esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Other Techniques for Resonant Frequencies Identification  
Because of difficulties using RAR in rigid structures, other signal analysis tools based on the similarity/dissimilarity of the recorded signals using the correlation have been utilized in this work.  
This way, the linear correlation between the signals of the selected Rbins has been calculated using the Pearson linear correlation coefficient (PLCC) [40]. The PLCC is a statistical quantity that estimates pairwise linear correlation between two signals. The coefficient ranges from \(-1\) to \(1\), indicating negative and positive total correlation, respectively, and \(0\) indicates no correlation at all.  
Figure 12 shows the PLCC correlation matrix, where it is observed that all the signals show positive correlation. Only the Rbin41 and Rbin42 are highly correlated, with a PLCC of approximately 95%, as can be intuited from Figure 11, where the similarity of the PSDs is shown. Among the other Rbins, the correlation is relatively low. We can attribute the high correlation to the displacement of the building with a higher SNR recorded by both bins.  
With the above, we have focused on the Rbin41 and Rbin42 to analyze their frequency content in a narrower band (Figure 13). As mentioned before, we can appreciate that the main peaks correspond to frequencies of 2.49 and 2.71 Hz, similar to those frequencies identified with the accelerometric network, and which more likely correspond to the first and second mode of the structure. However, only analyzing the PSDs does not make clear whether these frequency bands correspond to the resonant frequencies of the building. In addition, the third resonant frequency of 3.98 Hz, identified with the accelerometric network, is not present in the PSDs of the RAR Rbin signals.  
Figure 12: Linear correlation matrix between the signals of the selected Rbins. Darker blue indicates better correlation.  
Figure 13: The PSD calculated by the Welch method for the signals recorded from Rbin41 and Rbin42. The dashed lines correspond, from left to right, to the frequencies of 2.49 and 2.71 Hz.  
Thus far, we have focused on data recorded from each Rbin and tried to obtain features of the dynamical properties of the structure based on separately studying the outputs. However, the RAR allows synchronously measuring different points of the structure. This advantage is studied by two different multisignal analysis methods: the cross-power spectral density and the magnitude-squared coherence.
# Waste Cork in Metakaolin-Geopolymer Matrix: Physico-Mechanical Characterization
## 3 Results  
### Weight Loss upon Curing and Chemical Stability in Water  
Figure 3 shows the weight loss of the geopolymeric samples during the first 28 days of ageing. It can be observed that the presence of cork waste greatly affects weight loss, namely, the value of weight loss recorded for the GP0 reference geopolymer is 3.016%, and it increases at around 10% for formulations GP-2.4 CW (10.364%) and GP-4.8 CW (10.941%). This behavior is related to the capacity of cork to absorb water and then slowly release it. The high weight loss observed for the GP-9.1 CW sample (20.872%) was probably due to the excessive use of water to work the sample, as already mentioned in Section 2.2.  
Furthermore, the final step of geopolymerization is the condensation of two proximate groups of Si-OH or one Si-OH and one Al-OH with the elimination of a water molecule that can be eliminated during the natural drying of the paste via surface evaporation. As all the specimens were prepared with identical shapes and sizes, the surface area was exactly the same for all the specimens. The different rate of water loss via evaporation is then due to the specific microstructure of each single composite, with the GP-9.1 CW sample demonstrating the highest porosity (see also measurements of total porosity in Section 3.2). It is well-known in the literature that the water retention properties of samples depend on  
Figure 3: Weight loss of all geopolymers during the first 28 days of ageing.  
Figure 2: Images of the front and rear sides of the GP-9.1 CW sample used for thermal conductivity test.  
their pore size, with larger sizes giving both higher water absorption and water release rates [21]. It is evident how the presence of cork in the composites under investigation facilitates water loss, as reported by the water retention values measured after 28 days and presented in Table 2 for all the formulations. Furthermore, the minimum value of water retention recorded for the GP-9.1 CW sample could be attributed to a specific peculiarity of the inner bark of the cork oak (_Quercus suber_ L.), which consists of a structure of air-filled cells, resulting in a waterproof, light and flexible structure [22].  
