esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Dimensional Non-Destructive Beam Monitoring Detector for Ion Beams
## 3 Experimental Results
### Scattering Effects  
A dedicated experiment was conducted to determine the amount of beam scattering introduced by the \(\pi^{2}\) foil. The method, shown in Figure 4d, consisted in installing the UniBeM detector 60 cm downstream of the \(\pi^{2}\), in order to assess the effect of the foil on the beam profile measured by the UniBeaM. To this end, beam profile measurements were taken with the UniBeaM, with the foil in and out of beam. The results are reported in Figure 10. The vertical profile, which closely resembles a Gaussian distribution, was fitted obtaining standard deviations \(\sigma_{IN}=(3.3\pm 0.1)\) mm and \(\sigma_{OUT}=(1.7\pm 0.1)\) mm, for the situations with the \(\pi^{2}\) foil inserted and removed, respectively. The beam profile with the foil in beam can be thought of as the convolution of the beam profile with the foil out of beam and the probability distribution of the scattering introduced by the foil on a point-like beam. Assuming that this probability distribution is also Gaussian with standard deviation \(\sigma_{spread}\), the following relationship holds:  
\[\sigma_{IN}=\sqrt{\sigma_{OUT}^{2}+\sigma_{spread}^{2}} \tag{2}\]  
For our experiment we obtain \(\sigma_{spread}=(2.8\pm 0.2)\) mm. Considering the 60 cm drift space between the \(\pi^{2}\) scintillating foil and the UniBeaM detector, this is equivalent to 47 \(\upmu\)m per cm of drift space.  
This result was cross-checked against a simulation using the TRIM (Transport of Ions in Matter) software [24]. In the simulation, an 18 MeV point-like proton was set to impact a 15 \(\upmu\)m aluminium foil at an angle of 45\({}^{\circ}\). Since the thickness of the P47 coating is less than 1 \(\upmu\)m, it is considered negligible. The mean energy lost by the protons in the foil, as determined by the simulation, is of only 89.0 keV. After the foil, 60 cm of drift space in vacuum was simulated as air at a pressure of \(1.2\cdot 10^{-7}\) g/cm\({}^{3}\). The proton beam at the end of the drift space presented a Gaussian distribution with a spread of \((2.60\pm 0.05)\) mm in both the horizontal and vertical planes (Figure 11). This result deviates less than 10% from the measurement, well within the 25% tolerance on the thickness foil guaranteed by the manufacturer of the aluminium foil.  
Figure 10: Horizontal (**left**) and vertical (**right**) beam profiles measured by the UniBeaM, with and without the \(\pi^{2}\) foil inserted along the beam path. A Gaussian fit to the vertical profile is used to determine the amount of scattering introduced by the foil.
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 5 Case Study
### ESCR Is Low and 10% Changes in Load (ESCR = 1.7)  
Case 1 was simulated when the onshore AC system was relatively weak. Thus, when the ESCR of the AC system is 1.7, the variable load \(P_{2}+JQ_{2}\) is disconnected from the system when the system is under the stability condition. Figure 5 shows the changes in the AC-side MMC converter and onshore AC system when the system loses 10% of the load. Figure 5a shows the curve of the DC voltage of the AC-side MMC converter. From the figure, we observe that when the ESCR is 1.7, the corresponding \(H_{MMC}\) value is 0.361 s.  
When we increase \(H_{MMC}\) to 0.4 s, we find that the curve amplitude increases, which indicates improved support of the inertia control for realizing system stability. It can also be observed that the output increases as \(H_{MMC}\) increases, and the effective support of the inertia control for the system frequency regulation also increases. Figure 5b,c presents the active power and rotor speed of the SG in the AC system, respectively. It is clearly observed that when the system load is reduced, the SG's rotor-speed change and active-power change are effectively obtained.  
