esa-sceva/satcom-chunk-collection huggingface dataset:
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 4 CBIR Method in the AUV Collaborative Search Mission
### Construction of Behavior Models Based on Fuzzy Inference
#### 4.1.1 Fuzzification  
The fuzzification process involves defining several fuzzy subsets for each input variable within the fuzzy domain and assigning membership functions to these subsets. The membership function serves to describe the degree of mapping of each fuzzy subset within the domain, and it is typically presented in a graphical form.  
We define the fuzzy domain for NoCs as \([0,n-1]\), where n represents the total number of AUVs. The fuzzy domain for PRR is \([0,1]\), the fuzzy domain for DPP is \([-3000,0]\), the fuzzy domain for DPN is \([-1000,0]\), the fuzzy domain for detection error is \([0,10]\), and the fuzzy domain for detection error variance is \([0,3]\). The linguistic values for all these domains are represented as \"Large (L)\", \"Medium (M)\", and \"Small (S)\".  
Because the smooth curve characteristics of the Gaussian membership function are well-suited for handling continuous and fuzzy data, we choose the Gaussian membership function here. The mathematical expression of the Gaussian membership function is as follows:  
\[\mu(x)=\exp(-\frac{(x-c)^{2}}{2\sigma^{2}}) \tag{1}\]  
where \(x\) is the input value, c is the center of the Gaussian function, and \(\sigma\) is the standard deviation (which controls the width of the curve).  
For the output, we designed three behavioral outcomes \(A=\{a_{Com},a_{Pot},a_{Det}\}\), where \(a_{Com}\) is communication priority, \(a_{Pot}\) is positioning priority, and \(a_{Det}\) is detection priority. The membership function chosen for these outcomes is the triangular membership function, and the mathematical expression is as follows:  
\[\mu(x)=\begin{cases}0,&if\ x\leq a\ or\ x\geq c\\ \frac{x-a}{b-a},&if\ a<x\leq b\\ \frac{c-x}{c-b},&if\ b<x<c\end{cases} \tag{2}\]  
Figure 3: Flow of behavior models based on fuzzy inference.  
where \(a\) is the left endpoint (minimum value) with a membership degree of 0, \(b\) is the center point (maximum value) with a membership degree of 1, and \(c\) is the right endpoint (maximum value) with a membership degree of 0.  
Due to space limitations, we have presented the membership function curves for one input (NoCs) and the output separately, as shown in Figure 4.
## 2 App and Tools for the Collection and Elaboration of Raw GNSS Measurements
### Tools for the Elaboration of Raw GNSS Measurements  
In the experimentation, two tools were used: RTKLib (2.4.2.) and CSRS-PPP.  
RTKlib is an open-source software package for RTK-GPS developed by [PERSON] and [PERSON] [17; 18]. RTKLIB consists of a portable program library and several APs (utilising the library).  
The library implements fundamental navigation functions and carrier-based relative positioning algorithms for RTK-GPS with integer ambiguity resolution by LAMBDA. The software contains the following functions:  
1. AP Launcher (RTKLAUNCH);
2. Real-Time Positioning (RTKNAVI);
3. Communication Server (STRSVR);
4. Post-Processing Analysis (RTKPOST);
5. RINEX Converter (RTKCONV);
6. Plot Solutions and Observation Data (RTKPLOT);
7. Downloader of GNSS Data (RTKGET);
8. NTRIP Browser (SRCTBLBROWS).  
For analysis of post-processing of GNSS data, we used RTK in the RTKPOST GUI application of RTKLIB. Through the RTKPOST, it is possible to upload RINEX files of observation and navigation data. In particular, RTKPOST inputs the standard RINEX 2.10 or 2.11 observation data and navigation message files (GPS, GLONASS, Galileo, QZSS and SBAS) and can compute the positioning solutions through carrier-based relative positioning. It is also possible to choose different positioning modes including single-point, DGPS/DGNSS, kinematic, static, PPP-kinematic and PPP-static.  
CSRS-PPP is a web service ([[https://www.nrcan.gc.ca/maps-tools-publications/maps/tools-applications/10925](https://www.nrcan.gc.ca/maps-tools-publications/maps/tools-applications/10925)]([https://www.nrcan.gc.ca/maps-tools-publications/maps/tools-applications/10925](https://www.nrcan.gc.ca/maps-tools-publications/maps/tools-applications/10925)), accessed on 3 May 2021) (web app) offered by Canadian Geodetic Survey of Natural Resources Canada (NRCan), active since October 2003 [19]. The application allows users from all over the world to perform Precise Point Positioning by submitting the RINEX file of the observables produced by their own single- or dual-frequency receiver. Through a graphical interface, the users can choose the processing mode (static or kinematic), the reference frame of the output coordinates (NAD83 or ITRF14), and insert the Ocean Tidal Loading file (OTL). The results, once processed, are sent directly to the users via e-mail.  
