esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Anomaly Detection from Event Log
### Conformance Checking in a Business Process Area  
Conformance Checking involves the comparison of event data generated in the process with the defined process model. Initially, the log replay method, which replays a token in the process model was used for conformance checking [8; 28]. Later, the trace alignment method, which compares the symmetry between the modeled process and the event log recorded in the performance stage, was introduced and quickly established as the de facto standard in this field [10; 29; 30; 31; 32]. The traditional conformance checking method has been post-mortem analysis, which compares data collected for a certain period with the model. However, the recent emergence of Big Data and real-time data processing technology, prompted studies to detect anomalies online by comparing event logs generated in real time with the process model [16; 17; 33]. Furthermore, as the amount of data increases with the progress of the process, conformance checking by processing and analyzing large event logs has been researched [34; 35; 36]. Most studies on conformance checking have been conducted from the perspective of control flow, although a few of these studies have been conducted from the perspective of time, data properties, and data flow [37; 38; 39; 40; 41].
# An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design Optimization
## 4 Conclusions  
The novel hybrid metaheuristic algorithm FHGWJA combining the grey wolf optimizer (GWO) and JAYA was presented in this study. FHGWJA is an advanced grey wolf optimizer using elitist strategies and JAYA-based schemes to improve the current best record in each iteration. Exploration and exploitation are enhanced by forcing FHGWJA to increase diversity and select high-quality trial designs lying on descent directions. All operations carried out in FHGWJA to generate new trial solutions are computationally cheap because decision making mostly relies on evaluation of cost function rather than optimization constraints.  
The present algorithm was successfully tested in seven engineering problems including up to 29 optimization variables and 1200 nonlinear constraints. Test cases covered  
Figure 16: Comparison of convergence curves of FHGWJA and its competitors for the 200-bar optimization problem.  
various fields in robotics (i.e., 2D path planning), hydrology (i.e., calibration of a nonlinear hydrologic model), and hydraulic, mechanical, and civil engineering (i.e., shape optimization of a concrete gravity dam, optimal crashworthiness design, and weight minimization of a planar 200-bar truss structure).  
The extensive comparison with the optimization literature carried out in this study proved the superiority of FHGWJA over a huge number of competitors that were reported to have found the best available solutions for each test problem. In fact, the present algorithm always converged to or improved the target global optimum designs indicated in the literature. Remarkably, FHWGA was the 1 st or 2 nd fastest algorithm in all test problems. The present algorithm was considerably faster than the standard GWO and JAYA formulations, and advanced variants of its component algorithms, thus confirming the efficiency of the proposed hybridization scheme. Finally, FHGWJA was very robust, achieving a high rate of success in all test problems with low standard deviations on optimized cost and number of analyses required in the search process.  
In summary, FHGWJA is a very efficient tool for engineering optimization. Further research will investigate the suitability of FHGWJA for large scale optimization problems with thousands of constraints. Optimization with discrete variables also will be analyzed. In this regard, the optimal solution found with FHGWJA for the 200-bar truss problem was simply matched with a discrete set of available cross-sections obtaining competitive results with the best solution available in the literature.  
Conceptualization, methodology, validation, C.F. and L.L.; formal analysis, C.F. and C.I.P.; software, investigation, data curation, C.F.; writing--original draft preparation, C.F.; writing--review and editing, L.L and C.I.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data relative to this study are available upon reasonable request.  
The authors declare no conflicts of interest.  
References
## 4 Discussion  
The mathematical analysis of chemotaxis models shows a plenitude of spatial patterns such as the chemotaxis models applied to skin pigmentation patterns [7; 26; 27]. That leads to aggregations of one type of pigment cell into a banded spatial pattern and to other models applied to the aggregation patterns in an epidemic disease [28], tumor growth [29], angiogenesis in tumor progression [30], and mathematical modeling of macrophages as vehicles for drug delivery to the tumor, as was used in [9]. Recently, direct transport of bacteria-based drug delivery vehicles was developed in [31], and iron oxide (IO) nanoparticles are used to track the chemotaxis of macrophages for the drug delivery control process using MRI imaging and visualization [5]. Here we investigated numerically the Turing instability and pattern formation in two dimensions for the chemotaxis model to show that the cell density stabilizes at a certain moment, thus verifying the theoretical results about pattern formation using an appropriate numerical scheme. Indeed, Figure 5 shows the time evolution of the cell density at the point (3.275; 3.1) in the red line, (8; 7.5) in the blue line, and (4.975; 2.825) in the cyan line. We observe that the cell density at the three points varies in response to the gradient of the chemoattractant, which plays an essential  
Figure 4: Pattern formation for the model (3) at time \(t=14\) (**a**) with \(1.9\times 10^{-4}\leq u\leq 0.98\) and at time \(t=30\) (**b**) with \(3.9\times 10^{-3}\leq u\leq 0.832\).  
