esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Geometrically Exact 3D Beam Model and Variational Form for Assigned Strains
### Rotation Tensor  
Finite rotation algebra is involved in large deformation analysis of 3D beams. We refer readers to [17; 18; 21; 22; 23] among the great amount of work available in the literature on the topic.  
Finite 3D rotations can be described by an orthogonal tensor \(\mathbf{R}\) belonging to the nonlinear manifold \(\mathrm{SO}_{3}\). The properties \(\mathbf{R}^{-1}=\mathbf{R}^{T}\) and \(\det\mathbf{R}=1\) allow us to express \(\mathbf{R}\) in terms of only three parameters. A useful way to parametrize \(\mathbf{R}\) is the use of the rotation vector \(\boldsymbol{\varphi}\) because it lies in a vector space:  
\[\mathbf{R}(\boldsymbol{\varphi})=\mathbf{I}+a_{1}(\varphi)\mathbf{W}( \boldsymbol{\varphi})+\frac{1}{2}a_{2}(\varphi)\mathbf{W}(\boldsymbol{\varphi} )^{2} \tag{1}\]  
Figure 2: Wavelength variation as a function of the strain (image source: [[https://scienzapertutti.infn.it/schede-approfondimento/1669-fbg-cosa-sono-e-a-cosa-servono](https://scienzapertutti.infn.it/schede-approfondimento/1669-fbg-cosa-sono-e-a-cosa-servono)]([https://scienzapertutti.infn.it/schede-approfondimento/1669-fbg-cosa-sono-e-a-cosa-servono](https://scienzapertutti.infn.it/schede-approfondimento/1669-fbg-cosa-sono-e-a-cosa-servono)) (accessed on 20 July 2022)).  
where \(\varphi=\sqrt{\varphi^{T}\varphi}\) is the rotation magnitude and \(\mathbf{W}(\varphi)\) is the spin tensor corresponding to \(\varphi\), i.e., \(\mathbf{W}(\varphi)\mathbf{v}=\varphi\times\mathbf{v}\) for all vectors \(\mathbf{v}\), where \(\times\) denotes the cross product. As reported in [15], the trigonometric functions \(a_{1}(\varphi)\) and \(a_{2}(\varphi)\) are  
\[a_{1}(\varphi)=\frac{\sin\varphi}{\varphi},\qquad a_{2}(\varphi)=\frac{1-\cos \varphi}{\varphi^{2}}.\]  
This parameterization uses the minimal set of parameters to describe the rotation and gives a one-to-one correspondence between \(\mathbf{R}\) and \(\varphi\) in the range \(0\leq\varphi<2\pi\)[15; 18].  
For future use, we also introduce the second order tensor \(\mathbf{T}\) defined as  
\[\mathbf{T}(\varphi)=\mathbf{I}+a_{2}(\varphi)\mathbf{W}(\varphi)+\frac{1}{2} a_{3}(\varphi)\mathbf{W}(\varphi)^{2} \tag{2}\]  
with  
\[a_{3}(\varphi)=\frac{\varphi-\sin\varphi}{\varphi^{3}}.\]  
so that, as reported in [24], we have the equivalence  
\[\mathrm{axial}\Big{(}\mathbf{R}^{T}\mathbf{R}_{s}\Big{)}\equiv\mathbf{T}( \varphi)^{T}\varphi_{s}\]  
where \(\mathrm{axial}(.)\) denotes the extraction of the axial vector from a skew tensor.
