esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 3 Developments in Seismic Imaging
### Dense-Wide Aperture Acquisition  
The dense-wide aperture (WA) technique is a field data acquisition technique that involves deploying many closely spaced seismic sensors with a large array aperture [30]. One advantage of the dense-wide aperture technique is its ability to image complex subsurface structures, which can be difficult to image using narrow-aperture acquisition methods [31]. Additionally, by integrating seismic tomography and reflection seismology, dense WA arrays allow for the better detection of small-scale features such as thin layers and small faults [32].  
The geometry of dense WA differs from the common midpoint narrow aperture reflection surveying as it captures multifold reflection data covering a wide range of offsets, including deep-penetrating refracted waves suitable for first-arrival travel-time tomography [32]. An example is pictured in Figure 1. Data recorded by dense WA arrays are attractive because they contain more seismic phases than those recorded by traditional arrays and therefore can be processed using several imaging methods rather than only one, reducing the need of further field data surveys. For instance, dense WA profiles allow the use of refraction tomography or full-waveform inversion for estimating the seismic velocity models. This estimation is essential for achieving a reliable depth migration of seismic reflection data [33]. Moreover, dense WA arrays allow valuable structural information to be obtained from different imaging techniques, facilitating the integration of seismic tomography and seismic reflection data into the interpretation phase [31]. Ultimately, this integration can lead to an improvement of seismic interpretation.
# Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
###### Abstract  
The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users' resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity.  
Keywords:wireless resource management; deep reinforcement learning; unmanned aerial vehicles; wireless networks +
Footnote †: journal: Applied
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 4 Case Study
### Korean Power System
#### 4.3.2 Adequate Installed Capacity Reserve  
The Korean electricity market stipulates a reliability standard as a loss of load expectation (LOLE) of 0.3 days/year. An adequate installed capacity reserve (AACR) refers to the ratio of supply capacity to peak demand in the LOLE 0.3 condition. The AACR has profound financial relevance because it is linked to the settlement amount of the market participants. Therefore, in deriving the AACR, reproducibility and accuracy must be confirmed using a deterministic method, such as the proposed method in this study. The formulation of AACR is as follows:  
\[AACR=\frac{ICP+DR_{cp}+ND_{tr}-HVDC_{cp}}{D_{peak}}, \tag{22}\]  
where \(DR_{cp}\) is the contracted capacity for the demand response resources; \(ND_{tr}\) is the transaction volume of non-dispatchable resources at peak demand; \(HVDC_{cp}\) is the capacity of the HVDC, which transfers power from the main system to the sub-grid. The pump was assumed to be dispatchable regardless of the reservoir level. The output of non-dispatchable resources was considered to be the chronological patterns. The preventive maintenance schedule referred to the shutdown plan registered in the Generator Outage Management System (g-OMS) and the neglected temperature and load. Figure 3 presents a flow chart for determining an adequate installed reserve margin by adjusting the demand.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Indices**} & \multicolumn{5}{c}{**Uncertainty (\%)**} \\ \cline{2-6}  & **0** & **2** & **5** & **10** & **15** \\ \hline LOLH (hour/year) & 0.000966 & 0.000974 & 0.001018 & 0.001180 & 0.001506 \\ \hline EUE (MWh) & 0.118651 & 0.119550 & 0.125131 & 0.147038 & 0.191325 \\ \hline \hline \end{tabular}
\end{table}
Table 8: IEEE RTS 2020 reliability indices results considering the RE uncertainty.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fuel**} & \multirow{2}{*}{**Dispatchable**} & \multicolumn{2}{c}{**Total Capacity**} & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**\% of Total**} \\  & & **(GW)** & **Generators** & **(\%)** \\ \hline Oil & Y & 0.86 & 14 & 0.6 \\ LNG & Y & 41.20 & 96 & 30.7 \\ Coal & Y & 36.14 & 58 & 26.9 \\ Nuclear & Y & 23.25 & 24 & 17.3 \\ Pump & Y & 4.70 & 16 & 3.5 \\ RE & N & 26.30 & - & 19.6 \\ etc. & N & 1.82 & - & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The 2022 Korean power system generation mix.  
Table 10 lists the iterative process for adjusting the demand to meet the LOLE 0.3 condition. The demand was adjusted in each iteration, and the LOLE was calculated. As Iteration 15, LOLE satisfies 0.3, the reliability standard of the Korea Power System, and the adequate installed capacity reserve was calculated as 14.41%. A simulation time of approximately 1300 s occurred in the series of processes, and the results were determined within a reasonable time frame.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 3 Experimental Design
### Measurement Parameters  
While the performance metrics are important, equally relevant to the success and consistency of this study are the parameters against which the network values are measured. Lagos is an urban city, and the areas of Lagos are considered to have certain parameters in common. These parameters include the average distance between buildings (20 m) and the 10 m street width. Additionally, the operating frequency, working height of the transmitter, and the receiver to transmitting antennae distance were 1876.6 MHz, 30 m, and 1 km, respectively. Table 2 shows the measurement parameters and their corresponding values.
