esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### LEO/GEO-Integrated AOD Fusion Process
#### 3.2.2 Soft Data Construction  
Ground-based AOD observations acquired by AERONET and MAN are introduced to construct probabilistic soft data to reconcile the discrepancy from satellite-based AOD products, which is expressed as follows:  
\[AOD_{sat,x,y,t}=AOD_{ground,x,y,t}+\varepsilon_{x,y,t}\ \ \ \varepsilon\sim N \Big{(}\mu,\ \sigma^{2}\Big{)} \tag{10}\]  
where \(AOD_{sat,x,y,t}\) and \(AOD_{ground,x,y,t}\) are the satellite-based and AERONET or MAN AODs at the space-time coordinate (\(x,y,t\)), respectively, and the discrepancy (\(\varepsilon\)) approximately follows a normal distribution with mean (\(\mu\)) and variance (\(\sigma^{2}\)). Ground-based sites provide reduplicative point observations, whereas satellite observations provide images of a given area at a single time. Hence, to mitigate the influences of aerosol variability in space and time, the AOD values of the original satellite-based and merged data are extracted by spatially averaging 3 \(\times\) 3 pixels (at least 20% valid pixels) centered at each ground-based site. Furthermore, the corresponding ground-based AODs are calculated by temporally averaging observations within \(\pm\)30 min of the scanning time of each image [64]. Figure 3 depicts the Gaussian probability density functions of \(\varepsilon\) for satellite-based AODs, where the MODIS AOD product demonstrates the best data quality while the CALIOP AOD product generally possesses larger uncertainty than the MODIS and AHI AOD products due to the influences from surface reflection and sunlight [65; 66; 67; 68]. For pixels where satellite-basedAOD is valid, the Gaussian probability density soft data after discrepancy quantifying is expressed as follows:  
\[AOD_{soft,\ sat}\ \sim N\Big{(}AOD_{sat}\ \ +\mu,\ \sigma^{2}\Big{)} \tag{11}\]
## 4 Result  
Based on the results of organic carbon analysis, major element analysis, and trace element analysis of the 30 selected mudstone samples, the measured TOC (Total Organic Carbon) content of Ying IV mudstone in the Xuijaweizi Depression of the Songliao Basin during the Cretaceous period exhibits a range of 1.46-4.34 wt% (mean = 2.65 wt%, n = 30). The Loss on Ignition (LOI) values range from 5.07% to 10.27%, mean = 7.26% (Figure 2). By correlating the LOI with TOC content, a statistically significant positive relationship (R\({}^{2}\) = 0.903, \(p<0.01\)) was demonstrated (Figure 3). This suggests that higher organic matter content leads to higher LOI values. This relationship can be used as an initial indicator of lake productivity changes, which in turn reflects the impact of regional climate change on lake ecology [19, 20].  
The enrichment index of major and trace elements in the Yingcheng Formation mudstone is characterized by the Enrichment Factor (EF), which is used to quantify the enrichment degree of each element and its oxides. The average content of major elements in global average shale (AS) [21] is used as the reference value to determine the relative enrichment of elements, and this method can eliminate the interference of other factors. The EF is defined as  
\[\text{EFx}=(\text{X}/\text{Al})_{\text{Sample}}/(\text{X}/\text{Al})_{\text{ AS}}\]  
where wAl/wX represents the ratio of the mass fraction of element X to that of Al in the sample, and (wAl/wX)\({}_{\text{AS}}\) represents the ratio of the mass fraction of element X to that of Al in the global average shale. When the EFx value is greater than 1, it indicates enrichment, while a value less than 1 indicates depletion. Through comparison, it can be found (Figure 4 and Table 1) that the mudstone of Ying IV generally shows enrichment in Fe, P, and Mn, and depletion in Al, Mg, K, and Ti. The elements Si, Ca, and Na are enriched in the upper part of Ying IV mudstone but depleted in the lower part.  
Trace elements are highly diverse in the Earth's crust and have a wide range of concentrations. Compared to major elements, they are more sensitive to environmental changes and can provide rich geochemical and geological information. Among the 30 tested mudstone samples, as shown in Figure 5 and Table 2, Ying IV mudstone in the Xuijaweizi area is significantly enriched in Zn and Ba, while it is depleted in V, Co, and Ti. The elements Cr, Ni, Cu, Rb, and Sr exhibit slight concentration fluctuations with changes in depth.  
Figure 4: Enrichment map of major elements in Ying IV.  
Figure 3: Relationship between LOI and TOC.  
Figure 2: Relationship between LOI and depth.
## 8 Conclusions  
We studied the performance of a Wi-Fi-based vehicular ad hoc network (VANET) for deployment in shopping mall parking lots using field trial measurements and simulations. We developed an OPNET-based simulation model to validate the system's performance. The system's performance was validated by configuring and analyzing various simulation scenarios. The research findings reported in this paper provide some insights into the deployment of VANETs with the goal of enhancing road safety and communication efficiency in the WestCity shopping mall parking lot in Auckland.  
