esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 2 Advanced Technologies and Mechanisms in RF MEMS Switches
### Actuation Mechanism
#### 2.1.3 Electromagnetically Actuated RF MEMS Switches  
Figure 1c shows the structure of a typical electromagnetically driven RF MEMS switch. Electromagnetic RF MEMS switches, as a special type of latching switch, are characterized by a low loss, high linearity, and wide bandwidth [36; 37]. [PERSON] [38] demonstrated that on the microscale, electromagnetic forces dominate over electrostatic forces. The working principle of electromagnetic MEMS switches is based on the electromagnetic interaction between magnetic materials and active (coil) or passive magnetic field sources (permanent magnets). The electromagnetic force drives the movement of the beam membrane when direct current is applied to the coil.  
Using permanent magnets or ferromagnetic materials, electromagnetic MEMS switches can achieve a stable latched state without the need for external energy input; i.e., the beam membrane can maintain a fixed position, either up or down. In this case, the magnetomotive force generated by the permanent magnet forms the core part of the magnetic circuit. The magnetic field produced by the coil is opposite to the magnetization direction of the cantilever beam, achieving ON/OFF switching [39]. With the miniaturization of MEMS devices, the required magnetic force decreases. For instance, [PERSON] et al. [40] designed and fabricated a laterally driven magnetic MEMS switch, which can generate about 200 \(\upmu\)N of magnetic closure force under less than 0.7 V driving voltage and 13 mW power.  
Although electromagnetic MEMS have clear advantages over electrostatic and piezoelectric actuators in terms of force, polarity, and driving distance on the microscale, they are relatively slower, larger in size, and typically do not use CMOS-compatible manufacturing processes [40]. The compatibility of MEMS technology with magnetic materials is a key area of current research, with many research institutions around the world actively exploring this issue.
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 1 Introduction  
Phobos is one of the two moons of Mars. It was discovered in 1877 by [PERSON], an astronomer from the United States Naval Observatory. It moves deep within Mars' gravitational field [1; 2]. Its first close approaches were made by the Mariner 9 spacecraft in 1971. Many imaging data come from the Viking 1 and Viking 2 orbiters (data acquired in the late 1970s). Based on these data, the first shapes were interpreted, and dynamic models of Phobos were created. Close observations of Phobos were also made by the spacecraft Phobos 2 (100 km from landing on Phobos, 1988), Mars Global Surveyor (1996-2006), and Mars Reconnaissance Orbiter (since 2005). Currently, the only spacecraft that regularly encounters Phobos and provides extensive data about this satellite is Mars Express [3].  
Since its discovery, Phobos has garnered significant interest from astronomers and researchers, and its origin remains an open question. Scientists planning space missions have identified Phobos as a target for research, from which acquiring extraterrestrial samples might be \"relatively\" straightforward. Additionally, Phobos is perceived as a potential launch site for future human Mars exploration. It has been suggested that Phobos could serve as a \"pit stop\" for crewed Martian missions [2; 4]. Furthermore, measurements conducted during operations near Phobos and potential landings on its surface could fill gaps in the knowledge required for the exploration of Mars' moons. Using a penetrator, or other devices on its surface related to determining the mechanical properties of the material, would aid in understanding the geotechnical properties of regolith. A brought-back sample could help to determine the availability of resources as well as the mineral, elemental, andisotopic composition of the regolith. It would also provide a better characterization of the effects of cosmic weathering in the Martian environment. Additionally, the shape and volume could be much more accurately determined than they currently are [4].
## 2 Materials and Methods
### Detection of LPS-Induced NO Production  
RAW264 cells were activated by LPS to induce iNOS expression, and the intracellular production of NO was confirmed using a fluorescence-microscopy-based method. Briefly, cells were seeded in a 35 mm glass-bottom dish and incubated overnight. Cells were then treated with 0 or 1 \(\mu\)g/mL LPS for 24 h at 37 \({}^{\circ}\)C. Next, the cells were incubated with 10 \(\mu\)M diamofluorescein-FM diacetate (DAF-FM DA; Goryo Chemical Inc., Hokkaido, Japan) for 30 min at 37 \({}^{\circ}\)C to detect intracellular NO production. The DAF-FM DA fluorescence was observed using a CSU-10 confocal laser scanning unit (Yokogawa Electric Co., Tokyo, Japan) coupled to an Eclipse Ti-U inverted microscope with a PlanAPO 20 objective lens (Nikon Co., Tokyo, Japan) and a C5810-01 color chilled 3 CCD camera (Hamamatsu Photonics K.K., Shizuoka, Japan). The excitation and emission wavelengths for DAF-FM DA were 488 and 515 nm, respectively. Prior to DAF-FM DA treatment, the cells were incubated with 100 \(\mu\)M 2-(4-Carboxyphenyl)-4,4,5,5-tetramethylimidazololine-1-oxyl-3-oxide, sodium salt (carboxy-PTIO) (Dojindo Laboratories, Kumamoto, Japan), an NO scavenger, for 2 h at 37 \({}^{\circ}\)C.
