esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Datasets
### Remoted-Sensed Winds  
The task of a scatterometer is to measure global wind fields. The first spaceborne scatterometer mission was SeaWinds on the QuikSCAT satellite, which operated during 2002\(-\)2012. Since 2007, as the successors of QuikSCAT, the ASCATs onboard the Metop-A/B/C satellites have been releasing official products. Typically, ASCAT winds have a 12.5 km spatial resolution and two 550 km wide swath coverages in ascending/descending directions. The HY-2B scatterometer has also been releasing wind products along the same track since 2018. As concluded in [5], the HY-2B geophysical data records (GDRs) after systematic corrections using the MOE determination method have high accuracy. Scatterometer-based winds are valuable sources for the development and validation of SAR wind retrieval algorithms [11; 36; 37] due to their high levels of quality and fine spatial resolutions. The main limitation of ASCATs and the HY-2B is that they are unable to detect strong winds (>25 m/s). The spaceborne polarimetric microwave radiometer (i.e., WindSAT, which operated from January 2003 to November 2020) had the ability to monitor winds of up to 40 m/s [38]. Although the spatial resolution of WindSAT was 25 km, its swath coverage reached 350 km, allowing it to obtain wind fields in TCs, which benefited the research on TC dynamics [39; 40]. Figure 1 depicts the wind maps from the ASCATs onboard the Metop-A and HY-2B scatterometers and the maps from WindSAT for 7 September 2020 between (10\({}^{\circ}\)N, 100\({}^{\circ}\)E) and (60\({}^{\circ}\)N, 180\({}^{\circ}\)E). In this case, it can clearly be observed that there are gaps between the along-track swaths. Thus, it was necessary to reconstruct the wind field by fusing the three types of remote-sensed data.
## 2 Preliminaries
### Exploration Strategy  
The exploration rate represents the likelihood of an intelligent optimization algorithm selecting new solutions for exploration. A higher exploration rate implies a greater inclination of the algorithm towards exploring uncharted solution spaces, thereby increasing the chances of discovering optimal solutions. MOFECO-MS employs the crossover operator to enhance the algorithm's exploration capability. Since the decision variables of BITTP consist of two parts, the route plan and the item plan, these two components are separately updated during element updates.  
For the route plan, the partial mapping crossover operator (PMX) [27] is employed because it maintains population diversity and prevents the algorithm from converging to local optima. PMX facilitates diversity preservation by avoiding the generation of duplicate genes through mapping, thus enhancing both convergence and exploration capabilities.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Description** \\ \hline \(\mathbf{x}_{ij}(k)\) & The element to be updated \\ \(\mathbf{x}_{bj}(k)\) & The best element in the cycle \(j\) \\ \(\mathbf{x}_{bjr}(k)\) & The best element in any of the \(q-1\) cycles except the cycle \(j\) \\ \(\mathbf{x}_{npn}(k)\) & Any element in the current optimal solution set \\ \hline \hline \end{tabular}
\end{table}
Table 3: Symbols and description of the elements.  
As the item plan is binary encoded, the uniform crossover operator [28] is utilized. This operator exchanges genes at each locus of paired individuals with the same crossover probability, generating two new individuals. Uniform crossover enhances population diversity, facilitates gene communication and combination, and improves the algorithm's search efficiency and convergence rate.  
During the exploration phase, expanded and narrowed exploration strategies are employed to expedite and enhance the search process. Expanded exploration involves crossing element \(\mathbf{x}_{ij}(k)\) with the best element in each cycle, comprising \(\mathbf{x}_{bj}(k)\) and \(\mathbf{x}_{bj^{\prime}}(k)\). On the other hand, narrowed exploration entails crossing \(\mathbf{x}_{ij}(k)\) with \(\mathbf{x}_{\text{{ups}}}(k)\), with the frequency of these methods adjusted by probability \(p_{c}\). The pseudocode for the exploration phase is depicted in Algorithm 3. Notably, the probability of expanded exploration is \(p_{c}\), while the probability of narrowed exploration is \(1-p_{c}\).  
