esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 2 Exploring the Options
### Selection of the Correct Integrity Approach  
Table 1 below provides the justification for the proposed selection of option 2, user-based integrity, which has been chosen for further examination in this study and for upcoming projects within orbit demonstrators as it provides the best accuracy with the lowest TTA, very low on-board processing, and without the need for ISL. The downside is that it is highly demanding in the LEO-to-User bandwidth.  
\begin{table}
\begin{tabular}{c} _On-board processing:_ high.  
Three selection criteria were considered: performance, TTA, and number of on-board resources.
## 3 Result and Discussion
### Impedance Monitoring of the Needle Positioning
When the needle tip was positioned in the dermis from 0 s to 3.4 s, the measured impedance was higher than other tissue layers because of the low conductive nature of the dermis tissue and sweat dust, hair follicles in the dermis tissue [26]. As the needle tip was progressed into the hypodermis and paused for 4.04 s to 19.5 s to obtain the impedance of the hypodermis. As expected, the real part of the impedance at 50 kHz was significantly decreased due to the high conductivity of the hypodermis as compared to dermis [26]. Once the needle tip reached the muscle, it was paused for a period of 20.7 s to 36.9 s. The impedance value decreased again corresponding to the lower resistance of the muscle than other tissues layers [11]. When the needle was withdrawn from the muscle compartment to hypodermal tissue, the needle was paused for 37.4 s to 52.7 s, where the real part of impedance value was increased due to the high resistive properties of the hypodermis tissue as compared to muscle. And at last, when the needle was withdrawn into the dermal tissue, it was again paused for a period of 52.8 s to 70.09 s to measure the impedance. The tissue impedance was again increased due to the resistive structure of dermis tissue. Figure 5: Normalized (**a**) real and (**b**) imaginary part of the impedance with respect to the values of the saline (0.9% NaCl) solution from the measured spectra at three different positions in each tissue type. For future work, the current detection method needs to be improved by providing information about the lateral and spatial position of the needle tip. For this, the distribution of the electric fields or sensitivity fields in the complex tissue structure determined by different electrode configuration is required to be investigated. Several research groups [6; 17] reported the finite element simulation of the electric fields surrounding the needle tip in different tissue models. The electrical impedance tomography using a multi-electrode array is expected to provide the lateral and spatial information of the needle [27; 28]. Finally, the feasibility of the intra-articular injection therapy based on the impedance monitoring with the monopolar injection needle and the algorithm to detect the target tissue will be tested in an animal model and clinical trials.
## 6 Security Analysis
### Sustaining Mutual Authentication  
In the present proposal, the SB is authenticated by the CC via having the random value \(\alpha_{j}\) and the ID shadow's hash function of the SS verified in the message of authentication, where a genuine SB creates a valid message of authentication. Nonetheless, a random value of \(\alpha_{j}\) is considered in the authentication of the SS by the CC, where this value is computed from \(IDsbw_{j}\) (\(\alpha_{j}=IDsbw_{j}\oplus IDsb_{j}\)). The resulting hash is \(OHsb_{j}=h\) (\(IDsbw_{j}\mid\mid\alpha_{j}\)) and is expected to correspond to the ID shadow's hash function of the SB in the message of authentication \(OHsb_{j}=Hsb_{j}\). On the other hand, the CC's legitimacy can be authenticated by the SB using the random values of \(\beta_{j}\) and \(\alpha_{j}\), as well as the hash value \(Hcc_{j}=h\) (\(IDccw_{c}\mid\mid\alpha_{j}\mid\mid\beta_{j}\)), which are expected to correspond to the resulting hash value of \(OHcc_{j}\). By doing this, the proposed scheme suits the mutual authentication property.