## 5 Common Systems
### Timing and Synchronization
#### 5.1.7 CW Fiber-Optic Links for Clock Distribution  
For most diagnostic monitors along the machine, the kicker systems as well as the controls event timing distribution system, the drift and jitter requirements are less stringent. Therefore a distribution system was developed in collaboration with and purchased from SINTEC Microwave Systems GmbH, which is adapted for the needs of SwissFEL with respect to the number of clients, their location along the machine, the requested power levels and the jitter performance. Our clock signal at 142.8 MHz serves as input signal to a transmitter module, where the signal is amplified and split into 8 outputs. Each of these outputs is fed into an optical transmitter module where it modulates an optical carrier at 1310 nm wavelength. After transmission through the fiber connection, the optical carrier input signal is demodulated in a Receiver module, converted back to a 142.8 MHz electrical signal and amplified. Emanating from the 8 Receiver stations along the machine, a local coaxial T-shaped sub-distribution supplies several diagnostic monitors. The added jitter of the clock distribution in the offset range from 10 Hz to 10 MHz is 73 fs rms and lies well below the most demanding specified value among the clients of 200 fs (rms). This system is installed in SwissFEL, operational and remotely monitored with respect to RF power levels at the receiver stations, module temperatures, amplifier and laser diode currents.
## 1 Introduction
### Background  
Our work focuses on dual-model EHR architectures, which consist of separately defining the EHR schema (i.e., EHR archetypes) from the EHR data (i.e., EHR instances or EHR extracts). Specifically, a dual-model architecture defines two different models: first, a generic Reference Model (RM) designed to represent the most basic properties and structures of any EHR, examples are OpenEHR and ISO13606; and second, an Archetype Model (AM). Archetypes are detailed definitions of clinical concepts in the form of structured and constrained combinations of the entities of the reference model [9]. Archetypes are used to model concepts in the EHR, and EHR extracts are instances of archetypes containing the patient's EHR data.  
This work introduces a methodology to enrich a dual-model-based EHR with potentially relevant information from the Semantic Web. The Semantic Web incorporates semantic metadata from ontologies and terminologies into the Web. It defines URIs that link to data, and documents, which allow categorization of each datapoint in the Semantic Web with coded values from terminologies and ontologies. In addition, The Semantic Web establishes mechanisms to define semantic relationship between data. Metadata languages, such as \"Resource Description Framework\" (RDF) or \"Ontology Web Language\" (OWL), allow indexing of web information resources with knowledge representations and store them in documents. The Semantic Web provides machines with the capability to interpret and make inferences over the data [10]. It introduces mechanisms to retrieve data, such as \"SPARQL Protocol and RDF Query Language\" (SPARQL), and to perform data reasoning, such as \"Semantic Web Rule Language\" (SWRL).
# Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
## 4 Results and Discussion
### GMI and BER Performance  
The GMI versus ROP for four different kinds of PS-DMT signal schemes at IRs of \(4.9/4.5/3.9\) bits/QAM symbol are investigated and shown in Figure 9a-c, respectively. They indicate that Preq, WHT-based FDSP and Fast WHT-based PDSP schemes have almost the same GMI performance and outperform the original one, due to the relatively flat SNR distribution. As the theoretical maximum information rate can be given by GMI with an ideal FEC code, there is about \(0.3\) dB improvement for the receiver sensitivity in PS-DMT transmission system with the LDPC rate of \(9/10\).  
Figure 6: Spectra of the received signals with (**a**) original scheme, (**b**) Preq scheme, (**c**) WHT-based FDSP scheme and (**d**) Fast WHT-based PDSP scheme.  
The measured BER performance for four kinds of PS-DMT signal scheme versus ROP is investigated and shown in Figure 10. When the LDPC rate is set to 9/10 and IRs equal to 4.9/4.5/3.9 bits/QAM symbol, compared with the original scheme, the receiver sensitivity improvements of about 1 dB can be achieved at BER performance below 1e-3 for Preq/WHT-based FDSP/fast WHT-based PDSP schemes. Thus, the fast WHT-based PDSP scheme may be a good option for the implementation of the PS-DMT transmission systems with a large SC SNR fluctuation regarding computational complexity.  
Figure 8: The recovered 64 QAM constellations for (**a**) original scheme, (**b**) pre-equalization, (**c**) WHT-based FDSP and (**d**) fast WHT-based PDSP.  
Figure 7: The estimated SC SNR for four kinds of received PS-DMT signals.  
Figure 9: Measured GMI versus ROP for IRs of (**a**) 4.9, (**b**) 4.5 and (**c**) 3.9 bits/QAM symbol.