# Review of Photodetectors for Space Lidars
## 3 Space Lidar Detectors from Short-Wave to Mid-Wave Infrared
### HgCdTe APD Arrays for Surface Elevation Lidar
The maximum APD gain was measured to be about 100, and the electrical bandwidth was greater than 1 GHz from their 4 \(\times\) 4 pixel arrays.
# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 3 Target Classification in Passive Forward Scattering Radar
### Vehicle Classification Performance at Different Baseline Crossing Range  
This section analyzes how the different vehicle baseline crossing range, \(R_{R}\) (range from vehicle to the receiver) affect the vehicle classification performance does. It is known that from the FSR theory and as also discussed in Section 2, when a moving target crossing the baseline, the system will gain the enhancement in FS RCS, \(\sigma_{f}\) and is given by:  
\[\sigma_{f}=4\pi\frac{A^{2}}{\lambda^{2}} \tag{11}\]  
where \(A\) is the physical target area and \(\lambda\) the wavelength of the illuminating signal. The amplitude of \(\sigma_{f}\) will directly influence signal received at the antenna by:  
\[P_{R}=\frac{P_{T}G_{T}G_{R}\lambda^{2}\sigma_{f}}{\left(4\pi\right)^{3}R_{T}^ {2}R_{R}^{2}} \tag{12}\]  
where \(P_{T}\) is the LTE transmitted power from the base station, \(G_{T}\) is the LTE transmitter antenna gain, \(G_{R}\) is the receiver antenna gain. The range from the transmitter to the target is given by \(R_{T}\). The received signal is highly dependent onthe target size and range between the target and receiver. This data could reflect the signal power at the antenna. The received signal power is inversely proportional to the \(\mathrm{R_{R}}\) by the inverse square law. However, the range between the target and receiver is restricted in ensuring the FSR condition is met. Figure 12 shows the time domain signal scattered from vehicle in the Saloon category for different \(R_{R}\) with \(v=10\) km/h. Obviously, this signal is the output after all the hardware processing including first RF amplification, amplitude detection via diode, high order frequency filtering by LPF, DC blockage by HPF and low frequency amplifier. It can be seen that the amplitude envelope decreases as the range, \(R_{R}\) increasing. As the system is linear, the envelope does also represent the actual signal power pattern at the front end of the receiver. This signal is then used as the input to the classification system explained in Figure 8. The overall classification performance is tabulated in Table 5 and the location of training and testing data in the PC space is illustrated in Figure 13. As expected, highest classification is observed for \(R_{R}=5\) m, which follows the previous indicator explained in Section 3.1. The performance is slightly decreases as the range \(\mathrm{R_{R}}\) increases which reflect to the quality of the input signal to the classification system. It can be suggested that signal strength plays important role in the classification performance.  
Figure 12: Signal in time at the ADC output for vehicle crossing at different crossing range, \(R_{R}\).
