esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
## 4 Result and Discussion
### Experiment Results and Discussion
The comparison of the detection effectiveness of the proposed method with other methods on the ICDAR2015 dataset is shown in Table 5. The precision, recall, and F-Score of the proposed method in this paper were, respectively improved by 7.2%, 5.5%, and 6.3% compared to the EAST. Because the EAST had a small perceptual field and used text candidate frames, it was less effective in detecting long and slanted text. Compared with the segmentation-based PixelLink method, the precision and F-Score were improved by 7.9% and 2.2%, respectively. This was because PixelLink required geometric features as post-processing to detect segmented text and could not effectively handle text regions with complex backgrounds. Compared with the PANet model, the precision and F-Score were also improved by 6.8% and 1.6%, respectively. This was because the PANet did not make full use of the high-level semantic information, resulting in its failure to accurately detect some text regions with large text size differences. The comparison of the detection effectiveness of the proposed method with other methods on the ICDAR2017 dataset is shown in Table 6. Compared with PSENet, the accuracy and F-Score of the proposed method are improved by 7.9% and 2.2%, respectively. Because the PSENet network does not exclude the impact of light and complex backgrounds. Compared with the original DBNet model, the accuracy, recall, and F-Score increased by 2.2%, 1.1%, and 1.6%, respectively. Although our proposed method did not perform as well as some models in terms of recall, it outperformed other methods in terms of precision and F-Score, which fully demonstrated the high precision of our model in classifying textual and non-textual regions of images. Furthermore, it could fully utilize both high-level and low-level semantic information to achieve accurate localization of textual instances. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Methods** & **Precision (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline SCUT\_DLVlab1 [37] & 80.3 & 54.5 & 65.0 \\ ete\_ctcl01\_multi\_scale [37] & 79.8 & 61.2 & 69.3 \\ Corner [37] & 83.8 & 55.6 & 66.8 \\ Zhang et al. [38] & 74.9 & 61.0 & 67.3 \\ PSENet [6] & 77.0 & 68.4 & 72.5 \\ DBNet [10] & 81.9 & 63.8 & 71.7 \\ Ours & 84.1 & 64.9 & 73.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of the model proposed in this paper with other models on the ICDAR2017-MLT dataset. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Methods** & **Precision (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline EAST [21] & 83.6 & 73.5 & 78.2 \\ PixelLink [15] & 82.9 & 81.7 & 82.3 \\ RRD [33] & 85.6 & 79.0 & 82.2 \\ SPN [34] & 86.6 & 82.1 & 84.3 \\ TextSnake [35] & 84.9 & 80.4 & 82.6 \\ PANet [36] & 84.0 & 81.9 & 82.9 \\ DBNet [10] & 89.5 & 75.9 & 82.1 \\ Ours & 90.8 & 79.0 & 84.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of the model proposed in this paper with other models on the ICDAR2015 dataset.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 5 HARRF LNA Design
### X-Band LNA Design Procedure
#### 5.1.4 State-of-the-Art in X-Band LNA Design  
Table 1 provides a comparison of state-of-the-art LNAs operating in the target X-band frequency range. These designs vary in process technology, application, frequency band, gain, noise figure, and power dissipation. The proposed design in [15] leverages the 0.15 \(\upmu\)m InGaAs pHEMT process to achieve a combination of high gain (\(\sim\) 40 dB) and low noise figure (\(\sim\) 0.9 dB).  
Figure 11: Noise Figure of the X-band MMIC LNA circuit.  
Figure 10: Gain of the X-band MMIC LNA circuit.
## 3 Results
### Campaign 2: Class (March)  
Figure 3 presents an example of the diurnal variation of the PM mass concentration inside the classroom during March 2022. Owing to the COVID-19 pandemic, the number of students that can attend each lecture was restricted to ten, but the room can hold forty persons. On this day, the researcher entered the class at 11:00 and a lecture was held from 16:00 to 19:00 with 10 students present.  
The classrooms are heated with floor-mounted fan coils, from 08:00 to 12:00 h. The heating system appears to increase the PM concentrations above 4 \(\upmu\)m. The operation of fan coils disturbs the PM concentrations in the breathing zone. They affect the deposition of 2.5, 10, and 25 \(\upmu\)m particles, depending on the fan coil air discharge angle and the air flow rate [31].  
