esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of AI Techniques in SVAD
### Learning
#### 3.1.4 Supervised vs. Unsupervised vs. Semi-Supervised  
Supervised learning techniques for SVAD offer several advantages, including the ability to accurately identify and classify anomalies using labeled data and the ability to identify specific types of anomalies. These techniques are also useful for detecting anomalies in surveillance and security applications. However, a significant amount of labeled data is required, and these techniques can be sensitive to environmental changes, affecting their accuracy.  
Unsupervised learning techniques for SVAD offer advantages such as not requiring labeled data and the ability to detect anomalies in real-time. These techniques can also be used to identify patterns in the data that deviate from the norm and classify them as anomalies. However, unsupervised learning techniques are not able to identify specific types of anomalies and can also be sensitive to changes in the environment.  
Semi-supervised learning techniques for SVAD can use labeled and unlabeled data, allowing for accurate identification and classification of anomalies. These techniques can also be used to identify specific types of anomalies and detect anomalies in real-time. However, semi-supervised learning techniques require significant labeled data and can also be sensitive to environmental changes.  
In conclusion, supervised, unsupervised, and semi-supervised learning techniques each offer advantages and disadvantages when it comes to anomaly detection in SVAD. Each technique has its limitations, and the accuracy of the results can be affected by changesin the environment. Therefore, the choice of technique will depend on the specific needs of the application and the availability of labeled data.
## 2 Materials and Methods  
### Geodetic Technologies Supporting Blast Design  
The design of a blasting hole pattern that meets safety requirements for ongoing operations must be based on precise geometric measurements of the burden zone to be excavated from the active deposit. The selection of appropriate geometric parameters for the blasthole grid--in correlation with the type of explosive used and the charge configuration--is a key condition for ensuring operational safety while optimizing the utilization of detonation energy from the explosive charges placed in the holes.  
In the blast design process, geodetic tools play a critical role, particularly in the open-pit extraction of hard rock raw materials. Among the most commonly used devices are 3D scanners (e.g., laser total stations), which enable accurate mapping of surface topography and bench face geometry. Global navigation satellite systems (GNSSs) are also of significant importance, providing high-precision positioning of blastholes and other critical points within the mining area. Increasingly widespread is the use of UAVs, which, through rapid photogrammetric data acquisition, support the geometric inventory of the excavation site and enable efficient planning of blasting operations.  
Geospatial data obtained from these tools are increasingly being integrated directly into specialized blast design software, such as BlastPlan, JKSimBlast, QuarryX, Strayos, or O-Pitblast. These platforms allow for detailed modeling of the blast layout, forecasting of blasting outcomes (e.g., fragmentation, muckpile displacement, and vibration levels), and verification of compliance with applicable safety standards and environmental constraints.
## 2 Methods
### Imaging Spectrometer  
The Hyperspec imaging spectrometer (Headwall Photonics, Fitchburg, MA, USA) is based on the Offner design and enables spectral VNIR (418-918 nm, 10 nm full width at half maximum, FWHM) imaging at high spatial resolution through high efficiency f/2.0 telecentric optics, a high efficiency aberration-corrected convex holographic diffraction grating, and a C-mount objective lens (Cinegon f/1.4 8 mm, Schneider Optics, Hauppauge, NY, USA). The Hyperspec has operated on G-LiHT over a wide range of conditions, including harsh environments, providing stable radiometric results. The GSFC unit receives a full calibration annually at the GSFC Biospheric Sciences Laboratory's Radiometric Calibration Laboratory which maintains US National Institute of Standards and Technology (NIST) traceable calibration sources and detectors, and is additionally monitored regularly for radiometric and wavelength stability using a portable Teflon integrating sphere with Tungsten Hg/Ar lamps.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 4 Conclusions  
This paper proposed a line segment extraction algorithm suitable for indoor mobile robot grid maps, called RM-Line. The algorithm uses the grid map's connected domain, i.e., the area formed by the connected blank grids, to find the obstacle grids located at the edge of the connected domain. On this basis, a scanning model is used to pick out active points, i.e., obstacle points that may contain complete line information. Then, a ray model was proposed, and the active points were used as the starting point of the ray model to obtain line segments in the grid map.  
