esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Traditional Dark Channel Prior
### Estimation of Transmission Map  
Medium transmission \(t(\mathbf{x})\)[1; 5] is expressed by  
\[t(\mathbf{x})=\mathrm{e}^{-\beta t(\mathbf{x})}, \tag{2}\]  
Figure 1: Examples of original haze image and proposed haze-removal image and transmission map (\(\gamma=0.5\)).  
where \(\beta\) is the scattering coefficient of the atmosphere and \(d(\mathbf{x})\) is the depth at coordinate \(\mathbf{x}\). [PERSON] et al. [5] used DCP, indicating that at least one RGB colour channel within most local patch has a low-intensity value  
\[DC\left(\mathbf{J}(\mathbf{x})\right)=\min_{\mathbf{y}\in\Omega(\mathbf{x})} \left(\min_{c\in\{r,g,b\}}\left(J^{c}(\mathbf{y})\right)\right), \tag{3}\]  
where \(J^{c}(\mathbf{y})\) is a colour channel of haze-free image \(\mathbf{J}(\mathbf{y})\) at coordinate \(\mathbf{y}\) and \(DC\) is the dark-channel operator which extracts a mimimum RGB colour channel in a local patch \(\Omega(\mathbf{x})\) centered at coordinate \(\mathbf{x}\). From Equations (1) and (3) can be rewritten as  
\[DC\left(\frac{\mathbf{I}(\mathbf{x})}{\mathbf{A}}\right)=\tilde{t}(\mathbf{x} )DC\left(\frac{\mathbf{J}(\mathbf{x})}{\mathbf{A}}\right)+\left(1-\tilde{t}( \mathbf{x})\right), \tag{4}\]  
where \(\tilde{t}(\mathbf{x})\) is the coarse transmission map based on patch and the argument \(\mathbf{I}(\mathbf{x})/\mathbf{A}\) and \(\mathbf{J}(\mathbf{x})/\mathbf{A}\) are to be element-wise division. If \(\Omega(\mathbf{x})\) is set to large patch size (e.g., \(15\times 15\)), \(DC(\mathbf{J}(\mathbf{x})/\mathbf{A})\) should tend to be zero. Finally, the transmission \(\tilde{t}(\mathbf{x})\) can be estimated by Equation (5).  
\[\tilde{t}(\mathbf{x})=1-\omega DC\left(\frac{\mathbf{I}(\mathbf{x})}{\mathbf{ A}}\right). \tag{5}\]  
where \(\omega\) is the haze removal rate which is considered to the human perception for depth scene (0.95 in the [PERSON] et al. [5]). Since \(\tilde{t}(\mathbf{x})\) is calculated by each large patch to satisfy the DCP, \(\tilde{t}(\mathbf{x})\) is not smooth in edge region and the spatial resolution is lost. To solve the problem, [PERSON] et al. [5] refined the transmission map \(\tilde{t}(\mathbf{x})\) using image-matting processing [14] as post-processing. However, such processing requires high computational cost and several tens of seconds to execute the haze-removal method.
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 3 Survey of Signal Processing Units based on FBGs
### Optical Differentiators
Figure 7: First-order optical differentiator based on a transmissive FBG: **(a)** Grating period (black dashed line) and apodization (red solid line) profiles considering linearly chirped phase mask and representative spatial resolution (0.3 mm); **(b)** Targeted transmission spectral response, phase (dotted green line) and amplitude (dotted blue line) and corresponding spectral response limited to fabrication constraints, in phase (solid black line) and amplitude (solid red line). The FBG-based optical differentiator performance is mainly degraded for inputs whose bandwidth is below 0.5 THz, while preserving an excellent behavior for input pulses with a 3 dB-bandwidth ranging between 0.78 and 1.55 THz. This fact translates into a TBP-2 calculated as the ratio between the maximum and the minimum input pulse 3 dB-bandwidth with \(C_{C}\) higher than 99.5%). This degradation is predominantly attributed to imperfections of the fabrication-constrained device spectral response nearby the transmission resonance notch. Higher order (up to \(N=4\)) optical differentiators with optical bandwidth of 2 THz were also designed based on the same technique [61]. Results are depicted in Figure 9. Numerical simulations are employed to assess the output temporal waveforms when an input pulse, Gaussian-like, with 850 fs-FWHM is considered. The first- to-fourth order derivatives of the input pulse are shown in Figure 9b, where the output of the ideal differentiators are also depicted in black, validating their excellent operation. High-order differentiators and fractional-order differentiators (i.e., those in which the parameter \(N\) is not an integer number) have been also presented based on FBGs in transmission, but obtaining a smart-engineered apodization phase modulation, based on a two-step nonlinear optimization algorithm, instead of a linear chirp [62; 63].
