esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 TMS Study Protocol and EEG Recording  
### EEG Data  
For this study, we employed an H7 coil (Brainway Ltd., Jerusalem, Israel) [23] connected to a Magstim Rapid2 stimulator (Magstim, Spring Gardens, UK) positioned over the mid-line central area (i.e., 8 cm above the nasion along the main reference curve (nasion-inion) and conformal to the patient's head). The employed sham H7 coil is placed within the same helmet as the verum H7 coil and produces a similar acoustic artifact as well as scalp sensations by electrical stimulation, which, however, induces negligible E-fields in the brain. It is a realistic sham procedure that has been shown to maintain the blinding in the context of various clinical trials. For instance, in the study by [PERSON] et al. [24], the majority of the participants (75-88%) could not guess if they were assigned in the sham or verum stimulation groups. Accordingly, it was suitable for the purpose of the broader phase II diagnostic accuracy study and was therefore employed in the present study. Sham stimuli were administered at 30, 35, 40, 50, 60, 70 and 80% of maximum stimulator intensity (SI), as also at rest (SI = 0%). This procedure was performed two times for each subject, once without the auditory mask and once with the mask. During the TMS sessions, EEG was recorded with 60 Ag/AgCl pellet electrodes placed according to the International 10-10 system and connected to a TMS compatible EEG amplifier (eXimia, Nexstim Ltd., Helsinki, Finland). The EEG signals were analog band-pass filtered from 0.1 to 500 Hz and sampled with a 1450 Hz sampling frequency and 16-bit precision. In order to reduce the TMS-induced artifact, the EEG amplifier was temporarily blocked from 100 us before to 2 ms after the TMS pulse by a sample-and-hold circuitry [25]. A group of healthy subjects and one of patients with Genetic Generalized Epilepsy was used in this study. The data were collected in the context of a cross-sectional with delayed verification, phase II diagnostic accuracy study exploring the diagnostic and predictive potential of deep TMS with an H7 coil combined with high-density EEG in patients with Genetic Generalized Epilepsy. The paper describing this study is in preparation. Demographics of the participants are provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **N** & **Age:** & **Sex: M/F** & **\# of Stimulations:** \\  & & **Mean (Std)** & **Median (Range)** \\ \hline Healthy & 11 & 34.6(9.5) & 5/6 & 31 *, ([11, 97]) \\ Epileptic & 13 & 28.4(7.9) & 5/8 & 31 *, ([13, 81]) \\ \hline \hline \end{tabular}
* Median for all SI levels and subjects. The median per SI ranges from 29 to 32.  
\end{table}
Table 1: Subject Demographics.
## 4 Establishment of Simulated HSI Dataset Based on Experimental Measurement  
The goal of this section is to build a simulated HSI using samples with similar colors and spectra to test the classification ability of the wavelength selection algorithm LBI-BPSO DR results for similar color samples. Firstly, the multi-point hyperspectral data of the ground objects samples were measured, and these data were regarded as pure pixels in the simulated HSI. Then, the linear mixture model is used to calculate the mixed pixels of each abundance, and these data are used as mixed pixels in the simulated HSI. Based on these pure pixels and mixed pixels, four types of ground objects (three types of vegetation are classified into the same class) HSI can be constructed.
# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
###### Abstract  
Medical imaging is currently being applied in artificial intelligence and big data technologies in data formats. In order for medical imaging collected from different institutions and systems to be used for artificial intelligence data, interoperability is becoming a key element. Whilst interoperability is currently guaranteed through medical data standards, compliance to personal information protection laws, and other methods, a standard solution for measurement values is deemed to be necessary in order for further applications as artificial intelligence data. As a result, this study proposes a model for interoperability in medical data standards, personal information protection methods, and medical imaging measurements. This model applies Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) standards to medical imaging data standards and enables increased accessibility towards medical imaging data in the compliance of personal information protection laws through the use of de-identifying methods. This study focuses on offering a standard for the measurement values of standard materials that addresses uncertainty in measurements that pre-existing medical imaging measurement standards did not provide. The study finds that medical imaging data standards conform to pre-existing standards and also provide protection to personal information within any medical images through de-identifying methods. Moreover, it proposes a reference model that increases interoperability by composing a process that minimizes uncertainty using standard materials. The interoperability reference model is expected to assist artificial intelligence systems using medical imaging and further enhance the resilience of future health technologies and system development.  
