esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Geodesy  
Satellite altimetry has also become an effective remote sensing technique with important applications to geodesy [44]. Indeed, satellite altimetry has impact to geodesy by allowing mapping and monitoring a major part of the Earth surface and to make improvements on the measurements of the gravity field. The altimetric constellation with a number of successive and/or parallel missions with different orbit configuration and sampling characteristics are very supportive of geodetic applications.  
Figure 25: Maps of snow depth estimated from the combinaition of SARAL/AltiKa and CryoSat-2/SIRAL for November 2013, December 2013 and January 2014 [43].  
The case of SARAL/AltiKa in its SARAL-DP configuration is of particular interest in this regard, as it is covering the earth with a very fine resolution well suited.  
_Ability to Find Uncharted Seamounts_  
Seamounts--undersea mountains of volcanic origin--are obstacles to flow, sites of enhanced mixing, habitat for fish, and possible hazards to submarines. Small seamounts are more common than large ones. [PERSON] et al. [45] found that all seamounts 2000 meters tall and taller appeared to have been found by the satellite altimeters of that time, but suggested that there might be as many as one hundred thousand seamounts between 1000 and 2000 m tall that had escaped detection. Since only about eight percent of the ocean floor has been sounded to one nautical mile resolution [46], the discovery of new seamounts will come mainly from new satellite altimetry. However, small seamounts remain difficult to find because they create only small anomalies in sea level (Figure 26). [PERSON] [47] showed that the AltiKa altimeter on SARAL/AltiKa has much better precision in sea level measurement than previous Ku-band altimeters, and that this precision can be used to detect seamounts as small as 1 km tall. Marks [48] showed that a sea surface height profile made from the temporal median of SARAL/AltiKa repeat-track profiles can detect even smaller seamounts. It is now expected that SARAL's new geodetic mission will furnish data for finding many previously unknown seamounts. This will be a boon to scientists modelling ocean flow and mixing, to marine biologists and commercial fishermen, and perhaps also to navies that must navigate submarines.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 5 Lost Circulation While Cementing through Naturally Fractured Reservoirs
### Cement Slurries for Primary Cementing through Fractured Formations
#### 5.3.5 Fiber-Cement Slurry  
Cementing operations using advanced, chemically inert fibers mitigate lost-circulation problems without compromising operational efficiency or the quality of the slurry or set cement [49]. A fiber-cement slurry can offer better zonal isolation and strengthen the bond to the casing in HPHT deep wells and maintain cement plugs to control the lost circulation in fractured, high permeability zones. The main mechanism of the fiber is to create a mesh network that can improve the strength of the set cement as well as further control circulation losses [109; 110]. Polypropylene Fibers (PPF) added to the base cement slurry (class G cement) have a negligible effect on the fiber-cement slurry density, rheological properties, free water, and reduce the thickening time by 75% at a concentration of 0.75% BWOC. Polypropylene fibers have a significant effect on reducing the porosity and the permeability of the set cement. An increase of the cement compressive strength of 17.8% was achieved with the addition of polypropylene fibers at a concentration of 0.5% BWOC [111].  
The engineered silica-based fiber does not affect slurry properties, has better dispersibility compared to conventional fibers, flexibility, and the capability of creating a strong fibrous network, which in combination with the solid particles of a cement slurry features immediate sealing and retains circulation in the event of lost circulation [104].
# Optimal Power Flow Solutions for Power System Considering Electric Market and Renewable Energy
## 4 Numerical Results
### Obtained Results for the IEEE 30-Node System
#### 4.2.4 Results Obtained for Case 5  
In this section, JSA is implemented for determining optimal location and optimal size of three subcase--5.1, 5.2, and 5.3 with 2, 3, and 4 wind turbines, respectively. The optimal solution of the study cases, together with that of Case 2 and Case 3, are reported in Table 3. We can see that Node 5 is selected for all study cases. For using at least two WTs, Node 2 is always selected. Node 1 is selected for two Cases, with 3 and 4 WTs, while Node 10 is selected for the last case with 4 WTs. Clearly, Node 5 is the most prior location and Node 10 is the least prior location for installing a wind turbine with 10 MW. In summary, the important order for installing WTs is Node 5, Node 2, Node 1, and Node 10. With respect to the power, all wind turbines are suggested to be 10 MW.  
