esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimal Combination of Factors  
In an ideal situation, selected MQIs would show an optimal behaviour for a unique combination of factors (\(S_{f}\), \(T\) and \(M\)) in each contrast scenario (\(S0x\)). Nevertheless, they were most likely expected to reach their correspondent optimal at different combinations of factors. To deal with this possibility, two alternative approaches to the optimisation problem were addressed: _Coverage Priority_ and _Accuracy Priority_.  
* Under the _Coverage Priority_ approach, a combination of factors that provided the best coverage (ideally a 100% \(C_{f}\)) were extracted from the whole range of combinations. Then, smaller \(\sigma_{D}\) results were prioritised. If more than one candidate provided the maximum \(C_{f}\) and the minimum \(\sigma_{D}\) among the reduced set, the combination that credited the minimum \(\overline{D}\) was finally selected.
* Under the _Accuracy Priority_ approach, \(\sigma_{D}\) and \(\overline{D}\) results were arranged in 0.1 \(\upmu\)m ranges. The algorithm sought first for combinations of factors that provided responses for both \"accuracy\" MQIs within the first range (e.g., \(Min(\sigma_{D})<\sigma_{D}<Min(\sigma_{D})+0.1\)\(\upmu\)m).  
An additional condition was imposed so that results would simultaneously provide at least a 75% coverage. If one or more combinations were found matching both conditions, the one with the maximum \(C_{f}\) was chosen as the optimal combination. Nevertheless, if this were not possible, a second range (lower limit equal to the minimum response for each MQI and higher limit + 0.2 \(\upmu\)m higher) would be explored, and the search would continue until at least one valid combination was found.
# MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float
## 4 Experiments
### Detection of Blue Whales
#### 4.2.3 Implementation with MeLa  
The algorithm has been implemented with the MeLa language. The code is accessible on Github, see the Supplementary Materials section. For this application, the analysis estimates a processor usage of 2 pct. The autonomy of the float is found to be 4.6 years; this is less than the seismic detection application because the power consumption of the sensor is higher, due to a higher sampling frequency. The estimated amount of data transmitted each month is only 14 kB. This is much less than the seismic application, because only timestamps are sent through satellite communication. However, the probability to record a blue whales D-call is estimated to be much higher with five records per hour. If 20 s of sounds where recorded for each detection, the amount of data to transmit each month would be 56,047 kB. Transmitting such an amount of data by satellite increases the costs of satellite transmission and reduces the life time of the float to 0.7 years.  
Once a first version of the algorithm is ready we can compose it with the seismic application. A first error appears because the maximum duration of the dive and depth are not equal for the two applications, thus we defined both to 10 d and 1500 m depth. Subsequently, another error appears because the two applications use the same sensor, but at different sampling frequency. A solution could be to use decimation, but this has not been implemented in the language. Alternatively, we could adapt the seismic algorithm to support higher sampling frequency. Instead, we decided to use the two outputs of the hydrophone, one for low frequencies and one for high frequencies. However using the two outputs of the hydrophone has a noticeable effect on the power consumption. Instead of an autonomy of 5 and 4.6 years for the two separated applications, MeLa computes an autonomy of 2.8 years if the two applications are composed, to be installed on the same instrument. The processor usage is still very low (i.e., 2%), because neither applications require a lot of processing time.  
At this point, the algorithm has not been finalized, since several coefficients still have to be defined or refined. For example, the STA/LTA length or the checks done in order to validate that the signal is blue whale D-call. Simulating the algorithm on a personal laptop with experimental data allowed finalizing the algorithm without programming a real instrument. The finalized algorithm is the one described in the precedent section.
# Definition and Determination of Fin Substitution Factors Accelerating Thermal Simulations
## 2 Materials and Methods
### Analytical Formulas
#### 2.2.1 Surfaces of the Smooth and Finned Surface  
Figure 1 shows the designations of a flat plate and a vertical rectangular finned array. The area of smooth surface \(A_{0}\), which corresponds to the area of the finned array, is calculated as  
\[A_{0}=W\cdot L \tag{1}\]  
Concerning the finned array, the perimeter \(P_{f}\), the cross-sectional area \(A_{f,cs}\), the surface area \(A_{f,s}\), total fin area \(A_{f,s}\) and the fin base area \(A_{fb}\) are calculated using the formulas below. The end surfaces of the fin or the finned array are neglected.  