interoperability; de-identifiers; measurement uncertainty; standardization +
Footnote †: journal: Medical Imaging
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 3 Test Program
### Loading Protocol  
To investigate the mechanical properties of the VVSDSI bearing, tests were conducted following the loading protocol specified in the Chinese test method for seismic isolation rubber bearings [31].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Category & \(D\) (mm) & \(d\) (mm) & \(t\) (mm) & \(H_{0}\) (mm) & **Combination Form** \\ \hline Upper disc spring set & 250 & 127 & 10 & 17 & Double-stacked \\  & & & & & Triple-paired \\ Lower disc spring set & 250 & 127 & 10 & 17 & Quadruple-stacked \\  & & & & & Triple-paired \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of disc spring groups in VVSDSI bearings.  
Figure 7: Experimental setup and specimen.  
1. Ultimate load test: Based on the characteristics of the bearing, the displacement control method was used for loading. During the test, the specimen was slowly loaded from the initial state to the ultimate state (the upper and lower disc springs were completely flattened) and then unloaded. To ensure the accuracy of the results, the test was repeated 3 times, and all load-displacement curve data were recorded.
2. Cyclic load test: The bearing is cyclically loaded using the displacement control method. During the test, the preloading displacement is 16, 23, and 30 mm; the loading frequency is 0.02 Hz, and the loading amplitude is 1, 2, 4, and 6 mm. Each test is completed after three reverse loadings, and the load-displacement hysteresis curve data of the bearing are recorded.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 4 Verification of Mimblewimble
### Security Properties of Pedersen Commitments  
In MW transactions, input and output amounts are hidden in Pedersen commitments. In Section 2.3 we have introduced the definition of a commitment scheme (Definition 1). A commitment scheme is expected to satisfy the following two security properties:  
* Hiding: the receiver, who received the commitment, does not learn anything about the original value.
* Binding: after the commit stage, there is at most one value that the sender can successfully open. In the cryptocurrencies world, these two properties should be understood this way:
* Hiding: a commitment scheme is used to keep the transactions secure. The sender commits to an amount of coins and this should remain private for the rest of the network over time.
* Binding: senders cannot change their commitments to a different transaction amount. If that were possible, it would mean that an adversary could spend coins which have already been committed to an UTXO, what would allow to create coins out of thin air.  
There are two possible specifications for these properties. Computational hiding or binding is when for all polynomial time adversaries, they can break the security property with negligible probability. This asymptotic security is parameterized by a security parameter \(\lambda\) and adversaries run in polynomial time in \(\lambda\) and their other inputs. On the other hand, we talk about perfectly hiding or binding, when even with infinite computing power it would be not possible to break the security property.  
Notice that a commitment scheme cannot be perfectly hiding and binding at the same time:  
* If the scheme is perfectly hiding, there must exist several inputs committing to the same value. Otherwise, an adversary with infinite computing power attempting to find out which input committed to a certain output, could try all possible inputs finding out the corresponding output. This shows that this scheme cannot be perfectly binding.
* If the scheme is perfectly binding, it means that there is at most one input that committed to an output. Imagine an adversary with infinite computing power attempting to find out which input committed to a target output. It would be possible to try all inputs and find which one verifies the commitment. Thus, this scheme cannot be perfectly hiding.  
So, for cryptocurrencies systems is better to provide stronger security in order to guarantee the hiding property. In other words, we prefer a commitment scheme with computational binding and perfectly hiding. We can understand this by first assuming adversaries break the binding property. It means that they could create money from thin air from a certain point in time but this would not affect the blockchain history. On the other hand, if the adversary breaks the hiding property, history could be inspected and all the transactions revealed. That breaks one of the main principles of a privacy-oriented cryptocurrency.
