esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays
## 4 Results and Discussion  
### Utilization of Voxels and Signal Rays  
To compare the influences of different methods on the number of signal rays used, the experimental results were analyzed. Figure 5 shows the number of signal rays used in different schemes at day of year (DOY) 152, 2017, the upper one for the case of per 30 min, and the lower one for each tomographic solution. It can be seen that only a small part of the signal rays is used in Scheme #1 compared with the total number of signal rays observed. Scheme #2 improves the utilization of signal rays in every epoch and solution, and Scheme #3 has the same effect in improving the usage of signal rays, which illustrates that Scheme #3 can be a good comparison to Scheme #2. Moreover, the average number of signal rays observed, used in different schemes, can be seen in Figure 6. The figure illustrates that Scheme #2 improves the average utilization of signal rays used by 19.68% compared with Scheme #1. For Scheme #3, the percentage of increment is 22.2%.  
On the other hand, the number of voxels passing through by the signals is analyzed. The upper image in Figure 7 shows the number of voxels crossed by the signal rays once per 30 min between Schemes #1, #2, and #3, whereas the lower image shows the situation of every tomographic solution in that day. Evidently, the number of voxels crossed by the signal rays in Scheme #2 is more than that of Scheme #1, both for an epoch or a tomographic solution. For Scheme #3, it has the largest number of voxels crossed by signal rays in most cases, but it does not represent a good performance in penetrating voxels for Scheme #3. This is because the total number of voxels in Scheme #3 is 900, which is much larger than 560 in the other schemes. It may make more sense to focus on the percentage of the voxels crossed by signal rays.  
Figure 5: Number of signal rays used in Schemes #1, Scheme #2, and Scheme #3 per 30 min (upper) and each solution (lower) of DOY 152, 2017 (yellow triangles for the total number of signal rays observed).  
Moreover, the statistical results, which include the average number and percentage of voxels crossed by signal rays once per day in different schemes for the period of DOY 152-158, 2017, can be seen in Figure 8. The finding shows that the average number of Scheme #1 increases by 15.14% from 65.36% to 80.50% under Scheme #2. Additionally, Scheme #3 has the largest value in the average number of voxels, but its percentage (54.74%) of voxels crossed by signal rays is the smallest. For models with different total voxels, the percentage of voxels passing though by signal rays is more important than the number of that.  
Figure 6: Histogram for the average number of signal rays observed (yellow, the first bar in each set), used in Scheme #1 (blue, the forth bar in each set), Scheme #2 (red, the third bar in each set), and Scheme #3 (green, the second bar in each set) at DOY 152–158, 2017.  
Figure 7: Number of voxels crossed by signal rays in Schemes #1, #2, and #3 per 30 min (upper) and each solution (lower) of DOY 152, 2017.
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 3 Proposed VLC-Based IoT Scheme
### Device Management and Data Transport Operations
#### 3.3.3 VLC Data Transport  
In the proposed scheme, the VR data is classified into the following two types. One is location-specific data, which is contained in the VLC frames that VT transmits to VRs. The other one is service data, which is delivered through the VR service channel between AA and VR.  
Figure 10 shows the data transport operations, in which VR receives location-specific data from VT and service-specific data is exchanged between AA and VR. For the example of the _museum_ service, the VR device may be used to give detailed information on the concerned exhibition item (e.g., description on the item) to the visiting user. In this case, the location-specific data contains the IDs of AA and VT, the channel number of the target item, etc. Please note that such information is specific to a particular VT or item. The VR device might be a smartphone with a mobile guide application for the museum, or a dedicated device that is given by the museum. Based on the location-specific data, VR will try to get more detailed and additional information on the target item from AA, which is served by the service data of VR Service Channel.
# Resource Allocation for Relay-Based OFDMA Power Line Communication System
## 3 The Optimal Resource Allocation Strategy for a PLC System  
In this section, we will give the optimal resource allocation algorithm by considering both the transmit power and interference power constraints [33; 34; 35; 36]. Since problem (11) is a joint optimization problem, we can divide the complex problem into two subproblems to obtain the global optimal solution, i.e., the subcarrier allocation strategy and power allocation.
