esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### Datasets and Settings  
We performed evaluations over two datasets named HRSID [9] and SSDD [10]. HRSID was proposed by using images from 99 Sentinel-1B images, 36 TerraSAR-X, and 1 TanDEM-X image. These images were then cropped to \(800\times 800\) pixel images. Overall, there are 5604 images, including 16,951 ships. The ship resolution ranges from 1 m to 15 m. SSDD is a multi-size and multi-resolution dataset. It has 1160 SAR images, including 2456 ships. The sizes of the SAR images in SSDD range from \(7\times 7\) to \(211\times 298\) pixel images. In order to maintain consistency, we resized the images of the SSDD set to \(800\times 800\) pixels. All our experiments were implemented by using PyTorch 0.4.1 and CUDA 9.1. Our hardware platform consisted of an Nvidia Tesla V100 (32 G) GPU and an Intel Xeon Platinum 8260 CPU.  
For both datasets, HRSID and SSDD, we randomly select 90% of images for the architecture search and training. The remaining 10% of images are left for performance validation. The number of epochs is set to 100, and the batch size is set to 32 for the architecture search. After a neural network is derived by Darts_Tiny, sufficient trainings are performed, while the learning rate is initialized to be 1.  
The first 15 epochs of 100 are used to train the super neural network (line 2 in Figure 8). Remember that in the search process of Darts_Tiny (see Figure 8), we have three thresholds, \(T_{1}\), \(T_{2}\) and \(T_{3}\), for performing channel pruning. In our experiments, training and validation precision are expressed by F1. According to our experience, \(T_{1}\) is set to 3, and \(T_{2}\) is set to 75 such that only when the gap between training and validation precision is greater than 3 and the current validation precision (F1) is greater than 75, channel pruning is performed. The third parameter \(T_{3}\) is set to 70 such that after channel pruning, training is performed until F1 of validation recovers to 70. In our experiments, \(T_{3}=70\) can be easily reached after a few training epochs. We use the loss function of YOLOv2 [35] to implement \(Loss\_ce\) in Formula (8). The detection threshold \(IoU\) is set to 0.3. Unless otherwise specified herein, F1 is obtained by setting the threshold of confidence score to 0.15.
# A Parallel Algorithm Based on Regularized Lattice Boltzmann Method for Multi-Layer Grids
## 2 RLBM Based on Multi-Layer Grids
### RLBM Based on Multi-Layer Grids  
To facilitate the description of the model, the 2D grid is depicted in Figure 4.  
There are three layers of grids in Figure 4. The light blue frame represents the first layer, the brown frame indicates the second layer and the dark blue frame corresponds to the third layer. The two black circles denote the solid boundaries within the flow field. The first layer grid is half the size of the second, and this pattern continues.  
To facilitate grid division and parallel computation, this paper adopts a center-point format for storing grid point information. The resulting storage format is similar to a fork-tree structure [12], which facilitates programming to process.  
In a locally refined multi-layer grid structure, the ratio of the coarse grid size to the fine grid size is set to \(n=\delta\mathbf{r}_{c}/\delta\mathbf{r}_{s}=2\), where \(c\) represents a coarse grid and \(s\) represents a fine grid. Thus, in a 2D problem, a coarse grid can be subdivided into four fine grid cells. To ensure fluid viscosity consistency throughout the flow field, the following relationships need to be satisfied:  
\[\delta_{t}^{s}=\frac{1}{n}\delta_{t}^{c},\quad\tau^{s}=\frac{1}{2}+n\left(\tau ^{c}-\frac{1}{2}\right) \tag{16}\]  
Velocity continuity needs to be satisfied at the intersection interface of grids of different sizes. Therefore, it must be satisfied that the equilibrium state distribution equations have the same value on the grid intersections. The following relationship is obtained:  
\[f_{i,c}^{\text{neq}}=n\frac{\tau_{c}}{\tau_{s}}f_{i,s}^{\text{neq}} \tag{17}\]  
Figure 3: Flow chart of RLBM.  
