esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Verification and Small-Scale Demonstration
### Implemented Activities in the Use Cause  
As mentioned in Section 3.1, the use case prototype is built around 3 key activities: _setup phase_, _data log_, _data request_ but setup phase is currently innactive as the system is implemented progressively, in a modular fashion. Below, a succinct overview of the implementation of each activity is given.  
**Data log** During runtime a variety of data are stored in the MongoDb server. Live data are currently sent through an IGMAP interface to a Gmail account and then are scrapped through Python and the corresponding Gmail API. After the arrival of the data packets, a sensitization function formulates them into JSON and the Python script, using an HTTP _POST_ request and a predefined authentication key and forwards the data alongside with the id of the sensor in the database. The MAM service is abstracted through a mock up series of functions that semantically complete the aforementioned behaviors in the \(\mathtt{mam\_service.js}\).  
**Data request** A variety of CRUD operations in the backend service as well as the MAM service were implemented, so that the user can access the data in the MongoDB. The POST/GET requests, during the interaction with the PUG/Jquery front end, occur in an async format provided by Node.js and are served statically by Express. When a end user registers to the system, a process is initiated in the user.js file, performing a _POST_ request to the server, checking the validity of the data the user provided. In case the data are correct, a secure password is generated by the \(\mathtt{genSalt}()\) function of bcrypt cryptography library that is then hashed through bcrypt.hash() and stored through Mongoose in the MongoDb database through a \(\mathtt{newuser.save}()\) function. Finally, the page is refreshed in order to showcase the success of the registration. The new user is then granted user level status which enables the Dashboard and My Data tabs. When a user selects a field or a stream, a variety of _POST_ and _GET_ functions are initiated, and a filtered list of the requested data are served in the frontend by an async mongoose query. When a user buys a stream, the async function \(\mathtt{createOrderKeys}\) indicates the authentication token through OAuth, providing it along with the rest of the required information. The user can then initiate a transfer of funds through the IOTA network in the given address in order to complete the order. This functionality can be achieved via an external service or via a module given by the IOTA foundation.  
The backend maintains a service that checks the status of pending orders. When the appropriate funds are sent to the corresponding address, the authentication token of the message that lies in the transaction is parsed, and the same token is fetched through an \(\mathtt{order.findOne}()\) function from the database. If those two tokens match, the stream's id is attached to the buyer, and the pending order is deleted. The buyer can then access and download the data through the MyData tab on the front end.
## 2 Theoretical Background
### Permutation Entropy  
Entropy is a conventional approach to indicate the degree of uncertainty for a random system in the field of information theory. As entropy increases with the degree of randomness for an observed system, it can be utilized in a quantitative analysis for feature extraction in the field of PQD detection. Permutation entropy is one of most popular entropies in signal processing [22]. The temporal information of the monitored object is recognized according to permutation entropy by counting the ordinal patterns. Permutation entropy can estimate the complexity of a time-series after comparing the neighbouring values. By definition, permutation entropy is calculated by the time-series probability density function that is based on Shannon entropy. For a time domain series with a finite  
Figure 1: Flow chart of the VMD algorithm.  
length of \(N\), \(\{x(t)\}\) (\(t\) = 1, 2, \(\ldots\), \(N\)), a segment can be constructed by an \(m\)-order dimension. Each \(m\)-order segment is then sorted in an ascending sequence that is defined as permutation pattern \(\pi\). The m-order segment \(X\) is defined in Equation (5) while considering the time delay \(\alpha\):  
\[X_{i}^{m}=\{x(i+\alpha(j_{1}-1)),x(i+\alpha(j_{2}-1)),\cdots,x(i+\alpha(j_{m}- 1))\} \tag{5}\]  
From practical experience, the time lag \(\alpha\) is selected as 1 and the value \(m\) is from 3 to 7. Hence, the number of \(m\)-order permutation patterns \(\pi\) is the factorial of \(m\). The relative frequency of each permutation pattern \(\pi\) is calculated in Equation (6):  
\[p(\pi)=\frac{H\big{\{}X_{i}^{m}\,\text{{has}}\,\text{{type}}\,\pi,i\big{|}1,2, \ldots,N-m+1\big{\}}}{N-m+1} \tag{6}\]  
where \(H\) represents a number. Subsequently, permutation entropy \(PE(m)\) is calculated while using the probability density function \(p(\pi)\) for the relative frequency in Equation (7):  
\[PE(m)=-\sum\limits_{i=1}^{m!}p(\pi_{i})\log(p(\pi_{i})) \tag{7}\]
## 3 SF-PPP Performance Analysis of Multi-GNSS Stations
### SF-PPP Strategy  
Pseudorange and carrier-phase observations on L1/E1/B1f frequencies were utilized. The Wuhan University multi-GNSS precise orbit and clock products (denoted as WUM products hereafter) [36] were employed. As the WUM products are generated with dual-frequency observation combination, differential code bias (DCB) errors should be corrected in SF-PPP. The multi-GNSS DCB products released by CAS (Chinese Academy of Science) were used. Both the phase center offsets (PCO) and phase center variations (PCV) of the receiver and satellite antennas were considered and corrected by the IGS antenna model. The satellite cutoff elevation angle was set to 10\({}^{\circ}\). The hydrostatic tropospheric delay was calculated using the global pressure temperature (GPT) model [37]. The tropospheric wet delay was estimated as a process parameter with a prior variance of 0.5 m and process noise deviation of \(10^{-5}m/\sqrt{s}\). To map the zenith tropospheric delay to slant delay, the global mapping function (GMF) was adopted [38]. Some other error terms such as relativistic time delay, phase windup, and station displacement were also considered. The detailed processing strategy and model corrections in SF-PPP are summarized in Table 1.  