\[P_{f}=2H+B \tag{2}\]  
\[A_{f,cs}=B\ L \tag{3}\]  
\[A_{f,s}=(2H+B)\ L \tag{4}\]  
\[A_{f}=A_{f,s}\ n \tag{5}\]  
\[A_{fb}=b\ L\ n \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \(T\) & \(\rho\) & \(\
u\) & \(k\) & \(c_{p}\) & \(\beta\) \\ (K) & (kg/m\({}^{3}\)) & (kg/m s) & (W/m K) & (J/kg K) & (I/K) \\ \hline
273 & \(1.276\times 10^{0}\) & \(1.722\times 10^{-5}\) & \(2.436\times 10^{-2}\) & \(1.006\times 10^{3}\) & \(3.674\times 10^{-3}\) \\
293 & \(1.189\times 10^{0}\) & \(1.821\times 10^{-5}\) & \(2.587\times 10^{-2}\) & \(1.006\times 10^{3}\) & \(3.421\times 10^{-3}\) \\
373 & \(9.333\times 10^{-1}\) & \(2.190\times 10^{-5}\) & \(3.162\times 10^{-2}\) & \(1.011\times 10^{3}\) & \(2.684\times 10^{-3}\) \\
473 & \(7.359\times 10^{-1}\) & \(2.605\times 10^{-5}\) & \(3.823\times 10^{-2}\) & \(1.025\times 10^{3}\) & \(2.115\times 10^{-3}\) \\
573 & \(6.075\times 10^{-1}\) & \(2.981\times 10^{-5}\) & \(4.442\times 10^{-2}\) & \(1.045\times 10^{3}\) & \(1.745\times 10^{-3}\) \\ \hline \end{tabular}
\end{table}
Table 1: Temperature-dependent material properties of air [13].  
Figure 1: Schematic representation of a flat plate (**a**) and a vertical rectangular finned array (**b**).
## 2 Security Issues in WSN
### Vulnerabilities and Attacks
Excessive sensing requests transmitted from intruders may drain the energy of nodes. As deployed sensors are typically resource-constrained, unnecessary requests are too prejudicial for WSN. Security threats may have different impacts in specialized applications. In healthcare systems, for example, integrity and availability are central, since attacks, like DoS, can compromise the effectiveness of sensor networks and put people in danger [17]. For wireless image sensor networks, visual sensors capture images that potentially reveal sensitive information about individuals, such as their identities or interaction patterns [13]. Images reveal much more than just the obvious identity information, including clues about people's habits, preferences or social links, requiring privacy mechanisms. Wireless visual sensor networks may also be vulnerable to quality of experience (QoE) and quality of service (QoS) [18] attacks. QoE- and QoS-based optimization approaches have been proposed in recent years, and different levels of prioritization may be exploited for performance enhancement [19]. However, this information may also be exploited by intruders to gain prioritized access to network resources.
# Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction
## 4 Experiments
### Event Selection  
Generative model-based event prediction is a difficult task, which requires predicting the correct sequence of all words in the text. In real-world applications, the low accuracy of prediction results makes it difficult to apply models to specific prediction tasks. Because of the diversity of languages, sentences with a high prediction error rate may express correct semantics. At the same time, sentences generated by the model are often poorly readable. Therefore, we applied the model to the event selection task. Given a series of candidate real events, the model calculates the similarity between the candidate event and the predicted result to find the most likely next event. An example of this task is shown in Figure 5. Here, the similarity between the candidate event and the predicted result was evaluated by the Jaccard index, defined as follows:  
\[J\left(E_{i},E_{j}\right)=\frac{\left|W_{E_{i}}\cap W_{E_{j}}\right|}{\left|W_{ E_{i}}\cup W_{E_{j}}\right|} \tag{21}\]  
where \(W_{E_{i}}\) is the set of words in event \(E_{i}\). Based on the above ideas, our model was applied to the next event selection task that aims to estimate whether the model can correctly hit next events.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{4}{c}{**International News**} & \multicolumn{2}{c}{**China News**} \\ \hline \hline \multicolumn{2}{c}{**Long Event Link**} & \multicolumn{1}{c}{**Short Event Link**} & \multicolumn{1}{c}{**Long Event Link**} & \multicolumn{1}{c}{**Short Event Link**} \\ \hline \hline HRED & 45.63\% & 68.32\% & 59.51\% & 73.23\% \\ \hline HS-GRU & 42.50\% & 66.41\% & 57.43\% & 71.42\% \\ \hline \(\Delta\)\% & 3.13\% & 1.91\% & 2.48\% & 1.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error rate for events link with different length.  
