esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## Inverse Perspective Transform (IPT)
### Setting of a Region of Interest (ROI) for IPT  
This paper focuses on applying the IPT technique to the tunnel CCTV image, referred to as the original image (OI), and this involves the initial establishment of a region of interest (ROI) [29]. The ROI is defined as a quadrangular shape, conforming to the road width and CCTV installation spacing when the image is stretched and observed on a horizontal plane, as illustrated in Figure 2a [29]. In this context, Figure 2b serves as an OI coordinate system, its origin is at the top left corner, the rightward horizontal direction is denoted as the x axis, and the downward vertical direction is denoted as the y axis. Subsequently, upon transforming Figure 2b into a horizontal plane image, the term transformed image (TI) is introduced, with its origin located at the top left corner, as depicted in Figure 2a. The TI coordinate system is defined, with the x' axis indicating the rightward horizontal direction and the y' axis indicating the downward vertical direction. To generate the TI as shown in Figure 2a, the ROI must first be designated on the OI in Figure 2b by following the prescribed procedure described in Figure 3.  
Figure 3 shows the procedure for finding four points of interest for setting ROI for tunnel CCTV as a figure (Figure 3a) and a flow chart (Figure 3b). In Figure 3b, each process is described in detail as follows:  
(1) In the world coordinate system, the two red lines are parallel to the vehicle's driving direction, and are located at the edge of the road.  
(2,3) Green and blue lines are drawn which are perpendicular to (nearest between) the lines drawn in (1) in the world coordinate system.  
Through this process, the four orange points are found which the intersection of each of the lines, as shown in Figure 3a, and these points are used to define ROI. Using the coordinates of these four vertices of the ROI, an image conversion is conducted to transition from OI to a horizontal plane. The ROI, once converted to the horizontal plane image, takes the form of a rectangle.
## 2 System Model and Problem Description
### Problem Description  
Each person in the workplace environment would wear a bracelet that will be paired with their smartphone using Bluetooth technology to ensure that the person carries the smartphone during moving. This can be useful to notify the workplace manager when the person takes off the bracelet and the connection of the Bluetooth is terminated during the working hours. Different metrics are collected using the smartphone of the workplace manager in all the pre-defined geofences and are used for generating the unique GDS for each geofence. In addition, the manager of the workplace will generate a GDS for each building in the workplace environment.  
During working hours, the smartphones of the employees will collect the pre-defined metrics periodically at time intervals of \(T^{p}\) when the smartphone's accelerometer senses that the person is moving. This can help to reduce the processing overhead on the person's smartphone. After that, the collected metrics are used to generate the GDS for the geofence that the person is located in. Then, it will compare with the GDSs of their associated geofences that are defined in their own cluster to identify whether the person is in their cluster or not.  
Layered-Architecture Geofence Division (LAGD) method is considered in this paper. First, the generated GDS is compared with buildings' GDSs to identify whether the person is inside or outside the buildings. When there is a matching between the generated GDS and the GDS of any building, sort the set \(\mathcal{F}_{k}[n]=\{1,\,2,\,\ldots,F_{k}\}\) that contains the associated geofences for the person \(k\) at the time interval \(n\) that located inside this building in descending order based on the GPP. After that, the generated GDS is compared with the GDS of the current building and the GDS of all the associated geofences in \(\mathcal{F}_{k}[n]\) based on the sorted list considering starting with the GDS of the current building. In the case of mismatching, wait for a fixed time margin \(T^{s}\) and repeat the above steps. This can help to reduce the processing overhead at the employee's smartphone. In the case of matching between the generated GDS and the GDS of any geofence in \(\mathcal{F}_{k}[n]\), this indicates that the person is inside their own cluster of associated geofences. If the GDSs are mismatched during a pre-defined violation time interval \(T^{\triangledown}\), an alert will be generated to the manager of the workplace to inform them of this violation to take proper action. The functional block diagram for the proposed approach is illustrated in Figure 2.
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### Study Focus and Applications
#### 3.2.1 Classification  
Wetland cover classification arises from the monitoring and management needs to categorize wetland ecosystems into several types displaying different landscapes within wetlands. Traditional in situ surveys are too costly and slow to meet the demand for extensive and fast monitoring of wetland habitats [11]. The RS technology, in contrast, offers labor- and cost-effective advantages for mapping and assessing wetlands due to its intuitive observations of the ground in the broad coverage area, along with timely, regular, and rapid monitoring capabilities [53; 54; 55].  
