esa-sceva/satcom-chunk-collection huggingface dataset:
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 8 Future  
Regarding the complex and changeable environment underwater, there are various degrees of degeneration with underwater images. Additionally, as for large-scale scenes, a wireless distribution system [43] is needed to detect targets underwater. For a more efficient and detailed system, in the future we will integrate an image analysis module to judge different image problems and use different image enhancement algorithms to improve the image quality. Additionally, in order to improve the efficiency of the system, we will drive the AUV cluster to complete the target detection and calculate the point clouds separately with the AUV positioning technology [44], and then send the data back to the leader ship and perform the point cloud registration and fusion processes. For another typical problem with underwater images, due to waterproof housing being needed for underwater cameras, light will pass through three mediums, namely water, glass, and air, which cause twice the refraction before arriving at the optical sensor. In the future, we will take the refraction into consideration, which can improve the accuracy of the scene structure estimation.  
Conceptualization, Y.C. and W.G.; Funding acquisition, Y.C. and J.L.; Investigation, S.G.; Project administration, Q.L. and S.G.; Resources, W.G.; Validation, Q.L.; Writing--original draft, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61971206, No. 61631008, No. U1813217, No. U19A2061, No. 61772228, and No. 61902143), the Fundamental Research Funds for the Central Universities 2017 TD-18, the National Key Basic Research Program (2018 YFC1405800), Jilin Scientific and Technological Development Program (No. 2020122208 JC), the Education Department of Jilin Province (No. JJKH20211105 KJ),the Key Research  
Figure 9: (**a**) Model in the air. (**b**) Reconstruction result based on images in the air.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environment** & **Video** & **Point Cloud** & **Comparison** \\ \hline Enough light & 140 MB & 36.1 MB & \(-\)74.21\% \\ Insufficient light & 72.1 MB & 25.8 MB & \(-\)64.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data compression results.  
and Development Program of Jilin Province (No. 20210203175 SF), Foundation of Education Bureau of Jilin Province under Grant(No.JJKH20220988 KJ), Aeronautical Science Foundation of China under Grant(No.2019 ZA0R4001), National Natural Science Foundation of China under Grant, (No. 51505174), Interdisciplinary integration innovation and cultivation project of Jilin university under Grant(No. JLUXKJC2020105).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
###### Abstract
Urbanized areas are composed of heterogeneous surfaces, including different artificial materials and natural areas, and therefore, exhibit a complex landscape characteristic, making it difficult to map all LCU classes using a single index [40]. The main objective of this research work was to propose a multi-index-based SVM classification approach, for mapping seven different LCU classes, in complex urban areas. The research focused on separating the built-up areas and bare lands, in addition to providing an accurate and reliable LCU map, in three densely urbanized metropolitan cities of Turkey, using the Sentinel-2A imagery. The method design comprised using a few sets of specific indices, each group of which highlighted a generalized LCU category--built-up regions, vegetation covers, and water bodies. For the built-up category, the existing built-up indices and the normalized difference tillage index (NDTI) were used as the first components of the multi-index dataset, and their performances were evaluated. For the vegetation cover determination, NDVI, the soil adjusted vegetation index (SAVI), and the red-edge-based normalized difference vegetation index (NDVIre) were evaluated as the second component. The normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to highlight the water bodies as the third component. Several combinations of spectral indices and the original satellite images were classified using the same training sample set and supervised SVM algorithm, as it has a non-parametric, machine-learning-based structure and because of its reported performance in supervised, pixel-based algorithms, mentioned above [15; 16; 17]. Accuracy assessment was performed using stratified random points to evaluate the performance of the multi-index approach and its possible advantages over the traditional classification of the original spectral bands. The proposed approach was developed with the Sentinel-2A image of the metropolitan city of Istanbul and applied to two independent regions located in the metropolitan cities of Ankara and Konya, in Turkey, to validate the common usage and effectiveness.
