esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Structure and Working Principle
### Modeling of Energy Storage System
#### 2.2.1 Lithium-Ion Battery  
[PERSON] et al. presented analyses and economic comparisons of the efficiency and modes of existing energy storage equipment connected to a power grid [24]. From their work, the battery energy storage system (BESS) was deemed suitable for various distributed systems owing to its flexible capacity, power characteristics, and its relatively compact size. Among the many energy storage batteries, LiFeFO\({}_{\text{d}}\) batteries are widely used in ESSs owing to their long life and high energy density. In terms of battery model construction, relevant researchers have summarized various equivalent circuit models for lithium-ion batteries [25; 26]. Due to the main focus of this article being on the performance of composite energy storage systems under cyclic operating conditions, the accuracy requirement for system dynamic process response is not very high. Therefore, in the modeling process, the battery model is simplified to a Rint model, which ensures the accuracy of the model over a long time scale while reducing the complexity of the model.  
\[I_{\text{bat}}(t)=\frac{U_{\text{bat}}-\sqrt{U_{\text{bat}}{}^{2}-4R_{\text{bat }}P_{\text{bat}}(t)}}{2R_{\text{bat}}} \tag{2}\]  
Figure 2: Photovoltaic equivalent circuit diagram.  
here, \(I_{\text{bat}}\), \(U_{\text{bat}}\), and \(R_{\text{bat}}\) are the current, voltage, and internal resistance of the battery during operation, respectively.
## 2 Stochastic Green's Function Method for 3D Ground Motion Simulation and Its Improvement
### Phase Spectra of Small Earthquakes in Subfaults  
The random phase spectrum is generally used in the stochastic Green's function method. Due to the fact that the ground motion time history corresponding to a random phase spectrum is a random wave, the directivity effect of the near-fault ground motion cannot be reflected in the synthesized results, and the simulation accuracy is lower in the low-frequency range. Therefore, based on the similarity between the phase difference spectrum and the ground motion time history envelope curve [29; 30], the ground motion of the small earthquake in the first subfault satisfying the envelope curve is generated as follows:  
1. Using the time history envelope function which is based on scattering theory and proposed by [PERSON] [31; 32], set the phase difference probability density distribution consistent with the shape of the envelope curve;
2. Generate the phase difference randomly according to this phase difference probability density distribution;
3. Derive the phase spectrum of the small earthquake in the first subfault according to the phase difference;
4. After combining the phase spectrum with the amplitude spectrum, the ground motion of the small earthquake in the first subfault \(F^{K}_{11}(f)\) in the frequency domain can be obtained.  
[PERSON] et al. [33] found that seismic waves with the same propagation path have similar phase characteristics by analyzing the observed seismic records. Therefore, the idea of the modified empirical [PERSON]'s function method is used for reference, and Equation (7) is applied in this study to obtain the ground motions of the small earthquakes in other subfaults in the frequency domain:  
\[F^{K}_{ij}(f)=A^{K}_{ij}(f)\frac{F^{K}_{11}(f)}{|F^{K}_{11}(f)|} \tag{7}\]  
where, \(F^{K}_{11}(f)\) and \(F^{K}_{ij}(f)\) represent the ground motion (complex form) of the small earthquake caused by the rupture of the first subfault and the _ij_ subfault, respectively; \(\left|F^{K}_{11}(f)\right|\) is the amplitude of \(F^{K}_{11}(f)\) and is smoothed using a Parzen window, with a bandwidth of 0.05 Hz.  
Thus, the phase spectra of small earthquakes among different subfaults within a large fault can be correlated and have a certain degree of discreteness, and the directivity effect of the near-fault ground motion can be reflected. After performing the inverse Fourier transform on the complex Fourier spectra of small earthquakes in subfaults, the time histories of small earthquakes in subfaults can be obtained.
