esa-sceva/satcom-chunk-collection huggingface dataset:
# Reference Models for Digital Manufacturing Platforms
## 3 Reference Models Alignment
### Industrial Internet Consortium Reference Model
#### 3.4.4 Implementation Viewpoint  
The implementation viewpoint deals with the technologies needed to implement functional components, communication schemes, and procedures involved during their lifecycle. The implementation viewpoint defines three patterns for a coherent IIoT System implementation: three-tier architecture pattern, gateway-mediated edge connectivity and management architecture pattern, and the layered databus pattern.  
The three-tier architecture pattern divides the system into three tiers (edge, platform, and enterprise) and three networks (proximity, access, and service). The edge tier collects data from sensors, actuators, devices, control systems, or any asset in the proximity of the physical system, which are collectively called the edge nodes. For this purpose, the edge tier uses the proximity network. The access network enables connectivity between the edge tier and the platform tier. The platform tier consolidates operation processes, performs data analytics, and transformation functions with respect to data flows.  
It also offers management functions on edge nodes and dispatches control messages from the enterprise tier to the edge tier. The service network enables connectivity between the services in the platform tier, as well as between platform and enterprise tiers. The enterprise tier implements domain-specific applications, decision support systems, and provides user interfaces to end-users. It implements the rules and control logic of the system, and issues control commands to the platform and edge tiers.  
The gateway-mediated edge connectivity and management pattern and the layered databus pattern are more specific instances or variations of the three-tier architectural patterns that deal with connectivity solutions to interconnect the different tiers in specific network topologies, supported by the connectivity framework [45].
## 5 Conclusions and Prospective
### Summary of Conclusions from the Experimental Results  
The limitations observed in this study stimulated an experimental analysis of the classical JSP problem, which was augmented by taking into account the energy consumption during waiting times or machine shutdowns, as well as different machine speed configurations. After performing a study with nine augmented JSP instances and three types of algorithms (namely, NSGA-II, SMS-EMOA, MOEAD), we concluded that the best results were obtained by NSGA-II.A more thorough experimental study would require tuning the parameters of the algorithms under comparison. Nevertheless, an important conclusion can be drawn from the results obtained in our benchmark: the inclusion of machine velocity and idle/stop energy consumption data to the usual makespan minimization considered in naive JSP formulations may have a high impact on the schedule, costs, and sustainability of real-world manufacturing industries.
## 3 Fundamentals of GLI-Split
### Mobility Support  
In today's Internet, mobile IP is needed for communication with a mobile node (MN). The mobile node's home address serves as a stable reference address on the transport layer and for finding a rendezvous point with the mobile node on the network layer. If the mobile node leaves its home network, the mobile node's care-of-address indicates its location on the network layer. With upgraded GLI-nodes, locators in local or global addresses may change due to roaming without breaking transport connections because upgraded GLI-nodes use only identifier addresses on the transport layer so that mobile IP is no longer needed. However, GLI-Split allows improved mobility support only if two upgraded GLI-nodes communicate with each other and reside in GLI-domains. Any other communication patterns are supported by mobile IP.  
Mobility support with GLI-Split works as follows. The DNS stores a static home address of the mobile node which is used for mobile IP. This is a GLI-address and contains the identifier in the usual position. Thus, GLI-nodes can extract the identifier and get an appropriate locator for the mobile node. When a mobile node roams into a GLI-domain, it receives new local and global locators and updates the global mapping system and the local mapping system in the new domain. Furthermore, it informs all GLI-upgraded corresponding nodes (CNs) about its new global locator with mobility update messages so that the corresponding nodes can reach the mobile node again. If the mobile node and the corresponding nodes are in the same GLI-domain, the corresponding nodes may query the local mapping system to obtain the local locator of the mobile node to avoid triangle routing via the GLI-gateway (see Section 4.3.4). Then, both nodes communicate via a direct connection without triangle routing. A similar feature is provided by ILNP [22]. In contrast to GLI-Split, an upgraded mobile node in ILNP updates the DNS with its new address when roaming into a new network; interworking with non-upgraded nodes is not defined. In GLI-Split, the new global GLI-address of the mobile node is also used as care-of-address for communication with non-upgraded nodes using mobile IP.  
We highlight the benefits of the new mobility support offered by GLI-Split compared to mobile IP. Corresponding nodes can contact mobile nodes always directly without triangle routing over a home agent. This is an advantage since home agents may be far away and increase the latency. With mobile IPv6, such route optimization can be done under some conditions, but the first contact with the mobile node is always via the home agent. Furthermore, GLI-Split makes local moves of mobile nodes almost invisible to corresponding nodes in other domains. If the mobile node moves only within a GLI-domain, it receives a different local locator but keeps the same global locator so that corresponding nodes in different domains can continue to send to the same global GLI-address as before. Hence, the communication is hardly impaired by the location change.
