esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Concise Robust Path-Following Control with RRR
### Control Design
#### 3.2.3 Control Parameters Determining  
To guarantee the robust performance of the aforementioned control laws, a novel method to determine the design parameters is developed in this section. By virtue of the first-order CGSA [11; 26; 27], a simple linear robust controller \(v\) is obtained as following.  
\[\frac{1}{T_{L}s+1}=\frac{Gv}{1+Gv} \tag{33}\]  
\[v=\frac{1}{GT_{L}s} \tag{34}\]  
where \(1/T_{L}\) stands for the natural frequency of the control loop. For course control loop, it should be smaller than the interference band, i.e., the frequency band of wave 0.3-1.25 rad/s. For RRR control loop, \(1/T_{L}\) is recommended to be the natural frequency of ship roll motion, which can be estimated by empirical formula.  
Obviously, the transfer function models in Equations (5) and (6) are strictly rational proper fraction function, with a standard form as Equation (35). It is noteworthy that engineering control objects can usually be transformed into the form as Equation (35) by model reduction or Bode plots approximation. Then, a typical PID controller is obtained by substituting Equation (35) into Equation (34).  
\[G(s)=\frac{b}{a_{2}s^{2}+a_{1}s+a_{0}} \tag{35}\]where \(a_{0},a_{1},a_{2}\) and \(b\) are constant coefficients.  
\[v=\frac{a_{2}s}{bT_{L}}+\frac{a_{1}}{bT_{L}}+\frac{a_{0}}{bT_{L}s}=K_{d}s+K_{p}+K_ {i}/s \tag{36}\]  
Based on Theorems 1 and 2, the course and RRR control loops are uniformly asymptotic stable at set course \(\psi_{d}\) and roll angle \(\phi_{d}\) respectively. That is to say, the steady-state errors satisfy \(\underset{t\rightarrow\infty}{\lim}e_{c}(t)=0\) and \(\underset{t\rightarrow\infty}{\lim}e_{r}(t)=0\). Consequently, the integral control parameter \(K_{i}\) can be ignored. Then, Equation (36) can be rewritten as Equation (37).  
\[v=\frac{a_{2}s}{bT_{L}}+\frac{a_{1}}{bT_{L}} \tag{37}\]  
Defining \(1/T_{Lc},\ 1/T_{Lr}\) as the natural frequency of course and RRR loops, respectively, and substituting the parameters of Equations (5) and (6) into (37), one can get  
\[v_{c}=\frac{1}{KT_{Lc}}+\frac{T}{KT_{Lc}}s \tag{38}\]  
\[v_{r}=\frac{2\xi}{K_{\phi}\omega_{\phi}T_{Lr}}+\frac{s}{K_{\phi}\omega_{\phi} ^{2}T_{Lr}} \tag{39}\]  
Comparing Equations (38) and (39) with Equations (21) and (32), one can obtain the design parameters by solving the equation sets as follows  
\[\left\{\begin{array}{c}1+k_{c1}k_{c2}=\frac{1}{KT_{Lc}}\\ k_{c1}+k_{c2}=\frac{T}{KT_{Lc}}\end{array}\right. \tag{40}\]  
\[\left\{\begin{array}{c}1+k_{r1}k_{r2}=\frac{2\xi}{K_{\phi}\omega_{\phi}T_{Lr }}\\ k_{r1}+k_{r2}=\frac{1}{K_{\phi}\omega_{\phi}^{2}T_{Lr}}\end{array}\right. \tag{41}\]  
By virtue of ship maneuvering empirical knowledge and the first-order CGSA, the design parameters \(k_{c1},k_{c2},k_{r1},k_{r2}\) can be exactly determined in accordance with system parameters \(K,T,K_{\phi},\xi,\omega_{\phi}\), \(T_{Lc},T_{Lr}\), which have clear physical significance and domains. However, the optimal performances in different loading status or sea conditions cannot be guaranteed.
## 4 Review of Mask-Face Detection Methodologies
### Three-Class Mask-Face Detection Methodologies
#### 4.2.2 Data Balancing-Based Approaches  
Due to the imbalanced problem in the existing dataset, [23; 61; 62; 65] established datasets of three balanced categories by making more annotations for incorrect manually. More specifically, in [65], by performing enhanced data augmentation techniques, the training dataset represented an equal proportion of the three categories. In addition, their model was more robust in real scenarios owing to data augmentation. Their Faster R-CNN Inception ResNet V2 model finally achieved an accuracy of 99.8%.  
