esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effect of Strain Rate and BRB on Seismic Response of a CFST Frame Structure
### Analysis of the Influence of the Strain Rate on the Seismic Response of the CFST Structure  
In this subsection, the nonlinear seismic responses of CSFT-A are analyzed with and without consideration of the strain rate effect. The maximum roof displacement and the inter-story drift ratios (ISDRs) are used as the structural response indexes to extract the time history analysis results. Table 1 lists the maximum roof displacement and ISDRs of the structure with and without the strain rate effect and the corresponding differences. Figures 7-10 show the structural dynamic responses under the Taft ground motion with different peak ground accelerations (PGAs). The results show that, as the PGA increases, the differences between the maximum roof displacement and ISDRs of the structure with and without the strain rate effect gradually increase. For a PGA of 0.2 g, the structural response curve basically coincides with each other for the two cases, which indicates that the effect of the strain rate is not remarkable. This is because, under low seismic excitation, the concrete does not experience tensile cracking, and the overall structure is still in a linear elastic stage, so the strain rate effect is not remarkable. For a PGA higher than 0.2 g, the results considering and not considering the strain rate effect are very different. As seen in the table, when considering the strain rate, the roof displacement and maximum ISDR of the structure are much lower, and the corresponding maximum differences are 10.2% and 6.9%, respectively. In short, the influence of the strain rate on the structural response is not remarkable in the elastic stage. However, as the structure enters the nonlinear stage, considering the strain rate increases the strength and energy dissipation performance of the material and, thus, improves the structural resistance, which reduces the structural response. Considering the influence of the strain rate can more reasonably reflect the actual stress state of the structure and make the analysis results more accurate. Therefore, the influence of strain rate should be properly considered in structural seismic analysis and design. Figure 6: Layout of the exemplar structures (unit: mm).
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 5 Conclusions  
In terms of performance, ATTD achieved a truncation threshold with high correlations relative to the clairvoyant benchmark, similarly to the results obtained by the L-curve and GCV methods. Although this correlation decreases at high noise levels, the trade-off is justified by the reduced computational complexity. Additionally, compared to simpler methods with lower computational complexities, such as the fixed truncation threshold method and SURE, ATTD offers imaging performance approaching that of the clairvoyant benchmark.  
This contribution proposes a promising mechanism for determining a suitable truncation threshold for TSVD-based MMF-SPI. The effectiveness of this approach is demonstrated by its comparable performance to that of the clairvoyant benchmark, which can be obtained using traditional methods with high computational complexity or via prior target knowledge, outperforming traditional methods with less computation. This can be particularly observed in the case of the fixed truncation threshold and the SURE mechanism, especially  
Figure 6: Adaptive performance comparison under varying fiber configurations (noise levels) using the (**a**) diffusion model and (**b**) ATTD \(k_{\text{d}}\). Truncation threshold and Pearson correlation coefficients with the ground truth are marked in each image. Above each column, \((m,n)\) denotes the translation stage’s position (various noise levels).  
when the noise is substantial. Target insensitivity was verified via simulations, and all parameters can be determined in a self-contained manner.  
Conceptualization, Y.X. and J.L.; methodology, Y.X. and J.L.; software, Y.X., L.Y. and X.H.; validation, Y.X., L.Y. and X.H.; formal analysis, Y.X.; data curation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, J.L.; supervision, M.L., J.M. and L.G.; project administration, M.L., J.M. and L.G.; funding acquisition, J.L. and M.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported, in part, by the Fundamental Research Funds for the Central Universities (2023 RC28) and the National Natural Science Foundation of China (U2241209 and 61801040).  
Not applicable.  
