esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response Analysis of Structures Using Legendre-Galerkin Matrix Method
## 2 Basics for SDOF and MDOF Systems
### Governing Equation for SDOF Systems  
A structure with one degree of freedom with mass \(m\), stiffness \(k\), and damping \(c\) is assumed to be under dynamic load \(P(t)=P_{0}\sin(\Omega t)\) with excitation frequency \(\xi\) (Figure 1).  
The governing equation of this system is [1]:  
\[m\ddot{u}(t)+c\dot{u}(t)+ku(t)=P_{0}\sin(\Omega t) \tag{2}\]  
where Equation (2) is written with similar connotations to Equation (1). The exact solution to the motion equation of a SDOF system under harmonic load \(P_{0}\sin(\Omega t)\) with initial conditions \(u(0)=u_{0}\) and \(\dot{u}(0)=\dot{u}_{0}\) is the sum of the displacements of the transient state of the system (\(u_{transient}\)) and the steady state (\(u_{steady\,state}\)), and is expressed as [5]:  
\[u_{total}=u_{transient}+u_{steady\,state} \tag{3}\]  
in which \(u_{transient}\) and \(u_{steady\,state}\) are defined as:  
\[\begin{array}{l}u_{transient}=e^{-\xi\omega t}\Big{(}u_{0}\cos\omega_{d}t+ \frac{\dot{u}_{0}+u_{0}\omega_{d}^{2}}{\omega_{d}}\sin\omega_{d}t\Big{)}+\\ \frac{p_{0}}{k}\frac{e^{-\xi\omega t}}{\left(1-\beta^{2}\right)^{2}+\left(2 \xi\beta\right)^{2}}\left[2\xi\beta\cos\omega_{d}t+\frac{\omega_{d}}{\omega_ {d}}\left\{2\beta\xi^{2}-\beta(1-\beta^{2})\right\}\sin\omega_{d}t\right], \end{array} \tag{4}\]  
and  
\[u_{steadystate}=\frac{p_{0}}{k}\frac{1}{\left(1-\beta^{2}\right)^{2}+\left(2 \xi\beta\right)^{2}}\Big{\{}\left(1-\beta^{2}\right)\sin\Omega t-2\xi\beta \cos\Omega t\Big{\}} \tag{5}\]  
where \(\xi\) and \(\omega=\sqrt{\frac{k}{m}}\) are the damping ratio and natural frequency of the system, respectively, and \(\omega_{d}=\omega\sqrt{1-\xi^{2}}\) indicates the damped frequency. Finally, \(\beta=\frac{\Omega}{\omega}\) is the ratio of the excitation frequency to the system natural frequency.
## 2 Background
### Modelling Techniques Adopted--Fuzzy Approach  
[PERSON] introduced fuzzy logic as a powerful modeling technique that can be used for understating the uncertainty and qualitative aspects of human nature [35]. Fuzzy techniques have been widely utilized in several studies over the past decade. It has the ability to virtually connect humans to computers by analyzing linguistic inputs to stem numerical outputs [36]. Traditionally, a set of inputs has sharp and crisp boundaries, where elements are either in or out of a set, and the ranking of membership of a variable is zero or one [37]. However, in the real world, information is mainly ambiguous and incomplete. That is when fuzzy logic is applicable, as elements are allowed to have partial memberships ranging from zero to one (i.e., zero is no membership, and one is full membership) [38].  
[PERSON] et al. [23], has previously developed prediction models for the performance of construction organizations using the Artificial Neural Network (ANN) model and regression. A total of 18 factors were identified from the literature review. Based on the responses received from industry experts (5-point Likert scale was used), these factors were evaluated and allotted ranks using ANN training (i.e., ranking the factors to determine the relative importance of each variable and the highest impact on the model). Analysis of weights of the trained neural network is used to derive the contribution percentages. The higher value implies that the variable contribution to classification/prediction is also high. Based on the ANN rankings, nine factors with the highest contributing factor were shortlisted from the pool of 18 factors.  