## 4 Results
### RQ3: Challenges in University Campus Wawfinding Using Mobile Navigation Apps
The MazeMap group also referred to it when giving feedback \"MazeMap should also provide live location like Google Maps\". Although MazeMap provides the current location, most participants faced issues with accuracy. Most of the time, only \"?\" was shown instead of the exact current location when inside the building, which made orientation difficult as the participants could not understand where they were and where to go. Moreover, participants also complained that MazeMap does not automatically select the current location to create the route map to the destination; instead, you must enter the current location yourself, which is sometimes difficult when you do not know your exact location. Lastly, for \"university signage issues\", most challenges were reported by participants using Google Maps. This is because participants in this group relied more on university signage and physical maps on campus, which is also in line with the theme \"help and support for navigation\" and \"broken in-campus/indoor navigation\". The university signage issues included problems such as building names not being visible at the entrance or not highlighted, room numbers being randomly assigned and not intuitive, font size and color contract issues with the displayed room labels, no floor maps in some buildings, the physical maps inside the building not helpful to find the meeting rooms and lack of proper directions for each room. These problems are in the real world and not inside the map of navigation apps. The category \"technical issues\" were related to app restarting, GPS/in-building signal issues and internet connectivity. The participants using MazeMap reported more technical issues. There were problems with the app restarting and dropping directions, the GPS stopped working inside, and the indoor positioning system had some issues as participants experienced connection problems inside buildings. On the other hand, participants using Google Maps only had a few technical issues related to lag in GPS. Lastly, the \"mental and physical effort\" category included challenges related to physical exertion and time limit. Both groups (Google Maps and MazeMap) experienced similar challenges related to these themes. Participants were tired as they had to walk more when the app was not helping them or giving them information for quicker or easier paths (such as the availability of elevators instead of taking stairs). Similarly, most participants were stressed due to the time limit for finding a location which is similar to when you are running late for a meeting. Overall, from the qualitative analysis presented in Figure 2, analyzing the parameters in the category column (third column) shows that both the Google Maps and MazeMap groups had equal challenges for \"campus landscape related issues\", \"navigation app related issues\" and \"mental and physical effort\". However, the analysis highlights that participants using Google Maps experienced more \"Wayfinding issues\" and participants using MazeMap experienced more \"technical issues\".
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 1 Introduction
In the recent state of the art for point cloud detection, pillar-based deep learning methods have proven to be particularly effective [9]. We extend a pillar network with a multi-resolution neck to enable the detection of smaller objects, such as VRUs, with just a few points. The high-resolution features enable regions of interest (ROIs) to detect smaller  
Figure 1: The detection of vulnerable road users (VRU) from 4D radar data (colored dots) is particularly challenging due to the sparsity of radar sensor data, especially for small objects. With our SRFF, we present a method that specifically targets the detection (green boxes) of these objects. objects already in the head. In addition, we include our novel Sparsity-Robust Feature Fusion (SRFF), which combines high- and low-level features with a lightweight attention mechanism. Here, low-level features aid in better localization, while high-level features allow for better classification. The multi-level features each have different sparsities due to their effective receptive field. Our combination of features with different sparsities makes the network more robust for classes with few points. All of these characteristics enable us to accurately identify VRUs in 4D radar data. For instance, this capability allows for operation in adverse weather conditions where other sensor systems may not function correctly. Section 2 discusses the current state of the art and highlights the most important related works. Following this, Section 3 presents our proposed network and SRFF. Section 4 presents both the qualitative and quantitative evaluations, which are then discussed in Section 5.
## 4 Discussion  
### The Discussion of the Algorithm Accuracy  
Comparing simulation results (Tables 1-3) with experimental results (Tables 5 and 6), two points can be found. First, the results of the experiments and simulation are consistent with theoretical analysis, which can prove the feasibility of the MPSI algorithm. Second, the tracking accuracy of pitch angle, \(x\), \(y\) and \(z\) are reduced due to the existence of pitch angle and the lateral offset and the coupling relationship between \(H_{y}\) and \(H_{z}\), which can be compensated and optimized by the MPSI method. Compared to previous studies in references, the tracking accuracy has been improved 2 times by the dual receivers tracking system with the MPSI algorithm. Especially in the normal case, the MPSI algorithm improves the accuracy of tracking PIG by 10 times.  
There are theoretical bases for the minimum positioning error of the MPSI method.  
