esa-sceva/satcom-chunk-collection huggingface dataset:
# Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems
## 2 Background
### Intentional Agents for Fault Finding  
Agents were originally conceived as a way of computerizing tasks and processes in the way that humans typically might address them. [PERSON] et al. discuss cognitive agents who are able to apply expert reasoning, mimicking the activities of human investigators [45]. Like humans, agents are able to adapt their strategies autonomously to cope with changes in the environment they perceive. Agents have emerged as a promising approach to creating large distributed and self-adaptive systems including ICPSs [47; 48]. [PERSON] [49] and [PERSON] [50] provided an early background to intentional agents, introducing the concepts of Beliefs, Desires and Intentions (BDI). They described how agents form beliefs about the environment they are observing. Reasoning about those beliefs leads to the formation of desires to achieve goals that are fulfilled by pursuing intentional tasks. While ICPSs are deterministic, the world they operate in is not. Agents are one approach to working with devices that must bridge that temporal divide.  
Diagnostic approaches encountered employed agents in both Model-Based [41; 45] and Model-Free Artificial Intelligence fault-finding techniques [44; 51]. The most common scenarios describe agents capturing diagnostic data, reasoning about evidence and then facilitating fault management [52; 53]. [PERSON] and [PERSON] discuss encapsulating avionics domain knowledge for agents to create standard libraries of resources and techniques [54]. These are captured in failure-indicator matrices. They envisage the use of agent techniques to create a set of standard functions and interfaces that can be deployed as rule-based expert systems.  
A number of alternative agent frameworks have emerged since [PERSON] and [PERSON]'s original work. [PERSON] and [PERSON] [55] provide a comparison of multi-agent frameworks including GOMS [56] and Soar [57]. Both of these architectures approach agent interactions from a computational intelligence perspective. Later work by [PERSON], [PERSON] and [PERSON] led to the development of the GORITE (Goal ORIented TEams) Multi-Agent Framework [3; 5]. GORITE implements intentions as explicit activities and makes no distinction between goals and plans. This approach simplifies the specification of goals that can be shared by multiple agents and then executed independently. However, representing environment information in a format agents can use is complex [58; 59]. [PERSON] et al. comment that there is a need to be able to describe the activities required from agents more concisely using appropriate Domain-Specific Languages (DSLs) [3].
# Identification and Allelopathy of Green Garlic
###### Abstract  
Garlic and formulations containing allicin are used widely as fungicides in modern agriculture. However, limited reports are available on the allelopathic mechanism of green garlic volatile organic compounds (VOCs) and its component allelochemicals. The aim of this study was to investigate VOCs of green garlic and their effect on scavenging of reactive oxygen species (ROS) in cucumber. In this study, green garlic VOCs were collected by HS-SPME, then analyzed by GS-MS. Their biological activity were verified by bioassays. The results showed that diallyl disulfide (DADS) is the main allelochemical of green garlic VOCs and the DADS content released from green garlic is approximately 0.08 mg/g. On this basis, the allelopathic effects of green garlic VOCs in vivo and 1 mmol/L DADS on scavenging of ROS in cucumber seedlings were further studied. Green garlic VOCs and DADS both reduce superoxide anion and increase the accumulation of hydrogen peroxide of cucumber seedlings. They can also regulate active antioxidant enzymes (SOD, CAT, POD), antioxidant substances (MDA, GSH and ASA) and genes (_CscAPX_, _CsGPX_, _CsMDAR_, _CsSOD_, _CsCAT_, _CsPOD_) responding to oxidative stress in cucumber seedlings.  
green garlic volatile organic compounds; volatiles isolation and analysis; allelochemicals; GC-MS; biological activity; reactive oxygen species Article
# Air Conditioning Load Forecasting and Optimal Operation of Water Systems
## 2 Methods
### Load-Forecasting Model  
#### 2.2.1 Support Vector Regression Principle  
Support vector machines include classification support vector machines and regression support vector machines [17]. In this paper, the regression prediction model of air conditioning load forecasting is established. Therefore, the regression support vector machine is used. The essence of support vector machine is to find a hyperplane in multi-dimensional space to segment the samples to be studied so as to maximize the interval of each sub-sample after segmentation, which is transformed into a convex programming problem. Insensitive loss function is introduced into regression support vector machine \(\varepsilon\); if the absolute difference between the real value and the predicted value is not greater than \(\varepsilon\), then the loss value (the deviation between the predicted value and the real value) is 0.  
