esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Permeability vs. Tortuosity and Specific Surface Area  
According to the K-C equation, permeability is not only a function of porosity, but also of hydraulic tortuosity \(\tau\) and specific surface area \(S\)[1; 3; 5; 38]. Permeability is inversely proportional to the square of \(\tau\) and \(S\). The K-C equation was employed for an ideal porous medium, and many studies have shown that permeabilities calculated using this equation do not agree with the measured permeability of various rocks. Therefore, some modifications have been proposed in the literature [3; 5; 9; 39; 40].  
Figure 11 demonstrates the dependence of the permeability of sub-volumes on \(\tau\) (Figure 11a,c) and \(S\) (Figure 11b,d) for heterogeneous and naturally fractured samples before and after their dissolution. As shown in Figure 11, there is a clear relationship between permeability and \(\tau\) and \(S\). These relationships can be best described by power laws \(k\sim\tau^{-\alpha}\) and \(k\sim S^{-\beta}\), where \(\alpha\) and \(\beta\) are the power exponents for \(\tau\) and \(S\), respectively.  
The values of \(\alpha\) and \(\beta\) are much higher than those in the K-C equation: \(\alpha\) varies from 8.5 to 40.1 (Figure 11a,c), and \(\beta\) from 5.1 to 17.1 (Figure 11b,d). [PERSON] et al. also showed that there is a good power law relationship between \(k\) and \(\tau\), with the power exponent varying between 3.46 and 8.24 for sub-samples of anisotropic and heterogeneous tight sandstone [41]. [PERSON] and [PERSON] also obtained a power law relationship between permeability and tortuosity \(k=(0.15/\tau)^{9.09}\)[25] for various granular porous materials. Values of \(\alpha\) and \(\beta\) much greater than 2 indicate that the K-C equation is not an effective tool for predicting the absolute permeability of natural rock samples. Figure 11 also indicates the larger scattering in the distribution of \(k\) for the naturally fractured sample.
## 2 Electromagnetic Wave Propagation Behavior and the Principle of the Reflected Voltage
### Horizontal resolution  
The horizontal resolution of the electromagnetic wave of the ground penetrating radar is related to the size of the calculated first Fresnel zone. If the first Fresnel zone cannot provide enough reflected energy, the resolution of the object cannot be measured, as is shown in Figure 2.
## 1 Introduction
### Dynamic Adjustment of the Scaling Factor  
To obtain an efficient and robust FXAPL-I algorithm, the scaling factor must be adjusted manually, requiring a time-consuming and costly effort by expert human designers. To reduce this burden, [PERSON] et al. [16] introduced a novel strategy to automatically adjust the scaling factor and thus contribute to the rapid development of practical ANC applications. Their method to adjust automatically the scaling factor is based on the estimation of an auxiliary error vector \(\varepsilon_{ji}(n)\)[17]. For the FXAPL-I algorithm, the auxiliary vector is defined as \(\varepsilon_{ji}(n)=\textbf{X}_{ijk}(n)\textbf{e}_{k}(n)\). The algorithm for the coefficient update can thus be rewritten as follows:  
\[\textbf{w}_{ji}(n+1)=\textbf{w}_{ji}(n)+\sum_{k=1}^{K}\mu_{ijk}\varepsilon_{ji }(n) \tag{11}\]  
The dynamic scaling factor can then be expressed as follows:  
\[sf_{ijk}=sf_{max}\frac{\parallel p_{ijk}(n)\parallel^{2}}{\parallel p_{ijk}(n )\parallel^{2}+C} \tag{12}\]  
where \(p_{ijk}(n)\) is an estimation of \(\varepsilon_{ji}(n)\) that is obtained as \(p_{ijk}(n)=\alpha p_{ijk}(n-1)+(1-\alpha)\varepsilon_{ji}(n)\) with a smoothing factor (0\(<\alpha<\)1) and \(C\) is a parameter related to the projection order and is approximated by \(L_{ij}/\)SNR, where SNR is the signal-to-noise ratio. To guarantee stability, the maximum scaling factor parameter \(sf_{max}\) is set to less than 1. The proposed method to compute the step size \(\mu_{ijk}\) is thus as follows:\[\mu_{ijk}=\frac{\parallel\textbf{X}_{ijk}(n)\textbf{e}_{k}(n)\parallel^{2}}{ \parallel\textbf{X}_{ijk}^{t}(n)\textbf{X}_{ijk}(n)\textbf{e}_{k}(n)\parallel^{ 2}}\cdot sf_{ijk} \tag{13}\]  
In this work, the use of the automatically adjustment method contributes little to the overall computational complexity. However, its use potentially allows the rapid development of practical applications by minimizing the time-consuming effort of the designers.
