esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Test Research on the Propagation of Tsunamis and Their Interaction with Navigating Ships
## 3 Experimental Verification
### Free Attenuation Test  
Roll decay tests in calm water were undertaken; they were mainly useful for the verification of the ship loading conditions. A common method of assessing the damping present in ship rolling motion is to perform a free decay experiment in which, in the absence of waves, the ship is given an initial roll amplitude and then released. By processing the resulting decaying oscillatory trace it is possible to estimate quantitatively the degree of damping, even when this is nonlinear. The freedom degree of the ship in the vertical plane exhibits the recovery characteristic. The ship model deviates from the position of equilibrium under an external loading. When the external force disappears, the ship will return to the original position, which depends on its own recovery characteristic. In a free roll decay test, the model is heel in calm water to an initial heel angle and released; the model starts to perform free decaying oscillations with a frequency depending on the amplitude. Subsequently, the accuracy of the model test can be verified. The decay curve of the roll motion is shown in Figure 9. It can be seen from the test results that the decay of the ship meets the requirements of the loading condition, and the experimental data were used to tune the ship hydrodynamic inertia properties, the natural roll period, the roll damping, and the inertia.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & \multicolumn{2}{c}{**Test Results**} \\ \hline Rudder angle & \multicolumn{2}{c}{35\({}^{\circ}\)} \\ \hline Speed of ship & 0.4 m/s & 0.7 m/s \\ \hline Vertical intercept (m) & 4.81 & 5.63 \\ \hline Horizontal intercept (m) & 2.22 & 2.39 \\ \hline Turning radius (m) & 8.08 & 8.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Turning test data.  
Figure 8: Trajectory in the turning test recorded by Differential Global Positioning System (DGPS).
## 4 Results and Discussions
### Porosity Characteristics  
According to the test results for the helium porosity of coal samples, the porosities of the coal seams in the study area vary from 4.20% to 5.41%, suggesting that the coal seams are low-porosity reservoirs. From the C\({}_{13}\) to C\({}_{25}\) coal seam, the average porosities of coal seams increase first and then decrease (Figure 6), reflecting that the coal seams in the middle of P\({}_{3l}\) may have higher porosities.  
Previous studies have shown that the total porosity of coal is closely related to the coal rank. With the increase in coal rank, the total porosity of coal presents a U-shaped curve, and the turning point occurs at \(\overline{\text{R}}\)r, approximately 1.6% [43; 44; 45]. In other words, starting from lignite, the total pore volume of coal gradually decreases with the increase in the degree of coalification. When it reaches coking coal, the total porosity of coal reaches the minimum value and then gradually increases with the increase in the degree of coalification. The coals in the study area have a high degree of metamorphism, and the maximum vitrinite reflectance (R\({}_{o,max}\)) ranges from 2.59% to 3.10%, with an average of 2.82%, suggesting that the coals are lean-coal to anthracite in rank. Figure 7 shows that the porosity of coal in the study area is positively correlated with the R\({}_{o,max}\); that is, with the increase in thermal evolution degree, the porosity of coal tends to increase.  
Figure 6: Distribution ranges of porosities of different coal seams in the Longtan Formation of the Dacun Block.  
Figure 7: Relationship between coal porosity and R\({}_{o,max}\) in the Dacun Block.
# The Development of the Pipe Jacking Guidance Technology
## 3 Laser Target Method
### Measuring Principle  
The laser target method is to finally obtain the laser target position and the azimuth, pitch and roll angles of the pipe jacking machine by means of the laser beam and coordinate measurement function provided by the total station [8]. The total station emits a laser beam, which is illuminated onto the target prism below the laser target to obtain the absolute coordinates of the laser target. The laser beam is focused through a thin lens on the laser target to form a laser spot on the light-sensitive panel. The CCD camera collects the laser spot information and utilizes the horizontal and vertical position of the laser spot on the light-sensitive panel to obtain the azimuth of the current state. The laser target is equipped with a dual-axis inclinometer that is capable of measuring pitch and roll angles. Finally, the horizontal and vertical deviations between the central axis of the jacking pipe and the design route are calculated. The calculation process is as follows:
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Chemical Methods
#### 2.2.2 Thermal Decomposition Method  
The thermal decomposition method, also referred to as pyrolysis, was developed to overcome the limitations of the co-precipitation technique by utilizing non-aqueous solvents with elevated boiling points [27]. This method is employed to produce highly crystalline and uniformly sized MNPs [20]. Primarily, it involves the elevated temperature-induced decomposition of organometallic compounds in organic solvents, conducted in the presence of stabilizing agents, such as surfactants [19; 28; 29].  
