esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FlexMonitor
### Monitoring Strategy and Data Source
#### 3.2.2 Data Source  
According to the proposed monitoring strategies, there are mainly four kinds of monitoring data sources in FlexMonitor: the raw packets, the statistics data, the end-to-end measuring data and the network event triggering messages.  
Figure 3: A hybrid monitoring strategy instance.  
The characteristics of different monitoring data sources are listed as Table 1. Among these four kinds of monitoring data sources, the network event triggering message is a kind of efficient monitoring data source. When the collection part listens to the event triggering messages, it can directly obtain the occurrence information of the network event without further analyzing the event triggering messages. Through relying on the event triggers in the switches or end-hosts, the network monitoring can realize the corresponding network event detection, which may only incur limited event triggering messages into the network. It can be seen that the monitoring overhead can be greatly reduced in this way. The reduction of monitoring overhead can be reflected from two perspectives: On the one hand, for the reason that the collection unit of each event trigger would continuously poll the network state and the computing unit of each event trigger would also continuously judge whether the network condition meets the deployed event triggers in the targeted switches or end-hosts, the computing overhead in the controller can be almost entirely reduced. On the other hand, due to the decrease of the port or flow statistics polling messages, the communication overhead between the controller and switches is also greatly reduced. In addition, through defining appropriate triggering conditions, using the network event triggering messages as one of monitoring data sources can be helpful to enhance the real-time detection of corresponding network events. In practice, many network events can be easily described into event triggers. These network events include the detection of network congestion, abnormal traffic, elephant flows, network hot spots, etc. For example, regarding the elephant flow detection, the triggering condition can be defined as a specific elephant flow threshold and the basic monitoring data can be defined as the byte statistics for each flow. When there exists a flow where its byte statistics exceeds the pre-defined threshold, the switch would trigger an elephant flow notification message to the controller.  
According to the specific monitoring requirements of upper management applications, the collection part would continuously collect monitoring data from these different sources. In addition, FlexMonitor can use customized analysis algorithms to analyze and aggregate the received monitoring data. The upper management applications can obtain these required monitoring data through the northbound interface.
## 3 Recent Literature Analysis
### Cyber-Physical Systems
#### 3.2.3 Cyber Defense  
Building effective defensive measures against cyber-attacks was also a key theme within the novel CPS literature. There was a focus on research into CPS intrusion detection, and to counter certain attacker techniques. This area of CPS research included some machine learning implementations; however, it also identified challenges to these machine learning approaches to enable CPS defense.  
The introduction of federated deep learning is a key research step towards intrusion detection in Industrial CPS [49]. This challenge is exacerbated by the need to preserve privacy within such systems [49]. Intrusion detection is also applied to other CPS subsets, such as in healthcare, where deep belief network models are utilized to identify potential attacks against the system [19].  
Novel studies regarding trustworthiness within Industrial CPS rely on the optimization of defense resources utilizing machine learning-enhanced evaluation frameworks [50]. This has flow-on effects to response metrics and risk prevention, with further research opportunities in relation to human integrity problems [50].  
Intrusion detection learning through a Siamese convolutional neural network is another research area identified as novel, with such a model improving detection metrics and false alarm rates [51]. Additional benefits through few-shot learning are of particular value to this research, potentially enabling the network to identify anomalies relatively early in the leaning process compared to a standard neural network, which presents value in an evolving cyber threat context [51]. Few-shot learning presents research opportunities that extend beyond specific case studies and show usefulness in a variety of learning scenarios. In complex CPS where temporal and feedback components may induce unpredictability, research into few-shot algorithms has the potential to provide further robustness to detections.  
Within the recent novel literature, vulnerabilities to CPS discussed included solutions to prevent false data injection (FDI) attacks within nonlinear semi-Markovian switching CPS [52]. In a 5G context, research into Distributed Denial-of-Service (DDoS) attacks is addressed through the processing of real-time data through volutional neural networks to enable early detection [26]. Such studies bring forth questions regarding cross-applicability to other CPS outside of the mobile telephone subset. Specific attack technique discussions were not a major theme within the research; however, they highlight concerns relating to the current threat landscape.
