esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we introduced a new calibration approach that utilizes diffuse markers, invisible to the naked eye, on the DDHOE screen. Compared to our previous marker-less calibration, this calibration makes sure that the views are sent into the correct direction, without any distortion. It also lets the user freely place the projector, calibration-camera. and DDHOE-screen in the display setup, without having to worry about any positioning errors. Since the approach can be applied on a transparent display, and since it correctly aligns the virtual world with the real world, it proves to be a good calibration approach for when the transparent light field displays are to be used for Augmented Reality applications.  
As for now, we foresee a marker in one corner of every micromirror. In future work, we would like to reduce the number of markers, or spread the marker over the four corners of a micromirror. This way, we can further reduce the loss of the viewing zone, or limit the loss to regions from which the observer will likely not look towards the screen. Experiments to measure the aberrations exhibited by the micromirrors and the absolute errors of calibration will also be conducted in the future.  
Conceptualization, R.O. and B.J.J.; methodology, L.J.; software, L.J.; validation, L.J. and B.J.J.; formal analysis, L.J.; investigation, L.J. and R.O.; resources, Y.I. and K.W.; writing--original draft preparation, L.J.; writing--review and editing, B.J.J.; supervision, K.Y., G.L. and P.B.; project administration, K.Y. and B.J.J.; funding acquisition, K.Y. and B.J.J. All authors have read and agreed to the published version of the manuscript. This research was funded by Japan Society For Promotion Of Science (JSPS) Grant No. 18H03281. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
DDHOEDigitally Designed Holographic Optical Element SLMSpatial Light Modulator CGHComputer Generated Hologram
# Double Unloading Gas Control Technology for Fracturing Soft Coal Seams in Overlying Key Strata
## 2 Method
### Hydraulic Fracturing Radius Determination
#### 2.3.3 Determination of Fracturing Radius  
As shown in Figure 3, different colors are used to indicate different rock layers, the roadway section is 4.5 m wide and 3.5 m high. The fracturing test borehole, labeled as 1, is located in the roadway gang and has a height of 1.5 m. It has an azimuth angle of 0\({}^{\circ}\) (angle with the center line of the roadway), an inclination angle of 12\({}^{\circ}\), and a depth of 52 m. The observation borehole, labeled as 2, is positioned 3 m horizontally from the fracturing borehole. It has a height of 1.5 m, an azimuth angle of 20\({}^{\circ}\), an inclination angle of 12\({}^{\circ}\), and a depth of 55 m. The water seepage zone of the observation borehole after fracturing is labeled as 3. 4 shows a diagram of a hydraulic fracturing unit. The maximum pumping pressure of the emulsion pump is 30 MPa, which fulfills the requirement for the fracturing pressure of the key layer of the top plate. To achieve automatic water pressure sealing and maintain the water injection pressure, the MA ZF-A64-enhanced ME rubber water injection sealing device shown in Figure 5 was used, with a length of 2 m and a maximum expansion diameter of 120 mm. 6 shows a graph of the change in pressure in the pressure gauge with time during the fracturing process. The on-site water injection process is divided into four stages, as shown in Figure 3: (a) the water pressure naturally rises and reaches equilibrium at approximately 3.5 MPa; (b) the water injection pressure of the pump is manually increased to 20.15 MPa; (c) the water injection pressure is maintained at around 18 MPa until the pressure plummets to ensure continuous water injection and sufficient extension of rock cracks; (d) the water level in the hole is observed, and the pressure is manually released to switch off the injection pump. The comprehensive analysis of water injection pressure, water injection time, location of fracturing section, and water seepage section indicates that the sandstone fracturing pressure is 20.15 MPa, the sandstone holding pressure stage requires continuous water injection for 17.5 min, and the hydraulic fracturing radius is 15 m.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality  
MR is a technological paradigm that merges physical and digital worlds to create a unified and interactive environment where real-world and virtual elements coexist and interact in real-time. Positioned along the virtuality continuum [2], MR spans the spectrum between totally physical environments and fully immersive virtual realities, encompassing technologies such as AR, AV, and VR. Unlike purely virtual experiences, MR enables users to interact with real and virtual objects simultaneously, offering immersive and contextually enriched experiences. By leveraging advanced sensors, display systems, and processing power, MR enables seamless integration between digital content and the physical world, facilitating different applications in many areas, like education, healthcare, entertainment, and industrial training.  
