esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Cost Circularly Polarized Antenna for IoT Space Applications
## 4 Conclusions  
A comparison with a 4-element antenna available in the state-of-the-art is summarized in Table 2. As expected, the proposed work provides a better performance in RHCP gain and bandwidth for a smaller maximal dimension. It should also be noted that the use of a higher cost and lower-loss material (tan\(\delta\) = 0.001) would increase the antenna gain by 0.6 dB. Despite the use of a higher loss FR4 substrate, the proposed solution was successfully used to communicate with the LS1 LEO satellite from Lacuna [19] over a 617 km distance with 14 dBm output power.  
**Author Contributions:** Conceptualization, F.F.; methodology, F.F. and L.H.T.; software, F.F.; validation, L.H.T. and F.F.; formal analysis, F.F.; investigation, N.V.T.; resources, F.F.; data curation, L.H.T.; writing--original draft preparation, F.F.; writing--review and editing, L.H.T.; visualization, L.H.T.; supervision, F.F.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is funded by Graduate University of Science and Technology under grant number GUST.STS.DT2017-TT01.  
**Acknowledgments:** This research is funded by Graduate University of Science and Technology under grant number GUST.STS.DT2017-TT01. The CREMANT is acknowledged for support on radiation measurements. Lacuna is deeply thanked for the experiment using the LS1 satellite.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & [12] & [13] & [14] & [15] & **Proposal** \\ \hline
**Max. Dimension \({}^{\star}\)** & 0.19\(\lambda\) & 0.24\(\lambda\) & 0.25\(\lambda\) & 0.26\(\lambda\) & 0.21\(\lambda\) \\ \hline
**Center Freq. in MHz** & 919 & 866.5 & 915 & 915 & 868 \\ \hline
**Substrate tan\(\delta\)** & 0.002 & 0.007 & 0.008 & 0.02 & 0.02 \\ \hline
**Peak RHCP Gain in dBic** & 2.2 & 2 & 2.4 & \(-\)0.6 & 3.14 \\ \hline
**3 dB RHCP Gain Freq. BW** & 1.6\% & N.A. & 2.8\% & 2\% & 5\% \\ \hline \(-\)**3 dB Beamwidth** & 150\({}^{\circ}\) & 120\({}^{\circ}\) & 120\({}^{\circ}\) & 85\({}^{\circ}\) & 117\({}^{\circ}\) \\ \hline \end{tabular} \({}^{\star}\)\(\lambda\) is the wavelength at the center frequency.  
\end{table}
Table 2: Performance comparison with the state-of-the-art.  
Figure 11: Measured LHCP and RHCP Gain versus theta for phi = 0\({}^{\circ}\) and 90\({}^{\circ}\).
# Fast Ion Speed Diffusion Effect on Distributions of Fusion Neutrons
## 1 Introduction
From the point of view of the development of the physics basis, as well as the reliability and the computational speed of integrated modelling, the use of analytical results is beneficial. The formulae for distributions of fusion products found in [25] themselves are general, i.e., suitable for arbitrary distributions of velocities of fuel nuclei. Either distributions obtained experimentally can be plugged in, or those calculated numerically. In [25], a compact simplified analytical model of anisotropic fast ion distributions neglecting the speed diffusion effect on high-energy tails was applied as a sample case, which is convenient, being expressed in a closed form. The purpose of this work was to figure out to what extent such a simplification is plausible. The overall population of energetic particles strongly influences the neutron production [22]. Both in the DEMO-FNS classical tokamak [5] and in the FNS-ST spherical tokamak [4], contributions of beam-plasma interactions to the total fusion rates are either on par with or exceed thermonuclear rates, i.e., contributions of Maxwellian populations of fuel nuclei. It is therefore preferable to evaluate the sensitivity of neutron spectra to modifications of high energy ion distribution tails caused by speed diffusion. Section 2 describes the mathematical models used for this purpose, and Section 3 presents the results, followed by a summary given in Section 4.
