esa-sceva/satcom-chunk-collection huggingface dataset:
# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 2 Radiomics
### Prediction of Complications after AIS  
There are some studies that have used RFs to predict some complications in patients with AIS. Fu B et al. obtained an AUC of 0.96 in predicting malignant cerebral edema (MCE) using a combined model with RFs from NECT of patients with AIS [50]. Jiang et al. also predicted edema after acute ischemic stroke using MRI-based radiomics in 389 patients, with similar results [51]. Wen et al. developed a radiomic model to predict malignant acute middle cerebral artery infarction (mMCAi) based on CT radiomic features (NCECT and CTA). They obtained an AUC of 0.917 and 0.913 in the training and validation cohorts, respectively. They concluded that radiomic features could be an instrumental tool to predict the risk of mMCAi [52].  
On the other hand, Meng et al. attempted to predict hemorrhage transformation in AIS patients based on MRI radiomics features. They extracted 5400 radiomic features from 71 AIS patients. They performed manual segmentation. They obtained an AUC of 911 and an accuracy of 0.894 in predicting hemorrhage transformation with a combined model. They concluded that a combined model with MRI-based radiomics can provide important information to physicians in the diagnosis of AIS patients [53]. Xie et al. also developed a CT-based radiomics model to predict hemorrhagic transformation in AIS patients and achieved similar results (AUC of 0.845-0.750) [54].  
Finally, Liu et al. developed a radiomics model based on NCECT to predict hemorrhage expansion in patients with thrombolysis/thrombectomy related hemorrhagic transformation. They concluded that CT-based radiomics is valuable in predicting hemorrhage extension [55].
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 5 Experiment and Analysis
### Comparative Experiments and Analysis
#### 5.3.2. Results Analysis  
As can be seen in Figure 9, the following conclusions can be made.  
Case 1: The spatial and time query parameters remained. In this case: (a) when the amount of metadata was less than 7.5 million items, the time consumption of the LSI model-based retrieval method was a little less than that of longitude- and latitude-based data retrieval; (b) with the increase of the metadata volume, the LSI model-based data retrieval was more efficient than the longitude- and latitude-based data retrieval; (c) when the amount of metadata was less than 5.5 million items, the time consumption of LSI model-based metadata retrieval on a single Solr node was not very different from that of SolrCloud; (d) when the metadata volume increased, the retrieval speed differences between SolrCloud and Solr became larger; (e) as for the longitude- and latitude-based data retrieval on the single Solr node, its retrieval speed was much slower than that of our proposed metadata retrieval program; and (f) although the query time increased little with the increase of metadata volume in the HBase cluster, it was still larger than that of the LSI model-based method. This may be because HBase has to manually scan the entire database to get results if we try to \"filter\" based on a \"component\" of the key or any of the values [45]. SolrCloud, on the other hand, with its inverted index, can handle queries on any of the fields in any combination, and can simply blaze them fast.  
Case 2: The spatial query parameters remained but time frames changed. In this case: (a) with the increase of query time frames, the time consumed showed an upward trend as a whole, but this  
Figure 9: The results of all comparative experiments.  
was not obvious, not only for SolrCloud but also in the Solr single node--this type of situation could benefit from the inverted index of SolrCloud and Solr; and (b) the query time increased little with the increase of query time frames in the HBase cluster.  
Case 3: The time frames remained but spatial query parameters changed. In this case: (a) the time consumption increased with the increase of query spatial extent, regardless of using the LSI model-based metadata retrieval method or the longitude- and latitude-based data retrieval method; and (b) for both the SolrCloud/Solr and the HBase cluster, the query time growth rate of LSI model-based metadata retrieval method was greater than that of the longitude- and latitude-based data retrieval method. This may be because the comparison increased with the GeoSOT grid number increase. However, such a small flaw still did not affect the overall query efficiency of LSI model-based data retrieval method.  
In short, the results of all the comparative experiments proved the superiority of our proposed data retrieval program.
