esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Metrics in Human Mobility Data Network
#### 2.2.1 Human Mobility Data Sources
In addition, the problem of representativeness can be extensible to people left behind by the \"Digital Divide\" who are under-represented [39]. Second, the social network API allows access to a limited number of records [12]. Human mobility time series computed from insufficient samples tend to have more fluctuations, making the general pattern less recognizable and less reliable. Third, unlike the passively collected data from mobile telephone records, smart cards, and wireless networks, social media data have their own intrinsic active nature, as users must grant permission to share their data and determine the location accuracy of their posts, all depending on their personal settings [39]. The LBSNs dataset selected for our work is Gowalla. It is a location-based social networking website where users share their locations by checking-in. The friendship network is undirected and was collected using their public API [40], and consists of 196,591 nodes and 950,327 edges. We have collected a total of 6,442,890 check-ins of these users over the period of February 2009-October 2010. However, this dataset stores a large amount of data and we only consider data from March 2010 to October 2013. GPS-derived metrics are primarily used for population-level mobility analyses. Location data can, however, also be derived by triangulation from cell towers, from Bluetooth interactions, and from IP addresses via wi-fi connections. GPS data sources can also be complex, with multiple overlapping applications, providers, publishers, and aggregators [41]. Prerequisites to data sharing is another limitation. The benefits of using personal data should outweigh the risks to privacy. Baselines need to be established. To compare data with the same time window in previous years seasonal mobility patterns are important; however, most companies or providers do not store data for such a long time because of data retention policies. Clear communication of the baseline is important, including the uncertainty associated with it, so that decision makers can make sense of the changes they see in human mobility. To preserve privacy, data should be aggregated. However, policy makers and researchers will typically request high-resolution disaggregated data. Finally, the GPS dataset was obtained through a Freedom of Information Law (FOIL) request from the New York City Taxi and Limousine Commission (NYCT&L). The NYCT&L display trip data of Uber pickups in New York City [42]. This dataset consists of over 4.5 million Uber pickups from April 2012 to September 2013, and 14.3 million more Uber pickups from January to June 2013. For this article, we use the data from January to June 2013.
## 2 Materials and Methods
### Data Analysis  
We performed linear regression analysis to examine the correlations among the dose estimates of DOSIRIS and glass badge, the eye-lens doses measured by the two dosimeters, the numbers of procedures performed, and the lens and PET drug doses. Nurses' exposure was analyzed using 6-month lens doses, while RTs were evaluated using 1-month lens doses. In the analysis of RTs, we did not consider the variability among workers, focusing solely on the relationship among lens dose, number of procedures, and PET drug dose. For the nurses, annual doses were estimated based on doses received over a 6-month period; we also recorded whether the 20 mSv/year threshold was exceeded. The annual lens dose was calculated using Equation (1) [5; 21; 31]:  
\[\text{Estimated annual lens dose}=6\text{-month lens dose}\times 2 \tag{1}\]  
In the case of RTs, the personnel changed monthly. Therefore, we used the 1-month lens dose to calculate the annual lens dose as follows:  
\[\text{Estimated annual lens dose}=1\text{-month lens dose}\times 12 \tag{2}\]  
The numbers of procedures performed annually by nurses and RTs vary among facilities, depending on the performance of the equipment and the number of staff. The number of procedures required for the annual eye-lens dose to exceed 20 mSv was calculated as follows:  
\[\text{Number of procedures required for eye-lens dose to exceed 20 mSv/year}=20\text{/estimated annual lens dose}\times\text{estimated annual number of procedures} \tag{3}\]  
[PERSON]'s signed-rank test was used to compare the glass badge and DOSIRIS dose estimates. We chose this method based on a previous study that performed similar comparisons [21]. The test was performed on the 1-month lens dose data. _p_-values \(<\) 0.05 were considered statistically significant.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 8 Experiments and Discussions
### Test Moving Obstacle-Avoidance Ability  
To be able to verify the moving obstacle avoiding ability, in the first scenario, we simulate two obstacles with their initial position, velocity and size as Table 6.  
