esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Interpretation
### Minerals  
The mineral and carbonate content data of the Hengtongshan Formation can be seen in Table 1. The carbonate content is between 1.91 and 65.17 wt%, with an average of 11.65 wt%. The average carbonate contents of the first member, second member and third member of the Hengtongshan Formation are 18.58, 12.27 and 8.96 wt%, respectively. The overall trend is decreasing, but the differences are small. However, through targeted core observations, some samples with more than 50% carbonate content were found to be associated with carbonate dikes, resulting in high measured carbonate values. According to the XRD test, the mineral composition of the Hengtongshan Formation is primarily clay (average 38.62%), followed by quartz (average 35.54%, Table 1). The content of plagioclase is higher than that of K-feldspar, which is probably due to the loss of K-feldspar in the process of terrigenous elastic migration and diagenesis, and some samples feature values of less than 1% [11]. The calcite content is relatively high as a whole, with an average of 13.92%, which is highly consistent with the carbonate content observed in the previous test (Tables 1 and 2). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample ID** & **Depth (m)** & \begin{tabular}{c} **Total Clay** \\ **(wt\%)** \\ \end{tabular} & \begin{tabular}{c} **Quartz** \\ **(wt\%)** \\ \end{tabular} & \begin{tabular}{c} **K-Feldspar** \\ **(wt\%)** \\ \end{tabular} & \begin{tabular}{c} **Plagioclase** \\ **(wt\%)** \\ \end{tabular} & \begin{tabular}{c} **Calcite** \\ **(wt\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Dolomite** \\ **(wt\%)** \\ \end{tabular} &
\begin{tabular}{c} **Pyrite** \\ **(wt\%)** \\ \end{tabular} \\ \hline TD1-163 & 288 & 46 & 32 & 1 & 7 & 14 & & \\ TD1-171 & 278 & 43 & 37 & 2 & 7 & 11 & & \\ TD1-172 & 287 & 35 & 31 & 2 & 8 & 24 & & \\ TH-9 & 152.8 & 28 & 34 & 12 & 9 & 15 & 0 & 2 \\ TH-34 & 224.54 & 45 & 29 & 1 & 8 & 16 & 0 & 1 \\ TH-46 & 284.26 & 43 & 26 & 1 & 8 & 15 & 5 & 2 \\ TH-49 & 289.06 & 41 & 35 & 12 & 2 & 7 & 2 & 1 \\ TH-51 & 291.8 & 28 & 37 & 5 & 6 & 17 & 7 & 0 \\ TH-76 & 331.05 & 37 & 39 & 3 & 5 & 15 & 0 & 1 \\ TH-83 & 345.46 & 41 & 35 & 4 & 4 & 16 & 0 & 0 \\ TH-93 & 394.33 & 43 & 42 & 4 & 2 & 9 & 0 & 0 \\ TH-101 & 426.1 & 38 & 42 & 2 & 7 & 7 & 1 & 3 \\ TH-102 & 426.8 & 34 & 43 & 1 & 6 & 15 & 0 & 1 \\ average & 38.62 & 35.54 & 3.85 & 6.08 & 13.92 & 1.50 & 1.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the XRD results of the samples from the Hengtongshan Formation.
## 3 Analysis of Failure Characteristics of a Buried Waterstop
### Waterproof Capacity Analysis of the Waterstop
#### 3.3.2 Short-Term Resistance of the Waterstop to Seepage  
Taking working condition 1 as an example, the development and change of the maximum value of contact pressure of the waterstop with the increase in the deformation are plotted in Figure 16.  
Figure 16 shows that the overall contact pressure between the waterstop and the concrete increases with the increase in deformation. During tensile deformation, the connection between the first rib of the waterstop and the flank is the part with the highest contact pressure; during compression deformation, the contact position around the middle hole and the lining is the position with the highest contact pressure; when the settlement is deformed, the contact position between the flanks of the waterstop and the lining is the position with the highest contact pressure.  
