esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The frequency containment reserves (FCR) and frequency restoration reserves with automatic activation (aFRR) values of the isolated EPS were calculated and simulated. The potential of HVDC to stabilize and restore the parameters of an isolated EPS were investigated [8; 9; 10; 11; 12; 13; 14; 15; 16]. The main objective of this study was to investigate all possible and available means to prevent the isolated EPS from shutting down or splitting into separate, non-synchronously operating parts with a load of a certain magnitude. This study aimed to demonstrate that with sufficient active power, inertia in reserve, and with the FCR function of the generators enabled, it is possible to maintain stable EPS parameters in the event of a sudden and unexpected system disturbance [4; 5; 6; 17; 18; 19; 20]. Each and any isolated EPS must be prepared to independently ensure reliable energy sector operation very efficiently and cost-effectively. It needs to establish an internal, integrated market for the active power reserves of the isolated EPS that would stimulate competition among electrical energy producers which provide power reserve services. These reserves are to be used to compensate short-term power and energy shortages in the isolated EPS [21; 22; 23; 24; 25]. The rest of the paper is organized as follows: the structure of the analyzed EPS and its operating conditions are presented in Section 2, Section 3 is intended for calculation of the EPS inertia and permissible power imbalance, the impact of EPS response on \(k\) factor is analyzed in Section 4, the identification of abrupt EPS isolation is discussed in Section 5, the simulation results of EPS desynchronization and reaction of the isolated system to disturbances are presented in Section 6, synchronization of the EPS with a synchronous area is discussed in Section 7, the recommended sequence of actions after an abrupt and unexpected EPS disconnection is presented in Section 8, the results of the research are summarized and discussed in Section 9, and conclusions are presented in Section 10.
## 2 Deformation and State Description of a Segment  
### Segment Deformation  
The dielectric elastomer actuator can deform the body segment under the action of periodic excitation voltage [43]. Figure 1 shows the model schematic diagram for the deformed body segment driven by the dielectric elastomer actuators. When the tail of the body segment is fixed, the displacement of the body segment head is assumed as \(x(t)\), as shown in Figure 1a. The simplification model of the body segment is obtained, as shown in Figure 1b. \(F_{driven}(t)\) is the ultra-low frequency excitation. The body segment can realize reciprocating deformation under ultra-low frequency excitation. We refer to the results in reference [50], where four viscoelastic-spring components accurately describe the dynamic characteristics of the dielectric elastomer actuators.  
In the simplified model of one body segment with a dielectric elastomer actuator, as Figure 1b, \(F_{driven}(t)\) is the ultra-low frequency excitation. The mass of the body segment is assumed to be equivalent to the lumped mass \(m\); the equivalent stiffness of the dielectric elastomer actuator is given as \(k_{i}\); and the equivalent viscous coefficient is given as \(c_{i}\) (\(i\) = 1,2,3,4).  
The frictional resistance is defined as \(F_{fr}\), depending on the viscous forces. The ultra-low frequency excitation \(F_{driven}(t)\) drives the dielectric elastomer actuator. The dynamic model of the body segment is expressed as  
\[\begin{array}{c}\ddot{x}=-\frac{1}{m}\bigg{(}kx+\sum\limits_{i=1}^{4}k_{i}x_{ i}+F_{fr}\bigg{)}+\frac{1}{m}F_{driven}(t),\\ k_{i}x_{i}=c_{i}(\dot{x}-\dot{x}_{i}),\ i=1,2,3,4,\\ F_{fr}=\zeta sign(\dot{x}),\\ F_{driven}(t)=V(t)^{2}(ax+b).\end{array} \tag{1}\]  
Fixing the parameters is the same as the values in reference [46]: for the excitation voltage \(V(t)\), the deformation of the body segment \(x\) can be calculated from Equation (1). As the excitation voltage is \(V(t)=2(\sin(2\pi t-\pi/2)+1)\), the deformation of the body segment is shown in Figure 1c. In Figure 1c, the deformation of the body segment stays in periodic deformation under periodic excitation voltage. For the periodic deformation of the body segment, the locomotion of the earthworm-like robot can be realized.  