The addition of water during composite preparation was necessary to reach the proper workability of the paste. The consequence of such an addition of water was the dilution of the alkaline solution used as the activator of the entire process of geopolymerization, which could have prevented proper reticulation. For this reason, the confirmation of the degree of the reticulation of the geopolymeric aluminosilicate matrix was indirectly measured by the integrity test in water of the consolidated composites [23]. After the required 24 h of immersion in water, the samples were extracted and examined. All samples were found to be intact and resistant to finger pressure, indicating that the geopolymerization took place correctly. The eluates presented a yellowish color that became more intense as the percentage of cork increased, indicating the presence of natural tannins.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
###### Abstract  
In Large Intelligent Surface (LIS) systems, achieving accurate channel estimation is essential for enhancing communication quality and system efficiency. The main focus of this study is on using the Least Squares (LS) method to estimate pilot-based channels. It also looks at more advanced methods, like using low-density parity-check (LDPC) codes, antenna selection, and optimized pilot design, to make the method more accurate and effective. We employ orthogonal pilot sequences to reduce signal interference and improve pilot power to enhance estimation performance. Additionally, LDPC codes play a crucial role in eliminating noise and interference effects, thereby improving system reliability. We also propose selective configurations of LIS antennas to balance high performance with reduced computational costs. Collectively, these strategies lead to a significant reduction in the Bit Error Rate (BER) and a remarkable improvement in the overall system performance, offering a practical solution for complex LIS deployments.  
channel estimation; LS; LIS; complexity; SC-FDE; LDPC Article
## 2 Upper Bound Solution
### Trapezoidal Wedge Failure Mode  
Figure 3 schematically illustrates the failure mechanism in the trapezoidal wedge failure mode. In this mode, a trapezoid-shaped soil block forming an angle \(\theta\) with the ground surface moves at velocity \(V\).  
Under the trapezoidal wedge failure mode, the total \(P\) power is expressed as  
\[P=\big{(}F_{p}+W_{g}\sin\beta\big{)}V\sin\theta-W_{g}V\cos\beta\cos\theta. \tag{15}\]  
The dissipation rates \(D_{b}\) (along the slip surface \(B\)) and \(D_{s}\) (along the two side failure surfaces \(S_{1}\) and \(S_{2}\)) are given as  
\[D_{b}=c_{u}\frac{L}{\sin\theta}DV, \tag{16}\]  
\[D_{s}=2\bigg{[}c_{u}\bigg{(}LH-\frac{L^{2}}{2\tan\theta}\bigg{)}V\bigg{]}=c_{ u}L\bigg{(}2H-\frac{L}{\tan\theta}\bigg{)}V. \tag{17}\]  
The total dissipation rate \(D\) is then  
\[D=D_{b}+D_{s}=c_{u}\frac{L}{\sin\theta}DV+c_{u}L\bigg{(}2H-\frac{L}{\tan\theta }\bigg{)}V=c_{u}L\bigg{(}\frac{D}{\sin\theta}+2H-\frac{L}{\tan\theta}\bigg{)}V. \tag{18}\]  
Assuming that the total work is fully dissipated, the following equation is derived.  
