esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 New Taxonomy for Constructability Constraints and Features  
### Introduction of the New Taxonomy  
Using a taxonomy can aid in identifying and describing constructability constraint types systematically. Such taxonomy should be generic, structured, and project-independent. Considering the introduced examples from the case study earlier in this section and the similar examples and constraints from relevant projects, the typical constructability constraints and the associated features can be divided into three different levels:  
* **Micro level (component level)**, which includes components and their characteristics (e.g., shape and dimension).
* **Meso level (interrelationship level)**, which concerns relations between two or more components (e.g., distance, intersection, connection).
* **Macro level (contextual level)**, which deals with constraints related to the context of the overall design and requires consideration of the entire architectural concept (e.g., the number of types, and the position of components within the design).  
Figure 9 shows the developed taxonomy for constructability constraints, including their required parameters for feature-based modeling and computational implementation. This taxonomy is developed in alignment with the work of [13; 15; 65] on constructability and feature-based modeling.  
When analyzing and categorizing constructability constraints, it is crucial to differentiate between the cause (i.e., the design intent) and the effect (i.e., the constructability constraint). This distinction is necessary because sometimes the design intent might belong to a different level of taxonomy than the relevant constructability constraint. An example here is the \"inclined straight edge\" feature considered in the selected case-study project. In this project, the original design intent was to have a pattern with curved vertical elements across multiple floors. According to the new taxonomy, this design intent can be classified at the \"Macro Level\", as it considers the overall pattern of the facade panels. However, experts strongly advised against curved edges for these panels, as they are expensive to fabricate and not easy to install. As a result, a constructability constraint was determined to design facade panels with inclined straight edges. This constraint pertains to the \"shape\" of the building components, classified at the \"Micro Level\". Hence, each facade panel in a bay received a distinct angle of inclination as its feature.  
Figure 9: Taxonomy for classification of typical geometric constructability constraints and features.  
The new taxonomy can broadly classify most constructability constraints and features of prefabricated facades, independent of projects. The three subsequent tables provide examples from renowned projects and their respective constructability constraints, show-casing this flexible adaptability. In selecting these examples, special attention was paid to ensure that all had sophisticated design intents challenging construction and varied in scale, occupation purpose, and levels of complexity.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 5 Lost Circulation While Cementing through Naturally Fractured Reservoirs
### Two-Stage Cementing Technique  
The pressure acting on the loss zones during casing cementing can be reduced by using mechanical tools such as stage cementing collars in combination with a cementing basket or External Casing Packers (ECPs). Stage Cement Collars (SCCs) allow cementation in two or three stages, reducing dynamic and hydrostatic pressure in deeper formations. Below the SCC a cement basket is placed, and it supports the second-stage cement slurry, prevents its downward movement and thus its loss into the thief zone.  
External casing packers (ECPs) are also placed below the SCC, just above the loss zone and they are activated after the first stage of slurry pumping. ECP prevents the transfer of hydrostatic pressure of the second stage cement slurry column on deeper zones by expanding and sealing the annular space.  
Therefore, the cementation of the casing in case of partial and complete losses can be performed by replacing single-stage cementing, with two-stage cementing which involves placing a stage cementing collar (SCC) above the thief zone. The need to use a SCC and its placement depth should be determined based on the results of interval drilling, the presence of thief zones, and the severity of mud loss.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 6 Conclusions
### GNSS's Time Offsets  
GLOAASS time scale was misaligned with respect to GPS by about \(-\)400 ns until the summer of 2014, when an alignment procedure started, according to [8]. This procedure was completed at the beginning of 2015, so currently the GLONASS timescale is aligned with GPS. Despite this the alignment is not constant, and there may be non-predictable variations with amplitude up to 100 ns, like the one that happened in the second half of 2016.  
BeiDou timescale is not aligned with GPS. The time offset vary continuously, with an oscillating trend and amplitude of about 70 ns. Furthermore it is not centered to zero, but it is always positive, although a slow trend of the average value to zero has been detected.  
Galileo and QZSS time scales are aligned with GPS, although for Galileo two sudden variations, in November of 2015 and November-December of 2016, of about 60 and 30 ns, respectively, were noticed.  
In addition to the previous constellation-specific considerations, for each time offset a receiver-dependent Inter System Bias (ISB) which could reach several tens of nanoseconds was evident. This bias tends to be almost constant in time, and so it could be calibrated. Since ISB is receiver-dependent, an upgrade of receiver firmware can change its amount and a new calibration is required.  
The time offsets of 31 IGS/EUREF stations since 1 January 2014 were monitored. For each station, the potential discontinuities in time series of time offsets have been detected, and whenever possible thecause of discontinuity has been identified. Hence, for each continuity interval a relative-to-Septentrio calibration of time offsets has been evaluated.  
