esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Knowledge Gaps  
The literature review reveals the following knowledge gaps:  
1. [label=(0)]
2. Data augmentation methods that transform and amplify the data themselves, such as jitter and arrangement, need to learn about the distribution pattern and noise of the data themselves in advance, and it is difficult for them to retain the time dependence of the original data. In addition, enhancement methods based on multi-source heterogeneous data fusion have certain requirements for the number and availability of data sources. Therefore, it is difficult to use traditional data enhancement methods for the enhancement of time-series data with a single data source and complex internal correlations, such as in fault rate prediction for electromechanical equipment.
3. Although neural network models, especially LSTM, have been greatly optimized in terms of structural defects and have strong generalization capabilities and robustness, they have more hyperparameters, and the network scale is difficult to determine and optimize.  
This study proposes a CNN-LSTM prediction framework based on TimeGAN data enhancement and optimized with the sparrow search algorithm (SSA) to predict the fault rate of platform screen door systems. Through TimeGAN, data that conform to the time characteristics of the original fault data are generated, and the fault dataset is augmented. The high-dimensional features of the fault sequence are extracted by the CNN, and LSTM is selected for fault rate prediction due to the need for long-term sequence prediction. The SSA is used to optimize the model parameters to achieve an accurate prediction of the fault rate of platform screen door systems.
## 2 Materials and Methods
### Data Collection
#### 2.3.3 Data Processing
However, after discovering issues within some of the orthomosaics for harvest sites, the Volz datasets were processed with both the \"standard\" and \"alternative\" calibration methods at each KPIS to determine the best combination (Table 11). Orthomosaic composites, 3D Point Clouds, and DSMs were produced for each PWA and Volz dataset surrounding their given NDBM treatment. The best iterations of the PWA and Volz datasets were chosen as the basis for the land cover classifications. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Keypoint Image Scale** & **Calibration Method** & **Number of Calibrated Images** \\ \hline \multirow{3}{*}{PWA pre-burn (4/1/20)} & 1 & Standard & 615/621 (99\%) \\ \cline{2-4}  & **1/2** & **Standard** & **618/621 (99\%)** \\ \cline{2-4}  & 1/4 & Standard & 615/621 (99\%) \\ \hline \multirow{3}{*}{PWA post-burn (4/3/20)} & 1 & Standard & 377/378 (99\%) \\ \cline{2-4}  & 1 & Standard & 388/389 (99\%) \\ \hline \multirow{3}{*}{PWA post-burn (5/1/20)} & 1 & Standard & 309/310 (99\%) \\ \cline{2-4}  & 1 & Standard & 241/256 (94\%) \\ \cline{2-4}  & 1/2 & Standard & 241/256 (94\%) \\ \cline{2-4}  & 1/4 & Standard & 239/256 (93\%) \\ \cline{2-4}  & **1/2** & **Alternative** & **243/256 (94\%)** \\ \cline{2-4}  & 1/4 & Alternative & 239/256 (93\%) \\ \hline \multirow{3}{*}{Volz mid-cut (5/21/20)} & 1 & Standard & 149/159 (94\%) \\ \cline{2-4}  & 1/2 & Standard & 151/159 (95\%) \\ \cline{2-4}  & 1/4 & Standard & 146/159 (92\%) \\ \cline{2-4}  & **1/2** & **Alternative** & **154/159 (97\%)** \\ \cline{2-4}  & 1/4 & Alternative & 149/159 (94\%) \\ \hline \multirow{3}{*}{Volz post-cut (7/2/20)} & 1 & Standard & 155/157 (99\%) \\ \cline{2-4}  & 1/2 & Standard & 156/157 (99\%) \\ \cline{2-4}  & 1/4 & Standard & 154/157 (98\%) \\ \cline{2-4}  & **1/2** & **Alternative** & **157/157 (100\%)** \\ \cline{2-4}  & 1/4 & Alternative & 156/157 (99\%) \\ \hline \multirow{3}{*}{Volz post-cut 2 (8/26/20)} & 1 & Standard & 166/174 (95\%) \\ \cline{2-4}  & 1/2 & Standard & 168/174 (96\%) \\ \cline{2-4}  & 1/4 & Standard & 163/174 (93\%) \\ \cline{2-4}  & **1/2** & **Alternative** & **169/174 (97\%)** \\ \cline{2-4}  & 1/4 & Alternative & 163/174 (93\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Photogrammetric processing parameters tested for georeferenced orthomosaic generation at the PWA prescribed burn and Volz selective timber harvest sites.
