esa-sceva/satcom-chunk-collection huggingface dataset:
# An LLC Converter with Capacitive Insulation
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline  & **[18]** & **[17]** & **Conventional LLC** & **Proposed Method** \\ \hline \multirow{2}{*}{Rated power} & 36 W & 85 W & 150 W & 120 W \\  & (12 V, 3 A) & (165 V, 0.51 A) & (12 V, 12.5 A) & (12 V, 10 A) \\ \hline Efficiency at full load & 94.5\% & 85.5\% & 94.1\% & 94.3\% \\ \hline Efficiency at half load & 94.5\% & 84\% & 94.3\% & 93.7\% \\ \hline Switching frequency & \multirow{2}{*}{1.4 MHz} & \multirow{2}{*}{65 kHz} & \multirow{2}{*}{-100 kHz} & \multirow{2}{*}{-240 kHz} \\ at full load & & & & \\ \hline Active switches & GaN FET & Si-MOSFET & Si-MOSFET & Si-MOSFET \\ \hline Power density & 18.3 W/cm\({}^{3}\) & Not available & 1.81 W/cm\({}^{3}\) & 2.71 W/cm\({}^{3}\) \\ \hline Touch current test & \multirow{2}{*}{Not available} & \multirow{2}{*}{734 \(\upmu\)A} & \multirow{2}{*}{10 \(\upmu\)A} & \multirow{2}{*}{80 \(\upmu\)A} \\ (264 Vac, 60 Hz) & & & & \\ \hline High pot test & \multirow{2}{*}{Fail (3 kVac, 60 Hz, 60 s)} & \multirow{2}{*}{Not available} & \multirow{2}{*}{Pass} & \multirow{2}{*}{Pass} \\ (3 kVac, 60 Hz, 60 s) & & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed and conventional methods. Figure 16: Measured curves of switching frequency.
## 5 Performance Evaluation and Analysis
### Key Performance Indicators (KPIs)  
The optimization goal of this study is clearly defined in terms of key performance indicators (KPIs), which include the probability of resource collision (PRC), the packet reception ratio (PRR), and the radio resource utilization (RRU). These KPIs directly measure the performance improvements brought about by the proposed SEAC technique.  
All the results in this study demonstrate that the Shared Experience Actor-Critic (SEAC) method with \(N\) steps (\(N=10\)) consistently outperforms traditional methods like DS and SB-SPS. These trends are consistent with findings from other studies [2], where SEAC has shown improvements in reducing the PRC and enhancing the PRR. However, unlike the method in [2], which focuses solely on immediate rewards, our approach uses \(N\) steps to optimize both short-term and long-term rewards. This allows for more informed decision making and better resource management, leading to superior performance, particularly in high-density traffic scenarios.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 4 Experimental Test and Security Analysis
### Encryption Speed Test  
In the proposed algorithm, there are \(M\) loops for row-by-row encryption and \(N\) loops for column-by-column encryption, which are two parallel loops, so the total number of loops is \((M+N)\) and the time complexity of the algorithm is roughly expressed as O\((M+N)\). The common image encryption algorithm takes a single pixel as the processing unit, so the number of loops of the algorithm is equal to the number of pixels, that is, \((M\times N)\) times, so the time complexity is O\((M\times N)\). The average time (average of 100 encryption experiments) spent by the proposed algorithm in one round of encryption is shown in the last column of Table 7 (unit: millisecond). Compared with other algorithms reported in the literature, this algorithm has obvious advantages in encryption speed.
# Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
## 3 European Electronic Authentication Regulation
### Interoperability among EU Member States
A step in this direction is marked by Regulation (EU) 2019/1157, which mandates that ID cards are harmonized across the EEA, with a new common identity card model replacing the various formats. This harmonization, along with the progressive adoption of the eIDAS Regulation by Member States, will allow for the maximum scalability of the proposed solution across Europe. According to Table 1, MSs and Identity Providers are increasingly shifting away from traditional card-based eID schemes, which require card readers, toward mobile-based solutions, which are more practical for end users. The most recent schemes, where more eID means involve smartphones, clearly show this trend. For greater security against attacks, these eID methods can make use of software programs as well as hardware-embedded elements like SIM cards, Secure Element, and Trusted Execution Environment. However, technology and the specific mobile phone being utilized have a significant impact on how secure such an embedded device is.
