esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### SAR Data  
The main advantage of SAR sensors is given by their capability to acquire images both at night and under all weather conditions, thus filling the gap often resulting from cloudiness-corrupted optical datasets.  
Currently, the Sentinel-1 constellation of the Copernicus program is the only provider of SAR data, freely available through the Sentinel Scientific Data Hub [62]. The constellation is composed of two satellites, Sentinel-1A and Sentinel-1B, operating since 2014 and 2016, respectively. The twin satellites acquire C-band (central frequency of 5.404 GHz) SAR data all over the world, with a revisit time of 6 days over Europe and some hotspot areas, and 12 days in the rest of the world. Such a short repeat cycle increases the chance to collect free co-flood data that allow mapping inundated areas accurately.  
For our study, we used the Interferometric Wide (IW) swath acquisition mode, which captures three sub-swaths by employing the Terrain Observation with Progressive Scans SAR ((TOPSAR) [63]). IW is the main acquisition mode for the systematic monitoring of surface deformation and land changes, providing data with a 250 km swath and a spatial resolution of 5 m \(\times\) 20 m. In particular, we used the Single Look Complex (SLC) format, i.e., complex-valued images in the slant range by the azimuth imaging plane [64; 65]. We also used the ESA SNAP open source software for geocoding SAR data [66] and converting the pixel values from digital numbers (DNs) into measurements of the sigma naught values (in dB). To this end, the 3-arcsec SRTM DEMs have been used on the investigated areas.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Band Used** & **Spatial Resolution (m)** & **Revisit Time (Day)** & **Data Availability** \\ \hline MODIS-Terra/Aqua & R; G; B; NIR; SWIR(2) & 250 \(\div\) 500 & 1 & [69] \\ Proba-V & R; B; NIR; SWIR & 100 \(\div\) 300 & 5 \(\div\) 1 & [71] \\ Landsat-8 & R; G; B; NIR; SWIR(2) & 30 & 16 & [72] \\ Sentinel-2 (a/b) & R; G; B; NIR; RE(2); SWIR(2) & 10 \(\div\) 20 & 5 \(\div\) 10 & [62] \\ Sentinel-1 (a/b) & C & 5 \(\times\) 20 (SLC) & 6 \(\div\) 12 & [62] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the data used in our study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Band Name** & **Wavelength Interval** \\ \hline Blue (B) & \(\sim\)450–500 nm \\ Green (G) & \(\sim\)500–620 nm \\ Red (R) & \(\sim\)620–700 nm \\ Red Edge (RE) & \(\sim\)700–750 nm \\ Near Infrared (NIR) & \(\sim\)750–1400 nm \\ Shortwave Infrared (SWIR) & \(\sim\)1400–3000 nm \\ SAR-Band (C) & \(\sim\)5 cm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bans and wavelength used for this study.
## 3 Design Approach
### Open Source Network Stack
#### 3.2.2 Multi-Technology MAC Design Framework  
For the design of the MAC protocol, our board supports the TAISC framework [8], which is designed to simplify the design of time-critical MAC protocols. The TAISC framework provides interfaces towards all three supported radio chips (IEEE 802.15.4 2.4 GHz, IEEE 802.15.4 g sub-GHz, and IEEE 802.15.4 UWB). Each interface can run an independent MAC protocol, or novel multi-interface MAC protocols can be designed. Example MAC protocols that are provided include CSMA/CA, ALOHA, slotted ALOHA, IEEE 802.15.4e TSCH, ContikiMAC TODO:ref, and several TDMA variants. In addition, a multi-interface TDMA MAC implementation is available that combines the strengths of two radio interfaces: sub-GHz for reporting and TDMA slot allocation, and UWB for ranging (see Section 4).
