esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidisciplinary Design Optimization of Cooling Turbine Blade: An Integrated Approach with R/ICSM
## 3 Multidisciplinary Design and Optimization of Turbine Stage Blade
### Parameterized Modeling Method of Cooling Turbine Blade
#### 3.1.2 Parametric Modeling of Cooling Characteristics  
MDO requires the automatic updating of geometric models based on design parameters. This involves collaboratively integrating feature parameterization modeling for both blade air cooling characteristics and adjustments to blade shape.  
To mitigate the bending moment caused by centrifugal force in the turbine rotor blade, it is essential to distribute the center of gravity of the blade section radially along its height. The incorporation of typical cooling features can significantly alter the center of gravity of the blade section. Therefore, it is necessary to develop a parametric model for the hollow blade with variable wall thickness, which allows for swift adjustment of the center of gravity by altering the wall thickness distribution. Variable wall thickness encompasses three aspects: (1) variation of wall thickness dependent on radial height, (2) variation of wall thickness in the axial direction, and (3) independent variation of thickness at the pressure and suction sides. To accomplish this, the parametric modeling approach for the blade cavity in this study includes the following steps:  
1. Establish eight feature control points on the suction side profile, as illustrated in Figure 5.
2. Determine the corresponding eight feature control points on the pressure side profile using geometric constraints based on inscribed circles at the feature control points.
3. Connect the corresponding feature control points on the pressure and suction side profile and control the wall thickness at the pressure and suction sides using line length percentages.
4. Ensuring cavity smoothness by incorporating the wall thickness values at the first, third, and eighth feature control points on the pressure and suction sides as variables. These values are then interpolated through quadratic interpolation to determine the wall thickness at the remaining feature control points.
5. Generate cavity curves on the pressure and suction using spline curves.
6. Repeat steps (1) to (5) to obtain the cavity design sections corresponding to the external blade shape at the root, middle, and tip. The blade cavity is obtained using the UG curve group command.  
Figure 4: Radial stacking of turbine blade.  
Expanding on the parametric model for the variable wall thickness hollow blade, a feature-based parametric modeling approach was adopted to separately design the leading edge and dovetail structures. Subsequently, typical cooling features, including baffles, turbulence ribs, internal cooling root segments, and trailing edge holes, were iteratively enhanced. Through this iterative process, a comprehensive parametric model for the cooling blade was developed, as illustrated in Figure 6.
## 5 Comparisons and Experiment
### Proof of Concept Experiment and Discussion
#### 5.3.1 Experimental Setting  
The robot and the obstacle are modeled as a circle defined by its radius \(r_{Robot}\) = 0.2 m (Table 1), \(r_{Obstacle}\) = 0.1 m, respectively. The mobile robot is initially located at the origin of the global coordinate system and its initial forward moving direction is the + \(x\)-axis. The mobile robot is modeled as a kinematic unicycle (1) with curvature as a constrained input bounded by the maximum curvature (Figure 2) while the tangential speed is held constant with \(\rm{v_{x}=U=0.5~{}m/s}\). The robot pose is obtained by numerically integrating Equation (1), which is directly controlled by the path curvature or angular velocity with the maximum allowable curvature for the mobile robot set as 1.5 (1/m), as shown in Table 1. Thus, the angular speed is upper bounded by \(\omega\leq\kappa_{max}U=1.5*0.5=0.75\) rad /s. The pure pursuit command rate is 10 Hz, and the safety distance is \(r_{Safe}\) = 0.1 m, which is the robot displacement in a period to allow a sharp turn in case of an imminent collision. Given a safety distance \(r_{Safe}\) between a robot and a detected obstacle, the radius of the obstacle is enlarged to account for the robot radius as \(r_{Obs}\) = \(r_{Obstacle}\) + \(r_{Robot}\) + \(r_{Safe}\) = 0.4 m for guaranteed local obstacle avoidance. In our experiment, the look-ahead distance is equal to the robot radius \(L=r_{Robot}\) = 0.2 m. Note that if different subgoals between the obstacles are selected online for streamline changing from the current robot pose, we obtain topologically different paths with different curvatures for sequential obstacle avoidance in an environment with multiple obstacles. This is seen in the experiment in the following.  
