esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Considering the comprehensive influence of multiple process parameters in the application of laser processing on the scribing quality, many scholars also built prediction models through artificial intelligence [25; 26] and multiple data regression methods to predict and analyze laser parameters and scribing depth and obtained good prediction results [27; 28]. However, the method loses the physical law information of the laser scribing process and cannot clearly give the influence law of various laser parameters on the scribing depth. The model-based neural network needs a large number of sample data, and a small amount of sample data is difficult to support the accuracy of the results. In this paper, our goal is to establish the physical theoretical model of laser scribing depth and laser parameters and analyzes the influence and law of each parameter on the scribing depth through a single factor experiment. Through the regression of experimental data, the feasibility of the model is verified and analyzed, and the adaptive mathematical equations which can be used for the direct control of the laser CNC system are obtained. Therefore, the requirements of the laser scribing process, the physical theoretical model and the experiment of process parameters were studied. This paper is organized as follows. Section 2 analyzes the process requirements of laser scribing. Section 3 presents the laser scribing depth model based on thermal energy balance. Section 4 introduces the equipment and parameters of engraving experiment verification. In Section 5, the relationship between laser process parameters (laser power, frequency, speed, forward angle and side slope angle) with scribing depth is explored. Section 6 presents the concluding remarks of this study and the plan for future research.
###### Abstract  
Distributed generation (DG) has been incorporated into the distribution networks and, despite the rising prevalence of electric vehicle (EV) loads that are uncertain and cause substantial challenges in their operation, it is necessary to enhance the voltage profile, reduce power losses, and consequently improve the stability of whole networks. The recently proposed beluga whale optimisation algorithm is explored in the optimisation framework to determine the most suitable size of wind turbine generating systems (WTGS), while the optimum placements are determined by minimising the placement index (P-Index) using the distribution load flow (DLF) method. The voltage stability factor (VSF) is employed to formulate the P-Index to enhance voltage sensitivity in distribution systems. The main purpose of this article is to assess the influence of voltage-dependent, uncertain ZIP-form EV loads in order to analyse their potential in the active and reactive power operations of the distribution network while retaining the system voltage within a specified limit by significantly reducing system losses and taking distribution network-level constraints into account. The efficacy of the methodology is validated on the standard IEEE-33 test system by formulating two performance indices to determine a significant enhancement in convergence characteristics and a reduction in system losses.  
distributed generations; electric vehicle; beluga whale optimisation; optimal location; wind turbine generating system +
Footnote †: journal: Applied Sciences  
effects of EVs on the electrical power system and the integration of RESs in the networks, this problem has drawn great attention from researchers. In brief, EV load analysis is essential to determine any impacts on the power system and evaluate the effect of EVs on active distribution networks. This data collection would allow for the upgrading of the power grid to be planned for in the near future.  
Thus, the appropriate placement of DGs and the distribution of EV loads in the system are of utmost importance in distribution systems, and it is essential for a distribution system to utilise optimisation techniques to address the issue of distributed generation planning (DGP). To enhance the voltage profile and system stability of active distribution networks, a wide range of optimisation approaches and their combinations with analytical techniques have been devised.
# An Optimized Fractional-Order PID Horizontal Vibration Control Approach for a High-Speed Elevator
###### Abstract  
Due to factors such as uneven guide rails and airflow disturbance in the hoistway, high-speed elevators may experience significant vibrations during operation. This paper proposes an optimized fractional-order PID (FOPID) method to suppress vibrations of high-speed elevators. First, an accurate horizontal vibration model is established for the elevator car, in which the car frame and body are separate. Then, taking the control cost and the system performance as objective functions, we obtained an optimized FOPID controller based on multi-objective genetic algorithm optimization. Finally, the effectiveness of the controller in reducing elevator vibration was verified through numerical simulation. The results indicate that the horizontal acceleration controlled by the FOPID controller is reduced by about 68% compared to the case without a controller and about 25% compared to the conventional PID controller.  