The data and code underlying the results presented in this paper are not publicly available at this time but can be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MMF & Multimode fiber; \\ SPI & Single-pixel imaging; \\ CS & Compression sensing; \\ TSVD & Truncated singular value decomposition; \\ GCV & Generalized cross-validation; \\ SURE & [PERSON]'s unbiased risk estimator; \\ SV & Singular value; \\ ATTD & Adaptive truncation threshold determination; \\ BD & Bucket detector. \\ \end{tabular}
# Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomatages
## 2 Methods
### Frame Sorting  
#### 2.1.1 Disc and Fovea Detection and Frame Categorization  
We apply the Faster R-CNN [14] method, depicted in Figure 2, since it has a good balance between efficiency in computation and storage, and detection performance. It comprises a CNN for feature extraction, a region proposal network (RPN) for exploring the bounding box location of object candidates, and a Fast R-CNN classifier for the object candidates, and it is combined in an end-to-end fashion. For the CNN, we used the ResNet-50 [17], which was pre-trained on the ImageNet dataset [18]. We applied transfer learning by fine-tuning the network on 13,000 images that were centered on either the optic disc or the fovea, sampled from our private dataset described in Section 3.1. For the RPN, we fixed the size of the object bounding boxes, since the size of the optic disc and fovea are generally constant in the fundus images. The results are given as the 2-D position and size of the bounding box, and confidence score for each object class, namely the fovea and optic disc. Here, we select the object with maximum confidence if there are more than one detection, as well as apply a threshold value of 0.9 on the confidence score, in order to determine the fovea and optic disc, respectively. For the detected fovea and optic disc, we often only use their point coordinate that is defined as the center coordinate of the bounding box.  
After the fovea and the optic disc are detected, we categorize the frames into four categories, namely, frames \(\mathcal{F}^{f\&o}\) containing both the fovea and optic disc, frames \(\mathcal{F}^{f}\) containing only the fovea, frames \(\mathcal{F}^{o}\) containing only the optic disc, and peripheral frames \(\mathcal{F}^{p}\) that do not contain either one.
# Low-Density Geopolymer Composites for the Construction Industry
## 2 Materials and Methods
### Electromagnetic Shielding Measurements  
As a quick assessment, the experimental setup aims to measure the signal strength between the transmitter and the receiver at different distances. The electromagnetic shielding experiments were conducted using the shielded box method. The transmitter (signal source) was placed in a cubic steel box of 500 \(\times\) 500 \(\times\) 500 mm. Five out of six faces of the box were mounted with metal plates of 0.45 mm thick; then they were covered with aluminum foil with a thickness of 20 \(\upmu\)m. This setup eliminates the signal transmission from the transmitter to the environment through these five faces. The geopolymer sample with the carbon grid built-in was fixed to the remaining face (see Figure 3).  
Figure 3: The box used for electromagnetic tests.  
Figure 2: Sample GP (**a**), and thermal conductivity device: HFM436 Lambda (**b**).  
For the tests, three commercially available carbon net types (Hitexbau, Augsburg, Germany) were used, which differ in their grid openings: HTC 10/15-40, HTC 21/21-40, HTC 34/34-40. Their properties are shown in Table 2.  
A Qualcomm MDM9207 chipset with a Wi-Fi module was used as the signal generator. A device equipped with the MediaTek MT8735 chipset was used as the receiver, and the signal strengths were measured with the Wi-Fi Analyzer software. Both devices were tuned to a frequency of 2432 MHz. Measurements of the signal strength were performed between the transmitter and the receiver at specific distances.
# Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
## 4 Experiments and Analysis of Results
### Visualization of the Results  
Figure 5 shows the visualization of defect detection on the MCSD dataset for a qualitative assessment. Each row in the figure indicates a different type of defect. The outputs from YOLOv7-tiny, YOLOv5n, and YOLOv8 are displayed in the third, fourth, and fifth columns, respectively, and the first column displays the findings from our model and three comparable algorithms.  
Our model excelled notably in pinpointing small and subtle defects, as can be seen in the first column, particularly in challenging scenarios, such as edge anomalies or defects with contours that are not clearly defined against their backgrounds. This proficiency  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **4 B-Leakage** & **6 B-Pit** & **9 C-Mushy Spot** & **12 C-Chipping** & **14 D-Defect** & **16 D-Soiling** \\ \hline YOLOv5n & 82.4 & 95.0 & 84.3 & 89.9 & 75.0 & 82.4 \\ YOLOv7-tiny & 66.7 & 62.5 & 71.3 & 82.1 & 60.5 & 63.9 \\ YOLOv8n & 92.5 & 72.1 & 86.1 & 88.9 & 42.0 & 71.9 \\ Ours & 97.3 & 99.9 & 91.0 & 91.3 & 75.5 & 95.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative analysis of defect detection performance across YOLO models (AP %).  
