esa-sceva/satcom-chunk-collection huggingface dataset:
# Zirconium-Containing Metal-Organic Frameworks (MOFs) as Catalysts for Biomass Conversion
## 7 Conversion of Other Biomass-Derived Compounds
### Reduction of Carbonyl Compounds: Synthesis of Hydroxysteroids
Therefore, the use of A4 as a reaction substrate allowed us to evaluate the chemo- and regioselectivity of the MPV process using MOF-808 as a catalyst. To our delight, we observed the following: (i) the C=C bond in position 4 remains unaffected (100% chemoselectivity); (ii) no traces of monohydroylated products in position 3 were detected (100% regioselectivity); (iii) reduction of the 17-keto group produces almost exclusively epistestosterone (ET), with a diastereoselectivity > 95%; and (iv) dihydroxylated products were only detected at high A4 conversions and long reaction times. Nevertheless, after optimization of the reaction conditions, we were still able to attain an A4 conversion of up to 70-80% while maintaining an excellent selectivity higher than 80% to the monohydroylated product in position 17, forming ET almost exclusively. In order to understand the excellent results obtained for the MPV reduction of 17-ketosteroids to the challenging 17\(\alpha\)-OH derivatives over MOF-808, we performed temperature-dependent kinetic analysis to evaluate the reaction rate constants of E1 reduction with \({}^{1}\)PrOH and 2-BuOH. The apparent activation energies and the corresponding thermodynamic parameters were derived from the corresponding Arrhenius and Eyring-Polanyi equations. The analysis of the energy differences between the two transition states leading to 17\(\alpha\)-E2 and 17\(\beta\)-E2 revealed a dominant contribution from the entropic term when 2-BuOH was used as a hydride source, while the entropic and enthalpic terms partially neutralize each other when \({}^{1}\)PrOH is used, resulting in a lower diastereoselectivity [81]. These findings suggest that the selectivity to the 17\(\alpha\) isomer originates from the confinement of the reaction inside the MOF cavities, where Zr\({}^{4+}\) sites are located.
## 2 Materials and Methods
### Prediction and Optimisation Models
#### 2.3.4 HKOA-LSTM  
The KOA algorithm, introduced by [PERSON] and other researchers in 2023 [52], is a physics-based meta-heuristic inspired by [PERSON]'s rules of planetary motion, enabling the prediction of planetary positions and velocities at any specified moment. The algorithm employs a planet's position, mass, gravity, and orbital velocity as the four fundamental operating parameters, with each planet's position serving as a candidate solution and the optimal solution analogous to the sun. The algorithm particular to KOA is outlined as follows:  
Step 1: Initialisation. \[X_{i}^{j}=X_{i,\text{low}}^{j}+\text{rand}[0,1]\times\left(X_{i,\text{up}}^{j}- X_{i,\text{low}}^{j}\right) \tag{18}\]  
Figure 4: Schematic representation of LSTM architecture. \begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameters** & **Values** \\ \hline Layers & 1–4 \\ No. of neurones & 1–150 \\ Learning rate & 0.001–0.01 \\ Optimiser & Rmsprop \\ No. of batch size & 8–32 \\ No. of epochs & 50–100 \\ Time steps & 1–20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LSTM hyperparameter range. \(X_{i}\) denotes the ith planet (candidate solution) within the search space; \(X_{i,\text{up}}^{j}\) and \(X_{i,\text{low}}^{j}\) signify the upper and lower limits of the \(j\)th decision variable, respectively. Rand \([0,1]\) refers to a randomly generated number within the interval of 0 to 1. Step 2: Define F.  
