esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Pre-Processing  
Road images capturing a moving vehicle have limitations in terms of feature extraction due to various noises and diffuse reflections caused by lighting. Additionally, the quality of the captured road images deteriorates due to diffuse illumination and haze, making it difficult to accurately classify vehicle types and colors, especially when it comes to classifying vehicle colors. Therefore, the proposed method improves color contrast by reducing the image size and equalizing the color histogram during the pre-processing step. Additionally, it improves selective color contrast by estimating atmospheric light based on the dark channel before reducing atmospheric haze [4; 35].  
The proposed method uses a full HD road image with dimensions of a size of 1920 \(\times\) 1080 pixels as input. In order to reduce processing computations during vehicle detection, types and colors classification, the input image is resized to 960 \(\times\) 540 pixels using the nearest neighborhood interpolation. The RGB color space of the input road image is first converted to the Lab color space. Contrast-limited adaptive histogram equalization [31] is then applied only to the luminance layer (L-space). The image is then converted back to the RGB color space. This process only affects the pixel intensity by adjusting the luminance, while the original color is preserved. As the final step of pre-processing, the dark channel priority based dehazing algorithm [32] is applied to remove the haze phenomenon contained in the road image, and the input road image (\(I\)) is as shown in Equation (1).  
\[I(x)=J(x)I(x)+A(1-T(x)) \tag{1}\]  
Here, \(I\) is the observed distorted image, \(J\) is the original image before it is distorted by haze, \(A\) is the atmospheric light, and \(t\) is the transmission map representing the proportion of light that finally reaches the camera through the atmosphere. A portion of \(J(x)\) is lost as it passes through the atmosphere, leaving only a signal equal to \(J(x)I(x)\). In addition, the airlight (\(A\)) produced by the sun and other ambient light sources is partially mixed into the original image before being distorted, and finally obtained as the observation image \(I(x)\). The dehazing algorithm generates a dehazed image (\(J\)) by estimating the transmission map and the atmospheric light as shown in Equation (2). First, the atmospheric light (\(A\)) is estimated using the dark channel prior, and then the transmission map (\(t\)) is estimated.  
\[J(x)=(I(x)-A)/(\max(I(x),I0))+A \tag{2}\]  
Figure 2 shows the processing flow of the pre-processing step, which involves reducing the image size, emphasizing contact, and removing haze from the input road image.
## 3 Results
### Inducible TaPS Gene Expression and \(\beta\)-Patchoulene Accumulation  
To explore the biological function of TaPS, its gene expression pattern was analyzed. As indicated in Figure 2A, _TaPS_ exhibited low constitutive expression in wheat leaves, stems and roots. Upon elicitation, _TaPS_ gene expression was induced differentially in response to various treatments. UV irradiation and MeJA treatment elevated _TaPS_ gene expression only slightly. Alamethicin treatment and _F. graminearum_ spore inoculation drastically induced _TaPS_ gene expression, suggesting involvement of TaPS in biotic stress response. Consistently, herbivory with beet armyworm larvae feeding also strongly induced _TaPS_ gene expression in wheat leaves. Based on the gene expression analysis, alamethicin was the powerful elicitor to induce _TaPS_ gene expression, which then was used to treat wheat leaves for terpene analysis by GC-MS. \(\beta\)-patchoulene was clearly detected in treated wheat tissues (\(\sim\)24 ng/g FW), this for the first time, but not in controls (Figure 2B), consistent with inducible gene expression of _TaPS_. In addition, we also detected other sesquiterpenes in wheat leaves, suggesting the presence of unknown sesquiterpene synthases that await their identification in future investigation (Figure 2B and Figure S2).  
Figure 1: Wheat \(\beta\)-patchoulene synthase (TaPS) catalyzed \(\beta\)-patchoulene formation. (**A**) Gas chromatography-mass spectrometry (GC-MS) chromatogram of extract from the culture co-overexpressing TaPS and farnesyl diphosphate (FPP) synthase in _E. coli_. The empty vector (EV) control was also shown. The product \(\beta\)-patchoulene was labeled as peak 1 and the chemical structure was shown as well. (**B**) Mass spectrum of peak 1. The calculated retention index (RI) was shown.
# Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue
###### Abstract  
The detection of mental fatigue is an important issue in the nascent field of neuroergonomics. Although machine learning approaches and especially deep learning designs have constantly demonstrated their efficiency to automatically detect critical features from raw data, the computational resources for training and predictions are usually very demanding. In this work, we propose a shallow convolutional neural network, with three convolutional layers, for fatigue detection using electroencephalogram (EEG) data that can alleviate the computational burden and provide fast mental fatigue detection. As such, a deep learning model was created utilizing time-frequency domain features, extracted with Morlet wavelet analysis. These features, combined with the higher-level characteristics learnt by the model, resulted in a resilient solution, able to attain very high prediction accuracy (97%), while reducing training time and computing costs. Moreover, by incorporating a subsequent SHAP values analysis on the characteristics that contributed in the model creation, indications of low frequency (theta and alpha band) brain wave characteristics were indicated as prominent mental fatigue detectors.  
deep learning; CNN; fatigue; Morlet wavelet; EEG; time-frequency analysis Article
# Experimental Study on Renewable Porous Carbon Dioxide Adsorbent Materials for Space Shuttles
## 4 Conclusions  
The solid amine, which is a promising option to regeneratively remove CO\({}_{2}\) from space shuttles, was studied systematically. The amount and heat of CO\({}_{2}\) adsorption in the solid amine was tested experimentally. The water adsorption at different humidities was investigated, and increasing the humidity increased the amount of water adsorption in the solid amine. The effective thermal conductivity of the solid amine was also studied at different pressures in a N\({}_{2}\) atmosphere. The results showed that the effective thermal conductivity increased in the shape of an 'S'. At different atmospheres, such as air, N\({}_{2}\), and adsorbed CO\({}_{2}\) at different temperatures, the amount of adsorption played an important role in the effective thermal conductivity. The effective thermal conductivity of the solid amine increased with an increase in humidity. In fact, the effect of water vapour should be considered during CO\({}_{2}\) adsorption. In future, we can test the carbon dioxide adsorption capacity in solid amines under different humidity conditions.  
Data curation, T.L.; Formal analysis, H.K.; Writing--original draft, C.Z.; Writing--review & editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Open Fund of the Key Laboratory of Thermal Power Technology (No. TPL2019B02), the Natural Science Basic Research Program of Shaanxi (Program No. 2020 JQ-127).  
The authors declare no conflict of interest.
## 5 Experiments and Results
### Experiment on Standard Test Case  
#### 5.2.1 Calculation of Minimum Load Similarity Margin  
The minimum load similarity margin \(s_{\text{min}}\) is a parameter to affect the similarity matching accuracy. To determine \(s_{\text{min}}\), 365 load matrices of one year are chosen to be tested. Define \(P_{lossMGA}\) as the active power loss after optimized by the MGA method and \(P_{lossDO}\) as that after optimized by the BDO method without fine adjustment. To compare the active power losses of two methods, a factor named loss error is defined as follow:  
\[err=\frac{P_{lossBDO}-P_{lossMGA}}{P_{lossMGA}}\times 100\% \tag{35}\]  
Figure 7 shows the distribution of load similarity and loss error. As seen from Figure 7, the loss errors are consistently lower than 1%. Most of the points are centralized at the sector area divided by the two lines through point (0, 1). The point distributions approach high density when close to point (0, 1). As shown on Figure 7, the loss errors are lower than 0.5% when the load similarities are larger than 0.95. Twenty groups of optimization results are shown in Table 4. Compared with the optimization result of MGA, the active power losses of BDO are larger than those of MGA, but the loss errors are lower than 1%, which is acceptable. Thus, the minimum load similarity margin can be set to 0.95 in this paper.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 2 RADIANCE: Theoretical Basis and General Calculation Methodology
### Specifying Inputs and Interpreting Outputs
In the next section, we present the validation of the view factor results generated through RADIANCE by considering four simple examples.
