esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Improved Path Point Sampling Strategy
#### 2.2.2 Secondary Sampling  
After completing the initial sampling, a certain number of feasible paths are obtained. These paths serve as reference paths for the secondary sampling. The initial sampling points are cleared, and then secondary sampling is performed within a certain range of the reference paths, as shown in Figure 4. The process of secondary sampling can be represented by pseudocode.  
The pseudocode for secondary path point sampling is shown in Algorithm 3. The process starts by segmenting and sorting the path set, followed by clearing other path points in the sampling space (lines 2-5). The width of all path segments is then expanded (line 7), and the secondary sampling space is defined as the intersection of the path space and sampling space (line 8). Sobol sampling is conducted in the secondary sampling space (line 9). For each sample, it is necessary to determine whether the sampling point falls within an obstacle; if it does, the point is deleted; otherwise, it is retained and added to the point set \(V\). Finally, set \(V\) is returned as the result of secondary sampling (lines 10-17).  
Figure 3: Comparison of Sobol sampling and pseudo-random sampling.  
```
1:functionSICcondarySampling(\(X_{\textit{fun}}\), \(\pi\), \(n\), \(\textit{width}\))
2:\(\textit{path}\leftarrow\)IMTALPathSIComentation(\(\pi\))//Segmenting the path formed by standard FMT*
3:\(\textit{path}\_\textit{num}\leftarrow\)PATHSORIT(\(\pi\))//sort the path segments and return the total number
4:\(\textsc{C}_{\textsc{Lear}}(X_{\textit{fun}})\)//clearing initial sampling points
5:\(i=0\)
6:while(\(i<\textit{path}\_\textit{num}\))do
7:\(X_{\textit{Path}}\leftarrow\)ResZEPTATHWIDTH(\(\textit{width}\))//resize the width of the initial sampling paths
8:\(X_{\textit{Path}}\wedge\textit{True}=X_{\textit{Sample}}\)
9:\(S_{\textit{S}}\textit{S}_{\textit{S}}\leftarrow\)SORTSAMPLINC(\(X_{\textit{sample}}\), \(\textit{point}\_\textit{num}\))
10:if\(\textsc{Out}\)SupCognCLE(\(\textit{SecondaryPoint}\)) == TRIEthen
11:\(\textsc{D}_{\textsc{ELETE}}(\textit{SecondaryPoint})\)
12:else
13:\(V\leftarrow\)APPENDIX(\(\textit{SecondaryPoint}\))
14:endif
15:\(i=i+1\)
16:endwhile
17:return\(V\)
18:endfunction
```  
**Algorithm 3** Secondary Sampling  
From Figure 4, it can be seen that the secondary sampling path points are distributed around the initial feasible path, which effectively improves the quality of path searching.
## 2 Materials and Methods
### Quantification and Relative Odor Activity Value (rOAV) Calculation  
Xcalibur software (Thermo Fisher Scientific, Waltham, MA, USA) was used for GC-MS data analysis, including peak extraction, baseline filtering, calibration, peak alignment, deconvolution, and peak area integration. Volatile compounds were semi-quantified based on the peak areas of the internal standard. rOAVs were calculated by dividing the content of each volatile compound by its odor threshold in water [22]. Odor thresholds (OTs) in water were based on the literature [5; 15; 23; 24].
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 5 Conclusions  
Our research clearly shows that both UAV and satellite imagery have significant capability to detect plant stress in mature _P. radiata_ trees under controlled experimental conditions. This is evidenced by the deviation of spectral indices from both sensors from the \"no-change\" region and from close correlations with the field observations of tree health. This result suggests that a more comprehensive monitoring platform can be developed by combining both sensor technologies. This would aid precision forest management by enabling both coarse, broad-scale monitoring of disease progression and fine-scale changes that might indicate new or early disease outbreaks. We found that smaller scale instances of herbicide induced stress require finer spatial resolution imagery for detection. This supports our hypothesis that the higher resolution UAV imagery provides greater sensitivity to physiological stress than coarser satellite imagery. We have also defined the lower spatial bound for physiological stress detection for the RapidEye satellite. In our experiment, the lower spatial bound for stress detection was found to be the area represented by 4 or more trees, this equated to an area of approximately 77 m\({}^{2}\) on average. This finding suggests that, using this approach and the indices examined, the RapidEye sensor is sensitive to changes at a spatial resolution that is approximately twice the pixel size of the derived imagery. This may be useful for managers engaged in planning and acquisition of satellite imagery. Our experiment showed that the UAV and satellite imagery data used are complementary and can be used in combination to provide a more complete means of detecting and monitoring physiological stress patterns in forested ecosystems. This multi-sensor approach will lead to the augmentation and enhancement of current approaches based on a single platform.  
