esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Assessment**} & \multirow{2}{*}{**Weighted Kappa**} & \multicolumn{2}{c}{**Asymptotic**} & \multirow{2}{*}{**Z\({}^{\text{c}}\)**} & \multirow{2}{*}{**Sig.**} & \multicolumn{2}{c}{**95\% Asymptotic Confidence Interval**} \\ \cline{3-4}  & & & **Std. Error** & & & **Lower Bound** & **Upper Bound** \\ \hline \hline
**Location/End Level Apically** & & & & & & \\ \hline Between the two evaluators & 0.312 & 0.136 & 2.201 & 0.028 & 0.046 & 0.578 \\ Between evaluator 1 and consensus & 0.571 & 0.145 & 4.076 & \textless{}0.001 & 0.287 & 0.855 \\ Between evaluator 2 and consensus & 0.596 & 0.204 & 3.408 & \textless{}0.001 & 0.196 & 0.995 \\ \hline
**Fracture line involvement Endocrown/tooth structure** & & & & & & \\ \hline Between the two evaluators & 0.321 & 0.195 & 1.593 & 0.111 & \textless{}0.061 & 0.704 \\ Between evaluator 1 and consensus & 0.565 & 0.222 & 2.489 & 0.013 & 0.131 & 0.999 \\ Between evaluator 2 and consensus & 0.646 & 0.152 & 3.313 & \textless{}0.001 & 0.348 & 0.944 \\ \hline
**Catastrophic/non-restorability** & & & & & & \\ \hline Between the two evaluators & 0.255 & 0.176 & 1.428 & 0.153 & \textless{}0.090 & 0.600 \\ Between evaluator 1 and consensus & 0.315 & 0.144 & 1.990 & 0.047 & 0.033 & 0.598 \\ Between evaluator 2 and consensus & 0.787 & 0.139 & 3.509 & \textless{}0.001 & 0.513 & 1.060 \\ \hline
**Number of pieces and separation** & & & & & & \\ \hline Between the two evaluators & 0.734 & 0.122 & 4.714 & \textless{}0.001 & 0.494 & 0.974 \\ Between evaluator 1 and consensus & 0.782 & 0.118 & 5.077 & \textless{}0.001 & 0.551 & 1.013 \\ Between evaluator 2 and consensus & 0.944 & 0.056 & 6.121 & \textless{}0.001 & 0.834 & 1.053 \\ \hline
**Fracture direction** & & & & & & \\ \hline Between the two evaluators & 0.491 & 0.146 & 2.836 & 0.005 & 0.204 & 0.777 \\ Between evaluator 1 and consensus & 0.885 & 0.072 & 4.512 & \textless{}0.001 & 0.744 & 1.026 \\ Between evaluator 2 and consensus & 0.483 & 0.154 & 2.804 & 0.005 & 0.181 & 0.786 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Agreement between the two examiners and each one to the consensus for each assessment point using Kappa statistics. Figure 5: **(A,B)** Two specimens with different failure modes of conventional endocrown design and endocrown with proximal extension.