Figure 3: Pattern formation for the model (3) at time \(t=0.05\) (**a**) with \(3.10-3\leq u\leq 0.995\) and at time \(t=0.9\) (**b**) with \(2.10-5\leq u\leq 0.98\).  
role to stop the aggregation of the cells. Next, the cell density stabilizes at the three points when the time t is greater or equal to 23 s, and we obtain the same result for the other spot patterns. Consequently, this stabilizing proves that the volume-filling chemotaxis model (3) generates stationary spatial patterns. These results demonstrate that our proposed chemotaxis model has a great potential to improve drug delivery processes.
# Biomechanical Analysis of Posterior Ligaments of Cervical Spine and Laminoplasty
## 1 Introduction  
Cervical laminoplasty is a decompression procedure of the lamina for asymptomatic patients of cervical spondylotic myelopathy (CSM), cervical disc herniation (CDH), and cervical ossification of the posterior longitudinal ligament (C-OPLL) [1, 2, 3, 4]. The primary purpose of laminoplasty is to decompress the cervical spinal cord by widening the spinal canal, preserving the posterior anatomical structures as much as possible, and preserving the widened space stability [1]. It is a technique with excellent clinical and mechanical results [1, 5, 6]. Laminoplasty is divided into two types based on osteotomy: (1) Double-door laminoplasty and (2) open-door laminoplasty [1]. In double-door laminoplasty, the osteotomy is performed at the central lamina. In open-door laminoplasty, the osteotomyis performed at one side of the lamina. Both techniques are associated with reasonable clinical outcomes [7].  
However, some authors have reported increased kyphosis and axial pain postoperatively [8; 9; 10] due to the damage to the posterior cervical muscles and ligaments during the surgical procedure [8; 9; 10; 11]. Concerning these papers, few reports have examined the extent of biomechanical changes when laminoplasty with/without LF is performed on the cervical spine [12; 13; 14]. The LF was reported to restrain flexion equally in a porcine model [15]. In the biomechanical study, none of the studies reported the effect of preserving/resecting the LF. In a clinical study, [PERSON] reported postoperative axial pain as prevalent in up to 30% of the patients by injured LF [11]. The development of techniques to preserve the posterior ligaments has been reported [16; 17; 18; 19]. We hypothesize that when laminoplasty is conducted on the cervical spine model, the range of motion (ROM) and stress concentrations on the cervical spine may change with/without the LF, and the importance of posterior ligament for restoring/stabilizing the cervical spine will become evident.  
For this purpose, the C2-C7 three-dimension (3D) finite element (FE) model of the cervical spine was developed using CT scans of a healthy subject. The 3D FE model of the cervical spine was validated for ROM, intervertebral nucleus stress, and facet contact forces by comparing data with in vitro experiments. Later, the validated model was modified to simulate laminoplasty with and without LF.
## 4 Discussion
### Clinical Implications
#### 4.6.4 Preclinical Findings and Translational Potential  
The preclinical studies reviewed in this study demonstrate the potential for GTR membranes to support the regeneration of large-scale tissue defects and even complex organ systems [63]. These findings highlight the scalability and adaptability of GTR technologies for clinical applications. However, translating these preclinical successes to human populations requires rigorous clinical trials to evaluate efficacy, safety, and long-term outcomes [64; 65]. Clinicians must consider patient-specific variables, such as the degree of immunosuppression, comorbid conditions, and potential interactions with concurrent treatments, when implementing GTR-based therapies.
## 3 Results and Discussion  
### Effects of Permuted Numeric Labels on the Partition  
We wondered whether the correct assigning of numeric labels would alter the less possible the SC calculations, so we analyzed how the SC changes over the synthetic partitions with permuted labels. We began generating synthetic partitions in ascending and descending order, increasing both the number of classifications and the number of items, up to 100. It is important to highlight that the number of items belonging to each classification was kept constant. Because trying all the possible permutations for each vector would be implausible, we created a subset of 1000 permutations of each vector, then we calculated the mean SC (Figure 1, see Section 2). We observed that the mean SC became high when the number of items in the VP was greater or equal to 2 times the number of classifications, nevertheless, we obtained the highest SC when the numeric labels we assigned by sequential order, either ascending or descending (Figure 2).
