esa-sceva/satcom-chunk-collection huggingface dataset:
## EUV and SXR Full Field Microscopes Based on a Double Stream Gas-Puff Target Source  
### Full Field EUV Microscope  
The recently developed EUV microscope (scheme and picture are shown in Figure 3a,b) [71], having a total volume of (W \(\times\) D \(\times\) H) 100 cm \(\times\) 70 cm \(\times\) 161 cm, represents one of the most compact microscopes reported in the literature and was developed as a technological demonstrator, based on similar experimental systems [72].  
An infrared Nd:YAG laser NL 302 from Eksma, Vilnius, Lithuania (\(\lambda\) = 1064 nm, 500 mJ) energy, 4 ns time pulse duration, and intensity of 10\({}^{11}\) W/cm\({}^{2}\)), was focused using a proper lens (f = 25 mm) onto a double stream gas-puff target. The Ar plasma radiation, at the wavelength of 13.5 \(\pm\) 0.5 nm, was spectrally narrowed by an ellipsoidal off-axis mirror, coated with a Mo/Si multilayer mirror (MLM), having the function of condenser, reflecting the EUV radiation at 45\({}^{\circ}\) incidence angle. The longer wavelengths emitted from the Ar plasma (\(\lambda\) > 16 nm) have been cut employing a free standing 250 nm thick Zr filter (Lebow, CA, USA). The sample was imaged using an FZP objective onto the CCD camera. The system is equipped with differential pumping, which allows avoiding high gas pressure inside the vacuum chamber. During the test phases, a pressure of \(\sim\)10\({}^{-4}\) mbar was constantly maintained by using two scroll pumps and one turbo-molecular pump. The pressure inside the source chamber (of a few mbar during the source operation) was separated from the pressure inside the microscope chamber (where it is possible to achieve a pressure down to 10\({}^{-5}\) mbar) by using a cone-shaped differential pumping aperture (1 mm in diameter). The object was locked in place by a magnetic holder (KB25/M, from Thorlabs Inc., Newton, NJ, USA), and its position was finely adjusted using three-axis motorized translation stages, (8 MT173-20 from Standa, Lithuania), having a position accuracy of 1.25 \(\upmu\)m. A dedicated piezo-stage (LPS-45 from PI miCos GmbH, Eschbach, Germany), with a repeatability of 50 nm, was used for accurate positioning of the FZP along the optical axis. The chamber was equipped with a supplementary load lock, to easily replace the sample. The gas pressure was adjusted to obtain the highest photon flux in the focal plane of the condenser. Ar (working gas) pressure was set to 10 bar, and He (outer gas) pressure was set to 6 bar. To avoid nozzles damaging by the plasma generation, the focal spot was located 1.5 mm away from the nozzle plane. The time delays between the synchronization pulse from the pumping laser power supply and the opening times of the two valves supplying the nozzles (with Ar and He) were 100 \(\upmu\)s and 230 \(\upmu\)s, respectively, while the pulse width was 500 \(\upmu\)s for both valves. Finally, the time between the synchronization pulse and the laser pulse generation was set to 1 ms.  
Figure 3: (**a**) Scheme and (**b**) external view of the extreme ultraviolet (EUV) microscope. Figure modified, based on [71].
# Effects of Tunnel and Its Ventilation Modes on the Aerodynamic Drag of a Subway Train
## 3 Measurement Results
### Effects of Tunnel Ventilation Modes on the Temperature  
Figure 4 compares the time histories of the temperature measured by the thermocouple in the two ventilation modes. The mean temperatures inside the tunnel for recirculation and free-cooling modes were 29.05 \({}^{\circ}\)C and 29.80 \({}^{\circ}\)C, respectively. A higher temperature is expected in the free-cooling mode when no cooled air is pumped into the tunnel and heat generated by running trains raises the temperature in the tunnel. During measurements, the test train ran first in the recirculation mode of the tunnel and then in the free-cooling ventilation mode for only about 1 h. Therefore, the temperature rise in the free-cooling mode was only 0.75 \({}^{\circ}\)C, compared with the recirculation mode.  
