esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study
## 3 CPS-Based Reliability Approach
### Sensor Reliability Assessment  
One approach for assessing sensor reliability in key emerging applications, such self-driving in the automotive sector [5], is a model-based design for representing key sensor parameters. These parameters can be derived from monitoring processes where 'functional parameters' refer to the sensor characteristics and sensor lifetime, as well as to economic aspects (see Figure 1).  
With the aim of increasing the reliability of data collected by LiDAR, metrological assessment procedures should also be applied. Linear interpolation of measurements from three detectors arranged in series is a time-saving procedure for processing and reducing LiDAR data [68].  
All the major sources of potential error that influence point positioning accuracy have to be considered in the analytical derivations in order to determine the reliability of the achievable point positioning accuracy of LiDAR systems. Some of the random errors are reported in [69]. Some mathematical foundations are also provided for point positioning accuracy derived from the LiDAR equation, via error propagation:  
\[r_{M}=r_{M,INS}+R_{INS}^{M}\big{(}R_{L}^{INS}\cdot r_{L}+b_{INS}\big{)} \tag{1}\]  
where \(r_{M}\) represents the 3D coordinates of an object point in the mapping frame; \(r_{M,INS}\) represents time-dependent 3D INS coordinates in the mapping frame, provided by GPS/INS; \(R_{INS}^{M}\) is the time-dependent rotation matrix between the INS body and the mapping frame; \(R_{L}^{INS}\) is the boresight matrix between the laser frame and the INS body frame; \(r_{L}\) represents the 3D object coordinates in the laser frame; and, \(b_{INS}\) is the boresight offset vector.  
In addition, the calculation of the accuracy of the estimated location of an object using the LiDAR sensor can be performed by other key performance indices. For example, the use of the Distance Root Mean Squared (DRMS) measure for the data that are tracked on the x-y plane (2D) and the Mean Radial Spherical Error (MRSE) measure for the data that are tracked in the x-y-z space (3D) were reported in [27; 70]. Using derivable error formulas, any given random error and scan angle in the LiDAR range can be modelled and simulated. By doing so, the factors affecting LiDAR system accuracy can be analyzed [71].
# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 2 Materials and Methods
### Segmentation Labels  
The LUS video files must be labelled by a highly trained expert, but the procedure is not time-consuming. On the other hand, labelling a 2D dataset for basic semantic segmentation in LUS takes more time but requires only a low level of expertise. We educated a group of 80 volunteers, who drew rectangular bounding boxes around the objects of interest using a labelImg tool [15]. Labelling was the first step in the production of semantic segmentation masks.  
We randomly selected 6400 frames from 545 videos, and each volunteer labelled 80 frames. The objects of interest were the pleura, lung, rib, and tissue. Class pleura represents both the parietal and visceral pleura. In the LUS images, it is a shimmering white line connecting the lung and chest wall. Class lung is the area under the pleura between the anechoic shadows of the ribs. Class ribs represents the area of anechoic shadow. This artifact appears because sound waves are reflected well by the hard material of the ribs. Finally, the class tissue represents the chest wall, consisting of skin, fat, and muscle tissue.  
We produced binary semantic segmentation masks for the objects of interest by applying several computer vision algorithms to the areas delimited by the bounding boxes, see Figure 2. We assumed that the objects of interest differed from other objects in the bounding box by brightness. To create the segmentation mask for the lung class, we applied intensity thresholding in the bounding box with a threshold value determined by [PERSON]'s method. The initial result was refined by morphological dilation, and the holes in the mask were filled. Finally, the mask was returned to the coordinate system of the image. We repeated this process for each bounding box in an image. In the end, we kept only the largest contiguous region because some of the images contained multiple separate regions of the lung. We generated pleura masks using the same process. Similarly, masks were created for the rib class however, we inverted the mask produced by [PERSON]'s method and kept two separate regions.  
Figure 1: Total number of samples from each class (Lung Sliding Absence (LSA)/Lung Sliding Presence (LSP)) with the number of frames.
