esa-sceva/satcom-chunk-collection huggingface dataset:
# Electronic Voting System Using an Enterprise Blockchain
## 4 System Proposal
### Procedure  
The system must prevent unauthorized users from participating in the vote while providing the highest level of transparency. To identify a user, the identity provider (IdP)  
Figure 11: Election asset written in GO.  
Figure 10: NFT Control Access.  
must be used to certify its identity, IdPs are mechanisms widely used in authentication processes. During the elections, the authorization server may be the national digital identity provider available in many countries. For smaller or more specific scenarios, email can be used as a provider of signed tokens (e.g., JWT; JSON Web Token is an open standard based on JSON proposed by the IETF for the creation of access tokens) for addresses of eligible users.  
The CA of the SUFFRAGE organization is integrated with the IdP; this allows it to generate a cryptographic identity (public and private key pair) for each voter previously verified by the IdP; it is also integrated with the SoftHSM--a software emulation of an HSM--where module where said identities are stored, once inside SoftHSM, cryptographic material can never be extracted or viewed. In addition, the use of SoftHSM makes it possible to take advantage of some security functionalities that a true HSM offers, making the proposal more viable in the sense of cost, simplicity and ease of maintenance.  
The MINTER affiliation is the only one authorized at the chaincode level to mint the fixed number of ballots necessary for the voting process; these ballots can only be generated on a single occasion per election. In short, the entire sub-process means that the only participants authorized to mint the ballots are those governed by the SUFFRAGE organization; in the same way, only transactions issued by MINTER dApp will be allowed.  
The Election asset (see Section 4.2.2) stores some public data so all organizations can consult those data. The fields to be defined for the asset depend on each voting process. In this proposal, it is assumed that there is no objection in that the number of voters has information in the public domain and is stored in the ledger when a specific electoral process is created. For example, the number of voters in an election process for a particular professional or labor organization may be irrelevant to the rest of the citizenry, but the fact that these data are in the public domain is not a problem.  
Voting is done by issuing an anonymous transaction to the network blockchain that modifies some token states. A new state is stored by linking the identifier of the ballot with the transaction ID. This last step guarantees Verifiability, as this ID allows the system to track any transaction.
## 2 Materials and Methods
### Fourier Transform Infrared Measurements
149.5-150.8 \({}^{\circ}\)C; \({}^{1}\)H-NMR (600 MHz, DMSO-\(d_{6}\)) \(\delta\) 9.25 (s, 1H), 8.02 (d, \(J\) = 8.1 Hz, 2H), 7.91 (s, 1H), 7.86-7.73 (m, 3H), 7.73-7.61 (m, 4H), 7.41-7.40 (m, 2H), 2.67 (s, 2H), 0.95 (s, 9H); \({}^{13}\)C-NMR (151 MHz, chloroform-\(d\)) \(\delta\) 171.67, 158.56, 141.04, 138.39, 136.70, 133.83, 131.27, 131.05, 130.84, 129.74, 129.53, 129.31, 128.77, 127.81, 127.68, 127.42, 126.33, 126.31, 126.26, 126.26, 126.23, 126.20, 125.54, 125.35, 125.17, 123.55, 123.37, 44.68, 32.11, 29.82; IR (\(v\), cm\({}^{-1}\)): 3693.9, 2959.4, 2871.7, 1670.7, 1620.7, 1603.8, 1483.6, 1431.7, 1325.2, 1128.8, 1068.0, 1017.4, 829.5, 797.7, 761.9, 708.3, 690.9, 597.9; HRMS: \(m/z\) Cal