The air density \(\rho\) is related to its temperature by  
\[\rho=p/(R_{0}\cdot T), \tag{3}\]  
where \(p\) is the mean pressure inside the tunnel, \(T\) is the mean temperature in Kelvin and \(R_{0}\equiv 287.05\) J/kg\(\cdot\)\({}^{\circ}\)K is the specific gas constant. Since the train speed is relatively low, \(p\) is considered to be a constant. A 0.75 \({}^{\circ}\)C increase in temperature in the free-cooling mode produces a 0.26% decrease in \(\rho\), compared with that in the recirculation mode. On the other hand, \(\eta\) is also related to temperature from [PERSON]'s formula [24]:  
\[\eta=\eta_{0}\frac{T_{0}+C}{T+C}\left(\frac{T}{T_{0}}\right)^{3/2} \tag{4}\]  
where \(T_{0}=288.15\)\({}^{\circ}\)K is a reference temperature, and \(\eta_{0}=1.78\)\(\times\)\(10^{-5}\) kg/(m.s) is the reference viscosity at \(T_{0}\). \(C\) is [PERSON]'s constant, which is 120 for dry air. Based on Equation (4), a 0.75 \({}^{\circ}\)C increase in temperature in the free-cooling mode causes a 0.22% increase in \(\eta\), compared with that in the recirculation mode. It may be inferred that the effects of tunnel ventilation modes on temperature and subsequently on \(\rho\) and \(\eta\) are limited, thus not considered in the following discussions.
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 4 Data Analysis and Model Development
### Model 1: CNN-LSTM with ERP Segments
To address data imbalance between preference (positive) and non-preference (negative) classes, we applied focal loss, which places a greater emphasis on challenging samples. We used \(\alpha\) = 0.25 and \(\gamma\) = 2.0. After 30 training epochs, the model's performance metrics were as follows:  
* Recall: 98.76% (very high, rarely missing positive/preference cases);
* Precision: 72.11% (relatively low; some non-preference cases misclassified as preference);
* F1 score: 83.36%;
* Accuracy: 77.09%. Although precision was somewhat modest, the model's exceptionally high recall could be advantageous for real-time emotion monitoring applications.
## 3 Results and Discussion
### Effect of Thermal Processing on Soluble Sugars in Broccoli  
Steamed broccoli had the highest soluble sugars content (11.04 \(\pm\) 2.45 mg SucE/g fw), and the content was high in air-fried broccoli as well (8.23 \(\pm\) 4.96 mg SucE/g fw) (Figure 3). Water from steaming broccoli contained only 3% soluble sugars compared to associated tissue, as the tissue was not in direct contact with water. A previous study on bamboo shoots showed that the loss of soluble sugars was up to 14.39% during boiling and up to 17.42% during stir-frying [73]. The addition of oil contributes to the decrease in soluble sugar content by increasing the temperature and inducing Maillard reaction, which is consistent with our results (air-fried broccoli had 2.7 times more soluble sugars than pan-fried broccoli in oil). Since air-frying does not involve oil, as compared to pan-frying, the Maillard reaction is weaker, so more soluble sugars are present. Blanched broccoli had a higher soluble sugars content than broccoli which was blanched and then pan-fried, suggesting that pan-frying had a negative effect, which is consistent with the theory that the Maillard reaction degrades soluble sugars. [PERSON] et al. [12] obtained similar results. They found that steamed broccoli had the highest soluble sugars content, while the lowest retention was observed in boiled and stir-fried broccoli.  
Figure 3: Effect of different thermal processing techniques on soluble sugars in broccoli extracts. Values represent average \(\pm\) standard deviation of three biological and four technical replicates. Different letters indicate a significant difference among the values (one-way ANOVA, Duncan’s test, \(p\)\(\leq\) 0.05). SucE = sucrose equivalent; fw = fresh weight; BO = broccoli boiled; BOW = water after boiling broccoli; BL = broccoli blanched; BLW = water after blanking broccoli; ST = broccoli steamed; STW = water after steaming broccoli; AF = broccoli air-fried; PF = broccoli blanched and then pan-fried.  
