esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Notably, HL offers reliable and consistent readings, and is almost unaffected by surface roughness, although its readings can be influenced by factors such as moisture content, texture, and ambient temperature fluctuations [43; 44]. Recent studies have shown that, in rock materials, both HL and UPV values decrease with increasing porosity and crack density [45; 46; 47], suggesting that they could also be indirectly linked to hydric performance parameters such as capillary water absorption. However, the current literature emphasizes their role as predictors of mechanical strength [48; 49] and, to date, neither have been directly applied to estimate capillary water behavior. Yet moisture transport plays a critical role in stone degradation, as it can accelerate or trigger subsequent mechanical damage, particularly in thermally altered materials. In light of the increasing frequency and severity of wildfires, coupled with the critical need for reliable, non-destructive diagnostic methodologies, this study rigorously evaluates the viability of Leeb hardness and ultrasonic pulse velocity measurements as proxies for predicting the hydric behavior of carbonate stones subjected to high-temperature exposure. For this, a total of thirteen carbonate lithotypes, including marbles, limestones, a traverine, and a breccia, were studied before and after thermal exposure at 300 \({}^{\circ}\)C and 600 \({}^{\circ}\)C. These two temperature thresholds were selected to capture distinct stages of thermal alteration in carbonate stones. At 300 \({}^{\circ}\)C, thermal expansion of calcite may contribute to crack closure due to differential stress redistribution [50], whereas at 600 \({}^{\circ}\)C, the same process is more likely to promote the formation of new microcracks and porosity increase [48], although this is still well below the calcination temperature of calcium carbonate (approximately 897 \({}^{\circ}\)C) [51]. Together, these two conditions provide a representative spectrum into both moderate and advanced fire-induced degradation scenarios. Petrophysical properties such as open porosity, bulk density, and capillary absorption coefficient were measured. By bridging the gap between mechanical diagnostics and moisture behavior, this work contributes to the development of more comprehensive assessment strategies for stone conservation under changing climatic conditions.
## 1 Introduction
[PERSON] and [PERSON] [16] evaluated the analytical energy loss correlations used in the preliminary design for the air or gas radial inflow turbine in designing the s-CO\({}_{2}\) turbine. They found that the published loss model configurations may not suit off-design analysis for the s-CO\({}_{2}\) radial inflow turbine. They developed a new loss model configuration with less than a 2% difference in efficiency prediction over the CFD result. With the aid of the CFD method, [PERSON] and [PERSON] [17] studied the loss contribution for a 300 kW low specific speed s-CO\({}_{2}\) radial inflow turbine. Their results concluded that the end-wall viscous losses in the stator are dominated, which plays a more significant role than that for the gas radial inflow turbine. [PERSON] et al. [18] focused on the leakage loss in the impeller backface cavity for the s-CO\({}_{2}\) radial inflow turbine and revealed the relevance of the leakage loss to the labyrinth seal geometrical parameter. The present paper provides an optimization design platform based on the one-dimensional design method in [PERSON] et al. [19] to carry forward the efficient s-CO\({}_{2}\) radial inflow turbine design. An in-house code is developed in MATLAB language. The design variables are selected with nozzle velocity coefficient, rotor velocity coefficient, reaction degree, diameter ratio, absolute flow angle rotor inlet, and relative flow angle at the rotor outlet. The optimization function maximizes the total-to-static efficiency with aerodynamic and structural constraints. The build-in function, _fmincon_, is treated as the optimization algorithm. The optimization code is then applied to design the s-CO\({}_{2}\) radial inflow turbine with a power of 2.1 MW. The comparison of the total-to-static efficiency is performed between the CFD simulation and the one-dimensional calculation under the design working condition to validate the current code. The flow field and the performance are also analyzed in detail under the off-design working conditions.