Figure 5: Comparison of results of different models on the MCSD dataset.  
is evident in the rows showcasing these complex defect categories, in which our model consistently outperformed the others in detection clarity and accuracy.  
The second, third, and fourth columns validate the comparative performance of the other algorithms. While YOLOv5n, YOLOv7-tiny, and YOLOv8 exhibited varying degrees of success, they each demonstrate certain limitations in detecting less conspicuous defects--a critical aspect in which our model demonstrates its robustness.
# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 4 Results and Analysis  
### Correlation Analysis of Model Variables  
To analyze the correlation between SM and its predictors in different seasons and areas, the training set was divided into summer and autumn seasons. It was also divided into northwest (NW) and southeast (SE) areas based on a 3000 m elevation threshold, as shown in Figure 1a. The sample sizes for Total, summer, autumn, NW, and SE were 8670, 3465, 5025, 4219, and 4451, respectively. Figure 3 displays bar (a) and stacked bar (b) plots for the CC analysis. The stacked bars ignore the positive and negative CCs, and CCs with \(p\)-values more than 0.05 are in black. Figure 3 shows the following: (1) in the total set as shown in Figure 3a, all CCs except for the slope passed the significance test with a 95% confidence interval. Elevation, slope, and day sequence had large negative correlations with SM. The remaining predictors were positively correlated with SM, and the CCs from high to low were longitude, LST, EVI, ET, LCT, latitude, AM/PM, and NDVI. Although some predictors had low CCs, they would also work in nonlinear models. (2) The largest contributing factors were slope, elevation, day sequence, and longitude. These factors had the top four CCs in the total set and the subset, indicating that terrain and spatiotemporal information had a significant impact. Day sequence, ET, latitude, LST, and EVI were more effective in autumn than in summer and in the NW area than in the SE area. (3) In autumn, all \(p\)-values were less than 0.05. In summer, the \(p\)-values of several variables exceeded 0.05. Except for the slope, elevation, and day sequence, the LCT, AM/PM, and aspect were influential in summer, while others had low CCs. (4) Regionally, most of the CCs were relatively high in the NW area. In the SE area, predictors had little or no influence except for the elevation, longitude, and slope. (5) The EVI was a better representation of vegetation effects than the NDVI. The EVI and NDVI were more influential in autumn than in summer. The aspect had a greater influence in the NW area than in the SE area. Figure 3c shows the importance of each predictor in the RF model from high to low. It can be seen that the predictors with the highest impact were elevation, day sequence, latitude, longitude, and slope, and the impact degrees of these five features were much higher than those of the others. Thus, the SM downscaling in complex terrains should include the spatiotemporal information and terrains as inputs.
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 4 Results and Discussion  
In this section, the MPPT PV system depicted in Figure 1 is modelled and simulated using the MATLAB/SIMULINK software. The efficacy of the recommended RSA-MPPT controller is evaluated, and its performance is demonstrated through two simulation analyses, which are compared against DOA [20], WOA [23], GWO [17], and PSO [16] algorithms. Table 1 presents the settings of parameters, iteration number \(T\), and population size \(N\) of each simulated algorithm.  
The first simulation is conducted under uniform solar irradiance (USI) conditions, which entail slow and gradual changes in irradiance as well as rapid fluctuations. In the second simulation, the performance of the RSA-based MPPT controller is examined in various partial shading scenarios (PSCs).  
During all the simulations, the control of the boost and VSC is initially blocked for 0.05 s for the purpose of regulating the system. Following this, the blocking is removed, and the duty cycle of the boost converter is set to a fixed value of \(D\) = 0.5. This configuration persists until the MPPT is enabled at \(t\) = 0.4 s. The impact of this procedure is evident in the resulting waveforms.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
###### Abstract  
Global Navigation Satellite System (GNSS) is the most widely used Positioning, Navigation, and Timing (PNT) technology in the world today, but it suffers some major constraints. Locata is a terrestrial PNT technology that can be considered as a type of localised \"constellation\", which is able to provide high-accuracy PNT coverage where GNSS cannot be used. This paper presents a comprehensive literature review of the Locata technology and its applications. It seeks to answer questions, such as: (1) What is Locata and how does it work? (2) What makes Locata unique compared with other terrestrial positioning systems? (3) How has Locata been used in different applications for accurate PNT? (4) What are the current challenging issues that may restrict its further adoption for custom-grade navigation in urban environments?  