Figure 19 shows maximum profits of these study cases. As using more wind turbines, the maximum profit is higher. Case 3 with one wind turbine has more profit than Case 2 without wind turbine by $37. The profit of two WTs is $5093.7, which is more than the profit of one WT, $5035.8 by $57.8. Similarly, three WTs can reach greater profit than two WTs by $9.2, and four WTs can reach greater profit than three WTs by $26.2. The comparisons indicate that the placement of two wind turbines is the most effective and locations at node 5 and node 2 are the best for placing wind turbines. On the other hand, the use of four WT can reach greater profit than base system by $130.3, which is equivalent to 2.6% of the base  
Figure 18: Comparison of profits from Case 3 and Case 4.  
system. The profit improvement is not much, and it is also a problem to consider if the four WTs should be placed in the system for the purpose of benefit.  
Optimal control variable of wind turbines and optimal control variables of the IEEE 30-node system for different cases are reported in Table 10 in Appendix A.
# A Trustworthy Communication Hub for Cyber-Physical Systems
###### Abstract  
The motivation for this research arises from the challenges in the trustworthy communications related operation of cyber-physical systems (CPS), especially in the energy and mobility domains. The increasing amount of distributed energy resources (DERs) of prosumers and electric vehicles requires new ways for CPS communications to enable information exchanges for smart operation in peak consumption hours and balancing power levels in the energy grids in order to lower the energy cost. The huge number of mobile appliances and the related service providers do not serve properly the privacy of the owners, owing to the vertical silo type of operating model in industries. As the results of this research, we provide a trustworthy communication hub for CPS (CPS hub) for solving the challenges related to trustworthy communications between physical resources owned by different stakeholders. The CPS hub realizes the communication spaces concept, and enables combined trust and communications processes when dynamic resources owned by different stakeholders are exchanging information. The evaluations showed that the provided CPS hub enable information exchanges between distributed energy resources of different stakeholders, so that they can join the aggregation process for more flexible and efficient resource usage in energy markets. The CPS hub enable interaction between heterogeneous physical devices of multiple stakeholders to exchange information so that, for example, authorities can see the situation in the emergency area and, simultaneously, the policies of the owners can be taken into concern. Despite limited evaluation scenarios, it is shown that consideration of the ownership issues in the trustworthy communication for information exchanges between heterogeneous physical resources (devices) is possible and feasible. Several future research items, such as, for example, scalability; real-time and streams based operation; as well as consideration of the security, privacy, trust, and safety challenges, were detected. However, the evaluations showed that the constructed CPS hub contribute a set of very essential technical enablers for future smart CPS systems and create strong a basis for such future research towards a future smart society.  
Keywords: cyber-physical systems; machine-to-machine communications; Internet of Things; smart energy systems; smart mobility systems; communications; security; trust +
Footnote †: journal:
## 5 Conclusions  
A one-dimensional transient model for the analysis of regeneration systems for glass furnaces is presented. This lower order model that allows a quick simulation of the regenerative set performance uses heat transfer coefficient profiles obtained from the application of specific CFD models. The heat transfer coefficients are locally re-computed in the code, taking into account the local composition of the gas according to the WGR strategy. A gas emissivity routine previously developed by the authors is used for the above heat transfer model. The model has shown its ability to simulate the effects of regenerator geometry or inversion time change. It can therefore be routinely used for the optimization of the refractory material distribution along the axis of the regenerative chambers during the system design. The model, extended to take into account the WGR effects, is applied to compare the regenerative system performance change with respect to the standard design. The analyses show an increase of the thermal performance of the regenerator when the WGR system is activated (air outlet temperature increase of about 50 \({}^{\circ}\)C in the example). The model proved to be a useful tool for understanding and quantifying the additional thermal effect introduced with the addition of radiant gases into the chamber when operating in the air phase according to the WGR strategy.  
Data curation, [PERSON]; Funding acquisition: C.C.; Methodology, C.C.; Software, A.S.; Supervision, C.C.  
