esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis  
This paper presents an enhanced IEEE 33 power distribution system for the purpose of conducting arithmetic example analysis. The hybrid optimization method proposed in this paper is realized in MATLAB (2023b) by calling the CPLEX algorithm package. Three photovoltaic power supplies and two wind turbines are connected to ADN. The installation locations of PV are nodes 16, 22, and 24, corresponding to an installed capacity of 400, 400, and 400 kW; and the installation locations of the WT are nodes 13 and 20, corresponding to an installed capacity of 1000 and 500 kW. As indicated in the reference [4], the selected nodes for the SOP access points are 6, 16, 22, 24, and 33. These nodes exhibit significant voltage fluctuations. Node 6 is situated in the heavy load branch area, while nodes 16, 22, and 24 are DG access points and are situated near the end of the feeder. Node 33 is situated at the end of the feeder. The specific structure of enhanced IEEE 33 is illustrated in Figure 8. The parameters pertinent to the algorithm are presented in Table 1. In order to validate  
Figure 7: Flowchart for solving the bilayered regularization model.  
As illustrated in Table 3, the DG fluctuation indexes of Schemes 3, 4, and 5 are less pronounced than those of Schemes 1 and 2. This is due to the fact that Schemes 3, 4, and 5 are configured with a nodal flexibility resource, namely an ESS, which transfers active power from time to time through charging and discharging strategies. This enables the reduction of the peak-to-valley difference of the load and the satisfaction of the flexibility demand of the net load. In instances where the power generated by DG exceeds the load, the ESS is capable of storing the surplus energy. Consequently, Figure 10 illustrates that DG volatility may be negative in schemes that incorporate ESS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{3}{c}{**Flexibility Indicators**} \\ \cline{2-3}  & **DG Consumption Rate \(H_{\text{sw}}\)/\%** & **DG Volatility \(H_{bd}\)/\%** & **F\({}_{down}\)** \\ \hline
1 & 65.37 & 54.68 & 17.35 \\
2 & 86.01 & 38.82 & 36.08 \\
3 & 87.32 & 19.32 & 44.66 \\
4 & 94.88 & 6.71 & 54.24 \\
5 & 91.79 & 14.94 & 49.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of flexibility of different schemes.  
Figure 10: Comparison of DG volatility indicator.  
Figure 9: Comparison of DG consumption rate indicator.  
The values of the DG consumption rate index and the DG volatility index for Schemes 4 and 5 are greater than those for Schemes 1, 2, and 3. This is due to the fact that Schemes 4 and 5 are configured with network-type flexibility resources. In addition to node-type flexibility resources ESS, SOP can realize fine power control at the feeder level, coordinate various flexible resources, optimally adjust ADN's current distribution, improve the system's ability to adapt to the uncertainty of demand, and significantly enhance the ability of DG consumption.
## 4 Discussion  
In this paper, we presented a real-time train tracking algorithm that runs on the basis of one-second signals without delay. The algorithm is based on two main steps, i.e., the detection of vibrations and the tracking of trains in the signal. The performance of the algorithm was evaluated on two test sites, where one was in a tunnel with a standard cable trench and the other one was on an open track with the cable attached directly to the rail. In the tunnel, we measured a long distance which lead to occasionally lost tracks, especially on a stretch where the cable installation was sub-optimal for transferring vibrations from the train to the cable. On the open track with the cable mounted directly to the rail, we did not observe lost tracks during the test period, which is due to strong signals, because the observed cable length was considerably shorter than in the tunnel. Due to the increased noise with the cable installation method, we observed several artifact train tracks. Nevertheless, for both evaluated tracks we reached accurate tracks in more than 98% of the cases. We conclude from the results that the installation in a cable trench is advantageous for train tracking with a DAS system.  
For two shorter recordings, we presented the positional error, which was around 40 m, which was also in the range of the available ground truth. For more accurate evaluations of the positional accuracy of the tracking, it would be important to get better ground truth data and perform a highly accurate calibration of the fiber cable.  
