esa-sceva/satcom-chunk-collection huggingface dataset:
# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 4 Result and Discussion
### Training Details
#### 4.2.2 Image Resolution Transformations Based on Station Spacing  
Figure 9 shows the training details on the original datasets and three augmented datasets by using the station spacing strategy. It can be seen that four types of loss functions on the verification set decrease when the training epochs grow. The model's performance is not significantly improved when trained on the two times datasets. The values of _cls_loss_ that trained on four times and eight times datasets decrease rapidly compared to the origin and two times datasets, and the final value is basically the same. At the same time, we use precision, recall, and mAP to evaluate the performance of different augmented datasets. The precision, recall, and mAP have fluctuated during the model training on the original datasets. When the training datasets were augmented two times, four times, and eight times, these three metrics gradually became stable, proving that the augmented datasets can increase the robustness of the training model. Although the model can converge fastest and is the most stable at the eight times datasets, the values of precision, recall, mAP_0.5, and mAP_0.5:0.95 reach the maximum when the model training on the four times augmented datasets. The station spacing strategy considers the effect of the setting parameters of the GPR device, which can augment the datasets by setting different station spacing when collecting the data.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Moderate-Power Thrusters
#### 4.2.1 Hall Effect Thrusters
In studies in which conducting plates were placed throughout a vacuum chamber, it was observed that the thruster was sensitive to the bias of these plates, but the overall performance did not change greatly as the cathode was moved closer to them [64]. This indicates that the electrical effect of the vacuum vessel on the thruster exists, but may be subtle. Another open question in Hall thruster research is the nature of the various instabilities exhibited in these devices. The presence of a wide variety of oscillations in Hall thrusters has been known for decades [65], extensively examined theoretically [66; 67; 68; 69; 70], and studied in numerical simulations [71; 72; 73; 74; 75]. Despite the fact that many of these instabilities are expected to play a significant role in the fundamental operation of Hall thrusters, most of them are still poorly understood and inconsistently reproduced in simulations. A final aspect of moderate-power Hall thrusters that still requires research is lifetime qualification for these devices. Lifetime testing in ground facilities is still the primary tool for verifying the long-term performance of a thruster, which is logistically taxing due to stringent testing requirements [60] and ultimately delays the application of new designs. [PERSON] et al. echoed this sentiment in [76], opining that the standard 150% lifetime test requirement is impractical and further arguing the following points: standalone tests cannot establish reliability; more ambitious deep space missions increase the qualification demands on thrusters; and time-dependent failure modes may not be captured due to differences between ground and flight throttling profiles.
# Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City
## 2 Materials and Methods  
### Study Region and Soil Sampling  
The study region (114\({}^{\circ}\)24\({}^{\prime}\)-114\({}^{\circ}\)32 E, 36\({}^{\circ}\)33\({}^{\prime}\)-36\({}^{\circ}\)40\({}^{\prime}\) N) covers an area of about 160 km\({}^{2}\). It is located in Handan City, south of Hebei Province. It is an important national highway transportation hub city in China. The area features a continental monsoon climate with an average annual temperature of 13.5 \({}^{\circ}\)C. Handan city is known as the modern \"steel capital\". Its industry is developed, and it is also an important source of grain, cotton and other crops. The western part of the study area is an industrial zone, with and iron and steel group, fertilizer plant, asphalt-processing plant, refractory plant, metallurgical machinery plant, cooking plant, concrete company and other enterprises. Farmland is scattered in the eastern and southwestern parts, and a thermal power plant exists in the northeast. These activities cause heavy metals to enter the soil. The topsoil is comprised of loess and subclade.  
Based on the 1.5 km \(\times\) 1.5 km grid pattern of the whole study region, combined with the terrain distribution of the study area, 64 surface soil samples were collected. The coordinate of each sampling location was located by GPS. The distribution of sampling points is presented in Figure 1. Each sample consisted of five subsamples within 10 m of a specific sampling location. Each sample was packed in 1 kg and brought to the laboratory.
