esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles of the Method  
### Encryption in Spherical Diffraction Domain  
Since spherical diffraction (SpD) is an asymmetry diffraction propagation model [38], the SpD-based encryption should overcome the shortage of symmetry cryptosystem, which is based on traditional diffraction. According to the spherical diffraction theory [38; 39; 40; 41; 42], the object and the observation surfaces are concentric spheres. \(r\) and \(R\) represent the radii of the inner and outer surfaces of the concentric sphere, respectively. In this diffraction model, there are two models of propagation, namely the inside-out propagation (IOP) model and the outside-in propagation (OIP) model, as shown in Figure 1a,b, respectively.  
We use \(Q_{R}\) (\(\theta_{R}\), \(\varphi_{R}\)) and \(P_{r}\) (\(\theta_{r}\), \(\varphi_{r}\)) in the spherical coordinates to denote the object and the observation points for the OIP model, respectively. The spherical coordinate of \(Q_{r}\) (\(\theta_{r}\), \(\varphi_{r}\)) and \(P_{R}\) (\(\theta_{R}\), \(\varphi_{R}\)) denote the object and the observation points for the IOP model, respectively. \(\theta_{r}\) and \(\theta_{R}\) are in the range of \(-\pi\) to \(\pi\), \(\varphi_{r}\) and \(\varphi_{R}\) are in the range of \(-\pi\)/2 to \(\pi\)/2. If we represent the diffraction  
Figure 1: Spherical diffraction model: (**a**) inside-out propagation (IOP) model, (**b**) outside-in propagation (OIP) model.  
distributions on the inner and outer surfaces by \(u_{r}\) (\(\theta_{r}\), \(\varphi_{r}\)) and \(u_{R}\) (\(\theta_{R}\), \(\varphi_{R}\)), the diffraction integral formulas based on the Rayleigh-Sommerfeld integral equation of the two models can be written as  
\[u_{r}(\theta_{r},\varphi_{r})\ =\ C\iint\limits_{s}u_{R}(\theta_{R},\varphi_{R}) \cos\alpha\frac{\exp(ikd_{O_{R}P_{r}})}{d_{O_{R}P_{r}}}d\theta_{R}d\varphi_{R} \ =\ SpD_{r}(u_{r}(\theta_{r},\varphi_{r}))\cdot\ldots\cdot\text{OIP}, \tag{1}\]  
\[\cos\alpha\ =\ \frac{r-R\cos(\theta_{R}-\theta_{r})\cos(\varphi_{R}-\varphi_{ r})}{d_{O_{R}P_{r}}}, \tag{2}\]  
\[d\ =\ d_{O_{R}P_{r}}\ =\ d_{O_{r}P_{R}}\ =\ \sqrt{r^{2}+R^{2}-2 Rr\cos(\theta_{R}- \theta_{r})\cos(\varphi_{R}-\varphi_{r})}, \tag{3}\]  
\[u_{R}(\theta_{R},\varphi_{R})\ =\ C\iint\limits_{s}u_{r}(\theta_{r},\varphi_{r}) \frac{\exp(ikd_{O_{r}P_{R}})}{d_{O_{r}P_{R}}}d\theta_{r}d\varphi_{r}\ =\ SpD_{R}(u_{R}(\theta_{R},\varphi_{R}))\cdot\ldots\cdot\text{IOP}, \tag{4}\]  
where \(C\) denotes a constant and \(k\) denotes the wavenumber of the incident light. \(d\) denotes the distance between two points of \(P\) and \(Q\) on the object and observation surfaces. \(s\) represents the object surface.