**Author Contributions:** Conceptualization, J.P.D. and M.S.W.; Data Curation, J.P.D.; Formal Analysis, J.P.D. and G.D.P.; Investigation, J.P.D., G.D.P. and M.S.W.; Methodology, J.P.D.; Visualization, J.P.D.; Writing--Original Draft, J.P.D.; Writing--Review & Editing, G.D.P. and M.S.W.  
**Funding:** This study was funded as part of the Growing Confidence in Forestry's Future (GCFF) programme (Contract number: CO4X1306). GCFF was co-funded by the Forest Growers Levy Trust and the New Zealand Ministry of Business, Innovation and Employment (MBIE).  
**Acknowledgments:** The authors recognise the contributions of [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] who completed all field work and [PERSON] who helped develop the study methods. Scion colleague [PERSON] is acknowledged for offering advice on herbicide application rates. We are grateful to the forest managers for providing access and allowing us to complete this study in their forest.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### ROT and ROTI  
In order to detect, investigate and characterize the occurrence of ionospheric irregularities, we have used the Rate of change of the TEC Index (ROTI). The ROT and ROTI values are usually expressed in TECu/min. ROTI is defined as the standard deviation of the rate of TEC (ROT), and it is estimated by dual-frequency GNSS data with the time interval of 5 min by using Equation (3) [29]:  
\[ROTI=\sqrt{\langle ROT^{2}\rangle-\langle ROT\rangle^{2}} \tag{3}\]  
where \(\langle\cdot\rangle\) represents the temporal average. ROT is defined as the TEC variation rate of two successive epochs as stated in Equation (4) [29]:  
\[ROT=\frac{STEC_{t}^{i}-STEC_{t-1}^{i}}{k_{t}-k_{t-1}} \tag{4}\]where \(i\) indicates the observed satellite and \(t\) denotes the time of epoch. Hence, \(k_{t}-k_{t-1}\) is the time interval between the subsequent epochs.  
Depending on the ROTI value, the activity level can be classified in ranges such as: weak (if 0.25 \(\leqslant\) ROTI \(<\) 0.5); moderate (if 0.5 \(\leqslant\) ROTI \(<\) 1); and strong (if ROTI \(\geqslant\) 1), according to [PERSON] et al. [36].
# A Novel Visual SLAM Based on Multiple Deep Neural Networks
## 4 Experiments
### Validation Experiment  
To demonstrate the necessity of using the corresponding deep feature matcher in a visual SLAM system that uses a deep feature extractor, we compare our system with SuperPoint-SLAM [25] on selected sequences. Because SuperPoint-SLAM uses SuperPoint as our system (also using the same pre-trained model) but does not use a deep feature matcher, only the Euclidean distance between descriptors is used to match features.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sequence** & **Fuse Last and Reference** & **Reference** \\ \hline
00 & **1.0** & 1.2 \\
03 & **0.5** & 1.5 \\
04 & 0.2 & 0.2 \\
05 & **0.6** & 0.9 \\
06 & **0.7** & 0.8 \\
07 & **0.4** & 0.5 \\
09 & **3.1** & 3.3 \\
10 & **0.8** & 1.3 \\ \hline \end{tabular}
\end{table}
Table 2: Verifying fusion pose estimation in the KITTI odometry dataset using RMSE (m).  
Figure 6: The scene and trajectory of 04.  
Table 3 shows that using the corresponding deep feature matcher in a visual SLAM system employing depth features can significantly improve the estimation accuracy and stability of the system. For example, on Sequences 00 and 09, SuperPoint-SLAM cannot even run the whole sequence, while our system not only runs stably but also achieves good estimation accuracy. This is mainly due to the fact that our system uses SuperGlue which is adapted to SuperPoint. As shown in Figure 8a, the features extracted by SuperPoint can be correctly matched using SuperGlue. Correct matched features are the basis for accurate pose estimation. However, as can be seen in Figure 8b, the matching method used by  
Figure 7: Tracking the map points (green) of the reference frame and the temporary map points (blue) of the last frame, (**a**–**e**) in order away from the reference frame.  