According to the deformation of each segment \(x(t)\), the body length \(l(t)\) after deformation can be expressed as  
\[l(t)=l_{0}+x(t) \tag{2}\]  
where \(l_{0}\) is the original length of each body segment.
## 2 The Surrogate Model for Computer Experiments
### Space-Filling Design  
Conventional surrogate modeling uses the training samples at once to fit a response surface. The training samples are generated by design of experiments (DOE). A space-filling design is the most widely used in computer experiments and it uniformly fills the samples over the input space by maximizing the distance between the samples as far apart as possible (that is, uniformity). The space-filling designs include Latin hypercube designs (LHDs) [6], maximum LHDs [18], orthogonal LHDs [7], uniform designs [8], generalized LHDs [9], and maximum projection designs [19], according to their own criterion for uniformity. Among the various space-filling designs, the maximum LHD (Mm LHD) seems to be the most commonly used space-filling design in practice thanks to its simplicity and availability in software packages [19]. Mm LHD searches an LHD by maximizing the minimum distance among the training samples, and this can be obtained by minimizing the following criterion (Morris-Mitchel criterion).  
\[\min_{D}\left\{\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\frac{1}{d^{k}\big{(}\boldsymbol {x}_{i},\boldsymbol{x}_{j}\big{)}}\right\}^{1/k} \tag{10}\]  
where \(d^{k}\big{(}\boldsymbol{x}_{i},\boldsymbol{x}_{j}\big{)}=\Big{(}\sum\Big{(} \big{(}\boldsymbol{x}_{i}-\boldsymbol{x}_{j}\big{)}^{k}\Big{)}\Big{)}^{1/k}\).  
It is worth noting that space-filling designs were not originally designed for sequential surrogate modeling. Space-filling designs have been widely employed under a specific number of the training samples (due to a limited number of the model evaluation). In the sequential sampling method, the space-filling designs are typically used to generate initial training samples. Within the initial training samples, an initial surrogate model is constructed and used to select an infill sample for the sequential sampling method.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Integration Circuit Implementation  
The hardware experiments utilized simple test signals (sine, triangle, and square waves) with well-defined derivative relationships, allowing direct verification of the derivative recovery without requiring integration circuits. For sinusoidal inputs, the recovered cosine signals clearly demonstrate the expected 90\({}^{\circ}\) phase shift characteristic of differentiation. While integration circuits were not implemented in these experiments due to the predictable nature of the test waveforms, complex message signals would require an integration stage following demodulation to reconstruct the original signal from its recovered derivative. This approach maintains experimental clarity while acknowledging the integration requirement for practical applications involving arbitrary message signals.
## 3 Results  
### Regression Results  
Histograms for Hg, As, and Sb XRF results (in situ, ex situ-bulk, ex situ-sieved) and for AAS results were not normally distributed, and had a long tail distribution. The correlation fraction suggested the following correlations should be inspected further for Hg, unless otherwise noted:  
* AAS vs. in situ;
* AAS vs. ex situ-bulk;
* AAS vs. ex situ-sieved;
* ex situ-bulk vs. in situ;
* ex situ-sieved vs. ex situ-bulk;
* As ex situ-bulk vs. As in situ;
* As ex situ-sieved vs. As ex situ-bulk;
* Sb ex situ-bulk vs. Sb in situ;
* Sb ex situ-sieved vs. Sb ex situ-bulk.  
Unbalanced ANOVAs performed on the above-mentioned pairs indicated significant differences between pairs' means. This validated performing regressions (linear, semi-log, and log-log) on all nine pairs. Summary statistics of linear regressions comparing AAS results to the different XRF types can be found in Table 1. Linear regressions were the best correlations (\(p\)-values \(<\) 99% and coefficient of determination (R\({}^{2}\)) values \(>\) 0.70), with conversion factors ranging from 0.8-1.9. Because  ex situ-bulk and ex situ-sieved linear regressions had statistically insignificant (\(>\)90% CI) intercepts, linear regressions through the origin are presented in Table 1.  
