esa-sceva/satcom-chunk-collection huggingface dataset:
# Spreadsheets as Interoperability Solution for Business Process Representation
## 5 Mapping a Spreadsheet with a BPMN Model
### Transformation Procedure
```
Input: Spreadsheet table _Rows_ Output: Business process graph \(G_{P}\)\(G_{P}:=\) new Graph(0); \(\triangleright\) Add vertices to \(G_{P}\) foreach element \(r\) in Rowsdo  int[] phase = getPhase(\(r\).Order); ifindexOf(\(r\)) == 0 then \(G_{P}\).addVertex(\(r\).Order, phase, \(r\).Activity, type := \"Start Event\"); elseif\(r\).Terminated == \"yes\"then \(G_{P}\).addVertex(\(r\).Order, phase, \(r\).Activity, type := \"End Event\"); else \(G_{P}\).addVertex(\(r\).Order, phase, \(r\).Activity, type := \"Task\");  end if  end foreach\(\triangleright\) Create connections between related activities foreach vertex \(v_{1}\), \(v_{2}\) in \(G_{P}\) where \(v_{1}\)!= \(v_{2}\)do ifprecedence(\(v_{1}\),\(v_{2}\)) then \(G_{P}\).connect(\(v_{1}\),\(v_{2}\), \(v_{1}\).phase - \(v_{2}\).phase);  end foreach\(\triangleright\) Remove \"goto tasks\" and unnecessary connections foreach vertex \(v_{0}\) in \(G_{P}\)do  outEdges := \(v_{0}\).getOutEdges(); if\(v_{0}\).Activity.startsWith(\"goto\")then \(G_{P}\).connect(\(G_{P}\).getPredecessors(\(v_{0}\)), getVertex(\(v_{0}\).Activity.subString(\"goto\"),0); \(G_{P}\).remove(\(v_{0}\)); else \(G_{P}\).filterEdges(outEdges);  end if  end foreach\(\triangleright\) Add gateways to the model foreach vertex \(v_{0}\) in \(G_{P}\)do if\(v_{0}\).getSuccessors().count() > 1 then \(G_{P}\).createSplitGatewayStructure(\(v_{0}\)); elseif\(v_{0}\).getPredecessors().count() > 1 then \(G_{P}\).createMergeGatewayStructure(\(v_{0}\));  end foreach
```  
**Algorithm 1**Generating a business process graph based on a spreadsheet description.
# Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation
## 2 Review of Ensemble Empirical Mode Decomposition and Hilbert Square Demodulation
### Hilbert Square Demodulation  
Once a localized defect emerges in a bearing or gear, impulses occur every time the tooth or rolling element crosses the defective area; thus, the amplitude modulation occurs in measured signals. HSD, a type of non-stationary signal processing technique based on Hilbert transform (HT), has been investigated for feature extraction of the amplitude modulation signal [22]. The following is the basic principle of HSD.  
The reason for the phenomenon of amplitude modulation is that a high-frequency carrier signal is varied by a low-frequency modulating signal. Thus, the modulated signal could be the product of the modulating signal with the carrier signal. The modulating signal results from the impacts caused by defects of a bearing or gear, whereas the carrier signal is a combination of the resonance frequencies of the bearing. Therefore, the mathematical model of modulated signal can be described as:  
\[x(t)=\mathsf{s}(t)f(t) \tag{4}\]  
where \(f(t)\) is the high-frequency carrier signal and \(\mathsf{s}(t)\) is the low-frequency modulating signal.  
Given:  
\[Z(t)=x^{2}(t)+H^{2}[x(t)] \tag{5}\]  
where \(H[x(t)]\) is the Hilbert transform of \(x(t)\).  
