esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Direct Perturbation Method  
The direct perturbation method is based on the main idea that the response can be expressed through a Taylor-series expansion in terms of a set of zero mean random variables [26]. Let \(\mu\) and \(\sigma^{2}\) be the mean and variance operators, respectively. If all the uncertain variables \(X_{i}\) (\(i=1\ldots k\)) of the model are normally distributed and independent of each other, then the first-order approximation gives:  
\[Y_{lim}=Y\big{(}\overline{\mathbf{X}}\big{)}+\sum_{i=1}^{k}\left(\frac{\partial Y }{\partial X_{i}}\big{(}\overline{\mathbf{X}}\big{)}\right)\left(X_{i}-\mu_{X_{i}}\right) \tag{3}\]  
where \(\mathbf{X}\) = (\(X_{1}\), \(X_{2}\), \(\ldots\), \(X_{k}\)) is the set of the uncertain variables, and \(\overline{\mathbf{X}}\) is the design point (with the restriction \(\overline{X}_{i}=\mu_{X_{i}}\)). It is so possible to rewrite Equation (3) in function of the so-called sensitivity factor \(\beta_{i}\) as follows:  
\[Y_{lim}=Y\big{(}\overline{\mathbf{X}}\big{)}+\sum_{i=1}^{k}\beta_{i}\big{(}X_{i}- \mu_{X_{i}}\big{)} \tag{4}\]  
with \(\beta_{i}=\frac{\partial Y}{\partial X_{i}}\big{(}\overline{\mathbf{X}}\big{)}\). Thus, the mean value and the variance of \(Y_{lim}\) become:  
\[\mu_{Y_{lim}}=Y\big{(}\overline{\mathbf{X}}\big{)} \tag{5}\]  
\[\sigma^{2}_{Y_{lim}}=\sum_{i=1}^{k}\sum_{l=1}^{k}\beta_{i}\beta_{l}Cov[X_{i},X _{l}] \tag{6}\]  
Under the further hypothesis that the elements of vector \(\overline{\mathbf{X}}\) are fully uncorrelated, Equation (6) is simplified to:  
\[\sigma^{2}_{Y_{lim}}=\sum_{i=1}^{k}\beta_{i}^{2}\sigma^{2}_{X_{i}} \tag{7}\]  
This parameter reflects the analytical uncertainty of the model. Furthermore, one major advantage of this approach is its ability to estimate the first- and second-order statistical moments of the response variable without requiring the assumption of a specific probability distribution for the uncertain parameters. However, a limitation of the perturbation method is that its linear approximation may fail to accurately capture the nonlinear relationships between the model and the input parameters of vector \(\overline{\mathbf{X}}\)[29], a drawback that becomes especially significant in the presence of large uncertainties.
## 4 Discussion
### The Impact of Limited Input Data on Model Results  
In the ESTARFM, three input images are required. More basic images can provide more information about the changes and make the results more reliable. However, valid data are limited, which severely constrains the application of data fusion. The segmented fitting and numerical conversion process of the ESTARFM_p takes more into account the spatial information of the original images. So, in this section, an attempt was made to test whether the model could handle having a lack of coarse-resolution images on the target  
Figure 8: Fifteen sets of Chl-a results for 22 September derived using the ESTARFM-p model.  
date. To quantitatively test the degradation of accuracy when there are fewer inputs, the new model performed predictions with input images on a single date as an extreme case. The input data were the GOCI and Landsat pairs in Table 1, excluding those obtained on 22 September.  
The statistical coefficients of the results are shown in Figure 9. Taking the actual observations of Landsat as \"true values\", these results were calculated from the 15 different date combinations in Table 3 and single-date images in Table 1. Compared with the ESTARFM, the new model scheme had a better performance in all the indicators. The reason for this may be that the new model scheme adopts the numerical conversion method, which is more suitable for the characteristics of chlorophyll concentration data. The single-date results also show reasonable coefficients but with more fluctuations and outliers. Compared with the ESTARFM, the single-date results show a higher _RMSE_ and lower _CC_, which indicates that the number of input images has a great impact on the model results.  
To better illustrate this, areas near Shicheng Island and Wangjia Island were extracted, as shown in Figure 10. Compared with the Landsat observations, all three models managed to obtain variations in the Chl-a. The ESTARFM_p performed the best as it did not miss any of the peaks. The single-date model results show abrupt predictions and gaps in both numerical and spatial distribution. However, the general trends are still similar to the remote sensing observations. Therefore, although the accuracy of the model degrades when it lacks inputs, the results of the ESTARFM_p could still be taken into account in engineering problems that need less accuracy.
