esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating and Analyzing Long-Term Multi-GNSS Inter-System Bias Based on Uncombined PPP
## 6 Conclusions  
This paper focused on the estimation and analysis of the ISB parameters based on the uncombined PPP using the GLONASS, BDS, GALILEO and GPS systems. We first deduced the uncombined PPP function model of G + C + R + E four-system fusion processing. Aiming at the problem that there are much many parameters to be evaluated for the four system uncombined PPP and the inter-frequency deviation of the pseudo range of GLONASS system, we separate the pseudo range IFB parameter of GLONASS system of single epoch through theoretical derivation, simplify the PPP function model, improve the model strength and parameter estimation efficiency. And we then determined the processing model of the ISB parameters. Next, we used the MGEX station's 28-day data to estimate and analyze three kinds of ISB parameters. Our conclusions are summarized as follows.  
1. The uncombined PPP of the G + C + R + E system fusion provides high positioning accuracy. From the statistical results of the 28-day data, the rms of positioning deviation in the E, N and U  
Figure 12: Relationship between ISB parameters and receiver types.  
directions reached 4.6 mm, 3.4 mm and 8.5 mm, respectively, which can effectively guarantee the reliability and accuracy of the ISB parameter solution.
2. The intra-day variation of ISB was relatively stable, and the standard deviation of three kinds of ISB parameters were all less than 0.6 ns. Among them, the ISB parameter between the GALILEO system and GPS system was the most stable, and the standard deviation was the smallest, which may be related to the GALILEO system's good signal quality.
3. The mean ISB parameters value of single-day solution of each station is not stable in the time period, and the amplitude of jump is irregular (up to 60 ns); however, the ISB parameters of all stations show similar variation trends and amplitude on the same day. This situation is independent of receiver, which may be ascribed to satellite reference clock of different system.
4. There is a clear relationship between the ISB parameter and receiver type.  
We acknowledge the following limitations in this study. The receiver types of all MGEX stations have not been analyzed statistically, and the accurate relationship between the ISB parameters and receiver type have not been analyzed systematically. These issues will be the focus of future work.  
All authors contributed to this work. Conceptualization, F.Z., Formal analysis, X.Z., F.Z., Investigation, C.L., Methodology, F.Z., Validation, X.F., Writing--original draft, F.Z., Writing--review and editing, G.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant no. 41374041,41904039).  
The contribution of data from MGEX and the support from the software GNSSer ([[http://www.gnsser.com](http://www.gnsser.com)]([http://www.gnsser.com](http://www.gnsser.com))) are appreciated.  
The authors declare no conflict of interest.
## 3 Results
### Differential Scanning Calorimetry (DSC)  
The thermal properties by DSC of PLA bionanocomposites films are presented in Figure 3.  
The glass transition temperature (T\({}_{\text{g}}\)), crystallization temperature (Tc), melting temperature (T\({}_{\text{m}}\)), and associated enthalpies of samples are evaluated from DSC curves and are summarized in Table 3.  
The thermal parameters of neat PLA evaluated from the DSC, second heating scan consist in T\({}_{\text{g}}\) = 59.9 \({}^{\circ}\)C and T\({}_{\text{m}}\) = 152.1 \({}^{\circ}\)C, in good agreement with findings described by [PERSON] et al. [32]. With the introduction of plasticizer into polymeric matrix, the T\({}_{\text{g}}\) decreased (42.3 \({}^{\circ}\)C) suggesting the improvement in chain mobility. The PLA/ATBC sample showed the double melting temperatures (T\({}_{\text{m}}\)) at 145.8 and 131.7 \({}^{\circ}\)C. It is noted that a shoulder is observed as endothermic melting points for all samples: PLA/AgNPs 0.5% (131.0 \({}^{\circ}\)C); PLA/AgNPs 1% (134.4 \({}^{\circ}\)C), and PLA/AgNPs 1.5% (131.0 \({}^{\circ}\)C),  
Figure 3: Normalized Differential Scanning Calorimetry (DSC) curves recorded in the second heating of PLA bionanocomposites films and plasticized PLA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **T\({}_{\text{g}}\) (\({}^{\circ}\)C)** & **T\({}_{\text{c}}\) (\({}^{\circ}\)C)** & **\(\Delta\)H\({}_{\text{c}}\) (/g)** & **T\({}_{\text{m}}\) (\({}^{\circ}\)C)** & **\(\Delta\)H\({}_{\text{m}}\) (/g)** & **X\({}_{\text{c}}\) PLA (\%)** \\ \hline PLA/ATBC & 42.3 & 86.7 & 20.5 & 145.8 & 22.1 & 29.6 \\ PLA/Col 5 & 46.5 & 92.0 & 22.7 & 145.50 & & \\ PLA/AgNPs 0.5\% & 47.2 & 86.7 & 20.3 & 131.0 & 22.9 & 32.5 \\ PLA/AgNPs 1\% & 48.0 & 92.5 & 22.9 & 150.8 & 23.8 & 33.9 \\ PLA/AgNPs 1.5\% & 51.3 & 86.8 & 18.1 & 144.3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermal parameters evaluated from DSC thermograms and second heating scan.  