When the number of students increased from ten to thirty students, PM concentrations increased from 11% (PM\({}_{1}\)) to 68% (PM\({}_{10}\)) and CO\({}_{2}\) and TVOC concentrations increased by 223% and 65%, respectively, on average.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Office** & & **Lab** & & **Outdoors** & \\ \hline
**Variable** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multicolumn{7}{c}{**PM (\(\upmu\)g m\({}^{-3}\))**} \\ PM\({}_{1}\) & 8.56 & 0.54 & 4.85 & 0.94 & 3.33 & 0.08 \\ PM\({}_{2.5}\) & 10.34 & 0.91 & 5.57 & 1.42 & 5.00 & 0.19 \\ PM\({}_{4}\) & 15.54 & 1.78 & 7.12 & 3.63 & 8.53 & 0.86 \\ PM\({}_{7}\) & 20.93 & 2.22 & 8.54 & 7.91 & 13.25 & 1.47 \\ PM\({}_{10}\) & 22.90 & 2.43 & 9.13 & 11.30 & 16.48 & 1.68 \\ TSP & 27.36 & 3.42 & 10.18 & 25.15 & 26.75 & 5.26 \\ \hline \multicolumn{7}{c}{**Gaseous pollutants**} \\ TVOC (ppb) & 103.70 & 28.91 & 115.22 & 41.30 & 132.85 & 24.33 \\ CO (ppb) & 387.78 & 100.27 & 377.30 & 242.96 & 428.42 & 106.13 \\ CO\({}_{2}\) (ppm) & 530.23 & 67.33 & 532.29 & 90.78 & 517.25 & 147.03 \\ O\({}_{3}\) (ppb) & 10.30 & 4.10 & 9.04 & 5.54 & 12.66 & 16.41 \\ NO (ppb) & 9.07 & 3.41 & 8.34 & 5.43 & 12.15 & 7.67 \\ NO\({}_{2}\) (ppb) & 12.08 & 2.27 & 10.22 & 7.67 & 14.11 & 3.67 \\ \hline \multicolumn{7}{c}{**Microclimatic conditions**} \\ AT (\({}^{\circ}\)C) & 19.04 & 1.33 & 19.27 & 2.32 & 15.70 & 1.10 \\ RH (\%) & 38.60 & 1.76 & 43.76 & 6.14 & 45.56 & 4.73 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of indoor and outdoor atmospheric pollutant concentrations along with air temperature and RH (Campaign 1).  
Figure 3: Campaign 2, Class: PM mass concentrations and number of people present.
# Spatial Baseline Optimization for Spaceborne Multistatic SAR Tomography Systems
## 2 Baseline Equivalence Analysis of SMS-TomoSAR  
SMS-TomoSAR systems employ several small satellites \(S_{1},S_{2},\ldots,S_{N}\) in a formation to observe and measure the target area. In particular, SMS-TomoSAR could be considered to be monostatic SAR when an SMS-TomoSAR system employs only one satellite. Compared with monostatic SAR, SMS-TomoSAR obtains more spatial sampling in a single flight, which can reduce the number of revisits and increase the number of samples. Figure 1 shows the spatial geometry model of an SMS-TomoSAR system. The multi-base multi-pass tracks of an SMS-TomoSAR system is expressed as:  
\[f_{m}=\{r_{mn}|n=1,\;2,\;\ldots,\;N\}\;\;\;\;\;m=1,\;2,\;\ldots,\;M, \tag{1}\]  
where \(n\) is the \(n\)th satellite in the satellite formation, \(m\) is the \(m\)th flight of the satellite formation, and \(f_{m}\) is the track group of the \(m\)th flight of the satellite formation.  
TomoSAR uses beam-forming technology to obtain high-precision height resolution capability, but tomographic processing requires a large number of spatial samples in different perspectives. Currently, data acquisition systems mainly include single-antenna multi-pass SAR systems, multi-antenna multi-pass In-SAR systems, and single-pass antenna array SAR systems. Essentially, an SMS-TomoSAR system does not differ from other systems in data acquisition, so the multi-base multi-pass baselines of an SMS-TomoSAR system can be considered equivalent to a group of multi-pass baselines of monostaticSAR. Selecting the image obtained by the track \(r_{mn}|m=m_{0},n=n_{0}\) as the main image returns \(NM-1\) baselines from \(NM\) tracks.  
\[\{b_{mn}|m\
eq m_{0},\ n\
eq n_{0}\} \tag{2}\]  
The baseline in a single flight of a satellite formation is referred to as a same-track baseline, and these same-track baselines of \(M\) flights constitute a repeat-track baseline. Considering that there is no essential difference between the baselines of different institutional systems in terms of data acquisition, the repeat-track baseline of SMS-TomoSAR systems is equivalent to:  
\[\{b_{mn}|m\
eq m_{0},\ n\
eq n_{0}\}=\Big{\{}b_{p}\Big{|}p=1,\ \ldots,\ MN-1\Big{\}}. \tag{3}\]  
In addition, the proposed baseline equivalence analysis of an SMS-TomoSAR system is helpful to further explore and obtain the optimal baseline design method. Figure 2a shows the baseline equivalent diagram of an SMS-TomoSAR system.  