# Post-Disaster Recovery Monitoring with Google Earth Engine
## 2 Materials and Methods
### Methods
#### 2.3.2 Image Classification  
The final stacked/combined images for each time step consisted of seven Landsat bands (i.e., Blue, Green, Red, NIR, SWIR1, SWIR2, and TIR) and the computed indices (i.e., NDVI, EVI, SATVI, NDTI, and LSWI). The combined images were stored under the \"Asset\" tab of the GEE code editor platform to be later used for training and testing ROI selection/extraction.  
We collected regions of interest (ROIs)/training areas from the images, using the tools within GEE in a way that each class had at least 40 polygons. These selections were based on field verifications and visual interpretations from high-resolution Google Earth Pro historical images and high-resolution satellite images acquired by different sensors (i.e., WorldView1-3, GeoEye1, Pleiades), and Landsat true- and false-color images to obtain final robust training and testing datasets. From the collected ROIs, 70% were randomly selected for training the classifier method and the rest were used for testing purposes. We used Random Forest, as an ensemble machine learning algorithm, to classify the images based on the collected training samples. Random Forest has been widely used in RS image processing for different applications [59], including land cover and land use classification [60; 61], landslide detection [62], and hyperspectral image classification [63], due to providing efficient and accurate results. It is also one of the machine learning-based methods available and ready to use in GEE. This approach has also been used by other researchers and its ability to produce accurate classification results has been demonstrated [64; 65; 66; 67]. In addition, Random Forest was selected as the best classifier among several advanced algorithms, including SVM and neural networks, for classifying hundreds of different real-world datasets [68].  
Random Forest grows multiple decision trees on randomly selected training subsets. In addition, it takes the advantage of the bootstrapping approach to construct the decision trees from training samples and input variables at every node. The constructed numerous decision trees then go through the majority voting mechanism to obtain the final classification result. This strategy contributes to overcoming the limitations of single tree-based classifiers such as overfitting, and makes it less sensitive to noise. In the current study we executed Random Forest with 100 decision trees per class, a 0.5 fraction number to bag per tree, and 10 for the minimum size of a terminal node.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 5 Numerical Experiments with Practical Sound Fields
### Sound Propagation Problem in a Rectangular Room Including Acoustic Diffusers  
Figure 8a shows the analyzed rectangular room of 10.2 m \(\times\) 10.8 m surrounded by rigid boundaries with a source point and 27 receivers. The acoustic diffuser, which includes eight periods with a period of 1.2 m, is installed periodically in front of a wall with air spaces. Figure 8b presents details of diffusers. This room cannot be modeled with staircase approximation unless one uses very small rectangular elements because the diffusers comprise rigid reflectors of 0.25 m thickness inclined at various angles against the back wall. However, the proposed explicit TD-FEM can fit the inclined boundaries with fewer elements because of discretization using irregularly shaped FEs. We discretized this room using the irregular FEs with 0.017 m maximum edge length, which corresponds to 6.74 PPW at 3 kHz. The discretization result around the diffusers is shown in Figure 8c. The degrees of freedom (DOF) in this problem are 552,054. As a sound source signal, the Gaussian pulse with the upper-limit frequency of 3 kHz was used. The sound pressure waveforms were calculated at 27 receivers up to 0.1 s with the time interval of 1/120,000 s, which was determined from Equation (38) with the minimum edge length of used mesh. For the standard implicit TD-FEM, the same mesh and time interval were applied. Regarding the measurement accuracy, we use the relative error in sound pressure at each receiver between the reference solution and numerical solution as  
\[e_{\text{relative}}=\sqrt{\frac{1}{N_{\text{step}}}\frac{\sum_{i=1}^{N_{ \text{step}}}(p_{\text{reference}}(i)-p_{\text{FEM}}(i))^{2}}{\sum_{i=1}^{N_{ \text{step}}}(p_{\text{reference}}(i))^{2}}}, \tag{49}\]  
where \(N_{\text{step}}\) represents the total number of computed time steps. Also, \(p_{\text{reference}}(i)\) and \(p_{\text{FEM}}(i)\) respectively denote the reference solution and numerical sound pressure at \(i\)-th time step. Regarding the calculation of reference solution, sufficiently fine mesh of 15.3 PPW at 3 kHz was used.