Locata; PNT; terrestrial constellation 10 March 2019; Accepted: 14 April 2019; Published: 16 April 2019
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution  
### Early Foundations (1993-2007)  
The foundation of modern medical image analysis was solidified in 1993 when [PERSON] et al. introduced a systematic approach to CT-based diagnosis, highlighting the modality's superior contrast resolution and its capability to visualize internal organs clearly without structural overlap. This method proved particularly effective for diagnosing abdominal and pelvic trauma in selected patient populations, demonstrating distinct advantages over other diagnostic techniques [5]. Further advancements were documented in 2007 by [PERSON] et al. in a comprehensive study involving 245 patients with blunt abdominal trauma. Their retrospective analysis of CT scans correlated with surgical outcomes and clinical follow-ups established the reliability of CT for this purpose. The study reported a sensitivity of 97% and specificity of 95%, validating CT [6]. Concurrently, [PERSON] et al. (2007) focused on vascular emergencies in liver trauma, developing specialized CT protocols to identify and grade various conditions such as lacerations, parenchymal hematomas, contusions, and active bleeding. Their work emphasized the diagnostic precision of CT in assessing vascular complications and elementary lesions in liver trauma, contributing significantly to the management and treatment strategies based on early diagnosis and possible arterial embolization [7].
## 3 Methods
### Machine Learning Estimation of the PL
#### 3.1.2 Machine Learning PL Estimation  
As the IDP PL estimation is too time-consuming to be used in a GA-like optimization algorithm, we investigate if this advanced PL model can be well approximated by an ML model. Here, we applied the Ensembles Trees: Bagging algorithm [36; 37]. Aggregation bootstrap, or bagging, utilizes various categorization techniques and regression techniques for reducing the uncertainty connected with forecasting and thus boosts the prediction process. It is a straightforward idea: for the available data, several samples of bootstraps are taken, each bootstrap sample is applied to the prediction process, and then the observations are combined [37]. The ML algorithm can estimate the output (i.e., dependent variable) after being fed with the inputs (i.e., independent variables). To do this, with the desired accuracy, the algorithm must be trained/validated to learn which is the output that corresponds to the given inputs using a training dataset (number of samples where the inputs and the corresponding true output are known). During the training/validating process, the ML algorithm iteratively adjusts its coefficients to minimize \(MAE\) between the predicted and the true path loss using Equation (2)  
\[MAE=\frac{1}{N}\sum\limits_{i=1}^{N}{{|PL_{ML}}-{PL_{WHIP}|}} \tag{2}\]  
where \(N\) is the number of observations in the training/validating dataset, \({PL_{WHIPP}}\) is the PL estimated by WHIPP's IDP model for observation \(i\), and \({PL_{ML}}\) is the PL estimation made by the ML algorithm using a vector of inputs. To prevent overfitting during the training/validation process, we use the _k_-cross validation technique [38]. Cross-validation partitions the dataset in \(k\) subsets, while using _k-1_ subsets to train the network and the remaining _k-th_ subset to validate the algorithm and calculate the \(MAE\). The procedure is repeated \(k\) times until each subset of the \(k\) subsets has been used as the validation dataset. Another metric is R\({}^{2}\) (ranges between [0,1]), representing the proportion of the variance for a dependent variable (output) that is explained by an independent variable or variables (inputs) in a regression model.