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### Quality Analysis  
#### 2.6.1 Camera Calibration Accuracy Analysis  
To evaluate the accuracy of the calibration (Section 2.5.2), the RMS residual (with units in pixels) was used in each calibration solution to represent the re-projection errors for the tie points. These points were detected on the source images and averaged across all the images of the calibration group. The values corresponding to each item are listed in Table 7.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Internet of Things for Advanced Manufacturing  
The Internet of Things is not only transforming our homes and cities but also revolutionizes the manufacturing industry. Known as the Industrial Internet of Things (IIoT), this integration brings IoT technologies into advanced manufacturing processes, enhancing efficiency, reducing downtime, and improving overall productivity. As we can see, there are many different avenues where IoT is applied within manufacturing, each serving specific purposes. For example, imagine a factory floor where every machine is equipped with sensors that monitor temperature, vibration, and performance in real time. These sensors collect data and send them to a centralized system that uses advanced analytics to predict when a machine might fail or require maintenance. This predictive maintenance approach can prevent unexpected breakdowns, saving time and reducing costs. Furthermore, IoT devices enable the automation and remote control of manufacturing processes. Robots and automated systems can communicate with each other and adjust operations based on real-time data. For instance, if a production line is experiencing a bottleneck, the system can automatically reroute tasks or adjust production rates to optimize workflow. In advanced manufacturing, IoT is also enhances supply chain management. With IoT sensors tracking inventory levels, shipments, and logistics, manufacturers can maintain optimal stock levels and reduce waste. Additionally, IoT devices can ensure quality control by monitoring products throughout the production process, detecting defects early, and ensuring consistency [7]. All of these applications of IoT in advanced manufacturing aim to make the production process more efficient, reliable, and cost effective. Just like in smart homes and cities, the ultimate goal is to make life easier and more productive, not only for manufacturers but also for consumers who benefit from better products and services.
## 3 Research Model and Hypotheses
### Perceived Usefulness  
Perceived usefulness, an important factor in TAM, plays a non-negligible role in predicting behavioral intentions [17; 36]. In the context of research on online gamified education, students' perceived usefulness of new learning mode continues to be a part worth focusing on. The inclusion of game elements in an outcome-oriented learning process has been a topic of concern for educators in terms of whether the inclusion of game elements can promote good learning outcomes and learning experiences for students. Likewise, under the condition that game elements are combined with online instruction, learners' recognition of the \"usefulness of online gamified classes\" may further influence their behavioral willingness to participate in class. However, in real-life teaching and learning contexts, students' positive learning attitudes are not always maintained by \"perceived usefulness\", and the Task-Technology-Fit plays a significant role in determining the actual usage of game elements in learning situations [37]. As a result, we suggest that students' subjective perceptions of the usefulness of the gamified instructional model may influence students' perceptions of classroom learning tasks and their behavioral intentions to participate in gamified online classes. Hence, the following hypotheses are proposed:  
**Hypothesis 2 (H2):**_Perceived usefulness has a significant positive influence on students' intention to participate in an online gamified classroom._  
**Hypothesis 3 (H3):**_Perceived learning tasks have a mediation effect on the effect of perceived usefulness on students' intention to participate in an online gamified classroom._
# Quadcopters in Smart Agriculture: Applications and Modelling
## 2 Types of Quadcopters
### Commercial Quadcopters
#### 2.1.1 3 DR Iris+  
The 3 DR Iris+ includes a GoPro camera which allows it to capture high resolution images and videos. Many research papers use this type of drone for different agricultural tasks such as monitoring [38; 39], plant health assessment [40; 41], and chemical spraying [41]. For example, the authors in [40] used the 3 DR Iris+ equipped with a modified Canon S110 camera to capture aerial images of potato crops in Ventaquemada, Boyaca, Colombia. This approach enabled them to assess areas infected by fungus early on, allowing for timely interventions to prevent the spread of diseases and reduce yield losses.