# Hands-On Deformation of Volumetric Anatomical Images on a Touchscreen
## 7 Results
### Implementation Details and Performance  
We have implemented our work on a compute platform consisting of a 3.1 GHz Quad-core Intel (Santa Clara, CA, USA) Core i7-3770S CPU with 16 GB of memory, and a NVIDIA (Santa Clara, CA, USA) GTX670 graphics card. We have used a HANNspree (Taipei, Taiwan) 23-inch touchscreen with full 10-finger tracking. We perform volume rendering using VTK [53], and we also employ its ray-casting picking functions.  
For all the example manipulations, we have used an abdomen CT-scan with 5 million voxels. This dataset is publicly available under the OsiriX DICOM image library (ref. BRE-BIX) [54]. As a preprocess, we have scanned the opacity range of the model to identify the major anatomical elements and define transfer functions, as well as their mechanical parameters (i.e., Young modulus and Poisson's ratio). To apply the corotational coarsening deformation model, we have created coarse and fine tetrahedral meshes in the following way. First, we have created the coarse mesh as a regular tetrahedral mesh covering the volume image, producing a total of 6000 tetrahedra. Next, we have segmented bone material based on its opacity range, we have meshed the resulting bone surfaces, and we have created the fine tetrahedral mesh using TetGen by combining the bone nodes with a regular high-resolution grid. The fine mesh has a total of 281,000 tetrahedra.  
In the examples shown in the paper, the average performance of our method is of 10 fps. The computational cost is dominated by CPU-GPU data transfers, which take about 50 ms per frame, and are motivated by the lack of low-level access of the VTK API to the volume data. This cost could be drastically reduced with a fully dedicated implementation, and thus improve the overall runtime performance. With the current performance, and same as in other applications, latency makes users move more slowly than they would with a more responsive system.  
Within our algorithm, the cost of deformation is below 30 ms per frame, and resampling takes just 15 ms. The cost of other components, such as the selection of handles and the mapping of 2D gestures to 3D transformations, is negligible, thanks to the high efficiency of the methods we propose. For performance scalability, we refer the reader to the analysis carried out by [PERSON] et al. [1]. During manipulation and deformation, our method adds just two operations to their pipeline: (i) the mapping of 2D gestures to the 3D transformation of a handle, and (ii) the computation of forces due to the rigidly transformed handle, as in (10). The cost of these two operations is negligible compared to the rest of their pipeline.
# Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
## 3 Theory and Algorithms
### Rain Attenuation  
The rain attenuation can be calculated from the rain rate using the empirical model in the ITU-R P.838-3 recommendation [13; 14]. The specific attenuation (\(\gamma_{R}\)) and the total attenuation (\(A_{rain}\)) in dB is given using  
\[A_{rain}=\gamma_{R}r_{eff}=k_{p}R^{\alpha_{p}}\cdot rr_{0}, \tag{10}\]  
where \(R\) is the rain rate (mm/h), \(k_{p}\) is the coefficient of either vertical polarization (\(k_{V}\)) or horizontal polarization (\(k_{H}\)), \(\alpha_{p}\) is the coefficient of vertical polarization (\(\alpha_{V}\)) or horizontal polarization (\(\alpha_{H}\)), \(r_{eff}=rr_{0}\) is the effective path distance between two antennas and \(r\) is the distance factor.  
The distance factor \(r\) is determined using the ITU-R P.530-18 recommendation [14], given as  
\[r=\frac{1}{0.477r_{0}0.633R_{0.01}^{0.073\text{a}}f0.123-10.579[1-\exp(-0.024r _{0})]}, \tag{11}\]  
where \(R_{0.01}\) is the 1-min rain rate exceeded for 0.01% of the time. The rain rate \(R_{0.01}\) is determined from the cumulative distribution function (CDF) of rain rates, collected over a long period. In the case that rain statistics cannot be measured, an estimate can be calculated from the ITU-R P.837-7 recommendation [31]. The formula of the distance parameter was derived empirically. In the previous ITU-R P.530-17 release, the factor was limited to the maximum value of 2.5. However, experimental data from previous studies have shown that for short distant links (under 1 km) such a limit resulted in the underestimation of rain attenuation [22; 32]. For the current ITU-R P.530-18 model used in this study, the maximum limit of \(r\) has been removed. Using the values for 74.625 GHz and vertical polarization, \(k_{p}=k_{p}=1.0946\) and \(\alpha_{p}=\alpha_{v}=0.7118\). The \(R_{0.01}\) rain rate measured in 2022 at the experimental location is \(R_{0.01}=83.2\) mm/h, and this yields \(r=3.7075\).  
