esa-sceva/satcom-chunk-collection huggingface dataset:
# FREDY: Federated Resilience Enhanced with Differential Privacy
## 3 Proposed Framework
### Teachers Model Training  
Each individual teacher model is trained using its proprietary sensitive data, ensuring its confidentiality is maintained. Within the federated learning framework, the server orchestrates the training of these teachers, while the student remains in a state of readiness for the teacher training to conclude. At each iteration of the federated learning procedure, the teachers retrieve the global model (i.e., \(W^{\text{\it global}}\)) from the FL server. Employing the Stochastic Gradient Descent (SGD) training algorithm [49], they train this global model using their own private-sensitive training set, with a local batch size \(B\), local epochs \(E\) and learning rate \(\eta\). For each local epoch \(e\in E\) of the teacher training procedure, each teacher holds a local model which can described as  
\[\Delta W_{t}^{local}=\eta*\
abla J(W_{t}), \tag{2}\]  
where \(J()\) is the loss function of the model and \(\eta\) is the learning rate. Then, each client computes the update of the weights as  
\[W_{t+1}^{local}=W_{t}^{local}-\Delta W_{t}^{local}. \tag{3}\]  
Then, at the final local epoch, the teachers upload their updated weights \(W^{local}\) to the server and the global model Sis updated by aggregating the \(W^{local}\). We use (1) as the aggregation function of the updated weights from the teachers.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 1 Introduction
### Sea-Level Measurements  
There are several methods of measuring sea levels, which can be categorized into three types, surface-following devices, fixed sensors, and remote or mobile sensors.  
_Surface-following devices_: While tide poles are inexpensive to install, they are tedious measurements to make and require the wave averaging to be done by eye. Placing a float gauge [10] in a stilling well introduces systematic wave averaging. They are robust but require installation on a vertical structure and are high maintenance.  
_Fixed sensors_: Acoustic reflection and radar reflection methods are robust and low cost, but again they require vertical structures for installation. Pressure sensors have an advantage in that they do not need a vertical structure for installation. However, they require water density and wave corrections and are deemed to be high maintenance [10]. GPS reflectometry is cost-effective and works best where there is no drying out at low water. However, [PERSON] et al. [11] showed that with the latest GPS devices and in combination with improvements to the global satellite network, improved sea-level measurements could be made within intertidal zones. These methods require multiple, high-frequency sampling for the wave averaging.  
_Remote/mobile sensors_: Satellite altimetry gives global coverage of sea levels, but the coarse resolution makes it difficult to interpret readings in the intertidal zone. Surface GPS buoys were previously expensive to build and deploy. However, [PERSON] et al. [12] showed that low-cost GPS buoys could deliver accurate sea-level measurements and can be potentially installed in the intertidal zone.
## 5 Conclusions  
In this paper, a simple SFO estimation and compensation scheme is proposed for the asynchronous OFDM-based UWOC system. The coarse phase shift is obtained by the product of two phase-conjugated pilots, and then an averaging processor of multi frames is used for accurate SFO estimation. The experimental results demonstrate that the proposed scheme can achieve an estimation accuracy within \(\pm\)3 ppm under the SPO range of \(\pm\)1000 ppm. A standard \(\pm\)200 ppm SFO can be well-compensated with negligible EVM penalties after 1 m underwater transmission. Afterwards, the robustness is verified in the water with bubbles and sea salt, which shows the proposed algorithm has great application potential in a real underwater environment. Moreover, it can be concluded that our proposed scheme has low computational complexity and, thus, is a very promising solution for real-time OFDM-based UWOC systems. Our algorithm can also be extended to high-speed UWOC systems, and the corresponding experiments will be carried out the future work.  
Conceptualization, H.L.; methodology, H.L.; software, H.L. and J.Z.; validation, H.L. and T.C.; formal analysis, J.Z.; investigation, H.L. and J.Z.; resources, J.Z.; writing--original draft preparation, H.L.; writing--review and editing, H.L., J.Z., T.C., Z.W., B.C. and Y.L.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Multiply/Divide** & **Add/Subtract** & **arg\{**\}** \\ \hline
[26] & M/2 + 1 & M/2 \(-\) 1 & M \\
[30] & \(N_{d}+1\) & \(N_{d}-1\) & \(N_{d}\) \\
[22] & 4 & 1 & 2 \\ Proposed & 2 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Complexity comparisons of SFO estimation.  