###### Abstract  
Various approaches have been explored to address the path planning problem for mobile robots. However, it remains a significant challenge, particularly in environments where a multi-tasking mobile robot operates alongside stochastically moving humans. This paper focuses on path planning for a mobile robot executing multiple pickup and delivery tasks in an environment shared with humans. To plan a safe path and achieve high task success rate, a Reinforcement Learning (RL)-based double layer controller is proposed in which a double-layer learning algorithm is developed. The high-level layer integrates a Finite-State Automaton (FSA) with RL to perform global strategy learning and task-level decision-making. The low-level layer handles local path planning by incorporating a Markov Decision Process (MDP) that accounts for environmental uncertainties. We verify the proposed double layer algorithm under different configurations and evaluate its performance based on several metrics, including task success rate, reward, etc. The proposed method outperforms conventional RL in terms of reward (+63.1%) and task success rate (+113.0%). The simulation results demonstrate the effectiveness of the proposed algorithm in solving path planning problem with stochastic human uncertainties.  
reinforcement learning; double-layer control; path planning; mobile robots; human-shared environments; MDP; FSA
# Investigation of Impact Behavior of STS304L Steel Plate Under Cryogenic Temperature
## 2 Experiment Details
### Test Procedure and EBSD Analysis  
The test scenarios are listed in Table 2. The impact energy was varied from 50 J up to the point where cracks appeared to evaluate the temperature sensitivity of STS304L's mechanical behavior and energy absorption capacity under drop-weight impacts. The tests were conducted at room temperature (\(20\)\({}^{\circ}\)C), \(-100\)\({}^{\circ}\)C, and \(-193\)\({}^{\circ}\)C. In this study, the drop weight was fixed, while the drop height was adjusted to control the impact velocity. The velocity at impact was calculated using the following free-fall equation:  
\[v=\sqrt{2 gh} \tag{1}\]  
where \(v\) is the impact velocity, \(g\) is the gravitational acceleration (\(9.81\) m/s\({}^{2}\)), and \(h\) is the drop height. The test parameters, including the drop heights and corresponding velocities, are summarized in Table 2.  
EBSD analysis was conducted to investigate the correlation between plastic deformation and strain-induced martensitic transformation at different temperatures by measuring  
Figure 4: Schematic of the environmental chamber (highlighted in red).  
Figure 5: Time–temperature history measured by thermocouple.  
the martensite fraction in the radial direction. These results were used to assess the influence of temperature on the mechanical behavior of STS304L.  
To ensure proper alignment with the impact direction (thickness direction), the specimens were vertically hot-mounted. Surface preparation involved sequential grinding with SiC paper (400 to 2000 grit, 1 min per step), followed by fine polishing using diamond suspensions (3 \(\upmu\)m and 1 \(\upmu\)m) and colloidal silica (0.04 \(\upmu\)m) to eliminate surface scratches. Prior to analysis, the specimens were ultrasonically cleaned in water and ethanol. EBSD measurements were performed using a TESCAN MIRA 3 LMH In-Beam Detector (TESAN ORSAY HOLDING a.s., Brno, Czech Republic) under the following conditions: 20 kV accelerating voltage, \(\times\)500 magnification, and 1 \(\upmu\)m step size.
## 5 System Performance  
Significant rainfall events which occurred during the 2020-2021 tropical cyclone season provided a basis to operationally test the system and identify ongoing gaps for future development. In particular, the rainfall events on 18 December 2020, 7 January and 22 February 2021 proved useful in assessing the efficiency of the prediction model. Figure 7 illustrates the river levels recorded at Alaoa East and Lelata flow gauging stations from the 15 to 19 December 2020 [43], associated with the 18 December 2020 flooding. Higher rainfall was observed at Afiamalu rainfall station than Le Pue during this period highlighting the high local spatial variability in the catchment (as previously described in Section 3.2). Whilst the 5-, 10-, and 60-min rainfall intensities at Afiamalu were all less than 1-year return period flood, the 24-h rainfall accumulation of 392 mm from 2 pm on the 17 to 18 December was greater than a 25-year event (Table 5). For the same period, 316 mm of rainfall was recorded at the Le Pue site, equivalent to between a 5- to 10-year event [33].  
