esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Ethyl Ester (EE) Production from Palm Olein
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Derivative Production Conditions**} & \multicolumn{2}{c}{**Hydrolysis of Sardine Oil**} \\ \hline
**Immobilization Step (pH)** & **Incubation Step (pH)** & **Activity a ** & **EPA/DHA Ratio b ** \\ \hline
**T.0** & **7.0** & **1.38** \(\pm\) 0.12 & **1.90** \(\pm\) 0.10 \\
**7.0** & **8.0** & **1.22** \(\pm\) 0.05 & **1.83** \(\pm\) 0.05 \\
**7.0** & **10.1** & **1.13** \(\pm\) 0.08 & **1.86** \(\pm\) 0.03 \\
**8.0** & **8.0** & **1.01** \(\pm\) 0.08 & **2.11** \(\pm\) 0.02 \\
**8.0** & **10.1** & **1.04** \(\pm\) 0.04 & **2.32** \(\pm\) 0.15 \\
**Reference Cx derivative (pH 9.0) c ** & **10.1** & **1.10** \(\pm\) 0.07 & **1.70** \(\pm\) 0.07 \\ Reference CNBr derivative (pH 7.0) d  & **-** & **1.88** \(\pm\) 0.09 & **1.97** \(\pm\) 0.10 \\ \hline \hline \end{tabular}  
* Activity is defined as the umol of PUFAs (polyunsaturated fatty acids) = EPA (ciosapentaenoic acid) + DHA (docosahexaenoic acid), produced per minute and gram of derivative with 1 mg of protein in the biphasic system: 5 mL of cyclohexane, 5 mL of buffer tris–HCl 0.1 M at pH 7.0, and 0.50 mL of sardine oil at 25\({}^{\circ}\)C [52]. b Selectivity is defined as the molar ratio of EPA to DHA produced after 24 h of reaction [52]. c Derivative obtained after the initial immobilization of aminated BTL2 in absence of DTT for a duration of 1 h at pH 9.0 followed by an incubation step at pH 10.1 for a duration of 24 h. d Derivative obtained after chemical amination of the CNBr derivative of native BTL2 immobilized at pH 7.0 and 4.0 \({}^{\circ}\)C. Values are the mean of three different experiments where the standard deviation was never \(>\)5% of the mean value (here not shown for clarity).  
\end{table} 
Table 4: BTL2 immobilization on Gx and derivative properties. Interestingly, the EE14 h yield per mg of immobilized protein (BCA method, Section 3.4) for the more active covalent TLL derivative was 21.4, while for the commercial biocatalyst it was just 8.6. This higher specific activity for the novel biocatalyst implies savings in the quantity of enzyme needed which constitutes one of the main components of the cost for this kind of catalyst [9; 15].
# A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications
## 3 Moment Fitting for Enriched Spectral Elements
### Mass Lumping for Enriched Spectral Elements
Using different enrichment functions within an element is still possible, as long as these are applied on different nodes. Secondly, commonly used shifted enrichment functions [111, 112] cannot be applied, since they would lead to zero mass coefficients. In general, it should be noted that the variational formulation is abandoned by introducing a residual in Equation (30). For this reason, the accuracy of the novel procedure will be studied at both the element \begin{table}
\begin{tabular}{c c c c} \hline \(jk\) & **11** & **12** & **22** \\ \hline \(\mathcal{F}_{jk}\) & \(\rho\times\left\{{N_{1}}^{2},{N_{2}}^{2},\ \dots,\ {N_{n}}^{2}\right\}\) & \(\rho F\times\left\{{N_{1}},{N_{2}},\ \dots,\ {N_{n}}\right\}\) & \(\rho F^{2}\times\left\{{N_{1}}^{2},{N_{2}}^{2},\ \dots,\ {N_{n}}^{2}\right\}\) \\ \hline \(\mathcal{G}_{jk}\) & \(\left\{{1},\ \xi,\ \xi^{2},\dots,\ \xi^{m}\right\}\) & \(F\times\left\{{1},\ \xi,\ \xi^{2},\dots,\ \xi^{m}\right\}\) & \(F^{2}\times\left\{{1},\ \xi,\ \xi^{2},\dots,\ \xi^{m}\right\}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Definition of basis functions \(\mathcal{G}_{jk}\) for three instances of the moment-fitting Equation (29), whose results are applied in the integration of the respective function sets \(\mathcal{F}_{jk}\). \(\rho\) is assumed as constant within \(\Omega_{\text{e}}\). level (in the upcoming Section 3.6.1) and the system level (in Section 4.1)). Finally, we expect the presence of discontinuities and the manipulation of the mass matrix to have an important impact on stability, which is studied in Section 3.6.2.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 9 Performance Analysis