The specific attenuation in Equation (10) is determined using the rain rate. However, previous studies have shown that smaller rain drop sizes, associated with lower rain rates, cause higher attenuations in the E band [33]. Thus, without measuring the actual rain drop size distribution via an instrument such as a raindrop spectrometer, the ITU-R P.838 model may underestimate the specific attenuation. In a tropical location, the power budget needs to be designed for larger rain rates; then, the model should be sufficient.
## Abbreviations  
\begin{tabular}{l l} \(a_{r}\) & aspect ratio of the particles \\ \(\varphi_{b}\) & basic friction angle \\ \(C_{u}\) & coefficient of uniformity \\ \(r_{i}\) & degree of particle roundness \\ \(s\) & deviatoric stress \\ \(R\) & equivalent roughness \\ \(S\) & equivalent strength \\ \(d_{c}\) & final visco-elastic deviatoric strain for \(D=\frac{1}{2}\) \\ \(\varepsilon_{s}^{\mathrm{K}}\) & Kelvin deviatoric strain \\ \(G^{K}\) & Kelvin shear modulus \\ \(\varepsilon^{K}\) & Kelvin strain \\ \(\eta^{\mathrm{K}}\) & Kelvin viscosity \\ \(\sigma_{\mathrm{max}}\) & maximum principal stress \\ \(K_{\mathit{ref},n_{0}}^{\mathrm{M}}\) & Maxwell bulk modulus at reference pressure and reference porosity \\ \(K_{\mathit{ref},n}^{\mathrm{M}}\) & Maxwell bulk modulus at reference pressure \\ \(n_{2}\) & Maxwell bulk modulus power coefficient \\ \(K_{s}^{\mathrm{M}}\) & Maxwell bulk modulus \\ \(\varepsilon_{s}^{\mathrm{S}}\) & Maxwell deviatoric strain \\ \(G^{\mathrm{M}}\) & Maxwell shear modulus \\ \(\varepsilon^{M}\) & [PERSON] strain \\ \(\varepsilon_{v}^{M}\) & [PERSON] volumetric strain \\ \(E_{\mathit{ref},n_{0}}^{\mathrm{M}}\) & Maxwell Young modulus at reference pressure and reference porosity \\ \(E_{\mathit{ref},n}^{\mathrm{M}}\) & Maxwell Young modulus at reference pressure \\ \(n_{1}\) & Maxwell Young modulus power coefficient \\ \(E_{\mathit{ref}}^{\mathrm{M}}\) & Maxwell Young modulus \\ \(\sigma_{\mathrm{min}}\) & minimum principal stress \\ \(d_{x}\) & particle size with x\% passing \\ \(\varphi_{\mathrm{p}k}\) & peak friction angle \\ \(\varepsilon^{\mathrm{p}}\) & plastic strain \\ \(\varepsilon_{s,f}^{\mathrm{K}}\) & final visco-elastic deviatoric strain \\ \(n\) & porosity after compaction \\ \(n_{0}\) & reference porosity after compaction \\ \(p_{\mathit{ref}}\) & reference pressure \\ \(p\) & spherical stress \\ \(D\) & stress ratio \\ \(t\) & time \\ \(\varepsilon\) & total strain \\ \(\sigma_{c}\) & uniaxial compressive strength \\ \(\varepsilon_{s}^{\mathrm{se}}\) & visco-elastic deviatoric strain \\ \(\varepsilon^{\mathrm{se}}\) & visco-elastic strain \\ \(\varepsilon^{\mathrm{se}}\) & visco-elastic strain \\ \end{tabular}
# An Analytical Model for the Prediction of Emptying Processes in Single Water Pipelines
## 4 Analytical solution
### Reduction of the Second-Order ODE to a First-Order ODE  
To solve the IVP (Equations (13) and (14)), it is crucial to note the involvement of the term \(\Big{|}\frac{dL}{dt}\Big{|}\), which gives rise to two cases: \(L^{\prime}(t)\geq 0\) and \(L^{\prime}(t)\leq 0\).  