## 3 Results
### Evaluating the Efficiency of the YOLO v10 XE Model in Analyzing Building Roof Topologies
#### 3.2.4 ANOVA (Analysis of Variance)  
To further elucidate the efficacy of the YOLO model, an experiment based on an analysis of variance (ANOVA) was devised. This analysis assesses whether there are statistically significant differences in the effectiveness of the YOLO model between the two types of house roofs detected in the orthophoto images. Two classes of objects were included in this study: houses with gable roofs and others. The performance of the model was assessed using three metrics: precision, recall, and F1-score. The F1-score is a measure of the ability of the model to detect each class but realized for each subarea.  
The formulas and results of the ANOVA analysis are described in detail in Appendix A.2. The results indicate that the differences between roof types are on the verge of statistical significance (\(p=0.07\)), which is likely due to the dominance of gable roofs in the training dataset and the limited representation of other roof types. These factors emphasize the need for a more balanced and diversified dataset to improve the robustness of the analysis.  
The ANOVA analysis conducted allowed a detailed evaluation of the YOLO model, revealing differences in detection performance between the two roof types. These results suggest the need for further tuning of the model and the need to supplement the training data with examples of other roof types. Marginalizing these differences would produce more consistent results and improve the overall performance of the model.
## 2 Materials and Methods
### Model Development
#### 2.3.2 Performance Evaluation  
Four metrics were used in this study for injury severity classification, namely accuracy, true positive rate (TPR), false positive rate (FPR), and area under the curve (AUC)-ROC. The overall performance accuracy of the model was measured for a given test dataset by calculating the proportion of the correct predictive results of all samples. TPR characterizes the number of identified positive samples over all positive samples. FPR refers to the proportion of identified negative cases to all negative cases. AUC is a widely used indicator that quantifies the classification performance by calculating the area under the ROC curve, where ROC can be obtained by plotting TPR to FPR. The formulation of accuracy, TPR, and FPR is shown in Equations (13)-(15).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{13}\]  
\[TPR=TP/(TP+FN) \tag{14}\]  
\[FPR=TP/(TN+FP) \tag{15}\]  
where _TP_, _TN_, _FP_, and _FN_ are the number of true positive, true negative, false positive, and false negative samples, respectively.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 5 Visual Simulations and Analysis (Constellation Scenarios)
### Moon-Based Constellation Simulation  
The Moon's side of the Cislunar space, on the other hand, is the most critical area with strategic importance to reach deep space significantly in the near times. The Artemis missions are the most recent trials to retake humans to the Moon and make a habitation on it. The Artemis-2 will take the gateway to Near Rectilinear Halo Orbit (NRHO) and will need assistance to sustain for the long term.  
Figure 9: SRP analysis for Earth orbits (Cislunar scenario) [16]. (**a**) SRP Force variation-Normal operations (Newtons); (**b**) SRP Torque variation-Normal operations (N-m); (**c**) SRP Force variation-Critical conditions (Newtons); (**d**) SRP Torque variation-Critical operations (N-m).
###### Abstract  
To explore a more accurate prediction method for subgrade settlement induced by underpass construction, this study takes the existing railway project of Ningbo Yuanyi Road underpass as a case to construct a subgrade settlement prediction model based on the Mamba neural network. Using monitoring data collected using on-site automated monitoring robots as the data foundation, the prediction results of the improved transformer, long short-term memory (LSTM), time-series dense encoder (Tide), and decomposition-linear (Dlinear) neural networks are compared. The research results show that the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the proposed Bi-Mamba model are 0.279 and 0.276, respectively, demonstrating higher prediction accuracy than comparative models such as iTransformer and LSTM. Additionally, ablation experiments verify that the attention gating module in the model reduces the MSE by 9.1%, serving as a key component for improving accuracy. This study provides an advanced data-driven prediction method for subgrade settlement forecasting, offering technical references for similar engineering projects.  