[PERSON] analyzed the data and wrote the paper; [PERSON] conceived the model and supervised the work.  
The authors declare no conflict of interest.
Figure A1. GPS coordinates and clock differences. “max 5” refers to the maximum threshold of 5 m as a condition for coordinate rejection.  
Figure A2: GLONASS coordinates and clock differences. “max 5” refers to the maximum threshold of 5 m as a condition for coordinate rejection.  
Figure A3: BeiDou coordinates and clock differences. “max 5” refers to the maximum threshold of 5 m as a condition for coordinate rejection.
## 3 Results  
### Application of the Method  
#### 3.1.1 Extracted Sample Sets  
The proposed burned area mapping method was applied to the Hot Pot Fire, Range 12 Fire, and Rey Fire to extract the sample sets of five typical types of land cover. The relationship of reflectance at band 5 (1.24 \(\upmu\)m) and band 7 (2.13 \(\upmu\)m) of the extracted samples was investigated. Figure 5a,b show the scatter plots of band 5 and band 7 reflectance comparisons of the samples extracted by Box 1 and Box 2, respectively, in the Range 12 Fire. The samples of unburned types in Box 1 and Box 2 are in the upper left corner of the scatter plots, whereas the samples of the burned area in Box 1 are in the lower right corner of Figure 5a,b. Figure 5c is the scatter plot made by combining Figure 5a,b. The empirical formula with an intercept of -0.003 and a slope of 1.079 can effectively separate the burned area samples from unburned samples in the Range 12 Fire, so the samples below the line are extracted as burned area samples. Using the same method, we can determine the empirical formulas of the other two wildfire cases and then extract more samples of the burned area. The empirical formula and its coefficients \(\alpha\) and \(\beta\) of the three wildfires are shown in Table 5.  
The samples of vegetation, cloud, bare soil, and cloud shadow were also extracted from the three wildfire cases. Finally, the sample sets acquired from the Hot Pot Fire, Range 12 Fire and Rey Fire contained burned area pixels (1251), vegetation pixels (1101), bare soil pixels (898), cloud pixels (302), and shadow pixels (205); the total number of extracted samples is 3757.
## 1 Introduction
First, solid catalysts (PS-NHPI; Figure 2a) were obtained according to the procedure described in [24], by immobilizing NHPI on commercially available polymer supports, differing in (i) their functional group content (according to the manufacturer, this was in the range of  
Figure 1: Catalysts employed in SCILL systems reported in the literature (see text for further details). (a) SBA-15-TEMPO, (b) PS-TEMPO, (c) SiOCONHPI. 2.0-5.5 mmol Cl/g), (ii) their grain size (from 16 to 400 mesh), and (iii) the amount of divinylbenzene (DVB) used as a cross-linking agent (1%, 2%, or 5.5%). The obtained PS-NHPI material was characterized by SEM, FT-IR, and elemental analysis. Previously, catalysts of this type with 0.35-1.65 mmol NHPI/g were active for the oxidation of toluene and _p_-methoxytoluene using dioxygen and Co(OAc)\({}_{2}\) (as co-catalyst), either in acetic acid or without solvent, and the catalyst could be separated twice in reactions without a solvent [24]. Herein, the synthesized heterogeneous PS-NHPI catalysts were covered with a thin layer of ionic liquid, [emim][OcOSO\({}_{3}\)], which contained cobalt(II) chloride (Figure 2b). The activities of the obtained systems were tested in a model ethylbenzene oxidation reaction conducted at 80 \({}^{\circ}\)C under dioxygen atmosphere for 6 h. This work aims to compare the activity of various obtained catalysts PS-NHPI@CoCl\({}_{2}\)@[emim][OcOSO\({}_{3}\)]), referred to as SCILL/SILP systems (SILP = supported ionic liquid phase), with systems composed of heterogeneous PS-NHPI and CoCl\({}_{2}\) (PS-NHPI/CoCl\({}_{2}\)) or IL (PS-NHPI/IL) added separately to the reaction mixture. Their potentials for reuse was also tested.
