esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Hacker Intention Using Deep Packet Inspection
## 5 Discussion  
As indicated in the results, Random Forest and Logistics Regression metrics were high. Random Forest Classification provides support to handle imbalanced datasets and over-fitting, yielding a better-performing model [22, 23, 24].  
Although the Gaussian Naive Bayes processed the data in significantly less time, the precision and F1 scores were much lower than Logistic Regression and Random Forest. The lower F1 score indicates the model needs improvement in precision. The metrics for this model are lower than the others due to its inherent requirement for feature independence [25].  
A Random Forest algorithm was used for feature selection, reducing features for input to the models from 87 to 22 features. By steadily reducing the number of features, we were able to gauge the impact of the remaining features on the efficacy of models. We observed an inflection point, after selecting the top 22 features, below which model performance began to suffer. A reduction in the number of features also contributed to faster processing times.  
We propose that by analyzing the TCP flag states, presented through NFStream's deep packet inspection, we can build ML models to detect specific hacker behaviors, from which intent can be inferred at an early stage by characterizing the network behavior associated with certain hacker scans. Once intention is detected, appropriate means can be used to better thwart an attack, such as cyber deception.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 2 Sensor Technologies
### Temperature Measurement
#### 2.1.2 Mobile Sensing  
Mobile sensing systems such as Geoball and Geosniff are small spheres 25 mm in diameter and 8 g in weight that have an embedded transceiver, microcontroller, temperature sensor and power supply. These miniaturized sensors are inserted into the pipes of a geothermal underground structure and are then carried by the thermal fluid in order to monitor the thermal parameters of the fluid along the borehole heat exchanger. These sensor systems allow the acquisition of information on the thermal properties of the subsurface geological structure and their influence on the thermal properties of the borehole. A wireless data processing unit sends data and acquires configurational information prior to measurement and retrieves measured thermal data from a sensor inside the U-tube of the borehole heat exchanger [18].  
These types of sensors have demonstrable advantages of higher reliability and lower cost over standard sensors and fibre optics [13]. Fibre optic technologies such as high-temperature distributed acoustic sensors [25] are used for their unparalleled ability to discriminate and measure environmental variables such as strain, temperature and vibrations [26]. The purpose of the method is to improve the conventional thermal response test [18]. Compared to Pt100 sensors, mobile sensing systems are highly accurate and do not produce large, statistically significant measurement deviations [13].
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 7 Performance Evaluation
### Experimental Results
#### 7.3.1 Evaluation of the Smart Office Scenario
Figure 11 reports the average number of hops as a function of \(\omega\). Once more, the generic trend was that the number of hops increased with \(\omega\), except for SACAR-IR, DDR (since the logic behind them is to create and send a message only to destinations that are one hop away), and SACAR-HC, for which the average number of hops was stabilized around one for each value of \(\omega\). For GSaR, the number of hops was also stabilized around one because it is focused on different types of scenarios. Remarkably, and differently from the previous analysis in which the average latency was studied, SACAR-OCVN and SWR presented comparable results in terms of the number of hops. Figure 9: Overhead ratio (\(\theta\)) vs. frequency of message generation (\(\omega\)): smart office scenario. As a summary, after the evaluation of the proposed algorithms and their comparison with the benchmark ones in a small scenario such as a smart office, we can state the next remarks: (i) if most of the nodes are within the same range, the best solution is to use SACAR-IR; (ii) if (i) is not the case, SACAR-OCVN presents the best outcomes among the set of store-carry-and-forward routing algorithms on delivery probability and overhead ratio, although an increase in the average latency and in the number of hops is experienced; (iii) if a balanced trade-off between delivery success and latency is required, then the best option according to the obtained results is to use SACAR-HC. Having evaluated a small scenario, in the next section, we analyze the outcomes of the proposed solutions over a bigger one, both in terms of area to cover and in the number of nodes. Figure 11: Average number of hops (\(\gamma\)) vs. frequency of message generation (\(\omega\)): smart office scenario. Figure 10: Average latency (\(\tau\)) vs. frequency of message generation (\(\omega\)): smart office scenario.