Figure 8: BER performance versus water salinity under different SFO. Subfigures show the 16-QAM constellations when the SFO is (**a**) 1000 ppm; (**b**) 200 ppm; (**c**) 0 ppm.  
Funding: This work was supported in part by National Key Research and Development Program of China (2021 YFB2900800), the Science and Technology Commission of Shanghai Municipality (Project No. 20511102400, 20 ZR1420900) and 111 project (D20031). Conflicts of Interest: The authors declare no conflict of interest.
## 4 Implementation and Performance Analysis of CAES-Based Image Encryption
### Statistical Analysis by NIST  
In this section, the National Institute of Standards and Technology (NIST) [26] test suite was introduced to evaluate the randomness of the encrypted images. The OpenCV function library of Python was utilized to collect the three values of R, G, and B from image 1 in Figure 10; then convert the values from 0 to 255 into bytes for encryption, as shown in Figure 8; and then output the results as a NIST test file through the Numpy library. The NIST SP800-22 test consisted of 15 items. The method of detection compared the calculated outputted \(p\)-value for each item with a prescribed significance level of 0.01. If the outputted \(p\)-values were all greater than 0.01 for all of the test items, it passed the NIST test. The NIST test results shown in Table 1 were obtained with a stream length of 10,000,000 bits and a bit stream count of 30. The obtained data revealed that the encrypted data from the CAES algorithm passed the NIST SP 800-22 random number detection (\(p\)-value > 0.001) and that the test performance of each item was better than the original AES-CBC and AES-ECB. This result indicates that the presented CAES algorithm had a good randomness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Nist Test**} & \multicolumn{4}{c}{\(p\)**-Value**} \\ \cline{2-5}  & **AES-ECB** & **AES-CBC** & **AES-ECB** & **CAES-CBC** \\ \hline Frequency & 0.000000 & 0.468595 & 0.602458 & 0.862344 \\ Block frequency & 0.000954 & 0.534146 & 0.602458 & 0.350485 \\ Cumulative sums & 0.000001 & 0.949602 & 0.602458 & 0.999896 \\ Runs & 0.000000 & 0.407091 & 0.991468 & 0.299251 \\ Longest run & 0.000000 & 0.534146 & 0.739918 & 0.739918 \\ Rank & 0.000000 & 0.534146 & 0.350485 & 0.407091 \\ FFT & 0.000000 & 0.468595 & 0.407091 & 0.253551 \\ Non overlapping template & 0.000000 & 0.998205 & 0.991468 & 0.991468 \\ Overlapping template & 0.000439 & 0.000737 & 0.407091 & 0.911413 \\ Universal & 0.000000 & 0.804337 & 0.862344 & 0.862344 \\ Approximate entropy & 0.00000 & 0.407091 & 0.862344 & 0.299251 \\ Random excursions & 0.122325 & 0.484646 & 0.350485 & 0.534146 \\ Random excursions variant & 0.025193 & 0.980883 & 0.637119 & 0.911413 \\ Serial & 0.000000 & 0.350485 & 0.911413 & 0.084037 \\ Linear complexity & 0.000000 & 0.299251 & 0.299251 & 0.671779 \\
**Sum** & **0.148912** & **8.221956** & **9.617851** & **9.898687** \\
**Average** & **0.009927** & **0.548130** & **0.641190** & **0.659912** \\ \hline \hline \end{tabular}
\end{table}
Table 1: National Institute of Standards and Technology (NIST) randomness test.  
Figure 10: Visual effect of encrypted images.