## 4 Proposed Solution Procedure for Harmonic and Interharmonic Detection
### Solution Procedure of Proposed Harmonic and Interharmonic Detection Method  
This section illustrates the flowchart of the proposed solution procedure for harmonic and interharmonic detection, as shown in Figure 5, where SSWT is used as the demodulation method to identify magnitude- and frequency-modulated components in a time-varying waveform represented by TF ridge curves in the plot. The ridges can reveal the local features of the signal and show energy distribution over frequency changes from one instant to the next. Due to the lightly blurred ridge curves detected in the TF plane for harmonic or interharmonic estimations obtained by the proposed SSWT, the clustering method, DBSCAN, is then used to identify the ridge curves and more accurately detect the corresponding frequencies. After all the harmonic or interharmonic frequencies are identified, the inverse SSWT of (8) is then applied to obtain each reconstructed time-domain waveform and the associated magnitude of frequency component.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 3 Routing Implementation
### System Model  
Before we discuss the details of the novel coding-aware routing implementation, the system model and some crucial terms need to be introduced first. We construct the immovable nodes in a wireless ad-hoc network, which can support multiple multicast and unicast sessions. All nodes are in a promiscuous mode, such that each node can both overhear the data transmission and obtain the delivery probabilities of links with its one-hop neighbors. Each node has a buffer pool that can store the original and encoded packets that are sent and overheard. Moreover, the original or encoded packets in each buffer are used for encoding or decoding. The buffer is large enough to store the packets. The sources and destinations of each flow are randomly and independently selected, and the inter-flow encoding and decoding operations are XOR in this paper. The related CCAR terms are explained in Table 2 below.
# Creating Audio Object-Focused Acoustic Environments for Room-Scale Virtual Reality
## 5 Evaluation/Results
### List-Based System  
The authors compare two spatial implementations of an environment's object-based music to demonstrate the differences in spatial response. The music contains three elements: two drone-based sounds are accompanied by bass-drum-like collision sounds. In one version, the 3D binaural audio spatializer embeds the drone-like sounds in the same space as the visual objects, with the music becoming diegetic. In the second version, randomized, stereophonic amplitude-based panning locates these sounds outside the game world, with the music becoming non-digetic. In both cases, the sound of the collisions between the visual representations of the objects is kept in 3D binaural audio. The spatialization strategy implemented in the non-digegetic version shares similarity with the approach taken by [40] (see Section 2.4).  
While the music evokes a similar atmosphere in both cases, its spatial impression changes. The screencast first presents a fly-through through the environment using the non-digegetic version, followed by a version using the diegetic approach (Video S6: 06_digetic-Nondigetic.mp4). The diegetic approach makes the music localized and associates each sound with its visual representation. Due to the spatial correlation between the visual and aural representation, the collision sounds appear to be part of the music. The music becomes responsive to the environment's room reverberation and the player can approach the sounds contained in the music.  
This diegetic placement contrasts with the non-digegetic version. There is a spatial disconnect in the non-digetic version between the collision sounds, the drone-like sounds, and the acoustic environment's remaining sounds. Furthermore, due to this spatial disconnect, the collision sounds may not appear to be part of the music but rather merely the sounds of the colliding objects.  
The diegetic or non-digetic versions of the music may both be suitable; however, their narrative meaning may change [64]. Ultimately, whether the effect of the music's spatial dislocation appears problematic or appropriate would depend on the game's contexts, narrative goals, or overall approach, as suggested by [10].
## 3 Proposed Model  
### HDS Channel  
In this channel environment, the blurring effect and misalignment are represented by a point spread function (PSF), while the noise is characterized as additive white Gaussian noise (AWGN). The digital user signal \(u[k]\) is modulated into 2D data \(a[j,k]\) to ensure compatibility with the medium used in HDS systems. The output of the HDS channel is captured by a camera sensor and converted into a 2D array, forming a digital image of the data. The received signal \(y[j,k]\) is distorted in the following manner:  
\[y[j,k]=a[j,k]\otimes h[j,k]+w[j,k], \tag{31}\]  
where \(\bigotimes\) is the 2D convolution operator. \(h[j,k]\) is the discrete signal as a point spread function (PSF) of 2D interference in the HDS channel and can be calculated as below. Equation (32) represents the energy of \(h(x,y)\) in the bit region, which is detected by the camera sensor.  
\[h[j,k]=\int\limits_{-\dfrac{1}{2}}^{j+\dfrac{1}{2}}\int\limits_{k-\dfrac{1}{2}}^ {j+\dfrac{1}{2}}h(x,y)dxdy, \tag{32}\]  
where \(h(x,y)\) is a continuous PSF as the below definition.  
\[h(x,y)=\dfrac{1}{\sigma_{b}^{2}}\sin c^{2}\left(\dfrac{x-m_{x}}{\sigma_{b}}, \dfrac{y-m_{y}}{\sigma_{b}}\right), \tag{33}\]  
where \(\sin\) c(\(x,y)=(\sin(\pi x)/\pi x)(\sin(\pi y)/\pi y)\); \(\sigma_{b}\) is the blur grade; and \(m_{x}\) and \(m_{y}\) are the misalignments in the horizontal and vertical directions, respectively. In this paper, the PSF \(h[j,k]\) has a size of \(3\times 3\) array pixels. Finally, \(w[j,k]\) is the additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma_{w}^{2}\). In addition, SNR is defined as follows.  
