esa-sceva/satcom-chunk-collection huggingface dataset:
# A Single-Launch Deployment Strategy for Lunar Constellations
## 4 Analysis of the Constellation Deployment
### Discussion on the Results  
The analysis proves the suitability of the single-launch deployment strategy to reach the desired targets within one sidereal month and a total \(\Delta V\) less than 1 km/s for each satellite. Recalling that the injection into the equilibrium region is performed by the launch vehicle, the \(\Delta V\) that shall be provided by the satellite never exceeds 730 m/s, a value that can be provided by most small satellites. For instance, considering a small satellite of wet mass at launch of 100 kg, a monopropellant propulsion system providing a thrust \(T=20\) N with a specific impulse \(l_{sp}=230\) s can easily perform the maneuvers requiring a total propellant mass \(m_{p}=32.4\) kg, which is shown hereafter to be lower than that required by other innovative deployment strategies.  
It is worth now comparing the deployment strategy to other innovative ones available in the literature. [PERSON] et al. and [PERSON] proposed interesting solutions to deploy satellites from the same launch trajectory to orbits with different RAAN and inclination, respectively [56,57]. It should be noted that the solution by [PERSON] et al. addresses Earth constellations. Compared to these strategies, the one developed here offers the advantages of allowing the selection of both parameters in a single maneuver requiring 50 % or less of \(\Delta V\) for this task, leading to relevant savings in the propellant mass budget.  
[PERSON] et al. proposed the use of the CR3 BP and nodal precession due to gravitational perturbations to deploy multiple satellites in different orbital planes from the same launch trajectory [33]. Additionally, this method is designed for Earth constellations, but can be extended to lunar ones. Compared to it, the strategy proposed in this work is more rapid, because it does not require the nodal precession to select the RAAN, and can be obtained at slightly lower \(\Delta V\), because the CR3 BP solutions developed by [PERSON] et al. are constructed from libration point halo orbits that are characterized by a higher energy level with respect to that of low-energy captures (\(h=10^{-2}\)) considered here.  
A different solution is proposed by [PERSON] et al., who suggest the use of low-thrust propulsion and nonlinear orbit control to deploy a satellite constellation around Mars [58], with consequent substantial savings in the required mass of propellant. Even though it is not trivial to predict the \(\Delta V\) required for the deployment in the hypothesis of extending the single-launch deployment to Martian constellations, which is an actual possibility [59], it is reasonable to suppose that it would be greater than the one required using low-thrust propulsion. On the contrary, the deployment time of the low-energy strategy could reasonably be shorter and it requires fewer resources from the satellite platform.  
Figure 4: Satellites trajectories during Phase 2: detail of the lunar orbits in the rotating frame.
# Nanotechnology, Nanomedicine, and the Kidney
## 5 Nanotechnology for Renal Therapy
### Drug Delivery
#### 5.1.4 Small Molecule Pro-Drugs  
While methods of targeted delivery aim to deposit a bioactive molecule to a specific cell or tissue, pro-drugs are medications that are designed to be activated only after reaching their target [72]. This can reduce adverse side effects and increase the effectiveness of a drug while minimizing the total drug amount per body weight. Pro-drugs are widely used in cancer treatment, but pro-drugs have also been recently developed to deliver active compounds to the kidney.  
The quinolone antibiotic ciprofloxacin is commonly used to treat urinary tract infections and is metabolized in the liver. By conjugating ciprofloxacin to a synthetic lysin-rich peptide (KKEEE)3K, [PERSON] et al., were able to redirect the ciprofloxacin from the liver to the kidney [73]. They found that the radiolabeled pro-drug accumulated entirely in the kidneys can thereby deliver the antibiotic to the bladder with high efficiency.  
Urinary tract infections are often complicated by antibiotic-resistant bacteria. These bacteria widely express the enzyme \(\beta\)-lactamase, which inactivates \(\beta\)-lactam antibiotics, rendering penicillin and its derivatives ineffective. In an exquisite approach, [PERSON] et al., created an inert pro-drug by conjugating ciprofloxacin with a molecule that can be cleaved by the bacterial \(\beta\)-lactamase [74]. Cleavage of cephalothin, an analog to the \(\beta\)-lactam antibiotic cephalospor, activates the pro-drug bactericidal effect and selectively targets antibiotic-resistant bacteria where they accumulate.