It can be seen from above that short-term resistance to seepage is offered by the deformation contact pressure. Figure 16 shows that when the tensile deformation is greater than 2.7 mm, the compression deformation is greater than 4.7 mm, and the settlement deformation is greater than 2 mm, the short-term resistance to water seepage is greater than the long-term resistance. Subsequently, with increasing deformation, the short-term resistance to water seepage also continues to increase.  
Figure 16: The maximum contact pressure of the waterstop in working condition 1.  
Figure 15: Long-term resistance of the waterstop to seepage.  
The related results of working conditions 1 and 2 are shown in Table 4, where \(\rm P_{wl}^{st}\) is the long-term seepage resistance, the critical deformation \(\rm t_{c}\) is the deformation when the short-term seepage resistance is equal to the long-term seepage resistance, \(\rm Mises_{max}\) is the maximum von Mises stress on the waterstop, the allowable deformation \(\rm t_{a}\) is the maximum deformation when the hose can meet the service life requirements, and \(\rm P_{wlmax}^{lt}\) is the maximum short-term seepage resistance within the allowable deformation.  
In summary, the following conclusions can be drawn:  
1. The stress on waterstop I at the maximum tensile and compressive deformation does not reach the dangerous level. After the settlement deformation reaches 29 mm, the stress exceeds the dangerous level, which may make it difficult for the tunnel joint to meet the service life requirements.
2. When the deformation of the tunnel joint is greater than the critical deformation, the short-term resistance to water seepage is greater than the long-term resistance to water seepage. Therefore, if the waterstop can maintain a certain amount of deformation for a long time, it may be more conducive to the waterproofing of the tunnel joint.
3. Except for the long-term water seepage resistance, the difference between working conditions 1 and 2 is small, which means that the initial contact pressure has little effect on the waterproofing ability of the waterstop after deformation.
# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 6 Results
### Machine Learning Results  
The results of the RFR and MLPR models for all the different datasets that are described in Table 2 are presented in Figure 7. To simplify the representation of such a number of different datasets, the \(x\)-axis groups them by the tag \"dataset type\", as shown in the same table.  
Figure 6: Baseline results for each variable (\(ge1\), \(ge2\), \(ut\), \(lc\) and \(pv\)) with an aggregation of 15 min by rule and number of days.  
Figure 7: _Cont._  
To avoid the presence of negative metric values, the \(y\)-axis represents the absolute value of the \(CV(RMSE)\). Therefore, the best model is the one with the minimum value.  
Each of the points in the graph represents the error for a give dataset, while the red hyphens indicate the model with the minimum error value (i.e., the best model for each of the dataset types). As defined at the start of the present section, the variables \(ge1\), \(ge2\) and \(ut\) also include datasets with external baseline information (the expected load of the campus, i.e., \(lc\)). The variables \(lc\) and \(pv\) do not include any \"EXBL\" dataset, as this is not considered to be of interest.  
For the RFR models, these results indicate that the dataset types \"Db\", \"Db + EXBL\", \"DbWa\" and \"DbWa + EXBL\" are usually the best for all variables. The two exceptions are \(pv\), which is better forecasted by the dataset types \"DbWa\" and \"BlWa\", and \(lc\), whose best model for 15-min aggregations is obtained with the dataset \"CaIn\".  
For the MLPR models, the dataset types \"Bl\" and \"BlWa\" also obtain good performances for the variables \(ge2\) and \(lc\).  
Figure 7: Metrics for the RFR and MLPR models for each forecasted variable (\(ge1\), \(ge2\), \(ut\), \(lc\) and \(pv\)) with an aggregation of 15 min. The \(x\)-axis is grouped by dataset type. The left column corresponds to the RFR models, while the right column corresponds to the MLPR models. (**a**) \(ge1\) using RFR; (**b**) \(ge1\) using MLPR; (**c**) \(ge2\) using RFR; (**d**) \(ge2\) using MLPR; (**e**) \(ut\) using RFR; (**f**) \(ut\) using MLPR; (**g**) \(lc\) using RFR; (**h**) \(lc\) using MLPR; (**i**) \(pv\) using RFR; (**j**) \(pv\) using MLPR.  
