esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis Using Experimental Data
### Data Analysis  
To evaluate the acquisition performance of the multichannel combining ring vector sensor receiver using experimental data, SNRs and noise powers for various channels are first calculated from one hundred trials and are all shown in Figures 4 and 5, respectively. For the \(k\)th receiver channel, the averages of the hundred measured SNRs and noise powers are used to determine \(d_{k}\) and \(\sigma_{k}^{2}\), respectively, which in turn specify numerical values for \(\rho_{k}\) and \(\xi_{k}\), respectively. By substituting these in (13) and (14), \(P_{A}\) versus \(P_{FA}\) receiver operating characteristic curve for the ring vector sensor receiver using the multichannel combining method is obtained and graphed in Figure 6, together with the \(P_{A}\) versus \(P_{FA}\) curve for each individual channel of the ring vector sensor receiver (results of the maximum power selection method are discussed at the end of this subsection).  
We observe that the multichannel combining method for the ring vector sensor receiver offers the highest signal acquisition probability compared to each of its individual channels. Additionally, the \(x\) and \(y\) channels appear to exhibit higher signal acquisition probabilities than the \(p\) channel. To understand why the \(x\) and \(y\) channels outperform the \(p\) channel, we look at the measured SNRs. Figure 4 shows one hundred experimental data points for the SNR of each of the \(x\) and \(y\) vector components and the \(p\) scalar component measurements of the ring vector sensor receiver. We note that the SNRs of the vector components are higher than those of the scalar components. This can be attributed to the smaller noise powers of the vector components, according to the hundred noise measurements shown in Figure 5 for each component. A theoretical explanation for the lower noise powers of the vector components is that they do not receive noise from all directions, whereas the scalar component collects noise from various directions [12].  
Figure 4: Measured vector and scalar signal-to-noise ratios.  
Figure 5: Measured vector and scalar noise powers.  
Figure 6: Experimental signal acquisition performance curves for the ring vector sensor receiver.  
For the maximum power selection method, the \(P_{A}\) versus \(P_{FA}\) curve is shown in Figure 6, obtained using (16) and (17) and the experimental data. We notice that the selection method and the combining method exhibit a nearly similar performance. This is because there is at least one strong channel that individually provides high \(P_{A}\), and adding more channels slightly increases \(P_{A}\).
# Use of Evolutionary Computation to Improve Rock Slope Back Analysis
## 7 Case Study 2
### Results
#### 7.4.2 Back Analyzing \(k_{h}\) and \(\sigma_{ci}\) Slope Parameters  
The uncertainty parameter \(\sigma_{ci}\) of the upper and the lower sliding masses of the slope as shown in Figure 10 as well as the horizontal seismic coefficient (\(k_{h}\)) of the slope are considered for back calculation. Back calculation is accomplished by using the optimization algorithms (\(PSO\) and \(GA\)) to minimize the objective function (Equation (3)).  
Tables 16 and 17, respectively, show the back-calculated \(\sigma_{ci}\) values of the upper and lower sliding masses as well as the \(k_{h}\) value of the entire slope from \(GA\) and \(PSO\). The results show that the values of the back-calculated parameters obtained from the different optimization methods are different.  
4.3 Back Analyzing \(k_{h}\), \(\sigma_{ci}\) and Geological Strength Index (\(Gsl\)) Slope Parameters  
Herein, the uncertainty parameters \(\sigma_{ci}\) and \(GSI\) of the lower and the upper sliding masses as shown in Figure 10 as well as the \(k_{h}\) parameter of the entire slope are considered for back calculation. GSI is considered to be same for both the upper and the lower sliding masses. In his study, [PERSON] et al. [33] gave the same GSI value for the upper and the lower sliding masses. [PERSON] et al. [44] indicated that the GSI system works well for engineering geologists since it is consistent with their  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{\(T/H=\) **Infinity**} & \multicolumn{3}{c}{\(T/H=2.5\)} \\ \cline{2-4}  & \(\sigma_{ci}\) (\(MPa\)) & \(k_{h}\) & \(\sigma_{ci}\) (\(MPa\)) & \(k_{h}\) \\ \hline Material 2 & 69.22 & 0.011 & 65.64 & 0.07 \\ Material 3 & 43.8 & 0.011 & 41.68 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Back-calculated \(k_{h}\) and \(\sigma_{ci}\) from GA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{_Ttickness to Height Ratio (T/H)_} & \(GA\) & \(PSO\) \\ \hline
2.5 & 0.120 & 0.120 \\ Infinity & 0.045 & 0.045 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Back-calculated \(K_{h}\) values.  