\[F_{\text{g}_{i}}(t)=e_{i}\times\mu(t)\times\frac{\overline{M}_{s}\times \overline{m}_{\text{i}}}{\overline{R}_{i}^{2}+\varepsilon}+r_{1} \tag{19}\]  
\(\overline{M}_{s}\) and \(\overline{m}_{\text{i}}\) indicate the normalised values of \(M_{s}\) and \(\mathbf{m}_{i}\), which correspond to the masses of the sun \(X_{s}\) and the target planet \(X_{i}\), respectively; \(\varepsilon\) represents a minuscule value; \(\mu(t)\) is a function that exponentially diminishes over time t to regulate search precision; \(e_{i}\) signifies the orbital eccentricity of the planet; \(r_{1}\) is a randomly generated value within the range of 0 to 1; and \(\overline{R}_{i}\) denotes the normalised value of \(R_{i}\), representing the Euclidean distance from the sun \(X_{s}\) to the target planet \(X_{i}\). Step 3: Calculate the velocity of the target planet. \[V_{i}(t)=\left\{ \begin{array}{l}\ell\times\left(2r_{4}\overset{\rightarrow} {X_{i}}-\overset{\rightarrow}{X_{b}}\right)+\overline{\ell}\times\left( \overset{\rightarrow}{X_{a}}-\overset{\rightarrow}{X_{b}}\right)+\left(1- R_{i-norm}(t)\right)\\ \times\Gamma\times\overset{\rightarrow}{U_{1}}\times\overset{\rightarrow}{r _{5}}\times\left(\overset{\rightarrow}{X_{i,up}}-X_{i,low}^{\rightarrow} \right),\text{ if}R_{i-norm}(t)\leq 0.5\\ \\ r_{4}\times\mathcal{L}\times\left(\overset{\rightarrow}{X_{a}}-\overset{ \rightarrow}{X_{b}}\right)+\left(1-R_{i-norm}(t)\right)\\ \times\Gamma\times U_{2}\times\overset{\rightarrow}{r_{5}}\times\left( \overset{\rightarrow}{r_{3}X_{i,up}}-X_{i,low}^{\rightarrow}\right),\text{ Else}\\ \end{array} \right.
## 4 Results
### Model Validation  
To verify the explanatory capacity of the model, we superimposed on the map that shows the results of the ignition risk model the ignition points recorded in the 2016-2017  
Figure 6: Ignition risk map in the Maule region, Chile.  
season. These account for the distribution of the ignition points of the megafire in the Maule region (red points in Figure 7). In this comparison, it is important to consider that the modeling of the ignition risk was carried out based on data from 2013 and 2015. Figure 7 clearly shows that most of the points where a fire started are in areas the model classified as having a high probability of ignition. These results contribute to validating the model and provide useful information to risk management in the future.  
In addition, we compare the model results with the real ignitions between 2016 and 2020. Table 3 shows that 85% of fires in that period occurred in points classified as medium, high, and very high risk by the model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ignition Probability** & **Risk Level** & **Points Frequency** & **Ignitions 2016–2020 (\%)** \\ \hline
0–20 & Very low & 113 & 3.19\% \\ \hline
20–40 & Low & 424 & 11.99\% \\ \hline
40–60 & Medium & 728 & 20.58\% \\ \hline
60–80 & High & 941 & 26.60\% \\ \hline
80–100 & Very high & 1331 & 37.63\% \\ \hline  & TOTAL & 3537 & 100.00\% \\ \hline \end{tabular}
\end{table}
Table 3: Percentage of fire ignition during 2016–2020 according to model probability levels.  
Figure 7: Ignition risk map and ignition points during 2016–2017 season.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
### The Role of Technology in Maritime Safety  
Historically, technological advancements have played a pivotal role in improving maritime safety. The transition from sail to steam power, the introduction of radar [26; 27] and Global Positioning Systems (GPSs) [28; 29], and the development of advanced navigational aids have each contributed to safer and more efficient maritime operations [30; 31]. In recent decades, the integration of information technology and automation has further transformed the industry, enabling real-time monitoring [32] and control of ship systems [24; 25], enhanced communication [33; 34], and more sophisticated training simulations.  
