esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The findings reveal that the carotenoids in all organically grown rosehip flesh present significantly higher concentrations than in seeds. Furthermore, \(\beta\)-carotene was the predominant carotenoid component in rosehip flesh, and the significantly highest concentration was observed in RV species.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **TPC** & **TFC** & **TPAC** & **TCC** & **ACC** \\ \cline{2-6}  & \multicolumn{5}{c}{**Rosehip flesh**} \\ \hline TPC & 1 & 0.24 & 0.96 * & \(-\)0.79 * & \(-\)0.45 * \\ TPC & 0.24 & 1 & \(-\)0.50 * & 0.55 * & \(-\)0.05 \\ TPAC & 0.96 * & \(-\)0.50 * & 1 & \(-\)0.86 * & \(-\)0.39 \\ TCC & \(-\)0.79 * & \(-\)0.55 * & \(-\)0.86 * & 1 & 0.44 \\ ACC & \(-\)0.45 * & \(-\)0.05 & \(-\)0.39 & 0.44 & 1 \\ \hline Compound & \multicolumn{5}{c}{**Rosehip seeds**} \\ \hline TPC & 1 & 0.46 * & 0.98 * & \(-\)0.87 * & \(-\)0.28 \\ TPC & 0.46 * & 1 & 0.29 & \(-\)0.10 & \(-\)0.70 * \\ TPAC & 0.98 * & 0.29 & 1 & \(-\)0.91 * & \(-\)0.16 \\ TCC & \(-\)0.87 * & \(-\)0.10 & \(-\)0.91 * & 1 & 0.11 \\ ACC & \(-\)0.28 & \(-\)0.70 * & \(-\)0.16 & 0.11 & 1 \\ \hline \hline \end{tabular}  
* \(p\) \(<\)0.05. TPC: total polyphenols content; TFC: total flavonoids content; TPAC: total phenolic acids content; TCC: total carotenoids content; ACC: ascorbic acid content.  
\end{table}
Table 4: Correlations between biologically active compounds content in different organic rosehip flesh and seeds.  
Among the phenolic acids, \(p\)-coumaric and gallic acids dominated. Overall, the results of this study demonstrate that the highest content of total phenolic occurred in _Rosa canina_ fruit species. According to the flavonoids' composition, _Rosa villosa_ flesh and _Rosa canina_ seeds were found to be the most valuable, and they accumulated the significantly highest contents of these compounds. The content of ascorbic acids in _Rosa rugosa_ was highest both in flesh and seeds.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/15/5337/s1](http://www.mdpi.com/2076-3417/10/15/5337/s1)]([http://www.mdpi.com/2076-3417/10/15/5337/s1](http://www.mdpi.com/2076-3417/10/15/5337/s1)), Figure S1: Standard curves for all carotenoids identified in experiment in organic rose fruits and seeds experiment, Figure S2: Standard curves for all phenolic acids identified in experiment in organic rose fruits and seeds experiment, Figure S3: Standard curves for all flavonoids identified in experiment in organic rose fruits and seeds experiment.  
Conceptualization, J.K. and B.M.; methodology, J.K. and E.H.; software, J.K. and B.M.; validation, J.K., E.J., and N.V.; investigation B.M. and J.K.; data curation, J.K., N.V., and E.J.; writing--original draft preparation, J.K. and B.M.; writing--review and editing, N.V., E.J., and E.H.; visualization, J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System  
The block diagram of the proposed system is shown in Figure 1. Here, the system firstly captures the video signal, which is segmented into frames. The received frames are then inserted into the motion detection step, which combines the movement accumulation by estimating the difference between successive images together with the adaptive subtraction background. Then, the same frame is inserted into the color detection stage, which is determined using two rules defining the gray tones of the smoke. Then, it is inserted into the texture detection stage, to characterize the texture using the LBP and LBPV based on multiscale analysis. Finally, the regions that meet these three characteristics are considered as candidate regions, in which the presence of smoke might be found. These are finally analyzed to determine whether the expansion is upward, by temporarily overlapping blocks. The next sections describe in depth each block of the proposed scheme.  
Figure 1: Proposed system.