## 5 Conclusions  
This paper developed an integrated approach to optimize train stop planning and train timetabling with a demand-responsive mode in high-speed railways. Taking passenger demand with expected departure times and infrastructure conditions into account, we developed a mixed integer linear programming model to determine a high-quality service plan including stop patterns, arrival/departure times at stations of all trains and passenger ride plans. In this modeling process, minimizing the remaining passenger demand and total train travel time were considered to be the optimization objectives. Finally, the proposed method was applied to a real-scale instance study. A series of several experiments were set up to prove the effectiveness of the constructed model. The results show that a service plan suitable for passenger travel demand could be obtained by the proposed approach, while reducing the amount of remaining passengers and train travel time. Moreover, we separately analyzed the impact of variable weight coefficients \(\theta\) of the objectives on the results. The remaining passenger demand and train travel time are mutually interrelated, and operators need to find a balance between them.  
Future studies could pay attention to the following three areas: (1) In the method of this paper, the number and operation range of trains are fixed. In order to obtain a more flexible service plan, a variable number of trains and an unfixed scope of train operation need to be considered in future research. (2) More efficient solving algorithms could be developed to deal with the model. Although the proposed model could be solved using the exact solver, it performs poorly in terms of computational efficiency for large-scale application in the real world. (3) Only one train operation direction is considered in the process of modeling. The problem of scheduling in two directions with rolling stock needs to be studied in the future.  
Conceptualization, Y.L. and B.H.; methodology, Y.L.; software, Y.L.; validation, Y.L., R.Y. and P.Z.; formal analysis, Y.L.; investigation, R.Y.; resources, B.H.; data curation, P.Z.; writing--original draft preparation, Y.L.; writing--review and editing, B.H.; visualization, Y.L.; supervision, R.Y.; project administration, B.H.; funding acquisition, B.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fundamental Research Funds for the Central Universities (CN) (Award Number: 2020 JBZD007) and the National Natural Science Foundation of China (Award Number: 71971019).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion
However, the accuracy of separation is improved by using the proposed method. The separation result has better smoothness, which is consistent with the dispersion of the S\({}_{1}\) mode Lamb wave. The right part of the amplitude spectrum is slightly irregular, which means it has some minor errors. From Figure 5f, the time-frequency of the extracted S\({}_{1}\) mode signal is non-stationary. From Figure 5d, the central frequency of the extracted S\({}_{1}\) mode signal is 3.14 MHz, and the frequency range is from 2.7 to 3.55 MHz. The experimental results from Figures 4 and 5 show that the proposed method can separate the single mode contained in the multimodal mixed signals. The separated signals and spectra indicate that the results have better smoothness. The dispersion degree of the A\({}_{1}\) mode is slow, delaying on the time domain, and the time waveform changes less along the distance. On the contrary, the phase speed of the S\({}_{1}\) mode changes quickly in the low frequency band; the time signal changes significantly along the distance, and the amplitude becomes smaller and the duration longer with the increase of the distance. Figures 4d and 5d show the amplitude spectra of the separated A\({}_{1}\) mode and S\({}_{1}\) mode. Except for a slight irregularity at both ends, it is in line with the theoretical analysis of the Lamb wave dispersion. The A\({}_{1}\) mode center frequency is 2.98 MHz, and the S\({}_{1}\) mode is 3.14 MHz. In order to further verify the proposed method, the separated A\({}_{1}\) and S\({}_{1}\) modes are added and compared with the experimental original measured signal at the distance of 1.6 cm in Figure 6. From Figure 6a, to the time-frequency blind signal separation method, the reconstructed signal is very different from the original signal. The result is obvious because the extracted signal mode signals are inaccurate and incomplete. In this method, the dispersion of Lamb waves are not considered resulting in the inaccuracy and incompleteness of single mode separation. On the contrary, to the proposed method, from Figure 6b, it can be seen that the mixed signals after separation and addition is the same as the original signal. The start and end time points are also the same. The front part of the reconstructed signal is more consistent with that of the original signal, and only a few peaks are slightly larger than the original signal. The tail of the signal is slightly less than that of the original signal. Considering that the tail of the signal is almost the main component of the S\({}_{1}\) mode and its dispersion degree is relatively fast, there may have been incomplete compensation in the dispersion compensation, which might lead to errors in the signal separation. Figure 6c,d are the time-frequency distributions of the original Lamb wave signal and the reconstructed Lamb wave signal, respectively. It can be seen that their time-frequency energy distributions are consistent. The maximum energy distribution is about 4.5 us, which implies that the separated A\({}_{1}\) mode and S\({}_{1}\) mode are consistent with the original single modes.