# Estimation of Seismically-Induced Crest Settlement of Earth Core Rockfill Dams
## 4 Crest Settlement Calculations and Correlation with Ground Motion Parameters
The calculated settlements were compared with [PERSON]'s [1] data in Figure 6. The numerically calculated settlements fell within the range of measured values. The numerical settlements were lower than the measurements at low intensities (free-field \(PGA\) \(<\) 0.2 g). The differences in the dam dimensions, material properties, and underlying soil profiles may all have influenced the calculated settlement ratio. However, the results were very similar at \(PGA\)\(>\) 0.2 g. It was therefore further validated that the numerical model provided reasonable estimates of the crest settlement. It also highlighted that the difference in the definition of the settlement ratio did not have a pronounced influence on the calculated results for free-field \(PGA\)\(>\) 0.2 g. It also demonstrated that nonlinear dynamic analysis could be used as a seismic design tool to predict crest settlement and to evaluate the susceptibility of the dam damage. Figure 7 shows the calculated settlement ratios plotted against \(PGA\), \(PGV\), arias intensity (\(I_{a}\)), and \(M\). The multivariate regression analyses were performed to evaluate the goodness of fit. The functional form for the predictive equation of the crest settlement was as follows:  
\[Ln(s)=\alpha_{1}GM_{1}+\alpha_{2}GM_{2}+\alpha_{3}GM_{3}+\alpha_{4}GM_{4} \tag{7}\]  
where \(S\) is the settlement (%), \(\alpha_{1}-\alpha_{4}\) are the model coefficients, and \(GM_{1}-GM_{4}\) are the ground motion parameters. The coefficients and parameters are listed in Table 4. Also listed are the R\({}^{2}\) for each model. The model coefficients \(\alpha_{1}-\alpha_{4}\) and the coefficient of determination (R\({}^{2}\)) were calculated using multivariate regression analyses. Among the four GMs, \(I_{a}\) was revealed to have the strongest dependence on the settlement. For the single-parameter model, \(I_{a}\) was demonstrated to provide the best fit with the calculated settlements, resulting in R\({}^{2}\) = 0.82. \(PGA\) resulted in the second-best estimate of R\({}^{2}\) = 0.77. \(M\) was revealed to be positively correlated with the settlement, but produced the lowest R\({}^{2}\) of 0.33. The settlement prediction was further improved by using multiple parameters. When using \(PGA\) and \(I_{a}\), the uncertainty was reduced by 10.0% and 5.3% compared with the use of the single parameters \(PGA\) and \(I_{a}\), respectively. Figure 6: Comparison of crest settlement versus outdoor \(PGA\) data of ECRD presented in [PERSON] [1] and this study. The four-parameter model resulted in R\({}^{2}\) = 94.1%, which was 12.1% higher compared with the single-parameter model.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 3 LP-WAN Solutions for IIoT Services
### Weightless  
Weightless is the alliance name for a set of three LP-WAN open standards: Weightless-W, Weightless-N, and Weightless-P [21]. The three Weightless flavors work in sub-GHz bands, but each of them has its own particularities.  
The original Weightless-W standard makes use of the TV whitespace spectrum and provides a wide range of modulation schemes, spreading factors, and packet sizes. Considering all these features, and depending on the link budget, Weightless-W claims to achieve two-way data rates from 1 kbps to 10 Mbps with very low overhead. Due to the extensive feature set provided by Weightless-W, the edge-node's battery lifetime is limited to three years and the terminal cost is higher than that of its competitors. The communication between the edge-nodes and the base station can be established along 5 km, depending on the environmental conditions.  
In turn, Weightless-N uses a class of low-cost technology, very similar to that employed by Sigfox. Thereby, ultra-narrow band (DBPSK) modulation is adopted in order to provide unidirectional-only connectivity of up to 100 bps, exploiting ISM bands. This scheme is based on nWave's technology [22], which was donated as a template for the Weightless-N standard. Because of the simplicity of this solution, Weightless-N allows a battery duration of up to 10 years, very low cost terminals, and a long connection range similar to that reached by Weightless-W.  
Finally, the newest Weightless-P open standard is derived from the M\({}^{2}\)Communication's Platanus protocol [23]. This version gathers together the most proper characteristics of the previous standards, and it claims to be specifically focused on the industrial sector. Using a narrow-band modulation scheme (Gaussian Minimum Shift Keying, GMSK, and Offset Quadrature Phase Shift Keying, OQPSK) operating in 12.5 kHz channels, Weightless-P implements bi-directional communication with an adaptive data rate from 200 bps to 100 kbps. It supports both ISM and licensed spectrum operation. Aimed at providing the reliability demanded by some industrial applications, Weightless-P includes, by default, valued characteristics such as acknowledged transmissions, auto-retransmission, frequency and time synchronization, and channel coding, among others. Compared with the other Weightless standards, Weightless-P provides a more limited range of 2 km and its advanced features in comparison with Weightless-N permit a shorter battery lifetime of three years.  