## 3 Results and Discussion
### Prediction of WKU, WHAZU versus AR and \(P\)  
In this chapter we will deal with prediction of WKU and WHAZU vs. AR, \(P\) at non-measured parameters which give information on the quality of Picea abies cut on the upper board. The results we will be presented as a graph of output property vs. AR, \(P\) in categories of laser power at a cutting speed of 1.2\(\cdot\)10\({}^{-2}\) m\(\cdot\)s\({}^{-1}\).  
In Figure 2a,b we are show the dependence WKU and WHAZU vs. AR and \(P\) for cutting speed 1.2\(\cdot\)10\({}^{-2}\) m\(\cdot\)s\({}^{-1}\) in this order.  
From Figure 2a,b we can see that AR have effect on WKU only for laser power 200 W and don't have effect on WHAZU. WKU is changing in average from 0.64 mm to 0.81 mm which correspond to 26.5% which is above the measuring error of this dimension. Therefore, we can conclude that AR does have statistically significant effect on WKU. Change of WKU vs. AR at low power 200 W lies in fact that at 200 W is material removal rate MRR low thus heat transfer through higher thermal conductivity by higher AR is significant which leads to accumulation of heat in kerf region thus higher cutting kerf width which is proportional to mass of wood which is burned.  
At higher laser powers WKU don't change with AR and \(P\) which can be described by fact that at these values of \(P\) material removal rate MRR is at maximal value and therefore heat transfer in the cutting kerf region don't occur. This fact was also recorded by many authors in literature like as [PERSON] (16) on pine wood and [PERSON] (13) and [PERSON] (6). From the Figure 2 it is also good to see that laser power increases the cutting kerf width at upper board which was found out also by [PERSON] (8), [PERSON] (5) and [PERSON] (18).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Statistical** & **WKU** & **WKL** & **WKR** & **WHAZU** & **WHAZL** \\
**Parameter of** & **(10\({}^{-3}\) m)** & **(10\({}^{-3}\) m)** & **(-)** & **(10\({}^{-3}\) m)** & **(10\({}^{-3}\) m)** \\ \hline Slope (-) & 0.970 & 0.960 & 0.940 & 0.960 & 0.970 \\ \hline R\({}^{2}\) (-) & 0.950 & 0.980 & 0.933 & 0.960 & 0.970 \\ \hline RMSE (parameter units) & 0.010 & 0.004 & 0.012 & 0.001 & 0.002 \\ \hline Rel\_RMSE (-) & 0.018 & 0.012 & 0.035 & 0.009 & 0.013 \\ \hline \end{tabular}
\end{table}
Table 5: Statistical parameters of the ANN networks.  
From Figure 2b is good to see that laser power don't have significant effect on WHAZU which is caused by fact that at higher cutting speed upper board thickness is cut quicker and therefore heat propagation in HAZ region don't occur which is in good agreement with [PERSON] (6), [PERSON] (9) and [PERSON] (15). All presented results in Figure 2 is in good agreement with [PERSON] (7).
# Unsupervised Driving Situation Detection in Latent Space for Autonomous Cars
## 4 Solution Proposed
### Driving Situations  
This subsection introduces how we create and detect driving situations from observations of the car using a clustering technique to divide a training dataset. Our proposal is to cluster the observations into situations under an unsupervised approach. Unlike previous approaches, we perform the clustering in the latent space of the representations. Given that we are using an unsupervised approach, we require the images from the observations of a previously stored dataset. Next, we apply an encoder (\(\Phi_{\omega}\)) to the observations in order to convert those runs into a latent space representation, namely:  
\[\Phi_{\omega}(z):Z\to L \tag{11}\]  
where \(L\) is the latent space. This representation is made up of the main features extracted from the observations. Then, the latent space points are classified using a clustering algorithm. Figure 2 illustrates the clustering methodology. These clusters of similar points are what we call driving situations. In particular, we apply the K-means algorithm for clustering the inputs. The K-means tries to minimize the distance within clusters, i.e.:  
\[\arg\min_{s}\sum_{i=1}^{k}\sum_{l\in\Phi_{\omega}(S_{i})}\|l-\mu_{i}\|^{2} \tag{12}\]  
where \(l\) is the latent space representation of an observation.  