high-speed elevator; vibration suppression; fractional-order PID controller; multi-objective genetic algorithm
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 3 Materials and Methods
### [PERSON]'s Navigation Graphs  
[PERSON] considered a robot navigating through a field of disks that it detects with a local sensor [22]. Although this path is generally not optimal, [PERSON]'s conception of navigation graph is an important concept that we use in our method. Figure 1(a) illustrates how he defines the edges and nodes for his graph. \(A\) is the point of contact of a bitangent that arrives on \(O_{1}\) in the negative direction, and an unseen obstacle below \(O_{1}\). \(B\) and \(C\) are the points of contact of the bitangent from \(O_{1}\) to \(O_{2}\), both in the negative direction of the obstacle boundary, D and E are the points of contact of the bitangent from \(O_{1}\) to \(O_{2}\) that leaves \(O_{1}\) in the negative direction and arrives on \(O_{2}\) in the positive direction.  
The edges \(CBA\) and \(EDA\) are made up of an arc on the boundary of a disk concatenated with a rectilinear bitangent going to the next disk. The nodes are the point of departure of the boundary arcs. This graph is a directed graph because of the particular order selected for the edges' components. Figure 1(b) represents a [PERSON] path through a field of disks or different radii and positions. There are two points on which we will improve on his work: the obstacles should have arbitrary forms and the optimization should produce a globally shortest path.  
Figure 1: Paths through a field of obstacles shown in gray.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 3 Improved OMP Damage Identification Method Based on Sparsity
The larger this damage-factor variation element, the more severe damage to the substructure, the more significant its contribution to the structural frequency change, and the larger the corresponding residual vector. The magnitude of the residual vectors is also related to experimental measurement error. The larger the error, the higher the overall value of the residual vectors. Their difference is insignificant, which is unconducive for separating the damage elements and rearranging the sensitivity matrix. Therefore, the measurement error should be controlled to the maximum possible extent. Moreover, considering the nonlinear correlations and the error of linearity assumption, there is an iteration process in the calculation of \(\mathbf{\varepsilon}_{i}\) as shown in Equation (18). Take the \(s\)th iteration as example. \[\left\{ \begin{array}{c}\min\|\hat{\mathbf{\omega}}-\overline{\mathbf{\omega}}\left( \mathbf{\mu}_{0}^{s}\right)\|_{2}^{2}=\|\hat{\mathbf{\omega}}-\overline{\mathbf{\omega}} \left(\mathbf{\mu}_{0}^{s-1*}\right)-\mathbf{R}_{i}^{s-1*}\left(\mathbf{\mu}_{i}^{s*}-\mathbf{ \mu}_{i}^{s-1*}\right)\|_{2}^{2}\Rightarrow\mathbf{\mu}_{i}^{s*}\\ \\ \varepsilon_{i}^{s}=\overline{\mathbf{\omega}}\left(\mathbf{\mu}_{i}^{s*}\right)- \overline{\mathbf{\omega}}\left(\mathbf{\mu}_{0}^{s}\right)\end{array} \right. \tag{18}\]  
\(\mathbf{\mu}_{i}^{s-1*}\) is the damage factor when the \(i\)-th substructure is assumed undamaged in the \(s\)-th iteration and \(\mathbf{R}_{i}^{s-1*}\) is the corresponding sensitivity matrix. Based on this procedure, the frequency residual vector \(\mathbf{\varepsilon}_{i}\) is more accurate to the real value when on the \(i\)th substructure is damaged. In contrast to the OMP method of identifying the damage substructure location in the forward direction, the IOMP method developed in this study reflected two different identification criteria based on the residual vector variance criterion and residual vector correlation with the sensitivity criterion. The proposed method reverses selection, eliminates the damage-factor elements of undamaged substructures, and determines the number of damaged substructures using a specific threshold and the principal component analysis method based on singular value decomposition.
## 4 Experimental Results
### Inputs to Build VKGs: Ontology, Mappings, Queries, Databases, and Ontop
#### 4.4.1 Ontology  
Ontop uses RDFS [69] and OWL2 QL as ontology languages, guaranteeing that queries over the ontology can be rewritten into equivalent queries over the data alone. Ontop is also extended to support fragments of SWRL [70].  
Figure 7: A systematic process of federated ontology and federated virtualized knowledge graph (VKG) generation.  