Linear regressions comparing XRF results of different soil fractions (in situ, ex situ-bulk, and ex situ-sieved) had the strongest correlations (Table 2). Ex situ-bulk was the best predictor of ex situ-sieved across all three heavy metals of concern (Hg, As, and Sb),  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**XRF** & **N** & \(p\)**-Value** & **R\({}^{2}\)** & **Intercept** & **CF** & \(p\)**-Value of** & **Equation** \\
**Type** & & & & & & **Intercept** & \\ \hline In Situ & 79 & \(<\)0.0001 & 0.17 & 12 & 0.8 & 0.0013 & y=0.8x + 12 \\ \hline Ex Situ-Bulk & 154 & \(<\)0.0001 & 0.88 & 01 & 1.9 & NA & y = 1.9x \\ \hline Ex Situ-Sieved & 135 & \(<\)0.0001 & 0.87 & 01 & 1.6 & NA & y = 1.6x \\ \hline \end{tabular} \(p\)-value of intercept was insignificant.  
\end{table}
Table 1: Summary linear regression results comparing AAS to XRF. Conversion Factor (CF) is the slope of the regression line.  
with R\({}^{2}\) values ranging from 0.89-0.96 and \(p\)-values \(<\)0.0001. Linear regressions through the origin were preformed on As ex situ-bulk vs. As in situ, and Hg ex situ-sieved vs. Hg ex situ-bulk comparisons, as both had statistically insignificant (\(>\)90% CI) intercepts.
# Improvement of Light Extraction in Deep Ultraviolet GaN Light Emitting Diodes with Mesh P-Contacts
## 5 Conclusions  
We have fabricated DUV LED structures with the mesh p-GaN/ITO structure. Thanks to the lower light absorption at 280 nm, the mesh p-GaN/ITO DUV LED showed a 12% output power enhancement compared to the conventional DUV LED. The EQE value was as high as 6.3% at 20 mA. In addition, the notorious efficiency droop was not observed up to 120 mA, and the output power reached 32 mW at 120 mA. We also employed a simple 2D simulation model to study the optimum p-mesh contact structure, and revealed the balance between the light absorption and leakage current due to the high voltage. These improvements should facilitate highly efficient solid state DUV light sources penetrating into important health applications such as purification, disinfection, and sterilization.  
Conceptualization, S.-Y.K.; methodology, S.-Y.K., C.-J.C., Z.-T.H., and T.-C.L.; software, C.-J.C.; validation, S.-Y.K., C.-J.C., Z.-T.H.; formal analysis; S.-Y.K., C.-J.C., Z.-T.H.; resources, T.-C.L.; writing, S.-Y.K., C.-J.C., Z.-T.H.; visualization, Z.-T.H. and T.-C.L.; supervision, T.-C.L.; project administration, T.-C.L.; funding acquisition, T.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology (MoST) of Taiwan, under the project number MOST 109-2124-M-009-005.  
The authors would like to gratefully acknowledge Semiconductor Laser Laboratory, Nano Facility Center and Nano Device Laboratories for technical support.  
The authors declare no conflict of interest.  
Figure 5: (**a**) Schematic diagrams of the simulated structures. (**b**) Simulated L––V characteristics of the four structures. (**c**) Normalized electron current in the active region.
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 3 Independent Microphone Simulation  
### Model Description  
The microphone, as shown in Figure 7, is composed of a diaphragm and a back chamber. A vent is always machined to achieve a static pressure equilibrium in and out of the microphone. When a low-frequency sound excites the diaphragm deformation, the associated sound pressure in the back chamber will also experience pressure leakage and heat conduction effects, so that the phase response of the diaphragm deformation, which is in a linear relationship with the voltage output of the microphone, will be affected [30].  
The 1/12 model of the microphone is built as shown in Figure 8. The vent is divided into six sections, and the total acoustic impedance of the six sections is equivalent to the vent [31,32]. Only the effective volume of the air in the back chamber is modeled, and the parameters are consistent with those given in Table 2. Proper mesh in the diaphragm region is helpful in correct and fast simulation. Other regions are coarsely meshed, as shown in the figure below.  
