esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study proposed a method of screening the optimal technologies for site remediation. The method is rooted in the \"contamination source control--process blocking--in situ remediation\" site remediation principles and the distribution characteristics of contaminants, as well as relationships among different areas including the source zone, plume area, and potentially contaminated area. The clear spatial relationship concept promises to enhance the effectiveness of contaminated site remediation. When using the procedure in an in-production petrochemical-contaminated site where LNAPLs are present, a technology mix, which includes institutional control, risk monitoring, emergency response, multiphase extraction, interception ditch, monitoring of natural attenuation, hydrodynamic control, as well as some alternative technologies, was found, aiming at different locations and strata. The technologies in the mix may not be the final ones and should be tested using computer simulation or pilot scale tests before carrying out the remediation work. The proposed method only gave us a technical framework and should be tested and enriched in future studies.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app131911076/s1](https://www.mdpi.com/article/10.3390/app131911076/s1)]([https://www.mdpi.com/article/10.3390/app131911076/s1](https://www.mdpi.com/article/10.3390/app131911076/s1)), Table S1: The scores in environmental factors; Table S2: The scores in social factors; Table S3: The scores in technical factors.  
**Author Contributions:** Methodology, Z.Z., C.G., L.S. and Z.N.; Software, Y.X. and X.W.; Validation, Y.X. and X.W.; Investigation, S.Y., C.G., L.S. and Z.N.; Resources, Z.Z., C.G. and Z.N.; Data curation, Z.Z., C.G., L.S. and Z.N.; Writing--original draft, M.Z.; Writing--review & editing, M.Z., Z.Z., L.S. and Z.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 42007171; the Sinopec Science Department Project, grant number 322082; the Hebei Natural Science Foundation, D2022504009; the CAGS Research Fund, grant number SK202207.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** Author [PERSON], [PERSON] and [PERSON] were employed by the company SINOPEC Research Institute of Safety Engineering Co., Ltd. Author [PERSON] was employed by the company Hebei Geological Environment Monitoring Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Sinopec Science Department Project. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
# Research on Blockchain-Based E-Bidding System
## 3 Proposed E-Bidding System
### Problem Analysis  
1. Access to information  
Any authorized participants can invoke the \(SC\) to check the tender, bidding and review information. However, the public information hides their real identity information through ZKP and other privacy mechanisms. Instead, private transformation, public transformation, isomorphic graph generated by multiple transformations of random graph \(G\) and unique label are used to represent their identities. Therefore, except for the main body who released the information, the rest of the people could not know the association between the bidding information and \(B_{i}\), or the association between the evaluation opinions and \(E_{j}\).  
2. Data ownership confirmation  
After the bidding or review, in order to prevent information tampering and disclosure, the system still uses \(GI\)-\(ZKP\) technology to confirm the ownership of the information submitted by \(B_{i}\) or \(E_{j}\). The user uses the corresponding private transformation \(\alpha_{i}\) or \(\beta_{j}\) and the public transformation \(\omega_{i}\) or \(\varphi_{j}\) returned by the system to verify the compound isomorphism of the original graph \(G\). The result \(ResGB_{i}\) or \(ResGE_{j}\) of ZKP validation is compared with the previously generated compound isomorphism graph \(IsoGB_{ii}\) or \(IsoGE_{j}\). If the two graphs are the same, it indicates that the information was actually posted by the user.  
3. Duplicate submissions preventing  
For both \(B_{i}\) and \(E_{j}\), it is necessary to prevent them from submitting bidding information and review information repeatedly, otherwise it will lead to system disruption. When the system receives the bidding information of \(B_{i}\) or the review information of \(E_{j}\), it shall check whether the hash value of the label carried has been included in the list of issued labels. If not, it is proved to be the information submitted by an invalid user and can be ignored. If it is, check whether the hash value of the label is in the consumed label list. If it is still in the consumed label list, it indicates that it is a duplicate submission and will be rejected.