With the current advancement in Earth observation instruments (e.g., multi/hyperspectral, SAR, etc.), there are ever more types of airborne or space-borne images with different resolutions available (i.e., spatial/spectral/temporal resolution). This leads to significant demand for intelligent Earth observation based on RS imagery, enabling the smart identification and classification of wetland areas from airborne and space platforms. The problem of scene classification has emerged as an active research area in RS data analysis and has drawn remarkable attention in recent years, as it is essential for effectively interpreting remotely sensed images. It attempts to label given scene images correctly based on their contents with predefined semantic land cover categories [56]. Currently, satellite image data for Earth observation are being made continuously available to the public. Governmental programs, including the ESA's Copernicus and NASA, are putting considerable effort into making such data freely available for universities and institutions,  
Figure 9: Overview of the diversity of application domains of RS Earth observations in wetland areas.  
researchers, scientists, and technology experts to use in order to inspire innovation and entrepreneurship.
# 3D Printed Orthodontic Aligners--A Scoping Review
## 3 Results
### Summary of Findings
#### 3.2.6 Biomechanics  
Three studies investigated biomechanical aspects in terms of force and moment generation or FEM analysis. Forces and moments generated in vitro by directly printed Aligners from TC-85 (Graphy) were found to be lower, more consistent, and predictable compared to thermoformed Aligners made from ATMOS (American Orthodontics, Sheboygan, WI, UAS) or Zendura FLX (Bay Materials, Fremont, CA, USA) [54]. [PERSON] et al. measured in vitro forces and moments generated by TC-85 printed aligners and found that variations in thickness changed the amount of force and moments, but in a complex and unpredictable pattern. Nevertheless, they concluded with the possibility to improve anchorage by increasing aligner thickness where anchorage is needed, therefore reducing side effects [55].  
The mechanical behavior using a Finite Element Analysis (FEA) of directly printed aligners (Dental LT resin, Formlabs (Somerville, MA, USA)) compared to vacuum formed ones (Duran and Durasoft, both Scheu (Iserlohn, Germany)) seemed to be comparable in terms of stress distribution, whereas the geometrical accuracy was higher in the directly printed material [47].
## 4 Conclusions  
In summary, we grew a bulk GaN/AlN on CVD graphene, transferred on silicon substrate coated by an amorphous oxide layer. In comparison with our previous works [18; 28] on the study of nitrides epitaxy using a graphene buffer, in this research we clearly demonstrate that the number of graphene layers, the type of strain and defects are crucial for the high-quality epitaxy of GaN/AlN by the PA-MBE method. We found a strong correlation between the number of graphene layers and the quality of GaN/AlN grown on it. Bilayer and few-layer graphene were recognized as the most suitable buffers, which provide plasma-assisted molecular beam epitaxy of perfect GaN/AlN films. This fact can be explained by the difference in the FLG and MLG stiffness and defectiveness. We believe that our investigation contributes to the development of GaN-on-Si technology, which promises some benefits, such as low cost and high manufacturability. Due to its flat structure, moderate activity and tendency to non-covalent interaction, graphene probably can also be used for epitaxial growth of III-nitrides on other substrates which are amorphous of have a crystal lattice unsuitable for binding to III nitrides.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122211516/s1](https://www.mdpi.com/article/10.3390/app122211516/s1)]([https://www.mdpi.com/article/10.3390/app122211516/s1](https://www.mdpi.com/article/10.3390/app122211516/s1)).  
Conceptualization, N.I.K., A.S.G. and D.P.B.; methodology, D.P.B., A.S.G., I.V.K. and K.P.K.; software, D.P.B. and K.P.K.; validation, D.P.B., A.S.G., I.V.K. and K.P.K.; formal analysis, D.P.B., A.S.G., I.V.K. and K.P.K.; investigation, D.P.B., A.S.G., P.L.D., A.A.T., M.M.M. ([PERSON]), K.P.K., M.M.M. ([PERSON]) and P.S.D.; resources, N.I.K., A.S.G., I.V.K. and K.P.K.; data curation, D.P.B., A.S.G. and I.V.K.; writing--original draft preparation, D.P.B.; writing--review editing, K.P.K., A.S.G. and D.P.B.; visualization, D.P.B., A.S.G. and K.P.K.; supervision, N.I.K. and V.A.L.; project administration, N.I.K.; funding acquisition, N.I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in Supplementary Materials.  
