esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simple Model to Predict Machined Depth and Surface Profile for Picosecond Laser Surface Texturing
## 1 Introduction
The only necessary step for rapid application of the model is to carry out two baseline experiments to calculate the estimated simulation parameters. Hence, fabrication of micro-craters with pulse trains of two different numbers is initially performed to provide baseline values. The estimated simulation parameters are calculated using the obtained baseline values. Subsequently, the depths and profiles of created micro-channels with various scanning speeds or adjacent intervals are simulated using the developed model and calculated parameters. Corresponding experiments have been conducted to validate the developed mathematical model.
# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 1 Introduction
First of all, inappropriate sitting postural habits formed in childhood are not easily corrected when they become adults [20]. It implies that it is most likely that the wrong posture habit will be maintained even after becoming an adult. In addition, back pain caused by inappropriate posture in childhood is likely to develop again [21]. Thus, the posture classification study for children can help to prevent lumbar disease in the long term by encouraging the correct postural habit formation during childhood. Therefore, the objective of this study is to classify the sitting postures of children via conventional algorithms and deep learning-based algorithm using the body pressure distribution data from pressure sensors. The conventional machine learning algorithms used in this study are Naive Bayes classifier (NB), decision tree (DT), neural network (NN), and SVM. Through a comparison of the application of CNN and conventional machine learning algorithms on the data set obtained from the pressure distribution data for children, we investigate the effectiveness of our approach wherein the CNN algorithm is applied while ensuring reliability through a validation analyses (average value of accuracy = 0.953). The remainder of this study is organized as follows. Section 2 contains a literature review of the related studies and machine learning algorithms. Section 3 describes the methods of the experiment and data processing. Section 4 presents the results of the validation analysis for each machine learning algorithm and the further analysis results of the CNN algorithm are presented. Section 5 discusses the results of the machine learning algorithms used in this study and compares them with the results of the previous studies. Finally, the conclusions are summarized in Section 6 along with the applications of this study and the scope for future study.
# A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
###### Abstract  
Network slicing has become an unavoidable requirement for allocating 5G mobile network resources when sharing resources among devices that have varying needs. As a result, the virtual network slices get resources from the shared physical infrastructure that matches their needs. In order to maximize the use of shared resources, it is critical to provide an efficient virtual network embedding strategy for mapping each user's requests to a physical infrastructure. Virtual network embedding primarily deals with the two most important network parameters--node mapping and link mapping. This paper proposes the heuristic fuzzy algorithm for node mapping and [PERSON]'s algorithm for link mapping. The proposed fuzzy based multi-criteria decision making technique uses membership functions for node parameters to prepare node mapping. By determining the shortest path, [PERSON]'s algorithm is used to provide link mapping. The proposed strategy is tested under dynamic physical infrastructure conditions for validation. The average acceptance ratio, cost-revenue ratio, and average utilization of node capacity and link bandwidth are used to evaluate the performance of the proposed strategy. In addition, the obtained results are compared to the literature to show that the proposed strategy is effective.  
5G network; virtual network embedding; resource allocation; heuristic fuzzy; shortest path
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 5 Simulation Results
### Performance Evaluation of ASPF
In addition, the ASPF simulation results are shown in Figure 11. In Figure 11a, \(\Delta_{1}\) gradually decreases as the number of days increases, because the data in _group_ 1 has more accurate predictions than _group_ 2 from Table 5. As \(\Delta_{2}\) decreases in Figure 11b, the network updates continuously, and the number of similar days increases so that \(\Delta_{1}\) becomes smaller correspondingly. For Figure 11c,e, \(\Delta_{1}\) does not change, because in _group_ 2 and _group_ 3, the number of the similar days are less than in _group_ 1. \(\Delta_{2}\) in Figure 11d,f decrease gradually, and thus the accuracy of our method improves continuously. In summary, the proposed ASPF obtains high-precision prediction values in both _non-similar days_ and _similar day mode_ under different predicting methods. \begin{table}
\begin{tabular}{c c
## 3 Analysis Method of End Face Stress for Cycloid Gear
### Global Stress Scaling Factor  
Considering the size discrepancy at the millimeter scale between the shape and the unit circle, a global stress scaling factor is employed in this paper for normalization purposes. As depicted in Figure 6, by holding the distance between the fixed point and the point of applied force constant, the alterations in the shape of the force-applied region have negligible impacts on the resultant maximum equivalent stress. When ensuring consistency in the mesh count, as per the representation in Figure 7, an inverse relationship between the maximum equivalent stress value and side length is evident. Their product, however, demonstrates minimal fluctuations. From the above observations, during the mapping process, it is discerned that the factors affecting the peak stress predominantly hinge on variations in the geometric dimensions, with shape deviations playing marginal roles.  