Experimentally, we set the weight values when the robot detect collision is w\({}_{1}=0.3\), w\({}_{2}=0.15\), w\({}_{3}=0.33\), w\({}_{4}=0.22\). Figure 23 is a snapshot of the robot's movement from the starting point to the target based on tracking the previously created trajectory in the first scenario. In the process of moving in a given trajectory, the robot must dodge these simulated obstacles. When no obstacle is presented, the robot moves along the given trajectory. If any obstacle that falls into the scanning zone then the robot will check for collision against that obstacle. In the given situation, both obstacles certainly collide with the robot when it maintains the original trajectory. Therefore, when these obstacles are detected by the robot, the robot is forced to update offset angle to avoid obstacles while establishing a subtrajectory from this parameter. The offset angle is constantly updated until the detected obstacle leaves the robot's scanning zone. Right at that time, by re-path algorithm, the robot tries to return to the original trajectory. We also have some snapshot of the robot (Figure 24) that presents the performance of the robot in the second scenario without the presence of unforeseen obstacles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **1 st Obstacle** & **2 nd Obstacle** \\ \hline Initial position & (\(585,400\)) & (\(850,300\)) \\ Velocity & (\(-0.1,-0.5\)) & (\(0,0.1\)) \\ Size (cm) & 40 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Virtual obstacle parameters for testing moving obstacle avoiding ability in first scenario.  
Figure 23: Reference trajectory of the robot is the blue curve, detected obstacle has red circular boundary, non-observed obstacle has green circular boundary.  
Figure 24: Snapshot series of the trajectory tracking performance of the robot in workspace.
## 2 State of the Art
### Four-Dimensional Modelling
#### 2.2.8 Summary: 3D/4D Reconstruction Challenges  
* Three/four-dimensional modelling processes have to deal with non-linear historical spatial situations and must gain information from time-varying singular historical sources of heterogeneous quality.  
* Most approaches to automate the 3D/4D modelling process are optimized for specific purposes and therefore unable to cope with sparse samples and to detect differences with small variations.  
Consequently, current 3D/4D reconstructions of past buildings are almost exclusively performed in manual workflows and--except in specific cases--fail with automation.
## 4 Simulation and Discussion
### Simulation III  
This simulation is conducted in order to validate the robustness of the proposed approach by using the 3P, 3R, SCARA, UR5, and Barrett Technology's WAM7R robot manipulator. The difference between this simulation and the prior two is that 500 sets of target joint variables and 500 sets of initial joint variables are generated randomly. The end-effector configurations of the four robots are calculated by forward kinematics relative to the target joint variables. Thereafter, the inverse kinematics of the four robots are solved using the NICCD and NR method. The correct rate of solving the inverse kinematics of these four robots is shown in Figure 4. Indeed, from Figure 4, for the WAM7R robot manipulator, it is clear that the approach proposed in this paper has a correct rate that is smaller than that of the NR method. Moreover, from Figure 4, for the remaining four robots, it is clear that the approach proposed in this paper has a correct rate that is larger than that of the NR method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Stanford Arm**} & \multicolumn{4}{c}{**WAM7R**} \\ \cline{2-13} \cline{5-13}  & \(\mathbf{\theta_{1}(\text{rad})}\) & \(\mathbf{\theta_{2}(\text{rad})}\) & \(\mathbf{d_{3}(\text{mm})}\) & \(\mathbf{\theta_{4}(\text{rad})}\) & \(\mathbf{\theta_{5}(\text{rad})}\) & \(\mathbf{\theta_{6}(\text{rad})}\) & \(\mathbf{\theta_{1}(\text{rad})}\) & \(\mathbf{\theta_{2}(\text{rad})}\) & \(\mathbf{\theta_{3}(\text{rad})}\) & \(\mathbf{\theta_{4}(\text{rad})}\) & \(\mathbf{\theta_{5}(\text{rad})}\) & \(\mathbf{\theta_{6}(\text{rad})}\) & \(\mathbf{\theta_{7}(\text{rad})}\) \\ \hline \(\mathbf{\Theta_{\text{goal}}}\) & \(-0.1290\) & \(0.8754\) & \(100.0000\) & \(0.9256\) & \(0.2757\) & \(1.3889\) & \(0.4088\) & \(2.0346\) & \(-2.3493\) & \(-1.2559\) & \(-3.1283\) & \(2.8344\) & \(1.6732\) \\ \(\mathbf{\Theta_{\text{in}}}\) & \(-1.0520\) & \(2.2184\) & \(-0.3619\) & \(2.5406\) & \(-2.9331\) & \(0.2037\) & \(-0.5576\) & \(2.2993\) & \(2.6431\) & \(1.8048\) & \(-0.9740\) & \(-2.7061\) & \(1.6552\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The specific values of the target and initial joint variables of Stanford Arm and WAM7R manipulator.  