experience in describing rocks and rock masses during logging and mapping. Therefore, _GSI_ for material 2 and material 3 are considered to be the same during back calculation.  
Tables 18 and 19 respectively showed the back-calculated parameters of the slope obtained from _GA_ and _PSO_. The back calculated \(\sigma_{ci}\) and _GSI_ values of the lower and upper sliding masses of the rock slope obtained from the different optimization methods are different. Also the value of \(k_{h}\) obtained from the different optimization methods are different for the same thickness to height ratios. Table 18 shows the same \(k_{h}\) values considering \(T/H=\) infinity and \(T/H=2.5\) which is in contrast with phenomenon observed from the other tables where \(k_{h}\) values are always larger for \(T/H=2.5\). Considering parameter distributions in this case can help us to capture more reasonable strength parameters.
## 3 Results and Discussion
### Data Analysis and Modeling  
Data analysis and modeling involve the process of extracting, transforming, and loading data into a database. These data include three types of source:  
1. official (from the Institute of Public Health and competent institutions),
2. reliable (from private sources and industry), and
3. public (from the portal of news agencies and social networks).  
Given the diversity of data sources, formats, structures, and the frequency and level of data acquisition, appropriate mechanisms are provided for the transformation of raw data into appropriate models.  
Data modelling ensured that all available data was placed within a logical model implemented in the DBMS user environment. The proposed graphical and alphanumeric data model is consistent with the database model and the metadata catalog based on the Core Metadata for Geographic Datasets--ISO 19115: 2003 (E) standard. The geometric data model is based on available geometric types of the GML 3.1.1. standardized geometric data model (spatial topology), as well as associated complex types that enable the maintenance of GIS data using a time-domain (time topology) according to ISO 19107: 2003 (geographic information--spatial schema) and ISO 19108: 2002 (geographic information--temporal schema) standards [21].  
Generally, spatial data go through three phases. The first phase is their acquisition, the second one is storing in databases (to be accessible for analysis, search, querying, or further processing), and the third phase is their presentation (maps, tables, diagrams, text).  
Each of these phases has distinctive requirements on how to store and keep data, so the modelling of the data considers different models [23]:  
* acquisition-oriented data model--receives the data in sequences and according to the structure appropriate to the way and method of their collection;
* query-oriented data model--where the data are stored in the database so that they are accessible for query execution; and
* output-oriented data model--supports the display of results (outputs) of query operations in the form of tables, maps, reports, and other display formats.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 4 System Calibration
If, for some reason, such as logistical constraints, any back-to-back calibration cannot not be performed, the use of an over-the-air calibration method in postprocessing is required. This approach is feasible if the locations of the antennas are known. In the following sections, we will describe the steps taken to achieve a calibrated data set. Due to logistical problems, we were unable to perform a back-to-back calibration, and hence we resorted to a combination of post-back-to-back calibration with two of the units to compensate for cable lengths and signal processing time. Then, we applied an over-the-air approach to compensate for the CFO and propagation delay. All steps require that at least a portion of the measured scenario is _static_ so that we can assume that the CTF does not change during the calibration procedure. We ensured that we did not have any _time offset_ by syncing all computers to a local network time protocol (NTP) server. Then, we ensured that the FPGAs shared the same notion of global time. We also assumed that there was no sampling clock frequency offset. \begin{table}
\begin{tabular}{l l} \hline \hline
**System Errors** & **Source** \\ \hline Carrier Frequency Offset (CFO) & The oscillators do not provide the same frequency. \\ Clock Phase Offset & The PLLs lock on random—and different—phases. \\ Sampling Clock frequency Offset & The clock frequency of the ADCs are not the same. \\ Sampling Time Offset & The ADCs samples at different times. \\ Time Offset & The system does not share the same notion of time. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of measurement errors.