**Example 1**.: _The following ontology metadata captures the domain knowledge of running an example targeting the cardiovascular domain. It decides the concepts of cardiac diseases and cardiac patients with the following OWL axioms:_  
:Heart_Attack rdfs:subClassOf :Cardiovascular_Diseases. (1)  
:Cardiovascular_Diseases rdfs:subClassOf :Chronic_Diseases. (2)  
:Chronic_Diseases rdfs:subClassOf :Cardiac_Diseases. (3)  
:has_Cadiac_Diseases rdfs:domain :Patient_Role. (4)  
:hasCardiac_Diseases rdfs:Range :Cardiac_Disease. (5)  
:hasPatient_ID a owl:DatatypeProperty. (6)  
:has_Chest_Pain a owl:ObjectProperty. (7)  
In the example mentioned above, classes such as :_Heart_Attack_, :_Cardiovascular_Diseases_, and :_Chronic_Diseases_ are sub-classes of :_Cardiac_Diseases_ (_including other cardiovascular diseases_). The object property :_has_Cardiac_Diseases_ has class :_Patient_Role_ as its domain and :_Cardiac_Disease_ as its range. We also have a datatype property :_hasPatient_ID_.
# Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
## 3 Results
### Experimental Results of Binary Classification  
The binary classification outcomes for the bridging and non-bridging ST class are presented in Table 1. The model's effectiveness for bridging ST samples is declined at the 50 th, 100 th, and 250 th epochs. Specifically, precision values are decreased to 86.82% at the 100 th epoch and 85.42% at the 250 th epoch. Similarly, the lowest recall rates recorded are 90.00%, 90.93%, and 90.77% for the 50 th, 100 th, and 250 th epochs. The F1 scores are 88.45% at the 100 th epoch and 88.49% at the 250 th epoch. Overall, the mean values across these epochs are 87.08% for precision, 91.37% for recall, and 89.17% for the F1 score. Respectively, for the non-bridging ST class, the lowest recall rates are recorded at the 100 th and 150 th epochs, with values of 88.75% and 89.60%, respectively. In terms of precision, the model achieves 85.75% at the 150 th epoch and 86.50% at the 250 th epoch. The F1 score reaches 91.73% during the 50 th epoch and is improved to 92.85% at the 150 th epoch.  
Table 2 illustrates the performance metrics for binary classification across both categories, highlighting accuracy, precision, recall, and F1 score. The total accuracy achieved is 97.28%. The precision, recall, and F1 scores are 96.12%, 95.65%, and 97.77%, respectively.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Epochs**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1 Score**} \\  & **Brd** & **NBrd** & **Brd** & **NBrd** & **Brd** & **NBrd** \\ \hline
50 & 88.03\% & 91.45\% & 90.00\% & 93.25\% & 90.12\% & 91.73\% \\
## 2 Methodology  
### Experimental Photovoltaic Installation  
The experimental PV installation consisted of different, fully commercial modules located in the East of Poland (Latitude 51 \({}^{\circ}\)51 and Longitude 23 \({}^{\circ}\)10) is shown in Figure 1. Technologies of the modules used in the analysis and their nominal power were as follows: cadmium telluride (3.3 kWp), copper-indium-gallium-diselenide (3.72 kWp), and polycrystalline silicon (17 kWp). Detailed specification of each kind of module based on the PV manufacturer data sheet is presented in Table 1. The modules are oriented to the south and tilted at the optimum angle for the given latitude equal to 34\({}^{\circ}\). The installation is connected to the grid using the inverters. Both thin film installations are connected with the use of inverters with one maximum power point tracker (MPPT), while for pc-Si PV string the inverter equipped with two MPPT systems is used. The distance between the rows of panels equals to 6.3 m.  
At the location of the experimental system, solar radiation intensity on PV module plane, as well as the temperature of the modules, were measured. A solar radiation sensor based on the monocrystalline silicon solar cell is located in the centre of the installation and tilted at the same angle as the modules. Pt1000 resistance temperature detectors are attached to the back of the module. Detailed specification of the devices used is listed in Table 2.  
