esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Applications
### Rainfall Conditions  
The study area has a humid subtropical monsoon climate, with average annual rainfall ranging from 1341.4 to 2259.6 mm and overall rainfall showing uneven spatial and temporal distributions and high extremes [42]. According to the rainfall statistics for the last five years of the Anyuan County to Dingnan County Expressway Project, it is found that heavy rainfall occurs frequently in the region, especially in certain months, which is particularly alarming, such as the rainfall in May 2015, which was as high as 570 mm. And from April to June every year, which is the rainy season in Gannan, the rainfall in these two seasons accounts for nearly 80% of the annual rainfall, showcasing the abundant rainfall in the region. According to the climatic conditions of the project site, the simulated rainfall was set to 50 mm/d. The simulated slopes started to receive rainfall at 0 h, and the change in the rainfall intensity gradually increased from 0 to 24 h and reached a maximum at 24 h; at 96-120 h, the rainfall intensity gradually decreased, and at 120 h, the rainfall intensity reached 0, corresponding to the end of the rainfall event. The curve of the change in the rainfall intensity of the slope model is shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multirow{2}{*}{**Cohesion/RPa**} & \begin{tabular}{c} **Angle of Internal** \\ **Friction/** \\ \end{tabular} & \
\begin{tabular}{c} **Permeability** \\ **Coefficient** \\ **(\(\times\)**10** **-** **cms-1** \\ \end{tabular} &
\begin{tabular}{c} **Poisson’s Ratio** \\ **Easticity/MPa** \\ \end{tabular} \\ \hline Unimproved granite residual soil & 12.28 & 19.41 & 5.29 & 0.3 & 5 \\ Fly ash amended soil & 38.51 & 22.15 & 0.71 & 0.3 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical and mechanical parameters of finite element model materials.  
Figure 18: Slope calculation model.
## 2 Study Area and Dataset
The acquired Sentinel-1 dataset comprises ground range-detected (GRD) dual-polarized VV and VH data acquired from the ascending orbit, as well as dual-polarized HH and HV data from the descending orbit at a spatial resolution of 10 m. All four collected SAR backscatter layers are the average composite of the summer time series, which reduces the speckle noise level of the backscatter layers. Sentinel-1 data are collected approximately every 6 to 12 days, depending on the orbit and revisit schedule, ensuring frequent updates that contribute to temporal coherence in wetland monitoring applications. The acquired Sentinel-2 data encompass optical, red-edge, near-infrared, and shortwave infrared bands. It is noteworthy that, similar to Sentinel-1, the multispectral data are also an average composite of summer images with cloud cover below 10% and a spatial resolution of 10 m. Sentinel-2 operates with a revisit frequency of approximately 5 days, enabling more frequent observations that are crucial for capturing temporal dynamics and improving the model's ability to discern seasonal changes and wetland classifications. The Sentinel-2 true-color image of the study area and acquired ground truth polygons is depicted in Figure 1a.
# Dynamic Shear Degradation of Geosynthetic-Soil Interface in Waste Landfill Sites
## 3 Disturbed State Concept (DSC)  
The Disturbed State Concept and the disturbance function have been employed in the present study to understand the chemical effects on the interface. The early DSC theory was originated by [23] to characterize the softening behavior of an overconsolidated soil, whereby its behavior in a normally consolidated state was used as the reference state. This initial concept was later developed and unified as the DSC to model the behaviors of a wide range of materials [24]. The disturbance function is a key factor of the DSC, and it quantitatively defines dynamic shear stress degradation. The convenience and reliability of the DSC have been successfully verified by the previous research [14; 15; 16; 17], and the numerical formulation of dynamic interface behavior that was achieved with the DSC is a valuable accomplishment [7]. The details of the DSC and the disturbance function are described in previous papers [21; 25]. For the disturbance function theory, \(D\) can be defined based on the shear stresses of the interface that are from the cyclic simple shear test, as follows:  
\[D=\frac{\tau^{i}-\tau^{a}}{\tau^{i}-\tau^{c}} \tag{3}\]  
where, \(a\), and \(c\) denote the Relatively Intact (RI), observed, and Fully Adjusted (FA) states, respectively. As shown in Figure 2, these values are obtained from the stress-strain curves of the cyclic simple shear test.  