## 1 Introduction
As regards the watermarking model, [PERSON] gave the definitions of the watermarking schemes and provided an abstract model of watermark embedding, extraction, and robustness [30]. Regarding the embedding strategy, the quantization modulation has theoretically proved its superiority in robustness [31]. In terms of watermark capacity analysis, [PERSON] provided the closed formula for calculating the watermark capacities of the private Laplacian watermarking system [32]. According to the robustness analysis and computation, [PERSON] proposed the quantitative relationship between robustness and destructed watermark units [33]. It should be noted that there are still two drawbacks to realize the computation of robustness under image cropping attack: (1) Quantitative models regarding robustness and image cropping attacks have not been comprehensively established. Some analyses of watermarking algorithms, in fact, focus on the watermarking model [30], embedding strategy [31] or watermarking capacity [32], rather than the robustness. The work accomplished by [PERSON] simplified the attack parameter as the ruined watermark bits count [33], which bypassed the analysis of attacking properties and attacking strength; (2) The evaluation index of robustness is not universal, particularly in terms of comparisons. The commonly used indexes, including \(NC\) or \(BER\), only represent the robustness in one single experiment. In this sense, these indexes vary according to the experimental data and conditions. Consequently, each robustness result from the research cannot be literally and directly compared. Unlike the intentions of previous works, this study proposes a computation model to directly analyze and calculate the watermarking algorithmic robustness under image cropping attack. The computation model involves analysis principles, a robustness evaluation index and a computation method. The analysis principles are concluded from well-designed watermarking algorithms and a robustness index is defined based on probability. According to the proposed principles and index, a computation method is presented, based on the approaches of combinatorial mathematics and probability theory. In addition, the influences of different factors, related to the watermarking algorithm on robustness are further explored with the object of promoting the design of watermarking algorithms. The rest of this paper is organized as follows: Section 2 introduces the methodology of robustness computation model. In Sections 3 and 4, the experimental results and discussions are given respectively. Finally, conclusions are given in Section 5.
## 3 Protocol Specification  
### Network Model  
In this paper, we employ an elementary model for describing WSNs as undirected and weighted graphs. As such, let \(G=(V,E,W)\) be a simple weighted graph, in which \(V\) is the set of nodes denoted by positive integer numbers from one to \(|V|\), representing the sensor nodes and the sink. The set \(E\) is composed of links, here expressed by \(e_{i,j}\), where \(i,j\in V\). For each \(e_{i,j}\in E\), there is a \(w_{i,j}\in W\) attached, which denotes the energy-related function.  
Under this network model, a source node periodically senses and collects the data from the surroundings, then sends the data to the next hop until the data reach the sink node. The goal is to find an energy-efficient route such that the energy consumption is balanced and the lifetime of the network prolonged. Moreover, we assume that:  
1. All nodes are isomorphic;
2. Links are symmetric. If the target's transmitted power is known, nodes can calculate the approximate distance of senders according to the Received Signal Strength Indication (RSSI);
3. Depending on the recipient's distance, the node can adjust its transmit power.
4. We use a first radio model as described in [3].  
Based on these assumptions, the transmitter power level can be adjusted to use the minimum energy required to reach the intended next hop receiver, then the energy consumption rate per unit information transmission depends on the choice of the next hope node, i.e., the routing decision.
## 3 The Derivation of CH\({}_{4}\) Diffusivity and Permeability by MIP  
### CH\({}_{4}\) Diffusivity  
Gas diffusion in coal can be described as Fick's diffusion [35], which can be expressed as:  
\[F_{g}=-D_{e}\frac{\partial C}{\partial x} \tag{6}\]  
Figure 1: Experimental devices for CH\({}_{4}\) adsorption and diffusion.  
where \(F_{g}\) denotes the diffusive gas flux in the porous medium [M\(\cdot\)L\({}^{-2}\)\(\cdot\)T\({}^{-1}\)]; \(D_{e}\) denotes the effective gas diffusion coefficient [L\({}^{2}\)\(\cdot\)T\({}^{-1}\)]; \(C\) is gas concentration in the pore [M\(\cdot\)L\({}^{-3}\)]; and \(x\) is distance [L].  
Gas diffusivity (\(D^{\prime}\)) is defined as the ratio of the effective gas diffusion coefficient (\(D_{e}\)) to the gas diffusion coefficient in air (\(D_{a}\)), which can be expressed as an exponential function of porosity (analogous to [PERSON]'s law) [36]:  
\[\frac{D_{e}}{D_{a}}=D^{\prime}=\varphi_{a}^{m} \tag{7}\]  
where \(\varphi_{a}\) is gas measured porosity and \(m\) is cementation factor. In this work, the cylindrical shape for coal pores is assumed that the cylindrical diffusion paths prevail in the coals. The effective diffusion coefficient is expressed as [24; 37; 38; 39]:  
\[D_{e}=\frac{\varphi_{a}}{\tau}D \tag{8}\]  
where \(D_{e}\) is effective diffusion coefficient (m\({}^{2}\)/s), \(D\) is the gas diffusion coefficient (m\({}^{2}\)/s); \(\tau\) denotes the tortuosity factor [38]:  
\[\tau=0.223-1.13\varphi_{a} \tag{9}\]  
(applicable for \(0.05\leq\varphi_{a}\leq 0.95\)).  