Firstly, the dual receivers tracking system with the MPSI algorithm uses the symmetrical distribution of the radiation field of the transmitting coil to realize the four-dimensional tracking of the PIG, while the traditional AGM usually has key position points where errors may exist. The PIG transmitter, pipeline and soil can be regarded as a cylindrical symmetrical system, and an accurate analytical solution based on the magnetic dipole model can be obtained in principle. In theoretical analysis, some approximate processing in the process of analyzing the transmitter's radiation field will introduce errors. For instance, ignoring the length of the coil may cause errors in the analysis of the spatial distribution of the radiated magnetic field directly above the transmitter. The traditional AGM just makes use of these points where there may be analysis errors. On the contrary, with the transmitter center as the origin, from the perspective of electromagnetic fields, the symmetry of the entire system is inevitable. Therefore, it is no error that \(H_{y}\) has the characteristics of origin symmetry and \(H_{z}\) has the characteristics of axis symmetry. The MPSI algorithm is precisely based on this symmetry, so the accuracy is higher than traditional methods.  
Secondly, referring to (8), the MPSI algorithm has its own characteristics of filtering common-mode noise.  
Thirdly, unlike traditional methods that only use a single characteristic data point (zero magnetic field point) to locate the PIG, the MPSI algorithm utilizes the statistical characteristics of all data, which can reduce random errors and further improve accuracy.
# Fake Base Station Detection and Link Routing Defense +
## 3 Background and Primer
### 5G Cellular Network Architecture  
The 5G cellular telecommunication network has three main components: user equipment, base station, and core network. Figure 1 shows the high-level architecture diagram and the physical connection of these three components. The user equipment consists of a mobile device quipped with a universal subscriber identity module (USIM) that contains the subscriber-specific identity and network access-related identities (network ID, network public key, service type, etc.). The base station acts as a bridge gateway between the user equipment and the core network. The core network provides service connectivity to the user equipment by authentication management, identity management, and mobility management. The user equipment accesses the cellular network using a radio channel to connect with the base station and establishes a logical connection with the core network over the established radio connection to obtain the cellular service. In the 5G cellular networking protocol, the user equipment connects to the base station using radio resource control (RRC)-layer connection and to the core network using non-access stratum (NAS)-layer connection. An intermediary wired backhaul network of routers and switches connects the base station and core network, which is out of this research's scope.
# Transcriptomic Analysis of the Candidate Genes Related to Aroma Formation in _Osmanthus fragrans_
## 2 Results  
### Sequencing and De Novo Assembly  
The flowering process can be divided into four periods, that is, the xiangyan stage, the initial flowering stage, the full flowering stage, and the final flowering stage [23]. The amount of volatiles produced by _O. fragrans_ petals gradually increases, along with flower opening and reaches the highest level at the full flowering stage, then declines at the final flowering stage [6]. To obtain genomicsresources, to narrow down the special pathways, and to get candidate genes of interest in _O. fragrans_, the petals from three individual plants were collected at the initial flowering (S1), full flowering (S2), and final flowering (S3) stages (Figure 1). In total, nine RNA-Seq libraries were constructed using the above RNA samples and sequenced by Illumina HiSeq(tm) 4000.  
Approximately 532,121,476 raw reads were obtained and 523,961,310 high-quality clean reads remained after filtering. The sequencing result was assembled into a transcriptome using Trinity to act as a reference sequence for the subsequent analysis (Table S1). All transcriptomic data was deposited in the NCBI Sequence Reads Archive (SRA) under the accession number SRP143423. The longest transcript of each gene was a unigene. In total, there were 136,611 unigenes obtained with a maximum length of 16,876 bp, a minimum length of 201 bp, an average length of 792 bp, and an N50 length of 1424 bp (Table 1). The distribution of the unigene lengths is shown in Figure S1.
# Holographic Mixed Reality System for Air Traffic Control and Management
## 5 Design and Development Process
### Calculation of Scaling Factor  
In order to map the real-world locations in a mixed reality world, the actual distances were scaled down based on a derived scaling factor. The scaling factor decided the number of units in the real world represented by one unit in the mixed reality world. If the distance or units for representing the real-world distance in the mixed reality world is denoted by D\({}_{1}\) and actual real-world distance is denoted by D\({}_{2}\); the scaling factor can be derived using Equation (1).  
\[\text{Scaling Factor}= \frac{\text{D}_{1}}{\text{D}_{2}} \tag{1}\]  
\"D\({}_{2}\)\" depended on the level of detail in the view. There were two main views. A detail view that covered each minor visual detail of the airport and a map view that was an abstract representation of air traffic on the world map. It can be seen here that the detail view was more focused on closely located positions with less distance between the airplanes, in contrast to the map view. Therefore, it was required to rescale and remap comparatively larger distances for the map view than the detail view. As a result, for finding the actual distance to be considered for finding the scaling factor against each view, slightly different methods were used, as discussed below:
## 3 Results and Discussion
### Validation of the Developed Algorithm
#### 3.1.2 Validation of the Model with Geometric Nonlinearity  
Validation of the accuracy of the geometric nonlinearity of the model was performed for the undamaged plate-like structures.  