The disturbance function can be also expressed as the following equation:  
\[D=D_{u}\Big{[}1-e^{-A\xi_{D}^{Z}}\Big{]} \tag{4}\]  
where \(D_{u}\) is the disturbance function value under an FA state and, practically, 0.99 is applicable based on the previous research [26]; \(A\) and \(Z\) are the intrinsic material parameters that represent the damage growth and the decay processes, respectively [21]; and \(\xi_{D}\) is the deviatoric plastic strain trajectory that represents the accumulation of the plastic deformation in each cyclic stress-strain loop. Details for estimating the disturbance function and parameters are explained in previous studies [21; 25].
## 3 Results
### Color Values of SSEF at Different Conditions  
#### 3.2.1 Color Values at Different pH  
The sensitivity of the SSEFs with varying SWE concentrations to the pH variations was tested by immersing them in buffer solutions with pH values ranging from 1 to 14 (Table 2). Based on the color measurement at the same pH value, the L* of the SSEF with 0.15% SWE exhibited a significantly (\(p<0.05\)) higher value (65.86-74.97) than the films with 0.25% (64.75-73.56) and 0.35% (44.35-60.96) SWE. Conversely, the a* and b* values of the SSEF containing the highest concentration of SWE showed significantly (\(p<0.05\)) higher values (16.72-58.25 and 25.14-43.58, respectively) than the SSEFs with 0.25% (7.68-50.22 and 15.65-29.87, respectively) and the 0.15% (5.43-48.87 and 11.75-22.15, respectively) extract (Table 1). Consequently, the total color difference (\(\Delta\)E) of the SSEFs with the addition of 0.15%, 0.25%, and 0.35% SWE increased significantly (2.30-44.20, 8.37-43.90, and 3.77-45.86, respectively; \(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment** & **Thickness** & **Transparency** & **Tensile** & **Elongation at Break (\%)** \\ \hline
0.00 & 0.13 \(\pm\) 0.01 d & 3.46 \(\pm\) 0.01 d & 15.43 \(\pm\) 0.01 a & 11.23 \(\pm\) 0.01 c \\
0.15 & 0.17 \(\pm\) 0.01 c & 2.16 \(\pm\) 0.13 a & 10.15 \(\pm\) 1.1 b & 12.68 \(\pm\) 1.17 b \\
0.25 & 0.19 \(\pm\) 0.01 b & 1.26 \(\pm\) 0.03 b & 8.48 \(\pm\) 1.0 c & 14.80 \(\pm\) 0.96 a \\
0.35 & 0.22 \(\pm\) 0.01 a & 0.84 \(\pm\) 0.04 c & 7.70 \(\pm\) 0.7 c & 15.70 \(\pm\) 1.26 a \\ \(p\)-value & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 \\ \hline \hline \end{tabular} Remarks: Data are presented as mean \(\pm\) SD. A different superscript letter indicates a significant difference (\(p<0.05\)) in treatment.  
\end{table}
Table 1: Physical properties of SSEF with different SWE concentrations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Treatment**} & \multicolumn{4}{c}{**PhH**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** \\ \hline
0.15\% & \(\pi^{\prime}\) & 74.8\(\pm\) & 74.8\(\pm\) & 74.2\(\pm\) & 73.0\(\pm\) & 73.5\(\pm\) & 73.2\(\pm\) & 72.8\(\pm\) & 72.6\(\pm\) & 72.6\(\pm\) & 72.0\(\pm\) & 71.8\(\pm\) & 71.2\(\pm\) & 70.4\(\pm\) & 69.2\(\pm\)
## 4 Experimental Results and Analysis  
In this paper, the model and algorithm proposed for multi-satellite scheduling are verified by numerical examples as follows. By designing simulation scenarios and typical examples, the performance of the real-coding PBIL algorithm is analyzed by comparing it with binary-coding PBIL and the Genetic Algorithm (GA). Additionally, the influence of key parameters on algorithm performance, such as the probability correction coefficient, is analyzed.
# AE-CGAN Model based High Performance Network Intrusion Detection System
## 5 Experimental Environment  
### Dataset  
There are not many open datasets used for NIDS, and they are mainly based on KDD99 dataset [8], NSL-KDD dataset [9], Kyoto2006 dataset [30], and ISCX2012 dataset [31]. Researchers' assessment of existing datasets shows that they are mostly old and unreliable, and some lack traffic diversity and volume. There are also problems that do not reflect the current tendency to attack.  