Figure 1: Geographical distribution of 40 MGEX stations.
# Modeling of Fuzzy Systems Based on the Competitive Neural Network
## 2 Theoretical Framework
### Fuzzy Sets  
A fuzzy set is characterized by a membership function that assigns a degree of membership to each element of the universal set. The membership function represents the uncertainty or vagueness associated with the degree to which an element belongs to a particular set [16; 17].  
The most popular Type-1 FISs are Mamdani [27] and Takagi-Sugeno-Kang [28]. The Mamdani FIS [27] is a model based on fuzzy logic principles that facilitates the representation and processing of uncertain or imprecise information. It has been successfully applied in several domains, due to its ability to model complex systems and handle linguistic variables in a way that humans can interpret. In this system, antecedents and consequents are represented by fuzzy membership functions.  
The Takagi-Sugeno-Kang FIS, better known as Sugeno FIS, is a type of fuzzy logic system that uses linguistic rules and fuzzy logic principles to model and infer relationshipsbetween input and output variables. Introduced by [PERSON] and [PERSON] [28], the Sugeno FIS represents rules in an IF-THEN format, where the antecedents are formed using fuzzy sets and the consequents are linear functions. This model is widely applied in areas such as control systems, decision making, and pattern recognition due to its transparency and ease of interpretation.  
Researchers around the world have incorporated the fuzzy sets theory to solve classification challenges, more specifically, to classify features with a high degree of similarity or uncertainty for different datasets. In [6], the authors proposed a fuzzy clustering method using Interval Type-2 FIS combined with Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) clustering algorithms. In [29], optimization with swarm algorithms is applied to enhance an Interval Type-2 Fuzzy FIS for data classification. The proposal was tested on datasets from the UCI machine learning library and satellite image data. The results demonstrate that utilizing optimization algorithms can significantly improve accuracy in solving data classification problems. In [30], the authors introduce an unsupervised outlier detection algorithm which implements K-NN and fuzzy logic. Recognizing the limitations of unsupervised approaches in handling complex datasets, the authors provide a solution to this problem by proposing the utilization of K-NN rule and fuzzy logic for effective outlier detection. In [31], a hybrid method based on fuzzy logic and Genetic Algorithm applied to the text classification Twitter is proposed. In [32], the approach combines Fuzzy C-Means clustering and a fuzzy inference system for an audiovisual quality of experience. Experimental analyses indicate that the proposed framework outperforms methods without optimization and existing models, specifically MLP-based techniques.