## 4 Privacy in the Age of Openness, Web Services, Mash-Ups and Profiles
### Anonymity and Privacy Enhancing Technology  
While the new mobile and cloud-hosted technology paradigm was not yet defined, a number of research projects explored identity protection and privacy by means of higher cryptographic protocols. Many of the research efforts referred back to [PERSON]'s MIX concept [13, 26]. Their aim was the suppression of uncontrolled data release, along with strong confidentiality of communication relationships (\"unobservvability\"). Many new terms and definitions have been collected and structured by [PERSON] and [PERSON] [27]. A few alternative efforts to develop either a global, total infrastructure of identification, or a total, user-executed information control have been worked on by researchers. From the inventions made under the investigation of electronic cash [28], Brands credentials [29], the credential systems Idemix [30] and Microsoft's U-Prove [31] based on Brands were developed as protocols for anonymous or pseudonymous credentials [29, 30], following suggestions for transaction pseudonymity made by [PERSON] [32]. These credential systems are designed for unlinkable authentication and credential presentation. They were further researched with interdisciplinary efforts in building frameworks [33] for regulation, design, auditing and managing IT with guaranteed privacy properties and IdM systems with known risks. Large publicly funded research projects focused on user-centric privacy protection, on privacy-enhancing identity management, and on unobservable transactions on the Internet. Some of the results have made it into practice, such as the Tor project and AN.ON anonymization services [26], while a large part of the achievements is waiting for a debut with the user masses. In parallel, commercial developments have aimed at the re-introduction of a virtual security perimeter based on trusted hardware. In these, trustworthy software systems are expected to process data according to, and only according to, the processing policy that travels with the data [12]. Most of these developments targeted, however, the Internet paradigm based on personal computers. The largest part of the population these days is using services through mobile phone networks, an infrastructure that directly connects network access to identification, location and billing accounts.  
Facing these growing infrastructures of identification, one expects resistance from its users. However, only a specialized segment of the user population insists on the use of PET. Investigations frequently show that strong anonymity technology that requires personal discipline and imposes an extra burden upon users is only accepted by special-interest groups [34, 35, 36]. General user populations frequently show little acceptance of security or privacy measures that impair user experience. Special-needs target groups frequently classify IdM technologies as one of the main hindrances for universal access to applications [37].
# Evaluation of the Consistency of Three GRACE Gap-Filling Data
## 4 Triple-Collocation Method-Based Fusion on the Gap-Filling Data  
Here, we put forward a new strategy based on the triple-collocation method to generate a fused SHC gap filling product, which theoretically avoids random error better than any of the individual datasets. We first estimated the error variance of each degree and order based on the 10-year SHCs data from 2009 to 2018 and counted the median variance of each order. Note that the calculated error variance may occasionally be negative (less than 25%) due to increased correlation among the series, which will affect the calculation of weights. Therefore, to get rid of these negative values, we use the median error variance of each degree (l) to calculate the weight, which is used for all coefficients of the same degree (i.e., we assume the data quality is a function of only degree). Regarding the finite spatial resolution of the QE, only the low-degree terms (degree 2-15) were fused. Figure 11 shows the distribution of weight values for each degree of SHCs. Compared to IGG and SSA, the weights of QE are lower and gradually decline with an increasing degree. This result is consistent with our previous finding that the higher degree terms of the SHCs of QE may contain higher errors and should therefore be given smaller weights when performing the data fusion.  
Next, the weights calculated in Figure 11 were applied to fuse the SHCs of SSA, QE, and IGG. We only show the results of coefficients of order = 1 in Figure 12. The weighted average results retain the interannual signal to a large extent and provide better agreement with the IGG and SSA series and effectively avoid the detrimental effects of systematic bias in QE, such as C\({}_{2,1}\), C\({}_{5,1}\), C\({}_{13,1}\), and C\({}_{15,1}\). The fused series shows a large difference when the individual series are subject to high-frequency vibrations, such as C\({}_{5,1}\), C\({}_{7,1}\), C\({}_{13,1}\), and C\({}_{15,1}\), and the weighted average is evidently smoother, indicating the noise is suppressed to a certain extent. In some cases, the fused series can better maintain the seasonal and interannual variation patterns in the gap months. For example, the agreement for the results of C\({}_{7,1}\) is poor: IGG and QE appear to over- or underestimate the value of the gap period, and SSA, while retaining the long-term trend, loses much of the seasonality (Figure 12f). Nevertheless, the fused series retains both the long-term trend and part of the seasonal signal.  
Figure 11: Distribution of weight values for fusing the three SHC datasets used in this study.
## 1 Introduction
This study combines the efficiency of DSs with the advanced analytical capabilities of AI to verify the usefulness of HPC infrastructure for image processing and classification. The main objective of this paper is to compare the processing efficiency and image classification accuracy of a low-scalability DS-HPC (LSDS-HPC) infrastructure composed of three nodes equipped with NVIDIA GTX 1050 ti graphics cards. The main contributions of this work are as follows:* It demonstrates how an LSDS-HPC setup with recycled hardware can maximize the efficiency and accuracy of image classification. * It promotes the reuse of devices considered obsolete for complex tasks. * The results obtained aim to facilitate research in various areas, opting for the use of low-scalability distributed systems when a large budget is not available to acquire specific equipment for high-performance computing. * It allows researchers and scientists to choose a distributed training framework for their distributed deep learning models. This highlights the importance of reusing obsolete hardware to reduce e-waste in computer labs, research centers, companies, and industries. * Finally, empirical speedups across different frameworks and infrastructures are presented to reinforce the optimal choice of the distributed training framework.
