esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 8 Micromegas for UV Detection
### Applications of Micromegas Photodetector  
The Forfire detector is one example of an application of Micromegas used as a UV detector [113]. This detector uses a reflective CsI photocathode deposited on top of a mesh in a double-stage Micromegas (Figure 25). An optical MgF\({}_{2}\) lens is used to focus UV light from the infinite to the plane of the photocathode. In this configuration, the detector is sensitive to UV photons in the range of 180 nm \(\leq\) \(\lambda\) \(\leq\) 230 nm. This wavelength is only emitted by artificial light sources, like open fires, as the sunlight in the wavelengths of \(<\)290 nm is cut by ozone in the upper parts of the atmosphere.  
The use of a Micromegas gives additional advantages for a low threshold fire detector. Micromegas can provide a high gain as UV detector, which gives a sufficient signal-to-noise ratio even for the detection of single photoelectrons. Electric sparks, flames, or explosions can be detected in solar-blind operation. The spatial resolution of the Micromegas detector in combination with the UV lens, can be used to precisely localize the light source, in a low-cost and high-sensitivity camera, compared to commercially available silicon-based detectors.
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 2 Parallel Auto-Encoder Framework  
We observed that the frequency data and mode shape data in SHM are generally in two significant magnitude scales, for example, 7.6 and 0.9. It is improper to normalize them in the same magnitude scale at the same time in the pre-training stage as in [3; 34]. In order to tackle the data magnitude issue, a parallel auto-encoder framework is proposed in this section for the vibration-based damage detection/identification problem in structural health monitoring. As it is known that modal information (e.g., frequencies and their corresponding mode shapes) of a given structure will change with variations of its physical properties (e.g., stiffness mass), this damage identification problem can be formulated as a pattern-recognition problem. In the proposed approach, modal information will be utilized as input separately while the output will be the structural elementary stiffness parameters which indicates the structural health conditions. The motivation of the proposed parallel based model will be first described in Section 2.1, then the training methods and formulations of the proposed approach will be described in Section 2.2.
# Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
## 4 Numerical Experiments
### Synthetic Data Generation  
To generate synthetic data, four different deterministic functions for the applied current \(I(t)\) were run through the Hodgkin-Huxley Equations (18)-(21). The applied currents considered were:  
1. a constant current, where \[I(t)=2\;\;\text{mA/cm}^{2}\] (33)
2. a step function with one long pulse, such that \[I(t)\;=\;\begin{cases}0\;\;\text{mA/cm}^{2},&t\in[0,20)\\ 10\;\;\text{mA/cm}^{2},&t\in[20,160)\\ 0\;\;\text{mA/cm}^{2},&t\in[160,200]\end{cases}\] (34)
3. a \"pulsing\" step function with multiple short pulses, such that \[I(t)\;=\;\begin{cases}0\;\;\text{mA/cm}^{2},&t\in[20q,20+20q),\\ &q=0,2,4,6,8\\ 10\;\;\text{mA/cm}^{2},&t\in[20q,20+20q),\\ &q=1,3,5,7,9\end{cases}\] (35)
4. a sinusoidal function, where \[I(t)=10\sin(0.2t)+10\;\;\text{mA/cm}^{2}.\] (36)  
For each data set, measurements of voltage \(V(t)\) were taken at 2001 equidistant time instances over the interval \([0,200]\) and corrupted by Gaussian noise with zero mean and standard deviation 0.05. Figure 2 shows each data sets along with the corresponding applied current. Note that the gating variables \(n(t)\), \(m(t)\), and \(h(t)\) are unobserved states.
