esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Model  
### Models of Adsorbers  
The flow field of the adsorber was calculated using a two-dimensional, axisymmetric geometry in order to save computational time. The models of the adsorbers that were investigated are illustrated in Figure 1. The main structure of the radial-flow adsorber consists of a center flow channel, an adsorbent packing layer and an outer flow channel. The holes for gas flow distribution shown in Figure 1e are evenly distributed between the flow channel and the adsorbent loading layer. The x-axis is the radial coordinate and the z-axis is the axial coordinate, and the center of the end of the adsorber inlet is taken as the coordinate origin. The schematic of radial-flow adsorbers including the important sizes are shown in Figure 1f, and the structure parameters of the adsorber are shown in Table 1.  
Radial-flow reactors can be classified into z-flow and \(\pi\)-flow types depending on the flow directions in the center channel and in the annular channel next to the adsorbent layer. As shown in Figure 1, in the z-flow type, the flow directions in annular and center channels are the same, while in the \(\pi\)-flow type the two flow directions are opposite. Moreover, radial-flow reactors can also be classified into centripetal (CP) or centrifugal (CF) flow types depending on radial-flow direction. In the CP-flow type, the gas is fed to the annular channel and flows radially inward from the annular channel to the center channel, while in the CF-flow configuration, the gas is fed to the center channel and flows radially outward from the center channel to the annular channel. Therefore, four flow configurations, CP-z, CP-\(\pi\), CF-z, and CF-\(\pi\), are selected for modeling the radial-flow adsorbers as shown in Figure 1[14].
## 9 TR-ARPES Spectra Measurements
### TR ARPES Spectra of p-type GaAs (100)  
TR-ARPES spectra of p-type GaAs (100) valence band were measured with time delay steps \(\sim\)67 fs from time-zero to 1000 fs and larger steps up to a delay of 30 ps (Figure 13). The 50-fs excitation laser pulse (1.55 eV) irradiates the sample material with a fluence of 2 mJ/cm\({}^{2}\). The femtosecond EUV pulse with photon energy \(\sim\)20.15 eV (H13) probes the excited sample material with photon flux \(\sim\)10\({}^{9}\) ph/s (photon density \(\sim\)50 ph/\(\upmu\)m\({}^{2}\)/pulse).  
Figure 13a shows the ARPES spectra of the valence band of GaAs (100) at time zero (left image) and at time delay +650 fs (right image) after the excitation laser pulse. To further distinguish the influence of excitation pulse on the ARPES spectra, two typical electron Energy Distribution Curves (EDC) are plotted in Figure 13b, for two time delays: \(-\)500 fs (before excitation) and +5 ps (after excitation). It is clearly observed that the peak position of EDC is shifted to the higher kinetic energy side when the EUV probe pulse arrives at the sample after the excitation. We attributed this up-shifting phenomenon to the Surface Photo-Voltaic (SPV) effect. In general, the downward band bending on the p-type semiconductor can trap photo-excited electrons at the surface and drive photo-excited holes into the bulk. Such electron-hole separation could produce additional electric filed to transiently shift the band structure up in energy [49; 50].  
The shift of EDC peaks in valence band (called by SPV shift), induced by the excitation laser pulse, can be calculated from the temporal evolution of EDC:  
\[\text{SPV shift}=\text{KE}_{\text{EDC-PEAK}}\text{ (TDL)}-\text{KE}_{\text{EDC-PEAK}}\text{ (TZ)}, \tag{2}\]  
where \(\text{KE}_{\text{EDC-PEAK}}\) (TDL) is the kinetic energy (KE) of photoelectrons at the peak of EDC for a given time-delay (TDL); and \(\text{KE}_{\text{EDC-PEAK}}\) (TZ) is the value at the time zero (TZ). Figure 13c,d show the SPV shift of GaAs's valence band as a function of time delay between excitation laser pulse and EUV probe pulse. Within ultrafast rise-time \(\sim\)650 fs, the SPV shift reaches a maximum plateau of \(\sim\)80 meV, lasting \(>\)30 ps. The SPV error bars are obtained by averaging out the measured data over 50 cycles. The errors are minimized by measuring \(\text{KE}_{\text{EDC-PEAK}}\) at the peak of the EDC. The temporal step error bars are not shown because they are much smaller than the delay step \(\sim\)67 fs. Similar SPV measurements of photo-excited electron dynamics in p-type GaAs (100) have previously been reported [49; 50].  