## 3 Engineering Example
### Seismic Response Analysis of Shield Tunnel Based on Time-History Analysis Method  
#### 3.3.1 Calculation Model  
In order to further study the seismic response characteristics of the shield tunnel in the sea area of the nuclear power plant, a three-dimensional refined numerical model of a sinking tube-soil under a complex soil foundation is established based on the lateral and longitudinal response displacement method, taking into account the nonlinear characteristics of the soil mass, the structure-foundation infinite dynamic interaction, and the convection and pulse effects of flowing water inside the sinking tube. The equivalent linear unit and viscous elastic artificial boundary are created in the secondary development of ANSYS using UPFs; on this basis, the dynamic time-history analysis of the sinking tube tunnel under the action of ultimate ground motion is carried out as indicated in Figure 20, and the lining segment structure is indicated in Figure 21.  
The grid of the foundation is as uniform as possible, with different colors representing different material properties. The soil layer model extends 45 m along the left and right sides of the tunnel, the tunnel is buried at the depth of 9.3 m, and the bottom of the soil model is 80 m away from the center of the tunnel. The soil materials and layer thicknesses are silt for 13.85 m, muddy clay for 4.55 m, clay for 12.68 m, silty clay for 11.5 m, and moderately weathered rock for 50 m from the top to the bottom.
# Security and Ownership in User-Defined Data Meshes
## 6 Conclusions  
This paper introduces an innovative framework for securing the access and ownership of data meshes based on Blockchain and NFTs. The framework is applied on a data lake storage architecture, which may host big data at any scale, frequency and format, and utilizes semantic data blueprints for dynamically constructing data products in data meshes. These products are designed to meet user demands and ensure that stakeholders access specific areas of the data mesh as needed through the transfer of ownership. The integration of NFTs and Blockchain technology offers a novel approach to addressing ownership and governance concerns. A dedicated algorithm was developed for incorporating the ability to mint time-based NFTs, thus facilitating secure and transparent data ownership transfers. The proposed framework was demonstrated using a real-world case study from the smart manufacturing area. Specifically, a data lake was built to host data produced at a poultry meat production factory by several sensors and automated systems during the breeding process followed in the farms. Specific portions of data were selected to construct data products in a custom data mesh, which were then used as key elements for granting access and transferring ownership to authorized users via the execution of smart contracts and NFTs. The scenarios tested suggested successful behavior in terms of ease of use, transparency, and correctness. It should also be noted that users in the factory (workers and managers in breeding sites and production line) were able to easily follow the algorithmic approach of the proposed framework and apply its steps efficiently, greatly appreciating the ability to share data.  
Future research will focus on enhancing and automating parts of the framework by utilizing recommender systems driven by user preferences and/or the history of transactions for creating data products. Specifically, the system will be modified to employ advanced algorithms for analyzing user behavior and preferences to generate access recommendations. This will allow data products to be constructed upfront, thus speeding up the process for granting access and transferring ownership.  
Figure 11: Result for token#0 and token#1.  
**Author Contributions:** Conceptualization, M.P. and A.S.A.; methodology, M.P. and A.S.A.; software, M.P. and P.C.; validation, M.P., P.C. and A.S.A.; formal analysis, M.P.; investigation, M.P.; resources, M.P. and A.S.A.; data curation, M.P.; writing--original draft preparation, M.P.; writing--review and editing, M.P. and A.S.A.; visualization, M.P. and P.C.; supervision, A.S.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
## 4 Discussion
These disadvantages are due to the fact that (1) we wanted to focus on the most widely used approaches (with an addition of the less known FVP approach, which relies on a completely different principle), (2) time complexity analysis would first require optimization of the programming code behind the studied approaches, and (3) the remaining 19 sets of video recordings from LGI-PPGI-FVD are not publicly available due to the limited storage space on the server that data set's creators use, whereas PBDT-rPPG offers only a small number of videos covering the skin tone challenge. It is also to be noted that the success rate and the ROI size are the only parameters that directly assessed the performance of the studied approaches. However, even ROI sizes themselves do no tell anything about the quality of the skin mask (in terms of the ratio between the skin and non-skin pixels). In SNR and RMSE metrics, potential differences between the studied approaches are masked especially in the case of POS, which has been shown to exhibit the best overall performance [31] in extracting the rPPG signal from video recordings. The first future challenge is the optimization of the studied algorithms to allow their best performance. For example, (1) in VK-RGBHCbCr and especially in VK-Conaire, a combination of a sequence of morphological operations (erosion and dilation) could be used to further refine the ROIs (see Figure 1d,h,l for noisy ROIs identified by VK-Conaire); (2) in all approaches with image processing front-ends, identified ROIs could be divided into multiple ROIs (multiple ROIs would also enable measurements of additional physiological parameters [44]); and (3) in all approaches, lengths of the processing window lengths could be optimized. Additionally, we could test some other skin classification approaches, such as one class support vector machine-based classifier [45] or an approach based on active contours and Gaussian mixture models [32], which do not rely on simple pixel-wise segmentation. It is to be noted that both methods require robust face detection. The second challenge is related to the analysis of the performance of the studied approaches in cases when subjects are wearing masks. Due to the fact that masks are becoming a new reality of our everyday life amid the ongoing COVID-19 pandemic, this challenge is in our opinion relevant for the rPPG research community. Recently, [PERSON] et al. [46] created a publicly available data set containing recordings of 61 masked subjects together with an algorithm that adds a synthetic mask to a face on a selected video recording, which is, in our opinion, a valuable contribution for the rPPG community. The last challenge is related to the evaluation of various approaches for extracting rPPG signals from video recordings covering parts of the body other than face. These body parts have already been used for assessing peripheral hemodynamics [47; 48].
