esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Evaluation  
#### 2.4.1 Segmentation Metrics  
The performance of automatic ventricle segmentation methods was evaluated by comparing them to manual segmentations using several key metrics (Figure 1) in Matlab R2023a (The MathWorks Inc., Natick, MA, USA). In addition, the GT and automated segmentation volume was evaluated and compared to check for systematic differences between the segmentations in the test sets. Two widely recognized metrics, the Dice Similarity Coefficient (DSC) and 95 percentile Hausdorff distance (HD95), were used.  
The DSC is a measure of volumetric overlap and is calculated as twice the overlap of the two segments divided by the total size of both volumes combined. The Hausdorff distance is defined as the largest distance between the boundary points of one contour and the nearest point in the other. The HD is computed bidirectionally, from the automated segmentation to the GT, and vice versa, and the maximum of those two directions is taken. The 95 th percentile HD (HD95) is taken, as it is more robust to outliers.  
However, both DSC and HD95 have been shown to have little correlation to the time needed to correct the contours and, thus, clinical contour quality [19]. Therefore, the surface DSC and added path length (APL) metrics were added, as they have been shown to correlate strongly with the number of manual corrections required of the automated contours and thus to time-saving and clinical contour quality [20].  
The surface DSC was first proposed by [PERSON] et al. [21] and reports the accepted surface parts (the surface parts within a tolerance of the true boundary) compared to the total surface (sum of automatic contour surface area and GT surface area). The tolerance parameter of the surface DSC represents interobserver variations in segmentations and was set to 1 mm, as brain OAR delineation interobserver variability is often around this value [22].  
The APL calculates the total length of the boundary, which requires manual adjustment to meet institutional contouring guidelines. Again, a tolerance parameter of 1 mm was used to define acceptable deviation from the GT. Together, these four metrics comprehensively analyze both spatial overlap and boundary precision.  
While the DSC, HD95, and surface DSC were used to evaluate the contour quality of both ventricle and PVS segmentations, the APL metric was applied exclusively to ventricle evaluation. The PVS is not a manually delineated OAR but rather an expansion of the ventricle segmentation, meaning the APL does not provide correct information for this structure.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 3 Estimation of the Coefficients of Variation of Fracture Energy  
The term \"coefficients of variation\" is employed in the plural form in this context because, within the framework of the current probabilistic model, the coefficient of variation of \(G_{IC}\) varies based on the volume of each finite element. Consequently, determining these coefficients of variation stands as a primary challenge concerning the model's practical application and operational aspects.  
To achieve this objective, an inverse analysis necessitates an experimental study providing detailed information on macrorack propagation, including the evolution of macrorack length and opening. This experimental study must be conducted at a sufficient distance to yield representative data concerning the dispersion associated with this propagation. Additionally, it should facilitate the determination of the intrinsic \(G_{IC}\). However, determining these toughness characteristics experimentally poses a significant challenge.  
Previous studies have demonstrated that experimental tests need to be conducted on large concrete specimens to accurately determine these characteristics [9; 28; 32; 33; 34; 35; 36; 37; 38]. This necessity arises due to the high level of concrete heterogeneity associated with largeraggregate sizes. As a result, the process zone at the tip of the propagating macrorack can reach approximately 30 cm [28].  
The inverse analysis in this study relies on an experimental investigation conducted on a large-scale Double Cantilever Beam (DCB) specimen detailed in [28; 38]. The dimensions of the DCB specimen used in this experiment were 3.5 m in length, 1.1 m in width, and 0.3 m in depth.  
This particular DCB test conducted on such a substantial specimen stands as a unique case in current concrete studies. It is noteworthy because it provided genuine and intrinsic values of \(K_{IC}\) (mode I critical stress intensity factor) or \(G_{IC}\) (mode I specific fracture energy). It is important to emphasize that most other experimental studies have yielded values that are sensitive to scale effects rather than being intrinsic [28; 38]. Due to the computational demands associated with simulating this type of DCB test specimen, only one concrete composition was studied in our analysis.