This model and the CFD applications presented have been developed within the PRIMEGLASS European Project (LIFE12 ENV/IT/001020) framework by the research team from the University of Genova.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**WGR off**} & \multicolumn{2}{c}{**WGR on - Average WG Fraction 20\%**} \\ \cline{2-6}  & - & **Tot** & **S-1** & **S-2** & **S-3** \\ \hline Mass flow [kg/s] & 4.52 & 4.52 & 1.13 & 1.13 & 2.26 \\ WG mass fraction [\%] & 0 & 20 & 0 & 10 & 35 \\ Inlet temperature [C] & 150 & 205 & 147 & 186 & 243 \\ Outlet temperature [C] & 968 & 1018 & 983 & 1052 & 1019 \\ Thermal power \(P_{th}\) [MW] & 3.09 & 3.74 & 0.945 & 0.991 & 1.81 \\ Specific Thermal power [kW/m\({}^{2}\)] & 1.065 & 1.290 & 0.978 & 1.025 & 1.872 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model output of the Cold RC with the WGR system.  
Figure 13: Average temperature profiles across the Cold RC with the WGR system application.
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
## 3 ZnO Magnetoelectric Composites
### Discussion
To improve the ME performance, the coupling between the FM and PE phases of the composites needs to be maximized, combined with advanced magnetostrictive materials with improved magnetostriction and soft magnetic behavior. Moreover, ZnO nanowires and nanorods have not yet been fully explored in magnetoelectric applications, even though they represent the bulk of research around ZnO nanostructures. Until this point, only ZnO microrods were extensively utilized in the study of the interfacial interaction between ZnO and magnetic materials, [61; 80; 82; 83], specifically targeting sensor applications. Thus, a novel application of nanowire/nanorod composites, either in array form or in single nanowire configurations, may provide the means of bridging the performance gap, while also improving upon spatial resolution and sensitivity in sensor applications. Single ZnO nanowire-based magnetoelectric composites are still missing from the literature and, combined with the piezophotronic and the piezophotronic effect [86], can potentially improve upon the performance of current ZnO ME composites but also give rise to new effects through the interaction between ferromagnetism, photoexcitation, piezoelectricity and the semiconducting phenomena in ZnO.
## 1 Introduction
[PERSON] et al. [9] obtained magnetic flux gate second sampling data from nearly 100 geomagnetic stations nationwide to study the relationship between Mw > 6 events and polarization value anomalies in western mainland China (west of 110\({}^{\circ}\) E) from 2015 to 2018 and found that multiple stations in a certain range near the epicenter before the earthquake would have anomalies at the same time, and the anomaly area was proportional to the magnitude. The larger the area is, the higher the magnitude. The seismic correspondence rate (seismic anomaly/all anomalies) reaches 65% in six months and reaches 80% within one year [15]; these works illustrated that pre-earthquake anomalies do not only exist at the single station closest to the epicenter. In addition, [PERSON] et al. [16] also found groups of GVIP high anomalies at multiple stations in western China during the period of 2019-2020. Based on the above work, we aim to investigate the multistation geomagnetic vertical intensity polarization high-value anomalies before the occurrences of several strong earthquakes, such as the Yangbi Mw 6.4 earthquake in Yunnan, the Mado Mw 7.4 earthquake in Qinghai, and the Luvian Mw 6.0 earthquake in Sichuan during 2020-2021. The correlations between the simultaneous GVIP anomaly and the occurrence of subsequent earthquakes in western China from 2015-2021 are discussed.