For application in the railway sector, DAS shows great potential, especially when combined with other sensors for redundancy. In future research, we will evaluate methods for fusing different sensor modalities to increase the robustness of the tracking.  
The authors from NBG Fosa GmbH recorded the data. This involved the planning and calibration of the test sites, as well as understanding the signal behavior in different positions. The authors from AIF Austrian Institute of Technology GmbH developed the tracking algorithms and their implementation. Conceptualization, C.W. and A.P.; Data curation, G.N. and H.D.; Methodology, C.W.; Project administration, M.L. and H.G.; Software, A.P.; Supervision, M.L. and H.G.; Visualization, M.W.; Writing--original draft, C.W.; Writing--review & editing, M.L. and M.W. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the \"Wiener Wirtschaftsgantur\" through the research project \"FOS--Real Time Methods\" with the grand ID 1890985  
The authors declare no conflict of interest.
## 2 Materials and Methods  
Postimplant surgery cone beam computed tomography (CBCT) scans were retrospectively analyzed to assess the accuracy of computer-guided implant placement compared to the preoperative computer-digital planned implant position. In the period from December 2019 to December 2021, CBCT scans were collected from patients treated with computer-guided immediate or delayed implant placement of a single maxillary incisor. Subsequently, only patients who had been treated in the last 5 years and had undergone a postoperative CBCT scan were included in our analysis. Inclusions and exclusions criteria were summarized in Tables 1 and 2. All scans were transferred and assessed anonymously, with only the information about type of treatment recorded. For all patients, implant placement was conducted by the same operator using a 3D printed tooth-supported surgical guide. The digital planning of the implant placement was performed with a dedicated guided surgery software in which we imported the Digital Imaging Communication in Medicine (DICOM) data coming from the CBCT scan and the standard tessellation language (.stl) files recorded by digital impression of the jaws, obtained with an intraoral scanner (Cerec Omnicam, Dentsply Sirona, Charlotte, NC, USA). Digital data were matched to acquire the necessary anatomical information to perform a digital wax-up of the future definitive prosthesis in order to allow an ideal virtual implant placement (respecting the ideal loading axis and bone bioavailability). Implant placement was conducted by the same operator using a 3D printed tooth-supported surgical guide through an implant guided surgical kit (Neoss guide, Neoss Ltd., Harrogate, UK) and only bone level implants of the length 10 \(\pm\) 1 mm were included in our analysis. Evaluation of congruence between digital planning and in vivo postoperative implant placement was conducted by analyzing the mean deviation of the apico-coronal placement and implant axis in sagittal projection, the mean mesiodistal deviation of the implant with adjacent teeth at the level of the implant head and apex, in the axial position. Implant axis in sagittal projection is a parameter that depends on the vestibulo-palatal inclination and the apico-coronal depth of the implant. Statistical analysis was performed using a dedicated software (MATLAB, version 7.10.0, The MathWorks Inc., Natick, MA, USA). The null hypothesis to test was that no statistically significant differences between measurements collected in preoperative digital planning and in vivo implant placement and between immediate and delayed placement (_p_ < 0.05) were found.
# Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics
## 5 Evaluation and Discussion
### Limitations
#### 5.2.3 SA Server as a Trusted Third-Party  
As indicated in the evaluation using the framework by [PERSON] et al. [2], as presented in Table 4, the vulnerability of the SA server, functioning as a trusted third party in the authentication process, constitutes a potential weak point. A compromise of the SA server could result in the impersonation of user accounts. Subsequent research endeavours will seek to enhance the system model by either mitigating or entirely eliminating the influence of the SA server during authentication. The goal is to design a system where the compromise of the SA server does not translate into a compromise of the user's account.
