esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Future Directions
Moreover, Sentinel-2 SR observations are typically acquired at consistent local solar times, providing temporal regularity across scenes. Our model includes solar angle parameters, and is trained on a large, diverse dataset spanning a wide range of times, geographic locations, and atmospheric conditions. This diversity enables the model to implicitly learn the stable surface signal embedded in SR. Also, the intra-day variation in SR due to changes in solar geometry is generally small compared to the variation in SSI introduced by dynamic atmospheric conditions such as clouds and aerosols. In fact we evaluated the model's performance with and without the SR bands as input features. Figures 19 and 20 below show respectively the variation in R\({}^{2}\) and MSE evaluated on the testing set across the wavelength range for both approaches. It is evident that incorporating SR leads to improved model performance. This serves as an example of effectively downscaling coarse-temporal-resolution satellite data to derive parameters with high temporal resolution. While the use of SR as a stable predictor is both logical and empirically validated by the model's improved performance, it is not without limitations in the operational context. Satellite-derived data can be obstructed and unavailable in heavy, opaque cloud conditions. Since the SR input is available at a 5-day resolution and given that the temporal variability of SR is typically slower than the daily scale, occasional cloudiness on random days is unlikely to affect operation. However, in geographical locations that experience persistent cloud cover over extended periods (e.g., monsoon regions during monsoon seasons), the absence of recent SR observations could hinder the model's ability to continuously estimate SSI. This constitutes a key limitation in our approach. In such regions, an alternative may be to replace the missing SR instances with monthly averaged values. However, this should be done only after a careful analysis of the temporal variability of SR and its stability over time in the specific region. We also explored integrating split conformal prediction into our ML model to enable uncertainty quantification. This approach yielded statistically valid and meaningful uncertainty quantification across the majority of the spectral range without compromising predictive accuracy. However, for the lower wavelength bins, the resulting interval could often be disproportionately wide relative to the smaller target values, rendering them less informative. As part of future work, we aim to investigate more adaptive conformal prediction techniques that utilize scaled nonconformity scores, such as locally adaptive conformal regression [66], to improve the calibration of uncertainty estimates across the entire spectral range.
## 2 Communication Network Infrastructure
### Lagrange Subnet Routers (LSR)  
These satellites operate similarly to Low Earth Orbit Relay Satellites (LEO Relay Satellites) and are crucial as they will be responsible for relaying the information collected from the PMNs to the ABR satellite. Therefore, in terms of communication payload, they are equipped with transponders and signal amplifiers capable of fulfilling the data relay objective. The LSRs have solar power systems and batteries to ensure proper power supply for their payload and navigation system.
## 3 Results and Discussion
### Purification of Crude Phycobiliprotein Extract
#### 3.3.1 Purification of Phycobiliprotein-Rich Extract by Ammonium Sulphate Fractionation  
The ammonium sulphate precipitation technique is widely employed for protein purification and has also been used for PC. Ammonium sulphate fractionation was performed on the crude PC extract after the optimal extraction to remove impurities and improve the purity of the extract. An increase in ammonium sulphate content leads to protein precipitation, and a two-step precipitation was performed, first with 20% ammonium sulphate to remove impurities; in this step, a green precipitate was obtained and removed. As depicted in Figure 5, the purity after this step shifted from 1.12 \(\pm\) 0.06 to 1.44 \(\pm\) 0.06 (\(p\) < 0.05). In the following second step--precipitation with 70% ammonium sulphate-- a dark blue precipitate was obtained, drastically increasing purity to 2.32 \(\pm\) 0.06 (\(\sim\)two-fold increase).  
Previously, in studies using the same concentrations of ammonium sulphate (20% and 70%), authors have reported that the purity of crude PC extracts from Nostoc sp. increased from 0.6 to 0.9 (1.5-fold increase) after two-step fractionation [60]. Moreover, [PERSON] et al., utilising different ammonium sulphate saturation rates, reported that the 25-70% saturation led to the most significant increase (from 0.6 to 1.8, a three-fold increase) of _A. platensis_ extract [31].  