Figure 5d shows the frequency variation curve of the onshore AC system. Figure 5e shows the changes of MMC active power when the system is weak. As the load decreases, the active power is affected to a certain extent, but the normal operation of the system can still be guaranteed. As the system inertia constant increases, the system frequency stability is also greatly enhanced. The increase of the inertia constant can further reduce the oscillation of the system frequency. This once again verifies that the improved inertia control effectively supports the frequency stability of the system.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Rated Value** \\ \hline Rated AC rms voltage \(V_{rms}\) & 230 kV \\ Rated SG rms voltage \(V_{SG}\) & 230 kV \\ Rated VSC power \(S_{DC,tail}\) & 950 MVA \\ Rated DC voltage \(V_{DC}\) & 640 kV \\ Nominal DC voltage \(V_{T_{DC}}\) & 370 kV \\ VSC arm reactor \(L_{arm}\) & 50 mH \\ VSC converter reactor \(L_{f}\) & 1.25 mH \\ VSC reactor resistance \(R_{f}\) & 0.0005 \(\Omega\) \\ DC capacitor \(C\) & 2.8 mF \\ Submodule number \(N\) & 76 \\ Filter cutoff frequency \(f_{cut}\) & 2400 Hz \\ Nominal frequency \(f_{AC}\) & 50 Hz \\ \hline \end{tabular}
\end{table}
Table 1: System parameters.
# Cracking Behavior of Fiber-Reinforced Concrete Beams Made of Waste Sand
## 6 Conclusions  
Through the tests and analysis presented above, the following conclusions were formulated:  
1. The modification of the fine aggregate concrete mixture with steel fibers and super-plasticizer significantly improves the tensile and compressive strengths of the matrix. For the initially designed sand concrete of class C12/15, the compressive strengths of the fiber-reinforced sand concrete corresponding to classes C25/30 and C30/37 were achieved, specifically for sand concrete with fibers measuring 30/0.55 mm and 50/0.8 mm, respectively.
2. Analyzing the crack spacing on the section of constant moment, it can be observed that the best agreement between measurements and calculations was achieved using [PERSON]'s method [53]. This method produces a slight overestimation, averaging 19% above the measured values.
3. The highest correspondence between the calculated crack widths and the test results were obtained for the proposal included in [53]. The average percentage error was 38.4%, with the lowest standard deviation being 45.1%. In contrast, the method outlined in EC2 [52] showed the lowest agreement with the experimental results, yielding an average error of 75.5% with a standard deviation of 58.5%. Therefore, in this case, it is advisable to use the method proposed in [53], which yields satisfactory results.
4. The plain (commercial) concrete used in the tests, which had a declared compressive strength class of C35/45, exhibited higher strength properties compared to the two lower-class fiber-reinforced sand concretes (C25/30 and C30/37). Despite this, the widths of perpendicular cracks in the beams made of fine aggregate fiber-reinforced concrete and ordinary concrete were similar. Based on the tests conducted, it can be concluded that beams made of fine aggregate concrete (using waste sand) with steel fibers meet the requirements for ordinary concrete elements concerning the limit state of cracking.
5. The waste sand used in the tests proved to be a fully valuable fine-grained aggregate. Its use in the production of structural fiberconrete will help reduce the exploitation of natural resources and lower CO\({}_{2}\) emissions.  
Conceptualization, J.D., J.L.-B. and A.D.; methodology, J.D.; software, A.D.; validation, J.D., J.L.-B. and A.D.; formal analysis, J.L.-B.; investigation, J.L.-B. and A.D.; resources, J.D. and J.L.-B.; data curation, J.D.; writing--original draft preparation, J.D. and J.L.-B.; writing--review and editing, A.D.; visualization, J.L.-B.; supervision, J.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors are making the research results available. They are located on private computers. If you wish to access them, please contact the corresponding author.  
The authors would like to thank the employees and students involved in the research.  
The authors declare no conflicts of interest.
## 4 RoCoMAR Algorithm  
In this section, we describe the algorithms of RoCoMAR in detail. The RoCoMAR is comprised of _link reinforcement_ algorithm and _adaptive link maintenance_ algorithm, which allow RNs (robotic nodes) to be placed as relays at the appropriate positions and to adjust their positions and movements according to the change of network topology. Note that those algorithms work independently with route discovery algorithm. Therefore, RoCoMAR can use any existing routing protocols (e.g., AODV, DSR, and OLSR) to discover and establish the initial route from the source and destination.  