```
1:\(\mathbf{x}_{ij}(k)\), \(\mathbf{x}_{\text{{s}}_{\text{{i}}}t}(k)\), \(t=1,2,\cdots,q\), \(\mathbf{x}_{\text{{aps}}}(k)\)
2:\(\mathbf{x}_{new}(k)\)
3:if\(rand<p_{c}\rhd\)
4:\(rand\) is a random number between 0-1 then
5:\(\mathbf{x}_{new}(k)\Leftarrow\mathtt{Croc}(\mathbf{x}_{ij}(k),\mathbf{x}_{ \text{{s}}_{\text{{i}}}t}(k))\)
6:else
7:\(\mathbf{x}_{new}(k)\Leftarrow\mathtt{Croc}(\mathbf{x}_{ij}(k),\mathbf{x}_{ \text{{aps}}}(k))\)
8:endif
9:Return \(\mathbf{x}_{new}(k)\)
```  
**Algorithm 3** Exploration phase
# Fabrication of Silicon Carbide from Recycled Silicon Wafer Cutting Sludge and Its Purification
## 3 Results and Discussion  
### Raw Material Characterization  
In the present work, the starting materials were silicon sludge and pure carbon powder. Figure 2 shows the XRD pattern of the silicon sludge, with only silicon peaks visible. Figure 3 shows the scanning electron microscopy (SEM) image and EDS analysis results. Most of the particles were found to be silicon, as shown in Figure 3b. Moreover, it was found, from the SEM image in Figure 3, that the shape of the particles was elongated, irregular, and of diverse sizes, which corresponded to the particle analysis shown in Figure 4. The average particle size in the silicon sludge was 1.6 \(\upmu\)m, and most particles were under 3 \(\upmu\)m in size. Although only silicon was recognized in the XRD data shown in Figure 2, small amounts of carbon and oxygen were also detected, as shown in Figure 3. This was attributed to the coolant solution used with the diamond saw in the silicon ingot cutting process. It was estimated from the content of oxygen that the particle containing oxygen was not silicon oxide and was only partially oxidized. Since the amount of oxygen and carbon was quite small, a minor effect was expected in the synthesis of SiC in the present work.  
Figure 3: (a) SEM image of the Si sludge, (b) and (c) energy dispersive X-ray spectrometry (EDS) spectra for points 1 and 2, respectively.  
Figure 2: XRD pattern of the Si sludge which was used in the present study.  
The XRD pattern for carbon used in this work showed a strong, sharp peak near \(\sim\)20 = 26\({}^{\circ}\), which corresponds to the stacking of carbon-like crystalline graphite.
# A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Turkije)
## 2 Study Area  
The study area was Izmir province in Turkriye, located between 38.25\({}^{\circ}\) and 38.60\({}^{\circ}\) latitude and between 26.90\({}^{\circ}\) and 27.35\({}^{\circ}\) longitude. Izmir is the third biggest city in west Turkriye (Figure 1a), and has a population of nearly 4.4 million people [70]. The city is very well known, and hosts international cultural activities. Topographically, the altitude reaches 1500 m in the western part of the region. Although a 70\({}^{\circ}\) slope can be seen in the working area, the principal slope is between 0\({}^{\circ}\) and 35\({}^{\circ}\). Approximately 60% of this zone has a slope gradient <10\({}^{\circ}\). The climate characteristics in the study area are typical Mediterranean climate: seasons are relatively mild, rainy in winter, sunny and hot in summer. Precipitation amounts to 700 mm (27.5 inches) per year, and rain is most abundant in winter; in summer it rains very rarely [71].  
Geologically, the rock units can be grouped into three main groups: Bornova melange, Neogene sediment and Yamanlar volcanic rock. Apart from these three groups, alluvium and slope debris continue to be deposited [72]. The matrix of Bornova melange rocks located within the tectonic belt called the Izmir-Ankara Zone is composed of Upper Cretaceous-Paleocene sandstone-mudstone intercalations. In the matrix consisting of sandstone-shale intercalations, limestone, serpentinite, chert and diabase blocks of various sizes ranging in age from the Triassic to the Cretaceous can be found [73,74] (Figure 1b).  
