esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The DSM provides an advanced setup for POD of the RO receiver satellites in LEO, including the capability to routinely assess the uncertainties of the computed orbit data for climate-quality processing, which is presented in this paper. In order to ensure highly consistent and accurate RO-derived ECVs tied to the raw measurements, precise orbit positions, velocities, and clock estimates of the GNSS transmitter satellites and LEO RO receiver satellites need to be determined and their attributed uncertainty assessed. In this study we focus on the assessment of orbit position and velocity, whereas the satellites clock characteristics and stability will be discussed in a separate publication focusing on RO excess phase processing. Numerous past studies have evaluated the relationship between LEO orbit accuracy and atmospheric parameters derived from RO [3, 24, 25, 26, 27, 28, 29]. Based on the orbit accuracy specifications deduced from these studies, the POD processing presented in this study aims for a daily LEO orbit accuracy within 5 cm in position and 0.05 mm/s in velocity respectively, enabling highest quality RO retrieval results. In order to test these orbit requirements we routinely calculate different orbit solutions for mutual consistency check employing two independent POD software packages Bernese GNSS software v5.2 [30] (in short \"Bernese\" hereafter) and NAPEOS v3.3.1 [31], and use GNSS orbit and clock data from different orbit data archives, the Center for Orbit Determination in Europe (CODE) [32] and the International GNSS Service (IGS) [33]. The computation of estimated random and systematic orbit uncertainties is based on orbit inter-comparison between the different solutions, the analysis of satellite laser ranging (SLR) residuals, and random error propagation within Bernese. Finally, days exceeding the orbit accuracy target specifications are associated with increased uncertainty estimates and flagged for the subsequent occultation data processing. Building (also) on these POD uncertainty estimates, the uncertainty propagation along the RO level 1 and level 2 data processing chain (ODP in Figure 1) ultimately delivers thermodynamic ECVs jointly with uncertainty estimates. This processing leads to reference data products enabling added-value for climate monitoring and applications from the co-estimated uncertainties. Following this introduction, Section 2 starts with an overview of the processed missions. Section 3 introduces the data and software used followed by the description of POD and SLR technique, and the uncertainty estimation approach. Results are presented and discussed in Section 4. In Section 5 a summary and conclusion is provided.
## 4 Mechanical Effect Factors
### Mindlin's Solutions  
The displacement caused by point load at any point can be solved by using Mindlin theoretical solution in the elastic half-space shown in Figure 5[25]. The above four categories of force can be decomposed into vertical and horizontal forces, then brought into Mindlin solution to calculate.  
Figure 4: Four mechanical effect factors.  
When there is a vertical concentrated load \(P\) at burial depth \(d\), then the displacement \(S_{x(y),V}\) of x(y) direction and the displacement \(S_{z,V}\) of z direction at any point \(M\) (\(x\), \(y\), \(z\)) in space induced by the load \(P\) can be expressed as Equation (9):  
\[\left\{\begin{array}{l}Sx(y),V=\frac{Px(y)}{16\pi G(1-\mu)}\left[\frac{3-4\mu }{R_{1}^{3}}+\frac{(3-4\mu)(z-d)}{R_{2}^{3}}-\frac{4(1-\mu)(1-2\mu)}{R_{2}(R_{ 2}+z+d)}+\frac{6\mu z(z+d)}{R_{2}^{5}}\right]\\ Sz,V=\frac{P}{16\pi G(1-\mu)}\left[\frac{3-4\mu}{R_{1}}+\frac{8(1-\mu)^{2}-(3- 4\mu)}{R_{2}}+\frac{(z-d)^{2}}{R_{1}^{3}}+\frac{(3-4\mu)(z+d)^{2}-2 dz}{R_{2}^{ 3}}+\frac{6 dz(z+d)^{2}}{R_{2}^{5}}\right]\end{array}\right. \tag{9}\]  
where \(\mu\) is Poisson's ratio; \(G\) is soil shear modulus.  