Artificial intelligence (AI) represents the latest frontier in this evolutionary trajectory [35; 36]. AI technologies have the potential to revolutionize maritime operations by providing advanced analytics, predictive maintenance, autonomous navigation, and decision support systems [37]. Within the maritime industry, AI applications are increasingly being explored to address complex challenges such as route optimization, anomaly detection, and resource allocation [38]. The adoption of AI not only promises to enhance operational efficiency but also holds significant potential for improving safety outcomes by mitigating human error and augmenting decision-making processes [39; 40; 41].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Description** & **Impact on Safety** \\ \hline Human error [17] & Mistakes made by crew due to fatigue, miscommunication, or lack of training & Leading cause of maritime accidents \\ \hline Mechanical failures [18] & Equipment malfunctions or breakdowns & Can result in loss of vessel control or accidents \\ \hline Environmental hazards [19] & Adverse weather conditions, natural disasters, and navigational obstacles & Increase risk of collisions and grounding \\ \hline Communication barriers [20] & Language differences among international crews & Misunderstandings leading to operational errors \\ \hline Regulatory compliance complexity [21] & Challenges in adhering to international and local regulations & Potential legal penalties and safety lapses \\ \hline Cybersecurity threats [22] & Vulnerabilities to cyberattacks affecting navigational and operational systems & Can compromise vessel safety and security \\ \hline Cargo handling risks [23] & Dangers associated with hazardous materials and improper cargo stowage & Risk of fires, explosions, or environmental contamination \\ \hline Piracy and security threats [24] & Risks from piracy, terrorism, and other unlawful acts & Threaten crew safety and vessel security \\ \hline Fatigue and crew welfare [25] & Long working hours and stressful conditions impacting crew performance & Increase likelihood of accidents due to impaired judgment \\ \hline Aging Infrastructure & Use of outdated vessels and equipment lacking modern safety features & Higher susceptibility to failures and accidents \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key maritime safety challenges.
# Signal Enhancement of Helicopter Rotor Aerodynamic Noise Based on Cyclic Wiener Filtering
## 2 Theory
### Cyclic Wiener Filter Adaptive Algorithm  
The standard LMS adaptive iterative algorithm is used for cyclic Wiener filters. The iterative relationship of the LMS weight coefficients of the cyclic Wiener filter is:  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu[x_{ai}^{*}(n-k)e(n)] \tag{7}\]  
where \(\mu\) denotes the step factor and is adopted to adjust the weight coefficient; \(w\) denotes a weight vector representing filter coefficients; \(x^{*}\) suggests taking the conjugate of \(x\); \(x_{ai}\) represents the new signal after the frequency shift, a, of \(\alpha_{i}\). The iterative relationship of the weight coefficients is more intuitively demonstrated by expanding Equation (7) as:  
\[\left[\begin{array}{c}\left[\begin{array}{c}w_{a_{0},0}(n+1)\\ \vdots\\ w_{a_{0},M-1}(n+1)\end{array}\right]\\ \left[\begin{array}{c}w_{a_{1},0}(n+1)\\ \vdots\\ w_{a_{1},M-1}(n+1)\end{array}\right]\\ \vdots\\ \left[\begin{array}{c}w_{a_{L-1},0}(n+1)\\ \vdots\\ w_{a_{L-1},M-1}(n)\end{array}\right]\\ \end{array}\right]=\left[\begin{array}{c}\left[\begin{array}{c}w_{a_{0},0}(n )\\ \vdots\\ w_{a_{0},M-1}(n)\end{array}\right]\\ \left[\begin{array}{c}w_{a_{1},0}(n)\\ \vdots\\ w_{a_{1},M-1}(n)\end{array}\right]\\ \vdots\\ \left[\begin{array}{c}w_{a_{L-1},0}(n)\\ \vdots\\ w_{a_{L-1},M-1}(n)\end{array}\right]\\ \end{array}\right]+\mu e(n)\left[\begin{array}{c}\left[\begin{array}{c}x_{ a_{0}}^{*}(n)\\ \vdots\\ x_{a_{0}}^{*}(n-M+1)\end{array}\right]\\ \vdots\\ \left[\begin{array}{c}x_{a_{L-1},0}^{*}(n)\\ \vdots\\ x_{a_{L-1},M-1}^{*}(n-M+1)\end{array}\right]\\ \end{array}\right] \tag{8}\]