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 1 Introduction
The framework combines complementary methodologies to create a comprehensive optimization system: RSM establishes accurate mathematical models capturing the non-linear relationships between mix variables (W/C, sand ratio, AEA) and concrete performance indicators; NSGA-III efficiently explores the global solution space to generate Pareto-optimal alternatives that conventional methods cannot identify; CRITIC objectively quantifies performance indicator priorities through a data-driven analysis of standard deviations and interrelationships, eliminating the subjective bias inherent in traditional weighting methods; and VIKOR provides scientific decision-making for selecting the optimal compromise solution from complex multi-criteria alternatives. This integration creates a closed-loop \"modeling-optimization-weighting-decision\" system that resolves the fundamental trade-offs in concrete mix design for extreme environments. The framework systematically optimizes key performance indicators, including R\({}_{\text{f}}\)/R\({}_{\text{c}}\), dry shrinkage, and porosity, demonstrating both theoretical rigor and practical effectiveness for designing high-performance pavement concrete under the complex engineering conditions found in arid regions.
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 6 Conclusions  
In this paper, we study the design of the multi-UAV-based network infrastructure and propose a formation control algorithm. Based on the necessity of an instant and extensible network infrastructure, our algorithm dynamically transforms the network coverage of the infrastructure and leads to the enhancement of throughput. To grant the flexibility of the algorithm, we address the generic network model of the multi-UAV-based network infrastructure and derive protocol-specific parameters that could be applied to the algorithm. In addition, we have several research directions for future work:  
* Network protocol. Formation algorithm equalizes the congestion by moving the UAVs, which could possibly incur handover between the APs more frequently. Handover procedures might decrease the throughput of the infrastructure, so the protocol that supports fast recovery from the connection change is needed. Also, we observed that the IPv6 Routing Protocol for low-power lossy networks (RPL) [40] is a promising routing scheme to adapt to the multi-UAV-based network infrastructure. The network model utilizing RPL in the network infrastructure should be analyzed and the pros and cons should be addressed.
* Terrain. This paper assumes that the ground is flat. Although the altitudes of the UAVs are sufficiently high to ignore some elevation of the terrain, some environments with high elevations or buildings could affect the formation of the UAVs. To manage the WLAN network traffic, \(z\) axis change could not be enough to approach optimal formation, which needs to improve our formation algorithm.
* Evaluation. We evaluate our formation control algorithm by ns-3. Even though ns-3 is one of the well-known network simulation tool and continually revised for improvements, it could suffer from precision issues due to lack of real-world projection. To evaluate our formation control algorithm on a more practical testbed, a dataset extracted from real-world environments [41] could be desirable.  
We believe that our algorithm contributes to the increased availability of the multi-UAV-based network infrastructure in various situations, such as rescue or surveillance missions. Also, we hope  
Figure 9: Mobile device experiment scenario and the performance.  
that our novel approach to progressively controlling the formation of the mobile network nodes will present new possibilities for flexible network design.  
Conceptualization, S.P. and K.K.; Methodology, S.P.; Software, S.P.; Validation, K.K. and H.K. ([PERSON]); Writing--Original Draft Preparation, S.P.; Writing--Review & Editing, K.K., K.K. and H.K. ([PERSON]); Supervision, H.K. ([PERSON]).  
This research was supported by Nano UAV Intelligence Systems Research Laboratory at Kwangwoon University, originally funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD).  
The authors declare no conflict of interest.
# Chemical Migration from Wine Contact Materials
## 5 Wine Migration Studies on Chemical Contaminants
### Analytical Methods  
After the completion of the migration test, the sample, which is the food simulant or the wine, is subjected initially to the sample preparation procedure in order to extract the analytes, remove interfering matrix substances, and have pre-concentration of the target compounds. Afterward, the obtained extract is analyzed using a proper analytical technique to identify the compounds and quantify them. The selection of the sample preparation steps and the analytical technique to be applied depend on the nature of the compounds to be determined (analytes) and the nature of the sample. According to the studies performed for the investigation of migrating substances from cork and plastic tops of wine, gas chromatography (GC) and liquid chromatography (LC) coupled in all cases to mass spectrometry (MS) [11; 36; 71; 72; 73; 74; 75] were used for the determination of organic compounds, while Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) were used for the elemental analysis [76].