\[\big{(}F_{p}+W_{g}\sin\beta\big{)}V\sin\theta-W_{g}V\cos\beta\cos\theta=c_{u} L\bigg{(}\frac{D}{\sin\theta}+2H-\frac{L}{\tan\theta}\bigg{)}V. \tag{19}\]  
Thus, the soil thrust \(F_{p}\) is  
\[F_{p}=c_{u}L\bigg{(}\frac{D-L\cos\theta}{\sin^{2}\theta}+\frac{2H}{\sin\theta }\bigg{)}+W_{g}\bigg{(}\cos\beta\frac{\cos\theta}{\sin\theta}-\sin\beta\bigg{)}. \tag{20}\]  
Given that \(c_{u}\), \(H\), \(L\), \(D\), and \(W_{g}\) are known in Equation (20), the least upper solution obtained by setting the gradient of \(F_{p}\) with respect to \(\theta\) should be zero:  
\[\begin{array}{c}\frac{\partial F_{p}}{\partial\theta}=c_{u}L\bigg{(}\frac{ L}{\sin\theta}-\frac{2H\cos\theta}{\sin^{2}\theta}-\frac{2(D-L\cos\theta)\cos \theta}{\sin^{3}\theta}\bigg{)}\\ \hskip 14.226378 pt+W_{g}\Big{(}-\cos\beta\frac{\cos^{2}\theta}{\sin^{2}\theta }-\cos\beta\bigg{)}=0,\end{array} \tag{21}\]  
Figure 3: Schematic diagram of the trapezoidal wedge failure mode for a single-track system.  
which yields  
\[\sin^{2}\theta+\frac{2}{L}(H\sin\theta+D)\sqrt{1-\sin^{2}\theta}-\frac{W_{g}\cos \beta}{c_{u}L^{2}}\sin\theta-2=0. \tag{22}\]  
Equation (22) represents a nonlinear equation of \(\sin\theta\). This study employed the bisection method to solve it, with \(\theta\) ranging from \(\tan^{-1}(L/H)\) to \(90^{\circ}\). Once \(\theta\) is determined, the soil thrust \(F_{p}\) can be calculated using Equation (20).
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 4 Simulation Results
### Training Convergence  
Figure 3, illustrates the training metrics of a single classifier under the AWGN channel and how the training is performed for a random training SNR value \(\sigma_{tr}^{(n)}\in[19,23]\) dB. Each epoch is a full pass through the dataset, and each iteration is an update of the network parameters. Figure 3 also shows validation metrics, which are recorded each time the program validates the network. For example, the validation accuracy presents the classification accuracy on the validation set, which gives an idea of the generalization of the model. Final validation metrics are labeled \"Final\" in the plot.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Description** & **Case 1** & **Case 2** \\ \hline \(S\) & Number of training samples (for each class) & \(10^{5}\) & \(10^{4}\) \\ \(N\) & Number of training signals (for each class) & 2000 & 200 \\ \(\beta\) & Length of chaotic signals & 50 & 50 \\ \(w_{\gamma}\) & Number of validation samples (for each class) & \(3\times 10^{3}\) & \(10^{3}\) \\ Z & Number of test signals & \(10^{4}\) & \(10^{4}\) \\ \(a_{t}\) & Mini-batch size & 200 & 200 \\ \(H\) & Number of hidden units & 100 & 10 \\ \(E\) & Number of epochs & 20 & 500 \\ \(\eta\) & Learning Rate & \(10^{-2}\) & \(10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the parameters.  
Figure 3: Training metrics when the training SNR \(\sigma_{tr}^{(n)}\in[19,23]\) dB.  
The training accuracy represents the classification accuracy of each mini-batch during the training process. The smoothed training accuracy is less noisy than the training accuracy and makes it easier to observe trends. The training loss shows the value of the loss function, i.e., the categorical cross-entropy, on each mini-batch. The results show that the loss function converges rapidly within about 100 iterations.  
When the DLCSK system is trained at relatively lower SNRs (For instance, when \(\sigma_{tr}^{(n)}\in[11,15]\) dB), training accuracy converges more slowly. Since, the chaotic signals contain more stochastic features, the fluctuations of the curve and difficulty of training increases. However, for \(\sigma_{tr}^{(n)}\in[11,15]\) dB, the model can achieve a relatively good accuracy rate, and converge to a small final error.