###### Abstract  
Plasmonic metal nanoparticle (NP)-graphene (G) systems are of great interest due their potential role in applications as surface-enhanced spectroscopies, enhanced photodetection, and photocatalysis. Most of these studies have been performed using noble metal NPs of silver and gold. However, recent studies have demonstrated that the noble metal-graphene interaction leads to strong distortions of the graphene sheet. In order to overcome this issue, we propose the use of Ga NPs that, due to their weak interaction with graphene, do not produce any deformation of the graphene layers. Here, we analyze systems consisting of Ga NP/G/metal sandwich coupling structures, with the metal substrate being, specifically, copper (Cu) and nickel (Ni), i.e., Ga NP/G/Cu and Ga NPs/G/Ni. We experimentally show through real-time plasmonic spectroscopic ellipsometry and Raman spectroscopy measurements of the quenching of the Ga NP localized surface plasmon resonance (LSPR) depending on the wetting of the graphene by the Ga NPs and on the electron transfer through graphene. Theoretical finite-difference time-domain (FDTD) simulations supportively demonstrate that the LSPR in such sandwich structures strongly depends on the contact angle of the NP with graphene. Finally, we also provide evidence of the electron transfer from the Ga NPs into the graphene and into the metal substrate according to the work function alignments. These considerations about the contact angle and, consequently, geometry and wetting of the metal NPs on graphene, are useful to guide the design of those plasmonic systems to maximize electromagnetic enhancement.  
graphene; gallium nanoparticles; plasmonics; copper; nickel +
Footnote †: journal: Applied Physics Letters B
# Interferometric Wavefront Sensing System Based on Deep Learning
## 2 Method
### Wavefront Analysis Neural Network  
There is a problem in the method of extracting the wavefront phase based on the fringe patterns. The phase value calculated by the inverse trigonometric function arctan is limited to \((-\pi,\pi]\), which is not true; that is, there is a phenomenon of phase wrapping. So far, a lot of methods have been proposed to solve this problem. There are some good algorithms such as [PERSON]'s branch cut algorithm [15], the quality-guided algorithm [16], and the mask cut algorithm [17]. They all belong to path-tracking algorithms. This type of algorithm has very high requirements for the quality of the input image. It needs to select a suitable integration path in the phase map and bypass the noise area to prevent error transmission.  
In general conditions, noise is inevitable. The true phase map calculated by this type of algorithm is prone to large errors; thus, it is difficult to guide wavefront correction. When we detect the distorted wavefront, phase unwrapping is an indispensable part. Traditional algorithms have low accuracy when facing noisy images. Some algorithms with anti-noise capability often require a lot of calculation time, making it difficult for wavefront detection systems to achieve dynamic monitoring of the wavefront.  
After we get the true phase map, using mathematical expressions to fit the wavefront can better guide wavefront aberration analysis and correction. In optical measurement research, it is very common to use Zernike polynomials as the basis function for wavefront fitting. The reason is that Zernike polynomials fit the optical wavefront with high accuracy, they are orthogonal in the circular domain, and most of the optical instruments have circular apertures. What is more, there is a certain correspondence between the Zernike polynomial and the Seidel aberration. If we use orthogonal Zernike polynomial as the basis function to fit the phase change of the measuring beam wavefront, it can be expressed as Equation (6):  
\[\Delta\varphi(x,y)=\sum_{i=0}^{M}a_{i}Z_{i}(x,y) \tag{6}\]  
where the index \(i=1,\ 2,\ \ldots,M,Z_{i}\) is the ith Zernike polynomial, and \(a_{i}\) is the corresponding coefficient.  