depending on the application [12]. Rotor-based smart surfaces, in particular, excel in handling heavy loads and maintaining high-speed performance, making them the most suited solution for package handling [12]. Among these devices, traditional designs typically rely on assembling modular units, each one containing several motorized omnidirectional wheels [2; 9; 11] or single rotors with dual-axis motors [10; 13]. These fully or over-motorized solutions achieve precise control at the expense of increased design complexity and energy consumption [12]. To address these limitations, an under-actuated rotor system has been proposed, leveraging passive rotors and discrete axis control to simplify the design while maintaining functional capabilities [2; 14]. Building on prior work that introduced and validated the under-actuated rotor-based smart surface [2; 14], this paper shifts focus to the control methodologies required to make the system \"smart.\" In fact, effective control is essential for enabling the surface to perform tasks such as sorting and orienting objects.
## 3 Results and Discussion
### Classification Models Based on Selected Optimal Wavelengths  
Figure 5 shows the visual evaluation of the classification models for overfitting or underfitting, where each decision boundary is shown as a black line between colored regions. A decision boundary with a complex curved shape indicated an overfit model. The decision boundaries of the LDA with range normalization and SVM with raw data models (Figure 5a,c, respectively) are each a simple straight-line and there is as much separation between the two classes as possible. The decision boundaries of QDA with range normalization and RBF SVM with raw data has a curved line.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Pretreatment** & **1 st Band (nm)** & **2 nd Band (nm)** & **3 rd Band (nm)** \\ \hline \multirow{4}{*}{LDA} & Raw & 396 & 583 & 741 \\  & SNV & 396 & 453 & 846 \\  & Max normalization & 559 & 563 & 865 \\  & Mean normalization & 410 & 765 & 865 \\  & Range normalization & 554 & 966 & 985 \\  & Smoothing & 396 & 578 & 741 \\ \hline \multirow{4}{*}{QDA} & Raw & 420 & 635 & 640 \\  & SNV & 822 & 846 & 880 \\  & Max normalization & 712 & 789 & 899 \\  & Mean normalization & 640 & 918 & 956 \\  & Range normalization & 415 & 607 & 827 \\  & Smoothing & 420 & 631 & 990 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 4: Performance comparison of SFS using the classifiers of (**a**) linear discriminant analysis (LDA); (**b**) quadratic discriminant analysis (QDA); (**c**) support vector machine (SVM) and (**d**) SVM with radial basis function (RBF) kernel, with different data preprocessing methods for two-class classification.  
To completely classify the groups, a complicated decision boundary is required which leads to overfitting problems. The decision boundary of QDA and RBF SVM has a simple curve, which means the model is not over-fitted. Thus, most of the validation set samples (identified by 'x' markers in Figure 5) belong to the areas that are correctly classified. This result implies that the models are well generalized and will work on an unknown data set. However, in Figure 5f, the distribution of the data is linear indicating the linear decision boundary is a possible classifier for identifying two groups in these two cases. The decision boundary was a relatively more complex curve than a linear decision boundary, even though the validation sets are correctly classified (Figure 5), indicating that it does not guarantee performance using unknown data. The 3D hyperplane decision boundaries for SVM and RBF SVM are shown in Figure 6. The 3D decision boundary for SVM is a flat plane and has a good separation between two groups. However, the 3D decision boundary for RBF SVM consisted of a curved plane even though the distribution of the data is linear. Based on this result, it is not necessary to have a complex shaped model to distinguish between the two groups, and a simple linear or nearly planar decision boundary, as in the case of Figure 5 (when only two features are used), can provide a sufficiently effective and simple model that performs with high accuracy.  
Figure 5: _Cont._
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 2 USV Thruster Fault Feature
### Analysis of Thruster Fault Features  
To diagnose the selected faults (the breakage of the thruster blade and entanglement of floating objects), vibration, current consumption and rotational speed (revolutions per minute, RPM) of the thruster were selected as the fault features, in line with a previous study on the validation of fault feature selection for an underwater thruster [25]. In the proposed fault diagnosis method, correlations between data in fault conditions were analyzed to identify patterns, and faults were diagnosed by visualizing the identified patterns. Therefore, frequency-domain analysis, whereby the number of samples of data could be reduced, or filters with time delays were not applied; thus, the raw data-based method was used.  