# Surface Motion for P-Wave Scattering by an Arbitrary-Shaped Canyon in Saturated Half-Space
## 3 Methodology
### Wavefield Analysis  
#### 3.2.1 Free-Field  
Plane longitudinal fast P-wave \(\phi_{1}^{(i)}\) enters the half-space with circular frequency \(\omega\), incident angle \(\gamma_{1}\), and amplitude 1. Under the rectangular coordinate system \(x\)-\(o\)-\(y\), the incident plane fast P-wave is characterized as  
\[\phi_{1}^{(i)}=\exp[ik_{1}(x\cos\gamma_{1}-y\sin\gamma_{1})] \tag{20}\]  
When the incident wave propagates to the horizontal surface, it can produce a reflected fast P-wave \(\phi_{1}^{(r)}\), reflected slow P-wave \(\phi_{2}^{(r)}\), and reflected SV wave \(\psi_{t}^{(r)}\). The expression of its potential is as follows  
\[\phi_{1}^{(r)}=A_{1}^{P}\exp[ik_{1}(x\cos\gamma_{1}+y\sin\gamma_{1})] \tag{21}\]  
\[\phi_{2}^{(r)}=A_{2}^{P}\exp[ik_{2}(x\cos\gamma_{2}+y\sin\gamma_{2})] \tag{22}\]  
\[\psi_{t}^{(r)}=A_{t}^{P}\exp[ik_{t}(x\cos\gamma_{t}+y\sin\gamma_{t})] \tag{23}\]  
in which \(\gamma_{2}\) and \(\gamma_{t}\) are the angle between the reflected slow P-wave and the reflected SV wave and the horizontal line, respectively, which satisfies Snell's Law \(k_{1}\cos\gamma_{1}=k_{2}\cos\gamma_{2}=k_{1}\cos\gamma_{t}\). \(A_{1}^{P}\), \(A_{2}^{P}\), and \(A_{t}^{P}\) are the amplitudes of the corresponding reflected fast and slow P-waves and SV waves, respectively.  
Using Fourier-Bessel function expansion, Equations (20)-(23) can be expressed into Fourier-Bessel series, seeing Equations (25)-(27).  
\[\exp(\pm ikr\cos\theta)=\sum_{n=0}^{\infty}\varepsilon_{n}(\pm i)^{n}J_{n}(kr) \cos n\theta(\varepsilon_{n}=1\ \text{for}\ n=0;\ \varepsilon_{n}=2\ \text{for}\ n\geq 1) \tag{24}\]\[\phi_{1}^{(s)}=\sum_{n=0}^{\infty}A_{n}H_{n}^{(1)}(k_{1}r)\sin n\theta \tag{32}\]  
\[\phi_{2}^{(s)}=\sum_{n=0}^{\infty}B_{n}H_{n}^{(1)}(k_{2}r)\sin n\theta \tag{33}\]  
\[\psi_{t}^{(s)}=\sum_{n=0}^{\infty}C_{n}H_{n}^{(1)}(k_{t}r)\sin n\theta \tag{34}\]  
In the above formulas, \(\phi_{1}^{(s)}\), \(\phi_{2}^{(s)}\) and \(\psi_{t}^{(s)}\) are the potentials of the scattered fast P-wave, slow P-wave, and the scattered SV wave, respectively, and \(A_{n}\), \(B_{n}\), and \(C_{n}\) are the coefficients to be determined.  
The total wave field in saturated half-space can be obtained as follows  
\[\phi_{1}=\phi_{1}^{(i+r)}+\phi_{1}^{(s)} \tag{35}\]  
\[\phi_{2}=\phi_{2}^{(r)}+\phi_{2}^{(s)} \tag{36}\]  
\[\psi_{t}=\psi_{t}^{(r)}+\psi_{t}^{(s)} \tag{37}\]
## 3 Results and Discussion
### NPP Spatial Pattern and Land Use Change
There was a dramatic mutual transformation process between the two land types, indicating that land use in the study area has been significantly affected by human activities. In addition, 2856 km\({}^{2}\) of built-up area was transferred to grassland. This may be closely related to the Chinese government's strict control of 120 million hectares of arable land in recent years and its resolute clampdown on the illegal occupation of arable land. The area of grassland that was converted into built-up area was 3418 km\({}^{2}\), reflecting the blind pursuit of rapid expansion through urbanization in recent years, even at the cost of the environment.