# Deep Learning Methods for Bug Bite Classification: An End-to-End System
## 2 Materials and Methods  
Our research is focused on an image classification problem aiming to determine the bug that caused the bite on the basis of a provided image of the bug bite taken with a mobile phone. In this paper, we used various classification methods to address the bug bite classification task. The traditional methods that we tried, which were based on extracted SIFT descriptors, feature bagging, a support-vector classifier, a random forest classifier, and a grid search with 5-fold cross-validation, showed poor results. Thus, in this paper, we do not report and discuss these traditional approaches and their results.  
Recent studies have shown that deep learning techniques have taken the leading role in various directions of computer vision, such as segmentation, detection, classification, etc. [25]. This motivated us to apply CNN architectures to the bug bite classification task and to compare the results from their evaluation. In this paper, we report results regarding the models based on the following CNNs: (1) the lightweight MobileNet-v2 because it is particularly designed to fit the limited performances of mobile devices; (2) the very powerful but extensive VGG16 due to its simplicity and fast convergence in addition to its power; (3) Inception-v3 because of its computational efficiency, which does not compromise the performance; and (4) DenseNet169 due to its ability to increase the accuracy without reducing the performance while avoiding the risk of overfitting. We have also tried several other state-of-the-art strategies, including those provided by Google's service AutoMLVision [26], as well as different variants of Xception [27], but we do not report the results based on these architectures since they performed significantly poorly.  
Additionally, we propose a two-level approach, in which skin vs. non-skin images are filtered at the first level, while the bug that caused the bite is identified at the second level. At the second level, the pre-trained, transfer learning CNN models tuned for this specific task, including MobileNet-v2, VGG16, Inception-v3, and DenseNet169, are used and compared. Additionally, with an expectation for the improvement of the model performances, we propose an ensemble model built with model stacking. An end-to-end system that relies on this two-level approach is described in this section as well.  
For each of the applied methods, we use the training set for the training and optimization of the models. The test set is used out-of-sample for the evaluation of the models' performance.
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 4 Evaluation
### Controller Parameter Setting and Evaluation
#### 4.2.2 Evaluation of Temperature Setting Limit and Switching Time Limit Criterion  
Figure 8 shows the \(c^{*}(t)\) signal output of the PID controller and the MCA PID controller. The control signal input to the temperature chamber from the PID controller outputs a maximum temperature control signal of 50.08 \({}^{\circ}\)C at 10 s, which exceeds the temperature change limit of \(T_{\rm s}\leq\) 15 \({}^{\circ}\)C and does not meet the temperature setting limit criterion. For the TFB PID parameter adjustment method, there is a large mutation of the control signal. In the actual use process, it will cause direct damage to the thermostat control system. For the Fuzzy PID control method, the parameter adjustment process can be seen from the controller output signal. Its output signal is smaller than the original PID but cannot meet the system requirements. For the MCA PID controller, at 27.5 s, the output maximum temperature control signal is 13.09 \({}^{\circ}\)C, which meets the temperature setting standard. Combining the \(c^{*}(t)\) signal output of the several PID controllers, it is found that the MCA PID controller design method can control the output of the control signal to the thermostat well, and can limit the output of the large value control signal to ensure the safety and stability of the thermostat process.  
Figure 7: System response under different controllers.  