[PERSON]'s one-dimensional reality virtuality continuum provides an intelligible way to visualize the different concepts, as represented in Figure 1. The spectrum is a straight line with two ends. The physical and the virtual environments are placed on the line's left and right ends, respectively. AR lies towards the left end of the spectrum, while VR is at the right end. Although both these environments are MR systems, they are positioned differently on the \"Mixed Reality Spectrum\".  
The term \"Metaverse\" originates from the Greek prefix \"meta-\", meaning \"beyond\" or \"after\", combined with \"verse\", derived from \"universe\". This fusion represents a reality transcending the physical universe, encompassing immersive virtual environments or digital worlds. The Metaverse refers to a digital realm that operates in parallel with the physical world. Within this virtual space, users can interact and communicate with one another through personalized avatars, utilizing VR and AR technologies to immerse themselves in the experience. This concept is gradually becoming tangible, allowing individuals to feel present through the whole experience, even if they are physically elsewhere.  
The input devices, such as orientation trackers, magnetic trackers, and eye-tracking devices, coupled with the recent advancements in processing power, have made it possible to capture data about the physical environment and build a connection between humans, computers, and the environment by utilizing computer perceptions of the environment. The development of such systems has opened new doors for integrating virtual and physical worlds, creating a bridge between reality and virtuality, as shown in Figure 2.
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 2 Method and Experimental Results
### Proof-of-Concept Test Bench
The pipe and optical components are fixed on an optical breadboard mounted on a motorized, linear translation stage which, in turn, is connected to a large optical table. The breadboard can move along one direction around the stationary wire that is attached to the large optical table. This last feature will be used in the future to assess the feasibility of the sensor to measure transverse displacement of the electric field source. The first experimental results were obtained exploiting the working point WP1 and two different modulation amplitudes, \(E_{pp}=E_{\pi}\) and \(E_{pp}=0.6E_{\pi}\), in order to validate the analytical results. Figure 16 compares the laboratory measurements with the analytical results showed previously in Figure 8. The chosen modulation frequency of 600 Hz was far from the piezoamplifier limit to avoid errors on the measurement due to modulation distortion. The oscilloscope recorded the output signal at a 100 kHz sampling rate. The sensor measured an electric field ranging from 3 to 150 kV/m, limited at the lower end by the dynamic range of the acquisition system, and at the higher end by the maximum voltage we could apply to the rod.
## Abbreviations  
ADC Analog-to-digital converter  
AMCW Amplitude-modulated continuous wave  
ASPRS American Society for Photogrammetry and Remote Sensing  
BER Bit error rate  
CID Column identification number  
CRC Cyclic redundancy check  
DPIM Digital pulse interval modulation  
DPPM Differential pulse position modulation  
DH-PIM Dual-header pulse interval modulation  
DPTM Digital pulse time modulation  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Spreading Codes** & **OOK** & **PPM** & **DPPM** & **MPPM** & **DPIM** & **DH-PIM\({}_{2}\)** \\ \hline Block partitioning & **1:2:2:2:2** & **1:2:2:2** & **1:2:2:2** & **1:2:2:2** & **2:2:2** & **2:2:2** & **2:2:2** & **2:2:
# Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space
## 4 Discussion
### Extraction Error Analysis  
Taking the indoor corridor image as an example, when MSLSD constructs a multi-scale image space model and uses LSD algorithm to extract line features from down-scaled space, the extraction error of line features on the down-sampling image is larger than that on the original image. As is shown in Figure 18, Gaussian blur makes the image resolution lower; this is an indispensable step of model construction, causing the extraction error to be amplified.  
In the optimization and purification process, the proposed MSLSD method always takes the line segment on the original image as the reference, ensuring the extraction error from the low-resolution image is not introduced. Figure 19 is the result of MSLSD optimization, where we can see the method of this paper solved the line segmentation problem and ensured the accuracy of the final extracted line segments.  