# Failure Mode Analysis and Dynamic Response of a Coal Mine Refuge Chamber with a Gas Explosion
## The Finite Element Model  
### Finite Element Code and Arbitrary Lagrange-Euler (ALE) Method  
ANSYS/LS-DYNA is a finite element software that can be efficiently used in dynamic analysis of a number of models in most areas in engineering. LS-DYNA is a general purpose transient dynamic finite element program that specializes in high-rate related problems, such as explosion and impact. ANSYS adapts the LS-DYNA as the solver for these problems, however, it retains its own pre- and post-processes. In LS-DYNA, there are three classes of available methods: Eulerian, Lagrangian, and Arbitrary Lagrange-Euler (ALE) ones, for the generation of a grid or mesh [(20)]. When the deformation of structures is large, the grid that moves together with the medium in the Lagrangian framework will become highly irregular, which would affect both the efficiency of the method and its accuracy. The Eulerian method that employs stationary spatial grids can overcome this difficulty. The ALE algorithm consists of a classical Lagrangian step in which the mesh moves along with the modeled material, a rezone step in which the mesh is modified to preserve good quality through the computation of Eulerian time step, and a remapping step in which the solution is conservatively transferred from the old mesh to the new rezoned one and, thereby, performs the fluid-solid coupling transient analysis. In this work, the ALE method was employed to simulate the process that the explosive explodes, the propagation of the shockwaves through branch gallery and its junction in this process, and the interaction of explosion shockwaves with the branch gallery and its junction.
## 3 Cases and Measurements
### Measurements  
The wind resource in the three cases is usually measured at a site using a wind tower, equipped with wind speed and wind direction sensors. The locations of the wind towers are shown in Figure 1. The datasets of the three cases are obtained as follows:  
1. SJB: The measured data includes wind speed, wind direction and temperature data. The cup anemometers are placed at 30, 50 and 70 m, and the wind vines are placed at 30 and 70 m. The temperature observations are also used to obtain the temperature data at 30 and 70 m.
2. GFC: The cup anemometers are at 30, 50 and 80 m, and the wind direction is obtained with wind vines placed at 30 and 80 m. Unfortunately, there is no temperature sensor installed on the tower.
3. HNH: Wind speeds are measured by the cup anemometers placed at 10, 60 and 100 m. Wind vanes are placed at 10 and 100 m. The temperature data is also unavailable in the HNH area.  
The datasets at upper heights are used to verify the reliability of the wind speed extrapolation method. The measurement parameters of each case including height, time interval and data collection period are presented in Table 6.
# Current Context and Research Trends in Linear DC-DC Converters
## 6 LDO's Recent Research Trends
### Reducing Noise Levels Further  
The requirement for noise-free power supplies is increasing due to the rapidly increasing use of microcontrollers and processors in portable electronic devices. Increasing the PSRR is a commonly used approach to address this issue [45]. The higher the PSRR, the lower the ripples in output voltage [45]. PSRR is higher when the dropout voltage (voltage across the drain and the source of PMOS) is higher because when the voltage across the drain and the source of PMOS is high, the operating point is within the saturation region where the PMOS series resistance is lowest. When the dropout voltage is low, the operating point of PMOS moves to the triode region, where PMOS resistance is higher than the PMOS on resistance in the saturation region. With lower resistance, a higher level of noise will pass from the input to the output of LDO [46]. Therefore, there is a trade-off between the PSRR and the efficiency of LDOs [47]. Researchers are focused on developing synchronous digital LDOs to overcome this issue [48; 49]. A digital error amplifier is in the synchronous digital LDO, and an external sampling clock is used in the feedback controller and the comparator [50].