Bornova melange is represented by intercalations of sandstone-shale and the Kizil-kalesi formation that occurs in that matrix includes serpentinite blocks and limestone olistoliths (Figure 1b). Lower Miocene-early Pliocene Neogene sedimentary rocks are  
Figure 1: (**a**) Location map, (**b**) Geological map of the study area [75].  
angular discordantly overlie the Bornova melange that forms the bedrock in the study area. The Neogene sedimentary rock deposits are composed of limestone, conglomerate and marl units [76]. The slope angles of the Neogene sedimentary rock layers are close to the horizontal range, between 5-20\({}^{\circ}\). The Upper Miocene and Pliocene Andesitic Yamanlar volcanic rock discordantly overlies the Neogene sedimentary rock [72; 77]. The Yamanlar volcanic rock consists of andesite, auto-brecciated andesite, tuff and agglomerates. In the study, the agglomerate and tuff forming the Yamanlar volcanic rock is defined as volcano sediment and the andesite as volcanic rock (Figure 1b).
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 2 Literature Review
### Context Awareness and Intersimilarity in Complex HAR  
The role of context recognition in complex activity recognition cannot be overexpressed. Therefore, understanding the context in which an activity is performed can enhance the capability of HAR models to identify the complexity of such activity [28, 29, 30, 31, 32]. Researchers have, over the years, developed models for activity recognition using additional sensor signals other than the traditional inertial sensors (accelerometer, gyroscope, magnetometer, etc.) [29, 30, 33, 34, 35]. The rationale is to acquire as much information as possible from integrated or nearby ambient sensing devices to train deep neural networks.  
Using this rich contextual information, existing research works have been able to recognize different activities that are difficult to identify using fewer sensor modalities. In recent research conducted by the authors of [29], they used ambient signals from audio sensors combined with inertial signals to pretrain a CNN model for automatic human activity recognition. The work, however, used only audio signals as contextual information and a CNN model compared to the one presented in this article. Similarly, reference [30] combines ambient signals from audio sensors and video cameras in addition to other inertial sensors to detect the activities of the elderly to recommend early interventions and rehabilitation. To address the interclass similarity in HAR, [31] relied on camera signals and developed a model that explored hierarchical matching using a consistency correlation-driven feature selection. However, these two works have the potential for intruding on people's privacy.  
To address the same challenge, authors in [32] combine sensing information from multiple sensor modalities including traditional sensors (accelerometer and gyroscope) and ambient sensors (atmospheric pressure, temperature, and humidity sensors) with location information. However, compared to the work presented in this article, they only explored handcrafted feature extraction processes and traditional machine learning algorithms. One of the latest works, [33], added context information to enhance HAR accuracy in logistics using identities and location as contextual data. Unlike the works in [32, 33], we used environmental sensing data specifically, noise level and illumination conditions, as contextual information to enhance the accuracy of human activity recognition.  
In summary, the current work differs from these works in that we not only used inertial sensor signals from smartphones, but we also investigated the significance of context data in conjunction with the inertial sensors to address the challenge of interclass similarity in the datasets. Thus, in addition to the traditional inertial sensors, we proposed two (2) extra signals (environmental light conditions and noise level signals) to augment the traditional inertial sensing signals with contextual information, using a combination of CNN and LSTM algorithms to generate a more accurate HAR model.
