esa-sceva/satcom-chunk-collection huggingface dataset:
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
###### Abstract  
Coordinated contractions and motility patterns unique to each gastrointestinal organ facilitate the digestive process. These motor activities are coordinated by bioelectrical events, sensory and motor nerves, and hormones. The motility problems in the gastrointestinal tract known as functional gastrointestinal disorders (FGIDs) are generally caused by impaired neuromuscular activity and are highly prevalent. Their diagnosis is challenging as symptoms are often vague and difficult to localize. Therefore, the underlying pathophysiological factors remain unknown. However, there is an increasing level of research and clinical evidence suggesting a link between FGIDs and altered bioelectrical activity. In addition, electroeticals (bioelectrical therapies to treat diseases) have recently gained significant interest. This paper gives an overview of bioelectrical signatures of gastrointestinal organs with normal and/or impaired motility patterns and bioelectrical therapies that have been developed for treating FGIDs. The existing research evidence suggests that bioelectrical activities could potentially help to identify the diverse etiologies of FGIDs and overcome the drawbacks of the current clinically adapted methods. Moreover, electroeticals could potentially be effective in the treatment of FGIDs and replace the limited existing conventional therapies which often attempt to treat the symptoms rather than the underlying condition.  
functional gastrointestinal disorders; bioelectrical signals; slow waves; bioelectrical therapies; electroeticals +
Footnote †: journal: Applied Sciences
# Satellite Image Categorization Using Scalable Deep Learning
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.4 Classification  
The EfficientNet feature extraction network is concatenated with a flattened layer of hundreds of neurons with the ReLU activation. Finally, a SoftMax layer is attached for the classification of satellite images. The ReLU \((\mathcal{F}_{\mathcal{R}})\) and softmax \((\mathcal{F}_{\mathcal{S}})\) activations can be defined as follows:  
\[\mathcal{F}_{\mathcal{R}}(\mathrm{x})=\mathrm{max}(0,\mathrm{x}) \tag{13}\]  
\[\mathcal{F}_{\mathcal{S}}(\mathrm{x})_{\mathrm{i}}=\frac{\mathrm{e}^{\mathrm{ x}_{\mathrm{i}}}}{\sum_{\mathrm{j=1}}^{\mathrm{k}}\mathrm{e}^{\mathrm{x}_{\mathrm{j}}}} \tag{14}\]
## 1 Introduction
We demonstrate the use of hardware employing commonly clocked digital-to-analog converters (DACs) to transmit frequency diverse signals having fast time-varying coefficients, motivating further practical study of FDA architectures employing structured fast-time modulation.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 4 Results  
### Results of Cropland Extraction  
The sample library is automatically generated from external LULC maps, and features from Sentinel images are extracted and input into the Random Forest (RF) model. The model is trained and tested using an 80:20 split of the sample library data. This split is achieved by creating a field where each sample point is randomly assigned a value uniformly distributed between 0 and 1. Points with values greater than 0.8 are assigned to the test set, while those with values less than 0.8 are assigned to the training set. This study utilizes the confusion matrix and the Kappa coefficient to evaluate experimental accuracy, which are standard methods for assessing the accuracy of image classification [46]. The distribution of samples is shown in Figure 6. The Kappa coefficient for the cropland test samples is 0.82, and the overall accuracy is 90.8% (Table 4). The results of the cropland distribution extraction in Shanwei are shown in Figure 7. To validate the accuracy of the cropland extraction, the results were compared with those from the ESA, ESRI, and DW products in different terrain areas. The comparison results are shown in Figure 8.  