According to the Bedrosian product theorem [23], \(H[x(t)]\) can be written:  
\[H[x(t)]=H[\mathsf{s}(t)f(t)]=\mathsf{s}(t)H[f(t)] \tag{6}\]  
Thus, \(Z(t)\) can be expressed as:  
\[Z(t)=\mathsf{s}^{2}(t)\Big{\{}f^{2}(t)+H^{2}[f(t)]\Big{\}} \tag{7}\]Generally, the high-frequency carrier signal \(f(t)\) is a harmonic signal:  
\[f(t)=a_{1}\cos(\omega_{1}t) \tag{8}\]  
so the Hilbert transform of \(f(t)\) can be written as follows:  
\[H[f(t)]=a_{1}\sin(\omega_{1}t) \tag{9}\]  
The second term of Equation (7) on the right-hand side can be transformed as follows:  
\[f^{2}(t)+H^{2}[f(t)]={a_{1}}^{2}\cos^{2}(\omega_{1}t)+{a_{1}}^{2}\sin^{2}( \omega_{1}t)={a_{1}}^{2} \tag{10}\]  
so \(Z(t)\) finally becomes:  
\[Z(t)={a_{1}}^{2}s^{2}(t) \tag{11}\]  
It is clear that only the low-frequency modulating signal is left in Equation (11), so we can get the fault characteristic frequency by means of spectrum analysis.
## 4 Conclusions  
This study aims to improve the safety performance of a green low-carbon concrete by FA content based on mechanical property tests and microscopic results of FA content. The main findings were listed as follows.  
1. The optimal FA content in green low-carbon concrete is 20%. After 28 days of standard curing, the flexural strength and compressive strength of the concrete with 20% FA increased by 0.82 MPa and 4.32 MPa, respectively;
2. XRD analysis shows that the formation time of C-S-H and AFt phase in FA-doped concrete is earlier. In addition, the incorporation of FA promotes hydration reactions that rehydrate Ca(OH)\({}_{2}\) in the system;
3. SEM analysis revealed that the microstructure of the concrete mixed with 20% FA is mainly composed of fibrous C-S-H gels and rod-like ettrinigite, and the cement colloids and FA between the interfaces tend to be dense, ensuring the safety properties of the concrete;
4. This study only provides a qualitative description of the carbon footprint, and computational models should be used to quantify carbon reduction capabilities in the future.  
Conceptualization, Z.C. and M.L.; methodology, Z.C. and M.L.; formal analysis, Z.C. and M.L.; investigation, Z.C.; validation, M.L.; data curation, Z.C. and M.L. writing--original draft, Z.C. and M.L.; writing--review and editing, Z.C. and L.G.; visualization, Z.C. and L.G.; supervision, L.G.; resources, L.G.; project administration, L.G.; funding acquisition, M.L. and L.G. All authors have read and agreed to the published version of the manuscript.  
The study is supported by the Key Research and Development Program in Ningxia Hui Autonomous Region, grant number 2022 BDE02002, and the Key Research and Development Program in Xinjiang Uygur Autonomous Region, grant number 2022B01050.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 2 Literature Review
### Spatial Scope Definition and Circle Structure Analysis of HSR Station Areas  
Defining the spatial scope of the station area is the basis for the spatial development of research stations. Many studies define the spatial scope of the station area by creating different buffer zones and then comparing their development status [15]. Determining the spatial scope of the station area is the basis for studying the spatial development of traffic stations. Some scholars have carried out empirical analyses on various types of stations without considering traffic congestion; the traffic distance of driving for 4 to 6 minutes is the radius of the station area [16]. However, [PERSON] asserted that the spatial scope of HSR stations should be characterized according to the research purpose [17]. [PERSON] et al. noted that the delimitation of an HSR station area is closely related to its own attributes, the level of urban development, and the location and that these factors should be taken into account to delimit the spatial scope [18]. [PERSON] et al. studied Singapore rail transit and found that the capitalization income of houses within a radius of 600 m is 8.6% and that there is no significant impact on houses within a radius of 600-1600 m. Therefore, the spatial scope of rail transit is set to the maximum of 1600 m [19]. Compared with Western settings, due to increased population density and building area, the regional spatial scope of HSR stations in Asian settings may be smaller [20]. According to the development of different station areas, [PERSON] [21] and [PERSON] [22] divided station areas into three layers: 0-800 m as the core area, 800-1500 m as the integrated hub area, and 1500-2000 m as the area of influence. Domestic studies mostly refer to the \"three circles structure\" model to simply delimit the spatial scope of the station area [23]. Only a few studies comprehensively consider urban planning, natural conditions, and so on [24].