For the regression problem, the support vector machine model can be introduced. For a given sample G = {(x\({}_{1}\), y\({}_{1}\)), (x\({}_{2}\), y\({}_{2}\)),..., (x\({}_{n}\), y\({}_{n}\))}, the linear regression function of SVR air conditioning load forecasting is given by Equation (1)  
\[T(x)=\omega\Phi(x)+b \tag{1}\]  
where \(\omega\) is the weight vector, \(\Phi(x)\) is a nonlinear mapping function, and \(b\) is the offset vector.  
Since the relaxation degree on both sides of the spacing belt may be different, two relaxation variables \(\xi_{i}\) and \(\xi_{i}^{*}\) need to be introduced, and the expression of the optimized SVR air conditioning load model is given by Equation (2)  
\[T(x)=\min_{\omega,\xi_{1}\leq 2}\frac{1}{2}\|\omega\|^{2}+C\sum_{i=1}^{n}(\xi_{i }+\xi_{i}^{*}) \tag{2}\]  
where \(C\) is the penalty factor, which represents the size of the sample penalty when the model training load error is greater than \(\varepsilon\).  
After introducing Lagrange function into Equation (2), it is transformed into dual form and solved. The optimal solution of Equation (2) is \(\alpha^{*}=\left[\alpha_{1}^{*},\alpha_{2}^{*},\bullet\bullet,\alpha_{n}^{*}\right]\), \(\alpha=\left[\alpha_{1},\alpha_{2},\bullet\bullet,\alpha_{n}\right]\). So, the regression function is converted to Equation (3).  
\[T(x)=\omega^{*}\Phi(x)+b^{*}=\sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})\Phi(x_ {i})\Phi(x)+b^{*}=\sum_{i=1}^{n}(\alpha_{n}-\alpha_{n}^{*})g(x_{i},x)+b^{*} \tag{3}\]  
where \(g(x_{i},x)=\Phi(x_{i})\Phi(x)\) is called kernel function. Different forms of kernel functions generate different support vector machines. The commonly used kernel functions include radial basis function (RBF), polynomial function, perceptron (sigmoid) function, linear function, etc.
# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
## 3 Methodology
### Feature Extraction  
Features can be extracted from the decomposed signals that can be used further as input by the classification model to distinguish various signals--healthy and faulty, for instance. In this study, eight statistical features were used for feature extraction based on 16 sets of WPT Level 4 coefficients. Thus, after decomposition, for each coefficient, the statistical features in Table 3 were calculated. The total number of features totaled \(8\times 16=128\). The initial dataset for ignition timing variation and AFR variation contains 300 instances and that for engine misfire contains 200 instances for different conditions. Thus, we have the feature matrix of \(300\times 128\) or \(200\times 128\) depending on the type of fault.
## 4 Conclusions  
To gather maximum information concerning the health conditions of structures under various loading conditions, as well as to enhance the protection of sensors from external harsh environments, it is desirable to embed sensors into structures. Considering the three main drawbacks of SHM through embedded distributed FOSs, as listed in Section 1, FOSs are modeled as being embedded inside an adhesive joint between two CFRP laminates in a dual-sinusoidal pattern. The main conclusions are:  
* Based on an optimized set of sinusoidal placement parameters proposed in previous works [1; 2; 3], finite element modeling under various boundary conditions has been conducted.
* Numerical results have helped further identify the resulting strain patterns and sensing capabilities from the optimized dual-sinusoidal placement of FOSs in three loading cases.