# Study on the Method of Moving Load Identification Based on Strain Influence Line
## 4 Verification by Experiment
### Analysis of Experiment Results
#### 4.2.3 The Identification Results of Vehicle Weight  
As the vehicle speed has no obvious influence on the identification results of the vehicle weight, the vehicle speed was set as 1.33 m/s in the following analysis. The two-axle vehicle and three-axle vehicle were divided into four grades of weight in the experiment. Each grade of vehicle weight was tested in three lanes, and the load identification results were shown in Figure 17. It can be seen that no matter which lane the vehicle drives, the vehicle weight identification error of each sample can be controlled within \(\pm\)10%. The error of more than 90% of samples was smaller than \(\pm\)5%, and the error was relatively larger compared with the simulation results, which was within an acceptable range. In addition, the error fluctuation of load identification was small, and the variance was smaller than 2%. Compared with reference [26], the method proposed in this paper greatly improves the identification accuracy. Therefore, it can be considered that the load identification method considering the load transverse distribution was effective. The error sources should be analyzed in the following aspects: (1) The vehicle weight identification is based on the speed identification, so the error of vehicle speed will affect the result of vehicle weight identification, (2) although there are lane restrictions in the experiment, the vehicle's trajectory is not always in a straight line along the bridge span direction, and its trajectory is relatively random.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **1\# Beam** & **2\# Beam** & **3\# Beam** & **4\# Beam** & **5\# Beam** \\ \hline The strain integral coefficient (\(10^{-7}\)) & 8.11 & 8.33 & 8.04 & 7.23 & 7.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mid-span strain integral coefficient of each beam bottom.  
Figure 16: Influence of vehicle speed on load identification: (**a**) Two-axle vehicle, (**b**) three-axle vehicle.
## 3 Results
### MGWR Regression Results  
#### 3.3.1 General Effect of MGWR Regression  
After determining the fractal dimension of each subspace in Xingdi Village, this study employs a Multiscale Geographically Weighted Regression (MGWR) model to further investigate the influencing factors and patterns associated with mountainous settlements. MGWR can account for multi-scale effects in regression calculations by assigning independent spatial bandwidths to different influencing factors, thereby capturing the spatial heterogeneity characteristics among factors with greater precision. The previously calculated fractal dimensions of subspaces are utilized as the dependent variable. The graded proportions of slope and aspect within the subspace grids, along with elevation, are employed as independent variables for the MGWR regression. All independent variables have passed multicollinearity detection. The model fit is presented in Table 4.  
The MGWR model demonstrates favorable values for RSS and AIC. The R\({}^{2}\) value of 0.504 is attributed to the insufficient explanatory power of the independent variable (aspect) on the dependent variable (fractal dimension). In the MGWR model, different bandwidths are assigned to factors based on their spatial characteristics to more accurately reflect their spatial influence. Specifically, the slope factor is allocated a local bandwidth of 45, indicating that the influence of slope exhibits strong spatial heterogeneity and is more suitable for medium-scale analysis. In contrast, the effects of elevation and aspect are more extensive and display global consistency, thus being assigned global bandwidths for computation. The statistical data for the MGWR regression can be found in Table 5.  
The statistical analysis reveals that the slope variable (X\({}_{1}\)) and the elevation variable (X\({}_{2}\)) exert significant positive influences on the dependent variable (fractal dimension), with mean regression coefficients of 0.592 and 0.318, respectively. In contrast, the aspect variable (X\({}_{3}\)) exhibits a \(p\)-value exceeding 0.05, indicating that its effect on the dependent variable is not statistically significant.
## 2 Materials and Methods
### Data Preparation  
The Sentinel-2A and Landsat-8 images were processed using the Sentinel Application Toolbox version 8.0.0 (SNAP). All Sentinel-2A and Landsat-8 satellite image bands were resampled in SNAP using the nearest neighbour method into 20 and 30 m resolutions, respectively. The resampled images were mosaicked to cover PEI's provincial boundary using the SNFAP built-in raster mosaicking tool. Three Landsat-8 scenes were mosaicked to cover the entire Island. Two of these scenes were collected on 26 July 2019, and the other one was acquired on 19 July 2019. The satellite images were reprojected to a local coordinate system, imported in ArcGIS Pro, and used to create training data for the LULC maps.