# Seismic Risk Analysis of Existing Link Slab Bridges Using Novel Fragility Functions
## 2 Seismic Reliability Framework
### Fragility Functions  
Among the different probabilistic tools currently available, Multiple-Stripe Analysis (MSA) [41; 43] is selected in this paper due to its efficiency and accuracy in building seismic demand models (e.g., models relating IM values to structural response values \(D\)). In more detail, based on the outcomes of the extensive parametric study performed in [41], and in order to achieve a level of accuracy comparable to that of robust direct simulation methods (i.e., Monte Carlo-like methods), the following setup of analysis is adopted:  
* the IM hazard curve is discretized in 20 IM levels equally spaced in the log-log plane, thus MSA analysis is performed at 20 stripes;
* a set of 20 pairs (two horizontal components) of ground motion samples is used at each IM level, for a total amount of analysis equal to 400 for every case study; this setting reveals essential to properly account for the record-to-record variability typical of real earthquakes (i.e., ground motion features variability with the seismic intensity [44]);
* IM hazard curve truncation at sufficiently low MAF levels (i.e., \(10^{-5}\) year\({}^{-1}\)), with the aim of accounting for the seismic intensity variability through a properly wide interval of IMs, ranging from frequent to rare seismic events.  
Once data are collected from MSA analysis, Fragility functions \(F_{d}(im)\) can be analytically determined. It is recalled that, according to its usual definition [45], a seismic fragility function expresses the probability that an undesirable event will occur as a function of the value \(im\) assumed by the random value IM. In the following subsections, fragility function computation will be widely discussed based on both classical and innovative approaches.
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 1 Introduction
The third stage is feature extraction and classification. We use BAGRU to extract temporal features of CSI and RNet18 to extract spatial features. Finally, their extracted features are fused and input into softmax for classification. The main contributions of this work are as follows:  
* We have used the ubiquitous commercial Wi-Fi infrastructure to design a deep space-time gesture recognition method based on Wi-Fi signals. Due to low cost, no need to carry any equipment and no privacy leakage, it can be applied in many fields. * We propose a feasible feature extraction method RAGRU, which can extract temporal and spatial features from CSI data. We add an attention mechanism to this algorithm, which can give more important weights to important gesture features to obtain high-precision classification results. * We have conducted experiments in two natural environments on datasets of two environments collected by ourselves, verified the system's effectiveness for gesture recognition under Wi-Fi signals and evaluated the system's performance. The experimental results show that the average accuracy of the gestures was above 92% in both natural environments, which proves that the method has strong robustness and practicability in different settings. The remainder of the article is organized as follows. Part 2 introduces the existing gesture recognition technologies and compares their advantages and disadvantages. Part 3 presents the specific process and algorithm of the Wi-GC method. Part 4 introduces the experimental scenarios and parameters and analyzes the impact of various factors on the experimental results used to evaluate the method's overall performance. Finally, Part 5 summarizes the full text and gives an outlook on future research work.
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 3 Methodology
### Transfer Learning Solution Based on 2D-CNN
#### 3.3.2 Image Preparation  
To feed the data from the three systems into 2D-CNN, data instances need to be converted into images. Figure 10 illustrates the workflow needed to prepare the input images for the 2D-CNN. Starting from the symptom vectors of residual data, each case is first plotted as a line plot, and the area between the line and the horizontal zero axis is shaded to aid image recognition. On top of that, the choice of window size is carefully selected. In the ECS case, a threshold of 1.5% deviation from the healthy baseline is chosen. Any data instances below the threshold are considered healthy; hence, a fixed window size is applied to the final input images to avoid zooming into the minute deviations and causing misclassification. The data instances above are likely to be faulty cases; hence, a flexible zoom is allowed to fit the maximum and minimum in the image, which helps to amplify the fault signature. In the APU, due to a lack of consideration of healthy cases, the window size is determined with the aim of reducing the influence of an outlier parameter, ETC_S, which reaches a 100% negative in some cases where there is complete signal loss. An outlier threshold of 3% deviation from the healthy baseline is set so that any data exceeding this limit will have a fixed window size to focus on the parameters other than the outliner signal. The final input images are fed into 2D-CNN without an axis and labels, such as the ones shown in Figure 7, to allow the model to focus on fault patterns.  