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 3 GNSS Receiver Signal Processing
### GNSS Code-Based SPP and CRB  
Standard code-based SPP exploits only the code observables in (14). If we consider \(M\) visible satellites being tracked, we have a complete set of code observations \(\mathbf{y}_{\varrho}^{\top}=[\widehat{o}_{1},\dots,\widehat{o}_{M}]\), and the unknown parameters to be inferred are \(\mathbf{\gamma}^{\top}=[\mathbf{p}_{R}^{\top},\;c\delta t_{r}]\). We can also define the noise vector as \(\mathbf{n}_{\varrho}^{\top}=[\epsilon_{\varrho,1},\dots,\epsilon_{\varrho,M}]\), with covariance \(\mathbf{C}_{\mathbf{n},\varrho}\). The standard solution to this nonlinear estimation problem, \(\mathbf{y}_{\varrho}=\mathbf{h}\left(\mathbf{\gamma}\right)\ +\ \mathbf{n}_{\varrho}\), is (i) to linearize the model around an initial position \(\mathbf{p}^{0}\) and (ii) use an iterative weighted least squares (WLS) estimator [38]. Considering \(\delta t_{i}\) and \(\mathbf{p}_{T_{i}}\) as derived from the navigation message, the resulting linearized model can be written as:  
\[||\mathbf{p}_{T_{i}}-\mathbf{p}_{R}||\approx||\mathbf{p}_{T_{i}}-\mathbf{p}^{0 }||-\mathbf{u}_{i}(\mathbf{p}^{0})\delta_{p},\ \delta_{p}=\mathbf{p}_{R}-\mathbf{p}^{0},\ \mathbf{u}_{i}(\mathbf{p}^{0})=\frac{ \mathbf{p}_{T_{i}}-\mathbf{p}^{0}}{||\mathbf{p}_{T_{i}}-\mathbf{p}^{0}||}, \tag{16}\]  
\[\widetilde{\mathbf{y}}_{\varrho}\approx\widehat{\mathbf{H}}(\mathbf{p}^{0}) \delta\ +\ \mathbf{n}_{\varrho},\ \delta=[\delta_{p}^{\top}\ c\delta t_{r}]^{\top},\ \left(\widetilde{\mathbf{y}}_{\varrho}\right)_{i}=\widehat{\varrho}_{i}\ +\ c\delta t_{i}-||\mathbf{p}_{T_{i}}-\mathbf{p}^{0}||,\ \widehat{\mathbf{H}}(\mathbf{p}^{0})=\begin{bmatrix}-\mathbf{u}_{1}^{\top}( \mathbf{p}^{0})&1\\ \vdots&\vdots\\ -\mathbf{u}_{M}^{\top}(\mathbf{p}^{0})&1\end{bmatrix}, \tag{17}\]  
with \(\mathbf{u}_{i}(\mathbf{p}^{0})\) the unit steering vector to the ith satellite, evaluated at the position \(\mathbf{p}^{0}\). For the problem above, the solution can be found based on an iterative WLS adjustment (i.e., applying a Gauss-Newton method [38]), where the state \(\delta\) is updated with \(\widehat{d\delta}_{WLS}\) as:  
\[\widehat{d\delta}_{WLS}=\arg\min_{\delta}\{||\widetilde{\mathbf{y}}_{\varrho}- \widehat{\mathbf{H}}(\mathbf{p}^{0})\delta||_{\mathbf{W}}^{2}\}=\left( \widehat{\mathbf{H}}^{\top}(\mathbf{p}^{0})\mathbf{W}\widehat{\mathbf{H}}( \mathbf{p}^{0})\right)^{-1}\widehat{\mathbf{H}}^{\top}(\mathbf{p}^{0}) \mathbf{W}\widetilde{\mathbf{y}}_{\varrho}, \tag{18}\]  
where the optimal weighting is \(\mathbf{W}=\mathbf{C}_{\mathbf{n},\varrho}^{-1}\). In addition, the CRB for the SPP problem (i.e., denoted CRB\({}_{spp}\)) is given by the inverse of \(\mathbf{F}_{\mathbf{\gamma}|\mathbf{\gamma}}\left(\mathbf{\gamma}^{0}\right)=\widehat{ \mathbf{H}}^{\top}(\mathbf{\gamma}^{0})\mathbf{C}_{\mathbf{n},\varrho}^{-1} \widehat{\mathbf{H}}(\mathbf{\gamma}^{0})\), with \(\mathbf{\gamma}^{0}\) a selected value of \(\mathbf{\gamma}\).