CSRS-PPP, in processing the data, uses the best ephemerides available at that time:  
1. Ultra-fast (\(\pm\)15 cm), available every 90 min;
2. Rapid (\(\pm\)5 cm), available the next day;
3. Finals (\(\pm\)2 cm), available 13-15 days after the end of the week.  
Version 3 of the CSRS-PPP can achieve millimetre-level accuracies for long observations (24+ hours) in static mode. Accuracies of a few centimetres can be typically achieved with a 1 h observation [20].
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 2 Materials and Methods
### Scanning Mode  
In this study, the Lidar launched eight electromagnetic beams in different directions into the atmosphere by rotating the console to drive the telescope. The aerosol backscattered signals were received by the receiving telescope, and the echo signals were processed for high-altitude detection [39]. In order to synthesize the three-dimensional wind field in the space atmosphere, a series of beams with different directions were scanned via the Lidar to obtain the radial velocities in the different directions. The scanning mode of a Lidar system is divided according to the number of degrees of freedom. As shown in Figure 2, the beam orientation is defined in terms of the azimuth angle \(\theta\) and the elevation angle \(\varphi\). In the Vertical Azimuth Display (VAD) scanning technique, the measurement is made in at least three linearly independent directions, and when the wind fields are not uniform, the measurement errors will increase [40; 41]. When more than three directions are used, extended tests under uniform and stable conditions can be carried out [21].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Qualification** & **Specification** \\ \hline \multirow{4}{*}{Transmitter} & Operating wavelength & 1550 nm \\  & Pulse energy & 145 \(\mu\)J \\  & Pulse repetition & 10 KHz \\  & Pulse width & 200 ns \\ \hline \multirow{4}{*}{Transceiver} & Laser mode & Pulse \\  & Scan mode & Conical \\  & Elevation angle & 60\({}^{\circ}\) \\  & Start angle & 0\({}^{\circ}\) \\  & Step angle & 45\({}^{\circ}\) \\ \hline \multirow{4}{*}{Data-Acquisition} & Sampling frequency & 1 GHz \\  & Sampling points & 150 \\ \cline{1-1}  & Range resolution & 30 m \\ \cline{1-1}  & Blind range & 30 m \\ \cline{1-1}  & Gate number & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wind profile radar system parameters.  
Figure 1: Wind profile Lidar system.  
As shown in Figure 2, during the experiment, the Lidar operated in the VAD scanning mode for a period of 2.5 min. The elevation angle was fixed at 60\({}^{\circ}\). The azimuth angle changed from 0\({}^{\circ}\) to 360\({}^{\circ}\), with an angle step of 45\({}^{\circ}\). Eight-beam wind measurement laser radars in a total of eight directions, namely, 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), 315\({}^{\circ}\), and 360\({}^{\circ}\), were used. The radial spatial resolutions were set at 60 m in the range of 0-2.5 km [19].
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 2 Background and Problem Statement
### Topology Awareness  
Topology awareness helps improve the topology match performance between overlay networks and bearing networks, and an effective topology awareness methodology is network positioning. However, due to the complexity of existing bearing networks, the accuracy of network positioning is significantly impacted, thus the accuracy of topology awareness is degraded. We use an example to discuss the potential accuracy degradation. In order to simplify the network scenario, we use autonomous systems to represent the bearing networks. A simple but representative bearing network is depicted in Figure 2. For simplicity, the underlay devices are not revealed, and overlay nodes are deployed in AS1 and AS2, which have an exchange point (Er) in geographic location L1.  
We use the following example to discuss the positioning inaccuracy. Two overlay nodes (O1 and O2) are deployed in AS1 and one node (O3) in AS2. Overlay nodes O1, O2 and O3 are all in location L2, and the delays between three overlay nodes and Er are D(O1,Er), D(O2,Er) and D(O3,Er). Due to the fact that the packet transmitted from O3 to O1 has to pass through Er, the delay between O3 and O1 is estimated as D(O3,O1)=D(O3,Er)+D(O1,Er). The delay between O2 and O1 is small because they are in the same autonomous systems and close. Thus, the delay between O2 and O1 is far less than that between O3 and O1, that is, D(O2,O1)\(\leftrightarrow\)D(O3,O1). However, when O1 is chosen as the reference node in a network positioning method, the position of O2 would be close to O1 while O3 is distant from O1.  
