esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 1 Introduction
(2024) proposed an AI-based system for precise oil spill detection, while [PERSON] et al. (2024) conducted a survey on the You Only Look Once (YOLO) model for SAR image analysis [17; 18]. [PERSON] et al. (2023) examined polarimetric SAR satellite images using a DeepLabV3+-based model to monitor oil spill risks in coastal areas [19]. [PERSON] et al. (2023) introduced Sw-YoloX, which enhances the detection performance for sea surface objects [20]. [PERSON] et al. (2022) demonstrated the effectiveness of the DeepLabV3+ model for detecting oil spills using PlanetScope images [21]. These deep learning technologies are increasingly utilized in oil spill detection through remote sensing, supporting environmental monitoring and disaster response via complex data analysis. Convolutional neural networks (CNNs) are a deep learning algorithm particularly adept at recognizing patterns in images. Recently, Transformer models have been adopted and have demonstrated superior performance compared to traditional CNNs. However, objective comparisons of CNN and Transformer-based models for oil spill detection using high-resolution optical images have been infrequent. With the increasing number of small cube satellites and the continuous refinement of optical image resolution, it is necessary to implement a collaborative monitoring system that combines satellite SAR and optical sensors to achieve enhanced spatial and temporal coverage for detecting oil spills in high-resolution images. In this context, the objective of this study is to evaluate the prediction performance of existing CNN models and novel Transformer-based models for oil spill detection using high-resolution optical satellite images. PlanetScope images were utilized for deep learning semantic segmentation with DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2 Former, which employs a Transformer-based architecture for both encoding and decoding. To ensure an objective evaluation, the three models were subjected to k-fold cross-validation tests. Additionally, the pixels of the predicted oil spills were classified into oil types using histogram analysis.
## 4 Discussion of GFPS Scheme
\begin{split} J_{1}&=2\cos(x_{1}+1)x_{1}+2\sin(x_{1} +1)+1,\\ J_{2}&=-6\cos(2x_{2}-3)x_{2}-3\sin(2x_{2}-3)+1,\\ J_{3}&=-3\cos(3x_{3}-1)x_{3}-\sin(3x_{3}-1)+1,\\ J_{4}&=2\cos(x_{4}-4)x_{4}+2\sin(x_{4}-4)+1,\end{split}  \tag{29}\]  
According to the theoretical analysis, the controller and parameter update laws presented in (23) have the following form:  
\[ \begin{split} u_{1}&=J_{1}x_{4}-y_{4}+\hat{a}_{1}(y_ {1}-y_{2})-\hat{a}J_{1}(x_{1}-x_{2})+\sum_{i=1}^{4}k_{1i}e_{i},\\ u_{2}&=y_{2}+y_{1}y_{3}-J_{2}x_{1}x_{3}-\hat{b}_{1}y_ {1}+\hat{d}J_{2}x_{1}+\hat{c}J_{2}x_{2}+\sum_{i=1}^{4}k_{2i}e_{i},\\ u_{3}&=J_{3}x_{1}x_{2}-y_{1}y_{2}+\hat{c}_{1}y_{3}- \hat{b}J_{3}x_{3}+\sum_{i=1}^{4}k_{3i}e_{i},\\ u_{4}&=y_{1}y_{3}+J_{4}x_{2}x_{3}-\hat{d}_{1}y_{4}+ \hat{r}J_{4}x_{4}+\sum_{i=1}^{4}k_{4i}e_{i},\\ \hat{a}&=(x_{1}-x_{2})J_{1}e_{1}+(a-\hat{a}),\\ \dot{\hat{b}}&=x_{3}J_{3}e_{3}+(b-\hat{b}),\\ \dot{\hat{c}}&=-x_{2}J_{2}e_{2}+(c-\hat{c}),\\ \dot{\hat{d}}&=-x_{1}J_{2}e_{2}+(d-\hat{d}),\\ \dot{\hat{r}}&=-x_{4}J_{4}e_{4}+(r-\hat{r}),\\ \dot{\hat{a}}_{1}&=(y_{2}-y_{1})e_{1}+(a_{1}-\hat{a }_{1}),\\ \dot{\hat{b}}_{1}&=y_{1}e_{2}+(b_{1}-\hat{b}_{1}),\\ \dot{\hat{c}}_{1}&=-y_{3}e_{3}+(c_{1}-\hat{c}_{1}), \\ \dot{\hat{d}}_{1}&=y_{4}e_{4}+(d_{1}-\hat{d}_{1}), \end{split}  \tag{31}\]  
