esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Simulation of Blast Events  
The structural response to blast loads can be analyzed in several ways. Hydrocodes offer the possibility to perform complex computer fluid dynamic (CFD) analyses where the blast wave and the structure interact directly by calculating the blast wave propagation and the structural response coupled in a global computational model. However, such procedures are very computationally (time) intensive. Next to a coupled CFD, simplified procedures are suitable. These methods vary from the decoupled dynamic analysis of the pressure propagation and the structural response in two different calculation models to simplified dynamic methods that only calculate the structural response using a semi-analytically determined blast pressure load. The latter is a method that is frequently adopted in the blast analysis of building structures. Here, the pre-calculated blast pressure load is applied to the simulation model as a load time history without considering the effect of the structural deformation on the pressure propagation and distribution. Then,the structural response is determined. Due to the very short load duration of usually a few milliseconds, hydrocodes can also be efficiently employed for such simplified methods. However, static methods are used as well, in which the dynamic characteristics of the load and structural response are taken into account in a highly simplified manner using so-called dynamic increase factors.  
In this research, a simplified dynamic approach is applied with the hydrocode LS-DYNA [18]. The reason for choosing a simplified method and not the fully coupled CFD analysis is that this method is reproducible in practical engineering applications, and it is relatively easy to implement. Furthermore, the focus is also on finding an answer to the question of whether steel tanks are generally suitable for sustaining high-pressure and long-duration blast loads. Of course, this requires an adequate application of the pressure load, but the focus is less on blast wave propagation and more on the structural response. In addition, in chemical industry plants or LNG process unit design, an explosion pressure load is usually specified as a pressure-time history and less via a reacting quantity of explosive, whose pressure propagation must first be determined using CFD.  
The key challenge of the simplified dynamic calculation approach is to apply the adequate blast load to the overall tank structure as well as to capture the material behavior in a proper way. Finally, a static approach is used to evaluate the most suitable analysis method for tank design practice.
# Combined Effect of Ferrous Ion and Biochar on Cadmium and Arsenic Accumulation in Rice
###### Abstract  
Excessive accumulation of cadmium (Cd) and arsenic (As) in rice (_Oryza sativa_ L) poses a potential health risk to populations. Cd and As exhibit opposite geochemical behavior in paddy soil, using appropriate remediation materials to reduce their migration and inhibit their uptake by rice is a great challenge. A pot culture experiment was conducted to investigate the effects of application of silikworm excrement biochar (BC) and ferrous sulfate (Fe(II)) on available Cd and As in paddy soils and their uptake by rice. Results showed that the application of BC + Fe significantly accelerated the tillering of rice plants, and the addition of BC alone to soil did not have a significant effect on the pH of soil, while applied 1% (w/w) BC and 1% (w/w) Fe(II) (1 BC-1 Fe(II)) treatment could markedly reduce the soil pH. BC+Fe(II) could reduce the content of available Cd (reduced by 10%-23%) and As (reduced by 6%-33%) in soil. BC+Fe(II) has a distinct decreasing effect on the available As, thus inhibiting As uptake in rice tissues, and the effect was more obvious with an increasing mass ratio of Fe in BC+Fe(II) treatment. BC+Fe(II) decreased bioaccumulation factors (BF) of As compared to control and BC alone treatments. Compared with As, Cd was more readily transferred from the root to the shoot and accumulated in rice eventually. These findings provide a safe and reliable remediation strategy though application of BC+Fe(II) in Cd and As co-contaminated soil. However, the improvement effect of amendments should be paid a special attention on soil pH.  
silicworm excrement biochar; heavy metal; paddy soil; phytoavailability Article
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 2 Methodology
### Extraction of Repeways
#### 2.3.3 Comprehensive Analysis  
1. By utilizing the least squares method to fit the plane, we can obtain the plane formula \(ax+by+cz+d=0\) and the distance \(d\) between each point of the point cloud and the fitting plane.