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### Next-Generation AIoT in Aviation: FMDE, G-FMDE, and Satcom Integration
* The external analytical IT system, facilitated by the G-FMDE, opens possibilities for industry-wide data sharing and collaborative problem-solving on a global scale. Of course, there are some challenges in the discussed architecture:
* Handling and securing the transmission of over 40,000 parameters requires robust data management strategies and advanced cybersecurity measures, especially considering the use of satellite communication. * While highly beneficial, the implementation of Satcom and the processing of such large volumes of data may involve significant costs that need to be balanced against the benefits. * Combining data from multiple systems (FMDE, G-FMDE, and Satcom) and analyzing it across different platforms requires careful system integration and data standardization. * The extensive data collection and global transmission capabilities of this system may necessitate careful consideration of international regulatory requirements, particularly regarding data privacy and cross-border data transmission. As AloT technologies continue to evolve, we can anticipate further advancements in this architecture:  
* The real-time, global data availability could pave the way for AI-driven autonomous systems that can make instant decisions about flight parameters, routes, and even emergency procedures. * With the wealth of data available, predictive maintenance models could become so accurate that they virtually eliminate unscheduled maintenance, optimizing aircraft utilization and reducing costs. * The ability to analyze data across entire fleets in real-time could lead to optimized fleet management strategies, improving overall airline efficiency. * The comprehensive health monitoring could extend to cabin systems, allowing for preemptive addressing of issues that affect passenger comfort and satisfaction. The aircraft communication architecture featuring FMDE, G-FMDE, and Satcom represents the cutting edge of AloT application in aviation health monitoring by dramatically increasing the volume of data collected, providing global, real-time transmission capabilities, and enabling integrated analyses across multiple systems.
# Building Information Modelling Facility Management (BIM-FM)
## 6 Barriers Affecting the Implementation of BIM-FM
### Lack of Standardisation in BIM-FM  
Whilst part of the alignment issue is arguably due to a general misunderstanding of different sectors, it must also be acknowledged that there is also a lack of alignment between the official standards and practices for BIM, FM and AEC sectors. For instance, [PERSON] et al. [25] compared the BSI and ISO standards for asset management and BIM and found that there is no overall framework to link them, and they are often juxtaposed, as evidenced by [PERSON] and [PERSON] [12], who reviewed a BIM-FM platform for an airport which did not have a BIM created during its construction and found that the BIM team and FM team had contradicting requirements. Furthermore, [PERSON] et al. [78] found that many BIM models for design and construction are developed using organisation-specific naming conventions, which cause issues relating to data extraction and management when they are transferred to another organisation for the FM stage. Likewise, [PERSON] and [PERSON] and [PERSON] and [PERSON] [12; 66] identified the same issue, and [PERSON] and [PERSON] [66] go on to claim that even when there were consistent naming conventions, there was a lack of quality control to ensure the procedure was being followed.  
Whilst [PERSON] et al. [25] found that existing AM and BIM standards are often in opposition, it is also true that the existing ISO BIM standard [19] is not yet fully developed for FM.  
Therefore, it is not unreasonable to suggest that it would be simpler for BIM-FM standards and processes to aim for alignment with FM practices rather than trying to align new construction and FM practices.
## 3 Methods
### Construction of AGB Samples Based on LiDAR Data
#### 3.1.3 Construction of AGB Samples  
Following individual tree segmentation, matching each tree to a specific species is a crucial step. To achieve this, tree coordinates derived from point cloud data segmentation were integrated with GGP stand data and field sampling data that provide tree species information. This process links the structural attributes of trees (height and crown width) with their biological attributes (species). The study area predominantly consists of monoculture plantation forests, with each plot containing a single tree species. By spatially overlaying point cloud data with GGP stands data, the tree species information corresponding to each point cloud dataset was identified. Each species exhibits different growth patterns and biomass relationships, and the integration of tree coordinates with  
\begin{table}
\begin{tabular}{c c c} \hline
**Model Number** & **Model** & **Formula** \\ \hline
1 & Linear regression & \(\mathrm{H}=\mathrm{a}+\mathrm{b}\times\mathrm{DBH}\) \\
2 & Power function & \(\mathrm{H}=1.3+\mathrm{a}\times\mathrm{DBHb}\) \\
3 & Logarithmic model & \(\mathrm{H}=\mathrm{a}+\mathrm{b}\times\mathrm{lnDBH}\) \\
4 & Bates model & \(\mathrm{H}=\mathrm{a}\times\mathrm{DBH}/(\mathrm{b}+\mathrm{DBH})\) \\ \hline \end{tabular} Here, a and b are species-specific parameters, DBH is the diameter at breast height, and \(\mathrm{H}\) is the tree height.  
