esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis of the Designed Probes
### Simulation Analysis of Delamination Eddy Current Testing
#### 4.2.2 Simulation Analysis of Eddy Current Testing for Delamination of Various Locations  
According to the analysis in Section 3.1, at the excitation frequency used in the simulation, the skin depth is 7.1 mm. In theory, defects smaller than this depth can be effectively detected. To investigate the detection effectiveness of the designed probe for subsurface defects located deeper within the CFRP sample, simulations were conducted with different delaminations of the same dimension at various locations in the CFRP sample (Figure 5). Table 10 provides the specific parameters of the delamination. Perform top scanning of defects inside the CFRP sample using the designed probes, canning mode 1 was used for defects in the 0\({}^{\circ}\) layer, while scanning mode 2 was used for defects in the 90\({}^{\circ}\) layer. The detection results are shown in Figure 15.  
According to Figure 15, it can be observed that the curves of the detection voltage differences obtained from different scanning modes exhibit differences. This is caused by the inherent characteristics of the probe [35]. The designed probe successfully detected subsurface defects located deeper within the sample. From the pattern of voltage difference changes, for defects of the same dimension, the closer the defect is to the top surface of the sample, the larger the detection voltage difference peak obtained from top scanning,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Delamination Number** & **Delamination Parameter** & **Delamination Location** \\ \hline Delamination 7 & 15 mm \(\times\) 15 mm \(\times\) 0.1 mm & Layer 11 (0\({}^{\circ}\)) \\ Delamination 8 & 15 mm \(\times\) 15 mm \(\times\) 0.1 mm & Layer 12 (90\({}^{\circ}\)) \\ Delamination 9 & 15 mm \(\times\) 15 mm \(\times\) 0.1 mm & Layer 13 (0\({}^{\circ}\)) \\ Delamination 10 & 15 mm \(\times\) 15 mm \(\times\) 0.1 mm & Layer 14 (90\({}^{\circ}\)) \\ Delamination 11 & 15 mm \(\times\) 15 mm \(\times\) 0.1 mm & Layer 15 (0\({}^{\circ}\)) \\ Delamination 12 & 15 mm \(\times\) 15 mm \(\times\) 0.1 mm & Layer 16 (90\({}^{\circ}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Delamination at different locations.  
Figure 15: Detection voltage difference for delamination at different locations: (**a**) scanning mode 1; (**b**) scanning mode 2.  
resulting in a better detection effect. According to the results of the simulation experiments, the designed probes can detect the delamination at the bottom layer (Layer16) of the CFRP sample (Figure 5).
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 3 Results
### PCA-SVR Model Prediction Results
Both \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) could be used to predict the moisture content of straw compost to some extent based on the results of parameter selection. The prediction set \(R_{\mathrm{p}}\)2 of the SVR model based on full variables and developed with \(\varepsilon^{\prime}\) was the highest, reaching 0.9998. The prediction set \(R_{\mathrm{p}}\)2 of the SVR model based on selected characteristic variables by PCA and developed with \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) combined variables was 0.9877, and the RMSEP was the smallest, at 0.0026. However, the prediction set \(R_{\mathrm{p}}\)2 of the SVR model based on full-frequency variables and developed with \(\varepsilon^{\prime}\) was higher than the correction set \(R_{\mathrm{c}}\)2, which indicated that this parametric model may not be adequately generalizable. Additionally, combining two parameters rather than just one can result in complementing information, a thorough response to the inherent relationship between dielectric characteristics and moisture content, and an improved model prediction accuracy. Figure 8 displays the prediction results of PCA-SVR; the samples are concentrated near the regression line (y = x), and the prediction effect is more effective. Figure 9 displays the prediction error of the moisture content of the PCA-SVR model; the prediction errors are concentrated within \(\pm\) 0.6%. Therefore, the SVR model based on the combination of \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) variables and based on PCA was selected as the best model for predicting the moisture content of straw compost. Figure 8: Predicted results of the moisture content of the PCA–SVR model.
