esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental
### Filter Development, Test Methods, and Results
#### 3.1.5 Surface Flow Method  
At this stage of development, the proof of principle that the coated substrate provides an active NO\({}_{2}\)-sorbing filter in both static and through-flow configurations was supported. However, although potentially greater total surface area of filter might be required, the energy-saving benefits of using natural draughting (air flow induced by geometrical or thermal gradient design) to provide surface flow encouraged the investigation of methods to evaluate gas sorption via surface exposure alone.  
Surface flow analysis was developed in two steps in which the first was at a relatively low surface area capacity and was followed by higher surface area capacity to evaluate the capacity of the filter material for repeated cyclical exposure. The initial experiments were conducted using a traditional mechanical style flow meter, as shown schematically in Figure 17a and in practice in Figure 17b. The gas was inlet through a central vertical pipe, and it flowed back down through the surrounding volume in which the smooth two-side coated filter was placed. To increase contact area, the filter was pleated with successive parallel folds. The flow rate was controlled at \(v\) = 200 cm\({}^{3}\cdot\)min\({}^{-1}\) with an extended exposure time of 4 h.  
The method was used again to establish the role of moisture for the surface flow mode. The predominance of nitrite under pre-wetted conditions (approximately 50 w/w% water content) confirmed that the sorption mechanism was likely one of NO\({}_{2}\) gas dissolution when excess moisture was present, and it was dominant over the desired active salt conversion of the calcium carbonate, which was effectively suppressed (Figure 17c). Therefore, the localised humectant action of MNFC in the coating was confirmed to be ideal for promoting the desired nitrate formation rather than moisture saturation in both the through-flow and surface flow configurations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sample**} & **NO\({}_{2}\)-** & **NO\({}_{3}\)-** \\  & **(mg\(\cdot\)kg\({}^{-1}\)filter)** & **(mg\(\cdot\)kg\({}^{-1}\)filter)** \\ \hline NP coated exposed REGULAR & 36.82 & 41.34 \\ \hline NP coated exposed WET & 137.97 & 30.04 \\ \hline NP coated non-exposed BLANK & 14.67 & 31.25 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Nitrite and nitrate formation comparison as a function of moisture level on exposure to synthetic NO\({}_{2}\) relative to a non-exposed sample and non-pre-wetted (REGULAR) sample.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 3 Experiments
### Comparisons with State-of-the-Art Methods  
In this section, experiments are conducted on the conveyor dataset mentioned above, and our method is compared with the state-of-the-art approaches. Among them, UNet [28] is an improved FCN network, and Deeplab v3 is a very successful version of the image segmentation network Deeplab series [29].  
UNet is the cornerstone of medical image segmentation. In recent years, many effective methods in medical image field have been improved on UNet. It consists of two parts: a feature extraction part that increases the receptive field to obtain context information, and an upsampling fusion structure that can achieve precision positioning. We use the dataset to train the UNet network for 1000 epochs, and we save the best model to conduct experiments on the test images. Then we post-process the result of UNet by image erosion. The result images are shown in Figure 7, column 5.  
Besides, compared with UNet, the Deeplab series network is more popular in image segmentation tasks. Deeplab v3 proposes a more general framework that replicates the last block of ResNet and cascades it. An ASPP (Atrous Spatial Pyramid Pooling) structure with a BN (Batch Normalization) layer is used to increase the receptive field and extract more abundant features without increasing the parameters. We train the Deeplab v3 with the same training parameter settings and then test it. For the result of Deeplab v3, we use the least square method for linear fitting. The results are presented in Figure 8, column 6.  
Figure 7: Comparison of the results between the Canny and the proposed method. (**a**) The result of Canny. (**b**) The result of the proposed method.
## 2 Literature Review
### Problem Statement and Objective  
Due to the dispersed nature of the data accumulated from TBM tunneling, processing the input data and predicting the results, status of a TBM, and projection completion scenarios are difficult to understand for stakeholders. Furthermore, the prediction of TBM performance is a nonlinear and complex problem due to various geotechnical conditions encountered along the tunnel alignment [53]. Machine learning techniques have recently been adopted to solve this problem using only historical data to predict TBM performance. Feedback from real-time operational parameters and geological conditions has not previously been considered, which would influence the performance prediction accuracy of TBMs. An integrated application of digital twins and machine learning has the potential to solve such a problem.  