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 2 Related Work
### Crack Detection  
Early crack detection methods include threshold algorithms [13], feature clustering algorithms [14], and random forest algorithms [15]. These methods rely on manually designed prior information to construct detection algorithms. For example, [PERSON] et al. utilized wavelet analysis to extract crack edges, resulting in crack profiles with greater continuity compared to traditional edge-detection algorithms. However, such methods typically perform poorly in complex backgrounds and under varying lighting conditions, exhibiting limited generalization capabilities. With the rapid advancement of deep learning, convolutional neural networks (CNNs) have become increasingly prevalent in crack detection applications. For instance, [PERSON] et al. [16] employed Faster R-CNN for crack object detection, followed by segmentation of the cracks within the bounding boxes using a corrected tubular flow method. Although this approach achieved high crack segmentation accuracy, the segmentation algorithm still depended on prior information and lacked robustness against complex backgrounds. In recent years, end-to-end deep-learning-based segmentation models have begun to demonstrate significant potential. DeepCrack [17] is a typical example. This network effectively handles cracks of varying scales through multi-scale feature fusion and deep convolution, overcoming the limitations of traditional methods that rely on prior information design.
## 4 Results and Discussion
### Multiple Defects  
In practical engineering applications, a component probably contains multiple defects simultaneously. It should be pointed out that it is a difficult problem for an accurate inspection because multiple scattering will occur if the distance of the two defects is closed. Hence, three circular defects with different sizes were considered, as shown in Figure 13a. A central defect (marked as #1 defect) was located at (40,120) with a diameter of 3 mm. The second one with a diameter of 4 mm (marked as #2 defect) was horizontally shifted by 6 mm from #1 defect and the third one with a diameter of 5 mm (marked as #3 defect) was vertically shifted by 6 mm from #1 defect.  
Similarly, a successive-frequency FWI was applied and the reconstructed defects are shown as Figure 13b. From the tomograph, these three circular defects were clearly discernable despite complex wave scattering due to the presence of the three adjacent defects. Furthermore, both vertical and horizontal profiles were compared with the true model as shown in Figure 14. The good agreement proved that the proposed method was capable of accurately detecting multiple defects in complex parts.  
Figure 12: Inversion results for open notch cracks. Tomograph and error variation for (**a**,**b**) the horizontal notch, (**c**,**d**) the inclined notch at 45\({}^{\circ}\) and (**e**,**f**) the vertical notch.  
Figure 13: (**a**) True model and (**b**) FWI tomograph of three defects.  
In addition, it is much more common that different types of defects might exist in the structure at the same time. In order to extend this study to more practical applications, an additional case study was also carried out to evaluate the structure with coexisting circular holes and cracks. Here, the circular holes were set as the same as Figure 13a and an incline notch of 45\({}^{\circ}\) was considered. The inversion results are presented in Figure 15. The FWI tomograph clearly showed the notch crack and the disperse holes. Moreover, due to the fact that the crack was relatively distant from the dispersed circular holes, the cross section profiles were very close to the results with only holes or cracks. The good agreement demonstrated that the proposed method was able to inspect different types of defects at the same time with a satisfactory accuracy.  
Figure 14: Cross sections (**a**) through #1 and #2 defects and (**b**) through #3 defect along the horizontal direction. Cross sections (**c**) through #1 and #3 defects and (**d**) through #2 defect along the vertical direction.