In light of these findings, this paper introduces a methodology for computing the average radius of a polygon centered on its geometric centroid, which essentially serves to determine the scaling factor. The initial step merges the horizontal and vertical coordinates of each vertex of the polygon into a singular matrix. Subsequently, the geometric centroid of the polygon is ascertained. The ensuing step involves calculating the Euclidean distances from each vertex to the geometric centroid. The average of these distances renders the  
Figure 6: Influence of geometry on maximum equivalent stress (subjected to a force of 100 N vertically upwards with a mesh size of 0.1 mm).  
polygon's average radius. The core intent behind this strategy is to harness the average radius as a pivotal reference value during geometric scaling transformations.  
For a more formal representation, consider \(P\) as a two-dimensional polygon comprising \(n\) points, with the coordinates for each point delineated as (\(x\_i,\_y\_i\)), where \(i=1\), \(2\), \(\ldots\), \(n\). Consequently, the average radius \(r\_avg\) of the polygon \(P\) can be articulated as per Equation (16).  
\[r_{\text{avg}}=\frac{1}{n}{\sum_{i=1}^{n}\sqrt{\left(x_{i}-\frac{1}{n}{\sum_{j =1}^{n}x_{j}}\right)^{2}+\left(y_{i}-\frac{1}{n}{\sum_{k=1}^{n}y_{k}}\right)^{ 2}}}. \tag{16}\]  
Given the context established in this study, the stress transformation process is delineated from unit circle inversion mapping to the polygon. Hence, the scaling factor is determined as \(1/r\_avg\). Assuming that, during the variations in the geometric shape dimensions, the product of the stress and the scaling factor remains constant, this approach facilitates the determination of the stress across the cycloid gear cross section.
## 6 Sidelobe Reduction via Optimizing the Sensor Selection Algorithm  
The consideration of taking random samples of sensor locations provides additional degree of freedom for controlling the sidelobes. To achieve the desired mainlobe accompanied by reduction of the sidelobes, it is required to select a subset of connected and collaborative sensors from candidate sensors within the same coverage area of each \(\kappa\)th source-destination pair.  
This sampling is assumed to be as Latin distributed random variable which represents the whole coverage area considering the fluctuations/shadowing effects in the channel. It depends on the distance among the selected collaborative sensors, whereas the sensors are close to each other, while the base station is located far from these selected collaborative sensors. Moreover, the network can be viewed as homogeneous and the power attenuation due to different paths are unequal. Therefore, we can either adjust the gains of the receivers or the power/the number of the collaborative sensors participating in CB by adjusting the weights.
## 5 Real Experiment
### Flight Experiment with Real Data  
To further validate the image-motion compensation capability of the image-motion compensation mechanism, a flight experiment based on actual data was carried out. Figure 9a shows a partial image captured by the carrier at a flight speed of 242 km/h and a flight altitude of 3.17 km, with the aerial camera's roll angle being 4.2\({}^{\circ}\) to ground  
Figure 8: Images of the image-motion compensation experiment: (**a**) the real scene; (**b**) the resolution target images; and (**c**) the cross-target images.  
which met the design expectations. The obtained R-spectral band image, G-spectral band image, and B-spectral band image could be fused to obtain an RGB color image, as shown in Figure 10e.  
