esa-sceva/satcom-chunk-collection huggingface dataset:
# Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand
## 3 Results and Discussion
### Unconfined Compressive Strength
#### 3.1.4 Correlation Analysis Between Variables and Strength  
To quantitatively investigate the correlation between \(D_{\rm r}\), \(C\), \(d\), and UCS, a linear correlation analysis was conducted using Pearson's correlation coefficient [30], as shown in Equation (1).  
\[R(x,y)=\frac{\sum_{i=1}^{n}x_{i}y_{i}-n\overline{x}\overline{y}}{\sqrt{\sum_{i= 1}^{n}\left(x_{i}^{2}-n\overline{x}^{2}\right)\sum_{i=1}^{n}\left(y_{i}^{2}-n \overline{y}^{2}\right)}} \tag{1}\]  
where \(x_{i}\) and \(y_{i}\) are the observed values of the \(i\)-th sample, \(\overline{x}\) and \(\overline{y}\) are the means of the variables \(x\) and \(y\), and \(n\) is the sample size.  
As shown in Figure 7, the Pearson correlation coefficients reveal the following relationships: \(D_{\rm r}\) shows a weak negative correlation (\(-0.23\)) with UCS, suggesting that increased soil density slightly suppresses strength; \(C\) exhibits a moderate positive correlation (0.62), indicating that higher concentrations significantly enhance sandy soil strength; and \(d\) has a weak positive correlation (0.43), implying limited strength gain from extended curing beyond an initial period.
## 2 Experimental Procedures  
### Materials  
Powders with spherical particles and an average particle size of less than 45 \(\upmu\)m are typically selected for use in the MIM process [59]. In addition, some investigations indicate that metallic powders with lower particle sizes can increase surface finish, moldability, and sinterability [60; 61]. On the other hand, high-performance anisotropic permanent magnet manufacturing requires fine monocrystalline powders with a rare-earth-rich phase at grain boundaries. Here, a compromise has been reached using rare-earth-rich fine powder produced by hydrogen decrepetition.  
Strip cast flakes with a (Nd, Pr, Ce, Dy)\({}_{32.3}\)(Fe, Co, Al, Cu, Ga, Zr)\({}_{65.5}\)B (wt.%) composition were used for manufacturing the fine powder by hydrogen decrepetition (as described in [61]) and jet milling process under N\({}_{2}\) gas, which will be referred to as Nd-Fe-B in the following parts of the paper. The O\({}_{2}\) content of 0.35 wt.% was measured on powders sealed in a tin container to minimize powder oxidation during the transfer to the equipment. The size and distribution of the powder particles affect the final properties and can change the flow behavior when interacting with the binder. The particle size distribution can be seen in Figure 1, which had a d50 = 5.12 \(\upmu\)m. An SEM image of the powder is also presented in Figure 2, showing particles with an angular morphology.  
Figure 1: Particle size distribution of the Nd-Fe-B powder used in this study.  
The binder systems consisted of paraffin wax (PW) supplied by SER Wax Industry (Santena, Italy) as a flow enhancer, linear low-density polyethylene (LLDPE) from Exxon Mobil (Houston, TX, USA) as the backbone, and stearic acid (SA) from Sigma Aldrich (Saint Louis, MO, USA) as the surfactant. Compared to low-density polyethylene (LDPE), LLDPE has more short branches in the molecular chain structure, which gives LLDPE higher tensile strength and impact and puncture resistance. In addition, LLDPE decomposes by chain scission [62], which can be beneficial in minimizing carbon residues in the sintered magnets. For these reasons, LLDPE was selected for this investigation. The PW, LLDPE, and SA densities are 0.9, 0.925, and 0.85 g/cm\({}^{3}\) respectively. For feedstock manufacturing, the theoretical density of the Nd\({}_{2}\)Fe\({}_{14}\)B was considered, which is 7.6 g/cm\({}^{3}\).
