esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Methodology
#### 3.2.2 Synthetic Minority Oversampling Technique (SMOTE)  
In the case of fraud detection problems the minority class needs to receive special consideration, as it defines the phenomena which we aim to anticipate from a multitude of majority class structures that reflect correct processes. The performance of standard classifiers is biased towards the majority class, as they are programmed to minimize the  
Figure 1: Stages involved in this study.  
overall inaccuracy of classification regardless of class distribution. This bias problem can be overcome by excluding examples of the majority class, known as undersampling, or by including new examples of the minority class, known as oversampling. Due to the small size of our dataset, we chose the latter.  
An effective oversampling technique for producing new examples is SMOTE [62], which is one of the more advanced sampling methods. SMOTE can be implemented independently of the classifier being used. This algorithm addresses the challenge of overfitting caused by random oversampling. It relies on the feature space to create new instances with the aid of interpolation between positive instances that lie together. SMOTE starts by finding examples near the feature space, connecting the dots between the examples, and drawing a new instance at a point along that line [66]. In particular, a random sample of the minority class is chosen first. Then, for this sample, the value k of the nearest neighbors is found (usually k = 5). A randomly selected neighbor is chosen and a synthetic example is constructed at a randomly selected point in between two examples in the feature space.  
It is evident from prior studies that SVM, NN, DT, and LR have good performance for the task of fraud detection. For comparison with XGBoost, we selected SVM, DT, and LR. From the ensemble methods, we selected the RF and AdaBoost algorithms, as decision tree-based algorithms are considered best for small- and medium-sized data. Model training and testing of the comparison phase algorithms was performed with the help of the Scikit-learn package from Python.  
After the comparison phase, it was clear that XGBoost, a tree-based algorithm, was the best dataset. In the next stage, we optimized the XGBoost algorithm to obtain an optimal hyperparameter combination with the help of RandomizedSearchCV from Scikit-learn, which performs a randomized search of hyperparameters, to further enhance the performance of the algorithm. The estimator parameters used to implement these methods were optimized by performing cross-validation on the parameter settings search.
# Method for Improving the Reliability of SRAM-Based PUF Using Convolution Operation
## 4 Implementation Results
### The Results of Lightweight Scheme  
The lightweight scheme is suitable for IoT devices. Stable 256-bit information can be obtained from 64 \(\times\) 64 bit initial data using a new convolution. The size of the convolution kernel is 8 \(\times\) 8. The step size of this convolution is four bits, as shown in Figure 6. This lightweight scheme uses 64 bits SRAM to output a one-bit response. The hardware cost is half of the traditional Bose-Chaudhuri-Hocquenghem (BCH) codes scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Absolute Address** & **Total Number of Errors** & **Reliability (\%)** \\ \hline
0X1000000 & 26 & 99.8984 \\
0X10005700 & 29 & 99.8867 \\
0X10007000 & 77 & 99.6999 \\
0X1000 ABC0 & 16 & 99.9375 \\
0X1000B000 & 5 & 99.9805 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total number of errors and reliability of the five groups (redundancy coefficient = 0.05).  
Figure 5: The inter-chip HD in one-layer convolution.  
Ten groups of areas were selected with absolute addresses 0X10000000, 0X10001000, 0X10002000, 0X10003500, 0X10005700, 0X10007000, 0X10009000, 0X1000 ABC0, 0X1000B000, 0X10004A00. The redundancy coefficient was changed to 0.1 according to Formula 3. In Figure 7, a comparison of the inter-chip HD of three chips is presented. The results were all around 50%. The average inter-chip HD for all data was 50.195%, which indicates that the results of this scheme are unique. The result showed a large swing around 50%, which confirms the analysis provided in Section 3.3. The reliabilities of the three chips were 100% for all the above addresses.  
Different temperatures have a large impact on the stability of SRAM, so the initial SRAM values for three chips in the same region were tested at temperatures of \(-20\)\({}^{\circ}\)C, 40 \({}^{\circ}\)C and 80 \({}^{\circ}\)C. The data at 20 \({}^{\circ}\)C was used as the standard matrix and verification matrix. The algorithm above was used to process the same location data at different temperatures. The SRAM worked normally as it would at 20 \({}^{\circ}\)C at \(-40\)\({}^{\circ}\)C and 80 \({}^{\circ}\)C. As is shown in Table 3, the three chips produced the same results at different temperatures based on the verification matrices that were saved, indicating that the lightweight scheme was able to perform well at different temperatures.  
Figure 6: Convolution operation with self-filling.  
Figure 7: The inter-chip HD in the lightweight scheme.  