The visual interpretation comparison reveals significant inconsistencies among the three global land cover products in Shanwei. Overall, the ESRI result shows the smoothest representation, but it suffers from boundary inaccuracies, and small cropland areas are filtered out, leading to less comprehensive extraction results. The DW result exhibits considerable interior noise within cropland areas, undermining the morphological integrity of the cropland plots. In contrast, ESA performs better than the other two products in terms of both the completeness of cropland information extraction and the accuracy of boundary delineation in plains and coastal areas. However, cropland in mountainous regions was sometimes omitted, likely due to shadowing and layover in the SAR data used by ESA. The data source employed in this study is the same as ESA's, resulting in higher spatial consistency between the two. However, in mountainous areas, the method used in this study yields significantly better results than ESA. Overall, by integrating various LULC maps, this study has successfully extracted high-quality sample points in Shanwei, and the resulting cropland map more accurately reflects the actual distribution of cropland in the region.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 3 Preliminaries
### OFDM Signal Model  
Consider a general model of an OFDM system, which is shown in Figure 1. The \(n\)th sample of the transmitted baseband signals in the time domain can be described as  
\[s(n)=\frac{1}{\sqrt{N}}\sum_{k=0}^{N_{u}-1}c_{k}e^{2\pi kn/N},\ \big{(}-N_{cp} \leq n\leq N-1\big{)}, \tag{1}\]  
where \(N\) is the number of inverse fast Fourier transform (IFFT) points, and it also denotes the total number of subcarriers, \(N_{u}\) is the number of effective subcarriers, \(j=\sqrt{-1}\), and \(C_{k}\) is the modulated data sample on the \(k\)th subcarrier. To prevent ISI from destroying the orthogonality between the subcarriers, we usually put a copy of the last \(N_{cp}\) length samples of the OFDM symbol at the head of the time-domain symbol as a CP.  
At the receiver, the \(n\)th sample of the received signal is given by  
\[r(n)=\sum_{l=0}^{L-1}h_{l}s(n-\tau_{l}-d)\cdot e^{l2\pi fn/N}+w(n), \tag{2}\]  
where \(L\) is the number of the channel paths, \(\tau_{l}\) is the delay of the \(l\)th channel path, \(d\) is the integer-valued timing offset, \(f\) is the normalized frequency offset, and \(w(n)\) is the sample of AWGN.
## 3 Results and Discussion
### Applicability and Limitations  
Previous studies that applied methods similar to those of this study under natural light mainly focused on the analysis of the light environment with diurnal or seasonal changes, which can be affected by climatic and weatherly factors. The light environment in PFELs is determined by controllable light sources, so the potential for application of simulation methods is thought to be larger. In particular, because electrical lighting in PFELs consumes electrical power, which incurs additional costs, simulation studies are important not only for analyzing the light interception of plants but also for improving energy use efficiency. By introducing some variables related to the light environment to the simulation, we analyzed their effects on light interception, photosynthesis, and LUE, and this result could support the optimal light design in PFELs. Additionally, to our knowledge, this is the first study to adopt the optimal light intensity for achieving the maximum LUE, which can support efficient electrical energy management. It is expected that further studies on various types of crops, types of lights, and lighting methods can expand the applicability of our findings for user purposes. Meanwhile, the method for constructing structurally accurate plant models should be improved. Because cultivated crops in PFELs mainly have small and dense canopies, 3D-scanned or image-based plant modeling methods have limitations in describing the whole canopy without destruction. Additionally, compared with rule-based plant models, the description of model continuity in time series is difficult. Nevertheless, because structural accuracy in the plant model is important for precise light analysis, as described above, methods to combine the advantages of different modeling methods should be developed.
# GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks
## 3 Results
### Internal Validation  
This study conducted a systematic evaluation of the performance of the GraphEPN model through rigorous internal validation. The internal validation employed a five-fold cross-validation strategy, where the dataset was partitioned into five subsets, with one subset used for validation and the remaining four subsets used for training in each iteration. This alternating validation approach ensures the robustness of the model's performance across different data splits.  
To comprehensively assess model performance, we used the AUC and the AUPRC as the primary evaluation metrics. Additionally, to evaluate the relative performance of GraphEPN compared with other commonly used machine learning methods, we compared it with elastic net, gradient boosting, XGBoost, and logistic regression models [29; 30; 31; 32]. Additional file: Figures S1 and S2 illustrate the performance of each model across the five-fold cross-validation, whereas Figure 2a,b highlight the AUC and AUPRC results for GraphEPN in each fold.  