However, this advantage comes at the cost of the minimized interpretability of the model output. The black box quality of an ANN model makes it next to impossible to gain insight into a problem based on an ANN model. The regression technique allows the user to sequentially remove possible explanatory variables that do not contribute to the fit of the model [39]. Regression techniques permit hypothesis testing concerning both the univariate and multivariate association amongst each explanatory variable and the outcome of interest. However, it fails to recognize or identify the highly nonlinear factors or correlation among variables [39]. Human reasoning being more approximate than precise often makes it difficult to measure and determine the measure of factors affecting a particular cause.
## 3 Results
### FTIR Analysis
As shown in previous studies [35], FTIR spectra of clay minerals are complex, where both the position and the intensity of the IR peaks are dependent on the conditions of mineral formations and the presence of various impurity ions and elements within the lattice. The FTIR profile registered on the black pigment (Figure 4a) is clearly dominated by the absorption bands of calcite. Neither iron oxides nor quartz were found. Instead, manganese oxides may be present, as indicated by the IR bands centered around 1031, 490 (shoulder), and 423 cm\({}^{-1}\)[28; 36]. The small peak at 1627 cm\({}^{-1}\) along the broad absorption between 3600 and 3200 cm\({}^{-1}\), corresponding to stretching and bending vibrations of water molecules, infers the presence of hydrous Mn oxide. A manganese-rich ochre (black earth) may have been used for the black pigmented layer. As indicated by the existing literature, manganese ochres (also called wads) are composed of complex associations of manganese oxides and hydroxide minerals, and, in certain cases, also iron oxides [37; 38] (the presence of iron oxides, although not observed within the FTIR spectra, can be inferred via XRF analysis; see Section 3.2). Figure 4: FTIR spectrum registered on the black finishing layer (sample S7) (**a**) and on the plaster layers (**b**) (coarse plaster layer with black line; fine plaster layer with blue line). The absorptions of the various mineral components identified are marked on the spectra as follows: Q—quartz; H—hematite. Investigation of the coarse plaster layer (_arriccio_) indicated a similar composition to the one registered for the yellow ochre samples. Strong absorption bands ascribed to calcite were observed (1411, 871, 713 cm\({}^{-1}\)), as well as strong peaks assigned to hematite (Figure 4b). Silicates, namely, quartz, were also identified. Some clay minerals are also present, as inferred via the peaks at 3623 and 1011 cm\({}^{-1}\). FTIR spectra registered on the fine plaster layer (_intonaco_) highlighted the presence of calcite with very small amounts of mineral impurities such as iron oxides and possible traces of clay minerals.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 4 Experimental Evaluation
### Results and Discussion
#### 4.3.1 Packet Delivery Ratio (PDR %)  
Figure 6 shows the packet delivery ratio (PDR %) results of the three different mobility scenarios, 20, 40, and 60 m/s, versus 50 simultaneously moveable UAV nodes. The PDR of the proposed routing schemes offers better performance in all scenarios under different mobility. The benchmark routing protocols suffer and have low PDR; hence, their performance decreases due to the dynamic nature of nodes and unstable links during simulation time. In the 20 m/s mobility speed, the PDR. of the proposed scheme PHCC is at 95%, whereas the GEO-D routing protocol has a 60% PDR, though it offers better results in all benchmark schemes. In the comparison, at the start, AODV has a 70% PDR but its performance gradually decreases as the mobility increases due to the fact at high mobility, the topology of the UAV ad-hoc network is highly dynamic (unpredictable). Moreover, among our proposed techniques, PHCC gives 3.5% better PDR with static and 2% better PDR with random schemes. Our three proposed schemes offer better results in comparison with benchmark schemes. At 40 m/s, UAVs move at higher speeds; all routing protocols try to maintain the route. As the number of moving UAV nodes increases, the communication links between UAVs across the network change dynamically; as a result, the performance of other protocols decreases. At the start, GEOD tries to have 58%; as the number of moving UAVs increases, its performance decreases, just like other benchmark schemes. On the other hand, PHC-Static's performance also decreases due to mobility. At 60 m/s, which is the highest speed (suitable for military UAVs), the performance of all our proposed schemes is better than that of all other four-benchmark routing protocols. The GEO benchmark is for the 3D environment, but its PDR is less in our scenario due to the high-speed mobility under the different network environments. GEO cannot maintain the routes quickly; therefore, its PDR decreases. Moreover, the AODV topology scheme's performance is the worst because there is no stable path across the network. The proposed schemes store the position of each UAV in the routing table (unlike AODV, the UAVs have addresses); therefore, the source UAV knows the position of destination UAV and intermediate UAVs and, hence, can find the shortest path efficiently. Meanwhile, the benchmark schemes' performances degrade because of their unstable topology (AODV) and no information from UAVs out of communication range (position-based routing).