\begin{table}
\begin{tabular}{c l} \hline
**ML Classifiers** & **Key Parameters** \\ \hline kNN & n\_neighbors = 3 \\ \hline SVM & kernel = ’linear’, decision\_function\_shape = ’ovo’ \\ \hline DT & max\_depth = 4, \\  & criterion = ’entropy’ \\ \hline RF & n\_estimators = 30, \\  & max\_depth = 4, \\  & criterion = ’entropy’ \\ \hline
**Deep networks** & **Information of layers** \\ \hline \multirow{6}{*}{1D-CNN} & Input layer \\  & Conv1D layer (filters = 16, kernel\_size = 4, padding = ’causal’) \\  & Conv1D layer (filters = 32, kernel\_size = 6, padding = ’causal’) \\  & Conv1D layer (filters = 64, kernel\_size = 8, padding = ’causal’) \\  & MaxPooling1D layer \\  & Flatten layer \\  & Dense layer (units = no. classes, activation = ’softmax’) \\ \hline \multirow{6}{*}{2D-CNN} & Base model: \\  & ResNet101 from Keras Applications, pooling = ’max’ \\ \cline{1-1}  & Top layers: \\ \cline{1-1}  & Flatten layer \\ \cline{1-1}  & Dense layer (units = 256, activation = ’relu’) \\ \cline{1-1}  & Dense layer (units = no. classes for the target systems, activation = ’softmax’) \\ \hline \end{tabular}
\end{table}
Table 2: Details of the ML classifiers and deep models used.
# Performance Evaluation of Asphalt and Asphalt Mixtures Modified by Fuel-Resistant Admixture
## 3 Results and Discussion
### Performance of SMA-13 Mixture
#### 3.2.3 Marshall Stability Test  
The results of the Marshall stability test are depicted in Figure 12. The parallel experimental results satisfy the error tolerance requirements stipulated for repetitive tests in Standard JTG E20-2011 [18].  
Prior to immersion, the Marshall stability reached its peak at an FRM concentration of 1%, exhibiting a 21.07% improvement compared to the matrix asphalt. This improvement may be attributed to the enhanced rheological properties of FRMA in terms of strengthening the adhesion between asphalt and aggregates. The Marshall stability decreased with increased FRM concentration but still surpassed that of the matrix asphalt.  
After the oil erosion treatment, the specimens without FRM exhibited the minimum OR\({}_{\text{MS}}\), reaching only 24.5%, accompanied by a significant decrease in Marshall stability. In comparison to the control group, specimens containing 1% to 7% FRM showed a substantial increase in OR\({}_{\text{MS}}\), approximately 31.3%, 37.4%, 28.8%, and 23.2%, respectively. Therefore, the addition of FRM effectively mitigated the impact of oil erosion on the Marshall stability of asphalt mixtures. Due to the influence of changes in the rheological properties in asphalt and the uniform distribution of FRM in FRMA, OR\({}_{\text{MS}}\) exhibited a parabolic trend.  
According to Standard JTG F40-2004, the Marshall stability requirements for SMA mandate a minimum of 5.0 kN for base asphalt and 5.5 kN for modified asphalt [19]. Before oil immersion, Marshall stability of all specimens met the requirements. After oil immersion, the requirements for Marshall stability were maintained with FRM contents of 1% and 3%. Nevertheless, at an FRM content of 5%, Marshall stability closely aligned with the requirements.