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## Composition of a PMSM Drive System Based on Predictive Control
### Structure of a PMSM System
(**a**) Topology of voltage source inverter (**b**) Spatial distribution diagram of voltage vector. the forward Euler formula [68], combined with Equations (2) and (3). The equation in the continuous time domain can be rewritten as:  
\[ \begin{bmatrix}u_{d}(k)\\ u_{q}(k)\end{bmatrix} =\left[ \begin{array}{c}\frac{L_{d}}{T_{s}}\\ \frac{L_{q}}{T_{s}}\end{array} \right]\left[ \begin{array}{c}i_{d}(k+1)-i_{d}( k)\\ i_{q}(k)+1-i_{q}(k)\end{array} \right]+\left[ \begin{array}{cc}0\\ \omega_{e}\psi_{f}\end{array} \right]+\left[ \begin{array}{cc}R_{s}&-\omega_{ e}L_{q}\\ \omega_{e}L_{d}&R_{s}\end{array} \right]\left[ \begin{array}{c}i_{d}(k)\\ i_{q}(k)\end{array} \right] \tag{7}\]  
where \(T_{s}\) is the sampling time; \"\(k\)\" represents the current moment, and \"\(k+1\)\" represents the next instant. It is worth noting that the relationship between these motor equations and parameters is the basis of PMSM predictive control, not only for predictive current control, but also for predictive torque and predictive flux linkage control, etc.
# Flash Flood Early Warning System in Colima, Mexico
## 3 Results
### Drifter Retrieval  
The proposed tracking solution is comprised of three types of devices shown in Figure 21: RiverCore fixed nodes, drifters, and a RiverDrone. This would work by detecting drifters while they pass through certain fixed stations that act as checkpoints. Then, as their location gets recorded by the data acquisition platform, a sniffer possibly mounted on a drone can be sent to search for them near the last activated checkpoint.  
Drifters emit an 802.15.4 radio signal as they pass along the river path. When drifters pass below a RiverCore node, the signal is detected by a radio coordinator module which is attached to the fixed node so that the drifter location can be estimated in real-time.  
When drifters get lost, it is possible to track their last location between shorter segments of the river. In this way, when it becomes necessary to search for drifters, can be released to follow their track, starting immediately after they were detected by a fixed node. The sniffer on board will scan for drifters using a radio coordinator module, so the location of drifters can be reported in real-time, using a cellular communication module.  
The tracking system began field testing during the last trimester of 2019 and, currently, a second version using LoRa is under development. Both radio solutions will be tested to determine the best solution that suits the needs of the project.  
Figure 20: Calculated flow from data of nodes 5 and 14 during Narda hurricane, 23 September to 7 October 2019.  
When the sniffer detects a drifter, it sends the drifter's GPS information to the server through 3G technology. If the drifter is detected by a fixed node, only its ID is sent to the server to confirm what has occurred in its trajectory up to that point.
# Priority Pricing for Efficient Resource Usage of Mobile Internet Access
## 4 Convergence Analysis  
Equilibrium methods assume the existence of equilibrium and compute an optimal price vector to induce the system to converge on the equilibrium state. To analyze the validity of the equilibrium existence assumption, this study uses the price vector obtained from the equilibrium analysis to help compare the job arrival rate vector(s) computed from the SAM with that computed from the equilibrium analysis. More specifically, this section focuses on the question of whether a system with non-infinitesimal impact of users and periodic updates converges to a steady equilibrium state. I chose the following three Nash equilibrium models [22; 23], which all use a framework similar to mine:  
* Case 1: Profit maximization system with a single service level,
* Case 2: Net value maximization system with a single service level, and
* Case 3: Net value maximization system with two service levels.  
In all three cases, the value of a type-\(j\) job is expressed by the utility function shown in Equation (8).  
\[U_{j}(T)\;=\;V_{j}^{\prime}\Big{(}\lambda_{j}^{*}\Big{)}-v_{j}T, \tag{8}\]  
Figure 3: Examples of a state transition graph.  
where \(V_{j}^{\prime}\Big{(}\lambda_{j}^{*}\Big{)}\) is the marginal value function, \(v_{j}\) is the delay cost, and \(T\) is the job completion delay. The value function \(V_{j}\Big{(}\lambda_{j}^{*}\Big{)}\) represents the expected gross value per time unit gained by all type-\(j\) jobs when the effective arrival rate of type-\(j\) jobs is \(\lambda_{j}^{*}\). \(V_{j}\Big{(}\lambda_{j}^{*}\Big{)}\) is assumed to be monotonically increasing, strictly concave, and twice-differentiable. The value of a single type-\(j\) job is represented by a _marginal_ value function, \(V_{j}^{\prime}\Big{(}\lambda_{j}^{*}\Big{)}\). A job's value is assumed to decrease proportionally to the job completion delay, and therefore the delay cost, \(v_{j}\), is a constant.  