We first describe the link reinforcement algorithm, and then extend the discussion to the adaptive link maintenance algorithm. We also describe the PDR (packet delivery ratio) based link quality estimation, which is used in link reinforcement and adaptive link maintenance algorithms to quantify the quality of wireless links. Finally, we numerically analyze the link quality gain obtained by the link reinforcement processes.
# Hydrodynamics of an Airlift Column for Aeration in Molten Sulfur
## 4 Results and Discussion
### Liquid Circulation Velocity  
The liquid circulation is caused by the gas holdup difference between the riser and downcomer. The liquid circulation velocity \(U_{Lr}\) is a significant hydrodynamic parameter of the airlift column, which is associated with the liquid axial velocity \(V_{Lr}\) and gas holdup in riser \(\varepsilon_{r}\), \(U_{Lr}=V_{Lr}(1-\varepsilon_{r})\).  
Figure 10 shows the contours of the liquid axial velocity at different superficial gas velocities. The \(V_{Lr}\) increases with the \(U_{Gr}\). The growth rate of the \(V_{Lr}\) decreases when the \(U_{Gr}\) is larger than 0.0333 m/s. Figure 11 shows the contours of the \(V_{Lr}\) at different total column heights with \(U_{Gr}\) of 0.0389 m/s. The \(V_{Lr}\) increases with the total column height. This is due to the fact that as the total column height increases, the hydrostatic pressure increases, and consequently, the driving force generated by the differential pressure between the riser and the downcomer increases. Finally, the enhanced driving force is an increase in the liquid circulation velocity.  
Figure 8: Comparison of total gas holdup calculated by the correlations and CFD simulation at different gas superficial velocities \(U_{Gr}\).  
Figure 9: Comparison of total gas holdup calculated by the correlations and CFD simulation at different total column heights.  
When the superficial gas velocity increases, the liquid circulation velocity gradually increases when the gas holdup is high, as reported by previous studies [25; 26; 27; 28]. This is due to the fact that the displacement of the bubbles in the downcomer tends to increase with the superficial gas velocity. As seen from Figure 4, the gas holdup difference between the riser and downcomer decreases as the \(U_{Gr}\) increases. As a result, the liquid axial velocity no longer changes evidently.  
The comparison of the liquid circulation velocity calculated by the Chisti correlation, the proposed correlation, and the present simulation are shown in Figures 12 and 13. As shown in Figure 12, when the \(U_{Gr}\) is lower than 0.0389 m/s, the simulation results are larger than the results calculated by the two correlations.  
Figure 11: Axial liquid velocity distribution at different total column heights with \(U_{Gr}\) of 0.0389 m/s.  
Figure 10: Axial liquid velocity distribution with \(U_{Gr}\) at a total column height of 15 m.  
Figure 13 illustrates the results for different total column heights with \(U_{Gr}\) of 0.0389 m/s. The results calculated by the Chisti correlation fit in well with the simulation results, while the results calculated by the proposed correlation are slightly larger than the simulation results. The results calculated by the two correlations exhibit the same variation trends. When the total column height is less than 10 m and more than 15 m, the simulation results are larger and smaller than the results obtained using the two correlations, respectively.
# Study on Multi-Span Tension Coupling Relationship of Gravure Printed Electronic Equipment
## 4 Coupling Relation Analysis
### Varying Rotational Speed \(\omega_{2}\)  
Rotational speed \(\omega_{2}\) is the input, \(T_{2}\), \(T_{3}\), and \(T_{4}\) are the outputs, and \(T_{1}\), \(\omega_{1}\), \(\omega_{3}\), and \(\omega_{4}\) are constants. When the system is running stably, \(\omega_{2}\) generates a 0.04 r/min step increment, and the step responses of \(T_{2}\), \(T_{3}\), and \(T_{4}\) are shown in Figure 9.  
In Figure 9a, for span 2, the step increment of \(\omega_{2}\) causes a step change in tension \(T_{2}\), and its step response is consistent with the law described above, that is, the increase in discharge roller speed will lead to the increase in web tension in this span.  