## 3 Results  
The comprehensive demographic, laboratory, and clinical characteristics of SS patients are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **SS Criteria** & **Study Group** \\  & **(n = 50)** \\ \hline Age mean \(\pm\) SD years & 52 \(\pm\) 10.86 \\ Gender Female/Male, \(n\) & 43/7 \\ BMI & 42/8 \\ median (IQR) & 25.67 (8.36) \\ Disease duration, (range), years & (3 months–12 years) \\ \(<\)1 year, \(n\) & 33 \\ \(\geq\)1 year, \(n\) & 17 \\ Xerostomia, yes/no \(n\) & 31/19 \\ Xerophathmia, yes/no \(n\) & 31/19 \\ Arthalgia, yes/no \(n\) & 42/8 \\ Arthritis yes/no \(n\) & 13/37 \\ Fatigue yes/no \(n\) & 9/41 \\
**Laboratory tests:** & \\ median (IQR) & 16 (16) \\ Erythrocyte Sedimentation Rate (mm/h) & 0.53 (2.95) \\ CRP (mg/dL) & 3/47 \\ Anemia yes/no \(n\) & 4/46 \\ Leukemia yes/no \(n\) & 36 (70) \\ ANA positive, \(n\) (\%): & 10 \\ + (\(<\)1:160), \(n\) & 4 \\ ++ (\(<\)1:320), \(n\) & 22 \\ ANA negative, \(n\) (\%): & 14 (30) \\ \(-\)(\(<\)1:160) & ANA profile, \(n\) (\%): & 27 (54) \\  & anti-Ro/SSA antibodies, \(n\) (\%) & 8 (16) \\  & anti-La/SSB antibodies, \(n\) (\%) & 9 (18) \\ Rheumatoid factor (RF) positive, \(n\) (\%) & 13 (26) \\  & Focal lymphocytic sialdenitis (FLS) & 50 (100) \\  & positive, \(n\) (\%) & 50 (100) \\  & Focus score (FS), \(n\): & \\  & \(<\)0 & 3 \\  & 1 & 19 \\  & 2 & 11 \\  & 3 & 11 \\  & 4 & 6 \\  & USWSF positive, \(n\) (\%) & 23 (46) \\  & median (IQR) & 2 (2.5) \\  & [PERSON]’s test positive, \(n\) (\%) & 35 (70) \\  & Right eye median (IQR) & 5 (7) \\  & Left eye median (IQR) & 5 (7) \\
**Organ involvement:** & & \\ Fever yes/no \(n\) & 0/50 \\ Skin involvement yes/no, \(n\) & 14/36 \\ Weight loss yes/no, \(n\) & 1/49 \\ [PERSON]’s phenomenon yes/no, \(n\) & 1/49 \\ Salivary gland inflammation yes/no, \(n\) & 2/48 \\ \hline \hline \end{tabular}  
* Data presented as the median (interquartile range), mean \(\pm\) standard deviation (SD), or as \(n\) (\%); BMI, body mass index; CRP, C-reactive protein; ANA, antinuclear antibodies; PS, focus score; USWSF; unstimulated whole salivary flow rate.  
\end{table}
Table 2: Demographic, clinical, and laboratory characteristics of SS patients.  
on the patient's age (\(p\) = 0.08) or gender (\(p\) = 0.40). There were no differences between FS and ESR or between FS and CRP (\(p\) = 0.48 and \(p\) = 0.83, respectively). Xerophthalmia did not correlate with the patient's age or gender (\(p\) = 0.22 and \(p\) = 0.40, respectively). Dry eye did not correlate with ESR or CRP (\(p\) = 0.51 and \(p\) = 0.43, respectively). There was a statistically significant correlation between xerophthalmia and USWSF results (\(p\) = 0.02). There was no difference in FS between SS patients with dry eye and those without dry eye (\(p\) = 0.27). There were no differences in clinical symptoms, laboratory values, or serological values between patients with dry eye and those without dry eye. The detailed comparison between SS patients with xerostomia and without xerostomia is presented in Table 3.