Mamba; deep learning; railway engineering; subgrade settlement Article
# CNT-PUFs: Highly Robust and Heat-Tolerant Carbon-Nanotube-Based Physical Unclonable Functions +
## 6 Stable Key Extraction from CNT-PUFs
### Entrolment
Finally, we merged the CNT-PUF cells in Algorithm 2, yielding random responses of 0 s and 1 s, which assemble a PUF response. In this procedure, we assumed a Random Number Generator (RNG) that produces unpredictable and uniformly distributed random numbers \(\delta\in_{R}\{0,1\}\). The algorithm always selects the cell with the most-significant distance to \(I_{th}\) either from \(L_{0}\) if \(\delta=0\) or from \(L_{1}\), otherwise, until \(n_{bits}\) are generated, or one of the lists \(L_{0}\) or \(L_{1}\) is exhausted. The resulting list of cell indices forms the challenge \(C_{p}^{sel}\) of the CNT-PUF. The cells with the greatest distance from \(I_{th}\) are selected first because these cells indicate the highest stability and, thus, the lowest probability that some of the future responses will be located on the opposite side of \(I_{th}\). ```
1:procedureSelectRandomPUFCells(\(L_{0}\), \(L_{1}\), \(n_{bits}\))
2:return : \(C_{p}^{sel}\)
3:for\(i\in[0,1,\ldots,n_{bits}-1]\wedge i<|L_{0}|\wedge i<|L_{1}|\)do
4:\(\delta\in_{R}\{0,1\}\)
5:if\(\delta==0\)then
6:\((c_{id},r_{p}^{i},d_{min})\gets pop(L_{0})\)
7:else
8:\((c_{id},r_{p}^{i},d_{min})\gets pop(L_{1})\)
9:endif
10:\(C_{p}^{sel}\to append(c_{id})\)
11:endfor
12:endprocedure
```  
**Algorithm 2** Selection of random PUF cells using a Random Number Generator (RNG). The enrolment process must occur in a secure environment, typically after manufacturing. Furthermore, \(C_{p}^{sel}\) is transferred via a secure channel to the entity that will store the challenge for later use in requesting the PUF response during the reconstruction phase. As is the case for any other helper data scheme, the integrity of the challenge \(C_{p}^{sel}\) must be preserved. One method could be using message authentication codes to prevent unauthorised modifications of the helper data. Furthermore, we were able to generate fully stable responses based on the available dataset, making the response suitable for cryptographic applications. Because not only the distribution of conducting and non-conducting cells is random, but also the distance from the threshold \(I_{th}\) is unpredictable as well, the cell selection from \(L_{0}\) and \(L_{1}\) leaks no vulnerable information. Note that this algorithm is only executed once, typically after manufacturing. Therefore, the cells can be measured and ordered utilising high-resolution analogue-digital converters. Subsequently, the generated helper data are converted into a challenge. The reconstruction process, executed during runtime, is very lightweight, involving only comparison measurements with a fixed threshold, which can be implemented using a significantly less-complex circuit. A detailed explanation of this process is provided in the subsequent section.
# Compact Beam-Scanning Reflectarray Antenna with SLL Reduction Using In-Plane Panel Translations
## 3 Multi-Stage Design  
In order to achieve good scanning performance, the RA response needs to be tailored to the entire panel translation path, rather than to a fixed position and radiation angle. This path can be discretized into a set of positions \(\bar{d}=\{d_{0},d_{\pm 1},d_{\pm 2},\ldots\}\), each with a matching radiation angle \(\theta_{k}\). For every \((d_{k},\theta_{k})\) pair, a unified phase law can be calculated using (2). Then, a multi-focal optimization algorithm is required to calculate a single phase law for the RA panel that achieves a balanced radiation performance among several panel positions.  
Figure 1: Mechanical beam-scanning RA antenna using linear in-plane movements: antenna optics (**left**) and illustration of the relation between the panel position and the steering angle (**right**).  