Figure 3: The position and angle errors of the initial selected joint angles of the Stanford Arm and the WAM7R manipulator versus the number of iterations of the NICCD and NR methods.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 4 Interference Alignment in Cognitive Radio: Second Paradigm
### Leakage Interference
#### 4.2.2 Space-Time Water-Filling (ST-WF)
Instead of using energy detectors at the FAPs (despite their ease of implementation as well as low complexity), this work suggests the use of the GLRT detector, predominantly beacuse the GLRT detector is very adaptable and does not require a static threshold. The main objective is to maximise the average sum-rate of the macrocell base station (MBS) and the FAP's rate with negligible reduction in the PU's rate. This is achieved by the OIA technique using the TBF algorithm. The suggested OIA-TBF protocol makes use of the same frequency band of a pre-existing MUE to assure that no interference is levied on the MUE's performance for such a network and permits the opportunistic FAPs to transmit data for FUEs.
## 2 Materials and Methods
### EfficientNet  
EfficientNet [23; 24] is a family of CNNs that were specifically designed to be more efficient in terms of computation and parameter usage than existing CNNs. EfficientNet achieves this by using a novel scaling method that optimizes the network architecture (Table 1) based on the available computational resources.  
EfficientNet can also be used for video classification by adapting it to process multiple frames of a video sequence. One way to do this is to use a 3D CNN architecture, which can capture both spatial and temporal information from the video frames.  
To use EfficientNet for video classification, we can apply the 3D CNN to each frame of the video sequence and then combine the outputs from each frame to obtain a final classification. This can be achieved by either averaging the outputs or using an attention mechanism to focus on the most relevant frames.  
One advantage of using EfficientNet for video classification is that it is highly efficient in terms of computation and parameter usage. This can be important for applications where resources are limited, such as on mobile devices or in real-time video analysis. EfficientNet can also achieve high accuracy on a variety of image classification tasks, which suggests that it may also be well-suited for video classification.  
However, there are some challenges associated with using EfficientNet for video classification. One challenge is that video classification typically requires processing a large number of frames, which can be computationally intensive. Another challenge is that EfficientNet may not be as effective at capturing temporal information as other CNN architectures that were specifically designed for video classification, such as the Two-Stream CNN or the 3D ResNet.  
Overall, using EfficientNet for video classification can be an effective approach due to its efficiency and high accuracy in image classification tasks. However, it may require additional optimization and tuning to achieve an optimal performance on video classification tasks.
# Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold
## 3 Statistical Classifier and Adaptive Threshold
### Target Detection  
As shown in Figure 3 for target detection, detection point is calculated in the time and frequency domains and the sum of these two values will determine the final detection point. If condition (8) satisfied, then the target is detected.  
\[\begin{cases}ifa_{ttp}+A_{ftp}\geq 1\Rightarrow AcceptTarget\\ if_{atp}+A_{ftp}<1\Rightarrow\text{RejectTarget}\end{cases}, \tag{8}\]  
With respect to condition (8), the sum of two detection points directly, may increase the error detection. This fault usually is created due to climate changes, air, and water, the location of the sonar and etc. To reduce the error rate, detection point in both the time and frequency domain has been combined. Various methods for fusion are proposed. For example, [PERSON] et al. in [19] proposed new decision fusion to provide a systematic taxonomy of the viable detectors. In this paper, to reduce the error rate, detection point in both time and frequency domain has been combined by adaptive weigh. The weights are calculated by Wiener filter. In other hand, because of changing environmental conditions (depth, heat, pressure... ) in deferent points of sea the ambient noise signal is changed randomly. So for decrease effect of environmental conditions, winner filter is proposed. In this paper detection point (in both time and frequency domain) has been combined by adaptive weight that calculated by winner filter. This filter is updated by LMS algorithm.  
Figure 7 indicated calculation adaptive detection point by Winer filter.  
Figure 6: How calculate detection point in frequency domain.  