## 4 Correctness and Rationality of ANM
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Span–Width Ratio**} & \multirow{2}{*}{**Calculation Methods**} & \multicolumn{6}{c}{**Natural Vibration Frequencies (Hz)**} \\ \cline{3-8}  & & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** \\ \hline \multirow{4}{*}{6.72} & EBT & 23.71 & 86.42 & 172.52 & 270.09 & 372.26 \\  & ANM & 23.24 & 81.33 & 156.45 & 238.41 & 323.40 \\  & FEM & 23.85 & 83.43 & 159.55 & 240.98 & 322.92 \\  & \(e_{EBT}\)/\% & \(-\)0.59 & 3.58 & 8.13 & 12.08 & 15.28 \\  & \(e_{\Lambda\Lambda\Lambda}\)/\% & \(-\)2.58 & \(-\)2.52 & \(-\)1.94 & \(-\)1.07 & 0.15 \\ \hline \multirow{4}{*}{8.12} & EBT & 16.39 & 61.27 & 126.05 & 201.97 & 283.93 \\  & ANM & 16.07 & 58.57 & 116.34 & 181.44 & 250.03 \\ \cline{1-1}  & FEM & 16.62 & 60.26 & 119.32 & 185.23 & 253.47 \\ \cline{1-1}  & \(e_{EBT}\)/\% & \(-\)1.38 & 1.68 & 5.64 & 9.04 & 12.02 \\ \cline{1-1}  & \(e_{\Lambda\Lambda\Lambda}\)/\% & \(-\)3.28 & \(-\)2.81 & \(-\)2.50 & \(-\)2.05 & \(-\)1.36 \\ \hline \multirow{4}{*}{9.24} & EBT & 12.73 & 48.22 & 100.74 & 164.09 & 233.80 \\  & ANM & 12.57 & 46.47 & 94.22 & 149.29 & 208.17 \\ \cline{1-1}  & FEM & 12.95 & 47.87 & 96.83 & 153.06 & 212.54 \\ \cline{1-1}  & \(e_{EBT}\)/\% & \(-\)1.70 & 0.73 & 4.04 & 7.21 & 10.00 \\ \cline{1-1}  & \(e_{\Lambda\Lambda\Lambda}\)/\% & \(-\)2.88 & \(-\)2.92 & \(-\)2.69 & \(-\)2.47 & \(-\)2.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of natural vibration frequencies of a CBGCSW (simply supported).
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 4 Result Analysis and Discussion
### Integrated POD with Full LEO Constellation
#### 4.1.4 Integrated POD with Different Ground Network
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Satellites**} & \multicolumn{2}{c}{iGMAS} & \multicolumn{2}{c}{iGMAS +60 LEO-P} & \multicolumn{2}{c}{iGMAS +60 LEO-S} & \multicolumn{2}{c}{iGMAS +96 LEO-P} \\ \cline{2-9}  & **RMS** & **Percentage** & **RMS** & **Percentage** & **RMS** & **Percentage** & **RMS** & **Percentage** \\ \hline
**GPS** & 7.4 & – & 0.8 & 89.2\% & 0.7 & 90.5\% & 0.7 & 90.5\% \\ \hline
**GLONASS** & 9.7 & – & 0.8 & 91.8\% & 0.7 & 92.8\% & 0.7 & 92.8\% \\ \hline
**Gallieo** & 8.8 & – & 0.8 & 90.9\% & 0.7 & 92.0\% & 0.7 & 92.0\% \\ \hline
**BDS GEO** & 237.1 & – & 6.1 & 97.4\% & 3.8 & 98.4\% & 4.8 & 98.0\% \\ \hline
**BDS IGSO** & 6.4 & – & 1.8 & 71.9\% & 1.8 & 71.9\% & 1.8 & 71.9\% \\ \hline
**BDS MEO** & 10.4 & – & 0.8 & 92.3\% & 0.8 & 92.3\% & 0.8 & 92.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average 3D RMS values of GNSS orbit differences and improvement percentage with respect to the result of POD using ground iGMAS data (Unit: cm). Figure 9: Average 3D orbit differences RMS of GNSS satellites for the integrated POD using the iGMAS network. P and S denote the nearly-polar and sun-synchronous orbits, respectively. As shown in Figure 11, the estimated orbit of the integrated POD with 8 regional stations can achieve a centimeter-level accuracy, which is better than the POD result only using ground MGEX observations (see Table 4). This indicates that with the assistance of 60 LEO satellites, only using observations from regional stations can already obtain a relatively high-quality satellite orbit. A significant reduction in orbit difference can be recognized for all the GNSS satellites when the 8 regional stations are replaced by 8 global stations. We can find that the orbit accuracy of the integrated POD with 8 global stations is almost in line with the accuracy of the corresponding MGEX and iGMAS integrated POD schemes. The accuracy improvement can be attributed to the better performance of the globally distributed stations in anchoring the whole constellations compared to the regional stations. Slight orbit degradation for GNSS satellites is observed when the number of global stations is reduced to 4. The result shows that the POD of GNSS satellites has less dependence on the number of ground stations and is more sensitive to the distribution of ground stations after the inclusion of the large LEO constellation. Figure 11: Average 3D orbit differences RMS of GNSS satellites for the integrated POD with a few ground stations. Values on the top of each bar indicates the corresponding average 3D RMS. Figure 10: Distribution of a small number of ground stations.
