esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Method  
The differential equations were spatially discretized on a uniform mesh. Second-order central-difference and QUICK [32] schemes were used for the diffusion terms and convective terms, respectively. In this paper, line-successive over-relaxation (SOR) was used to solve the unknown terms, and the tridiagonal-matrix algorithm (TDMA) to solve the problem, where the solution was initiated at s\({}^{+}\) = 0 and iterated along main flow direction s\({}^{+}\) (Figure 1). The ratios of the maximal variations to the maximal values of the temperature, the volumetric proportion of the liquid phase of the PCM suspension, and velocity were monitored to determine the convergence of these parameters, where the convergence condition for each parameter was \(10^{-5}\).  
To test grid independence, the mean Nusselt numbers of the inner pipe walls in both the heating and the cooling section were used to calculate the deviation percentages. Numerical errors were estimated using the grid-convergence-index (GCI) method for the local Nusselt numbers of the heating and cooling sections. Deviation percentages and numerical errors were used to determine suitable grid points for our calculations. In our previous study [27], a grid of \(2001\times 201\) points was sufficient for zero wall thickness (\(t_{w}\) = 0). Therefore, in this study, a grid of \(2001\times 201\) points was used as the baseline, and the number of grid points was increased along the radial direction. The following loop configuration was used to test grids with different numbers of points, and \(AR\) = 1; \(t_{H}^{*}\) = 0.8; \(l_{z}\) = 2; \(Ste^{*}\) = 0.1; \(Sb^{*}\) = 0.6; \(Ra^{*}\) = \(10^{11}\); \(c_{v}\) = \(10\%\); and \(t_{w}\) = 0.5. A grid of \(2001\times 701\) resulted in less than 1% deviation in the mean Nusselt number, and less than 1% error in the energy-balance conditions (data not shown) of the walls in the heating and cooling sections. Therefore, a grid of \(2001\times 701\) was used in all simulations.  
The developed formulation and the incorporated numerical method were verified by simulating selected cases of heat transfer in a single-phase toroidal loop [33] and forced convection of the PCM suspension fluid in a straight circular pipe [34], as shown in Figure 2[27]. No significant difference was found among the results of the different studies, confirming the reliability of the simulation results in this study.
\({}^{2}\) Physics Department, Faculty of Science, South Valley University, Qena 83523, Egypt  
\({}^{3}\) Tokai Quantum Beam Science Center, National Institutes for Quantum Science and Technology (QST), Ibaraki 319-1106, Japan; [EMAIL_ADDRESS] (T.H. ); [EMAIL_ADDRESS] (T.S.) \({}^{4}\) Institute of Laser Engineering, Osaka University, Suita, Osaka 565-0871, Japan  
\({}^{5}\) UVOSR Synchrotron Facility, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki 444-8858, Japan; [EMAIL_ADDRESS] (M.K.J. [EMAIL_ADDRESS] (T.T. ); [EMAIL_ADDRESS] (M.F.) \({}^{6}\) Hirosimhan Synchrotron Radiation Center, Hiroshima University, Higashi-Hiroshima 793-0046, Japan  
\({}^{7}\) Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2-4, Ibaraki 305-8568, Japan; [EMAIL_ADDRESS]  
\({}^{8}\) Correspondence: khaled.ali.2 [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel. : +81-774-38-3425; Fax: +81-774-38-3425  
**Abstract:** One of the most noteworthy aspects of computed tomography (CT) based on the nuclear resonance fluorescence (NRF) transmission method is the isotope selectivity that makes it possible to discern an isotope of interest from other isotopes within a sample. We experimentally obtained a three-dimensional (3D) isotope-selective CT image based on the NRF transmission method (3D NRF-CT) for the enriched lead isotope distribution of \({}^{208}\)Pb in a cylindrical holder in a previous study. The cylindrical holder's diameter and height are 25 mm and 20 mm, respectively. The NRF-CT imaging technique requires a considerable data accumulation time. It took 48 h to obtain an image with a resolution of 4 mm/pixel in the horizontal plane and 8 mm/pixel in the vertical plane using a laser Compton scattering (LCS) gamma-ray beam with a beam size of 2 mm and a flux density of 10 photons/s/eV. Improving the NRF-CT image resolution