esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 3 System Description
All three are made of a flat copper conductor in the shape of inverted Vee and are tuned to the frequency of 5.40 MHz. The choice of the antenna design (inverted Vee) is based on its building facilities--they are easy to build as they only need a mast--and based on its gain, as it is similar to the horizontal dipole. Moreover, they are ideal for NVIS as they have a good radiation pattern in the upward direction. The frequency was chosen and calculated from the revised ionograms of Observatori del Ebre [27]; this frequency is lower than the cutoff frequency as it allows us to correctly receive ordinary and extraordinary waves from the ionosphere. Concretely, and because of the study of the ionogram, it was possible to determine that the ideal frequency for the transmission to make the wave rebound with the minimum of possible losses is 10% less than the critical frequency F2. The transmitter is less complex than the receiver as it only has one antenna. The reception part of the scenario is more sophisticated, as our experiment follows a single input multiple output design. The two reception antennas are set perpendicularly. This configuration together with a phasing network that delays one antenna from the other will allow the system to receive different polarizations at the same time, which is the main goal of the scenario itself. A phasing box was designed and implemented to realize the delays mentioned with coaxial delay lines. The box gets a total of four wires, two from each antenna; we get two identical signals for each antenna with a radio frequency splitter. The paths of the two antennas are the same, as follows: one feed line is connected directly to a RF combiner, and the one cable is lengthened with a quarter  
Figure 1: Schematic of the remote sensing platform. wave phasing line to provide a \(90^{\circ}\) phase shift. The output of the box gives us a phase difference between the dipoles antennas of either \(+90^{\circ}\) and \(-90^{\circ}\). For the designed system, the phasing network is for one single frequency. For a multiple frequency receptor, the phasing network should be done in digital. Figure 2 shows a diagram of the phasing network designed. The goal of the phasing network is to unify the two antennas (initially linearly polarized and orthogonal to each other) so that two circularly polarized antennas are created. Specifically, the polarization vectors of the antennas are the following:  
\[LHCP_{Antenna}=\hat{x}+j\cdot\hat{y}, \tag{1}\]  
\[RHCP_{Antenna}=j\cdot\hat{x}+\hat{y}, \tag{2}\]  
Equation (1) belongs to left-hand circular polarization, which corresponds to the ordinary wave in the Northern Hemisphere. Equation (2), the extraordinary wave, was studied, and it demonstrated that it has the lesser delay and RHCP in the Northern Hemisphere, while the ordinary wave has the greater delay and LHCP [18].
## 5 Stability Analysis
### Stability of the Closed-Loop System  
**Theorem 3**: _Consider the closed-loop system consisting of the USV dynamics (3), (9), the guidance laws (17), (18), the control laws (43), (49), the adaptive update laws (44), (50) and the neural shunting model (32). There exist appropriate designs \(\kappa\), \(\gamma\), \(k_{1}\), \(k_{ue}\), \(k_{re}\), \(\gamma_{u}\), \(\kappa_{u}\), \(\tilde{\xi}_{u}\), \(\gamma_{r}\), \(\kappa_{r}\), \(K_{eu}\), \(K_{er}\), \(\tilde{\xi}_{r}\), \(A\), \(B\), \(D\), such that all error signals in the system are UUB._  
Proof of Theorem 3: To analyze the stability of closed-loop system, construct the following Lyapunov function.  
\[V=V_{1}+V_{2} \tag{63}\]  
Its time-derivative is computed as:  
\[\dot{V}\leq -\gamma x_{e}^{2}-ly_{e}^{2}-(k_{1}-1)\psi_{e}^{2}-(k_{ue}-\frac{1} {2})u_{e}^{2}-(k_{re}-\frac{1}{2})r_{e}^{2}-\frac{1}{2}\kappa_{u}\phi_{u}^{2}- \frac{1}{2}\kappa_{r}\phi_{r}^{2}-(A_{r}-\frac{\sigma}{2}-\frac{1}{4})y_{r}^{2}\] \[-(K_{eu}-\frac{1}{2})e_{u}^{2}-(K_{er}-\frac{1}{2})e_{r}^{2}+\Lambda \tag{64}\]  
Thus, it can be seen that all signals in the closed-loop system are UUB.