For each case, I analyze whether the system converges to the equilibrium state, as assumed by the corresponding equilibrium model.
## 2 Materials and Methods
### Analysis of VOCs of Fermented Sugarane Samples  
Esters and alcohols were analyzed, identified, and quantified in cane wine samples that exhibited regular fermentation dynamics, achieved the highest sensory analysis scores, and exhibited no sensory or physicochemical defects. The control samples were the respective spontaneously fermented cane wines. This was accomplished using solid-phase headspace microextraction (HS-SPME) in combination with gas chromatography-mass spectrometry (GC-MS). This method is a straightforward and eco-friendly extraction and concentration analytical procedure. To identify and quantify each compound, a sequence of standard organic compounds was injected using the same extraction procedure. The identification of each compound was further supported by Kovats Indices (KI), which are derived from the retention time normalized to the adjacent eluting n-alkanes. KI values are independent of the analytical conditions, making them useful for identifying unknown compounds. Gas chromatographic analyses were conducted using an Agilent 5890 GC system coupled with an HP 5973 quadrupole mass spectrometer. An HP5-MS column (5% diphenyl-95% dimethylpolysiloxane, 30 m \(\times\) 0.2 mm, 0.25 \(\upmu\)m film, J & W Scientific, Folsom, CA, USA) was utilized. Ultra-high-purity helium was used as carrier gas, with water and oxygen traps (Supelco(r), Sigma-Aldrich Italia S.r.l., Milan, Italy) installed on the carrier gas lines. The oven temperature was initially set at 40 \({}^{\circ}\)C for 5 min, then increased at a rate of 5 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C and subsequently by 10 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C. The temperature was then held at these levels for a period of 10 min. The carrier gas (He) was utilized at 1 mL/min. The mass spectrum was recorded at an ionization voltage of 70 eV and an ion source temperature of 220 \({}^{\circ}\)C [40]. Samples were analyzed using the HS-SPME-GC-MS method with a PDMS-CAR-DVB fiber (Supelco, ITA). The extraction and purification procedure involved exposing the fiber to 5 mL of cane wine in a 40 mL vial sealed with a silica septum at 70 \({}^{\circ}\)C for 20 min. The fiber was then manually inserted into a GC inlet port that was equipped with a specific glass liner for SPME injection (0.75 mm i.d.). The desorption of the fibers was then conducted in the gas chromatograph inlet port for a period of 3 min at a temperature of 250 \({}^{\circ}\)C, utilizing the splitless injection mode. The compounds identified were then verified by comparing the mass spectrum profile with those of authentic reference compounds. In instances where reference standards were not available, the identification of the organic compound, using a mass spectrometry profile, was carried out by the comparison of the experimental mass spectrum with the NIST11 mass spectra library collection (minimum match quality \(\geq\) 90%) and considering the KI, also.
# Solution-Processed Memristor Devices Using a Colloidal Quantum Dot-Polymer Composite
## 3 Results
### Operation Mechanism of CdSe/ZnS QD-PMMA Memristors  
Concerning the set and reset processes, the operating mechanism can be described as follows. As shown in Figure 2a, due to the large difference between the lowest-unoccupied-molecular-orbital (LUMO) level of PMMA and the work function of Al, the PMMA is likely to act as a charge blocking layer. Additionally, the difference between the highest-occupied-molecular-orbital level of PMMA and the work function of ITO is large enough (\(\Delta\)E-2.5 eV) to effectively block the hole injection from the ITO electrode. When a positive bias is applied to the ITO electrode, electrons start to inject from the Al electrode into the CdSe/ZnS QD-PMMA layer and trapped at the CdSe/ZnS QDs (Figure 2b). Here, due to the large difference between the LUMO level of PMMA and the conduction band minimum of CdSe/ZnS QDs (2.2 to 2.6 eV) [28], it is likely that the trapped electrons are well-confined in the QDs. Then, when a sufficiently high bias is applied, the electric field formed between the QDs is increased, and consequently, the electronic occupation probability of the QDs increases [25], leading to a shift of Fermi level toward the LUMO level of PMMA [25]. This, in fact, may generate conducting filaments in the CdSe/ZnS QD-PMMA layer, resulting in the LRS [29]. On the other hand, when a negative bias is applied to the ITO electrode, electrons are de-trapped from the CdSe/ZnS QDs and the device returns to the initial HRS, as shown in Figure 2c.  