###### Abstract  
The assessment of rock brittleness holds significant importance for understanding and predicting the mechanical properties and engineering behavior of rocks. Due to the lack of a unified definition of rock brittleness, numerous evaluation methods for brittleness indexes have been proposed by scholars both domestically and internationally in recent decades, resulting in diverse evaluation outcomes. In this study, we first summarize the existing rock brittleness evaluation methods and highlight their respective advantages and disadvantages. Subsequently, considering the pre-peak plastic deformation of the rock mass, the pre-peak brittleness index factor is introduced. Furthermore, taking into account the total energy consumed by the rock mass for failure after the peak, the post-peak brittleness index factor is proposed. These two components of the brittleness index describe the characteristics of different stages of the stress-strain curve, leading to the development of a novel brittleness index. The proposed method is then applied to evaluate the brittleness of both red-bed sandstone and cyan sandstone, revealing the variation of rock brittleness under different working conditions. Finally, three existing evaluation methods are selected to validate the rationality of the proposed method. The results demonstrate that for red-bed sandstone, the proposed brittleness index exhibits maximum values under natural conditions at all confining pressures. The four brittleness indexes consistently characterize the brittleness of red-bed sandstone under natural conditions. Under saturated conditions, the brittleness indexes exhibit different patterns of variation. For cyan sandstone, the three brittleness indexes--B7, B9, and Bnew--exhibit a similar trend in characterizing the brittleness of cyan sandstone under natural conditions and freezing-thawing conditions, while the trend of B17 is essentially opposite to that of the previous three indexes. The research findings provide guidance for the assessment of sandstone brittleness.  
rock brittleness; plastic deformation; sandstone; energy dissipation; brittleness index
# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
## 3 Indoor Reconstruction Methodology
### Indoor Component Reconstruction
#### 3.4.1 Data Term  
The data term encloses the weights between the cells and the terminal nodes. Starting from the available data, some cells can be directly categorized as occupied (Figure 5a). In particular, all cells occupied by points belonging to the ceiling can be directly assigned to set T. In a similar way, cells bordering an occupied cell and separated from it by a detected wall segment are set as empty. For this reason, weights of edges joining the sink to cells labelled as occupied are set to infinite and, in a similar way, edges joining the source to empty cells are set to infinite. The weights of the remaining edges between cells and terminal nodes are presented by using a method based on the ray casting method inspired by that found in [13; 41]. This method essentially creates virtual rays connecting the scanner positions (which in the case of an MLS changes during the time) and a cell. If the rays intersect an edge detected as \"real\" in the previous step the tracing is stopped. On the other hand, in the case the ray traverses only \"pending\" walls the ray is cast. The rationale of this method is that the cells belonging to the interior space are more likely to intersect the rays than the exterior cells. In other words, the outer cells will have relatively low weights and the interior cells will have relatively large weights. More specifically, summarizing the previously listed cases we have:  
If the cell is classified as occupied starting from ceiling occupancy:  
\[D_{i}(l_{i})=\left\{\begin{array}{l}\infty\,\,l_{i}ext\\ 0\,\,l_{i}int\end{array}\right. \tag{7}\]  
If the cell is classified as empty starting from ceiling occupancy:  
\[D_{i}(l_{i})=\left\{\begin{array}{l}0\,\,l_{i}ext\\ \infty\,\,l_{i}int\end{array}\right. \tag{8}\]Otherwise:  
\[D_{i}(l_{i})=\left\{\begin{array}{ll}\frac{i_{num}}{max_{num}}\cdot s_{a}&l_{i} text\\ \left(1-\frac{i_{num}}{max_{num}}\right)\cdot s_{a}&l_{i}\text{\emph{eint}}\end{array}\right. \tag{9}\]  
where \(i_{num}\) is the intersection number of each cell, \(max_{num}\) is the maximum number of intersections in all cells and \(s_{a}\) is the reciprocal of each cell's area.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 5 Results and Discussion
### Numerical Simulation
Similarly, the PP method, which shows limited effect in the aligned dataset (\(p\)-value of 0.429), exhibits extreme sensitivity to the number of recordings in non-aligned conditions, as evidenced by a \(p\)-value of \(2.21\times 10^{-194}\). The Yoshida method maintains robustness across both conditions, with high \(p\)-values of 0.493 and 0.969 in the aligned and non-aligned datasets, respectively, indicating low sensitivity to the number of recordings.