\[\text{SNR}=10\log_{10}\left(\dfrac{1}{\sigma_{w}^{2}}\right), \tag{34}\]
## Materials and Methods
### Nutritional Indices and Fatty Acid Ratios  
Several FA's ratios and nutritional indices of milks from each of the feeding systems are reported. The summation of \(\Omega\) 6 and \(\Omega\) 3 and \(\Omega\) 9 are reported. The atherogenicity index (AI) and thrombogencity  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fatty Acid (\% of TFA)** & **CONC** & **PALM** & **SOYA** & **BEET** \\ \hline
**Caprylic Acid-C8:0** & \textless{}0.05 & 3.25 & \textless{}0.05 & \textless{}0.05 \\
**Capric Acid-C10:0** & \textless{}0.05 & 3.31 & \textless{}0.05 & 0.31 \\
**Lauric Acid-C12:0** & 0.16 & 47.24 & 0.29 & 2.36 \\
**Myristric Acid-C14:0** & 0.12 & 16.22 & 0.23 & 1.35 \\
**Pentadecanoic Acid-C15:0** & 0.06 & \textless{}0.05 & 0.14 & 0.32 \\
**Palmitic Acid-C16:0** & 13.53 & 8.47 & 11.85 & 29.61 \\
**Stearic Acid-C18:0** & 2.25 & 2.55 & 4.48 & 2.13 \\
**Oleic Acid-C18:1** & 27.36 & 15.81 & 20.71 & 20.65 \\
**Linoleic Acid-C18:2** & 48.26 & 2.58 & 47.39 & 34.28 \\
**Linoleic Acid-C18:3** & 2.82 & \textless{}0.05 & 9.44 & 4.72 \\
**Stearidonic Acid-C18:4** & 0.06 & \textless{}0.05 & 0.1 & 0.27 \\
**Arachidic Acid-C20:0** & 0.44 & 0.14 & 0.54 & 0.28 \\
**Gadoleic Acid-C20:1** & 0.46 & 0.11 & 0.29 & 0.38 \\
**Behenic Acid-C22:0** & 0.26 & \textless{}0.05 & 0.48 & 0.48 \\
**Lignoecirc Acid-C24:0** & 0.17 & 0.08 & 0.23 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Feed supplements mean major fatty acid triglyceride content (% of total fatty acids (TFA)). Parlour concentrate (CONC), palm kernel expeller (PALM), soya hulls (SOYA), and molassed beet pulp (BEET).  
index's (TI) outlined by [PERSON] and [PERSON] [14] are dietary risk indicators for cardiovascular disease. AI is based on the ratio of fatty acids with pro-atherogenic and those with anti-atherogenic properties. It is indicative of the propensity to inhibit aggregation of plague and diminish the levels of esterified FA, cholesterol, and phospholipids, thereby preventing the development of micro and macro-coronary diseases; while TI is based on the relationship between pro-thrombogenic and anti-thrombogenic fatty acids, and is indicative of the tendency to form clots in the blood [15].  