Among the recently released datasets, the UBSW-VB15 dataset [32] reflects the latest attacks. However, it is not suitable as the dataset of this study because the types of attacks are smaller than those of CICIDS2017 and the number of features is small.  
For this reason, this study uses the CICIDS2017 dataset with normal and most recent attacks similar to actual data. It consists of normal traffic and 15 types of attacks. Similar to the actual network, normal data accounts for more than 80% of the data and includes rare attacks such as Infiltration and Heartbleed attacks. Table 1 shows the types and ratios of the classes in the CICIDS2017 dataset.  
Dataset uses maxima-minimum normalization method to normalize [33] the characteristic value of the CICIDS2017 dataset to facilitate a comparison of results. \(X_{max}\) and \(X_{min}\) represent the maximum and minimum values of the original characteristic values, respectively.  
\[X_{norm}=\ \frac{X-X_{min}}{X_{max}-X_{min}} \tag{4}\]  
Conduct an experiment on a dataset that has been normalized by dividing it into 60% and 40% for training and testing, respectively.  
Figure 4: Structure of autoencoder-conditional generative adversarial networks (AE-CGAN).
# Satellite SAR Data-based Sea Ice Classification: An Overview
## 3 Interactive Sea Ice Charting: GIS-based Techniques
The \"Planeta\" technology implies a combination of automated and interactive techniques to process visible, infrared or microwave satellite data. The satellite images are pre-processed in automated mode. In the interactive mode, the ice parameters (age, concentration, ice forms, etc.) are interpreted on satellite images and displayed on the ice chart. In addition, the designed GIS-based software \"Planeta Monitoring\" employs supervised classifications based on Bayesian maximum likelihood for normal feature vectors distribution, and the K-means clustering algorithm [15].
# Battery-Free Pork Freshness Estimation Based on Colorimetric Sensors and Machine Learning
## 4 RF Harvesting Experiment  
### RF Harvesting Evaluation of the Collinear Dipole Antenna  
In the case of the RF harvesting system proposed in this study for food monitoring, measurements were not taken in an anechoic chamber. As measurements had to be taken in a normal environment, the distance from the RF source changed, and the average harvesting amount was computed after conducting multiple measurements. Performance evaluations of the antennas were carried out indoors in a space of 10 m (L) \(\times\) 10 m (W) \(\times\) 5 m (H). An axle cable was used to connect the antenna to the measuring unit (Agilent ENA Series Network Analyzer, Keysight Technologies, Elk Horn, IA, USA). An overall measurement system, including a network analyzer, an antenna, and an RF transmitter, was constructed on a wooden table placed in the center of the room. The proposed sensor tags took 150.8 s to charge up to 4 V at a distance of 50 cm from the RF source [24; 25; 26; 27].
## 5 Experimental Investigations
### Error Analysis  
During the error analysis phase, a detailed examination of the prediction results of the Auto-HPIF model was conducted to identify potential issues and areas for improvement. Figure 3 provides an example of a slot prediction error. Based on the user input sequence mentioned previously, it can be inferred that the user's intention involves controlling the air conditioner, which includes actions such as turning it on, adjusting the temperature, airspeed, and air direction. However, during the identification of the slot value \"cool mode at 22 degrees\", the model mistakenly identifies \"set\" as the B_offset slot label, resulting in an incorrect resolution of the entire slot value as \"22 degrees\". This error may be attributed to the model learning a linguistic pattern during training, where the term \"cooling\" becomes associated with the slot label and is subsequently considered a valid feature for prediction. Nevertheless, in real-world scenarios, such patterns may not be encountered frequently, leading the model to make erroneous predictions when faced with new situations.  
Figure 3: An example of an error slot label for Auto-HPIF on CADS datasets (The utterance” Turn on the air conditioner and set it to cool mode at 22 degrees. Also, adjust the fan speed to the second level.” is translated from Chinese. For specific examples in Chinese, please refer to Figure S2 in the Supplementary Materials).  