Various organometallic precursors of NPs include those synthesized with acetylketonates, N-nitrosophenylhydroxylamine, and carbonyls possessing various metallic centers such as Fe\({}^{2+,3+}\), Mn\({}^{2+,3+}\), Co\({}^{2+,3+}\), Ni\({}^{2+,3+}\). Stabilizing agents like fatty acids, oleic acids, and hexadecylamine are frequently utilized [30]. The type of the NPs depends on the chosen organometallic compound used as a source [17].  
The temperature requirement varies based on the precursor type [19]. To achieve a high degree of uniformity and a size ranging from 4 to 30 nm, the optimal temperature falls within the 100-350 \({}^{\circ}\)C range [31; 32]. Some authors consider that the temperature, reaction time, type of surfactants and solvents, and aging period should be adjusted according to the desired shape and size [33; 34]. For instance, NP size was controlled by adapting the reaction time, resulting in sizes of 4, 12, and 60 nm for times of 1, 10, and 24 h, respectively [17]. The reaction time also influenced the morphology of the NPs, a transition from spherical NPs (at an early stage) to cubic ones (over longer times) being observed [17].  
Significant findings include the synthesis of monodispersed iron oxide MNPs ranging from 6 to 20 nm through the polymer-catalyzed decomposition of Fe(CO)\({}_{5}\)[35; 36]. Iron oxide MNPs (IOMNPS) were synthesized from metal fatty acid salts, yielding nearly monodispersed NPs with adjustable sizes between 6 and 50 nm and shapes evolving from points to cubes with increasing reaction time [37].  
It is noted that any remaining surfactants may impact surface modification efficiency [20]. Oleic acid, hexadecylamine, and fatty acids are the most commonly used surfactants in this method [20].  
MNPs synthesized using this method exhibit promising potential as magnetic resonance imaging (MRI) contrast agents for diagnosing cancerous tissue [17]. Other advantages of this method include high crystallinity, dispersibility, uniformity, homogeneous shape, and a fine particle size distribution [38; 39].  
Despite these substantial advantages, this method requires extensive purification steps of the synthesized MNPs (especially for biomedical applications) [40] due to the production of toxic organic-soluble solvents [41]. Additionally, this method is time-consuming and has low product yields [20].
# Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande +
### Timing Needs and Requirements  
We will now focus on the design of the timing system for HK, which will play an important role in achieving the goal described previously. The lepton flavor identification depends on the reconstruction of the Cherenkov ring patterns (a sharp ring being the signal for a muon and a fuzzier one for an electron). The particle interaction vertices are reconstructed through the relative photon arrival timing between the PMTs. As a result a very good internal synchronization between all PMTs is needed and has been quantified to a stability under 100 ps, in relation with other reconstruction limitations. In addition, an external synchronization using GNSS-based time (or tags) is required to align to the accelerator site to open time windows to record all data when the neutrino beam goes through HK, but also to align to universal time UTC in the context of multi-messengers astrophysics. For instance, in case of a supernova burst, UTC synchronization can allow different neutrino observatories to point to the right region of the sky if the timing precision is sufficient for efficient triangulation. To achieve this, it is required to monitor HK timing with respect to UTC within 100 ns at least.