where \(k_{ij}\) (\(i=1,2,3,4;j=1,2,3,4\)) are real constants that compose the matrix \(K\); \(e_{i}=(y_{i}-h_{i}x_{i})\), \(i=1,2,3,4\); \(\hat{a}\), \(\hat{b}\), \(\hat{c}\), \(\hat{d}\), \(\hat{r}\), \(\hat{a}_{1}\), \(\hat{b}_{1}\), \(\hat{c}_{1}\) and \(\hat{d}_{1}\) are estimated values of the unknown parameters in systems (26) and (27). From the analytical result given in [38], GFPS between systems (26) and (27) will be achieved if the \(K\) matrix is chosen as  
\[K=\left[ \begin{array}{cccc}-1&1&0&0\\ -1&-1&1&0\\ 0&-1&-1&1\\ 0&0&-1&-1\\ \end{array} \right], \tag{33}\]  
which satisfies condition (24). However, the GFPS conditions obtained in this paper for systems (26) and (27) only require that all eigenvalues of \(K\) matrix have negative real parts. Numerical simulations for systems (26), (27) are carried out below by selecting  
\[K=\left[ \begin{array}{cccc}-1&1&0&0\\ -2&-1&1&0\\ 0&-1&-1&1\\ 0&0&-1&-1\\ \end{array} \right]. \tag{34}\]  
**Remark 2**. : _The \(K\) matrix (34) is not antisymmetric, which does not satisfy the first condition in (24)._
## 2 Model Setup and Validation
### Governing Equations
\(\delta\), \(W\) and \(H\) are the thickness, width, and height of the wall, respectively. The heat transfer across the wall is described in the following equation [18; 19]:  
\[\frac{\partial T_{\rm{s}}}{\partial t}=\frac{\lambda_{\rm{s}}}{\rho_{\rm{s}}C _{\rm{s}}}\bigg{[}\frac{\partial}{\partial x}\bigg{(}\frac{\partial T_{\rm{s} }}{\partial x}\bigg{)}+\frac{\partial}{\partial y}\bigg{(}\frac{\partial T_{ \rm{s}}}{\partial y}\bigg{)}+\frac{\partial}{\partial z}\bigg{(}\frac{ \partial T_{\rm{s}}}{\partial z}\bigg{)}\bigg{]} \tag{9}\]  
Convective heat transfer boundary conditions are adopted on the wall's inner and outer surfaces. For the inner surface, it is expressed as:  
\[-\lambda_{\rm{s}}\frac{\partial T_{\rm{s}}}{\partial x}\bigg{|}_{x=0}=h_{\rm{ i}}(T_{x=0}-T_{\rm{i}}) \tag{10}\]  
For the outer surface, it can be expressed as:  
\[\frac{\partial u_{\rm i}}{\partial t}+\frac{\partial\big{(}u_{\rm i}u_{\rm j} \big{)}}{\partial x_{\rm j}}=-\frac{1}{\rho}\frac{\partial p}{\partial x_{\rm i }}+\frac{\partial}{\partial x_{\rm j}}\Bigg{(}\
u\frac{\partial u_{\rm i}}{ \partial x_{\rm j}}-\overline{u^{\prime}{}_{\rm i}u^{\prime}{}_{\rm j}}\Bigg{)} \tag{2}\]0  
Moreover, adiabatic boundary conditions are used on the cross-sectional planes (\(y=0\) and \(y=W\), \(z=0\) and \(z=H\)) of the envelope [20]:  
\[\frac{\partial u_{\rm i}}{\partial t}+\frac{\partial\big{(}u_{\rm i}u_{\rm j} \big{)}}{\partial x_{\rm j}}=-\frac{1}{\rho}\frac{\partial p}{\partial x_{\rm i }}+\frac{\partial}{\partial x_{\rm j}}\Bigg{(}\
u\frac{\partial u_{\rm i}}{ \partial x_{\rm j}}-\overline{u^{\prime}{}_{\rm i}u^{\prime}{}_{\rm j}}\Bigg{)} \tag{2}\]1  
where \(\rho_{\rm{s}}\) and \(\lambda_{\rm{s}}\) are the density and thermal conductivity of the wall, respectively. \(T_{\rm{a}}\) refers to the temperature of the surrounding air (i.e., the outdoor air or the adjoining air). \(T_{\rm{i}}\) is the indoor air temperature. \(h_{\rm{i}}\) and \(h_{\rm{o}}\) are the heat transfer coefficients of inner and outer surfaces of walls, respectively.