## Abbreviations  
* AAPM TG218 American Association of Physicists in Medicine Task Group 218
* ALARA As Low as reasonably achievable
* ASC Aperture shape controller
* ASC_H Aperture shape controller high
* ASC_L Aperture shape controller low
* ASC_M Aperture shape controller medium
* ASC_VH Aperture shape controller very high
* ASC_VL Aperture shape controller very low
* CTV Clinical target volume
* DVC Dose volume constraint
* EPID Electronic portal imaging device
* ESAPI Eclipse scripting API
* GPR Gamma passing rate
* IMRT Intensity modulated radiation therapy
* IQR Interquartile range
* MCS Modulation complexity score
* mGPR Measured gamma passing rate
* ML Machine learning
* MLC Multileaf collimator
* Monitor units
* MUlimit Monitor unit limitation
* MRO Manual re-optimization
## 6 Conclusions  
In this paper, the effects of the tilted angle on the channel capacity of UWOC system with APD receiver adopting OOK modulation were analyzed. For this purpose, a mathematical system model suitable for describing the signal transmission in clear ocean water links with APD receiver and path loss with pointing error was established first; then closed-form expressions of the capacity lower bounds for UWOC with APD under the non-negativity, peak power, and average power constraints were derived in detail, which indicate that, no matter what the average-to-power ratios are, the capacity lower bounds are always monotonically increasing function with respect to the channel fading; based on these results, by relating the tilting angle of the receiver to the channel fading, an optimization problem of maximizing the channel capacity of UWOC system with APD via tilting  
Figure 4: Capacity lower bounds with respect to the coordinates of the receiver (**a**) before tilting the receiver; (**b**) after optimally tilting the receiver.  
Figure 5: Optimal tilting angle distribution versus the receiver position on the X-Y plane.  
the receiver was proposed and solved theoretically. Both analysis and simulation results show that we can reasonably tilt the receiver's plane according to the solved optimum tilting angle, thus greatly improving the channel capacity of UWOC system.  
Conceptualization, M.J.; methodology, M.J. and H.S.; software, H.S.; validation, P.H. and G.T.; formal analysis, Y.L.; writing--original draft preparation, H.S.; writing--review and editing, Y.L. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Fundamental Research Funds for the Central Universities (No. 2019B15714) and the National Natural Science Foundation of China (No. 61832005).  
The authors declare no conflict of interest.
# Application of the Extended Reality Technology for Teaching New Languages: A Systematic Review
## 3 Results
### Motivating Students to Learn a New Language  
[PERSON] and [PERSON] [29] examined the positive impacts of a VR creative project on English language learners' creative self-efficacy, inspiration, and motivation toward using VR technology in teaching university students. They found that VR technology can motivate students to participate in learning activities and it helped improve students' writing performance [36]. Studies point out that computer game-based lessons help with vocabulary acquisition, vocabulary learning and memory, vocabulary retention, writing tasks, and had a motivational effect on students' attitudes [26]. Virtual multimodal teaching and learning environments can invoke less proficient students' attention and motivation, and enhance collaboration and language performance [37]. Studies into the use of AR mobile language learning tools suggest that an AR-based learning tool 'Explorez' can help bridge the gap between gaming and education, making language learning more motivating and exciting [38].