Figure 12: ARPES spectra of Cu (111) band structure, along the \(\Gamma\)K-direction in the surface Brillouin zone. (**a**) HHG EUV beam with H13 (\(h\
u_{13}\) = 20.15 eV); and (**b**) He-I lamp (\(h\
u_{He}\) = 21.22 eV). Vertical axis: PE energy (eV) and horizontal axis: electron K-vectors (Å\({}^{-1}\)).
# A Systematic Review: To Increase Transportation Infrastructure Resilience to Flooding Events
## 3 Results
(2021) inspected delay as impacts on traffic corridors caused by high tide flooding in the East, Gulf, and Pacific coastal regions of the USA. This study utilized geospatial analysis for the representation of the flood impacts on road networks with traffic volume data [67]. [PERSON], et al. (2016) assessed the impacts of pluvial floods on a road network by utilizing geospatial tools. This study developed an algorithm to determine the start and end time of the flooding on the roadways. The results of the algorithm allowed this study to quantify the interruptions to the roadways in Shanghai, China [82]. Transportation network impacts of accessibility and mobility are crucial to evaluate as they represent the functionality of the roadways. These were measured by investigating delay, vehicle speed, or ability to traverse the road in the flood. [PERSON] and [PERSON] (2019) considered the roads (edges) and intersections (nodes) of road infrastructure to determine accessibility of each node in Zurich, Switzerland and determined that flood events affect topological properties of the roadways [74]. [PERSON], et al. (2005) considered the effects of climate change to analyze the impacts on the performance of an urban transportation network in the Boston Metro Area, USA. This study measured accessibility and mobility by considering increased delay and loss of trips [88]. Social and economic impacts were considered by some studies to investigate the impacts to accessibility and mobility, showing that flood impacts extend beyond physical attributes of the transportation infrastructure. [PERSON], et al. (2017) developed a correlation between depth of flood and vehicle speed in a case study in Newcastle upon Tyne, UK. This study revealed that there are wide variety of potential impacts of flood events on accessibility and mobility, such as with safety, disruption, and economic, and social impacts [80]. [PERSON], [PERSON], et al. (2021) found the impacts of floods by combining transit feed datasets, surveys, and flood maps to show disruptions from floods which led to delay in mobility and loss of accessibility to jobs, especially to low-income individuals. Floods impact individuals, particularly the disadvantaged, at a higher proportion in Kinshasa, Democratic Republic of the Congo [64]. [PERSON], et al. (2022) investigated social and economic vulnerabilities for the Mayurakshi River Basin, India. This study deployed questionnaire surveys to the general public for understanding their experiences with floods. This study also conducted spatial analysis for investigating flood depth, duration, and inundation area [89]. Table 5 represents the 25 studies related to the analysis of the vulnerability of transportation systems and elements related to flooding, which is research category D [90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114].
## 4 Results and Discussion
### Clustering Results Analysis
#### 4.3.1 Comparative Analysis of Intercity Trip Chains from a Temporal Perspective
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Beijing–Shijiazhuang**} & \multicolumn{2}{c}{**Shijiazhuang–Beijing**} \\ \cline{2-5}  & **May 2019** & **May 2021** & **May 2019** & **May 2021** \\ \hline Weekday & 3 & 3 & 3 & 3 \\ Gender & 1 & 1 & 1 & 1 \\ Type-O & 1 & 1 & 1 & 1 \\ Mode-O & 1 & 1 & 1 & 1 \\ Type-D & 0 & 1 & 0 & 1 \\ Age & 7 & 5 & 7 & 5 \\ MOT-O & 13 & 8 & 7 & 7 \\ Ds-O & 2 & 2 & 1 & 1 \\ Wt-O & 10 & 9 & 7 & 6 \\ Wt-D & 7 & 5 & 7 & 7 \\ Ds-D & 1 & 1 & 2 & 2 \\ Percentage & 52.6\% & 41.39\% & 45.4\% & 58.76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Beijing–Shijiazhuang direction in different years accounted for the most trip chain characteristics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Beijing–Tianjin**} & \multicolumn{2}{c}{**Tianjin–Beijing**} \\ \cline{2-5}  & **May 2019** & **May 2021** & **May 2019** & **May 2021** \\ \hline Weekday & 3 & 3 & 3 & 3 \\ Gender & 1 & 1 & 1 & 1 \\ Type-O & 1 & 1 & 1 & 1 \\ Mode-O & 4 & 4 & 1 & 1 \\ Mode-D & 1 & 1 & 1 & 1 \\ Type-D & 1 & 1 & 1 & 1 \\ Age & 6 & 5 & 6 & 7 \\ MOT-O & 8 & 7 & 10 & 12 \\ Ds-O & 2 & 2 & 1 & 1 \\ Ds-O & 2 & 2 & 1 & 1 \\ Wt-O & 5 & 7 & 6 & 6 \\ Wt-D & 7 & 6 & 5 & 6 \\ Ds-D & 1 & 1 & 2 & 2 \\ Percentage & 55.7\% & 54.99\% & 47.7\% & 49.