esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Best Practice
### Geometric Processing
#### Ground control points (GCPs)
(i) The manual distribution of GCPs in the field and setup of field-based GNSS equipment can be time-consuming (especially for complex/dangerous environments). (ii) GCPs are unsuitable for pushbroom imagery, as GCPs do not account for pitch, roll, and yaw distortions induced by UAV flight dynamics. Correcting for these distortions requires a very dense network of GCPs, and rigorous correction is not possible. (iii) GCPs are unsuitable for a point-measuring spectroradiometer, as the GCPs are not visible in spectral readings.
## 4 Proposed Algorithm: CMWN-DV-HOP
### Weighted Recursive Least-Squares Algorithm (WRLS)  
One of the main reasons that lead to ranging deviation from the DV-Hop is not enough to consider the information of all anchor nodes that can communicate with the unknown node. The present paper uses the weighted recursive least-squares algorithm to calculate the position information of each unknown node. The weighted recursive least-squares algorithm can reduce the amount of computation and provide a stable localization process in the event of node failure or the addition of new nodes in the network.  
The following details the idea of the weighted recursive least-squares scheme. Due to different measurement situations, the data obtained from these measurements influence the node's positioning differently. We assume that the data with high measurement accuracy have a higher weight than the data with low measurement accuracy. Weighting factors are multiplied on each component of the error vector \(E\), assuming they are \(w(1),w(2),\cdots,w(n-1)\), respectively. At this point, the error function \(J\) can be formalized as follows:  
\[J=b^{T}\textbf{W}b-2X^{T}\textbf{A}^{T}\textbf{W}b+X^{T}\textbf{A}^{T}\textbf{ W}\textbf{A}X \tag{12}\]  
Figure 4: Cyclotomic method (with unit circle as an example).  
Figure 5: Division of the hop count interval [0,1].
## 3 Results
### Evaluation of Cell Viability  
We tested the three propolisols extracts (PPF, PSDE and MPE) and several isolated propolis compounds (caffeic acid, \(p\)-coumaric acid, artepillin C and baccharin) for their toxicity. To do this, we performed an MTT assay on the BMDM with the same treatments used for evaluating the effects on cytokine secretion. The results showed no cytotoxicity, thus demonstrating that the cytokine results were not biased by possible toxic effects (Figure S5). Additionally, higher dosages of PSDE (100 and 300 ug/mL), MPE (300 ug/mL), caffeic acid (50 and 100 ug/mL) and \(p\)-coumaric acid (50 and 100 ug/mL) resulted in a significant increase in cellular viability when compared with the negative control (Figure S5, Supplementary Materials).  
An increase in cellular viability with high concentrations of propolisis and propolisis components was also observed in several other studies [62; 63], although some studies have reported cytotoxicity of propolisis extracts and of isolated compounds at high concentrations [64; 65]. An objective comparison between these studies concerning cytotoxicity is not possible because of differences between the methodologies employed, in the types of propolisis extracts, when they were applied, and other variables. These dissimilarities in the effects of increasing concentrations of propolisis and its constituents deserve further investigation.  
Figure 4: Cytokine concentrations (0–400 pg/mL) in triplicate (three lines) for each treatment: **M:** medium alone; **L:** after LPS (500 ng/mL) stimulation of bone marrow cell-derived macrophages; **1–6:** Polar propolisis fraction (PPF) (1, 10, 25, 50, 100 and 300 ug/mL); **7–12**:** Propolis soluble dry extract (PSDE) (1, 10, 25, 50, 100 and 300 ug/mL); **13–18**: Microencapsulated propolisis extract (MPE) (1, 10, 25, 50, 100 e 300 ug/mL); **19–22**: Caffeic Acid (10, 25, 50 and 100 ug/mL); **23–26**: \(p\)-coumaric acid (10, 25, 50 and 100 ug/mL); **27–30**: Artepillin C (10, 25, 50 and 100 ug/mL) and **31–34**: Baccharin (10, 25, 50 and 100 ug/mL).