Figure 2 illustrates the oscillatory behaviour of \(v=-L^{\prime}(t)\) around zero. This prompts us to redefine the initial problem, stated for all \(t>0\), into several issues over time intervals of the form \([t_{k},\,t_{k+1}]\). These intervals are determined by changes in the sign of \(v=-L^{\prime}(t)\). We start with setting \(t_{0}=0\) (where \(v(t_{0})=0\)).  
The value \(t_{1}\) is defined as \(t_{1}=\sup_{t}\{t>0:\,v(t)=-L^{\prime}(t)\geq 0\}\). Under this condition, it is ensured that \(L^{\prime}(t)\) keeps the same sign for all \(t\in(t_{0},\,t_{1})\), i.e., \(L^{\prime}(t)<0\). Also, the  
Figure 2: Visualisation of the intervals defined for each analysed case.  
continuity of \(v\) is guaranteed because of the nature of the physical phenomenon. Thus, [PERSON]'s theorem from calculus [24] states that, given a change of sign in \(v\), there has to be a value \(\overline{t}\) where \(v(\overline{t})\)=0. This value is precisely \(\overline{t}=t_{1}\) (i.e., \(v(t_{1})=-L^{\prime}(t_{1})=0\)).  
Once a solution for IVP (13) and (14) is obtained, the values \(L(t_{1})\) and \(L^{\prime}(t_{1})=-v(t_{1})=0\) can be computed.  
Because of the way it was defined, \(t_{1}\) is the smallest value of \(t\) such that \(t_{1}>t_{0}\) and \(L^{\prime}(t_{1})\leq 0\); then, a new value \(t_{2}\) can be set such that \(t_{2}=\sup\limits_{t}\{t>t_{1}:\,v(t)=-L^{\prime}(t)\leq 0\}\). Thus, a new IVP can be defined using Equation (13) and the initial conditions \(L(t_{1})\) and \(L^{\prime}(t_{1})=-v(t_{1})=0\).  
This process is repeated, solving the IVP given by Equation (13) on each interval \([t_{k},\,t_{k+1}]\) with initial conditions \(L(t_{k})\) and \(L^{\prime}(t_{k})=-v(t_{k})=0\), where \(t_{k+1}\) is set as  
\[t_{k+1}=\sup\limits_{t}\{t>t_{k}:\,v(t)=-L^{\prime}(t)\geq 0\,\,\,\text{or}\,\,\,v(t )=-L^{\prime}(t)\leq 0\}.\]  
Notice that, because of the continuous nature of \(v\), there has to be specific values \(\overline{t}\) where \(v(\overline{t})=0\) whenever \(v\) switches its sign, and those values are exactly \(t_{k}\) for \(k=0,1,2,...\)  
In the following, a solution will be presented for each of the two previously outlined cases.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 5 Conclusions and Suggestions  
In this study, we discuss the different parameterization of GPS/BDS PPP, where combined PPP between the traditional and a simplified model are compared. GPS/BDS PPP were performed with different models and scenarios using 1 month of GPS/BDS data from 11 IGS MGEX stations. Comparing daily static coordinate estimates of BDS-only, GPS-only and GPS/BDS combined solutions with the IGS daily estimates, the mean 3D RMSes were 3.7, 1.6 and 1.5 cm, respectively. In the kinematic case, the 3D RMSes were 17.0, 8.2, 6.5 cm. Magnitude distribution of the 3D RMS shows higher percentage of more accurate solutions in GPS/BDS combined solutions, which is due to the inclusion of data from different systems.  