## 1 Introduction  
X-ray free-electron lasers (XFELs) [1; 2; 3; 4; 5; 6; 7] have significant impacts on a variety of scientific fields. In particular, coherent X-ray pulses with an ultrafast temporal duration (<10 fs) have enabled observation of the electronic and structural dynamics of matter with an angstrom and femtosecond spatiotemporal resolution. Owing to the ultrashort pulses in the X-ray region, remarkable scientific progress has been reported in studies on capturing ultrafast dynamics in light-induced chemical reactions [8; 9; 10; 11; 12], phase transition [13; 14; 15; 16], coherent phonon oscillation [17], and spin states [18]. These studies had been carried out using pump-probe techniques combined with femtosecond optical lasers; therefore, the stabilities of not only the output power of the lasers but pointing and timing are crucial for the success of experiments. Recently, high-power laser systems based on fiber lasers have been developed for user experiments [19; 20; 21; 22] under high repetition rate operation of the megahertz order in the European XFEL [4] and the Linac Coherent Light Source II (LCLS-II) [3]. In XFEL facilities operating at several-tens to a hundred hertz--such as the LCLS [2], SPring-8 Angstrom Compact Free Electron Laser (SACLA) [5], the Pohang Accelerator Laboratory X-ray free-electron laser (PAL-XFEL) [6], and Swiss X-ray Free Electron Laser (SwissFEL) [7]--stabilization techniques adapted to synchronized optical laser systems are keys of precise measurement for pump-probe experiments.  
In SACLA, we installed a femtosecond optical laser system and constructed components of amplification, transport, pulse compression, and frequency conversion for time-resolved experiments. The operation of this laser system has been stabilized in terms of power and pointing by controlling conditions of temperature and air-flow in the optical path. Regarding the timing of the laser system,we had employed a synchronization system with direct detection of an optical pulse train, which has a jitter of \(\sim\)0.3 ps [23; 24; 25]. Recently, a feedback system using a \"balanced optical-microwave phase detector (BOMPD)\" working at a C-band frequency was developed and enabled reduction of the jitter down to \(\sim\)50 fs. In this paper, we introduce the optical components of the laser system and the results of the stabilized operation. The femtosecond pulses produced by the laser system are provided for user experiments by time-resolved X-ray absorption and emission spectroscopy, X-ray diffraction, and X-ray scattering.
# Advances in Biomaterials for Breast Reconstruction
### Modification of Filling Material in Breast Implant  
Silicone has been used as a filler since the first implant was introduced in the 1960s. Since the initial development, multiple modifications of filler characteristics have been made to achieve a more durable, consistent, and natural shape. In the past, the property of  
Figure 3: Scanning electron microscopy (SEM) images of implant surface. (**A**) Sientra Slimed foam textured implant surface (66\(\times\) magnification); (**B**) Allergan Biocell salt-loss textured implant surface (104\(\times\) magnification); (**C**) Mentor Siltex imprinted textured implant surface (100\(\times\) magnification); (**D**) Motiva SilkSurface textured implant surface (300 \(\upmu\)m scale). Based on [49; 50].  
Figure 2: The mechanism of bacterial attachment to the implant surface. Based on [48].  
the silicone filler in breast implants had been thought to be an important factor affecting the capsular contracture rate [54; 55]. However, by the evolution of fourth-generation implants, better safety had been explored, and long-term outcomes had been described [56; 57].  
Silicone is a mixture of semi-inorganic polymeric molecules composed of different length chains of polydimethylsiloxane monomers [30]. The property of the silicone can be varied by the polymer chain length and degree of crosslinking between polymer chains [58]. For example, in liquid silicone the length of the polymer chain is shorter on average, and the degree of crosslinking is minimal. However, in silicone gel, the length of the polymer chain and the degree of crosslinking is increased to maintain the implant's shape.  
The cohesiveness of the breast implant varies by manufacturer. According to a study by [PERSON] et al., the Allergan 410 implant had the highest cohesivity when compared with the Mentor CPG implant [56]. [PERSON] et al. reported that Sientra's form-stable implant had the least cohesiveness when compared to the Allergan 410 and Mentor CPG implants [59]. However, as there is no standardized grading system for implant cohesiveness, accurate comparison between various implants is difficult. Moreover, as cohesivity is not the only factor that affects implant stiffness, it is not appropriate to interpret high cohesiveness of the implant as high stiffness.