The proposed scheme can provide response information with 100% reliability using a convolution algorithm with redundancy. It does not require additional hardware resources and can adjust flexibly according to different needs.
# Sensitive Information Detection Based on Deep Learning Models
## 4 Experiments
### Experimental Results and Analysis
#### 4.4.4 Comparative Experiment  
To validate the detection effectiveness of the model proposed in this paper, under the same configuration conditions, we trained different object detection models using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **mAP0.5/\%** & **Params (M)** & **GFLOPs** & **Detection Time/ms** \\ \hline YOLOv8n+SPPF & 73.4 & 3.00 & 8.1 & 2.6 \\ YOLOv8n+SPPF & 73.8 & 3.00 & 8.1 & 2.4 \\ YOLOv8n+SimSPPF & 74.8 & 3.00 & 8.1 & 3.5 \\ YOLOv8n+SimSPPCSPC & 76.2 & 4.61 & 9.4 & 3.7 \\ YOLOv8n+SimCSPSPPF & 77.9 & 4.61 & 9.4 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of experimental data for different spatial pyramid pool layers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **mAP0.5/\%** & **Precision\%** & **Recall\%** & **Params (M)** & **GFLOPs** \\ \hline IS-YOLOv8n+SE [23] & 78.5 & 84.0 & 69.4 & 5.14 & 9.6 \\ +CA [24] & 78.1 & 83.3 & 68.0 & 5.14 & 9.6 \\ +EMA [25] & 78.6 & 81.8 & 69.2 & 5.13 & 9.6 \\ +CBAM [26] & 79.3 & 81.0 & 71.6 & 5.14 & 9.6 \\ +MCA [27] & 78.3 & 83.0 & 67.0 & 5.13 & 9.6 \\ +GAM [28] & 78.4 & 82.3 & 69.7 & 6.77 & 10.9 \\ +Sea Attention & 79.7 & 83.4 & 70.7 & 6.61 & 10.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experiments with different attentional mechanisms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Baseline** & **InceptionNext** & **SimCSPSPPF** & **C2F\_SAC** & **Sea Attention** & **mAP0.5\%** & **Precision\%** & **Recall\%** & **Params (M)** & **GFLOPs** \\ \hline  & & & & & 73.8 & 78.3 & 64.3 & 3.00 & 8.1 \\ YOLOv8n & ✓ & & & & 76 & 78.6 & 67.7 & 3.53 & 8.5 \\ YOLOv8n & ✓ & ✓ & & & 78.5 & 80.3 & 71.4 & 5.14 & 9.8 \\  & ✓ & ✓ & ✓ & & 79.2 & 88.7 & 67.3 & 5.13 & 9.6 \\  & ✓ & ✓ & ✓ & ✓ & 79 the same test set. As shown in Table 4, compared to other classical algorithms, such as YOLOv3-tiny [29] and RT-DETR [30], our model demonstrates higher accuracy in detecting sensitive content, particularly pornography. Although the parameter and computational requirements of YOLOv5n and the Gold-YOLO [31] algorithm are lower than our model's, their detection accuracy is significantly lower than that of our model. Furthermore, while the computational requirements of YOLOv9-c [32] and the RT-DETR algorithm are much higher than ours, their detection accuracy is still inferior to ours. In summary, the algorithm proposed in this paper outperforms other algorithms, such as YOLOv3-tiny and RT-DETR, exhibiting better performance in terms of detection accuracy and speed, making it more suitable for practical deployment.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 5 Case Studies
#### 5.1.3. Bicyclist--Driver Interaction Hypotheses  
In this study, we tested two hypotheses regarding bicyclist behavior and physiological response during a passing event.  
**Hypothesis 1:**_Effect of the Driver's Presence on Bicyclist's Lateral Position._  
* **Null Hypothesis (H\({}_{01}\)):** The bicyclist's lateral position is not affected by the driver's presence.
* **Alternative Hypothesis (H\({}_{11}\)):** The bicyclist's lateral position is affected by the driver's presence.  
The null hypothesis assumes that the presence of a passing vehicle does not influence the bicyclist's positioning within the bicycle lane. This is based on the premise that a dedicated bicycle lane provides a sense of separation and security, allowing cyclists to maintain a consistent lateral position regardless of external traffic conditions. However, in real-world scenarios, bicyclists tend to adjust their position in response to nearby vehicles, especially when they perceive the vehicle as being too close. This natural tendency to create a greater safety margin supports the alternative hypothesis, suggesting that a passing vehicle may cause the bicyclist to subtly shift within the lane.  