The distance between the robot's center and the obstacle's center is \(D\) = \(D_{Sensor}\) + \(r_{Obstacle}\) + \(r_{Robot}\), where \(D_{Sensor}\) is the range value measured by the sensor. In the case of a circular robot and a circular obstacle, which is the case studied in this paper, the collision-free criterion for safe navigation is that the distance \(D\) between the point robot (robot center) and the obstacle center is larger than \(r_{Obs}\), i.e., \(D\) > 0.4. In the experiment reported here, a left/right turn is the default action and must maintain at least 0.4 m to a detected obstacle to be avoided. A narrow passage is not considered in this proof of concept experiment, since this default action may cause navigation difficulty, if not an impossibility in such a situation.
# System Proposal for Mass Transit Service Quality Control Based on GPS Data
## 3 Preliminary
### Global Positioning System (GPS)
Given the different sources of error in the GPS coordinates, in the case of public transport vehicles, multipath errors are particularly relevant, especially when the vehicles run through urban areas. Considering the above explanation of GPS, the user accuracy depends on a combination of satellite geometry, URE, and local factors such as signal blockage, atmospheric conditions, and receiver design features/quality, adopting our system a maximum possible error in position data of 25 m, taking into account that the SIS URE Accuracy is behind 13 m during normal operations at any AOD and the metioned external factors (atmospheric conditions, multipath, technical manufacturer specifications,...); henceforth this maximum error will be represented by \(E\). Figure 1 is a map illustrating the position data captured by public transport buses that follow a route in an urban area. The blue dots represent the position data, and we can see that some of these coordinates stray from the road because of GPS data errors. It is often the case that, once the vehicle's GPS geolocation has been obtained, it has to be positioned on the transport network. This requires a geographic database that contains the positions of the different points on the transport network. To locate a vehicle on the network, the distance between its GPS position and the position of the different points on the transport network is used. This distance can be calculated using different formulae, all of which result in errors since the Earth is not a perfect shape; it is neither a perfect sphere nor a perfect ellipsoid. In general, the distance formula with the lowest error is the haversine formula [30], and is therefore used by the proposed system. Given two points, \(p1\) and \(p2\), with coordinates expressed in radians (\(lat_{1}\), \(lon_{1}\)) and (\(lat_{2}\), \(lon_{2}\)), the value of this distance is calculated using the following expression:  
\[Dist(p1,p2)=2\times R\times\arcsin(\sqrt{A}),\]  
where \(R\) is the radius of the Earth at the equator and \(A\), a factor calculated as follows:  
\[A=\sin^{2}\biggl{(}\frac{\Delta lat}{2}\biggr{)}+\cos(lat_{1})\times\cos(lat_{ 2})\times\sin^{2}\biggl{(}\frac{\Delta lon}{2}\biggr{)},\]  
\[\Delta lon=lon_{2}-lon_{1},\]  
\[\Delta lat=lat_{2}-lat_{1}.\]  
Figure 1: Examples of GPS location data for public transport buses on a fixed route. The positions are represented by the blue dots. This distance function produces an error, owing to the fact that the Earth is not a perfect sphere and the radius of curvature is not always the same. The maximum error occurs when the points are located at the greatest possible distance, i.e., in the antipodes--a distance of approximately 20,000 km--the maximum error being 2 km.
## 4 Experiment
### Result Analysis
#### 4.2.2 Quantitative Analysis  
The experimental data for TFN, LMF, MuIT, MISA, Self-MM, and MMIM come from [51]. For the other models, we conducted five experiments on each of the three datasets using publicly available source code and averaged the experimental results for each model. In all evaluation metrics, except for MAE, larger values indicate better model performance. The experimental results are compared in Tables 4-6.  
Table 4 shows the model's results on the CMU-MOSI dataset. Compared to the EMT model, CCDA improved by 0.009 on the regression metrics MAE and Corr. In terms of classification task metrics, CCDA improved by 0.6% on Acc-2 and Acc-5 and 0.7% on Acc-7 and achieved a 0.6% improvement in F1-score over the best model. Similarly, as shown in Table 5, CCDA's performance on CMU-MOSEI improved by 0.003 on MAE, 0.006 on Corr, 0.5% on Acc-7, 0.4% on Acc-5, 0.6% on Acc-2, and 0.7% on F1-score compared to EMT. Table 6 shows the experimental results of the model on CH-SIMS, where CCDA achieved better results on some metrics, such as 0.006 on MAE, 0.005 on Corr, 1.4% on Acc-3, 1.2% on Acc-2, and 0.9% on F1-score. However, its performance on the 5-classification task was slightly worse than that of the EMT model. We believe that while CCDA improves coarse-grained sentiment classification, it does not improve much for fine-grained classification.  