# An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients
## 1 Introduction
Artificial-intelligence-based approaches have been shown to significantly improve sensitivity and specificity in diagnosing COVID-19 pneumonia; machine learning approaches using convolutional network models have found promising results in distinguishing COVID-19 from other types of pneumonia [23; 24; 25]. This volumetric calculation reflects disease extension, and can be used in predicting disease severity, progression, and response to treatment [26; 27; 28]. Within this context, the reliability and repeatability of lung sub-volumes according to their density is a significant issue. The need for operator-independent approaches, possibly defining sub-regions with a clear link to functional meaning, is urgent. Thus, the principles aims of our study were:  
1. To develop an automated, operator-independent quantitative method to identify the different lung regions for COVID-19 patients, based on individually optimized Hounsfield unit (HU) thresholds; the proposed method is based on an intuitive, interpretable phenomenological characterization of lungs, with clear functional meaning;
2. To achieve a feasible implementation of the proposed method in such a way as to be potentially usable by other institutions;
3. To demonstrate the robustness of the method with respect to inter-scanner variability within a single institute;4. To report inter-patient distribution of the HU-based parameters extracted by our approach over a large single-center population of 166 patients during the first wave of the pandemic.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 4 Results and Discussions
### Comparisons with State-of-the-Art Methods in Kaggle Dataset  
To prove the practicality of our method, we compare the proposed approach with several GAN-based SR models, including SRGAN [3], ESRGAN [4], EEGAN [5], and EESRGAN [6]. In order to have a fair comparison, we retrain these models using the same dataset. As shown in Table 5, the proposed approach has the highest scores for all the performance indices in the Kaggle dataset, including PSNR, SSIM and FSIM. Moreover, in Figure 7, it shows the SR images generated by our approach are more realistic and competitive compared with other models. Figure 8 is an enlarged partial view of the SR result of each method in the Kaggle dataset.  
Figure 8: An enlarged partial view of the SR result of each method in the Kaggle dataset, where the best results are shown in red font.  
Figure 7: SR visual result comparison for all models in the Kaggle dataset, where the best results are shown in red font.
###### Abstract  
Background: We conducted a narrative review of studies analyzing the occlusal and mandibular plane angles after maxillary molar distalization using temporary skeletal anchorage devices (TADs). An original preliminary investigation was conducted on the occlusal and mandibular plane angle changes according to the design of TAD-supported distalization. Methods: We included 51 participants stratified into three groups (buccal TAD, lingual arch TAD, and pendulum TAD) who underwent lateral cephalography before and after treatment. The paired t-test and ANOVA were used to analyze the significant differences among the groups. Results: The pteryoid vertical to maxillary second molar (_p_ < 0.01) significantly differed before and after treatment within each group (group 1, 1.29 \(\pm\) 1.73 mm; group 2, 2.01 \(\pm\) 1.46 mm; group 3, 1.12 \(\pm\) 1.43 mm). The angle between the Frankfort horizontal and anatomical occlusal plane increased by 1.96 \(\pm\) 2.88\({}^{\circ}\) (_p_ < 0.05) and 2.51 \(\pm\) 2.57\({}^{\circ}\) (_p_ < 0.01) in groups 2 and 3, respectively. Conclusions: The measured variables did not significantly differ among the three groups. The Frankfort-mandibular plane angle and functional occlusal plane did not change after maxillary molar distalization. The anatomical occlusal plane is influenced by the anterior teeth and may be unrelated to maxillary molar distalization. Further studies are required to verify the exact relationship between maxillary molar distalization and occlusal plane angle.  