Figure 1: The framework of Software-Defined Service Overlay Network (SDSON) built on bearing networks. The bottom plane consists of underlay bearing networks. The middle plane is the forwarding plane, which consists of Relay Servers (relay forwarding nodes). The top plane is the control plane, which consists of Relay Controller (control node).  
Figure 2: The accuracy degradation in existing bearing networks due to the topology complexity.  
Then, the delay deviation between O2 and O3 comes up. Actually, they are in the same location and they should be close. The above delay deviation severely impacts the accuracy of topology awareness when network positioning methods are adopted.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 6 Analysis of Experimental Results
### Comparison of Performance
#### 6.1.1 Comparison with Base Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Method** & **Features** & **Accuracy** & **Precision** & **F1-Score** & **Recall** & **AUC** \\ \hline \multirow{6}{*}{DT} & - & 55 & 81.289\% & 87.272\% & 87.258\% & 93.080\% & 94.868\% \\  & Feature importance & 10 & 83.611\% & 89.503\% & 89.048\% & 93.663\% & 95.294\% \\  & RFE & 5 & 82.775\% & 88.696\% & 88.271\% & 92.427\% & 94.586\% \\  & RFECV & 41 & 82.976\% & 89.013\% & 88.524\% & 93.522\% & 95.188\% \\  & SPS (forward) & 5 & 88.455\% & 92.09\% & 91.925\% & 94.859\% & 96.201\% \\  & SPS (backward) & 10 & 88.687\% & 92.108\% & 92.132\% & 94.917\% & 96.244\% \\ \hline \multirow{6}{*}{RF} & - & 55 & 86.537\% & 91.012\% & 90.709\% & 94.513\% & 95.911\% \\  & Feature importance & 10 & 91.069\% & 94.001\% & 93.605\% & 95.684\% & 96.784\% \\ \cline{1-1}  & RFE & 10 & 85.262\% & 90.029\% & 89.967\% & 94.247\% & 95.7\% \\ \cline{1-1}  & RFECV & 39 & 86.705\% & 90.673\% & 90.629\% & 94.585\% & 95.967\% \\ \cline{1-1}  & SPS (forward) & 5 & 90.12\% & 94.38\% & 93.627\% & 95.465\% & 96.595\% \\ \cline{1-1}  & SPS (backward) & 5 & 92.026\% & 95.154\% & 94.457\% & 95.926\% & 96.95\% \\ \hline \multirow{6}{*}{KNN} & - & 55 & 70.038\% & 78.863\% & 77.96\% & 84.879\% & 90.055\% \\  & SPS (forward) & 10 & 82.413\% & 85.932\% & 86.993\% & 92.823\% & 94.851\% \\ \cline{1-1}  & SPS (backward) & 10 & 82.466\% & 85.524\% & 86.805\% & 92.607\% & 94.758\% \\ \hline \multirow{2}{*}{Stacking} & - & 55 & 97.264\% & 97.609\% & 96.992\% & 96.47\% & 97.951\% \\ \cline{1-1}  & feature selection & 10 & 97.183\% & 97.442\% & 97.065\% & 96.724\% & 98.075\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of multiclass classification performances for the base model and the best model applying each feature selection method. Figure 7: ROC curves of each classification for the base model and the model applying each feature selection method: (**a**) DT; (**b**) RF; (**c**) KNN; (**d**) Stacking. As a result, when feature selection was used, the performance was maintained and improved. In addition, the features that had a significant influence on the classification results varied for each classification algorithm. The classification using feature selection achieving the best performance for each classification algorithm shows a lower score than that of the stacking algorithm using the feature selection method based on frequency. This indicates that the classification performance is highly dependent not only on the combination of features, but also on the classification algorithm applied.
## 3 Results
### False-Negative Results  
Five thyroid cancers were found \"soft\" by the SE evaluation: three cases of PTC and two of microPTCs. Six malignant nodules appeared \"soft\" in SWE. One PTC and two microPTCs were in agreement with SE; the other three cases--one microPTC and two PTCs--were soft in SE. Thus, the disagreement between the two elastography assessments occurred in four cases of PTC and one of microPTC. Two of the PTCs misdiagnosed by SWE  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Benign** & **Malignant** & _p_**-Value** \\ \hline Mean EI & 16.17 (5.90–86.30) & 41.8 (16.66–118.66) & \(p\) \(<\) 0.0001 \\ Max EI & 24.5 (10.40–118.76) & 57.46 (22.80–223.33) & \(p\) \(<\) 0.0001 \\ QBox ratio & 1.2 (0.6–4.3) & 3.3 (0.8–9) & \(p\) \(<\) 0.0001 \\ SE Asteria score & 2 (1–4) & 3 (2–4) & \(p\) \(<\) 0.0001 \\ Strain ratio & 1.5 (0.57–10.63) & 6.1 (1.2–15.5) & \(p\) \(<\) 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of ultrasound SE and SWE elastography (median values and range interval) in the benign versus malignant thyroid nodules.  