**Hypothesis 2:**_Effect of the Driver's Presence on Bicyclist's Heart Rate._  
* **Null Hypothesis (H\({}_{02}\)):** The bicyclist's heart rate does not change in response to the passing driver.
* **Alternative Hypothesis (H\({}_{12}\)):** The bicyclist's heart rate increases in response to the passing driver.  
The null hypothesis assumes that since the bicycle lane is a designated space for cyclists, the driver's passing action should not induce a significant physiological stress  
Figure 7: Relationship between critical moment, distance, and heart rate in Case Study I: definition of before and after the critical moment.  
response. However, in real traffic environments, a vehicle passing at close proximity could trigger a stress response, leading to an increased heart rate. This can be attributed to perceived risk, unexpected movement, or subconscious fear of potential collision, even when the bicyclist is within a dedicated lane. The alternative hypothesis suggests that bicyclists exhibit a measurable physiological reaction when a vehicle passes closely, supporting the notion that proximity to motorized traffic can influence stress levels.  
By testing these hypotheses, our study aims to determine whether the presence of a human-driven vehicle influences bicyclist positioning and heart rate, even in the presence of a dedicated bicycle lane. These findings contribute to a better understanding of bicycle-vehicle interactions, which is critical for improving cycling safety, infrastructure design, and the realism of bicycle behavior models in traffic simulations.
## 3 Case Study: Entire Frame
### Results  
Despite having masked almost all pixels in the mountainous areas at Step 1-5 due to decorrelation, more than a million valid pixels, mainly distributed on the Kanto Plain and along northern basins and plains, still remain (Figure 6a). As a measure of InSAR data quality, we compare the LOS velocities with those stemming from the GNSS measurements with the same reference point of the 950217 GNSS station. The InSAR-derived velocities successfully image the broad tectonic deformation (i.e., LOS decrease in the northeast due to the combination of eastward and vertical uplift motions) and are largely consistent with the GNSS velocities (Figure 6a). The average and STD of the difference between InSAR and GNSS (n = 56) is 0.1 mm/yr and 1.9 mm/yr, respectively. We detect no significant systematic errors caused by factors such as ionospheric noise and orbital errors, despite no ionospheric correction having being applied in the LiCSAR products (Figure 6a,b). We also tried estimating the best-fitting quadratic polynomial surface through the differences between InSAR and GNSS velocities, at all GNSS stations, and subtracting it from the InSAR-derived velocities, which slightly decreased the STD to 1.6 mm/yr (Figure 6b). In this case, the impact of ionospheric noise and orbital errors is quite small, although, in some cases, these error sources could become significant, particularly at low latitudes on ascending tracks [61].  
We also compare the LOS time series data and find that the InSAR-derived results are quite consistent with the GNSS measurements, even at the farthest GNSS site (950179) located 150 km away from the reference (950217; Figure 6c, Figure S5 and S6). The STD of the difference of the time series at each GNSS station shows no clear correlation with distance from the reference point (Figure 6d) once the GACOS correction has been applied, and the average of the STD of the difference at all GNSS stations is 9.0 mm, which represents the root-sum-square of the uncertainties of the InSAR- and GNSS-derived time series for each epoch. We also calculated the STD of the GNSS measurements for each epoch from the differences from those of 30-days moving average, and the average of the STD among all GNSS stations is 5.0 mm. Considering these, the uncertainty of the InSAR-derived time series for each epoch is 7.5 mm.  
The results for Southern Tohoku demonstrate the potential of LiCSAR products and LiCSBAS to detect broad tectonic deformation independently without the help of GNSS and to contribute to dense and detailed tectonic strain mapping and global earthquake hazard assessment [62].
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 3 Research Methodology
### Existing Energy Consumption in an Educational Building in the University during the Fall Semester  
During the research, we adopted the quantitative approach, and we collected data on the energy consumption of a given building in the university. As an example, Table 1 presents the university's electricity consumption during the fall semester of 2017.  
The consumption illustrated in Table 1 results in a high electricity bill. Hence, to lower the electricity bill, the university opted for the development of an energy policy as well as an EMS to control the heaters. For this reason, the CASANET EMS project aimed to develop an energy-efficient control method to reduce energy consumption in buildings by using ICT. This project's outcomes are as follows: First, an intelligent framework that will monitor and control loads of the building is developed. Second, a database to archive user behavior and environmental data designed. Third, a software tool to control HVAC appliances is implemented.