# A Systematic Compact Model Parameter Calibration with Adaptive Pattern Search Algorithm
## 2 Problem Definition  
The compact model is computationally efficient description of the terminal properties of the electrical devices, such as diode, bipolar junction transistor and metal-oxide-semiconductor field-effect transistor (MOSFET) to simulate the behavior of VLSI system [13; 14; 15; 16; 17; 18; 19; 20; 21]. Restricting the direct current property, a simple mathematical representation of the compact model is as follows:  
\[Model(\overset{\rightarrow}{V},\overset{\rightarrow}{P})=\hat{I_{D}}, \tag{1}\]  
where the vector \(\overset{\rightarrow}{V}\) and \(\overset{\rightarrow}{P}\) are the terminal voltage biases, and parameters of the model, respectively, and the \(\hat{I_{D}}\) is the simulated drain current vector from the compact model, corresponding to bias conditions. Calibrating (extracting) the compact model parameter means adjusting the set of parameters to fit the simulation data derived from the model to measurement data of the transistor. The objective of parameter calibration is:  
\[Find\overset{\rightarrow}{P},\text{ }minimizing\text{ }\mathcal{L}(\overset{ \rightarrow}{P})=\sum\biggl{(}\frac{I_{D}-\hat{I_{D}}}{I_{D}}\biggr{)}^{2}, \tag{2}\]  
to minimize the mean of squared relative error (MSRE), a fitness function, between simulated and measured values, where \(I_{D}\) is the measured drain current, which is the target output vector for the optimization. MSRE is used as a fitness function, rather than MSE, due to the wide dynamic range of the drain current.  
Usually, model parameters for the given device are calibrated by the experts manually. Therefore, it is very tedious and time-consuming work and hard to deal with massive number of devices fabricated for the development of the new technology. A systematic extraction of model parameter is to find optimal algorithm to optimize the fitness of the model rapidly with practically low computational cost.
## 1 Introduction  
The advances of wireless communications and distributed signal processes have promoted the deployment of the wireless sensor networks (WSN) in recent years [1; 2; 3]. Usually, a sensor network employing a number of spatially-distributed sensor nodes is deployed in a surveillance region. These sensor nodes are able to make a decision locally and transmit such local decisions to a fusion center (FC), where a final situation assessment is derived according to the information from local sensors. The capability of detecting a target over a potential region can be significantly enhanced due to the collaborative detection from multiple local sensors. Such a capability, together with the flexibility and scalability of the WSN makes it an attractive solution to many surveillance applications, such as enemy intruder detection [4], health inspection [5], home automation [6] and environment monitoring [7]. Our goal in this paper is to develop a fusion rule at the FC to derive a final decision of the WSN by considering the uncertainty of the received data at the FC due to modulation, reception mode and the communication channel between the FC and sensors.
## 5 Results and Discussion  
### Evaluation Standards  
We use three metrics, accuracy (ACC), normalized edit distance (Norm_ED), and model testing time (Time), as evaluation criteria for the recognition networks. Accuracy is used to measure the effectiveness of recognition, and is defined in (6).  
\[ACC=\frac{N_{correctwords}}{N_{allwords}}\times 100\% \tag{6}\]  
where \(N_{correctwords}\) represents the total number of words recognized correctly and \(N_{allwords}\) represents the total number of words recognized.  
The normalized edit distances were summarized and divided by the number of test images. The obtained average edit distance was used as a metric, defined in (7)  
\[N\text{orm\_ED}=1-\frac{1}{N}\sum_{i=1}^{N}\frac{D(s_{i},\widehat{s_{i}})}{ \text{max}(s_{i},\widehat{s_{i}})} \tag{7}\]  
where \(D\) denotes the Levenshtein distance, \(s_{i}\) denotes the predicted text line, \(\hat{s_{i}}\) denotes the corresponding ground truth, and \(N\) is the total number of text lines.  
The experiments in this paper measure the performance of the network by the recognition accuracy, the model testing time, the normalized edit distances, and the number of parameters of the network.
# _k_-Center Clustering with Outliers in Sliding Windows
###### Abstract  
Metric \(k\)-center clustering is a fundamental unsupervised learning primitive. Although widely used, this primitive is heavily affected by noise in the data, so a more sensible variant seeks for the best solution that disregards a given number \(z\) of points of the dataset, which are called outliers. We provide efficient algorithms for this important variant in the streaming model under the sliding window setting, where, at each time step, the dataset to be clustered is the window \(W\) of the most recent data items. For general metric spaces, our algorithms achieve \(O(1)\) approximation and, remarkably, require a working memory linear in \(k+z\) and only logarithmic in \(|W|\). For spaces of bounded doubling dimension, the approximation can be made arbitrarily close to 3. For these latter spaces, we show, as a by-product, how to estimate the effective diameter of the window \(W\), which is a measure of the spread of the window points, disregarding a given fraction of noisy distances. We also provide experimental evidence of the practical viability of the improved clustering and diameter estimation algorithms.  