SuperPoint-SLAM produces more incorrectly matched pairs when matching the features extracted by SuperPoint. This introduces a larger error in the pose estimation.
# Aerodynamic Sensitivity Analysis for a Wind Turbine Airfoil in an Air-Particle Two-Phase Flow
###### Abstract  
In this paper, the Navier-Stokes equations coupled with a Lagrangian discrete phase model are described to simulate the air-particle flows over the S809 airfoil of the Phase VI blade, the NH6 MW25 airfoil of a 6 MW wind turbine blade and the NACA0012 airfoil. The simulation results demonstrate that, in an attached flow, the slight performance degradation is caused by the boundary layer momentum loss. After flow separation, the performance degradation becomes significant and is dominated by a more extensive separation due to particles, since the aerodynamic coefficient increments and the moving distance of separation point present similar variation trends with increasing angle of attack. Unlike the NACA0012 airfoil, a most particle-sensitive angle of attack is found in the light stall region for a wind turbine airfoil, at which the lift decrement and the drag increment reach their peak values. For the S809 airfoil, the most sensitive angle of attack is about 3\({}^{\circ}\) higher than that for the maximum lift-to-drag ratio. Hence, the aerodynamic performance of a wind turbine is very susceptible to particles. Based on the most sensitive angles of attack, the more sensitive scope of angles of attack of a blade airfoil and the more sensitive range of rotor tip speed ratios are predicted sequentially. The present study clarifies the principles for the performance degradation of a wind turbine airfoil due to particles and the conclusions are useful for the wind turbine design reducing the particle influences.  
Navier-Stokes equations; discrete phase model; air-particle two-phase flow; wind turbine airfoil; aerodynamic performance; flow separation +
Footnote †: journal: Applied Sciences
## 4 Flight Test Results and Discussion
### Detection Warning Analysis  
In this analysis, only the flight test data from the Remote ID module 1, which was in the controlled UAV, was used because only UAV 1 was controlled to initiate a conflict and then maneuvered to avoid it. The data were grouped based on the warning parameters (0: no warning, 1: conflict detection warning, 2: near-collision warning). The analyzed parameters were UAV height; course angle; yawing rate; and relative height, distance, and speed between the UAVs. The resulting boxplots are shown in Figure 14.  
The average values of UAV height and speed shown in Figures 13f and 14a decreased with increased warning levels. This indicates that the pilot turned the controlled UAV away to avoid conflict. Due to this turning, the height and speed were reduced with the  
Figure 14: Boxplots of the warning parameters analysis based on the UAV flight parameters: (a) height [m], (b) relative height [m], (c) course angle [deg], (d) yawing rate [deg/s], (e) relative distance [m], and (f) speed [m/s].  
increase in the warning level. The turning indication is also shown in the yawing rate plot of Figure 14d, where the highest average turn rate happens at warning level 1. This means that the pilot started the turn or changed the bearing angle at warning level 1. However, the course angle plot in Figure 14c was not the parameter that influenced the warning since it only indicated the direction angle of the flight movement.  
The UTM monitoring application was developed with the detection algorithm based on the distance measurement in lateral and vertical modes. This is clearly shown in the relative distance and relative height of Figure 14e,b, which indicates that the algorithm functioned well as intended. The average relative distance and relative height decreased with the increase in the warning level.
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 5 Discussion  
This paper performed an adaptive attack to the detection based on logit dispersion to prove its feasibility. The advantage of this method is that it can generate adversarial examples with similar logits distribution to normal speech. At least the current detection based on logits cannot detect the adversarial examples effectively. Moreover, these experiments give us some enlightenment. To protect the human-computer interaction system based on ASR, defenders should further investigate the methods using logits features for adversarial example detection. As mentioned in Section 4.4, the study of logit features with advantages in adversarial example detection for a specific model still has broad application prospects. Meanwhile, since the threshold is statistically obtained from the dataset, the detection method using threshold division cannot reasonably deal with the logits feature near that point. Designing a computational approach for discriminating adversarial examples near the threshold can further improve detection accuracy based on logit dispersion.  