In the experimental phase, this paper conducted comparative tests on the proposed RM-Line and the state-of-the-art CannyLine in grid maps of three different environments. The experimental results show that, for the grid map of mobile robots, the proposed RM-Line could obtain more accurate line segments and consume less time.  
This version of the algorithm only deals with line extraction on a 2D plane. In the next stage, we will try to extend the algorithm to 3D maps.  
Conceptualization, Y.Z.; methodology, H.L.; software, H.L. All authors read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Numerical Benchmark: Calibration of a SDoF Bouc-Wen Type Hysteretic System
Indeed, it can be clearly seen that 700 steps are sufficient to reach the steady state. Consequently, samples generated by the chains follow the posterior distributions. However, the sample points generated by the AIES MCMC algorithm have been post-processed carrying out the burn-in of the first half of sample points before convergence to avoid the pollution of the estimate of posterior properties. In our case, the quality of the generated MCMC chains can be judged satisfactory. Table 3 reports the result of the Bayesian inversion analysis: mean and standard deviation of the posterior 5%-95% quantiles of the distribution and Maximum A Posteriori (MAP) point estimate. The MAP can be assumed as the most probable parameter value following calibration. This value has been used as a best-fit parameter. Figure 4: Prior samples of the BWBN benchmark model parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Mean** & **Std. dev.** & **(0.05–0.95) Quant.** & **MAP** \\ \hline \(k_{i}\)\(\left(\mathrm{N}/\mathrm{m}\right)\) & 4.5 & \(0.18\times 10^{-1}\) & (4.4–4.5) & 4.4 \\ \(\beta\left(\mathrm{m}^{-1}\right)\) & 65 & 0.36 & (64–65) & 64 \\ \(\gamma*\) & \(-0.38\) & 0.25 & (\(-0.4\)—0.34) & \(-0.40\) \\ \(\alpha\) & 0.035 & \(0.53\times 10^{-2}\) & (0.027–0.044) & 0.031 \\ \(\sigma^{2}\) & \(2\times 10^{-4}\) & \(4.7\times 10^{-6}\) & (\(1.9\)–\(2.1\)) \(\times 10^{-4}\) & \(60\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Marginal posterior distribution of the BWBN benchmark model parameters. The model response prediction using MAP as a point estimate of the posterior distributions is plotted in Figure 6. It can be clearly seen how the inferred response reproduces the experimental record quite accurately, with a mean square error (MSE) over the time of \(2.007\times 10^{-4}\) (i.e., half of the average of the MSE computed on all the post-predictive model runs, MSE = \(4.0415\times 10^{-4}\)). In fact, we should not dwell on this specific prediction; rather, we should consider its confidence intervals. The latter tells us how uncertainties on model input propagate through the model. Figure 5: Trace plots of the Markov Chains and corresponding kernel density estimation (KDE) for each BWBN model parameter [6]: (**a**) \(k_{i}\); (**b**) \(\beta\); (**c**) \(\gamma\)*; (**d**) \(\alpha\); (**e**) \(\alpha^{2}\). From inspection of Figure 6 it can be concluded that uncertainties on the input parameters produce higher uncertainty of the response prediction in the lower amplitude regions rather than in the peak values. However, this is a satisfying result in earthquake engineering, where one is more interested in predicting maximum response quantities. The posterior distributions of the calibrated parameter are plotted in Figure 7.
## 2 Mathematical Models
### Summary of the Model Study
#### 2.4.1 Two-Phase Relative Permeability Model  
The Land model studies gas-water two-phase relative permeability in strongly water-wet porous media, assuming a monotonic relationship between the captured non-wetting phase saturation (gas in the model) and the initial gas saturation. It also assumes that the imbibition relative permeability scanning curve is reversible. The Land model is widely used for calculating residual non-wetting phase saturation, but has only been studied for a single cycle without considering multi-cycle scenarios. The Killough model introduces two methods for calculating imbibition-phase relative permeability, including parameter interpolation and normalization of experimental data. It considers the hysteresis effects in both the wetting and non-wetting phases. However, it does not account for multi-cycle experimental effects. The Carlson model proposes a hysteresis model for non-wetting phase relative permeability, assuming theoretically parallel self-scan curves and no hysteresis effect in the wetting phase. It modifies the Land model. The Beattie model considers hysteresis effects in both wetting and non-wetting phases and suggests that the imbibition scanning curve is irreversible. It is based on the normalization of boundary curves, assuming that each phase relative permeability scanning curve should approach its boundary curve during fluid injection processes. The Delshad model investigates oil-water two-phase hysteresis models developed based on capillary pressure and relative permeability in mixed-wet rocks. It assumes that the water saturation cannot be lower than the reverse water saturation from the main drainage to the main imbibition path.