[PERSON] et al. [61] also customized their dataset with equal amounts of data in three classes before designing their mask-face detection model. This dataset avoided the situation where three classes of detection results were quite different due to the imbalance of training data. First, they used the face recognition algorithm in OpenCV to locate and crop the faces in the images, and the obtained pictures passed through the lightweight network NASNetMobile [64] and the deep-structured ResNet50 [28], respectively. After comparison, the model achieved an accuracy of 92%.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
CONIKS introduces several innovative features that enhance the security and usability of key transparency systems:  
Transparency Tree  
CONIKS employs a \"transparency tree,\" a variant of the Merkle tree, to efficiently verify the inclusion of key entries while preserving user privacy [11]. This structure allows users to audit their key bindings and verify the correctness of their peers' keys without exposing sensitive information to third parties. Key Revocation and Updates  
CONIKS incorporates mechanisms for key revocation and updates, ensuring the long-term security of the cryptographic system [12]. Users can periodically update their keys, and compromised keys can be efficiently revoked, providing resilience against long-term attacks. Scalability  
Designed for scalability, CONIKS supports large-scale deployments in modern messaging systems [13]. Its efficient key verification protocols and privacy-preserving features make it a practical solution for real-world applications, addressing the limitations of existing key transparency solutions. CONIKS represents a significant advancement in key transparency, offering a robust and scalable framework for secure key management in end-to-end-encrypted communication systems [14]. Its unique contributions to privacy, scalability, and usability make it a compelling choice for developers and organizations who are seeking to enhance the security of their cryptographic systems.
## 4 Routing Strategies
### MCL Path Selection Strategy  
This strategy is based on the idea that the more a certain link is included in the chosen paths for source-destination pairs, the highest the blocking probability will be. This situation is represented on the small network of Figure 9(a) where, considering the paths between nodes 0\(\rightarrow\)3, 1\(\rightarrow\)5, 2\(\rightarrow\)3 and 2\(\rightarrow\)5, the SP algorithm can bring excessive load to the link 1-3 leaving other links underutilized. Therefore, paths for source-destination pairs should be selected with the objective of minimizing the blocking probability of the link with highest expected contention value, denoted by \(\zeta_{MAX}\). This is achieved by the following ILP optimization problem:  
\[\text{Minimize }\zeta_{MAX} \tag{1}\]  
Subject to  
\[\sum_{v\in\mathcal{V}_{s,d}}\sigma^{v}=1,\;\forall s,d\in\mathcal{N} \tag{2}\] \[\sum_{s,d}\sum_{v\in\mathcal{V}_{s,d}}\sigma^{v}\times p_{l}^{v} \times t_{s,d}\leq\zeta_{MAX},\;\forall l\in\;\;\mathcal{L}\] (3) \[\sigma^{v}\in\{0,1\};\text{non-negative integer: }\zeta_{MAX} \tag{4}\]  
where \(\sigma^{v}\) is a binary variable that indicates if \(v\) is used to carry bursts from node \(s(v)\) to node \(d(v)\). Equation (2) is a constraint stating that one path must be found for each pair of nodes. Each path is selected from the corresponding set \(\mathcal{V}_{s,d}\) of available paths. Equation (3) states that the expected congestion at a link must not exceed \(\zeta_{MAX}\).  
With this algorithm, and for the same scenario presented, we can see in Figure 9(b) that the traffic is now more evenly distributed, increasing the network utilization to avoid the highly congested situation presented in Figure 9(a). It should be noticed, however, that on this attempt to spread the traffic throughout the network some paths can became longer (like the case of path 2\(\rightarrow\)3). This observation was the driving force to the next strategy.
## 4 Discussion
### Measuring the Eccentricity of the Earth's Orbit  
During observation of the sun, we obtained the T\({}_{\rm B}\) increments observed on 27 December 2019, 1 January 2020, 30 June 2020, and 2 July 2020, which are quite close to the perihelion and aphelion; the statistical results are shown in Table 5. From Equation (14) to Equation (17), the T\({}_{\rm B}\) increments observed on the perihelion and aphelion days could be used to calculate the values of \(e\), Q and R, which were 0.0169, 3.44% and 6.6%, respectively. These results were consistent with previous studies [25; 30]. We can see that the average eccentricity estimated is relatively in good agreement with the 0.0167 given in the literature. The average variation of the Earth-Sun distance is 3.44% throughout the year. The intensity of the incident solar radiation at the aphelion should be about 6.6% less than at the perihelion.  