## 2 Results and Discussion
### Optimization of HS Collection for VOC Profiling
#### 2.2.1 HS Extraction  
Since the seedlings were alive in each vial just before HS collection, it was necessary to inactivate certain enzymes, such as lipoxygenase and hydroperoxide lyases, which contribute to VOC production. We compared each total ion chromatogram (TIC) obtained by applying the three extraction methods for the water and MS samples. CaCl\({}_{2}\) is commonly used to stop enzyme activity, as well as to promote the release of VOCs to the HS from the plant tissues [53]. Ethylene-diamine-tetra-acetic acid (EDTA) can also inhibit enzyme activity in the cell [54; 55]. In addition, the combined use of EDTA and CaCl\({}_{2}\) provides highly reproducible VOC emission from tomato fruit [39], which is important when conducting metabolomic analysis. Therefore, we added EDTA, followed by CaCl\({}_{2}\), to each HS vial for HS extraction. To validate the HS extraction method using EDTA and CaCl\({}_{2}\), we heated other sets of HS vials to 80 \({}^{\circ}\)C to quench the enzyme activity in the seedlings. HS-VOCs emitted from untreated seedlings were also collected. We used _Arabidopsis_ seedlings grown in water and MS conditions and performed HS-SPME-GC-TOF-MS analysis on these samples. A distinct pattern was seen in TICs of HS-VOCs, which were extracted by (i) adding EDTA solution followed by CaCl\({}_{2}\) and (ii) heating at 80 \({}^{\circ}\)C. TICs of HS-VOCs from treatment (ii) and those emitted from the untreated seedlings looked similar, particularly when the samples were grown in water (Supplementary Figure S1). Peaks detected in samples from treatment (i) outnumbered other methods (Supplementary Figure S2). On the basis of the results, we chose to use a combination of EDTA and CaCl\({}_{2}\) to extract HS-VOCs for further analysis.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
#### 4.1.4 Comparative Summary  
Table 4 provides a comparative analysis of NS-3, Cooja, and OMNeT++, summarizing their key features, limitations, and applicability for IoT and Space IoT simulations.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **NS-3** & **Cooja** & **OMNeT++** \\ \hline Programming Language & C++, Python bindings & Java, C & C++ \\ \hline Wireless Communication & Supports Wi-Fi, LTE, 5G; partial support for 6G under research & Primarily for low-power WSNs; limited LTE/5G support & Supports Wi-Fi, LTE, 5G via INET framework \\ \hline Routing Protocols & Implements various routing algorithms (AODV, OLSR, DSDV, DSR) & Supports basic WSN routing algorithms & Highly customizable; supports diverse routing models via extensions \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of NS-3, Cooja, and OMNeT++.
# Applications of Graphene-Based Nanomaterials in Environmental Analysis
## 3 Analytical Applications in Environmental Samples
### Applications of GO-Fe\({}_{3}\)O\({}_{4}\)  
[PERSON] et al. [76] synthesized mGO nanocomposites for the adsorption of PAHs with the help of MSPE from tap water, sea water samples and river water. For the extraction of the analytes, 40 mg of the sorbent were conditioned with 5 mL methanol and 5 mL deionized water and then dispersed into a 50 mL aqueous sample solution into an ultrasonic to form a homogenous suspension. Then, the solution was shaken for 10 min for the adsorption to take place and with the help of an external magnetic field at the bottom of the vial, the sorbent was isolated from the mixture. After 3 min, the liquid was decanted, and the adsorbent was held at the bottom by the magnet. Afterwards, the remaining precipitate was washed with 5 mL of 10% \(v/v\) ACN and the target organic pollutants were eluted with 2 mL of ACN:CH\({}_{2}\)Cl\({}_{2}\) (4:1) after ultrasonication for 3 min. Lastly, the 20 \(\upmu\)L of the effluent was injected to the HPLC-UV system for analysis. The LODs from this work were 0.09-0.19 ng/mL and the recoveries were between 86.3-103.2% for the tap water samples, 76.8-101.2% for the river water samples and 80.9-99.6% for the sea water samples. [PERSON] et al. [77] also used Fe\({}_{3}\)O\({}_{4}\)@GO as the sorbent for the MSPE of trace levels of 2,\(A\),_A_'-trichlorobiphenyl (PCB 28) from water samples and then determined by Gas Chromatography- Mass Spectrometry (GC-MS). The recommended method was found suitable for the preconcentration and immobilization of PCB 28 from large amounts of aqueous solutions, as well as the cleaning of polychlorinated biphenyl pollution. [PERSON] et al. [78] proposed a facile approach of preparing mGO composites and then using them in the MSPE of trace amounts of sulfonamides (sulfadiazine, sulfadimidine and sulfathiazole) in different water samples, determined by High Performance Liquid Chromatography-Diode Array Detection (HPLC-DAD). The results showed that the LODs
## 2 Background
### Content-centric Internet  
CDNs [7; 10] are an evolution of the client-server architecture introduced early in the emergence of the Web and designed to reduce the overhead of the content server by bringing (parts of) the content to the network edge, closer to the user, similar to a cache memory on a computer. Figure 1 shows the basic function of a CDN where the content is delivered from an edge node nearest to the user. In this figure, UserA receives content from its source server while UserB, requesting the same content, receives it from the cache linked to the edge router, ER1. Thus, content is pushed to the edge networks and is delivered as fast as possible through appropriate replication and caching technologies. For a highly solicited server, caching content close to the user at the edge of her network means improved response time, not only because of a reduced load on the main server but also because of reduced latency for the user. A CDN is deployed in partnership with ISPs which also benefit from having less traffic to relay.  
