esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Current Status and Limitations of Data Interoperability with Landsat and Sentinel
### Aquatic Applications: Potentials and Limitations
Such uncertainty products for Landsat-8 and Sentinel-2 are only possible when calibration uncertainties or instrument artifacts are well known. It is, thus, crucial to have accurate knowledge of radiometric performance of this constellation throughout its lifetime. Currently, preliminary vicarious calibrations [61] conducted for a handful of OLI [62] and MSI images [63] using in situ data autonomously measured at the Marine Optical Buoy (MOBY) and the Buoy for the Long Term Acquisition of Time Series (BOUSSOLE) site have yielded reasonable relative consistencies in TOA, \(R_{rs}\), and TSS products. Analyzing the near-simultaneous overpasses after vicarious calibration indicated that the corresponding products agree, on average, within 0.5% in TOA and 8% in using in situ measurements at the MOBY and the BOUSSOLE site with the red channel showing the largest differences. This largest discrepancy in the red channel is attributed to imperfect spectral-band adjustments in the red spectral bands. Figure 14: Time-series of chlorophyll-a products derived from Landsat-8 and Sentinel-2A/B. Figure 15 illustrates the Relative Spectral Responses (RSRs) (only within 600-700 nm) overlaid onto hyperspectral in situ \(R_{rs}\). where shifts in the spectral bands can be inferred [63]. The histograms in Figure 15 show the distributions of the ratios in the red channels, i.e., MSI to OLI, obtained from simulated/measured hyperspectral \(R_{rs}\) data [63]. The spread in the distribution indicates that if a median ratio (~0.9) is chosen for spectral band adjustments, errors are expected in intercomparisons. Overall, although the existing intercomparison exercise has minimal uncertainties [62; 64], there seems to be a need for further improvements in the treatment of differences in red channels in both the TOA and \(R_{rs}\) domains. With the foreseeable improvements, it is possible to utilize the approach to ensure consistency in radiometric responses in the future. Such efforts will become more critical as the instruments and onboard calibration devices age. Regardless of the radiometric performance of the instruments, it is worth noting that high-quality products (e.g., Chl) are nearly impossible over areas affected by haze in the sunlight region, i.e., the eastern portion of the scenes. In addition, differences in the SNRs may also contribute to differences in products at local scales, i.e., pixel-level. For instance, band ratio algorithms may accentuate random noise contributions leading to larger discrepancies in the products.
## 2 Lattice Boltzmann Method for Solids  
### Lattice Boltzmann Method  
The BE [34] describes the evolution of particles in the phase space and can be written as:  
\[\frac{\partial f}{\partial t}+\xi\cdot\
abla_{x}(f)+g\cdot\
abla_{\xi}(f)= \Omega(f,f), \tag{1}\]  
where \(g\) is the force field felt by particles, like gravity for mass particles, and the distribution \(f\) corresponds to the statistical distribution of particles over the phase space; so \(\xi\) is the particles speed, and \(\Omega\) is the collision-interaction operator representing the interactions between particles.  
Using the trapezoidal rule and a change of variable (\(f_{i}\) to \(\widehat{f}_{i}\) see Equation (A2) in Appendix A), a second-order discretization of the BE is done. Then, the time evolution can be simulated by a succession of the collision step and streaming step, described by the following famous equations which forms the core of the LBM and read:  
\[f_{i}^{\;c}(x,t)=f_{i}^{\;s}(x,t)-\Delta t\omega\Big{(}f_{i}^{\;s}(x,t)-f_{i}^ {\;(eq)}(x,t)\Big{)}-\Delta t\Big{(}1-\frac{\omega}{2}\Big{)}g_{i}(x,t), \tag{2}\]  
\[f_{i}^{\;s}(x+\xi_{i}\Delta t,t+\Delta t)=f_{i}^{\;c}(x,t). \tag{3}\]  
where the superscript \(c\) and \(s\) stands for collision and streaming, \(f_{i}\) and respectively \(g_{i}\) are the discretization over the velocity space of \(f\) and \(g\cdot\
abla_{\xi}(f)\) respectively, through a projection on the Hermite basis. \(\Delta t\) is the increment of time chosen, \(\xi_{i}\) is the result of the Gauss-Hermite quadrature, \(f_{i}^{\;(eq)}\) is the discretized equilibrium distribution function and \(\omega\) is the invert of the relaxation time for the system to converge toward the equilibrium. Onemight also note that in Equation (2) using a negative sign in the factor with the term \(g_{i}\) is not the most common convention, so readers should be careful to avoid sign mistakes.  