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 2 Routing in the Data Center
### Link-State Routing  
Link-state routing protocols, such as OSPF [14] and IS-IS [15], have been for many years, and continue to be, the state-of-the-art Interior Gateway Protocols (IGPs) for Internet Service Provider (ISP) backbones. Nevertheless, a link-state routing network protocol cannot easily scale beyond a thousand routers, mainly due to the Link State Advertisements (LSAs) flooding, even though they are hierarchical protocols by design [16]. Nowadays, data centers can easily accommodate tenths of thousands of servers, which means a few thousand switches/routers. For example, for fifty thousand servers, more than four thousand switches are needed (see the formula in [5]); more efficient Clos network realizations [17] claim to demand around 2400 switches for a hundred thousand bare metal servers. In the last several years, the industry and the research community have been working on efficient ways to reduce flooding in this type of protocols; for example, Openfabric is an IS-IS Optimal Distributed Flooding for Dense Topologies [9].
## 3 Proposed Approach
### Feature Extraction/Classification  
This phase represents the primary contribution of the present study. Figure 4 elucidates the architectural framework of the proposed feature extraction/classification methodology, denoted as Mean-CAM-CNN, which comprises two distinct branches: the global stage and the local stage. Each stage integrates a dedicated CNN. In the global stage, entire images belonging to the same class are input into the initial CNN to generate CAMs corresponding to each image. The average CAM is subsequently computed from the collective CAMs of images within the same class. Utilizing the average CAM, the significant (i.e., relevant) region of each global (i.e., original) image is extracted and utilized in the training/classification processes at the local stage. In summary, the implementation of Mean-CAM enhances predictive capabilities by directing the attention of the CNN to the most pertinent common region, thereby encapsulating the most relevant features.  
Figure 4 provides a comprehensive depiction of the Mean-CAM-CNN architecture, wherein ResNet-50 [29] serves as the foundational backbone model. The Mean-CAM-CNN is organized into two principal stages: global and local. Each stage consists of five convolutional blocks, incorporating batch normalization and rectified linear unit (ReLU) activation functions. These blocks are seamlessly connected to global max-pooling (GMP), a fully connected (FC) layer, and a softmax layer.  
In contrast to the global stage, the local stage is characterized by an input composed of local images restricted by the mask generated through the global stage. For illustrative purposes, the input image is superimposed onto the heatmap.  
Before diving into the global and local stages' technical details, we will first introduce the theoretical basis of the CAM methodology and the proposed frameworks: Mean-CAM and mask inference.  
Figure 3: Coloring, enhancing, and resizing ear images with the proposed DCGAN model: (**a**) original images; (**b**) enhanced images.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 3 Results
### Publication Trends over Time  
The literature on discrete event simulation (DES) in health services has experienced a remarkable growth in recent years. As illustrated in Figure 2, the number of publications in this area was merely 26 in 2010, but by 2022, the cumulative count had reached 616 papers across various journal types (excluding books and surveys). This exponential growth of DES publications in health research can be attributed to the expanding pool of adopters and dissemination channels for this research approach [19]. Among these publications, the majority (349 papers) take the form of case studies, highlighting the practical application of DES in analyzing healthcare systems.
## 4 Statistical Testing of ENMRA Algorithm
### System Model
#### 5.2.3 DS-CDMA Design  
The DS-CDMA system delivered low-packet retransmissions and high-channel reuse and thereby offset the multipath effect in WUSNs. It lessened the use of energy and thereby increased the network throughput. The multiuser access interference (MAI) [57] must be reduced if this technique is used in MI-USNs.
# A Review of Low-Frequency Active Vibration Control of Seat Suspension Systems
## 3 ANN Control  
Unlike a model-based controller, the ANN controller is data-based. It can be used to identify complex nonlinear objects that are difficult to accurately model and can be used as a controller for adaptive control. Compared with traditional controllers, ANN controllers have many features. They have powerful nonlinear processing capabilities and are well suited for dealing with problems with a large number of input variables, as well as multivariable output. In addition, the ANN can learn unknown information autonomously. However, for active vibration control of a seating system, the ANN system has not been widely accepted.  
