esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### How Spatial Resolutions Affect the SIF- EVI Relationships  
Figure 6 shows the SIF-EVI relationships at different spatial resolutions for forest ecosystems. It shows that with decreasing spatial resolution, R\({}^{2}\) increased. Higher correlations for each spatial resolution appeared on Julian day 193, 209, and 225 (July and August). [PERSON] and [PERSON] [28] and [PERSON] and [PERSON] [35] agreed that most ecosystems are highly heterogeneous at spatial scales larger than 5-10 km, which implies that the characteristic scale of most ecosystems is between 5-10 km. In this study, we found that similar R\({}^{2}\) values appeared at about 30 km, which indicated that the characteristic scale of SIF-EVI was about 30 km in the forest region studied. Figure 6 shows the obvious scale effect of SIF-EVI in forests.
# Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition
## 2 Related Work
### Segmentation Techniques in HAR  
Segmentation is essential in HAR for dividing continuous data streams into precise segments. Traditionally, wearable sensors like accelerometers provided clear, event-triggered data for straightforward boundary detection. However, the shift to radar-based HAR, which captures richer but noisier and more unstructured signals, introduced significant challenges.  
* Dynamic Segmentation: The authors in [29] utilized STA/LTA motion detectors and multi-task learning to enhance radar segmentation, effectively managing activities in mixed-motion scenarios. This built upon earlier methods, like Renyi entropy [30], which improved the detection of transitions in micro-Doppler signatures under noisy conditions.
* Fixed-Length Windowing: Addressing the limitations of traditional methods in multi-label HAR, refs. [9; 28] implemented fixed-length overlapping windows to better handle the classification of multiple concurrent activities. This technique ensures comprehensive coverage of activity boundaries, enabling more effective recognition of complex, overlapping behaviors.  
Despite advancements, segmentation in HAR remains challenging. Fixed windows often do not suit varied activity durations or datasets where activities overlap in segments, and traditional methods are not ideal for multi-label classification, which does not require defining the precise start and end of each activity. In response, our paper proposes a feedback-driven segmentation approach tailored for continuous, multi-label HAR. This innovative method dynamically adjusts window sizes based on performance analysis, effectively determining the optimal segment length without prior knowledge of activity durations and offering a scalable solution that more closely reflects the nuances of real-world behavior. Although dynamic segmentation does increase processing time, the performance improvement is substantial, justifying the additional computational cost.  
Figure 1: Depiction of multi-label classification in **(a)** computer vision and **(b)** HAR from time series data. This approach can identify multiple labels simultaneously, such as recognizing a cat, a plant, and a bench in a photograph. Similarly, it can determine that a specific time window, highlighted in orange, contains radar signatures indicative of activities like walking, sitting down, and sitting [25].
_applied sciences_  
_Article_  
**Chemical Mechanisms Involved in the Coupled Attack of**  
**Sulfate and Chloride Ions on Low-Carbon**  
**Materials: An In-Depth Study**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2,*(c) and [PERSON] 1(c)**  
1 Unite Mixte de Recherche-Materiaux pour une Construction Durable (UMR MCD), Universite Gustave Eifel, Cerema, F-7454 Marne-la-Vallee, France; [EMAIL_ADDRESS] (F.E.I.); [EMAIL_ADDRESS] (M.M.J.); [EMAIL_ADDRESS] (O.O.M.)  