* Nonetheless, it is worth noting that the modeling suggested is nothing more than a proof of concept that targets the best combination of variables, which will be further applied in a more realistic project related to real smart composite structures for offshore applications.
* The responses of dual-sinusoidal FOSs under tension, bending, and torsion agree with expectations and demonstrate their strain-measuring advantages.
* On the one hand, dual-sinusoidal placement retains the multi-axial strain-sensing capability from a single-sinusoidal placement; on the other hand, it provides enlarged strain-measuring areas.
* The strain measurements along dual-sinusoidal FOSs can be readily related to the corresponding strain components of host structures if the structures are primarily under in-plane strains; if out-of-plane shear strains become dominant or comparable to in-plane strains, it becomes difficult to do so.
* Based on this research work, the following future studies are worth conducting: (1) correlations between numerical and experimental results to be further established to more accurately interpret the strains measured by FOSs and reflect the mechanical responses of host materials; (2) the three key in-plane placement parameters to be further optimized quantitatively as functions of the length and width of host composite structures by conducting parametric studies to strike a balance between their resulting mechanical influences and sensing functions; (3) the out-of-plane placement of dual-sinusoidal FOSs to be further characterized. Note also that the thickness of adhesive joints (elasto-plastic effects) and temperature play an important role in strain measurements, and they will be addressed in further studies.  
conceptualization, M.D.-H.; methodology, H.S., M.D.-H. and V.C.; formal analysis, M.D.-H., V.C. and H.S.; writing--original draft preparation, H.S.; writing--review and editing, M.D.-H. and V.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Data Availability Statement: Data contained within the article will be made available on request. Conflicts of Interest: The authors declare no conflict of interest.
## 4 Conclusions  
The purpose of this publication is the parametric investigation of a solar-power co-generation unit, which can produce electricity and compressed green hydrogen. The total configuration consists of a PTC field, where solar irradiation is exploited, a thermal energy storage tank, an ORC unit, a PEM water electrolyzer to produce pure hydrogen, as well as a hydrogen compression and storage system. The analysis is focused on developing a system that will achieve high thermodynamic performance and economic viability. The unit has been investigated based on the climate data of Athens, Greece, while it can also be installed in a remote area or island. The most dominant conclusions of this analysis are described below:  
* MDM is selected as the most proper working fluid because achieves the highest exergy performance. * When the power input to the electrolyzer and the hydrogen production rate increases, the net electricity generation, energy efficiency, and exergy performance decrease, while the installation is less economically viable. * In the final unit design, the net electricity production is 12.32 kW and the electricity that feeds the electrolyzer is 11.5 kW, which leads to a hydrogen production rate of 0.205 kg/h. * The overall unit energetic performance is 14.03% and the exergy efficiency is 14.94%. * The PBP is calculated to be equal to 8.9 years, the NPV is 122 k\(\epsilon\), and the IRR is 12.92%. * The present installation promotes the goals of sustainability and green energy policies in the domain of green electricity production and energy storage. Finally, future research should concentrate on detailed dynamic analysis and the system control strategy. In parallel, the integration of other devices to produce additional outputs, such as water heaters for useful heating or absorption chillers for cooling purposes, as well as the installation of other solar collectors, should also be examined. Conceptualization, P.L., N.G. and E.B. ; methodology, P.L., A.K. and D.N.K. ; software, P.L., N.G. and E.B. ; validation, P.L., A.K. and D.N.K. ; formal analysis, P.L. and N.G. ; investigation, P.L., N.G. and E.B. ; writing--original draft preparation, P.L., N.G., A.K., D.N.K., E.B. and C.T. ; writing--review and editing, P.L., N.G., A.K., D.N.K., E.B. and C.T. ; supervision, E.B. and C.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Figure 12: Capital cost separation into parts. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data available after request. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
## 3 Simulation
### Simulation Results
#### 3.1.2 Microspheres  
Figure 8a shows the reconstructed and focused image of a glass microsphere. As shown in Figure 8b, \(x_{r}~{}=~{}\varnothing_{img}/2\) is the radius of a microsphere in the reconstructed image.  