Atherogenecity index (AI) and thrombogenicity index (TI) were calculated as described by [PERSON] and [PERSON] [14];  
\[\text{AI = \frac{\text{C12}:0+(4\times\text{C14}:0)+\text{C16}:0}{\text{n6 PUFA +n3 PUFA}+\text{MUFA}}} \tag{1}\]  
\[\text{TI = \frac{\text{C14}:0+\text{C16}:0+\text{C18}:0}{(0.5\times\text{ MUFA})+(0.5\times\text{n6 PUFA})+(3\times\text{n3 PUFA})+\left(\frac{\text{n3 PUFA}}{ \text{n6 PUFA}}\right)}.} \tag{2}\]
## 3 Results and Discussions
### Energy and Exergy Analysis of the RORC Configuration
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Components** & **E\text{{}_{F}} (kW)** & **E\text{{}_{P}} (kW)** & **E\text{{}_{d}} (kW)** & **E\text{{}_{L}} (kW)** & \(\epsilon_{k}\) \\ \hline ITC 1 & 541.20 & 238.28 & 6.46 & 296.45 & 44.03 \\ Pump 1 & 0.36 & 0.098 & 0.263 & – & 27.31 \\ Turbine & 84.74 & 74.10 & 10.63 & – & 87.45 \\ Evaporator & 268.33 & 238.38 & 58.06 & – & 88.83 \\ Pump 2 & 1.89 & 1.46 & 0.42 & – & 77.615 \\ Regenerator & – & – & 48.01 & – & – \\ Condenser & – & – & 17.77 & 77.79 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Exergy values for each component. heat gain cycle of the ORC can be observed, starting with the heat gained in the regenerator, and then passing through the three stages of the heat exchanger. According to the definition of input-output results, the fraction of exergy destroyed in each device was found and is represented in Figure 5. On the one hand, it can be appreciated that greater fractions of exergy are destroyed in the evaporator with a value close to 40.99%, followed by the regenerator with a value of 33.9%, and the condenser with a value of 12.55%. On the other hand, devices that destroy less exergy are the turbine with a value of 7.51%, the exchanger (ITC 1) with a value of 4.56%, and finally Pump 1 and Pump 2 with values of 0.186% and 0.3%, respectively. The components with greater irreversibilities such as the evaporator, regenerator, and condenser are the devices that have greater opportunities for improvement, although the components generate greater amounts of exergy due to interactions with other devices, the nature of the same, or the operating conditions.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 3 Kinetics Models and Their Development
### Dckm
#### 3.3.4 Benzene  
[PERSON] et al. [102, 123] developed an elementary reaction mechanism for benzene oxidation in SCW based on the low-pressure benzene combustion mechanism and submechanisms for the oxidation of key intermediates, which incorporated the aromatic to C-1, C-1, and H\({}_{2}\)/O\({}_{2}\) submechanisms. The benzene combustion mechanism was modified critically to meet the lower temperatures and higher pressures of SCWO, including (1) the adaptation of some recombination reactions for pressure; (2) the addition of a pressure-corrected C5H5/C5H6 submechanism; (3) the inclusion of bimolecular reactions and thermal dissociation reactions of C\({}_{6}\)H\({}_{5}\)OO; and (4) the insertion of reactions for phenyl radical (C\({}_{6}\)H\({}_{5}\)) degradation and rate coefficients predicted by CHEMDIS.  
Figure 13 shows the revised major oxidation pathways of benzene in SCWO [123]. The conversion to C\({}_{6}\)H\({}_{5}\) includes principal reactions while C\({}_{6}\)H\({}_{5}\)OH is minor for benzene decomposition. The model befits the experimental benzene and phenol concentration profiles and predicted the measured benzene conversion accurately, while the predicted CO and CO\({}_{2}\) concentrations were inconsistent with experimental measurement values due to the lack of chemistry for the further oxidation of the linear C\({}_{6}\), C\({}_{5}\), C\({}_{4}\) intermediate species to CO and CO\({}_{2}\). However, the model overcame the problems that the predicted oxidation rate of benzene was too fast and the concentration of CO was incorrectly predicted to exceed that of CO\({}_{2}\) since the two thermal decomposition reactions of C\({}_{6}\)H\({}_{5}\)OO were inserted.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 3 Proposed Method
### Loss Functions
#### 3.3.1 The Loss Function of G  
The loss function of G(\(L_{\text{G}}\)) consists of two parts which are the adversarial loss (\(L_{Adv}\)) and the content loss (\(L_{Con}\)). The calculation equation is illustrated in Equation (2):  
\[L_{\text{G}}=L_{Adv}+L_{Con}. \tag{2}\]  
Among them, the \(L_{Adv}\) is defined as follows:  
\[L_{Adv}=\frac{1}{N}{\sum}_{n=1}^{N}\left(D(F)-a\right)^{2}. \tag{3}\]  
The form of mean square error (MSE) loss is used to calculate the \(L_{Adv}\) and the parameter a is set approaching 1 since G wants to increase the discriminant result \(D(F)\) and generate the data which cannot be distinguished by D. The smaller \(L_{Adv}\) means the better-fused images generated by G.  
The \(L_{Con}\) is used to keep the correlations between source images and fused images, and has an influence on which kind of information will be merged from the source images in different modalities. In this paper, the structural similarity (SSIM) (S  
where \(\omega\) represents the weight and \(SSIM(\star)\) just implement the structural similarity operation in [65]. \(A\) and \(B\) are source images, and F is the fused image. With the join of \(L_{SSIM}\), visually pleasing fused images are always obtained, and the values are higher by objective evaluation metrics.  