Figure 8: The variation of the T\({}_{\rm B}\) increment as a function of time before the solar eclipse on 26 December 2019 and 21 June 2020 (red curve) as compared with that after the solar eclipse on 27 December 2019 and 30 June 2020 (blue curve). (**a**) 22.235 GHz, (**b**) 25.0 GHz, (**c**) 30.0 GHz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Average \(\Delta T_{\rm{sun}}\) (K)**} \\ \cline{2-4}
**Date** & **22.235 GHz** & **25.0 GHz** & **30.0 GHz** \\ \hline
27 December 2019 & 90.2 \(\pm\) 0.6 & 134.3 \(\pm\) 1.2 & 157.6 \(\pm\) 1.6 \\
1 January 2020 & 90.5 \(\pm\) 1.6 & 135.1 \(\pm\) 1.9 & 158.7 \(\pm\) 2.1 \\
30 June 2020 & 84.5 \(\pm\) 1.1 & 125.5 \(\pm\) 1.7 & 147.4 \(\pm\) 1.8 \\
2 July 2020 & 84.3 \(\pm\) 0.6 & 126.3 \(\pm\) 1.0 & 147.9 \(\pm\) 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average T\({}_{\rm B}\) increment during non-eclipse conditions.  
This method broadens the field usage of GMR, and it shows that the GMR can be used to measure the eccentricity of earth orbit accurately and even to monitor the variation of the solar radiation. The measurements of the solar T\({}_{\rm B}\) increment and eccentricity both depend on antenna errors, regarding its pointing towards the sun, and observation errors. Therefore, these effects have to be taken into account during observation and calibration. In order to reduce the observation error, the GMR underwent regular maintenance and the LN2 calibrations were performed twice a year.  
The antenna direction is an important factor of the observations, and it has to be calibrated. During the scanning observation, the maximum T\({}_{\rm B}\) increment can be received by the GMR when the antenna beam points to the center of the sun, and the biases of the antenna beam pointing between the peak T\({}_{\rm B}\) and the predicted sun position can be used to calibrate the antenna direction [17]. In addition, solar activity may be a factor influencing the observation results, an effect which we will study further though future long-term observation.
## 6 Conclusions and Future Perspectives
### Challenges  
#### 6.2.1 Interpolation-Induced Errors  
Interpolations are a commonly used preprocessing operation to prepare oceanographic data for DL model development. They are widely used to handle missing values, harmonize data from different sensors with varying spatial and temporal resolutions, generate training labels, and upsample low-resolution data. While these methods are efficient and effective, they inevitably introduce human-induced biases that can compromise data fidelity and degrade model performance. Firstly, interpolation is a mathematical approximation process that does not necessarily align with physical laws. For instance, in frontal zones where SST, SSS, and other ocean parameters change rapidly, linear interpolation may excessively smooth the data, obscuring critical physical transitions and preventing DL models from capturing essential dynamic processes. Besides, the reliability of interpolation is highly dependent on the quality, density, and spatial distribution of the original observations. For quality, if the original data contains systematic biases, sensor noise, or calibration errors, these issues will propagate through the interpolation process, amplifying errors in the generated data. Regarding density, in regions with sparse in situ observations or low satellite revisit frequency, interpolated outputs may generate unrealistic spatial patterns that cannot reflect real-world variations and mislead DL models to learn inaccurate spatial representations.
## 3 Results and Discussion
### Effects of Gas Components  
#### 3.3.1 Effects of O\({}_{2}\)  
As shown in Figure 10, the mercury removal efficiency of Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 in N\({}_{2}\) reached 73.4% at 120 \({}^{\circ}\)C. In this process, the lattice oxygen of the adsorbents played an important role. With O\({}_{2}\) concentration rising to 5%, the mercury removal efficiency showed a significant increase to 96.6%. This was because O\({}_{2}\) could compensate for the lattice oxygen consumed in the reaction, thus improving the outcome of Hg\({}^{0}\) capture [47].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**Concentration of the Oxygen**} & \multirow{2}{*}{**Co\({}^{3+}\)/Co\({}^{2+}\)**} & \multirow{2}{*}{**Fe\({}^{3+}\)/Fe\({}^{2+}\)**} \\ \cline{2-2} \cline{5-5}  & **O\({}_{\alpha}\)** & & & \\ \hline Fresh Co-ZSM-5 & - & - & 0.77 & - \\ Fresh Fe-ZSM-5 & - & - & - & 1.62 \\ Fresh Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 & 7.8\% & 92.2\% & 1.37 & 1.29 \\ Spent Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 & 5.9\% & 94.1\% & 0.84 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: XPS data of a series of samples.  
Figure 10: Effect of gas components on Hg\({}^{0}\) removal efficiency. T = 120 \({}^{\circ}\)C.  
Figure 9: XPS spectra of fresh Co-ZSM-5, Fe-ZSM-5, and fresh Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5: (a) Co 2p, (b) Fe 2p.