To mitigate such errors in future iterations, several measures can be implemented to improve the model's accuracy and robustness. Firstly, increasing the training data would be beneficial as it enables the model to learn from a more diverse range of examples and generalize better to novel scenarios. Additionally, introducing more features could give the model more context and relevant information for making accurate predictions. Furthermore, optimizing the model architecture can contribute to its overall performance by refining its underlying mechanisms and improving its ability to capture complex patterns and relationships within the data. Complementing these technical enhancements, incorporating manual review and feedback mechanisms can serve as valuable tools for monitoring the model's performance during its application. This iterative feedback loop would facilitate ongoing improvements based on real-world observations and enhance the user experience when the model is deployed in practical scenarios.
## 3 Results
### Discussion of Results
#### 3.2.1 Parametric Study of the ANN-PSO Hybrid Model for Queue Intensity  
The best-performing configuration for queue intensity prediction was obtained when the number of neurons was 7, swarm population size was 50, and acceleration factors were C\({}_{1}\) = 1 and C\({}_{2}\) = 2.5. Under these conditions, the model achieved a training R\({}^{2}\) of 0.9403 with an MSE of 3.8582, indicating strong learning capability. The testing R\({}^{2}\) value of 0.9164 suggests that the model effectively generalizes to unseen data, confirming its reliability in predicting queue intensity (Results highlighted in bold and italic in Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & **Swarm Population** & \multicolumn{2}{c}{**Acceleration Factor**} & \multirow{2}{*}{**Training \(R^{2}\)**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**Testing \(R^{2}\)**} \\
**Neurons** & **Size** & & & & & \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: Parametric study of the ANN-PSO hybrid model for queue intensity.  
A key observation from the results is that smaller swarm sizes yielded better performance in queue intensity prediction, while larger swarm populations introduced excessive exploration, leading to slightly higher errors. This finding highlights the importance of balancing exploration (global search) and exploitation (local search) in PSO-based optimization to maintain stable and accurate predictions. This aligns with the work of [PERSON] & [PERSON] [27], who emphasize that the balance between exploration and exploitation in PSO is achieved through the careful selection of parameters and methodology. They further note that conventional PSO parameters are often chosen to ensure convergence, reinforcing the necessity of parameter tuning in achieving optimal results.
# Dynamic Response of Drill Floor Considering Propagation of Blast Pressure Subsequent to Blowout
## 5 Structural Analysis
### Implementation to Finite Element Analysis
#### 5.1.2 Material Properties and Behavior  
The mechanical behavior of the materials that compose the drill floor was defined in accordance with the framework of the finite element analysis. Mild steel, S235, S355, and S460, are typically adopted for the offshore unit and were therefore selected as the structural material of the FE model in this study. The material properties of the selected materials were obtained from DNV Offshore Classification, \"Determination of Structural Capacity by Non-linear FE Analysis Methods\", and are summarized in Table 6.  
During the FE analysis, the drill floor behaved according to the elastic modulus until the stress reached the yield strength presented in Table 6, and in the plastic region of the stress-strain relation, each material comprising the FE model behaved according to a sequence of the piecewise linear plasticity model, which is commonly adopted for shell elements. From DNV Offshore Classification, an engineering stress-strain curve was obtained and converted to a true stress-strain curve based on the logarithmic relationships that have \(\sigma_{ture}=\sigma_{eng}\big{(}1+\varepsilon_{eng}\big{)}\) and \(\varepsilon_{ture}=ln\big{(}1+\varepsilon_{eng}\big{)}\). The obtained true stress-strain relation was applied to material behavior in the FE analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Material**} & \multirow{3}{*}{\begin{tabular}{c} **Elastic** \\ **Modulus** \\ **(GPa)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Density** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} } & \multicolumn{3}{c}{**Min.**} & \multirow{3}{*}{\begin{tabular}{c} **Min.** \\ **Vield** \\ **Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Critical** \\ **Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Vield** \\ **Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Critical** \\ **Strength** \\ **(MPa)** \\ \end{tabular} } \\ \hline S235 & 205 & 0.3 & 7850 & 235 & 360 & 0.20 & 236.2 & 432.6 & 0.1817 \\ S355 & 205 & 0.3 & 7850 & 355 & 470 & 0.15 & 357.0 & 541.0 & 0.1391 \\ S460 & 205 & 0.3 & 7850 & 460 & 540 & 0.10 & 462.8 & 595.4 & 0.0948 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summarization of material properties.  