Then, the Bosanquet relation was introduced to get gas diffusion coefficient [40]:  
\[\frac{1}{D}=\frac{1}{D_{a}}+\frac{1}{D_{KA}} \tag{10}\]  
Here \(D\) is gas diffusion coefficient [L\({}^{2}\)\(\cdot\)T\({}^{-1}\)], \(D_{a}\) is gas diffusion coefficient in air [L\({}^{2}\)\(\cdot\)T\({}^{-1}\)] and \(D_{KA}\) is Knudsen diffusion coefficient [L\({}^{2}\)\(\cdot\)T\({}^{-1}\)]. For \(D_{a}\), combined/effective diffusion coefficient of CH\({}_{4}\) in binary gases (CH\({}_{4}\) and Air) can be estimated from the Fuller theory [41],  
\[D_{a}=\frac{0.0101T^{1.75}\sqrt{\frac{1}{M_{CH_{4}}}+\frac{1}{M_{Air}}}}{P \Big{[}\big{(}\Sigma v_{CH_{4}}\big{)}^{1/3}+\big{(}\Sigma v_{Air}\big{)}^{1/3 }\Big{]}^{2}} \tag{11}\]  
where \(T\) is absolute temperature (K); \(M_{CH_{4}}\) is molecular weight of CH\({}_{4}\) (g/mol); \(M_{Air}\) is molecular weight of Air (g/mol); \(P\) denotes the pressure of CH\({}_{4}\) (Pa); \(\Sigma v_{CH_{4}}\) and \(\Sigma v_{Air}\) respectively represent molecular diffusion volume of CH\({}_{4}\) and Air (cm\({}^{3}\)/mol).  
And we can obtain the Knudsen diffusion coefficient (\(D_{KA}\)) with the parameter of the temperature (\(T\)), molecular weight of gas (\(M_{CH4}\)) and average pore size (\(d_{a}\)) of the porous medium, according to the following equation [23; 42; 43]:  
\[D_{KA}=48.5d_{a}\sqrt{\frac{T}{M}} \tag{12}\]  
\(T\) is temperature (K); M is gas weight (g/mol); and \(d_{a}\) is average pore diameter (m) from MIP:  
\[d_{a}=\frac{4V}{A}\times 10^{-6} \tag{13}\]  
\(V\) is total intrusion volume (mL/g); \(A\) is total pore area (m\({}^{2}\)/g).
# Experimental Study on the Desalination of Waste Leachate Using the Combined Freezing Method
## 3 Results and Discussion
### Comparison of the Different Desalination Methods  
Figure 12 shows the relationship between the ice production rate and the salt rejection under different methods, and the most intuitive phenomenon in the figure is that the salt rejection decreases with the increase in the ice production rate. It is found that the above methods have some variability in the desalination effect, and this variability is based on the similarities and differences in the desalination principles of the different methods, which are mainly concentrated in the stage of high ice production rates [34]. When the ice production rate is 40%, the salt rejection in descending order is the FGM > FCBM > FCM > FGBM. Overall, however, the fluctuation of the FCBM under different ice production rates  
Figure 11: Quality relationship between the ice crystal melt effluent and discharged concentrate.  
Figure 10: Desalination parameters of the FCBM for waste leachate.  
is relatively small, and its stability is higher. Compared with the FGM, the desalination process has a shorter duration. Although the FCM significantly diminishes the desalination duration, the salt rejection remains inferior to the FCBM at varying ice production rates. Therefore, the advantage of the FCBM lies in its ability to shorten the desalination process while improving the desalination efficiency.  
The FGM, FCM, and FBM are all methods that utilize cooling and freezing processes to achieve the icing of water molecules and separation of salt, differing only in operating principles and application scenarios, and their actual application requires a comprehensive consideration of factors such as the treatment scale, energy consumption, equipment cost, technical difficulty, and salt discharge to make a choice.
# Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities +
## 4 Tracking Radar Design
### Radar Block Diagram and Operation
#### 4.3.1 Transmitter  
A transmitter (_Tx_) generates the radar probing signals. Its subsytems are an RF generator, a power amplifier, and a master oscillator (PSC MO).  
A low-noise RF generator produces a CW triangular LFM signal and a trigger pulse (pulse), indicating the start of each slope of the triangular FM modulating signal. The parameters of the LFM signal are set by a main processing unit (MPU), which configures the RF generator through SCPI commands. To achieve a very high frequency stability, the RF  
Figure 5: General diagram of Cheia SST radar.  
generator is externally driven by the very low-noise, high-stability reference signal (fREF100) generated by a positioning-satellite-constellation (PSC)-controlled master oscillator (MO).  
The RF generator's CW LFM output signal is amplified to the required power level by a linear solid-state power amplifier (SSPA) and then applied to the LHCP port of a 6 GHz orthomode transducer (OMT) and radiated as left-hand circular polarization waves through the \(Tx\) antenna. To achieve a high linearity and a low additive phase noise, the SSPA is used at \(-3\) dB from saturation power.  
A part of the RF generator output signal is extracted in order to be used as a local oscillator (LO) signal in both receivers (main receiver and tracking receiver). The LO is extracted through a divider.  
The pulse trigger pulse starts ADC acquisition in both the main signal processor (SP) and the tracking signal processor (TSP), belonging to the receiver.
## 2 Materials and Methods
### Data
#### 2.2.10 LT_SIF  
The solar-induced chlorophyll fluorescence (SIF) data called LT_SIF from [70] were used in this study. The LT_SIF was developed to correct the temporal inconsistencies in the far-red SIF data from various satellites (GOME, SCIAMACHY, and GOME-2) using the time series of SIF data from the Sahara Desert as the benchmark. The three satellite sensors' distinct time series of SIF data were then combined to create LT_SIF, which stands for Long-Term Consistent Global SIF dataset and spans the period from July 1995 to December 2018 (termed as LT_SIF). We re-gridded the LT_SIF into 8\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) and only used the available data from 1995 to 2015 to calculate the correlation with SPEI.
# A Self-Color-Changing Film with Periodic Nanostructure for Anti-Counterfeit Application
## 2 Concept and Structure Design
### The Domain Setup  
This section describes the domain setup for the parametric studies of the proposed film. The two setup principles to achieve the new anti-counterfeiting properties are as follows.  
First, the setup method must be able to model objects that are smaller than the minimum wavelength of VIS (<400 nm) to allow us to control the optical properties of nanoparticles, such as color. The finite element analysis (FEA) method is suitable for this study [44]. FEA is a well-known technique with high potential for analyzing complex nano-scale structures [45; 46]. FEA has two essential setup processes: defining boundary conditions and mesh sizes.  
Second, the boundary conditions to be used must be characterized by a frequency-selective surface (FSS) behavior that allows the transmission or the rejection of wave to cause the self-color-changing of the film. The setup in this paper uses periodic boundary conditions on the side to create an array with infinite extent. In this study, we use COMSOL Multiphysics as a simulation tool.  
Our security film relies on FSS behavior for achieving spontaneous color change. Such behavior is dependent on the optical properties of the material. Therefore, the material properties are crucial to our studies. The boundary condition of the metal in FEA is usually defined with a perfect electric conductor (PEC) [45]. However, this paper will use the actual optical property of aluminum and PET obtained from [47; 48], respectively, instead of PEC, for the most realistic results.  
In Figure 1, the illumination of VIS starts from the upper perfectly matched layer (PML), traveling through the air layer down to the film, with an input power of 1 watt. Aluminum nanocubes are surrounded by PET and enclosed by an air layer. The air layer is sealed on the top and bottom with a PML boundary condition for absorbing all the reflected waves, assuming that the top and bottom regions of the film are infinite in extent [45]. The PML layers have a height of 19.25 \(\upmu\)m. All the domains are meshed by tetrahedral meshes with a maximum size of no more than 0.1 times the shortest VIS wavelength (or in this case 0.1 \(\times\) 400 nm). In our study, the largest mesh size is no more than 40 nm.  
In this research, parametric studies are as follows:  
1. Width (W) of the nanocube in the range of 10 to 200 nm;
2. Length (L) of the nanocube in the range of 10 to 200 nm;
3. Height (H) of the nanocube in the range of 10 to 200 nm.  
The parametric studies aim to find the best size of the nanocube to create an LPF.