## 3 Result and Discussion
### Latency Characterization
For instance, the authors in [31] improved healthcare access in remote areas by connecting urban and rural services but did not focus on real-time, personalized care, which is a key feature of our system. The study [32] enhanced telecare by managing uncertainties in patient data but did not fully address the need for stability and responsiveness in teleoperations. The work in [33] monitored health indicators like sleep and water usage to improve elderly care but lacked the AI-driven adaptability and personalization that our system provides.
## 5 Discussions
### Smart Detection Methodologies  
Fall signals are simply measured by body sensors, e.g., accelerometers, and sent to an external device for intelligent decision-making processing, like a PC with a wireless module such as Bluetooth, as presented in [86]. This external device is responsible for analyzing the acquired signals of the accelerometer sensors and, with a developed neural network classifier, the decision of fall detection is taken. In [87], the authors built a framework using a field-programmable gate array (FPGA) kit to apply the ANNs classifier to develop an embedded vision-based detection system of falls. A digital signal processor (DSP) was utilized to accelerate the ANNs functionality in the context-aware system, as described in [88]. A bio-inspired optical sensor has been used in this study. However, embedded wearable detection systems and solutions based on FPGAs and DSPs are still under development and not implemented as realized commercial systems yet. In most previous studies, wearables are just utilized for sensing and collecting data in the experiments. After finalizing the experimental phase, the recorded sample signals are extracted from the datasets for training and testing the proposed algorithm, either in a hardware or a software form using MATLAB and/or other programing languages like C++.  
Comparing neural network models with other machine learning and/or thresholding techniques is usually focused on the performance of the discrimination function only, while other technological features are ignored [37]. Therefore, physical characteristics of operation, e.g., memory size, consumption of energy and processor capacity, and running time/speed may affect the outcome performance metrics to evaluate these smart algorithms successfully. The execution time for the training phase of ANN models was estimated to be higher than other methods in a comparative study of six machine learning algorithms [89]. In contrast, the computational time of the testing phase for ANN models can have the same value as, or be even lower than, other methods to obtain the detection decision.
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 2 System Design
### Downstream Design
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Code** & \multicolumn{2}{l}{**MNZCC**} \\ \hline \(\lambda_{\text{dn}}/\text{\bf{{}_{up}}}\) **(nm)** & \(\text{\bf{U}}_{\text{1}}\) & \(\text{\bf{U}}_{\text{2}}\) & \(\text{\bf{U}}_{\text{3}}\) \\ \hline
1575/1260 & 0 & 0 & 1 \\
1575.4/1260.4 & 0 & 1 & 0 \\
1575.8/1260.8 & 1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MNZCC code construction for three users [26; 27; 28; 29; 30; 31; 23; 24]. Figure 2: Block diagram of (**a**) integrated PON-FSO system with VLC, (**b**) satellite communication system; TWDM-PON (**c**) Tx section for downlink and Rx section for uplink, (**d**) Rx section for downlink and Tx section for uplink; WDM-OCDMA (**e**) Tx section for downlink, (**f**) Rx section for uplink, (**g**) Rx section for downlink and Tx section for uplink, (**h**) Tx section with MNZCC code in downlink; (**i**) full-duplex UOWC NOMA-VLC system using RGB LEDs; (**j**) Tx timing section in WDM-OCDMA for uplink; (**k**) MNZCC decoder design. In addition to this, incoming coded input modulation was performed using PRBS-NRZ and MZM components for each user. Three output signals from each encoder were forwarded to a 3 \(\times\) 1 Ideal MUX as well as a 2 \(\times\) 1 ideal MUX. Then the signals passed through three bidirectional components of the ODN section for full-duplex transmission. The downlink signals at the Rx side are distributed into three parts by using a 1 \(\times\) 3 ideal DEMUX, as shown in Figure 2g. It is then forwarded to three OCDMA decoders (D1-D3) followed by a 6 \(\times\) 1 AWG MUX as well as a 1 \(\times\) 3 fork. For decoding, different types of WDM DEMUXs as well as WDM MUXs combinations were considered. Additionally, 1 \(\times\) 2 ideal DEMUX along with a 6 \(\times\) 1 MUX and 1 \(\times\) 3 fork components were utilized [11; 33]. Figure 2i illustrates the NOMA-VLC system based on RGB LEDs for UOWC using three red, green and blue LEDs to offer the required bandwidth and small beam divergence. For the downlink UOWC VLC system, NRZ-OOK modulation was incorporated to modulate three input LED (red, green and blue) signals. After that, the signals were multiplexed using a 1 \(\times\) 3 ideal MUX to transmit over ODN and VLC channels. The VLC signal was transmitted through a sea channel considering primarily weak turbulence, scattering as well as absorption losses employing a lens at the transmitter as well as receiver sides to center the incoming light in the water channel [11; 33].