## 2 Materials and Methods
### Design and Implementation of the Sensing Device
#### 2.2.2 Hardware Components  
In this research, we employ the Arduino Nano 33 BLE Sense board composed of embedded sensors to detect color, proximity, motion, temperature, humidity, audio, and more. The presence of the embedded sensors allows the board to manage numerous IoT and AI applications without requiring the presence of external sensors. This board is built upon the nRF52840 microcontroller and runs on Arm(r) Mbed(tm) OS. The processor has other important features such as Bluetooth(r) pairing via NFC and ultra-low mode energy consumption. For the sensorization of the device, reference made is to the APDS-9960 unit built into the Arduino Nano33 BLE Sense board, which features advanced gesture sensing, proximity sensing, digital ambient light sensing (ALS), and color sensing (RGBC). This modular unit has dimensions 3.94 \(\times\) 2.36 \(\times\) 1.35 mm, and incorporates an IR LED and a factory-calibrated LED driver.  
The proximity detection function provides the measurement of distance via the photodiode sensing of reflected IR energy from built-in LEDs. Detect/release events are interrupt-driven and occur whenever the proximity result crosses the upper and/or lower threshold settings. The IR LED intensity is factory trimmed to eliminate the need for end-equipment calibration due to component variations. The proximity results are further improved by automatic ambient light subtraction. The proximity results are affected by three basic factors: IR LED emission, IR reception, and environmental factors, including distance to the target and the surface reflectivity. The photodiode signal is combined, amplified, and offset adjusted to optimize performance. The colour and ALS detection feature provides red, green, blue, and clear light intensity data. Each of the R, G, B, C channels has a UV and IR blocking filter and a dedicated data converter producing 16-bit data simultaneously. This architecture allows applications to accurately measure ambient light and sense colour, which enables devices to calculate colour temperature and control display backlight.
# An Accelerated Slicing Algorithm for Frep Models
###### Abstract  
Complex 3D objects with microstructures can be modelled using the function representation (FRep) approach and then manufactured. The task of modelling a geometric object with a sophisticated microstructure based on unit cell repetition is often too computationally expensive for CAD systems. FRep provides efficient tools to solve this problem. However, even for FRep the slicing step required for manufacturing can take a significant amount of time. An accelerated slicing algorithm for FRep 3D objects is proposed in this paper. This algorithm allows the preparation of FRep models for 3D printing without surface generation stage. The spatial index is employed to accelerate the slicing process. A novel compound adaptive criterion and a novel acceleration criterion are proposed to speed up the evaluation of the defining function of an FRep object. The use of these criteria is significantly reducing the computational time for contour construction during the slicing process. The K-d tree and R-tree data structures are used as spatial indexes. The performance of the accelerated slicing algorithm was tested. The contouring time was reduced 100-fold due to using the novel compound adaptive criterion with the novel acceleration criterion.  
function representation; implicit surface; additive manufacturing; 3D printing; K-d tree; R-tree +
Footnote †: journal: Applied
# Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
## 5 Case Simulation  
### Algorithm Testing  
To test the performance of IDBO, six benchmark functions were selected, including two unimodal benchmark functions, two multimodal benchmark functions, two fixed-dimension multimodal benchmark functions, and a multi-objective function to comprehensively assess its optimization capabilities [28]. Through testing, the basic performance of IDBO can be validated. Benchmark functions are shown in Table 3.  