The CDN was an early form of _network overlay_, that is, an application-oriented network built on top of the Internet, with mission-specific nodes and dedicated topology. Another variant of the overlay concept is the Peer-to-Peer (P2P) network, which consists of distributed peers who simultaneously consume and contribute content. Similar to CDNs, the benefits of deploying relay nodes at the edge for P2P networks has also been demonstrated in the literature [11]. Such a network can be characterized as content-centric where the location/identity of a peer is not significant; a peer requests a specific content, the (overlay) network gathers chunks of that content from different sources, assembles and finally delivers the content to the requester.  
A Content-Centric Network (CCN) [12] pushes these principles further, by placing content at the focal point of communications, treating content as a primitive, decoupling content location from its identity and retrieving a content by its name only. Thus, a CCN-based Internet does not interconnect hosts; rather it delivers a requested content irrespective of content location. Furthermore, experimental research has demonstrated that a CCN behaves more energy-efficiently in delivering content than conventional CDNs and P2P networks [13].  
In this article, we are taking the general meaning of a CCN that deals with content rather than unprocessed data. Such content could be an image, audio or video, user-generated, media, or even a piece of code (e.g., script, \"app\"). In a proposed evolution of the Web, content becomes the main point of focus and can no longer be managed entirely on the server side, leading to a distribution of information across a CCN. A content does not of itself carry any value unless it can be utilized in a service. Our goal is to bring content to a location closer to the users and also add intelligence at the edge so that content could be transformed through value-added services.  
Figure 1: Generic architecture of the present internet.
###### Abstract
This novel measure enables the diagnostic validation of a model and full exploitation of scarce real world data. The complete approach is proposed and systematized in three phases. In the first phase, the BBN framework is employed for modelling influences among putative ship grounding risk factors and parametrization based on expert elicitation and historic data when available. The verification of the developed model is performed through sensitivity analysis. In the second phase, validation takes place through predictive and diagnostic inference on real scenarios. Predictive analysis of the BBN grounding model is performed to estimate the probability of grounding given evidence through forward evidence propagation, while for diagnostic inference, the newly proposed Lyapunov-based divergence measure is employed to identify risk factors. Within these settings, real world data can be efficiently exploited to validate the behavior of the model. Finally, in the third phase, the individual causal influence method [1; 10] is employed to rank accident risk factors. The full pipeline of the process is given in Figure 1, which differs from previous methodology in a way that it extends the common BBN modelling and verification procedures with validation on real case accidents. Motivation for this research on the maritime grounding risk of small passenger vessels in the fact that maritime traffic is continuously increasing in the Adriatic Sea due to the development of nautical tourism, with an evident increase in the number of small passenger vessels, their passenger capacity, and size. While to date the small number of accidents on the Croatian side indicate relative safety, novel developments and circumstances call for a safety review and risk factor analysis. Nowadays, the support to safety regulation and risk management depends on the existence of suitable maritime accident models and scientific risk analysis [18]. Although the Bayesian network framework has been developed for the grounding of small passenger ships in the Adriatic, the proposed approach can be applied to similar accidents and the research of other expert elicited Bayesian belief networks. The rest of the paper is organized as follows: Section 2 presents the mathematical and computational background of Bayesian network modelling adopted for maritime accident reasoning. In this section, the predictive inference measures used in this work are systematized and adopted to the context of maritime accidents, and the novel Lyapunov-based divergence measure for diagnostic inference is defined. Section 3 explicates the modelling and verification as the first phase of the integrative approach. The second stage of the approach, i.e., the validation, both predictive and diagnostic, on real case data is given in Section 4. The final risk factors assessment based on the developed model is presented in Section 5. The paper is concluded in Section 6.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 2 Data
### ICESat-2 Data  
ICESat-2 data has been provided by the National Snow and Ice Data Center (NSIDC). The database contains all released data products for the ICESat-2 mission; version 4 is used in this study. The ATLAS [7] data used includes the ATL03 Global Geolocated Photon Data [14] and ATL12 Ocean Surface Height [8] data products.  