# Human-Robot Cooperative Control Based on Virtual Fixture in Robot-Assisted Endoscopic Sinus Surgery
## 4 Experiments and Discussion
### Guiding Virtual Fixture Experiment  
Figure 10 shows the experiment scene of the GVF, which is a cubic polynomial curve. The purpose of this experiment is to evaluate the constraint performance of the GVF. The starting and ending coordinates of the curve in the figure are (147.3, 49.2, -2.5), (-117.8, 90.6, -30.3). The robot works in the admittance-based drag control mode, and is dragged from the starting point along the curve to the ending point, without GVF constraint and with GVF constraint. During the experiment, the trajectory of the endoscope tip is obtained through the navigation system.  
The experimental results are shown in Figure 11. It can be seen that the maximum deviation of the trajectory of the endoscope tip from the ideal trajectory is approximately 5.2 mm without the GVF constraint. After loading the GVF constraint, the maximum deviation from the ideal trajectory is  
Figure 10: The experiment scene of the GVF.  
Figure 9: Endoscopic sinus surgery assistant robot system.  
approximately 1.0 mm. The comparison results show that the GVF can effectively reduce the trajectory tracking error of the robot and improve the drag operation precision.
## 2 Materials and Methods
### Synthesis of Au@Ag Nanorods  
The synthesis of the Au nanorods (AuNRs) was completed according to the seed-mediated growth method, with some modifications [33]. First, the seed solution was synthesized by mixing 2.5 mL of 0.2 M of cetyltrimethylammonium bromide (CTAB) with 1.5 mL of 1 mM HAuCl\({}_{4}\). This process was followed by the fast addition of 600 \(\mu\)L of 0.01 M iced sodium borohydride (NaBH\({}_{4}\)) solution under vigorous stirring. The color of the solution tuned to dark brown, which indicates the formation of gold nano-seeds. Next, 180 \(\mu\)L of 15 mM silver nitrate (AgNO\({}_{3}\)), 1.25 mL of 15 mM HAuCl\({}_{4}\), and 11 mL of water were sequentially added into 12.5 mL of 0.2 M CTAB solution under vigorous stirring. Then 0.1 M ascorbic acid (AA) solution was slowly, drop by drop, added into this mixture to obtain the 'growth solution.' Lastly, 20 \(\mu\)L of the seed solution was mixed with the prepared growth solution under magnetic stirring for 15 s and then left to react for 1 h without stirring. The formation of AuNRs was indicated by the color gradually turning into dark purple. For purification, the resulting solution was centrifuged at 9000 rpm for 20 min and re-dispersed in water.  
For the fabrication of the core-shell Au@AgNRs, 2 mL of the purified AuNRs was sequentially mixed with 0.0728 g CTAB, 4 mL of water, 130 \(\mu\)L of 0.1 M AA, 140 \(\mu\)L of 15 mM AgNO\({}_{3}\), and 240 \(\mu\)L of 0.1 M NaOH under vigorous stirring. The color gradually turned to dark red, which indicated the deposition of a silver shell onto the AuNRs. Lastly, the NPs were centrifuged once at 7500 rpm for 20 min and re-dispersed in water.