# Detecting Inverse Boundaries by Weighted High-Order Gradient Collocation Method
## 5 Conclusions  
In this work, the weighted HG-RKCM is introduced to solve inverse Cauchy problems having multiply connected domains. In particular, for two multiply connected domains of different geometry, four different configurations have been provided to investigate the ability of the method in detecting the unknown boundary. Firstly, the weights for Neumann and Dirichlet boundary conditions have been numerically studied. Based on this, the stability of the method is tested by adding disturbance on both Neumann and Dirichlet boundary conditions. From the numerical results, high accuracy in the approximation is observed. For the cases of missing boundary on the circumference, two possible treatments have been adopted: spin of points on the circumference to avoid overlapping points in the domain and arrangement of one layer of ghost points on the known boundary. Lastly, the comparison of CPU time for solving various problems has come to the conclusion that the HG-RKCM is the most efficient method in the inverse analysis.  
Funding acquisition, methodology, supervision, writing-original draft, J.P.Y.; Formal analysis, H.F.S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of the Republic of China (Taiwan) under grant number MOST 107-2221-E-009-141-MY3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{s}\) & **0** & **1** & **2** & **3** \\ \hline \multirow{3}{*}{(a)} & \(u\) & 0.9392 & 0.9703 & 1.1766 & 1.2071 \\  & \(u_{,x}\) & 4.3020 & 3.8733 & 3.8337 & 4.9856 \\  & \(u_{,y}\) & 6.5409 & 6.4914 & 7.1475 & 7.2616 \\ \hline \multirow{3}{*}{(b)} & \(u\) & 1.2561 & 1.3938 & 1.4540 & 1.6904 \\  & \(u_{,x}\) & 5.5221 & 6.3356 & 7.0172 & 8.0663 \\  & \(u_{,y}\) & 8.7736 & 7.5323 & 7.7360 & 7.5732 \\ \hline \multirow{3}{*}{(c)} & \(u\) & 0.9973 & 1.0513 & 1.2115 & 1.4096 \\  & \(u_{,x}\) & 4.8008 & 5.1810 & 5.6641 & 7.2072 \\  & \(u_{,y}\) & 7.4692 & 8.0126 & 5.3486 & 8.0440 \\ \hline \multirow{3}{*}{(d)} & \(u\) & 5.6311 & 6.5935 & 10.9980 & - \\  & \(u_{,x}\) & 25.7791 & 34.7108 & 37.6828 & - \\ \cline{1-1}  & \(u_{,y}\) & 30.7985 & 34.9769 & 44.3546 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of maximal absolute errors (%) obtained by various \(s\): (a) unknown upper inner boundary; (b) three half unknown inner boundaries; (c) unknown inner boundaries; (d) half unknown outer boundary.  
Figure 18: Comparison of CPU time by different methods in the multiply connected domains: type two.
# Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone
## 3 Experiments and Results  
### Duty Cycle  
The \"duty cycle\" technique leads to non-continuous GNSS carrier phase tracking of smartphones [27], and a new feature of Mi 8 with the latest Android 9.0 operating system is \"Force full GNSS measurements\" for developers, which makes it possible to turn off the \"duty cycle\" when recording data [28]. After turning on the \"duty cycle\", the cycle slips rate increases significantly, from about 20% to 70%, which means that turning off the \"duty cycle\" may increase the data availability (Figure 1).
## 2 State of The Art
### Nonlinear Video Exploration  
Many systems have been proposed which help users explore time-based media [21,22], and video more specifically, in a modular and non-linear manner. [PERSON] et al. [23] propose a collaborative approach that enables different users to create a video that provides alternative paths to navigate the content to learn about a topic. [PERSON] and [PERSON] [24] provide table of content style links to navigate to different sections of a video. This idea is extended by [PERSON] et al., in their study authors use a chapter/section structure to provide a skimmable summary and thumbnail of a video segment to a user [25]. Similarly, [PERSON] and [PERSON] [26] design and evaluate an approach to create a table of content structure to non-linearly navigate a video for smart-phones and tablet devices. A widely used approach to enable users to non-linearly explore video content is creating hypervideos.