## 4 Results and Discussion  
### Experimental Environment and Parameters  
The experimental environment of this study is presented in Table 1. To fully leverage the capabilities of all models, training was conducted for 300 epochs. The batch size was uniformly set to 64, the maximum supported by the available GPU memory, to ensure fair comparisons across models. To prevent the models from becoming trapped in local optima during training, the cosine learning rate (cos-lr) was enabled. Furthermore, all other training parameters were kept at their default values, as specified by the YOLO algorithms. Further details on these parameters are provided in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Category** & **Detail** \\ \hline \multirow{4}{*}{Hardware} & CPU & Intel Core i5-12600 KF \\  & GPU & Nvidia RTX 2080 Ti 22 GB \\  & Memory (RAM) & 96 GB \\  & Storage & 1 TB SSD \\ \hline \multirow{4}{*}{Software} & OS & Windows 10 \\  & Development Environment & PyCharm 2024.1 \\ \cline{1-1}  & Accelerated Environment & CUDA 12.1 \\ \cline{1-1}  & Deep Learning Framework & Pytorch 2.3.0 \\ \cline{1-1}  & Python Version & 3.8.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Environmental configuration.  
Figure 5: The structure of the NAMAttention and SCDown block. (**a**) NAMAttention. (**b**) SCDown.
## Finite Element Models
### Material Constitutive Equations
#### 2.2.2 Material Modeling of Core Concrete  
The complex, nonlinear material behavior of concrete was described by the elasto-plastic damage model--concrete damaged plasticity, which was developed by [PERSON] et al. (2016) and elaborated by [PERSON] and [PERSON] (2017). This model assumes non-associated potential plastic-flow where the flow potential is defined by the Drucker-Prager hyperbolic function and the yield function. This assumption provides a realistic simulation of the concrete in compression, because a major point of departure of concrete behavior from metal is that it cracks in a quasi-brittle manner under relatively low stress. The tensile behavior is defined by a crack detection surface, which forms part of the yield function. On reaching the yield surface, a crack perpendicular to the maximum principal tensile stress is assumed to have formed and the stiffness is modified according to representing the behavior of such a crack.  
The damage plastic model with the modified Drucker-Prager yielding criterion and the non-associated flow rule that was defined in ABAQUS were used for concrete in this analytical model. The model allows inputting a multi-linear uniaxial compression stress-strain curve. In this paper, the authors proposed a practical equivalent uniaxial stress-strain constitutive model of the L-shape concrete-filled steel tubular structure, which had been verified in L-shaped concrete-filled steel tubular columns ([PERSON] et al., 2018; [PERSON] et al., 2018). The core concrete equivalent stress-strain curve was shown in Figure 2. The compressive behavior of the core concrete could be described, as follows:  
\[y=\left\{\begin{array}{l}2x-x^{2}\quad(x\leq 1)\\ \frac{x}{\beta_{v}(x-1)^{q}+x}(x>1)\end{array}\right. \tag{1}\]  
where: \(x\) is the normalized strain, \(x=\varepsilon/\varepsilon_{0}\), \(y\) is the normalized stress, \(y=\sigma/\sigma_{0}\), \(\sigma_{0}\) is the concrete compressive stress, \(\sigma_{0}=f_{c}\), \(\varepsilon_{0}\) is concrete strain at the concrete compressive stress, \(\varepsilon_{0}=(1240+1000\xi^{0.25}+12.7f_{c})\times 10^{-6}\), \(\eta\), \(\beta_{0}\), and \(\xi\) are coefficients, and \(\eta=1.6+1.5/x\), \(\beta_{0}=\frac{f_{c}^{0.1}}{1.35\sqrt{1+\xi}}\), \(\xi=f_{y}A_{s}/f_{c}A_{c}\).  
Figure 1: The stress-strain curve of steel.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 2 Comparative Study of BPv7 and IPv6 Functionalities  
As described in Section 1, we adopt BPv7 as a reference, as it represents the latest and most advanced iteration of the BP. Subsequently, we compare it to IPv6, focusing on their respective functionalities.  