## 4 Results
In real practice, it is necessary to ensure protection of biometric templates from compromise. For this purpose, an approach based on fuzzy extractors [19] or shallow artificial neural networks with automatic robust learning [28] is used at the stage of image classification. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Brief Information** & **Dataset** & **Accuracy, \%** \\
**About the Method** & & \\ \hline Authentication technique based on simple & & \\ cross-correlation values of PSD features extracted & EEG of 109 subjects from 19 EEG channels during eyes-closed and & the PhysioNet database & 98.04 [16] \\ eyes-open rest-state conditions. & & \\ \hline Deep neural network (DNN) is used to classify the & & \\ data. Optimization and regularization methods are & 109 individuals & 99.19 [29] \\ employed to improve the accuracy of the results. & & \\ \hline EEG-based cryptographic key generation systems. & The EEG data of 80 & \\  & subjects from AEEG & 97.42 [13] \\  & dataset & \\ \hline Automatic channel selection, wavelet feature & & \\ extraction and deep neural network (DNN) & DEAP multimodal dataset & 94 [18] \\ classifier. & & \\ \hline Biometric authentication method based on the & & \\ discrete logarithm problem and & & \\ Bose-Chaudhuri–Hocquenghem (BCH) codes. & & \\ \hline EEG-based cryptographic key generation. & 16 channels selected from & \\  & the DEAP dataset & \\ \hline EEG-based identification method using & & \\ code-modulated visual echo potentials (c-VEPs). & 25 subjects & 99.43 [21] \\ \hline Proposed method & 95 subjects & 99.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the obtained results with the results achieved in other studies. Figure 8: Probability of subject identification error as a result of performing a computational experiment.
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
## 3 Discussion
### NMR Porosity
#### 3.2.2 Movable Fluid Porosity
At present, in the logging of the sandstone reservoir, people often calculate the \(\varphi_{\text{FFI}}\) based on an empirical value of \(T_{\text{2C}}\). From the above analysis, the migration of water in the nanopores, micro-fissures, and fissures are different in centrifugation, so the \(\varphi_{\text{FFI}}\) may have some intrinsic relationship with the porosity of the nanopores, micro-fissures, and fissures. Based on the porosity data of 8 coal samples, the empirical equation between the \(\varphi_{\text{FFI}}\) and the three types of porosity was fitted as follows  
\[ \begin{array}{l}\varphi_{\text{CFFI}}=0.1565\varphi_{\text{P}}+0.4746\varphi _{\text{mf}}+0.9783\varphi_{\text{f}}\\ R^{2}=0.9972\end{array}  \tag{3}\]  
where \(\varphi_{\text{CFFI}}\) is the movable fluid porosity, %; \(\varphi_{\text{P}}\), \(\varphi_{\text{mf}}\), \(\varphi_{\text{f}}\) is the porosity of nanopores, micro-fissures, and fissures, %, and \(R^{2}\) is the correlation coefficient. Equation (3) shows that the contribution to the movable fluid porosity mainly comes from fissures, followed by micro-fissures, with the contribution of nanopores being the lowest. Table 3 showed that the movable fluid porosity calculated from the \(T_{2}\) spectra was very close to that of the laboratory, which indicated that the method can be used to calculate the movable fluid porosity of the reservoir in the field work through the \(T_{2}\) spectra of saturated samples. It needs to be explained that there are large differences in pore structure of the coal reservoir in different mining areas, and the fitting equation should be more accurate and reliable based on coal samples from the same mining area. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample ID** & \(T_{\text{2C}}\) **(ms)** & **FFI (\%)** & **BVI (\%)** & \(\varphi_{\text{FFI (\%)}}\) & \(\varphi_{\text{PVI (\%)}}\) & \(\varphi_{\text{CFFI (\%)}}\) \\ \hline CZ-1 & 1.78 & 17.95 & 82.05 & 1.48 & 6.78 & 1.36 \\ CZ-2 & 1.84 & 16.99 & 83.01 & 1.38 & 6.76 & 1.41 \\ CZ-3 & 1.88 & 16.93 & 83.07 & 1.33 & 6.50 & 1.36 \\ CZ-4 & 1.93 & 16.91 & 83.09 & 1.30 & 6.36 & 1.28 \\ WY-1 & 1.36 & 23.35 & 76.65 & 0.71 & 2.34 & 0.68 \\ WY-2 & 1.76 & 23.05 & 76.95 & 0.63 & 2.12 & 0.61 \\ YJL-1 & 6.94 & 28.79 & 71.21 & 4.35 & 10.77 & 4.26 \\ YJL-2 & 7.39 & 28.61 & 71.39 & 4.17 & 10.41 & 4.23 \\ \hline \hline \end{tabular} \(T_{\text{2C}}\) is the \(T_{2}\) cutoff value; BVI is bound water volume index; and \(\varphi_{\text{CFI}}\) is the movable fluid porosity by fitting equation.  