# Implementation of Fault-Tolerant Control for
## 5 Simulation Results
### Simulation 1  
In this part, the parameter selection of the extended state observer as seen in Equation (10) is explained. The total torque function at each joint is assumed to be  
\[\left\{\begin{array}{l}\tau_{1}^{t}=\tau_{1}\\ \tau_{2}^{t}=(1-\rho_{2}(t))\tau_{2}+f_{2}(t)\quad t>5\\ \tau_{3}^{t}=\tau_{3}\end{array}\right. \tag{45}\]  
where for loss of effectiveness, \(\rho_{2}(t)=0.4\sin(\pi t)\), and a fault function \(f_{2}(t)=60\sin(\pi t/2)\).  
For the extended state observer of Equation (10), the four parameters such as \(\alpha_{1},\alpha_{2},\alpha_{3}\) and \(\varepsilon\) should be suitably selected. The turning parameters in the set of \(\alpha_{1},\alpha_{2},\alpha_{3}\) that should satisfy the Hurwitz condition are quite related to the qualities of the observer such as the accuracy and peaking values. From this consideration, the turning parameters were set as \(\alpha_{1}=8,\alpha_{2}=28,\alpha_{3}=7\). For \(\varepsilon\), Equation (17) shows that the value of \(\varepsilon\) highly affects the behavior of the observer convergence. Therefore, three different values of \(\varepsilon=0.1,\ 0.01\), and \(0.001\) were considered in this simulation. In Figure 2b, it can be seen that the peaking value highly defends the selection of \(\varepsilon\). The smaller value of \(\varepsilon\) causes faster convergence, but the high magnitude peaking value in the fault estimation. As a trade-off between the convergence and the peaking value, \(\varepsilon=0.01\) was selected.
Sensors_**2012**, _12_, 10511-10535; doi:10.3390/s120810511  
_Setisos_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Cross Layer Adaptation of Check Intervals in Low Power Listening MAC Protocols for Lifetime Improvement in Wireless Sensor Networks**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2,3}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Computer Science Department, University Carlos III of Madrid, Avda. Universidad 30,  
Madrid 28911, Spain; E-Mails: [EMAIL_ADDRESS] (J.C.);  
[EMAIL_ADDRESS] (M.-C.M.)  
\({}^{2}\) Computer Science Department, University of Pisa, Largo B. Pontecorvo 3, Pisa 56127, Italy;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) ISTI-CNR, via G.Moruzzi 1, 56124 Pisa, Italy  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]l.: +34-91-624-91-43.  
_Received: 16 July 2012; in revised form: 16 July 2012 / Accepted: 27 July 2012 / Published: 2 August 2012_  
**Abstract:** Preamble sampling-based MAC protocols designed for Wireless Sensor Networks (WSN) are aimed at prolonging the lifetime of the nodes by scheduling their times of activity. This scheduling exploits node synchronization to find the right trade-off between energy consumption and delay. In this paper we consider the problem of node synchronization in preamble sampling protocols. We propose Cross Layer Adaptation of Check intervals (CLAC), a novel protocol intended to reduce the energy consumption of the nodes without significantly increasing the delay. Our protocol modifies the scheduling of the nodes based on estimating the delay experienced by a packet that travels along a multi-hop path. CLAC uses routing and MAC layer information to compute a delay that matches the packet arrival time. We have implemented CLAC on top of well-known routing and MAC protocols for WSN, and we have evaluated our implementation using the Avrora simulator. The simulation results confirm that CLAC improves the network lifetime at no additional packet loss and without affecting the end-to-end delay.  
**Keywords:** communication latency; synchronization; preamble sampling protocols; adaptive scheduling; wireless sensor networks
## 3 Results  
### Stability Analysis of Multi-Temporal Calibration Results  
According to the processing steps described in the previous section, the comprehensive gains of three CMOS sensors on board the OHS-F satellite were calculated. The SD and RB were also calculated using the comprehensive gains of different CMOS sensors on different dates. The stability of the calibration results of each CMOS sensor was analyzed based on these calculation results.