To ensure the robustness of the evaluation, the average values across the five folds were taken as the final evaluation metrics. The results show that GraphEPN outperforms all the other methods in terms of both the average AUC and average AUPRC, with values of 82.9% and 45.8%, respectively. Compared with the second-best model, XGBoost (with an AUC of 79.2% and a AUPRC of 38.9%), GraphEPN achieves improvements of 3.6% and 6.6% in these two metrics, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **AUC** & **AUPRC** \\ \hline AAP & 0.735 & 0.312 \\ AAP+Spatial & 0.802 & 0.389 \\ AAP+DSSP & 0.785 & 0.346 \\ AAP+DSSP+Spatial & 0.829 & 0.433 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of feature performance in predicting BCEs on SabDab\(\_\)665.
## 3 Results
### Model Development and Evaluation for the Subtropical Coniferous Forest
#### 3.1.1 EMGPP Models Using 3-Factor and 2-Factor for S/Q \(<\) 0.5 Conditions  
Generally, GPP, NEE and BVOC emissions in this coniferous forest show clear diurnal, seasonal and interannual variations and vary with key driving factors and atmospheric conditions, including PAR, air and soil temperature, precipitation, water vapor pressure, vapor pressure deficit (VPD), soil water content, cloud density, drought and other factors [35; 59; 71; 72; 73] and are higher around noon and in summer. Daily emission peaks occur around noon.  
Based on the above data criteria in Section 2.4, 91 measurements (\(n=91\) for S/Q \(<0.5\)) from 22 May 2013 to 31 December 2014 were selected in the model developments for using the 3-factor and 2-factor methods. These valid data were used together with the mean water vapor pressure at ground level (E) and the solar radiation measurements (PAR, S and Q) averaged over the duration of each NEE measurement to determine the coefficients B\({}_{\rm i}\) of the 3-factor and C\({}_{\rm i}\) of the 2-factor EMGPP models. Table 1 shows the results and statistical metrics, including coefficient of determination (R\({}^{2}\)), average and maximum of the absolute relative bias, \(\delta_{\rm avg}\) and \(\delta_{\rm max}\) (%), (\(\delta=1\,y_{cal}-y_{obs}\,|\,^{*}100/\,|\,y_{obs}\,|\), where \(y_{cal}\) and \(y_{obs}\) are calculated and observed GPP), normalized mean square error (NMSE = \((y_{cal}-y_{obs})^{2}/(\overline{y}_{cal}\times\overline{y}_{obs})\)) [74], standard deviations of calculated and observed fluxes (\(\sigma_{\rm cal}\) and \(\sigma_{\rm obs}\)), mean absolute deviations (MAD, in mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and percentage of mean measured value, %), and root mean square errors (RMSE, in mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) and %) in percentage of mean measured value.  
Figure 1 displays the calculated hourly GPP using 3-factor and 2-factor EMGPP models and measured GPP for S/Q \(<0.5\) conditions. Figure 2 shows a scatter plot of calculated versus measured hourly GPP. Generally, the 3-factor and 2-factor EMGPP models agree with the measurements for S/Q \(<0.5\) conditions.
## 3 Results and Discussion
### The Effect of Operation Parameters on AC-CoPcS Catalytic Performance
#### 3.2.3 The Effect of AC-CoPcS Catalyst Dosage (\(C_{\text{ca}}\))  
Figure 8 shows the effect of AC-CoPcS catalyst dosage (\(C_{\text{ca}}\)) on \(CPP_{\text{SMM}}\), \(Yield_{\text{DMDS}}\), and \(Purity_{\text{DMDS}}\). The \(CPP_{\text{SMM}}\) and \(Yield_{\text{DMDS}}\) increase gradually when the \(C_{\text{ca}}\) increases from 222.2 ppm  
Figure 6: The GC-MS spectra of the DMDS product under different reaction time. Experimental conditions: SMM (18.0 wt%) 450 g, \(RR\) = 600 r/min, \(C_{\text{ca}}\) = 888.9 ppm, \(T_{\text{re}}\) = 65 \({}^{\circ}C\), \(P\)(O\({}_{2}\)) = 0.9 MPa.  