## 6 Conclusions  
Coulombic efficiency and remaining capacity are the indicators for estimating battery performance and available capacity, respectively. To ensure safe operation and prevent excessive discharge, accurate SoC and SoH estimations are very important for lithium ion batteries. Thus, much research has been conducted to resolve these issues and increase battery performance. As battery manufacturing technology continues to progress, lithium ion batteries are used more and more. When the battery is discharging at a high rate under a low temperature, the heat produced during discharge will offset some influences from the environmental temperature. As a result, after discharging for a certain length of time, the amount of power delivered will drop while the voltage will conversely rise. In that case, SoC estimates voltage of battery with large errors. As the environmental temperature rises, the battery performance is visibly initially better than that under room temperature or low temperature. However, the battery's cycle life would be lower than that under room temperature. Therefore, under the processes of cyclic charging and discharging, different currents and temperatures produce significant changes in battery capacity. These factors must be considered for the implementation of the battery management system (BMS).  
This research used LiFePO4 batteries with 10 Ah capacity and 3.2 V rated voltage for testing under various temperatures and discharge rates. Under testing conditions of 400 cycles, the discharge voltages and currents of the batteries during the discharge process were recorded. The batteries were analyzed with different degrees of aging and their corresponding characteristics were gathered. The comprehensive model was able to follow the temperature and current changes to precisely estimate the SoC of the LiFePO4 battery. The results obtained indicate that the average error of the comprehensive model under various current loads and temperatures to be only 1.55%.  
The LiFePO4 batteries are manufactured in the same factory and should have a high degree of consistency. Therefore, when the error of one battery is higher than the rest, it can be considered a signal that the battery capacity is fading faster than before, indicating that the battery should be replaced immediately. Due to the model accuracy and stability shown during this paper, the model can be considered a useful tool for the control and performance analysis of a management system which includes a Li-ion battery.  
The author declares no conflict of interest.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 2 Methodology
### Trajectory Planning with Time Variant Safety Margin
#### 2.2.3 Supplementary Lateral Constraints  
The lateral constraints for lane change control is derived from the boundaries of lane width. In this part, the calculate of the position constraints used in lateral trajectory planning is investigated. Similar to those used for longitudinal trajectory planning, position constraint sequences \(y_{min}\) and \(y_{max}\) for lateral movement are given by  
\[y_{min}\left(k\right) =\begin{cases}\min\left[y_{min}^{t}\left(k\right),y_{min}^{s} \left(k\right)\right],\;k\leqslant k_{fin}\\ y_{min}^{t}\left(k\right),\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;k_{fin}<k \leqslant k_{end} \tag{20}\] \[y_{max}\left(k\right) =\begin{cases}\max\left[y_{max}^{t}\left(k\right),y_{max}^{s} \left(k\right)\right],\;k\leqslant k_{fin}\\ y_{max}^{t}\left(k\right),\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;k_{fin}<k \leqslant k_{end}\]  
The definitions of lateral constraints \(y_{max}^{t}\), \(y_{min}^{t}\) and \(y_{max}^{s}\), \(y_{min}^{s}\) are designed to keep \(V_{H}\) within the lane boundaries. A unified formula for these parameters are expressed by  
\[y_{min}^{g}\left(k\right) =y_{l}-\frac{w_{l}}{2}+\frac{w_{c}}{2} \tag{21}\] \[y_{max}^{g}\left(k\right) =y_{l}+\frac{w_{l}}{2}-\frac{w_{c}}{2}\]  
where \(y_{l}\) is the lateral position of the target lane centerline, and \(w_{c}\) is the width of host vehicle, \(w_{l}\) is the width of lane.  