This study proposes a digital twin-driven framework for TBM performance prediction, visualization, and monitoring through machine learning. The main contributions of this work are as follows: (i) based on different machine parameters and geological factors, different AI-based algorithms are used to predict the performance of TBMs using historical and real-time operational data; (ii) a virtual TBM model is constructed to provide an opportunity for end users to visualize, understand, and monitor each change in the state of TBMs; (iii) real-time information flow between physical and digital construction is established to ease schedule estimation; (iv) a framework is proposed to simultaneously predict, visualize, and monitor the performance of TBMs through digital twins and artificial intelligence.
## 3 Formation Controller Design  
The proposed virtual-structure-based formation control scheme is shown in Figure 2.  
For a group of \(N\) robots, the virtual-structure approach is applied to generate \(N\) basic desired trajectories \(\left[\begin{array}{cc}x_{di}&y_{di}\end{array}\right]^{T}\) for these robots to track, and the trajectories are parameterized by \(s\) in order to keep the group in formation. The desired velocities \(\left[\begin{array}{cc}v_{di}&\omega_{di}\end{array}\right]^{T}\) can be calculated based on the  
Figure 1: The kinematic model of a mobile robot.  
Figure 2: Formation control scheme.  
desired trajectories containing the desired velocities and trajectories. To achieve collision avoidance via the potential function, a trajectory generator is designed to replan the reference trajectories \(\left[\begin{array}{cc}x_{ri}&y_{ri}\end{array}\right]^{T}\) with the desired velocities \(\left[\begin{array}{cc}v_{di}&\omega_{di}\end{array}\right]^{T}\) and adjusted velocities \(\left[\begin{array}{cc}v_{ai}&\omega_{ai}\end{array}\right]^{T}\). Based on the reference velocities and trajectories, the tracking controller \(\left[\begin{array}{cc}v_{i}&\omega_{i}\end{array}\right]^{T}\) is designed to achieve good formation tracking performance. Moreover, the parameter \(s\) is designed to adjust the desired velocities to track with regard to the tracking errors of the whole formation, where the desired formation can be slowed down to wait for the robots to track when the system has large tracking errors. Therefore, good formation and tracking performance regarding the obstacles can be achieved by the proposed control design.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results
### BOSS Algorithm Comparison with Traditional Image Segmentation Algorithms
#### 3.4.1 Algorithms Comparison Procedure  
The rationale behind the algorithm comparison is described in this section. The comparison dataset was composed of 300 images and 300 reference masks. Each reference mask represented the desired result of the segmentation process. Every reference mask was manually obtained using Adobe Photoshop 2019 (Image->Adjustments->Threshold), selecting a suitable threshold level because of the varying noise conditions over the 300-image test set.  
Now, the scope compared the output of the generic segmentation algorithm with the corresponding reference mask. The procedure was as follows:  
Figure 11: Ursa Major constellation stars localization performed by the BOSS algorithm. Names and visual magnitudes of the segmented objects have been reported in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**ID** & **Name** & **Magnitude** \\ \hline
1 & [PERSON] & 1.95 \\
2 & [PERSON] & 2.42 \\
3 & [PERSON] & 2.34 \\
4 & [PERSON] & 2.25 \\
5 & [PERSON] & 1.75 \\
6 & Psi Ursae Majoris & 3.16 \\
7 & [PERSON] & 3.33 \\
8 & [PERSON] & 1.80 \\ \hline \end{tabular}
\end{table}
Table 3: IDs, names and visual magnitudes of the segmented objects in Figure 11.  
* The output mask was compared with the reference mask pixel by pixel;
* The number of FP, FN and TP were updated during the mask comparison;
* FP, FN and TP values were used to compute the Precision, Recall and F1 values (these indices were described in the previous Section 2.6_Comparison Indices_).  
This process was repeated for all the 300 images, and a final averaged value for the F1 index was obtained for the considered algorithm.  
This procedure was directly applied for the BOSS and Otsu algorithms because they do not need to be configured: Otsu does not need any configuration parameter, and the BOSS algorithm has its fixed value of \(p_{min}\) which was frozen during its design in Section 3.2.  