## 3 Conventional SFO Estimation Scheme
### Conventional Scheme A  
From previous work [23], we consider the post-FFT correlation between two OFDM symbols at subcarrier \(k_{i}\), which is given by  
\[\Lambda_{m}=\sum_{i=1}^{N_{c}/2}\hat{R}_{l}^{*}(k_{i+(m-1)N/2})\hat{R}_{l+D}(k _{i+(m-1)N/2}),\ m=0,1 \tag{3}\]  
with  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **PHY Rate** & **\# Repeat Copies** \\ \hline Mini ROBO & 3.8 Mbps & 5 \\ Standard ROBO & 4.9 Mbps & 4 \\ High-speed ROBO & 9.8 Mbps & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Robust orthogonal frequency division multiplexing (ROBO) mode in HomePlug Green PHY (GP).  
\[\begin{split}\hat{R}_{l}^{*}(k_{i})&=R_{l}(k_{i})/\hat{ X}_{l}(k_{i})\\ &=H_{l}(k_{i})e^{j2\pi\phi(k_{i})(IN_{u}+N_{g})/N}e^{j\pi(N-1)\phi(k_{i})/N}+I_{l}(k_{i})/\hat{X}_{l}(k_{i})+Z_{l}(k_{i})/ \hat{X}_{l}(k_{i})+\epsilon_{l}(k_{i})\end{split} \tag{4}\]  
where \(N_{c}\) is the number of non-masked carriers used for data transmission, \(k_{i}\) is the subcarrier index of the \(i\)-th non-masked carrier, \(\hat{X}_{l}(k_{i})\) is the decided data symbol in subcarrier \(k_{i}\), \(D\) is the time separation between the OFDM symbols, and \(\epsilon_{l}(k_{i})\) is the decision error given by  
\[\epsilon_{l}(k_{i})=H_{l}(k_{i})\frac{X_{l}(k_{i})-\hat{X}_{l}(k_{i})}{\hat{X }_{l}(k_{i})}e^{j2\pi\phi(k_{i})(IN_{u}+N_{g})/N}e^{j\pi(N-1)\phi(k_{i})/N}. \tag{5}\]  
When the additive noise is very large, the phase difference between two OFDM symbols is relatively small, which leads to poor frequency-offset estimation. To resolve this problem, \(D\)-symbol delay estimation is a promising strategy as adopted in (3). To obtain \(\hat{X}_{l}(k)\), we can use simple hard data decision. Practically, it is reasonable to use the hard decision as discussed in former work [23]. By using two consecutive data-recovered OFDM symbols, the basic DD concept can be extended to the OFDM-based PLC system. The estimate of \(\Delta_{s}\) is then obtained as  
\[\hat{\Delta}_{s}=\frac{N}{2\pi N_{u}}\frac{\arg\{\Lambda_{2}\}-\arg\{\Lambda_{ 1}\}}{k_{N/2+1}-k_{1}} \tag{6}\]  
where \(\arg\{x\}\) denotes the angle of a complex number \(x\). As discussed [23], Equation (6) provides an unbiased estimate in the presence of small SFO. More importantly, the unbiasedness of Equation (6) holds true when the subcarriers used for estimation have a symmetric and uniform distribution around the DC carrier. However, this condition is no longer met depending on the transmit spectrum mask, which results in biased estimates.
## 5 IoT Platform Communication with IoT Sensors and Applications
The data in the NIDD system are transferred to the servers (applications and services) using an SCEF or SGi interface. Before the data are delivered, the IP is encapsulated by P-GW in the SGi interface. The control plane optimization process includes the delivery of small NB-IoT data on the control plane using a signaling radio bearer without creating a data radio bearer in the radio link. The IP NB-IoT data are delivered to the MME as the NAS PDU and through the SCEF or S/P-GW. The SCEF allows the delivery of the non-IP data only. As already mentioned, the S/P-GW performs the first IP encapsulation stage of the non-IP data. With the optimization of the user plane, similarly to LTE, the NB-IoT data are delivered on the user plane. In both 'connected' and 'idle' states, the context of the user equipment is stored in the device and the base station to enable lower signaling rates [44].
## 4 Structural Sensitivity Analysis
### Sensitivity Analysis of the Maximum Deflection to the Bulk Density of the Main Beam  
The concrete is composed of coarse aggregate, sand, water, etc. in a certain proportion of mixing. The mixture ratio is determined in the design stage, but the amount of material and the specified value is difficult to be the same in the construction process, which will lead to changes in the concrete bulk density. For consideration of structural safety, it is necessary to analyze the sensitivity of the bulk density of the main beam.  