with the existing hardware is challenging. Therefore, we proposed an alternative method to improve the NRF-CT image resolution using the fusion visualization (FV) technique by combining the NRF-CT image including isotopic information with a gamma-CT image, which provides better pixel resolution. The 3D gamma-CT image for the same sample was measured at the same beamline BL1U in the ultraviolet synchrotron orbital radiation-III (UVOSR-III) synchrotron radiation facility at the Institute of Molecular Science at the National Institutes of Natural Sciences in Japan under similar experimental conditions except for the LCS gamma-ray beam flux and beam size. Obtaining a 3D gamma-CT image with a resolution of 1 mm/pixel took 5 h using an LCS gamma-ray beam with a beam size of 1 mm and a flux density of 0.7 photons/s/eV. The data processing of the FV technique has been developed, and the 3D NRF-CT image quality was improved. **Keywords:** computed tomography (CT); fusion visualization (FV); isotope selectivity; laser Compton scattering gamma-ray beam (LCS); nuclear resonance fluorescence (NRF)
## 4 Comparison between Two Models
### Acceleration Responses of the Main Structures
#### 4.3.2 Acceleration Amplification Factors  
The frequency response factor (FRF) is defined as the ratio of the input frequency to the fundamental frequency of the model, and is expressed by the following equations:  
\[\beta(x)=\frac{f_{i}}{f_{x}} \tag{4}\]  
\[\beta(y)=\frac{f_{i}}{f_{y}} \tag{5}\]  
where \(f_{i}\) is the frequency of harmonic, \(f_{x}\) corresponds to 22.1 Hz and 17.3 Hz for the HRM model and the JRM model, respectively; while \(f_{y}\) corresponds to 20.3 Hz and 20.2 Hz for the HRM model and the JRM model, respectively.  
Figure 10: Comparison of acceleration power spectral density ratio: (**a**) transverse excitation; (**b**) longitudinal excitation.  
Overall, the AAFs at the vault are all larger than those at the arch bottom, indicating a significant amplification effect at the vault. Under harmonic transverse excitation, the amplification factors of the hall opening section were larger than that of the normal section in both models, i.e., A1 and A3 and A2 and A4 were compared to show that there is an amplification effect at the hall opening section, and the comparison through A5 and A7 and A6 and A8 showed that there was no amplification effect in the platform opening sections. Under harmonic longitudinal excitation, there was no amplification effect in the opening section of the HRM model as both A1/A3 and A2/A4 were less than 1.0; however, in the JRM model, A2/A4 = 1.18, so there was an amplification effect in the opening section of its hall. It can also be observed that the amplification factors at the platform measurement points of the JRM model were less than 1.0 in the high-frequency region, indicating that there was a phenomenon of acceleration reduction, which may be due to the fact that the amplification factors are not affected by the radiation field.  
Figure 11: Acceleration time histories of the main structures: (**a**) Sine 12 Hz-x; (**b**) Sine 21 Hz-x; (**c**) Sine 12 Hz-y; (**d**) Sine 21 Hz-y.  
the joint sets consume more input energy under high-frequency excitation, reducing the seismic response of the station structure.
## 2 State of the Art
### Scope of This Work  
This research paper builds upon publications that examine the suitability of acoustic signals for mainly monitoring tool conditions. Existing work in the field of hobbing process monitoring is done for macro gears with a focus on utilizing structure-borne sound for condition monitoring and predictive maintenance applications. The current research expands upon these results by investigating the suitability of structure-borne sound signals for monitoring microhbobbing processes, as well as the direct monitoring of the quality parameters of complex geometries. This is of particular interest as microgears have a complex geometry and thus the manufacturing requirements often reach the technological limits with no readily available production-integrated measuring methods. The structure-borne sound is captured via intelligent sensor integration. For the data analysis, different classification algorithms are used, which are trained on the basis of labeled data and subsequently tested.