Inverters, as well as both mentioned sensors, were connected to the central data-logging computer system for synchronous data collection. All parameters, such as solar irradiance, DC generated electric power, and module temperature of each PV technology studied were measured at each 5 min during the considered year of 2015 and stored for the analysis. All necessary computations were carried out with the use of Matlab/Simulink software (MathWorks, Natick, MA, USA).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PV Technology** & \(\mathbf{P_{max}\) [W]} & **Area [m\({}^{2}\)]** &
\begin{tabular}{c} **Temp. Coefficient** \\ **of \(\mathbf{P_{max}\), \(\mathbf{\beta}\) [\%/\({}^{o}\)C]** \\ \end{tabular}} & **Efficiency \(\mathbf{\eta_{r}\) [\%]}** \\ \hline Cadmium telluride (CdTe) & 75 & 0.72 & \(-\)0.25 & 10.6 \\ Copper indium gallium & 155 & 1.25 & \(-\)0.31 & 12.6 \\ diselenide (CIGS) & 250 & 1.55 & \(-\)0.4 & 15.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PV modules specification data.  
Figure 1: Experimental installation consisting of different PV (photovoltaic) module technologies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solar Radiation Sensor** & **Temperature Sensor** \\ \hline Monocrystalline silicon cell (5 \(\times\) 3.3 cm) & \\ Temperature range: \(-\)20 \({}^{\circ}\)C to +70 \({}^{\circ}\)C & Range: \(-\)40 \({}^{\circ}\)C-70 \({}^{\circ}\)C \\ Radiation range: 0 to max. 1400 W/m\({}^{2}\) & \\ Tolerance of the irradiance sensor: \(+\)/\(-\)5\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of the devices used for irradiance and temperature measurements.
# Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing
## 7 Conclusions and Future Work  
The cloud infrastructure has grown more varied in terms of hardware due to cloud service providers' considerations regarding using accelerators, making resource management more difficult. It can be challenging to maintain QoS when operating a heterogeneous cloud architecture. This paper presents and discusses power and energy consumption models. These models were created for two virtual machines, each of which has heterogeneous GPUs from two generations (Fermi C2075 and K40c). Each GPU has a variety of unique characteristics and features. The models have been assessed by studies comparing predicted and actual values generated by models on a cloud testbed. Performance counters were used as inputs in shallow neural networks to test DNN power models. Secondly, employing hybrid inputs considerably improved the outcomes (performance counters, and GPU and memory utilization). Additionally, DNN energy models for two VMs have been presented. In each VM, comparisons between conventional and agnostic energy models with similar inputs have been made. Agnostic energy models that abstract the infrastructure heterogeneity for both VMs have demonstrated a small accuracy improvement with reduced inputs.  
The suggested future directions for extending this study are structured as follows: First, we will employ Deep Convolutional Neural Network (CNN) workloads to enhance the generalization of GPU energy models to apply to different types of GPU architectures. Second, we will consider other cloud infrastructure resources including CPU, main memory, and network traffic to estimate the total energy consumption of the VM. Another potential extension of this work is to increase the heterogeneity of GPU architectures, for example, through the consideration of GPU generations--from older to new to the newest, the Maxwell, Pascal, and Volta NVIDIA architectures running together in cloud-computing environments. Finally, based on the enhanced energy models, scheduling policies to reduce energy consumption, and then balancing the trade-off between energy and performance will be developed in a cloud-computing environment.  
Methodology, A.A.; Software, A.A.; Validation, Y.A.; Resources, Y.A.; Writing--original draft, A.A.; Writing--review & editing, K.D. and Y.A.; Supervision, K.D.; Funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Education of Saudi Arabia and funded by King Abdulaziz University under Grant G: 690-611-1443.  
Not applicable.  
Data Availability Statement: The datasets presented in this article are not readily available because they are part of an ongoing study. Requests to access the datasets should be directed to the corresponding authors.  
The authors declare no conflict of interest.