## 2 Materials and Methods
### Selection of the Streptomyces Strain  
The strains _Streptomyces griseus_ (_S. griseus_) KACC 20084, _Streptomyces griseus_ (_S. griseus_) KACC 20731, and _S. rimosus_ KACC 20082 were procured from the Korean Agricultural Culture Collection (KACC), Republic of Korea. The strain _S. avermitills_ KCTC 9063 was supplied by the Korean Collection for Type Cultures (KCTC), Republic of Korea. Each microorganism was cultivated on tryptic soy broth for four days at 27 \({}^{\circ}\)C with shaking (250 rpm). All _Streptomyces_ strains were analyzed for the production of geosmin and 2-MIB using SPME GC-MS (Table S1). For preparation of the VOCs, an aliquot of 80 \(\mu\)L of bacterial suspension was inoculated into a 20 mL clear vial with a PTFE septum and a screw cap containing 8 mL of tryptic soy broth. Inoculated and uninoculated broths were incubated for four days at 27 \({}^{\circ}\)C and then subjected to volatile profiling via HS-SPME GC-TOF-MS. Among these isolates, _S. rimosus_ KACC 20082 was selected for further study because it produced both geosmin and 2-MIB (Figure 1). The \(m/z\) values of the most abundant ion fragments were 95 for 2-MIB and 112 for geosmin.
## 4 Discussion
### Practice, Policy, and Future Research
#### 4.4.3 Future Research  
Expanding eye-tracking research to natural, uncontrolled environments is crucial for understanding real-world reading, comprehension, and attention processes, addressing the limitations of controlled settings. Including diverse demographics beyond university students is essential to capture broader variations in age, language, and cultural backgrounds.  
Research on synthetic data should prioritise enhancing realism by incorporating behavioural and environmental variability, such as blinking, pupil dilation, and eye redness. Tailoring synthetic data for specific contexts, like reading or mobile devices, and validating models against large-scale, real-world datasets will ensure reliability and practical application, addressing data scarcity challenges.  
Typography's role in comprehension and attention requires further study, particularly intelligent features like bolding and spacing across multilingual scripts. Integrating NLP to identify key words could enhance memory retention and productivity. Greater focus on diverse user needs, including individuals with dyslexia or ADHD, is needed, as these groups may exhibit unique eye movement patterns that current models fail to accommodate.  
Finally, exploring cognitive workload, attention, and typography in dynamic contexts, such as digital media and adaptive learning systems, could inform text layouts that optimise reading efficiency and comprehension across various environments.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15063308/s1](https://www.mdpi.com/article/10.3390/app15063308/s1)]([https://www.mdpi.com/article/10.3390/app15063308/s1](https://www.mdpi.com/article/10.3390/app15063308/s1)), Table S1: Extraction Table and MMAT.  
Conceptualisation, L.A., S.A., B.H., M.N., A.S., W.A. and Z.L.; methodology, D.P. and W.S.; software, L.A.; validation, L.A., S.A., B.H. and D.P.; formal analysis, L.A., S.A., B.H., M.N., A.S., W.A. and Z.L.; investigation, L.A., S.A., B.H., M.N., A.S., W.A. and Z.L.; resources, L.A., S.A., B.H., M.N., A.S., W.A. and Z.L.; data curation, L.A.; writing--original draft preparation, L.A., S.A., B.H., M.N., A.S., W.A. and Z.L.; writing--review and editing, L.A., S.A., B.H., W.S. and D.P.; visualisation, L.A.; supervision, D.P. and W.S.; project administration, D.P.; funding acquisition, D.P. All authors have read and agreed to the published version of the manuscript.  