# Underwater Optical Path Loss after Passage of a Tropical Storm
## 1 Introduction
The simulated results show that Super Typhoon Gochul largely reduced the maximum detectable depth of EOID by about 15 m in comparison to without typhoon activity. Because tropical storms affect UWOC only on the received power, a much simpler method than the EODES would be used. The differential-integral radiative transfer equation (RTE) is discretized into a set of algebraic equations. The classic Gauss-Seidel iterative method is used to solve the algebraic equations (called the RTE solver) and in turn to compute the received power [8]. Unlike uniform discretization of the angular space as in [9], an optimal non-uniform angular discretization scheme is used to consider the strong forward scattering characteristics of ocean waters. Since the optical path loss from transmitter to receiver directly affects the line-of-sight link, the optical path loss is calculated with and without typhoon passage using the RTE solver to identify a typhoon's effect on the UWOC line-of-sight link. The rest of the paper is organized as follows. Section 2 describes the selection of the typhoon influence area. Section 3 describes IOPs in the western Pacific Ocean observed by the NAVOCEANO Seagliders with and without typhoon passage. Section 4 depicts a two-dimensional RTE. Section 5 shows the impact of a typhoon's passage on the optical path loss. Section 6 presents the conclusions.
# Directional Support Vector Machines
## 9 Experiments
### Towards Deep Directional Classifiers
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{4}{c}{**(a) Accuracy**} \\ \hline
**Dataset** & **best-SVM** & **rMLP** & **dMLP** \\ \hline
**Colposcopy** & \(\mathbf{85.41}\pm 8.5\) & \(51.67\pm 18.1\) & \(81.26\pm 9.9\) \\
**Behavior**[14] & \(82.99\pm 3.8\) & \(81.85\pm 5.1\) & \(\mathbf{83.50}\pm 5.3\) \\
**Arrhythmia**[13] & \(\mathbf{78.59}\pm 5.2\) & \(70.17\pm 9.6\) & \(71.45\pm 5.1\) \\
**eBay**[16] & \(\mathbf{86.00}\pm 5.1\) & \(73.38\pm 21.7\) & \(84.86\pm 5.3\) \\
**Megasopores**[18] & \(\mathbf{76.28}\pm 3.7\) & \(76.01\pm 4.0\) & \(73.78\pm 4.4\) \\
**Characters**[15] & \(\mathbf{98.30}\pm 1.4\) & \(83.63\pm 7.2\) & \(92.07\pm 2.7\) \\
**OnlineNews**[19] & \(50.06\pm 4.9\) & \(52.09\pm 4.1\) & \(\mathbf{52.33}\pm 4.2\) \\
**Continents** & \(\mathbf{97.18}\pm 1.0\) & \(95.57\pm 1.6\) & \(96.56\pm 1.7\) \\
**Wall**[20] & \(82.97\pm 1.6\) & \(86.23\pm 1.2\) & \(\mathbf{87.03}\pm 1.7\) \\
**Temperature1** & \(69.33\pm 1.2\) & \(70.02\pm 2.6\) & \(\mathbf{70.03}\pm 2.0\) \\
**Temperature2** & \(79.83\pm 1.3\) & \(\mathbf{81.06}\pm 2.5\) & \(75.63\pm 4.6\) \\
**MAGIC**[17] & \(81.75\pm 1.3\) & \(73.20\pm 18.5\) & \(\mathbf{87.23}\pm 1.3\) \\ \hline \hline \multicolumn{4}{c}{**(b) Macro F\({}_{1}\)-score**} \\ \hline
**Dataset** & **best-SVM** & **rMLP** & **dMLP** \\ \hline
**Colposcopy** & \(\mathbf{85.17}\pm 8.6\) & \(46.36\pm 20.2\) & \(80.86\pm 10.2\) \\
**Behavior**[14] & \(53.29\pm 16.1\) & \(42.30\pm 13.6\) & \(\mathbf{58.03}\pm 15.3\) \\
**Arrhythmia**[13] & \(\mathbf{77.12}\pm 5.7\) & \(68.87\pm 10.0\) & \(69.76\pm 5.5\) \\
**eBay**[16] & \(\mathbf{85.77}\pm 5.1\) & \(72.30\pm 23.2\) & \(84.47\pm 5.4\) \\
**Megasopores**[18] & \(\mathbf{71.63}\pm 5.0\) & \(70.45\pm 5.7\) & \(68.61\pm 5.4\) \\
**Characters**[15] & \(\mathbf{98.29}\pm 1.4\) & \(82.56\pm 8.0\) & \(91.83\pm 2.9\) \\
**OnlineNews**[19] & \(\mathbf{48.30}\pm 5.7\) & \(49.00\pm 5.0\) & \(\mathbf{49.77}\pm 5.8\) \\
**Continents** & \(\mathbf{97.34}\pm 0.9\) & \(94.81\pm 2.3\) & \(96.30\pm 2.4\) \\
**Wall**[20] & \(85.14\pm 1.7\) & \(86.53\pm 1.3\) & \(\mathbf{88.11}\pm 1.8\) \\
**Temperature1** & \(48.83\pm 0.8\) & \(56.61\pm 7.4\) & \(\mathbf{60.91}\pm 5.2\) \\
**Temperature2** & \(74.21\pm 2.0\) & \(\mathbf{74.83}\pm 4.8\) & \(67.68\pm 5.0\) \\
**MAGIC**[17] & \(78.75\pm 1.5\) & \(69.16\pm 21.3\) & \(\mathbf{85.47}\pm 1.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average performance and standard deviation per model on 30 runs for 12 datasets, which are presented in increasing order of their respective cardinality. The evaluated models are: directional SVM with highest average accuracy (best-SVM), MLP trained on top of raw angle values (rMLP), and MLP trained on top of the feature transformation \(\phi(x)=(\cos(x),\sin(x))^{\intercal}\) (dMLP). Best results are given in bold.