For span 3, \(T_{3}\) first decreases and then returns to its original steady-state value after reaching its negative peak. The reasons for this phenomenon are as follows: roller 2 is the feed roller of span 3, and the increase in \(\omega_{2}\) will cause a negative step change of \(T_{3}\). Meanwhile, \(T_{2}\) is the input of span 3, and the \(T_{2}\) step increase caused by the increase of \(\omega_{2}\) is transferred to span 3, which compensates for the decrease in \(T_{3}\) and finally returns \(T_{3}\) to the initial steady state. \(\omega_{2}\) causes a \(T_{2}\) change, which is then propagated to the web of span 3. This is an indirect process that lags behind the direct change in \(T_{3}\) caused by the \(\omega_{2}\) change, so \(T_{3}\) will first rapidly decrease and then slowly increase.  
For span 4, the roller speeds at both ends remain constant, while \(T_{3}\) experiences tension fluctuations that decrease first and then increase. This tension fluctuation will be transmitted to span 4, causing \(T_{4}\) to experience fluctuations that decrease first and then increase. However, the amplitude of this tension fluctuation is smaller, and the arrival time of the peak value is delayed.  
In addition, it can be seen from Figure 9a that when the input is \(\omega_{2}\), for the transient response, the settling times of \(T_{2}\), \(T_{3}\), and \(T_{4}\) are 4.36 s, 6.50 s, and 8.38 s, respectively, which is the same as the previous input of \(T_{1}\) and \(\omega_{1}\), indicating that the settling time progressively increases. For the steady-state response, the change of \(\omega_{1}\) only affects the steady-state value of \(T_{2}\), without affecting the steady-state tensions in other spans.  
The experimental results in Figure 9b verify the correctness of the tension model when the input is the velocity of roller 2, and they also confirm the phenomenon of backward transmission of tension fluctuations.  
Figure 9: Step responses of web tension \(T_{2}\), \(T_{3}\), and \(T_{4}\) when the speed \(\omega_{2}\) has a 0.04 r/min step increment: (**a**) simulation; (**b**) experiment.
## 2 Materials and Methods
### Data Analysis  
R-R interval, ABM, and respiration time series data were divided into consecutive 5-min segments for the entire length, so that each segment includes 10 consecutive 30-s epochs used for sleep stage coding. Then, the sleep stage of segment was defined as those of the majority among 10 epochs, i.e., if the stage of \(\geq\) 6 epochs were the same, it was chosen as the stage of the segment. If there was no majority stage among 10 epochs, the segments were defined as transitional stages. In each 5-min segment, the maximum value of Act(\(t\)) were selected as ABM of the segment.  
R-R intervals in each segment were interpolated with a step function only using interval data consisting of consecutive normal beats and the interpolated signal was resampled at 1024 equidistant time points. After calculating heart rate from mean R-R interval, the resampled R-R interval time series were filtered with a Hanning window and converted into frequency domain by fast Fourier transformation. From the obtained power spectrum, Hsi was computed for each segment. After correcting for the losses of variance resulting from the sampling and filtering processes, the power of the very-low-frequency (VLF, 0.0033-0.14 Hz), low-frequency (LF, 0.04-0.15 Hz), and HF components and LF-to-HF ratio were computed. Additionally, the peak power of HF component was also calculated. The power of LF and HF components and peak HF power were transformed into the natural logarithmic values. As an index of nonlinear heart rate dynamics, scaling exponents (\(\alpha\)) were calculated by detrended fluctuation analysis [17; 18]. According to earlier studies [8; 9], we measured scaling exponents in the ranges of 30-60 s and 60-300 s as \(\alpha_{30-60}\) and \(\alpha_{60-300}\), respectively, so that they fit to 5-min segment lengths.  
Additionally, power spectrum of respiration was also calculated for each 5-min segment and the Hsi of respiration signal was also calculated in the same ways as that of the R-R intervals.
## 2 Related Works  
As a typical unsupervised learning algorithm, the GAN is mainly composed of two basic networks, the generator (G) and the discriminator (D).  
