esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Satellite Data  
#### 2.6.1 Planet Satellites  
Planet [34] is a commercial company running a constellation of small, standardized CubeSat 3U form factor (10 \(\times\) 10 \(\times\) 30 cm) nano-satellites of approximately 4 kg in mass. Planet currently operates more than 175 of the so-called 'Doves' PlanetScope (PS) nano-satellites, providing unprecedented daily nadir-pointing high-resolution land surface imaging of the entire earth. Planet's PS satellites provide images at a spatial resolution of 3 m (nadir ground sampling distance) in four spectral bands (RGB and NIR), with an equatorial overpass time of 9:30-11:30 a.m. (local time) [33], sometimes even three times a day (i.e., from different Doves). Planet Team currently provides a series of application programming interface (API) tools, such as Clips API [49].  
In this study, a total of 3015 clipped PlanetScope Ortho Tiles were downloaded for 2017 using the Clips API for the vineyard areas within the three regions of interest (Figure 1a). Tiles were downloaded as TIFFs, often including several files for a specific date with also missing images in some dates due to cloudy conditions (<10% missing data). Figure 11 shows a true RGB color Planet image of Mevo Beitar vineyard alongside the plots where \(\Psi_{\text{stem}}\) were measured (see Section 2.3), for comparison of scales.
# CourseKG: An Educational Knowledge Graph Based on Course Information for Precision Teaching
## 4 Experiments
### Entity Recognition Results
#### 4.2.2 Efficiency Analysis  
The accuracy and loss were also considered in the model training process. Figure 5 depicts the accuracy and loss curves, offering a visual representation of the model's performance throughout the training process. The accuracy curve shows the model's capability to accurately recognize data, whereas the loss curve reflects the incurred errors during training. Based on the results, both the loss and accuracy exhibit improvement as the number of epochs increases. This suggests that our model was progressively learning and refining its accuracy. After around 80 epochs, the model reached stable performance, indicating that additional training iterations may not yield substantial improvements. Consequently, the accuracy and loss curves illustrate that beyond approximately 80 training epochs, our model demonstrated strong convergence and excelled in performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dropout** & **Learning Rate** & **Acc (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \multirow{3}{*}{0.1} & 0.01 & 84.41 & 76.29 & 86.19 & 80.93 \\  & 0.001 & 85.89 & 77.15 & 87.23 & 81.88 \\  & 0.0001 & 86.21 & 79.61 & 88.01 & 83.60 \\ \hline \multirow{3}{*}{0.5} & 0.01 & 85.12 & 77.34 & 89.30 & 82.89 \\  & 0.001 & 86.43 & 78.79 & 90.08 & 84.06 \\  & 0.0001 & **88.63** & **80.96** & **90.56** & **85.49** \\ \hline \multirow{3}{*}{0.9} & 0.01 & 81.56 & 75.27 & 84.43 & 79.59 \\  & 0.001 & 82.79 & 78.87 & 86.17 & 82.36 \\ \cline{1-1}  & 0.0001 & 84.31 & 76.24 & 85.22 & 80.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of different parameters. The best results are emphasized in **bold**.
# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
## 3 Methodology
### Model Training  
Neural networks are recognized for their advantages in pattern recognition and data classification. Thus, our experiments are focused on comparing different neural network architectures using transfer learning for the specified application.  
The utilization of transfer learning can be justified based on its ability to leverage pre-trained models. This approach allows the model to inherit valuable knowledge and features acquired from large and diverse datasets, which can significantly expedite the training process and enhance performance, especially when the dataset is limited. Further, both compact and regular networks are used and compared because of the benefits of these diverse architectures. Compact networks are particularly advantageous in resource-constrained environments, such as mobile devices and edge computing. They strike a balance between model size and performance, making them efficient choices for specific applications. Additionally, compact networks are often less prone to overfitting, which is a crucial advantage when working with smaller datasets.  
Figure 4: Representation of benign and malicious MS Office files in grayscale image.  
Figure 5: Representation of benign and malicious PDF files in grayscale image.  
Figure 3: Feature extraction process of grayscale image for all file types.  
Both series and directed acyclic graph (DAG) architectures are also considered and compared due to the flexibility that these structures offer. Series architectures are typically used when data flow sequentially, allowing for straightforward modeling of linear dependencies. In contrast, DAG architectures enable more complex and interconnected relationships between layers, which can capture intricate dependencies and patterns in the data. By considering both options, we are able to compare which architecture works best for the specific task's requirements, ensuring that the model can effectively capture the dependencies inherent in the data.  