## 2 Modeling and Finite Element Analysis
### Gyroid Infill Geometry  
The gyroid surface ( see Figure 1) is one member in the mathematical family known as \"triply-periodic minimal surfaces\", and was first discovered by [PERSON] [37] in 1970. The gyroid is defined by an implicit trigonometric Equation (1).  
\[\sin\!\left(\frac{2\pi x}{\lambda}\right)\cdot\cos\!\left(\frac{2\pi y}{ \lambda}\right)+\sin\!\left(\frac{2\pi y}{\lambda}\right)\cdot\cos\!\left( \frac{2\pi z}{\lambda}\right)+\sin\!\left(\frac{2\pi z}{\lambda}\right)\cdot \cos\!\left(\frac{2\pi x}{\lambda}\right)=0 \tag{1}\]Upon its recent addition as an infill option in some slicers, the gyroid has produced much interest among hobbyist printers due to its beneficial properties such as near isotropy, quick print time, and strength [13; 14; 15]. For this research, the gyroid has been selected as the primary focus, and will be analyzed using the finite element method in order to develop predictive models of its behavior as the relative density varies, while the focus of this work is on the gyroid, the same method will be useful as additional 3D infills are implemented. In particular, infills with complex-shapes (such as other triply-periodic minimal surfaces) may warrant detailed investigation.  
It is important to note that there are two types of gyroid infill. The first, which is the focus of this work, is that defined by thickening the gyroid minimal surface. The second is a separate but related infill that has been investigated by [PERSON] et al. [38] and others. Rather than a thickened minimal surface, this second type is a lattice of intersecting curved struts defined by filling in the empty space that the gyroid surface encloses. This \"gyroid-strut\" model has great potential regarding continuously variable relative density [38], however, it has not yet been implemented in common slicers.  
In order to determine the elastic response of the gyroid infill, finite element analysis (FEA) was implemented to model a unit cell, similar to that done by [PERSON] and [PERSON] [10; 11; 12]. Unlike the rectangular infill, however, the gyroid is a complexly curved surface and cannot be reasonably simplified to a space-frame model. The logical first step in analyzing this shape, then, was to use a shell model.
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 2 Materials and Methods
### Antioxidant Activity Evaluation
#### 2.5.2 Cupric Ion Reducing Antioxidant Capacity (CURRAC) Microassay  
CURRAC assay was done as briefly described [PERSON] et al. [30]. Diluted extracts and Trolox solutions (67 \(\mu\)L) were pipetted in 96-well microplate in triplicate. Antioxidant capacity was evaluated after 30 min of reaction at room temperature between extracts and 61 \(\mu\)L 0.01 M CuCl\({}_{2}\), 61 \(\mu\)L 7.5 \(\times\) 10\({}^{3}\) M neocuproine in ethanol and 61 \(\mu\)L ammonium acetate buffer (1 M, pH = 7). Absorbance readings were made against a reagent blank at 450 nm. Calibration curve (y = 1.4453x + 0.0467), calculated using a range 0.05-0.6 mmol Trolox L\({}^{-1}\), showed good linearity (r\({}^{2}\) = 0.9968). Limit of detection was 11 \(\mu\)mol L\({}^{-1}\), and limit of quantification was 37 \(\mu\)mol L\({}^{-1}\). Results were expressed as mM Trolox Equivalents (TE) per gram of dry weight (mM TE g\({}^{-1}\) DW).