Figure 9a shows the calculated values of \(\mathrm{M_{L}}\) obtained with different object displacements. The values of \(\mathrm{M_{T}}\) as a function of the object displacements \(\Delta z_{o}\) are plotted in Figure 9b. The higher variation in the transversal magnification values is due to the low number of pixels used to draw the microsphere in the simulation. The simulated glass sphere's region of interest comprised only an area of \(115\times 115\) pixels.  
Figure 8: (**a**) Reconstructed and focused image of a glass microsphere and (**b**) profile graph of the horizontal yellow line that crosses the reconstructed image. \(\varnothing_{img}\) represents the diameter of the microsphere calculated as the full width at half maximum.  
Figure 7: (**a**) Longitudinal magnification, \(\mathrm{M_{L}}\), as a function of displacement distance \(\Delta z_{o}\). (**b**) Transverse magnification, \(\mathrm{M_{T}}\) as a function of \(\Delta z_{o}\) for three sparsity image criteria: Tamura coefficient (black squares), Gini Index (red circles), and entropy (blue triangles).  
The average and standard deviation of the transverse and longitudinal magnification values presented in Figures 7 and 9 were calculated, and the results are summarized in Table 1. The values with the least standard deviation are shaded.  
The Tamura coefficient and the entropy exhibit the smallest standard deviation, indicating that they are the most effective evaluation criteria for obtaining the best focused images. Additional processing is required for the reconstructed images before they can be evaluated using focusing criteria.
# A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis
###### Abstract  
Ferroptosis is a regulated form of cell death that involves iron-dependent lipid peroxidation. Ferroptosis-related genes (FRGs) play an essential role in the tumorigenesis of gastric cancer (GC), which is one of the most common and lethal cancers worldwide. Understanding the prognostic significance of FRGs in GC can shed light on GC treatment and diagnosis. In this study, we proposed a new gene co-expression network analysis method, namely EP-WGCNA. This method used Euclidean and Pearson weighted distance (EP_dis) to construct a weighted gene co-expression network instead of the Pearson's correlation coefficient used in the original WGCNA method. The aim was to better capture the interactions and functional associations among genes. We used EP-WGCNA to identify the FRGs related to GC phenotype and applied bioinformatics methods to select the FRGs associated with the prognosis (P-FRGs) of GC patients. Firstly, we screened the FRGs that were differentially expressed based on the TCGA and GTEx databases. Then, we selected the P-FRGs using EP-WGCNA, Cox regression, and Kaplan-Meier analysis. The prognostic model based on P-FRGs-Cox (_ALB, BNIP3, DPEP1, GLS2, MEG3, PDK4, TF_, and TSC22D3) was constructed on the TCGA-GTEx dataset. According to the median risk score, all patients in the TCGA training dataset and GSE84426 testing dataset were classified into a high- or low-risk group. GC patients in the low-risk group showed higher survival probability than those in the high-risk group. The time-dependent receiver operating characteristic (timeROC) showed that EP-WGCNA-Cox predicted 0.77 in the training set and 0.64 in the testing set for the 5-year survival rate of GC patients, which was better than traditional WGCNA-Cox (P-WGCNA-Cox). In addition, we validated that the P-FRGs were significantly differentially expressed in the adjacent non-tumor gastric tissues and tumor tissues by immunohistochemical strains from the Human Protein Atlas (HPA) database. We also found that the P-FRGs were enriched in tumorigenic pathways by enrichment analysis. In conclusion, our study demonstrated that EP-WGCNA can mine the key FRGs related to the phenotype of GC and is superior to the P-WGCNA. The EP-WGCNA-Cox model based on P-FRGs is reliable in predicting the survival rate of GC patients and can provide potential biomarkers and therapeutic targets for GC.  
gastric cancer; prognosis; ferroptosis; EP-WGCNA 1
## 4 Methodology  
In this section, we present the methodology employed in our study, encompassing various components essential for the accurate segmentation of retinal vessels. We begin by detailing the data augmentation techniques utilized to enrich the training dataset and enhance model generalization. Subsequently, we delve into the architecture of our proposed model, comprising three key modules: Channel-Attention and Spatial-Attention (CBAM), Joint Pyramid Upsampling (JPU), and Transformer. Each module contributes uniquely to the overall framework, facilitating robust feature representation, context aggregation, and segmentation accuracy. Through a comprehensive exploration of these components, we aim to provide a thorough understanding of our methodology and its implications for retinal vessel segmentation tasks.  