# Study of Standing-Wave Thermoacoustic Electricity Generators for Low-Power Applications
## 4 Results and Discussion
### Two-Stage Engine with Alternator Coupled in Push-Pull Arrangement
* In both the one-stage and two-stage engine simulations the operation volume velocities are almost equal (\(\,|\,U\,|\,\approx 9\times 10^{-2}\) m\({}^{3}\)/s). This circumstance derives from having fixed in simulations the peak-to-peak stroke to its maximum allowable value \(2\,|\,x_{max}\,|\). So, since the two engines work at comparable operation frequencies, the same volume velocities are involved (\(\,|\,U\,|\,=\omega\,A_{d}\,x_{max}\)). * The absolute value of the phase angle \(\phi_{pU}\) between the driving pressure and the volume velocity for which the impedance matching is realized does not change appreciably with the frequency and is approximately equal in the one-stage and two-stage cases, amounting to around \(82^{\circ}\). These circumstances imply that at a given frequency the two-stage engine performs worse than the one-stage counterpart since it produces a low acoustic power which is poorly absorbed be the LA as stated by the equation  
\[W_{a}=\frac{1}{2}|p_{1}-p_{2}||U|\cos(\phi_{pU}), \tag{26}\]  
In addition, the optimal load resistances \(R_{L}\) for which the impedance matching is realized are not related to the working frequency by the functional dependence shown in Figure 1, which contributes to the degradation of overall performance. The interpretation of the behavior of the efficiencies \(\eta_{ha}\), \(\eta_{ae}\) with \(f\) parallels the one made in the previous subsection.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 6 Multi-Operator Game for Channel Selection
### Game Formulation  
We define a multi-player game \(\mathcal{G}_{c}\) between the different present operators. The formulation of this game \(\mathcal{G}_{c}=\left\langle\mathcal{N},\prod_{i}S_{i}^{c},U_{i}^{c}\right\rangle\) is described, as follows:  
* A finite set of players \(i\in\mathcal{N}\), the set of operators.
* The action of a given player is the choice of the channel. The strategy chosen by an operator \(i\) is then \(\mathbf{x}^{i}=(x_{1}^{i},...,x_{C}^{i})\), with each \(x_{c}^{i}\) indicating whether it utilizes channel \(c\).
* For each player \(i\), the space of pure strategies is \(S_{i}^{c}\), such that \(S_{i}^{c}=\{\mathbf{x}^{i}\in\{0,1\}^{C}|\) (4d), \(\forall c=1,...,C\}\).
* A set of utility functions \((U_{i\in\mathcal{N}}^{c})\) that quantify players' profit for a given strategy profile.
# External Field Response and Applications of Metal Coated Hemispherical Janus Particles
## 2 Experiment  
### Sample Preparation  
#### 2.1.1 Unidirectional Arrangement of Hemispherical Particles  
Hemispherical polymethylmethacrylate (PMMA) particles were provided by Sekisui Plastics Co., Ltd. (Tokyo, Japan). The material of these particles is cross-linked PMMA, and it does not dissolve in organic solvent. The particle diameter is 2.5 um. The method for arranging particles is described in detail in [27]. Briefly, the hemispherical particles suspended in toluene were added in the water. It was sonicated to release aggregation of particles, and toluene and water were emulsified. After toluene was evaporated, the particles were arranged two-dimensionally at the air/water interface so that they were unidirectionally arranged. Then, they were transferred to a glass slide. Thus, hemispherical particles in the same direction were arranged two-dimensionally on a substrate. The degree of orientation is high, as demonstrated in Figure A1 in the Appendix A. By vacuum-depositing a metal on the hemispherical particles, Janus particles coated with metal only on the equatorial plane of hemispherical particles were prepared.  
Figure 1 shows the scanning electron microscope (SEM) (VE-9800; Keyence, Osaka, Japan) image of hemispherical particles coated with a 50 nm silver film, where silver is sulfurized with a smoke solution. There is no structural change on the spherical surface, while the structure of the equatorial plane is changed due to the sulfurization to silver sulfide. This image confirms that the metals were deposited only on the equatorial plane of hemispherical particles.