Therefore, selecting the line segment extracted from the original image as the benchmark in the optimization and purification process can ensure the line segment extraction accuracy. It does not introduce additional error generated by the low-resolution image line segment extraction when constructing the multi-scale image space.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size** & **Large Resolution** & **High Resolution** & **Medium Resolution** & **Low Resolution** \\ \hline \multicolumn{5}{c}{\(\sigma\) = 0.5} \\ \hline Pixels & \(>\)10\({}^{7}\) & 10\({}^{6}\)–10\({}^{7}\) & 10\({}^{5}\)–10\({}^{6}\) & \(<\)10\({}^{5}\) \\ \(t_{\text{max}}\) & \(>\)3 & 3 & 2 & 1 \\ \(N\) & \(>\)4 & 4 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The setting rules of model parameters.  
Figure 18: Multi-scale image space LSD line feature extraction of an indoor corridor. The left image is the original image. The middle image is the image with down-sampling once. The right image is the image with down-sampling twice.
## 2 Observational and Modeling Design
### Numerical Model Setup  
To place the WCR observations in a dynamically consistent 3D context that is continuous in time and is not limited to the area of radar echoes, we ran a WRF simulation at 1 km resolution. The WRF model output will be used to examine the evolution of the downslope wind storm and the vertical structure of the atmosphere over a domain much larger than the domain of the flight tracks. Here we use WRF version 3.7.1 [37]. Three nested domains are used, with horizontal grid spacing (\(\Delta\)x = \(\Delta\)y) of 9 km (d01), 3 km (d02), and 1 km (d03) (Figure 1a). The model uses 61 terrain-following vertical levels in all three domains. The vertical grid spacing is about 19 m near the surface and increases roughly exponentially with height, but remains less than 200 m below 2 km above ground level (AGL).  
The North American Model (NAM) reanalysis, available at three-hourly intervals with 12 km resolution, was used to drive the model's initial and boundary conditions. Fluxes in the PBL are parameterized with the Mellor-Yamada-Janjic scheme (MYJ, [38]), while horizontal diffusion is handled with a Smagorinsky first-order closure scheme (Table 1). The WSM 6-class graupel scheme [39] is used for cloud microphysical processes and atmospheric radiation is treated with the RRTM scheme [40] for longwave radiation and the Dudhia [41] scheme for shortwave radiation. The simulation was initialized at 0000 UTC on 11 January 2013 and carried forward for 30 h. The first few hours of the run are regarded as spinup time and the model output data are analyzed from 1800 UTC of 11 January to 0400 UTC of 12 January, matching the observational period. Only inner domain (d03) model data are analyzed here. The WRF data will first be validated against independent radiosonde data, and then will be used to describe the downslope wind storm and terrain induced gravity waves, as seen in a \(\Delta\)x = 1 km simulation.
# DC Voltage Induces Quadratic Optical Nonlinearity in Ion-Exchanged Glasses at Room Temperature
## 3 Results and Discussion
### The EFISH Phenomenon in IE Glasses
(**b**) Schematic orientation of the nonlinearity in the specimen before (up) and after (down) the application of DC voltage. of the glass at room temperature is extremely low. Note that applying DC voltage to the virgin glass gives no optical nonlinearity [18]. Another important observation from Figure 1a is that the angular positions of the MF maxima/minima drastically changed after the IE specimen was exposed to DC voltage; i.e., the incidence angles previously corresponding to the minimum signals became corresponding to the maximum ones, and vice versa. Such changes can be caused only by the \(\pi\)-phase-shift of the interfacial/subsurface SH signals. This is basically a change in the sign of the SON of the interfaces/subsurface layers. We schematically demonstrate this change in Figure 1b. In the case of the virgin or uncharged IE glass, the SON is purely the surface one, and its sign is defined by the orientation of the corresponding interface. The input interface is air-glass, while the output one is glass-air. Therefore, the signs of their initial nonlinearity should be opposite (see the upper scheme in Figure 1b). On the other hand, in the case of the EFISH, the signs of the SON at the upper and lower sides of the specimen are defined by the direction of the applied electric field, which is the same for both subsurface regions (see the lower scheme in Figure 1b). Therefore, the observed change in the SON sign fits perfectly into the EFISH hypothesis. These findings could lead to a relatively simple control of the nonlinear optical properties of K-waveguides in glass [3] using an external DC voltage. Also, this allows for the analysis of concentration and conductivity profiles, the gradient of which is responsible for the Maxwell-Wagner charge and electric field formation and, consequently, for the SHG, using high-resolution optical SH microscopy [36], rather than other, often destructive, techniques.