## 5 Elliptical Fiber Cross Section
#### Distributions of the Fiber's Cross Section with Different Orientations  
In this section, the effect of the elliptical cross-section orientation on the effective thermoelastic properties is analyzed. The calculated results of the effective coefficients are closely related to the fiber geometry, as mentioned in the previous section. For the elliptical cross section, it has been shown that \(\overline{C}_{2323}\) and \(\overline{C}_{1313}\), and \(\overline{\kappa}_{11}\) and \(\overline{\kappa}_{22}\), do not have the same values. Square periodic cells composed of thirty-six ellipses were considered to estimate the relation between the effective properties of the composite material and the orientation of the elliptical fibers. Consequently, the cell quarter must have a square with nine ellipses, as shown in Figure 11.  
The effect of having fibers with different orientations is determined by the rotation of the fiber's cross section with respect to the \(y_{3}\) axis. Different fiber orientations (\(\theta=0^{\circ},5^{\circ},10^{\circ},15^{\circ},20^{\circ},25^{\circ},30^{ \circ},35^{\circ},40^{\circ}\) and \(45^{\circ}\)) were considered, where \(\theta=0^{\circ}\) indicates an equally aligned fiber distribution, as shown in Figure 11a. Figure 11d illustrates the \(\theta=45^{\circ}\) orientation.  
Figure 10: Effective properties for different aspect ratios of the ellipse and \(\gamma=0.1\).  
Effective thermoelastic properties were calculated using an aspect ratio of \(\
icefrac{{a}}{{b}}=2\) and \(\gamma=0.2\). Figure 12a shows the effective coefficient values \(\overline{C}_{1313}\) and \(\overline{C}_{2323}\) for different orientations \(\theta\). The effective coefficient \(\overline{C}_{1313}(\overline{C}_{2323})\) increases (decreases) monotonically as a function of \(\theta\) until \(\overline{C}_{1313}\) has the exact same value as \(\overline{C}_{2323}\) for \(\theta=45^{\circ}\), where the composite material shows a transversely isotropic behavior as illustrated in Figure 12a. The thermal conductivity coefficients \(\overline{\kappa}_{11}\) and \(\overline{\kappa}_{22}\) show a similar monotonic behavior as a function of \(\theta\), reaching the expected transversely isotropic behavior for \(\theta=45^{\circ}\), as observed in Figure 12b.  
Figure 11: Fiber distribution in the cell quarter, (a) \(\theta=0^{\circ}\) alignment (equally aligned), (b) \(\theta=15^{\circ}\) alignment, (c) \(\theta=30^{\circ}\) alignment, (d) \(\theta=45^{\circ}\) alignment.
# Brief Review of Recent Developments in Fiber Lasers
## 4 Raman Fiber Lasers  
There are substantial white spaces in the wavelength spectrum where no RE-doped fiber lasers are available. Raman fiber lasers can cover these white spaces. In contrast to RE-doped lasers, Raman lasers are free from photodarkening.
## 3 Optimization of the NeuMF Model
### MLP (Multi-Layer Perceptron)  
MLP is designed to explore the interaction between users and items' latent features. Different from collaborative filtering of GMF, MLP is more flexible. MLP is not limited to the vector inner product,  
Figure 2: The structure of NeuMF [1].  
and can deeply learn the potential interaction between the user's hidden vector \(\mathbf{p}_{u}\) and the item's hidden vector \(\mathbf{q}_{v}\). The specific process of MLP is as follows:  
\[\begin{gathered}\mathbf{z}_{1}=\phi_{1}(\mathbf{p}_{u},\mathbf{q}_{v})=\left[ \begin{array}{c}\mathbf{p}_{u}\\ \mathbf{q}_{v}\end{array}\right]\\ \phi_{2}(\mathbf{z}_{1})=a_{2}\big{(}\mathbf{W}_{2}^{T}\mathbf{z}_{1}+\mathbf{b}_{2}\big{)}\\ \phi_{L}(\mathbf{z}_{L-1})=a_{L}\big{(}\mathbf{W}_{L}^{T}\mathbf{z}_{L-1}+\mathbf{b}_{L}\big{)} \\ \hat{y}_{uv}=\sigma\Big{(}\mathbf{h}^{T}\phi_{L}(\mathbf{z}_{L-1})\Big{)}\end{gathered} \tag{13}\]  
where \(\mathbf{W}_{\mathbf{x}}\) is the weight matrix of each layer, \(\mathbf{b}_{\mathbf{x}}\) is the bias vector of each layer, and \(a_{x}\) is the activation function of each layer. According to the literature [1; 23], we adopt the Rectifier (ReLU) as \(a_{x}\). The \(\hat{y}_{uv}\) is the output of the last prediction layer and \(\sigma\) (sigmoid function) is the activation function of the prediction layer. \(\mathbf{h}\) is the weight of the prediction layer and is trained by the loss function. MLP selects the four-layers (\(L=4\)) tower structure, and halves the layer size for each successive higher layer. Together with the final prediction layer, MLP has a total of five layers.