The experimental results show that the CCDA model using cross-modal attention and self-attention is able to learn intra- and intermodal dynamics. Dual-attention makes the model analyze the sample more comprehensively, and the cross-correlation loss enables some degree of interaction between different levels of dual-attention mechanism. In addition, the relevant coefficients guide the multimodal feature fusion stage, which allows the model to improve performance while increasing the model's generalization ability.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Models** & **MAE (\(\downarrow\))** & **Corr (\(\uparrow\))** & **Acc-7 (\(\uparrow\))** & **Acc-5 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** \\ \hline TFN [24] & 0.901 & 0.698 & 34.9 & - & 80.8 & 80.7 \\ LMF [25] & 0.917 & 0.695 & 33.2 & - & 82.5 & 82.4 \\ MuIT [30] & 0.846 & 0.725 & 40.4 & 46.7 & 83.4 & 83.5 \\ MISA [48] & 0.804 & 0.764 & - & - & 82.1 & 82.0 \\ Self-MM [49] & 0.717 & 0.793 & 46.4 & 52.8 & 84.6 & 84.6 \\ MMIM [51] & 0.712 & 0.790 & 46.9 & 53.0 & 85.3 & 85.4 \\ AMML [50] & 0.723 & 0.792 & 46.3 & - & 84.9 & 84.8 \\ EMT [33] & 0.705 & 0.798 & 47.4 & 54.1 & 85.0 & 85.0 \\ \hline Ours & **0.696** & **0.807** & **48.0** & **54.8** & **85.7** & **85.6** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiments on CMU-MOSI. Where \(\uparrow\) indicates that the higher the metric the stronger the performance of the model, and \(\downarrow\) is the opposite. Bold numbers indicate the model with the best results at that metric.
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 4 Practical Evaluation of Transport-Layer Options
### Orchestration Traffic Performance Using UDP  
In the previous sets we talked about the advantages that using UDP would provide to intermittently available SUAV platforms to disseminate NFV orchestration information. Nonetheless, to explore this possibility, we performed the same sets using UDP under those conditions to see if there is any real advantage against TCP. Figure 6 shows both cases in the same conditions of the previous tests.  
As expected, UDP produces a more constant and stable flow of traffic between the aircraft and the GCS as compared to TCP in both cases, more quickly reaching the target rate without being subject to the congestion control mechanisms of TCP. In both cases, there are time periods where the throughput drops throughout the simulation, as OLSR routing needs to converge after several links are broken due to certain units being replaced. As we can see in both situations, in UDP there is no \"recovery\" per-se (throughput decreases and increases back to its normal rate once the traffic flow is reestablished), so the GCS/SUAV will be receiving packets when there is an available route, no matter how much time passes: as long as the source is generating traffic and a route is available, orchestration traffic will be eventually received, no matter how much time it takes for the network to find an available path. This is completely opposite to TCP, as connections will be dropped if no path is found before their backoff timers expired, leaving the aircraft with periods without control information, severely harming its throughput.  
Nonetheless, in the saturation state there is a noticeable worse performance in this case as well. The reason is exactly the same as in the other protocol: if nodes cannot send traffic to the network, either because the channel is occupied or there is no path to the destination, then the units along the routing path will discard the packets arriving if their buffer is full. In contrast with TCP, since there are no retransmissions, the discarded packets are completely lost, which must be taken into account during the orchestration process since important information could be lost until the routing path is available again.  
Figure 5: Received throughput on SUAV (4,4) using TCP as transport-layer protocol under saturation conditions.