# An Improved Dictionary-Based Method for Gas Identification with Electronic Nose
## 2 Materials and Methods
### Improved Dictionary Learning Algorithm  
The traditional K-SVD dictionary learning algorithm updates the dictionary with only a single column of atoms in an iteration, which significantly slows down the process. The goal of this paper was to change the process of updating dictionaries by updating several atoms at the same time, so that unnecessary computations can be avoided. The least-squares approach was employed to tackle the issue, and the sparse matrix was decomposed using SVD to simplify the formula, further reducing the computational burden and improving the algorithm's learning effectiveness. Dictionary learning classification techniques often need initialization of the dictionary, sparse coding, and dictionary updates as well as classification. First, the dictionary is initialized by a normalized Gaussian random matrix. The number of atomic columns is set to \(k\) in the stage of updating the dictionary, and the reconstruction error is:  
\[\begin{array}{l}\|X-SW\|^{2}=\|E_{r}^{t}-\sum_{j\
eq i+1,\dots,i+r}^{k}s_{j} ^{(t-1)}w_{row}^{(t)}\,r\|^{2}\\ =\|E_{r}^{t}-S_{r}^{(t-1)}w_{row}^{(t)}\,r\|^{2},\end{array} \tag{6}\]  
Figure 4: A schematic diagram of the SRC.  
where \(E_{r}^{t}\) can be expressed as \(E_{r}^{t}=X-\sum_{i\
eq i+1,\ldots,i+r}^{k}s_{j}^{(t-1)}w_{row}^{(t)}\); \(W_{row\ r}^{(t)}\) is the \(t^{th}\) iteration of the sparse matrix from the row \((i+1)\) to the row \((i+r)\), and \(S_{r}^{(t-1)}\) is the \((t-1)^{th}\) iteration of the dictionary from the column \((i+1)\) to the column \((i+r)\). By applying the least-squares method in Equation (6), it can be expressed as:  
\[S_{n}=XW^{T}\left(WW^{T}\right)^{-1} \tag{7}\]  
By applying the SVD algorithm to decompose \(W\) in Equation (7), then it is expressed as follows:  
\[W=UQV^{T} \tag{8}\]  
where \(Q\) is a diagonal and full-rank matrix. By bringing Equation (8) into Equation (7), it can be expressed as:  
\[S_{n}=X\left(UQV^{T}\right)^{T}\left(UQV^{T}VQ^{T}U^{T}\right) \tag{9}\]  
where \(U\) is an orthogonal matrix. According to the Equation (9), it can be expressed as:  
\[S_{n}=XV^{T}Q^{-1}U \tag{10}\]  
Figure 5: The flowchart of the traditional K-SVD algorithms.
## 3 Time-Domain Analytical Solution of the Received Signal
### Time-Domain Analytical Solution of the Received Magnetic Induction Intensity Signal
#### 3.2.2 Time-Domain Analytical Solution for the Rising Half-Cycle
\(\Gamma_{BR0}(p)\) and \(\Gamma_{BRm}(p)\) are obtained by using the integral formula of the complementary error function [30], combined with the standard Laplace inverse transform formula [30], the Laplace inverse transform form can be obtained as:  
\[\Gamma_{BR0}(t)=\frac{2I\mu_{r}2\lambda_{i}^{2}}{\mu_{r}2\lambda_{i}^{2}-\
u \tau}\bigg{\{}e^{-t/\tau_{0}}[1-\frac{\sqrt{\
u\tau}}{\mu_{r}\lambda_{i}} \mathrm{erf}\sqrt{\
u t}]\ -e^{(\mu_{r}2-1)\lambda_{i}^{2}t/\tau}\mathrm{erfc}\left(\mu_{r} \lambda_{i}\sqrt{\frac{t}{\tau}}\right)\bigg{\}}-Ie^{-t/\tau_{0}} \tag{37}\]  
\[\Gamma_{BRm}(t) =\Psi\left(\mu_{r},\frac{\sqrt{\tau\
u}}{\lambda_{i}},2m\lambda_ {i}d,\frac{\lambda_{i}^{2}t}{\tau}\right)+\sum\limits_{n=1}^{2m}{C_{2m}^{n}(- 2)}^{n+2}\Phi_{n}\left(\mu_{r},\frac{\sqrt{\tau\
u}}{\lambda_{i}},2m\lambda_{ i}d,\frac{\lambda_{i}^{2}t}{\tau}\right) \tag{38}\] \[+\sum\limits_{n=0}^{2m-1}{C_{2m-1}^{n}(-2)}^{n+2}\Phi_{n+2}\left( \mu_{r},\frac{\sqrt{\tau\