## 3 Results
### Artificial Neural Network  
Considering the coefficient of determination \(r^{2}\), the root mean square error (_RMSE_), and the mean absolute percentage error (_MAPE_), an ANN with the best performance was obtained. The ANN with the best fitness for predicting the TRS (\(r^{2}\) = 0.99, _RMSE_ = 0.4365, _MAPE_ = 3.645833%) was composed of one input layer with three neurons, three hidden layers with ten, nine, and eight neurons, and an output layer with one neuron denoting the TRS (Figure 2). The ANN linear correlation is shown in Figure 3a, where most of the points are very close to the blue line, indicating good precision of the ANN model. The best conditions for obtaining the maximum amount of TRS were the following: a particle size of 43, a concentration of 6% acid, and 40 min, producing 18.6762 g/L TRS. Like the experimental results, Figure 3a-c show that the minimum values for particle size, acid concentration, and time produced minimum values for the TRS and vice versa, whereby, given the maximum values for the input variables, the ANN model predicted high values for the TRS. The ANN model could thus predict sugar production during chemical hydrolysis. For the optimal conditions obtained experimentally (particle size #50, 0.5N H2 SO4, and 20 min), the TRS was 16.41, while the ANN model's forecast was 16.40. However, it is possible to investigate optimal conditions even outside the range considered in the lab experiments.  
The optimal conditions used by the ANN model to obtain the maximum TRS (particle size #43, 0.6N H2 SO4, and 40 min) could be validated. However, due to the lack of screens with certain granularity, this was difficult. This is a limitation of this study, as the particle size could not be adjusted exactly to the size indicated by the ANN.  
Figure 1: Statistical comparison of the experimental values and the values predicted by the RSM model.  
To prevent overfitting, the artificial neural networks were evaluated using a subset of data not utilized for training purposes. The small size of the _RMSE_ and _MAPE_ and the proximity of \(r^{2}\) to 1 indicated that the ANN model could be generalized without overfitting.  
Figure 3: Linear correlation (**a**) and ANN plots for TRS with particle sizes of 16 (**b**), 30 (**c**), and 50 (**d**).  
Figure 2: ANN architecture of the model for obtaining the amount of TRS.
## 2 Materials and Methods
### Patient Selection and Randomization  
The study included 30 patients over the age of 18 who required surgical extraction of both impacted mandibular third molars. Inclusion criteria were the indication for bilateral third mandibular molar extraction, impacted mandibular third molars and bilateral similarity between the third molar positions according to [PERSON]'s classification. Non-inclusion/exclusion criteria were acute pericornitis in the region of the third mandibular molar, acute infection in the oral cavity, systemic diseases, the use of drugs that may affect wound healing (such as corticosteroids, immunosuppressive agents, chemotherapeutics), pregnancy and lactation, smoking, postoperative infection and dry socket. The present research was a prospective study with strict inclusion and exclusion criteria. The sample size for this study was determined according to [52].  
The indication for tooth extraction was established after taking a medical history, clinical examination and analysis of the OPT image of the patients. After establishing the indication for bilateral third mandibular molar tooth extraction and an informative interview, the participants signed an informed consent form for enrolling in the study. Enrolment of the patients and randomization of the alveoli was performed by a single doctor, who did not perform surgery and postoperative measurements.  