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Enhancing the EPR Effect by Targeting the Tumor microenvironment (TME)
#### 5.1.2 Enzyme-Activated Drug Delivery Systems  
Another strategy involves the use of TME-specific enzymes to activate drugs within delivery systems, thereby enhancing EPR-based drug delivery. This approach offers several advantages, including increased tumor drug accumulation, reduced systemic side effects, and improved treatment outcomes. For example, peptide-based doxorubicin (DOX) prodrugs have been designed to self-assemble into nanoparticles (NPs) that target tumors via the EPR effect. Once localized in the tumor, enzymes such as cathepsin B, which are overexpressed in the TME, activate the prodrug, releasing DOX directly into the tumor tissue. This targeted release mechanism minimizes off-target effects and maximizes therapeutic efficacy [170].
## 2 Materials and Methods
### Direct Shear Strength Measurement  
Direct shear tests were conducted at Zhejiang University on 3 June 2017 to measure the soil shear strength parameters using a self-made device (Figure 1). After calibrating the instrument, the soil samples in the annular soil collector were placed into the shear box, and the pressure of the cylinder was adjusted using the pressure-regulating valve. When the pressure met the requirement of the experimental setting, the reversing valve was opened and the air was filled in. Subsequently, the shear box was vertically compressed by the cylinder piston and the normal stress was loaded. After that, the mechanical test machine started moving upward at the speed of 0.08 mm min\({}^{-1}\), which pulled the wire rope and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Code**} & \multicolumn{3}{c}{**Coded Levels of Variables**} \\ \cline{3-5}  & & **Clay Content/\%** & **Moisture Content/\%** & **Wet Bulk Density/g\(\cdot\)cm\({}^{-3}\)** \\ \hline
1 & \(-1.682\) & 22.3 & 30 & 1.8 \\
2 & \(-1\) & 26.1 & 34 & 2.0 \\
3 & 0 & 30.8 & 40 & 2.3 \\
4 & 1 & 36.0 & 46 & 2.6 \\
5 & 1.682 & 40.6 & 50 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coded levels for different factors.  
thus moved the shear box down. Consequently, one direct shear test was completed when the shear displacement reached 4 mm, and the data of displacement as well as tension were recorded automatically.  
The shear tests for all 20 soil samples were performed with three replications under six different normal stresses, including 25, 50, 67, 83, 100, and 133 kPa, respectively. In total, 360 direct shear tests (20 coded levels \(\times\) 3 replicates \(\times\) 6 normal stresses) were performed. After all the tests were completed, the moisture content was measured again to ensure all soil samples were tested under the specific moisture content.  
During each direct shear test, when the shear displacement was 4 mm, the maximum force was taken as the maximum shear force of the soil sample under a certain vertical load, and the formula of the shear strength was as follows:  
\[\tau=\frac{F_{max}}{s} \tag{3}\]  
where \(\tau\) is the shear strength, \(F_{max}\) is the maximum shear force and \(s\) is the shear area.  
For each soil sample, the vertical stress and shear strength were drawn in the transverse and longitudinal coordinates, and the curves were fitted with a straight line. The angle of the straight line was the internal friction angle, while the intercept of the straight line in the longitudinal coordinates was the cohesion \(c\).