## 3 Experiments and Discussions
### Accuracy of Real-Time Vehicle-Borne PPP
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Direction** & **CAS** & **DLR** & **GFZ** & **WHU** & **WHU\_U** & **BRDC** & **B2b SDS** & **B2b GPS** \\ \hline E & 27.8 & 33.0 & 35.4 & 34.9 & 35.5 & 49.2 & 35.8 & 57.0 \\ N & 61.9 & 51.8 & 53.9 & 62.1 & 49.7 & 46.1 & 55.0 & 47.9 \\ U & 115.7 & 139.4 & 138.2 & 108.4 & 125.5 & 169.4 & 121.3 & 101.4 \\
2D & 58.9 & 61.4 & 64.5 & 71.2 & 61.1 & 67.5 & 65.7 & 74.5 \\
3D & 129.9 & 152.3 & 152.5 & 129.7 & 139.5 & 182.4 & 137.9 & 126.0 \\ \hline \end{tabular}
\end{table} 
Table 8: Position RMSE of vehicle-borne real-time dual-frequency PPP using different SSR products (unit: cm). \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Direction** & **CAS** & **DLR** & **GFZ** & **WHU** & **WHU\_U** & **BRDC** & **B2b SDS** & **B2b GPS** \\ \hline E & 80.0 & 88.6 & 54.3 & 79.4 & 73.9 & 111.8 & 87.1 & 42.3 \\ N & 53.9 & 72.0 & 53.1 & 156.7 & 57.3 & 72.7 & 32.9 & 47.8 \\ U & 88.6 & 87.8 & 60.0 & 178.6 & 57.3 & 126.3 & 103.5 & 129.2 \\
2D & 96.5 & 114.2 & 75.9 & 175.7 & 93.5 & 133.4 & 93.1 & 63.9 \\
3D & 131.0 & 144.0 & 96.8 & 250.5 & 109.6 & 183.7 & 139.2 & 144.1 \\ \hline \end{tabular}
\end{table} 
Table 9: Position RMSE of vehicle-borne real-time single-frequency PPP using different SSR products (unit: cm). It also can be found from Figure 15 that the accuracies of the real-time WHU orbits and clock products are similar to other ACs, but the positioning of single frequency PPP with WHU products is lower than that of other ACs. This is because the WHU BDS real-time clock products contain systematic errors. The RMSE of different BDS real-time clock products with respect to the WHU final clock product is presented in Figure 16. It can be clearly found that the RMSE value of WHU BDS real-time products is more significant than that of other products. Figure 16: The RMSE of real-time orbit clock errors. Figure 15: The average accuracy of positioning and real-time products.
## 7 Conclusions  
Mount Sinabung first erupted in 2010 after a long break of over 400 years, and this first eruption was followed by a series of six eruptions between 2013 until 2016. The biggest eruption occurred in 2014 and had a VEI of two, causing \(\sim\)16 deaths and the destruction of farmland and residents' houses by pyroclastic flows. Mount Merapi erupts roughly every five years because of the high magma activity in its magma chamber, as well as the magma pocket that formed during a past eruption. We analyzed the differences in the pyroclastic deposit flows between Mt. Sinabung and Mt. Merapi, using Landsat 7 and 8 imagery with Bands 7, 4, 2 and 7, 5, 3, respectively, to see the pyroclastic precipitates and deposits of sand and rocks.  
An artificial neural network method was used to analyze the pyroclastic precipitates and the pyroclastic precipitate area using Landsat imagery data taken annually from 2007 to 2017 for each volcano. The analysis showed that the pyroclastic sediment of Mount Sinabung spread to the east at the beginning of eruption and then widened to the southeast and south after the series of eruptions took place. The continuous eruption of Mount Sinabung caused the formation of pyroclastic deposits that accumulated around the body of the mountain. The pyroclastic sediment of Mount Sinabung tends to widen and is not transported far because of the direction of river flow on the mountain, which is not in the direction of the mountain's slope. In addition, the andesitic-rhyolitic (intermediate-acid) magma composition of Mount Sinabung prevents pyroclastic flow from being transported far, because the lava tends to be thick. In contrast, the pyroclastic flows of Mount Merapi flow to the south and to the southwest along the slope and river flow direction of Mount Merapi. Thus, the pyroclastic sediment of Mount Merapi does not accumulate much in the body of Merapi Volcano, but flows following the slope of the volcano. The composition of magma on Mount Merapi is also different from Mount Sinabung; Mount Merapi's magma is andesitic-basaltic (intermediate-base), so the flowing lava is thin and easily transported. Although the maximum VEI of Merapi is larger than that of Sinabung (VEIs of four and two, respectively), the pyroclastic precipitates of Mount Sinabung's eruption are wider than those of Mount Merapi (\(\sim\)18 and \(\sim\)17 km\({}^{2}\), respectively).  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015M1A3A3A02013416) and the Korea Meteorological Administration Research and Development Program under Grant KMIPA (2015-3071).  