W.S. received support from UKRI Innovate UK grant (grant number 10031483).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract
It may also improve the quality of research and make study outcomes more relevant to the patients, with more products developed in line with patients' needs [17; 22; 33]. A recent systematic review of attempts and initiatives about using PPI in benefit-risk assessment, published in late October 2020, concludes that patient preference elicitation tools are largely understood, and researchers and experts perform their use better. Unfortunately, despite the efforts identified and the initiatives undertaken, the pace of progress remains slow [34]. Moreover, evidence of proper use of these data in policy decision-making is lacking as PPI remains poorly implemented: therefore, many questions about PPI validity, representativeness, and robustness remain unanswered. Finally, there is a lack of guidance on conducting PPSs to inform decision making, possibly explaining the limited use of these studies [24]. However, most stakeholders consider PPI essential in informing future decision-making across the MPLC [35]. In addition, including PPI could improve the transparency and acceptability of regulatory or reimbursement decisions [36]. Given these considerations, there is a need for infrastructures that can monitor the contemporaneity and validity of the evidence. This study aims to provide a conceptual blueprint for an openly available and regularly updated patient preference management system for an integrated database (PPMSDB), collecting preference measures, characteristics, and meta-data, which could allow researchers to quickly overview a research field using the latest evidence and data to identify research gaps. Different initiatives [37; 38] address the methodological issues and provide recommendations and guidance on the design and conduct of PPI studies. [PERSON] et al. [18] have identified 32 methods for PPSs: 10 exploration and 22 elicitation methods. According to the type of data collection and analysis, these study methods were assorted to different groups. The exploration methods can be divided into three distinct groups--individual methods, group methods, and individual/group methods--while the elicitation methods can be classified into four distinct groups--discrete--choice based methods, ranking methods, indifference methods, and rating methods [18]. In conjunction with proper statistical analysis of quantitative preference measures, these aspects may facilitate formal evidence-based decisions and adequate consideration when conducting a structured decision-making process. The PPMSDB would enable the evaluation of previous studies and the continuous inclusion of new studies. As such, it would respond to the need for cumulative data aggregation approaches in addition to a retrospective summary of past studies.
## 1 Introduction
### Contributions  
As previously mentioned in Section 1.1, there have been considerable contributions in the literature to broadband communications. Nevertheless, the usual space constraints for publications have been preventing a comprehensive research work in which the assiduous efforts towards broadband communications development can be presented. Therefore, an all-inclusive tutorial in which the trends in broadband communications can be studied is highly imperative. Based on this, apart from being a good complement to contributions of the existing work, this paper also offers in-depth clarifications regarding different related concepts and architectures. In this regard, the main contributions of this tutorial paper are as follows.  
1. A comprehensive tutorial on various broadband technologies considering their capability in meeting the current and future network requirements is comprehensively studied, focusing mainly on the technological evolution and associated features. In this context, insightful analysis of their achievable rates, reaches, advantages, and limitations are presented.
2. Apart from the 5G-based FWA prospect that we consider, an extensive analysis of different FWA design considerations that can facilitate system planning and deployment are presented. Besides, related technical challenges are studied and we proffer viable solutions to address them.
3. Furthermore, an in-depth discussion on the 5G FWA field trials by various operators is presented. In this regard, we present different metrics for the FWA system performance evaluation.
4. A number of potential transport network solutions that are capable of supporting the 5G FWA deployment scenarios are presented and due attention is paid to their capability, advantages, and disadvantages. Based on this, we present a detailed classification of different transport network solutions considering their suitability for ultra-dense deployment and the number of subscribers that can be supported.
5. Apart from a comprehensive review of different RAN decomposition schemes that are capable of relaxing the transport network constraints, we expatiate the need for the RAN decomposition scheme. Furthermore, we present the effects of the RAN decomposition scheme on the 5G FWA transport network solutions with the main focus on their implementations and implications. Additionally, based on the 3 GPP WG3 5G, we evaluate and simulate the required transmission bandwidth for some split options. To ensure effective RAN virtualization and to meet the MFH requirements, we present a high-level concept of vRANs with PTN for attending adaptively to the dynamic nature of different use cases.