# Advances in Permeation of Solutes into Hair: Influencing Factors and Theoretical Models
## 3 Main Factors That Affect the Permeation and Absorption of Solutes in Hair
### Solutes
#### 3.2.2 Solute Hydrophobicity  
Some studies have reported that the permeation and absorption characteristics of solutes in biological substrates (e.g., hair, skin, and nails) are mainly related to the hydrophobicity of the solute [26; 49; 50; 51; 52]. Hydrophobic interaction is an important way that solute molecules and hair interact. [PERSON] et al. [53] found that the adsorption capacity of wool fibers for cationic surfactants increases with increasing hydrophobic structure. [PERSON] et al. [54] determined the absorption characteristics of cetyl trimethyl ammonium bromide (CTAB) and dodecyl trimethyl ammonium bromide (DTAB) in hair. The results showed that CTAB with strong hydrophobicity has greater absorption in hair than DTAB with weak hydrophobicity. [PERSON] et al. [55] found that when the solvent pH is 3.6 (close to the isoelectric point of hair), the hair fibers are neutral, the absorption of solutes in hair is mainly caused by the hydrophobic effect, and the absorption capacity increases with increasing molecular weight and hydrophobic structure of the solute.
## 4 Discussion  
[PERSON] and [PERSON] [36] report that the NDVI mainly reflects seasonal vegetation conditions, showing higher correlations with seasonal soil moisture stress, whereas the PDI does not show this relationship.  
The NDVI in this research was the vegetation index that performed better on coarser resolution than thinner spatial resolution, regardless of land use.  
Both sensing drought indices, the NDVI and the PDI can explain soil moisture variability in all the studied land uses. One study [37] showed significant negative correlations in spring, summer, and autumn between the NDVI and soil moisture, whereas farmland showed a significant positive correlation between NDVI and soil moisture in winter. In the current research, NDVI positively correlates with potato and pasture land uses and negatively correlates with bare soil, possibly due to the higher evaporation on bare soils (Figure 9).  
Bare soils are not affected by vegetation cover, so their reflectivity in red and NIR bands is only affected by the soil moisture content. If there is a decrease in soil moisture, the reflective+ity of the red and NIR bands increases [38]. When vegetation cover increases, reflectance in the NIR band is higher than in the red band. Where land use in the current study plot includes both soil and vegetation, the points scatter inside a triangular region in the NIR-R, as shown in Figure 5. These results on bare soils concur with laboratory reflectance studies [20].  
Spatial resolution clearly determines the ability of a sensor to generate the indices that can successfully approximate soil moisture.  
The NDVI produced no significant correlations with soil moisture on UAV images, whereas Planet Scope NDVI variants with their higher spectral and spatial resolution positively correlated with bare soils, concurring with [39]. The ease of calculating the NDVI and the high temporal resolution of the data may mean that Sentinel-2 Planet Scope may play a future role in early warning systems of drought, as it enables high-resolution vegetation condition monitoring, which may be useful in detecting the onset of agricultural drought.  
In regard to the SAVI, this can only be used with a significant correlation to estimate the soil moisture of bare soils.  
The NDWI was the index that performed best on detailed resolutions, especially to study the soil moisture of land use for vegetables such as potatoes, which is useful when considering precision agriculture.  
Observing Table 4, it can be seen that on pasture, the most significant correlations are found on coarser scales, whereas bare soil and potatoes have better results on detailed resolutions. These results show that UAV with multispectral cameras are useful to evaluate bare soil and potato soil moisture at detailed scales, and, above all, with the NDWI, SAVI and PDI.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 4 Methods for Measuring the SUHI
### Using LST as a Proxy of SUHI  
LST has been widely used to reflect SUHI variability, especially for local-scale studies based on Landsat series or ASTER data [87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107]. Such studies often concentrated on the LST variations and their relationships with spatial-temporal factors in a city [30]. The SUHI usually manifests itself in the form of hotspots [108] or higher LST of buildings/impervious surfaces than other surfaces [30]. The method can be employed to investigate the underlying driving mechanisms of SUHI at a local scale and, therefore, help urban planners formulate site-specific mitigation strategies. However, these studies usually do not estimate the intensity of SUHI, which makes comparisons of SUHIs among cities challenging.