The \(L_{Gnd}\) calculates the MSE of image intensity and gradient, so the Frobenius norm is introduced to express the loss simpler. The equation of \(L_{Gnd}\) is organized as follows:  
\[L_{Gnd}=\frac{1}{HW}(\gamma_{1}\|F-I\|_{F}^{2}+\gamma_{2}\|\
abla F-\
abla V\| _{F}^{2}+\gamma_{3}\|F-V\|_{F}^{2}+\gamma_{4}\|\
abla F-\
abla I\|_{F}^{2}), \tag{6}\]  
where \(\|\star\|_{F}\) represents the Frobenius norm, \(\
abla\) indicates the gradient computation. \(H\) and \(W\) represent the height and weight of the input images, respectively. We innovatively employ four parameters which are \(\gamma_{1}\), \(\gamma_{2}\), \(\gamma_{3}\), and \(\gamma_{4}\) to adjust the balance of \(L_{Gnd}\). The first two items take up a large proportion to keep more intensity information of infrared images and gradient information of visible images. However, in a few image pairs, contrary to what we have assumed, the infrared image includes more details and the visible image has strong intensities in some regions. Therefore, the last two items are added which can synthetically improve the robustness of the model, of course, \(\gamma_{3}\) is not more than \(\gamma_{1}\), \(\gamma_{4}\) is smaller than \(\gamma_{2}\). So, the fused image generated by G will contain much information of source images.
# A Dual-Gas Sensor Using Photoacoustic Spectroscopy Based on a Single Acoustic Resonator
## 1 Introduction
reported a multi-gas PA analyzer using a mid-infrared broadband source and an optical wheel, installing several optical filters for selecting different absorption lines of several gases for detecting CO, CO\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{6}\), C\({}_{2}\)H\({}_{4}\) and H\({}_{2}\)O gas [3]. In this paper, we present a compact wavelength modulation photoacoustic spectroscopy for the simultaneous measurement of CH\({}_{4}\) and NH\({}_{3}\) based on a single acoustic resonator using two DFB lasers emitting at 1512 and 1653 nm. Normally, multi-gas detection using several DFB lasers in the PAS approach is achieved by using several acoustic resonators [7], broadband source sensors [18] and an optical switch [7,20]. The motivation of our work is to simplify the system structure of optical absorption spectroscopy for multi-gas analysis. The developed compact PA spectroscopy has the advantage of simple structure (one resonator without using any optical switch) and broadband characteristics when combining two DFB lasers at different wavelengths. The performance of the developed instrument was evaluated for simultaneous detection of CH\({}_{4}\) and NH\({}_{3}\).
###### Abstract  
Modern low-cost electronic devices can achieve high precision for global navigation satellite systems (GNSSs) and related applications. Recently, the pseudo-range and carrier phase have been directly obtained from a smartphone to establish a professional-level surveying device. Although promising results have been obtained by linking to an external GNSS antenna, the real-time kinematic (RTK) positioning performance requires further improvement when using the embedded smartphone antenna. We first investigate the observation quality characteristics of the Xiaomi Mi 8 smartphone. The carrier-to-noise-density ratio of L5/E5a signals is below that of L1/E1 signals, and the cycle slip and loss of lock are severe, especially for L5/E5a signals. Therefore, we use an improved stochastic model and ambiguity-resolution strategies to improve the short-baseline RTK positioning accuracy. Experimental results show that the ambiguity fixing rate can reach approximately 90% in 3 h of observations when using the embedded antenna, while the GPS/Galileo/BDS single-frequency combination is more suitable for smartphones. On the other hand, convergence takes 10-30 min, and the RTK positioning accuracy can reach 1 and 2 cm along the horizontal and vertical directions, respectively, if ambiguity is resolved correctly. Moreover, we verify the feasibility of using a mass-produced smartphone for deformation monitoring. Results from a simulated dynamic deformation experiment indicate that a smartphone can recognise deformations as small as 2 cm.  
s _Applied Sciences_  
11 November 20213 November 20213 November 20213 January 2022  
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# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Image Encryption
#### 5.5.5 Synchronization  
Work on the synchronization of neural networks is plentiful. Researched techniques include Periodic Self-Triggered Impulsive (PSTI) control [245], neural activation function [246], and unbounded delay.  
The types of neural networks involved in synchronization research are also various. These include arrays of coupled jumping delayed neural networks [247], reaction-diffusion neural networks with mixed delays [248], and chaotic memristive multidirectional associative memory neural networks [249].