This work was carried out using the equipment of the MEPhI Shared-Use Equipment Center ([[http://ckp-nano.mephi.ru](http://ckp-nano.mephi.ru)]([http://ckp-nano.mephi.ru](http://ckp-nano.mephi.ru)), accessed on 31 October 2022).  
The authors declare not conflict of interest.
## 3 Design of Functional Modules
### UART Communication  
The UART communication module is divided into send and receive functions, as illustrated in Figures 8 and 9, and which are set out in further detail below:  
Figure 8: The ‘send’ function of UART communication.  
* The'send' process of UART communication
* Set variable.
* Temporarily save the data from the data transfer module (Transfer_Buffer) to OutData. Determine OutData.Length. If OutData.Length > 0, this means there are data in the serial port, and the workflow proceeds to Step 4. If OutData.Length \(\leq\) 0, the workflow ends.
* Send OutData via the serial port to the WSN coordinator.
* The'receive' process of UART communication
* Set variable.
* Calculate the length of serial port data and save to DataLen.
* Calculate the size of DataLen. If DataLen > 0, this means there are data in the serial port, and the workflow proceeds to Step 4. If DataLen \(\leq\) 0, the workflow ends.
* Save serial port data to InData.
* Save InData to the communication backup system (Backup Buffer).
# Study on the Mechanism of the Variable-Speed Rotor Affecting Rotor Aerodynamic Performance
## 3 Validation  
Figure 2 shows the NACA0012 airfoil dynamic stall validation under two freestream conditions: one represents no obvious dynamic stall and the other represents obvious deep dynamic stall, where the experimental data and Leishman solutions were derived from Reference [21]. In Figure 2a, the maximum AoA is not beyond the dynamic stall angle, so there is no leading edge vortex. Furthermore, the unsteady behavior is mainly a combination of the attached flow and trailing edge separation flow. When the AoA becomes larger, the deep dynamic stall appears as shown in Figure 2b. We can see that it differs greatly from Figure 2a. Due to the dynamic delay, the normal force increases further after AoA becomes larger than the static stall angle. Then, the boundary layer reversal point moves towards the leading edge and the vortex forms at Point 1. In this case, the vortex convects to the trailing edge at Point 2 before reaching the maximum AoA (Point 3); then, the \(C_{n}\) declines because of the vortex detachment. As the AoA decreases, reattached flow starts at Point 4. Compared with the experimental data and Leishman results, the present solutions have good correlations, and the modeling method is validated.  
Figure 3 shows a \(C_{n}\) validation of the NACA23012 airfoil dynamic stall in a low Mach number situation. Compared with the experimental data and Sheng results [26], the present solutions have  
Thanks to adequate public data, the current work used the Bo 105 helicopter as a base to implement the variable-speed rotor aerodynamic performance study; its rotor airfoil is NACA23012 and whose tail rotor airfoil is NACA0012. The key parameters for dynamic flight modeling of the Bo 105 helicopter were derived from Reference [28] and Reference [29] 263. Figure 4 shows a flight trim validation of the Bo 105 at sea level, compared with the flight test and Padfield results based on linear inflow modeling [29]. The take-off weight was 2200 kg. It can be seen that the collective feathering decreased at first and then increased as the flight speed increased. At a medium flight speed, the required rotor power was relatively low, as was the collective feathering. The present trim plot shows that in hover and low flight speed, there was a good correlation with the flight test, while in faster flight modes, the present collective feathering result was underestimated. During fast flight, the fuselage parasite power consumption dominates the rotor required power and increases with the cubic flight speed. Therefore, the deviation may be caused by an underestimated fuselage drag in fast flight; to simulate this, the current work applied the Padfield's fitting function. The lateral cyclic feathering correlated better with the flight test, owing to the wake method.
## 3 Results and Discussion
### Physicochemical Parameters Variation during Desalting Process
#### 3.2.2 Trimethylamine Content Variation  
Trimethylamine (TMA) is produced by the reduction in trimethylamine N-oxide (TMAO) because of microbial growth in fish muscle [29]. Figure 2B shows the results obtained for the TMA content during desalting at 4 and 20 \({}^{\circ}\)C. Initially, salted cod showed a TMA content of 0.043 \(\pm\) 0.004 mg TMA/g IS. The TMA content increased (\(p\) < 0.05) during cod desalting time, at both temperatures, to values of 0.076 \(\pm\) 0.008 mg TMA/g IS (20 \({}^{\circ}\)C) and 0.062 \(\pm\) 0.002 mg TMA/g IS (4 \({}^{\circ}\)C) after 4 h of desalting, maintaining (\(p\) > 0.05) similar values thereafter.  