# Low Power QC-LDPC Decoder Based on Token Ring Architecture
## 5 Conclusions  
This article presents an innovative idea of the QC-LDPC decoder design using the Token Ring architecture. The proposed architecture is oriented to reduce energy consumption. A significant reduction in dynamic energy consumption has been demonstrated with the decoder throughput remaining unchanged. Moreover, the analysis of the implementation results of decoders based on regular and irregular matrices \(\mathbf{H}\), as well as matrices with different submatrix sizes, allowed us to indicate an energy-saving configuration. Such a configuration is the regular code decoder with a sufficiently large submatrix size \(P\). It is likely that similar conclusions can be drawn for other QC-LDPC decoder designs, but we cannot claim that for sure.  
The architecture based on a Token Ring can undoubtedly be used in the implementations of QC-LDPC decoders as well as in many other modern digital systems. In the proposed solution, which is not optimized in terms of throughput, significantly lower losses of dynamic power were achieved combined with lower throughput. This type of implementation can be applied wherever the throughput is not a critical parameter of the designed device.  
The obtained results of the experiments indicate the general principles of selecting the \(\mathbf{H}\) matrix form, which has a positive effect on the power consumption of the designed decoder. It cannot be denied that it is difficult to judge whether the obtained results are universal in nature, or only apply to a specific family of FPGAs. It seems that they should be universal in nature, but confirming this would require experiments for several families of FPGAs. A comparison of the designed decoder with alternative solutions taken from the literature shows that the proposed solution has high implementation potential. It seems that it is also worth using them in ASICs, which do not have a number of the limitations characteristic of programmable systems.
## 2 Materials and Methods
### Method Validation
#### 2.5.3 Matrix Effect and Recovery  
Enhancement or suppression of signal on ESI-MS/MS response due to matrix components was evaluated by comparing the response of the analyte with respect to the response in the solvent. Extracted drug-free plasma samples obtained from six different sources were spiked with the analyte at concentrations equivalent to three QC levels and IS (100 ng/mL). Post extracted blank plasma and the pure standard solution containing the analytes and IS was also prepared at the same level of QC concentrations. To quantify for the loss of the compounds during the sample treatment process, the extraction efficiency (recovery) was estimated by comparing the analysis response (peak areas) of pre-extraction spiked serum with those obtained from post-extraction samples at the three QC concentrations.
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 3 Results
### Evaluation of the Proposed Algorithms on the MRIs from the BrainWeb Dataset
#### 3.1.1 No Bias Field Case  
Figure 3 shows the registration performance when there is no bias field in the MR images. The mean and variance of T-RMSE and I-RMSE for SSD, CC, RC, and MI similarity measures are shown separately in the top and the middle panels. The performance metrics are reported for LR-EMD, AFR-EMD, and the benchmark model. Without the bias fieldnoise, all the registration processes for all images have converged. AFR-EMD achieves similar performance to the benchmark intensity-based registration when there is no bias field noise. LR-EMD has a slightly higher error, particularly when RC has been used as the similarity measure. Both SSD and CC have an approximately 25% higher average error compared to the RC and MI similarity measures (both in T-RMSE and I-RMSE). Generally, MI achieves the highest accuracy and fastest convergence time among the similarity measures. The bottom panel in Figure 3 compares the running times for all the methods. There is no significant difference between the running times of our proposed methods and the benchmark method. Among the similarity measures, RC has the highest running time. Overall, when there is no bias field distortion, EMD-based registration algorithms perform similar to the benchmark intensity-based method.
## 2 Proposed Method  
### Conventional Monte Carlo Simulation (MCS) Framework for Direct Calculation  
Figure 1 shows a conventional framework for evaluating the seismic performance of bridge transportation networks based on the MCS approach. For direct calculation in a MCS framework, the network map should be reconstructed using geographic information system (GIS) data. Once an epicenter and earthquake magnitude are identified, the ground motion of the entire region of the target network can be calculated using the spatially correlated ground motion prediction equation based on the reconstructed network map. When the intensity of the ground motion is determined at the locations of bridge structures, the probabilities of bridges according to their damage states can be calculated from seismic fragility curves.  