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Beijing–Tianjin direction in different years accounted for the most trip chain characteristics. 2021. In the coordinated development of industries in the Beijing-Tianjin-Hebei urban agglomeration in 2021, Shijiazhuang may have undertaken male dominated industries such as manufacturing and infrastructure in Tianjin, leading to a surge in the demand for male labor commuting or deployment. The waiting time at the departure hub decreased. On the one hand, the experience of the Beijing-Tianjin intercity railway was extended to the Tianjin-Shijiazhuang line, with increasing train length and frequency during peak hours in 2021. On the other hand, the popularity of self-service ticketing and electronic tickets had increased, reducing the manual verification process. The waiting time at the arrival hub increased. Shijiazhuang may have implemented stricter arrival inspections in 2021, resulting in longer waiting times. For the Shijiazhuang to Tianjin direction, the waiting time at the arrival hub decreased in 2021 compared to 2019, with other changes being minimal. This was because the transportation system of Tianjin had become more efficient and convenient, and the manual verification process was reduced.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 2 Materials and Method  
### Data Processing Flow  
To identify stable points in a control network in the second and any subsequent phase, the following steps are proposed in this paper: (1) First, perform classical free network adjustment to arrive at the initial coordinates of the current phase; that is, only introduce the coordinates of one point and the azimuth of one side as the starting data to minimize the impact of starting data errors on the adjustment results. (2) Perform a coordinate similarity transformation between the coordinates of the current phase and the first phase, and absorb the overall parts of the changes between the two phases of data through four parameters, further eliminating the adverse impact of starting data errors on the adjustment results of the two phases, and dividing the control points into stable and unstable points based on the relative sizes of the residuals of the points after the similarity transformation. (3) Perform quasi-stable adjustment on the coordinate results of the points in the current phase. The technical route is shown in Figure 2.
# APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining
## 2 Related Works
### Graph-Based APT Attack Detection Methods  
In APT detection, researchers have integrated log analysis, graph models, and machine learning to improve detection precision:  
[PERSON] [12] proposes a two-stage machine learning framework that identifies abnormal inter-cluster invocation patterns by analyzing the spatiotemporal correlations between program execution paths and system events, effectively detecting collaborative attacks across hosts. UNICORN [2] develops Unicorn, a real-time APT detection system that converts audit logs into provenance graphs and extracts temporal-causal features. Capable of detecting APTs without prior attack knowledge, Unicorn achieves high accuracy and low false positive rates by leveraging dynamic feature engineering. [PERSON] [13] constructs a real-time detection system based on the kill chain model, mapping low-level system events to high-level attack scenarios via a High-Level Scenario Graph (HSG). This semantic mapping enables effective identification of multi-stage attack chains by bridging technical details with tactical-level behaviors. [PERSON] et al. [14] proposed a kill chain detection framework based on a layered architecture, which improves detection targeting by reducing the probability of cross-layer vulnerability reuse. However, this method relies on partial data and preset rules, and its generalization ability is limited in complex and dynamic network attack and defense environments, making it difficult to deal with new attack variants.  
These studies provide a multi-layer solution for APT detection from log modeling to semantic enhancement, but there is still room for optimization in terms of heterogeneous data fusion, dynamic environment adaptability, and low resource consumption. The Hypergraph Attention Network proposed in this paper further enhances the modeling ability of high-order behavior associations through a node-edge bidirectional attention mechanism, making up for the shortcomings of traditional graph models in complex semantic representation.  