## 3 Evaluation
### Elemental Image Compared to Viewpoint Image Resolution
This is because achieving accurate disparity typically requires a combination of a wide baseline and a high-resolution image. Since larger EIs demonstrate a greater baseline and a reduced number of texture-less EIs, as depicted in Figure 22, the results for high-resolution EIs are better than the accuracy in low-resolution images. Figure 21: The bar graph illustrates the performance of disparity calculated at five different resolutions. The graph shows that the EIs achieve the highest level of precision at a resolution of \(80\times 80\), followed by \(100\times 100\). Figure 22: (**a**) shows the output of \(20\times 20\) pixel EIs with a significant texture-less area, leading to an incorrect disparity computation. In (**b**), the outcome of \(100\times 100\) EIs taken from the same point with a wider baseline and a larger portion of the objects presented is a more accurate disparity.
## 2 Instrumentation Methods
### Compressor Instrumentation  
The compressor is instrumented to determine the total adiabatic efficiency and, by default, the total overall pressure ratio. The total overall pressure ratio [9] is defined as the ratio between the outlet total pressure (\(p_{2,t}\)) and inlet total pressure (\(p_{1,t}\)).  
The first method, presented in Figure 2a, through which the total overall pressure ratio can be determined, is to measure only the outlet total pressure (\(p_{2,t}\)) because the inlet total pressure (\(p_{1,t}\)) can be determined from the cell air pressure (\(p_{0}\)). Compressor total adiabatic efficiency [9] is the ratio between the total specific ideal work (\(l_{C,t,id}\)) and the total specific actual work (\(l_{C,t}\)). The total specific actual work [9] (\(l_{C,t}\)) is the difference between the outlet total specific enthalpy (\(h_{2,t}\)) and the inlet total specific enthalpy (\(h_{1,t}\)).  
\[\eta_{C,t}=l_{C,t,id}/l_{C,t}=(h_{2,t,id}(T)-h_{1,t}(T))/(h_{2,t}(T)-h_{1,t}(T)) \tag{2}\]  
In this case, in order to determine the total adiabatic efficiency, only the outlet total temperature (\(T_{2,t}\)) must be determined because the inlet total temperature (\(T_{1,t}\)) is similar to the cell air temperature.  
The second method, as shown in Figure 2b, shows that because of how the engine is constructed, there are situations when it is not allowed to install an outlet total pressure probe (\(p_{2,t}\)). In this case, it is mandatory to install an outlet static pressure probe (\(p_{2,S}\)) and it is necessary to know the cross-section (\(A_{2}\)) where it will be installed.
# A Simple Relationship to Estimate Parameters of the Optimum Compaction Point
###### 1 Introduction  
Mine site reclamation can be considered as very recent compared to the history of mine activities and to waste landfill management. Despite this discrepancy, mining regulations in developed countries (such as Canada) require the establishment of a plan of mine site reclamation and financial guarantees for the final reclamation of the mine site must be given before mine site development [1]. Thus, different scenarios of mine site reclamation were proposed [2], such as oxygen and hydraulic barriers.  
Different materials can be used for hydraulic layer barriers, among which one can find: (1) natural or treated clay, (2) geosynthetic clay liner (GCL), and (3) geomembranes (GM). Natural clayey (NC) or compacted material (compacted clay linear CCLs) layer covers consist of materials with stable mineralogy that are well known by geologists and engineers. Historically, clay covers have been the most commonly used material for hydraulic layer barriers [3]. During the construction of the hydraulic barrier, the control of some parameters such as compactive effort and molding water content is required. These last parameters affect the hydraulic conductivity [4; 5; 6], which is considered as one of the important key parameters for cover design. The required hydraulic conductivity for clay liners ranges from \(1\times 10^{-8}\) to \(1\times 10^{-10}\) m/s, respectively, for Japan and Denmark [7].  
To reach the target in terms of hydraulic conductivity, the cover must be compacted to a pre-determined dry unit weight that corresponds usually to a certain percentage of the maximum dry unit weight (\(\gamma_{dmax}\)). The last parameter that corresponds to the peak point of the compaction curve and having a water content corresponding to the optimum water content (\(w_{Opt}\)) can be evaluated using laboratory tests (Standard or Modified Proctor [8; 9]).  