## 1 Introduction
In addition, effective modeling is required due to the specific characteristics of WSNs, such as wireless communication between the sensor nodes and their constraints as small, low-power devices. Furthermore, common QoS requirements should be improved since WSNs have different QoS requirements for each application. Along with common QoS requirements, there is also a need for higher network throughput to minimize message delay, a need to improve data transmission reliability, and a need for network longevity. In order to satisfy these requirements, congestion and unnecessary traffic should be minimized and techniques to improve the reliability of data transmissions are required. Figure 1 illustrates that most transmission failures caused by congestion during data transmission in WSNs occur because of buffer drop and channel loss [14]. From this, it can be seen that network throughput decreases in the presence of congestion in the WSN, thus increasing the message delay. Therefore, in order to satisfy the common QoS requirements, study of congestion management, improvement of transmission reliability, and node energy management are required. Previous studies addressing data transfer or QoS guarantees have been divided into several categories: (1) techniques to reduce the amount of data generated by the sensor nodes [14, 15]; (2) techniques to detect and control congestion depending on the status of the sensor nodes' queues [16, 17]; (3) techniques to detect congestion using packet service times [18]; (4) techniques to adjust the throughput at the receiving node by passing the congestion state on to neighboring nodes [19]; and (5) techniques using packet priority [20]. These techniques all have the disadvantage of requiring an extra transmission to send a congestion signal. In addition, when congestion is detected, the throughput is reduced by dropping the packet and adjusting the sampling period of the node at which congestion occurred. Figure 1: Wireless Sensor Networks (WSN) Quality-Congestion dramatically degrades channel quality [14]. The purpose of this study is to maximize the utility of the network by predicting the occurrence of congestion through WSN modeling. In this paper, in order to detect congestion in sensor networks, generated periodic and non-periodic packets are predicted by node depending on the placement of the sensor nodes themselves. The prediction reliability can be improved by predicting the transmission time based on the network types. This study is organized as follows: In Section 2, related techniques are examined. In Section 3, the proposed WSN modeling techniques are explained. Section 4 describes an experiment measuring the accuracy of the proposed method. Finally, Section 5 presents the conclusions of this research.
# Detection of Parking Slots Based on Mask R-CNN
## 2 Research Status  
### Traditional Algorithms  
The traditional methods for feature utilization algorithms can be categorized into two types: slot-corner-detection-based [22; 23; 24; 25] and line-detection-based methods [26; 27; 28; 29; 30]. For instance, [PERSON] et al. [22] used the Fast detector to recognize corners to determine parking slots. [PERSON] et al. [23; 24; 25] used the corner features to identify slot patterns and parking slots. They used Harris detector to extract the lowest level features (corners) in the hierarchical tree structure. Although their results indicated a decent performance in parking-slot identification, it was hard to obtain the exact position and heading angle of corner features. The success rate also largely depended on the robustness of the corner detector.  
On the other hand, line-detection-based algorithms include Hough [26], Radon [27], RANSAC [28; 29; 30]. [PERSON] et al. [26] detected parking slots by finding parallel line pairs using a specialized filter and Hough transform. [PERSON] et al. [27] utilized a similar method that locates parallel line pairs using the Radon transform. [PERSON] et al. [28] also used RANSAC to detect separating lines in AVM images; parking slots were generated by pairing the combined separating lines based on the geometric constraints. The location and orientation of parking slots can be determined fast by line detection, however, it is susceptible to interference from external objects, such as lane lines, curbs, buildings, etc., around the parking slots. Therefore, complex filters or classification methods need to be designed.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 4 Results