When there is a horizontal concentrated load \(Q\) parallel to Y axis, pointing in the positive direction of the Y axis, then the displacement \(S_{x,H}\) of x direction, \(S_{y,H}\) of y, \(S_{z,H}\) of z at any point \(M\) (\(x\), \(y\), \(z\)) in space induced by the load \(Q\) can be expressed as Equation (10). While the horizontal concentrated load \(Q\) parallel to X axis, due to the symmetry of x and y, it only needs to exchange x and y in Equation (10).  
\[\left\{\begin{array}{l}S_{x,H}=\frac{Pxy}{16\pi G(1-\mu)}\left[\frac{1}{R_{1} ^{3}}+\frac{3-4\mu}{R_{2}^{3}}-\frac{6 dz}{R_{2}^{5}}-\frac{4(1-\mu)(1-2\mu)}{R _{2}(R_{2}+z+d)^{2}}\right]\\ S_{y,H}=\frac{P}{16\pi G(1-\mu)}\left[\frac{3-4\mu}{R_{1}}+\frac{1}{R_{2}}+ \frac{y^{2}}{R_{1}^{3}}+\frac{(3-4\mu)y^{2}}{R_{2}^{3}}+\frac{2 dz}{R_{2}^{3}} \left(1-\frac{3y^{2}}{R_{2}^{3}}\right)+\frac{4(1-\mu)(1-2\mu)}{R_{2}+z+d}(1- \frac{y^{2}}{R_{2}(R_{2}+z+d)})\right]\\ S_{z,H}=\frac{Py}{16\pi G(1-\mu)}\left[\frac{z-d}{R_{1}^{3}}+\frac{(3-4\mu)(z -d)}{R_{2}^{3}}+\frac{4(1-\mu)(1-2\mu)}{R_{2}(R_{2}+z+d)}-\frac{6 dz(z+d)}{R_{2 }^{5}}\right]\end{array}\right. \tag{10}\]  
Due to the location of load in Mindlin solution is at z axis, the coordinate of the formula above should be transformed to use when the location of load is at any point. The global coordinate system is xyz, let the local coordinate system be uvw and coordinate origin be \(O^{\prime}\) (\(m\), \(n\), \(k\)), shown in Figure 6. When coordinate is transformed, xyz are replaced by uvw, then let \(u=x-m\), \(v=y-n\), \(w=z-k\).
## 3 Unified White-Box ML Watermarking Framework
### Static White-Box Watermarking Schemes
#### 3.2.3 [PERSON] et al. Scheme (ResEncrypt) [55]  
ResEncrypt, proposed by [PERSON] et al. [55], is a static white-box watermarking scheme. Unlike the [PERSON] et al. and RIGA schemes that embed watermarks in filter parameter means, ResEncrypt inserts watermarks into kernel means to resist parameter-shuffling attacks. However, this strategy reduces the insertion capacity, prompting the authors to propose a DNN model called MappingNet to map fused kernels into higher-dimensional spaces.  
Feature extraction: The feature extraction of ResEncrypt \(\text{Ext}(\cdot,K_{\text{ext}})\) works as follows:  
1. Calculate the mean values of the kernel coefficients: \(\overline{W}_{ij}=\frac{1}{n\times d}\sum_{i=1}^{n}\sum_{j=1}^{d}W_{ijkh}\), obtaining \(\overline{\mathbf{W}}\in\mathbb{R}^{s\times s}\);
2. Use MappingNet \(F_{\text{map}}(\cdot)\) to map the fused kernel to higher dimensions: \(\mathbf{W}^{\text{map}}=F_{\text{map}}(\overline{\mathbf{W}})\).  
Projection function: Similar to [PERSON] et al., the projection function is  
\[\text{Proj}(\mathbf{W}^{\text{map}},K_{\text{Proj}})=\sigma(\mathbf{W}^{\text {map}}\times A)\in\{0,1\}^{l} \tag{13}\]  
where \(K_{\text{Proj}}=A\) is a secret random matrix.  
Advantages and limitations: ResEncrypt aims to resist parameter-shuffling attacks through kernel fusion. However, the shuffling attack is not effective, as it degrades model performance. Moreover, being based on the same strategy as [PERSON] et al., ResEncrypt suffers from similar problems: The projection function has low linear mapping capacity, making it vulnerable to overwriting attacks.
## 5 Simulation Results  
A DC/DC boost converter was selected for this validation. The applied boost converter design was illustrated in the previous section. A complete Simulink model of the MPPT controller is shown in Figure 6.  
The performance evaluation of the proposed enhanced P&O algorithm and comparison with the conventional P&O and IC algorithms were executed in MATLAB/Simulink in order to prove the GMPP tracking ability under various partial shading conditions. Different cases were considered in simulations where the GMPP location changes in P-V characteristics for weak and strong partial shading patterns to confirm the effectiveness of the algorithm.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 2 Full-Scale Test of Corrugated Steel
### Equivalent Stiffness Coefficient
#### 2.3.2 Analysis of Influence Factors  
CSPs exhibit varying sectional characteristics across different models, resulting in differing equivalent stiffness coefficients. Utilizing established and validated finite element numerical simulation methods, separate analysis models are developed for several prevalent types of CSPs. Subsequently, the general table of equivalent stiffness coefficients for CSPs is computed according to Equation (4), detailed in Table 2.  