meaning a bimodal distribution of crystallite size, different morphologies occurred in the heating and cooling process, and formation of disorder alpha phase [9].  
From Table 3, it is also observed that the Col 5 was incorporated into the plasticized PLA an increase of \(\mathrm{T_{g}}\) was obtained compared to that for the plasticized PLA-based bionanocomposites. This increase is accentuated by the introduction of AgNPs. \(\mathrm{X_{c}}\) evaluated from the melting enthalpy of samples (using the Equation (1)) showed higher values for bionanocomposites compared with those of PLA/ATBC and PLA/Col 5 samples.
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 4 Comparison of Selected AI Models
### 3D Object Detection  
The detection of 3D objects in the nuScenes set involves identifying and locating objects in three-dimensional space based on data from cameras (Figure 3). In this task, algorithms analyze images from cameras and then detect and describe objects such as vehicles, pedestrians, and road signs.  
The performances of various 3D object detection models evaluated on the nuScenes dataset are presented in Table 5. The models are compared based on several metrics, as follows: NDS (NuScenes Detection Score), mAP (mean Average Precision), mATE (mean Average Translation Error), mASE (mean Average Scale Error), mAOE (mean Average Orientation Error), mAVE (mean Average Velocity Error), and mAAE (mean Average Attribute Error), along with the year of publication and reference.  
The EA-LSS model demonstrates the highest performance, with an NDS of 0.78 and mAP of 0.77, showcasing commendable results across all metrics and introducing an edge-aware framework for enhanced object boundary delineation and spatial accuracy in 3D Bird's Eye View (BEV) contexts.  
Figure 3: Sample of NuScenes labels. Objects on a single image are colored in orange, while those on two consecutive cameras are shown in yellow [145].  
Other notable models include BEVFusion, which integrates multi-sensor data into a unified BEV representation to support concurrent tasks such as detection, segmentation, and tracking. FocalFormer3D employs a targeted mechanism to prioritize challenging instances within the 3D space, leveraging focal attention combined with transformer networks for an improved detection accuracy in complex environments. UniTR presents a unified transformer framework for multi-modal BEV representation, efficiently fusing data from LiDAR and cameras to produce comprehensive outputs.  
The 3D Dual-Fusion model enhances detection through a dual-query fusion approach, integrating both camera and LiDAR data to leverage the strengths of each sensor type. MGTANet incorporates a Long Short-Term Motion-Guided Temporal Attention mechanism to improve the detection of moving objects by encoding sequential LiDAR points. Center-Point focuses on center-based 3D detection and tracking to simplify object localization and association across frames. Lastly, SSN uses shape signatures to encode geometric features for robust multi-class object detection from point clouds.  
These models collectively contribute to advancements in 3D object detection by addressing a range of challenges through innovative frameworks and mechanisms tailored to enhance precision, recall, and overall detection performance.
# A Vision-Based Micro-Manipulation System
## 3 System Overview
### Entropy Maximization concerning the UCEV  
The UCEV is a key concept in contrast enhancement. It acts as a dynamic control parameter that adjusts pixel values in images to enhance contrast and improve visual perception. UCEV adaptively broadens or narrows pixel value distributions, intensifying the contrast between features and addressing issues like overly bright regions. This approach dynamically manipulates pixel distributions while preserving image integrity, offering adaptability across different image types. UCEV's automated approach eliminates the need for manual parameter adjustments, optimizing image quality by balancing contrast enhancement and image detail preservation through entropy maximization. This results in improved visual interpretation and image quality. The relationship of the UCEV adaptive  
Figure 2: FACE enhancement process.  
mechanism influencing every new pixel and harmonizing the pixel colors is expressed by Equation (5).  