In this study, the Wiener filter coefficients \(w\) and \(W\) (is shown in (9)) is calculated by LMS method.  
\[\begin{array}{l}\stackrel{{\rightarrow}}{{a}}_{ttp}=\left[a_{ttp_{ 1}}a_{ttp_{2}}\ldots a_{ttp_{m}}\right]\\ \stackrel{{\rightarrow}}{{A}}_{ftp}=\left[A_{ftp_{1}}A_{ftp_{2}} \ldots A_{ftp_{m}}\right]\\ \Rightarrow DP=\sum\limits_{i}w_{i}a_{ttp_{i}}+W_{i}A_{ftp_{i}}\quad i\leq m\\ \end{array}, \tag{9}\]  
where \(\stackrel{{\rightarrow}}{{a}}_{ttp}\) the detection point vector in \(m\) sequence of test input signals (which 10 thousands sample length in each sequence) in time domain, \(a_{ttp_{i}}\) is the detection point of \(i^{\text{th}}\) sequence in \(m\) sequences in time domain, \(\stackrel{{\rightarrow}}{{A}}_{ftp}\) is detection point in \(m\) sequence of test input signals in frequency domain, \(A_{ftp_{i}}\) is the detection point for \(i^{\text{th}}\) sequence in \(m\) sequences in frequency domain, \(w\) and \(W\) are the Wiener filter coefficients, and Detection Point (\(DP\)) is detection point after fusion. In this case, the condition (8) changes to condition (10).  
\[\begin{cases}ifDP\geq\frac{1}{2}\Rightarrow AcceptTarget\\ ifDP<\frac{1}{2}\Rightarrow\text{RejectTarget}\end{cases}, \tag{10}\]  
In other words, to estimate the target signal, \(m\) sequence of input signal (test) is investigated and eventually checking detection points in \(m\) sequences, the presence or absence of the target is determined.
## Abbreviations  
\begin{tabular}{l l} NMO & Neuromylitis optica \\ CNS & Central nervous system \\ PNS & Peripheral nervous system \\ ON & Optic neuritis \\ MS & Multiple sclerosis \\ AQP4 & Aquaporin 4 \\ AQP4-ab & Aquaporin 4- antibody \\ CSF & Cerebrospinal fluid \\ BBB & Blood-brain barrier \\ CDC & Complement dependent cytotoxicity \\ ADCC & Antibody-dependent cellular cytotoxicity \\ MAC & Membrane attack complex \\ AD & Autoimmune diseases \\ NAD & Neurological autoimmune diseases \\ ADEM & Acute disseminated encephalomyelitisitis \\ LTEM & Longitudinally extensive transverse myelitis \\ SLE & Systemic lupus erythematosus \\ GBS & Guillain-Barre syndrome \\ MG & Myasthenenia gravis \\ SS & Sjogen's syndrome \\ NMOSD & Neuromylitis optica spectrum disorder \\ Tfh & T follicular helper cells \\ MOGAD & Myrelin oligodendrocyte glycoprotein-associated disorders \\ CXCR5 & Chemokine C-X-C receptor 5 \\ CXCL13 & C-X-C motif ligand 13 \\ OAP & Orthogonal array proteins \\ MRI & Magnetic resonance imaging \\ NMR & Nuclear magnetic resonance \\ HDL & High-density lipoprotein \\ GFAP & Glial fibrillary acidic protein \\ ASC & Antibody secreting cell \\ NK & Natural killer cell \\ IL & Interleukin \\ JAK/STAT3 & Janus kinase/Signal transducer and activator of the transcription 3 protein \\ NFkB & Nuclear factor kappa-light-chain-enhancer of activated B cells \\ INF \(\gamma\) & Interferon-gamma \\ TNF \(\alpha\) & Tumour necrosis factor-alpha \\ Th17 & T helper cell 17 \\ Treg & Regulatory T cell \\ P2R & Purinergic receptors \\ GM & Gut microbiota \\ ABC-TP & Adenosine triphosphate-binding cassette transporter permease \\ EAE & Experimental autoimmune encephalomyelitisitis \\ IVMP & Intravenous methylprednisolone \\ PLEX & Plasma exchange \\ IA & Immunoadsorption \\ IVIg & Intravenous immunoglobulin \\ AZA & Azathioprine \\ RTX & Rituiximab \\ MTX & Mitoxantrone \\ TCZ & Tocilizumab \\ MMF & Mycophenolate mofetil \\ DHFR & Dihydrofolate reductase \\ FcRn & Neonatal Fc receptor \\ HSCT & Hematopoietic stem cell transplantation \\ AHSCT & Autologous hematopoietic stem cell transplantation \\ P13k & Phosphatidylinositol 3 kinase \\ \end{tabular}
## 3 Shake Table Model Tests
### Results and Analysis
#### 3.4.3 Acceleration Analysis  
Figures 11-13 illustrate the distribution of peak acceleration values in the X and Y axis along the tunnel section under different anti-vibration measures implemented in the model tests. The results revealed dynamic shifts in the peak positions of X-axis and Y-axis acceleration within the tunnel section when subjected to three different types of wave excitations, in contrast to scenarios lacking anti-vibration interventions. With the implementation of anti-vibration measures, the distribution of acceleration increments in the tunnel section exhibited a more uniform pattern, thus successfully managing the acceleration peaks in both the X and Y axes.  