Data augmentation is a crucial technique employed to enhance the diversity and size of the training dataset, therefore improving the performance and robustness of deep-learning models for retinal vessel segmentation. In this work, we have developed a data augmentation pipeline implemented using Python and leveraging the OpenCV and imgaug libraries. The pipeline consists of several augmentation techniques applied to the input fundus images and their corresponding vessel segmentation masks.  
First, we perform gamma correction to adjust the brightness and contrast of the images. Mathematically, gamma correction is defined as:  
\[I_{\text{corrected}}=I_{\text{original}}^{\gamma} \tag{1}\]  
where \(I_{\text{original}}\) represents the original pixel intensity values, and \(\gamma\) is the correction factor.  
Next, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve local contrast and enhance vessel visibility. CLAHE transforms the intensity distribution of the image to better utilize the dynamic range, therefore enhancing vessel features.  
Furthermore, various geometric transformations are applied to simulate different imaging conditions and orientations. These include rotation, flipping, elastic deformation, motion blur, grid distortion, and optical distortion. Mathematically, these transformations can be represented as affine transformations:  
\[\mathbf{X}^{\prime}=\mathbf{A}\cdot\mathbf{X}+\mathbf{t} \tag{2}\]  
Figure 1: Overall Flow.  
where \(\mathbf{X}\) represents the coordinates of the pixels in the original image, \(\mathbf{X}^{\prime}\) represents the coordinates after transformation, \(\mathbf{A}\) represents the transformation matrix, and \(\mathbf{t}\) represents the translation vector.  
Additionally, we incorporate median blur to reduce noise and random adjustments of brightness and contrast to simulate variations in imaging conditions.  
Overall, this data augmentation pipeline results in a more diverse and representative training dataset, enabling our deep-learning models to generalize better to unseen data and improve the accuracy of retinal vessel segmentation.
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
However, one of the main shortcomings is that NIRS is specially limited to the single-point detection of pre-selected areas in homogeneous products, which can hardly represent the whole nut sample [15]. The hyperspectral imaging (HSI) technique simultaneously integrates conventional spectroscopy and imaging to provide both spectral and spatial information from one target [16]. The hyperspectral image consists of a large amount of information (a 3D hypercube) which can be used to characterize the food more reliably than single imaging or spectroscopy technique. This leads to its special ability for visualizing the category or chemical composition distribution. In the last decades, HSI has received ample attention in agricultural products including meats [17; 18], cereals [19], fruits [20], vegetables [21], and nuts [22]. In the nut industry, HSI technique has been successfully applied to assess mildew damage [23], fungal contamination [24], moisture [25], and internal damage [26]. Recently, the quantitative or qualitative analysis of HSI mostly needs to be combined with chemometrics [27; 28]. With the application of chemometrics such as modeling, spectral preprocessing, and image processing, the performance may be improved [29]. However, to best of our knowledge, little work has been executed on identifying or authenticating different Chinese walnut varieties using this rapid and non-destructive HSI tool combined with chemometrics. Therefore, the specific objectives of this study are to develop and optimize methodology for identifying Ningguo and Lin'an Chinese walnut varieties by using a HSI system in the range of 400-1000 nm. Moreover, different chemometric methods including spectral pretreatments, wavelengths selection and modeling algorithms, will be tested to obtain a robust, reliable but simplified model with the optimal discriminatory ability. Consequently, the optimal simplified model will be attempted to apply back to visualize the classification maps of Chinese walnut varieties.