Niblack, LTA, WIT and LTS require an additional step before computing the final F1 value: the configuration parameters' optimization. Indeed all of them have at least one configuration parameter to be selected with a suitable criterion:  
* Niblack's configuration parameters are \(k\) and \(d_{Nb}\);
* LTA's configuration parameter is the Sensitivity;
* WIT's configuration parameter is \(\delta\);
* LTS's configuration parameters are \(\tau_{pre}\) and \(BKG_{0}\).  
By considering the generic configurable algorithm, the averaged F1 index was computed for every combination of the configuration parameters varying in their specific ranges. Indeed, every selected value for the configuration parameter brings the algorithm to be more or less severe in terms of segmentation performance and changes the final F1 index value. Results of this process, using commonly used values for the parameters, have been collected for each configurable algorithm in Tables 4-7. In the end, the six averaged F1 values for Otsu, BOSS and the optimized algorithms can be obtained and were reported in Table 8.
# Evaluation of Structural Behavior and Fatigue Performance of a KR-Type Rail Clip
## 2 Laboratory Measurements
### Assembly Test  
An assembly test was performed in the laboratory to evaluate the structural behavior of the rail clip during fastening, as shown in Figure 4. A torque of 250 \(\pm\) 25 N\(\cdot\)m was applied to the screw spike using a torque wrench until the central part of the clip came in contact with the guide plate. A combination of displacement transducers and rosette strain gauges was placed in specific locations of the clip to measure its global and local responses during the fastening procedure. The strain gauges were installed at highly curved locations where high stress concentration was expected.  
Figure 5 illustrates the global response of the clip. The vertical axis represents the torque applied to the screw spike for fastening the clip to the rail, while the horizontal axis represents the average vertical displacement at the central end of the clip, as shown in Figure 5. Elastic behavior was exhibited until the clip came in contact with the guide plate, at which point the torque applied was approximately 230 N\(\cdot\)m at the vertical displacement of 11.4 mm. From the rosette strain gauge measurements during the fastening process, the maximum principal strains at three locations (i.e., R1, R2, and R3 in Figure 6) were calculated and plotted as shown in Figure 6. During the unloading procedure, the strains upon complete removal of torque were measured. The presence of some residual strains indicated that the strains during tightening exceeded the yield strains, particularly at locations R2 and R3. It should be noted that such local plastic behavior exhibited by the clip during fastening would have been more obvious if the strain gauges were placed at locations with more severe stress concentrations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen** & **Yield Stress** & **Tensile Strength** & **Elongation at Failure** \\
**No.** & **(MPa)** & **(MPa)** & **(\%)** \\ \hline
1 & 1383 & 1517 & 11.0 \\
2 & 1379 & 1514 & 9.3 \\
3 & 1368 & 1497 & 8.5 \\ \hline \end{tabular}
\end{table}
Table 1: Steel mechanical properties.  
Figure 3: Stress versus strain curve (for coupon specimen 1).  
Figure 4: Assembly test setup.  
Figure 5: Global response of the clip (torque vs. vertical displacement).  
Figure 6: Strain measurements (torque vs. principal strains).  