ATL03 contains observations of each individual photon event and is used as the basis of higher-level data products produced for the ICESat-2 mission (such as ATL12). ATLASoperates at 10 kHz, which corresponds to a \(\sim\)0.7 m along-track nominal spacing. In this study, ATL03 is used to produce the models used in Section 3. ATLAS uses six beams to measure elevation, although three of them, designated the strong beams, have four times the energy of the weaker beams [14]. This is the nominal configuration; however, when the ocean surface is observed, only the strong beams are used to collect observations due to the lower reflection of the ocean surface compared with ice sheets [15]. The surface type is provided as a standard output of ATL03 and is used as a rough filter for surface ice and other surface types. We have used the ATL03 data to produce models estimating the significant wave height; these models are described in Section 3 (Methods). A detailed description of the data can be found in the data user guide at ([[https://nsidc.org/data/ATL03/versions/4](https://nsidc.org/data/ATL03/versions/4)]([https://nsidc.org/data/ATL03/versions/4](https://nsidc.org/data/ATL03/versions/4)), accessed on 26 August 2021).  
ATL12 contains the SWH product used in this study. The along-track spacing of the ATL12 data is variable, between 70 m and 7 km. This spacing is therefore highly dependent on the weather conditions [8]. Version 4 of the data includes a bathymmetric depth test for the data, thereby allowing one to exclude data when the depth is shallower than 10 m, according to the General Bathymetric Chart of the Oceans (GEBCO), to include only ocean waters [16]. As ATL12 is the standard data output for the significant wave height, no change has been made to this output. There is a short description of the method used to estimate the SWHs in ATL12 in Section 3 (Methods). A detailed description of the data can be found in the data user guide at ([[https://nsidc.org/data/ATL12/versions/4](https://nsidc.org/data/ATL12/versions/4)]([https://nsidc.org/data/ATL12/versions/4](https://nsidc.org/data/ATL12/versions/4)), accessed on 26 August 2021).
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 4 Challenges in Indoor Localization Systems  
### Signal Interference and Multipath Effects  
One of the primary challenges in indoor localization systems is signal interference and multipath propagation. Environments like hospitals, industrial settings, and military operations often have metallic surfaces, dense obstructions, and dynamic conditions that reflect and scatter signals, causing localization errors. For instance, in industrial environments, heavy machinery and metal obstructions significantly impact BLE and Wi-Fi-based systems, reducing their accuracy and reliability [26; 60]. Similarly, in healthcare settings, Wi-Fi signals suffer from interference caused by medical devices and dense infrastructure, which can affect real-time tracking performance [62].  
Multipath effects are particularly problematic for RFID and UWB-based systems. In healthcare applications, RFID tracking of patients or equipment can be disrupted by reflections from walls, beds, and other equipment, leading to inconsistent readings [23]. To mitigate these issues, advanced algorithms like machine learning-based filtering and data fusion techniques are employed to improve accuracy [60].
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 4 Experimental Results
### Discussion  
According to the experimental results and evaluations presented above, the validity and stability of our proposed model are fully verified, and the contributions of the proposed model can be summarized as follows.  