## 1 Introduction
CRAHNs are self-configuration and adaptive; hence, the mobile nodes must discover network resources and communicate accordingly. The system need to sense changing environmental conditions or changing capabilities and respond appropriately so that the entire system can operate efficiently [13]. Each node acts as a router and as a host, and the nodes can move in any direction, changing the network topology and, in some instances, partitioning the network. Hence, route maintenance in an intermittent network is a challenge. Node and spectrum mobility cause connection breakages which impacts negatively on data transmissions. Therefore, undelivered packets are stored in buffers for prolonged periods, which results in them being dropped. The increase in network traffic and the absence of centralized infrastructure increase network complexity in terms of routing challenges. Figure 1: Spectrum model depicting signals, noise, reversed bands, and the spectrum holes in \(k\) bands [6]. This study aims to minimize the signal overhead and improve network performance while ensuring that network resources are utilized efficiently [14]. This study proposes a routing scheme based on a cuckoo search cluster-based algorithm to address the routing challenge in ad hoc networks. Due to the increase in the request for spectrum and a need to transmit multimedia data at high rates, the need for robust and efficient routing schemes cannot be overemphasized [15]. The unique characteristics of ad hoc networks does not guarantee long-term network stability with low congestion and overhead. This study focuses on three routing metrics: the packet-delivery ratio, packet loss, and residual energy. Mobile nodes have limited energy resources; hence, the goal is to implement energy-aware routing scheme to extend the lifespan of a network [16]. The current routing schemes do not consider the energy of nodes in data transmission. Routing protocols play a significant part in CRAHNs, as the nodes are dynamic, and nodes assist in routing the data packets. Energy-efficient routing protocols are required in such a situation, particularly when there are no routers, base stations, and fixed infrastructure. Establishing an optimal and efficient path from source to destination is the main goal of routing protocols in CRAHNs. They must ensure that the network's lifespan is extended, and data transmission does not experience interference while minimum battery power is utilized. These objectives can be achieved by reducing the mobile node's energy consumption during both active and indolent transmissions.
## 3 Experimental Results and Analysis
### Running Time  
To compare the calculation complexity of different image registration algorithms, Table 2 shows the average running time of each algorithm running for 10 times, where all algorithms are implemented under a computer with Intel i7-6700 CPU, 32 GB memory and one NVIDIA GTX 1080 Ti GPU. It is seen that APAP algorithm runs slowest due to the use of the local homography matrix and ORB algorithm runs fastest among the traditional image registration algorithms. For learning-based image registration algorithms, Table 2 gives the running time when the algorithms are accelerated with one GPU, as well as the running time achieved without the GPU. It is seen that GPU can significantly speed up the learning-based algorithms. The running speed of GPU is much faster than that of CPU, and different neural network models achieve different running speeds, among which Xception runs the slowest and Googlenet runs the fastest. Because the [PERSON]'s and [PERSON]'s algorithms are only different in loss function and the neural network model is basically the same, the running time of the two algorithms are the same under the same conditions. The proposed algorithm involves the estimation of local homography matrices, so it runs slower than [PERSON]'s and [PERSON]'s algorithms under the same neural network.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithmic Type** & **Algorithm** & **RMSE** \\ \hline Pixel based & ECC & 18.13 \\  & SIFT & 5.077 \\ Feature based & ORB & 17.751 \\  & APAP & 4.458 \\ \hline \multirow{8}{*}{Learning based} & [PERSON] + VGG & 11.844 \\  & [PERSON] + Googlenet & 10.512 \\  & [PERSON] + Xception & 10.011 \\  & [PERSON] + VGG & 10.455 \\  & [PERSON] + Googlenet & 9.936 \\  & [PERSON] + Xception & 9.861 \\  & Proposed + VGG & 6.113 \\  & Proposed + & 4.344 \\  & Googlenet & 2.339 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMSE comparison of different image registration algorithms.
