esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Recognition Results and Performance Analysis
#### 3.1.3 Complexity Analysis of Models  
As PD detection instruments may be carried by operators to various environments for signal measurement, under the premise of ensuring the accuracy of network identification, the resource consumption and running speed of the model are also key indicators to measure the comprehensive performance of the model. To further compare and analyze the performance advantages of the MCNN model proposed in this paper compared with other models, the total parameters of seven models and the storage space occupied by the models are counted, and the results are shown in Table 4.  
As can be seen from the data in Table 4, the number of parameters of the MCNN model is only 8000, which is significantly smaller than that of the three types of depth convolution neural networks, while the other three compression models have a good performance in terms of spatial complexity. In addition, although the MCNN model takes up only 3.05 MB of memory, slightly more than the SqueezeNet1.0 model, it is still relatively small. Therefore, it is suitable for mobile devices or intelligent terminal instruments with limited volume and processor performance.  
If the reaction time of the model is too long in practice, it will be unable to deal with the PD timely and quickly. Therefore, in addition to the recognition accuracy and memory footprint of the model, the corresponding test time of different models will directly determine their application performance in the actual situation.
## 7 Conclusions  
In this paper a trajectory tracking problem for underactuated underwater vehicles moving horizontally using some inertial quasi-velocities is considered. The control scheme consists of a backstepping technique along with integral sliding mode control with IQV consideration. These quasi-velocities, resulting from the transformation of the equations of motion in velocity space, are crucial to the control process because the other methods have already been used many times. The feature that distinguishes the developed control scheme from others based on a combination of the aforementioned methods is the fact that the model parameters are used in the dynamic controller and thus the dynamic couplings. This is not just the well-known kinematic transformation because, as a result of the decomposition of the inertia matrix, the obtained IQV contain both kinematic and dynamic parameters. The stability analysis conducted in the sense of Lyapunov showed the feasibility and limitations in the applicability of the presented algorithm.  
Simulations were carried out taking into account the actual thrusters and velocity limits of the selected vehicles. The obtained numerical results, obtained for two vehicles with different dynamics, show that the proposed tracking control algorithm can effectively guide underwater vehicles to the desired trajectory and is robust to perturbations of model parameters and selected external disturbances functions. In addition, it is found that the proposed algorithm achieves better control performance than the chosen control scheme in tracking the trajectory of an underwater vehicle moving in the horizontal plane, given the assumed operational conditions.  
Based on the results obtained (theoretical analysis and simulation results), it can be concluded that the transformation of variables alone does not guarantee effective performance under different conditions, but should be incorporated into a control scheme that guarantees the expected performance. In turn, the transformation of variables should either improve this performance under certain criteria, or give some insight into the dynamics of the vehicle being regulated.  
Some comments are also made on the proposed algorithm using IQV in comparison with other control schemes. Further work may extend the results obtained to other vehicles, as the developed approach can also be applied to other types of underactuated systems for which it is possible to describe the dynamics using a symmetric inertia matrix.  
The work was supported by Poznan University of Technology Grant No. 0211/SBAD/0122.  
Not applicable.  
The author declares no conflict of interest.
## 2 Cellulose Nanomaterials
### Cellulose Nanocrystals (CNCs)  
The term cellulose nanocrystals, or even crystalline nanocellulose (CNC), describes the rod-like shaped nanoparticles obtained from different sources, such as cotton, wood pulp, plant residue, etc., and this is the most common type of nanocellulose [3].  
These nanostructures are produced with an extraction process consisting of two successive steps: the initial pre-treatment of the raw material and the following hydrolysis into CNCs. In the first step, raw materials are treated to remove the impurities and all other chemical constituents present in cellulose sources, such as hemicellulose and lignin; to achieve this, alkaline and bleaching treatments are carried out [32]. Then, hydrolysis is carried out to obtain the nanocellulose crystals; this step is based on a strong acid process that uses especially sulfuric acid (H\({}_{2}\)SO\({}_{4}\)), hydrochloric acid (HCI), or phosphoric acid (H\({}_{3}\)PO\({}_{4}\)), [27]. The acid hydrolysis is useful for removing, under controlled conditions, the disordered and amorphous regions of the cellulose fibers, leaving the crystalline domains, which then take on the characteristic crystalline shape, with a crystalline index of 54-88% [6; 33]. Sulfuric acid hydrolysis is the most efficient and widely used extraction method and, during the isolation treatment, it allows for a better dispersion of cellulose crystals in polar solvents due to the introduction of sulfate half ester groups on the cellulose chain, which, with a negatively charged surface coating, enhance intermolecular repulsive interactions [10].  