As plotted in Figure 7, at the state of the maximum cantilever, as the increased bulk weight of the main beam leads to the increasing dead weight and the original prestressed reinforcing bars are insufficient, the closing opening of each span will bend down with the  
Figure 6: Full-scale finite element model.  
increasing bulk weight, and the trend of bending down is linear. The relationship between the deflection and bulk density of each span is different. The deflection at the second span is smaller than that at the other four spans. With the variation of the bulk density, the variation of the maximum deflection at the mid-span position of each span increases (or decreases) with the increasing (or decreasing) bulk density, as shown in Figure 8. Under the condition of the maximum cantilever stage, the variation of maximum deflection at the mid-span position of each span varies linearly with the bulk density. Comparatively, the deflections at the first and second spans are less sensitive to the variation of the bulk density than the other three spans. The deflection at the fifth span is the most sensitive, i.e., the deflection changes 26% for every 5% change in bulk density. Above all, the change in bulk density has a great influence on the linear shape of the bridge, which will lead to a great deviation between the linear shape of the completed bridge and the theoretical situation. Therefore, the sensitivity analysis of bulk density provides guidance and a decision-making basis, which is helpful to optimize the design, improve the engineering quality, and ensure the safety of the project.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 4 Problem Formulation and Performance Metrics  
Based on the model and various performance metrics proposed in the previous section, we divide this section into four parts:  
* Calculate the uplink transmission latency between the UAV and each vehicle based on the UAV-Assisted Vehicular Channel Model from the previous section.
* Calculate the overall MAC layer service time based on the uplink transmission latency and the MAC protocol.
* Calculate the queuing time and overall system dwell time based on the Preemptive-Resume Priority rule and service time.
* Calculate the BSM transmission interval for vehicles within the UAV coverage area based on the system dwell time.
* Next, we will introduce these three parts separately. Let T denote the total communication delay between vehicles and the UAV.
## 3 Results and Discussion
### Influence of Free Surfaces on the Performance of Cutting Head  
#### 3.4.1 Free Surface at the Top Side of Rock Mass  
According to [PERSON] et al. [18], the free surface on rock mass would be helpful for the reduction of cutting force on the conical pick. However, the reduction of cutting force in conditions with different free surfaces might be different. When the boundary condition of rock mass is like that shown in Figure 3b, and the top side of the rock mass is the free surface, deformations of rock mass in the y- and z-direction are shown in Figure 16. For the deformation of rock mass shown in Figure 16, it could be found that:  
Figure 15: Stress and strain of rock under clockwise and anti-clockwise cutting directions. (**a**) effective stress; (**b**) maximum principal stress; (**c**) minimum principal stress; (**d**) effective plastic strain.  
1. For the deformation of rock mass in the \(y\)-direction, they can be divided into two parts based on sign: in the regions near the top of the rock mass, the displacement of the rock mass is positive; In other areas, the displacement of the rock mass is negative;
2. For the deformation of rock mass in the \(z\)-_direction_, the displacement of the rock near the free surface is the highest, while it gradually decreases as it moves away from the free surface at other positions;
3. The deformation of the rock mass near the rear side is negative, while deformation close to the front side is positive. The reasons for this phenomenon are as follows: (1) The rock mass close to the front side is more prone to deformation due to its proximity to the free surface, and the deformation follows the positive direction of the \(z\)-_axis_. (2) During the cutting process, the end plate of the cutting head would compress the rock mass close to the rear side, causing it to move along the opposite direction of the \(z\)-axis. However, due to the fully constrained rear side of the rock mass, the element displacements of the rock mass close to the rear side are smaller than the element displacements of the rock mass close to the front side.  
Figure 17 shows the comparisons of cutting force in the \(y\)-directions if the top side of the rock mass is a free surface. According to the figure, the cutting force in the \(y\)-direction under the clockwise cutting condition is higher than that under the counterclockwise cutting condition. In terms of their mean results, the mean cutting force under the clockwise cutting process is about 1961 kN while the mean cutting force under the counterclockwise cutting condition is about 1845 kN. By comparison of the mean cutting force under the counterclockwise cutting condition, the mean cutting force under the clockwise cutting condition increased by about 116 kN, an increase of about 6.28%.