First, isotropic beams with dimensions of 400 mm \(\times\) 25 mm \(\times\) 0.4 mm, referencing the work of [PERSON] et al. [42] were considered. These beams were assigned isotropic material properties of [PERSON]'s modulus (\(E\)) = 200 GPa and [PERSON]'s ratio (\(\
u\)) = 0.3. One end of the beam was fixed, while the other end was free and subjected to a perpendicular point load. To account for geometrical nonlinearity, multiple load cases were simulated to record displacements. The resulting data exhibited close agreement with the experimental findings of [PERSON] et al. [42], as shown in Figure 2a.  
Both the linear and nonlinear models of functionally graded straight beams were validated using results from [PERSON] and [PERSON] [43]. [PERSON] and [PERSON] presented non-dimensionalized displacement data across various non-dimensional loading conditions for multiple volume fractions. Our formulation was found to be in excellent agreement with their results, as illustrated in Figure 2b.  
Furthermore, the validation of the linear model for functionally graded curved beams, presented by [PERSON] et al. [44] was carried out. [PERSON] et al. presented non-dimensionalized maximum displacement data for multiple volume fractions and various curvature values. The present formulation consistently exhibited strong agreement with their findings, as indicated in Figure 2c.  
Finally, the nonlinear model for functionally graded curved beams has been validated using data from [PERSON] et al. [45]. [PERSON] et al. provided non-dimensionalized maximum displacement data under multiple non-dimensional loading conditions for various curvature values. Present formulation demonstrates robust alignment with their results, as depicted in Figure 2d.  
Figure 2: Validation of developed model with various results available in open literature. (**a**) Straight isotropic cantilever beam with multiple loads ([PERSON] et al. [42]). (**b**) straight functionally graded cantilever beam for both linear and nonlinear results ([PERSON] and [PERSON] [43]). (**c**) Curved functionally graded linear cantilever beam with various curvatures ([PERSON] et al. [44]). (**d**) Nonlinear Curved functionally graded cantilever beam with various curvatures ([PERSON] et al. [45]).  
Overall, the results in this section closely match those presented in the literature, in contrast to the previous section. This is primarily because these results are generated for an undamaged plate. Therefore, the outcomes are comparable in accuracy, given that other parameters such as size, boundary conditions, and material properties remain unchanged.
## 5 Experiments
### Experimental Results  
During the loading process, multiple measurements are taken every half an hour, the measured values of the temperature sensor were recorded and averaged. The results are shown in Figure 18. By comparing the two groups' temperature rise test results, it can be found that the temperature curve near G1835 is recorded by the 1\(\#\) sensor; the maximum temperature of the unmodified gear is 71.2 \({}^{\circ}\)C; the modified gear is 67.1 \({}^{\circ}\)C, a decrease of 4.1 \({}^{\circ}\)C; and the reduction rate is 5.8\(\%\). The temperature curve near the G1735 recorded by the 2\(\#\) sensor is the highest; the maximum temperature of the unmodified gear is 74.2 \({}^{\circ}\)C; and after modification, it is 65.1 \({}^{\circ}\)C, which is a decrease of 9.1 \({}^{\circ}\)C and a reduction rate of 12.2\(\%\). The temperature curve near the sun gear recorded by the 3\(\#\) sensor shows that the maximum temperature of the unmodified gear is 57.6 \({}^{\circ}\)C and that after modification, it is 54 \({}^{\circ}\)C, a decrease of 3.6 \({}^{\circ}\)C and a reduction rate of 6.3\(\%\).  
Figure 16: Modified tooth surface of gears were (**a**) ground, (**b**) measured, and (**c**) tested for load contact pattern.  