## 5 Potential Use Cases and Applications
### Operator and Third-Party Services  
Typically, operators of mobile networks offer and supply IEC service. However, a third party could supply these services. For example, third-party cloud service providers are entities that provide services and resources for hosting IEC applications, while they are not traditional network operators. There are many examples of third-party providers such as location and facility management companies, neutral vendors, tower owners, and vehicle fleet management companies. Some of these service introduced by third parties as follows:  
* _Autonomous vehicles:_ The autonomous platoon of truck convoys was one of the first use cases of autonomous vehicles [79; 80; 81; 82]. So a group of trucks moves closer to each other in a group, which saves fuel costs and reduces congestion. With advanced computing, it is possible to remove the need for drivers in all trucks except for the front-end trucks, because the trucks will be able to communicate with each other with extremely low latency. In addition, automated vehicles are powerful enough to manage all kinds of on-board computing tasks and well-connected enough to interact with more than one network or device [83]. These robotic vehicles will be in constant contact with the world while making split-second decisions based on information from smart sensors.
* _Industrial IoT:_ Actually, IoT devices and processes that included into this category are often referred to as the Industrial Internet of Things (IIoT). Safety is one of the fundamental issues that needs attention in the sector of Industry [84; 85; 86]. By using intelligence edge computing technologies and hardware it enables, safety levels could be improved and also provide analysts with real-time information about equipment, machines, tools and vehicles so that workers can work in a safe environment [87].
* _Big Data Analytic:_ This case, by using a pool of cloud-based service for external vendors depending on the collection of massive information (such as video, sensor data, etc.) from different devices, where these data are being analyzed before being sent to the central servers. These applications could be run in a single location (i.e., on a single host), distributed over a specific region such as campus or for the entire network. To support the restrictions imposed on a party that is requesting third-party service, it is necessary to run in all required sites (IEC hosts) applications.
* Tracking of locations: the main use of such cases enable real-time, network-metric tracking of active terminal equipment (regardless of GPS) using \"best-in-class\" geolocation algorithms [1]. In addition, deployment in the IEC system provides an efficient and scalable solution with local processing. It also enables these services to businesses and consumers (for example, on primary adherence), or in retail venues, locations and in different coverage areas where GPS services are not capable [88].
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 3 Methodology
### Model Construction
#### 3.3.3 The Pabs Model  
To enhance the timeliness of crop phosphorus nutrition diagnosis and support large-scale phosphorus management, we developed a corn phosphorus absorption (Pabs) model. The phosphorus standard accumulation (Psa) and phosphorus actual accumulation (Paa) in corn plants were calculated by multiplying the critical phosphorus concentration (Pc) and the plant's phosphorus concentration (P) by its aboveground biomass (Bio), as shown in Equations (3) and (4):  
\[\mathrm{P_{sa}=a\;*\;Bio^{1-b},} \tag{3}\]  
\[\mathrm{P_{aa}=P\;*\;Bio,} \tag{4}\]  
The Pabs model was derived by subtracting the standard phosphorus accumulation from the actual phosphorus accumulation, as indicated in Equation (5):  
\[\mathrm{Pabs}=(1-\mathrm{PNI})\Big{(}a\;*\;Bio^{1-b}\Big{)}, \tag{5}\]where 1 represents the value of PNI under the optimal phosphorus nutrition state of the plant [11], recorded as PNItarget.  
To further optimize the model's accuracy, we incorporated the phosphorus use efficiency (PUE) [47], as given by Equation (6):  
\[\text{PUE}=\frac{(\text{AP}-\text{PB})}{\text{PF}}\times 100\% \tag{6}\]  
where AP represents the treatment of corn with phosphorus fertilizer, PB represents the treatment of corn without phosphorus fertilizer, PF is the amount of phosphorus fertilizer used for corn, and the value of PAE in this study is 37%.  