The comparison between the RFR and MLPR techniques for the five forecasted variables shows that the errors of the MLPR models are less than those of the RFR models for almost all variables (\(ge1\), \(ge2\), \(ut\) and \(lc\)), except for \(pv\), which is better forecasted using the RFR models.
## 2 Computational Details
### Molecular Dynamics  
Molecular dynamics were used to study the stability of the protein-ligand complexes. Molecular docking results were taken as the input structures for the simulations. Molecular dynamics simulations were performed using the CHARMM36 force field [49] and the NAMD software [50]. The resulting structures from the docking process were taken as the starting point for the simulation. Missing residues of the PDB: 2 QMJ file were added using MODELLER [51]. Only these residues were relaxed while the rest of the atomic positions remained intact. The force field parameters for the ligands were built with the ACPYPE program, this is a tool based on Python to use ANTECHAMBER, in order to generate topologies for chemical compounds for a variety of molecular dynamics programs, including CHARMM [52; 53]. Geometry optimization and charges were obtained using SQM (part of the AmberTools [54]) with the SCC-DFTB Hamiltonian [55].  
The protein-ligand systems were solvated (TIP3P water model) in a box with periodic boundary conditions extended to 20 Angstrom in each direction. Appropriate amounts of Na+ and Cl\(-\) ions were added to neutralize the charge using VMD [56]. Energy minimization was then carried out using the conjugate gradient algorithm during 20,000 steps. After minimization, the systems were heated to 300 K during 500 ps in the NVT ensemble. Finally, the systems were maintained at 300 K and 1 atm during 150 ns using the Langevin piston Nose-Hoover method [57; 58]. Smooth Particle Mesh Ewald (PME) protocol was used for the long-range electrostatic interactions [59]. All the MD simulations were carried out using a 1 femtosecond time step, a 10 A cutoff for non-bonded interactions, and the SHAKE algorithm turned off. Visualization and analysis were performed using VMD. The evaluation of the root-mean-squared deviation (RMSD), distribution of distances from some protein residues to the ligands, and the hydrogen bond analysis during the simulation, were carried out also using VMD. In order to evaluate all the molecular properties of interest over a representative statistical sample, coordinates and velocities were recorded every 4000 configurations, corresponding to 4 ps. Then, averages and time evolution of properties reported here were calculated in the aforementioned snapshots.
## 5 Conclusions  
Traditional structural analysis software is based on Thimhosenko's stiffness matrix and, therefore, only considers shear effects on the vertical deformations of beams. In fact, the same rotations obtained by these programs are independent of whether this deformation is considered or not. This simplification might lead to significant errors in the simulation of the rotations in some structures (such as deep beams), as well as the wrong identification of the mechanical properties of the structural elements from the measurements on site (inverse analysis). Despite its important role, no other work studying the calculation of shear rotations in deep beams was found in the literature. To fill this gap, this study proposes a simple and intuitive method to calculate the shear rotations in both isostatic and statically redundant beams for first-order shear deformation theory. No examples of frames composed of deep beams are presented here. However, this methodology can also be applied to such structures.  
A set of parametric analyses were carried out to determine the slenderness ratios from which the shear rotations could be neglected in different structural systems. In addition, the errors in the inverse analysis of deep beams were parametrically studied. The importance of the proposed methodology was highlighted in the simulation of a construction stage of a composite bridge. The obtained results show the important role that shear rotations play in deep beams. The effect of prestressing on shear rotation will be addressed in future studies.  
Conceptualization, methodology, software, writing--original draft preparation, formal analysis and validation, S.E.; investigation and writing--review and editing, J.A.L.-G.; resources and writing--review and editing, H.M.; supervision and project administration, J.T. All authors have read and agreed to the published version of the manuscript.  