The multi-focal phase law should mitigate the beam de-focusing effects as the panel is translated, equalizing the performance across the entire translation range. In doing so, a drop in maximum gain and increased beam distortion are to be expected compared to a unifocal non-scanning design, as the panel cannot be fully tailored to any one specific focal position.  
In multi-focal designs, introducing a carefully selected phase reference \(\Delta\phi_{k}\) to each unifocal phase law before the optimization process can yield significant performance improvements [19; 24]. The selection of these phase references can be integrated into the optimization, but this typically requires a complex optimization algorithm to handle the simultaneous tuning of the entire phase law (i.e., the phase response of every cell), in addition to the phase references. This is a slow and complex process that can impose a limit on the number of panel positions being considered. An alternative is introduced in [17] with the multi-feed phase-only optimization algorithm (MF-POO), which will be used as part of the design process presented below. This algorithm is a multi-focal technique that integrates the phase references into the optimization process. However, it does so without requiring a large optimization process, as it relies on a series of much faster single-parameter optimizations to iteratively find the optimal solution. This reduces computational costs, while enabling the optimization of many panel positions to ensure stable antenna performance. MF-POO also introduces a weighting factor \(w_{k}\) to each configuration being optimized (i.e., each panel position for the presented antenna geometry). These can be adjusted to control the influence of each unifocal phase law in the optimization process, which in turn enables the fine control over the scan loss for a set of panel positions. Because of its fast convergence and the ability to control the scanning losses, MF-POO will be selected over other multi-focal optimization algorithms for the optimization process described below.
# Vibration Analysis and Empirical Law Definition for Different Equipment in a Civil Construction
## 2 Materials and Methods  
### Case Study  
A cut and cover tunnel was planned to be constructed parallel to a heritage construction site, around 100 m away, in northwest of Asturias, Spain (Figure 1). The excavation was done by a mechanical method, using different excavators equipped with a V-type bucket, a ripper and a hydraulic hammer as well as a bulldozer and a vibrating roller for the management of the backfill material. The hydraulic hammer was subsequently replaced by a vibration ripper due to operational considerations, such as the weakness of the rock mass, during the excavation process.  
The rock mass of the case study site is composed of Ordovician shale materials (dark shale from the Luarca Formation) which are found in a massive scale and with an 80\({}^{\circ}\) dip. Several uniaxial compression strength tests were done, obtaining a value between 12.5 and 23 MPa. Besides, the rock mass is highly fractured with several joint directions, having a medium to low rock mass rating (RMR), between 21 and 40, classified as poor-quality rock mass.  
The rock mass excavatability can be classified as diggable and/or rippable under the criterion of [PERSON] [41]. These authors define, on the Hoek-Brown abacus, the zones in which different excavation methods can be used: digging, ripping, breaker hammer and blasting. The GSI index and the point-load strength Is are the representative parameters of the rock mass.  
In this case, the rock mass can be considered blocky/disturbed, with a GSI between 20 and 40. On the other hand, an Is value between 0.5 and 1 can be deduced from the rock compressive strength. Consequently, the rock mass can be classified as diggable and/or rippable (Figure 2). These assumptions were verified during the field work, where the excavation was carried out almost totally with excavators and rippers.  
On the other hand, the backfill material was spread and compacted in different layers as the base and sub-base for the infrastructure. It is a material with a granulometry that ranges between 80 and 0.08 mm, mainly composed of sand and gravel, with less than 2.4% of fine elements. The density during the test was around 1.8 tr/m\({}^{3}\).  
Figure 1: Case study. (**a**) Cut and cover tunnel under construction. (**b**) Historical site where the field instrumentation was placed.  
Figure 2: Excavatability of the shale rock mass for Is \(<\) 3 according to [PERSON] and [PERSON] [41].
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Ablation Study  
In the ablation study, the influence of various components on model performance is verified, including the color transfer (in Section 3.3), loss function design (in Section 3.5), and data augmentation (in Section 3.6).