## 3 Experiments
### Experiment Results
#### 3.4.2 Prediction Results in the Beijing Metro  
The performance of prediction results in different time intervals in the Beijing metro is summarized in Table 8. The numbers in bold refer the better results between prediction with station classification, and prediction without station classification. The prediction results of MAE, RMSE, and WMAPE with classification in 15 min and 30 min time intervals are better than the prediction results without classification. Only RMSE in 10 min time intervals presents a slightly worse performance. It may be affected by an emergency, which causes the volume of inflow to suddenly decrease at some stations. This influence is more obvious for the passenger flow in smaller time intervals. Take the inflow at No.1 Stations in the Beijing metro, for example. Figure 6a,b illustrates the inflow and outflow in 10 min and 30 min time intervals, respectively. The inflow with a 10 min time interval clearly shows that the flow suddenly drops to zero shown in the dashed circle in Figure 6a. When the time interval increases to 30 min, the passenger flow is substantially reduced caused by the passenger flow dropped sharply to zero in the 10 min time interval, which is shown in the dashed circle in Figure 6b. Because RMSE is more affected by such outliers, poor RMSE results occur, especially in the scenario of 10 min time interval.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline \multirow{2}{*}{**Time Interval**} & \multirow{2}{*}{**Prediction Mode**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-5}  & & **MAE** & **RMSE** & **WMAPE** \\ \hline \multirow{3}{*}{10 min} & Prediction (with classification) & **17.2781** & 29.6929 & **9.668\%** \\  & Prediction (w/o classification) & 17.6344 & **29.6219** & 9.911\% \\  & Prediction (with classification) & **22.1919** & **37.9693** & **8.269\%** \\  & Prediction (w/o classification) & 23.0008 & 38.7386 & 8.589\% \\  & Prediction (with classification) & **32.5245** & **58.1694** & **6.120\%** \\  & Prediction (w/o classification) & 34.1360 & 60.1340 & 6.428\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction results in the Beijing Metro.  
Figure 6: Inflow at No.1 Stations in the Beijing metro. (**a**) 10 min time interval; (**b**) 30 min time interval.  
In summary, classification can still improve the prediction results for a suddenly decreasing flow. In most scenarios, the larger the time interval is, the better the improvement effects.
# Key Technologies for THz Wireless Link by Silicon CMOS Integrated Circuits
## 2 300 GHz Band Transceiver Using Silicon Integrated Circuits
### 300 GHz Band Transmitter Using Silicon Integrated Circuit
Since frequencies of these desired signals and undesired signals are close to each other, it is difficult to remove only undesired signals by using a bandpass filter. Therefore, as shown in Figure 6, two signal paths, an \"IF \(-\) LO\" signal and an \"IF \(+\) LO\" signal, are generated and given to the square mixer after being amplified. Then, these outputs are given to the balun. Finally, only the desired signal remains, and undesired signals are canceled by extracting the differential signal. Figure 4: Issues for connecting the final-stage mixers in parallel in a 300 GHz band CMOS transmitter. For a two-input one-output mixer, the layout becomes complicated, making parallel connection difficult. Figure 5: 300 GHz band CMOS transmitter using mixers in the final stage. Power amplifiers cannot be used in 300 GHz band CMOS integrated circuits, but output power can be increased by connecting the final-stage mixers in parallel. The parallel connection in the integrated circuit can be laid out easily by superimposing the local oscillation signal (LO) on the modulated intermediate frequency signal (IF) in the final stage mixer. A 300 GHz band transmitter was fabricated in a 40 nm CMOS process. The chip micrograph of the transmitter and the measurement results are shown in Figure 7a. The fabricated 300 GHz band CMOS transmitter achieves a maximum data rate of 105 Gbit/s with 32 quadrature-amplitude modulation (QAM) at 21 Gbaud (gigabaud). With 128 QAM at 3.52 Gbaud, on the other hand, 24.64 Gbit/s can be generated using the 4.5 GHz frequency bandwidth. A graph superimposing spectrum obtained when six channels of signals of 4.5 GHz bandwidth are generated at different frequencies is shown in Figure 7b. As a result, the transmitter can generate a signal having a total bandwidth of 27 GHz (= 4.5 GHz \(\times\) 6 channels) by limiting the channel band. This corresponds to 12.5 channels of the 2.16 GHz bandwidth used in the 60 GHz band. The maximum output power of the fabricated CMOS transmitter is \(-\)5 dBm.