## 3 Optimal Model  
### Objective Function  
Based on the cost-benefit analysis, an optimization capacity model of BESS aiming to maximize the net income of wind-energy union system is proposed in this part.  
\[\max f=S_{ave}+C_{serve}-C_{ost} \tag{14}\]  
(1) Investment cost of BESS \(C_{ost}\)  
The amortized capital cost model of BESS in [26] is adopted and modified as the cost function to be minimized, where the influences of the depth and times of the charge/discharge on the equivalent loss of cycle life are taken into account. The cost function can be written as:  
\[\begin{array}{l}C_{ost}=\frac{C_{c}(p,n)}{365}(\alpha^{s}\cdot P_{cap}+ \beta^{s}\cdot S_{cap})\\ \left\{\begin{array}{l}C_{c}(p,n)=\frac{p(1+p)^{T_{life}}}{(1+p)^{T_{life}}-1} \\ \beta^{s}=\frac{C_{c}}{T_{life}}+C_{OM}\\ \alpha^{s}=r\cdot\beta^{s}\end{array}\right.\end{array} \tag{15}\]  
(2) Directly benefit of saving wind curtailed energy \(S_{ave}\)  
Wind power curtailment will be mitigated by BESS shifting this part of energy during wind anti-peaking periods. So that the reduction amount of wind curtailed energy with and without considering BESS can be regarded as the direct benefit of the wind-energy union system. During a scheduling period, it can be expressed as Formula (15):  
\[S_{ave}=\rho^{w}(\int_{t=1}^{T}{(P_{wloss,t}-P_{wloss,t}^{B})\Delta t}) \tag{16}\]  
(3) Additional benefit of auxiliary service compensation \(C_{serve}\)  
This paper proposes that the auxiliary service cost of wind power integration eased by BESS should be as a part of the revenue of wind-energy union system. Based on the analysis of Section 2.2, the ancillary service costs of the \"net load\" fluctuation curve with and without BESS can be calculated, as follows:  
\[C_{serve}=C_{serve}^{Wind}-C_{serve}^{BESS} \tag{17}\]
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 4 Incremental Frequent Itemset Mining with Map-Reduce
### The Algorithms
#### 4.2.5. Reuse of Discarded Information  
During the MR algorithm, we generate many itemset candidates which were \"locally\" frequent in some Splits, but discarded in the end as they were not globally frequent. We propose to keep the previously discarded itemsets. For this, we keep the non-frequent itemsets (\(NFI(\mathcal{D})\)) in another file that will be created during the process of FIM on \(\mathcal{D}\). In our algorithm from Section 4.2.1, we require this file as an additional input (Previous Knowledge). The algorithm will \"join\" \(\text{FI}(\Delta)\) with \(FI(\mathcal{D})\cup NFI(\mathcal{D})\). With this additional information available, there is a higher chance of an itemset to have an exact count so it will not need to be accounted. As we join itemsets from \(\text{FI}(\Delta)\) with \(NFI(\mathcal{D})\), we are reducing the counts over \(\mathcal{D}\). As the old \(\mathcal{D}\) tend to be much larger than \(\Delta\), reducing (and possibly eliminating completely) the count improves the run time of the algorithm. It is important to mention that NFI tends to be much smaller than all possible itemset combinations of \(\mathcal{D}\), and we can keep them as they were already calculated and saved by at least one mapper anyway.  
Figure 8: Optimized incremental algorithm with reduced overhead.
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
###### Abstract  
An aging society increases the demand for solutions that enable quick reactions, such as calling for help in response to events that may threaten life or health. One of such events is a fall, which is a common cause (or consequence) of injuries among the elderly, that can lead to health problems or even death. Fall may be also a symptom of a serious health problem, such as a stroke or a heart attack. This study addresses the fall detection problem. We propose a fall detection solution based on accelerometer data from smartphone devices. The proposed model is based on a Recurrent Neural Network employing a Gated Recurrent Unit (GRU) layer. We compared the results with the state-of-the-art solutions available in the literature using the UniMiB SHAR dataset containing accelerometer data collected using smartphone devices. The dataset contains the validation dataset prepared for evaluation using the Leave-One-Subject-Out (LOSO-CV) and 5-Fold Cross-Validation (CV) strategies; consequently, we used them for evaluation. Our solution achieves the highest result for Leave-One-Subject-Out and a comparable result for the \(k\)-Fold Cross-Validation strategy, achieving 98.99% and 99.82% accuracy, respectively. We believe it has the potential for adoption in production devices, which could be helpful, for example, in nursing homes, improving the provision of assistance especially when combined into a multimodal system with other sensors. We also provide all the data and code used in our experiments publicly, allowing other researchers to reproduce our results.  
full detection; accelerometer; cellphone; UniMiB SHAR; recurrent neural networks 2025  
30 May 2025  
11 June 2025  
14 June 2025
## 2 Materials and Methods  
### Materials  
The volcanic rocks utilized in this experiment were collected from the Pulu Volcanic Zone in Hotan, Xinjiang (Figure 1), and were lithologically classified as porous basalts, appearing dark gray in color, exhibiting slightly porous textures.  