## 3 Results and Discussion
### Process Impact Comparison Immediately after Processing
In the control samples, seven volatile compounds were selected with the VID procedure. On the one side, three of these compounds, (_E_)-2-hexenal, \(\alpha\)-terpineol and hexyl acetate were selected with a positive VID value. This shows that these compounds are detected in higher amounts in the control samples or in another words they are detected in lower amounts in the processed samples and thus are possibly decreased due to the applied pasteurization techniques. In line with that statement, two of these compounds, (_E_)-2-hexenal and \(\alpha\)-terpineol, are detected at significantly lower amounts in thermally processed apple juices compared to other samples. (_E_)-2-hexenal is one of the volatile compounds reported to have a significant aroma relevance in apple juices. Hence, it seems that conventional thermal processing exerts a negative impact on some aroma relevant apple juice volatile compounds. [PERSON] et al. (2019) also reported that the aroma of apple juice seem to be more affected due to thermal processing compared to HPP, in particular causing increased formation of compounds responsible for cooked notes. On the other side, four volatiles, 2-methylbutan-1-ol, 2-methylpropyl acetate, hexan-1-ol and 3-methyl-2-buten-1-yl acetate, were selected with a negative VID values showing that these compounds are detected with a lower abundance in the control samples compared to processed ones. In relation to that, some of these volatiles were significantly increased after PEF and HPP pasteurization. The amount of 2-methylpropyl acetate seems to be increased after HPP. Hexan-1-ol, terpinen-4-ol and butyl propanoate were detected with higher amounts after PEF processing. In general, immediately after processing, thermal processing seems to reduce the amount of some odor-relevant volatiles compared to HPP and PEF pasteurization technologies. However, since the quality of processed juices further changes during shelf life, in the next section, the processing impact was investigated during refrigerated storage.
## 1 Introduction
The main focus of this review paper is the existing techniques and design strategies for PLS optimization, optimization problems, and the solutions that are related to wireless PLS. Moreover, it inclusively discusses the applications of several enabling and computing technologies that could improve the corresponding research challenges. In order to address the limitations of existing optimization challenges, ML and AI technologies need to be efficiently integrated into 5G networks in order to produce better security and resource management. The use of ML and AI technologies within the field of mobile communication infrastructure has made significant progress in ensuring security, reliability, and resource allocations in a dynamic, robust and trustworthy way [33; 34; 35; 36]. The contributions of this review paper are as follows. First, we introduced fundamental principles and different channel models. In this context, we briefly reviewed the common scenarios and limitations of different security methods to understand the basic theories that are related to PLS. Second, the information-theoretic security metrics and application scenarios in PLS were investigated to provide a brief insight into secure design strategies. Third, the potential research directions and technical challenges in PLS from the perspectives of security design and optimization approaches were widely discussed. Finally, we reviewed the potential advantages of using ML and AI enabling technologies to further improve conventional security technologies. In summary, this review paper identifies optimization challenges in terms of secure resource allocation and signal processing techniques and presents potential solutions through the introduction of AI- and ML-based technologies. The structure of this review paper is outlined as follows. The abbreviations that are used throughout the paper are described in Table 2. Then, Section 2 introduces the fundamental concepts of PLS; specifically, the general system model, adversary model and different types of wiretap channel models. The performance metrics that are used in PLS to evaluate the security design strategies are discussed in Section 3. In Section 4, the research directions and technical challenges are investigated; specifically, the main issuesand technical challenges of system design and optimization techniques, which are discussed from the viewpoints of secure resource management and signal processing techniques. Section 5 explains the paradigms of ML and AI within 5G networks and their applications in physical layer design and optimization. In Section 6, we present notable future directions and open challenges, followed by a conclusion in Section 7.
## 3 Results
### Aviation Technical Support as a Service
#### 3.15.2 Tier 2: Airline Fleet Management  
Each airline operates as a centralized node for its respective fleet, aggregating data from individual aircraft.  
At this level, data from the entire fleet are analyzed to identify trends, optimize maintenance schedules, and improve overall operational efficiency.  
Machine learning models analyze aggregated data to predict maintenance needs across the fleet.  
Decision-support systems help manage and allocate resources effectively, ensuring that maintenance activities are timely and disruptions are minimized.
## 1 Introduction
### Literature Review
Many researchers are experimenting with different applications by developing new algorithms such as the Skill optimization algorithm [23], Equilibrium optimization [24] and so on. In this manuscript, an engineering application is presented using a hybrid algorithm, that is, the Particle Swarm Optimization and Gravity Search Algorithm (PSOGSA), to estimate the parameters of monocrystalline solar cells and polycrystalline cells. The results obtained using PSOGSA are compared with those obtained using Particle Swarm Optimization (PSO) [25], Gravitational Search Algorithm (GSA) [26], Multi-Verse Optimizer (MVO) [27], Sine Cosine Algorithm (SCA) [28], Heap Based Optimizer (HBO) [29], and Political Optimizer (PO) [30]-based techniques to check the effectiveness of the PSOGSA.