Figure 1: Spatial geometry model of a spaceborne multistatic synthetic aperture radar tomography (SMS-TomoSAR) system (normal-slant-range (nsr) direction).  
Figure 2: (**a**) Baseline equivalent schematic diagram of an SMS-TomoSAR system; (**b**) Schematic diagram of the symmetric-geometric baseline model.
# Analysis of Liquefaction in Tailings Deposits by Fem Modeling of Underained Cyclic Triaxial
## 5 Strategy and Characteristics of Numerical Modeling
### Behavior of the Main Variables That Govern the UBC3D-PLM Model
#### 5.3.2 Parameter \(f_{dens}\) and Factors \(m_{e},n_{e}\), and \(n_{p}\)  
Studies indicate that the variation of this parameter and these factors influences the representation of liquefaction [36; 49]. Accordingly, the effect of the parameter \(f_{dens}\) was first evaluated using the default values for the factors (\(m_{e}=0.5\), \(n_{e}=0.5\)_and_\(n_{p}=0.4\)). The results are presented in Figure 9a, which shows the effects of different values of \(f_{dens}\). As can be seen in the figure, at lower values, liquefaction is reached in fewer cycles and the excess pore pressure is overestimated. This indicates that \(f_{dens}\) is directly related to the cycle in which liquefaction is reached.  
Figure 8: (**a**) Initial degradation of tension states for different values of the moduli \(K_{B}^{e}\), \(K_{G}^{e}\), and \(K_{G}^{p}\) obtained using different values of \(N_{1\omega}\). (**b**) The effect on \(R_{u}\) of using the parameters \(K_{B}^{e}\), \(K_{G}^{e}\), and \(K_{G}^{p}\) calculated using \(N_{160}=15.5\) compared with the experimental curve for test CTX 2.  
Figure 9: (**a**) The effect of varying the parameter \(f_{dens}\), comparing values of 0.45, 0.68, and 1.0 (default). (**b**) A comparison between the default parameters and two different sets of parameters with the same combination of factors \(m_{e}\), \(n_{e}\), and \(n_{p}\) and values of \(f_{dens}\) of 0.68 and 1, respectively.  
Additionally, the recommendations of [22; 37] are followed, although the value of the plastic exponent \(n_{p}\) is modified to better represent the cycle when liquefaction is reached. Furthermore, to adjust the curve, we propose modifying the elastic exponents \(m_{e}\) and \(n_{e}\) since these factors change the values of the moduli \(K_{G}^{e}\) and \(K_{B}^{e}\) expressed in Equations (1) and (2), respectively; therefore, if the values of \(K_{G}^{e}\) and \(K_{B}^{e}\) are decreased, the value of \(R_{u}\) does not increase significantly since these parameters modify the primary yield surface, which acts in the first cycle and when the peak surface is reached.  
Finally, Figure 9b shows the effect of changing the densification parameter, comparing values of \(f_{dens}\) of 0.68 and 1 with the values of the factors being kept constant. It can be seen that a greater accuracy is obtained for a value of \(f_{dens}\) of 0.68, both in terms of the number of cycles taken to reach a liquefied state and in the magnitude of the excess pore pressure during the cycles prior to failure. However, an abrupt increase is still observed in cycle 22, which significantly increases the post-liquefaction plastic strains. This effect can be controlled by the parameter \(f_{post}\).
# Augmented Reality for PCB Component Identification and Localization
## 4 Materials and Methods
### AR System Architecture  
The AR system was developed using UnityTM2021.3.9f1 (Unity Technologies, San Francisco, CA, USA), in conjunction with the Mixed Reality Toolkit (MRTK) (Microsoft Corporation, Redmond, WA, USA) and VuforiaTM Engine (PTC Inc., Boston, MA, USA). This configuration enabled real-time spatial anchoring, gesture-based interaction, and image-based tracking on a MicrosoftTM HoloLensTM 2 (Microsoft, Redmond, WA, USA); headset. Pattern-based image targets were generated from the PCB layout using VuforiaTM's model target generator to achieve a stable and precise alignment between digital overlays and physical components.  
The system architecture included the following components:  
* Hardware: MicrosoftTM HoloLensTMTM 2
* Development Environment: UnityTM2021.3.9f1;
* AR Libraries: MRTK for spatial interaction and UI integration;
* Tracking Engine: VuforiaTM Engine for image-based marker tracking.  