Figure 7: The distribution (box plot) of elastography parameters in patients with benign vs. malignant histopathology results for strain elastography: (**a**) strain ratio (Asteria score could not be represented as box plot); for shear-wave elastography: (**b**) Max elasticity index (Max SWE) measured in kPa; (**c**) Mean elasticity index (Mean SWE) measured in kPa; (**d**) QBox ratio (SWE ratio).  
using the Mean EI parameter would have been correctly classified if the Max EI parameter was used.  
Figure 9 illustrates the elasticity images of a thyroid nodule with a low risk in 2B that appeared soft in both SE (a) and SWE (b) and was revealed by the pathology report to be a classic variant of a papillary thyroid cancer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **AUC** & _p-Value_ \\ \hline B-mode & 0.764 & - \\ \hline Mean EI (SWE) & 0.912 & 0.0131 \\ \hline Strain ratio (SE) & 0.905 & 0.0023 \\ \hline B-mode + SWE & 0.886 & 0.0003 \\ \hline B-mode + SE & 0.861 & 0.0012 \\ \hline EI—elasticity index; SWE—shear-wave elastography; SE—strain elastography; AUC—the area under the receiver operating characteristic curve. \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance of strain and 2D shear-wave parameters alone and as additional evaluation for conventional ultrasound (2B), being compared to 2B evaluation alone.  
Figure 8: The receiver operating characteristic curve for the diagnostic performance of thyroid cancer through shear-wave elastography (SWE) with the parameter Mean elasticity index (SWE Mean), strain elastography (SE) with the parameter strain ratio, conventional US (2B), 2B combined with SWE and 2B combined with SE.
# Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers' Behavior
## 2 Material and Methods
### Data Analysis Methods  
The study aimed to assess the influence of left-turn guide line settings on novice drivers' behavior by analyzing vehicle trajectory characteristics in various scenarios. For ease of analysis, it is necessary to standardize the displacement data in the left-turn vehicle trajectories of all participants across all scenarios. Based on this standardized data, the \(X\)-axis is set perpendicular to the direction of the intersection's entry lane (lateral direction), and the \(Y\)-axis parallel to the direction of the intersection's entry lane (longitudinal direction). Based on the \(X\)-\(Y\) coordinate system, basic descriptive statistics methods were used to conduct statistical analysis on the left-turn vehicle trajectory data. Specifically, an error band plot of the participants' left-turn vehicle trajectory data was then generated to statistically analyze the fluctuations in their left-turn trajectories. There have been numerous studies on left-turn driving behavior and vehicle characteristics [28; 29; 30; 31; 32; 33]. Some focus on vehicle control systems to explore dynamic responses during turns, while others examine driver behavior to analyze trajectory characteristics. Regardless of the approach, steering angle and speed are consistently key evaluation indicators. Drawing from relevant research [30; 31; 32], this study adopted commonly used evaluation metrics such as steering angle, difference in speed, and position change of vehicle trajectory to analyze the impact of guide line settings on the left-turn driving behavior of novice drivers.  
Additionally, the Wilcoxon rank-sum test was used to analyze the standard deviation of vehicle trajectories in different designed scenarios for all participants. This was done to determine the effectiveness of left-turn guide lines and evaluate whether guide line settings would lead to significant differences in convergence of vehicle trajectories for novice drivers. Eye-tracker data were utilized to assess the effect of guide lines on novice drivers' gaze areas and fixation times, evaluating the efficacy of guide lines in assisting novice drivers during left turns.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)  
This section describes additional elements of the key recovery system model using proxy re-encryption, excluding the common elements of the two models proposed in Section 3.1.