### App.Any.Run
The results of the experiment showed accurate detection results for normal/malignant files, and the results were successfully derived by conducting a dynamic analysis of suspicious files. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Division**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Headers**} & \multirow{2}{*}{**PID**} & \multicolumn{2}{c}{**Process**} & \multirow{2}{*}{**URL**} \\  & & & & **Name** & \\ \hline \multirow{4}{*}{HTTP} & 34594 ms & POST 200 & 2440 & Firefox.exe & [[http://ocsp.digicert.com/](http://ocsp.digicert.com/)]([http://ocsp.digicert.com/](http://ocsp.digicert.com/)) (8 July 2011) \\  & 34192 ms & GET 200 & 2440 & Firefox.exe & [[http://detectportal.firebox.com/success.txt?ipv4](http://detectportal.firebox.com/success.txt?ipv4)]([http://detectportal.firebox.com/success.txt?ipv4](http://detectportal.firebox.com/success.txt?ipv4)) (27 April 2019) \\ \multirow{4}{*}{Request} & 34185 ms & GET 200 & 2440 & Firefox.exe & [[http://detectportal.firebox.com/success.txt](http://detectportal.firebox.com/success.txt)]([http://detectportal.firebox.com/success.txt](http://detectportal.firebox.com/success.txt)) (9 July 2016) \\  & 34179 ms & GET 200 & 2424 & Opera.exe & [[http://crl3.digicert.com/DigiCertHighAssuranceEVRootCA.crl](http://crl3.digicert.com/DigiCertHighAssuranceEVRootCA.crl)]([http://crl3.digicert.com/DigiCertHighAssuranceEVRootCA.crl](http://crl3.digicert.com/DigiCertHighAssuranceEVRootCA.crl)) (21 February 2012) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: HTTP request packet analysis results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Division**} & \multirow{2}{*}{**Protocol**} & \multirow{2}{*}{**PID**} & \multicolumn{2}{c}{**Process**} & \multirow{2}{*}{**IP(USA)**} & \multirow{2}{*}{**Domain**} \\  & & & **Name** & & \\ \hline \multirow{4}{*}{Network} & TCP & 1116 & Chrome.exe & 142.250.181.228 & www.google.com (9 October 2020) \\  & TCP & 1116 & Chrome.exe & 216.582.121.42 & clients2.google.com (9 October 2020) \\ \multirow{4}{*}{Connect} & TCP & 2440 & Chrome.exe & 34.107.221.82 & detectportal.firebox.com (12 October 2020) \\  & TCP & 2424 & Opera.exe & 185.26.182.94 & certs.opera.com (12 October 2020) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Network connection packet analysis results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Division**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Status**} & \multirow{2}{*}{**Domain**} & \multirow{2}{*}{**IP**} \\  & & & & \\ \hline \multirow{4}{*}{DNS} & 34117 ms & Responded & certs.opera.com (12 October 2020) & 185.26.182.94, 185.26.182.93 \\  & 33124 ms & Responded & www.google.com (9 October 2020) & 142.250.181.228 \\ \multirow{4}{*}{Request} & 33122 ms & Responded & accounts.google.com (9 October 2020) & 142.250.186.109 \\  & 33115 ms & Responded & clients2.google.com (9 October 2020) & 216.58.212.142 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: DNS Requests packet analysis results.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 3 Results
### Visualization Example
#### 3.1.2 Satellite Removal Strategy  
Based on MP1, MP2, S4 and \(\sigma_{\phi}\), G10 was considered to be the satellite affected most by scintillation; G21 was the most affected according to ROTI only on 13 September 2017 at SAOOP station. As shown in Figure 4, the original and G10-removed PPP height (a) and 3D (b) errors were compared based on RMSE on 13 September 2017 at SAOOP station. It can be observed from Figure 4 that the original height and 3D errors display variabilility during the first ten hours. By comparison, the RMSE of both height and 3D errors barely changed after G10 was removed, which meant removing G10 did not contribute to error mitigation though it was the satellite with strongest scintillation according to MP and scintillation parameters. During the scintillation period, there were 8 to 11 visible satellites including G10. Thus, removing G10 hardly affected the availability of satellites and the positioning algorithm. Furthermore, another scintillation satellite G21 was removed according to ROTI and the same comparison was conducted that is shown in Figure 5. On this occasion, the RMSE of height and 3D errors were slightly improved, where improvement rates of results shown in Figure 5,b are 5.5% and 4.6%, respectively.  
In order to investigate the influence of satellite removal on the GDOP, the GDOP time series plots before and after removing G10 or G21 are shown in Figure 6. The GDOP values mostly remain below 5 and all the GDOP values remain below 8. The GDOP values slightly increased during the first six hours in both graphs and in the last half hour in Figure 6. As  
Figure 4: Comparison between (**a**) the original and G10-removed RMSE of the height error, (**b**) the original and G10-removed RMSE (m) of the 3D error on 13 September 2017 at SAOOP station. The period affected by scintillation is framed in a rectangle (0.5 to 10 UT).  
Figure 5: As Figure 4 except G21 was removed rather than G10.  
aforementioned, there were 8 to 11 satellites available during the scintillation period. In this case, removing a single satellite has little effect on the satellite geometry and thus, DOP.