Both IPv6 and BPv7 are message-oriented protocols whose data unit structures are designed with modularity, facilitating extensibility through their respective extension mechanisms. In IPv6, the fundamental data unit is the packet, which can be augmented with extension headers to support additional functionalities such as fragmentation, routing, or hop-by-hop options. Similarly, BPv7 employs bundles as its primary data unit, which can be extended with Extension Blocks to incorporate various optional features. This common approach underlines the similarity between the two protocols and is a valuable resource to take into consideration for future extensions, or the porting to IPv6 of already existing BPv7 optional features such as Bundle Protocol Security (BPSec) [15]. While both BPv7 and IPv6 share a modular structure and design in their use of Extension Blocks and extension headers, respectively, their approaches diverge significantly in other areas. For example, BPv7 employs schedule-aware routing and has optional status reporting, which are crucial tools for disrupted networks. In contrast, IPv6 relies on well-established routing protocols like Open Shortest Path First (OSPF) and Border Gateway Protocol (BGP), which are unfit for challenged networks. DTN-compliant algorithms such as Contact Graph Routing (CGR) [16] would need to be implemented for IPv6 networks. Fragmentation handling also differs: IPv6 uses the Fragment Header, whereas BPv7 employs a custom fragmentation mechanism. Furthermore, BPv7 replaces the traditional hop limit of IPv6 with a lifetime expiration system for bundles, which is better suited for cases where messages may spend large periods in node storage awaiting a forwarding opportunity. Key features such as status report and store-and-forward mechanisms, native in BPv7, are entirely absent in IPv6. In general, the comparison highlights that while BPv7 and IPv6 share some design patterns, for the latter to operate in a DTN scenario the lack of a store-and-forward mechanism and a signaling mechanism must be addressed. The next subsections detail the related challenges in data and control planes.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 3 Opportunities Given by System-Level Testing
### Opportunities Given by TID Tests
#### 3.1.2 New Insight into Details of the System's Radiation Performance  
Results of the system-level radiation tests may be the source of valuable information about the system details, and different PoL test campaigns also gave such information.  
In the X-rays test (already mentioned in Section 3.1.1) it was possible to reach in relatively short time (around 8-10 h of irradiation) the total dose level high enough that the functional failure of two PoL modules was observed: above 400 krad and above 475 krad irradiation steps. More than 30 different electrical parameters were measured during each irradiation step of the TID tests, giving good observability during this experiment. Thanks to this good observability, it was possible to track the most sensitive component in the system (power MOSFET) and its most critical parameter, the MOSFET switching time (see Figure 2 for the recording of MOSFET driver waveforms in one of the irradiated PoL 2D boards and Figure 3 for the degradation of the MOSFET switching time in all three PoL modules irradiated with X-rays).  
This test has shown that system-level TID irradiation may be useful to (1) assess which parts should be replaced in the system to extend its radiation performance, (2) evaluate general radiation performance (TID level that the system may survive), (3) to define error signatures for the system. Such TID system-level test might be an interesting tool for verification of mature designs, but also evaluation at the early development phase of product/project.  
Figure 3: MOSFET switching time as a function of the total ionizing dose for two PoL 2D boards and one PoL 3D SiP module (after [22]).  
Figure 2: MOSFET driver waveforms recorded at steps 0, 200, 400 krad (X-rays irradiation of the PoL 2D board), after [22]. Black colour marks switching time for each irradiation step. Switching time is approximately 3 times longer for 200 krad (than prerad) and more than 15 times longer for 400 krad (than prerad). The device failed after the next irradiation step, i.e., it was not functional at the irradiation step of 450 krad.