# Person Search via Deep Integrated Networks
## 3 Overview of the Deep Integrated Networks for Person Search
### Pedestrian Detection Network
#### 3.1.2 Region Proposal Network
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sharing** & **Kernel** & **Kernel** & **Stride** & **Max Pooling** & **Stride** \\ \cline{1-1} **Conv1** & 96** & \(7\times 7\) & \(2\) & \(3\times 3\) & \(2\) \\ **Conv2** & 256** & \(5\times 5\) & \(2\) & \(3\times 3\) & \(2\) \\ **Conv3** & 384 & \(3\times 3\) & \(1\) & – & – \\ **Conv4** & 384 & \(3\times 3\) & \(1\) & – & – \\ **Conv5** & 256 & \(3\times 3\) & \(1\) & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network configuration for the Zeiler and Fergus network [65]. where \(d(w_{A})\) and \(d(h_{A})\) are the scale parameters. The linear transformation could be assumed, and Equations (4) and (5) can be modified as follows when the difference between \(A\) and \(G\) is small [35]:  
\[\left[ \begin{array}{c}x_{G}\\ y_{G}\end{array} \right]=\left[ \begin{array}{cc}t_{x}&0\\ 0&t_{y}\end{array} \right]\left[ \begin{array}{c}w_{A}\\ h_{A}\end{array} \right]+\left[ \begin{array}{c}x_{A}\\ y_{A}\end{array} \right] \tag{6}\]  
\[\left[ \begin{array}{c}w_{G}\\ h_{G}\end{array} \right]=\left[ \begin{array}{cc}exp(t_{w})&0\\ 0&exp(t_{h})\end{array} \right]\left[ \begin{array}{c}w_{A}\\ h_{A}\end{array} \right] \tag{7}\]  
The shift and scale parameters \(\Delta=\left[t_{x},t_{y},t_{w},t_{h}\right]\) can be learned as follows via linear regression:  
\[A=L\Phi(X) \tag{8}\]  
where \(L\) denotes the parameters of the linear regression model, \(\Phi(X)\) is a tensor with a size of \(W\times H\times 36\), and each element of the tensor corresponds to four parameters of the \(k\) rectangles. The model parameters can be obtained by minimizing the loss function to mimic the regression error between the estimated results \(L\Phi\left(X^{i}\right)\) and the ground truth \(G^{i}\)[35]:  
\[L^{*}=\underset{L}{argmin}\sum_{i=1}^{N}\parallel G^{i}-L^{T}\Phi\left(X^{i} \right)_{\parallel 2}+\lambda\parallel L\parallel_{2} \tag{9}\]  
where \(\lambda\) is a trade-off between the regression error and the regularization term \(\parallel L\parallel_{2}\) that gives a constraint for the elements of \(L\) to avoid overfitting. After obtaining the probabilities and refined coordinates for each rectangle via two branches [49], the first 6000 rectangles were selected according to the sorted softmax score. Also, each rectangle's coordinates in the original input image \(A^{i}_{IMG}\) are obtained by inversely mapping according to the refined coordinate \(\overline{A}^{i}_{RPN}\). Rectangle \(i\) is discarded if coordinates \(A^{i}_{IMG}\) are out of the image bounds. Non-maximum suppression [16] was then applied to reduce the number of rectangles. The first 300 rectangles from the remaining rectangles based on the softmax scores were output as proposals. In short, the rectangles with unreasonable size, a low probability of an object, or which were out of image bounds were discarded, and only 300 proposals were further processed in the following layers of the pedestrian detection network.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & Analog-to-Digital Converter \\ BCH & Bose-Chaudhuri-Hocquenghem Codes \\ BPSK & Binary Phase Shift Keying \\ DAC & Digital-to-Analog Converter \\ DPSD & Doppler Power Spectral Density \\ ECC & Error-Correction Coding \\ ICI & Inter-Carrier Interference \\ IFFT & Inverse Fast Fourier Transformation \\ ISI & Inter-Symbol Interference \\ OFDM & Orthogonal Frequency-Division Multiplexing \\ PDP & Power Delay Profile \\ PRBS & Pseudo-Random Binary Sequence \\ RS & Reed-Solomon Codes \\ SFCF & Space-Frequency Correlation Function \\ STCF & Space-Time Correlation Function \\ STCF & Space-Time-Frequency Correlation Function \\ TVIR & Time-Varying Impulse Response \\ TVR & Transmitting Voltage Response \\ UAC & Underwater Acoustic Communication \\ \end{tabular}