Given the diversity of the pre-trained model architectures compared, we use accuracy as the basis for determining the top methodology. The input to the models are the extracted grayscale images, which are pre-processed before being fitted in order to align with the input requirements of the model. For example, pre-trained models have specific criteria related to image color scales and dimensions. Consequently, all feature images generated needed to be standardized by concatenating them into a three-dimensional format representing the RGB color scale. Further, these images have to be resized as per the model's requirements, as previously specified in Table 1.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.10 DDoS (Distributed Denial of Service) Attack Resistance  
DDoS attacks include replay, false data injection, de-synchronization, flooding, jamming, etc. The above security analysis shows that this scheme can achieve mutual authentication and session key security. Therefore, the proposed scheme can resist false message injection attacks, which are mainly against unauthenticated and unencrypted communication processes.  
The above analysis shows that the proposed scheme can also resist the desynchronization attack. At each step, the entity verifies the validity of the received message, which ensures the consistency of the messages between the two sides of the communication. Consistency verification can also resist the jamming attack.  
After receiving the authentication request, the data concentrator first checks the timeliness of the message, and directly discards the messages that do not meet the timeliness requirement, thus resisting the DoS attack by replaying the old message. Next, the data concentrator authenticates the smart meter. The verification of the authentication requestonly involves the Chebyshev polynomial and the hash operation with low computational overheads, which requires very few computing resources, and discards the packets that fail to verify and terminate the session at the same time, which can alleviate the impact of the flooding attack. A further defense scheme against DDoS attack is given in Section 7. Therefore, the proposed scheme can resist or mitigate the above DDoS attacks.
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 2 Background
### K-Nearest Neighbor  
The KNN model is one of the simplest and most straightforward supervised learning models in machine learning. The key idea of this algorithm is to decide a predicted value based on the labeled data points of the training set that are near the query data point. KNN starts by loading the training data points in memory. Then, the classification task is completed by finding the nearest \(K\) data points. Finally, a vote of the \(K\) closest points to the query point will determine the class of the query data point. One critical decision that needs to be made is the selection of the distance function. Several distance functions have been proposed to compute the distance between two data points; however, the most common methods are cosine similarity and Euclidean distance.  
The Euclidean distance can be calculated by subtracting the training data point from the point to be classified, as in Equation (4):  
\[E(x,y)=\sqrt{\sum_{i=0}^{n}(x_{i}-y_{j})^{2}}. \tag{4}\]  
The calculation of the marginal probability can be ignored in this model, as it has the same value as in the GNB calculations. In this calculation, we determine the data point class based on the best posterior probability value. Although KNN contains a limited number of hyperparameters (i.e., the k-value and distance function), which makes it a simple model, the \(K\)-value can dramatically affect the model performance. Similar to DT, the KNN model has a large computation cost if the dataset is large.
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 5 Results
### Multi-Temporal Analysis
#### 5.2.3 Seasonal Deformation Coincident with Wetland Areas
Clays, silts and other poorly consolidated sediments are fine-grained and highly compressible, and therefore, subsidence in the unconfined aquifer is most likely to be coincident with these deposits [43]. Displacements may also arise due to shallow (top metre) shrinkage and swelling of the deposits in response to changes in water content [80]. However, it is not immediately evident why the three wetland/swamp regions identified in Figure 4 exhibit subsidence, whereas others do not, despite being associated with the same surface geology. We calculate displacement time series at five other wetland areas throughout the Perth Basin, but all show cumulative displacements of less than 5 mm over the observation period, despite changes in hydraulic head at nearby wells comparable to those shown in Figure 6. This disparity may be linked to variations in hydraulic conductivity between wetland areas and the unconfined aquifer or the occurrence/absence of sediments that are more susceptible to seasonal swelling and shrinkage. Other factors found to control the spatial distribution of deformation include the distribution of extraction wells, the thickness of deposits [77] and the presence of barriers to groundwater flow [81].