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 3 Optimizing Cloud-Based Streaming Internal Bandwidth: Case of an Unpopular Video
### SASST Principle
#### 3.1.1 SST Principle  
Each streaming node (SN) acts as a proxy node that loads the stream (back-end stream) from the storage node in the cloud and forks it to the clients requesting it. When the first streaming node requests the back-end stream, the storage node begins streaming it internally in the cloud network through a multicast complete stream (CS). When another streaming node later requests the same back-end stream, it will be asked (by the storage node using a manifest file, for example) to share the CS with the first streaming node and will be assigned a new multicast FTS stream to recover the missed video part (as it comes later than the first SN). The video content downloaded from the shared CS is buffered in a circular buffer, while the video content downloaded from the FTS is immediately relayed to the front-end clients. When the missed part is completely recovered, the FTS is closed, and the streaming continues through the circular buffer. This is the principle of the original version of SST. Recall that a slotted time axis is considered where all the requests coming within the same slot are served through the same CS and/or FTS starting from the beginning of the next slot.  
Figure 2 portrays the SST principle. It contains four subfigures numbered from 1 to 4 illustrating how SST deals with late requests. As shown in subfigure 1, the first SN is served through a CS.  
Then, as portrayed in subfigure 2, the second SN comes two slots later than the first SN. The second SN is assigned, as portrayed in subfigure 3, an FTS to serve the missed video slots 1 and 2 while buffering video slots 3 and 4 in a circular buffer from the ongoing  
Figure 2: Illustration of the SST principle and how it handles late requests. In (**1**) the first SN request arrives and gets a CS. In (**2**) the second SN request arrives late for two slots. In (**3**) the Streaming node opens a FTS for the second SN to get the two missed slots while sharing the CS with first SN. In (**4**) the FTS is closed and the second SN continues getting the video from its buffer.  
CS. Finally, as missed slots 1 and 2 are recovered, the FTS is closed, and video streaming to clients continues through the circular buffer, as shown in subfigure 4 of Figure 2.  
The circular buffer serves clients from one end while being filled through the CS from the other end. In other words, it serves slot 3 while being filled with slot 5, it serves slot 4 while being filled by slot 6, and so on, until the end of the requested video file. Thus, the buffer size of the second SN illustrated in Figure 2 never exceeds 2 slots, which is equal to the missed slot duration.
## 3 Current Data
### Active Microwave Scatterometer  
As shown in Figure 4, current scatterometer data are collected only by ESA and EUMETSAT's ASCAT. The C-band ASCAT provides continuity for the AMI sensor aboard ERS-1 and ERS-2. Backscatter image data products are available through the NASA SCP ([[http://www.scp.byu.edu/data/Ascat/SIR/Ascat_sir.html](http://www.scp.byu.edu/data/Ascat/SIR/Ascat_sir.html)]([http://www.scp.byu.edu/data/Ascat/SIR/Ascat_sir.html](http://www.scp.byu.edu/data/Ascat/SIR/Ascat_sir.html))). In addition, the Indian Space Research Organization and NASA's OSCAT operated from 2010 until February 2014, when it suffered an instrument failure. The Ku-band OSCAT provided continuity for QuickSCAT, although at a slightly different incidence angle. Backscatter image data products are available through the NASA SCP [[http://www.scp.byu.edu/data/OSCAT/SIR/OSCAT_sir.html](http://www.scp.byu.edu/data/OSCAT/SIR/OSCAT_sir.html)]([http://www.scp.byu.edu/data/OSCAT/SIR/OSCAT_sir.html](http://www.scp.byu.edu/data/OSCAT/SIR/OSCAT_sir.html))). Derived products for both sensors are included in Section 5 (see Table 2 and [[http://www.scp.byu.edu/derived.html](http://www.scp.byu.edu/derived.html)]([http://www.scp.byu.edu/derived.html](http://www.scp.byu.edu/derived.html))).
## 4 Results
### Backscattering Coefficients According to Rice Parameters  
Figure 5a shows the behavior of VV and VH backscattering coefficients during the entire rice growth. As expected, it can be observed that the VV backscatter values are higher than the VV polarization. In the vegetative stage (16 days to 65 days after sowing), the dynamic range of VV and VH backscatters is very high (about 7 dB). In the reproductive stage (65 days to 100 days), the VH backscatter tends to saturate, whereas the VV goes down. In the last ripening stage (after 100 days), both VH and VV backscatters tend to slightly decrease.  