The boundary conditions of the vent and surrounding walls both for the frequency domain and the transient simulations are the same as the settings of the pistonphone. The velocity excitation is also similar to the settings of the pistonphone, except that a 0.5 \(\upmu\)m amplitude excitation is applied to the microphone diaphragm to produce a 32 Pa associated sound pressure at the ideal sealing and adiabatic conditions.  
Figure 8: **(a)** The FEA model of the microphone. (**b**) Sound pressure distribution for the heat conduction and leaking condition at 0.1 Hz, in which the sound pressure decreases to almost 0.  
Figure 7: Assembly drawing of a condenser microphone.
## 2 Background and Related Works
### Label and Sparse Regularization Autoencoder (LSRAE)  
[PERSON] et al. proposed LSRAE, a structure that combines supervised learning methods with the autoencoder, which is unsupervised learning, to improve the performance of classification tasks. They thought that the overfitting and the irregular information problem of classification, which are chronic problems of autoencoders, occur because the relation between the target data and the input data is not considered at all in the reconstruction process. Therefore, they solved the problem by introducing two main methods: the first is adding a label regularization term to the objective function to reduce the difference between the actual label and the desired label, and the second is using the extreme learning machine (ELM) [35] for fast optimization. Label regularization term (\(I_{label}\)) of the LSRAE is formulated as follows:  
\[J_{label}=\frac{1}{2n}\sum_{i=1}^{n}\parallel L-T\parallel^{2}=\frac{1}{2n} \sum_{i=1}^{n}\parallel H\hat{\beta}-T\parallel^{2} \tag{5}\]  
where \(L\) is the actual label as the output of the autoencoder, \(T\) is the desired label, and \(n\) is the number of input samples. Figure 1 shows a way to utilize the ELM on the autoencoder. The blue lines connect the hidden layer and the output layer. The red lines connect the hidden layer and label layer. SoftMax classifier is adopted for the classifier. In the training process, they compute the reconstruction loss, the weight decay term, and the sparse regularization term, in order. Then, they use the concept of ELM to calculate the real label. The real label is used to obtain label regularization. The LSRAE learns through backpropagation by obtaining the squared error of the expression as Equation (5). It can restrict overfitting of the autoencoder that focuses only on reducing reconstruction loss. It improves the classification accuracies, and they also use the sparse constraint to restrict useless nodes. The simple objective function of LSRAE is calculated as follows:  
\[J_{sparse}=\sum_{j=1}^{s}KL(\rho||\hat{\rho}_{j}) \tag{6}\]  
\[J_{LSRAE}=J_{AE}+\lambda J_{label}+\beta J_{sparse} \tag{7}\]  
where \(J_{AE}\) is the loss function of the basic autoencoder, and \(\lambda\) and \(\beta\) are coefficients.
## 4 Application to Plastic Facial Phantom
### Experimental Results of the Proposed Registration Strategy  
For initial registration (coarse registration) of the point cloud acquired from the medical image and patient space, four coordinate points on the facial surface that could easily be identified were roughly selected. The locations of the four feature points in this study were the tip of the nose, the tips of both eyes, and the glabellar. Subsequently, the transformation matrix (rotation and translation) of the two spaces was extracted using the singular value decomposition method to perform the initial registration. Figure 7 illustrates a typical case of initial registration and shows the difference with ICP precision registration. The difference in the position of the point cloud between the initial registration and the ICP registration in all experimental cases using the plastic facial phantom was found to  
Figure 6: Apparatus and experimental setup (**A–C**).  
be about 2 mm on average. Successful initial registration was performed, leading to ICP refinement optimization without local minima.  
Figure 8 shows a comparison of the results of the conventional and proposed registration methods using experimental data from the plastic facial phantom. For the optical tracking system, the average SRE value using the conventional method was 1.36 mm, while the proposed registration method showed a reduced average SRE value of 1.07 mm and an increase in accuracy of about 21% (\(p<0.01\)). Additionally, the SRE value in the proposed registration method decreased by about 15% compared to the conventional registration method, and the difference was significant (\(p<0.01\)).  