The authors are indebted to the projects PID2021-126405 OB-C31 and PID2021-126405 OB-C32 funded by FEDER funds--A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/, BIA2017-86811-C2-1-R and BIA2017-86811-C2-2-R funded by FEDER funds.  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Surface Topographical Control of a Liquid Crystal Microlens Array Embedded in a Polymer Network
###### Abstract  
A novel approach for fabricating a microlens array with a tunable surface topographical structure and focal length is proposed in the present study. The microlens array was manufactured through the photoinduced molecular reorientation of nematic liquid crystals (LCs) stabilized by a polymer network. The fabricated microlens array had a mountain-shaped topographical structure due to the accumulation of polymers and LC molecules. The molecular orientation of the LC inside the microlens was disordered, while the outer side of the microlens was ordered. The thermal expansion of the polymer network and the phase transition of the LC molecules within the microlens array allowed the surface topographical structure and the focal length to be reversibly tuned under heat treatment. The results of this research work will enable future implementations to provide a thermally tunable microlens array.  
nematic liquid crystals; microlens array; polymer network; topographical control; focal length +
Footnote †: journal: Applied Physics Letters
## 3 Literature Review
### Life Cycle Assessment (ISO 14040s)  
Life cycle assessment (LCA), as defined by ISO 14040 [31], identifies the environmental load generated throughout all stages of products or systems and evaluates its impact on the environment to identify measures for reduction and improvement [32]. The assessment categories cover six major environmental impacts: global warming potential, abiotic depletion potential, ozone depletion potential, acidification potential, eutrophication potential (EP), and photochemical oxidation creation potential [10; 18].  
In Korea, assessments are conducted as per the LCA preparation guideline for additional items in the innovative design field under the Green Standard for Energy and Environmental Design (G-SEED) [10]. The assessment categories of building LCA are divided into four stages based on the system boundary [18]. The production stage (A1-A3) encompasses the environmental impacts generated throughout all stages, including raw material extraction, transportation, and manufacturing, involved in producing construction materials used in building construction. The construction stage (A4-A5) covers the environmental impacts generated during the transportation of construction materials and the actual construction process. The operation stage (B1-B7) includes the environmental impacts resulting from the use of operational energy throughout a building's lifespan, as well as those caused by the replacement of construction materials during repairs and maintenance. The disposal stage (C1-C4) covers the environmental impacts generated during the demolition of buildings, transportation of waste construction materials, and disposal of waste construction materials [33]. According to the G-SEED LCA guideline, the LCA results must include the production stage (A1-A3), construction stage (A4-A5), replacement (B4) and use of operational energy (B6) during the operation stage, and demolition (C1), transportation (C2), and disposal (C4) during the disposal stage. In addition, the embodied carbon associated with the production stage (A1-A3), construction stage (A4-A5), and disposal stage (C1-C4) pertains solely to building materials and does not include equipment materials such as mechanical and electrical systems [34; 35]. Figure 3 shows the system boundary according to the LCA preparation guideline.  
In LCA, the production stage is calculated by applying the environmental unit of each material to the quantity of materials used in the building. This is achieved by converting various construction materials into mass units (tons) by product category [33]. The major construction materials, which account for over 99% of the cumulative mass contribution, primarily include ready-mixed concrete, rebar, cement, masonry, insulators, aggregates, stone, glass, and tiles [35].  
Figure 2: Status of environmental product declaration (EPD)-certified products in Korea.
# A Blockchain-Enabled Framework for Improving the Software Audit Process
## 2 Preliminaries and Related Work
### Software Auditing
#### 2.1.2 Advantages of Doing Software Audits  
There are several advantages to conducting SA. Some of the most significant are described here.  