The change of variable used in the Equation (A3) also has an impact on the macroscopic variable. With the use of Equation (A2), one can compute:  
\[\sum_{i=0}^{q}f_{i} =\rho, \tag{4}\] \[\sum_{i=0}^{q}\xi_{i}f_{i} =\widehat{\rho v}=\rho v-\frac{\Delta t}{2}g_{i},\] (5) \[\sum_{i=0}^{q}\left(\xi_{i}-v\right)\otimes\left(\xi_{i}-v\right) f_{i} =\widehat{\Pi}=p^{(0)}-\left(1+\frac{\Delta t\omega}{2}\right) \Pi^{(1)}-\frac{\Delta t}{2}(g\otimes v+v\otimes g). \tag{6}\]  
In the last expression, \(\Pi^{(1)}\) is given by Equation (A27). These previous sums justify the correctional terms in the macroscopic speed and the \"lattice viscosity\" that has to be compensated when fitting the physical cinematic viscosity with the collision frequency \(\omega=\frac{2c_{s}{}^{2}}{c_{s}{}^{2}+2v}\). In the previous formula, \(c_{s}\) is the celerity of sound and is obtained by \(c_{s}{}^{2}=\frac{1}{3}R\theta\), where \(\theta\) is the temperature.  
Using [PERSON]'s approach [35], the projection of the forcing term on the Hermite basis at second order reads:  
\[g_{i}=\frac{w_{i}\rho}{c_{s}{}^{2}}\xi_{i}\cdot g+\frac{w_{i}\rho}{2c_{s}{}^{4 }}\bigg{[}(v\cdot\xi_{i})\xi_{i}-\frac{v}{c_{s}{}^{2}}\bigg{]}\cdot g. \tag{7}\]
# LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification
## 1 Introduction
LCF design models combine local context features and global context features to infer sentiment polarity of targeted aspect. * We introduce SRD to evaluate the dependency between contextual words and aspects. SRD is significant for figuring out local context, and features of contextual words in SRD threshold will be preserved and focused. * This paper implements CDM and CDW layer to enforce LCF design models to pay more attention to local context words of specific aspect. The CDM layer focuses on local context by masking output representations of less-semantic-relative contextual words. The CDW layer weakens the features of less-semantic-relative contextual words according to SRD. * Experiments conducted for ablated LCF design models to evaluate the significance and effectiveness of LCF design architectures. Besides, extra experiments are also carried out to evaluate the effectiveness of different SRD thresholds. Figure 1: Different influence of context words on sentiment polarity prediction. Only the impact of typical sentimental context words is demonstrated.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.8 Energy-Efficient Localization Based Routing Protocol (E2 LRP)  
In [154] the author proposed an energy efficient routing protocol based on localization (E2 LRP) for UWSNs. The proposed routing protocol uses three different kinds of beacon communicating nodes: (i) Unknown communicating nodes, (ii) Promoted beacon communicating nodes and (iii) Original communicating nodes. At three edge locations, a surface-based unknown communicating node can upgrade to a promoted beacon communicating node. The Tx anchor node follows normalized advancement connection matrix model, to forwarding communicating data packets to sensor Rx node. Furthermore, using the normalized advancement metric model, a next forwarder node is select on the basis of the distance from Tx anchor node to sensor Rx node and remaining energy of the network.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 4 Experiment
### Ablation Study
#### 4.5.2 Necessity of Regularization Map  
The regularization parameter \(\epsilon\) is applied in the guided filtering algorithm to regulate the extent of filtering. In more detail, a larger \(\epsilon\) causes more blurriness, while a smaller \(\epsilon\) preserves finer information in the image. To demonstrate the importance of the regularization map proposed in this paper, we apply various \(\epsilon\) values and the regularization map \(\mathcal{M}\) to the model for comparison. \(\mathcal{M}\) is the case where the parameter is a matrix. This means that when the parameter is \(\mathcal{M}\), each pixel has its unique regularization parameter. A visual example is given in Figure 6. The results indicate that the blurriness is significant when \(\epsilon=10^{-3}\), diminishing the clarity of the pillar's contours. Employing \(\epsilon=10^{-5}\) preserves more texture information but is less effective in removing noise from the wall area. Although smoothing is moderate when \(\epsilon=10^{-4}\), a closer inspection of Figure 6b,d reveals that a pixel-wise regularization map not only eliminates noise but also upholds more texture features in the dark areas. In general, using a regularized parameter matrix \(\mathcal{M}\) can enhance the effect more accurately than using a single parameter value.