Based on the characteristics of vehicle tire dynamic, the constraint of lateral acceleration is related to that of longitudinal acceleration. If we define the limitation of acceleration \(a_{dyn}\) from the tire friction dynamic, \(ay_{H}\) should conform to the following constraint:  
\[\left|ay_{H}(k)\right|\leqslant\sqrt{{a_{dyn}}^{2}-ax_{H}(k)^{2}} \tag{22}\]
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 3 Performance Analysis  
In this section, we will analyze the BER, outage probability, and achievable rate performance for the WPCN-based UWAC systems. Perfect channel state information (CSI) is assumed to be available for both the H-AP and terminal node for coherent detection. We first derive the PDF expression for the instantaneous SNR of information transmission in phase II. We then derive closed-form expressions of the average BER and the outage probability.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 5 Results and Discussion
### Impact of Available Resources  
We also investigate the influence of the available resources on packet success probability. Both LTE-V2X and NR-V2X use the same number of resources for a fair comparison, as the purpose is the comparison of the ability to reject multiple access interference. However, the availability of allocable resources in a real-world case could be higher for NR-V2X due to the different options for bandwidth and MAT. For this analysis, we again consider three distinct distances between the transmitter and the receiver (i.e., 20 m, 50 m, and 100 m). The number of interfering vehicles is 15 and the number of available resources in the selection window ranges from 7 to 15. The validation of the proposed approximation is reported in Figures 6 and 7, which show the performance of LTE-V2X and NR-V2X, respectively, and again demonstrates a good accuracy of the MMA approximation.  
Figure 5: Packet success probability as a function of the number of vehicles.  
Figure 6: Packet success probability as a function of the number of resources.  
The performance of the NR-V2X is also compared with that of the LTE-V2X in Figure 8. As expected, the probability of success increases as the number of available resources increases. This is because, for a fixed number of interfering vehicles, the increase in available communication slots in the selection window implies a reduced impact of interference. It is to be noted that, when the number of resources is equal to the number of vehicles (i.e., 15), each vehicle has the option to reserve a dedicated communication slot, so interference does not affect the performance, which is mainly determined by the distance between the tagged vehicle and the platoon head. Finally, we observe that the NR-V2X provides a higher success probability than LTE-V2X.
# Additive Manufacturing as an Alternative to Core Sampling in Concrete Strength Assessment
## 5 Conclusions  
This study demonstrates that concrete produced with up to thirty percent ash-and-slag replacement can satisfy the strength and density requirements for low- to medium-rise construction in seismic zones while delivering significant sustainability benefits. Across 108 paired tests, the compressive strength measured on 70 mm cubes consistently exceeded the in situ (core) strength, yet the cube-to-core ratio remained narrowly confined: the transition coefficient stabilised between 0.82 and 0.85, with an overall mean of 0.83 at 28 days. Applying a simple linear correction (slope 0.97 and intercept -4.9 MPa) reduced the prediction error to less than 2 MPa, well below the \(\pm\)10% safety margins embedded in most structural codes, thereby eliminating the need for destructive coring in routine quality control. Regression benchmarking on both physical data and statistically perturbed \"synthetic campaigns\" showed that ordinary least squares already achieves engineering-grade accuracy (RMSE \(\approx\) 1.8 MPa, R\({}^{2}\) > 0.999), while more complex learners add no tangible benefit--evidence that the cube-core relationship is fundamentally linear within the tested process window. A practical four-step protocol--casting cubes during printing, curing them alongside the structure, testing at the design age, and multiplying by the calibrated coefficient--can therefore be implemented immediately, saving time, labour and materials. Future research should extend the calibration to geopolymer binders, high-volume recycled aggregates and larger robotic systems, and should couple real-time porosity scans and fresh-state rheology with machine-learning refinements to push the residual error below 1 MPa and enable automated rejection of out-of-spec layers during construction. Overall, the work establishes a reliable, non-destructive pathway for verifying the strength of additively manufactured concrete elements, advancing both sustainability and structural safety in 3D-printed infrastructure.