Comparing GPS/BDS combined PPP coordinate estimates between the traditional and simplified models, mean biases of the differences are \(-0.6\), \(-2.5\), and \(0.4\)\(\upmu\)m and RMSes are 1.1, 3.9, and 1.9 \(\upmu\)m in the North, East, and Up components, respectively. The analysis of parameter and pseudo-range residual differences between the two models show the same quantity with deviation at the \(\upmu\)m level for: (1) epoch-wise sum of station clock and GPS ambiguity; (2) epoch-wise sum of station clock, ISB, and BDS ambiguity; (3) epoch-wise sum of station clock and GPS pseudo-range residuals; (4) epoch-wise sum of station clock, ISB, and BDS pseudo-range residuals; (5) epoch-wise double difference of ambiguity and pseudo-range residuals. The overall comparisons verify the equivalence of the position estimates derived from the traditional and simplified GPS/BDS combined PPP models.  
The IGS community is acknowledged for providing RINEX data and orbit and clock products. This research is supported by 100 Talents Program of the Chinese Academy of Sciences, the National Natural Science Foundation of China (NSFC) (No. 11273046), the National High Technology Research and Development Program of China (Grant No. 2013 AA122402 and 2014 AA123102).  
[PERSON] proposed the research topic, developed the algorithms and designed the experiment; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] collected the data and performed the experiment; all the authors contributed to writing and improving the paper.  
The authors declare no conflict of interest.
## 3 Preliminaries  
In this section, we introduce the notation that we use in this paper, which we summarize in Table 1.  
We use \(\mathcal{U}\) and \(\mathcal{I}\) to denote all users and all items, respectively. We use \(\mathfrak{S}\) to denote the collection of item sets. The numbers of users and items are denoted by \(n_{u}\) and \(n_{i}\), i.e., \(n_{u}=|\mathcal{U}|\) and \(n_{i}=|\mathcal{I}|\). For different sets of items, the number of items can be different. Thus, we use \(n_{s}\) to denote the maximum length of item sets. We use \(R^{s}\) to denote a collection of rating scores and use \(r^{s}_{u}\) to denote the rating that user \(u\) provides to the set \(S\in\mathfrak{S}\). To distinguish with the true rating, we use \(\hat{r}^{s}_{u}\) to represent the predicted rating score. Similarly, the predicted rating score of user \(u\) to item \(i\) is denoted by \(\hat{r}^{i}_{u}\). We use \(x^{s}_{ui}\) as an indicator variable that determines whether item \(i\) in the set \(S\) will be selected in the subset for user \(u\) or not.  
We denote the dimensions of embeddings by \(d\). We write \(\mathbf{p}_{u}\in\mathbb{R}^{d}\) and \(\mathbf{q}_{i}\in\mathbb{R}^{d}\) as the embeddings for user \(u\) and item \(i\), respectively. We pack all the embeddings of users to form the matrix \(P\in\mathbb{R}^{n_{u}\times d}\). Similarly, we can define matrix \(Q\in\mathbb{R}^{n_{i}\times d}\) by packing the embeddings of all items. \(P\) and \(Q\) are used to predict item-level preferences in the rating prediction module. We also define the matrix \(P^{\prime}\in\mathbb{R}^{n_{u}\times d}\) and \(Q^{\prime}\in\mathbb{R}^{n_{i}\times d}\) to select subsets in aggregating item-level ratings for set ratings. Noteworthily, these two groups of user and item embeddings are specially designed for different purposes, namely, rating prediction and subset selection. We also define \(\mathbf{w}_{u}\in\mathbb{R}^{n_{s}}\) as the personalized positional weights for user \(u\). We learn for each user \(n_{s}\) positional weights, one for each position in the set. We can define matrix \(W\in\mathbb{R}^{n_{u}\times n_{s}}\) by packing all the weights.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(\mathcal{U}\) & collection of users \\ \(\mathcal{I}\) & collection of items \\ \(\mathfrak{S}\) & collection of item sets \\ \(S\in\mathfrak{S}\) & one set of items \\ \hline \(n_{u}\) & number of users, i.e., \(n_{u}=|U|\) \\ \(n_{i}\) & number of items, i.e., \(n_{u}=|I|\) \\ \(n_{s}\) & maximum length of item sets \\ \(d\  
We define the problem of set-based preference learning (SPL) formally: _given a collection of set ratings, the goal of SPL is to accurately learn the user's preferences for individual items without the direct supervision of item-level feedback_.  