# Camera Calibration with Phase-Shifting Wedge Grating Array
## 3 Experiments
### PWG Arrays VS Checkerboard and Circle Patterns  
In this experiment, checkerboard and circle patterns were selected as contrasting objects, which also designed to have 6 \(\times\) 6 feature points with same locations. Both of the performances for three-step and four-step PWG arrays were explored. The four patterns were displayed on the LCD separately. Through adjusting the location of the camera, we collected four groups of pattern images from 10 different viewpoints. We obtained nine images per viewpoint: one for checkerboard, one for circle patterns, three for three-step PWG arrays, and four for four-step PWG arrays. Figure 4 shows the captured images of the four patterns.  
After capturing the calibration target images, we extracted the feature points from the images: the corners for checkerboard with Camera Calibration Toolbox for Matlab [21], the centers for circles with OpenCV function _findCirclesGrid_[23], and the centers for three-step and four-step PWG arrays with the feature detection method in Section 2.3. Figure 5a-c show the three-step PWG array images and Figure 5d shows their wrapped phase with detected feature points.  
Figure 4: The captured images of different patterns. (**a**) Checkerboard; (**b**) circles; (**c**) three-step PWG arrays; and, (**d**) four-step PWG arrays.  
Figure 6 shows the re-projection errors of feature points for the four patterns. The results clearly show that PWG arrays have an improvement in RMSEs than checkerboard and circle patterns. Table 1 shows the intrinsic parameters estimated from the four patterns. The calibration results are very close to each other. The tangential coefficients as well as the skew angle parameter are extremely small. It is sufficient to keep radial coefficients for nonlinear distortion. The RMSEs in the presented method are smaller than checkerboard and circles patterns and the four-step PWG arrays are more accurate than three-step PWG arrays. To our knowledge, four-step pattern always recover the wrapped phase with higher precision than three-step pattern. Thus, the calibration results of PWG arrays are more accurate than checkerboard and circle patterns and the calibration accuracy will be higher and higher with the step increases.  
Figure 5: Feature detection example. (**a–c**) three PWG arrays; and, (**d**) the wrapped phase with detected feature points (red cross ‘+’).  
Figure 6: _Cont._
# Efficient Removal Of U(VI) Ions from Aqueous Solutions by Tannic Acid/Graphene Oxide Composites
## 3 Results
### Regeneration Experiment  
The stability of the resultant TA/GO composites was evaluated by multiple adsorption/desorption cycles. It is evident from Figure 9 that the U(VI) adsorption capacity of TA/GO did not change greatly. After the cycle test, TA/GO composites were separated and dried, and the weight was calculated as 115 mg (a loss of about 4%). It indicates that TA/GO can be reused as an effective adsorbent to remove a large amount of U(VI) ions from aqueous solutions.  
Figure 8: Selective sorption capacities of coexistent ions on TA/GO composites at C\({}_{\text{initial}}=1.0\times 10^{-4}\) M.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sorbents** & **Experimental Conditions** & **C\({}_{\text{s, max}}\) (mg g\({}^{-1}\))** & **Ref.** \\ \hline Activated Carbon & pH = 3.0 T = 293 K & 28.3 & [36] \\ PPY & pH = 5.0 T = 298 K & 28.6 & [15] \\ GO nanosheet & pH = 5.0 T = 293 K & 97.5 & [41] \\ PPY/GO & pH = 5.0 T = 298 K & 147.06 & [15] \\ PANI & pH = 3.0 T = 298 K & 31.42 & [16] \\ TA/GO & pH = 3.6 T = 298 K & 87.8 & This study \\ TA/GO & pH = 5.0 T = 298 K & 266.56 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of U(VI) sorption capacity of TA/GO composites with other sorbents.