## 4 Conclusions  
In this study, via the ALD preparation experiments and SEY measurements, we analyzed the SEE characteristics and energy spectra of silicon-based MgO coatings under the condition of increasing coating thickness, and we carried out a detailed theoretical study on the influence of MgO coating thickness on SEY. Four conclusions can be drawn from the experimental and theoretical researches. First, with the thickness rise of the MgO coating samples, coatings' SEY curves tend to approach the SEY curve of pure MgO; at the same time, the influence of substrate on SEY decreases gradually. Second, the SEES results show that the majority of the emitted electrons are true secondary electrons, and the yield of backscattered electrons just accounts for a little proportion in SEY. Third, the semi-empirical SEE theory developed for double-layer structure is suitable to interpret the SEY regularities of MgO/Si structures with increasing thickness, and the simulation results are well consistent with the experimental phenomena. Fourth, the increase of the top layer's thickness will make the motion path become longer for the excited inner secondary electrons generated in the substrate, further leading to a result that the SEY contributed by the substrate become fewer. This work deeply studied the SEE characteristics for typical MgO/Si double-layer structures, and theoretically revealed the SEY dependence on thickness of the top coating, as well as the influence of the substrate on SEY. This study makes remarkable sense for the subsequent researches on the applications of nanoscale MgO coatings, and provides considerable guidance for the applications of high SEY materials.  
Conceptualization, X.Z. and J.G.; methodology, X.L., J.G. and W.Z.; software, D.W.; validation, X.Z., X.L. and R.Z.; formal analysis, R.Z.; investigation, J.G.; resources, R.Z.; data curation, X.Z.; writing--original draft preparation, X.Z. and J.G.; writing--review and editing, X.Z. and X.L.; visualization, D.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (grant number 2016 YFB030380), and the Scientific Instrument Developing Project of the Chinese Academy of Sciences.  
Not applicable.  
All data included in this study are available upon request by contact with the corresponding author.  
The authors declare no conflict of interest.
## 3 Pavement Dynamics Theory and Finite Element Model Verification
### Finite Element Model Verification  
In the field of road engineering research, ABAQUS software (version 6.10) has become an essential tool for analyzing the dynamic response of asphalt pavement structures under moving loads. As a widely used large-scale finite element analysis software, ABAQUS has been proven in numerous studies to provide high reliability and accuracy in pavement structure dynamic response analysis [23; 24; 25; 26; 27].  
To validate the developed model, its results were compared with those from the literature [28]. This reference used a semi-analytical numerical procedure to compute the dynamic response of a three-layer pavement system under elastic and viscoelastic conditions, subjected to non-uniform contact pressure and irregular tire impressions. The results were then compared in detail with finite element simulations.  
In this study, ABAQUS was used to establish a finite element model considering the viscoelastic properties of the surface layer and non-uniform tire load distribution, simulating the tire's interaction with the pavement. The surface asphalt layer was modeled as a linear viscoelastic material using the Prony series representation available in ABAQUS. The viscoelastic behavior was defined by inputting time-dependent shear relaxation modulus values, which describe how the material response evolves under loading. The pavement structure and material parameters are detailed in Tables 1 and 2, and the non-uniform load distribution is shown in Figure 3. The DLOAD subroutine was employed to simulate the dynamic application of non-uniform tire loads over time on the pavement. Figure 4 presents a comparison between the finite element results, considering the viscoelastic characteristics of the surface layer, and the semi-analytical solution from the literature [28]. The comparison shows that the simulated and reference vertical strain values at a depth of 0.2 m exhibit consistent trends, with closely matching peak values. The relative error in peak strain is approximately 2.4%. This result effectively validates the model and provides reliable theoretical support for the subsequent analysis of the deflection slope characteristics of inverted asphalt pavement structures.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Structural Layer** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** & **Density (kg/m\({}^{3}\))** & **Thickness (m)** \\ \hline Surface layer & 4000 & 0.35 & 2400 & 0.2 \\ Base layer & 1000 & 0.3 & 2000 & 0.4 \\ Subgrade & 200 & 0.4 & 1500 & \(\cdots\) \\ \hline \end{tabular}
\end{table}
Table 1: Pavement material parameters.  
\begin{table}
\begin{tabular}{c c c} \hline
**\(\tau\)i** & **gi** & **Instant Modulus (MPa)** \\ \hline
1000 & 0.000541 & \\
100 & 0.000902 & \\
10 & 0.002705 & \\
1 & 0.007663 & \\
0.1 & 0.025243 & 10,000 \\
0.01 & 0.071131 & \\
0.001 & 0.148753 & \\
0.0001 & 0.185896 & \\
0.00001 & 0.457167 & \\ \hline \end{tabular}
\end{table}
Table 2: Surface-layer asphalt mixture Prony series parameters.