As mentioned earlier, high PC recovery is also important, besides purity, when developing a purification strategy. As can be seen in Figure 5B in this study, the following approach resulted in small losses during fractionation, less than 4% in the first step and \(\sim\)7% of the total after the second step. This result is in close agreement with a previous study by [PERSON] et al., where the authors reported a 91.2% PC recovery after precipitation  
Figure 5: Purity after ammonium sulphate fractionation (**A**). Phycocyanin and alolphycocyanin recovery after purification with different ammonium sulphate concentrations (**B**). Image of the purified extract after 70% ammonium sulphate (**C**). Values are expressed as mean \(\pm\) standard deviation, n = 3. Different lowercase letters indicate significant differences (\(p\) < 0.05).  
with ammonium sulphate (50%) [61]. Nevertheless, it is worth mentioning that ammonium sulphate fractionation has also been combined with other purification techniques, including dialysis, ion exchange chromatography, and gel filtration, to obtain even higher purity of reactive-grade (>3.9) [62; 63]. Increasing the purity of the extract ensures the removal of contaminants and impurities, enhancing the quality of the obtained extracts. Extracts with high purity allow researchers to evaluate compounds' potential benefits and bioavailability. However, using a combination of these purification techniques leads to a lower recovery, which makes such purification methods extremely expensive and consumes a lot of time and resources.
## 2 Materials and Methods
### Airborne Hyperspectral Imagery (HSI)

Table 1: Date of SASI HSI image acquisitions with corresponding solar zenith angle (SZA), solar azimuth angle (SAA), and solar azimuth angle relative to aircraft (sensor) heading (RAA). All flights were conducted at an altitude of approximately 1000 m AGL. Angles are reported in degrees. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **SZA** & **SAA** & **RAA** \\ \hline \multicolumn{4}{c}{**Spring**} \\ \hline
15 April 2016 & 54.7 & 182.1 & 16.9 \\
20 April 2016 * & 35.5 & 157.0 & 172.1 \\
11 May 2016 * & 30.1 & 150.5 & 169.0 \\
24 May 2016 * & 26.4 & 154.1 & 174.9 \\ \hline \multicolumn{4}{c}{**Summer**} \\ \hline
10 June 2016 & 24.7 & 149.5 & 171.8 \\
23 June 2016 * & 29.0 & 131.3 & 151.8 \\
19 August 2016 & 48.6 & 133.3 & 147.8 \\ \hline \multicolumn{4}{c}{**Fall**} \\ \hline
23 September 2016 & 35.2 & 216.9 & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Date of compact airborne spectrographic imager (CASI) HSI image acquisitions with corresponding solar zenith angle (SZA), solar azimuth angle (SAA), and solar azimuth angle relative to aircraft (sensor) heading (RAA). Flights were conducted at an altitude of approximately 1000 m AGL. * Denotes full mosaics of 12 flight lines. For the mosaics, the values reported are for the flight line centered over the eddy covariance tower. All angles are reported in degrees. assessment and correction of the spectral alignment and a removal of signal offsets inherent in the recorded digital pixel values (i.e., electronic offset, dark current, frame shift smear, scattered light, and, for the CASI, only a second-order diffracted light correction) [38; 39; 40; 41]. Further preprocessing steps included spectroradiometric calibration, spectral resampling to remove the laboratory-identified spectral smile, and geocorrection. A single CASI flight line (23 September 2016) was not geocorrectable due to an error in the IMU data. For the four CASI mosaics, we used a \"minimize view zenith angle\" option to select which of the duplicate pixels located in the overlap between adjacent flight lines are applied in the resulting mosaic imagery. During geocorrection, all HSI flight lines were resampled to 1 m pixel size. Mission planning attempted to acquire the HSI lines coincident with the Sentinel-2A satellite overpasses in 2015 and 2016, and utilized a planned ground-track designed to optimize the alignment with respect to the solar azimuth angle (SAA) to minimize cross-track illumination effects. Weather was taken into consideration to avoid acquisition during cloudy conditions. All lines were acquired within 2.5 h of solar noon. For the individual SASI and CASI flight lines, the atmospheric correction was performed with the FLAASH module in the ENVI 5.4.1. For the CASI mosaics, atmospheric correction was applied to the individual flight lines using ATCOR4 4.7.0 for flat terrain prior to the application of a cross-track illumination correction and image mosaicking [31].