# Study on Asymmetric Support of Anchor Cable with C-Shaped Tube in Inclined Coal Seam Roadway
## 3 Roadway Deformation Law in Inclined Coal Seam
### Supporting Reinforcement Scheme  
The coal seam primarily experiences the shear failure phenomenon of the 9-8032 roadway, combined with the _ACC's_ high shear performance. The _ACC_ asymmetric support reinforcement scheme is proposed, and the supporting effects of asymmetric support and _ACC_ supporting components are investigated. The _ACC_ symmetric support reinforcement scheme and the anchor cable asymmetric support reinforcement scheme were designed to compare the supporting effects.  
Two types of _ACCs_ were used in the reinforcement scheme, which are as follows:  
1. A \(\Phi\) 21.8 mm \(\times\) 10,300 mm _ACC_: it consists of a 21.8 mm \(\times\) 10,300 mm anchor cable and a 2000 mm C-shaped tube.
2. A \(\Phi\) 21.8 mm \(\times\) 4300 mm _ACC_: it consists of a 21.8 mm \(\times\) 4300 mm anchor cable and a 2000 mm C-shaped tube.  
1. Supporting reinforcement Scheme 1 To keep the initial support method unchanged, four _ACCs_ are added in each row on the top plate. The middle two _ACCs_ are 10,300 mm in length, and the _ACCs_ near both sides of the side are 4300 mm in length; the spacing is 1200 mm, and the row spacing is 800 mm.  
On the right side of the roadway, each row receives an addition of four _ACCs_, while on the left, each row receives an addition of two _ACCs_. The length of the _ACCs_ is 4300 mm, and the row spacing is 800 mm.  
The _ACC_ prestress is 200 kN. The first reinforcement scheme is shown in Figure 15a,c.
2. Supporting reinforcement Scheme 2 On the basis of unchanged initial support, the position and arrangement of the roof support are the same as in Scheme 1.  
On each row on either side of the roadway, three _ACCs_ with a length of 4300 mm are added, with a spacing of 800 mm and a row spacing of 800 mm. The angle between the _ACC_ near the roof and the floor and the horizontal direction on both sides is 15\({}^{\circ}\).  
Figure 14: Original rock stress isoline distribution diagram of surrounding rock before roadway excavation.  
The anchor cable prestress is 200 kN. The second reinforcement scheme is shown in Figure 15b,d.  
(3) Supporting reinforcement Scheme 3  
The supporting position and arrangement of roof and side are the same as in Scheme 1. The \(ACC\) is replaced with an anchor cable of the same length.
## 4 Conclusions  
In this paper, an indoor visible light fingerprint localization method based on MPA-LSSVM is proposed. A fingerprint database is constructed by using an illuminometer to receive light intensity data from LEDs, and a marine predator algorithm is introduced to optimize the hyperparameters of the least squares support vector machine to construct a localization model with the optimal hyper-parameters to achieve localization and reduce the time overhead. Experiments were conducted in an indoor environment of 1 m \(\times\) 1 m \(\times\) 1 m, and the results showed that the average error of the proposed method was 0.041 m, and the localization error of 96.7% of the test points did not exceed 0.1 m. Compared with several other algorithms, the MPA-LSSVM-based localization method has a higher localization accuracy as well as fewer iterations, which achieves high-precision localization and can meet the needs of most localization services. However, the methodology of this study still has some limitations: The experimental platform is relatively small; therefore, follow-up study should consider gradually expanding the experimental platform. Additionally, the study only discusses the situation of localization in two-dimensional planes; therefore, future studies should consider the situation of localization in three-dimensional space. Finally, the background of this paper is the LoS link; in follow-up studies, it should be considered to include the not-line-of-sight (NLoS) situation.  
Conceptualization, Y.M.; methodology, Y.M.; software, Y.M.; validation, Y.M.; formal analysis, Y.M.; investigation, Y.M.; resources, Y.M.; data curation, Y.M.; writing--original draft preparation, Y.M.; writing--review and editing, Y.M. and Y.D. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location Algorithm** & \
\begin{tabular}{c} **Max Localization** \\ **Error/m** \\ \end{tabular} &
\begin{tabular}{c} **Average Localization** \\ **Error/m** \\ \end{tabular} & **Iteration Times** \\ \hline MPA-LSSVM & 0.1213 & 0.041 & 21 \\ \hline GA-LSSVM & 0.1391 & 0.057 & 37 \\ \hline PSO-LSSVM & 0.1702 & 0.085 & 45 \\ \hline \hline \end{tabular}  
* The other three scenarios are discussed next, which include the following:  
One LED (lamp A) is on;  
Two LEDs (lamp A and lamp B or lamp A and lamp C) are on;  
Three LEDs (lamp A, lamp B, and lamp C) are on.  