# Mechanical Behavior of Steel Fiber-Reinforced Lightweight Concrete Exposed to High Temperatures
## 1 Introduction
[20] studied the influence of different steel fiber types on the flexural behavior of NWC, and the test results showed that HF performed better than crimped shape steel fiber and straight steel fiber. On the other hand, according to [PERSON] et al. [21], the improvement of straight steel fiber on the tensile behavior of ultra-high performance concrete was greater than that of HF, and the improvement of micro straight steel fiber was also greater than that of HF. The above researches indicate that the influence of different types of steel fibers on the mechanical properties of concrete is related to the type of concrete matrix used. It is well known that the mechanical and physical properties of concrete will degrade to some extent under fire or high temperatures. In extreme cases, post-fire concrete structures without maintenance and retrofitting are no longer suitable to bear any service loading. Existing references show that the type, porosity, and mineral composition of LWA have essential influences on the residual properties of LWC after exposure to high temperatures, and the mineral composition of LWA determines the difference in thermal expansion between cement mortar and aggregate and the development of cracks in the interfacial transition zone [22,23,24,25,26]. As for the influence of steel fiber on concrete after exposure to high temperatures, [PERSON] et al. [27] studied the influence of temperature on the mechanical properties of plain NWC and fiber-reinforced NWC. The results showed that the presence of steel fiber improved the residual splitting tensile strength and the elastic modulus of NWC after exposure to high temperatures. According to [PERSON] et al. [28], the addition of steel fiber significantly improved the residual compressive strength and tensile strength of RPC after exposure to high temperatures. However, there is a lack of research on the residual mechanical performance of LWC and SFLWC exposed to fire and the performance of LWCcontaining various steel fiber types under fire. The current work aims to evaluate the effect of steel fiber on the residual mechanical behavior of LWC subjected to fire. Two steel fiber types were applied in this work, including crimped shape steel fiber (CF) and HF. An ALWC matrix with industrial sintered expanded shales as fine and coarse aggregates and a SLWC matrix with sintered fly ash round particles as coarse aggregates were prepared and compared with the NWC. The elastic modulus (stiffness), splitting tensile strength, post-fire compressive strength, compressive toughness, and compressive stress-strain relationship as well as the ultrasonic pulse velocity (UPV) of different mixes were evaluated. Based on the test data, the residual mechanical properties of plain LWC (including ALWC and SLWC) and SFLWC at different temperature levels were expressed as functions of temperature. These series of prediction equations will promote the usage of steel fiber and LWC in fire-resistant applications in the construction industry.
## 2 Literature Review  
In the last few decades, researchers discovered more characteristics of volatility in terms of long-range dependence and multifractality of the data in numerous fields including DNA sequences [3; 4; 19], climatology and hydrological time series [11; 20; 21], and stocks and other financial market data [6; 7; 22; 23]. In most of these studies, the complex long-range correlations and multifractality behaviors of the time series were measured by the so called Hurst exponent, which was originally developed in hydrology for the practical matter of determining optimum dam sizing for the Nile river's volatile rain and drought conditions [22; 24].  
The long-range correlations can be captured using several methods, including Rescaled Range Analysis (R/S) [22], Detrended Fluctuation Analysis (DFA) [3], Wavelet analysis [25], Multifractal Detrended Fluctuation Analysis [25], and so on. [PERSON] et al. [26] compared various methods on Hurst exponent and pointed that DFA is superior to other methods since it is less influenced by the time series finite size than others. As a generalization of DFA, MF-DFA is a popular method for the nonstationary time series, which has been applied with great success in several areas of research. Several literatures have also demonstrated the possibility of detecting the multifractal properties in time series through MF-DFA method [9; 27].  