2. The error \(\sigma\) of the distance d obtained from (1) can be calculated for fitting the plane point cloud. If each point \(d_{i}\) is far from \(2\sigma\), delete it, otherwise keep it.
3. By performing a principal component analysis on the noise-removed point cloud data, the parameters of the fitting plane are obtained, and the eigenvalues are sorted by the basis size.
4. The ropeway point cloud is segmented and extracted according to the linear features of the points, and the extracted ropeway points are saved as ropeway points and the non-ropeway points as out-ropeway points.
# Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review
## 4 Graphene-Based Composites
### Graphene@Iron Composites  
#### 4.1.1 Keywords Analysis of Graphene@Iron Composites Articles  
Keywords analysis is important in evaluating the area of interest in the articles. It helps to identify the differences and research growth in a particular studied area. Moreover, the co-occurrence analysis of keywords shows the relationship build due to various keywords. Based on the selected articles of Graphene@Iron composites, keyword analysis of the selected articles was made by VOSviewer where the mapping is shown in Figure 6.  
The first cluster with red nodes was assembled across the term \"microwave absorption\" having a maximum occurrence of 3. In the same cluster, \"Fe\({}_{3}\)O\({}_{4}\) nanoparticles\", \"ferric oxide\", \"graphene nanoplate\", \"hybrid material\", \"impedance matching\", \"microwave absorption\", \"porous graphene\" exists with just one occurrence each. The following cluster demonstrates the interest of researchers in microwave absorption of the included composites.  
Similarly, the green cluster around the keyword \"nanocomposite\" having a maximum cooccurrence of 3. The same cluster consists of keywords \"EMI shielding effectiveness\", \"Fe\({}_{3}\)O\({}_{4}\) @gnp hybrid\", \"nanocomposite\", \"shielding effectiveness\" and \"spinel\". Likewise, the third cluster is blue coloured with the keyword \"reduced graphene oxide\" with a maximum occurrence of 3. While the last yellow cluster is closely linked with the blue cluster having keywords \"electromagnetic interference shielding\", \"magnetite\", \"natural rubber\" and \"segregated network\" with 1 occurrence each.
## 4 Discussion
### Clinical Implications and Applications in Real-World Settings  
The mechanisms underlying readmission are complex and remain incompletely understood. In addition to physiological factors and medical conditions, a variety of psychological, social, and economical factors may intertwine with each other to cause readmission. As shown in Table 3, variables related to socioeconomic statuses such as education and occupation, and variables regarding social support such as ED arrival mode and patient's main caregiver, were more or less associated with the risk of readmission. Moreover, without adequate hospital discharge planning and transitional care interventions, patients may be readmitted after discharge from acute stroke care even though their medical conditions have been properly treated. For example, family members may lack the training, skills, and support services to provide caregiving for disabled stroke survivors, and therefore, bring patients back to the hospital. Adequate preparation and support for transition from acute stroke care to home may be required to reduce readmissions [24].  
Previous studies have shown that a substantial proportion (up to 12.9%) of readmissions were potentially preventable [16; 35]. Even though hospital discharge planning has the potential of preventing readmissions [36], one of the key steps is to identify patients at risk of readmission, preferably in the early stage of admission. The prediction models in this study were developed using only variables available upon admission and, therefore, can be used to estimate the probability of readmission soon after patients are admitted to the hospital. A clinical decision support system employing the ML prediction models developed in this study can facilitate clinicians to identify patients likely to experience readmission upon hospital admission. In this way, early targeted interventions for patients at high risk of readmission can be enabled. Certainly, these predischarge interventions should be tailored to the individual patient according to the estimated risk of readmission and may include patient needs assessment, patient education, medication reconciliation, the arrangement of early outpatient follow-up, and referrals to home health rehabilitation [37; 38; 39]. Furthermore, after hospital discharge, at-risk patients identified by the prediction models can be closely monitored to detect in time whether they are having problems and about to bounce back [40].