Considering the influence of computer vision, researchers usually use the perturbation size to evaluate speech adversarial examples. Adversarial examples with minor adversarial perturbations seem more normal and are less likely to be noticed. However, this is not enough. Our detection fully demonstrates that the adversarial examples generated based on classical C&W methods have universal logit features, and they are easily detected if no measures are taken. In addition, we also notice that the Logits-Traction adversarial example introduces more noise than the C&W adversarial example. This paper quantifies this drawback by comparing the Signal-to-noise ratio (SNR) of the Logits-Traction adversarial example with the C&W adversarial example. The calculation method is shown in Equation (9).  
\[SNR=10 log_{10}\frac{P_{x}}{P_{\delta}} \tag{9}\]  
where \(P_{x}\) and \(P_{\delta}\) mean energy of raw audio and added adversarial perturbation, respectively. Using the Librspeech dataset, we calculated the average SNR of the above C&W adversarial examples and Logits-traction adversarial examples with the same speech to the same target phrase. The average SNR of C&W adversarial examples was \(-6.605\). The average SNR of Logits-Traction adversarial examples was \(-7.299\). This means that the Logits-Traction method introduces more adversarial perturbation. This is caused by the more restrictive adversarial example search space of the method in this paper. It seems that the logits dispersion is inversely proportional to the size of the adversarial perturbation, and we will explore this conjecture further in subsequent studies. We suggest that the logit dispersion should be used as one of the evaluation indicators in the follow-up research. The logits dispersion of adversarial examples should be in the range of normal speech.
## 4 Methodology
### Data Encryption: Pre-Processing
#### 4.2.1 Handling Missing Values  
To ensure the integrity of the dataset, missing values were handled using mean imputation, where any missing data points were replaced with the mean value of the corresponding feature. This approach helps maintain the dataset's structure without introducing significant biases [39; 40].
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
## 2 Materials and Methods
### SAR Laboratory Measurements  
The objective of this study is to calculate the SAR from measurement data obtained from two different signal generators operating at 900 MHz and 1800 MHz (Everest GSM Simulators 900 CW4 and 1800 CW2[29]) and then assess the SAR on different tissues such as muscle, brain, and fat through simulations. SAR measurements were conducted at a standardized SAR laboratory[30], and signal generators with monopole antennas were used as RF-EMF sources with different frequencies. The laboratory is 4 \(\times\) 4 \(\times\) 5 m\({}^{3}\) in dimensions and has three surfaces covered with insulating material as shown in Figure 1.  
There are tissue-equivalent fluids in the laboratory that allow SAR measurements at different frequencies. In this study, 900 MHz and 1800 MHz tissue equivalent fluids were used based on a specific recipe. Before using these fluids, a liquid calibration test must be performed according to the relevant standards. The relative permittivity and conductivity values of these liquids must be in accordance with these standards with a maximum tolerance of 5%. The results of the test were validated using a vector network analyzer.  
The real part of the relative permittivity (\(\epsilon^{\prime}\)), the imaginary part of the relative permittivity (\(\epsilon^{\prime\prime}\)), and the conductivity (\(\sigma\)) values of two different liquids that were used in the laboratory and the simulations are given in Table 3 and are in accordance with the standards.  
Measurement setup consists of monopole antennas for 900 and 1800 MHz and generators. Monopole antennas of the generators are placed to the head region of the phantom with an angle of about 15\({}^{\circ}\). In the SAR laboratory, an electric field probe is positioned on the six-axis robot arm. Data are then collected in x, y, and z coordinates. The robot software defines the area to be scanned by the electric field probe, including the tracing paths and axial steps. Surface scanning is then conducted at 123 points with 8 mm intervals between  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Exposure Scenario** & **Frequency** & **Whole Body Average** & **Local Head/Torso SAR** & **Local Limb SAR** \\  & & **SAR (W/kg)** & **(W/kg)** & **(W/kg)** \\ \hline General Public & 100 kHz–6 GHz & 0.08 & 2 & 4 \\ \hline \end{tabular}
\end{table}
Table 2: SAR limits.  
Figure 1: SAR laboratory and phantom.  
each scan. First, the highest SAR value in the phantom is determined with the help of field scanning. Afterward, the scanning process is continued by performing three-dimensional cube scanning. SAR values of different output powers are obtained for the fixed antenna position with the help of openSARY4 software. This process is performed separately for the 900 MHz and 1800 MHz frequencies.