Figure 2: pH (**A**), trimethylamine content ((**B**), mg TMA/g IS), and soluble protein ((**C**), mg albumin/g IS) in cod fish during the desalting time (hours). Different letters (a–d) denote significant differences (\(p\) < 0.05) between desalting times. The symbol \({}^{*}\) denotes significant differences between desalting temperatures.  
During the first hour of desalting, the values obtained at 20 \({}^{\circ}\)C were statistically higher (\(p<0.05\)) than those obtained at 4 \({}^{\circ}\)C, suggesting again that microbial growth is lower when cod samples were desalted at a lower temperature. In fact, these results were in accordance with those previously discussed (pH value of cod samples), since a correlation between the pH values and TMA content was observed (r\({}^{2}\) = 0.981).  
Some studies in the literature correlate the TMA level with sensorial rejection, indicating 5 mg TMA/100 g as a possible sensorial limit [30]. In this study, TMA values were within the range of 1-3 mg TMA/100 g of desalted cod, showing that the TMA content in desalted cod samples was not high enough to cause a sensorial rejection.
## 2 Collision Avoidance Method
### Assessment Methods on the Collision Risk
#### 2.1.2 Closest Point of Approach  
The closest point of approach, also known as CPA, is the point where the OS is closest to the TS at any time. Current position, course, and velocity for both the OS and the TS were used in order to compute CPA as shown in Figure 4.  
Figure 3: Ship safety domain proposed by [PERSON] and [PERSON].  
Figure 2: Different safety criterion of ship safety domain: (**a**) Own-Ship (OS) domain is not violated; (**b**) Target-Ship (TS) domain is not violated; (**c**) neither OS nor TS domain is violated, and (**d**) both domains do not overlap.  
Position, course, and velocity of the OS are given as \((x_{o},\ y_{o})\), \(\Phi_{o}\), and \(V_{o}\). Likewise, position, course, and velocity of the TS are given as \((x_{t},\ y_{t})\), \(\Phi_{t}\), and \(V_{t}\). The relative moving parameters can be computed as  
\[D_{r}=\sqrt{\left(x_{t}-x_{o}\right)^{2}+\left(y_{t}-y_{o}\right)^{2}} \tag{1}\]  
\[V_{r}=V_{o}\times\sqrt{1+\left(\frac{V_{t}}{V_{o}}\right)^{2}-2\times\frac{V_ {t}}{V_{o}}\times cos(\Phi_{o}-\Phi_{t})} \tag{2}\]  
\[\Phi_{r}=cos^{-1}\times\left(\frac{V_{o}-V_{t}\times\cos(\Phi_{o}-\Phi_{t})}{V _{r}}\right) \tag{3}\]  
where \(D_{r}\) denotes the relative distance between the OS and the TS, \(V_{r}\) denotes the relative velocity, \(\Phi_{r}\) denotes the relative course. The distance between the OS and the TS at CPA and the time until CPA are computed as  
\[D_{CPA}=D_{r}\times sin(\Phi_{r}-\alpha_{t}-\pi) \tag{4}\]  
\[T_{CPA}=D_{r}\times cos(\Phi_{r}-\alpha_{t}-\pi)/\ V_{r} \tag{5}\]  
where \(D_{CPA}\) is the distance between the OS and the TS, and \(T_{CPA}\) is the time to closest point of approach at that time, \(\alpha_{t}\) denotes the azimuth of the TS, and \(\alpha_{r}\) denotes the relative bearing.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
###### Abstract  
With the growing complexity and frequency of network threats, anomaly detection in network traffic has become a vital task for ensuring cybersecurity. Traditional detection approaches typically rely on statistical features while overlooking the interaction patterns and structural dependencies among traffic flows. In addition, network traffic data are distributed across heterogeneous devices and domains, where centralized training methods face significant challenges such as data leakage and data silos. To address these issues, we propose a network traffic anomaly detection method based on cross-domain federated graph representation learning. In this method, network traffic is modeled as a graph, and a feature-structure decoupling design is adopted to separate the encoding and learning of graph topology and node attributes. Only structural information with minimal sensitive content is transmitted to the central server, whereas sensitive node attributes are preserved and processed locally to enhance privacy protection. Furthermore, a cross-gated feature fusion mechanism is introduced to enhance the expressive interaction between features and to generate graph-level embeddings for anomaly classification. To further improve the model's generalization across domains, a cross-domain structural guidance mechanism is implemented on the server side, which integrates structural information from multiple domains to guide the training of local models. Comparative experiments with other methods demonstrate that the proposed approach achieves superior performance in distributed network traffic anomaly detection scenarios.  