# Modeling the Health Benefits of Superblocks across the City of Los Angeles
## 2 Materials and Methods
### Intermediate Effects  
A series of intermediate effects have been used to connect the Superblocks to health outcomes. For example, the superblocks in the City of Barcelona were expected to improve the habitability of public spaces, advance sustainable mobility, increase urban green spaces and promote residents' participation. All of these intermediate effects will help to promote health outcomes [21]. Previous research summarized the neighborhood-level intermediate effects in terms of air and noise pollution reductions, traffic safety improvements, more walkability options, new and improved recreational spaces, rising house values, better sense of community and security and stronger social networks [22].  
Specifically, Superblocks aim to limit vehicle mobility, increase urban green spaces and decrease air pollution. There is a growing body of evidence that has revealed statistically significant associations between air pollution and many respiratory diseases including asthma and allergies (e.g., Ref. [23]. Meanwhile, limiting vehicle mobility and increasing urban green space also indirectly promote traffic safety, walkability and active recreation. These outcomes not only reduce risk of traffic injury but also promote physical and social well-being [24].  
In this study, we chose increasing urban green space (indicated by tree canopy) as the Superblock intervention and air pollution reduction (indicated by NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\) and O\({}_{3}\)) as the intermediate effects to model the health and economic benefits (indicated by the hospital admissions that would be avoided) of the LA Superblocks Model. Table 1 lists all the indicators.
## 3 Materials and Methods
### Metabolites Identification, Data Processing and Multivariate Analysis  
#### 3.7.1 SPME Coupled to GC/MS Analysis  
Chromatographic peaks were firstly deconvoluted using AMDIS software (www.amdis.net, accessed on 18 January 2022) prior to spectral peaks matching. Additionally, relative content of each metabolite was used for data abundance extraction, being based on area normalization of each peak to total peak areas. The average response per three biological replicates was calculated per each metabolite. The identification of VOCs was based on different parameters, i.e., retention time, Kovat retention index with the aid of a series of co-chromatographed alkanes (\(\mathrm{C_{8}}\)-\(\mathrm{C_{20}}\)), spectrum matching to NIST database, and standards whenever available [15].  
Untargeted MVDA, including PCA and HCA, were employed for samples dataset classification in the context of cinnamon botanical origin (_cassia_ vs. _verum_ vs. _tamala_ vs. _iners_), genuine _C. verum_ adulteration with _C. cassia_ (CA vs. CV and CVM), cinnamon different commercial products (CP-1 vs. CP-2 vs. CP-3 vs. CP-4), and finally authenticated drugs vs. commercial products following same parameters described in [23]. Additionally, supervised OPLS-DA was conducted for the same dataset to aid in marker identification and confirm modeling results derived from PCA analysis. SIMCA-P version 14.0 software package (Umetrics, Umea, Sweden) was used for modeling and all variables were mean-centered and scaled to Pareto Variance. Asides, heatmap analysis for peaks identified based on retention times (min) was conducted by MetaboAnalyst (version 5.0), choosing the option of top 25 ANOVA differential intensity.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 6 Discussion  
This paper developed a method for estimating long-term and short-term ground deformation induced by shield driving. The long-term settlement can be calculated by using the classic Gaussian-type displacement profile. The fundamental issue with its application lies in the representational value of the prediction parameters (i.e., volume loss and trough width parameter). At the same time, the highly empirical nature of the parameters, as well as the context of the local geotechnical conditions, should also be taken into account. The variability of these prediction parameters cannot be ignored and should be carefully considered when selecting reasonable values. In this study, detailed statistics results about the prediction parameters, based on experiences obtained from Changzhou, are given, and they can provide rational guidance in predicting the long-term settlement in similar water-rich soft strata. In addition, an analytical method for predicting short-term ground deformation induced by shield driving is also presented in this paper. The prediction of short-term deformation is of great importance for the cases where differential settlements need to be considered, such as when assessing the safety of ground buildings closely spaced.  
The use of empirical and analytical methods is sufficient to estimate the ground settlements in most cases of EPB shield tunnels. Compared to establishing a complicated numerical model, calibrating the existing model based on local empirical data from real-scale projects is more reasonable and acceptable.