Figure 9 shows the control of the working state of the thermostat by the \(c^{*}(t)\) signal output by various controllers. The PID controller produces a heating control signal at 10.0 s and a cooling signal at 10.01 s, remaining inactive for only 0.3 s between 66.7 and 67 s, and failing to satisfy the required short adjustment time \(t_{s}\geq 5\)s. For the TFB PID controller design method, although it has better dynamic performance in Figure 7, its control signal direction changes frequently. For the Fuzzy PID control method, the direction of the control signal frequently changes due to the automatic adjustment of the controller parameters. For the above controllers, the direction of the output signal frequently changes, which will speed up the loss of the thermostat and even cause damage to the compressor. The MCA PID control method proposed in this paper, from 106.0 s to 117.6 s, is the shortest time to maintain a working state. The time interval is 11.6 s, which satisfies the set minimum adjustment time. It can ensure the incubator's long-term stable operation and improve the system's reliability.  
Figure 8: PID controller output signal.  
Figure 9: Ethanol thermostat working condition.
## 5 Materials and Methods
### Gene Cloning and Sequence Analysis  
In order to clone ICPs genes from BtSU4 strain, the Roche/454 technology was used to sequence the complete genome of this strain. After genome assembling, performed by Roche 454's [PERSON], the contig00204 (GenBank accession number MT905434) was detected, containing two ICPs genes by using the Basic Local Alignment Search Tool (BLAST, [[https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+](https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+)]([https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+](https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+))) and an in-house Bt toxin genes database. The two _cry_-type genes were deposited in GenBank with the following accession numbers AY897354 and EU381044. The new Cry proteins were annotated by using the Conserved Domains Database (CDD) search ([[https://www.ncbi.nlm.nih.gov/cdd/](https://www.ncbi.nlm.nih.gov/cdd/)]([https://www.ncbi.nlm.nih.gov/cdd/](https://www.ncbi.nlm.nih.gov/cdd/))). The Phylogenetic analyses of the amino acid sequences were conducted by Neighbour-joining [28] and MEGA X [29].  
Figure 5: Insecticidal activity data of Cry8 proteins published before and in this work [12; 20; 21; 22; 23; 24; 25; 26]. The “A” and “L” labels after the insect name means the “Adult” or “Larvae” stages of those insects.
# Soil Depth Prediction Model Using Terrain Attributes in Gangwon-do, South Korea
## 2 Methodology  
### Study Area and Terrain Attributes  
The study area is selected as Gangwon-do in South Korea (Figure 1), with a total area of 16,827.93 km\({}^{2}\) and a population of 1,539,521 (population density of 90 people/km\({}^{2}\)). Gangwon-do has the most mountainous region and the lowest population density in Korea [42]. To estimate the terrain attributes of the study area, a digital map (scale 1:25,000) of data from the National Geographic Information Institute [43] is used. Terrain attributes are derived using ESRI's ArcGIS (ArcMap 10.1). Definitions and results of each derived terrain attribute are shown in Table 1.  
The SLOPE is defined as the angle between the tangent to the terrain surface and the horizontal plane and is a variable that determines the flow velocity by gravity [44].  
\[\beta=\arctan\Bigl{(}\sqrt{p^{2}+q^{2}}\Bigr{)}\text{ where }p=\frac{2\pi}{qx}, \ q=\frac{3\pi}{qy}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Terrain Attributes** & **Define** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of Terrain Attributes in the study area and its derived results.  
Topographic Wetness Index (TWI) is an index of the water content in the ground, and it can determine the spatial pattern of water flow [45; 46]. TWI was developed by [PERSON] [47] in TOPMODEL and is defined as a function of the upstream contributing area per unit area orthogonal to the slope and the flow direction.  
\[\ln\left(\frac{\kappa}{\ln\beta}\right)\]  
Where \(\alpha\) is the slope area of the unit grid length, \(\beta\) is the slope at a point on the surface.  
Sediment Transport Index (STI) was proposed by [PERSON] [48], and is an indicator of the erosion and sedimentation process. It is defined by the non-linear equation of the specific catchment area and slope.  
\[LS=\left(\frac{A_{S}}{2\Gamma 13}\right)^{0.6}\left(\frac{\sin\beta}{0.0896} \right)^{1.3}\]  
where\(A_{S}\) is a specific catchment area, \(\beta\) is the slope at a point on the surface.  