Cellulose nanocrystals have many interesting properties, such as a good stability, wide surface area, higher tensile strength (10 GPa), high elastic modulus (140-150 GPa), and several optical qualities.  
Their dimensions depend on the nature of the starting material, but they are certainly in the nanometer range. In particular, the length and width differ depending on temperature, time, and purity of the cellulose source [7; 9]. For example, CNCs obtained from plants have a diameter of 3-5 nm and length of up to 100-300 nm, whereas the CNCs collected from tunicates have a diameter of 10-20 nm and length of up to 500-2000 nm [9]. The morphological characteristics of CNCs are studied by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) or dynamic light scattering (DLS) [3].  
Table 1 shows the dimensions of cellulose nanocrystals as a function of the acid used for the hydrolysis and the source, highlighting the various parameters.
# Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
## 3 Simulation Results  
### Simulation Parameters  
#### 3.1.1 Characteristics of Ajisai for TLTT  
Ajisai is the nickname of the Japanese geodetic satellite EGS (Experimental Geodetic Satellite) operated by the Japan Aerospace Exploration Agency (JAXA). The satellite was launched into the circular orbit with an altitude of 1500 km and inclination of 50 degrees in Aug. 1986; it was one of three payloads of the test flight on the H-1 rocket [7].  
The shape of the Ajisai is a hollow sphere with a 2.15 m diameter; it is covered with 1436 corner cube reflectors for laser ranging and 318 mirror panels for photometric observation. The surface size and radius of curvature of the reflective mirrors are 393 cm\({}^{2}\) at maximum and 8.35-8.70 m, respectively. These mirrors are placed in 14 rings on the surface of the Ajisai satellite with 2.15 m diameter; the latitude angle of each mirror on the 8.5 m radius sphere is maintained on the surface of Ajisai satellite to reduce the actual radius to 1.08 m. The allocation of convex mirrors on Ajisai was designed to provide solar reflection to the ground observer at 3 times per rotation period by installing three mirrors in the same ring to have an identical latitude angle [7,10]. The key satellite parameters used for TLTT simulation are listed in Table 1.  
The initial spin rate of the Ajisai was 40 rotations per minute (rpm), chosen to stabilize the attitude of the hollow sphere; this rotation can provide observability of flashing light at a repetition rate of 2 times per second for the reflecting mirror configuration. The satellite spin period has slowly changed due to the effects from gravitation, solar radiation and irradiation pressure [13, 14, 15]. Following a recent analysis, the Ajisai spin period was found to double every 46.6 years; the current value is approximately 2.4 s [16,17]. Therefore, the current rate of light flashing has changed to 1.5 Hz for a spin rate of 25 rpm, from 2 Hz for 40 rpm.  
The reflectivity of Ajisai's mirror is a key parameter that determines the performance of the observation of reflected light. The convex mirrors of Ajisai were coated by silicon oxide for protection on the aluminum base; the reflectivity was in a range from 0.85 to 0.92 (mean 0.898) before launch. Based on analysis of the hyper temporal light curves of the Ajisai over 2 years from October 2015, the specular coefficients of the reflecting mirror were determined in the range of 82.3% to 88.2% (mean 85.3%), which shows that there has been a very small degradation of reflectivity of Ajisai's convex mirrors over the last 30 years.
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 4 Materials and Methods
### Configuration and Pre-Optimisation  
The necessary data for the 3D real-sized model of the city of Coimbra were obtained using a Blender plugin called BLosm, which extracts data from OpenStreetMap. This plugin utilises predefined rules to generate 3D structures such as buildings, areas and roads from OSM data, assigning appropriate colours to each component with respect to predefined rules from OSM as available in this link: [[https://wiki.openstreetmap.org/wiki/Simple_3D_Buildings](https://wiki.openstreetmap.org/wiki/Simple_3D_Buildings)]([https://wiki.openstreetmap.org/wiki/Simple_3D_Buildings](https://wiki.openstreetmap.org/wiki/Simple_3D_Buildings)) (accessed on 28 October 2024). BLosm added a new panel in Blender, allowing users to select the desired area and specify options for extracting specific elements. Figure 6 shows a screenshot taken from Blender.  
Following the data extraction with BLosm, modifications were made in Blender, such as converting curved roads to mesh objects and reduce the triangle count by using modifiers called \"Decimates\". These modifiers attempted to reduce the triangle count by preserving the geometrical shape. The buildings extracted were initially grouped as one mesh object. A function was applied to separate each building into its own object, leading to an increase in the number of buildings/objects to approximately 16,000 or more. This significantly increased memory and CPU usage in Unity, as each building had its own components and properties. To mitigate this issue, all the buildings that were not impacted by the flooding, based on their height location and a maximum realistic water height for the flooding, were grouped into a single object, reducing the number of individual parts up to 2000. Blender's scripting capability was necessary to achieve this. This realistic flooding height was chosen as the lowest possible to include most of the buildings. For instance, a flooding with a height of 10 m is almost impossible for a flooding that comes from a river overflow.  