Figure 17: Layout position of temperature sensors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Kinematic Viscosity @40 \({}^{\circ}\)C** & **Kinematic Viscosity @100 \({}^{\circ}\)C** & **Density @15 \({}^{\circ}\)C** & **FZG Scuffing Test Grade** \\ \hline Value & 220 mm\({}^{2}\)/s & 39 mm\({}^{2}\)/s & 1026 kg/m\({}^{3}\) & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of ISO-VG220 synthetic lubrication oil.  
It can be seen that as the input power and the time increases, the flash temperature of the gears after optimization are overall lower than unmodified gears, with a maximum temperature reduction of 9.1 \({}^{\circ}\)C and the maximum reduction rate of 12.2%. Since the flash temperature value is indirectly measured, there are other energy dissipation factors in the system. Although there are some discrepancies between the tests and the simulation for flash temperature and reduction rate, the overall flash temperature reduction trend is consistent, which can prove that the proposed gear modification method for reducing flash temperature and decreasing meshing power loss are effective.
# Guided Semi-Supervised Non-Negative Matrix Factorization
## 2 Related Work
### Guided NMF  
Due to the fully unsupervised nature of classical NMF, the generated topics may suffer from redundancy or lack of cohesion when the given data set is biased towards a set of featured words [8; 9; 15]. Guided NMF [15] addresses this by guiding the topic outputs through incorporating flexible user-specified _seed word_ supervision. Each word in a given list of \(s\) seed words can be represented as a sparse binary _seed vector_\(\mathbf{v}\in\mathbb{R}^{d}\), whose entries are zero except for some positive weights at entries corresponding to the seed word feature. The corresponding binary _seed matrix_\(\mathbf{Y}\in\mathbb{R}_{\geq 0}^{d\times s}\) can be constructed as  
\[\mathbf{Y}=[\mathbf{v_{1}},\cdots,\mathbf{v_{s}}]\in\mathbb{R}_{\geq 0}^{d\times s}.\]  
For a given data matrix \(\mathbf{X}\) and a seed matrix \(\mathbf{Y}\), Guided NMF seeks a dictionary matrix \(\mathbf{W}\), coding matrix \(\mathbf{H}\), and topic supervision matrix \(\mathbf{B}\) by considering the optimization problem  
\[\operatorname*{argmin}_{\mathbf{W}\in\mathbb{R}_{\geq 0}^{d\times k},\mathbf{H}\in \mathbb{R}_{\geq 0}^{k\times s},\mathbf{B}\in\mathbb{R}_{\geq 0}^{k\times s}} \underbrace{\frac{1}{2}\|\mathbf{X}-\mathbf{W}\mathbf{H}\|_{F}^{2}}_{\text{ classical NMF}}+\underbrace{\frac{\lambda}{2}\|\mathbf{Y}-\mathbf{W}\mathbf{B}\|_{F}^{2}}_{\text{ guiding}}, \tag{4}\]  
where \(\lambda>0\) is a regularization parameter. Matrices \(\mathbf{W}\) and \(\mathbf{H}\) can be interpreted in the same way as classical NMF. Matrix \(\mathbf{B}\) can help identify topics that form from the influence of seed words.
## 3 Experimental Process and Results
### Experimental Results Based on Simulated Seismic Profile Signals
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **PSNR** & **SNR** & **RMSE** & **SSIM** \\ \hline WT & 19.432 & 9.364 & 6.354 & 0.793 \\ Unet & 21.954 & 12.114 & 3.849 & 0.823 \\ UNet++ & 22.332 & 12.039 & 3.749 & 0.819 \\ TUnet & 25.137 & 14.082 & 2.354 & 0.846 \\ SUnet & 24.955 & 13.864 & 1.831 & 0.838 \\
**SRUnet** & **26.473**\(\pm\) 0.134 & **14.672**\(\pm\) 0.143 & **1.096**\(\pm\) 0.036 & **0.877**\(\pm\) 0.006 \\  & [26.339, 26.607] & [14.529, 14.815] & [1.060, 1.132] & [0.871, 0.883] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation indicators of each model when the decimation ratio is 0.5. Figure 16: Changes in evaluation indicators for each model when the decimation ratio is 0.3. Figure 17: Reconstruction results, residual signals, and corresponding F-K spectra under a decimation ratio of 0.5.  
and the optimized visualization results are shown in Figure 18, Changes in evaluation indicators for each model when the decimation ratio is 0.