## 5 Performance Evaluation
### Dataset
#### 5.1.2 PRECON Dataset  
The PRECON dataset [49] was collected over a period of one year, capturing electricity consumption patterns for 42 residential consumers (\(\mathcal{C}_{precon}\)) with varied demographics in Lahore, Pakistan, at 1 minute intervals. The dataset spans 321 days and was collected between 2018 and 2019.
## 4 Formulation of the Problem  
Currently, the plane curved CSW of composite curved girder bridges is considered as an orthotropic open circular cylindrical shell, and equivalent orthogonal open cylindrical shell can be regarded as a continuous, homogeneous, orthotropic and perfectly elastic body, as shown in Figure 3. Applying Equation (17) to the nonlinear term in Equation (14) and only considering the pure shear state results in \(P_{xy}=tp_{xy}=-t\tau_{xy}\). Then neglecting the higher order term in Equation (15), the global elastic shear buckling control differential equation of a PCCSW can be written as follows:  
\[D_{x}\frac{\partial^{4}w}{\partial x^{4}}+D_{xy}\frac{\partial^{4}w}{\partial x ^{2}\partial y^{2}}+D_{y}\frac{\partial^{4}w}{\partial y^{4}}+2P_{xy}\frac{ \partial^{2}w}{\partial x\partial y}-\frac{t}{R}\frac{\partial^{2}\phi}{ \partial y^{2}}=0 \tag{18}\]  
\[\frac{1}{E_{x}}\frac{\partial^{4}\phi}{\partial y^{4}}+\frac{1}{E_{y}}\frac{ \partial^{4}\phi}{\partial x^{4}}+\frac{2}{E}\frac{\partial^{4}\phi}{\partial x ^{2}\partial y^{2}}+\frac{1}{R}\frac{\partial^{2}w}{\partial y^{2}}=0 \tag{19}\]The governing differential Equations (18) and (19) can be reduced to an isotropic cylindrical shell equation, that is, a Donnell equation [26], which shows the rationality of the governing differential equations. In addition, the equivalent stiffness of the CSW used in the design guideline for composite bridges in Japan can be expressed as follows [27]:  
\[\begin{array}{l}D_{x}=\frac{E_{s}l^{3}}{12(1-\
u_{s}x^{2})}\\ D_{y}=\frac{s}{l}\frac{E_{s}(l^{3}+th_{r}^{3})}{6}\\ D_{xy}=\frac{s}{l}\frac{E_{s}l^{3}}{6(1+\
u_{s})}\end{array} \tag{20}\]  
Here, \(E_{s}\) and \(\
u_{s}\) are the elastic modulus and Poisson's ratio of steel, respectively, \(h_{r}\) is the corrugation depth of the CSW, \(s\) is the total folded panel segment length of single periodic corrugation, and \(l\) is the projection length of \(s\) in relation to the longitudinal curved axis.
## 4 Design of the Control Algorithm
### Feedback Control Module  
The feedback controller belongs to the classical linear regulator. In order to ensure the stability of the OES, the order of the feedback controller should not be high. For this reason, a PI controller is usually adopted, and it can be expressed as  
\[C_{\rm p}(s)=\frac{K_{\rm PS}+K_{\rm I}}{s} \tag{11}\]  
where \(K_{\rm P}\) and \(K_{\rm I}\) represents the proportional gain and integral gain, respectively.  
The input of the controller (11) is derived from the error between the reference speed \(v_{\rm r}\) and the speed \(\hat{v}\) observed from the SAKF, then, the output \(u_{\rm C}\) can be obtained using a Tustin transformation with the sampling period \(T_{\rm s}\) as:  
\[u_{\rm C}(k+1)=u_{\rm C}(k)+K_{\rm P}(v_{\rm r}(k+1)-\hat{v}(k+1))+K_{\rm I}T_ {\rm s}(v_{\rm r}(k)-\hat{v}(k)). \tag{12}\]