The main mineral composition is medium laboradorite, ordinary pyroxene, and small amounts of olivine and magnetite. Its chemical composition is shown in Table 1. Using a Hitachi S-4800 scanning electron microscope (SEM) (Hitachi High-Tech Corporation, Tokyo, Japan), we observed that the volcanic rock powder predominantly consists of irregular particles with a rough surface and multi-angular shapes (Figure 2a). The positions and intensities of the diffraction peaks in the X-ray diffraction (XRD) pattern indicate the high crystallinity of the sample (Figure 2b).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Test Items** & **Loss** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **R\({}_{2}\)O** \\ \hline Xinjiang Hotan Volcanic Rock & 1.88 & 55.97 & 15.80 & 7.98 & 6.96 & 3.84 & 0.13 & 3.00 & 3.80 & 5.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of volcanic rocks in the Hotan Region, Xinjiang (%).  
Figure 2: The SEM (**a**) and XRD (**b**) images of volcanic rock powder with a specific surface area of 480 (m\({}^{2}\)/kg).  
The cement utilized in this experiment is P-I type silicate cement, with a strength grade of 42.5 MPa, complying with the Chinese standard GB175-2023 [40]. The chemical composition and physical properties are presented in Tables 2 and 3.  
The chemical reagents used in the test are six kinds including Ca(OH)\({}_{2}\), CaSO\({}_{4}\), Na\({}_{2}\)SO\({}_{4}\), CaO, CaCO\({}_{3}\), and CaSO\({}_{4}\cdot 2\)H\({}_{2}\)O. The purity of the selected chemical reagents is more than 99%, and the dosage is calculated as a percentage of the mass of the gelling material.
## 4 The Developed Context Model
### Relevant Aspects of the Context Model
#### 4.5.2 The contexts depend on the metadata values  
The metadata elements characterise the WSN's status, or in other words, the WSN's status is obtained using metadata. But these metadata are not previously assigned to the contexts. Thus, the contexts could change depending on the metadata values. An example is related with the NodeNeighbours metadata element (Table 3). If the NodeNeighbours = 12, then the network context will be high density of nodes. In this context the node will select the best path to send the sensing data to the sink node. Meanwhile, if the NodeNeighbours = 2, the network context will be low density of nodes. Finally if the NodeNeighbours = 0, the context will be isolation node and the interoperability will be interrupted. The node must adapt itself in order to overcome this status and to avoid the sensing data lost.  
4.5.3. The contexts depend on the level of approximation  
As an example we will use an essential context component: the location or \"where\". The meaning of \"where\" could change according to the level of approximation (Table 4). If a node has a GPS sensor, it will be possible to attach the spatial coordinates to other sensor measures such as temperature, light, humidity. This spatial location will belong to the sensing contexts. On the other hand, if the GPS sensor is tracking the node trajectory, the observed location becomes a part of the node contexts. Based on the individual node location it will be possible to define the network coverage area, the extension, density, parent location, encounters between nodes, and detention areas that belong to a network context.
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### Implementation of LoRaWAN  
In the LoRaWAN scenario, as shown in the Figure 14, the topology used is the star. This way, all the end-nodes in the network will be directly connected to the LoRaWAN gateway. In this case the two main components are (1) LoRaWAN end-node and (2) LoRaWAN gateway. The end-node has the same role as the end-node in the mesh, however it is not a router for the other end-nodes that are on the network and do not reach the gateway. The gateway plays the same role as the mesh coordinator, because it is responsible for sending and receiving packets to the SMs and will be connected directly to the EPC server.  
The LoRaWAN topology requires that all end-nodes have connectivity directly to the LoRaWAN gateway, that is, if they do not have this connectivity, the device will be outside the coverage area of a given gateway and will look for another gateway that is closer to its coverage area. This way, the end-node will look for other coverage areas, if it finds any other gateway this end-node will connect to it and the communication will be performed again. Thus, it can be said that an end-node should not necessarily be fixed to a given gateway. It only needs to contact a gateway that is configured to send my data to the network server.