Keywords:distalization; maxillary molars; temporary anchorage devices; occlusal plane +
Footnote †: journal: Medical Conference
# Planar Delaunay Mesh Smoothing Method Based on Angle and a Deep Q-Network
## 2 Smoothing Method Based on Angle and Deep Q-Network
### Workflow of the Algorithm
#### 2.3.3 Step 2: Training of DQN Model
\begin{table}
\begin{tabular}{c c c} \hline \hline
**State** & **Reward Function** \\ \hline Normal & \((q_{v,t}-q_{v,t-1})\times 100\) \\ Node quality exceeds the threshold & 10 \\ The agent moves out of the inner polygon & \(-10\) \\ The agent moves out of the polygon & \(-100\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reward function setting for DQN model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline learning rate & \(\alpha\) & 0.01 \\ decay rate & \(\gamma\) & 0.9 \\ epsilon & \(\epsilon\) & 0.9 \\ number of times experience pool D starts to replay & N & 500 \\ batch size & m & 32 \\ number of intervals for updating objective function & C & 300 \\ maximum number of steps in a single round & T & 200 \\ maximum number of rounds & M & 1000 \\ optimizer & & RMSProp \\ activation function & & ReLU \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of parameter setting for neural networks. * Calculate the node quality \(q_{v,t-1}\) and \(q_{v,t}\) before and after the agent moves and set the reward value to \((q_{v,t}-q_{v,t-1})\times 100\);
* Determine whether the current node quality meets the quality threshold. If it does, terminate this round of training and start a new round;
* Determine whether the intelligent agent has moved out of the inner polygon. If so, give a reward of \(-10\) and continue training;
* Determine whether the agent has moved out of the polygon. If so, give a reward of \(-100\) and terminate this round of training. Otherwise, continue;
* Store \(\{S_{t},\ A_{t},R_{t},S_{t+1}\}\) as an array, and store the array in the experience playback pool. Update status \(S_{t}\) to \(S_{t+1}\);
* Randomly sample m samples \(\big{\{}S_{j},\ A_{j},R_{j},S_{j+1}\big{\}}\) from the experience playback pool D, where \(j=1,2,\ldots,\) m. Calculate the current target Q-value \(y_{j}\) using the following Equation; \[y_{j}=\left\{\begin{array}{ll}R_{j}&,\ \ \text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \
## 3 The Diurnal Reference Check (DRC)
### Checking Daily Segments for Systematic Errors  
To check daily segments for systematic errors, we compare \(T_{NIGHT}\) with \(T_{L4}\). This is a more adequate use of the 'foundation' \(T_{L4}\) than comparing each individual \(T_{IS}\) with \(T_{L4}\) (as it is done e.g. in the current 'Quam QC). The threshold for this comparison accounts for the geographical variability of the \(T_{L4}\) uncertainty. Specifically, this threshold is set to be proportional to the spatial standard deviation (SD) of the analysis SST within the \(5^{\circ}\times 5^{\circ}\) neighborhood of each grid node over each month. The estimates of the \(T_{L4}\) uncertainty, \(S_{L4}\), are obtained for any location by interpolating the gridded monthly SDs.  
The condition for comparing \(T_{NIGHT}\) versus \(T_{L4}\) takes the following form:  
\[\gamma_{1}S_{L4}<T_{NIGHT}-d(D)-T_{L4}<\gamma_{2}S_{L4} \tag{1}\]  
The function \(d(D)\) accounts for the dependency of \(M(T_{NIGHT}-T_{L4})\) on \(D\), corresponding to curve 1 in Figure 7, and the \(S_{L4}\) is a function of geographical coordinates. The coefficients \(\gamma_{1}\) and \(\gamma_{2}\) are selected based on the analysis of \(T_{SAT}-T_{IS}\) statistics over 2012-2018 and all matchups, both nighttime and daytime, from the MDS listed in Table 2. Figure 8 shows these statistics as a function of \(\gamma\):  
\[\gamma=(T_{NIGHT}-d(D)-T_{L4})/S_{L4} \tag{2}\]  
In Figure 8a, the medians \(M(T_{SAT}-T_{IS})\) depend on \(\gamma\), and their shapes are similar for all satellites. This confirms a direct link between the \(\gamma\) and systematic errors in \(T_{IS}\). Based on the dependencies in Figure 8a, we select \(\gamma_{1}=-1.7\) and \(\gamma_{2}=1.5\), which corresponds to the \(\pm 1\) K limits for \(M(T_{SAT}-T_{IS})\). Figure 8b shows the RSDs, \(S(T_{SAT}-T_{IS})\) as functions of \(\gamma\). The selected values of \(\gamma_{1}\) and \(\gamma_{2}\) correspond to the RSDs of \(\sim 1\) K at \(\gamma_{1}=-1.7\) and \(\sim\)0.6 K at \(\gamma_{2}=1.5\). Figure 8c shows the fractions of \(T_{IS}\)'s in all daily segments as a function of \(\gamma\). The above selection of \(\gamma_{1}\) and \(\gamma_{2}\) results in rejecting \(\sim\)0.25% of \(T_{IS}\)'s belonging to the segments with \(\gamma<\gamma_{1}\) and \(\sim\)0.2% of \(T_{IS}\)'s in the segments with \(\gamma>\gamma_{2}\).