In Figure 11, it was observed that under El-Centro wave excitation, the maximum and minimum values of acceleration in the X axis were \(7.2\) m/s\({}^{2}\) and \(-7.1\) m/s\({}^{2}\), respectively, without seismic measures. After grouting, installing anti-seismic joints, and incorporating a shock absorption layer, the maximum value of acceleration in the X axis decreased to \(5.2\) m/s\({}^{2}\), \(5.2\) m/s\({}^{2}\), and \(6.3\) m/s\({}^{2}\), respectively, with reduction ratios of \(-27.8\%\), \(-27.8\%\), and \(-12.5\%\), while the minimum value of acceleration in the X axis was \(-5.2\) m/s\({}^{2}\), \(-5.2\) m/s\({}^{2}\), and \(-6.5\) m/s\({}^{2}\), respectively, with reduction ratios of \(-26.8\%\), \(-26.8\%\), and \(-8.5\%\). Without seismic measures, the maximum and minimum values of acceleration in the Y axis were \(5.3\) m/s\({}^{2}\) and \(-5.2\) m/s\({}^{2}\), respectively. After grouting, setting seismic joints, and incorporating a shock absorption layer, the maximum acceleration in the Y axis decreased to \(2.3\) m/s\({}^{2}\), \(4.8\) m/s\({}^{2}\), and \(4.8\) m/s\({}^{2}\), respectively, with reduction ratios of \(-56.6\%\), \(-9.4\%\), and \(-9.4\%\), while the minimum acceleration in the Y axis was \(-2.8\) m/s\({}^{2}\), \(-4.9\) m/s\({}^{2}\), and \(-4.8\) m/s\({}^{2}\), respectively, with reduction ratios of \(-46.2\%\), \(-7.55\%\), and \(-7.7\%\), respectively.  
Figure 11: Distribution of (**a**) X-axis and (**b**) Y-axis acceleration peak value along tunnel longitudinal direction under El-Centro wave.  
\(-43.8\%\), \(-15.8\%\), \(-15.8\%\), respectively, and the reduction ratio of the minimum acceleration in the Y axis was \(-37\%\), \(-11.1\%\), \(-14.8\%\), respectively.  
Furthermore, the analysis of the peak position of acceleration indicated that the peak position of acceleration at the fault interface section changed after the installation of seismic joints and shock absorption layers.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 6 Land-Based Systems Implementation-Premise: Ultrasonic Broken Rail Detector (UBRD)
### UBRD Updates  
To update the UBRD, a new piezoelectric transducer has been developed, thanks to numerical modeling and measurement techniques. This improvement and the use of new digital signal processing techniques have been incorporated into a new version of the UBRD system that allows for doubling the distance between transmit and receive stations [51].  
[PERSON] et al. have proposed a method to integrate the UBRD system with a _train detection_ (TD) system [54]; the resulting system is able to distinguish the following three different  
Figure 16: Burst injection scheme [50].  
Figure 15: Block diagram of the UBRD [53].  
track conditions: _free, busy_, and _broken rail_. This is made possible by extracting, from the ultrasonic guided waves, the following three features: (a) _the RMS value_ and (b) _the energy_ in the time-domain; (c) _the frequency component with the highest amplitude_ in the frequency domain. In addition, _temporal_ and _spatial dependencies_ of the signals are taken into consideration. High-level signal processing techniques, such as deep learning algorithms, are also employed to deal with varying environmental situations and conditions. The suitability of this approach has been assessed through experimental tests.  
In Ref. [67], the same authors as Ref. [54] have proposed to exploit the _variational mode decomposition_ (VMD) algorithm to de-noise and reconstruct ultrasonic guided wave signals. The VMD method is a quasi-orthogonal signal decomposition technique for non-recursively decomposing a multi-component signal into a finite number of compactly band-limited _intrinsic mode functions_ (IMFs). The VMD algorithm is used to decompose ultrasonic guided wave signals into the fundamental, harmonics, interharmonics, and non-stationary disturbances, and can be used in conjunction with the method developed in Ref. [54].