Figure 7: The effect of reaction time on \(CPP_{\text{SMM}}\), \(Yield_{\text{DMDS}}\), and \(Purity_{\text{DMDS}}\) using the AC-CoPcS catalyst. Experimental conditions: SMM (18.0 wt%) 450 g, \(RR\) = 600 r/min, \(C_{\text{ca}}\) = 888.9 ppm, \(T_{\text{re}}\) = 65 \({}^{\circ}C\), \(P\)(O\({}_{2}\)) = 0.9 MPa.  
(\(\mu\)g/g, 0.1 g/450 g) to 888.9 ppm and then begins to decrease continuously when \(C_{\text{ca}}\) continues to increase to 1555.6 ppm. The optimum AC-CoPcS catalyst dosage is 888.9 ppm where maximum \(CPP_{\text{SMM}}\) and \(Yield_{\text{DMDS}}\) of 98.7% and 86.8% are achieved. When the \(C_{\text{ca}}\) increases, the more active spot of the catalyst and reaction surface involves, so the reaction rate of Equation (6) process accelerates, resulting in a sharp increase of the \(CPP_{\text{SMM}}\) and \(Yield_{\text{DMDS}}\) at first. However, when it reaches, and even exceeds, the appropriate dosage, too much heterogeneous solid matter may hinder the gas-liquid mass transfer between SMM and oxygen, so the \(CPP_{\text{SMM}}\) and \(Yield_{\text{DMDS}}\) decrease gradually. However, \(C_{\text{ca}}\) seems to have no effect on the purity of the DMDS product (Figure 8), and the \(Purity_{\text{DMDS}}\) is nearly constant and its value of all the samples is above 99.5%.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 1 Introduction  
The rapid digitalization and interconnection of industrial processes has significantly boosted efficiency and productivity. However, such advancement also introduces increased cybersecurity risks, particularly in the process industry, which is crucial to global supply chains and public health.  
To mitigate these risks, industry standards such as ISO/IEC 27001 [1] and frameworks such as the NIST Cybersecurity Framework [2] emphasize the importance of a multilayered strategy [3, 4] involving continuous monitoring and responsive actions.  
Despite these guidelines, current cybersecurity and reliability techniques often lack the flexibility to adapt to new and emerging threats [5]. These techniques can struggle with early detection, especially in complex systems, where software bugs, device malfunctions [6], flawed security policies [7], and human error introduce significant vulnerabilities [8, 9].  
This paper addresses these challenges by exploring the application of process mining techniques to monitor ICS event logs and detect anomalies even when such attacks are masked by normal operational data. We propose a novel methodology that leverages digital twins and process mining to uncover cyberattacks, providing a robust tool for security operators to understand and prevent potential impacts on industrial assets. Our approach not only enhances existing cybersecurity measures but also introduces a practical solution for real-time threat detection in industrial environments.
# Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence +
## 4 Global Convergence of RFA and Extensions
### Global Convergence of RFA  
Theorem 1 states the convergence of RFA.  
The RFA algorithm converges to a global optimal solution of Problem (3).  
Considering that all the formulas, from Problem (3) to Problem (12) in the previous section, are all transformed equivalently, we only prove the convergence of Problem (12) here. Specifically, we show that \(\widetilde{\mathcal{L}}\) of Problem (12) has a lower bound, which gradually decreases with the iterative process.  