## 4 Conclusions  
This work looks at the benefit of including GLONASS in integrated GPS/INS systems, especially in urban canyon environments. The effect of using different vehicle motion constraints was also considered. Data was collected in downtown Calgary and processed using various configurations. For the data analyzed herein, the main conclusions are as follows:  
* In environments where satellite visibility is insufficient for standalone GNSS positioning approximately 50% of the time, the benefits of GLONASS are minimal in a loosely coupled implementation. However, in a tightly coupled implementation, GLONASS provides considerable improvements. In this respect, these results are similar to previous results obtained with GPS/INS systems.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{_RMS_} \\ \cline{2-10}
**Configuration** & \multicolumn{3}{c}{**Position Error (m)**} & \multicolumn{3}{c}{**Velocity Error (m/s)**} & \multicolumn{3}{c}{**Attitude Error (deg)**} \\ \cline{2-10}  & _East_ & _North_ & _Up_ & _East_ & _North_ & _Up_ & _Roll_ & _Pitch_ & _Yaw_ \\ \hline LC GPS/INS & 37.8 & 72.2 & 20.8 & 3.30 & 4.06 & 0.68 & 1.9 & 2.5 & 41.2 \\ LC GG/INS & 36.6 & 41.0 & 17.3 & 2.24 & 2.41 & 0.63 & 1.8 & 2.5 & 33.8 \\ LC GG/INS YVH & 37.1 & 42.5 & 3.2 & 2.14 & 2.41 & 0.10 & 1.1 & 1.1 & 16.0 \\ TC GPS/INS & 11.8 & 18.4 & 15.8 & 1.08 & 1.44 & 0.57 & 2.2 & 1.5 & 33.9 \\ TC GG/INS & 8.6 & 13.8 & 14.1 & 0.84 & 1.03 & 0.47 & 1.6 & 1.8 & 27.8 \\ TC GG/INS YVH & 6.2 & 13.5 & 4.1 & 0.41 & 0.87 & 0.11 & 1.0 & 1.0 & 8.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison between LC and TC configurations for the three segments.  
Figure 18: Comparison between LC and TC architectures in terms of position, velocity and attitude RMS errors (Segment 3).  
* In environments when standalone GNSS positioning is possible 70% of time or more, inclusion of GLONASS in a loose integration provides similar performance to the GPS-only tight coupling system. This suggests that a simpler system is possible without sacrificing navigation performance simply by adding GLONASS measurements. In turn, this has direct benefits to system development time and cost.
* In benign environments where GNSS solutions are available most of the time, including GLONASS provides little benefit since the system is already dominated by the GNSS errors since free-inertial navigation is unnecessary.
* Including the GNSS-derived azimuth aiding and the velocity/height pseudo-observations produces significant performance improvements regardless of the integration strategy. Furthermore, results are quite similar between the loose and tight integrations in this case, thus further blurring the benefits of the tight integration approach.  
Based on these results, inclusion of GLONASS observations does offer some significant advantages over GPS-only integrated systems. Although not quantified here, similar results would also be expected by including data from other GNSS as well (e.g., Galileo).
# Performance Optimal PI controller Tuning Based on Integrating Plus Time Delay Models
## 2 Preliminary Theory
### Lag-Dominant Systems  
Given a system approximated with a FOPTD model  
\[H_{p}(s)=\frac{K}{1+Ts}e^{-Ts}, \tag{11}\]  
where \(K\) is the process gain, \(\tau\) is the time delay and \(T\) is the time constant. The system in Equation (11) may be defined as lag-dominant when \(T>\tau\) which is the instance for numerous systems. It is known that, when \(T\gg\tau\) then Equation (11) may be approximated with an IPTD model (see [6; 7]). From Equation (11), we write,  
\[H_{p}(s)=\frac{K}{T}\frac{1}{s+\frac{1}{T}}e^{-Ts}. \tag{12}\]  
Figure 2: Consider a control feedback system where the plant model is described by the process model, \(H_{p}(s)\), PI controller, \(H_{c}(s)=K_{p}\frac{1+Ts}{Ts}\), and the disturbance model, \(H_{v}(s)\), where disturbance \(v\) at the input when, \(H_{v}(s)=H_{p}(s)\), and at the output when, \(H_{v}(s)=1\). Input \(u\), output \(y\) and reference \(r\).  
Hence, when the system is lag-dominant and \(T\) \"large\", we may approximate Equation (12) as an IPTD system (Equation (1)) where \(k=\frac{K}{T}\) is the gain velocity (slope) and \(\tau\) the time delay.