u}}{\lambda_{i}},2m\lambda_{i}d,\frac{\lambda_{i}^{2 }t}{\tau}\right)\]  
where \(\Psi(a,b,k,t)=\frac{2\pi e^{-t}I}{b}[\varphi_{0}(-b,k,t)-\varphi_{0}(b,k,t)]\), \(\Phi_{n}(a,b,k,t)=\frac{a^{n+1}e^{-t}I}{2b}[\phi_{n}(a,-b,k,t)\)\(-\phi_{n}(a,b,k,t)]\). By substituting Equations (35)-(38) into Equation (16), the following formula can be obtained. It has been verified that this equation achieves the same result as the Section 3.2.1. \[B_{ref}(t)=\sum\limits_{i=1}^{\infty}{\frac{\mu_{0}e^{-\lambda_{i}x_{k}}Y( \lambda_{i})}{h^{2}j_{0}^{2}(\lambda_{i}h)}\cdot\Gamma_{B}(t)} \tag{39}\]
## 3 Proposed Methods
### Stereo Taillight Matching
#### 3.2.1 Stereo Taillight Matching Procedure  
After applying taillight detection to the left image \(I_{L}\) and the right image \(I_{R}\) independently, two lists of detected taillights \(\mathbf{L}=\{L_{1},L_{2},...,L_{m}\}\) and \(\mathbf{R}=\{R_{1},R_{2},R_{3},...,R_{n}\}\) are obtained. In the stereo taillight matching stage, our goal is to determine the list of taillight correspondence \(\mathbf{M}=\{(L_{k},R_{k})|L_{k}\in\mathbf{L},R_{k}\in\mathbf{R}\}\) that satisfies the condition that each combination \((L_{k},R_{k})\) refers to the same taillight in the real scene, also understood as \(L_{k}\) and \(R_{k}\) being matched with each other, so that the world coordinates of this taillight can be estimated using the stereo triangulation approach.  
The procedure of the proposed stereo taillight matching method on each image pair (\(I_{L}\), \(I_{R}\)) is explained as follows. Iteratively, each taillight combination (\(L_{i}\), \(R_{j}\)) will be considered. At first, the function matchingFeatureExtraction is applied to the combination (\(L_{i}\), \(R_{j}\)) to extract the feature vector \(matchVec\), which is further fed into a pre-trained matchingClassifier. This classifier returns a \(status\) with two possible values of match and not match, which represent whether \(L_{i}\) and \(R_{j}\) match each other. If they are predicted as matched, (\(L_{i}\), \(R_{j}\)) will be added to \(\mathbf{M}\). Additionally, \(L_{i}\) will be  
Figure 14: Out-of-view phenomenon: (**a**) left image; (**b**) right image.  
Figure 13: Occlusion phenomenon: (**a**) left image; (**b**) right image.  
removed from \(\mathbf{L}\) and \(R_{i}\) will be removed from \(\mathbf{R}\). The procedure then continues considering another combination until there are no more possible combinations remaining from \(\mathbf{L}\) and \(\mathbf{R}\). The entire flow of this procedure is described in Figure 15.
## 1 Introduction  
Energy is a critical foundation for social development. As global fossil fuel supplies dwindle and environmental protection pressures increase, comprehensive energy systems characterized by multi-energy flow coupling, multi-system integration, and multi-regional coordination are anticipated to mitigate the current pattern of extensive energy utilization and the conflicts between environmental protection and economic development [1]. In the resource-rich Yangtze River Basin, WSHP energy stations represent an innovative technological approach to the creation of a comprehensive urban energy supply. These stations utilize the Earth's water bodies as heat exchange sources and employ heat pumps to provide cooling and heating for buildings, offering advantages such as high operational efficiency and low costs. This method significantly contributes to large-scale carbon emission reductions in new urban developments [2; 3; 4].  