### Land-Based UBRD System with Early Rail Defect Detection Capability: Performance Analysis
#### 9.4.2 Defect Detection in the Presence of Small Defects  
The performance of the SVD, ICA, and dimensionally-reduced ICA algorithm was tested also by considering a dataset composed only of the measurements corresponding to the initial evolution of the defect. When the defect appears, it is supposed to reflect only a small amount of the impinging UGW. Therefore, the corresponding dataset is composed of the measurements in which the peak of reflection caused by the defect is small in amplitude. In this case:  
* From the outcomes of SVD analysis, it has been inferred that the presence of an artificial defect cannot be easily detected. Moreover, selecting the singular values characterized by the highest M-K score does not guarantee defect detection; furthermore, in these conditions, the defect cannot be located by analyzing the components of the singular vectors.
* The analysis of the results generated by the ICA have proven that the defect signature is spread over multiple independent components, whose associated weights exhibit a monotonic increase, proportional to defect growth.  
All of these results have led to the conclusion that ICA with dimension reduction performs better than the SVD algorithm in the presence of EOCs when attempting to detect the growth of small defects [65].
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 5 Conclusions  
In this paper, an autonomous drone-based infrared thermography system is proposed for automatic detection and localization of defective PV modules in a PV power station. The drone system is mounted with a dual camera setup consisting of a thermal camera and an RGB camera. The proposed drone system can fly autonomously without the need of manual control by using our automatic flight path planning algorithm. In addition, our system can detect and precisely locate the exact location of the faulty PV modules. Contrary to most previous methods, we tested our approach on a real 1-MW power plant location in Suncheon, South Korea. Our results demonstrate that the proposed method can accurately detect and locate faulty modules autonomously.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Condition** & **No. of Frames** & **N/A (Sunlight)** & **Shadow** & **Pollution** & **Unknown** & **Total** \\ \hline
1 & No control & 400 & 60 & 10 & 2 & 2 & 14 \\ \hline
2 & Angle control & 400 & 0 & 17 & 3 & 1 & 21 \\ \hline
3 & Angle control & 400 & 0 & 1 & 0 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of hotspot detection and recognition.  
Figure 16: Hot spots caused reflection due to sunlight, solid on PV surface, and shadows.  
Figure 15: Hotspots caused by unknown reason: 98.6 °C.  
**Author Contributions:** Conceptualization, H.J.; methodology, S.-W.L. and H.J.; software, C.H. and H.J.; validation, S.P., H.J. and S.-W.L.; formal analysis, S.-W.L.; investigation, C.H.; resources, S.-W.L.; data curation, H.J.; writing-original draft preparation, C.H. and S.P.; writing-review and editing, C.H. and S.P.; visualization, S.P.; supervision, H.J. and S.-W.L.; project administration, H.J.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1F1A1062075).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Stability Analysis of VSC-HVDC with Improved Virtual Impedance Control Strategy
### Impact of DC Side Capacity  
Figure 12 shows the Nichols plots of the open-loop transfer function \(T_{m}\) under a different capacitance of the DC side capacity when the transmission power is \(-500\) MW. The control parameters are \(k_{prodc}=15\), \(k_{intc}=100\), \(k_{i}=10\), \(R_{eq}=0.5\)\(\Omega\), and \(L_{eq}=0.002\)\(\Omega\). The blue, red, and yellow lines in Figure 12 represent the Nichols plots of \(T_{m}\) at the DC side capacity of \(300\), \(450\), and \(600\)\(\mu\)f, respectively. As shown in Figure 12, the Nichols plot gradually approaches \((-180^{\circ},0)\), with a decrease in the DC side capacity. The Nichols plot encircles \((-180^{\circ},0)\), and the system is predicted to be unstable when the DC side capacity is reduced to \(300\)\(\mu\)f.  