\end{table} 
Table 3: Pore characteristic parameters of the coal samples.
## 2 Methods
### Comparison to ImageNet and without Pretraining  
To assess the added value of domain-specific, endoscopic pretraining, the exact same neural network architecture is used to train models without pretraining and pretraining with ImageNet. As described in Section 2.5, 5 different models are trained with GastroNet from different initializations. To facilitate a fair comparison, a _no-pretraining ensemble_ is created using 5 different random weight initializations and an ImageNet ensemble is created using the model pretrained on ImageNet with 5 different random seeds. Table 1 in the results section shows the averaged metrics of the 5 models as well as the ensemble predictions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Average** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline No pretraining & 69.7\({}_{(\pm 2.2)}\) & 68.1\({}_{(\pm 3.4)}\) & 70.6\({}_{(\pm 3.3)}\) & 69.2\({}_{(\pm 2.1)}\) \\ ImageNet & 74.1\({}_{(\pm 5.1)}\) & 77.3\({}_{(\pm 5.8)}\) & 72.2\({}_{(\pm 9.3)}\) & 74.1\({}_{(\pm 3.9)}\) \\ GastroNet & **79.0\({}_{(\pm 2.1)}\)** & **83.1\({}_{(\pm 5.1)}\)** & **76.6\({}_{(\pm 3.0)}\)** & **79.6\({}_{(\pm 2.4)}\)** \\ \hline
**Ensemble** & & & & \\ \hline No pretraining & 73.6\({}_{(117/159)}\) & 69.5\({}_{(41/59)}\) & 76.0\({}_{(76/100)}\) & 72.6 \\ ImageNet & 81.8\({}_{(130/159)}\) & 79.7\({}_{(47/59)}\) & 83.0\({}_{(83/100)}\) & 81.3 \\ GastroNet & **83.6\({}_{(133/159)}\)** & **83.1\({}_{(49/59)}\)** & **84.0\({}_{(84/100)}\)** & **83.5** \\ \hline \hline \end{tabular}
\end{table}
Table 1: NBI-zoom video classification results. The top three rows show the average performances of the 5 obtained models, standard deviation is added between the brackets. The bottom three rows depict the performances of the ensemble predictions. The highest scores per metric are indicated in boldface, for both evaluation approaches.
# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 3 Experimental Results and Analysis
### Comparative Experiments and Analysis of Results  
The key to the spatially adaptive filtering process is the ability to consider whether the two image pixels of an interferometric data stack are statistically homogeneous. Once a suitable estimation window is defined for each image pixel, the noise in the magnitude data can be removed, the interferometric phase values can be filtered, and the coherence values of the interferogram can be correctly estimated by the careful selection of the SHP [10]. Both the conventional serial and parallel processing approaches adopted in this study use a search window of 15 \(\times\) 21 and a DS candidate point threshold of 25 as the parameter conditions for DS candidate point extraction.  
To properly evaluate the accuracy and performance of the spatial adaptive filtering using the parallelization approach proposed in this paper, the interferogram phase was first serially filtered using the conventional filtering approach, and the time required for the algorithm to complete the processing and the filtered phase information were counted. The interferogram was then processed by parallelizing the spatial adaptive filtering in the same manner. In this process, the results of the completed processing of the split data are collected by the master node and written to the disk by the collected RDD, which is the  
Figure 8: SqueeSAR data processing flow.  
same as the writing method of the conventional filtering algorithm; thus, they only differ in terms of data processing over the entire process.  
Due to the limited hard disk space, we cropped a smaller area of the image strips to verify the proposed parallelized processing scheme. The size of the entire experimental area was 2600 \(\times\) 11,800 pixels, and the data were divided into 72 (8 \(\times\) 9) PATCHs for processing based on StaMPS. In Figure 9, we present the annual average subsidence rate map of the study area after the phase optimization and time series solution, based on the SqueezeSAR algorithm, which jointly processes the DS and PS points and, finally, collects the information of a total of 2,401,517 PS points over the entire study area through time series analysis. As this study focuses on parallelization, little analysis has been conducted on the subsidence area in the study area.