This setup allowed the AR interface to present spatially accurate digital cues and annotations aligned with physical PCB features, facilitating guided component identification.  
Figure 1: Overview of the research design and workflow.
## 2 CFD Methodology and Numerical Modeling
### Mesh Analysis  
In the study, the hexahedral type of mesh is applied to discretize the entire computational domain. Finer mesh sizes are used near LED light walls, crop area surfaces, and airflow inlet and outlet surfaces. To ensure that the numerical results are not affected by the size and number of mesh, the results are simulated for three cases where the number of mesh is 552,789 (Coarse mesh), 1,984,032 (Basic mesh), and 2,847,910 (Refined mesh). The variation in air velocity and temperature along the \(z\)-axis direction for y = 0.6 m and x = 0.55 m in the numerical model is compared in Figure 3. As shown in Figure 3, the difference between the velocity and temperature predictions of the refined and basic meshes is minimal. Therefore, in order to achieve an optimum between the accuracy of the simulation results and computational efficiency, the model
## 3 Test Results and Analysis  
### Strength Comparison of Grounded Reinforcement  
The results of the uniaxial compressive strength tests of the intact specimens before and after grouting are listed in Tables 2 and 3, respectively, and the errors and histograms are analyzed in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grouting Material** & **Specimen** & **Uniaxial** & **Average Uniaxial** & **Peak Strength** & **Residual Strength** \\
**Number** & **Compressive** & **Compressive** & **Enhancement** & **Enhancement** \\
**Strength** & **Strength** & **Coefficient** & **Coefficient** \\ \hline \multirow{3}{*}{Ultra-fine cement slurry} & A-1 & 24.103 MPa & \multirow{3}{*}{22.107 MPa} & \multirow{3}{*}{\(-\)18.083\%} & \multirow{3}{*}{16.931\%} \\  & A-2 & 15.896 MPa & & & \\ \cline{1-1}  & A-3 & 26.323 MPa & & & \\ \hline \multirow{3}{*}{C-S slurry} & B-1 & 16.354 MPa & \multirow{3}{*}{21.378 MPa} & \multirow{3}{*}{\(-\)20.784\%} & \multirow{3}{*}{13.075\%} \\ \cline{1-1}  & B-2 & 22.487 MPa & & & \\ \cline{1-1}  & B-3 & 25.293 MPa & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Uniaxial compressive strength test results of grouted specimens.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Peak Strength** & **Residual Strength** & **Average Peak Strength** & **Average Residual Strength** \\ \hline A-1 & 27.595 MPa & 19.078 MPa & & \\ A-2 & 21.697 MPa & 13.925 MPa & & \\ A-3 & 31.666 MPa & 23.685 MPa & & \\ B-1 & 22.323 MPa & 14.718 MPa & & \\ B-2 & 24.386 MPa & 18.192 MPa & 26.987 MPa & 18.906 MPa \\ B-3 & 31.293 MPa & 22.314 MPa & & \\ C-1 & 26.481 MPa & 18.791 MPa & & \\ C-2 & 29.697 MPa & 18.768 MPa & & \\ C-3 & 27.743 MPa & 20.681 MPa & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Uniaxial compressive strength test results of intact specimens.  
Uniaxial loading is biased toward testing the bonding properties between the rock mass and the slurry [10]. Compared with the residual strength of the intact specimens, the residual strength of the ultra-fine cement grouting specimen increased by 16.931%, the residual strength of the C-S grouting specimen increased by 13.075%, and the residual strength of the MARITHAN(r) grouting specimen increased by 39.998%. The results show that compared with ultra-fine cement slurry and C-S slurry, MARITHAN(r) has better permeability and can better penetrate into the specimen and fill up many tiny cracks and fissures inside the specimen, thus better bonding the cracks and fissures.  
As can be seen from Figure 6, the grouting reinforcement methods can improve the residual strength of the specimens to a certain extent, but compared to the intact specimens, the peak strengths of the three types of grouted specimens are still reduced, and their peak strength enhancement coefficients are negative, indicating that there is a limit to the increase in strength depending on the grouting method.
# On the Quest for Ophthalmological Biomarkers for Long COVID: A Scoping Review
## 2 Materials and Methods
### Study Eligibility  
Broad inclusion criteria were only scientific articles that focused on ophthalmological signals (e.g., eye movement/tracking, pupillary light reflex, and visual evoked potentials) and long COVID in the English language. Those included studies had to be accessible to at least one of the authors.  