The final optimized phosphorus absorption model is represented in Equation (7):  
\[\text{P}_{\text{abs}}=\frac{\text{a}*\text{Bio}^{1-\text{b}}\big{(}\text{PNI}_ {\text{target}}-\text{PNI}\big{)}}{\text{PUE}} \tag{7}\]
# Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
## 4 Result and Analysis
### Tie Points  
Since the in-situ measurements are too scarce, ground truth data can hardly be found for appropriate places and times. To validate the final result, we selected 178 pairs of tie-points manually, which are evenly distributed in the final velocity field, both spatially and numerically. Almost all of these points are distinct features in the ice surface, including crevasses, moraine, deformation, and ice-shelf front. Velocities from tie points are compared with those extracted from the MOA by automated tracking in Figure 4. Showing a strictly linear relationship between them, the velocity result from tracking MOA agrees quite well with the tie point velocity, not only in the \(X\) and \(Y\) dimensions  
Figure 3: Start points and corresponding streamlines in (**a**) the Larsen-C Ice Shelf, (**b**) the Filchner-Ronne Ice Shelf, (**c**) the Donning Maud Land, (**d**) the Amery Ice Shelf, and (**e**) the Ross Ice Shelf, with transparent MOA tacking velocity field overlapping on the enhanced MOA 2009 image as the background.  
but also in the magnitude and direction domains. Statistical comparison shows that determination coefficients are all close to 1, and RMSEs are roughly 50 m\(\cdot\)a\({}^{-1}\), the same order as the original spatial resolution of the MODIS image taking five years time span, although the largest outlier goes to the extreme reaching 200 m\(\cdot\)a\({}^{-1}\). The mean direction difference is less than 1 degree with an RMSE of 8.4 degree. A variety of dynamic parameter can be derived based on the velocity field, among which the strain rate is of particular significance [63]. The above performance suggests that such parameters in the ice shelf area can be roughly estimated from our velocity result since the spatial distribution of errors is uniform and unbiased. However, noting that the tie-point pairs mentioned above are mostly derived from distinct features, those RMSEs are presumed to be underestimated since the majority of tracking pixels from indistinct features are not included in this comparison. Hence, the statistics can only represent areas with highly recognizable surface features. The different performance between tie-points and randomly sampled points in comparison with MEaSURE is followed in Section 5 later.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 3 Experimental Results  
### Data  
The test data are three image strips of KOMPSAT-3 product level 2R over Romania, as the specifications are listed in Table 1. The acquisition dates are 8 and 24 April and 4 May 2018. The strips have long image line sizes up to 60,000-70,000 pixels with an image swath width of 24,060 pixels. Each image stripe is made up of three image scenes with over 20,000 image lines each. The acquisition geometry includes incidence and azimuth angles. Strips #1 and #3 have similar geometry and a low incidence angle. Small incidence angles of Strips #1 and #3 produce a small GSD (Ground Sample Distance) than Strip #2 with a relatively large incidence angle. Note that the azimuth angle of Strip #2 is in an almost opposite direction from those of the others.  
Figure 5 shows the three data strips. Strip #2 is located in the center with partial overlap with the other strips.
# An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark
###### Abstract
Our main focus was on the effort needed to apply this programming model to the given applications and on the performance itself. Some sequential codes require certain transformations to produce correct parallel programs. In addition, to improve the results, several optimizations were performed in sequential code after the directives were applied. We primarily focused on comparing existing CUDA and OpenCL implementations with the new OpenACC implementations. The main contributions of this paper and additions to the field are summarized as follows. * We implemented applications from the Parboil benchmark suite using OpenACC and made them publicly available for further research. * We evaluated OpenACC implementations of the applications from the Parboil benchmark suite and compared them to their CUDA and OpenCL counterparts. * We discussed the programming effort needed to parallelize the application on the GPU using the directive-based programming model compared to low-level programming models. * We proposed several recommendations and optimizations that can be used to utilize the OpenACC programming model more efficiently. Related work, our motivation, the choice of benchmark applications, and the results are discussed in the rest of the paper. The paper is organized as follows. In Section 2, we review papers and results relevant to the topic at hand. In Section 3, we briefly describe the OpenACC programming model and its applications in GPU computing. Also, we discuss benchmark suites for GPU computing in general. Section 4 describes the Parboilbenchmark suite and its applications which we parallelized via OpenACC. Implementation details were given in Section 5. Results and discussion are presented in Sections 6 and 7. The Section 8 briefly concludes the paper with directions for future work.