* SA aid in maintaining software quality and identifying improvement opportunities. It allows an organization to maintain all programs functioning smoothly. The audit may reveal the necessity to acquire additional technologies that may further enhance the software's quality;
* During an audit, the status of existing licenses may be determined, allowing for more efficient software use. This will guarantee that the company maximizes the use of its existing licenses. The audit will also determine whether or not the licenses are current;  
Figure 2: Software audit taxonomy.  
* If the program needs certain proprietary tools to work effectively, it is important to undertake a comprehensive audit to determine whether the tools you want to acquire will be compatible with all the ones already existing. An audit at this point will guarantee that you acquire products that are suitable for company objectives and improve business operations;
* During the audit, it is possible to determine whether the software conforms to industry standards, and if it does not, the audit may recommend improvements that would enhance the program.
## 5 Conclusions  
Based on the dynamic mesh and UDF technology, the dynamic characteristics of the opening process of the molten salt swing check valve are simulated and analyzed, and a topological optimization method based on the thermal-fluid-mechanical coupling is proposed to optimize the disc and achieve improvement in the dynamic characteristics of the check valve. The specific conclusions can be summarized as follows:  
1. The flow analysis reveals that the special flow channel structure design of the molten salt swing check valve reduces the flow dead zone and avoids the accumulation of molten salt crystals.
2. The spherical valve structure, obtained by the topology optimization method, reduces the mass of the valve motion components by 57.76% while meeting the strength and stiffness requirements, and has a sufficient reverse-sealing performance.
3. The opening dynamic characteristics of the molten salt swing check valve have been significantly improved with the optimization of the valve disc. The full opening angle is increased from 61.5\({}^{\circ}\) to 67.5\({}^{\circ}\), and the time to reach the steady state is shortened from 0.18 s to 0.139 s. The positive resting pressure difference of the valve is reduced by 5 kPa, the minimum opening differential pressure is reduced by 8.9 kPa, and the flow characteristics are smoother after optimization while avoiding valve oscillation during the opening.
4. The topology optimization method is used to improve the mass and shape of the check valve disc, which has a significant effect on the optimization of the dynamic characteristics of the check valve. It provides new ideas for the industry to solve undesirable problems such as check valve disc oscillation, valve disc knocking on the valve cavity, and strange noises during operation.  
Conceptualization, S.L. and T.M.; methodology, T.M. and S.L.; software, T.M. and H.S.; data curation, M.Y.; writing--original draft preparation, T.M.; writing--review and editing, H.S. and Z.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Double First-Class Key Program of Gansu Provincial Department of Education, and Gansu Province Science and Technology Program Funding (Grant No. 22 CX8 GA125).  
**Institutional Review Board Statement:** Not applicable.  
Figure 22: Optimized valve disc opening angle and pressure difference curve.
## 1 Introduction
Only during the recent decades, thanks to availability of powerful computers, and advancement in the technologies, these techniques have become widespread, and several approaches have been proposed to obtain 3D models of rock faces by remote sensing data. Among these, terrestrial laser scanners (TLS) and unmanned aerial vehicles (UAV) have undoubtedly proved to be among the most efficient and reliable tools, and are being today used in many stability analyses worldwide [37; 38; 39; 40]. In this paper, we present some considerations about the use of surveys from these techniques, through production of dense point clouds and the related semi-automatic acquisition of discontinuity data for stability analysis, based upon some experiences carried out in the last years. Advantages and drawbacks of the single techniques, and of their integration as well, will be illustrated through two significant case studies in southern Italy, concerning a cliff in pyroclastic rocks in Campania and a natural karst cave in Apulia. Choice of the sites was dictated by two common situations in southern Italy, that is instability problems along high sea cliffs and the possibility of failures from natural or artificial sinkholes. This latter case, in detail, was selected to highlight the importance of high-resolution analysis and studies in carbonate rock masses, and the need to include in the analysis the role played by karst processes, which very often is underestimated or neglected. After introductory sections describing the techniques and the instruments used, the two case studies are presented, before reaching the conclusions where our considerations about pros and cons of the techniques are illustrated.