## 5 Results
### Overall Effect Sizes
This paper utilized funnel plots and fail-safe N statistics to detect publication bias and heterogeneity [109,148]. The X-axis of funnel plots is the converted Fisher Z effect value, and the Y-axis is the standard error, visually revealing the relationship between the two statistics. Small sample studies are commonly distributed at the bottom of the funnel plots, whereas large ones exist at the top. Figures 4-6 show funnel plots of the three pairwise relationships. In meta-analysis, a funnel plot is a graphical representation used to assess the presence of publication bias and to visualize the distribution of study effect sizes relative to a measure of precision, such as the standard error. In this plot, individual studies are typically depicted as white circles, with their horizontal position representing the estimated effect size and their vertical position reflecting precision (where studies with greater precision appear higher in the plot). The white diamond, on the other hand, represents the pooled or overall summary effect estimate along with its associated confidence interval. The width of the diamond conveys the precision of this aggregate measure. However, clearly detecting publication bias from the funnel plots can be difficult. To avoid the influence of subjective factors, this paper adopted the Begg test and Fail-safe N to obtain accurate outcomes from the quantitative perspective. According to Table 5, the \(p\)-values in the three pairwise relationships were non-significant at the 10% level [149,150], revealing that the publication bias will not affect the meta-analysis results. In addition, the values of Fail-safe N represent the number of studies required to refute significant meta-analytic means [151]. A more prominent Fail-safe N value indicates less possibility of publication bias. The formula can calculate the critical value (critical value = 5 \(\times\) sample size + 10). The fail-safe N values were larger than their corresponding critical values, suggesting that the meta-analysis results were reliable. \begin{table}
\begin{tabular}{c c c c} \hline
**Direct Effects** & **Number of Studies** & **Fail-Safe N** & **Begg \(p\)-Value** \\ \hline PU–BI & 43 & 31,161 & 0.125 \\ PEOU–BI & 43 & 24,596 & 0.173 \\ SI–BI & 12 & 3186 & 0.392 \\ \hline \end{tabular}
\end{table} 
Table 5: The analysis of publication bias in primary studies. Figure 4: Funnel plot of standard error by Fisher’s Z of PU–BI. Figure 5: Funnel plot of standard error by Fisher’s Z of PEOU–BI. Figure 6: Funnel plot of standard error by Fisher’s Z of SI–BI.
# A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys
## 2 Development of the GPS Time-Synchronization Method  
### Time-Synchronization Requirements for High-Density Seismic Array Survey  
The accuracy of the time-synchronization is a design index proposed based on the seismic wave velocity and the exploration depth [18; 19]. In order to investigate the impact of time accuracy on seismic surveys, a simplified model of a double-receiver seismic reflection survey is established, as shown in Figure 2. Seismic waves are excited by the source and vibrate the elastic medium. They are reflected through the stratigraphic interface and received by the receiver A and receiver B. Then, the travel time difference from the source to node A and node B can be expressed as follows:  
\[\Delta t=t_{2}-t_{1}=\frac{2\sqrt{(L_{1}+L_{2})^{2}+H^{2}}}{v}-\frac{2\sqrt{L_ {1}^{2}+H^{2}}}{v} \tag{1}\]  
where \(L_{1}\) is half the distance from source O to the receiver A, \(L_{2}\) represents that half the distance from source O to the receiver B minus \(L_{1}\), \(t_{2}\) represents the travel time from the receiving point B to the source O, \(t_{1}\) represents the travel time from the receiving point A to the source O, \(H\) represents the depth of stratigraphic reflection interface, and \(v\) represents the seismic wave speed. Assuming that \(H\) is 100 m, \(v\) is 4 km/s, \(L_{1}\) is 50 m and \(L_{2}\) is 2 m, and the travel time difference \(\Delta t\) is 0.45 ms. The travel time difference between node A and node B is 0.45 ms with the given arrangement. In actual exploration work, the time accuracy should be less than 10% of the travel time difference, which is 45 \(\upmu\)s, to ensure the accuracy of the seismic data interpretation.  
It can also be seen that if the time-synchronization error is 1 ms, the estimated error of the stratigraphic interface of the seismic interpretation is about 4 m. Therefore, it is necessary to ensure that the time-synchronization accuracy is as high as possible to ensure strict data alignment and the quality of seismic data interpretation. The nodal cable-less storage-type seismic data acquisition system performs long-term, uninterrupted acquisition tasks under the condition of continuous source excitation, which requires the sensor nodes to be always kept synchronized. As the acquisition time increases, the frequency drift of the crystal oscillator on the acquisition station will bring significant time error accumulation [20; 21]. The time-synchronization system should be able to continuously correct the time errors that occur to avoid error accumulation.
## 4 Experiments
### Application and Datasets
#### 4.2.5 Mobile Health Dataset  
This mHealth dataset was collected from 10 users with four different types of sensors, i.e., three-axis accelerometer sensors, three-axis gyroscope sensors, three-axis magnetometer sensors, and two-lead electrocardiogram sensors, to record 12 different activities at a 50 Hz sampling frequency. Some of the activities included standing still, sitting and relaxing, lying down, walking, climbing stairs, waist bend forward, the frontal elevation of arms, etc. It is a 12-category, well-balanced dataset.