## 5 Simulation Analysis
### Simulation Results of S11 Reflection Coefficient for Block  
The graph illustrates the S11 reflection coefficient for an antenna system with a block (Figure 13) spanning a frequency range of 1 GHz to 15 GHz. The S11 coefficient quantifies the power reflected to the source, acting as a vital metric for evaluating the efficacy of impedance matching, particularly in antenna or network configurations. The X-axis represents frequency in GHz, whilst the Y-axis illustrates the reflection coefficient in decibels (dB), with lower values indicating less signal reflection and enhanced impedance matching. The solid green line in the graph denotes the observed S11 values derived from empirical testing, while the dashed red line illustrates the simulated S11 values acquired from computational modelling. Both curves exhibit many troughs at distinct frequency locations, signifying diverse amounts of reflection over the frequency spectrum. A notable dip occurs at roughly 4 GHz when the measured and simulated S11 values decrease to about \(-40\) dB, indicating near-optimal impedance matching at this frequency. Additional dips are seen at 5 GHz, 9 GHz, and 13 GHz, but the depth and precise frequency locations exhibit slight variations between the measured and simulated outcomes. These discrepancies presumably result from actual defects in the physical system or the intrinsic limits of the simulation model. Notwithstanding these slight discrepancies, the overarching  
Figure 12: S11 Reflection coefficient without block.  
pattern between the measured and simulated data is analogous, corroborating the model's precision throughout most of the frequency spectrum. This research is crucial for assessing the performance of systems such as antennas because reducing the S11 reflection coefficient within specific frequency bands may markedly boost energy transmission and improve the system's overall efficiency.
## 4 Forecast for the Future Period
### New Prediction for World LNG Fleet  
In February 2024, the total number of LNG ships amounted to 701, an increase of 5% compared to 2023 [7]. This figure includes chartered ships in operation worldwide, ships awaiting loading and delivery of their first cargo, and ships that have been converted into floating terminals for the reception, storage, and regasification of liquefied natural gas (FSRU).  
The year 2023 was characterized by significant disruptions in the international shipping market. From the third quarter onward, a drought in Panama lowered the water level of Lake Gatun, the main water source of the Panama Canal, and reduced the number of daily transits, forcing US LNG cargoes to take the longer route around the Cape of Good Hope to Asia. The situation improved in early 2024 when rainfall increased again. At this point, however, passage through the Red Sea was hampered by attacks on ships by Houthi rebels in Yemen. In February, LNG ships began to avoid the Red Sea and thus also the Suez Canal on their way to Asia and Europe. The market overcame these challenges in part through trade swaps and other optimization strategies [7].  
Based on the information on the current status of the LNG fleet, a forecast was drawn up, which can be seen in Figure 10.  
Figure 10: Number of LNG vessels in use with future prediction [7; 30].  
Figure 9: LNG trade volume in MT with future predictions [7; 30].  
Figure 10 shows a forecast for the future period in which the actual number of LNG ships will continue to rise, with the number of ships reaching 900 by 2029 and continuing to increase.
# Organic Crystals for THz Photonics
## 5 Applications of Organic THz Sources
### Building and Construction Sector  
Although the reports on possible applications of THz technology in different sectors constantly increase, the construction and building industry lags behind them. However, several studies demonstrated that THz spectroscopy and imaging have promising potential and provide many opportunities for applications in construction and building materials characterization [217]. For instance, structural analyses of various thermal building insulation materials such as polymer foams, an aerogel composite, and a microencapsulated phase-change material by using a DSTMS-based THz-TDS and THz pulsed imaging were performed [175, 176, 218]. The insulating materials ensure low thermal conductivity and thermal transmittance to buildings. These characteristics are highly dependent on the thickness and structural properties of materials (e.g., homogeneity, porosity, humidity, density, inclusions, defects, etc.). Studies revealed that the absorption coefficient of polymer foams in the THz range is inversely related to the thermal conductivity of these foams [175]. The obtained THz  
Figure 16: THz absorption spectra of a reference material PABA (para-aminobenz acid) and several explosives (HMX, RDX, PETN) measured with a DSTMS-based THz-TDS system [215]. Copyright 2014 Society of Photo Optical Instrumentation Engineers (SPIE).  