Regarding security, the three Weightless versions provide end-to-end network authentication and 128 bit AES encryption.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Season of Capture**} \\ \cline{2-6}
**Fish Species** & \begin{tabular}{c} **Spring** \\ **(_n_ = 268)** \\ \end{tabular} & \begin{tabular}{c} **Summer** \\ **(_n_ = 278)** \\ \end{tabular} & \begin{tabular}{c} **Autumn** \\ **(_n_ = 267)** \\ \end{tabular} & \
\begin{tabular}{c} **Winter** \\ **(_n_ = 262)** \\ \end{tabular} &
\begin{tabular}{c} **_p_ Value** \\ \end{tabular} \\ \hline \multirow{2}{*}{_Dicentrarchus labrax_} & 1.81 & 2.50 & 0.37 & 0.04 & \\  & (0.00–3.89) & (0.00–5.00) & (0.00–0.90) & (0.00–0.11) & 0.491 \\ \hline \multirow{2}{*}{_Engraulis encrasicolus_} & 95.36 a & 94.43 a & 55.96 ab & 44.35 b & \\  & (61.38–129.33) & (59.04–129.82) & (31.18–80.75) & (21.60–67.09) & 0.030 \\ \hline \multirow{2}{*}{_Merlucius merlucius_} & 6.46 & 6.29 & 3.86 & 1.68 & \\  & (2.25–10.68) & (2.42–10.15) & (0.62–7.10) & (0.37–2.89) & 0.504 \\ \hline \multirow{2}{*}{_Mullus Barbatus_} & 5.52 & 9.12 & 3.48 & 1.00 & \\  & (1.27–9.77) & (3.28–14.96) & (0.66–6.30) & (0.08–1.92) & 0.430 \\ \hline \multirow{2}{*}{_Sardinella aurita_} & 1.00 & 1.79 & 0.86 & 0.00 & \\  & (0.02–1.98) & (0.05–3.54) & (0.00–1.76) & (0.00–0.00) & 0.226 \\ \hline \multirow{2}{*}{_Scomber japonicus_} & 223.41 ab & 276.33 a & 154.07 bc & 99.15 c & \\  & (156.42–290.39) & (206.20–346.47) & (92.72–215.42) & (58.11–140.18) & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prevalence (%) and mean abundance with 95% confidence intervals (calculated using the bootstrap method) of anisakid L3 type I larvae in the fish species, analyzed with respect to the microhabitat of parasitation. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Prevalence**} & \multicolumn{2}{c}{**Mean Abundance with 95\% CI**} \\ \cline{2-5}
**Fish Species** & **Viscera** & **Flesh** & **Viscera** & **Flesh** \\ \hline _Dicentrarchus labrax_ & 9.17\% & 7.34\% & 1.01 (0.33–1.69) & 0.18 (0.03–0.34) \\ _Engraulis encrasicolus_ & 50.00\% & 45.54\% & 59.79 (47.21–72.36) & 13.63 (9.20–18.06) \\ _Merlucius merlucius_ & 26.79\% & 18.75\% & 4.02 (2.59–5.45) & 0.55 (0.31–0.80) \\ _Mullus barbatus_ & 25.00\% & 19.00\% & 3.76 (2.29–5.23) & 1.02 (0.49–1.55) \\ _Sardinella aurita_ & 10.53\% & 0.00\% & 0.92 (0.38–1.46) & 0.00 \\ _Scomber japonicus_ & 74.07\% & 65.74\% & 179.78 (149.63–209.92) & 8.46 (6.71–10.21) \\ _Scomber scombrus_ & 68.00\% & 61.00\% & 242.16 (195.91–288.41) & 7.66 (5.77–9.55) \\ _Solea vulgaris_ & 0.00\% & 0.00\% & 0.00 & 0.00 \\ _Spara aurata_ & 4.55\% & 3.64\% & 0.66 (0.08–1.25) & 0.17 (0.00–0.35) \\ _Trachurus trachurus_ & 68.18\% & 59.09\% & 129.97 (103.90–156.05) & 5.54 (3.56–7.51) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prevalence (%) of anisakid L3 type I larvae in the fish species, analyzed with respect to the season of capture. Values followed by the same letters are not significantly different.
## 4 Experiments
### Sensitivity Analysis and Ablation Study  
In this experiment, we performed a sensitivity analysis of TAPT to evaluate the effect of the number of inner-loop updates on ASC performance, and the results are shown in Table 5 and Figure 5. According to the table, TAPT is less affected by the number of inner loops in ASC accuracy compared to MAML. Also, the lowest accuracy of TAPT is better than that of all existing few-shot ASC schemes using five inner-loop updates, as shown in Table 3. This indicates that the proposed scheme is more robust to the hyperparameters compared to other learning schemes.  
Next, as an ablation study, we compare the ASC performance of MAML and TAPT according to the number of inner-loop updates. The comparison between MAML and TAPT in Tables 3 and 4 might be unfair, because TAPT adjusts its parameters once more for initialization before the inner-loop update. However, even TAPT with one inner-loop update provides 46.08% better ASC performance than the original MAML with five inner-loop updates, as shown in Table 5. This is attributed to the task-adaptive transformation of TAPT that allows DeepASC to quickly adapt to unseen tasks.