## 2 Materials and Methods
### MODIS data  
In order to examine the effectiveness of \(C_{d}\) in \(R_{nd}\) estimation with satellite data, the model developed by [PERSON] et al. [11] ([PERSON]'s model hereinafter) to calculate \(R_{ni}\) directly from MODIS top-of-atmosphere (TOA) data was applied in this study(Table 2). Therefore, the instantaneous TOA reflectance and radiance data with a spatial resolution of 1 km from MOD/MYD021 KM, the corresponding geolocation and view geometry information including the solar zenith angle (SZA), solar azimuth angle (SAA), sensor zenith angle (VZA), sensor azimuth angle (VAA), and height from MOD/MYD03, as well as the cloud mask from MOD/MYD35 at all 105 sites during 2000-2017 were extracted. For comparison, only the clear-sky MODIS observations, which were determined by MOD/MYD35 with \"confident clear\" or \"probably clear\", were used in this study. After matching, a total of 1022 \(R_{nd}\) samples were obtained for comparison, and all other samples (No. of MODIS TOA samples = 131,563) were applied for training [PERSON]'s model. All MODIS data were obtained from the NASA Remote Sensing Imagery Data Download Web site ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/))), and [PERSON]'s model was implemented on Python 3.6.
## 3 Influence Factors of \(R_{s}\)
### Influence of Environmental Factors on \(R_{s}\)
#### 3.1.5 Salinity  
The salinity impact on the \(R_{\mathrm{s}}\) values of chemicals by the POCIS is related to the properties of the compounds, in particular to chemical groups. In general, the hydrophilicity of some organic pollutants would be reduced with the increase of salinity, affecting the partition coefficient of the pollutants. [PERSON] et al. [64] found that with increasing salinity, the \(R_{\mathrm{s}}\) for acidic compounds exhibited subtle change, whereas for basic compounds they showed an obvious reduction. [PERSON] et al. [32] explored the effect of salinity in the range of 0-35% on the \(R_{\mathrm{s}}\) of the POCIS, with the results showing that the maximum \(R_{\mathrm{s}}\) value was achieved at a salinity of 14%.  
The influence of salinity on the \(R_{\mathrm{s}}\) might be explained by the salting-out effect [82]. This effect increased with the size of the solute molecule and decreased with the polarity of the solute molecule [83]; thus, for most polar compounds in POCIS, the salting-out effect should not be statistically significant.  
Accordingly, caution should be taken in the application of freshwater-derived \(R_{s}\) values to the marine environment due to the difference in partition coefficients of chemicals between membrane water in both environments [84; 85]. Nevertheless, the impact mechanisms of salinity and the accurate salinity dependence of the \(R_{s}\) value are still unknown and require more investigation.
## 3 Results
### Molecular Docking  
Molecular docking was successfully applied to determine the affinity of a-tomatine and chlorhexidine compounds for MMP-2, MMP-8, and MMP-9 enzymes. The MMP-2 and MMP-8 enzymes were imported together with the cognate ligand, i.e., the ligand present in the protein structure in the Protein Data Bank. The cognate ligand was removed from the protein structure, and the accuracy of the molecular docking process was checked via docking again [41; 42]. The results obtained from this docking were in direct agreement with the position of the incoming ligand in the crystal structure and showed extremely low deviation. The low deviation values indicated docking extremely close to the crystal data, i.e., the experimental result. This indicated that docking using these settings would yield results extremely close to reality for a-tomatine and chlorhexidine. The validation image of the MMP-2 and MMP-8 enzymes and the RMSD value, which gives the mean value of the deviation, are given in Figure 1. Since MMP-9 does not contain a cognate ligand, validation was not performed (Figure 2).  