\end{table}
Table 2: Tree height–DBH growth models.  
species information ensures that each tree is associated with its corresponding species, which is critical for subsequent accurate AGB estimation using tree height-DBH growth models and species-specific AGB equations.  
In this study, point cloud data from individual tree segmentation were gridded at a 30 m resolution. Based on species-specific information, the optimal tree height-DBH growth model was selected for each tree species. Tree heights derived from LiDAR data were then used to calculate the DBH of each tree. Subsequently, the AGB of individual trees was estimated using the species-specific AGB models listed in Table 1. Finally, the total AGB for each grid was computed by summing the AGB of all individual trees within that grid. The AGB of each sample was calculated using Equation (1) and expressed in grams per square meter (g/m\({}^{2}\)). To ensure the uniform distribution of vegetation within the sample and reduce the influence of mixed information, samples located at the edge of the stand, as well as near roads and buildings, were removed.  
\[\text{AGB}=\sum_{\text{i}=1}^{\text{n}}\text{Ma}_{\text{i}}/\text{S} \tag{1}\]  
Here, AGB is the sample aboveground biomass, Ma is the individual tree aboveground biomass, S is the grid area, and i is each tree.
# Dentin Bond Strength of Calcium Silicate-Based Materials: A Systematic Review of In Vitro Studies
## 4 Discussion
### Final Irrigant  
The composition and structure of root dentin, along with its permeability and solubility characteristics, can undergo changes when irrigated with various chelating agents [16]. These alterations impact the bond strength of root canal filling materials to dentin, influencing the sealants' interaction with dentin [16]. Enhancing the surface roughness of root dentin contributes to increased adhesion, which can be attributed to a higher surface energy and bonding area [16]. Furthermore, the choice of irrigant, its concentration, and application time can significantly influence dentin wettability [16].  
In a comparison of nine articles that categorized samples based on the final irrigant used in each intervention group, it was observed that 17% EDTA as a final irrigant increased the adhesion strength of the CSCs [16; 29; 43; 47]. The alkaline pH of EDTA induces an abundance of hydroxyl ions, leading to smear layer dissociation, reduced chelation with calcium ions, and a slow dissolution of inorganic and organic components, resulting in higher surface roughness and favorable wettability, all contributing to elevated bond strength values. Smear layer removal facilitates closer contact between the cement and root dentin, a prerequisite for optimal adhesion [16].  
Additionally, the application of a diode laser after conventional irrigation (NaOCl + EDTA) increased bond strength values, likely due to morphological and chemical changes in the root dentin post irradiation [17]. Conversely, activating irrigants with sonic or ultrasonic devices showed no significant effects [40].
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 2 Materials and Methods
### Application of the GuSAC Algorithm  
During the mobile 3D laser scanning process, in addition to collecting tunnel lining structure point clouds, non-tunnel structural point clouds such as tunnel pipelines, grouting holes, tunnel lining joints, and some discrete noise generated by equipment vibrations are also collected. The GuSAC algorithm designed in this paper, compared to the traditional RANSAC fitting method, recognizes the regional geometric features of the tunnel, removes non-structural point clouds and noise, and outputs point cloud data that only contains the tunnel lining.
## 2 Methods
### Data Collection
#### 2.2.1 Preparation for Data Collection  
Kinematics: A 10-camera motion capture system (VICON Motion Systems Ltd., Oxford, UK) was used to record the upper-body kinematics. Participants were prepared for testing with nine small, spherical reflective motion capture markers placed along each arm and shoulder area based on the upper-body modeling (Vicon Nexus 2.12) with Plug-in-Gait (Vicon Motion System, Oxford Metrics Group Ltd., Oxford, UK): C7, right back over the scapula, sternum, both AC joints, both elbows (lateral epicondyle of the humerus), and medial surface of both wrists at the ulna (see Figure 1). Motion capture data were collected at 120 Hz.  