A local grid refinement method based on distribution functions and bi-directional coupling was developed by [PERSON] and [PERSON] [23]. In combination with the evolution of the RLBM, the paper establishes the relationship between the fine grid and the coarse grid.  
\[f_{i,c}^{+}=f_{i,s}^{\text{eq}}+n\frac{\tau_{c}-1}{\tau_{s}-1}(f_{i,s}-f_{i,s}^ {\text{eq}}) \tag{18}\]  
\[f_{i,s}^{+}=f_{i,c}^{\text{eq}}+\frac{\tau_{s}-1}{n(\tau_{c}-1)}(f_{i,c}-f_{i, c}^{\text{eq}}) \tag{19}\]  
where \(f_{i,c}^{+}\) is obtained by interpolating [40] the coarse grid through the fine grid and \(f_{i,s}^{+}\) is obtained by interpolating the fine grid through the coarse grid. To ensure the moments of the coarse and fine grids are identical, the coarse grids perform one collision and streaming, while fine grids require two collisions and streamings.  
The interpolation process between grids of different scales is shown in Figure 5. Firstly the grid information needs to be initialized and a pre-collision step is performed. Subsequently, the coarse grid undergoes one collision and streaming, while the fine grid undergoes two collisions and streamings. An interpolation between fine and coarse grids is conducted. This process is repeated until termination.
## 1 Introduction
The lifetime of a TBC system is dependent not only on microstructure and chemistry but also on the interface topology between the bond coat and the ceramic top coat, as it influences the growth dynamic [14], locations of high stress concentration locations [15], interfacial stress distribution, and crack propagation behaviour [16], which determines the TBC degradation mechanism and the overall performance of the thermal barrier coatings as a consequence [17]. Therefore, the surface roughness and the influence of the bond coat on TBC lifetime are of special interest [18]. Recently, it has been proven that the nanosecond pulsed laser texturing technique used to manufacture micro-hole texture on the surface of the bond coat leads to the design of high-durability TBCs [16]. Surface roughness also plays a key role in cyclic oxidation in the presence of CMAS (calcium-magnesium-alumino-silicate) [19]. The wettability and spreading dynamics of CMAS on different materials and coatings depend on their chemistry and surface roughness [20]. The pores and gaps between the columnar structure provide a convenient corrosion channel for molten CMAS [21]. However, the results demonstrate that the wettability and the actual contact area between the coatings and CMAS can be reduced by mechanical sanding and decreasing surface roughness, leading to delayed CMAS [22]. Furthermore, the roughness of the substrate influences the adhesion of the coating, an important parameter to consider for the use of thermal spray coatings during thermo-mechanical loading conditions [23]. The main mechanisms of adhesion can be described as mechanical locking, physical, diffusive, and chemical [24]. The adhesion strength of TBCs varies depending on the substrate materials and coating, the coating technique used, the coating application parameters, the substrate surface treatments [25], environmental conditions, and subsequent heat treatment [26]. In TBCs, the chemical adhesion mechanism of YSZ top coat to alumina bond coat is based on the formation of solid solutions between Al\({}_{2}\)O\({}_{3}\) and ZrO\({}_{2}\)[6]. Adhesion strength is an important parameter that characterises the resistance of the ceramic top coat to spallation [27]; therefore, adhesion testing is one of the crucial methods available to depict the durability of TBCs [28]. Unfortunately, little research has been conducted on the relationship between the surface roughness of the bond coat and the adhesion of 7 YSZ EB-PVD TBCs. In this paper, 7 YSZ EB-PVD TBCs were deposited on VPA bond coats with four roughness treatments. The roughness and microstructure of the 7 YSZ top coat under different bond coat roughness treatments were studied. The purpose of this paper was to investigate the influence of the roughness of the bond coat on the adhesion of 7 YSZ TBCs prepared by EB-PVD.
# Fuzzy Neural Networks--A Review with Case Study
## 3 The Selected Fuzzy Networks
### ANFIS Fuzzy Network
The membership functions (mf0, mf1, mf2) correspond to Gaussian-shaped distributions, each representing a different class of flowers in the Iris dataset: Setosa, Versicolor, and Virginia. The Gaussian shapes provide smooth transitions between the classes, a characteristic feature of fuzzy logic systems. For smaller input values, mf0 dominates, while mf1 and mf2 take over as the input values increase. This structure reflects how the network maps the input features to specific classes. The overlap between the membership functions in the middle range (e.g., around 5.5) illustrates the fuzzy nature of the classification, where more than one rule can be activated simultaneously. The final classification result is determined by a weighted combination of these activated rules. The parameters of the Gaussian functions (mean and standard deviation) are adjusted during the training process to optimize the system's performance and improve classification accuracy. In the presented example of the Iris dataset, these membership functions demonstrate ANFIS's ability to distinguish between different classes of flowers based on the input features. The smooth transitions between the functions allow the model to handle uncertainties and borderline cases effectively, ensuring accurate classification of the samples into the correct flower classes.
# Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks
###### Abstract  
A number of mathematical methods have been developed to make temporal signal analyses based on time series. However, no effective method for spatial signal analysis, which are as important as temporal signal analyses for geographical systems, has been devised. Nonstationary spatial and temporal processes are associated with nonlinearity, and cannot be effectively analyzed by conventional analytical approaches. Fractal theory provides a powerful tool for exploring spatial complexity and is helpful for spatio-temporal signal analysis. This paper is devoted to developing an approach for analyzing spatial signals of geographical systems by means of wave-spectrum scaling. The traffic networks of 10 Chinese cities are taken as cases for positive studies. Fast Fourier transform (FFT) and ordinary least squares (OLS) regression methods are employed to calculate spectral exponents. The results show that the wave-spectrum density distribution of all these urban traffic networks follows scaling law, and that the spectral scaling exponents can be converted into fractal dimension values. Using the fractal parameters, we can make spatial analyses for the geographical signals. The wave-spectrum scaling methods can be applied to both self-similar fractal signals and self-affine fractal signals in the geographical world. This study has implications for the further development of fractal-based spatiotemporal signal analysis in the future.  
Keywords:Fourier transform; wave spectrum scaling; fractal dimension; spatial signal; traffic network; Chinese cities +
Footnote †: journal: Applied Sciences
## 4 Computation of \(\epsilon\) Proper Equilibria
### Mixed 0-1 Quadratic Formulations
Hence, there exists only two probable outcomes when computing \(\epsilon(\sigma)\) for tiny values of \(\sigma\). The first possible outcome corresponds to the case where \(\epsilon(\sigma)\) is found to be bounded from below by a strictly positive value \(\tilde{\epsilon}\). The second possible outcome corresponds to the case where \(\epsilon(\sigma)\) converges to zero. **Case 1**: \(\epsilon(\sigma)\geq\tilde{\epsilon}\) When \(\epsilon(\sigma)\) is found to be bounded from below by an \(\tilde{\epsilon}\) strictly positive indicates that there are no \(\epsilon\)-proper equilibria close to \((\hat{X}_{1},\ldots,\hat{X}_{n})\), for \(\epsilon\) values less than \(\tilde{\epsilon}\). Therefore, \(\hat{X}=(\hat{X}_{1},\ldots,\hat{X}_{n})\) would not be proper. Now, let us assume that \(\epsilon(\sigma)\) converges to some value \(\tilde{\epsilon}>0\), when \(\sigma>0\) converges to 0 as in \((\Omega_{>})\). We then define a 0-1 mixed quadratic program under the same conditions of \(\Omega_{\epsilon}^{\sigma}\), but with \(\epsilon\leq\tilde{\epsilon}/2\) and seeking to maximize \(\sigma\). When the optimal objective value of this program is found to be equal to _zero_, we can obviously conclude that it is not possible to obtain a sequence \((X_{1}(\sigma),\ldots,X_{n}(\sigma))\) of \(\epsilon\)-proper equilibria converging to the Nash equilibrium \(\hat{X}\). Hence, the Nash equilibrium \(\hat{X}=(\hat{X}_{1},\ldots,\hat{X}_{n})\) is not proper. Proposition 4: _Consider the following 0-1 mixed quadratic program:_  
\[ \begin{array}{ll}(\Delta_{\bullet})&\max_{\epsilon,\sigma}\ \sigma\\ &\text{subject to}\\ &\hat{x}_{ik}-\epsilon\leq x_{ik}\leq\hat{x}_{ik}+\epsilon,\ \ \ k\in\{1,2,\ldots,m_{i}\},\ \ \ i\in N,\\ &0\leq\epsilon\leq\tilde{\epsilon}/2,\\ &(X_{1},\ldots,X_{n})\in\Omega_{\epsilon}^{\sigma}.\end{array} \]  
_If the optimal objective value of \((\Omega_{\bullet})\) is zero for a given \(\tilde{\epsilon}>0\), the Nash equilibrium \(\hat{X}=(\hat{X}_{1},\ldots,\hat{X}_{n})\) is not proper._  
Proof: If the optimal objective value of \((\Omega_{\bullet})\) is equal to 0, it is not possible to obtain a sequence \((X_{1}(\sigma),\ldots,X_{n}(\sigma))\)\(\epsilon\)-proper converging to \((\hat{X}_{1},\ldots,\hat{X}_{n})\). Therefore, the Nash equilibrium \(\hat{X}=(\hat{X}_{1},\ldots,\hat{X}_{n})\) is not proper. At this stage, a numerical noise may appear with infinitesimal values of \(\epsilon\). It is risky to rely just on the nonlinear optimization results to detect \(\epsilon\)-properness of a Nash equilibrium. The following mixed 0-1 linear formulations are proposed to overcome this problem.