first, second, and third natural frequencies through modal analysis were 871 Hz, 1025 Hz, and 1272 Hz, respectively.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 2 Principle of Signal Component Analysis for PWM and PPM  
In this section, the components of PWM and PPM signals are analyzed, starting with the PWM signal and its Fourier series analysis, followed by the same analysis for the PPM signal. It is well-established that square waves with identical frequencies but varying duty cycles exhibit identical magnitude spectra, differing only in their phase spectra. PWM, a form of phase modulation, modulates the phase of the signal in each cycle in accordance  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref. No.** & **Authors/Title (Year)** & \
\begin{tabular}{c} **Application/System** \\ **Context** \\ \end{tabular} & **Advantages** & **Limitations** &
\begin{tabular}{c} **Comparison to Proposed** \\ **Method** \\ \end{tabular} \\ \hline
[6] & [PERSON] et al. (2022) & \
\begin{tabular}{c} **Overview of PPM** \\ developments and \\ experiments \\ \end{tabular} &
\begin{tabular}{c} **Broad analysis;** \\ discusses BER/SNR \\ trends \\ \end{tabular} & No specific hardware design or simplification & Proposed work contributes design and theoretical modeling validated by experiments \\
[8] & [PERSON] \& [PERSON] of OOK, PPM, and DPIM & PPM has best power efficiency & Requires complex synchronization for PPM & Proposed method avoids synchronization overhead by leveraging simplified analog processing \\
[9] & [PERSON] (2022) & Remote communication with frame head/tail for sync & Frame sync via Gray code; low BER & Relies on FPGA and digital logic & Proposed design avoids digital complexity, using analog VCO + monostable \\
[10] & [PERSON] (2022) & PPM system modeled with AWGN and BER analysis & Theoretical BER validation via simulation & No hardware implementation or simplification focus & Proposed work validates both in MATLAB and on physical circuit level (MATLAB version R2024b Update 5 (24.2.0.2871072)) \\
[13] & [PERSON] et al. (2018) & Combines OFDM with PWM/PPM for VLC & Handles PAPR well; improves BER complexity & OFDM integration increases circuit complexity & Proposed system is free of OFDM blocks and uses lower-power analog modules instead \\
[19] & [PERSON] et al. (2013) & Analog FM demodulation via PWM generation and LIF & Simple architecture; adjustable gain & Limited to audio signals & Inspired FM-to-PPM block structure in the proposed work with general signal applicability \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)with the information signal. Similarly, PPM is another phase modulation technique in which the phase varies with the information signal.
## 5 Adsorption Isotherms Models
### Adsorption Kinetic Models  
Adsorption kinetics explains the rate of uptake of a solute from an aqueous solution onto the given adsorbent. It gives details about the rate of adsorption and factors affecting the overall rate of reaction and the mechanism used in the adsorption process. Different kinetic models such as [PERSON]'s pseudo-first-order, pseudo-second-order, [PERSON] kinetic equation, and parabolic diffusion model are used to study adsorption kinetic phenomena.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme.** & **Adsorbents** & **Metal/Dye** & **Isotherm** & **References** \\ \hline \multirow{2}{*}{1} & Chitosan/polyacrylonitrile & \multirow{2}{*}{Rhodamine B dye} & Langmuir adsorption & \multirow{2}{*}{[22]} \\  & semi-IPN hydrogel & & isotherm & \\ \hline \multirow{2}{*}{2} & Thiourea-modified & \multirow{2}{*}{Malachite green} & Freundlich isotherm & \\  & poly(acrylonitrile-co-acid) & & & \\ \hline \multirow{2}{*}{3} & Polyacrylonitrile/polyamidoamine & \multirow{2}{*}{DR80 and DR23} & Langmuir adsorption & \multirow{2}{*}{[76]} \\  & composite nanofibers & & isotherm & \\ \hline \multirow{2}{*}{4} & Nanoporous & \multirow{2}{*}{DB78} & Langmuir adsorption & \multirow{2}{*}{[96]} \\  & polyacrylonitrile/calcium & & isotherm & \\ \hline \multirow{2}{*}{5} & Anionic functionalized & \multirow{2}{*}{BCB} & Langmuir adsorption & \multirow{2}{*}{[64]} \\  & polyacrylamide fibrils & & isotherm & \\ \hline \multirow{2}{*}{6} & Poly(methacrylic-co-acrylonitrile) & Cd\({}^{2+}\), Cr\({}^{3+}\), MB, R6G, and & Langmuir adsorption & \multirow{2}{*}{[115]} \\  & acrylonitrile & paraquat & isotherm & \\ \hline \multirow{2}{*}{7} & Modified PAN/SiO\({}_{2}\) & \multirow{2}{*}{Th\({}^{4+}\), U\({}^{6+}\), Cd\({}^{2+}\), and Ni\({}^{2+}\)} & Langmuir adsorption & \multirow{2}{*}{[129]} \\  & composite nanofiber & & isotherm & \\ \hline \multirow{2}{*}{8} & Poly(acrylonitrile-co-acrylamidopropyl-trimethylammonium chloride) & \multirow{2}{*}{As\({}^{5+}\), Cr\({}^{6+}\), and Cr\({}^{3+}\)} & Langmuir adsorption & \multirow{2}{*}{[117]} \\  & acrylamidopropyl-trimethylammonium chloride & & isotherm & \\ \hline \multirow{2}{*}{9} & Activated nanofibuc & Ni\({}^{2+}\) & Freundlich isotherm & \multirow{2}{*}{[136]} \\  & polyacrylamideonitrile & Pb\({}^{2+}\) & Langmuir adsorption & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Various adsorption isotherm models of the metal ion and dye sorption on modified polyacrylamide adsorbent.  