According to the analysis presented in Table 2, the \(\eta\) of CSPs are primarily influenced by thickness and pitch depth (the amplitude of corrugations). To better understand the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **150-50** & **200-55** & **300-110** & **380-140** & **400-150** \\ \hline
3 & 1.008 & 1.008 & 0.897 & 0.826 & 0.780 \\
4 & 1.001 & 1.005 & 0.924 & 0.849 & 0.811 \\
5 & 0.994 & 1.000 & 0.928 & 0.879 & 0.850 \\
6 & - & 0.996 & 0.929 & 0.883 & 0.856 \\
7 & - & 0.991 & 0.934 & 0.885 & 0.864 \\
8 & - & 0.986 & 0.932 & 0.893 & 0.875 \\
9 & - & 0.980 & 0.929 & 0.892 & 0.879 \\
10 & - & 0.975 & 0.927 & 0.890 & 0.881 \\ \hline \hline \end{tabular}
\end{table}
Table 2: General equivalent stiffness coefficient table of CSP.  
variation pattern of \(\eta\), we introduce the concept of the \"Ratio of Depth to Pitch\" (RDP), defined as follows:  
\[RDP=\frac{Depth}{Pitch}\times 100\% \tag{5}\]  
The magnitude of RDP correlates directly with the intensity of CSP's wave fluctuations; a higher RDP indicates more pronounced waves, whereas a lower RDP signifies gentler undulations. Upon analysis, it becomes evident that CSPs' overall equivalent stiffness coefficient varies in response to factors such as thickness and RDP, as depicted in Figure 6.  
From Figure 6, several observations can be made: (1) For a given thickness, increasing RDP decreases \(\eta\). This indicates that higher RDP values correspond to more pronounced wave fluctuations in CSP, reducing the effective stiffness-contributing regions within the wave structure. (2) When RDP exceeds 0.333, \(\eta\) shows a positive correlation with thickness; conversely, for RDP values \(\leq\) 0.333, \(\eta\) exhibits a negative correlation with thickness. (3) The influence of thickness and RDP on \(\eta\) aligns closely with findings by [PERSON] et al. [25], who used a sectional parameter analysis method, highlighting RDP as the primary influencing factor.
## 5 Conclusions
A better than 2.122 m RMS of the orbit-only URE is obtained for all BDS-2 satellites. As for clock performance, except for C02 with a 3.493 m RMS, all satellites achieve a <2.357 m RMS. Since the clock error dominates global-average URE and worst-case URE, the performance of C02 is the worst, with an RMS of 4.152/5.311 m for these two criteria, respectively. For other satellites, <3.279/3.334 m RMS were obtained for the global-average URE and worst-case URE, respectively. Though a significant orbit difference exists between the GEO and IGSO/MEO satellites, the orbit difference does not affect the SIS UREs. The outcomes of the six-year BDS-2 broadcast navigation message performance assessment in this study will undoubtedly bring users a clearer understanding of BDS-2 broadcast navigation messages and, thus, more widespread BDS-2 PNT applications. [PERSON] drafted the manuscript and was responsible for the writing of the source code. [PERSON] conceived the experiment and the manuscript. [PERSON] collected the experimental data and validated the result. [PERSON] edited and reviewed the manuscript. This work has been supported by National Natural Science Foundation of China (Grant No. 41504027), Natural Science Foundation of Hubei Province (Grant No. 2017 CFB520) and Fundamental Research Funds for the Central Universities (Grant No. 2042017 kf0201). The authors would like to thank the IGS MGEX project and Wuhan University for providing the experimental data. The authors declare no conflict of interest.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 4 Insole Sensor Technology
### Sensor Size and Number  
The primary design consideration is the sensor size, which is directly related to the quality of raw data collected [5]. According to [PERSON] et al. [5], an insole sensor should possess a compact size, lightweight construction, and high flexibility. Additionally, it must exhibit an appropriate force-sensing range and reliable responsiveness under extreme conditions. The material and design of the device should be suitable for its intended application area, and the system's scanning speed must meet the application's specific requirements.  