## 2 Materials and Methods
### Method Validation
#### 2.6.2 Precision and Accuracy  
A sample of Guotai was spiked with three concentrations of standard solution for precision and accuracy tests according to the guide. The intraday precision was evaluated using GC\(\times\)GC-TOFMS analysis of the same sample three times on the same day. The inter-day precision was determined by repeating the intraday precision study on three different days. All analyses were performed in triplicate and the precision was calculated using the relative standard deviation (RSD, %) of those values. The recovery was determined through the calculation of the deviation percent between the calculated value and the nominal value.
## 2 Materials and Methods
### Experimental Procedure
To avoid any possible influences of the stickers on the measured parameters, the measurements were applied to the central part of each sample and far from the sticker locations. The downward global solar radiation fluxes (\(SR\)) were measured using a CMP3 solar meter (Kipp & Zonen B.V. Inc., Bohemia, NY, USA), and the ambient air temperature and relative humidity were measured using a thermo-hygrometer DMA033 (LSI-Lastem, Milano, Italy). The solar meter and thermo-hygrometer were fixed over a wooden stand with a height of 1.8 m, positioned beside the greenhouse prototypes. These sensors were calibrated before use by means of a supplier. The measured parameters were recorded every 1 min, averaged over 5 min intervals, and saved in a data logger (LI-1400, 9 channels, LI-COR, Inc., Lincoln, NE, USA).
# Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data
## 3 AES-128 Algorithm  
On 15 April 1997, the National Institute of Standard Technology (NIST) launched the activity of soliciting AES (Advanced Encryption Standard), which aims to replace the DES (Data Encryption Standard) cryptographic algorithm and determine a new data encryption standard, with a message block length of 128 bits and a key length of 128/192/256 bits or 16/24/32 bytes, faster than triple DES and at least as secure as triple DES. Until 2 October 2000, NIST announced the Rijndael algorithm proposed by Belgian scholars [PERSON] and [PERSON] as the new AES [29]. The Rijndael block cipher algorithm integrates security, high performance, and adaptability. According to the different key lengths, it is recorded as AES-128, AES-192, and AES-256, respectively. This paper takes the most widely used AES-128 algorithm as the research object and carries out security analysis combined with deep learning and SCA technology.  
The state of the AES-128 algorithm corresponds to a 4 \(\times\) 4 = 16 byte matrix, and the encryption process needs to go through 10 rounds of function iteration operations. Except for the 10 th round, every other round needs to go through SubByte, ShiftRow, MixColumn, and AddRoundKey. The tenth round does not include MixColumn. Among them, SubByte is the only nonlinear transformation in the AES algorithm, which constitutes the nonlinear substitution layer of the AES algorithm, that is, the S box. The ShiftRow and MixColumn transformation constitute the linear mixing layer of the AES algorithm, that is, the P box. Therefore, the AES algorithm is a typical SP (substitute-permutation) structure. In this paper, for the 16-bytes data blocks of the AES algorithm state, the \"divide and conquer\" strategy is adopted to analyze and crack each byte one by one and to finally realize the recovery of all keys. The pseudocode of the AES algorithm is shown in Algorithm 1 below.  