To get the unknown value of \(a_{i}\), a lot of methods have been proposed, such as the least-squares method, Gram-Schmidt orthogonal method [18], Householder transformation method [19], etc. However, these methods have problems such as poor fitting accuracy or slow fitting speed, which makes them difficult to be used in actual wavefront detection systems. Traditional methods can no longer meet the requirements of high-precision dynamic wavefront measurement. To get an algorithm with high accuracy and faster calculation speed, we introduced the state-of-the-art deep convolutional neural networks.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 3 The Compression Framework
### Reduction of the Primary Data
#### 3.1.2 The Autoencoder  
An autoencoder is a neural network that is trained to reproduce the input at its output while going through a bottleneck. Internally, it has hidden layers and a final bottleneck hidden layer \(h\) that describes a code used to represent the input. The network may be viewed as consisting of two parts: an encoder function \(h=f(x)\) and a decoder that produces a reconstruction \(r=g(h)\). If an autoencoder succeeds in simply learning to set \(g(f(x))=x\) everywhere, then it is not especially useful. Instead, autoencoders are designed to be unable to learn to copy perfectly. Usually, they are restricted in ways that allow them to copy only approximately. The model is forced to prioritize which aspects of the input should be copied, and therefore it often learns useful properties of the data.  
We obtain the encoded coefficients (the latent space) using standard loss measures in the AE. Consequently, the PD errors are minimized by the AE while learning a good codec. Error guarantees in the form of bounded reconstruction error (at a given level of reduction) on the PD will be achieved by applying error-bounded lossy compressors to the residuals. The loss function of the AE network compares the coefficients of the Tucker decomposition with the reconstruction from the bottleneck latent space:  
\[\text{Loss}=\text{MSE}\Big{(}X,\hat{X}\Big{)}, \tag{5}\]  
where \(X\) is the input Tucker coefficients and \(\hat{X}\) is the reconstructed coefficients.  
We use an AE which constrains the number of nodes present in the hidden layers of the network, limiting the amount of information that can flow through the network. By penalizing the network according to the reconstruction error, our model can learn the most important attributes of the input data and how to best reconstruct the original input from an encoded state. Ideally, this encoding will learn and describe latent attributes of the input data. Therefore, the bottleneck layer contains the data compression.
## 4 Discussion
### Considerations for Detection Limitations and Pipeline Materials  
The proposed model operates based on the data reported by in-line inspection (ILI) tools. If corrosion defects are covered by rust, biofilms, or scale, these surface layers may influence the accuracy of the inspection by interfering with the tool's ability to detect, locate, and measure defects. Additionally, if a defect is too small to be detected by the tool, it is inherently excluded from the analysis. Once a defect is detected and reported, the proposed model's alternatives (DENC and DBSCAN-based) process it as a discrete feature represented as a point on a planar surface, emphasizing its spatial positioning rather than its physical dimensions. Consequently, the model's effectiveness is not dependent on defect size but rather on the consistency of defect locations across inspections. However, since the model's framework is extensible to other clustering and feature matching techniques, its dependence on defect size and/or location may vary.  
Moreover, different pipeline materials exhibit varying responses to corrosive environments. While these responses indeed affect corrosion type, density, distribution, and severity, the model is designed with a parametric and extensible structure, potentially allowing it to accommodate these complexities. The process governing clustering, classification, and feature matching can be adjusted to account for material-dependent corrosion behaviors. However, it is important to acknowledge that the case study presented in this research is based on an API 5L X52 steel pipeline. While the model is adaptable, its performance and behavior may vary across different materials and corrosive environments.
# Power Train System Control of Electric Loader Based on Positive Flow System
## 2 Working Condition Analysis  
(1) Typical working conditions of the loader  
The typical working conditions of the loader mainly include V-type, T-type, and L-type, as shown in Figure 1. The V-type is that in which the loader drives to the material pile with no load, retreats to the origin with full load after shoveling and loading, drives to the loading vehicle at a certain angle, and then returns to the origin for the next cycle. The T-type is that in which the loader turns 90\({}^{\circ}\) from the origin and drives to the material pile. After shoveling and loading, it reverses to the origin and turns 90\({}^{\circ}\) in the opposite direction to unload, and then drives back to the origin to prepare for the next cycle. The L-type drives to the material with no load. After shoveling and loading, the loader reverses back to the origin, turns 90\({}^{\circ}\) and drives to the loading vehicle for unloading, and then returns to the origin with no load for the next cycle.  