An ANN controller (Figure 26) was proposed by [PERSON] and [PERSON] (2008) [73] to control a nonlinear vehicle model having 8 DOF. In this study, the active seat suspension system control was combined with an active vehicle suspension system control. The ANN controller could solve the nonlinear problem caused by the vehicle system under excitation disturbances. The ANN model was trained with errors between actual and expected output results by using backpropagation. The results demonstrated that the ANN controller performs well at controlling seating system vibration.  
The control system for an active seat suspension combining an ANN module, the active force control (AFC) method, and a PID controller (Figure 27) was proposed by [PERSON] and [PERSON] (2015) [74]. In the design of the ANN model, a hidden layer structure of 10 neural units was considered to have the best performance. Reverse learning was used to train the ANN model to estimate the mass of the seat-occupant system. In addition, the PID controller was designed to work with minimal disturbance and at low speed, and the AFC controller was used to improve the robustness and vibration control performance of the control system. In this controller, the system error was taken as the input, and the estimated mass was set as the output. The Levenberg-Marquardt algorithm was used to provide numerical solutions for nonlinear minimization. After comparison with the PID controller, the ANN control performed better than the other controllers.  
Figure 26: An integral controller based on artificial neural networks (ANNs) for active chassis suspension and active seat suspension system controls [73].
## 3 Results and Discussion
### Performance Decay Prediction of the Anode Recirculation Mode  
Different from DEA mode, the independent variable in anode recirculation mode is changed to current density, anode stoichiometry and time, and the dependent variable is changed to voltage. The operating conditions of the 12 processes in anode recirculation mode are shown in Table 3. Comparison of two-hidden-layer M-ANN with ReLU activation function and that of two-hidden-layer M-ANN with sigmoid activation function predictive values and true values of voltage change evolution for the PEM fuel cell with the anode recirculation mode are shown in Figures 9 and 10. The mean and maximum relative errors by two-hidden-layer M-ANN with ReLU activation function are 0.143% and 0.458%. The mean and maximum relative errors by two-hidden-layer M-ANN with sigmoid activation function are 0.155% and 0.359%.  
Changing the independent and dependent variables is to explore whether the M-ANN can continue to perform well when different independent variables are used. The comparison between Tables 3 and 5 shows that M-ANN has strong robustness. Compared with DEA mode, the performance change evolution in anode recirculation mode is simpler, with the slope of rising or fall remaining almost constant, and the voltage changes little. However, the mean relative error in anode recirculation mode is similar to that of DEA mode. In this case, we adjust the number of training epochs to within 2000. However, it does not yield a superior model. The most likely reason is that the prediction performance of two-hidden-layer M-ANN has reached the limit. Perhaps when using a three-hidden-layer M-ANN model, the prediction results will be better. However, the two-hidden-layer M-ANN is more complex than the three-hidden-layer M-ANN, so it needs more data to train the model to get better results.  
Figure 8: ReLU activation function and sigmoid activation function.  
Figure 10: Two-hidden-layer M-ANN with sigmoid activation function. Comparison of predictive values and true values of current density change evolution for the PEM fuel cell with the anode recirculation mode. (**a–l**) represent the 12 transient processes. (**a–h**), (**i,j**) and (**k,l**) are the train set, validation set, and test set, respectively.  
Figure 9: Two-hidden-layer M-ANN with ReLU activation function. Comparison of predictive values and true values of current density change evolution for the PEM fuel cell with the anode recirculation mode. (**a–l**) represent the 12 transient processes. (**a–h**), (**i,j**) and (**k,l**) are the train set, validation set, and test set, respectively.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types
### Radar Sensors  
Radar sensors use microwave signals that have the potential to collect crop data in heavy clouds and/or haze conditions. The SAR estimates crop parameters and biophysical status using backscattered signals, the accuracy of which depends on sensor characteristics, such as wavelength, incident angle, and polarization. The collection procedure is based on the following four methods: (1) SAR backscattering technique; (2) SAR polarimetry technique; (3) SAR interferometry technique; and (4) polarimetry SAR interferometry technique. The main principles, challenges and solutions of each technique are given in the following section.
# Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360
###### Abstract  
Fairness Artificial Intelligence (AI) aims to identify and mitigate bias throughout the AI development process, spanning data collection, modeling, assessment, and deployment--a critical facet of establishing trustworthy AI systems. Tackling data bias through techniques like reweighting samples proves effective for promoting fairness. This paper undertakes a systematic exploration of reweighting samples for conventional Machine-Learning (ML) models, utilizing five models for binary classification on datasets such as Adult Income and COMPAS, incorporating various protected attributes. In particular, AI Fairness 360 (AIF360) from IBM, a versatile open-source library aimed at identifying and mitigating bias in machine-learning models throughout the entire AI application lifecycle, is employed as the foundation for conducting this systematic exploration. The evaluation of prediction outcomes employs five fairness metrics from AIF360, elucidating the nuanced and model-specific efficacy of reweighting samples in fostering fairness within traditional ML frameworks. Experimental results illustrate that reweighting samples effectively reduces bias in traditional ML methods for classification tasks. For instance, after reweighting samples, the balanced accuracy of Decision Tree (DT) improves to 100%, and its bias, as measured by fairness metrics such as Average Odds Difference (AOD), Equal Opportunity Difference (EOD), and Theil Index (TI), is mitigated to 0. However, reweighting samples does not effectively enhance the fairness performance of K Nearest Neighbor (KNN). This sheds light on the intricate dynamics of bias, underscoring the complexity involved in achieving fairness across different models and scenarios.  
reweighting samples; bias mitigation; fairness AI; AIF360; traditional machine learning +
Footnote †: journal: Applied Sciences
# Electrochemical Biosensors Based on Conducting Polymers: A Review
## 4 Conducting Polymer-Based Electrochemical Biosensors
### Conducting Polymer-Based Immunosensors
The monoclonal anti-CA 125 antibodies were covalently immobilized on poly(anthranilic acid) using EDC-NHS and labeled with gold nanoparticles before being used to capture and detect CA 125 using electrochemical impedimetric measurements with a good sensitivity and reproducibility which matches the request of clinical needs (cancer antigens CA 125 are cancer biomarkers). Similarly, [PERSON] et al. developed an impedimetric immunosensor for the sensitive, specific, and label-free detection of human serum albumin (HSA, a valuable clinical biomarker for the early detection of chronic kidney disease) in urine. To enable efficient antibody immobilization and improved sensitivity, the carbon working electrode was sequentially modified with electropolymerized polyaniline and electrodeposited gold nanocrystals (Figure 5). Indeed, polyaniline (b) and Au nanocrystals (c) were successively electrodeposited on the screen-printed working electrode(a). Then, oxidized HSA antibody (d) and BSA (e) were immobilized onto the polyaniline-Au layer. Finally, the evolution of impedance (g) was used to quantify the amount of HSA (f). The normalized impedance variation during immunosuppressive increased linearly with HSA concentration and the biosensor displayed highly repeatable and highly specific response to HSA concentration [122]. Recently, the first immunosensors based on conducting polymer nanostructures using template methods have been developed. For example, an immunosensor for the determination of alpha-fetoprotein (AFP) was fabricated based on the three-dimensional macroporous polyaniline doped with poly (sodium 4-styrene sulfonate) by using a hard-template method [123]. The 3D macroporous PANI possessed large surface area, high conductivity and many functional groups, which allowed the immobilization of anti-AFP. Based on differential pulse voltammetry measurements, the prepared AFP immunosensor showed a wide linear range for AFP from 0.01 to 1000 pg/mL, with a detection limit of 3.7 fg/mL. Table 3 summarizes the performances of these conducting polymer-based immunosensors.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 5 Experiment and Analysis
### Limitations of the Proposed Method  
In this section, we added additional experiments to discuss the influence of a large amount of vertical edge information in the images on algorithms, such as radiator and fence scenes. Figure 16 shows an example for Fence, in which stripe noise with \(\eta\) = 0.05 is added. The PSNR and SSIM results are shown in Table 9. From the results, we can see that our proposed method still has good ability to remove stripe in the scene with rich vertical information. In essence, stripe noise has its own characteristics. It can be seen that the intensity of each column of stripe noise is about the same, and the columns are significantly different between columns, and the shape is different from other types of noise. Therefore, noise can be effectively processed. However, it can also be seen from the correction image that the similarity between stripe and vertical edge will affect the correction result and produce artifacts, which is also our next research direction.