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 3 Methodology
### Label-Setting KSSP Algorithm
Let the maximum number of simple paths to any node be \(k^{\prime}\). Therefore, the maximum size of the set \(T\) is finite (\(|T|\leq nk^{\prime}\)), and the algorithm terminates in at most \(nk^{\prime}\) iterations. Detailed proof is available in Appendix A. \(\square\)  
Proposition 3: The worst-case time complexity of the label-setting algorithm is pseudo-polynomial. \({}_{\blacksquare}\)  
Proof: The algorithm can be broken down into four major parts and analyzed separately. Initialization--Lines 1-11 are constant time operations and can be completed in \(O(1)\) time. Lines 12-13 take \(O(n)\) time to initialize the label indices for the nodes in the network. Node selection--Let us assume that \(T\) is implemented using a sorted list. The minimum cost label in line 15 can be found in \(O(1)\) time. From Proposition 2, the maximum number of temporary labels created is \(nk^{\prime}\), and in the worst case, line 15 is executed \(O(nk^{\prime})\) times. Moving the minimum cost label from \(T\) to \(P\) and incrementing the permanent label index (lines 16-19) finishes in \(O(1)\) time. Arc processing: The loop in line 20, in the worst case, executes \(O(mk^{\prime})\) times. The cycle check in line 21 takes \(O(n)\) time. A temporary label can be created in \(O(1)\) time (lines 22-25). Insertion of a new label into \(T\) takes \(O(nk^{\prime})\) (the maximum size of \(T\)) time (line 26). The total complexity of the loop from lines 20 to 28 is \(O(mk^{\prime}(n+nk^{\prime}))\). Termination check: the check for termination conditions in line 29 takes \(O(n)\) time. Thus, the overall time complexity of the algorithm is \(O(mnk^{\prime 2})\) using a sorted list for the temporary labels. It reduces to \(O(mk^{\prime}(n+\log k^{\prime}))\) using a min heap (see Section 3.4). A detailed analysis is provided in Appendix A. \(\square\)  
The label-setting algorithm is highly flexible and can be readily adapted for solving each of the variants of the KSSP problem defined earlier through suitable modifications to the termination condition (line 29) as per Table 1. For all the problems, the algorithm must terminate when there are no more temporary labels to process (i.e., \(T\) is \(\varnothing\)). But it can also terminate early when all the required paths are found. For the KSSP-OA problem, this is when \(k\) permanent labels are found to each destination node \(i\) (i.e., \(p_{i}\geq k\)). For the KSSP-OM problem, the algorithm can stop earlier than the KSSP-OA problem since there are fewer destinations considered. In the case of the CCSSP-OA problem, the algorithm can stop if all paths, whose cost is less than or equal to the given budget \(\alpha\), are found (i.e., when the cost of the next permanent label is greater than \(\alpha\)).
## 2 Related Work
use the migration ratios between adjacent urban cells to obtain \(\alpha\) locations for roadside units, which maximize the number of vehicles having at least one vehicle-to-infrastructure contact opportunity [24]. The results show that full knowledge of vehicle trajectories is not necessary for a close-to-optimal deployment. In [25], content delivery networks are developed in the context of vehicular networks to measure the content delivery, and the deployment of roadside units is improved to support the dissemination of various contents requiring different levels of performance. In [26], aiming for \(\rho\) percent of vehicles encountering roadside units in time intervals shorter than \(\tau\) seconds, the Gamma Deployment metric is formalized by developing an Integer Linear Programming formulation, and a heuristic solution is proposed for large instances, which shows small deviations for short inter-contact time guarantees. To achieve short download delays, [PERSON] et al., in [27], replicate contents to lightweight low-cost roadside buffers based on an analytical model of download delay of files. These works focus on the deployment of roadside units, but how to better the utilization of existing infrastructure is still an open issue. Since the above approaches utilize the mobile vehicles other than the roadside infrastructures to replicate and carry contents, their performances are greatly constrained. Therefore, the key insight of this article is to explore the rich resources of the fixed roadside units to improve the efficiency of data acquisition in cooperative vehicle-infrastructure systems.
## 3 Results
### Comparison of ANFIS and ANN Performance Results
Since ANN lacks an integrated fuzzy logic component, it may not effectively process the nuances that ANFIS can capture. Furthermore, ANFIS has the practical advantage of being easier to implement. The creation of fuzzy rules does not require extensive experience with the process [37]. In conclusion, the hybrid structure, flexibility, and ability to manage uncertainties are the key reasons why ANFIS outperforms ANN. These features make ANFIS a more suitable choice for modeling nonlinear data and analyzing dynamic systems.