Using the probabilities, a large number of samples of bridge damage states are generated. In each damage state sample, the performance and flow capacity of bridge structures are determined according to the damage states and can be utilized as input data for bridge transportation network analysis through the determined damage states. For this purpose, traffic analysis of a bridge transportation network requires basic information such as network topology data and number of lanes, link type (roadway or expressway), vehicle speed limit, and time required to pass each link for each bridge. If the damage states and flow capacity of each bridge facility are determined, the travel time of the source node to the destination node can be calculated through traffic analysis, and the summation of total travel time between all nodes is defined as TSTT. One of the widely used programs for traffic analysis is EMFE/4 developed by the INRO company, Canada. It is a software package of travel demand modeling that enables the TSTT calculation of an entire network at the macro level. EMFE/4 considers the specific roadway network with each bridge facility and the travel demand of traffic analysis zones based on traditional four-step travel demand modeling, which enables stable macro function analysis. When bridges are damaged due to an earthquake, the capacity of road links, in which the damaged  
Figure 1: Conventional Monte Carlo simulation (MCS) framework for direct calculation.  
bridges are included, is reduced. Subsequently, the optimized outcomes of traffic assignments are changed in the form of increasing TSTT. By comparing the TSTT of each scenario, the seismic risk of bridge transportation networks can be assessed. It has been adopted to estimate the performance of a transportation network in several studies [15; 16; 17], and we also adopted the EMME/4 software to calculate TSTTs in this study. If the mean value of TSTT calculated in the \(i\)th sampling satisfies a convergence criterion, the average total travel time and relative importance measure for each scenario are calculated, and the entire algorithm ends. If not, an iterative process of calculating TSTT with the \((i+1)\)th sample is required.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 6 Conclusions  
This paper has discussed the survey related to the models for the UWA channel problems and its challenges, and the techniques associated with the physical layer for the transmission of data over such channels. The essential issues of the UWA channel, such as delay spread, Doppler, waves, motion, distance, and SNR are summarized. This paper has addressed the modulation techniques used in UWCs over the physical layer. Additionally, it provided knowledge about the new UWC schemes, such as the SMTs which are mainly used on the IoUTs. This paper shows the satisfaction conditions of reliable UWAC using different modulation techniques. It reviewed the main characteristics of modulation technologies over the UWA channel that have been considered as UWAC physical layer techniques for UWWC: (1) underwater multicarrier communication modulation, such as OFDM (TDS-OFDM, CP-OFDM, ZP-OFDM, MIMO-OFDM, GFDM, UF-OFDM, and CPM-OFDM); (2) FBMC; (3) MIMO; (4) SMTs; and (5) OFDM-IM. Modern UWC technologies of researchers have also been presented. It is evident from this study that OFDM, GFDM, FBMC, MIMO, and MIMO-OFDM are used to achieve high spectral efficiency in UWAC. UF-OFDM and OFDM surpassed all the traditional modulation techniques to achieve low computational complexity. High energy efficiency techniques are essential for UWCC as they are battery-based communications. It is observed from this study that OFDM-IM, SMTs, MIMO, CPM-OFDM, and UF-OFDM outperform all the other modulation technologies in terms of high energy efficiency.  
Conceptualization, N.U.R.J. and H.E.; methodology, N.U.R.J. and M.S.; software, N.U.R.J. and H.E.; validation, S.A., H.S. and A.B.M.A.; formal analysis, N.U.R.J. and A.H.; investigation, N.U.R.J. and H.E.; resources, H.S. and A.B.M.A.; data curation, N.U.R.J., M.S. and H.E.; writing--original draft preparation, N.U.R.J. and H.E.; writing--review and editing, N.U.R.J., S.A. and H.E.; visualization, M.S., S.A. and A.B.M.A.; supervision, H.S. and H.E.; project administration, H.S. and H.E.; funding acquisition, H.S. and H.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Natural Science Foundation of China under Grants 62271426. This work was supported partially by the National Telecom Regulatory Authority, Egypt, through the Project \"AQUA-EloUTs: Effective Internet of Underwater Things for The Aquaculture Industry\". This paper is based upon work supported by Science, Technology & Innovation Funding Authority (STDF) under grant number 46143.  
The authors declare that the data used to support the findings of this study will be available from the corresponding author upon request.  
The authors declare that they do not have any conflict of interest.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 4 Discussion  
Here, by comparing three similar sensors with different canopy perspectives--UAV-based RGB imagery, tower-based PhenoCam, and ground-based, below-canopy NDVI hemispherical imagery--we show strong, positive correlations among the rate and magnitude of canopy greening consistent across all sensors, regardless of the perspective which they view the forest canopy. Spatial resolution affects canopy structural variables derived from below-canopy hemispherical imagery and UAV imagery (e.g., LAI and gap fraction), but we demonstrate that these structural attributes can still be accurately inferred in temperate, broadleaf systems. Our analyses show that all three sensor platforms considered here effectively approximate phenological indicators, with above- and below-canopy methods estimating canopy closure earlier than the oblique-angle tower-based PhenoCams, but later than MODIS derived NDVI.