Figure 4: An example of model outputs. Previous four events in the event link are shown on the left. The real last event and the prediction results for different model are shown on the right.  
Referring to the method of [PERSON] et al. [31], the candidate event set was built in the test set. The test set was randomly divided into subsets, each of which contained 50 event links. For each subset, the last event of each event link in the subset was selected as a candidate event. Finally, each event link in subsets had a candidate event set of size 50. We used the hit rate to evaluate different models. We compared the _hit rate_ of current result in candidate events with similarity in the Top 1, Top 5, and Top 10. Table 5 shows experimental results.  
Compared to the GRU and HRED models, the hit rate of our model is also improved in the two datasets. It further proves the importance of the combination of time-based information and prior knowledge for event prediction, which also improves the application value of the model.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 2 Deep Learning-Based Medical Image Analysis  
Medical image analysis plays a crucial role in various aspects of healthcare, including diagnosis, treatment, and health monitoring. It involves the processing and interpretation of images obtained from different modalities to gain insights into the human body and aid in medical decision-making. The integration of deep neural networks and computer vision techniques in medical image analysis has revolutionized the field, enabling earlier and more accurate diagnoses, improved treatment outcomes, and ultimately contributing to the advancement of healthcare and reduction in mortality rates. The development of medical imaging has been shown to extend human life expectancy, according to an intriguing study in [29]. Ultrasonography (US), CT scans, X-rays, PET, and MRI are the medical imaging techniques that are most frequently employed. These image modalities are significant for the detection, segmentation, and classification or diagnosis of anomalies because they offer vital functional and anatomical details about various body organs. Figure 4 displays a typology of significant medical imaging modalities.  
Figure 3: A wide range of cryptography application areas in deep learning-based medical image analysis covered in this survey.  
Medical image analysis is primarily used to support radiologists and clinicians in making accurate diagnoses and disease prognoses. Deep learning is used to perform a variety of tasks in medical image analysis, the most essential of which are classification or diagnosis, detection, and segmentation. PET models powered by AI have already demonstrated their ability to execute segmentation, detection, and classification tasks more effectively and objectively than skilled physicians [31; 32; 33]. Additionally, it is anticipated that the next generation of healthcare systems will include completely automated intelligent medical picture diagnosis systems. Few survey publications [12; 34; 35; 36; 30] have been written about deep learning-based medical image analysis.
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
## 7 Results: Time-Delay Estimation Accuracy Limit for Some Representative GNSS Signals
### GPS L1 C/A vs. GPS L5  
The other GPS civil signals do not exploit BOC-type modulations but in contrast use: (i) a BPSK(10) for the GPS L5; (ii) a BPSK(0.5) for the GPS L2C if we take one of the two codes; or (iii) a BPSK(1) for the GPS L2C considering both CM and CL codes. Then, the time-delay performance obtained with the L2C is either the same or slightly lower than the one obtained with the legacy L1 C/A signal. The CRB and MLE results in Figure 5 show that using such fast BPSK obviously also improves the time-delay estimation capabilities. Considering again the SNR\({}_{\rm out}=25\) dB operation point, we have now for the GPS L5 a standard deviation of 64 cm, being even better than the GPS L1C and Galileo E1 OS components. However, again, a 3\(\sigma\) error of 1.9 m cannot be seen as a precise positioning solution.
## 3 Results and Discussion
### DFT Study
#### 3.5.2 Global Reactivity Descriptors  
Table 4 summarized the reactivity descriptors values, and all reported data are in electron volt (eV). The stability of the molecule is determined by the \(\upeta\) value, whilst the \(S\) parameter provides information about chemical reactivity of substance. Molecules **4**-**12** have lower estimated \(\upeta\) than other examined molecules in the gas phase, indicating their low stabilities. Compounds **4**-**12** have a greater \(S\) parameter of 0.025-0.028, indicating their higher charge-transfer mechanism than that of the other substances. The \(\chi\) was studied for  
Figure 3: ABTS radical scavenging activity of **1**–**14**. Data are means \(\pm\) standard deviation of triplicate experiments.  
a better understanding of the charge-transfer reaction. The molecules abilities to attract electrons are represented by their negative of \(\upmu\). Accordingly, compounds **1**, **2**, **13**, and **14** have low electronegativity, indicating that they are better at giving electrons rather than grabbing them and thus showing low antioxidant activity. The chemical reactivity characteristics of **3-12** showed that they are more suitable targets for electron-scavenging processes. It is clear that all targets are highly polarized, demonstrating that the polarity of the environment will have a significant impact on the scavenging electron response. The values of dipole moments are listed in Table 5.