## 1 Introduction
used acoustic travel time tomography to assess a mass concrete dam, presenting cross-sectional images of the structure to locate cracks and other damages [19; 20]. However, the conventional ultrasonic CT has its own limitations including heavy workload, a complex reconstructed algorithm, and relatively low-accuracy images [21; 22], which is a huge challenge to the damage inspection of the whole STS structure of Shenyang Metro line 9. Thus, the overarching goal of the research effort is to develop an effective and accurate ultrasonic inspecting method to reduce the workload and time for the assessment of the STS structures before their service. To achieve this ambitious functionality, compressive sampling (CS) theory was proposed to improve both the measurement stage and the imaging stage of the conventional ultrasonic CT technique. In the rest of the paper, the ultrasonic CT technique and basic formula will be introduced briefly in Section 2. Then, in Section 3, CS theory is applied for the reconstruction of ultrasonic CT, and the whole conventional ultrasonic CT program is improved and redesigned to shrink the measurement number in the measurement stage. The internal damage information is reconstructed by using the \(\ell_{1}\)-norm minimization algorithm via fusing measurement data and solving optimization problems. In Section 4, three different numerical models with various damage conditions are completely reconstructed with accurate resolutions based on the proposed algorithm. Additionally, experimental testing of the proposed ultrasonic CT and the conventional one was employed to investigate the concrete-filled steel tube of Shenyang Metro line 9 in Section 5. Finally, conclusions are drawn and the scope of future work is discussed in the last section.
# Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
### Automatic Emotion Recognition System
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Technique** & **Dataset** \\ \hline [PERSON] et al. [45] & SVM & JAFFE \\ \hline [PERSON] et al. [46] & Patch based, SVM & JAFFE \\ \hline [PERSON] et al. [47] & GL Wavelet, KNN & JAFFE, CK, MMI \\ \hline [PERSON] et al. [48] & GF, MFFNN & JAFFE, Yale \\ \hline [PERSON] et al. [49] & HOG, SVM & JAFFE \\ \hline [PERSON] et al. [50] & DCT, SVM & JAFFE, CK \\ \hline [PERSON] et al. [51] & KLSWFDA, SVM & JAFFE, Yale, FD \\ \hline [PERSON] et al. [52] & WPLBP & JAFFE, CK+, MMI \\ \hline [PERSON] et al. [53] & SWLDA, HRCF & JAFFE, CK+, MMI, Yale \\ \hline [PERSON] et al. [54] & HOG, SVM & Not mentioned \\ \hline [PERSON] et al. [55] & Local prominent directional pattern (LPDP) & CK+, MMI, BU-3 DFE, ISED, GEMEP-FERA, FACES \\ \hline [PERSON] et al. [56] & Average weighting + CNN & Real time \\ \hline [PERSON] et al. [57] & Feature extraction, SVM & CK+, JAFFE, MMI \\ \hline [PERSON] et al. [58] & Multi-scale action unit (AU)-based network (MSAU-Net) & Self-generated 10,371 images and 1491 video clips \\ \hline [PERSON] et al. [59] & GAN & Multi-PIE, BU-3 DFE, and SFEW \\ \hline [PERSON] et al. [60] & CNN & FER13, CK+, JAFFE \\ \hline [PERSON] et al. [61] & CNN & KDEF, RAFD, RAF-DB, SFEW, and AMFED+ \\ \hline [PERSON] et al. [62] & Ghost-CNN & RAF-DB, FER2013 and FERPlus \\ \hline [PERSON] et al. [63] & CNN & JAFFE, CKUTKFace \\ \hline [PERSON] et al. [64] & CNN & CK+ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing FER. It has been observed that a very minor misalignment can cause displacement of the sub-regional location of the facial feature and can cause misleading placement in classification. Research has focused on the selection of patches of the facial features and has demonstrated high accuracy for an automatic facial expression recognition system. We, therefore, have selected salient facial features in our proposed automatic facial expression recognition system and have used the histogram of orientation gradients (HOG) technique to accurately identify the facial features.
## 2 Methodology
### Determination of Periodicityand Response Time of Water-Level Fluctuation  
#### 2.4.1 Periodicity  
The frequency analysis of the river stage and the riparian groundwater level was carried out using the fast Fourier transform algorithm (FFT) in MATLAB. FFT is applied to transform signals from time domain to frequency domain for quickly analyzing the frequency characteristics of stationary or nonstationary signals. FFT is an optimization algorithm of discrete Fourier transform (DFT). The DFT value \(X(k)\) can be defined as follows [39]:  
\[X(k)=\sum_{n=0}^{N-1}x(n)W_{N}^{nk},\ (k=0,\ 1,\ 2\ldots N-1), \tag{1}\]  
where \(x(n)\) is the value in the \(n\)-th place of a sampled timeseries, \(N\) is the length of the series, \(W_{N}\) is the rotation factor equal to \(exp(-j2\pi/N)\), and \(j\) is the imaginary unit, \(j^{2}=-1\).  