## 5 Discussion  
The proposed InSAR-RiskLSTM model introduces an advanced approach to railway deformation risk prediction by integrating spatial attention mechanisms with LSTM-based temporal modeling. Compared to traditional machine learning methods, such as decision trees and support vector machines, which primarily rely on handcrafted features and struggle with capturing sequential dependencies, our model effectively learns both spatial correlations and long-term deformation trends. Similarly, while existing deep learning approaches like CNNs and standard LSTMs have been applied to geospatial risk assessment, they often fail to fully utilize the spatial heterogeneity present in InSAR-RiskLSTM addresses this limitation by incorporating spatial attention, which dynamically  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Scenario** & **Accuracy (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Robustness Index** \\ \hline Flat Terrain & 93.8 \(\pm\) 0.4 & 91.5 \(\pm\) 0.5 & 92.1 \(\pm\) 0.4 & 1.00 \\ Mountainous Terrain & 92.4 \(\pm\) 0.6 & 90.2 \(\pm\) 0.7 & 90.8 \(\pm\) 0.6 & 0.98 \\ Urban Railway & 91.1 \(\pm\) 0.5 & 89.0 \(\pm\) 0.6 & 89.5 \(\pm\) 0.5 & 0.97 \\ Rainy Condition & 90.5 \(\pm\) 0.7 & 88.2 \(\pm\) 0.8 & 88.7 \(\pm\) 0.7 & 0.96 \\ Snowy Condition & 89.9 \(\pm\) 0.8 & 87.6 \(\pm\) 0.9 & 88.0 \(\pm\) 0.8 & 0.95 \\ Dry Condition & 93.5 \(\pm\) 0.5 & 91.2 \(\pm\) 0.6 & 91.7 \(\pm\) 0.5 & 1.00 \\ \hline Average & 91.9 & 89.6 & 90.1 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance evaluation across different railway conditions.  
prioritizes high-risk areas, leading to improved predictive accuracy and robustness across varying railway conditions. Beyond theoretical advancements, the model offers practical applications for railway infrastructure monitoring and risk mitigation. It can be utilized by railway operators, infrastructure maintenance agencies, and government regulators to enhance predictive maintenance strategies. By providing early warnings for potential deformation, the model enables more efficient resource allocation, reducing maintenance costs and minimizing disruptions in railway operations. Additionally, the model's ability to integrate real-time InSAR data enables continuous monitoring, making it suitable for large-scale deployment in national railway networks.  
From a commercialization perspective, InSAR-RiskLSTM has potential applications in predictive infrastructure management platforms, where it can be integrated with existing railway monitoring systems. Companies specializing in geospatial analytics, engineering consulting, and transportation safety could leverage this model to offer advanced risk assessment services. Furthermore, with increasing investments in smart infrastructure and digital twin technologies, this model can contribute to automated railway health monitoring systems, supporting decision-making in both the public and private sectors. The adaptability of the framework also enables potential extension to other critical infrastructure domains, such as highways, bridges, and pipelines, where deformation monitoring is essential for long-term structural safety.
## 3 Results and Discussion
### Changes in Soil Chemical Properties
Next, we measured the potential changes in soil mineral nitrogen (NH4+ and NO3-) after 6 months of soil incubation under climate change simulation. Concentration of NH4+ represents the net result from the process of ammonification, which tends to increase the concentration of NH4+, and the process of nitrification, which tends to decrease it. As can be seen in Figure 2a,b, NH4+ levels were most affected in the calcareous organic soil, in particular after incubation at 10% WHC. Here, NH4+ levels were highest (37.1 \(\pm\) 4.1 mg kg-1), compared to other soil samples, with the greatest decrease after incubation. Temperature seemed not to affect the concentration of NH4+, as did soil moisture. In contrast, the levels of NO3- increased in almost all treatments, as expected from the nitrification process (Figure 2c,d). The highest relative increase was measured in calcareous conventional soil under combined stress situation (drought and temperature rise), but the highest value was obtained in the calcareous organic soil incubated at the same combined stress situation. In general, nitrification was sensitive to soil moisture: dryness caused a lower production of NO3- than soils at optimal moisture (i.e., 40% WHC). Finally, we also measured the content of available phosphorus (Olsen P) because it is also connected to TOC and DOC. The selected vineyard soils showed a wide range of available P concentrations: from the usual P-depleted Slovenian soils [59], such as in conventional calcareous soils (8.36 \(\pm\) 0.40 mg kg-1), to much higher values in young acid organic vineyard soils (118.01 \(\pm\) 0.58 mg kg-1). Although a comprehensive global study of 760 soil samples demonstrated a negative impact of an increase in mean annual temperature on available soil P both directly and indirectly, through a decrease in organic C, organic P,  
Figure 2: Ammoniacal and nitric nitrogen and Olsen P in soils before and after 6 months’ incubation under no stress (19 \({}^{\circ}\)C and 40% WHC), moisture (19 \({}^{\circ}\)C and 10% WHC), temperature (29 \({}^{\circ}\)C and 40% WHC), and combined stresses (29 \({}^{\circ}\)C and 10% WHC). Different letters denote significant differences between treatments within each soil type and management, according to [PERSON]’s multiple comparison test (\(p\) < 0.05). and primary mineral P, and an increase in soil sand content [60], [PERSON] and Cleveland [61] demonstrated with laboratory experiments on soils from a range of ecosystem types from the tropics to the Arctic that soil P availability increases with temperature. In our case (Figure 2e,f), the 6-month incubation of soils simulating climate change resulted in a slight increase in available P content in all selected samples.