From the benchmark function tests, IDBO showed clear advantages in both speed and accuracy. In tests on the unimodal benchmark functions \(F_{1}\) and \(F_{2}\), IDBO quickly converged to a stable state in the early stages of iteration and was much faster than other algorithms, including DBO. The results showed that IDBO had excellent speed and accuracy. For the multimodal functions \(F_{3}\) and \(F_{4}\), which have multiple local optima where other algorithms often become stuck, IDBO maintained good performance. It effectively avoided these traps  
Figure 5: Flowchart of IDBO.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Dim** & **Range** & **Optimal Value** \\ \hline \(F_{1}=\sum_{i=1}^{t}\left(\sum_{j-1}^{j}x_{j}\right)^{2}\) & 30 & \(\left[-100,100\right]\) & 0 \\ \(F_{2}=\sum_{i=1}^{t}\left[(x_{i}+0.5)\right]^{2}\) & 30 & \(\left[-100,100\right]\) & 0 \\ \(F_{3}=\sum\limits_{i=1}^{t}\left[x_{i}^{2}-10\cos(2\pi x_{i})+10\right]\) & 30 & \(\left[-5.12,5.12\right]\) & 0 \\ \(F_{4}=\dfrac{1}{4000}\sum\limits_{i=1}^{t}x_{i}^{2}-\sum\limits_{i=1}^{t} \cos\left(\dfrac{x_{i}}{\sqrt{i}}\right)+1\) & 30 & \(\left[-600,600\right]\) & 0 \\ \(F_{5}=\sum\limits_{i=1}^{11}\left[a_{i}-\dfrac{x_{1}(b_{i}^{2}+b_{i}x_{2})}{b _{i}^{2}+b_{i}x_{3}+x_{4}}\right]^{2}\) & 4 & \(\left[-5,5\right]\) & 0.0003 \\ \(F_{6}=-\sum\limits_{i=1}^{5}\left[(X-a_{i})(X-a_{i})^{T}+c_{i}\right]^{-1}\) & 4 & \(\left[0,10\right]\) & \(-10.1532\) \\ ZDT 1 & 2 & \(\left[-4,4\right]\) & \(\left(0,1\right)\) to \(\left(1,0\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Benchmark functions.  
and found better solutions, with better fitness function values than other algorithms. In tests with fixed-dimension multimodal functions \(F_{5}\) and \(F_{6}\), IDBO performed especially well, particularly with \(F_{6}\), where other algorithms became stuck in local optima. IDBO not only escaped the local optima but also quickly approached the global optimal solution, maintaining its advantage in complex search spaces. From the results of the multi-objective test function ZDT1, it can be observed that the approximate Pareto front is relatively close to the true Pareto front in most regions, which indicates that IDBO exhibits good convergence and diversity in the multi-objective optimization process, effectively approximating the true optimal solution set. A comparison of the results of different test benchmark functions is shown in Figure 6.
## 5 Evaluation and Testing
### Data Integration and Interoperability Tests Using Ontop SPARQL Endpoint to Evaluate Federated VKG
\begin{table}
\begin{tabular}{l c} \hline \hline
**Syntax** & **Query** & **Execution Time** \\ \hline PREFIX _xml_: \textless{[[http://www.3.org/2002/07/owl#](http://www.3.org/2002/07/owl#)]([http://www.3.org/2002/07/owl#](http://www.3.org/2002/07/owl#))>} & \\ PREFIX _rdf_: \textless{[[http://www.3.org/1999/02/22-rdf-syntax-ns#](http://www.3.org/1999/02/22-rdf-syntax-ns#)]([http://www.3.org/1999/02/22-rdf-syntax-ns#](http://www.3.org/1999/02/22-rdf-syntax-ns#))>} & \\ PREFIX _xml_: \textless{[[http://www.3.org/XML/1998/namespace](http://www.3.org/XML/1998/namespace)]([http://www.3.org/XML/1998/namespace](http://www.3.org/XML/1998/namespace))>} & \\ PREFIX _obda_: \textless{[https://w3 id.org/obda/vocabulary#](https://w3 id.org/obda/vocabulary#)>} & \\ PREFIX _Cautiouscaller_: \textless{[[https://www.semanticweb.org/abid/ontologies/2023/7/CardiovascularDiseaseOntology#](https://www.semanticweb.org/abid/ontologies/2023/7/CardiovascularDiseaseOntology#)]([https://www.semanticweb.org/abid/ontologies/2023/7/CardiovascularDiseaseOntology#](https://www.semanticweb.org/abid/ontologies/2023/7/CardiovascularDiseaseOntology#))>} & \\ PREFIX _cro_disseases_: \textless{[[http://www.semanticweb.org/abid/ontologies/2023/7/CardiovascularDiseaseOntology#](http://www.semanticweb.org/abid/ontologies/2023/7/CardiovascularDiseaseOntology#)]([http://www.semanticweb.org/abid/ontologies/2023/7/CardiovascularDiseaseOntology#](http://www.semanticweb.org/abid/ontologies/2023/7/CardiovascularDiseaseOntology#))>} & \\  & select Treatment \textless{Treatment_Id} \textless{Treatment_Date} \textless{ dosage} \textless{duration} \textless{medication} where \textless{} \textless{treatment} \\  & a \textless{vo}\_disseases\_Treatment\_cov, diseaseshas\_Treatment\_Id \textless{Treatment_Id} & \\ \textless{}  
Figure 11 provides a comparative synthesis with existing approaches Ontop and SQL for data retrieval with time over databases. It defines their efficiency gap in query execution times during information retrieval processes. The bar chart highlights that SQL translations exhibit significantly faster execution times compared to Ontop mappings. The disparity highlights the performance metrics and the urgent need to optimize Ontop configurations to improve performance and achieve greater efficiency in data retrieval workflows. It also explains the optimization of Ontop and its configuration for performance gains in the information retrieval process over distributed data sources. The shorter execution times of the Ontop technique in terms of distributed data fetching over centralized data fetching using SQL are quite significant. The Ontop results highlight its efficacy in efficiently processing queries, showcasing its advantage over SQL in query retrieval, efficiency, and performance in distributed environments.
## 4 Voltage Stress Calculations across Power MOSFETs, Diodes, and Capacitors  
When designing a DC-DC converter, engineers perform voltage stress calculations for MOSFETs, diodes, and capacitors to determine the maximum voltage these components experience under various operating conditions. These calculations are essential for selecting components with sufficient voltage ratings, ensuring the converter's reliability and longevity.  
Utilizing Equations (26) and (27), it becomes feasible to determine the voltage stress across D\({}_{1}\) and D\({}_{2}\). Notably, this voltage was found to be very small and dependent on the Vg over the period wherein the Vg ranged from 25 V to 40 V. Furthermore, Equations (28) and (29) contribute to the understanding of the voltage stress across D\({}_{3}\) and D\({}_{4}\).  
\[V_{D_{1}}=\frac{Vg}{(1-D)} \tag{26}\]  
\[V_{D_{2}}=\frac{Vg}{(1-D)} \tag{27}\]\[V_{D_{4}}=\frac{2 Vg}{(1-D)} \tag{28}\]  
\[V_{D_{3}}=\frac{2 Vg}{(1-D)} \tag{29}\]  
\[V_{D_{5}}=\frac{4 Vg}{(1-D)} \tag{30}\]  
\[V_{D_{6}}=\frac{4 Vg}{(1-D)} \tag{31}\]  
\[V_{D_{7}}=Vo-\frac{4 Vg}{(1-D)}\text{ as average value} \tag{32}\]  
To further enrich the analysis, Equations (30)-(32) offer insights into the determination of VD5, VD6, and VD7, providing a comprehensive view of the voltage distribution across various components within the proposed converter.  
\[Vsw_{1}=\frac{2 Vg}{(1-D)} \tag{33}\]  
\[Vsw_{2}=\frac{6 Vg}{(1-D)}\text{ as average value} \tag{34}\]  
Leveraging Equations (33) and (34) facilitates the calculation of the voltage stress across Sw1 and Sw2, revealing that these values are very small when the proposed converter supplies a high load current.  