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 1 Introduction
### Related Research Work
CNNs excel at extracting spatial features from data, but they do not capture temporal dependencies effectively. This limitation affects the ability to fully recognize patterns in uninterrupted data, such as AE signals in pipeline diagnostics. To solve this issue, the CNN network is incorporated with LSTM. LSTMs are specifically designed to capture long-range dependencies and temporal sequences in data, making them optimal for analyzing time-series information. By combining CNN with LSTM, the feature extraction process is enhanced, using CNN for spatial pattern recognition and LSTM for temporal pattern recognition. This hybrid approach enhances the precision and efficiency of leak detection in pipelines, providing a more comprehensive analysis compared to using CNN alone. The latent spaces derived from the combined CNN and LSTM models are further optimized using a GA to accurately assess the health state of the pipeline. The key contributions of this study are outlined below. 1. A Gaussian filter is used to enhance variations in color intensity due to energy change across different scales and frequencies in AE scalograms. 2. A deep learning framework is introduced, combining enhanced AE scalograms with CNN-LSTM models and a genetic algorithm (GA) for feature optimization, with the goal of improving the identification of pipeline operating conditions. 3. The proposed approach is validated using real pipeline data, showcasing its effectiveness across varying leak scenarios, fluid types, and pressure conditions. The composition of this work is organized as follows: Section 2 outlines the proposed model and the sequential DL models for leak identification. The experimental setup is discussed in Section 3. Section 4 comprises the results, while Section 5 consists of the discussion. Finally, the conclusion and future directions of this study are described in Section 6.
## 4 EMS Optimization
### Comprehensive Analysis  
The analysis of the energy management system (EMS) across 24 h reveals significant insights into battery energy and power dynamics, grid power and pricing fluctuations, hydrogen energy and power management, load variations, and PV power integration.  
* Battery Energy and Power: Battery energy fluctuates from an initial level of approximately 1.8 kW-h, dropping to around 0.5 kW-h by the 13 th h, and then increasing to about 3.7 kW-h by the end of the 24 h. Meanwhile, battery power exhibits a rapid initial increase to 4 W within the first hour, with intermittent decreases to 0, 0.5 W, and 1 W, but generally maintains close to the upper limit of 4 W. These patterns illustrate how the EMS optimizes energy storage, discharge, and grid interactions for efficiency and stability.
* Grid Power and Pricing: Grid power starts at 75 kW, experiences fluctuations peaking at approximately 30 kW, and ends at \(-5\) kW by the 24 th h. Concurrently, grid pricing begins at 91 USD/kWh, reaches a peak of 111 USD/kWh around the 22 nd hour, and returns to 91 USD/kWh by the end of the period, ultimately reaching 0. These results show how demand fluctuations, market dynamics, and operational modifications impact grid power supply and pricing, which in turn impacts EMS strategies for optimizing battery energy storage systems (BESSs) and hydrogen energy storage systems (HESSs).
* Hydrogen Energy and Power: Hydrogen energy levels start at 4 kW-h, decrease to 1 kW-h, peak at 8 kW-h, and exhibit fluctuations throughout the day. Hydrogen power output predominantly stays at 40 kW, briefly dropping to \(-40\) kW, reflecting periods of intense activity and the effective management of hydrogen production and consumption by the EMS. This variability is crucial for optimizing HESS integration and ensuring efficient energy storage and supply.
* Load: The load begins at 34.9 kW, peaks at 35.5 kW around the 12 th h, decreases to 35.1 kW by the 17 th h, and stabilizes at 35 kW by the 24 th h. These variations underscore the EMS's capability to balance demand and supply, optimize load distribution, mitigate peak demand stress, and maintain grid stability.
* PV Power Integration: PV power starts at 0 kW, peaks at 48 kW by the 10 th h, and stabilizes thereafter. Battery power maintains a steady rise to 45 kW, while hydrogen power fluctuates between \(-48\) kW and 48 kW. Grid power varies between \(-50\) kW and 75 kW, with load power remaining constant at 40 kW. This integration highlights the significance of optimizing renewable energy integration for overall EMS performance and reliability by showing how the EMS dynamically controls energy sources and demand to maintain a stable and efficient energy supply.  
The EMS's adaptive strategies for managing diverse energy sources and demands are critical for enhancing efficiency, stability, and reliability in the energy system.