EMG: A Trigno Wireless EMG System (Delsys, Inc.) was used for acquisition of muscle activity at 1200 Hz. Adhesive pre-gelled Ag/AgCl surface EMG electrodes (inter-electrode distance: 10 mm, Delsys Inc., Boston, MA, USA) were placed on the muscles of the participant's dominant arm: biceps brachii (BB), triceps brachii (TB), lower trapezius (LT). The positioning of the electrodes was in accordance with SENIAM guidelines [27].
## 4 Materials and Methods
### SLC10A1 Sequencing and Genotyping
#### 4.5.4. Genome-Wide SNP Microarray  
Additionally, information on 5 _SLC10A1_ SNPs of the HumanHap300 Genotyping BeadChip (Illumina Inc., San Diego, CA, USA) was available [92]. Moreover, whole-genome genotyping of more than 700k SNPs was performed using the Infinium(tm) Global Screening Array-v2.0 (Illumina Inc.).  
Location of NTCP Variants in the Human NTCP Structure and In Silico Prediction of Their Functional Effects  
The variants p.R185C and p.S213R were located on the three recently reported NTCP structures [46; 47; 48]. Functional effects of both variants were predicted using the following 6 different algorithms as provided by the Ensembl Genome Browser ([[https://www.ensembl.org](https://www.ensembl.org)]([https://www.ensembl.org](https://www.ensembl.org)), release 106, last accessed on 6 June 2022): Sorting Intolerant From Tolerant (SIFT), Poly-Phenotyping-2 (PolyPhen-2), Combined Annotation-Dependent Depletion (CADD), Rare Exome Variant Ensemble Learner (REVEL), Meta Logistic Regression (MetaLR) and MutationAssessor. Variants were considered as tolerated when scores were \(\geq\)0.05 (SIFT), \(\leq\)0.5 (PolyPhen, REVEL, MetaLR, MutationAssessor) or <30 (CADD).
# Adaptation of the Marine Bacterium _Shewanella baltica_ to Low Temperature Stress
## 2 Results and Discussion
### Expression of S. bultica Cold-Induced Proteins in Response to Low Temperatures  
A typical bacterial response to a temperature downshift is associated with an increase in the production of cold induced proteins (Cip, also referred to as Csp--cold shock proteins) [45]. These small proteins have a conserved nucleic acid binding domain and are widespread in nearly all tested bacteria (independently of their habitat) [46]. These proteins mainly play a role of nucleic acid chaperones, affecting transcription initiation under conditions when stabilization of mRNA due to low temperature would hamper gene expression; however, some Csp may be active under other stress conditions as well [46; 47]. Two of those proteins have been identified in _S. oneidensis_ as responding to cold stress, mostly at the acclimatization stage [23], yet their cellular level may remain elevated for longer periods of time. Orthologs of those proteins in _S. baltica_ are encoded by Shew185_1705 and Shew185_1464; both of them were significantly upregulated at 90 and 180 min after onset of cold stress in our study (Supplementary Table S1). This indicates that under these conditions _S. baltica_ adjusts and regulates the level of gene expression also by affecting the mRNA stability.
# Introduction of a New Index of Field Operations Efficiency
## 3 Results  
### Effect of Field Shape on FTE  
Figure 10 provides the average, minimum, and maximum FTE values for all tested scenarios in each template field shape (Figure 8). The specific scenarios for which the FTE receives the minimum and maximum values in each individual field template, are given in Table 2. Significant variations in the average value of FTE were found between these six fields. As expected, odd shapes, such as REN, BPL are characterized by lower average values of FTE when compared to more regular shapes, i.e., SQR, STD, R21 and R41. The same findings are derived by other studies [7,22]. The FTE also varied significantly in the same field shape when using different setups of test parameters. The discrepancy between the minimum and maximum FTE in the same field shape is between 15% (in the case of SQR field) and 23%. (in the case of the BPL field).  
Figure 10: The minimum (min), average (ave), and maximum (max) field traversing efficiency (FTE) for each template field.  
Figure 9: Three conventional fieldwork patterns, (**a**) AB—pattern, (**b**) SF—pattern and (**c**) BL—pattern used in the tests.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 2 Related Works
### Handover for Vehicular VLC  
Although VLC is emerging as a viable option for vehicular networks, the number of research groups dealing with handover in VLC vehicular scenarios is very limited. In the last years, few works have been published in this area. [PERSON] et al. have studied the channel for indoor scenarios using Jointly Transmission COMP (JT-CoMP) in the two APs [28]. The results show an improvement in the link data rate. They have also evaluated this scheme in vehicular scenarios using the same methodology. As a result, they noticed some problems with its execution. So, they adapted the scheme to decrease the impact of the vehicle speed variations. Their algorithm incorporates dynamic steps on the HOM and TTT [21].  