k-center with outliers; effective diameter; big data; data stream model; sliding windows; coreset; doubling dimension; approximation algorithms +
Footnote †: journal:  
00 000-0001-5000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-0000-00000-00000-00000-00000-00000-00000-00  
The decision versions of the \(k\)-center problem and its variant with \(z\) outliers are NP-complete [12]; hence, only approximate solutions may be returned within reasonable time bounds. In this paper, we present approximation algorithms for the \(k\)-center problem with \(z\) outliers in the sliding window setting. Moreover, as a by-product, we also derive an algorithm to estimate the \(\alpha\)-effective diameter of the window, which is an interesting measure of the spread of a noisy dataset.
# Industrial Metaverse: A Comprehensive Review, Environmental Impact, and Challenges
## 6 Innovative Security and Privacy Threats  
The IM is not a utopian digital environment. It constitutes digital representations of real industrial environments, systems, assets, and spaces that users can control, communicate with, and interact with. This leads to many security and privacy challenges. It is a physical-digital fusion and human augmentation for industrial applications. Instead of being a closed platform run by a single corporate entity, it is an integrated ecosystem of businesses and consumers, which results in many security and privacy issues [108]:  
* Data security and cybersecurity risks: With the increased reliance on interconnected systems and data sharing, the IM raises concerns about data security and cybersecurity risks. To this end, more and more devices, as well as platforms are increasingly becoming interconnected. Practically, this increases the risk of cyber threats and data breaches. Safeguarding sensitive data is, thus, imperative to protect enterprises, governments, and individuals.
* Privacy implications and regulatory compliance: The IM also brings to the fore challenges with regulatory compliance and privacy issues. Thus, with companies collecting and analyzing huge amounts of data, there is a need to ensure that the privacy of individuals is respected and protected. Optimizing innovation and privacy is a challenge that needs to be addressed.  
* Avatar authentication issue: Increasingly, digital avatars such as faces, videos, and voices are employed in the virtual world, which is a form of Metaverse, where user authentication and verification are common in comparison to the real world. Realistically, attackers can make identical sounds and movies by mimicking the appearance of the real user using sophisticated AR and VR tools and devices coupled with AI bots. Consequently, the security and privacy of avatars remain a major concern.
# Study on Mechanism of Stick-Slip Vibration Based on Torque Characteristics of PDC Bit
## 4 Study on Torsion Variable Characteristics of PDC Bit Drilling Drill String  
The fundamental reason for SV of PDC bits is that the fluctuation of bit working torque leads to the instantaneous change in the bit rotating speed. With the rotation of the drill string system, this energy is stored in the form of elastic deformation. When this deformation reaches a certain degree, it is enough to overcome the rotation retardation of the rock on the bit, and this energy will be released instantaneously in the form of a cycle of SV. Therefore, the torsion variable of the drill string system is important in the relationship between torque and speed. A structural schematic diagram of the rock-bit-drill string system is shown in Figure 5.  
The formula of the drill pipe rotation variable in the above figure is shown below:  
\[\varphi=\frac{Tl}{GI_{p}}\]  
\(\mathrm{I_{p}=\frac{\pi\mathrm{D}^{4}}{32}}\big{(}1-\alpha^{4}\big{)}\)--Cross-sectional moment of inertia;  
\(\alpha=D/d\);  
T--Working torque of drill bit;  
L--Drill pipe length;  
\(\mathrm{G=\frac{E}{2(1+\varepsilon)}}\)--Shear modulus;  
E--Elastic modulus;  
\(\varepsilon\)--Poisson's ratio.  
Figure 4: Comparison of “penetration depth–torque” characteristic relationship curves of PDC bits of three different sizes. (**a**) Lanea50 rock block; (**b**) Mimosa70 rock block; (**c**) Baobab100 rock block; (**d**) Mimosa130 rock block; (**e**) Mimosa170 rock block; (**f**) 124\({}^{\prime\prime}\) transverse comparison of bit torque characteristics.  
According to industry standards (SY/T 5561) [41], the elastic modulus of drill pipe material corresponding to G-grade reinforced steel pipe material has a Poisson ratio of 0.3. The length of the drill pipe was set to 1000 m according to the specific working condition, and the torsion variables were, respectively, determined according to the three bit sizes used in the five strata and rocks.  