These can be merged into a weighted average or used to represent aggregated patch mosaic features. The importance of landscape ecology has been influenced by this appraisal of the terrain's richness [35]. With different natural algorithms, these landscape-level measures can yield comparable or duplicate information. The amount and spatial distribution of a single patch are represented by landscape-level metrics, which can be considered as a fragmentation index [36]. As a result, it is critical to understand what kind of measure we are dealing with (patch, class, and landscape). Even though they are all relevant, changes in land use/cover are a major environmental issue that contributes to landscape fragmentation, habitat loss, and climate change [37; 38]. Since the 1990s, landscape metrics have become ubiquitous instruments for monitoring, analyzing, and planning landscape patterns, thanks to substantial improvements in landscape ecology. Landscape metrics are widely acknowledged as a helpful and significant tool for tracking, identifying, and assessing the types of changes that occur in the landscape [39]. Landscape metrics are quantitative indicators of a landscape's structural composition and spatial organization. Landscape metrics analysis is the most essential tool for studying landscape patterns and quantifying spatial variation in landscape ecology [40]. In this paper, we conduct a comprehensive analysis of landscape fragmentation in the Sonipat district by calculating landscape metrics at three distinct levels: patch, class, and landscape. These metrics encompass various landscape pattern indicators that are crucial for understanding and managing ecosystem processes. Such processes are essential for the long-term sustainability of local natural resources, particularly in regions experiencing rapid urbanization and land use changes. By combining several landscape pattern metrics, we develop a detailed typology that captures the complexities of landscape fragmentation. This typology serves as a valuable tool for evaluating and guiding policies and programs aimed at managing and maintaining the minor yet critical characteristics of the landscape. Our study provides insights into how urban expansion and population growth impact natural resources, helping policymakers and stakeholders implement more effective strategies for sustainable land management and conservation in the Sonipat district and similar rapidly developing areas. While this study focuses on the spatial heterogeneity of the Sonipat district, the analytical approach and insights have broader applicability across diverse geographical regions and contexts. In rapidly developing areas worldwide, shifts in spatial heterogeneity often signal underlying changes in land use patterns, biodiversity, and ecological resilience. By quantifying spatial heterogeneity through metrics such as fragmentation, patch size, and edge density, this research provides a framework that can be adapted to various landscapes facing developmental pressures, from urban expansion in metropolitan regions to agricultural intensification in rural areas.
# Methods for Determining Polycyclic Aromatic Hydrocarbons (PAHs) in Milk: A Review
## 4 Conclusions  
Although the levels of PAHs typically detected in milk and dairy products are relatively low, their presence poses genotoxic and pro-inflammatory risks. This concern is amplified by findings from some studies indicating that these levels are occasionally exceeded. Dairy products are a dietary staple for the most vulnerable populations, such as children and seniors, for whom even small amounts of PAHs may be harmful. Considering the lifelong exposure to PAHs--beginning from birth--it is crucial to minimize their presence in everyday life.  
Food regulations concerning PAHs are more comprehensive in the EU than in many other countries. However, expanding the scope of legislation to include a broader range of products could further enhance consumer safety. That said, EU citizens currently enjoy higher levels of protection in food safety, including dairy products, than in other regions.  
It is necessary to continue researching methods of detecting PAHs in milk and dairy products. The complexity of this matrix and the behavior of PAHs makes adequate measurements difficult. However, modern chromatographic methods--GC and HPLC--allow for the detection of deficient concentrations of hydrocarbons. Further advancements in detection methods depend significantly on developing efficient extraction techniques. Emerging approaches like HeadSpace analysis (HS), solid-phase microextraction (SPME), and solid-phase extraction (SPE) show great promise.  
The dairy industry is pivotal in ensuring product safety by developing strategies to reduce PAH levels. This includes addressing both the environmental contamination and PAHs generated during technological processes. Researchers can support these efforts by advancing knowledge and working toward standardized, practical methods for reducing and detecting PAHs in dairy products.  