## 4 Simulations and Results
### Simulation Analysis
#### 4.2.4 CSK Spread-Spectrum Coding System Simulation  
The simulation results for the CSK spread-spectrum coding system under a Gaussian channel and multipath channels are shown in Figures 11 and 12. In these simulations, \(M=16\), so 16 different spreading codes with a 5 th-order M-sequence and 2-bit cyclic shift are used. The bandwidth \(B=2\) kHz, carrier frequency \(f_{c}=2\) kHz and sampling frequency \(f_{s}=16\) kHz.  
Figure 8: BER performance of DSSS coding systems in multipath channel.  
Figure 7: BER performance of DSSS coding systems in Gaussian channel.  
Figure 11: BER performance of CSK spread-spectrum coding systems in Gaussian channel.  
Figure 10: BER performance of M-ary spread-spectrum coding systems in multipath channel.  
Figure 9: BER performance of M-ary spread-spectrum coding systems in Gaussian channel.  
The simulation results are consistent with the theoretical analysis in that the direct spread coding system has the best coding gain while the soft spread coding system has limited coding gain. Spreading coding systems using short LDPC codes (CCSDS codes) achieve higher coding gains than spreading coding systems using other channel coding algorithms such as Turbo codes and convolutional codes. Under \(1\times 10^{-4}\) BER conditions, the code length is the same as 256 and the code rate is \(1/2\) for both Turbo and LDPC codes. Compared to Turbo codes, the CSK spread-spectrum system with CCSDS codes has a gain of about 0.3 dB.
## 3 Results
### Assessment of Intensity Variability and Segmentation Performance
#### 3.2.2 Comparison Amongst Brain Extraction Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Cortical Grey Matter**} \\ \hline  & \multicolumn{2}{c}{**SPM-BFC**} & \multicolumn{6}{c}{**N4 ITK**} \\ \hline  & **BET2** & **SWS** & **HD-BET** & **SynthStrip** & **BET2** & **SWS** & **HD-BET** & **SynthStrip** \\ \hline \multirow{3}{*}{Manual} & \(p=1.000\) & \(p=1.000\) & \(p=0.017\) * & \(p<0.001\) * & \(p=1.000\) & \(p=0.952\) & \(p<0.001\) * \\  & \(r=0.230\) & \(r=0.007\) & \(r=0.474\) & \(r=0.884\) & \(r=0.101\) & \(r=0.072\) & \(r=0.252\) & \(r=0.654\) \\ \cline{2-6}  & - & \(p=1.000\) & \(p=1.000\) & \(p<0.001\) * & - & \(p=1.000\) & \(p=1.000\) & \(p<0.001\) * \\ \cline{2-6}  & - & \(r=0.237\) & \(r=0.244\) & \(r=0.654\) & \(r=0.137\) & \(r=0.187\) & \(r=0.589\) \\ \cline{2-6}  & SWS & - & - & \(p=0.014\) * & \(p<0.001\) * & - & \(p=0.319\) & \(p<0.001\) * \\ \cline{2-6}  & - & - & \(r=0.482\) & \(r=0.891\) & - & - & \(r=0.323\) & \(r=0.726\) \\ \cline{2-6} HD-BET & - & - & - & \(p=0.066\) & - & - & \(p=0.076\) \\ \cline{2-6}  & & - & - & \(r=0.410\) & - & - & - & \(r=0.403\) \\ \hline \hline \multicolumn{6}{c}{**White Matter**} \\ \hline  & \multicolumn{2}{c}{**SPM-BFC**} & \multicolumn{6}{c}{**N4 ITK**} \\ \hline  & **BET2** & **SWS** & **HD-BET** & **SynthStrip** & **BET2** & **SWS** & **HD-BET** & **SynthStrip** \\ \hline \multirow{3}{*}{Manual} & \(p=0.283\) & \(p=0.404\) & \(p=1.000\) & \(p=1.000\) & \(p=0.031\) * & \(p=1.000\) & \(p=1.000\) & \(p=1.000\) \\  & \(r=0.331\) & \(r=0.309\) & \(r=0.216\) & \(r=0.237\) & \(r=0.446\) & \(r=0.244\) & \(r=0.144\) & \(r=0.057\) \\ \cline{1-1}  & \(p<0.001\) * & \(p=0.003\) * & \(p=0.002\) * & - & \(p<0.001\) * & \(p<0.001\) * & \(p=0.008\) * \\ \cline{1-1} BET2 & - & \(r=0.640\) & \(r=0.546\) & \(r=0.568\) & - & \(r=0.690\) & \(r=0.589\) & \(r=0.503\) \\ \cline{1-1} SWS & - & - & \(p=1.000\) & \(p=1.000\) & - & - & \(p=1.000\) & \(p=1.000\) \\ \cline{1-1} HD-BET & - & - & - & \(p=1.000\) & - & - & \(r=0.101\) & \(r=0.187\) \\ \cline{1-1} HD-BET & - & - & - & \(p=1.000\) & - & - & \(p=1.000\) \\ \cline{1-1} HD-BET & - & - & - & \(r=0.022\) & - & - & - & \(r=0.086\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Multiple pairwise comparisons (Bonferroni correction) between BE methods (Manual, BET2, SWS, HD-BET, and SynthStrip) for each RBV and BFC, reporting the \(p\)-value (\(p\)) and the post hoc effect size (\(r\)).
## 3 Experimental Procedures and Results
### Artificial Generation of Bearing Faults  
Collecting motors with bearing failures from factories is difficult and time-consuming. Thus, in this study, one out of several kinds of scratch faults was generated artificially on the surfaces of the outer raceways of bearings.  
The single scratch (SS) and full scratch (FS) shown in Figure 3 were considered basic-scratch faults. The SS had a length of 10 mm, a width of 0.5 mm, and a depth of 0.5 mm. The lengthwise direction was the circumference of the outer raceway. The scratch was located at the center width of the bearing thickness. The FS was formed with a file; it was 12 mm in length, 8 mm in width, and 0.5 mm in depth.  
Additionally, faults with multiple concentrated scratches (CS) were considered. Parallel scratches were generated on the outer raceway. Each scratch had a length of 10 mm, width of 0.5 mm, and depth of 0.5 mm. The concentrated scratches with two parallel scratches were indicated as C2S and four parallel scratches as C4S. The dimensions were stated for C2S as two parallel scratches 0.5 mm apart and, for C4S, as four parallel scratches 0.5 mm apart on the outer raceway of the bearing.  
A healthy motor was tested first for reference. Then, the healthy bearing (H) was replaced with a faulty one for further testing.  
Figure 3: Basic scratch faults: (**a**) single scratch (SS) and (**b**) full scratch (FS).  
Figure 2: Experimental setup of motor experiment.
## 2 Materials and Methods  
### Chemicals and Instruments  
All reagents and solvents were from Merk (formerly Sigma-Aldrich, Saint Louis, MO, USA) and were purified by standard procedures. AAEA was prepared by known procedures [32] while M4 was prepared as previously described [31]. Organic solutions were dried over anhydrous magnesium sulphate and evaporated using a rotatory evaporator operating at a reduced pressure of about 10-20 mmHg. The melting ranges of solid compounds in this study were determined on a 360 D melting point device, resolution 0.1 \({}^{\circ}\)C (MICROTECH S.R.L., Pozzuoli, Naples, Italy). Melting points and boiling points are uncorrected. The FTIR spectra were recorded as films or KBr pellets on a Perkin Elmer System 2000 instrument (PerkinElmer, Inc., Waltham, MA, USA) while ATR-FTIR analyses were carried out using a Spectrum Two FT-IR Spectrometer (PerkinElmer, Inc., Waltham, MA, USA). \({}^{1}\)H and \({}^{13}\)C NMR spectra were acquired on a Bruker DPX spectrometer (Bruker Italia S.r.l., Milan, Italy) at 300 and 75.5 MHz, respectively. Mass spectra were obtained with a GC-MS Ion Trap Varian Saturn 2000 instrument (Varian, Inc., Palo Alto, CA, USA; EI or CI mode; filament current: 10 mA) equipped with a DB-5 MS (J&W) capillary column.  