## Materials and Methods
### Response Surface Methodology
and xi cannot be expressed by the linear Taylor expansion, then the second-order transformation of Equation (2) needs to be further performed to obtain the following second-order model Equation:  
\[y=\beta_{0}+\sum\
olimits_{i=1}^{k}\beta_{i}x_{i}+\sum\
olimits_{i=1}^{k}\beta _{ii}x_{i}^{2}+\sum\
olimits_{i<j}^{k}\beta_{ij}x_{i}x_{j}+\varepsilon, \tag{3}\]  
In the Equation, \(\beta_{i}\) is the linear effect of the independent variable \(x_{i}\); \(\beta_{ij}\) is the interaction between the independent variables; \(B_{ii}\) is the secondary effect of \(x_{i}\). This can be expressed as a matrix:  
\[Y=\beta_{0}+X^{\prime}b+X^{\prime}BX+\varepsilon, \tag{4}\]  
In the Equation, \(X=(x_{1},x_{2},\cdots,x_{k})^{T}\); \(b=(\beta_{1},\beta_{2},\cdots,\beta_{k})^{T}\) is the regression coefficient matrix; B is the k-th order symmetric matrix. If the fitting is successful and the response reaches the best, then the following Equation is satisfied:  
\[\frac{\partial Y}{\partial X}=\mathsf{b}+2\mathrm{BX}=0 \tag{5}\]  
The solution X in Equation (5) is called the stable point, and the transformation can be obtained as:  
\[X_{0}=-\frac{1}{2}B^{-1}b, \tag{6}\]  
Substituting Equation (6) into Equation (4), the predicted response value at the stable point is obtained as:  
\[Y_{0}=\beta_{0}+\frac{1}{2}X_{0}^{\prime}b, \tag{7}\]  
There are many types of design models for response surface analysis, such as the central composite, Box-Behnken design, D-optimal design, and uniform design models. The specific design schemes of the various models are different, meaning the numbers of tests required are also different. It is necessary to select the appropriate model for the design according to the actual needs.
## 4 Comparison of Classical and Computer-Aided Reconstruction Results in Case 1
### Results of the Comparison Using Dlib-Based Face Recognition  
The comparison of the results from facial soft tissue reconstruction of the victim in Case 2 with two seized photographs of the deceased person (not shown to protect the individual's right to privacy) was carried out using computer vision, employing three different face recognition models: Dlib [75], VGG-Face [77], and GhostFaceNet [78]. The precise results of the computer-vision-aided comparison between the face of the person in question and the reconstructed 3D face in Case 2, normalized and rounded to the fourth decimal place, are shown in Table 9.  
The comparison of the reconstructed face in Case 2 with two reference photographs (Image 1: frontal passport-style; Image 2: slightly lateral view) yielded normalized similarity scores above the model-specific thresholds, indicating no similarity between the faces. All scores were normalized such that a value of 1.0 corresponds to the respective model's verification threshold.  
For Image 1, the models produced normalized distances above 1.0, suggesting that the face in this frontal image does not match the reconstructed face closely. The closest match was achieved by VGG-Face (Euclidean: 1.1396) and GhostFaceNet (Euclidean: 1.1664), with Dlib showing a slightly higher value (Euclidean: 1.2827).  
For Image 2, GhostFaceNet displayed a much higher distance (Cosine: 1.4823), indicating a clear mismatch. VGG-Face and Dlib both returned similar values (Euclidean: 1.1382 for VGG-Face and 1.2950 for Dlib), still above the threshold and suggesting no significant similarity.  
In conclusion, all models returned distances that exceeded the normalized threshold of 1.0, implying that the reconstructed face does not exhibit a strong resemblance to the reference images of the deceased person, according to the evaluated face recognition models.  
To provide context and improve the interpretability of the similarity scores, we additionally compared the passport photograph of the identified individual with a photo of a subject who is subjectively perceived as clearly dissimilar (e.g., female, more angular facial features, different hair color, mouth, and nose shape). Interestingly, the resulting Euclidean distances (VGG-Face: 1.1883; GhostFaceNet: 1.1732) were only slightly higher than those observed in the comparisons with the reconstructed face. Cosine distances were more distinct (VGG-Face: 1.4213; GhostFaceNet: 1.556), indicating greater dissimilarity. However, this comparison shows that absolute distance values do not differ as strongly as one might expect based on human perception. It also highlights that these similarity metrics, especially when close to model thresholds, must be interpreted cautiously, as they are not linearly scaled or normally distributed and do not directly correspond to perceptual similarity.
## 2 Related Work
Recently, the concept of intelligent communication has attracted a considerable amount of attention, and its use of machine learning-based methods to deal with optimization problems in communication systems has achieved superior performance. In [33], an optimal hybrid precoding scheme based on hybrid cross entropy (HCE) is designed to maximize the total achievable rate. In [34], the authors design the selection and precoding matrices jointly for millimeter-wave systems. The proposed framework contains a neural network (NN) based on deep reinforcement learning (DRL) and a deep deployment NN. In [35], a user grouping algorithm based on channel gain and correlation is designed, and then a beam space orthogonal simulation precoder is obtained using DRL-based beam selection.