# Algorithms Utilized for Creep Analysis in Torque Transducers for Wind Turbines
###### Abstract  
One of the fundamental challenges in analyzing wind turbine performance is the occurrence of torque creep under load and without load. This phenomenon significantly impacts the proper functioning of torque transducers, thus necessitating the utilization of appropriate measurement data analysis algorithms. In this regard, employing the least squares method appears to be a suitable approach. Linear regression can be employed to investigate the creep trend itself, while visualizing the creep in the form of a non-linear curve using a third-degree polynomial can provide further insights. Additionally, calculating deviations between the measurement data and the regression curves proves beneficial in accurately assessing the data.  
Keywords:data analysis algorithms; creep study; torque transducer; least squares method; regression; measurement uncertainty +
Footnote †: journal: _Algorithms_
## 3 Wireless Sensor Network and Wireless Sensor Node Deployment
### Analytical Network Model for Node Placement
* Computational geometry approach is frequently used in WSN coverage optimization. The most commonly used computational ones are the Voronoi diagram and Delaunay triangulation [38]. In our case, the vineyards are usually placed in a flat field where there are not high drops. Moreover, both the radio coverage area and the sensing area are circular areas. So the easiest deployment system for the sensing coverage is the grid-based method and this was applied in our case. The sensing region is usually divided into two sub-regions: the uncovered region (whose points cannot be sensed) and the covered region (whose points can be sensed). In our deployment we try to have an uncovered region as close to zero as possible. In order to deploy the wireless sensor cameras we will assume that the radio coverage radius is equal or higher than two times the sensing coverage radius (that is, using previous nomenclature, \(c\geq 2\cdot s\)). This will allow us to have full coverage of the ROI while maintaining radio connectivity between wireless sensors. Figure 4 shows eight wireless sensor cameras (in red). The sensing area of each sensor is drawn by blue circles and the radio coverage area (two times the sensing coverage area) is drawn in yellow. This system is also called hexagonal grid. It allows covering all the sensing field while maintains each sensor connected with at least two or three neighbors. Bearing in mind that c is the radio coverage radius and s the sensing coverage radius, the distance between sensors to avoid holes without sensing field is \(\sqrt{2}\cdot s\). Now, we can estimate the overlapping sensing area (see A in Figure 4) taking into account the circular segment equation [39]. This is given by Equation (5):  
\[A=0.864~{}s^{2} \tag{5}\]  
The hexagonal grid is the worst among all since it has the biggest overlapping area [40], but it is the one that covers the entire sensing field. The area covered by all the WSN as a function of the number of nodes \(n\) when \(n\geq 2\) in the WSN is expressed by Equation (6):  
\[A=n\cdot\pi\cdot s^{2}-0.864\cdot a_{n} \tag{6}\]  
where \(a_{n}=\sum_{i=1}^{n}(a_{n-1}+(-1)^{n})\), \(n\geq 2\) and \(a_{1}=1\).
## 1 Introduction
None of these studies focused on the measurement of the attenuation (or absorbing) coefficient (\(\alpha\)) or the molar attenuation coefficient (\(\epsilon\)) of the compounds according to the Lambert-Beer law \(A=-\log T=\epsilon dc=\alpha d\), where \(T\) is the transmittance, \(c\) is the concentration, and \(d\) the thickness of the sample. Furthermore, the THz refractive index of the saccharides was rarely measured, although this measurement is easy for systems based on THz time-domain spectroscopy (THz-TDS), which allows the detection of the amplitude and phase of the coherent THz radiation [2; 33]. The coherence of the THz radiation poses a crucial challenge when extracting optical parameters from the transmission data in THz-TDS, leading to FP interference effects due to the multiple reflections within planar samples. Several authors in the past have proposed different numerical methods to eliminate FP effects in the spectral data [34; 31; 35; 36; 37; 38]. However, in all the previous described studies on the THz spectra of saccharides, although flat and parallel-faced samples were used, no mathematical techniques for the elimination of FP effects were employed. In this work, we used THz-TDS to measure the transmission spectra of the previously cited seven saccharides in their solid state from 0.3 to 3.3 THz. The samples were carefully prepared as pellets made of saccharides and polyethylene (PET) powders. We eliminated the FP effects and estimated the volume fraction of air within the pellets in order to obtain a quantitative estimation of the absorption coefficient (in cm\({}^{-1}\)) and the refractive index as a function of the frequency. The DFT simulations were carried out by using a state-of-the-art method for the calculation of H-bonding interactions, which drives the collective motion of organic molecules. Simulation results provided the frequency of the vibrational modes and allowed us to characterize them in terms of intermolecular translations, intermolecular librations, and intramolecular vibrations.
# Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses
## 3 Methods
### Principal Component Analysis (PCA)  
The results obtained from the SBAS need to be interpretable, since thousands of pixel values have displacement values for a certain time span. PCA is a useful method to analyze time series data in the form of observations (M) and variables (N). It has advantages in allowing us to quickly visualize the results, and to analyze the correlation between variables and observations on a simple map that has a low dimension and an optimal view for a variability criterion. Here, the vertical displacement deduced from SBAS are on matrix or a table, in which the observations are displacements (mm) of different locations and the variables are different acquisition dates. The observations and dates are kept in individual rows and columns of the matrix, respectively. The general form of the PCA in this study can be defined as follows:  
\[X=TP^{\prime}+r \tag{6}\]  
where \(X\) is the PCA matrix that contains the time series, \(T\) is a matrix of scores and \(P\) is a matrix containing the loading factors (this matrix is transposed), and \(r\) is the residual or unexplained portion of the results. It must be noted that PCA originates from a projection method. If the data are too complex, the results may lead to misinterpretation, because PCA does not allow testing of the hypotheses. One solution could be to select different observations or variables from the SBAS results and run the model each time, as long as such manipulations (adding or removing observations/variables) are justified in the interpretation. Here, since the size of the preliminary SBAS matrix was too large, we randomly selected 5000 SBAS time series candidates for the PCA matrix (\(X\)). \(T\) and \(P\) were estimated from the least squares method, and each PC had one score and loading factor.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
###### Abstract
Furthermore, as another extension of our previous work [5] and as a possible practical application of our work, we sketch a timing channel that could be developed from a recent overshadowing attack [6] and involves a scenario of a flying drone that is remotely controlled and an attacker near the controlling person can stealthily transmit data to the drone or a confederate within eye sight of the drone. As another practical application of our research, we perform both simulation and, as an extension of our previous work [5], real-world experiments comprising a base station and mobile device to demonstrate the feasibility and practical application of our approach and to evaluate our prototype steganographic channel with respect to steganographic bandwidth, robustness, stealthiness, and steganographic cost, i.e., its influence on the carrier. Beyond an application scenario, we investigate countermeasures to demonstrate that such covert channels can be eliminated by careful design. The remainder of this article is structured as follows: Section 2 summarizes background information on the protocols used in 5G-air interfaces, network steganography, and related work about steganographic channels in 4G and 5G radio communication. Section 3 describes our methodology followed to analyze the protocols used in 5G-air interfaces in view of steganographic channels with the help of hiding patterns. Section 4 presents the design of a storage covert channel in the Packet Data Convergence Protocol (PDCP) of the 5G-air interface and a sketch of a timing channel. In Section 5, we evaluate the storage covert channel with simulations and real-world experiments. Section 6 presents our conclusions and gives an outlook on our future work.
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Auxiliary for External Circuits
#### 3.4.2 Predistortion Network  
Owing to the nonlinear characteristics of the TL, the base current generates harmonics and intermodulation distortion during input, among which the IMD3 has the greatest impact on the link transmission signal.  
To suppress signal distortion, [PERSON] et al. first reported a TL predistortion network technique based on the Schottky diode nonlinear compensation [76]. The modulated signal undergoes a 180\({}^{\circ}\) phase change after passing through the Schottky diode. Subsequently, it is superimposed with the signal passing through the delay line, which can use the phase difference to cancel the IMD3 component of the modulated signal. However, a one-Schottky diode circuit can limit the bandwidth owing to the use of phase shifters. To overcome this, a dual Schottky diode circuit can be used, which utilizes two Schottky diodes with an antiparallel structure to reduce the intermodulation distortion component by adjusting their bias currents.  
Without the Schottky diode predistortion network, the output spectrum exhibits a magnitude difference of 15.92 dBc between the input frequency and the IMD3 component. Because the IMD3 of the TL varies with the bias current, adjusting the bias voltage of the Schottky diode to make its nonlinearity the same as that of the TL can reduce the IMD3. After joining the network, the IMD3 component was notably reduced. The amplitude difference between the input frequency and the IMD3 component is 29.48 dBc under the single Schottky network and 31.45 dBc under the double Schottky network.  
After adding the predistortion network, the IMD3 component was reduced and the bandwidth was increased. One and dual Schottky diode predistortion techniques increased the bandwidth to 12 and 13.8 GHz, respectively. Under the same bias condition, the bandwidth without predistortion network analysis was only 10.96 GHz. However, when the predistortion network was introduced, the relaxation peak amplitude increased. This increase needs to be reduced for the optimization of this technology.  
Figure 12: Low-frequency auxiliary AC signal to improve TL optical bandwidth circuit diagram [74]. Reproduced from Ref. [74], with the permission of AIP Publishing.