A larger sensor may underestimate peak pressure values, while smaller sensors can make it challenging to track the displacement of points of interest during gait accurately. Consequently, it is recommended to use smaller sensors in an array configuration [16].  
As for the number of sensors, a simplified design with the minimum number of integrated sensors is preferred, as cost, power consumption, and robustness are the primary considerations [3]. However, as the resolution of insole pressure sensors improves, the approach is transitioning towards integrating a higher density of sensors within the insole. This allows comprehensive data collection across the entire foot, enabling the identification of pressure hotspots through software analysis during signal processing [33]. Other authorsargue that increasing the number of sensors allows the collection of a greater volume of information, thereby enhancing the accuracy of gait assessment. Besides sensor quantity, other factors influencing the accuracy of assessment outcomes include the precision of gait measurement parameters and the amount of data collected [25]. Unfortunately, the increase in the sensor number is reflected in the energy consumption and in the computational cost to analyze and process the signals to converge to a solution [16].  
The plantar surface can be segmented into distinct regions to gather more pertinent data for pathology analysis [16]. Next, as an example, we can observe in Figure 8, the segmentation, the divided zones and the position of each sensor in an insole.  
Thus, the minimum number of recommended sensors is fifteen. Still, this number can vary according to foot size, leading to the displacement of key pressure points [34]. For example, in [PERSON] et al.'s [14] work, six piezoresistive sensors were installed beneath the primary weight-bearing areas of the insole, specifically the big toe, the first and fourth metatarsal heads, the midfoot, and the heel.
## 3 Materials and Methods
### Atrial Fibrillation Detection  
In this section, we present the application of the proposed framework for detecting atrial fibrillation from short single-lead ECG recordings. Due to the existence of alternate rhythms that exhibit heartbeat patterns similar to atrial fibrillation, atrial fibrillation may be misdiagnosed when based on short-term detection. We propose a privacy-preserving solution to distinguish atrial fibrillation from normal rhythms, other abnormal rhythms, and noisy ECG recordings.  
In the current methodology, atrial fibrillation detection models are trained offline using publicly available datasets. Given the proposed framework and the features of the MORE encryption scheme, there are two possible training scenarios. Specifically, AI models can be trained offline on unencrypted data and then deployed to the cloud for inference on encrypted data, or they can be trained directly on encrypted data. The second approach is particularly useful in real-world scenarios in which confidential health-related training datasets are not made publicly available to third parties (i.e., they do not leave the data provider). Using the proposed framework, the training dataset is encrypted at the data provider (e.g., hospital), and only the encrypted version is transferred and stored on the cloud for privacy-preserving AI model training. Moreover, this approach favors on-line training, in which AI models are retrained and fine-tuned as new data becomes available.  
Following the strategies used in the research community to detect cardiac arrhythmias, we consider two approaches: (i) a classical machine learning solution based on feature engineering, and (ii) a deep learning end-to-end approach where the detection is based on the raw ECG signal. To address the problem of confidentiality of personal data, homomorphically encrypted data are used for both training and prediction. The proposed deep learning-based ciphertext data analysis is presented in Algorithm 4 where all operations performed during training and prediction are formulated in accordance with Section 3.1. In the following subsection, we describe the dataset, the experimental setup, including the proposed models, and their performance.
## 4 Results and Discussion
### Average and Weighted Average Entropy
As seen in this figure, sporadic AE signals are received for the majority of life as dislocation pileups and breakaways occur at multiple potential crack initiation sources. After a crack initiates and continues to grow to 1 mm, a common transition length between small and large cracks [46; 47; 48; 49; 50; 51; 52; 53], the average and weighted average entropy sharply decreases as shown in Figures 13a and 14a. This finding confirms that entropy decreases after a crack has initiated and that AE signals are more frequent and less disordered. The average and weighted average entropy trends then increase as the crack grows towards fracture as seen in Figures 13b and 14b, again agreeing with the hypothesis. Thus, entropy trends based on accounting for current and previous AE signals may be able to indicate when a crack initiates and grows to a small length, as characterized by a  
Figure 11: Average entropy trends versus fatigue cycles and hit count over the first 500 cycles: (a) average entropy versus cycles; and (b) average entropy versus hits. Figure 12: Weighted average entropy trends versus fatigue cycles and hit count over the first 500 cycles: (a) weighted average entropy versus cycles; and (b) weighted average entropy versus hits. sharp decrease, and when a specimen is near fracture, as described by a sharp increase. The weighted average entropy has the same general trend as average entropy in all graphs but is more irregular during initial fatigue cycles. In the end, there are slight differences between average entropy and weighted average entropy, but both show a noticeable decrease prior to a 1-mm crack followed by an increase as the specimens fracture. Thus, the average or weighted average entropy from AE signals may be able to clearly indicate when an incipient crack is initiating in general metal structures.