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 4 Experimental Results  
### FPGA Verification Results  
In this paper, the proposed universal code generator structure is implemented using Verilog HDL and simulated using Vivado 2018.3. To test and validate the experimental results, the spread spectrum code generation for all the code generators is implemented using MATLAB R2021a. The generated codes are then compared with the spread spectrum codes produced by the FPGA, confirming the accuracy of the generated codes. Figure 12 illustrates a partial spread spectrum code waveform obtained from the FPGA, where each rising clock edge corresponds to the output of one bit of the spread spectrum code.
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 4 Experiment and Results  
### Experimental Platform  
In this chapter, we describe the proposed 1D-CNN demodulator implementation using the Xilinx KC705 evaluation board. The block diagram of the experimental platform is shown in Figure 10. As shown in this diagram, a PC with a Keras neural network toolkit was used to generate the network parameters, which were later provided to a Xilinx KC705 evaluation board. The training data set \(X_{i}\) was provided in the neural network training process. In addition to the evaluation board itself, another three devices were applied to generate modulated data, which were the BPSK generator, FPGA mezzanine card (FMC) sampling subsystem, and additive white Gaussian noise (AWGN) generator.  
The experimental condition was set as follows: (1) carrier frequency was \(f_{\text{c}}\) = 10 MHz; (2) symbol rate was \(r_{b}\) = 5 Msps; (3) sampling frequency of the system was \(f_{\text{s}}\) = 80 MHz.
# Quantum LDPC Codes Based on Cocyclic Block Matrices
## 3 Quantum Codes Based on Cocyclic Quasi-Jacket Block Matrices  
Motivated by the center weight Hadamard matrices [26], a family of orthogonal matrices called Jacket transform was proposed [27], which is widely employed in signal processing, encoding, mobile communication, etc. [29; 30; 31; 32]. Especially, Hadamard and DFT matrices belong to this kind of Jacket matrix.
## 2 Materials and Methods
### Model Overview
#### 2.1.4 The Temperature and Greenness model (TG)  
The TG model [28] assumes that daytime land surface temperature (LST) was correlated with Ta, VPD, and PAR, and estimates GPP with EVI and LST as follows:  
\[GPP\ =\ (scaledLST\times scaledEVI)\times m \tag{4a}\] \[scaledLST\ =\ \left\{\begin{array}{cc}\min\left[\left(\frac{ LST}{30}\right),\left(2.5-\left(0.05\times LST\right)\right)\right]&\left(0< LST<50\right)\\ 0&\left(LST<0\ \text{or}\ LST>50\right)\end{array}\right.\] (4b) \[scaledEVI\ =\ EVI-0.1 \tag{4c}\]  
where the \(scaledLST\) is defined as the minimum of two linear equations. \(scaledLST\) reaches a maximum of 1.0 when LST = 30 \({}^{\circ}\)C and a minimum of 0 when LST declines to 0 \({}^{\circ}\)C or increases to 50 \({}^{\circ}\)C. The \(scaledLST\) accounts for the drought conditions with high temperature and high VPD stress [28]. \(m\) is a scalar determined by the model calibration (the same for the other statistical models). The calculation method of \(m\) is detailed in Section 2.3.