# Integrated Module Antenna for Automotive UWB Application
## 3 Conclusions  
This paper proposes an integrated module antenna with a monopole and patch array for automotive UWB applications. The proposed antenna is designed into single substrate. The monopole is designed with modified ground stubs for UWB localization requiring wide coverage in the horizontal plane. Moreover, the patch array is designed with additional ground stubs and parasitic radiators for rear passenger detection sensor in the vehicle requiring relatively narrower beamwidth and greater gain than the monopole. The modified ground stubs and parasitic radiators are applied to compensate the degradation of the antenna performances due to limited antenna design space of the module and coexisting ground layer. The size of the entire antenna structure is 35 mm \(\times\) 65 mm \(\times\) 1.156 mm. The proposed antenna designed on the multi-layered FR-4 substrate with a dielectric constant of 4.3. The bandwidth of the monopole is 6.14-8.24 GHz, and the patch array is 6.95-8.47 GHz. The isolation between the monopole and the patch array is less than \(-\)23 dBi in the target band. The proposed antenna is verified with its simulation and measurement. Furthermore, the simulations of the antenna with vehicle model are also conducted to verity its feasibility on actual vehicle. In order to compare the proposed antenna with the reference papers, the properties of each antenna are summarized in the Table 7.  
As shown in Table 7, for an antenna designed on a single substrate, the proposed antenna has the advantage of covering three bands, with compact size, low complexity, and two types of patterns. Future directions of research would be to expand the operating bandwidth of the rear passenger detection antenna (patch array), which would allow the 6.5 GHz band to be used, such as the localization antenna (monopole antenna). Based on this work, the proposed antenna could be applied as a multi-function antenna for automotive UWB applications with low costs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Patch Array (8.0 GHz)** & \\ \hline  & Left side & Right side \\ \hline  & & \\ \hline  & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 2 Materials and Methods
### PNT Payloads  
Space-based navigation systems utilize stable atomic clocks aboard satellites in well-defined orbits, emitting signals to Earth. These satellites establish a space-time reference frame, offering time and positioning information. A minimum of four satellite signals is necessary to accurately determine both position and time. Additional satellite signalsenhance precision and aid in detecting discrepancies and potential issues within the data. For such systems, a time epoch, defined with a specified reference frame that includes a location (e.g., center of Earth) and starting time (e.g., UTC), is necessary.  
The philosophy behind this kind of systems is based upon integrating atomic frequency references (AFRs) aboard satellites that maintain a consistent frequency output, which is utilized by other electronic components to generate signals for transmission to Earth. Code data and timestamps are implemented into the signals before their final transmission to the ground. However, noise, timing variability, and environmental influences that are beyond the AFRs impact the overall performance of the system.  
The clocks onboard must consistently deliver a precise and stable frequency along with a clearly defined time epoch. Any inaccuracies or instability in the clock time, or unpredictable frequency drifts, will result in accumulating errors in the pseudorange solution. To illustrate, one could consider that the GNSS demands such stable clocks that, for instance, to achieve a 1 m precision in measurements, a timing uncertainty of approximately 3 nanoseconds is required. Maintaining such timing uncertainty for a day necessitates a fractional frequency instability of approximately \(3.5\times 10^{-14}=\left(\frac{3\,\mathrm{ns}}{86,400\,\mathrm{s}}\right)\). This level of stability is attainable with high-quality atomic clocks, but not with other existing clock technologies or oscillators [2].
## 3 The Investigated Rotor System and Its Modeling
### The Investigated Rotor System  
The investigated rotor system consists of a rigid disc and flexible shaft (Figure 6). At both its ends, the shaft is coupled with the rigid stationary part by rolling element bearings inserted in squeeze film dampers lubricated with magnetorheological oil. The rotor is loaded by its weight and the disc unbalance.  
The rotor turns at constant angular speed. At a certain moment of time, it starts to accelerate, and its speed rises. Then, it continuous in rotation at constant angular velocity.  
The task is to determine the time history of the applied current to minimize the amplitude of the rotor vibration during its acceleration.  
In the computational model, the rotor is represented by a Jeffcott one. The rolling element bearings and the stationary part are considered as absolutely rigid. The magnetorheological squeeze film dampers are implemented in the computational model by means of force couplings. The material damping of the shaft and damping of the disc caused by the environment, as well as the damping of the cage springs, are considered viscous and negligible, respectively.