## 3 Simulation Results
### Influence of Multifrequency Ultrasonic Combination on Transient Cavitation Threshold
The simulation results in Figure 3 are just opposite to those in Figure 2. When the lowest frequency of the multifrequency combination is the same as that of single-frequency ultrasound, the transient cavitation threshold of the multifrequency combination is higher than that of the single-frequency ultrasound. The simulation results in Figure 3 show that the transient cavitation threshold change from the single-frequency ultrasound to the dual-frequency combination is stronger than that from the dual-frequency combination to the triple-frequency combination, which is consistent with the experimental results of [PERSON] [13] and other researchers [37; 38] who used multifrequency excitation to enhance cavitation. Figure 3: Transient cavitation threshold of single-frequency (1.4 MHz), dual-frequency (1.4 + 2.0 MHz), and triple-frequency (1.4 + 2.0 + 2.6 MHz) in different media: (**a**) water; (**b**) blood; (**c**) brain; (**d**) liver. Figure 2: Transient cavitation threshold of single-frequency (2.6 MHz), dual-frequency (2.6 + 2.0 MHz), and triple-frequency (2.6 + 2.0 + 1.4 MHz) in different media: (**a**) water; (**b**) blood; (**c**) brain; (**d**) liver.
## 7 Discussion  
The presented study describes how the ensemble configurations changed over time according to transmission requirements as well as multiplex forming mechanisms. The evaluated configuration had 12 services, including 10 regular audio broadcasts and 2 additional data services. According to available data, the target configuration should have 12 regular audio services, including 10 national and 2 regional broadcasts.  
The obtained subjective results concerning the current multiplex ensemble configuration show that DAB+ proved to be superior relative to simulcasted FM programs, as described in Table 7. As observed, listeners favor the digital standard, mostly for its clarity and resistance to noise and interference. It is worth mentioning that a MOS score of above 4.0 (good to excellent quality) is considered as a requirement necessary to fulfill the so-called broadcast quality criterion.  
In most cases, the differences in subjective judgement, concerning the quality of offered services in the current as well as previous study, were quite similar. A comparison of obtained results, with respect to changes in bitrate, is described in Table 8.  
The biggest difference, caused by raising the bitrate from 104 to 112 kbps (Regional PL), was equal to 0.25. However, the largest observed difference, in this case not related with bitrate adjustments, was equal to 0.52 (Talk 2). This may be caused by, e.g., different types of transmitted content compared to previous studies or the change in music taste among participating individuals. The major difference, caused by raising the bitrate from 104 to 112 kbps (Regional PL), was equal to 0.25. However, the principal observed difference, in this case not related with bitrate adjustments, was equal to 0.52 (Talk 2). This may be caused by, e.g., different types of transmitted content, compared to previous studies, or the change in music taste among participating individuals.
## 3 The Time Source Techniques
### Global Navigation Satellite Systems
#### 3.1.1 Timing Mechanism  
GPS is a satellite-based network comprising 24 satellites that provide worldwide coverage. These satellites orbit in Medium Earth Orbit (MEO) with the altitude of about 20,200 km (12,550 miles), each completing two orbits around Earth daily. The GPS constellation is organized into six equally spaced orbital planes, with four satellites in each plane, ensuring that users can view at least four satellites from almost any location on Earth.  
GPS satellites continually transmit their spatial coordinates and time information in two frequency bands. A GPS receiver, connected to an antenna, uses signals from at least four satellites to calculate its 3D position and precise time.  
Each GPS satellite is equipped with atomic clocks, typically cesium-based, which provide highly accurate timing. This time information is embedded in the broadcast signals, enabling receivers to determine the exact time of transmission. By solving time-of-arrival equations from four or more satellites, receivers can determine their positions and correct any internal clock offsets.  
A timing and synchronization module equipped with a GPS receiver can leverage this global time reference to precisely correlate, trigger, and timestamp measurement data. GPS time is maintained by multiple atomic clocks on each satellite and is referenced to the Master Clock (MC) at the United States Naval Observatory (USNO), known as UTC (USNO). This global synchronization allows users to access accurate time without maintaining atomic clocks, as all GPS receivers worldwide are synchronized to the same time signal [43].