## 3 Machinery Monitoring  
In this chapter, we introduce a novel method for monitoring the status of machinery. The monitoring process involves the following steps: (1) collection of signals pertaining to machinery status using add-on sensors; (2) filtering and normalization of collected signals; (3) extraction of feature values and the export of each piece of data as a data point using windows with a designated range; (4) dimensionality reduction using LDA; and (5) input of results into neural network for training and generation of neural network parameters to enable the recognition of data pertaining to machinery status.
# Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials
## 1 Introduction
Utilizing interval analysis methods, [PERSON] [17; 18; 19] examined the effects of uncertainties in several design parameters on rotors, including stiffness, damping, Young's modulus, cracking, and coupling misalignment. The interval analysis approach allows for the omission of specific probability distribution models for the uncertain parameters, requiring only the definition of their upper and lower fluctuation bounds. However, in scenarios characterized by multi-source uncertainty, this method necessitates the computation of combinations of specified integration points, such as Gaussian integration points, for multi-dimensional interval variables, thus significantly increasing the computational workload. To enhance computational efficiency, dimensionality reduction can be achieved through sparse generalized polynomial chaos expansion (gPCE) or polynomial dimensional decomposition [20; 21; 22]. [PERSON] [23] employed a polynomial dimensional decomposition method to analyze the uncertainty in the response of a nonlinear rotor system, achieving effective dimensionality reduction. Nonetheless, when considering the nonlinear factors of the rotor system (e.g., nonlinear oil film damping), balancing computational efficiency and accuracy remains an area requiring further investigation. The remainder of this paper is organized as follows: Section 2 presents the proposed methodology; this is followed by Section 3, which introduces an example of the research subject, namely a dynamically similar rotor tester for a turboshaft engine. Section 4 then discusses the results of the numerical analysis. Finally, Section 5 concludes the paper.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 4 Experimental Results and Analysis
### Experimental Results
#### 4.2.2 Comparison with Other Models  
In order to evaluate the performance of the model, we used mAP (mean average precision) and FPS (frames per second) as the evaluation indicators for ship detection. The mAP is the average of the average accuracy of detecting each class. Because there are only ships in our experiment (i.e., only one class), the mAP was equal to the average accuracy (AP). FPS represents the number of images that can be successfully processed in one second.  
We compared the performance of YOLOv3, YOLOv4, YOLOv3-tiny, YOLOv4-tiny, SSD, and Faster R-CNN by assessing their performance in mAP and FPS. The results are shown in Table 2. It was apparent that the performance of SSD and Faster R-CNN was poorer than YOLO, and YOLOv4-tiny achieved the best performance with regards to mAP. The mAP of YOLOv4 and YOLOv3 were higher than YOLOv4-tiny and YOLOv3-tiny, respectively. The YOLOv3-tiny's FPS performance was the best. The network structures of YOLOv4 and YOLOv3 are complex and have many parameters, which is suitable for more complex tasks and a larger variety of detection tasks. However, because the goal of this paper was to detect only one class of target, i.e., ships, the YOLOv4, and YOLOv3 networks were not necessary. This was especially true when considering that these models are larger and require more powerful computing platforms. The embedded computing devices deployed on spaceborne SAR satellites must be lightweight and, therefore, less powerful. Therefore, YOLOv3-tiny and YOLOv4-tiny were better suited for our on-board detection mission, and, of the two, YOLOv4-tiny was selected because it had a higher mAP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **FPS** & **mAP(\%)** & **Model Size (MB)** \\ \hline YOLOv3 & 64 & 88.82 & 234.9 \\ YOLOv4 & 55 & 93.21 & 244.2 \\ YOLOv3-tiny & 348 & 91.16 & 33.1 \\ YOLOv4-tiny & 339 & 93.46 & 22.4 \\ SSD & 76 & 75.46 & 93.2 \\ Faster R-CNN & 17 & 88.75 & 527.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different methods in FPS, mAP, and model size.  