### \(H_{2}\)S and COS Concentration
#### 3.3.1 Variation of Oxygen Ratio \(n_{\rm O2}\)  
Figure 11 shows the measured H\({}_{2}\)S and COS concentrations as well as the equilibrium concentrations, calculated with FactSage(r) (see Section 2.6), over the oxygen ratio \(n_{\rm O2}\). All concentrations are presented in water free (wf) and N\({}_{2}\)-free basis. For oxygen ratios from 0.20 to 0.28, H\({}_{2}\)S concentrations between 2000 \(\times\) 10\({}^{-6}\) m\({}^{3}\) m\({}^{-3}\) and 3000 \(\times\) 10\({}^{-6}\) m\({}^{3}\) m\({}^{-3}\) were measured at the respective conditions of 847 \(\pm\) 5 \({}^{\circ}\)C. From the measurements of this work, it is hard to deduct a specific trend for H\({}_{2}\)S, but it can be noted that the two points at higher \(n_{\rm O2}\) show slightly higher concentrations. For COS only one measurement was performed of 60 \(\times\) 10\({}^{-6}\) m\({}^{3}\) m\({}^{-3}\) at \(n_{\rm O2}\) = 0.2 mol mol\({}^{-1}\).  
The equilibrium predicts a slight increase of H\({}_{2}\)S and COS concentration with \(n_{\rm O2}\). This is related to higher feed of oxygen to the gasifier at higher \(n_{\rm O2}\), leading to the formation of H\({}_{2}\)O and CO\({}_{2}\). This drives Equations (5) and (6) away from sulfur capture following [PERSON]'s principle.
## 4 Dataset Description and Preprocessing
### Preprocessing
However, the method requires sufficient data points within the rolling window to provide reliable statistics, and the presence of outliers within the window may influence the computed rolling mean and standard deviation. Overall, the algorithm provides a robust framework for anomaly detection. It ensures adaptability across various domains and datasets by leveraging local statistics and normalization. The dataset was then divided into training and testing subsets, allocating approximately 80% of the data for training and 20% for testing to validate the model's performance. Figure 2: Correlation metrics. ```
0:
* \(C(t)\): Time-series data of closing prices. * \(w\): Rolling window size (\(w=30\)). * \(z_{\text{threshold}}\): Z-Score threshold (\(z_{\text{threshold}}=1\)). 0:
0:
* \(\mu(t)\): Rolling mean of \(C(t)\). * \(\sigma(t)\): Rolling standard deviation of \(C(t)\). * \(Z(t)\): Z-Score of \(C(t)\). * \(A(t)\): Abnormality indicator; \(A(t)=1\) if \(|Z(t)|>z_{\text{threshold}}\), otherwise \(A(t)=0\). 1: Step 1: Calculate Rolling Mean
2:for each time point \(t\) in \(C(t)\)do
3: Compute the rolling mean \(\mu(t)\) over a window of size \(w\): \[\mu(t)=\frac{1}{w}\sum_{i=t-w+1}^{t}C(i)\]
4:endfor
5: Step 2: Calculate Rolling Standard Deviation
6:for each time point \(t\) in \(C(t)\)do
7: Compute the rolling standard deviation \(\sigma(t)\): \[\sigma(t)=\sqrt{\frac{1}{w}\sum_{i=t-w+1}^{t}(C(i)-\mu(t))^{2}}\]
8:endfor
9: Step 3: Calculate Z-Score
10:for each time point \(t\) in \(C(t)\)do
11: Compute the Z-Score \(Z(t)\): \[Z(t)=\frac{C(t)-\mu(t)}{\sigma(t)}\]
12:endfor
13: Step 4: Identify Abnormalities
14:for each time point \(t\) in \(C(t)\)do
15:if\(|Z(t)|>z_{\text{threshold}}\)then
16: Set \(A(t)=1\) {Mark as abnormal}
17:else
18: Set \(A(t)=0\) {Normal point}
19:endif
20:endfor
```  
**Algorithm 1** Calculate Z-Score and detect abnormalities in cryptocurrency data