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 2 Materials and Methods
### Calculation of Loose Speed  
It can be obtained from Formulas (6) to (8) that the amount of the loss of interference \(\delta_{\omega}\) of the high-speed motor rises with the increase in the rotational speed. With the rotational speed increasing, the residual interference of the matching surface of the magnet and the sheath is less than the minimum effective interference \(\delta_{\mathrm{min}}\). Slip and relative rotation will occur, and even serious consequences of separation and loosening might be caused. Therefore, in order to ensure the normal operation of the motor, the maximum speed that can be withstood under the designed interference should be calculated.  
When \(\Delta-\delta_{\omega}\geq\delta_{\mathrm{min}}\), slip does not occur, and the rotational speed is loosened at this time. Combined with Formulas (6)-(8), the rotational speed [9]\(n\) is calculated by:  
\[n\leq\frac{30}{\tau}(\Delta-\delta_{\mathrm{min}})^{2}\left[\begin{array}{c} \frac{4\rho_{\mathrm{s}}}{8 gE_{s}}\cdot\left(\frac{d_{m}}{2}\right)^{3}(3+v_{s }+1-v_{s})\\ -\frac{2\rho_{m}(1-v_{m})}{4 gE_{m}}\left(\frac{d_{m}}{2}\right)^{3}\end{array} \right]^{-\frac{1}{2}} \tag{10}\]  
The diameter of the magnet is 28.5 mm, and the shaft length is 70 mm. The outer diameter of the sheath is 36 mm, and the shaft length of the sheath is 90 mm; the length of the interference fit between the end cover and the sheath is 10 mm; the motor rated power is 26 kW; and the speed is 30,000 to 100,000 rpm.  
Combining the above conditions, the effective value range of the design interference is 3.04 to 85.28 \(\upmu\)m, and the minimum design interference can bear the loose speed of 137,628.82 rpm.  
The reliability of loose speed calculated by the empirical formula still needs to be verified. It is verified by simulation in this paper, which is described in detail below.
## 2 Materials and Methods
### Data Calibration and Validation  
The convergence level between observational data and modeling results was calculated using statistical parameters [31; 37]. In this study, the Root Mean Square Error (RMSE) was employed to assess the model performance or prediction, utilizing the following equation:  
\[RMSE=\sqrt{\frac{1}{n}{\sum(y_{i}-x_{i})}^{2}} \tag{8}\]  
where \(x_{i}\) is the observational data; \(Y_{i}\) is the modeling result; and \(n\) is the total number of data points.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
###### Abstract  
We consider fault detection in a hydraulic system that maintains multivariate time-series sensor data. Such a real-world industrial environment could suffer from noisy data resulting from inaccuracies in hardware sensing or external interference. Thus, we propose a real-time and robust fault detection method for hydraulic systems that leverages cooperation between cloud and edge servers. The cloud server employs a new approach that includes a genetic algorithm (GA)-based feature selection that identifies feature-to-label correlations and feature-to-feature redundancies. A GA can efficiently process large search spaces, such as solving a combinatorial optimization problem to identify the optimal feature subset. By using fewer important features that require transmission and processing, this approach reduces detection time and improves model performance. We propose a long short-term memory autoencoder for a robust fault detection model that leverages temporal information on time-series sensor data and effectively handles noisy data. This detection model is then deployed at edge servers that provide computing resources near the data source to reduce latency. Our experimental results suggest that this method outperforms prior approaches by demonstrating lower detection times, higher accuracy, and increased robustness to noisy data. While we have a 63% reduction of features, our model obtains a high accuracy of approximately 98% and is robust to noisy data with a signal-to-noise ratio near 0 dB. Our method also performs at an average detection time of only 9.42 ms with a reduced average packet size of 179.98 KB from the maximum of 343.78 KB.  
hydraulic system; edge computing; feature selection; fault detection; genetic algorithm; long short-term memory; autoencoder +
Footnote †: journal: Applied Physics Letters
## 3 DAS Field Site Deployment
### Data Acquisition  
In the experiment, active sources were used to generate seismic waves and included a single 7257-kg Envirovibe P-wave Vibrator and an Envirovibe Shear (S) Wave Vibrator. Both vibe types (P and S) used a controlled vibratory sweep which had a linear pilot driving signal, i.e., 10-150 Hz, 16 s active sweep time with a 3 s listen time. Thus, for each active source, an uncorrelated record produced in these tests was 19 s temporal length. Four (4) sweeps per Vibroseis Point (VP) were generally used, with 16 sweeps per VP used on certain special VP locations (such as Zero-Offset) with sweep frequency from 10 to 150 Hz.  