Figure 2: Effect of different thermal processing techniques on total glucosinolates in broccoli extracts. Values represent average \(\pm\) standard deviation of three biological and four technical replicates. Different letters indicate a significant difference among the values (one-way ANOVA, Duncan’s test, \(p\)\(\leq\) 0.05). fw = fresh weight; SinE = sinigrin equivalent; BO = broccoli boiled; BL = broccoli blanched; ST = broccoli steamed; AF = broccoli air-fried; PF = broccoli blanched and then pan-fried.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 3 Design Methodology
### Resonant Constellation Generation and Merging  
Following the determination of constellation size \(n_{sat}\), all constellations are generated via \(\mathbf{C}_{n_{t}}^{n_{sat}}\), i.e., \(n_{sat}\) orbits are selected from the \(n_{t}\) orbits in \(\mathbf{\widehat{C}}\). Note that the satellite counts \(n_{sat}\) and the number of orbits in the combination must satisfy a specific relationship: if \(n_{t}<n_{sat}\), the current combination \(\mathbf{\widehat{C}}\) is discerned. Assume \(h\) valid combinations meet the quantity requirements. For the \(q\)-th combination \(\mathbf{\widehat{C}}_{q}\), all generated combinations are stored in matrix \(\mathbf{A}_{q}\), where each row of \(\mathbf{A}_{q}\) represents a constellation. The row vector \(\mathbf{a}=\left[\begin{array}{cccc}o_{1}&o_{2}&\ldots&o_{n_{sat}}&p_{0}\\ \end{array}\right]\), termed the constellation vector, records the orbit types and baseline period corresponding to the constellation. This constructs a constellation matrix \(\mathbf{A}\), containing all \(n_{sat}\)-satellite constellations formed by the combinations \(\mathbf{\widehat{C}}_{q}\), with a total count of \(h\).  
\[\mathbf{A}=\left[\begin{array}{cccc}\mathbf{A}_{1}&\mathbf{A}_{2}&\ldots&\mathbf{A}_{h}\\ \end{array}\right]^{T} \tag{27}\]  
In this study, taking four-satellite constellations as an example, the above procedures generate a total of 6922 constellations. Among these, some constellations share identical orbital-type compositions but differ in their baseline periods. To further merge and reduce the computational scale, such constellations are represented by the one with the smallest baseline period, resulting in a submatrix \(\mathbf{\widehat{A}}\) of the constellation matrix \(\mathbf{A}\). Assuming the final merged constellations number is \(G\), the following can be obtained:  
\[\mathbf{\widehat{A}}=\left[\begin{array}{cccc}\mathbf{a}_{1}&\mathbf{a}_{2}&\ldots&\mathbf{ a}_{G}\\ \end{array}\right]^{T} \tag{28}\]  
Finally, based on the baseline period in the constellation vector \(a_{g}\), the corresponding resonant orbit combination \(\mathbf{\tilde{C}^{\prime}}\) is retrieved from the resonant orbit set \(\mathbf{\widehat{C}}\). Then, according to the orbit types specified in \(a_{g}\), the corresponding initial orbital parameters can be extracted from \(\widehat{C}\), where \(g=1,2,\ldots,G\), thereby forming the constellation matrix.  
\[\mathbf{D}_{g}=\left[\begin{array}{cccc}\mathbf{b}_{1}&\mathbf{b}_{2}&\ldots&\mathbf{b}_{n_{sat }}\end{array}\right]^{T} \tag{29}\]  
By performing a row-wise traversal of \(\widehat{\mathbf{A}}\), a comprehensive set of all generated constellation matrices is obtained.  
\[\mathbf{\tilde{D}}=\left\{\begin{array}{cccc}\mathbf{D}_{1}&\mathbf{D}_{2}&\ldots&\mathbf{D }_{G}\end{array}\right\} \tag{30}\]  
This completes the entire process of extracting all resonant constellations from the orbit library. Through the described algorithm, a total of 1229 resonant constellations are generated.