Randomization was carried out by coin flipping. It was determined that the first patient included in the study would have the preparation of L-PRF applied to the alveolus of the right lower third molar; the next patient enrolled had the preparation of L-PRF applied to the alveolus of the left lower third molar, and this pattern was used throughout the research. L-PRF was alternately placed in the region of alveolus 38 or 48, as participants were included in the study. Both the dentist who performed the periodontal pocket measurement and the researcher who analyzed CBCT scans were blind regarding the site of the L-PRF application.  
The control group in the study consisted of 30 dental alveoli in which the preparation of L-PRF was not applied, while the experimental group included 30 alveoli in which the preparation of L-PRF was applied after tooth extraction.
## 2 Problem Formulation
### FLSs  
The FLSs used to approximate uncertain model dynamics \(f_{vi}\) are \(f_{\omega i}\) in (1), and the properties of FLSs can be seen in Lemma 1.  
**Lemma 1** ([26]).: _Let \(f(x)\) be a continuous function on a compact set \(\Lambda\). For any constant \(\varepsilon\), there always exists a FLS \(\hat{f}(x\Big{|}\Theta)=\Theta^{T}\varsigma(x)\) satisfying_  
\[\sup\limits_{x\in\Lambda}\left|f(x)-\Theta^{T}\varpi(x)\right|\leq\varepsilon \tag{3}\]  
_where \(\varpi(x)=\left[\varpi_{1}(x),\varpi_{2}(x),\ldots,\varpi_{N}(x)\right]^{T}/ \sum\limits_{i=1}^{N}\varpi_{i}(x)\) is the fuzzy basic function vector and satisfies \(\varpi^{T}(\cdot)\varpi(\cdot)\leq 1\). \(\Theta=\left[\Theta_{1},\Theta_{2},\ldots,\Theta_{N}\right]^{T}\) is the ideal weight vector and \(N\) is the fuzzy rule number._  
**Remark 1**.: _In this paper, we use a FLS to approximate the unknown continuous functions in (2). Note that neural networks and type-2 FLSs have also the approximation property of Lemma 1. Therefore, the FLSs used in this paper can be replaced by other approximators._  
**Control objective:** For NMRs (1) with full-state constraints, this paper develops a fuzzy adaptive ET formation control method such that the following hold:  
1. All the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB).
2. All robots will follow the leader by keeping a desired formation pattern, i.e., the angle and relative distance errors converge asymptotically to zero without violating the angle and relative distance constraints.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 5 Conclusions  
In this study, we investigated image degradation modeling to promote the performance of SR models in real-world scenarios. We took image degradation modeling as the problem of learning a conditional probability distribution of degraded LR images given the HR input. The conditional-normalizing-flow-based method IDFlow was proposed to learn the distribution from existing real-world SR datasets. Given one HR image, IDFlow can generate a series of realistic LR images by transforming latent variables sampling from a simple distribution. Large-scale synthetic HR-LR pairs can be generated, alleviating the problem of an insufficient number and a diversity of existing real-world SR datasets. Quantitative and qualitative experiments show that not only can IDFlow produce realistic degraded LR images close to real-world images, but it also improves the generalization ability of SR models. SR models trained with IDFlow can produce richer image textures and clearer patterns in SR results without visible noises, resulting in a better SR performance than state-of-the-art SR methods on real-world noisy images. Therefore, the superiority and effectiveness of the proposed IDFlow is well demonstrated.  
[PERSON] proposed the research idea of this paper and was responsible for writing, conceptualization, and methodology. [PERSON] conducted literature research and data curation, and verified the experimental results. [PERSON] was responsible for the visualization of experimental data and chart making. The paper was mainly written by [PERSON], with the participation of [PERSON] and [PERSON]. Supervision and review of the manuscript as well as funding acquisition were completed by R.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51675265, the Advantage Discipline Construction Project Funding of University in Jiangsu Province grant number PAPD, and the Independent Research Funding of the State Key Laboratory of Mechanics and Control of Mechanical Structures, grant number 0515K01.  
The authors declare no conflict of interest.