According to publicly available information, the image-motion compensation accuracy of the Global Hawk camera, CA-295, DB-110, LOROP, and VisionMap A3 has reached sub-pixel level, typically within the range of 0.1 pixels to 0.5 pixels [33]. This high-precision image-motion compensation enables the above-mentioned aerial cameras to obtain stable and clear images in high-altitude, high-speed flight, and dynamic environments. Furthermore, based on experimental results, the image-motion compensation accuracy based on the technology in this paper is better than 0.5 pixels, which also reaches the advanced level. This paper can provide technical support for the fields of electro-optical reconnaissance and mapping, and can also expand this technology to various types of electro-optical payloads.
# Recent Progress in Synthesis and Photonic Applications of Two-Dimensional Bismuthene
## 2 Structure and Properties of Bismuthene
### Nonlinear Property  
The nonlinear properties of bismuthene have attracted significant attention due to their ability to provide spin-orbit interaction, a high nonlinear refractive index, and semimetallic bonding [41; 42]. The nonlinear optical properties of bismuthene have been investigated by the measurement of spatial self-phase modulation (SSPM) [24]. As shown in Figure 1e,f, the diffraction rings of 2D bismuth have been observed in the visible wave range. It should be noted that under different irradiation wavelengths of 532 nm and 633 nm, the number of diffraction rings all demonstrate a quasi-linearly increasing trend with an increase in laser power density. Meanwhile, the slope of the relationship between the ring numbers and light intensity decreases with increasing laser wavelength, indicating that the SSPM of bismuthene is caused by the electronic transition. The third-order nonlinear susceptibility (e.s.u.) of bismuthene was calculated by  
\[\chi_{total}=\frac{c\lambda n_{0}}{2.4\times 10^{4}\pi^{2}L_{eff}}\frac{ dN}{dI}\]  
where \(n_{0}\) is the linear refractive index, \(\lambda\) is the wavelength, and \(L_{eff}\) is effective optical propagation length, respectively. It is worth noting that the third-order nonlinear susceptibility (e.s.u.) and nonlinear refractive (\(10^{-6}\) cm\({}^{2}\)/W) of bismuthene are much lower than those of 2D black phosphorus (BP) [43], revealing bismuthene as a suitable candidate for optical communication, as well as for all optical switching applications. In addition, the saturable absorption ability is also investigated. The saturation intensity at 1.5 um is approximately equal to 30 MW/cm\({}^{2}\), and the modulation depth is up to 2.03%, respectively. By applying bismuthene as a saturable absorber, a mode-locked fiber laser was fabricated, demonstrating a large signal-to-noise ratio of up to 55 dB and a repletion rate of 8.83 MHz, which will be discussed in detail in subsequent sections of this review.
## 3 Product Service System Case Study
### Case Study Assumptions  
Several assumptions were made to establish the boundary within which the case study is valid, and to reflect reality. Simulations, subsequent optimizations, and analysis were performed within the framework of the assumptions.  
\(\bullet\) Definition of availability: in this case study, the availability of the PSS is defined as follows. When the PSS shuts down due to a spare part failure, the part is replaced with a fresh part from the field repair kit. If there is no replacement part present in the field repair kit inventory, it must be shipped from the central warehouse. This is recorded as a PSS down incident. The availability metric is the percentage of all PSS part requests that is fulfilled by immediate replenishment from the field repair kit. For example, if there were 100 requests for various spare parts, and 95 of them were fulfilled from the field repair kit without placing emergency order from the central warehouse, the availability of the PSS is 95%.  
\(\bullet\) PSS and production system configuration: for the case study, the production system consists of two PSSs of the same type in a serial configuration. The field repair kit optimized in the case study is configured to service both PSSs.  
\(\bullet\) Field repair kit spare parts composition and quantities: the design team provided the list of spare parts in the field repair kit, along with their respective quantities and the estimated life. For the case study, all parts identified by the design team were included in the optimization. A total of 55 spare parts were included in the field repair kit.  
\(\bullet\) Periodic PSS usage: the PSS usage is defined as the number of prints produced per time period (month in this case study). For the case study, the nominal usage is set to 8.0 million, as provided by the design team responsible. The usage data was derived from past historical data of customers who have used similar type of PSS. Additionally, it was assumed that all spare parts fail based on PSS usage, not based on time.  