From the above stability analysis, it can be concluded that there are three main factors affecting the stability of the system--power flow [24], main circuit parameters, and controller parameters. Among the main circuit parameters, the most important factors are the length of the circuit and the capacitance of the DC side. According to the stability, analysis results in 4.4 and 4.5. The stability margin of the system can be improved by increasing the DC capacitance and DC cable length. When the main circuit parameters are determined and the power flow is determined, the stability of the system can also be improved by optimizing the controller settings.
## 5 Simulation Results
### Robustness  
The robustness of the algorithm was measured by executing the simulation 100 times. Each time, the robot must reach a randomly-generated pose that is within its configuration space and it reachable within 150 steps. If the controller takes longer, the simulation is restarted. Three control points were used. One interesting configuration is shown in Figure 8. The controller managed to achieve this bent arch configuration within 150 steps.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multicolumn{3}{c}{**Arrangement**} \\ \cline{2-4}  & **1** & **2** & **3** \\ \hline
1 & 70 steps & 64 steps & 46 steps \\
2 & 93 steps & 59 steps & 35 steps \\
3 & 185 steps & 147 steps & 92 steps \\ \hline \hline \end{tabular}
\end{table}
Table 4: Steps required to complete three reaching tasks using three arrangements of control points.  
Figure 7: These joint positions correspond to reaching task 1. The larger jitters are due to exploratory behavior, which is triggered to collect more information about the robot’s local kinodynamics.  
It was observed that 82% of the runs, the controller manages to reach to the target pose. However, in 18% of the runs, the controller did not successfully converge to the solutions that brought the robot's end effector to the desired poses. This can occur when the optimization is stuck in a local minimum, which results in an inaccurate actuation signal that does not optimally move the control points towards the target poses.
# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
## 1 Introduction
In Reference [31], WLAV is converted into the WLS, and the denominator of the weight function is modified by an exponential function with the square of the normalized residuals to improve the robustness of the estimator. Furthermore, the residuals are normalized by the internal student residual method. In the current distribution system, SCADA-based state estimators are not accurate enough to capture the system states because of the second-level refresh rates. Although PMU measurements provide millisecond update rates, the system observability cannot be guaranteed. Meanwhile, there may be bad data in SCADA and PMU measurements. To ensure the accuracy of DSSE and effectively track system state changes, a hybrid state estimator for a medium voltage distribution network is proposed based on SCADA and PMU measurements in this paper. At the time of SCADA data acquisition, the improved robust estimation combining the SCADA and PMU measurements is performed. During each iteration, the internal student residual method is used to unite the accuracy of the residuals at the same scale and the robust thresholds are adjusted by the normalized residuals adaptively to further eliminate the effect of bad data. Then according to the fast refresh frequency characteristics of the PMUs, the linear state estimation (LSE) is performed at the time of PMU data acquisition based on the results of the previous estimation time and the PMU measurements, which can quickly capture the changes of operation states in the distribution system. The main contributions of this paper include:  
1. To solve the problems of measurements with different refresh frequencies, a hybrid state estimator based on the improved state estimation and LSE is proposed, which makes full use of the PMU measurements to fast track distribution system states and guarantee the timeliness of real-time measurements. 2. To eliminate the influence of the bad data, the normalized residuals and adaptive thresholds are introduced to improve the conventional robust estimation method. Furthermore, the performance of the improved robust estimation method is compared with other robust estimation methods. The following parts of this paper are organized as follows: Section 2 introduces the multi-sampling period data fusion in the distribution system. Section 3 introduces the proposed method and Section 4 shows the simulations, followed by conclusions in Section 5.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 5 Malware Detection Techniques
### Heuristic-Based Malware Detection  
Heuristic analysis is a method for identifying malicious software by examining its signature characteristics. The utilization of conventional virus detection methods to detect malicious software involves the comparison of the code in a program to prior signatures of viruses that have been catalogued in a database. The signature recognition method, though still useful and employed, has seen its effectiveness increasingly limited in light of the emergence of new and contemporary threats since the early 2000s which have persisted into the present. to address this problem, a heuristic approach was created principally to identify the similarity of features between previously known and unknown malware instances. One of the few strategies employed to handle the burgeoning amount of risks presented by the continually developing cyber-criminal milieu is heuristic assessment [46; 51; 52]. Schema for the heuristic-based malware detection technique is given in Figure 8.  