Figure 1: General procedure of fault diagnosis.
# Magnetic Force Enhancement Using Air-Gap Magnetic Field Manipulation by Optimized Coil Currents
## 4 Optimization Problem Formulation  
The design goal of this work is set to find the optimal coil current distribution maximizing the average force \(F_{avg}\) acting on the plunger during its movement, as shown in Figure 3. To achieve the design goal, the optimization problem is formulated as  
\[\text{find }\beta_{i} \tag{7}\]  
\[\text{Maximize }F_{avg} \tag{8}\]  
\[\text{Subject to }\mathbf{KA}=\mathbf{f} \tag{9}\]  
\[\sum_{i=1}^{N}\bigl{|}\beta_{i}\bigr{|}\leq\beta_{total}^{*} \tag{10}\]  
\[-\beta_{max}\leq\beta_{i}\leq\beta_{max} \tag{11}\]  
In the benchmark actuator model in Figure 3, the objective function (8) is calculated as the average of the \(x\) directional (i.e., tangential direction to the air-gap line) magnetic force \(F_{x}\) at 21 equally spaced plunger locations:  
\[F_{avg}=\frac{1}{21}\sum_{m=1}^{21}F_{x}^{m} \tag{12}\]  
Here, the magnetic force \(F_{x}\) is calculated using [PERSON]'s stress tensor formulation in (1). In (9), the element stiffness matrix \(\mathbf{K}\) and force vector \(\mathbf{f}\) are derived using the finite element formulation of  
Figure 5: The plot of the current \(\mathbf{I}_{0}\) in (5) with respect to the design variable \(\beta_{i}\)  
[PERSON]'s equation in (2). The design variable \(\beta_{i}\) control the distributions of current density \(\textbf{J}_{a}^{i}\) and relative permeability \(\mu_{r}^{i}\) through (3) and (4), which are related to the magnetic flux density **B** through (2). The magnetic flux density **B** is then connected with the objective function \(F_{avg}\) through (1) and (12). The constraint in (10) is applied to confining the total number of coil turns below the target value \(\beta_{total}^{*}\). The constraint in (11) is for setting the maximum value of a number of coil turns as \(\beta_{max}\).  
In this work, the objective function (8) and the system of linear Equation (9) are computed using the finite element commercial software, COMSOL V5.3. The optimization problem (7)-(11) is solved using the globally convergent method of moving asymptotes (GCMMA) algorithm. The GCMMA code in MATLAB R2013b is combined with COMSOL for the implementation.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
###### Abstract  
As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling methods are higher-accuracy but ignore the computational speed. Combining these two types of methods to obtain fast and accurate recognition results remains a challenging problem. Motivated by this, in this study, a novel Manifolds-based Low-Rank Dictionary Pair Learning (MbLRPL) method was developed for a set-based video recognition/image set classification task. Specifically, each video or image set was first modeled as a covariance matrix or linear subspace, which can be seen as a point on a Riemannian manifold. Second, the proposed MbLRPL learned discriminative class-specific synthesis and analysis dictionaries by clearly imposing the nuclear norm on the synthesis dictionaries. The experimental results show that our method achieved the best classification accuracy (100%, 72.16%, 95%) on three datasets with the fastest computing time, reducing the errors of state-of-the-art methods (JMLC, DML, CEBSR) by 0.96-75.69%.  
set-based video recognition; image set classification; manifold learning; fast and accurate classification; discriminative dictionary learning
## 2 Materials and Methods
### Electrical Characterization  
The total voltage and the capacitor voltage associated with the electric charge estimation were measured using a high voltage probe (Rigol-RP1018H) and a differential voltage probe (Micsig, DP10013). Both voltage waveforms were recorded using an oscilloscope (Rigol, DS1054z, 50 MHz, 1 GS/s). The measured voltage by the differential probe across the capacitor (4.7 nF) is proportional to the electric charge passing through the electrode. The measured total voltage and electric charges values were plotted in a charge-voltage figure (Lissajous curve) as shown in the Figure 2.