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 3 Proposed Framework
### Mask R-CNN  
Mask R-CNN, akin in architecture to Faster R-CNN, can perform object detection and semantic segmentation simultaneously. It utilizes RoIAlign for more accurate segmentation of object boundaries within the region of interest (RoI), enhancing accuracy. However, enhanced accuracy in pixel-level segmentation requires substantial GPU memory and computational capability [23]. Mask R-CNN consists of three main components:  
1. Region Proposal Network (RPN): Like Faster R-CNN, Mask R-CNN begins with an RPN to suggest object regions in the image, predicting locations where objects are likely to be present.  
Figure 1: Object-detection framework for identifying bridge damage by integrating deep learning combination–modules [12].  
2. RoIAlign: This accurately maps the RoI. It replaces RoIPool to improve pixel alignment accuracy, enabling accurate mask prediction during instance segmentation.
3. Mask Head: This component predicts the segmentation mask at the pixel level for the object within each RoI, along with the object's bounding box. It consists of a CNN-based fully convolutional network that generates the mask using the feature map within the given RoI.  
The architecture of Mask R-CNN is shown in Figure 2. Mask R-CNN's capacity to detect objects and predict accurate segmentation masks for each instance makes it useful for various computer vision tasks, ranging from pedestrian detection and segmentation by autonomous vehicles to tumor analysis in medical imaging. Models pre-trained on large datasets such as Common Objects in COntext (COCO) are provided for Mask R-CNN, along with code in deep learning frameworks such as TensorFlow and PyTorch.
## 6 Future Instruments for Astrophysics and Fundamental Physics Research
### Instruments for Particle-Physics And Cosmology
The analysis of the residual background composition in the X-IFU focal plane assembly [168], for example, revealed that a considerable fraction of the background is induced by secondary electrons coming from the niobium shield surrounding the detector. Passive low-Z shields to interpose between the detector and the Niobium shield will help to reduce this component. A cryogenic anti-coincidence detector mounted in the proximity of the main array, as currently under testing in the demonstration model for X-IFU [162; 169], is designed to reduce the contribution from high energy particle of about a factor of 30 around \(5\,\mathrm{keV}\). One should realize, however, that the volume and mass constraints in space applications are very stringent. In a ground-based experiment like IAXO, the passive and active background shielding can be designed to be much more efficient. Correlation analysis between a large array of very sensitive pixels will help as well in reducing the background. (iii) TESs have been successfully employed on ground telescopes as very sensitive bolometers in a series of CMB experiments [189; 190; 191]. One interesting overlap between the TES-bolometers technology and the X-ray instruments discussed so far is the development of the 40-400 GHz instruments on board of the LiteBIRD mission [3; 192], recently selected by the Japanese space agency (JAXA) as a mission to be launched in 2027. LiteBIRD is a Lite satellite for the studies of B-mode polarization and Inflation from cosmic background Radiation Detection. It aims to detect the footprint of the primordial gravitational wave on the CMB. If successful, it will open up a new era in observational cosmology. The LiteBIRD detectors core technology will be an array of more than 4000 TES bolometers [193], divided between the low-, mid- and high-frequency bands, and read-out with MHz-FDM [194]. The current baseline foresees TESs with a normal resistance of about \(1\,\Omega\), a time constant of \(0.1\)-\(2.2\,\mathrm{ms}\) and saturation power around \(0.7\)-\(0.9\,\mathrm{pW}\). From the point of view of the read-out, the LiteBIRD TES-bolometers are very similar to the high-aspect ratio and high normal resistance TESs currently under development at SRON for the MHz-FDM read-out of X-IFU-like detectors, discussed in Section 4.2. Using MHz-FDM read-out similar to the one shown in Section 5.2, with demonstrated low SQUID current noise of \(\sim\)\(5\,\mathrm{pA}/\sqrt{\mathrm{Hz}}\), we believe a multiplexing factor of 72 pixels per SQUID chain is feasible.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 5 Formation Flying Nanosatellite Bus