Figure 7: Signature-based Malware Detection Schema.  
Assessment of Heuristic-Based Malware Detection  
A heuristic-based model can be utilized to generate signatures, allowing rules to be constructed from texts and behaviours. This technique employs Application Programming Interface (API) calls, Configuration Files (CFG), n-grams, Opcode sequences, and composite elements to produce a signature. Despite the possibility of heuristic-based detection to detect some previously unidentified malware, it is not able to identify all emerging malware variants. The false positive rate of heuristic-based methods is particularly elevated.
## 4 Results of Experiments  
### Performance Matrix  
This research utilized multiple performance metrics to evaluate the effectiveness of the models in sentiment analysis [41]. These metrics offer a comprehensive insight into the models' performances across different dimensions. Below are the definitions and equations for each parameter:  
True Positive (TP): This refers to instances where the classifier correctly identifies a positive review. For example, when a review is positive, the classifier accurately predicts it as such.  
Figure 4: The architecture of the Proposed Arb-MCNN-Bi Model for Arabic sentiment analysis.  
True Negative (TN): TN denotes cases where negative reviews are correctly identified as negative.  
False Positive (FP): FP occurs when a review is incorrectly predicted as positive when it is negative.  
False Negative (FN): FN happens when a review is wrongly classified as negative despite it being positive.  
Accuracy measures the overall correctness of the model and is calculated by:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{Total Reviews}} \tag{1}\]  
Precision indicates how many of the positive predictions were positive and is calculated by:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
Recall, also known as sensitivity, measures the ability of the model to detect positive reviews and is calculated by:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
F1-Score is the harmonic mean of precision and recall, providing a balance between them, calculated as:  
\[\text{F1}-\text{Score}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}\times\text{Recall}} \tag{4}\]
# A Study of Constitutive Model of Rock Damage under the Joint Effect of Load and Moisture
## 4 The Damage Constitutive Model of Water Containing Sandrock Specimens
### The Verification of Damage Evolution Law of Water-Bearing Rock  
Based on the average strength \(\overline{\sigma}\) and the average strain (\(\overline{e_{\omega_{t}}}\)) of the sandstone specimens at different water contents (\(\overline{\omega_{t}}\)), the corresponding m\({}_{1}\) and m\({}_{2}\) values can be obtained, as shown in Tables 3 and 4. The further stress-strain experimental curves and theoretical damage curves of sandstone specimens are shown in Figures 9 and 10, respectively.  
(1) Uniaxial compression status  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **B1** & **B2** & **B3** & **B4** & **B5** & **B6** \\ \hline \(t\)**f**h** & Dry & 0.75 & 1.5 & 6 & 24 & 720 \\ \(\overline{\omega_{t}}\)/\% & 0 & 1.688 & 2.007 & 2.212 & 2.619 & 2.749 \\ \(\overline{\varepsilon_{w_{1}-10}}\) & 11.243 & 10.759 & 11.148 & 11.343 & 11.582 & / \\ \(\overline{\mu_{2-10}}\) & 5.184 & 2.911 & 2.713 & 2.393 & 2.097 & / \\ \(\overline{\varepsilon_{w_{1}-20}}\) & 13.445 & 13.793 & 14.137 & 13.890 & 15.424 & 13.050 \\ \(\overline{m_{2-20}}\) & 3.628 & 3.004 & 2.958 & 2.550 & 2.351 & 2.839 \\ \(\overline{\varepsilon_{w_{1}-30}}\) & 15.637 & 15.691 & 16.922 & 17.725 & 18.333 & 14.760 \\ \(\overline{m_{2-30}}\) & 4.471 & 3.294 & 2.925 & 2.237 & 2.596 & 2.962 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Value of \(m_{2}\) under triaxial compression status.  