Settings whose reliability was proven through validation studies were used to dock the a-tomatine and chlorhexidine molecules to the MMP-2, MMP-8, and MMP-9 enzymes. The docking scores of the ligands for which molecular docking calculations were successfully performed are shown in kcal/mol in Table 6. In the molecular docking calculations, a-tomatine showed a higher affinity for all enzymes than chlorhexidine. The a-tomatine compound obtained scores of \(-9.358\), \(-9.663\), and \(-7.996\) kcal/mol for the MMP-2, MMP-8, and MMP-9 enzymes, respectively. The chlorhexidine compound obtained scores of \(-7.673\), \(-7.132\), and \(-5.102\) kcal/mol for the MMP-2, MMP-8, and MMP-9 enzymes, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sound**} & \multicolumn{2}{c}{**Eroded**} \\ \hline  & **24 h** & **6 Months** & **24 h** & **6 Months** \\ \hline a-Tomatine & 65/3/2 & 69/1/0 & 67/2/1 & 67/3/0 \\ \hline Chlorhexidine & 64/5/1 & 67/2/1 & 64/4/2 & 69/1/0 \\ \hline Control & 63/5/2 & 64/3/3 & 62/5/3 & 67/2/1 \\ \hline \hline \end{tabular}  
* The numbers represent the number of sticks that showed adhesive-mixed/cohesive-in-composite/cohesive-in-dentine failures for all subgroups.  
\end{table}
Table 5: Number of specimens according to the fracture pattern mode of each subgroup *.  
Figure 2: Validation image and RMSD values of MMP-2 and MMP-8 enzymes. The red-colored ligand is the molecular docking ligand. The green-colored ligand is the position in the crystal structure.
# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 4 Experiments and Results
### Experimental Results
To verify this, the RTAB-Map application was installed on an iPhone 12 Pro with a LiDAR sensor, and the indoor environment shown in Figure 11. The RTAB-Map application for iOS was released in June 2021 and is an RGB-D based on loop closure detection, stereo, and a LiDAR graph-based ROS SLAM system. Loop closure detection uses the BoWs approach to determine the likelihood that a new image comes from a previous or new location. In addition, when loop closure is detected, it detects the previous location and draws a map, enabling real-time mapping for a large-scale environment [32]. Since the RTAB-Map SLAM system can draw a more accurate map through a depth sensor, it is estimated that adding depth data to the system in this paper will enable a clearer and more accurate AR system to be implemented in a mobile environment. Therefore, in this paper, further research and development will be conducted using the iPhone's LiDAR sensor by changing the existing Android-based data collector to an iOS-based data collector. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **RMSE (m)** & **MEAN (m)** & **STD (m)** \\ \hline MH\_01(Easy) & 3.110 & 2.863 & 1.213 \\ MH\_03(Medium) & 3.562 & 3.230 & 1.502 \\ MH\_04(Difficult) & 3.308 & 2.976 & 1.445 \\ V1\_01(Easy) & 0.912 & 0.832 & 0.375 \\ V1\_02(Medium) & 1.796 & 1.675 & 0.650 \\ V1\_03(Difficult) & 1.589 & 1.412 & 0.729 \\ V2\_01(Easy) & 1.611 & 1.559 & 0.406 \\ V2\_02(Medium) & 1.327 & 1.244 & 0.463 \\ V2\_03(Difficult) & 0.939 & 0.854 & 0.390 \\ Average & 2.017 & 1.849 & 0.797 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Absolute translation RMSE, mean, and standard deviation. Figure 10: Optimization results: (**a**) before optimization; (**b**) after optimization.
# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
###### Abstract  
Information-centric networking (ICN) is an emerging network architecture that has the ability to share data on the network layer. It brings great benefits such as in-network caching, mobility support, and inherent security. Our proposed algorithms provide cost-efficient solutions for deploying name-resolution servers in 5G edge networks. They also have the potential to be applied to other scenarios, including the cloudlet placement problem in edge computation and the controller placement problem in software-defined networking.  