Obtaining uniform and stable switching characteristics is a prerequisite for realizing highly reliable memristor devices. To evaluate the operational stability of the CdSe/ZnS QD-PMMA memristors, the I-V characteristics were measured repeatedly for 50 times and the variation of the switching behavior was analyzed. Figure 3a shows the I-V curves obtained at the 1 st, 25 th, and 50 th measurements. As displayed, the device exhibited stable switching characteristics during the repetitive operation with the current switching ratio in the range of 10\({}^{3}\)-10\({}^{4}\). Figure 3b,c show the corresponding cumulative distribution of currents at LRS and HRS, and the variation of set/reset voltages, respectively. Particularly, the current at the LRS was in the range of 2.0-6.0 \(\times\) 10\({}^{-10}\) A, and at HRS, the current was in the range of 0.7-7.3 \(\times\) 10\({}^{-7}\) A. Additionally, the set and res  
Figure 2: (**a**) An energy band diagram of the CdSe/ZnS QD-PMMA memristor. The change in the energy band diagram of the device during (**b**) set, and (**c**) reset process.  
in the ranges of 2.09 \(\pm\) 0.159 V and \(-\)1.26 \(\pm\) 0.307 V, respectively. Nonetheless, these results show that the CdSe/ZnS QD-PMMA memristors exhibit relatively stable switching characteristics.
###### Abstract
The initialization operation of the K-means++ was used to obtain a relatively optimal cluster center points group as one initial particle of the Particle Swarm Optimization (PSO). Then, the parameters of the PSO algorithm were optimized by a fuzzy system and the GA algorithm was implemented to search for better clustering centers. Finally, the optimal clustering center points were found after several iterations. Three unsupervised evaluation indexes, the Separation (SP), Silhouette coefficient (SC) and the Sum of Squared Error (SSE), as well as two cluster similarity indexes Centroid index (CI) and Point-level centroid similarity index (CSI), were used to evaluate the clustering results. Five taxi GPS datasets were selected and compared with GAK, PSO based K-Means (PSOK), PSO based constraint K-Means (PSOCK), PSO based Weighted K-Means (PSOK), PSO-GA based K-Means (PSO-GAK), and K-Means++ algorithms to verify the validity of the proposedalgorithm. The innovations and main contributions of this paper are as follows:  
* A novel FPSO-GAK algorithm based on hybrid \"fuzzy system-particle swarm-genetic\" was designed to obtain excellent non-homogeneous search capability. * The PSO algorithm, fuzzy system algorithm, and genetic algorithm were organically combined to obtain the optimal initial clustering centers. * Three unsupervised evaluation indexes (SP, SC and SSE) and two cluster similarity indexes (CI and CSI) were employed to evaluate and analyze the clustering results. * The comprehensive experiment was designed and implemented to verify the effectiveness of the novel FPSO-GAK clustering algorithm.
## 4 Discussion
Having insight into which days and how many in a year are irregular can help to better manage buildings from the technical and the real-estate perspective. Methods proposed in this study could help with the EU's goal of developing a sustainable energy system by increasing the ability of building systems to adapt to their occupancy. The prediction of occupancy number and level could be used as an input to the building performance simulations. Comparing the overall building energy consumption with predicted occupancy could enable tracking the energy consumed per person, which could be used for optimising HVAC system operation. Occupancy information is vital for the demand response in buildings, where knowing the occupancy patterns can help with predicting the energy need and further optimise the performance with a smart energy system. Furthermore, the real-estate field can benefit greatly by having continuous insight into building occupancy, which can be used for office space management. Managing office space and its usage is becoming more critical because of the rise in popularity of remote work and high real-estate costs. Remote work has been growing for a while, and recently during the COVID-19 pandemic, it has become the new norm. Its popularity is expected to continue even after the end of the pandemic. Employees will want to continue enjoying the convenience of remote work; at the same time, the fear of potential new virus outbreaks could demand a need for a reasonable distance of workers in the office. Consequently, office space will have a lower occupancy rate, which could make them a target of cost-saving measures for companies, because of the resulting economic recession after the pandemics. Scope for future work is to assess the scalability of the proposed methods, on an area with a different size than the case study floor and in other buildings. Especially the unsupervised method, since it does not require continuous ground truth data collection, which makes it easier for commercial applications. Although, lighter methods for verifying an unsupervised method should be studied, which could include even manual building walkthroughs. Enriching the proposed unsupervised method by using multiple predictors or even including other widely available signals in building such as Wi-Fi, should be investigated. The unsupervised method should be tested in smaller areas, such as different floor wings, or office departments, which could increase the granularity and the value of insight. Furthermore, the novel concept in machine learning called transfer learning should be investigated. With the transfer learning concept, a supervised model trained on one building could be transferred to a similar building and therefore reduce the need for a good ground truth dataset for every building.