###### Abstract  
Biofouling in the membrane bioreactor (MBR) is a bottleneck in operation. This study explores the use of _cis_-2-decenoic acid (CDA) to mitigate biofouling. CDA is a signaling molecule known to disperse biofilms, which is reported to reduce the extracellular polymeric substances (EPS) of biofilms and make them less chemically resistant. In our experiments, CDA 300 nM was used for the backwashing biofouling in MBR, and backwashing with CDA followed by 0 to 500 mg/L sodium hypochlorite (NaOCI) was also performed. The synergistic effect of CDA and NaOCI in alleviating biofouling was observed at CDA 300 nM and 100 mg/L NaOCI. However, controversial phenomena occurred under other conditions. An increase in biofilm removal efficiency with higher concentrations of NaOCI was not observed. Instead, the fouling rate increased at a 200 mg/L NaOCI condition compared to the control condition (i.e., DW washing). This phenomenon is hypothesized to result from the antagonistic interaction between the dispersion induction by CDA and the stress induced by NaOCI. This study specifically demonstrated the efficiency of two-stage backwashing with CDA and NaOCI in various aspects. The results of this study are expected to be utilized for optimizing MBR backwashing protocols.  
cis-2-decenoic acid; membrane bioreactor; biofouling; sodium hypochlorite; backwashing; extracellular polymeric substances +
Footnote †: journal: Applied sciences
## 4 Experiment Verification
### Measurement Error Analysis  
As the data volume is huge in full-field temporal-spatial deflection measurement of large structures, it poses great difficulties to the calibration of the dynamic displacements of such a large number of measure points at the same time. To verify the measurement precision of STMM, two specific scenes were designed. One scene is to verify the transient full-field displacements of the beam at any time, and another scene is to verify the dynamic displacement-time curve at any point on the beam. In the first scene, the full field deflection curve of simply supported beam can be calculated theoretically with Equation (4) if the loading location is known, and the deflection curve can also be measured by STMM at the same time, when the toy truck traveling at the loading location. The errors of all 3770 STMM measurement points can be obtained by comparing the theoretical and experimental deflection curves. In the second scene, the dynamic displacement-time curve of any point along the beam can be measured by both a laser ranger and a corresponding one-dimension STMM, and similarly the error between the two motion tracking methods can be calculated.
## 2 Methodology
### Signal Processing Based on Wavelet Packet Transform (WPT)  
Because of the dispersion and multimode characteristics of ultrasonic waves and the effect of damages in pipes [53; 54], the receiving signals are very complex. Therefore, wavelet packet transform (WPT) was used to analyze the receiving signal and feature extraction [55].  
For a given scaling function and wavelet function, the dual scale equations are defined as [56]:  
\[\left\{\begin{array}{l}u_{2n}(t)=\sqrt{2}\sum\limits_{k\in Z}l(k)u_{n}(2t-k) \\ u_{2n+1}(t)=\sqrt{2}\sum\limits_{k\in Z}m(k)u_{n}(2t-k)\end{array}\right., \tag{15}\]  
where \(Z\) is a set of integers, and \(l(k)\) and \(m(k)\) are a pair of conjugate filters that satisfy the relation \(l(k)=(-1)^{k}m(1-k)\).  
From Equation (15), a signal can be expanded according to the wavelet packet basis function:  
\[x(t)=\sum\limits_{k\in Z}C_{n,j}^{k}2^{k/2}.u^{n}(2^{k}t-j), \tag{16}\]  
where \(C_{n,j}^{k}\) is wavelet packet decomposition coefficient, that is wavelet packet transform:  
\[C_{n,j}^{k}=2^{k/2}\int\limits_{R}x(t)u^{n}(2^{k}t-j)dt. \tag{17}\]In the damage detection of anti-corrosion pipes, the receiving signal can be decomposed by \(l\)-layer WPT, and \(2l\) subsets \(\{C_{1},C_{2},\cdots,C_{2^{l}}\}\) are obtained. Each subset represents the wavelet packet decomposition coefficient in a frequency band. Then, the wavelet packet energy of each subset can be calculated by  
\[E_{i}=\sum_{m=1}^{m}C_{i,m}^{2}, \tag{18}\]  
where \(C_{i,m}\) is the \(m\)th wavelet packet decomposition coefficient of the \(i\)th subset.  
The wavelet packet energy of the frequency band indicates the strength of the band in the signal. The wavelet packet energy of subsets is calculated after the WPT. The frequency band with larger wavelet packet energy is extracted as signal characteristics to reduce noise and interference.