[PERSON] applied the artificial neural networks model for detecting pyroclastic flow deposit area and wrote the paper. [PERSON] designed the volcanic experimental equation corresponding to volcano eruption. [PERSON] suggested the idea, organized the paper work and interpreted the results of volcanic activities. All of the authors contributed to the writing of each part.  
The authors declare no conflict of interest.
## 3 Results
### Molecular Docking Studies  
#### 3.3.1 Molecular Docking for the Enzyme \(\alpha\)-Glucosidase  
In silico molecular docking was performed for the \(\alpha\)-glucosidase receptor to identify the binding energy, binding affinity, and binding interaction at active sites of all the tentative compounds identified by GC-MS, along with the standard Acarbose.  
The docking results of \(\alpha\)-glucosidase revealed that 2-hydroxy-n-(2-phenylethyll) benzamide and lactose were the best-docked compounds. The binding affinity of 2-hydroxy-n-(2-phenylethyll) benzamide (\(-\)8.1 kcal/mol) is equal to standard Acarbose (\(-\)8.1 kcal/mol), while lactose showed 7.1 kcal/mol. Table 6 shows the docking results (binding interactions, energies, and amino acids) of best-docked compounds, and the sites of interactions between the enzyme and ligands are shown in Figure 1.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Sr No.** & **Compounds** & **H-Bond Interacting Amino Acids** & **Binding Affinity (kcal/mol)** \\ \hline
1 & 2-hydroxy-n-(2-phenylethyll)benzamide & LYS A:242, GLY A:274 & \(-\)8.1 \\ \hline
2 & Lactose & ALA A:270, ASN A: 275, ASN A:277, GLY & \(-\)7.1 \\  & & A:274, PHE A:246, TYR A:249 & \\ \hline
3 & Acarbose (standard) & GLN A:392, LYS A:395, THR A:448, PHE & \(-\)8.1 \\  & & A:282, SER A:145 & \\ \hline \end{tabular}
\end{table}
Table 6: The binding affinity of ligands, bond interaction, and bond number of GC–MS identified compounds against diabetes with \(\alpha\)-glucosidase.  
\begin{table}
\begin{tabular}{l l l l l} \hline \(\alpha\)**-Glucosidase Inhibition** & \multicolumn{2}{c}{**Acetylcholinesterasesterase Inhibition**} & \multicolumn{2}{c}{**Tyrosinase Inhibition**} \\ \hline _Z. pentandra_ & **Standard (Acarbose)** & _Z. pentandra_ & **Standard (Eserine)** & _Z. pentandra_ & **Standard (Kojic Acid)** \\ \hline
10.0 \(\pm\) 0.08 & 5.87 \(\pm\) 0.01 & 38.3 \(\pm\) 0.08 & 1.21 \(\pm\) 0.02 & 20.7 \(\pm\) 0.07 & 1.04 \(\pm\) 0.02 \\ \hline \end{tabular}
\end{table}
Table 5: IC\({}_{50}\) values of enzyme inhibition of ethanolic extract of _Z. pentandra_ (\(\mu\)g/mL).
###### Abstract  
Estimating the compressive strength of high strength concrete (HSC) is an essential investigation for the maintenance of nuclear power plant (NPP) structures. This study intends to evaluate the compressive strength of HSC using two approaches: non-destructive tests and concrete core strength. For non-destructive tests, samples of HSC were mixed to a specified design strength of 40, 60 and 100 MPa. Based on a dual regression relation between ultrasonic pulse velocity (UPV) and rebound hammer (RH) measurements, an estimation expression is developed. In comparison to previously published estimation equations, the equation proposed in this study shows the highest accuracy and the lowest root mean square error (_RMSE_). For the estimation of compressive strength using concrete core specimens, three different concrete core diameters were examined: 30, 50, and 100 mm. Based on 61 measured compressive strengths of core specimens, a simple strength correction factor is investigated. The compressive strength of a concrete core specimen decreases as the core diameter reduces. Such a relation is associated with the internal damage of concrete cores and the degree of coarse aggregate within the core diameter from the extracting process of the cores. The strength estimation expressions was formulated using the non-destructive technique and the core strength estimation can be updated with further test results and utilized for the maintenance of NPP.  
Keywords:high strength concrete; nuclear power plant; estimation of compressive strength; Nondestructive test; dual regression; concrete core; strength correction factor +
Footnote †: journal: Applied Sciences