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
## 2 Overview of Nanomaterials in Crani Tissue Engineering
### Ceramic Nanomaterials  
Ceramic nanomaterials share characteristics of both metallic and nonmetallic elements. These nanomaterials are hard and brittle, have electrical insulation properties, and can tolerate harsher chemical environments than metallic and polymeric nanomaterials [21]. Ceramic nanomaterials display good biocompatibility, osteoconductivity, osteoinductivity, biodegradability, restorability, and hydrophilicity [21]. Examples of ceramic nanomaterials include bioactive glass, nanohydroxyapatite (nHA), and calcium phosphate, among many other oxides, phosphates, nitrides, and carbides. Because ceramic nanomaterials are similar in composition to inorganic components found in human bone, they have great potential in tissue engineering and are often used as scaffolds [21].
## 5 Conclusions  
Achieving superior quality standards and meeting consumer requirements are primary objectives for modern agricultural companies. The study's findings underscore the potential of integrating SA-driven quality control and advanced information and communication technologies (ICT) to enhance defect detection in strawberry processing.  
The developed software solution, leveraging YOLOv8 and YOLOv9 models, demonstrates significant promise in real-world applications. A real-life case study that classified strawberries by detecting defects such as grey mold, rot, and mechanical damage provided valuable insights into the system's effectiveness. Performance metrics of various YOLOv8 models revealed critical differences in detection accuracy, speed, and computational complexity, as summarised in Table 2. The YOLOv8n model, with its balance of precision and recall, emerged as the most effective in terms of overall performance and computational efficiency.  
The novelty of our research can be underlined from multiple angles: (1) Integration of Advanced Algorithms: We employ the latest YOLOv8 algorithms (n, s, m, l, and x) for defect detection, which have not been extensively applied in the context of agricultural product inspection. These algorithms offer enhanced speed and accuracy, making them highly suitable for real-time applications. (2) Edge Device Deployment: The use of a Dahua 2 MP network bullet camera with full-color technology, combined with a local server (Lenovo W541) for real-time processing, represents a significant advancement. This setup allows for immediate defect detection and response, which is crucial for maintaining the quality of perishable goods like strawberries. (3) Real-World Validation: The implemented system was tested in a real-world environment, proving its reliability and effectiveness. This practical validation underscores the applicability and robustness of our solution in operational settings. (4) Interdisciplinary Approach: Our research bridges multiple disciplines, including computer vision, machine learning, and agricultural engineering. This interdisciplinary approach enhances the overall impact and applicability of the research, providing a robust framework for future advancements in the field.
###### Abstract  
This article presents the results of deformation characteristics and crack stress thresholds of cement stone under different acid treatment times. A series of true triaxial tests were conducted on cement stones under four sulfuric acid treatment times (0, 7, 14, and 28 days). The microstructures of cement stone under different acid treatment times were examined. In the range of 0 to 14 days, the damaging effect of sulfuric acid dominated, and the structural integrity was damaged by sulfuric acid. In the range of 14 to 28 days, the strengthening effect of calcium sulfate precipitate dominated, and the structural integrity was enhanced by calcium sulfate precipitate. The cement stone exhibited anisotropic deformation characteristics under true triaxial stress states. With an increase in acid treatment time, the deformation modulus of cement stone first decreased and then slightly increased. The crack stress thresholds first increased and then decreased with an increasing intermediate principal stress coefficient. With an increase in acid treatment time, the crack stress thresholds first decreased and then slightly increased. Substantially, the ratio of crack stress thresholds was analyzed. The proportion of the unstable crack growth stage and crack growth stage increased with minimum principal stress. Additionally, the proportion of the unstable crack growth stage and the crack growth stage first increased and then decreased with increasing acid treatment time. However, the proportion of the stable crack growth stage had the opposite variation law to that of the unstable crack growth stage and crack growth stage.  
true triaxial test; intermediate principal stress; cement stone; acid corrosion; crack stress threshold +
Footnote †: journal: Applied Sciences