Finally, we analyzed the compatibility of five selected theoretical propagation models (Free Space, Shadowing Path Loss, Egli, Hata, and COST 231). The results obtained have shown that the best-fit model (a close match with the measurements) is based on the measurement scenario/location. For instance, the \"Free Space\" model is one of the best-fit models for the scenario in which measurements are conducted from Level 1 of the car park to the roadside. The received signal strengths in the parking lot ranged from \(-45\) dBm to \(-92\) dBm. We also examined the coverage distance for the successful transmission of warning messages, achieving up to \(57\) m, \(17.5\) m, \(9.4\) m, and \(68\) m at parking levels 1, 2, 3, and the roadside, respectively. These findings are valuable for configuring similar networks and aiding the decision-making of administrators. We emphasized the impact of the separation distance and received signal strength on system performance, highlighting the need for practical measurements and comprehensive analysis beyond theoretical models. The challenges of identifying and addressing misbehavior in VANETs, particularly for safety and congestion avoidance applications are suggested as key areas of future work.  
Conceptualization, N.I.S. and F.A.; Methodology, N.I.S. and F.A.; Software, N.I.S.; Validation, N.I.S. and F.A.; Formal analysis, N.I.S. and F.A.; Investigation, F.A.; Resources, N.I.S.; Data curation, N.I.S. and S.G.; Writing--original draft, F.A.; Writing--review & editing, N.I.S. and S.G.; Visualization, N.I.S., F.A. and S.G; Supervision, N.I.S.; Project administration, N.I.S.; Handling reviewer comments, N.I.S. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within this article.  
The authors declare no conflict of interest. We also confirm that our paper has not been previously published and is not currently under consideration by any other journals.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 2 Literature Review
### Hub-and-Spoke Transportation Network Design  
On passenger transportation hub/location issues, [PERSON] and [PERSON] [25] formulated a hub-and-spoke location problem for the transportation network. In their proposed model, the authors consider a network of public transportation, such as bus and subway systems. They explain that a passenger might use some links to arrive at the hub network, traverse some links on the hub graph, and then exit the hub graph, either for his/her destination or to a spoke facility, which would be his/her final destination. It is obvious that the passenger determines the origin and the destination, and then the model solves the rest of the problem. In other words, once the model receives the origin/destination data, it solves a transportation problem to find the optimum route for the passenger to pass to arrive at the destination, considering cost function minimization. They develop an advanced version of the Benders decomposition, using some accelerated techniques such as the one explained by [PERSON] and [PERSON] [26], and also develop a heuristic algorithm to further improve the solution algorithm. The capacity of a spoke link can also be limited. [PERSON] et al. [27] propose a network in which the capacity of a spoke link is a function of the incentive provided to transporters.  
Regarding the inter-hub cost coefficient, the literature contains two lines of research: (i) articles that consider a cost coefficient if a passenger/flow is transported via the hub link, and (ii) articles that do not consider such coefficient. The researchers in the first group argue that the major point of scaling a facility to a hub facility is to consolidate the transportation flow and benefit from the economy of scale. Despite this, several articles fail to represent that concept. [PERSON] [28] discusses this issue, surveys the literature, and prepares a list of several articles that consider cost coefficients (as a result of economies of scale) for a hub link, which, in fact, has less flow than several other non-hub links in the same network. This reveals the fact that such an assumption/formulation is not always valid. [PERSON] et al. [29] explain that considering a constant, unique cost factor for hub links may not be an appropriate approach for this topic, based on the research articles in the literature. They provide several examples of research that considers the cost efficiency factor for a hub link, which has less passenger flow than some non-hub links.
## 1 Introduction
Furthermore, the existing method has shortcomings, such as failing to consider the conjugate relationship of angular frequencies, resulting in poor SSO parameter identification accuracy, and only being able to shorten the data window to 2 s.  
Classic ERA technology achieves precise and convenient extraction of modal parameters by simplifying the process of matrix decomposition. However, it also has obvious shortcomings. One needs a data window of 1 s to ensure the accuracy of the parameter identification results, and the other fails to take into account the conjugate relationship of angular frequencies. During the course of this research, we conduct our analysis, dependent upon a fixed model. However, in actuality, the actual waveform is changing, and thus the model should also change. The use of an extended data window will further constrain us within the fundamental assumption that the model is fixed and unchanging. Consequently, in practical applications, shortening the data window is particularly important. But, the research uncovered that the ERA's rate of identification accuracy will be significantly decreased after compressing the data window. Based on this, we have optimized the algorithm to ensure that it can maintain exceptional accuracy even under the condition of a shortened data window, which forms the core starting point of all of our work. In this paper, the existing ERA is improved by fitting equations from the aspect of solving the angular frequency, and the angular frequency obtained by the improved algorithm meets the constraint of pairwise conjugation. This improves the accuracy of SSO parameter identification and further shows that the 200 ms data window can be used for SSO parameter identification. Moreover, using a 200 ms data window in this paper can capture the dynamics of SSO to the order of milliseconds, which can minimize the limitations caused by the assumption that the model is fixed within the data window. Finally, the fitting-based ERA method is simulated using synthesized and measured PMU data. The simulation results show that the fitting-based ERA can shorten the data window duration to 200 ms and ensure the accuracy of SSO parameter identification. This paper is organized as follows. Section 2 studies the synchrophasor model and modal parameter extraction in the ERA. Section 3 introduces SSO parameter identification of the fitting-based ERA. In Section 4, the proposed method's performance is evaluated using the synthesized and measured PMU data.