2 Laboratoire Experimentation et Modelisation pour le Genie Civil et Urbain (EMGCU), Universite Gustave Eifel, F-77454 Marne-la-Vallee, France  
*Correspondence: [EMAIL_ADDRESS]  
**Abstract: This study aims to analyze the individual and combined chemical attacks of sulfate and chloride ions on cementitious materials and assess the efficiency of some selected additives (fly ash, blast furnace slag, and metakaolin) in countering this combined attack. This research is conducted in the context of construction in marine environments, where reinforced concrete structures are often subject to significant challenges due to early exposure to sulfate and chloride ions. This early exposure results in concrete expansion, cracking, and, ultimately, the corrosion of steel reinforcements. Nevertheless, the interaction between sulfate ions, chloride ions, and the cementitious matrix remains poorly understood. Previous research has drawn conflicting conclusions, with some suggesting that sulfate ions mitigate chloride attacks, while others have come to the opposite conclusion. During this study, experimental investigations were conducted by immersing powders obtained from crushed ordinary Portland cement (CEM I) paste specimens, as well as binary, ternary, and quaternary blends, in sulfate, chloride, and sulfate-chloride solutions over the course of 25 days at an early age. Results from different characterization techniques (thermogravimetric analysis, Fourier Transform Infrared spectroscopy, Raman spectroscopy, etc.) indicate that chloride ions delay the formation of ettringite, while the presence of sulfate ions accelerates the chloride attack by limiting the formation of Friedel's salt. The Mercury Intrusion Porosimetry test confirmed these results by showing a pronounced increase in specimens' porosity after exposure to solely sulfate after 25 days, compared to the ones exposed to both sulfate and chloride ions. Furthermore, the incorporation of multiple additives, particularly in ternary and quaternary blends, demonstrates the enhanced durability of the studied samples. This was confirmed by a Fourier Transform Infrared spectroscopy analysis, which indicated a delayed ettringite formation in these mixtures. This delay was further affirmed by the complete depletion of sulfate ions in the sulfate solutions upon contact with powders derived from the 100% CEM I paste.**  
**Keywords: durability; sulfate; chloride; supplementary cementitious materials; early age; coupling**
## 5 Experiments
### Sensitivity Analysis
#### 5.3.4 Sensitivity Analysis on the Transport Demand Ratio  
In this section, we conduct 6 experiments, aiming to illustrate the performance of the bi-objective MIP model in solving instances with different transport demand ratios. The ratio is denoted as \(\varphi\), where the original weekly transport demand for an OD pair is \(D_{\text{old}}\), the actual transport demand is \(D_{\text{old}}^{\text{actual}}\), and \(\varphi=D_{\text{old}}^{\text{actual}}/D_{\text{old}}\). To investigate the impact of varying the transport demand of all OD pairs on the model's effectiveness, we design instances by altering \(\varphi\) from 0.6 to 1.6 with a step size of 0.2 while keeping other parameters unchanged. Figure 9 shows the Pareto frontiers of all instances.  
Figure 8: The Pareto frontiers with different charter ratios.  
The sensitivity analysis with respect to the transport demand ratio demonstrates the profound impact of market size on the shipping line's operational scale and profitability. As the transport demand ratio increases from 0.6 to 1.6, representing a market shift from low to high demand, the Pareto frontier exhibits a significant and consistent outward expansion. The entire set of non-dominated solutions moves upwards and to the right, indicating that a high-demand market environment enables the simultaneous achievement of both higher total profits and greater transport volumes. At low demand levels (e.g., \(\varphi=0.6\)), the frontier is tightly constrained, positioned close to the origin, reflecting a market where limited cargo availability severely caps the potential for both revenue and service scale. Conversely, as demand grows, the frontier expands dramatically, unlocking a much larger space of high-performance strategic options.  
This outward expansion is driven by a fundamental shift in the company's core challenge, which necessitates a corresponding shift in management strategy. In a low-demand scenario, the primary challenge of the shipping company is to find enough cargo to fill its ships, often forcing it to accept less profitable OD pairs to maintain fleet utilization.  
Figure 9: The Pareto frontiers with different transport demand ratios.  
In a high-demand scenario, the market offers more cargo than the fleet can physically transport. This surplus of demand grants the shipping company a powerful strategic advantage by enabling it to prioritize more profitable OD pairs.  