## 1 Introduction
In recent years, remote sensing methodologies for Earth observation have gained more and more attention and appreciation, also consolidated by good results, so far achieved as tools for the monitoring of a wide range of deformation phenomena occurring on Earth's surface [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Remote sensing tools, indeed, have become rapidly one of the most important and valuable methods for the prevention and investigation of instabilities, thanks to their rapidity of acquisition, the easy management of the data and for the relative low costs as well. Among them, Differential SAR Interferometry (DInSAR) is widely implemented both for research aims and for commercial use, because it enables to obtain displacement velocity maps and time series of wide areas with sub-centimeter accuracy. In the last decade, also monitoring of linear infrastructures is strongly enabled and advisable by using DInSAR technique, thanks to the availability of historical images since 1992 and to a very high resolution and shortened revisiting time of the most recent satellite missions [18; 19; 20; 21; 22; 23; 24]. In particular, the launch of SAR sensors which operate at 3 cm wavelength in X-band, i.e., COSMO-SkyMed, covering wide areas (\(40\times 40\) km) with higher spatial resolution (\(3\times 3\) m) and reduced revisiting time (\(4\)-\(16\) days), hasenhanced DInSAR capability for displacement monitoring to an early detection of problems that might affect the linear infrastructure. In this work, a general approach to investigate preliminary cause-effect relationship, as indicator of vulnerability of linear infrastructures affected by slope instability, has been provided. The aim is to exploit the availability of very high-resolution satellite data, integrated with results of field campaigns to investigate, through a qualitative matrix-based approach, the vulnerability of linear infrastructures affected by geological events. The Integrated Procedure for MONitoring and Assessment of Linear Infrastructure SAfety (I-Pro MONALISA) is a semiautomatic GIS tool, using DInSAR and geomorphological data, combined with results of in situ surveys, to identify, along linear infrastructure, sections characterized by different levels of vulnerability. It represents a very fast tool, which may, in a preliminary and wide-area analysis, reduce costs and time of investigation, supporting public administrations in planning maintenance and rehabilitation activities for those areas affected by landslides interacting with linear infrastructures. The paper is organized as follows: I-Pro MONALISA tool is described in detail in Section 2, by defining input, procedure, and output; in Section 3 the test-area used to calibrate the proposed approach is introduced; DInSAR dataset and available information surveyed by field campaigns, as well as a discussion of the outcomes are described in Section 4 and in Section 5; finally, Section 6 summarizes the main conclusions of the paper.
## 3 Various System Configurations Studied
### Configuration Type I: A Single Common DCB Source  
The DTLI drive circuit configuration powered by a single common DCB source (Type I) is shown in Figure 7, where a two-level voltage inverter is connected to each side of the OEWM.  
Consequently, the DTLI configuration uses twelve semiconductor switching devices (typically IGETs or MOSFETs) and twelve antiparallel diodes. This configuration relies on the combination of the space vectors from both inverters, resulting in 64 (\(2^{3}\times 2^{3}\)) possible switching combinations distributed across 19 distinct spatial locations. In this configuration, the set of space vector hexagons generated by the slave inverter 'orbits' around the space vector hexagon of the master inverter. This DTLI configuration is similar to that of a three-level inverter, as shown in Figure 8. It consists of three hexagons with vertices of different lengths: a vertex of length \(2/3V_{dc}\) (hexagon ABCDEF), a vertex of length \(2/\sqrt{3}V_{dc}\) (hexagon HJLNQS), and a vertex of length \(4/3V_{dc}\) (hexagon GIKMPR). Table 2 summarizes the classification of these three hexagons into different vector groups.  
Figure 6: Three types of DTLI configurations for an OEWM.  
Figure 7: Configuration of a DTLI powered by a common DCB.  
However, the DTLI configuration powered by a single DC bus source inherently leads to the presence of ZSV, resulting in excessive current and voltage THD. This phenomenon results in high losses, increased temperature, and undesirable vibrations within the machine [97]. Additionally, this configuration is also affected by the presence of CMV.
## 3 Results
### Decomposition VOC Database

+++  
Carboxylic acids (C\({}_{2}\)-C\({}_{6}\) volatile fatty acids), longer chain acid esters (C\({}_{6}\)-C\({}_{8}\)), and monoterenoid ketones were almost exclusively identified using SPME collection. Major compounds in these groups were 2-methylbutanoic acid, 3-methylbutanoic acid, hexanoic acid methyl ester, and heptanoic acid methyl ester. SPME and sorbent tubes collected a range of aldehydes; however, saturated aldehydes were exclusively identified using SPME. During the bloat stage (day 2), SPME collected more compounds across the VOC profile than sorbent tubes. However, from day 4 onwards both techniques collected comparable numbers of VOCs demonstrating their complementarity. Indole, 2-pentylfuran, nonanal, 2-heptanone, 1-phenylethanone and hexane were the most frequently identified compounds in common between techniques. Occasionally a compound was not detected on a single experimental day using one technique, but was identified using the other technique. For example, 2-heptanone was consistently identified from day 8 onwards when the techniques are viewed collectively, however was identified intermittently by each collection technique. This suggests that the field conditions may have been unfavourable for one technique over the other on that particular day. When the profiles from both collection techniques are combined, overall trends become identifiable in several compound classes. Esters were predominantly identified in early decomposition (_i.e._, the first two weeks). As decomposition progressed, esters with fewer carbons decreased and longer chain esters remained. Ketones and hydrocarbons exhibited the opposite trend. Short chain ketones and hydrocarbons found early in decomposition remained stable while the number of more complex ketones and hydrocarbons increased in later decomposition.