# Evaluation of the Adhesive Potential of Bacteria Isolated from Meat-Related Sources
## 2 Material and Methods
### Bacterial Strains and Inoculation  
Fourteen food-borne bacteria were tested in this study (Table 1), namely _Aeromonas veronii_, _Aeromonas salmonicida_ I, _Aeromonas salmonicida_ II, _Serratia liquefaciens_, _Citrobacter braakii_, _Ralnella aqutilis_, _Escherichia coli_, _Pseudomonas putida_ I, _Pseudomonas putida_ II, Pseudomonas fluoreseces, _Pseudomonas fragi_, _Lactobacillus plantarum_, _Lactobacillus curutus_, and _Lactobacillus sake_. They were collected from food manipulated in Key Laboratory of Meat Products Processing, Nanjing Agricultural University, China. The strains stored at \(-80\)\({}^{\circ}\)C were each revived twice on TSA plates or MRS plates for the Lactobacillus isolates. A single colony of all the strains was selected and cultured in TSB or MRS broth, and then incubated at \(30\)\({}^{\circ}\)C for 18 to 24 h, allowing the concentration of bacteria to reach \(10^{8}\) CFU mL\({}^{-1}\). The activated bacterial culture was then used to prepare a bacterial suspension. An aliquot (\(200\)\(\mu\)L) of each culture was transferred into fresh TSB or MRS, respectively, and incubated at \(30\)\({}^{\circ}\)C for 20 to 24 h. Cells were harvested by centrifugation (Avanti J-E, Beckman Coulter, Brea, CA, USA) at \(12{,}000\times g\) for 5 min and then washed three times with \(0.1\%\) BSP. The pellet was resuspended using \(0.1\%\) BSP to a final concentration adjusted to \(10^{9}\) CFU mL\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Strain Number**} & \multirow{2}{*}{**Bacteria Isolates**} & \multirow{2}{*}{**Source**} & \multicolumn{3}{c}{**Affinity/\%**} \\ \cline{3-5}  & & & **Xylene** & **Ethyl Acetate** & **Chlororform** \\ \hline NCM1577 & _L. curatus_ & Cooked ham & 4.42 \(\pm\) 4.64 a & 6.90 \(\pm\) 2.61 bc & 3.88 \(\pm\) 1.43 a \\ NCM1578 & _L. sake_ & Cooked ham & 19.28 \(\pm\) 2.92 b & 4.09 \(\pm\) 1.52 abc & 4.08 \(\pm\) 2.63 a \\ NCM1579 & _L. plantarum_ & Cooked ham & 16.02 \(\pm\) 3.16 b & 0.75 \(\pm\) 0.35 a & 15.64 \(\pm\) 2.97 b \\ NCM1580 & _S. liquefaciens_ & Cooked ham & 75.64 \(\pm\) 6.16 f & 3.39 \(\pm\) 1.55 ab & 63.96 \(\pm\) 2.19 cd \\ NCM1594 & _C. braakii_ & Chilled chicken meat & 73.55 \(\pm\) 6.05 f & 3.09 \(\pm\) 2.60 ab & 15.45 \(\pm\) 1.54 b \\ NCM1582 & _E. Coli_ & Chilled chicken meat & 29.85 \(\pm\) 6.35 c & 6.44 \(\pm\) 1.07 bc & 22.77 \(\pm\) 3.30 b \\ NCM1583 & _R. aqutilis_ & Cooked ham & 56.52 \(\pm\) 13.87 e & 17.40 \(\pm\) 2.71 d & 59.45 \(\pm\) 12.52 c \\ NCM1586 & _P. putida_ I & Chilled chicken meat & 72.37 \(\pm\) 8.05 f & 31.47 \(\pm\) 7.73 f & 70.04 \(\pm\) 9.51 d \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of 14 isolated strains and their affinity for different solvents.