## 2 Materials and Methods  
The GPM-CO is a Low Earth Orbit (LEO) non-sun-synchronous satellite equipped with the two most advanced active and passive microwave sensors specifically designed for precipitation monitoring and estimation: the JAXA nadir-scanning DPR, operating at the Ku-band (13.6 GHz, 245 km swath) and at the Ka-band (35.55 GHz, 125 km swath), and the NASA GMI conical-scanning radiometer, with 53.1\({}^{\circ}\) viewing angle and a 885 km swath [29; 30]. GMI offers the most appropriate set of microwave frequencies for precipitation retrieval, with 10 dual-polarization (V and H) window channels at 10 GHz, 19 GHz, 36.5 GHz, 89 GHz, and 166 GHz, and three single-polarization (V) channels in the microwave water vapour absorption bands, one at 23.8 GHz and two pass-bands located symmetrically about the peak of the 183.31 GHz (183.31 \(\pm\) 3 GHz and 183.31 \(\pm\) 7 GHz) [36].  
In the GPM-CO database used for the analysis, each precipitating event is identified by the so-called precipitation feature (PF), a concept that was first introduced in late 1990s for other satellite-based observations. The PF database is freely available at [[http://atmos.tamucc.edu/trmm/data/](http://atmos.tamucc.edu/trmm/data/)]([http://atmos.tamucc.edu/trmm/data/](http://atmos.tamucc.edu/trmm/data/)) (accessed on 26 April 2021), and it is described in Reference [37]. The PF database V05 (covering the period from March 2014 through the year 2018) has been used in the study. PF definition involves two steps using Level-1 and Level-2 data. Level-1 data are obtained by processing both GMI and DPR products, after co-location of radiometer and radar data and a parallax correction (to account for their different scanning geometries). In order to identify the PFs, it is necessary to assemble the single Level-1 pixels into Level-2 data. Different criteria are used to assemble the Level-1 pixels, which can be based on DPR and GMI parameters, highlighting that there is more than one way to define PFs. For the following analysis, the PFs are defined by grouping all the adjacent Level-1 pixels with surface precipitation rate greater than 0.1 mm/h, as retrieved by the GPM-CO Ku-band radar. The number of adjacent pixels defines the area of a PF. This PF class (labeled as \"kurpf\" in the dataset) includes almost all events, from shallow rain to the deep convection or heavy precipitation cases, and it provides both radar-based and radiometer-based parameters for a better and more complete analysis of the different precipitation systems.
## 2 Materials and Methods  
Feature learning in PointNet is mostly carried out with only single-point XYZ features, without considering other local features extracted from the point cloud. This limits PointNet's ability to identify and summarize detailed information. To solve these problems, we propose the MSMF-PointNet model to improve the performance of PointNet. MSMF-PointNet consists of three main parts.  
1. Selection of multi-neighborhood features  
Firstly, the point cloud and the remote sensing image are registered. After registration, the spectral information of each point in the point cloud is fuse to the corresponding remote sensing image to obtain the basic features, namely, the coordinates (XYZ) and color (RGB) of the point cloud.  
Multiple features of the point cloud data are extracted using the spherical neighborhood method. Some common features, such as roughness, are selected. Covariance-based features are mainly calculated using the covariance-based eigenvalue \(\lambda_{1},\lambda_{2}\), and \(\lambda_{3}\), where \(\lambda_{1}\geq\lambda_{2}\geq\lambda_{3}>0\). A total of four point-cloud features with strong descriptive ability are selected: omnivariance (\(O_{\lambda}\)), planarity (\(P_{\lambda}\)), linearity (\(L_{\lambda}\)), and verticality (\(V_{\lambda}\)).
2. Expression of multiscale features  
The neighborhood scope of the target point is determined. In the process of feature calculation, the K value needs to be set in the K-neighbor method, while the radius R needs to be set in the spherical neighborhood method. The characteristics obtained are different depending on the size of the selected neighborhood. The minimum scale is generally larger than the minimum density of the point cloud, the maximum scale can be defined according to the average width of the analyzed building, and the interval size of the scale can be increased in turn according to the minimum scale. Two scales are comprehensively selected, with R values of 0.8 m and 1.2 m, respectively.