## 2 Structural Design
### Hopping Mechanism Design
#### 2.1.4 Structural Improvement  
\(P_{7}\) and \(P_{8}\) (Figure 4c) in the bilateral symmetric gear-crank-rocker mechanism are set as the output. The motion trajectories of points \(P_{7}\) and \(P_{8}\) are circular arcs with points \(P_{5}\) and \(P_{6}\) as their arc center, respectively. However, this output trajectory cannot meet DR.3. Therefore, additional motion components should be added to meet the required foot motion trajectory. Since the required hopping mechanism in this paper is a bilateral symmetric mechanism, and it does not contradict with DR.1 and DR.4, then the additional number of moving components \(n^{\prime}\), the number of lower pairs \(p^{\prime}_{\text{L}}\) and the number of higher pairs \(p^{\prime}_{\text{H}}\) must satisfy the following Equation (3):  
\[3n^{\prime}=2p^{\prime}_{\text{L}}+p^{\prime}_{\text{H}} \tag{3}\]  
After full investigation of common mechanism, the gear-linkage mechanism was finally selected as the additional moving component for the hopping mechanism. To ensure that the structure of the hopping mechanism is compact without changing its symmetry properties, a symmetrical gear-linkage mechanism with the least number of components, that is, the symmetrical gear-three-bars mechanism shown in Figure 4d, was selected. The output bars \(P_{5}P_{7}\) and \(P_{6}P_{8}\) were extended to bars with three revolute pairs, the newly added revolute pairs in which are articulated to the non-gear ends of the symmetrical gear-three-bar mechanism. Therefore, the whole bilateral symmetric gear-crank-rocker mechanism finally evolved into a symmetrical double-gear-pair ten-bar mechanism with a single DOF, as shown in Figure 4d, which was consistent with DR.1 to DR.4.
## 7 Conclusions and Future Work  
Conventional CS data-gathering schemes design the sparse represent basis such as DCT, DFT, and wavelets transform do not consider the network topology and sensor nodes' spatial information. Therefore, in our mechanism, diffusion wavelets based on sensor nodes' degree and different nodes' distance considering the above factors are proposed. Additionally, to further reduce the transport costs in WSNs, a sparse measurement matrix is utilized and MST and modified ant colony routing are jointly applied to mitigate energy consumption and balance the network load, especially lowering  
Figure 17: Network lifetime compassion of different data-gathering schemes vs. number of CS measurements.  
Figure 18: Network lifetime compassion of different data-gathering schemes vs. number of sensor nodes.  
the transmission costs for those nodes nearest the sink node. Experimental results have shown that our sparse basis can sparsity the signal well. Our methods can also accurately reconstruct the original signal. Moreover, the reconstruction error of our scheme is less than DFT. Compared with existing data-gathering approaches, our proposed algorithm not only minimizes the energy consumption of the network, but prolongs the network lifetime. Furthermore, our algorithm has a theoretical guarantee of recovering the original signal, and the sink node needs to gather \(M\ =\ O(K\log(M/K))\) measurements so as to recover the original signal.  
Future work should consider the following aspects: On the one hand, sensor node readings have not only spatial correlation, but temporal correlation, so our future work will extend the spatial-temporal filed. On the other hand, [43] demonstrates that optimization projection will generate better recovery performance than random projection, so a possible extension to this work will consider how to design the optimization projection so as to reduce the energy consumption.  
This work was supported by the National Natural Science Foundation of China (51504214, 61771417, 51504255), the Fundamental Research and Development Foundation of Jiangsu Province (BE2105040), the Natural Science Foundation of Jiangsu Province of China (BK20150204), the National Key Research and Development Program (2016 YFC0801403), and a China Postdoctoral Science Foundation Grant (2015M581884).  
[PERSON] designed the algorithm, performed the theoretical analysis, and wrote the entire manuscript. [PERSON] implemented the simulation and contributed to the manuscript preparation. [PERSON] contributed to polishing the revised manuscript and provided suggestions on simulation evaluation.  
The authors declare no conflict of interest.
# Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation
## 3 Numerical Calculation Results
### Viability Verification
Figure 3: Numerical calculation process of a large depth-of-field digital holography of multiplanar objects when subjected to inverse cross-correlation. (**a**) Optical setup. (**b**) Multi-plane objects. (**c**) Cross-correlation coefficient matrix. (**d**) Different plane ranges of different objects. By following the aforementioned computational procedures, the inverse cross-correlation technique can be used to realize accurate numerical solutions in the large-depth-of-field digital holographic imaging of multiplanar objects. This method combines optical principles with computerized image processing to obtain information about an object from multiple aspects so as to reconstruct a high-quality image of a multiplanar object, thus providing a powerful solution for achieving a more accurate and detailed imaging of multiplanar objects.