Figure 5: General workflow and links between each component of the project. Gear wheels represent the libraries that provide most of the solutions used.  
An instance of a Vuforia AR camera and image target GameObject is sufficient to run a prototype application in Unity. After importing the image target from the database created through the Vuforia platform, it can be selected and displayed in the scene as a real-world reference. This image target reference is used to adapt the 3D objects in the editor, providing some insights into the proportions for the final application.
# Design Study of a Large-Angle Optical Scanning System for MEMS LIDAR
## 5 Conclusions  
This work designed an angle extension optical system for a MEMS mirror whose mirror diameter is 5 mm and maximum mechanical rotation angle is \(\pm 5^{\circ}\). In this design an \(f\)-\(\theta\) lens group is used for flat field scanning and a symmetrical cemented lens group is used for angle expansion. The design theory was derived in detail and a simulation was carried out by ZEMAX software. The designed system realized a scanning angle as large as \(57^{\circ}\) and is in good linear relation with the tilt angle of the MEMS mirror. At a target distance of 100 m, the maximum RMS radius of the light spot is only 7 cm, and the beam divergence angle is less than 0.5 mrad. The total length of the optical system is less than 12 cm, which is easy to install and greatly improves the space and volume for the whole system. This optical scanning system also shows significant application potentialities for other 3D imaging LIDARs.  
Conceptualization, Y.P. and M.Y.; methodology, Z.B.; software, K.Z. and Y.S.; validation, M.Y., Y.P. and Z.B.; formal analysis, Y.P.; investigation, K.Z. and Y.P.; resources, Z.B. and Y.P.; data curation, M.Y.; writing--original draft preparation, Y.P.; writing--review and editing, M.Y.; visualization, M.Y.; funding acquisition, Y.P. and Z.B. 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 61905063, and 61905061); the Natural Science Foundation of Hebei Province, China (grant number F2020202055).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Data Analysis, Experimental Design, and Method Validation
### Validation and Method Monitoring  
The validation of a developed model, whether for calibration or classification, is a crucial step in the application of any vibrational spectroscopy technique. Many studies often rely solely on cross-validation to evaluate the model's performance, both qualitatively and quantitatively. However, independent validation is highly recommended and necessary to ensure the model's robustness. This can be performed by incorporating samples that were not part of the model's development process, such as those from different geographical regions or harvests. After the model is both developed and validated, continuous monitoring of its accuracy and reliability is essential. This ongoing evaluation should be part of the model's lifecycle, as variations in sample characteristics or processing conditions may significantly influence the predicted results [57]. Cross-validation is commonly employed to estimate the performance of calibration and classification models when external validation sets are not available. Among various approaches, k-fold cross-validation and leave-one-out cross-validation (LOOCV) are widely used. These methods reduce the risk of overfitting and offer robust error estimation, especially in studies with a limited sample size. Furthermore, independent validation using external datasets, such as samples from different regions, harvest periods, or food batches, provides an additional layer of reliability. Model robustness should also be periodically re-evaluated to account for potential shifts in food matrix characteristics over time.
## 2 Theoretical Background  
### Limit-State Function  
A limit-state function is a mathematical expression that defines a limit or boundary separating the safe or non-failure state from the failure state of a structure or system. It is a fundamental concept used to assess the safety and reliability of structures or components subject to various uncertainties, such as material properties, loads, dimensions, and environmental conditions. Consider a system in which the state is represented by a random vector of variables \(\mathbf{X}\in D_{\mathbf{X}}\subset\mathbb{R}^{M}\). Two distinct domains, \(D_{s}\) and \(D_{f}\), can be defined within the state space \(D_{\mathbf{X}}\), corresponding to the safe and failure regions, respectively. In essence, the system is deemed to be in a state of failure if the current state \(\mathbf{x}\) is within \(D_{f}\), and it is considered to be operating safely if \(\mathbf{x}\) is within \(D_{s}\). This classification allows for the development of a limit-state function, referred to as \(g(\mathbf{X})\). The surface in \(M\) dimensions defined by \(g(\mathbf{X})=0\) is termed the limit-state surface, serving as the dividing line between the safe and failure domains illustrated in Figure 1.