# Ferroelectric Transition in Sr- and W-Doped BaTiO3 Solid Solutions
###### Abstract  
We synthesized Sr- and W-doped BaTiO3 (BTO) polycrystals by using a solid-state reaction method. The X-ray diffraction results showed that Sr and W atoms occupied the Ba and Ti sites in tetragonal BTO, respectively, and there were changes in the lattice constants and the volumes in the Sr- and W-doped BTO. We found a change in the latent heat and the Curie temperature (T\({}_{\text{C}}\)) during the transition between the ferroelectric and paraelectric phases while increasing the contents of Sr and W in the Sr- and W-doped BTO. This can be explained by the fact that the doping of Sr and W atoms in BTO prevented a distinct transition between the ferroelectric tetragonal and paraelectric cubic structures by decreasing the c/a ratio to a value close to unity. This study shows a way toward a strategy for modulating a crystal structure by using proper dopants for future applications in ferroelectricity-based devices.  
ferroelectricity; BaTiO3; substitution; Curie temperature +
Footnote †: journal: Applied Sciences
# Frequency Division Multiplexing of Terahertz Waves Realized by Diffractive Optical Elements
## 1 Introduction
The shifts will be more significant for higher orders of diffraction, however at the cost of the strongly diminished diffraction efficiency. Therefore, we decided to focus on the 1 st order of diffraction. In such an approach, all other diffraction lines should be considered as a source of power losses, which can be reduced with the proper design of diffraction grating. Binary phase grating with 0-\(\pi\) phase jump forms only odd orders of diffraction and redirects 40.5%, 4.5%, and 1.6% of the incoming power to the 1 st, 3 rd, and 5 th orders of diffraction, respectively. Separation of the diffraction orders of the diffraction grating is observed in the far field (Fraunhofer region), which in the case of the THz wavelengths and aperture close to 100 mm lies several meters behind the structure. To overcome this issue and minimize the setup, an additional converging lens has been combined with the grating. In such a case, the diffraction orders are separated in the focal plane of the lens. Application of the DOEs is also advantageous, due to simple and cost-efficient manufacturing methods. It turns out that some polymers, e.g., Nylon 12 (PA12), Polypropylene (PP), or High Impact Polystyrene (HIPS), have a low absorption coefficient and refractive index in the range of 1.5 to 1.6 in the THz band [30; 32]. Thus, those materials can be used in the manufacturing process of optical structures for the THz spectral range. The lenses can be fabricated from filament, powder, or resin using the respective 3D printing methods (additive manufacturing methods): Fused Deposition Modeling (FDM*) [33; 34; 35], Selective Laser Sintering (SLS) [36; 35], and Digital Light Processing (DLP) [35; 37]. Additive manufacturing in many cases can guarantee high resolution and good quality of diffractive optical elements (DOEs) as well as a low cost of the production process [35]. In this paper, we present four DOEs, designed for four different focal lengths of 100 mm, 200 mm, 300 mm, and 400 mm. All structures have been designed for the DWL of 1 mm (which corresponds to the frequency of 300 GHz). All structures were simulated for the DWL as well as for other wavelengths. The structures with focal lengths of 200 mm and 400 mm have been chosen for experimental evaluation. All structures have been manufactured by means of SLS additive manufacturing from Nylon 12 (PA12). Design, modeling, and manufacturing of the DOEs are more precisely described in the next section.