# Regeneration Performance of Activated Carbon for Desulfurization
## Results and Discussion
### SO2 Adsorption
#### 3.2.3 Effect of Oxygen Content on SO\({}_{2}\) Adsorption  
To investigate the influence of O\({}_{2}\) contents on the SO\({}_{2}\) adsorption efficiency, experiments were carried out under conditions of different O\({}_{2}\) content (0, 2.5, 5, and 7.5% O\({}_{2}\)). Below 5%, it was observed from Figure 6 that with the increase in O\({}_{2}\) content, the desulfurization efficiency increases. It indicated that the present of O\({}_{2}\) could improve the desulfurization efficiency. This can be explained as follows: if oxygen and water vapor exist in the process of SO\({}_{2}\) adsorption, SO\({}_{2}\) will be oxidized to sulfuric acid and introduced into the pores of the AC, which improves the desulfurization efficiency of AC [28]. When the pore widths in the range of microporous to mesoporous increase, the capacity for storing gas and liquid may be improved [20]. The desulfurization efficiency above 5% was less. It could be shown that a large number of oxides were produced due to the combination of too much O\({}_{2}\) with the active components on the AC surface, thereby the AC fails to combine with more active sites, thereby its desulfurization efficiency decreases [29]. Therefore, we selected a fixed condition of oxygen content of 5% for the rest of the experiments.
## 3 Optimization Model
### MOGA Module
#### 3.3.1 Optimization Model  
Considering the multi-objective optimization problem with different scales and units, the nondominated sorting genetic algorithm 2 (NSGA-II) [50] was adopted in order to minimize the three objectives listed in Equations (11)-(13). Furthermore, because of deficiencies with respect to depicting the optimal Pareto front, the weighted-sum genetic algorithm approach [51] was not an option for the multi-objective optimization problem. Several optimal solutions for multi-objective problems were obtained with the following objectives and constraints.  
Objectives:  
\[\textit{Minimize}\ f\text{1}(s,x)\text{: }T_{a} \tag{11}\]  
\[\textit{Minimize}\ f\text{2}(s,x)\text{: }Ct \tag{12}\]  
\[\textit{Minimize}\ f\text{3}(s,x)\text{: }Mx \tag{13}\]  
where _Ta_ is the actual construction time, _Ct_ is the total cost, _Mx_ is the worker utilization fluctuation moment, \(s\) is the shifting time set, and \(x\) is the predecessor option set.  
Constraints:  
\[Ta\leq\textit{Maximum}\ Contract\ days \tag{14}\]  
\[r_{xt}\leq MWA_{x} \tag{15}\]  
\[LPF\leq 10\%\ of\ Contract\ price \tag{16}\]  
where \(r_{xt}\) is the worker demand type-_x_ on day \(t\), _MWA_x is the maximum worker availability of resource type \(x\), and _LPF_ is the late penalty fee.  
This study considers four types of resource, namely general workers, skilled carpenters, sanitary plumbers, and electricians. _LPF_ was calculated from the construction contract, and was not allowed to be greater than 10% of the total contract cost, otherwise the contract will be terminated.
## 3 Results
### Progeny Production for Bioassay 2
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**55\%**} & \multicolumn{2}{c}{**75\%**} \\ \hline  & **20 \({}^{\circ}\)C** & **25 \({}^{\circ}\)C** & **20 \({}^{\circ}\)C** & **25 \({}^{\circ}\)C** \\ \hline false indigo & \(140.55\pm 8.33\) Ah & \(65.65\pm 11.10\) Agh & \(140.88\pm 1.44\) Af & \(145.17\pm 15.77\) Bj \\ false indigo-DE & \(68.33\pm 5.12\) Ad & \(25.11\pm 3.50\) Ae & \(130.88\pm 1.36\) Be & \(139.12\pm 16.88\) Bi \\ false indigo–wood ash & \(23.12\pm 7.65\) Abc & \(11.88\pm 2.77\) Ac & \(115.88\pm 9.12\) Bd & \(100.11\pm 12.66\) Br \\ Canada goldenrod & \(136.36\pm 5.88\) Bh & \(70.66\pm 5.55\) Af & \(108.55\pm 12.14\) Acd & \(130.77\pm 15.66\) Bh \\ Canada goldenrod–DE & \(115.91\pm 7.33\) Afg & \(28.21\pm 35.5\) Ae & \(100.66\pm 14.12\) Acd & \(25.88\pm 2.88\) Aa \\ Canada goldenrod–wood ash & \(17.66\pm 2.33\) Aab & \(8.13\pm 1.32\) Abj & \(99.17\pm 5.99\) Ac & \(100.88\pm 8.88\) Rf \\ control DE & \(11.30\pm 2.74\) Aa & \(1.02\pm 0.04\) Aa & \(80.16\pm 6.66\) Bb & \(90.15\pm 5.66\) Be \\ control wood ash & \(21.13\pm 2.97\) Ab & \(1.05\pm 0.06\) Aa & \(40.12\pm 5.66\) Ba & \(30.77\pm 5.66\) Bb \\ staghorn sumac & \(120.11\pm 7.99\) Ag & \(95.11\pm 6.66\) Bi & \(130.88\pm 5.56\) Ae & \(65.11\pm 5.12\) Ad \\ staghorn sumac–DE & \(80.40\pm 2.55\) Ac & \(45.44\pm 5.44\) Af & \(115.15\pm 6.65\) Bd & \(68.56\pm 5.10\) Bd \\ staghorn sumac–wood ash & \(30.30\pm 1.66\) Ac & \(2.15\pm 12.2\) Aa & \(130.77\pm 7.69\) Be & \(50.42\pm 5.12\) Be \\ tree of heaven & \(136.4