MF-DFA is a good method to study the characteristics of time series of stock market and complex traffic flow. For example, [PERSON] et al. [28] and [PERSON] et al. [29] stated that MF-DFA is an acceptable choice to study comparative efficiency and the multifractality of stock markets. They found that the Islamic stock markets' adjustment to speculative activity is, in fact, higher than their conventional counterparts, and all stock market returns exhibited multifractal features. Besides, some scholars used MF-DFA to examine the highway traffic flow time series in Beijing, Shanghai and other places and discovered that the long-rangedependence behavior is ubiquitous in time series of road traffic flows. Moreover, the length of the time scale was significantly impacted on the multifractal characteristics of traffic flow sequences [30; 31; 32].  
At present, a broad consensus has emerged that long-range dependence and multifractality are somewhat realistic phenomena in traffic flow series [15; 33; 34]. However, there is no research on the long-range correlations of the time series of maritime traffic flow. Therefore, motivated by the importance of temporal structure and long-range correlations for modeling and prediction of maritime ship flow series, we investigated the complex temporal structure and long-range correlation behaviors of the ship flow sequences of container ports from the multifractal perspective using the MF-DFA method. Evaluation of such results for container ship flow in different time scales and different container ports will facilitate the production of more insights on evolution dynamics of these ports and global trade.
## 1 Introduction  
In recent years, the number of human-made objects launched into space has continued to grow [1, 2, 3], increasing the danger of the near-Earth debris environment and increasing the risk of in-space collisions [4, 5, 6, 7]. Mitigation regulations [8] and strategies [9] have been and are continuously being proposed, and active [10] and passive [11, 12] debris removal and post-mission disposal technologies are under development. It is clear that the risk of debris impacts shall be considered in the development of a space mission: a collision can cause damage to spacecraft subsystems and affect their functionality [13], up to the complete loss of the vehicle. Eventually, critical events may lead to the partial or complete fragmentation of the impacted bodies [14].  
In this context, on one hand, dedicated protections are designed to shield satellites from hypervelocity impacts; ground tests are usually employed to assess their capability to mitigate collisions [15, 16] and to protect spacecraft components [17, 18]. Furthermore, critical elements are usually subjected to similar tests to evaluate their survivability in the debris environment [19]. On the other hand, it is crucial to understand the physical processes involved in spacecraft collisions through numerical simulations and ground-based experiments. Currently, spacecraft collisions and large fragmentation events are evaluated mostly with empirical or semi-empirical tools (e.g., NASA SBM [20], FASTT [21, 22], IMPACT [23, 24]), as numerical simulations based on the impact physics (e.g., hydrocodes) are usually too complex and resource-consuming to investigate a wide range of collision scenarios. Ground tests of complex satellite models [25, 26, 27] usually require large impact facilities and a large amount of worker hours for fragment collection and analysis [28].  
The Space Debris group of the University of Padova has investigated both these topics in collaboration with national and international partners; this paper presents the experimental and numerical research activities performed by the group in the last 10 years. The next section introduces the Hypervelocity Impact Facility employed for the experimental investigation, consisting of a two-stage Light-Gas Gun capable of accelerating projectiles up to 100 mg at a maximum speed of 5.5 km/s. The purposes of the experimental campaigns, which are presented in Section 3, are (i) the evaluation of the ballistic response of satellite structures and components subjected to impacts with small debris of mm size, (ii) the development of new shields with enhanced shielding capabilities, (iii) the investigation of fragmentation processes at component and system level, and (iv) the development of smart systems for in situ small debris impact detection. The second part of this work describes the impact modelling research (Section 4), focusing on simulations of collision scenarios involving both basic elements and full satellites (Section 5). In particular, the collected results can be compared to those of current breakup models such as the NASA SBM, and to derive new semi-empirical formulations of the fragmentation process.