Given that the PRMIP model yields optimal solutions identical to those of the full MIP model (as established in Section 3.3), the detailed numerical results for the two most extreme scenarios of each of the four sensitivity analyses are presented for the PRMIP model in Tables A1-A8 in Appendix B.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 3 Performance Evaluation
### Simulation Results
#### 3.2.1 Probabilities of False Alarm and Detection  
The performance of a detector can be depicted by the receiver operating characteristics (ROC) curve, which illustrates the relationship of false alarm probability \(P_{fa}\) and detection probability \(P_{d}\). Here, detection probability \(P_{d}\) can be obtained as:  
\[P_{d}=P(H_{1}\big{|}H_{1})=1-P(H_{0}\big{|}H_{1})=1-P_{md} \tag{37}\]  
In the following Monte Carlo simulations, a dataset that conforms to a standard normal distribution is employed. The false alarm probability of fused detection is counted based on the detection results. Then, a bias, denoted as \(A\), is added to the dataset to simulate the anomaly of measurements. For the first detector, \(A\) is added to the difference of mean value \(\bar{x}-\bar{y}\). The results of two detectors and fused detection are counted. The results are shown in Figures 4 and 5.  
Figure 4 shows the simulation results of false alarms. The horizontal axis represents \(P_{fa}^{(i)}\), while the vertical axis shows the rate of false alarms that happened in simulations. Here, the rate of false alarm is denoted as \(R_{fa}\); it is tallied in Monte Carlo simulations. Two red curves in Figure 4 give the rough bounds of \(P_{fa}\), which has been described in Equation (16). The blue curve is the \(R_{fa}\) of fusion. It can be observed that fused \(R_{fa}\) is between two boundaries. Moreover, in a practical range, \(R_{fa}\) is less than \(P_{fa}^{(i)}\), which is plotted in the green curve, and this means that false alarms are reduced by fusion.  
Figure 4: False alarm probability of fusion.  
Figure 5: ROC curves of detections.  
ROC curves are plotted in Figure 5. In the simulations, two different normalized biases \(A\) are tested, and the corresponding results are plotted in dashed lines and solid lines. In both cases, blue curves are higher than the corresponding red and green curves. This means that the detection performance is improved by the fusion. It should be noted that the ROC curves of fusion take account of the reduction of false alarms, which is shown in Figure 4. Simulation results with other \(A/\sigma^{2}\) are similar to them.  
Actually, the improvement of detection performance is reasonable, because the data size of multiple detections' fusion is much larger than the data size of single detection, while the fusion does not extend the time of detection.
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 6 Conclusions  
In response to the problems of complex wiring and poor flexibility in traditional wired testing systems for aero-engines, a wireless transmission aero-engine pressure testing system based on FPGA was designed and implemented. The system integrates a front-end memory and a back-end testing station and combines LoRa wireless communication technology with the FEC algorithm. Through testing in complex environments, this system can monitor and record the pressure parameters measured in aero-engine tests, completely store valid data, and significantly improve the reliability and anti-interference ability of data transmission. In addition, the system solves problems such as wiring difficulties, management complexity, and insufficient scalability. The system features simple operation, a high level of automation, as well as high-precision testing and high reliability. It can adapt  
Figure 15: Curve diagram of pressure signal.  
to complex and harsh working environments and has high scientific value and practicality in the aviation and military industries.  
Conceptualization, S.G. and Z.W.; methodology, S.G.; software, Z.W.; validation, S.G.; formal analysis, Z.W; investigation, H.Z.; resources, H.Z.; data curation, S.G.; writing--original draft preparation, S.G.; writing--review and editing, S.G.; visualization, H.Z.; supervision, H.Z.; project administration, H.Z.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under grant number 62201523.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest.
# Computational Modeling of the Thermal Behavior of a Greenhouse
## 3 Results
### Case Studies
#### 3.2.5 North-Facing Wall Material and Thickness  
In order to evaluate the role of the thermal mass in the thermal performance of the greenhouse, solid granite rock walls with 40 cm, 60 cm, and 100 cm thicknesses were considered for the north-facing wall. The \"grounded\" boundary condition was also applied, meaning the outer edge of the granite is in contact with ground terrain/soil instead of outdoor ambient air.  