We can easily see that for any \(\widetilde{\mathbf{W}}\), it satisfies that \(0\leq\frac{\operatorname{tr}(\widetilde{\mathbf{W}}^{T}\widehat{\mathbf{S}}_ {t}\widetilde{\mathbf{W}})}{\operatorname{tr}(\widetilde{\mathbf{W}}^{T} \widehat{\mathbf{S}}_{t}\widetilde{\mathbf{W}})}\leq 1\), so that the lower bound of \(\widetilde{\mathcal{L}}\) is 0.  
Next, we prove that the objective value of Problem (12) gradually decreases with the iterative process of RFA. Defining  
\[\tilde{\eta}_{t}=\frac{\text{tr}(\widehat{\mathbf{W}}_{t-1}^{T}\widehat{\mathbf{S }}_{t}\widehat{\mathbf{W}}_{t-1})}{\text{tr}(\widehat{\mathbf{W}}_{t-1}^{T} \widehat{\mathbf{S}}_{T}\widehat{\mathbf{W}}_{t-1})}, \tag{15}\]  
we then have  
\[\text{tr}(\widehat{\mathbf{W}}_{t-1}^{T}(\widehat{\mathbf{S}}_{t}-\widehat{ \eta}_{t}\widehat{\mathbf{S}}_{T})\widehat{\mathbf{W}}_{t-1})=0. \tag{16}\]  
However,  
\[\widetilde{\mathbf{W}}_{t}=\underset{\widetilde{\mathbf{W}}^{T}\widetilde{ \mathbf{W}}=\mathbf{I}_{t}}{\text{argmin}}\text{tr}(\widetilde{\mathbf{W}}^{T} (\widehat{\mathbf{S}}_{t}-\widehat{\eta}_{t}\widehat{\mathbf{S}}_{T}) \widetilde{\mathbf{W}}). \tag{17}\]  
Therefore,  
\[\text{tr}(\widetilde{\mathbf{W}}_{t}^{T}(\widehat{\mathbf{S}}_{t}-\widehat{ \eta}_{t}\widehat{\mathbf{S}}_{T})\widetilde{\mathbf{W}}_{t})\leq 0, \tag{18}\]  
and  
\[\widetilde{\eta}_{t+1}=\frac{\text{tr}(\widetilde{\mathbf{W}}_{t}^{T}\widehat {\mathbf{S}}_{t}\widetilde{\mathbf{W}}_{t})}{\text{tr}(\widetilde{\mathbf{W}} _{t}^{T}\widehat{\mathbf{S}}_{T}\widetilde{\mathbf{W}}_{t})}\leq\widetilde{ \eta}_{t}. \tag{19}\]  
Thus, we prove that \(\widetilde{\eta}_{t}\) gradually decreases with the iterative process, and Theorem 1 holds.  
According to Theorem 1, we can see that RFA can converge to the global optimal solution. In addition, for given intrinsic and penalty graphs and the relational matrix, the computational complexity of the RFA algorithm is only \(\Theta(N)\), where \(N\) is the total number of data. That also illustrates the efficiency of our algorithm.
## 3 Results  
### Performances of Various LUE-GPP Models  
#### 3.1.1 Six Big-Leaf LUE-GPP Models  
The seasonal changes and the correlation between daily \(GPP_{\text{EC}}\) and \(GPP\) estimates by the six big-leaf LUE-GPP models in the orchard ecosystem from 2018-2020 are shown in Figure 1. The models well simulated the seasonal variations in \(GPP_{\text{EC}}\) during the growing seasons, with \(R^{2}\) values ranging from 0.666-0.729. However, all \(GPP\) estimations were overestimated, as shown by a linear slope of \(<\)1 between simulated \(GPP\) and \(GPP_{\text{EC}}\). This phenomenon was notable during the mid-growing seasons.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**Optimal Big-Leaf LUE-GPP Model**} & \multicolumn{2}{c}{**Two-Leaf LUE-GPP Model**} \\ \hline \multirow{2}{*}{**Parameters**} & **Power Function** & **Exponential** & **Power Function** & **Exponential** \\  & **Form** & **Function** & **Form** & **Function** \\ \hline A\({}_{1}\) & 1.536 & 2.703 & 1.315 & 9.776 \(\times\) 10\({}^{-10}\) \\ B\({}_{1}\) & 3.010 \(\times\) 10\({}^{-9}\) & \(-\)1.011 & 0.391 & \(-\)1.999 \\ A\({}_{2}\) & & & 1.447 & 2.9547 \\ B\({}_{2}\) & & & 3.38 \(\times\) 10\({}^{-6}\) & \(-\)0.411 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The fitted parameters of clearness index (\(K_{\text{t}}\))-based dynamic LUE constructed in optimal big-leaf light use efficiency-based GPP (LUE-GPP) model and two-leaf LUE-GPP model.  