## 4 Real-Time Experimental Tests
### Discussion  
For \"real-time exp\" with optimization, selling energy to the grid is preferred to make profits based on the coefficient \(k_{D}\) to minimize the total cost. Thus, with optimization gives better energy cost than without optimization. Furthermore, the EV energy distribution can be considered for \"real-time exp\" with optimization to be better than without optimization.  
To sum up, \"with opt\" performs better than \"w/o opt\" in minimizing the total cost of the IIREVs, and for the EV energy distribution, the results are satisfying with optimization,  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Case Operation** & **Grid Cost (cC)** & **Storage Cost (cC)** & **EV Shedding Cost (cC)** & **Total Cost (cC)** \\ \hline Real-time exp w/o opt & 109.83 & 6.17 & 40.72 & 156.73 \\ Real-time exp with opt & 54.88 & 5.73 & 0 & 60.91 \\ Opt for real conditions & 47.75 & 5.61 & 0 & 53.37 \\ \hline \end{tabular}
\end{table}
Table 5: Energy system cost—experimental test 2.  
Figure 26: EV energy distribution for “real-time exp” (**a**) without optimization and (**b**) with optimization—experimental test 2.  
which is not the case without optimization, as the share of storage and grid energies are higher than the share of PV energy.
# An Open-Source Algorithm for 3D ROck Slope Kinematic Analysis (ROKA)
## 2 The ROck Slope Kinematic Analysis (ROKA) Algorithm
### Preprocessing, Real Discontinuity Intersections Calculation  
Preliminarily, before the Kinematic Analysis (KA) process, the real discontinuity intersections are calculated taking into account the position and dimension of the discs representing the discontinuities (Figure 4) and using the function 'IntersectionCalculator.m'. In particular, it consists of the following:  
1. calculation of the intersection line between the two infinite planes that fit two discontinuities using the Hessian normal form [39; 40];  
Figure 3: Example of the calculation influenced by the scan-radius. (**a**) For each point of the 3D point cloud, (**b**) a moving scan-sphere, with a scan-radius defined by the operator, (**c**) selects all the points used to calculate the local orientation of the slope. (**d**) A circular scan-plane, centered onto the analyzed point and oriented as the local portion of the slope (previously calculated using the scan-sphere), is defined. It will be used to verify if the discontinuity planes or intersections intersect the surface of the slope.  
2. exclusion of the intersection lines that cannot be formed by the calculated circular discontinuities that have a finite dimension;
3. calculation and exportation of the dimension, orientation, and position of the possible \"real\" discontinuity intersections (Figure 4).
# Numerical Estimation of Bending in Holographic Volume Gratings by Means of RCWA and Deep Learning
## 3 Results  
This section presents the results of our study on modelling and predicting bending phenomena in holographic volume gratings. We begin by evaluating RCWA-SM for modelling diffraction efficiency in gratings with varying degrees of bending, comparing with [PERSON]'s method and Split-Field Finite-Difference Time-Domain (SF-FDTD) simulations. Following this, we explore deep learning approaches to estimate bending parameters. Specifically, we compare two architectures: an FNN and a CNN-FNN hybrid designed to  
Figure 3: Schematic of FNN and 1D CNN architectures. (**a**) FNN’s schematic, with weight matrices represented by the network between neurons (circles). (**b**) A 1D CNN’s schematic, with a kernel of 3 units represented in both convolution layers shown.  
predict bending polynomial coefficients, optical depth, and index modulation based on diffraction patterns.