The material properties of drill string steel pipe are listed in Table 3.
###### Abstract  
The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data. First, we compared the data removal and imputation methods, and the method of imputing missing data was found to be more effective. Then, in order to recommend a suitable model, we created a total of 126 models (missing imputation: single imputation, multiple imputation, K-nearest neighbor imputation; data preprocessing: standardization, principal component analysis, partial least square; and predictive method: multiple regression, machine learning, deep learning) and compared them using R\({}^{2}\) and mean absolute error (MAE) values. Finally, we investigated variable importance using the best prediction model. The results identified the best model as a combination of multivariate imputation using Bayesian ridge as the missing imputation method, standardization for data preprocessing, and an extremely randomized tree as the predictive method. Among the odor compounds, Methyl mercaptan, acetic acid, and dimethyl sulfide were identified as the most important odor compounds in predicting complex odors.  
odor substance; variable importance; missing data; imputation; odor prediction Article
# Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking
## 6 Conclusions  
We found that applications that appear in future networks require new QoE-centric business models that must be used for service providers in the proposed contract with their customers. In this paper, we propose an approach to ensure customer satisfaction, which corresponds to the quality of  
Figure 34: Applying fuzzy logic and machine learning techniques for the future IBN.  
service, taking into account the expectations of the customer QoE requirements. This approach fits well with the IBN network concept, which is able to calculate the behavior of applications and customers, training, and reconfiguring during operation, in order to minimize operating costs, improve the QoE, reliability, and security of network processes. The developed method is based on new algorithms of data flow routing with load balancing and a billing system. The use of the billing system will allow customers to adaptively order the required quality of service for evaluation from 1 to 5 through personal online offices at a certain price and receive a high quality of experience under conditions of high network load.  
A network simulation model has been developed with the ability to switch between two service methods (traditional and customer-oriented). The advantage of this model is the ability to explore new solutions by integrating algorithms into the network core. The simulation results show that compared to the traditional SLA method, our proposed method provides the necessary customer requirements for the ordered quality of service. Using this method, the provider will be able to make its network customer-oriented. Using this method on existing networks, by improving the QoS of priority services, it is possible to release the network resources, which were used ineffectively, to connect new customers. By using this method with the construction of a future IBN network, the provider will be able to automate many routine things, reducing the cost of maintenance staff. The main tasks of network configuration and support, which are performed by system administrators, will move to the centralized network element of the controller.  
Conceptualization, M.B. and P.V.; methodology, H.B. and M.K.; software, A.P.; validation, I.K. and V.R.; formal analysis, I.K.; writing--original draft preparation, M.B. and H.B.; writing--review and editing, M.K. and P.V.; visualization, M.B.; supervision, M.B.; project administration, P.V.; funding acquisition, P.V. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the project No. 0120U102201 \"Development the methods and unified software-hardware means for the deployment of the energy efficient intent-based multi-purpose information and communication networks\" and by the project No 0120U100674 \"Development of the novel decentralized mobile network based on blockchain-architecture and artificial intelligence for 5G/6G development in Ukraine\".  
The authors declare no conflict of interest.
# On the Feasibility of Interhemispheric Patch Detection Using Ground-Based GNSS Measurements
## 4 Results and Discussion
### The Analysis of Patch Occurrence with Relative STEC Values.
On the other hand, it should be remarked that for such a depleted ionosphere, the other structures may fulfill this condition, and be improperly classified as patches. This seems to be especially important for GNSS data, in which the total amount of plasma in the slant direction is measured. Similar to the southward IMF case, the structures in the southern hemisphere were stronger and reached up to 10 TECu. More interestingly, these structures were also observed on the nightside (Figure 4). The results for the further part of the recovery phase depicted another sequence of patches (\(\sim\)8:00-16:00 UT on 19 March), which occurred for slightly negative IMF Bz. Although the structures were much weaker (up to 15 TECu and 30 TECu in the northern and southern hemispheres, respectively), their generation seems to be similar to the second sequence observed during the storm. According to Figure 8, these patches are probably related to the enhancement of VTEC that was observed at noon mid-latitudes about six hours earlier. Unfortunately, the unfavorable data distribution in the southern hemisphere prevents the more detailed evaluation of ionospheric conditions in this case.