# Development Trend of Electronic Nose Technology in Closed Cabins Gas Detection: A Review
## 3 Concept and Principle of the Electronic Nose
### Basic Mechanism  
The gases in closed cabins can be identified quickly by the electronic nose and it is not necessary to classify all volatile compounds one by one. In the electronic nose detection process, in this case, the volatile compounds are converted into electronic signals in the form of digital outputs of the electronic nose sensor array [51]. Electronic noses enable real-time detection of air in closed cabins and do not require a complex sample pre-treatment, making them well suited for air detection in closed cabins. The electronic nose has a wide range of built-in gas sensors, which can be changed depending on the situation [18], thus creating a large amount of usable data. The main differences in gas sensors are in terms of sensitivity, response time, data error, etc.  
The reflection of the sensor array is determined by the variation of the parameters of the constituent gases in the cabin, which is essential for the development of an electronic nose with high selectivity and low production cost for online evaluation. After calibration, the electronic nose can be used for the accurate identification of hazards and warning gases in closed cabins [19]. However, electronic noses are not as effective as existing organic gases. However, electronic noses provide information based on existing gaseous organic compounds, which are intended to be analyzed using statistical methods.
# A Theoretical Foundation for Context-Aware Cyber-Physical Production Systems
## 4 CPPS Models  
In this paper, to study the Deadline Awareness Problem and the Future States Awareness Problem of CPPS, we adopt a variant of timed Petri nets called discrete timed Petri nets (DTPN) to construct models for CPPS. We follow a bottom-up approach to building models for CPPS by constructing individual models of the entities in CPPS first and then constructing the overall model for CPPS, taking into account the interactions between resources and operations in the workflows. The models of individual entities in CPPS are divided into two categories: task subnets and resource subnets. To present the modeling of CPPS, we list the symbols and notations used in this paper in Table 1.  
A CPPS consists of different types of entities, including resources and tasks. We propose task subnets and resource subnets for different types of resources and tasks. Task subnets and resource subnets in CPPS are described by a class of discrete timed Petri nets (DTPN) [9]. Resources and tasks in a system are represented by tokens in a DTPN. The number of tokens in each place can be represented by a vector called a marking. An event in a system is called a transition in a DTPN. The occurrence of an event changes the marking of a DTPN. A DTPN evolves from the initial marking due to the firing of transitions. In the DTPN model, we assume the time horizon is divided into \(\Pi\) periods and the duration of each period is \(\delta\). The choice of \(\delta\) depends on its application. Note that the method proposed in this study works regardless of the choice of \(\delta\) so long as \(\delta>0\). The value of the firing time of a transition is described by \(\mu\delta\), where \(\mu\in Z\) and \(Z\) is the set of nonnegative integers. Note that continuous time Petri nets are limiting cases of the discrete time Petri nets as \(\delta\to 0\). A formal definition of DTPN is as follows. Definition 1: We assume that the time horizon is divided into \(\Pi\) periods and the duration of each time period is \(\delta\). A DTPN is a five-tuple \(G=(P,T,F,m_{0},\mu)\) which consists of a set of places: \(P\), to describe the states, a set of transitions; \(T\), to represent events, flow relation; \(F\subseteq(P\times T)\cup(T\times P)\), to characterize the connection between places and transitions, an initial marking; \(m_{0}\), to describe
## 2 General Context: Global Exposure Data for Risk Assessment  
At a global scale, natural disaster financing or insurance mechanisms are in big need of datasets for risk assessment, especially for developing countries where organized, systematic data collection cannot always be guaranteed due to lack of resources or structural issues (see [3; 4; 5]). In particular, the activation of rapid post-event financial protection needs quick and ready-to-implement response to natural hazards. The use of local data for this purpose hinders full deployment of balanced insurance coverage due to various problems: time constraints (e.g., time consumed to collect and process local data), reliability and precision issues (to what extent can a large pool of data collected by different agents be trusted?), homogeneity issues (different countries may imply different standards), and so on. In this context, leveraging global datasets could be part of the solution--global data sets, often available at a reasonable price or sometimes even for free, represent a good starting point for local assessment if combined in the correct manner with local data. Unlike for traditional catastrophe risk modelling, catastrophe models used within parametric programs by investors and/or insurers to finance disaster risks require the data be available and reliable not only for the present time, i.e., for real-time operational use, but also for historical periods. These models are used both as the basis for loss estimation in (near) real time and for risk assessment to verify solvency or to make the product quotations. Therefore, it is a fundamental requirement that the data used for both purposes are statistically homogeneous, in order to avoid biasing the analysis. Even for a conventional risk model for the Caribbean area, exposure data, i.e., geographical maps of assets exposed to threats, are a fundamental component; in our case, the exposure information consists of maps with geographical distribution of the different types of crops. To this purpose, the goal was not to generate the best exposure data in absolute terms but rather exposure data whose quality level was consistent with:  
* the general model used for overall risk assessment, and
* the accuracy level of the other datasets incorporated in our production.  