## 4 Experimental Results
### Performance Evaluation and Discussion
#### 4.3.2 Performance Evaluation on SCUT-FBP5500  
Table 7 presents the performance evaluation of three CNNs on the SCUT-FBP5500 dataset. The evaluation was conducted using five-fold cross-validation, and the models were trained with different regression-loss functions. The results indicate that the L2 loss function outperforms the L1 loss, and both L2 loss and Log-cosh loss yield comparable results. This can be attributed to the fact that the data distribution in the SCUT-FBP5500 dataset aligns closely with normal distributions.  
When using the L2 loss function, the average performance of AlexNet was a PC of 0.9127168, an MAE of 0.2555128, and an RMSE of 0.3195926. VGG16-Net achieved an average PC of 0.9275814, an MAE of 0.2269836, and an RMSE of 0.2855518. FIAC-Net obtained an average PC of 0.9251664, an MAE of 0.2080304, and an RMSE of 0.2668852.  
Meanwhile, the average results for the L1 loss function showed that AlexNet achieved a PC of 0.8944518, an MAE of 0.2473232, and an RMSE of 0.321819. VGG16-Net obtained an average PC of 0.9106684, an MAE of 0.2479496, and an RMSE of 0.3149184. FIAC-Net achieved an average PC of 0.9123388, an MAE of 0.215242, and an RMSE of 0.284987.  
When using the Log-cosh loss function, the average performance of AlexNet was a PC of 0.9118102, an MAE of 0.2467066, and an RMSE of 0.3108374. VGG16-Net achieved an average PC of 0.9218162, an MAE of 0.2252112, and an RMSE of 0.2884092. FIAC-Net obtained an average PC of 0.9239984, an MAE of 0.2113572, and an RMSE of 0.2707102.  
Furthermore, the proposed ensemble average model, which combined the different loss functions, achieved an average PC of 0.9305098, an MAE of 0.2028174, and an RMSE of 0.2614154. These results indicate that the ensemble model slightly outperformed the L2 loss function in terms of PC, MAE, and RMSE. Consequently, this suggests that the proposed approach can potentially enhance the performance of FBP. Figure 7 illustrates the model predictions obtained by applying the fine-tuned FIAC-Net with the validation fold on the SCUT-FBP5500 dataset. Meanwhile, Figure 8 displays a visual representation of the utilized loss functions incorporated with the fine-tuned CNNs. The results indicate that the proposed ensemble loss function with the fine-tuned FIAC-Net achieves a competitive performance in predicting beauty scores, thus highlighting its efficiency.  
Figure 7: SCUT-FBP5500 tested samples on FIAC-Net. G, ground truth; P, model-predicted score. (**a**) Accurately predicted instances and (**b**) inaccurately predicted instances.  
Figure 8: A scatter-plot representation of samples of predicted beauty scores against actual beauty scores on SCUT-FBP5500. (a) AlexNet, (b) VGG16-Net, and (c) FAIC-Net.
# A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
## 2 Materials and Methods
### CT Phantom Image Augmentation
#### 2.2.1 Spatial Resolution Image Augmentation  
For spatial resolution, inspired by the need to evaluate the ability of equipment to distinguish small objects at spatial resolution, we initially implemented erosion and dilation techniques. An example of this implementation is illustrated in Figure 3. However, irrespective of the parameter adjustments, the resultant images did not reflect abnormalities in the CT equipment, i.e., they were not clinically obtainable. Subsequently, given the inability to discern the shape of the circle even in the presence of noise, we resorted to noise addition techniques.  
Images processed with Gaussian filtering and dynamic histogram equalization were obtained for clinical phantom image tests, serving as benchmark values for pass and fail criteria. Regarding Gaussian filtering, a pixel size set above 1.01 was deemed indistinguishable by experts. Thus, Gaussian filtering was applied with pixel sizes of 1.5, 1.7, and 2.0 to generate failing images, as depicted in Figure 3. In the case of dynamic histogram equalization, images were considered failing if the alpha value, representing the sub-histogram emphasis, was set below \(-0.5\). Accordingly, values of \(-0.5\), \(-0.7\), and \(-1.0\) were applied during dynamic histogram equalization to obtain the failing images in Figure 4.  