3. The construction of the MSMF-PointNet network  
The LiDAR point cloud assigned a priori to produce the 3D urban land cover is classified using a 3D DNN. The LiDAR point cloud is sparse and irregular, which renders traditional convolution methods unusable. PointNet [13] represented pioneering work on point clouds to overcome this problem, and its hyper-parameters and structure were redesigned to classify urban LiDAR point clouds in this study.  
In the next section, the steps for point-cloud classification and segmentation are described in detail by combining multiple features of neighborhood sampling with multiple scales to construct a deep neural network.
## 4 Hazards Analysis
### Landslide Hazard Assessment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **Weights (AHP)** & **Class** & **Ranking** & **Normalized Values (\%)** \\ \hline  & & \(<\)2\({}^{\circ}\) & 1 & 16 \\ \cline{3-4}  & & 2-5\({}^{\circ}\) & 2 & 33 \\ \cline{3-4} Slope & 0.25449 & 5–15\({}^{\circ}\) & 3 & 50 \\ \cline{3-4}  & & 15-25\({}^{\circ}\) & 4 & 66 \\ \cline{3-4}  & & 25-35\({}^{\circ}\) & 5 & 83 \\ \cline{3-4}  & & \(>\)35\({}^{\circ}\) & 6 & 100 \\ \hline  & & Carboniferous, Triassic siltstones & 1 & 25 \\ \cline{3-4} Lithology & 0.19809 & Neogene formations & 2 & 50 \\ \cline{3-4}  & & Flysch, clastic & 3 & 75 \\ \cline{3-4}  & & Recent loose and Pleistocene formations & 4 & 100 \\ \hline  & & No-till arable land, beaches, sand dunes, sand bars, salt flats, coastal lagoons, inland and coastal wetlands & 0 & 0 \\ \cline{3-4}  & & Port, recreational facilities & 1 & 16 \\ \cline{3-4} Land Use & 0.15603 & Olive growes, vineyards, natural pastures, sclerophyllous vegetation, transitional shrub–forest & 2 & 33 \\ \cline{3-4}  & & Agriculture with significant areas of natural vegetation, discontinuous urban development, broadleaf forests, coniferous forests, mixed forest & 3 \\ \cline{3-4}  & & Pastures, complex cropping patterns & 4 & 66 \\ \cline{3-4}  & & Mining locations & 5 & 83 \\ \cline{3-4}  & & Sparse vegetation areas & 6 & 100 \\ \hline Distance from & & \(>\)200 m & 0 & 0 \\ \cline{3-4} Faults & 0.11037 & \(<\)200 m & 1 & 100 \\ \cline{3-4} Distance from the & & \(>\)50 m & 0 & 0 \\ \cline{3-4} Road Network & 0.08869 & \(<\)50 m & 1 & 100 \\ \cline{3-4} Distance from the & & \(>\)50 m & 0 & 0 \\ \cline{3-4} Drainage Network & 0.05761 & \(<\)50 m & 1 & 100 \\ \hline Precipitation & 0.05027 & \(<\)1000 mm & 1 & 100 \\ \hline  & & \(<\)15 m & 1 & 25 \\ \cline{3-4} Soil Depth & 0.03666 & 15–30 m & 2 & 50 \\ \cline{3-4}  & & 30–75 m & 3 & 75 \\ \cline{3-4}  & & \(>\)75 m & 4 & 100 \\ \hline  & & \(>\)1.5 & 3 & 42 \\ \cline{3-4}  & & 0–1.5 & 2 & 28 \\ \cline{3-4} Slope Curvature & 0.03126 & 0 & 1 & 14 \\ \cline{3-4}  & & \(-\)0.5–0 & 4 & 57 \\ \cline{3-4}  & & \(-\)1.5—0.5 & 5 & 71 \\ \cline{3-4}  & & \(-\)2.5—1.5 & 6 & 85 \\ \cline{3-4}  & & \(<\)\(-\)2.5 & 7 & 100 \\ \hline Slope Aspect & 0.01652 & Others & 1 & 50 \\ \cline{3-4}  & & N-NW & 2 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter ranking used to obtain the Landslide Hazard Map.