## 3 Results
### Second Experiment
Based on the data presented in Table 8, it can be observed that the UGD reached the lowest difference between precision and recall on the eight data sets. The 1 CT1P approach obtained the lowest value of difference on the other data sets. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Data Set** & \multicolumn{3}{c}{**1 CT1P**} & \multicolumn{3}{c}{**GD**} & \multicolumn{3}{c}{**UGD**} \\ \hline  & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline occupancy\_12 & 0.606 & 0.726 & 0.660 & 0.630 & 0.809 & 0.708 & 0.707 & 0.720 & **0.714** \\ occupancy\_13 & 0.880 & 0.998 & 0.935 & 0.920 & 0.997 & 0.957 & 0.926 & 0.997 & **0.960** \\ occupancy\_14 & 0.652 & 0.727 & 0.688 & 0.613 & 0.813 & 0.699 & 0.718 & 0.736 & **0.727** \\ occupancy\_15 & 0.457 & 0.687 & 0.548 & 0.584 & 0.791 & 0.672 & 0.681 & 0.682 & **0.681** \\ occupancy\_23 & 0.907 & 0.998 & 0.950 & 0.898 & 0.998 & 0.945 & 0.926 & 0.998 & **0.960** \\ occupancy\_24 & 0.636 & 0.686 & 0.660 & 0.625 & 0.836 & 0.715 & 0.707 & 0.741 & **0.724** \\ occupancy\_25 & 0.368 & 0.567 & 0.446 & 0.587 & 0.787 & **0.672** & 0.658 & 0.648 & 0.653 \\ occupancy\_34 & 0.854 & 0.976 & 0.911 & 0.894 & 0.998 & 0.943 & 0.920 & 0.997 & **0.957** \\ occupancy\_35 & 0.915 & 0.995 & **0.954** & 0.899 & 0.998 & 0.946 & 0.908 & 0.998 & 0.951 \\ occupancy\_45 & 0.637 & 0.659 & 0.647 & 0.630 & 0.828 & 0.716 & 0.702 & 0.743 & **0.722** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Values of precision (P), recall (R) and F-measure (F) obtained by the 1 CT1P, GD and UGD methods on individual data sets concerning occupancy detection. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison** & **Adjusted \(p\)-Value** & \multicolumn{2}{c}{**Statistical Significant Difference**} \\ \hline UGD vs. GD & 0.27474 & NO \\ UGD vs. 1 CT1P & 0.00013 & YES \\ GD vs. 1 CT1P & 0.00545 & YES \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of conducted statistical test. The non-parametric Friedman Aligned Ranks test with Holm post-hoc multiple comparison was conducted for the data presented in Table 8 with the significant level \(\alpha\) = 0.05. The obtained results were presented in Table 9.  
Analysing the information presented in Table 9, it can be stated that the Unequal Geometrical Divide method achieves significantly lower values of difference between precision and recall than the Geometrical Divide method and 1 CT1P in occupancy detection process, which is grounded on the imbalanced data set. On the other hand, there is no statistically significant difference between the results obtained by the Geometrical Divide and the 1 CT1P methods.
## 3 Major Bioactive Compounds and Potential Applications
### Polysaccharides  
Marine microalgae are a valuable source of bioactive polysaccharides, particularly sulphated polysaccharides and \(\beta\)-glucans, which have shown wide-ranging pharmacological effects. These include antiviral, antioxidant, anti-inflammatory, immunomodulatory, and metabolic regulatory activities that support their application in medicine, cosmetics, and functional foods [92].  
In cyanobacteria like Spirulina and green microalgae such as _Chlorella pyrenoidosis_, \(\beta\)-glucans constitute up to 25-35% of total dry biomass, depending on strain and culture conditions. These \(\beta\)-glucans enhance host immunity by activating macrophages, NK cells, and dendritic cells through receptors such as lectin-1, triggering downstream NF-\(\kappa\)B and MAPK pathways [93]. \(\beta\)-glucan supplementation (250-500 mg/day) has shown clinical efficacy in lowering blood lipids, enhancing innate immunity, and improving glycaemic control [94].  