Figure 1: Structure of 4-AMBSTY HCl (M4).  
Elemental analyses were performed with an EA1110 Elemental Analyser (Fison Instruments Ltd., Farnborough, Hampshire, UK). UV-Vis spectra were acquired using a UV-Vis spectrophotometer (HP 8453, Hewlett Packard, Palo Alto, CA, USA) equipped with a 3 mL cuvette. HPLC analyses were performed on a Jasco model PU-980 instrument (JASCO Corporation, Hachioji, Tokyo, Japan), equipped with a Jasco Model UV-970/975 intelligent UV/Vis detector (JASCO Corporation, Hachioji, Tokyo, Japan) at room temperature. A constant flow rate (1 mL/min), UV detection at 254 nm, a 25 cm \(\times\) 0.46 cm Hypersil ODS 5 mm column, and a mixture acetonitrile/water 6/4 as eluent were employed for the acquisitions. GC-FID analyses were performed on a Perkin Elmer Autosystem (Varian, Inc., Palo Alto, CA, USA), using a DB-5, 30 m, diameter 0.32 mm, film 1 mm capillary column. Column chromatography was performed on Merck silica gel (70-230 mesh). Thin layer chromatography (TLC) was carried out using aluminum-backed silica gel plates (Merck DC-Alufolien Kieselgel 60 F254, Merck, Washington, DC, USA), and the detection of spots was made by UV light (254 nm), using a Handheld UV Lamp, LW/SW, 6 W, UVGL-58 (Science Company(r), Lakewood, CO, USA). Optical microphotographs were obtained with a Zeiss Axioskop instrument. Sieving was performed with a 2000 Basic Analytical Sieve Shaker-Retsch apparatus.  
Potentiometric titrations were carried out using a Hanna Micro-processor Bench pH Meter (Hanna Instruments Italia srl, Ronchi di Villafranca Padovana, Padova, Italy), which was calibrated using standard solutions at pH = 4, 7, and 10 before titrations. Lyophilization was performed using a freeze-dry system (Laboconco, Kansas City, MI, USA). Centrifugations were performed on an ALC 4236-V1D centrifuge at 3400-3500 rpm.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 10 Proposed Solution: Background, Implementation, and Simulation Results
### Data Collection on ns-3  
Researchers using ns-3 can use a mechanism called _tracing_ to capture any data generated by simulations. With this subsystem, the user can connect a function to any _trace source_, which outputs the data to the connected function when a condition is satisfied (e.g., every time a user transmits). This paper uses the trace source _ReportCurrentCellRsrpSint_. It returns linear SINR values for all users from a specific eNB at each LTE subframe (every 1 ms). This trace source is implemented in the PHY layer from the LTE module. For the throughput, the trace source _RxPDU_ notifies the amount of received Protocol Data Units (PDUs) by the Radio Link Control (RLC) entity.  
The function connected to the first trace source stores SINR values from all eNBs and, every 40 ms, calculates the average SINR of the system on that interval (accumulated sum divided by the number of samples). Similarly, the second trace source is connected to a function that stores the data in text files. These files contain the number of PDUs and bytes transmitted and received. The throughput is calculated by dividing the total amount of received bits by the transmission interval in seconds.