Sources were run across various geometrical configurations (Figure 5), including Walk-Away Lines, Walk-Around Circle, Zero-Offset, and Walk-Along (next to trench), each with a 10-m vibrator point interval. In some instances, a 20-m VP interval was used. In some limited special cases, an instantaneous thump type \"sweep\" was used as an additional experimental source input. The acquisition was done to meet the objective of gathering data to assess system characteristics, namely SNR, directional sensitivity (azimuthal), and spatial and temporal reflectivity resolution.  
The DAS interrogator was used for this test with a 10-m gauge length and 0.2 m sampling interval. To increase SNR, DAS data was stacked with 16 repeated shots. The use of various active sources and acquisition techniques aimed to provide a comprehensive understanding of the system's characteristics and performance, and to evaluate the effectiveness of DAS in monitoring subsurface changes in the well.  
Figure 4: Installation strategy simulation and method for optimum data acquisition for DAS VSP.
## 1 Introduction
Although some 3D models based on physical prototype of the coupling coils are presented, the accuracy of the model has not been verified by measurement. Thus, the credibility of the subsequent simulation results cannot be guaranteed. When studying the magnetic field distribution, there is no typical test method to explain the field, so there is no description of the magnetic field located in different areas of the vehicle among these studies. Therefore, a more convincing and typical test method is needed. In addition, when studying various offset conditions of coils, most scholars only pay attention to the changes of mutual inductance and coupling coefficient of the coupling coils, and do not explain the relationship between the current amplitude and phase of the coupling coils, which can significantly affect the distribution of magnetic field most. In addition, for studying the magnetic law of the coupling coils, only the magnetic field distribution caused by the current at basic frequency is considered, and the influence of other frequencies like side-band frequencies, the current harmonics and electromagnetic interference (EMI) noise current brought by the power devices in WCS are not studied. In this paper, a 3.7 kW magnet resonant WCS for EV is designed. The overall electrical model of the WCS is established in ANSYS Simplorer to implement the electric performance. The 3D models of coupling coils and vehicle body are established in ANSYS Maxwell and verified by physical prototype. In the case of coupling coils alignment and five coil offsets, the magnetic field distribution is obtained by simulation. The values of magnetic field at the seven test points are compared with the limits specified in the standard SAE (Society of Automotive Engineers) J2954 [21] to analyze the simulation results at test points. In addition, the changes in the mutual inductance and coupling coefficient, the relationship between peak value and phase current, and other parameters' changes brought by the offset are analyzed. Besides the magnetic field due to the fundamental wave current at basic frequency, the magnetic field due to harmonics wave current of the primary and secondary side of coils are obtained separately. In addition, the measurement results of near magnetic field distribution and far magnetic field at the odd frequency of 85 kHz are obtained. An experimental bench for the proposed 3.7 kW WCS is built to validate the rightness and feasibility of the simulation results.
## 2 Literature Review
### Synopsis on The BSC
This advantage makes the BSC a strategy implementation (and change) tool, since it alerts managers to the Critical Success Factors (CSFs) and encourages them to make decisions in line with the organisation's vision and strategy [20]. CSFs are therefore valued by customers and promote the creation of value for the organisation and differentiation from the competition. This tool allows for better management and performance assessment, as it provides relevant, concise and balanced information, supported by indicators from four perspectives [58]. In order to allow efficient management, the cause-effect relationships highlight the activities or the activities that are at risk of not meeting the strategic objectives [15; 59; 60; 61]. It also highlights the integration of the learning and growth perspective and the internal perspective, which enables a clear and fair assessment of employees and their belonging processes in order to determine incentives, rewards and progress of managers. Although the BSC has existed for decades [62; 63; 64; 54], recent studies have shown that it continues to be a fundamental management tool for companies that intend to add critical value to their business [8]. This critical added value has been demonstrated in recent studies [5; 25; 31; 65; 66]. For example, [16; 67] advocated the position that the BSC items are capable of promoting learning and the alignment of employees with the companies' strategy, provided that the same items are used in a dynamic and interactive way; [PERSON] et al. (2019) [53; 68; 69] considered that the implementation of the BSC in organisations has a relevant influence on strategic decision-making processes and [PERSON] (2019) [37; 70; 71] proved that the use of the BSC reduces the subjectivity surrounding this process; [PERSON] et al. (2018) [72; 73; 74] postulated that companies using the BSC demonstrated greater capacity to implement innovations at the operational level. As for [PERSON] et al. (2022) [31], the authors suggest that measurement of organisational performance through the BSC provides fundamental information for decision making, resource allocation, planning and control, influencing employee behaviour towards alignment with organisational objectives and also provides effective communication of corporate strategy [8] in order to influence people, processes and infrastructures with it [75; 76; 77]. Therefore, the original [PERSON] and Norton indicators are considered for the measurement of the four perspectives based on the critical factors [45; 78]. The following subsection reviews the literature on the four perspectives of the BSC.
# Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data
## 2 Materials and Methods
### Calibration Method  
A three-layer backpropagation artificial neural network (ANN, Figure 5) was used to consider the multiple environmental factors that affect low-cost air temperature sensors. The hidden layer had 13 neurons with the weight decay of 0.1, which were determined by 10-fold cross-validation of datasets obtained at each location. The input data were the air temperatures measured by the low-cost sensor, humidity, solar radiation, azimuth and elevation. The solar radiation (\"SP-215\", Apogee Instruments, Inc., Logan, Utah, USA) and humidity (\"SHT-71\", Sensirion AG, Staefa, Switzerland) sensors were connected to the sensor nodes, while the solar azimuth and elevation were based on the latitude and longitude obtained by the GPS units on the sensor nodes. Linear regression-based calibration was also conducted to compare the performance of our method with that of a conventional method. All calculations were conducted using R Version 3.3.3 (R Core Team, Vienna, Austria) [30]. Source codes developed for this research are available online [29].
# Parameterized Optimization in Uncertain Graphs--A Survey and Some Results
## 5 Parameterized Complexity of Probabilistic-Core Problem
### Parameterized Complexity of the Probabilistic-Core-RF Problem  
We show that the Probabilistic-Core-RF problem is W[1]-hard. The reduction that we show for the W[1]-hardness is similar to the hardness result of the Individual-Core problem shown by [PERSON] et al. (2016) (they call this the \((d,\theta)\)-Core problem). The reduction is from the Clique problem which is as follows: given a graph \(G=(V,E)\) and an integer \(k\), decide if \(G\) has a clique of size at least \(k\). The Clique problem is known to be W[1]-hard ([PERSON] and [PERSON], 2017).  
The Probabilistic-Core-RF problem is W[1]-hard for the parameter \(d\).  
Let \(\langle G=(V,E),k\rangle\) be an instance of the Clique problem. The output of the reduction is denoted by \(\langle\mathcal{G}=(V,E,p),d,\theta\rangle\), and it is an instance of Probabilistic-Core-RF problem. The vertex set and edge set of \(\mathcal{G}\) are same as \(V\) and \(E\), respectively, \(d=k-1\) and \(\theta=2^{-\binom{k}{2}}\). Further, for each edge \(e\in E\), define \(p(e)=\frac{1}{2}\) in \(\mathcal{G}\). Now we show that the Clique problem on the instance \(\langle G,k\rangle\) and the Probabilistic-Core-RF problem on the instance \(\langle\mathcal{G},d,\theta\rangle\) are equivalent.  
We prove the forward direction first. Let \(K\subseteq V\) be a \(k\)-clique in \(G\). The set \(K\) is indeed a \(d\)-core since every vertex \(v\in K\) has \(k-1\) neighbors in \(K\). The probability that \(K\) is a \(d\)-Core in a random sample from the possible worlds of \(\mathcal{G}\) is \(2^{-\binom{k}{2}}\). Thus, the set \(K\) is a feasible solution for the instance \(\langle\mathcal{G},d,\theta\rangle\) of the Probabilistic-Core-RF problem.  
Now we prove the reverse direction. We claim that any feasible solution \(K\subseteq V\) contains exactly \(d+1\) vertices. If \(K\) has less than \(d+1\) vertices then \(K\) cannot form a \(d\)-core in any possible world. Therefore, we consider the case in which that \(K\) has more than \(d+1\) vertices, and each vertex in the set \(K\) has degree at least \(d\). Consequently, the number of edges in any possible world in which \(K\) is a \(d\)-core is at least \(\frac{d(d+2)}{2}=\frac{d(d+1)}{2}+\frac{d}{2}=\binom{d+1}{2}+\frac{d}{2}\). Then, the probability that the set \(K\) is a \(d\)-core in \(\mathcal{G}\) is at most \(2^{-\binom{d+1}{2}-\frac{d}{2}}<\theta\), and this contradicts the hypothesis that \(K\) is a feasible solution. It follows that any feasible solution \(K\subseteq V\) contains exactly \(d+1\) vertices and each vertex has degree \(d\), thus \(K\) is a \(k\) clique. Hence the theorem.