## 3 Results and Discussion of CNN Models Classification
### Framework and Implementation Details
#### 3.2.4 Model Evaluation and Interpretation  
The models were evaluated using 5-fold cross-validation, with accuracy, precision, recall, and F1-score as the primary metrics. Grad-CAM and LIME were applied both to correctly classified and misclassified samples, providing insights into model behavior and highlighting areas for potential improvement.  
By combining the power of CNN architectures with explainability methods like Grad-CAM and LIME, the study not only achieved reasonable classification accuracy, but also offered transparency into the decision-making process, which is crucial for industrial applications.
## 3 Results and Discussion
### Comparison of the Three Methods  
This section presents a comparative analysis of three methods. It is important to consider the suitability of each method for different purposes. The response spectrum is commonly used in the design phase, while nonlinear dynamic analysis and pushover analysis are used for verification purposes. An integrated approach, as described in Section 2.1, can also be used with the aforementioned nuances.  
Each of the three methods has applications in various contexts, each with its advantages and disadvantages. The advantages and disadvantages of spectral modal analysis, non-linear dynamic analysis, and pushover analysis differ in terms of computational cost, precision, and applicability. These terms should be understood as the calculation time using the same structural model, equipment and software tool; the adequacy of the model's response to reality; and the ease or difficulty of applying the model and/or accessing the necessary information. Therefore, in no case should one method be discarded or preferred over another, as each has its own context of suitability. Although absolute terms cannot be established due to variability in computer tools, structures, types of FEM discretisation, required precision, imposed time steps, and other factors, it is possible to identify some relative advantages and disadvantages for the same site and structure:  
* Spectral modal analysis does not require the availability of an accelerogram, allowing for analysis using an elastic response spectrum. However, certain site parameters, such as maximum ground acceleration, among others, need to be known.
* Spectral modal analysis has lower computational cost compared to non-linear dynamic analysis and pushover analysis. However, in terms of precision, its results may be more limited.  
Figure 11: (**A**) Pushover hinges analysis. Mode Y, step 22. (**B**) Pushover hinges analysis. Mode Y, step 63.  
* Non-linear dynamic analysis offers a precise dynamic representation of the structure, which is ideal for vibration studies and dynamic responses. However, it typically requires higher computational cost and the availability of suitable accelerograms, as well as an appropriate time interval to increase precision.
* Pushover analysis allows for nonlinear analysis of the structure, accurately detecting the formation of plastic hinges under imposed displacement. It is crucial to have an adequate number of time steps for a maximum imposed displacement and, therefore, achieve the desired precision.
* The results obtained for spectral modal analysis (with a 5% damping ratio and high ductility) and non-linear dynamic analysis for the 2011 Lorca earthquake are presented in Table 2, corresponding to the reference node.  
All analysed methods (spectral modal, non-linear dynamic analysis, and pushover analysis) require prior eigenvalue analysis to obtain the vibration modes of the structure, which is essential for successfully and accurately conducting subsequent analyses.
# Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance
## 3 Quadrotor Attitude Control System Design
### ESO Parameter Tuning with CMA-ES Algorithm
#### 3.4.4 Adapting the Covariance Matrix  
The covariance matrix \(\mathbf{C}_{g}\) determines the search directions. Two methods to update the \(\mathbf{C}_{g}\): rank-1 update and rank-\(\mu\) update are presented in this section. The rank-1 update means that we should search new points in the direction of the new mean value:  
\[\mathbf{p}_{g+1}=\left(1-c\right)\mathbf{p}_{g}+\sqrt{c\left(2-c\right)}\sqrt{\mu_{ \omega}}\frac{\mathbf{m}_{g+1}-\mathbf{m}_{g}}{\sigma_{g}} \tag{16}\]where \(\mathbf{p}_{g}\) is the evolution path, \(c\) is the learning rate of the evolution path update, and \(\mu_{\omega}=\frac{1}{\sum_{i=1}^{n}\omega_{i}^{2}}\). The rank-\(\mu\) update means we should search new points in the direction of the search points with low costs. As a result, the covariance matrix \(\mathbf{C}_{g}\) updates with the equation below:  
\[\mathbf{C}_{g+1}=\left(1-c_{1}-c_{\mu}\right)\mathbf{C}_{g}+c_{1}\mathbf{p}_{g+1}\mathbf{p}_{g+ 1}^{T}+c_{\mu}\sum_{i=1}^{\mu}\left(\frac{\mathbf{x}_{i;\mu}-m_{g}}{\sigma_{g}} \right)\left(\frac{\mathbf{x}_{i;\mu}-m_{g}}{\sigma_{g}}\right)^{T} \tag{17}\]  
where \(c_{1}\) and \(c_{\mu}\) are the learning rates for the rank-1 update and the rank-\(\mu\) update.