Through the DFT mentioned above, spectrum values can be calculated, and the spectrum sequence can be obtained. In order to optimize the calculation efficiency, a sampled timeseries with the length of \(N\) is usually divided into two subsequences with the length of \(N/2\) according to the symmetry and periodicity of \(W_{N}\). As a result, a pair of DFT units can reduce the computation amount from \(N^{2}\) to \(Nlog_{2}N\), which is the reason why FFT is performed quickly. After the FFT calculation, the spectrum diagram can be plotted with signal frequency as the horizontal axis and amplitude \(X(k)\) as the vertical axis. The dominant oscillation frequency of the river stage or the groundwater level corresponded to the maximum spectrum value in the diagram; accordingly, the dominant period could be determined.
## 3 Results
### Energy Evolution Characteristics of CBM Specimens  
In the process of material properties changing, its essential energy is also transformed. Therefore, studying the energy evolution law of CBM specimens is helpful to further explore its deformation characteristics under external forces. From the above, it is found that the CBM specimens undergo deformation processes such as dense compression, elastic deformation, plastic deformation, damage accumulation and destruction during uniaxial compression. In the above deformation process, the total energy of the external force input is converted into elastic strain energy and dissipated energy, as shown in Formula (3). The elastic strain energy is stored inside the material as a potential energy, while the dissipated energy is consumed as the material is damaged. Therefore, by exploring the changing laws of elastic strain energy and dissipated energy, the degree of damage inside the sample can be effectively characterized.  
\[U=U_{e}+U_{d}, \tag{7}\]  
In the above formula, \(U\) is the total input energy under the action of external force, \(U_{\mathrm{e}}\) is the elastic strain energy, and \(U_{\mathrm{d}}\) is the dissipated energy.  
Under uniaxial compression, only the axial pressure needs to be considered, and the total input energy density of the CBM specimen can be obtained as follows:  
\[U=\int_{0}^{\varepsilon_{c}}\sigma_{1}d\varepsilon, \tag{8}\]  
Figure 7: Variation in cumulative acoustic emission ringing counts with fractal dimension.  
The elastic strain energy can be obtained by replacing the unloading elastic modulus with the elastic modulus of the stress-strain curve, while the dissipated energy can be obtained by substituting Equations (4) and (5) into Equation (3).  
\[U_{e}=\frac{\sigma_{1}^{2}}{2E_{u}}\approx\frac{\sigma_{1}^{2}}{2E} \tag{9}\]  
Through the above calculation, the energy evolution characteristics of CBM samples affected by fractal dimension under CO\({}_{2}\)NBW mixing and conventional water mixing are obtained as shown in Figures 8 and 9. In the above study, the loading process of CBM samples can be divided into five stages. It can be seen from Figures 8 and 9 that the energy evolution curves show different change trends in the five stages. In the 0-a stage, the stress effect on the internal energy of the material is not obvious, and the dissipated energy increases most slowly. The a-b stage is the linear elastic deformation stage, and the stress-strain curve in this stage mainly shows a nearly linear growth. From the perspective of energy change, the dissipated energy maintains a low growth rate, while the total energy mainly increases significantly in the form of elastic strain energy
# A Literature Review of Parameter-Based Models for Walkability Evaluation
## 3 Results and Discussion
### Utah Household Travel Survey  
Based on the UHTS (Utah Household Travel Survey) tool, which combines physical activity parameters and convenience for walking (Walkability), the authors [83] of this research examine the interdependence of these two observed topics. According to this research, the aspect of convenience for walking is observed through five groups of indicators (5D's): building Density (Density), Diversity in types of buildings (Diversity), Design and functionality of the infrastructure (Design), Accessibility (Destination accessibility), and Distance from transit and public points (Distance to transit). The results indicate that the optimal range of observation of the built environment is from 800 to 1600 m distance, that is, up to a 20 min walk from a pedestrian's point of view. Additionally, land use and socio-demographic status are dominant factors in determining the level of physical activity, while building density and design (urban plan) are key elements for promoting physical activity.