## 4 Discussion
### Strategies to Improve Gluten-Free Dough Rheology and Bread Characteristics
#### 4.1.5 Hydrocolloids
The results showed that xanthan gum improved batter consistency the most (double the control), and resulted in the highest increase in bread volume (+18.30% compared to the control). Moreover, the addition of 0.5% xanthan gum increased average cell size, lowered crumb firmness, and slowed staling. Other authors have highlighted that xanthan gum may have a promising effect on dough rheology. In [PERSON] et al. (2007) [33], the authors assessed the effect of hydrocolloids on dough rheology and bread quality parameters, in formulations based on rice flour, corn starch, and sodium caseinate, using pectin, CMC, agarose, xanthan gum, and oat b-glucan (1% and 2% _w/w_ rice flour basis). The analysis evaluated the rheological behavior of dough with a farinograph and a rheometer, and highlighted that xanthan gum had the most pronounced effect on viscoelastic properties, producing strengthened doughs. The formulation resulted in a farinograph curve typical of wheat flour doughs. Moreover, the addition of xanthan gum was consistent with the highest increase in dough elasticity and resistance to deformation. Despite these impressive results regarding dough rheology, no improvement was observed with respect to bread, which was characterized by low loaf volume and high crumb firmness. It should be noted that these results conflict with those reported in [PERSON] et al. (2010) [32]. It is possible that the lack of improvement is due to the formulation. In [PERSON] et al. (2007) [33], the authors highlighted a decreasing trend with an increasing level of hydrocolloids (from 1% to 2%), but did not test the specific case of 0.5% xanthan gum. Similar observations were reported by [PERSON] and [PERSON] (2011) [34], who assessed the effects on dough rheology and bread characteristics related to the addition of HPMC, xanthan gum, \(\kappa\)-carrageenan, and guar gum at 1%, 1.5% and 2% (_w/w_) in formulations based on corn starch and rice flour. Like [PERSON] et al. (2007) [33], [PERSON] and [PERSON] (2011) [34] highlighted an improvement in dough rheology and consistency, but not bread characteristics. The best results were obtained with HPMC, and guar gum performed better than xanthan gum. Different results were reported for millet-based pasta, where the addition of 2% xanthan gum was consistent with the highest increase in elasticity, and network strengthening [35]. Hydrocolloids have been widely studied in gluten-free formulations, and this literature review suggests that HPMC is the most promising option; nevertheless, further studies are needed to understand the topic in greater depth.
## 4 Discussion
As shown in Figures 5 and 6, the downregulation of lipogenesis-related factors such as proprotic convertase subtilisin/kexin type 9 (PCSK-9), apolipoprotein B (APOB), sterol regulatory element-binding protein 1 (SREBP-1), fatty acid synthase (FAS), sterol regulatory element-binding protein 2 (SREBP-2), and low-density lipoprotein receptor (LDLR) by FRVE inhibited lipid accumulation and synthesis in liver tissue. In contrast to the FRVE-treated group, the HFD-fed control group overexpressed all these lipogenic factors. SREBPs are transcription factors that bind to the sterol regulatory element DNA sequence, and SREBPP-1 and -2 regulate the lipogenesis targeting genes [33; 34; 35]. SREBPs upregulate the expression of HMG-CoA reductase (HMGR) required for cholesterol synthesis and fatty acid synthase (FAS) required for fatty acid synthesis [36]. Furthermore, SREBPP protects apolipoprotein B from proteasomal degradation, thereby increasing hepatic apoB lipoprotein [37]. ApoB is expressed in the liver and is associated with cardiovascular disease, hyperlipidemia, and NAFLD [38]. ApoB is the structural protein of very-low-density lipoprotein cholesterol (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL), which are incorporated into chylomicrons [39]. PCSK9, one of the factors regulated by SREBPP, is secreted by hepatocytes and inhibits the uptake of LDL by targeting LDLR degradation, and possibly lipogenesis [40]. Two clinical studies have demonstrated that patients with NAFLD show increased levels of PCSK9 [40; 41]. In this regard, we evaluated the expression of LDLR and SREBPP-1 along with PCSK9 in livers of HFD-fed mice by IHC. As shown in Figure 6, the HFD-fed control group showed high expression of PCSK9 mRNA and low levels of LDLR in the liver tissue. In contrast, the FRVE-treated group showed increased protein levels of LDLR and decreased protein levels of SREBPP-1 in the liver tissue (Figure 6). Moreover, we observed an increase in the phosphorylation of AMPK in the FRVE-treated group but not in the HFD-fed control group, which was consistent with our previous data, where FRVE increased the levels of phosphorylated AMPK in OA-induced HepG2 cells [5]. Phosphorylated AMPK plays an essential role in regulating lipid metabolism. Activated AMPK leads to the suppression of lipogenesis-enhancing factors like SREBPP-1 [42; 43; 44]. In our previous study, we demonstrated attenuation of FRVE-induced AMPK activation and recovery of FRVE-induced decrease in SREBPP-1 levels by the AMPK antagonist compound C in OA-induced HepG2 cells [5].