\[x^{\prime}_{i}=x_{i}+\alpha\sum_{j=1}^{k}u_{ij}(x_{i}-c_{j})\ \forall i=1\dots n \tag{5}\]  
where \(\alpha\) symbolizes the step size used in a line search that diverges from central cluster points through a fusion of fuzzy directions. A positive value of \(\alpha\) increases image dispersion, enhancing contrast, while a negative \(\alpha\) contracts pixel dispersion, potentially improving overly bright pictures. In the final contrast enhancement step, Equation (6) maximizes the global measurement, entropy \(J\).  
\[\underbrace{Max}_{\alpha}J(\alpha)=-\sum_{x\in\Omega}\left[p(x^{\prime}( \alpha))\log_{2}p(x^{\prime}(\alpha))\right. \tag{6}\]  
where \(p\) represents probability density, and \(x^{\prime}\) is a set of image pixels obtained from Equation (5). This measurement assesses the unpredictability of pixel distribution. The analysis employs a grid set \(\Omega\) for computing image entropy. A higher entropy value signifies greater randomness in pixel distribution, indicating significant pixel variance. This optimization process, as expressed in Equation (6), leads to an improved image contrast. An illustration of the proposed FACE image enhancement method is depicted in Figure 3.
# Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa
###### Abstract  
African landscape fires are widespread, recurrent and temporally dynamic. They burn large areas of the continent, modifying land surface properties and significantly affect the atmosphere. Satellite Earth Observation (EO) data play a pivotal role in capturing the spatial and temporal variability of African biomass burning, and provide the key data required to develop fire emissions inventories. Active fire observations of fire radiative power (FRP, MW) have been shown to be linearly related to rates of biomass combustion (kg s\({}^{-1}\)). The Meteosat FRP-PIXEL product, delivered in near real-time by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF), maps FRP at 3 km resolution and 15-min intervals and these data extend back to 2004. Here we use this information to assess spatio-temporal variations in fire activity across sub-Saharan Africa, and identify an overall trend of decreasing annual fire activity and fuel consumption, agreeing with the widely-used Global Fire Emissions Database (GFEDv4) based on burned area measures. We provide the first comprehensive assessment of relationships between per-fire FRE-derived fuel consumption (Tg dry matter, DM) and temporally integrated Moderate Resolution Imaging Spectroradiometer (MODIS) net photosynthesis (PSN) (Tg, which can be converted into pre-fire fuel load estimates). We find very strong linear relationships over southern hemisphere Africa (mean \(r=0.96\)) that are partly biome dependent, though the FRE-derived fuel consumptions are far lower than those derived from the accumulated PSN, with mean fuel consumptions per unit area calculated as 0.14 kg DM m\({}^{-2}\). In the northern hemisphere, FRE-derived fuel consumption is also far lower and characterized by a weaker linear relationship (mean \(r=0.76\)). Differences in the parameterization of the biome look up table (BLUT) used by the MOD17 product over Northern Africa may be responsible but further research is required to reconcile these differences. The strong relationship between fire FRE and pre-fire fuel load in southern hemisphere Africa is encouraging and highlights the value of geostationary FRP retrievals in providing a metric that relates very well to fuel consumption and fire emission variations. The fact that the estimated fuel consumed is only a small fraction of the fuel available suggests underestimation of FRE by Spinning Enhanced Visible and Infrared Imager (SEVIRI) and/or that the FRE-to-fuel consumption conversion factor of 0.37 MJ kg\({}^{-1}\) needs to be adjusted for application to SEVIRI. Future geostationary imaging sensors, such as on the forthcoming Meteosat Third Generation (MTG), will reduce the impact of this underestimation through its ability to detect even smaller and shorter-lived fires than can the current second generation Meteosat.  
biomass burning; SEVIRI; fuel consumption; fire radiative power; vegetation productivity; MODIS 2018, 10, 1591; doi:10.3390/rs10101591
## 4 Design of TLQoS
### Hybrid Potential and Routing Loop Avoidance
However, to avoid the routing loops or avoiding a packet roaming unnecessarily around the network we impose two conditions:  
* _Condition 1_: A node will not choose a neighbor from which it just received a packet. * _Condition 2_: Whenever the hop-count value of a packet exceeds a maximum hop-count threshold, \(HC^{max}\), the parameter \(\gamma\) is changed as \(\gamma=\gamma-0.1\) for every hop the packet traverses until it reaches to the \(BC\). Condition 1 prevents a packet to be exchanged between two nodes back and forth, thus avoids routing loops. Condition 2 avoids a packet moving around a large number of hops, also prevents routing loops. With the decreasing value of \(\gamma\), the time-invariant potential will be dominating that drives the packet to be closer to the \(BC\) following the shortest path route.