Figure 7: Sample images from the SAR ship dataset.
## 4 Experiments and Analysis
### Ablation Studies
#### 4.3.3 Scale Shift for Foggy Weather Adaptive Detection  
There was a potential scale shift between the source and target domain datasets. To investigate the effect of image scale on our method, we changed the size of the image in the target domain, and the scale in the source domain was fixed at 1504 pixels.  
In Figures 7 and 8, the loss variation and performance comparison of different cases in the training stage are illustrated, respectively. We analyzed the effect of the scale shift on the detection performance in these three cases.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **mAP(\%)** & **Gain** & \(AP_{0.50.95}\)**(\%)** & **Gain** \\ \hline _Source only_ & 28.7 & +0.0\% & 15.7 & +0.0\% \\ _Ours w/\(\mathcal{L}_{cls}\)_ & 38.0 & \(\uparrow\) +9.3\% & 21.8 & \(\uparrow\) +6.1\% \\ _Ours w/\(\mathcal{L}_{dis}\)_ & 43.3 & \(\uparrow\) +14.6\% & 25.8 & \(\uparrow\) +10.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison among the different loss functions on the cross-attention strategy transformer.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **mAP(\%)** & **Gain** & \(AP_{0.50.95}\)**(\%)** & **Gain** \\ \hline _Ours w/o trans_ & 36.1 & +0.0\% & 21.3 & +0.0\% \\ _Ours w/trans_ & 40.9 & \(\uparrow\) +4.8\% & 23.7 & \(\uparrow\) +2.4\% \\ _Ours w/trans+attention_ & 43.3 & \(\uparrow\) +7.2\% & 25.8 & \(\uparrow\) +4.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The validity of the proposed module on the Cityscapes to Foggy Cityscapes scenario. _‘trans’_ and _‘attention’_ represent the transformer encoder layer and convolutional block attention module, respectively.  
We plotted the detection performance at different image scales by changing the scales of the target domain images in Figure 9. As shown in Figure 9, by changing the scales under the same experimental conditions, the model achieved better results at all scales. Hence, our method was effective at solving the scale shift problem.  
Figure 8: The performance comparison between different cases in the training stage. We chose three kinds of experimental setup for comparison: (1) Ours w/trans (Orange), (2) Ours w/trans+attention (Green), and (3) Source only (Blue).  
Figure 7: The training loss variation between the different cases. We chose three kinds of experimental setup for comparison: (1) Ours w/trans (Orange), (2) Ours w/trans+attention (Green), and (3) Source only (Blue).
# Prosthesis Embodiment in Lower Extremity Limb Loss: A Narrative Review
## 5 Discussion
### Future Work  
First, despite ownership being the main factor in the prosthesis embodiment of the lower limb, very few studies have been conducted on this topic. The apparent contradiction between preferring a natural-looking limb and a brushed metal-looking limb suggests that much is not understood about the topic.  
Studies suggest a link between embodiment and prosthesis use, suggesting that studies showing increased embodiment can serve as a proxy for reduced abandonment. However, a direct connection between embodiment and long-term outcomes remains unproven, and the studies included in this review did not comment on abandonment. Further, what differentiates between a prosthesis that is used versus one that is abandoned is multi-factorial and might be measured better by the sum of multiple PROMs, pointing to the addition of a more complete concept of embodiment when assessing overall prosthesis satisfaction. It may be worthwhile to study how PEmbs-LLA is used in conjunction with other PROMs in clinical settings mentioned in [18], such as the Trinity Amputation and Prosthesis Experience Scales-Revised (TAPES-R), the Prosthesis Evaluation Questionnaire (PEQ) and Satisfaction with the Prosthesis Score (SAT-PRO), Beck's Depression Inventory, and the 36-Item Short Form Survey (SF36).  