In summary, existing APT detection methods face critical limitations in behavioral modeling and community analysis, including vague behavior boundary division where traditional graph methods rely on strict community partitioning and overlook nodes' participation in overlapping multi-stage attack behaviors, leading to semantic information loss and high false positive rates. Additionally, binary edge-based graph models fail to represent composite operations involving multiple entities, making capturing high-order semantic correlations of attack behaviors difficult. Meanwhile, the spatiotemporal dependencies of multi-modal data in kernel logs are inadequately handled, and traditional rule-based matching or shallow learning cannot mine deep cross-modal correlations. Furthermore, the random seed selection in traditional community detection for large-scale log processing causes instability and high computational overhead, making it challenging to meet real-time detection requirements.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing
### Covalent Organic Frameworks
The research presented an effective assay for PSA detection in the range of 100 fg mL\({}^{-1}\) to 10 ng mL\({}^{-1}\), utilising an optimum combination of the superior electron transfer of phosphorene, effective enrichment of methylene blue in the COF, and the excellent catalytic activity of Fe\({}_{3}\)O\({}_{4}\) molecules [276]. Two-dimensional COF layers often exhibit a great amount of attraction, preventing the formation of stable thin films. It is also possible to generate COFs from three-dimensional kenaf stem-derived microporous carbon by soaking it in a reactive liquid [277]. Yang et al. used this technique to generate COFs, which were subsequently integrated with electrodes to fabricate electrochemical sensors [278]. Copper nanoparticle deposition on these COFs enabled glucose detection, whereas platinum nanoparticle-deposited COFs allowed the determination of H\({}_{2}\)O\({}_{2}\). A similar metal nanoparticle doped COF was proposed by Gu's group for the detection of aflatoxin B1 [279]. In this work, AuNPs were embedded in COF, which was further functionalised with molecularly imprinted poly(\(o\)-aminothiophenol). The piezoelectric sensor showed linear responses in a wide concentration range (50 pg mL\({}^{-1}\)-75 ng mL\({}^{-1}\)) with significant recoveries (87.0-101.7%) in real samples [279]. In another study, AuNP-doped COFs were used for sensing cardiac troponin I (cTnI) [280]. This sandwich-type immunosensor also included toluidine blue as an electron mediator. The toluidine blue-AuNP-COF labels in association with primary antibody functionalised Au-TiO\({}_{2}\) nanoparticle doped polypyrrole provided electrochemical responses in a broad concentration range (500 fg mL\({}^{-1}\)-10 ng mL\({}^{-1}\)) and a low LOD (170 fg mL\({}^{-1}\)) [280]. In addition to the aforementioned research, several other studies, listed in Table 5, have been carried out using nanogels, dendrimers, hyperbranched polymers, and COFs for the early diagnosis of cancer, neurological disorders, and diabetes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor Platform/Label** & **Analyte** & \ \begin{tabular}{c} **Detection** \\ **Mechanism** \\ \end{tabular}  & \begin{tabular}{c} **Detection Range** \\ \end{tabular}
## 3 Results and Discussion
### Blackcurrant Total Phenolic, Antioxidant Capacity, and Ascorbic Acid Changes During Storage  
Since there were no significant differences in total phenolics and antioxidant capacities caused by pretreatments (Table 1), the storage effects were analyzed for each characteristic, including the total phenolic content and antioxidant capacities assessed through the DPPH assay and FRAP assay.  
On average, the total phenolics of blackcurrants ranged from 454.71 to 524.09 gAE/mL, and the statistical \(p\)-value was close to 0.05. From the changes in total phenolic contents across the storage (Table 3), the phenolics on day 20 were significantly lower than  
Figure 4: UVC pretreatment effects on blackcurrant mold and yeast population during storage. Each treatment contains three replicates. Bars indicate the mean \(\pm\) standard deviations of the replicates. \(p\)-values indicate the differences among treatments on the sampling day; ns indicates not significant.  
the total phenolics on days 0, 5, and 10. This indicated that along with the storage, the total phenolic contents decreased.  
Although there were no significant changes in antioxidant capacities under different pretreatments (Table 1), similar to the changes in phenolics, both DPPH and FRAP assays detected a decrease in antioxidant capacity during storage. DPPH decreased from 4.14 TEAC mM/mg on day 0 to 3.97 TEAC mM/mg on day 20. There were significant differences among the early storage days (days 0, 5, and 10) and the late storage days (days 15 and 20). The FRAP assay also detected a decreasing trend, from 321.97 to 215.10 Fe\({}^{2+}\) equivalent nmol/mg, across the storage period. It also revealed statistical differences on each sampling day, indicating that the FRAP sensitivity was higher than that of the DPPH assay in this study. Many studies have also highlighted the differences between these two methods, which may be due to their detection of different targets [47]. In summary, the antioxidant content of blackcurrants decreased over time under UVC pretreatments.  