However, the actual operation of WSHP energy stations presents numerous challenges. These include high specialization, complex systems, remote water intake, and a decentralized distribution of heat exchange stations, which collectively complicate operation and maintenance management and resource scheduling, significantly reducing energy utilization efficiency. The traditional management mode of these stations typically involves the independent monitoring of electrical equipment within the system, relying on periodic manual inspections and data analysis for stage-based maintenance. This approach fails to ensure the safety and stability of the energy system. It is a deterministic operating management mode based on process flows and human experience, lacking proactive equipment wear prediction, dynamic energy consumption data statistics, accurate energy demand forecasting, and timely responses to demand in changing environments. Consequently, this results in passive management, low efficiency, and during long-term operations, numerous factors such as surface water temperature, water flow rate, and building load characteristics influence the energy utilization efficiency. Current energy information management methods provide insufficient guidance for future operational trends [5; 6].  
With the rapid advancement of technologies such as cloud computing, mobile internet, big data, 5G, and artificial intelligence, the energy sector is undergoing a digital transformation. Digital technologies are reshaping the production, operation, and transmission modes of traditional energy systems, forming a new energy network system that integrates new energy technologies with information technology. This new system aims to enhance management efficiency and production effectiveness while ensuring safe and stable operations, thereby facilitating an early transition to green and low-carbon energy solutions. By employing various digital methods, it is feasible to transition from the traditional deterministic operation of mechanical systems to intelligent operation under uncertainty management, ultimately achieving the goal of developing information-based, digitalized, and intelligent energy stations [7; 8; 9].
## 5 Security Analysis
### Data Integrity  
Data integrity guarantees that data or information being transferred is never corrupted during data transmission and storage process. Although data confidentiality guarantees that only intended parties obtain the un-encrypted plain data, it does not protect data from being altered. Given the RSA encryption of numerical information as an example, a hacker or malicious user can do linear operation on the ciphertext and change the value even without breaking the key. When a sensor node is captured, the intruder is assumed to access all the available security information, such as cryptographic keys. Two conclusions can be put forth as two lemmas:  
**Lemma 1**.: _Assume that \(A_{c}\) is compromised and there are additional at most \(T-1\) collaborating compromised nodes among the neighboring nodes of \(A_{c}\) and \(A_{n}\). Then, any false data injected by \(A_{c}\) are detected by the \(A_{n}\)'s neighboring nodes only._  
**Proof of Lemma 1.** The neighboring nodes of \(A_{c}\) verify all the data broadcasted by \(A_{c}\), each monitoring node of \(A_{c}\) also aggregates the entire data by itself and then computes a subMAC for the aggregated data. If \(A_{c}\) injects false data, it can be detected by \(A_{n}\)'s neighboring nodes that are the MFN-group members of the monitoring nodes of \(A_{c}\). Since the subMACs of the plain aggregated data are verified by \(T\) neighboring nodes of \(A_{n}\), \(A_{c}\) needs at least \(T\) compromised monitoring nodes to inject false data.  
**Lemma 2**.: _Assume that \(A_{c}\) and \(A_{n}\) are not compromised, even if all forwarding nodes of \(A_{c}\) are compromised, false data that they inject are detected by \(A_{n}\)._  
**Proof of Lemma 2.** Those forwarding nodes that are the MFN-group members of \(A_{c}\)'s monitoring nodes verify the transferred data, but they do not compute new subMACs for the verified data. Thus, an attacked forwarding node that injects false data cannot add a new subMAC for its false data. Because all the forwarding nodes of \(A_{c}\) are assumed to be compromised, the false data injected by these compromised nodes are not detected during data forwarding. This implies that \(A_{n}\) receives the false data. In Algorithm 3, aggregators verify the received data using the subMACs computed by the backward aggregators. Therefore, when \(A_{n}\) receives the false data from compromised forwarding nodes, \(A_{n}\) fails to verify the subMAC computed by \(A_{c}\), which is assumed to be not compromised.