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 2 Materials and Methods
### Batch Least Squares Solution of the Considered Factor Graph  
When ignoring the motion constraints in Equation (7), we find the trivial solution which is called the _snapshot solution_. For this particular solution, the snapshot covariance matrix for state \(x_{j}\) accross all epochs from \(1\) to \(K\) is:  
\[\mathbf{p}_{j}^{(snap)}=\mathbf{diag}(\sigma_{j,1}^{2},\ldots,\sigma_{j,K}^{ 2}) \tag{8}\]  
The solution of the factor graph mixing both snapshot GNSS factors and motion factors is referred to as the _batch solution_. The batch solution covariance matrix is obtained with the following equation.  
\[\mathbf{p}_{j}^{(batch)} =(\mathbf{H}^{T}\mathbf{Cov}(\mathbf{n}_{j})^{-1}\mathbf{H})^{-1} =\left(\begin{bmatrix}\mathbf{I}_{K}&\mathbf{H}_{\mathbf{m}}^{T}\end{bmatrix} \left[\begin{array}{c|c}\mathbf{p}_{j}^{(snap)}&\mathbf{0}_{K\times K-1}\\ \hline\mathbf{0}_{K-1\times K}&q^{2}\mathbf{I}_{K-1}\end{array}\right]^{-1} \begin{bmatrix}\mathbf{I}_{K}\\ \mathbf{H}_{\mathbf{m}}\end{bmatrix}\right)^{-1}\] \[=\left((\mathbf{p}_{j}^{(snap)})^{-1}+\frac{1}{q^{2}}\mathbf{H }_{\mathbf{m}}^{T}\mathbf{H}_{\mathbf{m}}\right)^{-1} \tag{9}\]  
The inverse of the batch solution covariance is a so-called a tri-diagonal matrix [8].  
\[\mathbf{p}_{j}^{(batch)}=\left[\begin{array}{ccccc}\frac{1}{q^{2}}+\frac{1} {\sigma_{j}^{2}}&-\frac{1}{q^{2}}&0&\cdots&0\\ -\frac{1}{q^{2}}&\frac{2}{q^{2}}+\frac{1}{\sigma_{j}^{2}}&-\frac{1}{q^{2}}& \ddots&\vdots\\ 0&\ddots&\ddots&\ddots&0\\ \vdots&\ddots&-\frac{1}{q^{2}}&\frac{2}{q^{2}}+\frac{1}{\sigma_{j\times 1}^{2}}&- \frac{1}{q^{2}}\\ 0&\cdots&0&-\frac{1}{q^{2}}&\frac{1}{q^{2}}+\frac{1}{\sigma_{j\times 1}^{2}} \end{array}\right]^{-1} \tag{10}\]
###### Abstract  
Inspections of industrial and civil infrastructures are necessary to prevent damages and loss of human life. Although robotic inspection is gaining momentum, most of the operations are still performed by human workers. The main limiting factors of inspection robots are the lack of versatility as well as the low reliability of these devices, since they need to operate in a non-structured environment. In this work, a novel Hybrid Platform for inspection in industrial contexts is proposed, focusing on the design and testing of the Crawler Unit. The goal is to solve versatility related issues exploiting modularity and self-reconfigurability. The Hybrid Platform consists of three main systems: a mobile Main Base and two Crawler Units. Each would operate independently, accomplishing specific tasks. Docking interfaces, on each device, allow the systems to reconfigure into different robots. The Crawler Unit operates in constrained environments and narrow spaces. The Main Base patrols wide areas and deploys the Crawler Units near the inspection site. For dealing with challenging conditions, the two Crawler Units can dock together, reconfiguring into a snake-like robot. Additionally, once docked to the Main Base, the two Crawlers can operate also as robotic arms, providing manipulation abilities to the platform. The first version of the Crawler Unit exhibited an interesting performance over flat and uneven terrains. To extend the mobility of this robot, a second version was developed, introducing some innovations in the system design. These innovations provided the Crawler Unit with advanced mobility in the vertical plane, thus allowing the robot to deal with more complex scenarios such as crossing gaps, overcoming obstacles and lifting the modules.  
robotics; mobile robot; inspection robot; self-reconfigurable robot; modular robot; hybrid platform +
Footnote †: journal: Applied  
Acticle Design of the Crawler Units: Toward the Development of a Novel Hybrid Platform for Infrastructure Inspection.