K-means requires as input the number of target clusters; however, in our problem, we want to automatically detect the number of situations. To address this issue, we perform an elbow [29] and silhouette analysis [30] to determine the number of clusters, \(k\). According to the inflection point of the curve conformed between the number of clusters and the sum of the squared distance between the centroid and each cluster member, we select the adequate number of centroids as the number of driving situations detected. Once we have the corresponding number of driving situations, we take each centroid and make it go through a decoder that allows us to have a reconstructed image that can help us to have an understanding of what features were taken into account to make the clustering of each situation.
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 2 Authentication Process
### Cloud Authentication Mechanism  
Four processes comprising the access control [40] mechanism are best described by identification, authentication, authorization, and accounting, as proposed by [PERSON] et al. [41]. Identification refers to uniquely defining and describing each user of the cloud system with an associated set of credentials. An individual must provide a credential for establishing their identity in an authentication process. Different systems may necessitate various forms of credentials. An individual is frequently required to produce a credential as a password in today's computer and communication networks. Some systems may even demand the individual to have more than one credential to be provided as proof of the user's genuineness [42]. The entire cloud authentication mechanism for users can be broadly categorized into three phases as described by [PERSON] et al. [43], namely, (i) Registration, (ii) Log-in, and (iii) Verification. The authentication process can involve two parties, the Supplicant and the Authenticator. However, depending on the presence of a Responder that represents a trusted third party (TTP) [44], the authentication system could have three essential components [45]. After authentication, authorization is the next phase. Authorization is a process that deals with access restrictions and limitations on resources. Authorization explicitly specifies what each user may and may not do or access. In other words, authorization grants or restricts users' access to available resources. Accounting is the fourth component of access control [46]. Accounting keeps track of who enters the system, what they do, and at what time.
## 4 Description of Setup, Control Mode, and ROV Trajectories for Real Experiments
### Trajectories  
The experimental results of cable control validation were obtained during a series of tests carried out in the pool of the CIRS (Underwater Robotics Research Center) in Girona in the context of a TNA (TransNational Access) European project.  
Two kinds of ROV trajectories were defined to represent two configurations where cable control must be useful to reduce risks of cable snagging or entanglements as below.  
* A linear trajectory where the ROV goes forwards and then backwards First, a forward thrust command of 25% is sent for 15 s (between keyframes 1 and 2). Then, a zero command is sent for 5 s (between points 2 and 3). Finally, a backward thrust command of 25% is sent for 10 s (between points 3 and 4).
* A curvilinear S-shape trajectory comprising the following steps :
* a forward thrust of 40% for 15 s (between points 1 and 2);
* a right turn composed of a yaw thrust of 11.25% combined with a forward thrust of 40% for 6.5 s to turn right (between points 2 and 3);
* a left turn with the same thrust command values as the left turn, for 6 s (between points 3 and 4);
* a forward thrust of 40% for 4 s (between points 4 and 5). All these trajectories were associated with 1.5 m depth control. The ROV was open-loop-controlled to follow these trajectories by driving the corresponding thrusters. For each trajectory, three kinds of cable management were tested:
* Passive slack cable, where cable of sufficient length was deployed in the water from the beginning.
* Passive taut cable, where the cable is stretched between the feeder and the ROV when the ROV moves away from the feeder, which means that the buoy-ballast system is also completely stretched and has no real influence on the ROV. The feeder is not powered. When the ROV comes closer to the feeder, the cable is no longer taut, and the buoy gap tends to be reduced to a minimum.
* Cable control, using buoy gap control mode. A 25% and 40% forward thrust is associated with a buoy gap of about 0.7 mn and 0.8 m, respectively, according to the steady-state model.
# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 4 Security Issues in Cloud Computing Infrastructure
### Application-Level Issues
#### 4.2.1 Availability Issues  
The cloud should be available at all times, and robust to any security threat or user misbehavior. This is a basic, yet difficult, problem in the cloud. Compared to multi-clouds, the issue of availability in a single cloud is very critical. The application availability refers to making it accessible to the user [35]. Distributed denial-of-service attacks (DDoS) are one of the major threats to an application's availability on the cloud. These attacks have become complex and continue to develop rapidly, making it more difficult to identify and tackle them [37]. The major aim for the attacker is to block legal users and take control over resources and services so that the customer will not be able to use the application hosted on the cloud.
## 3 Efficient Energy Aware Routing Protocols: R-EERP and S-EERP
### Avoiding Sending the Duplicated Data  
In a sensor network, close-by nodes may fall into the same cluster and sense similar data. Then they may try to send their data to the base station. In our proposed solution, as the cluster-heads receive data from the nodes, an XOR operation is performed on the received data with the waiting data in order to check if they are the same. If the newly received data exists among the previously sensed data, it is ignored, because the same data has been sensed before and is being kept waiting at the cluster-head to be sent to the sink. In this way, by waiting the data and comparing the incoming data with the waiting data, redundant transmissions (transmitting the same data) is prevented. This reduces the volume of traffic transported across the resource-scarce wireless sensor network. It also means less processing at the base station. If all the received data is the same with the first waited data, none of the new data is sent to the sink. In this way, the whole network consumes less energy.  
There are various methods that can be used to compare the new data with the waiting data. If the information is ON/OFF information (_i.e.,_ it can be represented with a single bit), then we can apply simply an XOR operation. It will XOR a new coming bit together with the existing bit (waiting data); if the result is one, the new data will also be sent (Table 1) besides the waited data in the cluster head.  
Other data filtering techniques can be used as well for different data types. XOR is good for Boolean data. For byte or integer data, binary comparison can be performed and filtering out can be decided if the difference is less than a certain value.
## 2 Heliostat Orientation Control  
The orientation control system is presented in Figure 1. The control system modifies the angular position of two DC motors connected to the axes of the heliostat through two worm drive mechanisms to guide the heliostat to the desired position. A microcontroller unit (MCU) calculates the position of the sun and the desired angles of the heliostat in order to reflect the solar radiation on a specific target by using the geographic position of the heliostat and the current time and date values. Afterwards, a position control algorithm calculates the error between the desired and current angular position of the heliostat axes by using two rotary encoders in order to obtain a control signal which orients the heliostat by using two motor drivers that allow the bidirectional control of the DC motors.
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 5 Experiment
### Evaluation Metric  
In this paper, we use the widely used evaluation metrics in recommendation algorithms: root mean squared error (\(RMSE\)) and mean absolute error (\(MAE\)) to evaluate the performance of recommendation algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **NC** & **WI** & **Epinions** & **LastFM** \\ \hline \# Users & 22,737 & 8386 & 18,088 & 1874 \\ \# Items & 12,502 & 4593 & 261,649 & 2828 \\ \# Ratings & 225,580 & 80,643 & 764,352 & 71,411 \\ Density of relations & 0.0793\% & 0.2093\% & 0.0161\% & 2.0286\% \\ \# social relations & 111,394 & 34,099 & 355,813 & 25,174 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of Evaluation Datasets.  
Figure 2: Framework of graph embedding model based collaborative filtering algorithm.  
\[RMSE =\sqrt{\frac{\sum_{(i,j)\in R_{test}}|r_{ij}-\hat{r_{ij}}|^{2}}{|R_{ test}|}} \tag{9}\] \[MAE =\frac{\sum_{(i,j)\in R_{test}}|r_{ij}-\hat{r_{ij}}|}{|R_{test}|}, \tag{10}\]  
where \(r_{ij}\) denotes the actual score value, \(\hat{r_{ij}}\) denotes the score value predicted by the system, and \(|R_{test}|\) denotes the number of records in the test dataset. the smaller the \(RMSE\) and \(MAE\) values, the better the recommendation performance of the recommended algorithm.