Figure 9 visually illustrates the difference in SRE between the conventional and proposed registration methods for representative experimental data from 20 replications. The colormap shown on the right side of the figure indicates that the SRE increases as the color goes from blue to red (i.e., blue represents zero). Both the optical and electromagnetic tracking systems produced a wide distribution of blue across the facial surface using the proposed registration method compared to the conventional method, and a significant reduction in errors (red). In the rest of the experiments, both the optical and electromagnetic systems produced similar results (color distributions).  
Figure 8: Comparison of SRE between the conventional and proposed registration strategies using the plastic facial phantom.  
Figure 7: Coarse registration (representative trial) (**A–D**).  
Table 1 shows a comparison between the registration methodologies (conventional method vs. proposed method) and the TRE value according to the location of the diseased area. Compared to the existing method, the proposed registration method in this study showed reduced TRE values regardless of the location of the target.
# Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
## 4 Empirical Study  
### Software Simulation  
To assess the effectiveness of the trust and reputation model proposed, a custom software simulator has been developed to imitate intersection traffic. All AVs \(\{V_{1},V_{2},\ldots,V_{N}\}\) in the simulator are united into a system where each element can communicate with each other to organize the traffic. However, it is necessary to take into account that some AVs can transmit incorrect data (intentionally or because of technical problems) thereby provoking the road collapse. Our trust and reputation model is intended to prevent such actions and provide secure information interaction in the traffic system. The description of the software testing ground is given below:  
* The software testing ground is represented as matrix and is divided into square elementary sectors.
* The road map is known to all the AVs.
* There are only straight roads and their coordinates coincide with the coordinates of the elementary sectors located in the same row or column.
* Each road must belong to either vertical or horizontal type.  
The testing ground includes the following set of parameters: \(\{c,r,R\}\), where \(c\) is the number of columns that define the field; \(r\) is the row amount defining the field; \(Rd=\{Rd_{1},Rd_{2},\ldots,Rd_{n}\}\) is the set of roads where the AV can move. In its turn, each road is characterized by a set of parameters \(\{t,d,E\}\), where \(t\) is the road type (vertical or horizontal); \(d\) is the road direction (passing or oncoming);and \(E\) is the set of elementary sectors defining the roadway. The model of AV (MAV) consists of the following characteristics:  
* \(m\) is the set of elementary sectors, based on a road map, and the planned start and final positions of the MAV.
* \(s\) is the speed; speed is understood as the amount of elementary sectors crossed by a MAV per one conditional discretized time segment.
* \(RT\) is the sequence of steps for the MAV to go through the planned path (calculated on the basis of \(m\) and \(s\); one step is passed in one conditional discretized time segment).
* There is an array of reputation values of other MAVs for each discretized time segment \(t\) from the beginning of the interaction \(t_{1}\) to the discretized time segment preceding the current interval \(t_{current-1}\): \(R=[[R_{V_{1_{t_{1}}}},R_{V_{2_{t_{1}}}},\ldots,R_{V_{N_{t_{1}}}}],[R_{V_{1_{ t-1}}},R_{V_{2_{t_{current-1}}}},\ldots,R_{V_{N_{t_{current-1}}}}]]\).
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 2 Motion and Distortion Induced Artefacts
### Involuntary Eye Motion  
Involuntary movements of the eye, or the retina in particular, can lead to significant distortions within the OCT volumetric data acquisition [21] which, in some cases, are easily seen in the raw data. An example of a distorted OCT volume is shown in Figure 1. The represented planes can be taken as reference for the direction nomenclature used in this review. In this unregistered OCT volume, acquired with a custom made device at Medical University of Vienna (SS-OCT at 1050 nm with 200 kHz A-scan rate, 200 Hz B-scan rate) [22], the distortions are visible in the slow and coronal (en face) planes. The fast plane is often considered artefact free, as the frame rate acquisition in modern devices is faster than the motion expected to be observed in the retina. Nevertheless, axial motion, respiration, vascular pulsation, and involuntary fixational eye movements [11; 12; 13], described in the following subsections, may all contribute to coronal and axial distortions of the OCT data.