## 3 Mechanical Connectors to Enhance the Interface of Concrete Overlays
### Mechanical Connectors Analyzed  
In this work, two types of mechanical connectors to sew the interfaces of concrete overlays were analyzed. The first one corresponded to a hexagonal-head galvanized-steel screw commonly used for fastening applications in cracked and uncracked concrete. Its appearance and main characteristics are shown in Figure 2a and Table 3, respectively. A similar screw was studied in Reference [46] for strengthened beams. This element can be placed quickly (less than 30 s per piece) and is designed to generate low expansion forces. That makes it especially suitable to reduce the traffic opening time and minimize the damage in the bottom layer during its placement.  
Figure 3a represents the forces acting in the screw anchor when the interface is subjected to an external tensile load. Analogous to the resistance mechanism of a foundation pile, the screw resists transmitting both friction forces to the original pavement layer and anchorage forces to the reinforcement layer. The resultant of the tensile force that loads the transversal area of the core of the screw is equal to the external tensile force applied on the interface.  
Figure 1: The failure mode of anchors (Source: Model Code 2010).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Unit** & **Value** \\ \hline Height & (mm) & 80 \\ Outer diameter of thread & (mm) & 7.85 \\ Core diameter & (mm) & 5.85 \\ Nominal tensile strength & (MPa) & 930 \\ Stressed cross-section & (mm\({}^{2}\)) & 26.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the screw anchors.  
Figure 2: Mechanical connectors: (**a**) Screw, (**b**) \(\prod\)-shaped bended rebar.  
On the other hand, Figure 3b schematizes the same interface but subjected to a horizontal shear force. In this case, the screw is capable of withstanding the loads by transferring contact forces whose resultants are of the same magnitude and direction opposite to that of the applied ones. The shear stresses that appear in the cross-section of the screw at the interface compensate for these loads.  
The second connector was a \(\prod\)-shaped bended rebar 8 mm in diameter, 250 mm length, 150 mm height, 50.2 mm\({}^{2}\) cross-section, and 500 MPa nominal tensile strength (see Figure 2b). These bent rebars were placed in the most damaged zones of bonded whitetopping on asphalt concrete which required full-depth reconstructions. These reconstructions consisted of a bi-layer concrete pavement where the bottom asphalt was fully replaced by lean concrete and the top layer corresponded to an overlay. These rebars were selected because of their availability, their low cost, and the possibility to put them in the fresh lean concrete.
## 4 Simultaneous Utilization of Multiple RANs within a 3 GPP Framework
### Simultaneous Utilization of 3 GPP-Based and Wi-Fi Access Networks over 5 GC or EPC  
Commencing with Release 16 and onwards, a transport layer-level solution referred to as ATSSS was initially introduced to integrate non-3 GPP access networks (e.g., Wireless Local Area Network (WLAN)) into the 3 GPP ecosystem. This innovation facilitates the distribution of data traffic related to multiple applications through various pathways, encompassing both a 3 GPP access network (e.g., NG-RAN) and a non-3 GPP access network (e.g., WLAN) within the framework of the 5G system. In the subsequent sections, we delve into the distinct functions of ATSSS in each phase, enabling the simultaneous distribution of data traffic across both 3 GPP and non-3 GPP access networks, integrated into either 5G Core (5 GC) or Evolved Packet Core (EPC) network.
## 4 WEAMR Overview
### Network Clusters  
Due to the availability of gateways, we can efficiently organize and manage the sensor nodes in a network. Each node would have to associate to a particular gateway making it the default gateway. All the nodes associated to one gateway form one cluster. Please note that gateway and cluster head are the same name used interchangeably. One of the important benefits of the clustering approach is to facilitate efficient and reliable communication using gateway hotlines. For example, one set of nodes is sensing the environment to get the data and then that data is being sent to another set of nodes or a sink for further computation. The default gateways can provide a fast and reliable routing mechanism to communicate this data. Nodes become the part of a cluster depending upon their hop count from the gateway. The nodes would get associated to any gateway by using the information in the Router  
Figure 2: WSN with Hotlines Architecture.  
Advertisement (RA) message. The routers would send router advertisement (RA) messages to help the nodes to associate to a particular gateway. When a sender node receives RA messages from several routers, it would differentiate the messages with help of current time to live (Cur TTL) field.