Figure 6: Vaisigano flood decision support system (FDSS) web-portal interface tools (example forecast event only). In this example, time series of forecast flood peak and magnitude at river gauge sites along with corresponding maps/tables of flood hazard and human safety at building/road locations are displayed.  
The data at this and subsequent events on 7 January 2021 and 22 January 2021 have been used to validate and improve the prediction accuracy of the FDSS system by increasing the number of events in the FDSS system.  
Figure 7: (**a**) Forecast rainfall for 9 am on 18 th December 2020 local Samoan time (WRF model issued at 0000 UTC on 17 th December 2020). (**b**) Observed 5-min and (**c**) 10-min river stage at Lelata and Alaoa East river gauging sites, and weighted rainfall for Alaoa East associated with the 18 th December 2020 flooding.  
the amount of data that the 10-min and 1-h unit hydrographs are based upon. That is, these recent extreme events have extended the pool of data from which the hydrographs are derived.  
Modelled stage at Alaoa East are also shown in Figure 7. It should be noted that the river stage is based on area-weighted rainfall for the Alaoa East catchment and correlation of modelled and observed flows yielded an R\({}^{2}\) value of 0.87 for this event.
## 3 Results
### Interpretable Modeling Approach
#### 3.3.4 SHAP Analysis for Model Interpretation  
Figure 6 presents the SHAP waterfall plot and decision plot of the RF model. Figure 6a indicates that the model's average predicted classroom engagement was 0.404. Subject-specific software and management software notably increased student classroom engagement, followed by mobile devices. In contrast, insufficient or low-level website usage negatively impacted engagement, while multimedia technology exhibited minimal influence. Figure 6b further illustrates that management software and subject-specific software were the most influential predictors of student classroom engagement. Higher levels of subject-specific software and management software usage corresponded to increased predicted engagement levels and vice versa. Although websites, mobile devices, and multimedia materials exerted relatively minor effects, they functioned as auxiliary facilitators of engagement for certain student subgroups.  
Figure 5: Contour plots depicting the interaction effects of digital technology tools on student engagement: (**a**) subject-specific software and management software, (**b**) subject-specific software and website, (**c**) subject-specific software and mobile devices, (**d**) management software and website, (**e**) management software and mobile devices, and (**f**) website and mobile devices.
## 6 Complexity Analysis of Recurrence-Analysis-Based Detector  
Theoretical and experimental analyses show the superiority of the RAD compared with the Energy Detector (ED) and the Cyclostationary Feature Detector (CFD) in a low SNR scenario detection process. The complexity of the algorithms is measured through the number of complex multiplications that the algorithms have to perform for the calculation of the test statistics [60]. In this section, we provide the complexity analyses of the ED, CFD, and RAD.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 4 Simulation and Evaluation
### Testing Subsystems  
This section provides an independent analysis of the output characteristics of PEMFCs and lithium batteries. The loading current for the fuel cell ranges from 10 to 400 A, while the loading current for the lithium battery ranges from 10 to 300 A. The lithium-ion (Li-ion) battery has a discharge rate of 25 C.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{P_{load}}\) **(kW)** & \(\mathbf{P_{FC}}\) **(kW)** & \(\mathbf{P_{bat}}\) **(kW)** \\ \hline (0, 20] & 10 & \(P_{load}-P_{FC}\) \\ (20, 40] & 30 & \(P_{load}-P_{FC}\) \\ (40, 60] & 50 & \(P_{load}-P_{FC}\) \\ (60, 80] & 70 & \(P_{load}-P_{FC}\) \\ (80, 100] & 90 & \(P_{load}-P_{FC}\) \\ (100, 120] & 110 & \(P_{load}-P_{FC}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Power allocation of the proposed SM method for vehicles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Parameter** & **Value** \\ \hline  & Number of cells & 455 \\  & Voltage range & 0–450 V \\  & Current range & 0–400 A \\ PEMFC stack & Rated power & 100 kW \\  & Hydrogen pressure & 5 bar \\  & Air pressure & 5 bar \\  & Working temperature & 293.15–33.15 K \\ \hline  & Open circuit voltage & 300 V \\ Lithium-ion battery & Nominal capacity & 50 Ah \\  & Initial impedance & 10 m\(\Omega\) \\ \hline DC/DC converter & Efficiency & 98\% \\ \hline DC bus & Bus voltage & 580 V \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter configuration of the proposed hybrid power system.  