(1) The new model is regularized by a Gaussian kernel, which avoids the expensive computation associated with reinitialization. It follows that the model has low computational complexity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{2}{c}{**C-V**} & \multicolumn{2}{c}{**LBF**} & \multicolumn{2}{c}{**SBGFRLS**} & \multicolumn{2}{c}{**LIF**} & \multicolumn{2}{c}{**LSACM**} & \multicolumn{2}{c}{**SPFLIF-IS**} \\ \cline{2-13} \cline{2-13}  & **DICE** & _JSI_ & _DICE_ & _JSI_ & _DICE_ & _JSI_ & _DICE_ & _JSI_ & _DICE_ & _JSI_ & _DICE_ & _JSI_ \\ \hline
3063 & 0.9779 & 0.9568 & 0.9576 & 0.9186 & 0.9728 & 0.9470 & 0.8962 & 0.8119 & 0.9774 & 0.9557 & 0.9783 & 0.9575 \\
8068 & 0.9780 & 0.9570 & 0.9555 & 0.9149 & 0.9785 & 0.9579 & 0.8673 & 0.7657 & 0.9710 & 0.9436 & 0.9827 & 0.9660 \\
29030 & 0.9525 & 0.9093 & 0.9432 & 0.8925 & 0.9709 & 0.9435 & 0.8106 & 0.6815 & 0.9626 & 0.9280 & 0.9743 & 0.9500 \\
41004 & 0.9763 & 0.9537 & 0.9565 & 0.9166 & 0.9775 & 0.9560 & 0.8769 & 0.7808 & 0.9741 & 0.9494 & 0.9791 & 0.9590 \\
46076 & 0.9763 & 0.9537 & 0.9512 & 0.9070 & 0.9753 & 0.9518 & 0.8392 & 0.7230 & 0.9692 & 0.9402 & 0.9783 & 0.9575 \\
207056 & 0.9677 & 0.9375 & 0.9305 & 0.8700 & 0.9774 & 0.9558 & 0.8183 & 0.6924 & 0.9785 & 0.9579 & 0.9826 & 0.9659 \\
296059 & 0.9470 & 0.8994 & 0.9276 & 0.8650 & 0.9684 & 0.9387 & 0.8283 & 0.7069 & 0.9741 & 0.9495 & 0.9742 & 0.9498 \\
299091 & 0.9708 & 0.9433 & 0.9595 & 0.9221 & 0.9750 & 0.9512 & 0.8448 & 0.7313 & 0.9694 & 0.9406 & 0.9759 & 0.9530 \\ \hline Mean & 0.9683 & 0.9388 & 0.9477 & 0.9008 & 0.9745 & 0.9502 & 0.8477 & 0.7367 & 0.9720 & 0.9456 & 0.9782 & 0.9573 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _DICE_ and _JSI_ values of the results of image segmentation on fifteen Berkeley color images.  
Figure 7: _JSI_ values of the image segmentation results using the four models for Berkeley color images.  
(2) Our proposed model makes the best use of global and local image information. The model solves the problem of not accurately segmenting intensity-inhomogeneous images faced by the traditional image segmentation model and overcomes the shortcoming that local image information is sensitive to the initial contour and noise.  
(3) Compared with the existing C-V, LBF, SBGFRLS, LIF, and LSACM models, the SPFLIF-IS model exhibits high robustness to the initial contour and noise and quickly and accurately segments inhomogeneous and multiobjective images.
# Multidisciplinary Optimization of Aircraft Aerodynamics for Distributed Propulsion Configurations
## 4 Multidisciplinary Analysis and Optimization in Aero-Propulsive Design
### Interaction of Aero-Propulsion  
To ascertain the interaction and aero-propulsive performance of various configurations, 59 BWB civil aircraft with DPs were selected for analysis (refer to Table A1). Configurations with fewer samples, such as jet wings, have been excluded. The wing areas of these BWBs are from 236\(\sim\)1670 m\({}^{2}\). Three types of propulsor locations are included. They are BLI, pylon-above-wing (PA), and under-wing (UW). The numbers of cases are 20, 32, and 7, respectively. Among them, there are two thrust-split samples, both of which are driven by three engines and nine fans. The number of propulsors is from 2\(\sim\)18, and the Mach number of each propulsor for each BWB is systematic. In all cases, it is less than or equal to 0.85.  
Figure 4 shows numerous scatterplot matrices about the integration of BWB and DP systems. Orthogonal correlation plots show that BLI's performance is closely linked to PA and UW. However, PA and UW are only weakly correlated with each other. The figure shows that each sample has pros and cons. None is much better than the others. The DP location, which has provided a long flight range, high L/D ratio, and bearing capacity, requires more fuel, a higher Mach number, and a larger wing area.