## 2 General Considerations  
### Topologies  
As already mentioned, there are many different power electronic applications available that can benefit from such a light/no-load WBG operating upgrade. The study at hand deals primarily with  
Figure 1: Example efficiency curves of (**a**) a standard 3-phase 10 kW IGBT inverter and (**b**) a combination of both, SiC and IGBT inverter where a 1 kW SiC inverter is operating during load conditions of 0 to 1 kW and the 10 kW Si–IGBT inverter for any mode >1 kW.  
three-phase systems, which can be implemented as either 2-level or multilevel topology. In terms of multilevel converter topologies, the discussions are limited to 3-level t-type solutions for this publication. However, it has to be noted that this does not limit the applicability of the proposed concept to higher-order multilevel systems. Figure 2a,b shows two versions of a three-phase grid-connected power electronic inverter based on Si-IGBTs (either two- or three-level), which can be equipped by a SiC MOSFET half-bridge circuit (one stage for each phase).  
The enhanced circuit benefits from already existing passives, i.e., input filter, DC-link capacitor, etc. which can be utilized in terms of operation. For grid-connected applications, SiC is currently the most effective choice if single-stage voltage characteristics, cost, and switching losses are considered. Due to its unipolar and ohmic characteristics, SiC MOSFETs come with lower conduction losses up to a specific current level compared to the classical IGBT. During partial load conditions, IGBTs are suffering from their diode-like _V/I_ output characteristics, which makes them inefficient for lower current values. Therefore, if the efficiency of the inverter drops below a critical value (which can be specified in terms of power or input current), the Si-IGBT devices are deactivated, and the equipped SiC inverter takes over the handling of the energy transmission from input- to output-side. Thus, the solicited energy transfer can still be maintained and guaranteed under improved system efficiency performance (as illustrated in Figure 1). If a lower DC-link voltage than 600 V is required (e.g., for the current state of the art motor drive inverters), all the implemented SiC devices could be substituted by GaN transistors (Figure 3a). This brings an additional improvement in terms of semiconductor device efficiency but currently comes with higher chip cost, which delays the aspired amortization timing. Therefore, also 600/650 V SiC MOSFETs (as shown in Figure 3b) can be applied for those solutions.  
Figure 2: Three-phase grid-connected (**a**) 6-switch or (**b**) t-type IGBT-based rectifier/inverter with equipped 3 different SiC half-bridge legs for each converter input phase.
# BIZON-UGV for Airport Pavement Testing: Mechanics and Control
## 1 Introduction
The system consists of one measuring wheel buried in the ground to form a groove of a few centimeters with controlled hydraulic pressure. The measuring system is rigidly coupled with the BIZON platform chassis; thus, very important from the control and navigation point of view is that the navigation algorithms have been supplemented with software mechanisms to protect from a failure of the measurement system during a planned mission. Briefly, the software elements continuously analyze the measuring wheels' actual position by linear encoders inbuilt to hydraulic actuators and the current cruise deviation generated by the control systems. In specific conditions, the measuring wheels are raised up on command by the mission plan operator, sometimes caused by the vehicle stopping. The platform is also equipped with a BZYG-unmanned aerial device, which allows the introduction to the geotechnical industry of an innovative method that will be significantly better than currently available methods. The result of the study of the state of the art and  
Figure 1: BIZON—unmanned ground platform view, front view, and rear view. analysis of solutions offered by competing entities showed a lack of a product with similar features and functionalities on a global scale. The state-of-the-art UGV modelling, control, and application analysis provides a broad spectrum of both model-free and model-based approaches [15]. Many propositions, e.g., existing control methods, are still based on typical PID controlled autopilots considering motion limits or control saturation. In this paper and research, using both advantages, we proposed a hybrid method in which the proportional-integral-derivative system with feedforward control, abbreviated as the PID-FF control technique, is used to control the UGV's position and speed. At the same time, the suitably introduced FF component will increase the precision level in tracking the UGV. The novelty and added value of our work is the development of an original autopilot conception based on cascade hybrid PID-FF equipped with a saturation and filtering subsystem, as well as comparative simulation tests for the problem of stabilization of the device's orientation and position. The paper is organized as follows. Section 2 presents the ground vehicle mechanics model with strain simulations. Section 3 describes the UGV model considering non-holonomic constraints and autopilot design. UGV is used in simulation experiments, and its comprehensive report and analysis can be found in Section 3. Finally, the conclusion is drawn in Section 4.