amplitude images (see Figure 17) clearly visualized different manufacturing imperfections and internal structures such as beads structure, solvent distribution, air gaps and fibre direction in layers of aerogel blanket [175; 176]. Based on the observation of additional spectral features during the solidification and liquefying of a phase-change material, THz spectroscopy was demonstrated to be a suitable method to characterize specific properties related to the freeze-thaw process [175]. This is especially important when phase-change materials are incorporated within a polymer matrix in order to increase the energy efficiency of insulation materials. In addition, a periodical fibrous structure of an aerogel composite was also observable in THz spectra, where several spectral peaks were present separated by a distance corresponding to the diameter size of fibers [218]. These examples show that THz-TDS and THz pulsed imaging based on organic THz sources have a potential to become a suitable non-destructive testing method when evaluating the existing building materials as well as when developing novel high-performance fibrous, cellular or granular materials. THz-based methods can provide important construction-material information, especially when combined with other non-destructive testing techniques using ultrasound, microwaves, infrared waves or X-rays.
## 2 Definition of Activity Recognition problem
### Feature selection for activity recognition problems  
Feature selection could be described as a single objective optimization problem where given a set of features \(F\), a subset \(f^{*}\) has to be determined, such:  
\[J\left(f^{*},E\right)=\max_{f\subseteq F}J\left(f,E\right) \tag{2}\]  
where \(J:F\times\Psi\rightarrow\mathbb{R}\) is an utility function that measures how good is a feature subset \(f\subseteq F\) for classiffing a set of points \(E\in\Psi\), being \(\Psi\) the space of training examples. In a supervised context, where the class of the points in \(E\) is known, the goodness of the subset \(f\) could be interpreted as the accuracy of a classifier built using the attributes in \(f\), while in an unsupervised context the goodness of \(f\) could be interpreted as the quality of the generated clusters.  
The need for feature selections comes from the fact that available training instances usually contain highly correlated and/or noise-dominated attributes. These attributes have to be identified and removed, because they don't provide useful information for classification and their use only increase the classification cost, even dropping the classification accuracy.  
The feature selection problem has been widely studied in the literature. A taxonomy of the existing techniques could be established according to the evaluation criteria used to score the candidate feature subsets [14]: (1) _wrapper_ methods, where a classifier is trained to evaluate  
candidate subset; and (2) _filter_ methods, where a measure of the predictive power of the feature subset is calculated, without training any classifiers. _Filter_ methods are quicker and more general than _wrapper_ methods, but the optimality of the obtained feature subsets could not be ensured even using exhaustive search methods.