## 3 Results and Discussion
Therefore, the adjustment range of the molar ratios of BA/St/DVB is quite narrow in order to achieve high open-cellular extent and high toughness simultaneously in polyHIPE membranes. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline Sample & BA/St/DVB & \begin{tabular}{c} **E a** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **\(\sigma\) b** \\ **(kPa)** \\ \end{tabular} & \begin{tabular}{c} **\(\epsilon\) c** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **M\({}_{\text{T}}\) d** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **BET** \\ **(m\({}^{2}\)-g\({}^{1}\))** \\ \end{tabular} & \begin{tabular}{c} **P\({}_{\text{A}}\) (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **P\({}_{\text{T}}\) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **P\({}_{\text{C}}\) (\%)** \\ \end{tabular} \\ \hline RAFT-2 & 9/2/1 & 2.04 \(\pm\) 0.34 & 347 \(\pm\) 4.31 & 24.00 \(\pm\) 3.26 & 47.60 \(\pm\) 7.88 & 3.42 & 67.21 & 75.69 & 88.80 \\ RAFT-3 & 7/4/1 & 10.09 \(\pm\) 1.82 & 819.62 \(\pm\) 33.31 & 17.50 \(\pm\) 3.90 & 93.04 \(\pm\) 12.28 & 3.85 & 69.79 & 75.57 & 92.35 \\ RAFT-4 & 5/6/1 & 51.10 \(\pm\) 2.68 & 664.4 \(\pm\) 41.37 & 1.60 \(\pm\) 0.66 & 6.24 \(\pm\) 3.45 & 5.19 & 76.65 & 75.71 & 100 e \\ \hline \hline \end{tabular}  
* Young’s modulus, b ultimate tensile strength, c elongation at break, d modulus of toughness, e Deviation in the theoretical calculation, \(P_{\text{C}}\) is recorded as 100% when P\({}_{\text{A}}\) is greater than P\({}_{\text{T}}\).  
\end{table} 
Table 3: The structure and mechanical properties of polyHIPE membranes with different molar ratios. Figure 9: SEM images of polyHIPE membranes with different molar ratios of BA/St/DVB prepared by RAFT polymerization: (**a**) 9/2/1, (**b**) 7/4/1, and (**c**) 5/6/1. The heat stability of RAFT-2, RAFT-3, and RAFT-4 was also analyzed. The TGA results show that the initial decomposition temperatures of the samples all exceed 300 \({}^{\circ}\)C, and the heat resistance is improved by increasing the St content, as shown in Figure 12. Moreover, no shrinkage or yellowing phenomenon is observed after 200 \({}^{\circ}\)C treatment in air for 30 min, as shown in Figure 13, proving excellent thermal stability of all the samples. The synthesized polyHIPE membranes have wide application prospect in the field of lithium ion batteries as separators due to their high open-cellular structures and high toughness. Since the traditional polyolefin separators possesses poor heat stability, the superior heat stability of the polyHIPE membrane can effectively delay the thermal runaway and thus improve the safety of lithium ion batteries. Figure 11: Pore size distribution curves of polyHIPE membranes with different molar ratios of BA/St/DVB prepared by RAFT polymerization. Figure 10: The stress–strain curves of polyHIPE membranes with different molar ratios of BA/St/DVB prepared by RAFT polymerization.
## 2 About the Chosen Digital Signature Algorithms
### Sphincs+
#### 2.3.2 Advantages and Disadvantages According to the Authors  
The essence of Sphincs+ lies in its dichotomy: while it arguably represents the most cautious approach to post-quantum digital signature architecture, it pays the price in efficiency, in terms of both signature size and computational speed.  
Disadvantages:  
* Signature size and speed: Sphincs+ is not designed to be the fastest or the smallest, although it does offer trade-offs between these two metrics.  
Figure 4: Runtime benchmarks for Sphincs+, Sphincs+–Haraka on AES-NI, Sphincs+-Sha-256 on AVX2, Sphincs+-Shake256 on AVX2 [25].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Public Key Size** & **Secret Key Size** & **Signature Size** \\ \hline Sphincs+128s & 32 & 64 & 7856 \\ Sphincs+128f & 32 & 64 & 17,088 \\ Sphincs+192s & 48 & 96 & 16,224 \\ Sphincs+192f & 48 & 96 & 35,664 \\ Sphincs+256s & 64 & 128 & 29,792 \\ Sphincs+256f & 64 & 128 & 49,856 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key and signature sizes in bytes [25].  
Advantages:
* Minimal security assumptions: the security is entirely predicated on well-understood hash functions, avoiding new computational hardness assumptions.
* Easily analyzed attacks: the current state-of-the-art attacks, both classical and quantum, can be straightforwardly analyzed, enabling precise security quantification.
* Small key sizes: Sphincs+ boasts compact public keys, a benefit in scenarios where public keys are often transmitted.
* Overlap with XMSS: the scheme harmonizes well with XMSS, facilitating their combined use in specific applications like VPNs.
* Reusable building blocks: speed enhancements in underlying hash functions directly translate into Sphincs+ performance gains.