The results of indoor air temperatures of the greenhouse for these case studies are shown in Figures 21 and 22, respectively, for the middle days of January and July.  
The July values show that the wall thickness affects the obtained air temperatures, with the minimum value, at 05:00, increasing from 21.4 \({}^{\circ}\)C to 23.7 \({}^{\circ}\)C when the wall thickness increases from 40 to 100 cm. At 14:00, the maximum indoor air temperature of the greenhouse shows a difference of 2 \({}^{\circ}\)C between the maximum and minimum wall thicknesses but, in any case, below the temperature of 56.7 \({}^{\circ}\)C of the reference case at the same time. In January, the thermal mass of the granite wall allows the minimum indoor air temperature of the greenhouse, reached at 08:00, to be 3.6 \({}^{\circ}\)C above the reference greenhouse and 6.3 \({}^{\circ}\)C above the outdoor temperature regardless of the thickness considered for the wall. The maximum indoor air temperature reached at 15:00 is similar to the reference  
Figure 21: Indoor air temperature on 15 January for different wall thicknesses.  
Figure 22: Indoor air temperature on 15 July for different wall thicknesses.  
greenhouse's value and is also unaffected by the thickness of the granite wall, remaining at 23.7 \({}^{\circ}\)C above the outdoor ambient temperature at that time.  
Overall, the air temperature values show the ability of the high thermal inertia of the large granite wall to act as a heat reservoir that releases heat during the colder periods and absorbs heating during the hotter ones. However, it also shows that the wall thickness should be balanced with a careful airflow rate to limit the maximum air temperatures reached.
## 5 Machine Learning
### Results  
This study built a dataset using the target area's geotechnical properties and compression index data. The dataset was applied to machine learning algorithms (RF, XGB, and LGBM), and RMSE and R\({}^{2}\) were used to determine the model with the best performance. Model overfitting was avoided when the difference in R\({}^{2}\) between the training set and testing set was less than 0.1. Table 8 shows the results of the machine learning models for predicting the compression index. In terms of RMSE, the model using the RF algorithm had the lowest RMSE (0.1669). The R\({}^{2}\) was also the best, although it was close (0.72). However, the performance of the XGB and LGBM models was not significantly different from the RF model. When looking at these results from a computer science perspective, this is likely due to the fact that a small amount of data was used.  
Table 9 shows the hyperparameters of the models. A compression index regression model was built based on the RF model. Figure 4 shows the importance of the influencing factors used in the analysis through the feature importance provided by RF. Natural water content showed the highest importance in predicting the compression index. Liquid limit and plastic limit had relatively low importance, as in the linear regression analysis results. Compared to the results of a previous study that used data samples from multiple countries to predict the compression index of soft ground based on machine learning (R\({}^{2}\) = 0.93), the model proposed in this study shows relatively low results [39]. This was likely due to the number of data samples used in the analysis and the regional characteristics. The formation of soft ground is highly influenced by geographical characteristics such as parent rock  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{\(\boldsymbol{R^{2}}\)} \\ \cline{2-5}  & **Train** & **Test** & **Train** & **Test** \\ \hline RF & 0.1341 & 0.1669 & 0.82 & 0.72 \\ \hline XGB & 0.1403 & 0.1715 & 0.80 & 0.70 \\ \hline LGBM & 0.1401 & 0.1702 & 0.80 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model results.  
and flow rate. Therefore, it would be better to develop a customized model for predicting geotechnical properties by subdividing the region.  
Figure 5 shows the RF model's compression index predictions for the target area and the actual data. Since most of the data are concentrated in the center, the model can be used to predict a rough compression index.