Figure 4: Temporal variation of the rice height and biomass versus to acquisition dates of Sentinel-1 data.  
Figure 5b shows a scatter plot on VV and VH backscattering coefficients distribution together with their Pearson correlation coefficient \(R_{P}\) according to vegetative, reproductive and ripening stages. Although they are highly correlated at each stage, if we put all together in a full cycle, the Pearson correlation is decreased to 0.52.  
In Figure 5c,d, the behavior of VV and VH backscattering coefficients is similar with respect to both rice height and biomass. Both VH and VV backscatters increase strongly in the vegetative stage. On the other hand, in reproductive and ripening stages, while VH backscatter still slightly grows, the VV visibly decreases.
## 3 Comparison between Flow-3D, MIKE 3 FM, and 1D Exact Riemann Solver Predictions
### Computational Costs  
The validity of the 1D and 2D Shallow Water equation are compared with a known analytical solution or experimental data, which has been performed by many other researchers (see for instance [27; 46]), and in the study about applying 3D models to simulate near-field dam-break flows in References [3; 47]. All these comparisons showed that full 3D RANS models performed better than the 1D and 2D models, but the expensive computational effort of the 3D RANS models cannot be neglected.  
A comparison of the required computational time and the number of grids was made for the simulations. The MIKE 3 FM model has considerably lower computational costs, and it was 24 times faster than the Flow-3D in all cases (Table 5). All of the numerical simulations were performed using an Intel(r) Core(tm) i5 PC. The comparison between the simulated and experimental results, as showed for the first test case in [PERSON] and [PERSON] [30], clearly shows that the 3D RANS approach has the ability to represent the water surface profiles quite well during the entire dam-break process, immediately during the initial stage and after the gate collapse, and it reproduced the free-surface profiles of the front wave well for dry- and wet-bed conditions while the wave-front modelled by the  
Figure 13: Comparison of simulated velocity profiles at various locations upstream and downstream of the dam at \(t\) = 0.8 s, 2 s, and 5 s for water depth ratios \(\alpha\) = 0.1 by [PERSON] and [PERSON] [30]: (a) P1(\(-\)1 m); (b) P3 (+0.2 m); (c) P5 (+1 m); and (d) P6 (+2 m).  
MIKE 3 FM model for the wet-bed case was a rectangular jump rather than a curved surface. For the dam-break flow under a dry bed, the results of the free surface profiles during the late stage using the MIKE 3 FM approach can be considered reasonable relative to the experimental measurements. The performances of the MIKE 3 FM models in relation to the water depth variations and velocity variation for the dry- and wet-beds were satisfactory. The previous research results show the practical advantage of using the MIKE 3 FM model to compute the water levels and velocity for large-scale dam-break problems [48]. A possible dam-break flow application in which the SW 3D mode might be needed is used for computing the hydrodynamic characteristic of a dam-break wave in large domains, and the water surface profiles of the front wave during the earliest stage is not very important. These two three-dimensional models of Flow-3D and MIKE 3 FM could be efficiently and effectively applied in the near-field region, and if given the computational effort and efficiency required by each method, the MIKE 3 FM approach should be considered a major candidate for computations involving large domains, leaving the RANS approach for fine calculations in which knowledge of the 3D structure of the flow is required. Often, in practical applications, both these requirements are necessary.