## 3 Results  
### Primary enrollment Cohort  
A total of seven patients with pathology-confirmed glioblastoma with a mean age of 66.9 (\(\pm\)10.6) years, 57.1% male, were included in the pilot study of 64-gradient-direction QSI. Tumor locations were predominantly left-sided (5/7, 71.4%) with most located in the temporal or parietal lobes. Three patients (42.9%) had multifocal disease at presentation. Other population characteristics can be found in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Patient Characteristics (\(n\) = 7)** & **\(n\) (\%)/Mean (\(\pm\)SD)** \\ \hline \multicolumn{2}{c}{Demographic Details} \\ Age (Years) & 66.9 (\(\pm\)10.6) \\ Gender (Male) & 4 (57.1%) \\ \hline \multicolumn{2}{c}{Treatment Details} \\ Surgical Resection (vs. biopsy) & 6 (85.7%) \\ Chemotherapy (Temozolomide) & 7 (100\%) \\ Radiation & 7 (100\%) \\ \hline \multicolumn{2}{c}{Pathology Details} \\ MGMT Promotor Methylation & 4 (57.1%) \\ IDH Wildtype & 7 (100\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rhode Island Hospital primary glioblastoma cohort patient characteristics. \(n\) = 7 total patients with newly diagnosed glioblastoma who underwent generalized Q-space imaging (3T, 64 gradient directions, b = 1000 s/mm\({}^{2}\)) between 2018 and 2019.  
Tumor-associated tractography generated a consistent pattern of projecting tracts oriented outwardly from the tumor surface (defined in diffusion imaging by core-shell phenomenon, as described previously by [PERSON] et al.) ([PERSON] et al., 2018). Tumor ROI varied in size according to tumor volume, with a mean volume of 18.7 (\(\pm\)4.2) cm\({}^{3}\). The associated mean overall tract length was 23.2 (\(\pm\)3.1) mm, while the mean projecting tract length was 38.9 (\(\pm\)4.9) mm. The workflow for generating tumor-associated tractography as well as several representative examples of the observed pattern are depicted in Figure 1.  
In instances of multifocal tumor present on presentation (\(n=3\)), projecting tractography was anecdotally observed to span between foci of tumor ROI (Figure 2). Given that longitudinal, diffusionally coherent tractography bundles in the brain usually correspond to white matter structures, this observation raises the possibility of functional connectivity between regions of distinct tumor foci in instances where patients present with multifocal disease. Taken together, these findings show that, using QSI, we can define tumor-associated tracts extending from tumors and, in the case of multifocal tumors, observe possible functional connectivity between distinct foci.
## 3 Results
### Mechanical Properties and Water Vapor Permeability (WVP)  
The incorporation of neon extracts into pectin-/gelatin-based packaging (FPC) significantly influenced the mechanical and barrier properties of the material (Table 3). The addition of neon extracts increased the elongation (E) of the packaging, likely due to the interaction between the hydroxyl groups of the phenolic compounds and the polymeric side chains. As a result, FPC + HNE-PLE and FPC + HNE-MAE exhibited greater flexibility compared to FPC.  
Among the analyzed samples, FPC + HNE-PLE demonstrated higher tensile strength (TS), greater flexibility, and lower water vapor permeability (WVP) (Table 3).  
Figure 1: FTIR spectrums of food packaging containing neon extracts (**a**,**c**) and without neon extracts (**b**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Packaging** & **T [mm]** & **E [\%]** & **TS [Mpa]** & **WVP (g\_mm/d\_m\_2\_kPa)** \\ \hline FPC & 0.11 \(\pm\) 0.00 \({}^{\text{a}}\) & 8.00 \({}^{\text{a}}\)\(\pm\) 0.0 \({}^{\text{a}}\) & 13.76 \(\pm\) 0.79 \({}^{\text{a}}\) & 5.2 \(\pm\) 0.4 \({}^{\text{a}}\) \\ FPC + HNE-PLE & 0.10 \(\pm\) 0.01 \({}^{\text{a}}\) & 10.33 \(\pm\) 0.21 \({}^{\text{b}}\) & 16.45 \(\pm\) 2.25 \({}^{\text{b}}\) & 2.0 \(\pm\) 0.2 \({}^{\text{b}}\) \\ FPC + HNE-MAE & 0.16 \(\pm\) 0.04 \({}^{\text{a}}\) & 9.50 \(\pm\) 0.21 \({}^{\text{c}}\) & 9.24 \(\pm\) 2.01 \({}^{\text{c}}\) & 6.9 \(\pm\) 1.7 \({}^{\text{a}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean values and standard deviation of mechanical properties (T: thickness, E: elongation, TS: tensile strength) and water vapor permeability (WVP) of FPC + HNE-PLE, FPC, and FPC + HNE-MAE.  