In some situations, for example, at the prefeasibility stage of a project (before conducting the required tests), it may be useful to estimate \(\gamma_{dmax}\) and \(w_{Opt}\) to obtain an early overview of the conditions that can be anticipated. In this regard, various predictive modelshave been developed to estimate \(\gamma_{dmax}\) using routinely available data such as the liquid limit (\(w_{L}\)) and the plastic limit (\(w_{P}\)), the pasticity index (\(I_{p}\)), gravel content, sand content, and fine-grained content [10; 11; 12; 13; 14; 15; 16; 17; 18]. The proposed equations were based on multilinear regression or linear regression and their results were validated using literature data.  
In this study, a new approach was used with the objective to estimate the optimum compaction point parameters with limited parameters and with higher regression coefficients.
## 4 Discussion
As can be seen in Figure 4, for incident X-ray fluxes above \(10^{32}\) photons/cm\({}^{2}\)s, both time profiles for these states become wider, however the ratio of their widths remains constant and is equal to about 1.4. The observed increase in the width for the OPA and TPA processes may be considered as a first indication that the saturation regime for nonlinear interaction is approached [15; 16]. The atomic state response and state populations no longer follow the incident photon pulse profile, and therefore cannot be evaluated with a simple Gaussian-like approximation. Indeed, the saturation of absorption effects were reported for metallic Fe at X-ray flux intensities of 8.4 \(\times\)\(10^{34}\) photons/cm\({}^{2}\cdot\)s [16]. At the present point, the studies of such effects need more theoretical investigation, as well as experimental verifications. Though the present simulations were performed with a simplified Gaussian-like pulse structure, this is still a good approximation for single-shot pulse shapes expected during self-seeded operation of an XFEL [6; 14]. In addition, since the experimental data was taken in a cumulative measurement where many pulses were summed together to improve the signal-to-noise, a Gaussian pulse shape is a good representation of the average incident pulse shape. We show that the reported narrowing of atomic states populated through nonlinear interaction relates to a square-dependence of the applied X-ray pulse intensity not to the incident X-ray pulse shape. Further research should address other phenomena that may be related to the single-pulse interaction with SASE-like structure. For example, one may investigate the structure and envelope of the applied X-ray pulse. Moreover, different parameters describing the SASE structure of the incident beam may be evaluated, like spike width and spike separation in the time domain. We anticipate that these effects will start to become significant as they approach the timescales of atomic core-hole lifetimes. For any such single-shot simulations and experiments not only does the experiment require precise measurement of the incident beam properties, but the simulation parameters need to be carefully tuned to match the specific XFEL facility at which the experiment takes place. [PERSON] acknowledges the National Science Centre (NCN), Poland for support under grant no. 2015/19/B/ST2/00931. [PERSON] the Polish Ministry of Science and Higher Education for support from the budget for science in 2016-2019 under grant IDEAS Plus II HPI 2015000164. **Author Contributions:** [PERSON] conceived and designed the project, [PERSON] delivered the rate equations and [PERSON] executed calculations and prepared the figures as well as the initial version of the manuscript. All authors participated in result discussions and manuscript preparation. [PERSON] wrote the manuscript with input from [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Analysis
#### 2.3.3 Determination of Phenolic Compounds by HPLC  
The separation of anthocyanins, pyranoanthocyanins, flavonoids, phenolic acids, and flavan-3-ols in wine was carried out in a Waters Acquity Arc liquid chromatograph (Waters, Milford, MA, USA) equipped with a Waters 2998 diode array detector (Waters, Milford, MA, USA). The column used was a Poroshell120 EC-C18 core-shell column (150 mm \(\times\) 2.1 mm, 2.7 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA). The composition of the mobile phases was: 1% formic acid in water (A) and 1% formic acid in 1:1 (\(v/v\)) methanol: acetonitrile (B). The elution gradient started with 100% A for 2 min, linear increase from 0 to 15% B in 33 min, from 15 to 21% B in 15 min, and from 21 to 30% B in 20 min followed by washing and re-equilibration of the column. The column oven was maintained at 55 \({}^{\circ}\)C, the flow rate was 0.3 mL/min, and the injection volume was 5 \(\upmu\)L.  