Figure 10: _Cont._  
Figure 9: The stress-strain experimental curves and theoretical damage curves of sandrock containing water under uniaxial compression status. (**a**) Dry Sample; (**b**) Sample Submerged for 0.75 h; (**c**) Sample Submerged for 1.5 h; (**d**) Sample Submerged for 6 h; (**e**) Sample Submerged for 24 h; (**f**) Sample Submerged for 720 h.  
According to Figures 9 and 10, under both uniaxial and triaxial loading conditions, the overall shape of the theoretical rock damage curve is similar to the overall shape of the experimental curve, indicating that the theoretical curve of water-containing rock damage evolution can better reflect the sandstone damage under different water contents. Before reaching peak strength, the rock deformation exhibits linear elasticity, and after peak strength, the strength decreases rapidly. With the increase of confining pressure, the theoretical damage curve gradually slows down, which proves that the ability of rock to resist damage increases gradually with the additional increase of confining pressure.
## 4 Road Sensing
### Detecting and Monitoring Moving Vehicles
#### Passive Infrared (PIR) Sensors  
PIR sensors measure the IR radiation emitted by heated bodies, though usually they are commercially available as motion detectors which perform a binary detection of vehicles, causing a loss of detail in signatures. Some sensors though, such as those from the IRA-E700 series do not behave as binary detectors, providing detailed signatures, which can be seen in [65]. PIR sensors and detectors have been frequently used for tracking people but rarely for vehicles [63]. In comparison with other sensors they offer an intermediate range of detection which makes them suitable for multi-lane detection. They can be mounted overhead or at the sideways, though overhead mounting offers better performance. On the other hand, the downsides of PIR devices are that they are affected by atmospheric conditions such as rain, snow or fog; and that the presence of humans and animals triggers false detections. It should be mentioned that PIR sensors usually require Fresnel lenses to improve long distance detection, which can also be used to change the field-of-view of the sensor.
## 1 Introduction  
Polymer flooding is a well-established tertiary recovery mechanism and mature enhanced oil recovery (EOR) process. As water flooding has been adopted in most mature fields, adding polymers to the injected water costs less than other chemical EOR methods, which require, for instance, the use of surfactants. Polymer flooding has been the most widely used chemical EOR process since the 1960s [1]. As the mode of action, polymer increases viscosity in the injected aqueous phase,generating higher ultimate recovery due to better sweep efficiency and improved mobility control [2]. Increasing the viscosity of the injected aqueous phase produces a favorable mobility ratio (MR):  
\[M_{R}=\left(\frac{k}{\mu}\right)_{D}/\left(\frac{k}{\mu}\right)_{d} \tag{1}\]  
where the numerator refers to the polymer as displacing fluid and the denominator represents the oil as displaced phase. In Equation (1), \(k\) defines the relative permeability (to oil or polymer) of the porous media, and \(\mu\) represents the bulk viscosity of the fluid (oil or polymer). Polymer flooding develops a favorable mobility ratio and improves the volumetric sweep efficiency, leading to the elimination of the fingering effects and resulting in late breakthrough.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 3 The Compression Framework
### Reduction of the Primary Data
#### 3.1.1 Data Transformation  
The Tucker decomposition [34] is widely used in data reduction by considering a subset of coefficients that carry most of the information in the dataset and eliminate lower-rank coefficients, which typically has no adverse effect on tensor reconstruction. The original Tucker decomposition can be viewed as a higher-order singular-value decomposition (HOSVD), and we do not consider more complex variants such as HOOI [35] in this paper. It decomposes a tensor \(T\) of order \(N\) into a core tensor with the same order while generating \(N\) orthonormal bases. If we consider a dataset as a tensor \(T\in\mathbb{R}^{d_{1}\times d_{2}\times d_{3}}\) of order 3, its Tucker decomposition is \(T=\mathcal{T}\times_{1}U^{(1)}\times_{2}U^{(2)}\times_{3}U^{(3)}\), where \(\mathcal{T}\in\mathbb{R}^{d_{1}\times d_{2}\times d_{3}}\) is the core tensor which, as a lower rank approximation of \(T\), gives a representation basis for it. Unitary or factor matrices contain basis vectors that help project \(T\) into the basis space \(\mathcal{T}\).