}\) we apply a corresponding normalisation. The definition of the normalisation time interval \(\Delta t\) can be seen in Figure 2.  
Figure 3: Example of data-density distribution of a 30-min time interval of LiDAR staring mode measurements in the original \(u_{r}\)-\(\alpha\) frames of reference. Iso-lines show levels of probability of occurrence of the measurement with in a bin of 0.32 m/s width and 0.2 dB height.  
The normalisation is independent of data-density calculation methods which will be presented in the following. The use of the data-density approach may as well be applied without prior normalisation.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 2 Related Works
### ElGamal Public-Key Cryptography  
ElGamal public-key cryptography is an asymmetric encryption algorithm, which was proposed by ElGamal [29] in 1985. Different from RSA, ElGamal encryption is probabilistic, meaning that one plaintext can be encrypted to many possible different ciphertexts. The security of ElGamal encryption depends upon the difficulty of solving the discrete logarithm problem. The applications of ElGamal encryption are described as follows.
# Magnetic Nanowires
## 3 Results
### Tunable Magnetic Anisotropy of Magnetic Nanowire Arrays
These interesting properties of Co\({}_{x}\)Pt\({}_{1-x}\) alloys are related to its ordered face centered tetragonal (fct) phase (L1\({}_{0}\)) which exhibits very large magnetocrystalline anisotropy energy (up to 5 \(\times\) 10\({}^{7}\) erg\(\cdot\)cm\({}^{-3}\) around the equiatomic region). For this purpose, we used a thin anodized aluminum oxide film template (1.5 \(\upmu\)m thick) on a doped silicon wafer, which affords the thermal durability of the nanoholes for high temperature process after electrodeposition. The as-made electrodeposited 80 nm Co\({}_{47}\)Pt\({}_{53}\) nanowires were magnetically soft as shown by the hysteresis loops in Figure 8a. The large packing in the array (close to 50%) induces a strong interwire dipolar coupling that overcomes the shape anisotropy, thus resulting in an easy axis  
Figure 7: **(a)** effective field as a function of the pH for 30 nm Co nanowires electrodeposited at current densities of 5 mA/cm\({}^{2}\) (squares) and 50 mA/cm\({}^{2}\) (circles). The dashed line shows the expected value of \(H_{\text{EF}}\) for an array of Co nanowires (\(P\approx 3.5\%\)) assuming no contribution from magnetocrystalline anisotropy; **(b,c)** TEM images and corresponding diffraction patterns of two 30 nm Co nanowires electrodeposited at pH 5.2 at current densities of **(b)** 5 mA/cm\({}^{2}\) and **(c)** 50 mA/cm\({}^{2}\) (from [29]). Figure adapted from ref. [29] with permission of the author. perpendicular to the wire axis. Appropriate thermal treatment at 700 \({}^{\circ}\)C has contributed to ordering the alloy nanowires. X-ray diffraction patterns have confirmed that the L1\({}_{0}\) ordered phase was successfully obtained in such CoPt nanowires [28]. Hysteresis loops of the annealed sample measured parallel and perpendicular to the wires are displayed in Figure 8b. Magnetic hardening is developed after annealing at 700 \({}^{\circ}\)C with coercive fields as large as 11 kOe at room temperature. No anisotropy exists between the two directions, although the length/diameter ratio of the wires is larger than 10. This is because the L1\({}_{0}\) CoPt has a strong MC anisotropy, much higher than the shape anisotropy. In addition, the nanowires are textured along the <111> direction which makes an angle of 55\({}^{\circ}\) with the (001) easy axis, resulting in an absence of anisotropy.
# Roadway Backfill Mining with Super-High-Water Material to Protect Surface Buildings: A Case Study
## 4 Engineering Application
### Analysis of Surface Subsidence  
The mining of the working face lasted for half a year, and the result of monitoring after three months of mining ended were taken as the final surface subsidence (Figure 6). Based on the data analysis and calculation, the accumulated maximum subsidence is 27 mm after roadway backfill mining. However, the maximum horizontal deformation and surface slope only reach 0.3 mm/m and 0.7 mm/m respectively, lower than the critical value shown in Table 2. From the field observation, no obvious cracks were found on the surface and buildings after the implementation of roadway backfill mining. This proves that this mining method can effectively protect surface buildings and the eco-environment, as do other similar cases [37,38].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Buildings and Its Characteristics**} & \multicolumn{3}{c}{**Allowable Deformation Value**} \\ \cline{2-4}  & **Horizontal Deformation (mm\(\cdot\)m\({}^{-1}\))** & **Slope (mm\(\cdot\)m\({}^{-1}\))** & **Surface Curvature (10\({}^{-3}\cdot\)m\({}^{-1}\))** \\ \hline Brick-concrete structural buildings & 2.0 & 3.0 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Allowable surface deformation value for safe use of brick-concrete structural buildings.  
Figure 5: Curve results of roadway deformation: (**a**) convergence of the two sides; (**b**) convergence of roof-floor.  