Figure 16: (a) Geometry model and (b) finite element model of drill floor.
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 3 Proposed Detection Framework
### Data Preprocessing and Feature Engineering
#### 3.3.1 Data Transformation Using Label Encoding for Non-Numeric Data
This spatial pattern learning mechanism prevents the model from being misled by the artificial ordinal relationships in label encoding. Second, our normalization strategy distributes encoded values across the full pixel intensity range (0-255), ensuring no category is assigned extreme values that might be interpreted as more or less important, thereby maintaining balanced feature representation. Therefore, since label encoding maintains dimensional efficiency without increasing feature dimensions, and the inherent characteristics of CNN architecture in processing images can preserve the advantages of label encoding while mitigating its disadvantages, label encoding was applied to the data transformation process in this study. Table 3 shows the results of converting categorical features to numeric features. As shown in the table, each categorical value is mapped to a specific numeric value consistently. The 'flag' feature's 11 unique categorical values are transformed into evenly distributed numeric values between 23 and 253, preserving the distinctiveness of each category. Similarly, the'service' feature, with its numerous unique values, is mapped to values starting from 3 and incrementing appropriately, ensuring each service type maintains a unique representation in the generated images. This systematic encoding approach allows the CNN to potentially identify patterns associated with specific network services and flags that may be indicative of malicious traffic. \begin{table}
\begin{tabular}{l l c} \hline \hline
**Feature Name** & **Categorical Feature** & **Numeric Feature** \\ \hline \multirow{3}{*}{protocol\_type} & tcp & 85 \\  & udp & 170 \\  & icmp & 255 \\ \hline \multirow{6}{*}{flag} & SF & 23 \\  & S0 & 46 \\  & REJ & 69 \\  & RSTR & 92 \\  & SH & 115 \\  & RSTO & 138 \\  & S1 & 161 \\  & RSTOS0 & 184 \\  & S3 & 207 \\  & S2 & 230 \\  & OTH & 253 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of converting categorical features into numeric features.
# The Role of the Directivity of Various THz Detectors in Multiplexing Systems
## 3 Results  
### Characterisation of the Detectors  
First, all four investigated detectors were characterized for the acceptance angle of the incoming radiation. The comparison of the angular dependencies observed for the FET, QOD, WR34 and WR51 detectors can be seen in Figure 5.  
As can be seen, the widest acceptance band is observed for the FET detector. The other three are much more sensitive to the angle of incident radiation. The calculated 3 dB bandwidth (the average angle at which the registered signal decreased twice) for the FET, QOD, WR34 and WR51 detectors is equal to \(69^{\circ}\), \(12^{\circ}\), \(11^{\circ}\) and \(16^{\circ}\), respectively.  
In the next step, the frequency dependence of the angular acceptance was also tested for all investigated detectors. The results are presented in Figure 6.  
For the QOD, WR34 and WR51 detectors, changes in the angular acceptance band can be noticed only for relatively large shifts in the frequency (in the order of tens of GHz). The general tendency for all of these detectors is that the higher the frequency of radiation, the narrower the angular acceptance band. The situation is different for the FET detector, where small shifts in the radiation frequency (in the order of a single GHz) result in significant distortions of the angular acceptance characteristic. Most probably it is the combined effect of the patch antenna the detector is equipped with, and three wires connected to  
Figure 4: Simplified schematic diagram of the experimental setup. (MS)—Measured Structure, (F1)—100 mm focal point, (F2)—700 mm focal point.  
Figure 5: The comparison of the normalized angular acceptance characteristics obtained for the FET (at 260 GHz), QOD (at 140 GHz), WR34 (at 260 GHz) and WR51 (at 140 GHz) detectors, marked with the blue, orange, green and red lines, respectively.  
the transistor terminals (visible in Figure 1c). These wires have lengths in the order of the wavelength of the incoming radiation and thus may form an unintentional antenna with unspecified characteristics and strong frequency dependence. The possible influence of the shape and direction of the beam has been experimentally eliminated by upside down inversion of the FET detector (Figure 7).  
As can be seen in Figure 7, the angular acceptance characteristic obtained with the rotated detector is axially symmetric to the nominal one with respect to the \(\theta=0^{\circ}\) line.