# 3D Reconstruction Based on Iterative Optimization of Moving Least-Squares Function
## 4 Experiments
### Evaluation Metrics  
In the paper, we use three commonly used evaluation metrics to evaluate the results of our experiments. The evaluation metrics are described in detail as follows:  
Chamfer Distance (CD2): As one of the most frequently used metrics in 3D surface generation, the chamfer distance between point clouds provides a simple and intuitive way to compare the similarity between two sets of point clouds. A higher CD2 indicates a larger difference between the two point clouds, potentially signifying poor reconstruction or significant shape deviations. When the CD2 is small, it indicates that the geometry of the reconstruction is closer to the original model, and the reconstruction effect is relatively better.  
\[CD2=\frac{1}{|S_{1}|}\sum_{x_{i}\in S_{1}}min_{y_{i}\in S_{2}}\|x_{i}-y_{i}\|_ {2}^{2}+\frac{1}{|S_{2}|}\sum_{y_{i}\in S_{2}}min_{x_{i}\in S_{1}}\|y_{i}-x_{ i}\|_{2}^{2}. \tag{15}\]  
Normal Consistency (NC):  
\[NC=\frac{1}{|S_{1}|}\sum_{i=1}^{|S_{1}|}|n(x_{i})\cdot n(S_{T}(x_{i}))|+\frac{ 1}{|S_{2}|}\sum_{i=1}^{|S_{2}|}\big{|}n(y_{i})\cdot n(S_{Q}(y_{i}))\big{|}, \tag{16}\]  
where \(n(x_{i})\) represents the normal of point \(x_{i}\), \(n(y_{i})\) represents the normal of point \(y_{i}\), \(n(S_{T}(x_{i}))\) represents the normal of the point \(x_{i}\) in the generated surface at the nearest neighbor of the ground truth surface, and \(n(S_{Q}(y_{i}))\) represents the normal of the point \(y_{i}\) in the ground truth surface at the nearest neighbor of the generated surface. Based on the quantification results of the NC, the consistency between the model output normal and the GT normal is evaluated.  
F-score: Expressed as a combination of accuracy and recall, it is the harmonic average of precision and recall at a particular threshold \(t\).  
\[F-Score=\frac{2P(t)R(t)}{P(t)+R(t)}, \tag{17}\]  
where \(P(t)\) represents the precision, and the distance from the threshold \(t\) is generally 0.40. \(R(t)\) represents the recall.  
\[P(t)=\frac{100}{|S_{1}|}\sum_{x_{i}\in S_{1}}\big{[}min_{y_{i}\in S_{2}}\big{\|} x_{i}-y_{i}\big{\|}\big{]}, \tag{18}\]  
\[R(t)=\frac{100}{|S_{2}|}\sum_{y_{i}\in S_{2}}\big{[}min_{x_{i}\in S_{1}}\big{\|} y_{i}-x_{i}\big{\|}\big{]}. \tag{19}\]where \(t\) is the distance from the reconstructed point set to the real point set (that is, the threshold t is 0.40).
## 3 Results and Discussion  
### Optimum Binder Content (OBC)  
Table 4 describes the OBC and binder amount for the five mixes. Overall, the OBC for RAP mixes were lower than that of R0 in which the OBC of RAP mixes gradually decreased as the RAP content increased. This was probably due to an increment in the percentage of fine particles and the amount of WEO. However, the differences in OBC for all the mixes were relatively small. For example, the OBC for the R100 mix was roughly 8% lower than that of R0 in which other studies also reported the same trend [51,85,86,87,88]. In addition, the amount of binder used in RAP mixes was drastically reduced. For instance, only 13.6 g of binder was used to prepare the ACW14 samples of the R100 mix, which was 79.7% lower than the amount of binder used to prepare R0 samples. This is due to  
the combination of WEO and CRM in the mixes. The use of WEO effectively softens and rejuvenates the aged binder while enhancing the workability of RAP mixes [23, 42, 43, 89]. In addition, CRM enhanced the stability, efficiency, and consistency of the asphalt [23, 61]. Moreover, the inclusion of RAP could have influenced the stability and flow properties of the mixes. Therefore, it was demonstrated that the stability and flow values of RAP mixes were more load-bearing and more flexible compared to R0. The Marshall stability and volumetric properties of the five mixes with respect to the five binder contents are included in Appendix A.