Figure 1: Seasonal variations (**left**) and the relationships (**right**) between the gross primary productivity derived from EC measurement (\(GPP_{\rm EC}\)) and estimated values by the six big-leaf light use efficiency-based models (EF-LUE in (**a**,**g**), MODIS in (**b**,**h**), VPM in (**c**,**i**), MVPM in (**d**,**j**), TEC in (**e**,**k**), CASA in (**f**,**l**)).  
In the 2018 growing season, the six big-leaf LUE-GPP models exhibited various model performances, with \(R^{2}\) values ranging from 0.711-0.790, \(MAE\) from 1.377-2.441 g C m\({}^{-2}\) d\({}^{-1}\), _RMSE_ from 1.808-3.308 g C m\({}^{-2}\) d\({}^{-1}\), \(\eta\) from 0.226-0.563, \(d_{1}\) from 0.607-0.776, and _GPI_ from \(-\)2.048 to 0.875 (Table 4). Overall, the EF-LUE model outperformed the other five models, reaching a _GPI_ of up to 0.875 (1) (bracketed figures represent rankings).  
The model performances varied across the growing seasons and between years (Tables 4 and 5). For example, _GPI_ calculated by the EF-LUE model was 1.37 (2) in 2019 and 1.26 (1) in 2020. The daily average \(GPP\) of the EF-LUE model was 6.27, 5.62, and 6.09 g C m\({}^{-2}\) d\({}^{-1}\), respectively, which are similar to \(GPP_{\text{EC}}\) (6.77, 5.88, and 6.00 g C m\({}^{-2}\) d\({}^{-1}\)) during the 2018-2020 growing seasons (Table 5). Overall, the EF-LUE model was superior to the other five big-leaf models in terms of accuracy and stability.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
###### Abstract  
In gasoline engines, the combustion process involves a flame's propagation from the spark plug to the cylinder walls, resulting in the localized heating and pressurization of the cylinder content ahead of the flame, which can lead to the autopignition of the gasoline and air. The energy release from the autopignition event causes the engine cylinder to resonate, causing an unpleasant noise and eventual engine damage. This process is termed as knock. Avoiding knock has resulted in limiting the maximum engine pressures, and hence limiting the maximum efficiencies of the engine. Modern engines employ knock sensors to detect resonances, adjusting the spark plug timing to reduce pressures and temperatures, albeit at the expense of engine performance. This paper sets out to review the different signals that can be measured from an engine to detect the start of knock. These signals traditionally consist of the in-cylinder pressure, the vibrations of the engine block, and acoustic noise. This paper reviews each of these techniques, with a focus on recent advances. A number of novel methods are also presented, including identifying perturbations in the engine speed or exhaust temperature; measuring the ion charge across the spark plug leads; and using artificial intelligence to build models based on engine conditions. Each of these approaches is also reviewed and compared to the more traditional approaches. This review finds that in-cylinder pressure measurements remain as the most accurate for detecting knock in modern engines; however, their usage is limited to research settings. Meanwhile, new sensors and processing techniques for vibration measurements will more accurately detect knock in modern vehicles in the short term. Acoustic measurements and other novel approaches are showing promise in the long term.  
Keywords:engine knock; signal processing; vibration measurements; audio measurements; pressure measurements +
Footnote †: journal: _Signals_