### Subsystems Design
#### 5.2.3 Communication Subsystem (COMS)  
Transceivers for each band are based on software-defined radio (SDR), which simply changes its RF features in orbit. Figure 18 shows the frequency, data rate, and modulations for each RF application. Most communications are established in the UHF and S-bands. In particular, the S-band transceiver includes two modems in one unit for either the telemetry and telecommand (TMTC) or the crosslink (inter-satellite link, ISL), saving internal space and power consumption. UHF communication was adopted for early orbit operations and back-up communication for contingencies. Finally, X-band communication is available for future applications but is not currently utilized. The RF link budget analysis was conducted to ensure link availability in orbit, as summarized in Table 10. For both UHF- and S-band communications, the link budget should be higher than 6 dB; for the X-band, it should be higher than 4 dB. By applying the specifications of each device, the link budget meets the requirements of data rate and modulations; an S-band crosslink would be available in the range of 1000 km.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & **Budget (m/s)** & **\(\Delta\)V (m/s)** & **Attempt (Times)** & **Total \(\Delta\)V (m/s)** & **Margin (\%)** \\ \hline Drift Recovery & 3.0 & 2.55 & 1 & 2.55 & 15.0 \\ Station Keeping (1) & 0.5 & 0.38 & 1 & 0.38 & 24.0 \\ Reconfiguration & 2.5 & 0.11 & 20 & 2.20 & 12.0 \\ Residual and Disposal & 0.6 & & & & 100.0 \\ \hline
**Accumulated \(\Delta\)V (m/s)** & 6.6 & & & 5.13 & 22.3 \\ \hline \hline \end{tabular} (1) This stage includes the station-keeping maneuvers for maintaining inter-satellite distance.  
\end{table}
Table 8: Propellant budget for each orbit maneuver scenario.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Operation Scenarios**} \\ \cline{2-7}  & **LEOP (3)** & **Maneuver** & **Standby** & **Mission** & **Comm.** & **Safe** \\ \hline
**Power Generation (W)** & 5.81 & 17.31 & 21.64 & 17.31 & 17.31 & 21.64 \\
**Power Consumption (W)** & 3.56 & 9.29 & 5.81 & 16.24 & 9.70 & 4.47 \\
**Discharge (Wh)** & \(-\)7.18 & \(-\)16.64 & \(-\)11.71 & \(-\)26.67 & \(-\)17.37 & \(-\)9.01 \\
**Charge (Wh) (1)** & 3.14 & 13.17 & 19.76 & 13.17 & 13.17 & 19.76 \\
**Margin (Wh)** & \(-\)4.03 & \(-\)3.47 & 8.05 & \(-\)13.50 & \(-\)4.20 & 10.75 \\
**Depth of Discharge (\%) (2)** & 5.51 & 4.75 & & 18.45 & 5.74 & \\ \hline \hline \end{tabular} (1) The actual charge values are estimated at end-of-lifetime with conversion efficiency of buck-boost converters and man-made performance degradations. (2) The battery pack capacity is 77 Wh. (3) The orbit scenario is assumed to detumbling periods after separation.  
\end{table}
Table 9: Electrical power budget for each operation mode.
## 2 Analysis of the State of the Art
### Wurfl  
The Wireless Universal Resource FiLe (WURFL) is an XML based open source database which contains the characteristics and capabilities of a wide range of devices. These capabilities are classified into several groups. These groups are just a simple way to understand WURFL and its data. Its API is very easy to use and it has the advantage of providing a hierarchy able to _infer_ several capabilities for devices which are still not present in the file.  
The following are several capabilities used by [PERSON] _et al_. [68] for Imhotep, a framework which aims to ease the development of accessible and adaptable user interfaces:  
* display: It contains information about the device screen, as the resolution, number of columns, orientation, _etc._
* image_format: Supported image formats.
* playback: Supported video codec.
* streaming: Available streaming capabilities.
* sound_format: Supported audio formats.  
WURFL has become the de-facto standard for mobile capabilities. Nevertheless, there are several free and open source alternatives that are growing within the community very fast.