**Input.** Collection of users \(\mathcal{U}\); collection of rating scores \(R^{s}\).  
**Output.** A personalized scoring function that maps each item to a real value score for each user:  
\[\hat{r}_{u}^{i}=f(u,i|R^{s},\mathcal{U},\mathfrak{S},\mathcal{I}) \tag{4}\]  
\(\hat{r}_{u}^{i}\) is the predicted rating score of user \(u\) for item \(i\).
## 9 Concluding Remarks  
The fractional model for the nanofluids with kerosene oil and water base materials is presented. The thermal endorsement of the nanofluid particles was additionally impacted by slip effects, mixed convection, and magnetic forces. The computations were simulated with the Atangana-Baleanu (AB) and Caputo-Fabrizio (CF) fractional tools. The main results from current investigation are:  
* The thermal characteristics of heat transfer could be magnificently improved in the presence of copper oxide and silver nanoparticles.
* A relatively improved profile of velocity was observed for the copper-water-based suspension compared to the kerosene-oil suspension.
* A lower velocity rate was observed for the volume fraction and increasing values of fractional parameters.
* The increasing thermal outcomes were predicted for the suspension of kerosene oil and silver nanoparticles compared to copper oxide and water-based material.
* Decaying thermal outcomes were observed when the fractional parameters and Prandtl number were varied.
* The numerical values for the temperature profile were more impressive compared to the velocity profile using [PERSON] [31] and [PERSON]'s [32] integration techniques.
* These results can be further extended by incorporating various thermal features such as entropy generation, exponential heat sources, activation energy, joule heating, and bioconvection and by using different non-Newtonian models.  
Conceptualization, O.T.B., S.U.K.; methodology, A.R.; software, A.R.; validation, M.I.K., A.N.; formal analysis, N.B.K.; investigation, S.U.K.; resources, M.I.K.; data curation, O.T.B.; writing--original draft preparation, A.R., S.U.K.; writing--review and editing, M.I.K., E.S.M.T.-E.; visualization, E.S.M.T.-E.; supervision, M.I.K.; project administration, M.I.K., S.U.K.; funding acquisition, E.S.M.T.-E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All the data are clearly available in the manuscript.  
The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work (grant code: 22 UQU4340531 DSR04).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c} **Skin Friction by** \\ **Tzou [32]** \\ \end{tabular} \\ \hline
0.1 & 0.768353 & 0.762176 & 0.964697 & 0.9650723 \\ \hline
0.3 & 0.706666 & 0.701632 & 0.985097 & 0.9855834 \\ \hline
0.5 & 0.658185 & 0.654153 & 1.2411443 & 1.247146 \\ \hline
0.7 & 0.614201 & 0.611155 & 1.410835 & 1.417757 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical analysis for Nusselt number and skin friction by both numerical algorithms.
# Hyperledger Fabric Access Control for Industrial Internet of Things
## 2 Preliminary
### Industrial Internet of Things (IIOT)
#### 2.4.1 IIoT Concept  
IIoT indicates a network with billions of industrial devices, factories, and machines filled with sensors and connected to the internet for collecting and sharing data. The advent of tiny low-cost sensors and high-bandwidth wireless networks now means that even the smallest devices with a certain level of intelligence, can monitor, collect, and share data with other devices. All the collected data can be analyzed to improve the efficiency of business processes. IIoT can help companies better understand their business processes, and by analyzing data from sensors, it can improve the efficiency of their processes and even open new revenue streams. IIoT brings greater insight to broad supply chains, allowing businesses to coordinate and increase efficiency.