## 3 Research Ranges, Objects and Methods
### Research Methods
#### 3.3.3 Panel Tobit Regression Analysis  
To identify the driving factors for the adoption of green residences, this paper proposed the hypothesis that these factors are linearly related to the green residence development in cities and their driving forces are related to the star levels of green residences. This study constructed a short panel empirical model of the effects of the aforementioned economic factors, social environment, the real estate market, government measures, and climatic condition on the development of green residences with different star ratings in 42 cities.  
As an innovation, green residence is currently in the early stage of development [9], and 55% of the 1008 quantity samples of green residences selected in this paper had a value of 0. In this case, the OLS model cannot obtain a consistent estimation, whether using the entire sample or excluding the subsample after zero [42]. To address this problem, this paper treated the G\({}^{\prime}\)-score of green residences as censored data and adopted the panel Tobit model to construct the regression equation. If city \(i\) did not have a green residence in year \(t\), this study specified it as the left censored value, and the baseline structure for a left censored Tobit model with panel data can be described as follows:  
\[y_{it}^{*}=\alpha_{i}+x^{\prime}{}_{it}\beta+d_{it}\gamma+w_{j}+ \varepsilon_{it} \tag{7}\] \[y_{it}=\begin{cases}y_{it}^{*}&if\ y_{it}^{*}>0\\ 0&if\ y_{it}^{*}\leq 0\end{cases}\]  
where \(y_{it}\) represents the G\({}^{\prime}\)-score of green residences in city \(i\) in year \(t\); \(\alpha_{i}\) is the intercept term, reflecting the heterogeneity of the individual differences in cities; \(x_{it}\) is a independent variable including economic, social, and real estate market. To alleviate the influence of heteroscedasticity, autocorrelation, and multicollinearity of explanatory variables, this paper adopted logarithmic processing for absolute value variables. \(d_{it}\) is a dummy variable representing government measures; \(\beta\) and \(\gamma\) are vectors of estimated coefficients; \(w_{j}\) is the climate region's fixed effect; \(\varepsilon_{it}\) is the robust standard error term, \(\varepsilon_{it}\sim N\big{(}0,\sigma_{\varepsilon}^{2}\big{)}\).
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
There are several different feature extraction techniques utilised during the creation of deepfake detection techniques and tools, Table 4 summarises the selected paper's techniques with a description. \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Citation** & **Feature Extraction Technique** & **Description of the Feature Extraction Technique** \\ \hline [PERSON] et al. [31] & Hybrid CNN-LSTM with PSO algorithm & The CNN and LSTM are pre-trained on a dataset to extract facial landmarks before the PSO algorithm finetunes the weightings and biases of the CNN and LSTM architecture. \\ \hline [PERSON] and [PERSON] [32] & Pre-trained CNN (ResNeXt-50) with LSTM & Utilises a ResNeXt-50 RNN model for feature extraction from video frames with an LSTM layer with a 2048 input vector shape and 0.4 ReLU dropout rate to capture temporal discrepancies between frames. \\ \hline [PERSON] and [PERSON] [33] & Hybrid CNN-LSTM architecture & Utilises three convolutional layers with LSTM with max pooling and the ReLU activation function. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Various feature extraction techniques used in the selected publications.
## 3 Results and Discussion
### Determination of Particle Distribution  
For post-extraction samples containing significant amounts of dry sediment, DLS particle size studies were conducted (Table 5). The homogeneity of the distribution, the surface area of the particles, and the Dx (10, 50, 90) values were established (Dx represents the appropriate threshold value, where 10%, 50%, and 90% of particles are less than or equal to this value). These studies, like sieve size measurements from optical microscopy and SEM, confirmed the effective separation of the material into individual fractions. Depending on the source of the samples, there are differences in their specific surface area and homogeneity; for example, the CZT2 sample from snow deposits is characterized by less homogeneity and also the largest specific surface area of the 25-40 \(\upmu\)m fraction.  