Stream Power Index (SPI) is an index indicating the erosion risk of potential flows on a terrain surface. When the catchment area and slope increase, the SPI also increases as the flow velocity due to gravity increases [45; 46] The SPI is defined by the following equation.  
\[SPI=\ln\left(A_{S}\times\tan\beta\right)\]  
Curvature has an important effect on landslides and is classified into three types: convex, plane, and concave. In general, it is considered to have a negative effect on the landslide due to ponding caused by more extreme rainfall penetration in the case of the concave curvature relative to that of the Convex [49].  
Specific catchment area (SCA) is defined as the value obtained by dividing the upslope area by the contour width and is a value commonly used in hydrology to analyze the flow of water on the slope [50].  
\[A_{s}=\lim_{w\to 0}\left(\frac{\Delta}{w}\right)\]  
\begin{table}
\begin{tabular}{c c} \hline
**Terrain Attributes** & **Define** \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 2 Related Works
However, it has a limited ability to learn the long-term temporal information essential for hand gesture recognition. Among the various types of gestures, hand gestures are the most expressive way of interacting more naturally when communicating with computers. Therefore, recently, hand gesture recognition has motivated new technologies in the area of computer vision. Previous studies have been proposed to solve hand gesture recognition tasks such as the glove-based approach [37], device-related methods [38] and the data glove-based method to address the issue of external sensors that enable us to monitor a user's hand motions more frequently [39]. Apart from such methods, nowadays, deep learning-based models are utilized to solve the hand gesture recognition and classification problems more efficiently and accurately [40, 41]. Furthermore, they have been successfully implemented in CNN-based hand gesture recognition problems, too [42, 43]. In addition, LSTM has become an important part of deep learning models for human action recognition [10, 11]. Enhanced methods, such as Bidirectional RNN [12], hierarchical RNN [13], and Differential RNN (D-RNN) [14] were proven to recognize gestures. The work of [PERSON] et al. [15] proposed a combination of 3 DCNN and RNN, with fully connected spatiotemporal features transferred into RNN. Even though 3 DCNN can model fine motion, it cannot support long variation because this depends on the size of the filter. A practical approach is to model a short temporal snapshot of videos by averaging the prediction from a single frame and a stack of several optical flow frames after passing two replicas of 2 DCNN. In this study, we also adopt a two-stream network combining CNN layers on top of LSTM using optical flow and RGB videos as input. LSTM layers consist of blocks, which, in turn, consist of cells. Each unit or cell within the layer has an internal cell state and outputs a hidden state. However, the cell state is the key to the LSTMs unit. The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates. Each cell has its inputs, outputs, and memory. Cells that belong to the same block share input, output, and forget gates. However, each cell can hold a different value in its memory. Nevertheless, the memory within the block is written to, read from, and erased all at once. This denotes that the information inside a cell state is a necessary thing among the sequential classifications. The blocks sharing the layers that they receive inputs from and feed their output to, make up a layer.
## 3 Performance Analysis for Underwater CVQKD
Figure 6b compares the secret key rate for different beam width \(\omega_{0}\) and maximum beam divergence \(\theta_{0,max}\) when the receiver aperture and FOV are set to 2 inches and \(180^{\circ}\), respectively. We find that the secret key rate and transmission distance are less affected by the beam width but are greatly affected by the maximum initial divergence angle, which corresponds to the results of the light intensity distribution in \[I_{AB}=\frac{1}{2}\log_{2}\frac{V_{B}}{V_{B|A}}=\frac{1}{2}\log_{2}\frac{V+ \chi_{tot}}{1+\chi_{tot}}, \tag{5}\]0 A large maximum beam divergence makes the beam reaching the reception plane more divergent, thereby resulting in a decreased value of \(\eta_{r}\).
# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 2 Materials and Methods
### UCE Preprocessing Techniques  
Variations of preprocessing methods investigated in this work include \"no preprocessing\", FFTs, variations in the length of the FFT, and variations in the window function applied to the raw time-domain data samples prior to applying an FFT.
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Computational Component Optimization  
In the field of image inpainting, common computational components in deep learning model algorithms include convolution operations and attention mechanisms, which play crucial roles in the inpainting process. Many methods have been developed to refine the convolution operations and attention mechanisms, aiming to achieve better feature extraction and representation, ultimately enhancing the inpainting effect.
# Examining the Optimization of Spray Cleaning Performance for LiDAR Sensor
## 3 Results and Discussion
### Optimized Cleaning Model for the Entire Target Area
#### 3.1.2 Results of the Second Spray  
To calculate the cleaning rate of the second spray, the quadratic model was most suitable, and it exhibited high explanatory power with an R-squared value of 0.9205 and adj. R-squared value of 0.8902. The adeq. Precision of the second spray model was 16.6642, which is considered appropriate as it exceeds 4. Variance analysis results of the second spray model are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **R-Squared** & **Adj.** & **Adeq.** & **F-Value** & **_p-Value_** \\
**Response** & **R-Squared** & **R-Squared** & **Precision** & **F-Value** & **_p-Value_** \\ \hline First spray & 0.8666 & 0.8158 & 13.9004 & 17.06 & \textless{}0.0001 ** \\ \hline \hline \end{tabular}  
* _p \(<\) 0.05._  
\end{table}
Table 3: Variance analysis results of the first spray model.  
In the second spray model, the primary terms of the spray time and washer pressure were significant at a significance level of 5%, and the quadratic term of the washer pressure was significant at a significance level of 5%. When the regression coefficients of the factors were estimated based on the method of least squares, the quadratic equation was expressed as  
\[\begin{array}{l}\text{\it Cleaning Rate (Second spray)}=-8.1076+12.9788\times\text{\it spray time}+12.6699\times\\ \text{\it washer pressure}-0.7990\times\text{\it washer pressure}^{2}\end{array} \tag{2}\]  
The 3D surface and 2D contour graphs drawn based on the quadratic equation of the second spray model are shown in Figure 10. The cleaning rate significantly increased with the washer pressure, and it tended to increase with the spray time. Initially, the cleaning rate increased alongside the target point, but it decreased as the target point further increased. Conversely, the cleaning rate barely changed with the increase in spray angle.  
As observed from the ANOVA results, the washer pressure had the largest impact on the cleaning rate, and the cleaning rate increased according to the spray time. The washer pressure quadratically affected the cleaning rate.
## 4 Results of the Research
### Loaded Car Analysis
#### 4.2.2 Analysis on Curved Tracks
Periodic solutions last up to \(v=105\) m/s, and then their bifurcation to other periodic solutions appears but with lower values of \(\mid y_{lu}\mid max\) and p-t-p \(y_{lu}\) (approx. p-t-p \(y_{lu}=0.004\) m). The maximum value of velocity for which a stable solution exists is \(v=108\) m/s. In the next step, the lateral stiffness was reduced to 80% of its nominal value (\(k_{zy}=44.5\cdot 10^{5}\) N/m). As a result, there was a dramatic rise in the critical velocity up to \(v_{n}=73.3\) m/s. Periodic solutions last up to \(v=104\) m/s, and then their bifurcation to other periodic solutions appears, however, with lower values for \(\mid y_{lu}\mid max\) and p-t-p \(y_{lu}\). The highest velocity value at which stable solutions existed was \(v=109\) m/s. Concluding this part of the study, one can state that a reduction in the primary suspension's lateral stiffness \(k_{zy}\) by a dozen percent compared to its nominal value brought about favourable effects on the vehicle motion on the ST and CCs as well. To supplement studies on the influence of \(k_{zy}\) on the vehicle model's solution stability, how reducing \(k_{zy}\) affects the tested car model's properties when it is empty should be checked.