Given this context and the linked requirements, we came up with the following guidelines for our work:  
1. it was important to balance the different components of the risk model in order to avoid mixing datasets that score too differently in terms of accuracy and precision; in this case, finer data or finer models would indeed be underused;
2. our goal was to hit the optimal trade-off between data availability and the specific needs of the vulnerability component in order to capture the main factors affecting risk.  
In the following chapter, we will specify the case analyzed, and present our solution, specifically developed from the given conditions and the guidelines above.
# Comparative Study on Physicochemical and Nutritional Qualities of Kiwifruit Varieties
## 3 Results
### Vitamin  
In different varieties, the VE content, VC content, VB1 content, VB2 content and VB6 content were significantly different in terms of statistical significance. The highest VE content was 2.29 mg/100 g from 'Jinyuan' (hybrid), which was about eight times greater than that of 'Cuiyu', with the lowest content of 0.28 mg/100 g. 'MHYX' had a far higher VC content than other samples. The highest VB1 and VB2 content were both observed in 'Hongyang', with contents of 0.04 mg/100 g for both. The VB6 content ranged from 0.07 mg/100 g (Y2, 'G3') to 0.04 mg/100 g (G6, 'Huate').  
On the one hand, red-fleshed kiwifruits had a statistically significantly higher VE content and VB1 content than green-fleshed kiwifruits, as shown in Figure 4A,C, respectively. However, as seen in Figure 4B, green-fleshed kiwifruits had a far higher VC content than the other flesh colors. _A. eriantha_, which is green-fleshed, is regarded as 'the king of VC', which may be the cause of these outcomes. Both VB2 and VB6 content had no trend, with no statistically significant differences in all three flesh colors in Figure 4D,E.  
On the other hand, although _A. eriantha_ had the lowest content of VE, it had the highest VC content, which was about nine times higher than the average for other species (109.22 mg/100 g, 88.34 mg/100 g and 81.39 mg/100 g), as seen in Figure 4F,G. The VB1 content of _A. chinensis_ was significantly higher than that in the other three species, and the VB2 content of _A. eriantha_\(\times\)_A. chinensis_ displays the same trend in Figure 4H,I. The content of VB6 in _A. eriantha_ was significantly lower than that in other species, and the value of _A. eriantha_\(\times\)_A. chinensis_ was similar to that in parental plants (Figure 4)). A previous study reported that the VC contents of 'Hongyang', 'Cuiyu' and 'Jinkui' in Shaanxi were 100.00 mg/100 g, 88.58 mg/100 g and 111.02 mg/100 g, respectively [34]. In this study, the VC contents of the three varieties from Hubei were about 1.5 times greater than those from Shaanxi. The VB (VB1 + VB2 + VB6) contents of _A. chinensis_ and _A. chinensis_ var. _delicious_ in this study were 0.14 mg/100 g and 0.13 mg/100 g, respectively. Compared with one previous study [20], they increased by 8% and 14%, respectively. In summary, the vitamin content of kiwifruits even from the same variety varies in different geographical environments, which was speculated as one of the impact factors in vitamin content.