# Analysis of Dynamic Behavior of Spray Boom under Step Excitation
## 3 Results and Discussion
### Analysis of Influence of Boom Sprayer Speed on Vibration of the Spray Boom  
When the boom sprayer passes through the obstacle at a certain speed, it is as if it is subjected to a step excitation, and the upward force of the obstacle on the boom sprayer can be determined by the multi-body simulation on RecurDyn. The speed of the boom sprayer is about 6 km\(\cdot\)h\({}^{-1}\) in the field operation. On RecurDyn, the force of the front axle when the boom sprayer passed through a 0.1 \(\times\) 0.1 m obstacle at 4, 6 or 8 km\(\cdot\)h\({}^{-1}\) was simulated. Results show the upward force on the front axle is about 4800, 5200, 6320 N, and the time for the front wheels to pass through the obstacle is about 0.09, 0.06, and 0.045 s, respectively. Relevant parameters were substituted into the vibration model of the spray boom, and the vertical vibration displacement curves of the extremity of the spray boom at 4, 6, and 8 km\(\cdot\)h\({}^{-1}\) were plotted (Figure 12).  
When the boom sprayer passes through the obstacle, the vertically vibrating spray boom returns to its equilibrium position in about 5 s (Figure 12). After the spray boom is impacted, the vibration decay speed is related to the material characteristics and the vibration characteristics of the suspension of the spray boom [11,25,26]. Therefore, during simulation, the vertical vibration curve of the spray boom in the previous 5 s was taken. Comparison of vertical vibration curves at three speeds demonstrates that with an increase in the traveling speed of the boom sprayer, the amplitude of vertical vibration at the extremity of the spray boom decreases, and the vibration period is shortened. However, when the traveling speed is accelerated from 6 to 8 km\(\cdot\)h\({}^{-1}\), the downtrend of vertical vibration amplitude at the extremity of the spray boom and the downtrend of vibration period are slowed down. This further proves that the spray boom and suspension can  
Figure 11: Model predicted values and experimental values of vibration response of the extremity of the spray boom when the boom sprayer passes through obstacles at 8 km\(\cdot\)h\({}^{-1}\).  
drive the spray boom to follow the low-frequency components of the ground, while isolating the fluctuation and inclination of high-frequency components corresponds to the characteristics of the weak damping structure of the spray boom [5; 11; 26]. When the boom sprayer passes through the obstacle at a speed of 4 km\(\cdot\)h\({}^{-1}\), the vertical vibration amplitude at the extremity of the spray boom maximizes to 0.638 m upward. Due to the differences and vibration superposition between the upper and lower vibrations of the boom sprayer body and the vertical vibration frequencies of the spray boom, the spray boom is interfered by the upper and lower vibrations in the vertical vibration and the elastic deformation of the spray boom, resulting in small amplitude fluctuation at intervals [26; 27].
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 5 Results and Discussion  
### Quantitative Results  
With the semantic fusion process introduced in Section 3.4, the coarse semantic 3D point cloud was generated. Its quantitative result is denoted as the 3D baseline in Table 2. To be more specific, most points in 3D baseline are correct, yet with outliers and errors. The evaluation result of 3D baseline's re-projection map demonstrates that the 3D baseline is much better than 2D in both PA and F1-score. Figure 6a,b illustrate this fact vividly, where _Vehicle_ is segmented badly in 2D segmentation and segmented much better in 3D baseline.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Accuracy(\%)** & **Precision(\%)** & **Recall(\%)** & **F1 score(\%)** \\ \hline Building & 95.60 & 98.25 & 94.87 & 96.53 \\ Vegetation & 89.85 & 76.96 & 71.24 & 73.99 \\ Vehicle & 97.95 & 67.09 & 22.02 & 33.15 \\ Road & 87.91 & 52.58 & 73.84 & 61.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of 2D semantic segmentation.  
Figure 5: Visualization of PKU-M1.(**a**): A sample image of PKU-M1, (**b**): ground truth of (**a**), (**c**): prediction of (**a**), and (**d**): 3D re-projection map of (**a**). Since the re-projection map (**d**) is quite sparse, we use Pixel Accuracy to compare the re-projection map and the ground truth map. **Grey**: Building, **Green**: Vegetation, **Blue**: Vehicle, **Pink**: Road, **Black**: Background. Best viewed in color.  