The multifaceted nature of embodiment demands progress across multiple domains, with standardization of assessment scales emerging as a critical priority. This standardization is essential for a meaningful comparison of possible interventions, especially given the limited pool of patients available to test new developments. In addition to the lack of standardization, as highlighted by [75], few studies have examined how embodiment evolves over time or tracked long-term results, underscoring the need for more longitudinal research. Finally, while a prosthetic device that incorporates all of the technologies surveyed in this review has been designed, it has not yet been built. It remains to be seen how all the parts come together as a whole. These metrics are necessary for systematic reviews to recommend updates to the standard of care.  
In addition to addressing embodiment, there are practical limitations of these techniques that can be improved. Future work is underway to reduce the risks for patients with peripheral vascular disease for both the AMI approach and OI. Given that diabetes, peripheral vascular disease, and neuropathy are the leading causes of lower-limb amputation, managing the risk of infection would greatly improve the applicability of such techniques to improve embodiment and reduce abandonment.
## 4 Seismic Response Analysis of Isolated Structure Considering Negative Stiffness
### Seismic Response  
Three natural seismic waves are used for time history analysis: EL Centro, Pasaden, and Santa Barbara Courthouse. The artificial waves synthesized from the isolation response spectrum are REN1 and REN2. The specific information of each natural seismic wave is shown in Table 2, and the response spectrum after peak shaving is shown in Figure 20. The peak value of the seismic wave is taken as 0.3-0.6 g. The models used for analysis are mainly the traditional lead rubber bearing isolation model (LRB), the isolation model with negative stiffness and lead rubber bearing synergy (NSD), and the isolation model with damping negative stiffness and lead rubber bearing synergy (DNSD). Figure 19: Simplified mechanical model of the negative-stiffness device. Figure 18: Equivalent simplified model of isolated structure. Table 3 shows the comparison of absolute acceleration response and displacement response results of LRB and DNSD isolation layers under the action of local seismic waves. It can be seen from Table 2 that, when the seismic peak value is 0.3 g, 0.4 g, 0.5 g, and 0.6 g, the acceleration response of DNSD relative to LRB decreases by 14.31%, 10.16%, 14.23%, and 16.20% on average; the displacement response of the isolation layer decreases by 12.44%, 8.72%, 10.17%, and 12.21% on average; and under all working conditions, the acceleration response of DNSD relative to LRB decreases by 13.73% on average, and the displacement response of the isolation layer decreases by 10.89% on average. \begin{table}
\begin{tabular}{c c c} \hline
**Earthquake Name** & **Recording Station** & **PGA/g** \\ \hline Imperial Valley-02 & El Centro Array \#9 & 0.839 \\ Kern County & Pasadena—CIT Athenaeum & 0.176 \\ Kern County & Santa Barbara Courthouse & 0.322 \\ REN1 & — & 0.200 \\ REN2 & — & 0.200 \\ \hline \end{tabular}
\end{table} 
Table 2: Seismic-wave recording parameters. Figure 20: Seismic wave response spectrum (after peak shaving). Table 4 shows the comparison of acceleration-response and displacement-response results of LRB and NSD isolation layers under the action of local seismic waves. According to Table 3, when the seismic peak value is 0.3 g, 0.4 g, 0.5 g, and 0.6 g, the acceleration response of NSD relative to LRB decreases by \(-2.50\%\), 0.07\(\%\), 4.45\(\%\), and 10.66\(\%\) on average; the displacement response of the isolation layer increases by 7.08\(\%\), 6.02\(\%\), 3.98\(\%\), and 4.91\(\%\) on average; and under all working conditions, the acceleration response of NSD relative to LRB decreases by 3.17\(\%\) on average, and the displacement response of the isolation layer increases by 5.50\(\%\) on average.