\[Vc_{1}=Vg \tag{35}\]  
\[Vc_{2}=\frac{2 Vg}{(1-D)} \tag{36}\]  
\[Vc_{3}=\frac{2 Vg}{(1-D)} \tag{37}\]  
\[Vc_{4}=\frac{4 Vg}{(1-D)} \tag{38}\]  
\[Vc_{5}=V_{o} \tag{39}\]  
Equation (35) succinctly represents the voltage across C1, confirming its equality to the Vg. Moving forward, Equations (36) and (37) articulate the voltage stress across C2 and C3, respectively. Similarly, Equations (38) and (39) elaborate on the voltage stress across C4 and C5. A noteworthy observation emerges from the calculations, indicating a significant reduction in voltage on the MOSFETs and diodes when the converter is tasked with supplying a load of 325 W. This reduction is particularly pronounced when L3 and L4 operate in DCM, while L1 and L2 function in CCM.
# Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation
## 2 Parameter Estimation Problem and System Poles
### Parameter Estimation Procedure  
For the system described by state space model (Equation (1)):  
\[\begin{split}\frac{\mathrm{d}}{\mathrm{d}t}& \boldsymbol{x}=\boldsymbol{f}(\boldsymbol{x},\boldsymbol{u}|\boldsymbol{ \Theta})\\ &\boldsymbol{y}=\boldsymbol{g}(\boldsymbol{x},\boldsymbol{u}| \boldsymbol{\Theta})\end{split} \tag{1}\]where \(\mathbf{f}:\mathbb{R}^{n\times m}\mapsto\mathbb{R}^{n}\) and \(\mathbf{g}:\mathbb{R}^{k\times m}\mapsto\mathbb{R}^{k}\) are state and output functions with state variables \(\mathbf{x}\in\mathbb{R}^{n}\), output variables \(\mathbf{y}\in\mathbb{R}^{k}\), and input variables \(\mathbf{u}\in\mathbb{R}^{m}\) and where \(\mathbf{\Theta}=[\theta_{1}\quad\theta_{2}\quad\dots\quad\theta_{l}]\in\mathbb{S }\subset\mathbb{R}^{l}\) is the parameter vector, the parameter estimation problem is defined as follows.  
Given the discrete observed (measured) data \(\mathbf{y}[i]\) and known inputs \(\mathbf{u}(i)\) at time instants \(i=pT_{s},\ p=1,2,3\dots q\), with the step time \(T_{s}\) that corresponds to the sampling frequency \(F_{s}=\frac{1}{T_{s}}\), the problem of finding the optimal parameter set \(\hat{\mathbf{\Theta}}\) that minimizes (or in some cases maximises) the given criterion function \(J_{c}(\mathbf{\Theta})\) is called the parameter estimation problem for nonlinear state space models.  
The criterion function \(J_{c}(\mathbf{\Theta})\) is most commonly selected as the sum of squared residuals (also known as least squares criterion) presented in Equation (2), but there are cases where different functions such as maximum likelihood criterion, or least absolute differences criterion are used. More on criteria functions can be found in [17].  
\[J_{c}(\mathbf{\Theta})=\sum_{p=0}^{q}\left[\mathbf{y}[p]-\mathbf{y}(pT_{s})\right]^{2} \tag{2}\]  
In Equation (2) the \(\mathbf{y}[p]\) are the measured samples of the output variables and the \(\mathbf{y}(pT_{s})\) are the computed output variables obtained by some method of computation from Equation (1).  
The problem of parameter estimation is therefore separated into two different steps. The first step is computing the dynamical system response \(\mathbf{y}(pT_{s})\) and the second is solving the optimization problem shown in Equation (3).  
\[\hat{\mathbf{\Theta}}=\underset{\mathbf{\Theta}\in\mathbb{S}}{\operatorname{argmin}} \,J_{c}(\mathbf{\Theta}) \tag{3}\]  
Computing the system response \(\mathbf{y}(pT_{s})\) can be done by numerical solving of Equation (1) with a trial set of parameters \(\mathbf{\Theta}=\mathbf{\Theta}_{trial}\), by computing the response analytically or by estimating the response based on observations (\(\mathbf{y}^{\prime}[p]\)) with methods such as Kalman Filters (extended Kalman for nonlinear systems).  
Solving the optimization problem (Equation (3)) is completed using one of the available and applicable optimization methods that can be gradient based or numerical methods for approximating global minima.