graph neural network; federated learning; network traffic anomaly detection
# The Use of Correlation Features in the Problem of Speech Recognition
## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **q = 0.1** & **q = 1** & **q = 10** \\ \hline Decision tree (depth 10) & 35.75\% & 78.25\% & 92.50\% \\ \hline Support vector machine & 37.00\% & 75.50\% & 93.25\% \\ \hline Fully connected neural network (3 layers, 20-20-20 neurons) & 45.75\% & 87.25\% & 94.25\% \\ \hline Recurrent neural network (recurrent layer 24 neurons) & 54.25\% & 83.75\% & 95.50\% \\ \hline Long short term memory network (LSTM layer 24 neurons) & 49.75\% & 80.50\% & 96.00\% \\ \hline
1D convolutional neural network (16 filters) & 39.50\% & 86.25\% & 94.00\% \\ \hline Fully connected network based on correlation function & 57.25\% & 92.25\% & 96.50\% \\ \hline Recurrent network based on correlation function & 59.00\% & 93.25\% & 97.25\% \\ \hline LSTM network based on correlation function & 52.25\% & 94.00\% & 97.50\% \\ \hline Convolutional network based on correlation function & 51.00\% & 91.00\% & 95.75\% \\ \hline Convolutional network based on correlation portrait & 53.25\% & 96.75\% & 97.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Noisy messages recognition. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Processing Time, ms** \\ \hline Decision tree (depth 10) & 2.35 \\ Support vector machine & 8.94 \\ Fully connected neural network (3 layers, 20-20-20 neurons) & 246.80 \\ Recurrent neural network (recurrent layer 24 neurons) & 320.55 \\ Long Short Term Memory Network (LSTM layer 24 neurons) & 654.35 \\
1D Convolutional Neural Network (16 filters) & 806.20 \\ Fully Connected network based on correlation function & 246.80 + 18.40 \\ Recurrent network based on correlation function & 654.35 + 18.40 \\ Convolutional network based on correlation function & 806.20 + 18.40 \\ Convolutional network based on correlation portrait & 2325.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition time.
## 3 Results and Discussion
### Photoconversion Mechanism of DHSAN into SAN
As underlined in the literature concerning the pH-dependent equilibrium between the two forms of SAN, namely, iminium cation and alkanolamine, this equilibrium is shifted towards the iminium form in biological environments by the interaction in terms of intercalation with DNA [57]. Indeed, the SAN charged form can bind to DNA, establishing intercalative interactions with its bases, whereas intercalation is precluded to the alkanolamine neutral form, as proved by a number of biophysical experiments, due to its non-planar structure having a partial saturation [58].
# Towards Adaptive Multipath Managing:
###### Abstract  
With the large scale deployment of multihomed mobile computing devices in today's Internet, the Multipath TCP (MPTCP) is being considered as a preferred data transmission technology in the future Internet due to its promising features of bandwidth aggregation and multipath transmission. However, MPTCP is more likely to be vulnerable to the transmission quality differences of multiple paths, which cause a \"hot-potato\" out-of-order arrival of packets at the receiver side, and in the absence of a related approach to fix this issue, serious application level performance degradations will occur. In this paper, we proposes MPTCP-LM\({}^{3}\), a Lightweight path Management Mechanism to aid Multihomed MPTCP based mobile computing devices towards efficient multipath data transmission. The goals of MPTCP-LM\({}^{3}\) are: (i) to offer MPTCP a promising path management mechanism, (ii) to reduce out-of-order data reception and protect against receiver buffer blocking, and (iii) to increase the throughput of mobile computing devices in a multihomed wireless environment. Simulations show that MPTCP-LM\({}^{3}\) outperforms the current MPTCP schemes in terms of performance and quality of service.  
wireless multipathing; multihomed mobile computing device; multipath TCP; receiver buffer blocking; path management +
Footnote †: journal: Applied Sciences