FPC + HNE-PLE showed a lower WVP value (2.0 g\(\cdot\)mm/d\(\cdot\)m\({}^{2}\)\(\cdot\)kPa) than the control (FPC, 5.2 g\(\cdot\)mm/d\(\cdot\)m\({}^{2}\)\(\cdot\)kPa), suggesting reduced permeability. Conversely, FPC + HNE-MAE demonstrated a higher WVP value (6.9 g\(\cdot\)mm/d\(\cdot\)m\({}^{2}\)\(\cdot\)kPa) than the control, indicating increased permeability. HNE-PLE significantly reduced (\(p\geq 0.05\)) the WVP of FPC, while HNE-MAE did not result in a statistically significant difference (\(p\geq 0.05\)).  
These findings suggest that PLE, due to its higher extraction capacity compared to MAE (Table 2), was able to isolate biomolecules from neem leaves with both hydrophilic and hydrophobic characteristics, leading to the observed reduction in WVP for FPC + HNE-PLE.
# Experimental Investigation of Mechanical Properties of Clay-Cement Slurry Containing Graphene Oxide
## 3 Results
### Anti-Permeability Performance of Grouting Reinforcement
#### 3.2.3 Multiple Regression Analysis of Permeability Coefficient Influencing Factors
The equation is a nonlinear equation, taking the logarithm on both sides:  
\[\log K=\log\text{A}+\text{B}\cdot\log\text{O}+\text{C}\cdot\log H+\text{D} \cdot\log\text{N} \tag{9}\]  
\(k=logK\), \(a=\log\text{A},\;o=logO,\;h=logH,\;n=\log N\). The equation is transformed into a linear equation:  
\[k=a+\text{B}\cdot\text{o}+\text{C}\cdot h+\text{D}\cdot n \tag{10}\]  
The partial regression coefficients B, C, D, and E of constant A can be obtained by using the multiple linear regression method to analyze the experimental data. The standardized regression coefficient Beta can be obtained by standardizing the partial regression coefficients:  
\[k=-1.258070+0.612667o+14.719298h-0.348333n \tag{11}\]  
\[b_{o}=0.923,\;b_{h}=0.112,\;b_{n}-0.105,\;\text{R}^{2}=0.875\]  
where \(b_{o},\;b_{h},\;b_{n}\) are the standardized regression coefficients Beta of grouting volume to OPC content, GO content, and gradation, respectively, and R\({}^{2}\) is the multiple correlation coefficient. After transformation, the regression equation can be obtained:  
\[K=0.055\cdot\text{O}^{0.613}\cdot\text{H}^{14.712}\cdot\text{N}^{-0.348} \tag{12}\]  
According to the analysis, the multiple regression analysis shows that the coefficient of multiple determination is 0.875, indicating a strong correlation between the grouting reinforcement material and the OPC dosage, GO dosage, and gradation. The standardized analysis of partial regression coefficients shows that \(|b_{o}|>|b_{h}|>|b_{n}|\). This indicates that the OPC dosage has the most significant influence on the permeability coefficient of the grouting reinforcement material, followed by the GO dosage. On the other hand, geological conditions have the weakest impact.
## 3 Theoretical Performance Limits
### Simulation Validation  
This subsection conducts Monte Carlo simulations to verify the theoretical performance bounds obtained above. The parameter values are set as follows. The BS power limit is \(P_{T}=2W\) while the number of channels \(K=256\). The user interest obeys a truncated normal distribution in \([1,10]\) with mean 6 and variance 1. The file size of contents in the server obeys a uniform distribution in \([1,50]\), the user behavior follows a Bernoulli process, the number of users is \(U=10\), the amount of content is \(F=500\), and the length of each user's recommended list is \(N=50\).  
Figure 3 shows the total user interest as a function of transmission delay with different outage probability threshold \(\xi\). The simulation performance curves are obtained by running the above-proposed algorithms for 100 random snapshots and taking the average over the results. In addition, the theoretical upper and lower bounds are also calculated and shown. According to Equation (17), the upper bound of total user interest is calculated to be 3874.7, which is not a function of the transmission delay. The lower bound of the total user interest is calculated according to Equation (20) and shown to be a function of the transmission delay. We can see that the theoretical upper and lower bounds depict a feasible region, which is an accurate description of the performance areas of the UiL content delivery system.