```
//AES-128 Cipher //in: 128 bits (plaintext) //out: 128 bits (ciphertext) //Nr: number of rounds, Nr = 10 //Nb: number of columns in state, Nb = 4 //w: expanded key K, Nb * (Nr + 1) = 44 words, (1 word = Nb bytes) state = in; AddRoundKey (state, w [0, Nb - 1]); for round = 1 step 1 to Nr - 1 do SubBytes (state); // Attack Point, at round 1. ShiftRows (state); MixColumns (state); AddRoundKey (state, w [round * Nb, (round + 1) * Nb - 1]); end for SubBytes (state); ShiftRows (state); AddRoundKey (state, w [Nr * Nb, (Nr + 1) * Nb - 1]); out = state;
```  
**Algorithm 1**Preatocode of the AES-128 algorithm
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 3 Experiment and Results
### Dataset  
The data used in this paper were the defects on the surfaces of aero-engine components collected with an industrial camera in repair factories and garages, and these images were screened and sorted in the post-processing process. As shown in Figure 5, the data contained four defect types: crack, gap, pit and scratch. There were 1080 original images. In order to prevent over-fitting in the training process, we used data enhancement methods to expand the samples. First, independent target cropping was used to extract several small target defects in the images to strengthen feature recognition. Then, the images were horizontally flipped to enrich the defect features, and the exposure of the images was adjusted to expand the images under different exposure levels. Gaussian noise was also added to the images to enhance the robustness of the model. We obtained a total of 3500 original and enhanced images. The expanded samples were divided into a training set, validation set and test set according to the ratio 6:2:2. Finally, 2100 images for the training set, 700 for the validation set and 700 for the test set were obtained. The images in the dataset had different sizes during training in order to adapt to the structure of the network; YOLOv5 can resize input images to 640 \(\times\) 640. Since the YOLOv5 algorithm uses adaptive image scaling, there were no image distortion problems. After the training, the images were automatically resized to the original size for display.  
Figure 4: The architecture of the YOLOv5s-KEB model.  
Figure 5: Types of defect. (**a**) Crack; (**b**) gap; (**c**) pit; (**d**) scratch.  
LabelImg labeling software was used to generate xml files containing the image path, labeling area and label type for the real frame of the defect labeling in the image, as shown in Figure 6. The \"size\" item recorded the width, height, size and number of channels of the original image, and the \"object\" item recorded the category of the annotation defects and the horizontal and vertical coordinates of the upper left and lower right corners of the annotation box. Finally, the xml annotation files were converted into txt files, as required by the YOLOv5 model, before training with the dataset.
## 3 Results and Discussion
### ASE&C Technology Coupled with RO
Despite its low market price in comparison to other elements (0.67-2.02 EUR/kg, [21]), its elevated concentrations in seawater and brine make this element economically interesting. The approximation was also calculated for seawater with the available data. The annual benefits obtained ranged (for Na, Ca, Mg, Li and B recovery) from EUR 3 million to 9 million, based on 75-87% Na recovery and 8-20% Li recovery. The strong uncertainty regarding the availability of Li and the pegmatite ones has raised interest in brine/seawater as a new potential source [25]. Although Li concentration in these materials seems very low (average 1.24 g/m\({}^{3}\), [21]), it can increase significantly after treatment with RO or other methodologies such as ASE&C, reaching economically feasible extraction from brines and solids (in the case of the ASE&C). Li extraction from brines, such as electrochemical extraction, is more developed from the point of view of environmental friendliness and competitiveness [30].
## 5 Conclusions  
Recent developments in remote sensing data and newly developed algorithms mean that observing field scale water balance components has now become feasible. Combining the different parts of the water balance means that assessing whole farm water use efficiency as well as crop water productivity are now within reach. However, some significant challenges remain in relation to quantifying runoff and crop yield and the availability of validation data at the correct scales to verify remote sensing observations. There is a need to collaborate at a global scale to combine existing data and deliver products that are scalable from the point scale to the enterprise level for different levels of input data accuracy and have quantified uncertainties.  
Conceptualization, R.W.V. and I.F.; methodology, R.W.V., I.F., J.B., J.D., P.C.-L. and F.S.; formal analysis, R.W.V., I.F., J.B., P.C.-L. and F.S.; writing--original draft preparation, R.W.V., I.F., J.B., J.D., P.C.-L. and F.S.; writing--review and editing, R.W.V., I.F., J.B. and P.C.-L. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme (H2020-SPACE-2018-2020) under grant agreement No. 870344.  
Not applicable.  
The authors declare no conflict of interest.