# The Complex Relationship between Virulence and Antibiotic Resistance
### Antibiotic Inactivation
#### 2.4.2 Changes in Cell Permeability and Efflux  
Bacterial efflux pumps have evolved as a protective mechanism in both gram-positive and, particularly, gram-negative [30; 80] bacteria to maintain cell homeostasis and communication by actively pumping out solutes, metabolites, quorum sensing molecules, and toxins, specifically antimicrobial compounds [30; 96; 97; 98; 93]. There are five families of bacterial drug efflux pumps: the ATP-binding cassette (ABC) superfamily [99], the major facilitator superfamily (MPS) [100], the multidrug and toxic compound extrusion (MATE) family [101], the small multidrug resistance (SMR) family (a subgroup of the drug/metabolite transporter superfamily [102]), and the resistance-modulation-division (RND) superfamily [103; 104]. However, multiple drug resistance can be acquired via up regulation in a single efflux pump, making this a highly effective system [105; 106; 107; 30]. [PERSON] et al. evaluated a library of antimicrobial compounds, finding that all but 2 of the 33 compounds tested were susceptible to efflux by the endogenous multidrug efflux system in _E. coli_[108]. This lack of drug specificity facilitates the synergistic coupling of efflux up regulation with other resistance mechanisms, particularly changes in outer membrane permeability and will likely negatively impact drugs in development.
## 2 Pointing Strategy and Model Establishment
### Dynamic Model
#### 2.2.3 Mechanism Dynamics Model  
The angular acceleration of the telescope pointing mechanism can be obtained according to the angular momentum theorem:  
\[\dot{\mathbf{H}}_{\mathrm{O}}=\dot{\mathbf{J}}_{t}\ddot{\mathbf{\theta}}_{ \mathrm{O}}, \tag{3}\]  
where \(\mathbf{\theta}_{\mathrm{O}}\) is the rotation angle of the pointing mechanism, and \(\dot{\mathbf{J}}_{t}\) is the moment of inertia. According to the conservation of angular momentum, \(\dot{\mathbf{H}}_{\mathrm{O}}=\) external torque--internal torque. Consider using a preliminary second-order model to describe the dynamics of the telescope pointing mechanism:  
\[\begin{array}{l}\ddot{\mathbf{\theta}}_{\mathrm{O}}=-2\omega_{\mathrm{N}} \xi\dot{\mathbf{\theta}}_{\mathrm{O}}-\omega_{\mathrm{N}}^{2}\mathbf{\theta}_ {\mathrm{O}}+J_{t}^{-1}(\mathbf{M}_{\mathrm{T}}^{\mathrm{O}}+\mathbf{D}_{ \mathrm{T}}^{\mathrm{O}})-J_{t}^{-1}(\mathbf{M}_{\mathrm{T}}^{f}+\mathbf{M}_{ \mathrm{T}}^{\mathrm{SO}}+\mathbf{M}_{\mathrm{T}}^{\mathrm{EO}})\\ \omega_{\mathrm{N}}=\sqrt{\frac{\mathrm{K}}{I}},\ \xi=\frac{\mathrm{B}}{2\sqrt{K}\dot{ \mathbf{\theta}}_{t}}\end{array}, \tag{4}\]  
where \(\omega_{\mathrm{N}}\) is the natural frequency, \(\xi\) is the damping coefficient, and K, B are the stiffness and damping, respectively. \(\mathbf{M}_{\mathrm{T}}^{\mathrm{O}}\) is the driving torque generated by the piezoelectric actuator, \(\mathbf{D}_{\mathrm{T}}^{\mathrm{O}}\) is the external disturbance torque acting on the pointing mechanism, primarily due to the gravity gradient torque, \(\mathbf{M}_{\mathrm{T}}^{f}\) is the internal friction torque of the telescope pointing mechanism, \(\mathbf{M}_{\mathrm{T}}^{\mathrm{SO}}\) is the spacecraft reaction torque on the telescope pointing mechanism, and \(\mathbf{M}_{\mathrm{T}}^{\mathrm{EO}}\) is the reaction torque of the test mass (TM) on the telescope pointing mechanism. Since the TM is located inside the telescope pointing mechanism, the magnitude of the reaction torque on the mechanism is equal to the torque applied to the TM.