## 3 System Model and Preliminaries
### Tracing Rays in a Layered Underwater Environment  
Ray tracing is referred to the process of predicting the location of acoustic signal at different points and deducing a propagation path. Since sound is characterized as a sequence of pressure waves that propagates in underwater environment, ray tracing attempts to find the path taken by each ray where a ray has an infinitesimal beam width. Moreover, since the beam width of the ray is approaching zero, a ray is not allowed to spread along its propagation path. Therefore, a ray trace is represented using a curve where the slope along the curve is SSP-dependent. When a ray crosses a layer with a linear gradient \(g\), it experiences similar refraction conditions at each point within that layer that are governed by the law of refraction (Snell's law) and represented mathematically as:  
\[\xi=\frac{\cos\theta_{0}}{c_{0}} \tag{2}\]  
Figure 3: 3D layered model with a node \(n\) located at the origin of the coordinate system.  
where \(\theta_{0}\) and \(c_{0}\) are the ray gazing angle and SS observed at some point, respectively. Hence, the propagating ray within a layer will bend with a constant rate and produce a curved path that can be approximated by a circular arc with radius of [25]:  
\[R=\frac{1}{g\xi} \tag{3}\]
# Aerospace
## 7 Venus Small Satellite Constellation
### Mission Requirements  
1. The mission should insert a set of five 25 kg SmallSats to 200 \(\times\) 2000 km near-polar orbits with inclinations of 80 deg., 85 deg., 90 deg., 95 deg., and 100 deg.
2. The total \(\Delta V\) available for targeting and orbiter deflection maneuver is 45 m/s.
# 5G UFMC Scheme Performance with Different Numerologies
## 3 Numerical Results and Discussion  
In this section, we carry several simulations for different FFT sizes and M-QAM values. In all cases, the Dolph-Chebyshev filter has a 60-dB side-lobe attenuation.  
Simulation results clearly show that the BER performance of the UFMC modulation scheme is increased when the FFT size increases, as it is seen from the comparison of Figures 4-7. BER values are comparable to the ones found in the literature [12].  
The most important result is that, in the case of 2048 FFT, different Dolph-Chebyshev FIR filter lengths do not affect the BER performance both for the 64 and 256 QAM. These results open the way for using UFMC, mainly the 256 QAM, without taking into consideration the filter length that could insert computational complexity in the UFMC transceiver.  
Power Complementary Cumulative Distribution Function (CCDF) curves provide critical information about the signals encountered in 5G systems. These curves also provide the peak-to-average power data needed by component designers. Within this regulation, we performed CCDF calculations for different combinations of modulations, used FFTs, and Filter Lengths [14].  
Figure 4: UFMC BER versus (Eb/N0) dB for various Filter Lengths in the case of 1024 FFT and 256 QAM.  
Figure 3: Power Spectral Density (PSD) plots versus normalized frequency for (**a**) OFDM and (**b**) UFMC modulation schemes.  
Figure 5: UFMC BER versus (Eb/N0) dB for various Filter Lengths in the case of 2048 FFT and 256 QAM.  
Figure 6: UFMC BER versus (Eb/N0) dB for various Filter Lengths in the case of 1024 FFT and 64 QAM.  
Figure 7: UFMC BER versus (Eb/N0) dB for various Filter Lengths in the case of 2048 FFT and 64 QAM.  
In Figures 8-11, we have shown the CCDF of the PAPR curves for 64 QAM, and 256-QAM, respectively, and 1024 and 2048 FFT for different filter lengths. In all cases, filter length impacts the system performance in different ways. Thus, it is critical to calculate the CCDF values for a filter length that is comparable to the channel length (or CP length in CP-OFDM systems). This argument was also proposed in the SoTA UFMC system [7; 15; 16; 17].  
Figure 12 shows UFCM CCDF versus PAPR (dB) for Filter Length equal to 40 and different FFT and QAM. As it is seen, different FFT lengths affect the system performance in terms of CCDF values.  
Figure 11: UFCM CCDF versus PAPR (dB) for various Filter Lengths in the case of 2048 FFT and 64 QAM.  
Figure 10: UFCM CCDF versus PAPR (dB) for various Filter Lengths in the case of 1024 FFT and 64 QAM.
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 2 Literature Review  
In this section, we briefly survey the existing resource allocation solutions in the LoRaWAN network. We divide these solutions into three broad categories: Interference-based, link- and system-based, and mathematical model-based approaches. The primary aim of these approaches is to enhance the packet success ratio.