## 4 Discussion
value, calculated in the optimal variant, does not always have to be lower than the average geometric coefficient value obtained in the continuous variant. It is noticeable for area A that the average PDOP value in the optimal variant is greater by approx. 6% than that in the continuous variant. As regards the availability of GNSS data, it is high during both measurement campaigns and ranges from 96 to 97%. The highest positioning availability (approx. 99%) was obtained in the area A. However, the lowest positioning availability (92-95%) was noted for the area C.  
In addition, it was decided to compare the average PDOP values obtained for the two commonly used positioning solutions, i.e., GPS and GPS/GLONASS. Based on Figure 15, it can be observed that the geometric coefficient values were definitely higher for the single-system solution than for the dual-system solution. The differences between them amounted to 23.67% for the first measurement campaign and 34.04% for the second measurement campaign. As regards the changes in the PDOP values in individual measurement areas (A, B and C), they were similar to each other. During the first measurement campaign, these changes ranged from 18.52 to 28.10%, while during the second measurement campaign, they oscillated between 33 and 43%. Therefore, it can be concluded that the average PDOP values in individual areas differ by only a few percent from the average geometric coefficient values calculated for all campaigns. Significant differences were observed for the positioning availability. For the GPS solution, this parameter ranged from 87.88 to 89.81%, while for the GPS/GLONASS variant, it oscillated between 96 and 97%. The greatest changes were noted for the area C, characterised by a very large number of field obstacles. In this case, the positioning availability fluctuated around 75%, while for the GPS/GLONASS solution, it reached a value of almost 92%.
## 3 Channel Model
### FSO Channel
#### 3.1.5 Doppler Effect  
We assume that in order to ensure the information transmission of the AWACS, the relay formation composed of UAVs and the AWACS remains relatively stationary at all times. Therefore, we do not take into account the Doppler effect in the AWACS-to-UAV link.  
The Doppler frequency shift between UAV-to-GS links is induced by the relative motion between the mobile GS and the UAVs, which is expressed as \(\Delta f=vf_{FSO}\cos(\xi_{GS})/c\), where \(v\) is the relative velocity between the \(i\) th UAV and GS, \(f_{FSO}\) is the optical carrier frequency, and \(c\) is the speed of light. The current designs of FSO receivers can correct for frequency shifts up to \(\pm 15\) GHz [17]. In other words, for an optical signal with \(\lambda=1550\) nm, if \(v<6458\) km/h, the resulting Doppler shift can be corrected. It is evident that the current UAV and mobile GS (i.e., vehicle) cannot exceed this value in terms of their relative speed. Therefore, the Doppler effect of the UAV-to-GS links can be ignored.
# Context-Aware Caching Distribution and UAV Deployment: A Game-Theoretic Approach
## 2 System Model and Problem Formulation
### Problem Formulation
Thus, we express the channel power gain as follows:  
\[g_{u_{m}m}=h\left(P\left(LOS\right)d_{u_{m}m}^{-a}+\eta P\left(NLOS\right)d_{ u_{m}m}^{-a}\right). \tag{12}\]  
The good arrangements of UAVs' caching contents and service locations will achieve an appropriate trade-off between a high successful communication probability and low transmission overhead. Therefore, we design the ratio of users' successful communication probability and transmission overhead as the performance indicators of UAVs' actions. We denote the performance function of UAV \(n\) as:  
\[u_{n}\left(a_{1},a_{2},...,a_{N}\right)=\sum\limits_{m\in S_{n}}\frac{P_{success,m}}{r_{m}(a)}, \tag{13}\]  
where \(S_{n}\) denotes the set of users that UAV \(n\) serves, \(r_{m}\) is the transmission overhead of user \(m\) to obtain requested files specified in Reference [24], \(P_{success,m}\) is the successful communication probability from UAV \(n\) to user \(m\). Thus, the performance of all UAVs can be measured by:  
\[U\left(a\right)=\sum\limits_{n\in\mathcal{N}}u_{n}\left(a_{1},a_{2},...,a_{N}\right), \tag{14}\]  
where \(a=\left(a_{1},a_{2},...,a_{N}\right)\) denotes the joint strategy profile of all UAVs. Therefore, the optimization object is to find the optimal strategy profile \(a^{*}=\left(a^{*}{}_{1},a^{*}{}_{2},...,a^{*}{}_{N}\right)\) which can maximize the performance of all UAVs, i.e.,  
\[ \begin{array}{rl}a^{*}=\arg_{a}\max U\left(a\right)=\arg_{a}\max\sum\limits_{n \in\mathcal{N}}u_{n}\left(a_{1},a_{2},...,a_{N}\right),\\ s.t.\quad c1:\ \ |c_{n}|=F\leq|f|\,,\\ c2:\ \ \ P_{success,m}>P_{threshold},\end{array}  \tag{15}\]  
where the condition \(c1\) reflects the fact that UAVs' cache space does not exceed the number of system files, \(P_{threshold}\) is the communication probability threshold, the condition \(c2\) indicates that the solution must ensure users have appropriate communication probability to communicate.
# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
## 5 Results and Discussions
Figure 10: Linear deviation from the reference path analyzed across four segments: (**a**) start to point 1 in remote-control mode; (**b**) point 1 to point 2 in autonomous mode; (**c**) point 2 to point 3 in remote-control mode; (**d**) point 3 to point 4 in autonomous mode. issued brake commands, the delay resulted in increased stopping distance, as the vehicle continued to travel at its last speed before responding to the command. Figure 12 demonstrates how braking distance under delay conditions correlates with speed and latency (400-500 ms), especially in the 0-3 m/s range. At this latency, the braking distance is nearly equal to the total distance required to stop, meaning sudden obstacles within that range could result in collisions before the operator visually registers the hazard. For latencies below 400 ms, operators may perceive obstacles in time but may still struggle to brake within the short available distance, requiring evasive steering. In some cases, the controller may be switched temporarily to autonomous mode to reduce reaction time. Based on transition tests, the highest latency recorded was 316.65 ms, and the average was 109.70 ms. With a vehicle speed of 3 m/s, braking distances at these latency values were 2.37 m and 1.75 m, respectively, as shown in Figure 13a,b. If an obstacle appears within Zone B (before the braking distance), a collision is likely to happen unless evasive action is taken. Conversely, obstacles in Zone A (after the braking point) can be safely avoided by braking alone.
# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
###### Abstract  
The temporal and spatial variability of the Antarctic coastline is a clear indicator of change in extent and mass balance of ice sheets and shelves. In this study, the Canny edge detector was utilized to automatically extract high-resolution information of the Antarctic coastline for 2005, 2010, and 2017, based on optical and microwave satellite data. In order to improve the accuracy of the extracted coastlines, we developed the Canny algorithm by automatically calculating the local low and high thresholds via the intensity histogram of each image to derive thresholds to distinguish ice sheet from water. A visual comparison between extracted coastlines and mosaics from remote sensing images shows good agreement. In addition, comparing manually extracted coastline, based on prior knowledge, the accuracy of planimetric position of automated extraction is better than two pixels of Landsat images (30 m resolution). Our study shows that the percentage of deviation (<100 m) between automatically and manually extracted coastlines in nine areas around the Antarctica is 92.32%, and the mean deviation is 38.15 m. Our results reveal that the length of coastline around Antarctica increased from 35,114 km in 2005 to 35,281 km in 2010, and again to 35,672 km in 2017. Meanwhile, the total area of the Antarctica varied slightly from \(1.3618\times 10^{7}\) km\({}^{2}\) (2005) to \(1.3537\times 10^{7}\) km\({}^{2}\) (2010) and \(1.3657\times 10^{7}\) km\({}^{2}\) (2017). We have found that the decline of the Antarctic area between 2005 and 2010 is related to the breakup of some individual ice shelves, mainly in the Antarctic Peninsula and off East Antarctica. We present a detailed analysis of the temporal and spatial change of coastline and area change for the six ice shelves that exhibited the largest change in the last decade. The largest area change (a loss of 4836 km\({}^{2}\)) occurred at the Wilkins Ice Shelf between 2005 and 2010.  
Antarctic coastline; coastline extraction; remote sensing; Canny algorithms; ice shelves 2019 11 1844 10.3390/rs11161844