As shown in Figure 1, the G takes the random noise z as an input, maps it to the streaming space of the training dataset via the neural networks, and generates the corresponding simulation results G(z). The D, on the other hand, needs to differentiate between input data from different sources in order to identify their degree of truth and falsity, and both of them improve their respective performance in an adversarial learning manner.  
GANs use an adversarial loss function to drive \(G\) and \(D\) learning, whose functional expression can be defined as  
\[\underset{G}{\underset{D}{\text{\emph{max}}}}V(D,G)=E_{x\sim P_{data}(x)}[logD (x)]+E_{z\sim P(z)}[log(1-D(G(z)))] \tag{1}\]  
Among them, \(G\) and \(D\) represent the generator and discriminator, respectively. \(G(z)\) represents the sample generated by the \(G\) given random noise z. \(D(x)\) represents the probability that the real data sample \(x\) is recognized as true by \(D\). \(D(G(z))\) represents the discrimination probability of the generated sample, that is, the \(D\)'s ability to detect fake samples generated by the \(G\). \(P_{data}(x)\) and \(P(z)\) represent the distributions of real data and random noise, respectively. Through several training iterations, under the effect of gradient descent and backpropagation, G and \(D\) will reach the state of Nash equilibrium. In other words, the \(G\) can generate samples that are similar to the real data, while the \(D\) cannot distinguish between real and generated data when the network parameters can be saved and made available for subsequent reconstruction tasks.  
Moreover, a conditional GAN (CGAN) can achieve target generation by feeding the conditional variable \(c\) and random noise \(z\) into the generator [28]. The architecture of CGAN is shown in Figure 2.  
Its objective function can be defined as follows:  
\[\underset{G}{\underset{D}{\text{\emph{max}}}}V(D,G)=E_{x\sim P_{data}(x)}[logD (x|c)]+E_{z\sim P(z)}[log(1-D(G(z|c)))] \tag{2}\]  
where \(c\) represents the conditional information and \(G(z|c)\) represents the sample generated by the \(G\) given the condition \(c\) and the random noise \(z\). This enables the \(G\) and \(D\) to generate and discriminate the samples based on the given condition information so that the characteristics of the samples in the generating process can be controlled to achieve the conditional generation.  
Figure 1: The architecture of GANs.  
In summary, GANs demonstrate superior applicability for the efficient and fine construction of complex geological bodies. Additionally, the incorporation of conditional data, which typically represents various actual observations, ensures the accuracy and reliability of the modeling results.
## 4 Conclusions  
Compact dual-port and quad-port MIMO antennas have been developed to enhance overall efficiency of 5G Midband (n77/n78/n79/n96/n102) and UWB communication systems, offering improved diversity and better isolation. Both designs provide a broad impedance bandwidth of 8.2 GHz, spanning 2.6 GHz to 10.8 GHz, with reflection coefficient (S\({}_{\text{XX}}\)) consistently below -10 dB across the band. Both configurations demonstrate excellent strong inter-element isolation, where coupling (Sxy) remains under -15 dB at lower bands and drops beneath -20 dB at elevated frequencies. The MIMO antennas, both two-port and four-port, show excellent performance with low ECC, high Diversity Gain, and minimal MEG difference. TARC remains below the threshold, and signal delay variation is kept under 0.25 ns. These metrics ensure stable and efficient MIMO operation. Overall, the proposed antennas are compact and efficient solutions for 5G mid-band (C-band) and UWB MIMO systems. Future work will scale UWB-MIMO to larger arrays (4 \(\times\) 4, 8 \(\times\) 8) using advanced isolation methods (metamaterials, EBG, neutralization lines) and flexible, reconfigurable substrates for wearable and adaptive systems.  
Conceptualization, K.R.; Methodology, K.R.; Software, T.C.; Writing--original draft, K.R.; Writing--review & editing, S.G.; Supervision, S.G. All authors have read and agreed to the published version of the manuscript.  
No external sources of funding were received for this research.  
All original findings discussed in this study are contained within the article and any additional questions can be addressed to the corresponding author.  
The authors declare no conflict of interest.