Positioning experiments are carried out in these three cases, and the parameter settings as well as the positioning reference point settings were consistent with those in the previous paper, and each experiment was repeated 10 times, and the optimal results were taken for comparison. The average positioning errors for each case and for 90 percent of the positioning points are given in Table 5.  
\end{table}
Table 4: The positioning error and iteration times of three algorithms.
## 2 Data
### VIIIRS I-band Sensing Geometry and Active Fire Data  
S-NPP VIIRS is also in a sun-synchronous orbit, with a repeat cycle of 16 days [45]. Very similar to Aqua MODIS, S-NPP VIIRS flies over the equator at approximately 1:30 (descending orbit) and 13:30 (ascending orbit) local time [45]. The VIIRS imagery-resolution bands sense 32 375 m-pixel lines per scan with a field view of 112.56\({}^{\circ}\), and each scan line is composed of 6400 pixels with a swath width of 3000 km [45; 50]. The I-band pixel size generally increases along scan direction from nominal 375 m at nadir to \(\sim\)806 m at the scan edge (Figure 1) [45]. The growth rate of VIIRS pixel size with view angle is largely reduced relative to MODIS because of the VIIRS pixel aggregation scheme that aggregates three scan-direction samples from nadir to scan angle of 31.72\({}^{\circ}\) (three-sample zone) and subsequently two samples to scan angle of 44.86\({}^{\circ}\) (two-sample zone) and one sample to the scan edge (one-sample zone) [45]. Pixel size varies sharply at the boundaries between aggregation zones (Figure 1). Due to the onboard bow-tie deletion algorithm, repeat detections of the same fires from VIIRS adjacent scans are significantly fewer than MODIS [32]. Moreover, the wider swath width (3000 km), compared with MODIS swath (2340 km), enables VIIRS to provide a daily full coverage of the globe without any gaps. Thus, VIIRS can observe fires inside the equatorial MODIS swath gaps (Figure 2).  
The S-NPP VIIRS 375 m Level-2 active fire product (VNP14 IMG) for the first time improves the daily global active fire data to a sub-kilometer resolution. As with MODIS Level-2 active fire data, VNP14 IMG, a swath-level product (6 min), is produced in VIIRS I-band geometry. For each fire pixel, VNP14 IMG provides detection time, coordinates, and confidence, VZA, solar zenith angle, brightness temperatures at the I-4 (3.550-3.937 \(\upmu\)m) and I-5 (10.560-12.428 \(\upmu\)m) imagery bands, and day/night flag [41; 51]. Due to a relatively low saturation temperature of 367K, the S-NPP VIIRS I-4 band can be easily saturated over powerful fires, which makes it very difficult to directly retrieve FRP from radiance at I-4 band [51]. Thus, FRP recorded in the VNP14 IMG product is indirectly estimated using radiances of the collocated 750 m fire pixel and ambient non-fire pixels at the 4-\(\upmu\)m Moderate-resolution band (M-13, 3.987-4.145 \(\upmu\)m) [50; 51]. This study obtained the 375 m VNP14 IMG product from NASA's LAADS for the year 2017.  
Figure 2: Daytime active fire detections from Aqua MODIS (**a**) and Suomi National Polar-orbiting Partnership (S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) I-band (**b**) on January 16, 2017. The shading areas show areas observed by MODIS in three consecutive Aqua orbits across Africa. Note that VIIRS observations have full coverage and the gaps from MODIS are added in (**b**) for comparison purpose.
# Adaptive Versioning in Transactional Memory Systems +
## 5 Optimizations on Basic Adaptive Versioning
### Better Time Barrier Design  
The pseudocode of the better time barrier design is given in Algorithm 3. The objective of better time barrier design is to increase concurrency as opposed to the basic time barrier design. For this, we allow each transaction to start its execution (with possibly new versioning method) as soon as it becomes ready rather than waiting for other in-flight transactions to complete. Figure 5 illustrates the idea of better time barrier design. Consider a transaction \(T\). Let \(S_{j}\) be a set of transactions arrived before \(T\). Suppose current versioning method for executing transactions in \(S_{j}\) is \(V_{cur}=Lazy\) and new versioning method computed for transaction \(T\) is \(V_{new}=Eager\). Suppose the versioning method changed to \(Eager\) from \(Lazy\) after the transactions in \(S_{j}\) started execution (but not completed yet) and before \(T\) starts execution. In the basic time barrier design, \(T\) has to wait until all the transactions in \(S_{j}\) finish execution. In the better time barrier design, we ask \(T\) to start execution as soon as it is ready. If \(T\) does not conflict with any transaction in \(S_{j}\), then \(T\) continues its execution until it commits, otherwise, \(T\) aborts. In order to detect the possible conflict of \(T\) with the transactions in \(S_{j}\), we add a 1-bit _modify flag_ to each memory address that is going to be updated by the transactions in \(S_{j}\). The modify bit associated to a memory address is set to 1 at the start of a transaction if it is going to be updated and is reset back to 0 at the time of commit. If \(T\) conflicts with \(T^{\prime}\
otin S_{j}\), it is handled as per the contention management technique adapted in the design (e.g., suicide, kill etc).