# Analysis of Score-Level Fusion Rules for Deepfake Detection
## 4 Experimental Results
### Results
#### 4.2.2 Cross-Dataset Scenario
In this scenario, the fake distributions of the single methods have two modes, one centered on score 0 and one centered on score 1. The first one with a higher density represents incorrectly classified samples and is the cause of the performance drop in this scenario. An optimal fusion method should therefore leave the distribution of the real scores unchanged, which correctly have a unimodal distribution with an average of 0. It should also reverse the trend of fakes and increase the density of mode with a mean equal to 1. As shown by Figure 7, none of the fusion methods analyzed could achieve these difficult objectives since the real and fake starting distributions are greatly overlapped. However, it is of interest in the design of a deepfake detector to highlight certain clues:  
* Non-parametric fusion rules fail to separate the two distributions. Due to the complete control of the experimentation, it is possible to note that, in contradiction with our earlier findings [10], the Maximum rule does not bring benefit in the detection of never-seen-before deepfake samples. In fact, this function is limited to shifting the mode score towards 1. * Weighted average-based methods lower both the fake mode densities and the real mode density, thereby, resulting in distributions with high variability. This results in a slight improvement in cross-dataset performance, which is appreciable particularly when the operational point is set at the EER. * Methods based on classification models increase the density of the fake mode with a mean equal to 1 (resulting in a distribution with a lower variability) but decrease the density of the real mode by increasing its variability. This results in a slight improvement in the cross-dataset performance, which is appreciable particularly when the operational points are stringent from the point of view of the FPR (e.g., the threshold at 1 % FPR). To sum up, the analysis of the cross-dataset scenario confirms the improvement in the deepfake detection by means of the fusion rules based on the weighted average and classification models while demonstrating the lack of effectiveness of the non-parametric fusion approach. Moreover, the analysis in the cross-dataset scenario confirmed the ability of the fusion methods to improve the overall performance of the deepfake detection systems--also with respect to the variability introduced by the reference scenario. Accordingly, the designer should prefer fusion rules based on the weighted average, especially the Correlation-based Avg, when the threshold is set on the EER, or prefer fusion rules based on classification models, especially on Complement NB, with stringent operational thresholds. Figure 7: Cont.
## 3 Results and Discussion
### Permeability  
According to our former research [13], the matrix of the innovative pervious concrete has a very low porosity; its permeability mainly depends on the reserved top-bottom interconnected pores. Therefore, its permeability coefficient is almost entirely determined by the size and distribution of the pores and has little relationship with the mix proportion. The advantage is that the permeability coefficient of IRPC is higher and more controllable than traditional pervious concrete. The permeability coefficients of IRPC under abrasion and sulfate attack were revealed in Figure 5. The permeability coefficients of all groups were about 7 mm/s, much higher than the minimum requirement of pervious concrete (0.5 mm/s). With the increase of sulfate wetting-drying cycles, the permeability coefficients of IRPC slightly reduced. This may be due to the slight expansion of IRPC's volume after sulfate attack [38], leading to the reduction of its pore volume and permeability coefficients. Moreover, it can be found that the abrasion slightly reduced the permeability of IRPC. This is caused by the accumulation of debris in the pores from abrasion, but the reduction in the permeability coefficient is quite slight. Compared with the top-bottom interconnected pores of IRPC, the zigzag pores of traditional pervious concrete are more likely to be clogged by debris accumulation [39], resulting in the decline of permeability.  
Traditional pervious concrete cannot guarantee both strength and permeability performance. [PERSON] et al. [22] prepared recycled pervious concrete using waste glass and RCA. When NA was fully replaced by RCA, the compressive strength and permeability coefficient of recycled pervious concrete are about 20 MPa and 2.3 mm/s, respectively. When using 50% waste glass cullet and 50% RCA as aggregates, the compressive strength of recycled pervious concrete increased to 22 MPa, but the permeability coefficient was reduced to 1.2 mm/s. IRPC can possess the compressive strength of 40 MPa and the permeability coefficient of 7 mm/s even at a 100% replacement rate. Therefore, in terms of the strength and permeability performance, the combined use of innovative pervious concrete as the matrix and RCA as the aggregate is attractive and promising to produce IRPC with good strength and permeability.