## 2 Overview European XFEL  
X-ray FEL radiation is characterized by its ultrashort pulse duration, high pulse energies and a high degree of coherence. Scientific applications of soft and hard X-ray FEL radiation make use of these properties, in particular in the investigation of ultrafast processes in atoms, ions, simple and very complex molecules, clusters or condensed matter. The high pulse energies allow the collection of meaningful data sets from single pulses, thereby enabling the study of non-reversible processes. Coherence properties are exploited in imaging techniques that aim to obtain atomic spatial resolution for weakly scattering systems, in part combined with a corresponding temporal resolution [16]. Finally, the very high X-ray pulse energies, which combined with ultrashort pulse durations correspond to very high peak powers of up to several tens of GW, promise to enable access to new information of excited solids through non-linear X-ray scattering [17].  
There are several classes of these experiments requiring not only a high peak brilliance, but also high average brilliance. Here European XFEL has a clear advantage compared to other X-ray FEL facilities. Examples comprise studies of ultra-dilute systems, very small cross-section processes, non-linear X-ray processes, or particle-particle/particle-X-ray coincidence spectroscopy experiments. Furthermore it is possible to use subsequent X-ray pulses to probe equilibrium dynamics at frequencies up to 4.5 MHz, and beyond [18].  
To enable the use of the highest repetition rates in a pulse-resolved (non-integrating) manner has been the biggest instrumentation challenge for European XFEL. Such a mode of operation requires that diagnostic and X-ray detection systems operate at repetition rates of up to 4.5 MHz. In addition, optical lasers used to excite samples in a well-controlled manner and sample injections systems need to accommodate these high event rates. The development of this non-standard high repetition rate instrumentation is one key expertise of European XFEL and its partners.
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 5 Performance Evaluation
### Data Delivery Ratio  
In this section, we compare the performance of the proposed algorithm EDDA with other data dissemination schemes, such as GPSR, epidemic routing, SW and CCR, in terms of data delivery ratio. We also show how the data delivery ratio is affected by the number of maximum message copies and the number of vehicles. Here, we use the number of vehicles to indicate the network size.  
Figures 4 and 5 show the delivery ratio as a function of the number of message copies when the number of vehicles is fixed as 300. It compares data dissemination performance under different numbers of message replicas in the network. Figure 4 shows the results in a regular urban environment, and Figure 5 describes the case of a highway. It is easy to see that when the number of message copies is low, the epidemic algorithm achieves the best delivery ratio. However, as the number of message copies increases in the network, the network traffic dramatically grows in epidemic routing, as well as the number of data collisions increases and the data delivery ratio reduces. As a result, the performance of epidemic routing decreases and gradually underperforms the other algorithms. On the contrary, EDDA outperforms the other compared algorithms when more messages are spread in the network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation area & 2000 m \(\times\) 2000 m \\ Simulation time & 1 h \\ Vehicle communication range & 300 m \\ Vehicle velocity & [30, 120] km/h \\ Number of Vehicles & 100, 300, 500 \\ Number of message copies & [50, 500] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the simulations.  
Figures 6 and 7 show the delivery ratio as a function of the number of vehicles. In the simulations, we fix the maximum message copies that can be replicated as 300, and the number of vehicles is set as 100, 300 and 500. Figure 6 shows the results in urban VANET, while Figure 7 describes the results of highway. We can see that the delivery ratio increases when there are more vehicles in the area. This is because when there are very few vehicles on the roads, it may be hard to find the nearby forwarding vehicles, and hence, messages are dropped after the wait time. When there are more vehicles on the roads, the network connectivity becomes better as the vehicle density is higher, and the frequent node mobility will help carry and forward the packets that temporarily reach the sparse area.  
Figure 4: Delivery ratio vs. number of message copies in urban VANET.  
Figure 5: Delivery ratio vs. number of message copies on the highway.