**Abstract:** Information-centric networking (ICN) is an emerging network architecture that has the potential to address demands related to transmission latency and reliability in fifth-generation (5G) communication technology and the Internet of Things (IoT). As an essential component of ICN, name resolution provides the capability to translate identifiers into locators. Applications have different demands on name-resolution latency. To meet the demands, deploying name-resolution servers at the edge of the network by dividing it into multilayer overlay networks is effective. Moreover, optimization of the deployment of distributed name-resolution servers in such networks to minimize deployment costs is significant. In this paper, we first study the placement problem of the name-resolution server in ICN. Then, two algorithms called IIT-DOWN and IIT-UP are developed based on the heuristic ideas of inter-layer information transfer (IIT) and server reuse. They transfer server placement information and latency information between adjacent layers from different directions. Finally, experiments are conducted on both simulation networks and a real-world dataset. The experimental results reveal that the proposed algorithms outperform state-of-the-art algorithms such as the latency-aware hierarchical elastic area partitioning (LHP) algorithm in finding more cost-efficient solutions with a shorter execution time.  
5G; Information-Centric Networking; name resolution; placement optimization; multilayer overlay network +
Footnote †: journal: Applied  
Article
## 2 Algorithm for Optimal Deployment  
The algorithm to obtain the optimal deployment scheme is composed of four sub-categories. First, underwater acoustic channel modeling is carried out using a ray-based acoustic model for the targeted area using the ocean environment database including bathymetry, sound speed profiles, and sediment types. Second, the communication performance is estimated using the array processing technique of a single-input multiple-output (SIMO) system. The signal received at each array channel is simulated via a convolution of the communication source signal with the simulated channel impulse response. Third, the PS for the targeted area is derived using the estimated communication performance. Lastly, the optimal deployment scheme for the sensor nodes is determined using the VFPSO method based on the estimated PS.
###### Abstract  
The extended finite element method (XFEM) is efficient in simulating crack initiation and its evolution process for reinforced-concrete (RC) structures due to its ability to solve fracture problems. Moreover, the multiscale numerical simulation helps understand global and local failure behavior of RC structures simultaneously. In this study, the XFEM-based multiscale modeling approach was proposed to investigate the monotonic and hysteretic performance of RC columns. Firstly, two-scale models composed of fiber beam elements and XFEM-based solid elements with homogeneous material assumptions were established using compiled material subroutines for fiber beam elements. Secondly, the accuracy of XFEM-based two-scale analysis in predicting the hysteretic behavior of tested RC columns was verified by comparing the crack morphology and load-displacement curve obtained from tested specimens under different axial compression ratios (ACRs) and two-scale models using the concrete damaged plasticity (CDP) model. Thirdly, multiscale models of RC columns were constructed with fiber beam elements, XFEM-based solid elements and mesoscopic concrete models composed of mortar, interfacial transition zone (ITZ) and aggregates with different geometric shapes and distribution patterns. Finally, the XFEM-based multiscale simulation was employed to investigate the influence of mesoscale structure variation of concrete on both global behavior and local failure patterns of RC columns subjected to monotonic loading. The simulation results of multiscale models established with CDP model and XFEM were comparatively discussed in depth. The XFEM-based multiscale simulation developed in this study provides an efficient modeling approach for investigating the stochastic nature of cracking behavior in RC columns.  
e 8 October 2020; 10, 7899; doi:10.3390/app10217899  
1
# Hybrid-Attention Network for RGB-D Salient Object Detection
## 3 Evaluation Measures and Implementation Details
### Implementation Details  
We implement HANet using the popular PyTorch 1.2.0 library in Python. We apply Adam optimization with learning rate of 0.001, which is reduced by a factor of 2 if no improvement is observed in the validation performance over five consecutive epochs. The NJUD dataset [31] containing more than 2000 images and the NLPR dataset [32] containing 1000 images corresponding pixel-level ground truths are used to evaluate the proposed HANet. We follow the datasets splitting scheme proposed in [18; 21], 80% are used for training and the remaining 20% for test. All the images are resized to \(224\times 224\) pixels. The network is trained over 100 epochs with early stopping, and a minibatch of 2 images is used at every training iteration. In this study, HANet was trained on a computer equipped with an Intel i7- 7750H CPU at 2.21 GHz and an NVIDIA GeForce GTX TITAN Xp GPU.