It can be seen that no matter the kind of working condition, the operation process of the loader is cyclic. During load shoveling, the actions of the loader include bucket lowering, bucket retracting, and bucket lifting.  
(2) Load characteristics of hydraulic systems  
This paper mainly studied the upper working devices of the loader. Therefore, the traveling parts were not taken into consideration. The pressure curve of the main pump for upper devices of a loader in the working process is given in Figure 2. As can be seen, the load pressure fluctuates greatly in the shoveling process. Therefore, to improve the energy saving and ensure the controllability of the system, efficient matching of flow and pressure of hydraulic system through power train system is crucial.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 6 Conclusions  
In this paper, we proposed a double rate localization algorithm with one anchor for multi-hop underwater acoustic networks. The algorithm proposed a double rate scheme which separates the localization procedure into two modes, low-rate transmission mode and high-rate transmission mode, which can both increase the ranging measurement accuracy in multi-hop networks and maintain the transmission rate. Meanwhile, the algorithm also proposed an optimal references selection scheme used to find the best reference nodes in the algorithm that increase the localization accuracy for underwater networks with only one anchor. Simulated and experimental results demonstrated the performance advantage of the proposed DRL algorithm for underwater networks over the classic methods in various aspects. In the future, the localization algorithm for mobile UANs will be studied and we will improve the designed UANs system to accomplish more function.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61571367, 61671386). [PERSON], [PERSON], [PERSON] and [PERSON] designed the algorithm and the experiment; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper.  
The authors declare no conflict of interest. The founding sponsors have the role in the design of the study and in the decision to publish the results.
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 4 Methodology
### ELi and IDSER Detector
#### 4.4.2 EL Feature Extraction  
The first step for a web application to execute an expression is to parse it to an AST. The AST can represent the grammatical structure and semantic information of code well, so we extract the features of EL at the AST level. OGNL and SpEL are different expression languages that need to be parsed by different interpreters: OGNL is parsed by the parseExpression method provided by the OGNL library, and SpEL is parsed by the SpelExpressionParser class of the Spring Framework. The AST of OGNL is similar to that of SpEL in structure, so we choose the same traversal strategy: traverse AST with the depth-first search algorithm to find the nodes related to the \"+\" operator, method invoke, and field reference, then extract features from these nodes.  
However, we face two challenges when extracting features: (1) identifying the location of the expression in the packet, and (2) determining whether the OGNL interpreter or the SpEL interpreter is used to parse the expression. For the first challenge, we find that the expression may appear anywhere in the packet, making it impossible to locate the expression through simple regular matching (which may result in some malicious expressions being missed). As a response, we have to treat all fields and values of the packet as expressions. For the field, we find that the length of a normal field is usually not very long; however, if it contains an expression, especially a malicious one, its length tends to be much longer to achieve a specific function. Therefore, according to the experience, we set the threshold of string length to 30, and if the length of the field exceeds 30, it is regarded as an expression. For the value, we treat all of them as expressions. For the second challenge, we use these two interpreters to parse the same content separately.  
The reason for addressing these two challenges in the above manner is as follows: Whether it is the ONGL interpreter or the SpEL interpreter, their input parameters are strings. If a string, be it a field or a value, can be successfully parsed by an interpreter, it indicates that the string adheres to the syntax rules of the expression language. For example, if a field is a word, the expression interpreter will treat it as a variable during parsing. For benign strings (even if they are not expressions), this module will not extract malicious features from them after parsing, and thus it does not affect the judgment of the machine learning model.  
Notably, we find that some malicious expressions may exist in the URL, such as abc/{expression}/xyz. So, we need to perform string standardization to process slashes and backslashes that may exist in fields and values, splitting the string into {abc, expression, xyz} without destroying the structure of the expression.