An alternative solution is presented by [PERSON] et al. [46; 47]. They create a handover scheme named \"Flight\" based on link aggregation using Multipath TCP protocol (MPTCP). In this scheme, the vehicle can support two VLC links working at the same time, each one with its own address. One provides communication while the other serves as a backup. The protocol patrols the link status using the ARP message. When the system cannot provide a round trip communication, it faces a link failure, and then it uses the auxiliary link. The route modifications are performed in the Transport layer, where the packet's destination address is changed. This minimizes the disconnection time. During this period, the vehicle nodes can detect a new AP to establish another backup link [46; 47]. The outage time depends on the ARP frequency. For this scheme, the user requires two receivers and two transmitters to hold simultaneously the communication in both links.  
A system-level simulation tool provides a common way to validate new handover schemes. Due to the relative novelty of VLC, there are not many software libraries of this technology for this kind of simulator. While some research groups have created some ns3-based modules to evaluate VLC links [48; 49], none of them provide official support. However, these modules mostly consider only the link physical layer properties ignoring protocols from the link and upper layers. Some additional research works include evaluations of the multiple access scheme on the standard IEEE 802.15.7 under diverse conditions without evaluating the VLC link [50; 51; 52].
## 3 Results
### Afterberg Limits of DKM  
#### 3.2.1 Effect of Diatom Content  
The Afterberg limits of DKM samples with varying diatom contents are presented in Figure 6, along with the values of \(w_{\rm L}\), \(w_{\rm p}\), and \(I_{\rm P}\) for different DKMs from previous studies [6, 12, 31, 32, 33, 34]. The more diatoms in the DKM, the higher \(w_{\rm L}\) and \(w_{\rm p}\) are. The Afterberg limits of DKM with a diatom content of 0-60% are lower than DKM with a diatom content of 80-100%; for example, sample 60D-40K has \(w_{\rm L}\) = 70% and \(w_{\rm p}\) = 46%, while sample 80D-20K has the higher values of \(w_{\rm L}\) = 128% and \(w_{\rm p}\) = 98%. However, although \(w_{\rm L}\) and \(w_{\rm p}\) of the DKM change significantly with diatom content, \(I_{\rm P}\) remains relatively stable because the increments in \(w_{\rm L}\) and \(w_{\rm p}\) are approximately the same. Notably, \(w_{\rm L}\) and \(w_{\rm p}\) increase remarkably when the diatom content increases from 60% to 80%. Some previous studies [12, 35] suggested that the correlation between increasing \(w_{\rm L}\) and \(w_{\rm p}\) and diatom content is because \(w_{\rm L}\) and \(w_{\rm p}\) are affected by the unique microstructure of diatom frustules.  
The relationships between \(I_{\rm P}\) and \(w_{\rm L}\) for various DKMs are shown in Figure 7 as a Casagrande plasticity chart, along with data points for natural diatomaceous sediments from China [5], Korea [3], Japan [2], and Columbia [4]. The data points in Figure 7 for DKM samples move rightward with increasing diatom content, indicating that the plasticity of a DKM is hardly affected by its diatom content. Additionally, all of the DKM data points fall below the A-line, whereas the data of natural DSs are above the A-line and approach the U-line, as shown by the colored areas in Figure 7. The higher positions of the data  
Figure 6: Values of \(w_{\rm p}\), \(w_{\rm L}\), and \(I_{\rm P}\) for DKM samples versus diatom content. Data sources: DKMs [6, 12, 31, 32, 33, 34].  
points for natural DS compared with those for artificial DKM samples may be due to the existence of high plasticity mineral montmorillonite [1; 35]. Figure 8 shows the relationships between \(I_{\rm P}\) and clay content for DKMs and natural DSs, where the slope of the curve represents soil activity \(A_{\rm d}\). The \(A_{\rm d}\) value increases with the clay fraction and the DKM data points change from the line of \(A_{\rm d}=0.5\) to \(A_{\rm d}=1\), which is consistent with the findings of [PERSON] et al. [12].