The particle size distribution obtained by extraction with isopropanol and dichloromethane is presented in Figure 7. The size of the majority of samples falls within the range of 0.01-0.5 \(\upmu\)m for the isopropanol fraction and 0.01-0.3 \(\upmu\)m for the dichloromethane fraction. Depending on the source of the material, particles of varying sizes are washed out. The extraction of sands with isopropanol (SZY, UMU, BIS) resulted in the acquisition of particles with sizes predominantly between 0.04 and 0.05 \(\upmu\)m. However, when the starting material was snow, the sizes were CZT2 0.055 \(\upmu\)m, CZT1 1.0 \(\upmu\)m, and DK11 0.1 \(\upmu\)m, respectively. Subsequent washing with dichloromethane leads to the extraction of smaller particles, with their sizes being more similar to each other.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Fraction**} & \multirow{2}{*}{**Homogenity**} & \multirow{2}{*}{**Surface Area**} & \multirow{2}{*}{**Dx (10)**} & \multirow{2}{*}{**Dx (50)**} & \multirow{2}{*}{**Dx (90)**} \\  & & & **[m\({}^{2}\)/kg]** & & [\(\upmu\)m] & [\(\upmu\)m] \\ \hline \multirow{4}{*}{SZY} & \textless{}25 \(\upmu\)m & 0.901 & 1329 & 1.93 & 7.73 & 24.0 \\ \cline{2-7}  & 25-40 \(\upmu\)m & 0.765 & 553.2 & 4.01 & 42.3 & 109 \\ \cline{2-7}  & 40–100 \(\upmu\)m & 0.925 & 511.7 & 4.15 & 58.2 & 165 \\ \hline \multirow{4}{*}{CZT2} & \textless{}25 \(\upmu\)m & 0.871 & 1409 & 1.91 & 6.93 & 20.4 \\ \cline{2-7}  & 25–40 \(\upmu\)m & 1.569 & 909.4 & 2.60 & 15.54 & 83.5 \\ \cline{2-7}  & 40–100 \(\upmu\)m & 3.007 & 144.1 & 62.4 & 229 & 2230 \\ \hline \multirow{4}{*}{UMU} & \textless{}25 \(\upmu\)m & 0.733 & 1031 & 2.54 & 10.8 & 27.9 \\ \cline{2-7}  & 25–40 \(\upmu\)m & 0.567 & 321.6 & 10.6 & 48.4 & 105 \\ \cline{2-7}  & 40–100 \(\upmu\)m & 0.957 & 489.8 & 3.77 & 59 & 168 \\ \hline \multirow{4}{*}{BIS} & \textless{}25 \(\upmu\)m & 0.676 & 998 & 2.68 & 10.9 & 26.4 \\ \cline{2-7}  & 25–40 \(\upmu\)m & 0.702 & 367.8 & 8.4 & 46.3 & 113 \\ \cline{1-1} \cline{2-7}  & 40–100 \(\upmu\)m & 0.554 & 182.6 & 22.6 & 120 & 251 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sediment particle size distribution based on DLS.  
Figure 7: Particle size distribution in solutions after extraction with isopropanol and dichloromethane.
## 4 Discussion on Regional Dust Events
### Dust Outbreaks over China
The dust plumes are advected up to the Pacific Ocean; their transport occurs mainly in the free troposphere as shown in Figure 29. The role of the topography (Japan and North Korean mountain ranges) is certainly of major importance to lift the dust up to the top of the boundary layer and drive their injection within the free troposphere. The contribution of the topography on air pollutant dispersion in this region has already been identified in previous studies [144]. Other processes are dominant in the vertical redistribution of pollution in the mid latitudes: frontal activity and deep convection are usually identified, for instance the frontal systems, especially the so-called warm conveyor belts (WCBs) associated with extratropical cyclones. The role of the WCB is considered as the dominant advective transport mechanism for boundary-layer ventilation and transport of air pollutants into the free troposphere [139; 145] and confirmed in a study in Asia for a specific dust event [146]. Our simulation reveals very fine structures of the desert dust conveyed by the WCB even if these processes are large-scale events (Figure 29). In the AeroCom Project [104], simulations with global models run at coarse resolutions show how the boundary-layer mixing plays a dominant role for sea salt and mineral dust, which are emitted only from the surface, dry deposition and below-cloud scavenging were identified as important processes for the vertical distribution of mineral dust. On 19 March 15:00 UTC, low dust concentrations integrated over the boundary layer can be observed in the center of the cyclonic structure in relation with the sharp decrease of the marine boundary layer. During the studied period, a series of low-pressure systems over the Pacific lifts the dust plumes up to the free troposphere. This transport of dust in the free troposphere far away over the Pacific Ocean is confirmed by the CALIPSO data shown in Figure S15 in Supplementary Materials with a dust plume located between 4 and 6 km in altitude very well reproduced by CHIMERE. Figure 28: Arrival of the second dust episode over Beijing. Longitudinal cross section of total dust concentrations fields (Altitude versus longitude) at 39.9” N latitude from 110” E to 120” E passing over Beijing (B) in blue letters) from 16 March 18.00 UTC to 17 March 18.00 UTC. The dotted line represents the height of the boundary layer, the dashed lines represent the fraction of fine dust in percent. The full sequence of dust transport for the three episodes is depicted in Figures S16 and S17 in Supplementary Materials.