Sulphated polysaccharides, especially from diatoms (_Navicula_, _Cochlodinium_, _Sphacerlaria_), exhibit strong antiviral activities against HSV, HIV, and influenza viruses. _Porphylidium_ produces sulphated polysaccharides at levels exceeding 20% of biomass, which show potent antiviral and immunomodulatory effects. These compounds mimic heparan sulphate and block viral entry into host cells without cytotoxicity, with IC\({}_{50}\) values as low as 0.6 \(\mu\)g/mL against HSV-1 [95]. They also act as skin moisturisers and anti-inflammatory, forming protective biofilms and modulating inflammatory enzymes like COX-2, making them ideal ingredients in anti-ageing and soothing skincare formulations [96].  
In functional foods, algal polysaccharides have been shown to influence lipid metabolism and gut microbiota. In an in vivo mouse study, daily intake of polysaccharides from Spirulina and _Chlorella_ significantly reduced weight gain, inflammation, and liver fat accumulation while improving microbial diversity and short-chain fatty acids production [97].  
From an industrial perspective, techniques like ultrasound-assisted extraction, enzyme hydrolysis, and ethanol precipitation are optimised to retain activity and maximise yield. Additionally, chemical modifications such as sulphation and carboxymethylation enhance solubility and bioactivity, particularly for pharmaceutical formulations. Structural parameters, like degree of branching and sulphate content, play a key role in modulating the antiviral and immunomodulatory properties of these compounds [98; 99].  
In summary, microalgal polysaccharides, particularly \(\beta\)-glucans and sulphated polysaccharides, represent a versatile group of compounds with therapeutic applications spanning infectious diseases, metabolic syndrome, skin care, and gut health. Their functional diversity and safe profiles support growing interest in integrating them into intraveticals, dermatocosmetics, and pharmaceutical products.
## 5 Discussion and Limitations  
Although the coupled results of SSA decomposition are consistent with the actual data, the first and last parts of the individual decomposition results do not fit perfectly with the real situation, although the effect of the error on the time period of surface deformation in this paper is relatively small; when the time series is shorter, this error will inevitably be spread to the corresponding time period of the study, and the method of optimizing away this imperfect fit is a fundamental means of solving the problem of the large error. In addition to this, extending the time series as much as possible is a direct means of minimizing the error, as well as more accurately identifying the data for the trend term, thus further improving the accuracy of the residual term.  
Due to the limitation of the features in the study area, the PS-InSAR method is not good for the deformation detection in the non-building area. Of course, it can be combined with the SBAS-InSAR method, and the SBAS method can be utilized to fill in the area with poor spatial continuity of the PS points, but the poor correlation of the SAR data at this location will still lead to the lack of data or distortion in these areas. It is necessary to further obtain the surface deformation information from other observation means to improve the monitoring accuracy.  
Figure 8: (**a–d**) Shows the zone maps of four randomly selected highly sensitive buildings, the white dots are the PS points, the cumulative deformation values of the PS points on each building are indicated in (**i,j**), the gray background is the rainy season, the box marks out the obvious anomalous values, and the actual houses corresponding to (**a–d**) are indicated in (**e–h**). Situation.  
Most areas of Zhengzhou City are covered by soft soil, loess, and general soil, which mainly contain clay minerals, such as illite, kaolinite, and montmorillonite. For some houses with unstable foundation structures, such as ground cracks, the presence of these minerals may lead to abnormal uplift of the house due to the infiltration of precipitation into the underground of the house, which may cause the soil to absorb water and expand. For some houses with old or damaged structures, precipitation may often exacerbate structural damage, leading to abnormal settlement of the houses.  
Although the scheme provided in this paper identifies areas with anomalous values of surface deformation and utilizes a more stringent metric (anomalies of three or more times) to identify buildings with highly sensitive surface deformation, assessing the potential hazards of a building requires further access to its geologic movement and displacement characteristics of the site; mechanical parameters of the building materials; load-bearing properties; and distribution and interrelationships between the structural (columns, beams, and load-bearing walls) and non-structural (cladding, distribution) elements, as well as other building data. Therefore, the program can only be used as an auxiliary tool in the search for potentially damaging buildings.