# A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization
## 3 Proposed Localization Algorithm
### Hybrid Bald Eagle Search Optimization Algorithm
#### 3.2.6 Time Complexity Analysis  
Supposing the population size of HBES is \(n\), the dimension of the search space is \(d\), Then the Tent mapping initialization complexity of the population is \(O(nd)\). In addition, the maximum evaluation times \(T_{max}\) also influence the time complexity. The computation complexity of the fitness value is \(O(nd)\), the complexity of the three-position update is \(O\big{(}nd+n^{2}\big{)}\), the sorting complexity of the fitness value of the algorithm is \(O\big{(}n^{2}\big{)}\), and the control parameter \(r\), \(\omega\), updates the complexity of the algorithm is \(O(t_{1}+t_{2})\). Generating reverse populations and sorting complexity is \(O\big{(}nd+n^{2}\big{)}\). Then, the time complexity of HBES is:  
\[\begin{array}{l}O(\text{HBES})=O(2 nd)+O(T_{max})O\big{(}4 nd+4n^{2}\big{)}+O( t_{1}+t_{2})\\ \approx O(2 nd)+O(T_{max})O\big{(}4 nd+4n^{2}\big{)}\end{array} \tag{29}\]  
However, the original BES algorithm has the same initialization population \(n\), maximum evaluation times \(T_{max}\), and the dimension of search space \(d\) as the proposed HBES. The time complexity of the initialization phase is \(O(nd)\); calculating the fitness of all agents is \(O(nd)\); the position update is \(O\big{(}nd+n^{2}\big{)}\). Consequently, the time complexity of the BES algorithm is:  
\[O(\text{BES})=O(2 nd)+O(T_{max})O\big{(}3 nd+3n^{2}\big{)} \tag{30}\]  
In summary, the time complexity of the HBES algorithm is slightly higher than that of BES algorithm for the same number of iterations. However, the optimization capability of the advanced algorithm is superior to BES, and both of them are in the same order of magnitude. Speed of optimization, optimization capability, and robustness of the proposed method can be proved in the following experiments, including the 23 benchmark functions test, and TDOA problems test, respectively.
## 1 Introduction
This offers the advantage of measuring the gap at a constant position during the welding process to obtain a fundamental understanding of the ongoing processes over time [15; 16] and to avoid a negative effect of misalignment between laser beam and butt joint position [22]. The use of automated actuators is advantageous regarding reproducibility and reduced rejection [23] in general. The further development of intelligent welding fixtures enables precise welding processes with improved versatility [24] by combining actuators and sensors. This allows for an automatic clamping force and position correction for assembly [25; 26] or the adjustment of the clamping intensity during milling [27]. However, the process of laser-beam welding requires high-speed control systems from data acquisition over data processing to the mechanical intervention in the process due to the high processing speeds, as shown for the welding of galvanized steels in lap joints [28]. Clamping devices combining actuators and sensors would be able to provide a further knowledge on the process besides the avoidance of seam imperfections. The process behavior can be described in more detail by measuring specific parameters over time. In the case of welding high-alloy steels in butt joint configuration, distortion and resulting gap sizes are key parameters to ensure a stable process. This paper demonstrated the development of a clamping device for butt joints enabling the time-dependent acquisition of gap sizes and occurring forces due to thermal distortion in laser-beam welding by fixture integrated sensors. The additional integration of mechanical actuators in the clamping system further enabled the development of closed-loop controls utilizing gap size, occurring forces, or both combined as controlled variables for the purpose of an adaptive clamping device. The findings offer starting points for a further improvement of the welding process by adaptive and intelligent clamping devices, jigless welding and fixture design.
# A Notched Long-Period Fiber Grating Magnetic Field Sensor Based on Nanoparticle Magnetic Fluid
## 1 Introduction
For instance, the spatial anisotropy caused by the formation of magnetic chains contributes to birefringence, and the field-dependent optical transmission modulation results from the formation of magnetic columns [20]. With these useful magneto-optical properties, a lot of MF-based devices have already been developed, including switches, modulators, magnetic field sensors, tunable filters, and so forth [21].