\(\bullet\) Onsite conditions: availability of PSS also depends on other aspects, such as experience of PSS operation and maintenance staff and production equilibrium. It was assumed that the level of expertise of operation and maintenance staff was competent to upkeep the PSS at the desired performance level, as it typically is for real onsite facilities. Additionally, it was assumed that the production equilibrium is maintained.  
\(\bullet\) Simulation assumption: for the Monte Carlo simulation, spare parts life distribution and periodic PSS usage distribution were provided by the design team. Additionally, simulation time was set to be equal to the typical contract term agreed between the company and the customer. For this case study, the contract term was set to five years, with a single simulation period equal to one month.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 3 Background
### Hierarchical Clustering  
Hierarchical clustering (see, for example, [PERSON] (2000)) is a method of cluster analysis that seeks to build a hierarchy of clusters. This technique involves the iterative fusion or division of clusters based on a measure of similarity or dissimilarity between data points. In agglomerative hierarchical clustering (bottom-up approach), each data point starts as its own cluster, and pairs of clusters are merged sequentially based on a linkage criterion (such as single, complete, or average linkage) until all data points form a single cluster. The process is typically visualized in a dendrogram, where the vertical axis represents the distance at which clusters are merged.  
Conversely, in divisive hierarchical clustering (top-down approach), a single cluster containing all data points is progressively split into smaller clusters.  
A key advantage of hierarchical clustering is that it does not require a predefined number of clusters, allowing for a flexible analysis of the data's structure. However, it is computationally intensive, particularly for large datasets, and is sensitive to noise and outliers, which can distort the clustering outcome.  
The distance metric (e.g., Euclidean or Manhattan) and linkage method selected significantly influence the final clustering solution, and their choice depends on the data's characteristics.
## 4 Performance Evaluation in Edge Scenarios
### Performance in Network Programming
#### 4.4.2 CPU Resource Usage  
The CPU usage of the Gramine and Occlum frameworks is evaluated during network programming to address resource constraints in edge scenarios, where computational resources are limited. The evaluation focuses on the Lighttpd web server running within enclaves, with its CPU consumption monitored from an external terminal. To ensure the accuracy of the experimental results, the system cache is cleared prior to each test. The terminal used for testing is set to measure CPU usage every second. Results are shown in Figure 6. For Occlum, since all processes are initiated by a parent process, only the CPU usage of its child processes is measured. In contrast, for Gramine, the Lighttpd service is managed by the Platform Abstraction Layer (PAL) loader, making it the primary target for monitoring. The results show that both frameworks exhibit similar trends, with higher CPU usage during the initial runtime phase. However, as the server stabilizes, Occlum achieves slightly lower steady-state CPU overhead compared to Gramine. Overall, Occlum has a lower CPU overhead, but the initialization of the Lighttpd environment causes a significant number of context switches, resulting in the TEE's CPU overhead being much higher than that of a native Linux environment.
## 3 Methodology
### Selection of the NN Input Data  
A crucial issue in the design of a NN algorithm is the selection of appropriate inputs. The information considered as input for the two NN algorithms presented in this study, one for day time and the other one for night time, is now described. According to [32; 33] a first piece of information directly linked to the probability of a cloud of being rainy is represented by the Cloud Water Path (CWP). The CWP is the amount of water vertically integrated in the cloud, and given some assumptions about cloud vertical structures, it is related to the cloud optical thickness and to the droplet effective radius where the effective particle radius is defined by the ratio between the third and the second power of the droplet spectrum [34]. To characterize the geometrical thickness of a cloud, the integration of the extinction coefficient integrated over the cloud geometrical thickness, called Cloud Optical Thickness (\(\gamma\)), is also used. To retrieve CWP using SEVIRI data, reflectance of VIS0.6 and NIR1.6 can be used [6; 32]. High values of VIS0.6 reflectance correspond to high optical depth of cloud and low values of NIR1.6 reflectance indicate large particles in the cloud. This means that a large CWP is obtained when high values of VIS0.6 coincide with low values of NIR1.6. During the night, the cloud optical thickness is well defined by brightness temperature difference BITR10.8-BTIR12.1 [35], while to infer information about microphysical and optical cloud properties, the brightness temperature difference BITR3.9-BTIR10.8 and BITR3.9-BTWV7.3 may be used [36; 37; 38]. Another distinctive aspect of the raining clouds is the thermodynamic phase.  