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 2 Related Work
### Deep Learning-Based Alzheimer's Disease Classification Methods
#### 2.1.3 Other Deep Learning Methods  
Besides CNN and GNN-based methods, other deep learning methods have also been applied to AD diagnosis. In this section, we will discuss some of these methods and their applications in this field. [PERSON] et al. [23] proposed a learning framework based on Recurrent Neural Networks (RNNs) to extract sequence features from dynamic FC networks using rs-fMRI data for brain disease classification. This method, in contrast to CNN approaches, considered time dynamics and time steps, achieving an accuracy rate of 92.8% for AD classification. [PERSON] et al. [24] proposed a method using stacked sparse autoencoders to classify preprocessed rs-fMRI images. Evaluating the proposed model using k-fold cross-validation resulted in an accuracy rate of 77.13%. This method is characterized by analyzing brain regions using network learning weights, but the performance of autoencoders in feature extraction and dimensionality reduction may be limited by training data quality and model architecture. [PERSON] et al. [25] proposed a novel framework based on Finer-DBN, which, compared to traditional CNNs, can directly judge whether a sample is healthy based on the difference between reconstructed data and original data without the need for multiple model tests, achieving an overall accuracy of 78.36% and an F1 score of 76%. However, the DBN used may require a large amount of training data and computational resources and is sensitive to the selection of hyperparameters. Furthermore, the Finer-DBN framework may not fully utilize multimodal data and spatial information.
## 1 Introduction
### Synthetic Aperture Radar (SAR) Satellites
For this, high resolution spaceborne RADARSAT 1 SAR images collected during the active Bora period of the year 2003, created a series of high-resolution maps of 300 m dimension. Along with the previous observations on Bora winds, in this study, it was understood that along the Italian coast, several images show a wide (20-30 km) band of northwesterly winds that abruptly change to northeasterly Bora winds further the offshore. It was concluded by meteorological model that northwesterly winds are consistent with those of a barrier jet forming along Italian Apennine mountain chain.
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 2 Related Work
### Graph-Based Algorithms  
Most graph-based algorithms extract superpixels by optimizing a cost function defined on a graph model. Normalized cut (Ncut), proposed by [PERSON] et al. [26], is a classical image segmentation method. This method treats pixels and the similarity between each pixel pair from the image as nodes and edge weights in the graph, respectively. Then, superpixels are generated by minimizing a normalized sum of total edge weights. Although Ncut can express the global relationship between pixels, the boundary adherence performance is poor, and the computational complexity becomes progressively higher as the size of the image or the number of superpixels increases. To accelerate this process, the Felzenszwalb-Huttenlocher method (FH) [27] was proposed. FH defines a predicate that measures the evidence of boundaries to guide the merging of two nodes on the graph. Compared with Ncut, this method has better efficiency and boundary adherence, but the final superpixels have irregular shapes and sizes.  
The aim of Ncut and its optimized version is to achieve global optimization, which leads to high resource consumption. Spectral clustering (SC) [28] maps data into another feature space by utilizing a feature function, in which graph-cut methods are applied to generate the optimal result. However, eigendecomposition is a complex pre-processing method. [PERSON] and [PERSON] [29] have proposed linear spectral clustering (LSC) to accelerate superpixel segmentation. LSC replaces eigendecomposition with K-means clustering, in order to optimize the objective function and demonstrate the underlying mathematical equivalence between K-means and NCut. Although LSC reduces the amount of calculation, the similarity measure and restricted search range limit the boundary adherence capability.  
The random walk (RW) method [30] labels pixels using the probability of non-labeled pixels reaching different objective regions for the first time. This method only considers the first arrival probability and the local relationship between pixels and their corresponding seeds, which may lead to irregular regions in the final superpixel map. [PERSON] et al. [13] have proposed lazy random walks (LRW) to generate compact superpixels by considering self-loops and the commute time. In addition, LRW utilizes a relocation and splitting mechanism to ensure adherence to weak boundaries. As the entire graph must be traversed in each iteration, the computational cost is very high.