## 2 Development of the System for Point-Data Pre-Processing
### Module 1--3D Filtering  
3D digitization most often results in numerous unwanted points. These points frequently belong to objects which surround the object being digitized, such as fixtures, measurement table, or some other part of the assembly to which the digitized part belongs. However, in the case of non-contact methods, such as the laser triangulation, those points can originate from objects located further away. To some extent, the unwanted points can also be the result of measurement errors (due to operator errors, system-specific errors and/or errors due to specific nature of the digitized object, some external disturbance _i.e._, vibrations), _etc_. Those points, as described in the Introduction section, have to be eliminated in order to maintain quality of surface reconstruction. The module for 3D filtering of errors was developed to allow elimination of unwanted points. The module consists of three tools, which perform:  
1. [label=()]
2. Volumetric filtering;
3. Filtering by segmented line;
4. Module for the system of point data;
5. Module for the system of point data;
6. Module for the system of point data;
7. Module for the system of point data;
8. Module for the system of point data;
9. Module for the system of point data;
10. Module for the system of point data;
11. Module for the system of point data;
12. Module for the system of point data;
13. Module for the system of point data;
14. Module for the system of point data;
15. Module for the system of point data;
16. Module for the system of point data;
17. Module for the system of  
* **Elimination of particular points (by selection).**  
**The volumetric filtering method is based on forming a rectangular volume (Figure** 2**(a)) defined by the length, width and height of scanning (**\(x\)**-,**\(y\)**-, and** \(z\)**-axis). Any point data falling outside/inside (optional user choice) this volume are considered as unwanted and eliminated. This is a rough method and is used to eliminate extreme errors or point data belonging to fixtures and the measuring table, which may occur during 3D digitization.**  
**The segmented line filter is based on staggered lines which are composed of line segments, defined by the knot points (**\(P_{i}\)**) in the selected plane** \(x\)**-,** \(x\)**-,** \(z\) **or** \(y\)**-.** **After the filter line has been generated, point data are eliminated from one of its sides on the bases of the user choice (Figure** 2**(b)).**  
**Figure 2. (a) Principle of the volumetric filter. (b) Principle of the segmented line filter. (c) Algorithm of the module for 3D point (error) filtering.**  
The third tool allows individual selection of a larger number of points which could not be eliminated using the first two tools. Selection is performed in a graphical mode, from the point cloud. The three tools can be used each by itself, or in any other combination, iteratively, as shown by the algorithm in Figure 2(c).
## 4 Data Interpretation and Future Implications
Therefore, it is clear how variable the remediation performance could be according to different environmental conditions. Cross-sections of subsoil and permeability are illustrated in Figure 8. Figure 8a shows how the moisture influences the VOC extraction in mostly uniform and permeable soils. In the borehole P036B, the water reduced the permeability to gas flow by occupying more pore space. Therefore, when the water saturation is high (on the order of 70% to 80% of the pore space), the gas phase is mostly disconnected into bubbles, and the gas flow is orders of magnitude less than the flow through dry soil. This involves a reduction of the system effectiveness. It was observed that heterogeneous geologic conditions, with the presence of low permeability layers overlying zones of higher permeability, could reduce the system effectiveness. In Figure 8a, it is  
Figure 8: Stratigraphic column description for samples P036A, P036B, S291, S037, SP028, and S293 (uncertainty in the profiles at the bottom of the wells are indicated by dashed lines). shown how layers with different permeability could affect the total remediation efficiency for both light and heavy hydrocarbons. For boreholes with interbedded sand, silt, or clay (such as S291 and SP028) (Figure 8b,c), SVE induces flow through the sand that bypasses silt and clay lenses because of their higher moisture contents and lower absolute permeability. In other terms, sand is dominated by a significant airflow, while a much slower process of diffusion dominates the contaminated silts and clays, if any gas flow occurs. The ability of SVE to remove volatile contaminants from highly permeable soils (intrinsic permeability K \(>\) 10\({}^{-8}\) cm\({}^{2}\)) is well established in literature [33; 34; 35]. In these cases, the extracted concentrations may fall rapidly during the initial SVE operations, but once the SVE operations cease, contaminant vapor concentrations slowly reach the initial condition by diffusion from the silty/clay (i.e., less permeable soils act as a secondary source of contamination). In such cases, the removal can often be improved by modifying the extraction strategy (e.g., operating different or new wells, transitioning to a lower extraction rate, pulsing SVE operation). In particular, the rebound test provides valuable data for assessing the time of operation to achieve performance objectives as well as a benchmark for assessing progress toward cleanup during subsequent operations [36]. Consequently, soils with low air permeability are more difficult to remediate and exhibit reduced removal efficiency [35]. SVE is generally most effective in uniform soils with good conductivity and low moisture content. Furthermore, an excess of moisture can also cause a decrease in the microbiological activity. [PERSON] et al. [37] demonstrated through laboratory measurements and mathematical simulations that even small changes in temperature and moisture content can influence microbial activity, affecting the overall efficiency of the remediation operation.