It may be assumed that if both liquid (droplets) and ice water (crystals) coexist then the cloud is more likely to bring rain [32; 33; 35; 39; 40]. Two good indicators of the thermodynamic phase of the cloud are the cloud top temperature (CTT) (provided by BTIR10.8) and the brightness temperature difference BITIR8.7-BTIR10.8, used concurrently [33; 41]. To take into account the different temporal signature of convective (and orographic) processes with the weather fronts, [18; 42] suggest to consider not only the current value of BT of a pixel, but also the value corresponding at the previous five acquisitions. Furthermore, to minimize geolocation and synchrony problems between SEVIRI and GPM dataset and to provide useful texture information of the clouds, average and standard deviation values over boxes of \(3\times 3\) and \(5\times 5\) surrounding the pixel were added to the input vector. On the base of the previous considerations, for each day, the input vector of the two NNs, for day-time and for night-time, are listed in Tables 3 and 4, respectively.
## 4 Numerical Validation
### Example 2: A Forced System with Cubic Nonlinearities
To assess the precision of the proposed approach, the frequency amplitude response curves were next computed using the following system parameter values: \(m_{1}=1\) kg, \(m_{2}=1.5\) kg, \(k_{1}=2\) N/m, \(k_{2}=3.5\) N/m, \(k_{3}=5\) N/m, \(c_{1}=0.066\) N-s/m, \(c_{2}=0.099\) N-s/m, \(\varepsilon_{1}=1\) N/m\({}^{3}\), \(\varepsilon_{2}=1\) N/m\({}^{3}\) and \(Q_{1}=0.2\) N. The computation is performed starting with \(\omega_{f}=0\), with the initial conditions given by \(\big{(}x_{1},\ x_{2},\ \dot{x}_{1},\ \dot{x}_{2}\big{)}=\big{(}0,\ 0,\ 0,\ 0\big{)}\) and the driving frequency, \(\omega_{f}\), increased gradually at small incremental driving frequency step values of \(\Delta\omega_{f}=0.05\). The steady-state vibration amplitude of the previous solution is used as the initial condition to obtain the corresponding numerical integration solution at the next \(\omega_{f}\) value. Figure 6 shows the frequency-amplitude response diagrams from the numerically computed form, Equation (36), and from the corresponding equivalent representation forms (42). Notice that the numerical integration of (42) slightly differs from the numerical integration solutions of Equation (36) near to the second resonance region; however, the nonlinear modal equations capture the dynamics of the original system well. The LLE average values for Expressions (36) and (42) are \(\lambda_{LLE}=-0.0229\) bits/second, and \(\lambda_{LLE}=-0.0226\) bits/second, respectively. These computed LLE average values are almost the same, which confirms the validity of the proposed conjecture. Next the undamped and unforced case was considered with the above system parameter values and \(\big{(}x_{1},\ \dot{x}_{1},x_{2},\ \dot{x}_{2}\big{)}=\big{(}0,\ -0.1,\ 0,\ 0\big{)}\). Then, the transformation relationships (42) were used to plot the time-amplitude response curves. Notice from Figure 7 that the linear combination solution (37) describes the qualitative and quantitative system dynamics well. In fact, almost periodic solutions can be observed when the modal displacements are transformed back into the original coordinate systems by using the relationships (37). Therefore, it is concluded that the equivalent representation form of Equation (36) validates the conjecture, since the numerical integration solutions follow the dynamic behavior, observed during the numerical integration of the original equations of motion, well. As a final example, the nonlinear absorber system introduced by [PERSON] in [50] was examined.