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
###### Abstract  
Acoustic holography is an essential tool for controlling sound waves, generating highly complex and customizable sound fields, and enabling the visualization of sound fields. Based on acoustic sieve metasurfaces (ASMs), this paper proposes a theoretical design approach for zero-thickness broadband holograms. The ASM is a zero-thickness rigid screen with a large number of small holes that allow sound waves to pass through and produce the desired real image in the target plane. The hole arrangement rules are determined using a genetic algorithm and the Rayleigh-Sommerfeld theory. Because the wave from a hole has no extra phase or amplitude modulation, the intractable modulation dispersion can be physically avoided, allowing the proposed ASM-based hologram to potentially function in any frequency band as long as the condition of paraxial approximation is satisfied. Using a numerical simulation based on the combination of the finite element method (FEM) and the boundary element method (BEM), this research achieves broadband holographic imaging with a good effect. The proposed theoretical zero-thickness broadband hologram may provide new possibilities for acoustic holography applications.  
acoustic holography; metasurface; broadband hologram; ultrathin hologram; sound field control Article
## 3 Results
### \({}^{166}\)Ho Monte Carlo Simulations vs. \"Classical\" Dosimetric Approaches  
The dosimetric results for \({}^{166}\)Ho obtained from the MC simulation versus the tested approaches (Table 2) are shown in the correlation plot in Figure 2.  
The relative uncertainty of the MC absorbed dose values at a 95% confidence level was analogous to the previously analyzed paper on \({}^{90}\)Y [9], with a relative uncertainty of the mean absorbed dose of less than 5% for all the simulated LS cases.  
All tested methods severely underestimate the MC results, with an RD of approximately \(-\)64% for MIRD, \(-\)93% for kST, \(-\)56% for \(kST_{L}\), \(-\)76% for kLT, \(-\)68% for \(kLT_{221}\), and \(-\)60% for \(kLT_{L}\). The RD results are reported in Table 6, while  
For kST\({}_{L}\) and kLT\({}_{L}\), which showed a lower RD with respect to the reference MC simulation, an example slice in the coronal view of the AD distributions and the DVHs were compared with the MC data (Figure 3) for LS = 10% only.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Lung Shunt** & **MC** & **MIRD** & **kST** & **kST\({}_{L}\)** & **kLT** & **kLT\({}_{221}\)** & **kLT\({}_{L}\)** \\ \hline
10\% & 6.2 & 2.2 & 0.5 & 2.7 & 1.4 & 1.9 & 2.4 \\
20\% & 12.0 & 4.4 & 0.9 & 5.3 & 2.8 & 3.8 & 4.9 \\
30\% & 17.9 & 6.5 & 1.3 & 7.9 & 4.3 & 5.7 & 7.3 \\
40\% & 23.9 & 8.7 & 1.8 & 10.5 & 5.7 & 7.6 & 9.7 \\ \hline \end{tabular}
\end{table}
Table 7: Lungs’ \(\overline{AD}\) of the reference MC simulation along with the tested methods’ \(\overline{AD}\) for all the LS cases in units of Gy per GBq of administered activity, as visually represented in Figure 2.  
Figure 3: Example slices in the coronal view of the AD spatial distributions (left) for kLT\({}_{L}\) (**a**), kST\({}_{L}\) (**b**), and MC (**c**), along with their respective color scales, are shown, whereas the plot (right) reports the corresponding DVH (**d**). All dosimetric approaches show significant heterogeneity in the AD spatial distribution. The values of the AD maps and the DVH are given in Gy per GBq of administered activity.  
Figure 2: Correlation plot of the mean absorbed dose in the lungs (\(\overline{AD}\)) per GBq of administered activity, obtained from MC simulations with the reference phantom (x-axis), compared with those obtained using the methods listed in Table 2 (y-axis). Each point in the data series represents an increasing LS value (10%, 20%, 30%, and 40%), with a line representing the linear interpolation of each dataset, provided as a qualitative visual guide only.  
The homogeneity of the distribution varies for each method due to the different radiation transport computations performed by each approach, with HI values of 4.6 for KST\({}_{L}\), 5.6 for kLT\({}_{L}\), and 6.1 for MC.