# Deepwater 3D Measurements with a Novel Sensor System
## 2 Materials and Methods
### Underwater 3D Sensor System
#### 2.1.2 Software Architecture  
In order to manage the control of all functions of the sensor system and data transfer, from the image recording to the monitoring of the final 3D model of the observed scene, a software concept was developed, as shown in the scheme of Figure 2, and implemented.  
This concept was realized in such a way that certain data were available in real-time to support rough 3D reconstruction result monitoring with a time delay below 200 ms. This short latency time is necessary because incomplete 3D measurements must be prevented. If the operator identifies the need for a repeated measurement because of incomplete 3D data results, they can immediately navigate the ROV with the sensor system back to the relevant position.  
Figure 2: Software architecture.
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
## 3 Results
### Validation of the Generation and Detection of Photoelectrons  
We first verified the feasibility of generating photoelectron beams using the current platform. The energy of the electron excited by the photoelectric effect is determined by the difference between the energy of the photon and the work function of the metal plate. Therefore, the voltage applied to the metal surface will not affect the initial energy and quantity of photoelectron but only affect the motion state of photoelectron after emission and the size of photocurrent. As shown in Figure 3, when the UV lamp was turned off, the MCP had no output signal with varying V\({}_{\text{M-Z}}\) (from \(-100\) V to \(500\) V). In comparison, turning on the UV lamp directly resulted in a slight rise in the MCP background signal, which was probably caused by some UV photons being reflected into the MCP channels. In addition, the signal intensity was increased with increasing V\({}_{\text{M-Z}}\), implying that the signal was generated by the MCP detecting the incident photoelectrons. That is, a higher V\({}_{\text{M-Z}}\) is believed to enhance the kinetic energy of photoelectrons, leading to an increased MCP response.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 2 Related Basic Theory and Algorithm  
Based on the two main problems mentioned before, relevance and computational complexity, the related theory for solving the problems are listed in this section. First, the maximum information coefficient (MIC) is used to measure the relevance between features and features, and the relevance between features and categories. Second, an intersection operation is employed so that it can avoid high computational complexity. Third, in order to reduce data dimensions for further and to shorten the training time of classification model, the PCA algorithm is utilized.
## 5 Results and Discussion
### General Remarks  
The comparative evaluation of the results obtained between undamped and damped case, discussed in terms of displacement and base shear, has led to the following interpretations:  
* It is well known that the structural response reduction increases as the mass of TMD increases, but this mass has a limit in practice, due to geometrical and economical constraints. That is the reason why the mass ratio is not considered as an important value to optimize, and therefore, it is hard to achieve high reduction values practically. The results show that TMD systems are not effective for low and mid-rise buildings, because both the displacement and base shear values are barely affected, unlike high-rise building values. In fact, TMD are motion-based systems that demonstrate how their effectiveness is very limited for rigid buildings. As for the high-rise building, even though damped case with PTMD provides less reduction compared to the two other damped cases, it is considered acceptable and more suitable for this kind of structure.
* Structural strengthening with viscous damper systems is defined by the desired additional damping fixed in the preliminary design. From the results obtained, it has been observed that the structural response with the viscus dampers decreases well, showing better performance in terms of the displacement and base shear. In addition, viscous dampers are velocity-dependent systems, where its effectiveness increases with high velocities, usually for flexible buildings. Even though these systems are considered effective for the three studied buildings, they are considered more suitable for mid-rise buildings.
* Friction dampers' incorporation into the structures reduces considerably the building's response after optimizing dampers slip forces, their numbers, and locations under all earthquakes and types of buildings considered. It can be seen from the results obtained that the friction dampers are effective for both rigid and flexible buildings.
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Vibration Sensors in Port Crane Monitoring  
Vibration sensors are integral to RNDT of port cranes, enabling the detection of dynamic mechanical responses such as gear misalignment, bearing wear, or structural im-balance. These sensors support predictive maintenance by providing real-time data on operational integrity and fault development in crane systems. This section outlines the working principles, sensor types, and application relevance for port crane environments.