Figure 3: Spatial resolution erosion (**a**) techniques; spatial resolution dilation (**b**) techniques.
## 1 Introduction
The introduction of signal preprocessing, windowing functions, spectral clustering algorithms, and fast truncated singular value decomposition techniques can partially improve the effectiveness of the stochastic subspace identification method. However, the stochastic subspace identification (SSI) method still has problems such as mode omission and false modes in practical applications. This paper combines the random decrement technique (RDT) under non-stationary excitation with the covariance-driven stochastic subspace identification (SSI) method to propose the improved RDT-SSI method. Firstly, use the random decrement technique of non-stationary excitation to obtain the free attenuation correspondence of the structure, and use it to construct the Hankel matrix. Take the covariance as the statistic, use the non-correlation of signal and noise to remove the noise, and finally use the singular value decomposition technique to identify the modal parameters of the structure. This method can effectively improve the problems of mode omission and false modes. This method is applied to the actual health monitoringof stadium grids, and can also obtain better identification results in frequency, damping ratio, and vibration mode, while also significantly improving computational efficiency.
## 4 Experiments
### Quantitative Comparisons
#### 4.2.5 Speed Analysis  
The major computational cost of the CFSA tracker is the generation of candidate proposals and estimation of object position, the speed of the proposed method is about 18.5 fps without code optimization. The speeds of other trackers are shown in Table 5. We can observe that CSK tracker is faster than all of other trackers with the running speed of 269 fps. The CFSA tracker ranks 6 th among all trackers and 4 th in correlation filter based trackers. Compared with other correlation filter based trackers, CFSA tracker achieves a balance between running speed and tracking performance and can be extended to a parallel implementation to optimize its efficiency.
# Petrophysics Parameter Inversion and Its Application Based on the Transient Electromagnetic Method
###### Abstract  
The transient electromagnetic (TEM) method is a widely used geophysical technique for detecting subsurface electrical structures. However, its inversion results are typically limited to resistivity parameters, making it challenging to directly infer key petrophysical properties, such as water saturation and porosity. This study proposes a petrophysics parameter inversion approach based on TEM data. By constructing multiple geoelectric models with varying porosities and water saturation values for numerical simulations, the results demonstrated that both the forward and inversion responses of the TEM field maintained errors within 5%. The inversion procedure begins with the reconstruction of the subsurface resistivity distribution, which reliably reflects the true geoelectric model. Based on the inverted resistivity, the water saturation and porosity parameters are subsequently estimated. The inversion results closely match the overall trend of the actual model and exhibit a clear response at the target layer. Finally, the proposed method is applied to a field test at the Tongxin Coal Mine. By integrating subsurface electrical responses with geological data, the spatial distributions of water saturation and porosity within the coal-bearing strata were delineated. This provides a scientific basis for the detailed characterization of the physical properties of coal and surrounding rock, as well as for understanding the development of pores and fractures in underground strata.  
t +
Footnote †: journal: applied sciences  
Article
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## Appendix A  
CellTracker is an Android application built on Google's official Geolocation Application Programming Interface (API), designed to obtain the user's location even when GPS is not available (Figure A1). It interacts with cell towers through HTTPS POST requests and receives a response in JSON format, containing the latitude, longitude, and estimated accuracy radius (Figure A2) [40].  
The app also displays extended mobile network information, including the BS ID, MNC, TAC, CI, and PCI; the available mobile network types, such as NR, LTE, GSM, and HSPA; and associated parameters like EARFCN, TA, RSRP, RSRQ, and RSSNR, as well as the downlink and uplink band frequencies. These data are extracted using Android's telephony and location APIs, which provide access to telephony services and device connectivity information.  
The availability of this information may vary depending on the mobile network operator, privacy policies, and the Android OS version installed on the device. CellTracker is publicly available for download.  