# Minkowski and Galilei/Newton Fluid Dynamics: A Geometric 3 + 1 Spacetime Perspective
## 4 Conclusions
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Symbol** & **Description** & **References** \\ \hline \(\lambda\) & Affine parameter & Section 2.1 \\  & (Units of time/mass) & \\ \(\mathcal{X}(\lambda)\) & Worldline & Section 2.1 \\ \(\mathbf{p}\) & Particle four-momentum vector & Equations (3) and (4) \\ \(m\) & Particle rest mass & Section 2.1 \\ \(\tau\) & Proper time measured by particle & Sections 2.1–2.4 \\  & (\(\mathrm{d}\tau=m\,\mathrm{d}\lambda\)) & \\ \(\mathbf{u}\) & Particle four-velocity vector & Equations (5) and (6), \\  & & Equations (20) and (34) on \(\mathbb{M}\), \\  & & Equations (40) and (50) on \(\mathbb{G}\) \\ \(\mathbf{v}\), \(\mathbf{v}_{q}\) & Particle three-velocity vector measured by \(\Theta\) & Equations (6)–(8) \\  & (Tangent to \(\mathcal{S}_{t}\)) & Equation (79) on \(\mathbb{M}\), \\  & & Equation (80) on \(\mathbb{G}\) \\ \(\Lambda_{\mathbf{v}}\) & Particle Lorentz factor on \(\mathbb{M}\) measured by \(\Theta\) & Equation (18) \\ \(\mathbf{q}\) & Particle three-momentum vector measured by \(\Theta\) & Equations (21), (22) and 35) on \(\mathbb{M}\), \\  & (Tangent to \(\mathcal{S}_{t}\)) & Equations (41), (42) and (51) on \(\mathbb{G}\) \\ \(\epsilon_{q}\) & Particle total energy on \(\mathbb{M}\) measured by \(\Theta\) & Equation (21) \\ \(e_{q}\) & Particle kinetic energy measured by \(\Theta\) & Equation (36) on \(\mathbb{M}\), \\  & & Equation (53) on \(\mathbb{G}\) \\ \(s\) & Particle three-momentum vector measured by \(\tilde{\Theta}\) & Equation (67) on \(\mathbb{M}\), \\  & & Equation (68) on \(\mathbb{G}\) \\ \(\epsilon_{s}\) & Particle total energy on \(\mathbb{M}\) measured by \(\tilde{\Theta}\) & Equation (67) \\ \(\mathbf{v}_{s}\) & Particle three-velocity vector measured by \(\Theta\) & Equation (90) on \(\mathbb{M}\), \\  & & Equation (91) on \(\mathbb{G}\) \\ \(e_{s}\) & Particle kinetic energy measured by \(\tilde{\Theta}\) & Section 3.3 on \(\mathbb{M}\), \\  & & Section 3.5 on \(\mathbb{G}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Geometric entities characterizing particles on Minkowski and Galilei/Newton spacetimes.
# Improving Structural Power Content Analysis Robustness for Satellite Navigation Applications +
## 6 Conclusions  
In this paper, the performances of different suggested filter combinations to mitigate false alarms due to jamming and their effect on spoofing detection were evaluated. It was seen that the DAM operation in the SPCA algorithm induced a significant peak in the DC channel for different jammers, for which a DC notch filter was suggested. Additionally, it was noted that the current noise filter implementation using a comb filter could be susceptive to fluctuations in noise, whereas a filter with a higher equivalent bandwidth, such as a cyclic notch filter, could suppress these fluctuations.  