Furthermore, as shown in Table 2, the Pixel Accuracy of 3D baseline is 87.76%, and the F1-scores of _Vehicle_, _Vegetation_, and _Road_ are relatively low. The refinement methods introduced in Section 3.5 are denoted as Local, Global, and Local+Global in Table 2. Local, Global, and Local+Global methods in Table 2 have been fully tested, and we put the best results under various parameters to this table. With refinement, the F1-score of Vehicle significantly rises, while Building, Vegetation, and Road also have increased scores. In addition, the Local+Global optimization approach is better than the Local or Global approach in each semantic category. It leads to the conclusion that the Local+Global approach outperforms any single Local or Global approach.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 2 The Proposed Methodology
### Novelty of the Proposed SP-SSA Method  
The proposed approach integrates SSA and superpixel segmentation for the first time to extract improved the spatio-spectral features from HSI. Reconstruction of object-specific spatial sections, rather than the entire image, may be more effective. Hence, in the proposed work, 2D-SSA is applied individually to each superpixel segmented region to extract the local contextual information accurately. The pseudo-code for the proposed SP-SSA algorithm is outlined in Algorithm 1.  
```
Input: HS image, \(H\in\mathbb{R}^{n\times b}\)  Ground Truth \(GT\)  number of superpixels: K  Embedding Window Size:\(M_{x}\times M_{y}\)  Eigen Value Grouping: \(EV\) Output: Classification Map \(clsmap\) generated by SVM.
1:for\(b=1\) to \(B\)do
2: Perform SLIC superpixel segmentation to obtain segmentation map \(L\) from \(h^{b}\) containing \(K\) superpixel segments \(L\) = SLIC (\(h^{b}\),\(K\)) (as outlined in Section 2.1)
3:for\(k=1\) to K do
4:\(h^{b^{\prime}}_{k}=reconstruct2 DSSA(L,M_{x},M_{y},EV)\) (as outlined in Section 2.2)
5:endfor
6:endfor
7: Obtained Reconstructed HSI \(H^{{}^{\prime}}\in\mathbb{R}^{n\times b}\)
8:\(clsmap\) = SVM (\(H^{{}^{\prime}}\), GT)
```  
**Algorithm 1**Proposed SP-SSA algorithm for HSI classification.  
For each superpixel, the reconstruction (\(reconstruct2 DSSA\) (Algorithm 1)) is applied to the rectangular Region of Interest (ROI) surrounding the superpixel (Figure 4). The ROI is created based on the location information of the pixels available in that particular segment. Only the reconstructed pixels specific to those pixels in the selected superpixel are  
Figure 3: Implementation of 2D-SSA on a HSI scene (**a**) Original scene at 667 nm. (**b**) 1 st component grouping. (**c**) 1–5 th component grouping. (**d**) 1–10 th component grouping, where \(M_{x}\) = 5, and \(M_{y}\) = 5.  
stored as spatial features, while the remaining reconstructed pixels in the ROI are discarded as they do not belong to the superpixel under test. The same procedure is applied to all other superpixels, and the HS image is reconstructed using the proposed SP-SSA approach. This procedure collects local object-specific superpixel-based spatial features for each band in the image.
## 3 System Overview  
The proposed system is implemented to achieve high room level localisation accuracy and reliability. To this end, six steps need to be taken to identify the trajectory of the mobile user, as presented in Figure 1. The first step is to segment the indoor area into several cells and randomly assign multiple RPs within each cell. Then, training data collection is carried out by fusing RSS measurements taken at all RPs within each cell by all contributed devices. A multivariate linear regression is conducted to calibrate the RSS measurements collected from different devices. The missing RSS values are replaced by the new data estimated by the EM imputation method. The fifth step is to exploit the MVGMM to construct the probabilistic radio map for each cell based on the calibrated training data. Lastly, an online matching process is performed which is to fit the runtime observation into the distribution model of each cell, and feed into the Viterbi Algorithm to backtrack the trajectory of the user.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## Materials and Methods  
### Field Site and In Situ Crop Measurements  
Multi-temporal field campaigns, carried out during a 2017/2018 winter wheat growing season, involved acquiring sets of ground measurements in coordination with UAV-mounted multispectral camera observations over an experimental field trials site.