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 3 Results  
The aim of this section is to present the case study under analysis and discuss the obtained results. First, the analyzed MEMG is introduced, highlighting its main components and their interconnections within the three energy domains considered--namely, electricity, thermal energy, and natural gas--as described in Section 2. The data used for modeling each subsystem are then presented, along with their respective sources. Subsequently, the methodological assumptions adopted to evaluate the techno-economic performance of the MEMG with and without a BESS are outlined. The analysis considers a matrix of BESS configurations with varying power and energy ratings to assess the incremental economic value brought by storage and to identify the optimal BESS size. The results are then discussed in terms of economic indicators--such as net present value (NPV) and annual cost savings--as well as the resulting energy flows within the system. Finally, for a selected BESS configuration, a sensitivity analysis is carried out on two key drivers of the electricity bill: the DAM price and the power-based tariff design. Regarding the DAM price, the analysis explores how increasing the daily price spread affects the system's incentive to perform pure energy arbitrage and whether, at certain levels, the system begins to exploit the incentives associated with NIE. In parallel, a sensitivity analysis is conducted on the power-based tariff structure. In the current electricity bill, the capacity component includes metering and network charges applied to the monthly peak power withdrawal, but this flat structure does not accurately reflect the real costs of grid development, which are mainly driven by the system-wide annual peak demand. In response to this issue, several European countries--such as Belgium, Spain, and Slovenia [38]--have recently adopted differentiated power-based tariffs that vary according to the season and time of day, introducing a ToU capacity-based charge. This means that peaks occurring during system-wide peak hours are charged at a higher EUR/kW rate than those during off-peak periods when the grid is less stressed. Following this approach, the analysis examines how the MEMG's optimal scheduling responds to varying degrees of power-based tariff differentiation, specifically observing how strongly the MEMG is incentivized to shift its peak power withdrawal away from system peak hours.
## 2 Materials and Methods  
Samples were prepared using 1-butyl-3-methylimidazolium trifluoromethanesulfonate ([BMIM][TFS], \(>\)95%, Fluka, Morris Plains, NJ, USA), and nanosized-aluminum oxide (nano-Al\({}_{2}\)O\({}_{3}\), <50 nm, Aldrich, St. Louis, MO, USA). The nano-Al\({}_{2}\)O\({}_{3}\) powders (ca. 0.006 g) were mixed with [BMIM][TFS] (ca. 0.06 g), followed by sonication for 30 min, centrifugation, several washes of the precipitate with ethanol (0.1 mL for a single wash), centrifugation, and drying under vacuum for 3 h. The infrared spectra of samples measured at ambient pressure were taken by filling samples in a cell characterized by two CaF\({}_{2}\) windows without the spacers. A diamond anvil cell (DAC) of Merrill-Bassett design with a diamond culet size of 0.6 mm was used to generate pressures of up to ca. 2 GPa. Two type-IIa diamonds were used for mid-infrared measurements. Infrared (IR) spectra of the samples were measured on a Fourier transform spectrophotometer (Spectrum RXI, Perkin-Elmer, Naperville, IL, USA) equipped with an LITA (lithium tantalite) mid-infrared detector. The infrared beam was condensed through a 5\(\times\) beam condenser onto the sample in the diamond anvil cell. To remove the absorption of the diamond anvils, the absorption spectra of the DAC were measured first and subtracted from those of the samples. Samples were contained in a 0.3-mm-diameter hole in a 0.25-mm-thick Inconel gasket mounted on the DAC. To reduce the absorbance of the samples, CaF\({}_{2}\) crystals (prepared from a CaF\({}_{2}\) optical window) were placed into the holes and compressed to be transparent prior to inserting the samples. A sample droplet filled the empty space of the entire hole of the gasket in the DAC, which was subsequently sealed when the opposing anvils were pushed toward one another. Typically, we chose a resolution of 4 cm\({}^{-1}\) (data point resolution of 2 cm\({}^{-1}\)). For each spectrum, 1000 scans were compiled. Pressure calibration was performed following [PERSON]'s method [19; 20].