# Bifunctional Heterogeneous Catalyst for Biodiesel Production from Waste Vegetable Oil
## 3 Results and Discussion
### Reusability  
The economic potential of CaO/Al2O3 catalyst was evaluated via the reusability tests, which represents its capacity to perform the same catalytic activity conducted on WSO and WPO. The methanol-to-oil molar ratio of 9:1 and 4 wt % catalyst loading at 65 \({}^{\circ}\)C for 4 h utilized to achieve the initial FAME yield was sustained. Figure 3 shows the reusability of the CaO/Al2O3 catalysts. It was observed that the FAME yield displayed a descending trend due to the influence of deactivation or a decrease in the number of catalyst active sites as well as the reduction in the weight of the catalyst as a result of process operations, which is common to heterogeneous catalysts [30; 32]. The maximum FAME yields of 98 and 80% were attained in the use of WSO for the first and second run, respectively. Conversely, for WPO, the first and second maximum FAME yields of 89 and 77% were recorded respectively. The active sites' deactivation with time could be due to various possible phenomena, such as coking, sintering, poisoning or leaching [30; 33]. The blockage of the active sites by poisoning is evident when the process involves used oils with impurities in the form of pigments, food debris, oxidized fats, non-hydratable lecithins and various other non-lipid substances [33]. The loss of catalytic activity can also be attributed to leaching during the process, which increases the contamination of the product and the operational cost on account of substituting the catalyst [30; 32; 34].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fatty Acids** & **wt \%** \\ \hline
2,2,4,6,6-Pentamethylheptane & 0.223 \\ n-Nonadecane & 0.3331 \\ S-Butylbenzene & 0.2269 \\ n-Octadecane & 0.4567 \\
2,4-Di-tert-butylphenol & 1.9877 \\ n-Docosane & 0.4907 \\ Hexadecanoic acid or Palmitic acid & 40.5223 \\ Heptadecanoic acid or Margaric acid & 0.8483 \\
11,14-Octadecationoic acid or Linoleic acid & 7.689 \\
8-Octadecenoic acid or Oleic acid & 40.8909 \\ Octadecanoic acid or Stearic acid & 6.3312 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characterization of of WPO.
## 4 Discussion  
### Dynamics of the LUEmax  
The LUEmax is a fundamental characteristic of plants [75]. However, at different scales, the LUEmax of vegetation is mainly influenced by species, growth stage at the leaf level, and light intensity, which may cause it to show obvious spatial heterogeneity [76]. According to the findings of this paper, the LUEmax varies significantly across different growth stages and under various growth conditions. During the early growth stages of plants, the LUEmax tends to be relatively low, as young plants have not yet fully developed their photosynthetic systems. As plants grows, the LUEmax gradually increases, reaching  
Figure 9: (**a**) The q values of the factors; (**b**) the explanatory power of the interaction between factors. (The darker the color, the stronger the importance of the variable in (**b**)).  
its peak during the growth cycle. This change reflects plants' ability to continually optimize its photosynthetic system throughout the growth process. Each biome possesses structural characteristics unique to its environment [77]. For example, shrublands typically feature sparse, open canopies with short plants, smaller leaves, and predominantly sunlit foliage. In contrast, evergreen broadleaf forests are dense, with minimal gaps, tall trees, and larger leaves, resulting in a high proportion of shaded leaves. The LUEmax of plants presents obvious spatial heterogeneity and varies for different vegetation cover types [2]. Research on cropland and mangrove forests has demonstrated that LUEmax differs among various vegetation types and exhibits dynamic changes across different seasons. Specifically, these results show that LUEmax varies among crop types and is dynamic within a crop season. LUEmax undergoes dynamic seasonal variations in different climatic environments even for the same vegetation type [78; 79]. This finding is in agreement with the results obtained in this study. According to the findings of this paper, herbaceous plants generally show lower LUEmax values, while tree species tend to have higher LUEmax values. The variability in LUEmax across different vegetation types reflects the distinct physiological and ecological strategies adapted to their specific environmental conditions. Ecosystems with the same vegetation type exhibit similar LUEmax variations despite significant spatiotemporal differences [80], indicating the feasibility and reliability of upscaling site-based LUEmax for regional or global GPP prediction. These differences highlight the dynamics of LUEmax, enabling models to better capture the adaptive responses of leaves to short-term environmental fluctuations [81].