Conceptualization, K.Z.; methodology, K.Z.; investigation, K.Z.; resources, K.Z.; data curation, K.Z.; writing--original draft preparation, K.Z., E.G.-H., M.Z., A.W. and A.P.; writing--review and editing, K.Z., E.G.-H. and M.Z.; visualization, K.Z.; supervision, A.W. and A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
## 5 Performance Evaluation and Comparative Analysis
### Comparative Analysis  
The proposed framework demonstrated superior performance across multiple metrics when compared to existing frameworks in the literature. Notably, it explored interference mitigation via the SDN underlay network, which aligns with the theoretical framework presented in [12]. In comparison to the SDN overlay model proposed in [14], which achieved a maximum packet delivery rate of approximately 96% and dropped to as low as 81% under some test conditions, our framework consistently achieved 100% packet delivery rate across all tests (Figure 13). This confirms its enhanced reliability. In terms of resource management, the proposed framework also performed better than the RAN slicing model proposed in [22], which supported only two classes of IoT--massive IoT and URLLC. Unlike [22], our framework provides more granular classification for critical vehicular data types within the URLLC class. At an average flow rate of 2.3, 1.4, and 0.6 PPS for Camera, LiDAR, and RADAR sensors, respectively, our framework maintained a system utilisation of approximately 1; in contrast, they reported less efficient system utilisation values of 1.3, 1.4, and 1.5 at the same flow rates. In addition, our in-vehicle traffic classification model achieved 100% accuracy, outperforming the in-network ML classifier proposed in [23], which reported an average accuracy of 93.5%. Furthermore, it exceeded Deep Learning (DL) models, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN) when applied for flow-based classification in [18] (Figure 14). Whilst AI models offer significant flexibility for dynamic network management in next-generation networks, we recommend that such approaches are reserved for complex, multi-factor resource management tasks. Fundamental network operations, such as traffic classification, can be handled more efficiently using pro-active SDN controller algorithms (as demonstrated in this study) or direct configuration on OpenFlow switches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **TEST 1 (S)** & **TEST 2 (S)** & **TEST 3 (S)** \\ \hline CAMERA & 0.55 & 0.29 & 0.21 \\ LIDAR & 0.99 & 0.47 & 0.34 \\ RADAR & 2.02 & 1 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Overall delay per packet for vehicular data pre-processing and gNB offloading (s).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **TEST 1 (S)** & **TEST 2 (S)** & **TEST 3 (S)** \\ \hline CAMERA & 931.8 & 530.8 & 430.6 \\ LIDAR & 1293.3 & 520.7 & 420.6 \\ RADAR & 1092.5 & 711.4 & 530.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average delay at gNB during offloading (s).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Type** & **TEST 1 (S)** & **TEST 2 (S)** & **TEST 3 (S)** \\ \hline CAMERA & 0.12 & 0.07 & 0.06 \\ LIDAR & 0.28 & 0.11 & 0.09 \\ RADAR & 0.35 & 0.23 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average delay per packet at gNB during offloading (s).
## 3 Results and Discussion  
### Coating of Fe-ZSM-5 on Polymeric Woven Tubes  
Different binding materials were applied for coating the Fe-ZSM-5 powder on the polyethylene tubes. Of these materials, we only discussed the binding materials that showed considerable results. Ethyl vinyl acetate (EVA) and poly vinyl alcohol (PVA) with different ratios of ethanol were applied, although EVA has not been presented (not shown).  
Furthermore, PVA has better binding than EVA, and the epoxy coatings are displayed in Figure 2, which allows for a comparison in terms of binding. From the SEM images of coatings and the TGA analysis (Figure S3), it is evident that PVA is not as good as the epoxy coating material, because the ZSM-5 particles were submerged completely in the PVA coated tubes and lost their characteristics. Therefore, only the epoxy was further studied with different mixing ratios of the catalyst (hardener), which is shown in Figure 3. These micrographs demonstrated that the 1:1 ratio of epoxy and catalyst is better (as Fe-ZSM-5 morphology is visible, which shows that it is firmly attached on the tubes), which will be used for coating materials in the next few studies.  
Figure 2: SEM images (100 and 30 microns) of Fe-zeolite socony mobil (ZSM)-5 coated tubes with different coating materials, namely: (**a**) bare tube, (**b**) polyethyl vinyl alcohol (PVA) + Fe-ZSM-5 tube, and (**c**) epoxy + Fe-ZSM-5 tube.
## 2 Bidirectional DC-DC Converters
### Classification of Bidirectional DC-DC Converter
#### 2.1.1 Non-Isolated Bidirectional _DC_ to _dc_ Converter (_Nibdc_)
This issue can be prevented by employing a control plan, for example, voltage and current control techniques, but this can compromise the intricacy of the system and increase the related cost [18,49]. This topology involves the combination of two or more converters in parallel; it has a relative phase shift of \(360^{0}/\mathrm{n}\). Some of the advantages of interleaved converters include solving the issue of output current ripples, current splitting (_I/n_), better system productivity, higher power density, and better thermal capacity. Owing to the current splits in the parallel routes, there is a lower rate of conduction losses, and fewer switches are needed. The presence of the interleaving strategy reduces the current and voltage ripple at the input of the _DC-DC_ converter without increasing the switching losses; thus, the efficiency of the system is higher [50]. There is only one type in this topology, which is a two-phase interleaved non-isolated bidirectional _DC-DC_ converter; it comprises two output stages, with \(180^{\circ}\) out of phase. A simplified two-phase interleaved _DC-DC_ converter circuit is shown in Figure 9. Note that the interleaved half-bridge converter is the commonly used topology [31,51,52] as it offers better voltage transformation even when the size of the converter is small; it also reduces switching losses. However, this converter has some drawbacks, such as high cost as a result of the higher number of elements and its complex control technique [53]. Figure 8: Switched capacitor of bidirectional _DC-DC_ converter [18]. Figure 7: Cascaded bidirectional buck–boost converter [48].