## 4 Experimental Verification
### Discussion  
* The above results show that when the S0 single-mode lamb wave is used as the detection fundamental wave, the method of using the probability scan matrix to locate the microcrack can effectively locate and image the microcrack. This method has good robustness and accuracy, and is only sensitive to microcracks.
* The limitation of the method in this paper is that a microcrack can be effectively positioned only when two or more scanning centerlines pass through it, although there is an error when the microcrack is not at the focus. However, by moving the scan matrix and comparing the amplitude of the imaging core, it can still accurately locate the microcrack.
* In order to better solve the above shortcomings, for small area detection, the array can be set closer and the scanning path can be increased. For wide area detection, this method can be used as a qualitative test method, as long as the possibility of microcrack is detected within the area, further narrowing the range for secondary scanning.
# A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions
## 3 Materials and Methods
### Evaluation Metrics  
In this study, to systematically assess the reliability of the enzyme kinetic parameter DLERKm prediction model, we employ four statistical evaluation metrics: the root mean square error (RMSE) to reflect the overall deviation level between predicted and experimentally measured values, the mean absolute error (MAE) to demonstrate prediction accuracy from the perspective of absolute deviation, the coefficient of determination (\(R^{2}\)) to evaluate model fitting performance by quantifying the proportion of explained variance in dependent variables, and the Pearson correlation coefficient (PCC) to specifically assess the strength of linear correlation between predicted and truth values. The mathematical formulations of these metrics are defined as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}, \tag{11}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|, \tag{12}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y})^{2}}, \tag{13}\]\[\text{PCC}=\frac{\sum_{i=1}^{n}(y_{i}-\tilde{y})(\hat{y}_{i}-\tilde{y})}{\sqrt{\sum_ {i=1}^{n}(y_{i}-\tilde{y})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\tilde{y})^{2}}}, \tag{14}\]  
where \(n\) is the number of samples in the test dataset, \(y_{i}\) is the true Km value in the test dataset, \(\hat{y}_{i}\) is the predicted Km value, \(\tilde{y}\) is the mean of the true Km values in the test dataset, and \(\hat{y}_{i}\) is the mean of the predicted Km values.
# Solar Energy in Urban Planning: Lesson Learned and Recommendations from Six Italian Case Studies
## 4 Results and Discussion
### SINFONIA Bolzano  
#### 4.2.1 Overview  
The SINFONIA case study is the result of a five-year European project aiming to implement large, scalable, and integrated energy solutions in mid-sized cities. Bolzano, the capital of South-Tylor province (Trentino Alto Adige), was selected together with Innsbruck (Austria) as a testbed for this initiative to understand the replicability of the proposed solutions in other European cities [51]. The project aimed to achieve the following:  
* Obtain between a 40% and 50% reduction in primary energy use and a 20% increase in RES share for the selected districts in the two pilot cities of Bolzano and Innsbruck.
* Prove the feasibility of large-scale energy measures, retrofitting, grid optimization, and district heating and cooling systems in mid-sized European cities.
* Define district typologies and refurbishment models to ensure scalability and replicability. These last two aspects are supported by five \"early adopter\" cities where to test the initial outcomes of the two pilots.
* Develop a network to support and engage other European cities in strengthening their smart-energy solutions.  
In this framework, refurbishment interventions were undertaken for six residential complexes in Bolzano to improve their energy performance and indoor comfort. The initiative is in line with the municipal goal to reduce energy use and increase the share of RES. The presented case study focuses on one of these interventions, consisting of two buildings blocks of 36 flats each, located in the south part of the town (Figure 7).  
4.2.2. Challenges, Issues, and Decision Strategies  
The location of the building complex has been one of the main challenges in the development of the project. Its proximity to the side of a mountain represented an accessibility issue during the refurbishment phase. Other challenges included the limited surface for the integration of solar energy systems, which was also due to the proximity to the mountain slope toward the south and the presence of overshadowing due to four stairwell towers above the roof levels. Finally, the intervention should have the minimum possible impact on the tenants, as all refurbishment activities have been carried out with lived-in flats. Regarding the decision strategy, targets for the energy concept were set at the beginning to reach: (i) a final energy balance \(\leq\) 22.5 kWh/(m\({}^{2}\) yr); (ii) DHW from renewable energy sources \(\geq\) 9 kWh/(m\({}^{2}\) yr); and (iii) PV installation \(\geq\) 53 kW for the entire complex. Furthermore, the optimization of the solar systems and the improvement of interior daylight conditions were also investigated. An Integrated Design Process (IDP) was followed to assure good stakeholders' involvement.