## 3 Results and Discussion
### Assessment of TP Ecotoxicity (In Silico)  
Given the emerging nature of TPs as pollutants, it is crucial to assess their ecotoxicity to understand their potential impact on aquatic ecosystems. The ECOSAR software was used for this purpose. The TP structures were entered into the system in the form of SMILES. Acute toxicity was assessed on the basis of LC50/EC50 values generated by the program, while chronic toxicity was assessed on the basis of ChV values. The results for three different aquatic organisms (Fish, Daphnide, and Green algae) were divided based on the toxicity criteria established by the European Union (No. 1272/2008/EC) [57], similar to a previously published study [20] (Figure 7). According to ECOSAR's QSAR models,  
Figure 6: Time profiles of the dominant transformation products.  
there is a mathematical relationship between the associated log of the estimated toxicity values (mmol/L), which indicate the concentration of a chemical at which a particular effect (such as 50% mortality) is observed. What is more, the logK\({}_{\rm ow}\) values were also predicted (EPI Suite KOWWIN prediction). Given that the toxicity levels were between 0.0 and 100, a drug may be categorized as (i) very toxic (toxicity between 0.0 and 1.0), (ii) toxic (toxicity between 1 and 10), (iii) harmful (toxicity between 10 and 100), or (iv) non-toxic (toxicity > 100).  
SDX is of particular concern due to its action as an aniline, as it has a mild short-term toxicity, but shows severe chronic toxicity in the case of Daphnia. When taking into consideration the distinct transformation pathways, some patterns can be observed. Cleaving the S-N bond of sulfonamides has been reported to lead to less toxic products [58]. In our case, TP156 seems to be slightly more toxic than its parent molecule, while the following product of this pathway, TP142, is the most toxic of the group, exhibiting strong long-term toxicity to all three organisms and short-term toxicity to Daphnia and green algae. The final product of this pathway, TP132, is considered as non-toxic. Interestingly, this indicates that the fragmentation of the heterocyclic ring can lead to drastically less toxic products [46]. The increased toxicity of TP142 is expected, as in cases of -NH2-selective degradation processes, more toxic species are generated [46,59].  
Interestingly, the dominant TP of the desulfonation pathway, TP247, exhibits equal toxicity with SDX, while its hydroxylated form, TP263, is slightly toxic in terms of acute toxicity towards Daphnide. Generally, TPs generated from desulfonation can be highly variable and unpredictable, as it is likely highly dependent on the specific structures of the heterocyclic and aniline rings [46,60]. The remaining TPs of this pathway, TP148 and TP180, are less toxic and non-toxic, respectively.  
Finally, the hydroxylation pathway proved to be the less toxic one, with all of its TPs (TP327, TP302, TP290, and TP244) being less toxic than the parent compound, which seems to be generally the case with hydroxylation products [46,61].
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 2 Multi-Frequency RTK Positioning Mathematical Model
### Integer Ambiguity Resolution  
Once the estimated states obtained are updated in the Kalman Filtering, the float solutions can be obtained in RTK positioning. The fixed solution can be derived by the least-square ambiguity decorrelation adjustment (LAMBDA) method. The solution process for the fixed solution is mathematically a mixed-integer least squares problem. The parameters to be solved are partly float values and partly integers.  
\[\tilde{N}=\underset{N\in Z}{\text{argmin}}((N-\widetilde{N})^{T}Q_{N}^{-1}(N -\widetilde{N})) \tag{22}\]\[\hat{R}=\widetilde{R}-Q_{RN}Q_{N}^{-1}(\widetilde{N}-\hat{N}) \tag{23}\]  
where \(\hat{N}\) represents the most appropriate integer vector, \(N\) is the float carrier-phase ambiguities, and \(\widetilde{N}\) and \(Q_{N}\) are the integer ambiguities and their covariance. \(\hat{R}\) and \(\widetilde{R}\) are the fixed and the float solution of the rover antenna position, respectively \(Q_{RN}\) is the ambiguities and coordinates covariance matrix. In order to increase the ambiguity fixing rate and shorten the ambiguity fixing time, a partial fixing strategy is introduced in this research. A partial subset is selected for fixation in the full set of ambiguity according to a certain criterion. Generally, the elevation of the satellite is used as the selection criterion, and if the satellite's elevation is lower than the pre-set threshold, its ambiguity is not fixed. The ambiguity of satellites with elevations greater than the threshold is selected and arranged in ascending order of the variance of the original ambiguity parameters to form a new ambiguity subset [15].  