The voltage and power outputs of both types of batteries are shown in Figure 6. Specifically, to protect the proton exchange membrane of the fuel cell, the maximum loading current is set to 400 A. The output voltages and power of both systems also meet the expected conditions.
# The Classification of Inertinite Macerals in Coal Based on the Multifractal Spectrum Method
## 2 Materials  
According to International Commission for Coal Petrology (ICCP) standard, coal is classified across three main maceral groups; i.e. vitrinite, liptinite, and inertinite [27]. Macerals of inertinite mainly come from woody fiber of plant or fungus [28]. The plant cellular structure of fusinite is relatively complete, and some of them have clear intercellular space and cellular wall. The cells of the pyrofusinite are crushed and shattered to present the shape of \"arc\" or \"star-like\", while the oxyfusinite has an unbroken cellular structure that exhibits a sieve shape. Semifusinite, the transitional maceral between telinite and fusinite, is located in the form of irregular strips. Secretinite is generally a product of silk carbonization reaction of secretions (tannin, resin, etc.), and few of them are derived from gelation of humus coal. Besides, the microscopic images are irregularly elliptical. Funginite is mainly derived from the remains of fungi or the secretions of higher plants, and has a honeycomb-like or reticulated multicellular structure inside. Additionally, the outer shape is flattened circular or ring-shaped due to extrusion. The cellular structure of the macroinite has a high protrusion and is generally an irregular matrix. A fragment of the inertinite group of particles have a particle size of less than 30 \(\upmu\)m, angular or irregular in shape, and has no generally cellular structure. Most of the micrinites are distributed in asphaltene or mineral asphaltene with minor particle size and often small, nearly circular particles. Note that for fusinite, the two sub-macerals named pyrofusinite and oxyfusinite will be analyzed together with other six types of macroals in our work, as the texture differences are significant and obvious.  
From Figure 1, we can observe that there are some morphological differences among different macroals of inertinite in coal. However, their textures are fairly clear with singularity and conspicuous self-similarity. For such non-stationary structures, MF-DFA analysis can characterize them more effectively and show better processing power. In view of this, this paper performed the method of MF-DFA on each maceral image. For implementation, we used inertinite image data with 60 grayscale microscopic images of \(227\times 227\) pixels in size per group. The size was chosen to ensure that each image contained only one specific component, which is beneficial for subsequent feature extraction and classification experiments.
## 6 Conclusions  
In order to explore the characteristics of the wind environment above the bridge deck near the pylon zone and guide the arrangement of wind barriers for improving driving safety in this zone, the flow visualization and wind speed near the inverted Y-shaped bridge pylon zone were studied. Combined with the wind tunnel test and numerical simulation, specific distribution patterns were found, and based on these characteristics, two safety evaluation indicators that consider vehicle driving stability were proposed. The main conclusions are summarized as follows:  
1. The wind environment above the bridge deck near the pylon zone has the obvious characteristics of a typical three-dimensional flow field controlled by the flow around the pylon and girder. The influence range of the flow around the girder is dominant within 0.8 times the girder height in the vertical direction, and the flow around the bridge pylon is dominant within 3 times the pylon width in the longitudinal direction.
2. The wind velocity above the bridge deck is small behind the pylon columns and suddenly increases outside the pylon, and the maximum value appears near a distance equals to the pylon column width from the center of the bridge pylons. The sudden change caused by the flow around the pylon is unfavorable for driving safety under a crosswind. The wind tunnel experiment and the numerical simulation results both confirm the flow pattern and wind speed distribution. This distribution pattern of the wind environment can effectively guide the arrangement of wind barriers near the bridge pylon zone.
3. To effectively evaluate the wind barrier effects, this paper proposes a double-index evaluation method that contains the ratio of maximum influence coefficient \(R\) and the safety factor of response time \(K\). With the wind distribution pattern and the proposed evaluation scheme, the optimal scheme for the wind barrier arrangement of Sutong Bridge can be determined, and the driving safety significantly improved.  