Specific inclusion criteria were as follows:  
* Studies of clinical populations being investigated for long COVID.
* Studies using automated technology for measuring the above symptoms.
* Studies reporting ophthalmological parameters and outcome measures relevant to the clinical management of patient populations.  
Broad exclusion criteria were non-English language studies, non-primary, or non-peer reviewed research (dissertations, conference proceedings, and reviews, etc.), with reviews being inspected for potential overlaps to avoid any duplication. Ultimately, no reviews were included in the final selection.  
Specific exclusion criteria were:  
* Studies on fundoscopy.
* Studies on post-vaccination populations.
* Studies on healthy populations (unless healthy control comparator data were reported separately in a study also reporting on a patient population).
## 2 Related Work  
In recent years, research on resource management, task offloading, and collaborative computing in satellite networks and STIN has made significant progress. Existing studies can mainly be categorized into three areas: resource scheduling optimization, collaborative computing strategies, and task offloading methods. Although resource scheduling optimization focuses on the efficiency and flexibility of resource allocation, it still faces challenges when dealing with dynamic network environments and complex task demands. Collaborative computing strategies enhance computing performance through cooperation between different nodes, but optimization issues remain when addressing the heterogeneity between satellite platforms. Task offloading methods alleviate terminal load by offloading tasks to satellites or ground stations, but challenges in optimizing offloading decisions and task distribution mechanisms remain. Overall, existing research has yet to fully consider the cross-optimization of these methods, and there is considerable potential for further exploration.
# Total Ionizing Dose Effects in Advanced 28 nm Charge Trapping 3D NAND Flash Memory
## 4 Conclusions  
In our work, the impact of TID effects caused by Gamma rays on the performance of a 28 nm 3D CT NAND Flash memory, operating in both TLC and SLC modes, was meticulously analyzed. The experimental data reveal that the RBER, standby current, and read operation current escalate with increasing doses, paralleled by a rise in the number of bit flips. This indicates that the severity of the TID effect intensifies with higher doses, adversely affecting memory performance. The threshold voltage's decline with  
Figure 10: Erase block and program page timing versus dose. (**a**) Erase block time in TLC mode. (**b**) Erase block time in SLC mode. (**c**) Program page time in TLC mode. (**d**) Program page time in SLC mode.  
dose escalation is a critical factor contributing to the increase in the RBER. Moreover, threshold voltage shifts influence both erase and program times. Additionally, annealing time impacts performance, with some functionalities not reverting to pre-irradiation levels after annealing. Our work identifies the worst operating modes and bias states of a 28 nm 3D CT NAND Flash memory, which can provide guidance for spatial applications of this product. In the absence of capacity requirements, the optimal operating mode of this flash memory is SLC mode.  
The work presented here was completed via collaboration between all authors. Conceptualization, X.Z.; measurements and validation, X.Z., Y.W. and R.M.; formal analysis, X.Z, C.L. and T.W.; investigation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z.; supervision, M.H.; project administration, L.X.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 1 Introduction  
The technique of positron emission particle tracking (PEPT) has led to a paradigm shift in the measurement of flow behaviour in multiphase systems with high concentration of solids. PEPT can be applied to opaque experimental systems, as it relies on the detection of penetrating radiation from a tracer particle. The time-series location data produced by PEPT can also be used to plot high-resolution, time-averaged, or Eulerian maps of flow behaviour (e.g., [1, 2]); additionally, it provides detailed Lagrangian paths of the tracer particle (e.g., [3, 4]). For the three-phase mixture of mineral froth flotation, PEPT has revealed the paths and time-averaged flow profiles of particles of different mineral species (e.g., [5, 6, 7, 8, 9]) and water [10] inside a flotation vessel. It has also been used to track the interactions between particles and bubbles [11].  
However, questions still remain about whether high-frequency components of flow paths can be tracked effectively with PEPT. Some applications have removed these components as noise to focus on macroscale behaviour (e.g., granular flows [12]), while others suggest that the high-frequency component of the motion displays microscale behaviour (e.g., liquid flows [3]). This discrepancy has potential impact for applications of PEPT: either useful information about particle behaviour is being disregarded unnecessarily or information is output that could be a misinterpretation of the underlying particle behaviour.  
In order to investigate the high-frequency component of the tracer particle's motion, a model of the positron emission tomography (PET) scanner at PEPT Cape Town was developed, which produces simulated trajectories of a tracer particle moving on paths with characteristics of eddies and high-frequency fluctuations in turbulent flow. After validation with experimental data, the location data from the simulations can be compared to the ground truth path input to the simulation to determine the ability of PEPT measurements to represent turbulent flow paths.