# Composite Foundation Settlement Prediction Based on LSTM-Transformer Model for CFG
###### Abstract  
Roadbed construction typically employs layered and staged filling, characterized by a periodic feature of 'layered filling--filling interval'. The load and settlement histories established during staged construction offer crucial insights into long-term deformation under filling loads. However, models often rely solely on post-construction settlement data, neglecting the rich filling data. To accurately predict composite foundation ground (CFG) settlement, an LSTM-Transformer deep learning model is used. Five factors from the 'fill height-time-foundation settlement' curve are extracted as input variables. The first-layer LSTM model's gate units capture long-term dependencies, while the second-layer Transformer model's self-attention mechanism focuses on key features, efficiently and accurately predicting ground settlement. The model is trained and analyzed based on the newly constructed Changsha-Zhuzhou-Xiangtan interior railway section CSLLXZQ-1, which has a CFG pile composite foundation. The research shows that the proposed LSTM-Transformer model for the settlement prediction of composite foundations has an average absolute error, mean absolute percentage error, and root mean square error of 0.224, 0.563%, and 0.274, respectively. Compared to SVM, LSTM, and Transformer neural network models, it demonstrates higher prediction accuracy, indicating better reliability and practicality. This can provide a new approach and method for the settlement prediction of newly constructed CFG composite foundations.  
L +
Footnote †: journal: Applied sciences
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Signal Processing
#### 2.1.2 Mass Balance  
The working principle of mass balance method for leak detection is straightforward [17, 18]: if a leak occurs, the mass balance equation (accounting for the input and output mass flow rates and also the line pack variable rate) presents a systematic deviation. Although simple and certainly reliable, the primary difficulty in implementing this principle in practice derives from the huge variations experienced by the line pack term. This effect implies a very long detection time and, therefore, a frequently unacceptable leaked mass before an alarm is declared. Although the principle of mass balance method is simple, this method is very sensitive to arbitrary disturbances and dynamics of the pipeline, which may lead to false detection issues.
###### Abstract
As the focus of this research is mainly on masonry structures or combined masonry and concrete/timber structures, the most important NDT methods for existing masonry structures can be summarized: visual inspection [19], rebound hammer [20; 21], measurement of reinforcement location [22], stress wave transmission [23], ultrasonic velocity testing [24], sonic velocity testing [25; 26], surface penetrating radar [25; 27], infrared thermography [28], flatjack tests [29; 30], damage identification using impact vibrations [31], acoustic emission [32], etc. The basic advantages and disadvantages of the mentioned methods are described in Table 1. More detailed information can be found in [33]. The issue of seismic vulnerability assessment and restoration of underperforming existing buildings is a very significant and difficult problem [34]. To develop efficient assessment methods, it is necessary to draw knowledge and information from various other scientific fields. The seismic vulnerability requires specialized technical skills [35]. The preliminary work on seismic assessment was based only on visual inspection of the buildings [19; 36], NDT [15; 24; 32; 37; 38], and the experience of an engineer. The last decade has seen a growth in the technological advancement of various instruments that can considerably help in the estimation of structural safety and seismic behavior of existing structures. The applicability of the thermal camera for building diagnostics and detection of energy-related building defects has been investigated by numerous authors [39; 40; 41; 42; 43; 44]. The importance close-range remote sensing and using multispectral cameras for damage assessment and crack detection on buildings was emphasized by numerous researchers [45; 46; 47]. In the last few years, a growing number of studies have shown the great potential of hyperspectral cameras for detection and mapping cracks on buildings [48; 49]. Unmanned aerial vehicles (UAV) can be used for i.e., disaster management [50; 51], crack identification using image processing [47; 52], seismic vulnerability [53], architectural assessment of heritage sites and structures [54], and structural assessment using image processing techniques [55]. UAV-based photogrammetry [56; 57; 58] and LiDAR (Light Detection and Ranging) [59; 60; 61] devices are indeed used after earthquakes to scan affected areas, but this method is mostly used for larger areas only. The advantages and disadvantages of the new technologies which should complement traditional methods are given in Table 2. Mentioned techniques are especially proven to be good at a post-earthquake evaluation of the complete urban/rural areas and buildings individually.