## 2 Background Preliminaries
### Overview of the Perception Layer Threats and Attacks
The network layer, which connects the perception layer to the upper layers, is vulnerable to selective forwarding (a form of DoS attack), the cloning of ID and Sybil, wormhole attacks, sinkhole attacks, blackhole attacks, spoofed, alter, and reply routing info attacks, as well as eavesdropping, DoS, and MitM. While our study focuses on cyber attacks, the perception layer is subject to non-cyber attacks. The study presented in [23] focused on the vulnerabilities and threats associated with the various IoT layers and used the term edge to describe the perception layer. It argued that the greatest threat at the perception layer is not cyber but is rather electronic, such as jamming, or kinetic, such as physical attacks with the intent to damage, degrade, disrupt, or destroy the sensors, actuators, machines, controllers, and other devices at the perception layer. Attacks such as side-channel analysis, fault injection, and voltage glitch attacks are physical or hardware attacks [24]. However, this study focuses on cyber attacks on the perception layer. Generally, in perception layer attacks, the attacker aims to gain unauthorized access to control the IIoT devices to launch cyber attacks. While there are several types of attacks against IioT devices depending on the specific IioT layer, distributed denial of service (DDoS), data theft through keylogging, tracking through fingerprinting, and the scanning of open ports are some common attacks on IioT devices that are carried out by attackers through the use of botnets [21]. Other attacks against IioT devices include blackhole, Sybil, clone, and sinkhole attacks.
## 3 Samples and Methods  
### Sample Information and Sample Preparation  
The core samples used in this study were collected from the Es4x tight sandstone reservoir interval in the Bonan Sag. The samples are cylindrical plugs, each with a diameter  
Figure 1: Location map of the study area: (**A**) geographic location of the Bohai Bay Basin in eastern China; (**B**) location of the Jiyang Depression in the Bohai Bay Basin (red rectangle); (**C**) structural units and major faults in the Bonan Sag.  
of 2.5 cm and a length ranging from 2.5 to 8.0 cm. The porosity and permeability values in Table 1 were measured using a helium porosimeter (Model AP-121-003-1, \(\pm\)0.01% accuracy) and a permeability measurement instrument (Model ECK-III, \(\pm\)0.001 \(\times\) 10\({}^{3}\) um\({}^{2}\) accuracy), following the SY/T 5336-2006 standard measurement workflow, with porosity values ranging from 7.26% to 22.41% and permeability between 0.401 \(\times\) 10\({}^{-3}\) um\({}^{2}\) and 56.380 \(\times\) 10\({}^{-3}\) um\({}^{2}\).  
The crude oil used in the experiments has a density of 0.8469 g/cm\({}^{3}\), a dynamic viscosity of 3.32 MPa at 50 \({}^{\circ}\)C, freezing point of \(-\)11 \({}^{\circ}\)C, wax content of 14.78%, sulfur content of 0.72%, combined saturate and aromatic component of 48.89%, and asphaltene and resin content of 36.33%.  
All core samples were cleaned for 48 h using a Soxhlet extractor with a mixture of petroleum ether, ethanol, and toluene (in a 1:1:2 ratio) at 120 \({}^{\circ}\)C. Subsequently, the core plugs were sectioned into three parts: upper section A, middle section B, and lower section C (Figure 2). Section A, a 3-5 mm thick sample with a flat bottom and a fresh fracture surface, was used for ESEM to evaluate the wettability of the pore walls. Section B, a 2-3 mm thick circular thin slice, was polished with 1000-grit sandpaper for CA measurements to assess the macroscopic wettability of the rock surface. Samples from the CA measurements were then sliced into thin sections for SEM-AMICS scanning. Section C, a cylindrical plug with a length of 2.0-4.9 cm, was utilized for SI combined with NMR experiments to determine the macroscopic wettability of the samples. The brine used for SI was a 20 g/L NaCl solution, and the crude oil was supplied by the Exploration and Development Research Institute, Shengli Oilfield Company, SINOPEC, sourced from the E54x tight sandstone reservoir in the Bonan Sag.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **Well** & **Depth/m** & **Formation** & **Porosity/\%** & **Permeability/10\({}^{-3}\) μm\({}^{2}\)** \\ \hline C1 & Y161 & 3482.5 & Es4x & 7.26 & 0.401 \\ C2 & L176-1 & 2881.9 & Es4x & 22.41 & 56.380 \\ C3 & Y172 & 4042.1 & Es4x & 9.85 & 0.674 \\ C4 & L68 & 3376.9 & Es4x & 14.04 & 0.560 \\ \hline \end{tabular}
\end{table}
Table 1: Information from samples investigated.  
Figure 2: Schematic diagram of core plug sample preparation. (**A**) sample for pore-scale wettability observation, (**B**) sample for meso-scale wettability observation, (**C**) sample for macro-scale wettability observation.