Compounds were identified by comparing their UV spectra recorded with the diode array detector and those reported in the literature. In addition, an HPLC-MS analysis was conducted to confirm each peak identity. For this, a liquid chromatograph coupled to a QDA mass detector (Waters, PA, USA) was used. Elution was performed with the HPLC analysis conditions detailed above. The mass spectrometer operated in positive-ion mode for anthocyanin and pyranoanthocyanin confirmation, with a capillary voltage of 1.5 kV and in negative-ion mode for flavonoid, phenolic acid and flavan-3-ol confirmation, with a capillary voltage of 0.3 KV. In both modes, the cone voltage was 30 V, and the desolvation temperature of 350 \({}^{\circ}\)C. Mass scans (MS) were measured from \(m/z\) 100 up to \(m/z\) 1200.  
The quantification of different phenolic compounds was conducted by external standards. Concretely, anthocyanins and pyranoanthocyanins were quantified at 520 nm as malvidin-3-glucose chloride. Gallic acid and flavan-3-ols were quantified at 280 nm, using the pure compounds as external standards. The hydroxycinnamic derivatives were quantified as caffeic acid at 320 nm and flavonols at 360 nm as quercetin-3-glucoside.
## 2 Methodology
### Orbit Determination  
The process of experimental orbit determination makes use of the orbit observables acquired by the two main baselines of the interferometer: Main-Secondary 1 and Main-Secondary 2. The third possible baseline Secondary 1-Secondary 2 is redundant, and it is used only to check the consistency of the interferometric observations.  
The orbit determination module represented in Figure 2 uses two models. The first one estimates the orbit of the satellite by means of a geosynchronous dynamic model, which considers the \(j_{2}\), \(j_{22}\), \(j_{3}\), and \(J_{4}\) effects, Moon and Sun perturbations, and the Solar Radiation Pressure (SRP) [30]. The estimated trajectory of the spacecraft is propagated from an initial state (3D position and velocity) by means of a Runge-Kutta-Fehlberg 7(8) integrator [31].  
The output of the GEO dynamical model is combined with the second model of the orbit determination module, which estimates the phase observations that the interferometric measurement system described in Figure 2 would generate. They are the result of the distance differences between the estimated position of the satellite and the position of the antenna receivers, which are well determined from geodetic measurements. Once the \(\alpha_{\tau 0}\) phase term from Equation (9) is removed after calibration, the measured interferometric phase (\(\alpha_{ij}\)) is expressed as:  
\[\alpha_{ij}(t)=\frac{2\pi}{\lambda}\cdot n\cdot\left(\rho_{i}(t)-\rho_{j}(t)\right) \tag{13}\]  
The measured interferometric phase (\(\alpha_{ij}\)) is related to the distance between the spacecraft and the receiver (\(\rho\)) in terms of the range difference between the satellite and the pair of antennas (\(\rho_{i}-\rho_{j}\)), where \(\lambda\) is the wavelength of the emitted signal [26]. The atmosphere has a nonunit refractive index \(n\), which must be considered in order to express the distance between the satellite and the receivers in terms of the electrical path length [32]. Hence, each interferometric baseline yields a set of estimated phase observations for a single estimated trajectory.  
An iterative process based on the batch least-squares estimation technique [33] minimizes the Mean-Squared Error (MSE) between the measured (\(\alpha_{k}\)) and the estimated (\(\hat{\alpha}_{k}\)) interferometric observations as Equation (14) details, where \(n\) is the number of samples. The sampling rate of the interferometer is 1 sample/s. The resulting estimated trajectory corresponds to the one that generates the simulated phase observations that fit best to the measured phase observations.  
\[\text{MSE}=\frac{1}{n}\sum_{k=1}^{n}\left(\alpha_{k}-\hat{\alpha}_{k}\right)^{2} \tag{14}\]
# Blockchain-Assisted Verifiable and Multi-User Fuzzy Search Encryption Scheme
## 4 Definitions of Algorithm and Security Model
### System Model  
The model is based on the following roles, as shown in Figure 2.  