The four combinations of these filters were tested on simulated data and on publicly available spoofing datasets, and it was seen that the DC notch filter had a positive impact on the achieved false alarm rate of the algorithm with respect to the desired false alarm rate that is used to calculate the test statistic threshold. The cyclic notch noise filter also contributed to this impact positively, albeit less significantly. From these results, it could be concluded that both suggested filter changes add to the jamming resistance of the SPCA algorithm, as it appears to be able to approximate its desired false alarm rate better with both changes applied than with the original filter settings (comb filter and no DC filter) in jamming scenarios. In terms of spoofing detection, it was found that due to the (partial) removal of some of the spectral lines from the DAM output due to the DC notch filter, the SPCA algorithm becomes slightly less sensitive. Based on the focus of the application, a filter combination could be selected that yields the performance that is needed.  
Jamming model research, filter implementation, algorithm analysis, tests: J.R. and A.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
TEXBAT data available at [[https://radionavlab.ae.utexas.edu/texbat/](https://radionavlab.ae.utexas.edu/texbat/)]([https://radionavlab.ae.utexas.edu/texbat/](https://radionavlab.ae.utexas.edu/texbat/)), accessed on 14 July 2023.  
All authors were employed by the company S[&]T. The authors declare no conflicts of interest.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 2 Related Work
### Resource Scheduling for Single Satellite  
Researchers have conducted in-depth studies on the satellite resource scheduling problem and achieved specific results. In particular, the scheduling methods of single-star resources are studied [4; 5], mainly on earliest mission priority, satellite resource scheduling mechanism, heuristic algorithms, high-level priority algorithms with time estimation and critical path algorithms [6; 7; 8; 9; 10; 11]; these methods are the simplest, fast and effective resource scheduling methods. However, dealing with a large data volume of resource request queues, these methods often fail to schedule resources effectively and may result in many resource request tasks waiting for a longer time or failing to achieve the best overall resource scheduling.
## 2 Experimental  
### Preparation of Fn-CaP Donor Films on Optical Stamp Using the Biomimetic Process  
Fn-CaP donor films were prepared on an optical stamp through a precursor-assisted biomimetic process [30; 32], as detailed in the previous report [16]. Different from the previous work [16], a PET substrate was used as a transparent support, one-side of which was coated with a 1-mm-thick PDMS sheet. Hereafter, the PDMS-coated PET substrate is referred to as an optical stamp (denoted as PDMS/PET). Then, a thin carbon film with a thickness of about 50 nm was prepared on the PDMS surface of the optical stamp via vapor deposition. The thin carbon film was formed as a sacrificial layer, which absorbs laser light, thereby causing laser-induced forward transfer of the Fn-CaP film. The carbon-preformed optical stamp (denoted as C/PDMS/PET) was subjected to the oxygen plasma treatment for surface activation. The plasma-treated C/PDMS/PET substrate was alternately dipped in 0.2 M CaCl\({}_{2}\) and 0.2 M K\({}_{2}\)HPO\({}_{4}\)3H\({}_{2}\)O aqueous solutions for CaP precoating and, subsequently, immersed at 25 \({}^{\circ}\)C for 5 h in a supersaturated CaP solution for film growth. Fn (from bovine plasma, Sigma-Aldrich) was supplemented at a final concentration of 40 \(\mu\)g/mL to the CaCl\({}_{2}\) and K\({}_{2}\)HPO\({}_{4}\)3H\({}_{2}\)O solutions and to the supersaturated CaP solution for Fn immobilization within the film [16]. Fn is a biofunctional protein, which promotes cell adhesion and spreading. We previously confirmed that Fnis immobilized within apatite films formed using a similar precursor-assisted biomimetic process [32]. After the film formation, the substrate was washed with ultrapure water and freeze-dried before use (denoted as Fn-CaP/C/PDMS/PET donor sample). For comparison, a Fn-CaP film was prepared using a carbon-coated PET substrate without the PDMS layer, which is denoted as Fn-CaP/C/PET donor sample. The surface morphology of donor films was monitored by field emission-SEM (S-4800, Hitachi High-Tech Corporation, Tokyo, Japan). The film thickness was also estimated through the depth measurements of the laser-irradiated spots on the donor films after the LIFTOP or LIFT process.