Figure 6: Sketch of measured subsidence of observation lines.
## 5 Experimental Validation
### Experimental Results
#### 5.2.2 Sound Source Imaging Cloud Maps  
In this section, the identification results of three sound sources at frequencies of 300 Hz and 500 Hz using different methods are presented under the conditions of no flow velocity and 40 m/s flow velocity, respectively. The working condition, 40 m/s@300 Hz, is used as an example to give the sound source imaging cloud maps of different methods, shown in Figure 12.  
As shown in Figure 12, the sound source location and amplitude identification results of the different methods are significantly different.  
SBL successfully identifies the locations of the sound sources near the three real sources. However, due to the base mismatch error, the sound sources are located at grid points near the real sound sources, resulting in certain location and amplitude errors. In addition, there are some pseudo-source interferences.  
Constrained by its limited resolution performance, OMP shows serious location deviations for Source A and Source B, which are closely distributed, and the total amplitude error is also relatively large.  
Figure 11: Time–frequency spectrum of a single channel signal under the working condition of 40 m/s@300 Hz. (**a**) Time domain spectrum. (**b**) Power spectrum. 300 Hz is marked with a red circle.  
FISTA can find the general locations of the source distributions, but the hotspot areas are too large and the source locations are ambiguous. The width of the main flap is far beyond the actual area of the source distributions and there are many pseudo-sources, making it difficult to recognize the specific locations of the real sources accurately. The amplitude reconstruction error is significant due to severe energy dissipation.  
For the off-grid-\(GL_{1}\) method, there is a significant difference between the identified and actual source locations and a loss of Source C. In addition, the strength of sources is much lower than the actual strength. This is due to the uneven penalty of the \(L_{1}\) norm regularization. Moreover, due to the increase in the dimension of the variables to be solved, the off-grid method is more sensitive to actual noise interference.  
RPCA-SBL performs best among the comparison methods. Due to the removal of random noise interference, its sound source identification position and amplitude errors are lower than SBL. However, its search range is still limited to the grid position.  
VG-SBL breaks through the limitations of the fixed grid model on sound source locations and removes pseudo-source interference. In the iterative process, the distribution of real sound sources is gradually approximated, thus achieving the lowest source identification error and better visualization.  
Figure 12: Sound source identification results of 40 m/s@300 Hz: (**a**) SBL; (**b**) OMP; (**c**) FISTA; (**d**) off-grid-\(GL_{1}\); (**e**) RPCA-SBL; (**f**) VG-SBL. The different colors in the figure correspond to the colorbar on the right side of the figure, representing different amplitudes.
## 3 Trilateration and PSO Algorithms  
Once the estimated distances from the receiver to each LED are obtained, the localization of the target can be formulated as an optimization problem [45]. The pseudocode of the PSO is shown in Algorithm 1. The mathematical expression proposed for this problem is expressed by  
\[\sum_{i=1}^{3}{(d_{pi}-d_{i})^{2}}, \tag{14}\]  
where \(d_{pi}\) represents the distance from a test point \(P(x,y,h)\) to LED \(i\). This point varies in the search for a value of \(d_{pi}\) that minimizes Equation (14). To obtain an optimized solution, the utilization of a PSO algorithm is proposed. The steps to follow can be divided into two parts.
# Recent Advances and Challenges in the Seismo-Electromagnetic Study: A Brief Review
## 1 Introduction  
Earthquake forecast is one of the most challenging scientific issues. The key to earthquake forecasting is to find the precursors that help predict impending earthquakes [1]. Science presented the great unsolved scientific mysteries of our time in the Special Issue marking the journal's 125 th anniversary. Whether earthquake precursors that can help in prediction exist is one of them. To date, there have been many studies on earthquake forecasts, and most of them adopt a catalog-based probabilistic approach [2; 3; 4; 5]. Although a certain amount of precursory information is available in earthquake catalogs [6], earthquake forecasting, especially short-term prediction, is still far from adequate [7; 8]. Nevertheless, non-seismological approaches may provide additional useful information and broaden the knowledge for prediction [9; 10]. The content of earthquake precursors includes many physical parameters such as mechanical deformation, gas emissions, groundwater level variations, ground temperature variations, fluctuations of the electromagnetic field, and so on. The detection of electromagnetic perturbations before fault ruptures was proposed as a useful way to monitor crustal activities and it has been proven to be a promisingphenomenon preceding earthquakes. To date, there have been many reports on electromagnetic field changes associated with earthquakes in a very wide frequency range from megahertz to quasi-DC [11; 12; 13]. These phenomena, known as seismo-electromagnetic phenomena, have been intensively studied for some decades [14; 15; 16; 17; 12] all over the world, from measurements on the ground to space detection. Based on the observation instruments in different spheres of the earth, seismo-electromagnetic phenomena can generally be classified into electric and magnetic changes in the lithosphere (measurement on the surface and/or underground), perturbations in the lower atmosphere (measurement above the surface and below the ionosphere), and the upper atmosphere (partially ionized or fully ionized regions).