## 1 Introduction
The dynamic centrifuge model test can be used to simulate the prototype self-weight stress and replicate in situ stress conditions. The test results can be used to verify the reliability of a numerical simulation; because of its operability and repeatability, it is an important technique in research into the effects of seismicity on slopes. From the perspective of the interaction between homogeneous sand and a pile, the former studies mainly focus on the seismic response characteristics of a slope reinforced by stabilizing piles [21; 22; 23], the seismic response characteristics of inclined pile foundations [33], and the influence of liquefaction on pile foundations through centrifugal shaking table model tests [34; 35; 36], inter alia. From the perspective of the interaction between homogeneous clay and a pile, [PERSON] and [PERSON] [24] conducted centrifuge model tests to study the seismic strengthening mechanism of stabilizing piles by assessing the deformation profiles of slopes reinforced by stabilizing piles under seismic load. [PERSON] and [PERSON] [37] studied the interaction between friction piles and soft clay under seismic load in centrifuge model tests. The aforementioned dynamic model tests of slope stabilizing piles, and their mechanism of action, are mainly based on the dynamic response characteristics of sand, clay, and other homogeneous slopes; however, there are few studies on the dynamic response of a deposit slope with underlying bedrock and the working mechanism of the coarse-grained rock and soil mixture deposits, thus making it difficult to meet the needs of engineering practice. Therefore, in this research, two sets of centrifuge shaking table model tests were conducted to study the seismic response of a deposit slope with underlying bedrock and with stabilizing pile reinforcement and a similar unreinforced slope. The distributions of bending moments and lateral pile-soil load under continuous multi-level seismic loading were further explored. In the present work, the results are presented in terms of prototype-scale.
# Preliminary Field Results of a Dedicated 5G Positioning Network for Enhanced Hybrid Positioning +
## 3 Experimental Methodology
### Performance Metrics  
The 5G-ranging performance assessment within this paper is based on the variability of the measurements over a period of 1 s. This assessment indicates the ranging precision of downlink UE measurements from the flying 5G BS.  
Let us define the pseudorange measurement at the \(n\)-th time instant as follows:  
\[\rho(n)=\|\mathbf{x}_{\text{UAV}}(n)-\mathbf{x}_{\text{UE}}\|^{2}+c\cdot( \delta t_{\text{UE}}(n)-\delta t_{\text{UAV}}(n))+\varepsilon(n), \tag{1}\]  
where \(\mathbf{x}_{\text{UAV}}(n)\) is the three-dimensional (3D) position of the UAV, \(\mathbf{x}_{\text{UE}}\) is the 3D position of the static UE, \(\delta t_{\text{UE}}(n)\) is the receiver clock offset, \(\delta t_{\text{UAV}}(n)\) is the flying BS clock offset, and \(\varepsilon(n)\) is the pseudorange error due to noise and multipath. As one can observe from this expression, the main sources of pseudorange variability are the slow-moving distance \(d(n)=\mathbf{x}_{\text{UAV}}(n)-\mathbf{x}_{\text{UE}}{}^{2}\) due to UAV hovering movements, the BS and UE clock offsets, and the noise and multipath errors. The variation in these error sources can be calculated for each measurement as  
\[\Delta\rho(n)=\rho(n)-\mu_{\rho}\, \tag{2}\]  
where \(\mu_{\rho}\) is the average of all pseudoranges over the measurement period of 1 s. Note that the 50 s measurement set is split in subsets of 1 s, and \(\mu_{\rho}\) is computed for each subset. Thus, in the absence of large pseudorange outliers, the pseudorange variation \(\Delta\rho(n)\) is a suitable performance metric to assess the ranging precision of the 5G measurements.  
As a complementary metric, the 5G downlink PRS reference signal received power (RSRP) in [11] is computed to further assess the results in terms of propagation conditions.