Interestingly, for the specific antioxidant ascorbic acid, a slight increase was detected over the storage period, with changes of about 4 to 5 mg/100 g. Its concentration typically decreases during storage in other fruits due to oxidation and other degradation processes [48]. In certain circumstances, ascorbic acid can be converted back from its oxidized form, dehydroroscorbic acid, potentially leading to an increase in the amount of ascorbic acid [49]. Whether the scenario is the same in this study, further chemical analysis of ascorbic acid in blackcurrants shall be conducted.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 4 Discussion
### Performance Improvements in Critical Operational Periods  
The performance gap between our weighted fusion methodology (2.33% MAPE) and direct aggregation (3.09% MAPE) is particularly pronounced during peak load periods and extreme weather conditions. This improvement is operationally significant because accurate peak load forecasting directly impacts grid reliability and operational safety. The traditional direct aggregation approach consistently underestimates peak loads by up to 7.5% during critical high-demand periods, which could lead to insufficient generation capacity and potential grid instability. In contrast, our weighted fusion approach maintainssignificantly better tracking of actual load patterns, with peak-hour MAPE values reduced by 62-69% across our evaluation periods.  
This enhanced performance during critical operational periods is primarily attributable to the dynamic weight redistribution implemented by our PCA-based methodology. As demonstrated in the seasonal weight analysis, our approach effectively recognizes the changing forecasting strengths of different provinces under varying operational conditions, assigning higher weights to more reliable regional forecasts during each specific period. This adaptive weighting is particularly effective during extreme high temperature conditions (August), where weighted fusion reduced MAPE by approximately 10-12% versus direct aggregation.
# Synovial Sarcoma of the Extremities: A Literature Review
## 3 Treatment
### Surgical Treatment  
In the past, the main surgical procedure that was used for the removal of a soft tissue sarcoma of the lower limb was amputation, which provided low recurrence rates but sacrificed functional integrity.  
The recent evolution of the surgical techniques, together with the adjuvant methods, especially radiotherapy, made the so-called conservative surgery possible for the treatment of synovial sarcomas of the extremities. A wide surgical excision with negative margins is the procedure of choice. Negative margins are extremely important in order to decrease the rate of local recurrence and the overall survival rate. No specific guidelines exist regarding the ideal dimension of the margin that should be resected around the tumoral mass for synovial sarcoma. Usually, for small (<5 cm), superficial tumors, a wide excision  
Figure 8: Microscopic findings—classical synovial sarcoma with two main subtypes: biphasic and monophasic appearance, and with two typical cell types: spindle cells and epithelial cells (fusocellular proliferation with myxoid areas, detail), which is rare (HE staining, 20\(\times\)).  
Figure 9: Microscopic finding—monophasic subtype of a synovial sarcoma with mesenchymal spindle cell elements and fusocellular proliferation (HE staining, 40\(\times\)).  
with negative margins of approximately 1-2 cm is considered sufficient. In large synovial sarcomas with difficult localizations, achieving a proper negative margin without affecting important neurovascular structures may be difficult. In these situations, very close or microscopically positive margins may occur and the use of adjuvant therapies becomes mandatory in order to avoid local recurrences.  
Several methods of tissue reconstruction are available now in order to preserve a satisfactory functional status of the limb following excision.  
Use of non-vascularized or vascularized bone grafts, bulk allografts and oncologic endoprosthetic implants are now available for skeletal reconstruction following large ostearticular resections.  
Sacrificing the major vessels may be required for sarcomas that are surrounding these structures. Following this, arterial reconstruction with an autologous saphenous vein or synthetic grafts is mandatory in order to maintain the viability of the limb.  
Soft tissue reconstruction may also be required following the excision of synovial sarcoma. Several technical options are available in the surgeon's armamentarium, such as skin grafts, loco-regional and free flaps for coverage of the residual defect or tendon and muscle transfers for the restoration of motor function.  
Coordinated multidisciplinary surgical work is usually needed, especially for large sarcomas with problematic localizations in order to achieve a satisfactory resection, together with a good functional result and a satisfactory cosmesis of the limb.