# Review of Web Mapping: Eras, Trends and Directions
## 3 Web Mapping Eras of Development
### Web Mapping Eras
#### 3.2.5 Collaborative Web Mapping Era  
The increased focus on the user experience that resulted from the _interactive web mapping era_, paved the way for this new era. The start of the Web 2.0 developments opened the way for more users to participate in both mapping retrieval and data creation activities. Web 2.0, a term coined by [PERSON] [54], refers to the read-write web where user interaction is extended to, not only the choice of parameters for the selection and viewing of content, but also the creation of user-generated content [10]. [PERSON] [21] gave the first systematic and complete definition of Web 2.0, highlighting that, instead of simply disseminating information created by a webmaster, \"the central principle behind the success of the giants born in the Web 1.0 era who have survived to lead the Web 2.0 era appears to be this, that they have embraced the power of the web to harness collective intelligence\".  
The emergence of Web 2.0 in the mapping domain can be dated to 2005, when both new web mapping applications and new communities behind those applications have appeared. It is worth noting that, in their \"Web Squared\" Report, [PERSON] and [PERSON] [55] mention many examples related, directly or indirectly, to web mapping. This gives a clear hint about the importance acquired to geospatial information in contextualizing collaborative user-generated content. In the previous years, in fact, big web mapping collaborative platforms were put in action both by private companies (such as Google Map Maker--now integrated into Google Maps--and Microsoft Bing Maps) and by communities (such as OpenStreetMap).  
OpenStreetMap (openstreetmap.org) was founded in 2004 as one of the first widespread efforts to provide a mapping platform for volunteered data capture [56]. What started as a mapping exercise for the United Kingdom spread rapidly across the entire world. Geospatial data are redistributed under the ODbL license: users are free to share, create and adapt the database as long as they attribute, share-alike and keep open.  
Another example of a collaborative mapping platform is Wikimapia launched in 2006 [57]. The goal was to build a participative map where people worldwide could describe the whole world. Data are released under CC-BY-SA license, even if, being mainly derived from Google Maps, there can be problems in reusing them in some countries, depending on the specific jurisdiction.  
Some new terms, such as Crowdsourcing and Volunteered Geographic Information (VGI), were coined. VGI became integrated into many desktop and mobile apps so that the user communities are not only consumers of geospatial information and maps, but also producers that contribute to the shared knowledge of our world [58]. This fed into the generation of communities linking to and utilizing a host of related technologies including mashups, blogs, wikis, social networking and shared atlas/mapping portals.
# Multihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes
## 6 Conclusions  
The establishment of downstream routes represents the final stage of the WSN formation process. Its behaviour differs from the multicast control messages exchanged to obtain synchronization and form the downstream routes, since the sending of DAO messages can be performed on multi-hop links, where the interfering nodes vary with each retransmission. In this work, an equation that models the timing to form the downstream routes using the RPL DAO message is proposed. This message, unlike the previous phases of node deployment, is transmitted in unicast and must be sent from the new node to the root, so that in those cases where they are not within the same coverage area, a multi-hop communication will be performed.  
The communications architecture used is designed to meet the requirements of industrial applications through the use of the TSCH mechanism. This provides determinism and reliability, while the RPL protocol allows routing tables in WSN networks to be built and maintained autonomously. The use of the proposed analytical model and the study that accompanies it allows us to know in detail which aspects are more relevant during the network formation phase, allowing better configurations to be chosen in order to speed up this process. With a bad configuration of the protocol stack parameters, network formation can take many minutes or may even fail to connect, resulting in a loss of resources and energy dedicated to the process of connecting the devices.  
From the results obtained from the simulations, it has been verified that the analytical model follows the same behaviour as the simulated networks, whose variations have focused on determining the impact of interfering messages in RPL on the different hops of a link between node and root. This interference produced in the RPL traffic plane considerably affects the connection time and efficiency in the exchange of messages, so that if measures such as sending DAO-ACK or allocating more resources to RPL are not adopted, the construction of downstream routes may not be completed correctly.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Simulation results for three hops.  
**Funding:** The research leading to these results is part of the i4\(Q\) project that has received funding from the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement No. 958205.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.