The ongoing researches should be investigated considering the change of vehicle aerodynamic load influenced by the wake of the pylon column under crosswinds.  
Conceptualization, D.W. and A.C.; Data curation, D.W.; Formal analysis, Y.Z. and M.S.; Funding acquisition, D.W. and M.S.; Investigation, Y.Z.; Methodology, D.W. and M.S.; Project administration, A.C.; Resources, D.W.; Software, Y.Z. and M.S.; Supervision, A.C.; Validation, Y.Z., M.S. and A.C.; Visualization, Y.Z.; Writing - original draft, D.W. and M.S.; Writing - review & editing, D.W., Y.Z. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number [51778472]) and the program of China Scholarships Council (grant number [201906260185]). The authors would also like to express their thanks to the Jiangsu Sutong Bridge Co., Ltd., for support and assistance during the experiments.  
The authors declare no conflict of interest.  
Figure 20: The final arrangement scheme of wind barriers on Sutong Bridge.
# Propagation of Optical Coherence Vortex Lattices in Turbulent Atmosphere
###### Abstract  
Propagation properties in the turbulence atmosphere of the optical coherence vortex lattices (OCVLs) are explored by the recently developed convolution approach. The evolution of spectral density distribution, the normalized \(M^{2}\)-factor, and the beam wander of the OCVLs propagating through the atmospheric turbulence with Tatarskii spectrum are illustrated numerically. Our results show that the OCVLs display interesting propagation properties, e.g., the initial Gaussian beam distribution will evolve into hollow array distribution on propagation and finally becomes a Gaussian beam spot again in the far field in turbulent atmosphere. Furthermore, the OCVLs with large topological charge, large beam array order, large relative distance, and small coherence length are less affected by the negative effects of turbulence. Our results are expected to be used in the complex system optical communications.  
optical coherence vortex lattices; atmospheric turbulence; evolution properties; free-space optical communications +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Analysis of Watermarking Capacity  
The watermark capacity represents the maximum amount of a watermark that can be embedded into data. In this paper, we used the coordinates and attributes of elements as embedding targets. Movable elements can carry two bits of watermark information, whereas immovable elements can carry at least one bit of watermark information. Therefore, the watermark capacity of our proposed method is theoretically larger than the total number of elements in BIM data. However, some unstable elements are unsuitable as watermark carriers, and it is necessary to ensure that multiple elements correspond to one watermark bit. Thus, the watermarking capacity is generally smaller than the total number of elements. In this section, we examine the relationship between the watermark length and the robustness in our proposed watermarking algorithm. Using the BIM data from Section 4 as experimental data, we conducted watermarking experiments on model translation, detail level change, graphical element, and numerical model separation attacks at watermark lengths of 32, 64, 128, 256, and 512. Table 10 and Figure 12 show the experimental results.  
In Table 10 and Figure 12, it can be observed that the robustness of the watermark against element addition and deletion as well as pattern separation attacks decreased continuously as the watermark length increased from 32 to 512, except for translation and detail level change attacks, which did not affect the watermark extraction. Once the watermark length exceeded 50% of the total number of elements of the gymnasium and structure model, which occurred at a length of 512 bits, the ability of the watermark to resist various attacks was significantly reduced. Specifically, when encountering element deletion, the proportion of the extracted watermark was only 20%, and the NC value almost approached the threshold value set in this paper of 0.75. Thus, to ensure the robustness of the watermark algorithm, it is crucial to limit the length of the watermark sequence to be less than the total number of elements of the BIM data. In this study, a binary sequence of 288 bits was used as the watermark, indicating that to fully embed the BIM data and achieve high robustness, it should contain at least 576 elements. Therefore, the watermark capacity of the proposed method needs to be enhanced, and further investigation will be conducted in future research.  
Figure 12: Results of attacks under different watermark lengths. (**a**) Results of 100-m translating attack. (**b**) Results of changing level of detail. (**c**) Results of deleting 20% of elements. (**d**) Results of adding 20% of elements. (**e**) Results of FBX format data. (**f**) Results of JSON format data.