## 4 Damping Force Characteristics  
Total damping force of the proposed MRD-PSF is generated by the pressure difference between top and bottom chambers. If the damper is in a quasi-static motion and mechanical friction is neglected, it is calculated as follows.  
\[F_{total}=P_{1}A_{p}-P_{2}\Big{(}A_{p}-A_{r}\Big{)}=\Delta PA_{p}+P_{2}A_{r} \tag{36}\]  
\[P_{1}=P_{2}+\Delta P,\ \ P_{2}=P_{a}+\Delta P_{a}\cong P_{a} \tag{37}\]  
where, \(P_{1}\) is the pressure of the upper chamber, \(P_{2}\) is the pressure of the lower chamber, \(A_{p}\) is the area of the piston, \(A_{r}\) is the area of the piston rod and \(P_{a}\) is the pressure of the air chamber. Total damping force is composed of damping force and air spring force.  
\[F_{total}=F_{d}+F_{air}\ \text{where}:F_{d}=\Delta P\cdot A_{p},F_{air}=P_{a}A_{r} \tag{38}\]  
Among many operation flow modes in Table 1, Mode 1 and Mode 2 are suitable to the proposed MRD-PSF in the sense of the operating principle described in Section 2. When the stroke velocity is low, only the main orifice is opened to realize high damping coefficient (Mode 1) and the bypass orifice is opened at the breaking point to provide low damping coefficient (Mode 2). In this case, damping force at the breaking point is \(\Delta p_{c,B}\cdot A_{p}\). To evaluate the damping characteristic of the proposed damper, computer simulations are conducted. The design parameters used in the simulation are shown in Table 2. These parameters are chosen by considering the level of the damping force required  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Main Orifice** & **Bypass Orifice** & **Conditions** \\ \hline Mode 1-1 & Partially open & Close & \(\Delta P_{c,M1}<\Delta P<\Delta P_{c,B}\) \\ Mode 1-2 & Fully open & Close & \(\Delta P_{c,M2}<\Delta P<\Delta P_{c,B}\) \\ Mode 2-1 & Partially open & Open & \(\Delta P_{c,B},\Delta P_{c,M1}<\Delta P\leq\Delta P_{c,M2}\) \\ Mode 2-2 & Fully open & Open & \(\Delta P_{c,B},\Delta P_{c,M2}<\Delta P\) \\ Mode 3 & Close & Open & \(\Delta P_{c,B}<\Delta P<\Delta P_{c,M1}\) \\ Mode 4 & Close & Close & \(\Delta P<\Delta P_{c,M1},\Delta P_{c,B}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Several types of flow mode.  
for a middle-sized vehicle suspension system. The weight of the proposed MRD-PSF is 2.9 kg and the required electrical power with maximum operating current 1A, resistance of Coil 2 (5.4 \(\Omega\)) and resistance of Coil 1 (2.1 \(\Omega\)) is less than 10 W.
# Privacy-Aware Cloud Architecture for Collaborative Use of Patients' Health Information
## 3 Proposed Architecture
### Architecture Fundamental Aspects  
There are two fundamental aspects of the proposed architecture design that enable it to store and share patient health-related information in a privacy-preserving manner. The first fundamental aspect is structuring patient information into categories. This aims to eliminate the exposure of information that is not needed during instances of medical treatment.  
Structuring patient information also contributes to allowing patients to have means of control over who can access their information while it is stored on the cloud. The second fundamental aspect is the use of a searchable symmetric encryption scheme (SSE) [59], which enables searching through encrypted information without decrypting it. The objective of the searchable encryption scheme is to store patient information on the cloud without allowing the cloud provider to learn the content of the stored information.