Figure 1: p-stable element mapping.  
1. Data Owner \((DO)\). \(DO\) extends beyond individual entities to data centers that are responsible for the integration of file information owned by multiple data owners. This implementation realizes the application of a multi-owner scenario from another perspective.
2. Trusted Authority \((TA)\). As an authenticating entity, it generates attribute private keys for data users and transmits system public parameters and other relevant information to the users securely.
3. Data Users \((DU)\). \(DU\) need to apply for their attribute private keys based on their attributes as well as reliable information, then generate and send search trapdoor to the smart contract account for searching transactions through query requirements.
4. Smart Contract \((SC)\). As a decentralized component of the system, \(SC\) stores secure index tree \(Index_{tree}\) and verification information of data owners, mitigating the excessive and uncontrolled authority of the cloud server. The search contract performs the initial segment of the entire search transaction and ensures the verification process for \(DU\).
5. Cloud Server \((CS)\). \(CS\) has powerful storage as well as computational capabilities, and stores the ciphertext data files outsourced by \(DO\), and provides \(DU\) with the second part of the search service based on the transaction initiated, it may have the malicious behavior of tampering with the user's search results.  
The workflow of the system is as follows.  
\(DO\) transmits parameters, public keys, etc., to \(TA\) initially (Step 1), firstly categorizes all large collections of files, encrypts classified file collection \(D=\{F_{1},F_{2},\ldots,F_{n}\}\) and outsources the ciphertext set \(C=\{C_{1},C_{2},\ldots,C_{n}\}\) to \(CS\) (Step 2). To achieve access control and efficient search, \(DO\) constructs a file index tree and sends the index tree to the contract storage address (Step 3). \(DU\) send a registration request to \(TA\) (Step 4) and receives attribute private keys and search keys (Step 5). Then the \(DU\) generates a search token locally based on query keywords and algorithms disclosed by \(TA\) and sends it through a transaction to \(SC\) (Step 6). \(SC\) executes a search to obtain a search list and sends the information to the user (Step 7). The ciphertext of file identifiers \((FID)\), the ciphertext set \(C=\{C_{1},C_{2},\ldots,C_{n}\}\) and transaction information is sent to \(DU\) (Step 8). Then \(CS\) returns to \(DU\) the ciphertext  
Figure 2: System model.  
data of the search transaction (step 9). \(DU\) can verify the search results locally, and only users whose attribute private key satisfies the access control can finally decrypt the file and malicious servers will receive penalties. (Step 10).
# A New SORM Method for Structural Reliability with Hybrid Uncertain Variables
###### Abstract  
(1) Background: in practical applications, probabilistic and non-probabilistic information often simultaneously exit. For a complex system with a nonlinear limit-state function, the analysis and evaluation of the reliability are imperative yet challenging tasks. (2) Methods: an improved second-order method is proposed for reliability analysis in the presence of both random and interval variables, where a novel polar transformation is employed. This method enables a unified reliability analysis taking both random variables and bounded intervals into account, simplifying the calculation by transforming a high-dimension limit-state function into a bivariate state function. The obtained nonlinear probability density functions of two variables in the function inherit the statistic characteristics of interval and random variables. The proposed method does not require any strong assumptions and so it can be used in various practical engineering applications. (3) Results: the proposed method is validated via two numerical examples. A comparative study towards a contemporary algorithm in state-of-the-art literature is carried out to demonstrate the benefits of our method. (4) Conclusions: the proposed method outperforms existing methods both in efficiency and accuracy, especially for cases with strong nonlinearity.  
structural reliability; hybrid uncertainty; SORM; random and interval variables; polar space +
Footnote †: journal: Applied sciences