The app exports observation data in.csv format, recording information about the interaction between the UE and nearby BSs. Each record includes fields such as the date, time, MCC, MNC, CI, LTE RSRP, network type, provider, Phone_Lat, Phone_Lon, and accuracy (m), among others, organized by the observation epoch. The provider column identifies the source of the location data: GPS refers to the phone's GNSS sensor, network indicates a position provided by the mobile network, and WiFi refers to a position computed from nearby access points (if supported by the smartphone).
## 5 Analysis of Simulation Results
### Impact of Temperature on Horizontal Displacement of Crown Beam and Waist Beams  
Since the crown beam and the waist beams were connected to the retaining piles, the displacements of the crown beam and the waist beams were inevitably affected by the retaining piles. Figure 23 indicates that after the temperature rose, the crown beam and waist beam \(1\) moved outside the foundation pit, while the middle of waist beam \(2\) moved inside the pit, which was consistent with the measured displacement curves of the retaining piles. Due to the small earth pressure behind the pile, the overall displacement of the crown beam was toward the outside of the pit; the overall average displacement of the crown beam was about 0.95 mm, and it did not basically deflect. Because of the jacking force on the adjacent waist beam, the end displacement of waist beam \(1\) was significantly larger than its middle displacement. Indeed, the end displacement of waist beam \(1\) could reach 0.86 mm, while the displacement of the middle point of it was lower than 0.1 mm.  
Figure 22: The shear force (_SF_)–temperature relationship of (**a**) pile 1, (**b**) pile 2, and (**c**) pile 3.  
Moreover, its deflection enlarged with the temperature rise. The end displacement of waist beam 2 was completely in the opposite direction of its middle displacement; in fact, the end displacement of waist beam 2 was 0.83 mm, while the displacement of the middle point of it was \(-0.32\) mm. The displacement of the middle point of waist beam 2 was larger because it was not restricted by soil. Moreover, its deflection was most severely affected by the temperature.
# Permittivity of Undoped Silicon in the Millimeter Wave Range
## 2 Permittivity and Measurement Techniques  
Dielectric permittivity is a macroscopic description of many microscopic processes, such as dipole relaxation, lattice vibration, and electronic polarization. These processes take effect at different characteristic frequency ranges, as illustrated in Figure 1.  
The permittivity is actually a complex quantity. The imaginary part is due to dielectric loss. One representative mechanism of loss is the dipole relaxation [1]. When a dipole tries to follow the alternating electric field, friction between dipoles would cause loss of energy. Other mechanisms include conductive loss and lattice loss. These mechanisms can be incorporated into a single parameter the complex permittivity  
\[\varepsilon_{\mathrm{r}}=\varepsilon^{\prime}\mathrm{r}-\mathrm{j}\varepsilon^ {\prime\prime}\mathrm{r}, \tag{1}\]  
where the real part is the relative permittivity and the imaginary part is referred to as the loss factor. Since these mechanisms fall in different frequency ranges, the permittivity exhibits a dependency on frequency  
\[\varepsilon_{\mathrm{r}}(\omega)=\varepsilon^{\prime}\mathrm{r}(\omega)- \mathrm{j}\varepsilon^{\prime\prime}\mathrm{r}(\omega). \tag{2}\]  
Referring back to Figure 1, it has to be noted that the ticks on the axes are not to exact scale, only for illustrative purposes.  
Methods of dielectric measurement shall vary with frequency due to the frequency dependent and electrical size effects. Several methods can be used for dielectric measurement in the millimeter wave range, such as resonator method, transmission line method, and free space method [18]. The resonator technique is an efficient way for low-loss single frequency measurement. The transmission line method imposes considerable challenge on sample preparation with the increase of frequency. Therefore, the free space system is more preferred for measurement spanning over a whole frequency band.  
Free space measurement requires ideally a plane wave system. However, plane wave is merely an ideal model that can be approximated using the far field of an antenna, but the far field would require a space too large to use. Alternatively, we used a quasi-optical (QO) system to create a quasi-plane wave for dielectric measurement. The QO technique is particularly suitable for millimeter wave system due to its low-loss and wideband nature. It is also possible for multi-polarization measurement. In addition, the size of a QO system is controllable.