Among all the models, the rate of adsorption is generally studied using the [PERSON] pseudo-first-order model and pseudo-second-order model [133].
## 2 Materials and Methods
### Verification of Numerical Simulation Reliability  
To verify the simulation results, we attempted to compare them with the experimental results. Three full-scale tunnel fire experiments were conducted in a highspeed railway tunnel by [PERSON] et al. [36]. The flame height, longitudinal distribution of smoke temperature, and longitudinal smoke velocity were studied. In this research, the ambient temperature was 20.7 \({}^{\circ}\)C. Test 3 in this paper had a longitudinal ventilation velocity of 0 m/s, a fire area of 2.0 m\({}^{2}\), and a fire scale of 4 rectangular oil trays filled with 92-octane gasoline, each with a thickness of 2 cm. Based on the full-scale tunnel fire experiment, the results were compared with simulation results in this work for a special test, as shown in Figure 3. This figure shows that the FDS predictions match the experimental results well.  
The experimental results for the longitudinal distribution of the maximum temperature rise in fire smoke in a tunnel at 120 s from [36] are compared with the numerical results, as shown in Figure 3. At each data point, the simulation results of FDS were in good overall agreement with the experimental values, typically within a 5% deviation. However, an error margin of 8% was observed at a distance of 5 m from the fire source. These discrepancies become negligible away from the fire source because of complex turbulent and heat transfer phenomena that occur near the fire source. These differences are mainly because the wind  
Figure 3: Comparison of the temperature rise in T6 from [PERSON]’s experiment [36] and the simulation results of this work.  
Figure 2: Vertical temperature distribution 10 m downstream of the fire source under different grid sizes for a curvature radius of 800 m and a ventilation of 2 m/s at 150 s.  
velocity in the FDS model was set as 0, which continuously varied during [PERSON]'s experiment. Overall, the comparative results prove that the FDS predictions match the experimental results well.
# Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model
## 12 Conclusions  
Offshore wind farms are much more costly than onshore wind farms of equal capacity. The cost and efficiency of an offshore wind farm are determined by a variety of factors, including the installation sites of the wind turbines, the number of wind turbines, the cable connection topology, long-term wind conditions, and others. Since the number of variables for wind farm planning is large, computational optimization is needed to search an optimal solution for wind farm layouts. This investigation reviewed significant issues around the design of an offshore wind farm. First, numerical modeling of the wind turbine wake represents a fundamental step in overall design optimization for an offshore wind farm. Next, various wind-turbine layout optimization methods were presented. Most algorithms that have been developed for wind turbine layout optimization, such as genetic algorithms, are based on heuristic procedures. However, numerous up-to-date optimization techniques have been developed for wind farm layout. The internal electrical system of an offshore wind farm and its connections are also important for wind farm design. Furthermore, wind farm design should consider system reliability as well as economics, carbon emission, and safety.  
Wind farm equivalent is another important topic for wind power simulations because it reduces a large amount of simulation time for wind power integration. Numerous equivalent wind-farm models have been developed, which can capture the real steady-state and dynamic characteristics of a wind farm. Notably, accurate nonlinear wake models and additional functions for frequency regulation or fault ride through should be considered in the equivalent model of an offshore wind farm.  
Investigation, Y.-K.W. and J.-J.-Z.; writing--original draft preparation, Y.-K.W. and W.-C.W.; writing--review and editing, Y.-K.W.; project administration, Y.-K.W.; funding acquisition, W.-C.W.  
This work was financially supported by the Ministry of Education of Taiwan. Project title: Higher Education Sprout Project, Ministry of Education, Taiwan, and the Ministry of Science and Technology (MOST) of Taiwan under Grant 105-3113-E-194-001-CC2. Project title: Development of Integration Technology for Large-Scale Offshore Wind Farms in Taiwan (3/3).  
The authors declare no conflict of interest.