## 6 Conclusions  
In this paper, an ultra-wideband, fully-differential quadrature signal generation network is described and analyzed in detail. In order to generate an I/Q signal with perfectly matched I/Q balance in the entire 5G frequency band, a transformer-based quadrature hybrid is cascaded to suppress I/Q imbalance. Multiturn transformers are stacked vertically to enhance the coupling coefficient of the transformer-based quadrature hybrid. The layout of the cascaded transformer-based quadrature hybrid is carefully arranged to minimize intrinsic passive loss, as well as undesired I/Q imbalance. Magnetic coupling is minimized by adding ground wall shield between neighboring circuit blocks; thus, the quadrature hybrids can be placed in a compact manner to save chip area. Designed in GlobalFoundries 45 nm CMOS SOI process, our proposed ultra-wideband quadrature signal generation network shows excellent I/Q balance performance. The output I/Q magnitude mismatch is lower than 0.5 dB at 16 GHz -60 GHz, and the phase mismatch is lower than 2\({}^{\circ}\) from 16.5 GHz to 54.7 GHz. The IRR is higher than 40 dB from 21.5 GHz to 53.5 GHz. The input return loss is better than \(-\)10 dB at 22-45 GHz, with signal attenuation varying from 5.74 dB to 7.4 dB (including 1:2 power splitting and loss from passive balun). The results of our proposed I/Q signal generation network validate its ability to provide an ultra-precise, fully-differential quadrature signal with compact chip area, low loss and ultra-wide bandwidth.  
Conceptualization, investigation and writing--original draft preparation, Y.Z.; methodology, writing--review and editing and supervision, X.L. ([PERSON]), Z.L. and X.L. ([PERSON]); methodology, supervision and funding acquisition, Q.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 62101038 and Grant 61801027 and the Beijing Nova Program of Science and Technology under Grant number Z191100001119078.  
The authors declare no conflict of interest.  
Figure 16: Comparison of wideband IRR performances [12; 17; 18; 20].
## 2 Signal Analysis and Neural Network
### Hilbert-Huang Transform (HHT)
#### 2.1.2 Empirical Mode Decomposition  
EMD is the signal processing before HHT, which decomposes the signal into a combination of IMF. Due to the HHT's restrictions on the instantaneous frequency, if the general signal data are directly used, the complete and correct instantaneous frequency cannot be obtained. The steps of EMD screening IMF are listed as follows:1. Input the original signal \(x(t)\) to find the local maximum and local minima. Connect their values to upper envelope \(H(t)\) and lower envelope \(L(t)\), respectively;
2. Calculate the average of the upper envelope \(H(t)\) and the lower envelope \(L(t)\) to get the mean envelope \(m(t)\);
3. Subtract the original signal \(x(t)\) from the mean line \(m(t)\) to get \(h(t)\);
4. Check whether \(h(t)\) meets the conditions of the IMF. If not, go back to _Step 1_ and replace \(x(t)\) with \(h(t)\). Rescreen until \(h(t)\) meets the conditions and termination of IMF, and store \(h(t)\) as the component \(C_{i}\) of IMF;
5. Subtract the \(h(t)\) from original signal \(x(t)\) to get \(R(t)\);
6. Check whether \(R(t)\) is monotonic function or not. If yes, stop decomposition. If not, repeat _Step 1_ to _Step 5_.  
Therefore, the original data can be decomposed into n IMFs and a trend function, and we can perform HHT on the IMF for signal analysis. The flowchart of the EMD is shown in Figure 1.
# Investigations of the Windage Losses of a High-Speed Shrouded Gear via the Lattice Boltzmann Method
###### Abstract  
To suppress the adverse effect of the gear windage phenomenon in the high-speed aeronautic industry, a shroud as an effective alternative strategy is usually to enclose gears to reduce the windage behaviors of high-speed gears. To deeply understand these no-load power losses, this paper proposes a new simulation methodology based on the Lattice Boltzmann method to study the windage losses of a shrouded spur gear and conducts a series of numerical studies. The models reproduce a shroud spur gear varying radial and axial clearances to evaluate the influence of casing walls on windage losses. The simulation results were then compared with experimental values, showing a satisfactory agreement. Furthermore, a torque containment factor integrating the air compressibility at high Mach numbers is introduced to represent the reduction in torque (windage power losses) for the shrouded gear compared to the free case, and the theoretical formulae for predicting windage power losses are further improved for better applicability as the tight shroud approaches the gear during the preliminary design stage.  
windage losses; shroud; high-speed gear; torque containment factor; air compressibility Article