The ratio-factor ratio, defined as the ratio of the weight sum of the squared residuals by the second-best solution \(\widetilde{N_{\text{sec}}}\) to one by the best \(\widetilde{N_{\text{min}}}\), is used to check the reliability of the solution. It characterizes how close the floating-point solution is to the optimal integer vector [16].  
\[R\_best=\frac{\widetilde{(N_{\text{sec}}-\widetilde{N})}^{T}Q_{\widetilde{N} }^{-1}(\widetilde{N_{\text{sec}}}-\widetilde{N})}{\widetilde{(N_{\text{min} }-\widetilde{N})}^{T}Q_{\widetilde{N}}^{-1}(\widetilde{N_{\text{min}}}- \widetilde{N})}\geq k \tag{24}\]  
where \(k\) is an empirically given constant, which is usually set to 3.  
To evaluate the RTK performance, the ambiguity resolution fixing rate is defined as follows [14]:  
\[\text{fixing rate}=\frac{n_{fix}}{n} \tag{25}\]  
where \(n_{fix}\) is the correct fixed solution number and \(n\) is the total epoch number. The correct fixed solution implies that the ratio value is higher than 3.0.
# Universally Composable Oblivious Transfer with Low Communication
###### Abstract  
In this paper, a universally composable 1-out-of-N oblivious transfer protocol with low communication is built. This protocol obtained full simulation security based on the modulo learning with rounding (Mod-LWR) assumption. It can achieve universally composable security in the random oracle machine (ROM) model by combining random OT based on the key exchange protocol with the authentication encryption algorithm. It can be proven to resist static adversary attacks by simulating all corruption cases. Based on computer simulation and detailed mathematical derivation, this protocol was practicable and had better efficiency and lower communication.  
oblivious transfer (OT); universal composability (UC); learning with rounding assumption; random oracle model (ROM); static adversary attacks +
Footnote †: journal: Applied Sciences
## 3 Stability Model Test of the Support System
### Model Foundations
#### 3.5.2 Physical and Mechanical Properties of Model Foundation Soils  
After conducting excavation tests, samples were extracted from the model foundation at depths of 20 cm, 50 cm, and 90 cm near the junction of the two test areas. These samples were then subjected to density, moisture content, direct shear, triaxial unconsolidated undrained shear (UU), and consolidated undrained triaxial shear (CU) testing. Figure 8 illustrates the distribution of density and water content along the depth of the model foundation soil.  
The average density of the model foundation soil exhibited a slightly increasing trend from top to bottom, which was 1840, 1860, and 1870 kg/m\({}^{3}\), respectively; the average water content showed a \"C\" pattern distribution from top to bottom, indicating better drainage conditions in the upper and lower parts of the soil, with slightly lower water content than the middle part. The average value of the middle part of the soil (32.9%) was similar to both the liquid limit of the original soil (32.4%) and its natural water content (34.5%) numerically. It is noteworthy that the liquid limit (32.4%) and natural water content (34.5%) of the original soil are comparable in numerical value.  
The average shear strength indicators suggest that the cohesion ranges from 3.0 to 4.8 kPa and the internal friction angle ranges from 5.6 to 6.0\({}^{\circ}\) when utilizing a direct shear test. Alternatively, adopting the unconsolidated-unconfined triaxial test yields a cohesion  
Figure 8: Distribution of (**a**) soil density and (**b**) water content along depth in model foundation.  
Figure 7: \(Q\)-\(s\)-\(t\) relationship during preparation of model ground for case A1 and A4.  
range of 5.5-6.2 kPa and an internal friction angle range of 2.3-3.9\({}^{\circ}\). The coefficient of variation for the shear strength index is small, indicating good homogeneity and proximity to in situ soil test results.