## 3 Sensor Scheduling  
Surveillance is an important task that can be effectively performed by an intelligent network of sensors. For example, satellites can be equipped with cameras to monitor Earth surface for different events, such as forest fire, border crossing or enemy hostile activity. Another example is traffic monitoring at the roads and intersections. Many scientific publications have recently appeared in the literature due to increasing interest to the problem of finding optimal schedule for sensors [20; 21; 22; 23; 24; 25]. Most common technological and budget constraint is a low number of sensors to monitor all the objects of interest simultaneously. Thus, finding the schedule that reduces potential loss of limited observations is a task of high importance. This section provides a review of recently developed optimization based methods for building optimal schedule of sensor surveillance.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### Hadoop
#### 4.1.4 Fault Tolerance  
Apache Hadoop is highly fault tolerant. HDFS layer ensures fault tolerance using data replication. Data blocks stored in DataNodes are replicated and distributed across the cluster to provide reliability and high availability. Prior to the 2.x, the NameNode was a single point of failure in an HDFS cluster, with the release of Hadoop 2, HDFS high availability enables multiple standby NameNodes to run on a single HDFS cluster in an active/passive configuration. A failure can arise at three levels: task level, slave node level, master node level. If a task fails at the slave node, it reports back to the resource manager before exiting which in return allocates the failed task to another machine in the cluster and marks up the free slot on slave for another task. If a task fails at the master node, that task can be restarted in the same node from the last check point state since the master takes periodic check points of all the master data structures. When a slave node fails, the RM at the master node will detect this failure by timing out its heartbeat response. The RM will assign failed node tasks to some other idle node in the cluster and remove the failed node from a pool of resources. If the fault is transient, the NM at slave cleans up its local state, resynchronize itself with RM and redo its work done during the fault. RM failure was a single point of failure before Hadoop 2.x. However, the high availability feature enables running a redundant resource manager in standby mode that takes up in case of active resource manager failure.
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## Materials and Methods
### Validation and Field Condition Assessment  
EAMENA uses a standardised recording methodology that allows information about a site and its condition to be recorded in the field and later entered directly into our open-source, online database [1].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case Study** & **Date Earlier** & **Date Later** & **Threshold Value** & **Reducer** & **Redueer Scale** & **Buffer Radius** & **Input Points** & **Changed Points** \\ \hline
**Aawan** & October-December 2018 & October-December 2019 & 0.2 & Any non-zero & 10 & 100 & 279 & 73 \\ \hline
**Jufza** & March-May 2019 & March-May 2020 & 0.2 & Any non-zero & 10 & 100 & 82 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of change detection.  
Figure 2: Change detection workflow using Google Earth Engine.  
This standardisation makes data comparable, both with other database records, but also with the results of the change detection workflow.  
The results of the change detection were checked by comparing the Sentinel-2 composites manually with the change layer at each buffer and by checking Google Earth imagery from corresponding dates (see Table 2). In some cases, where there was no corresponding Google Earth imagery, we used PlanetScope imagery. An error matrix was prepared for each study area, allowing the proportions of false positives and missed instances of change to be quantified [30].  
We visited a sample of the sites in the field in both Egypt and Libya in 2020 to validate the automated approach and adjusted the error matrices accordingly. Sites were selected for validation based on their accessibility and the urgency of their condition; for example, several sites in the Aswan area were in the process of being destroyed, due to construction work, but this work also meant that we could not get close enough to some sites. Validation fieldwork in the Aswan area was undertaken in January-February 2020 in collaboration with the AKAP project and local antiquities inspectors. In Libya, field validation was more limited, due to the Covid-19 pandemic, but a sample of sites was visited by EAMENA partner and controller of archaeology in the south of Libya, [PERSON] in June-July 2020.
# Post-Cyclic Mechanical Behaviors of Undisturbed Soft Clay with Different Degrees of Reconsolidation
## 4 Post-Cyclic Mechanical Characteristics of Soft Soil
### Effective Stress Paths  
The shape and development trend of effective stress path during the post-cyclic shear process is analyzed. Figures 12-14 show the undrained shear effective stress paths (ESPs) curves under different experimental conditions. The critical state line (CSL) in \(p^{\prime}\)-\(q\) space can be determined by the best linear fit with the effective stress path endpoints of test ST-20, ST-40, and ST-60. The critical stress ratio \(M\) given by the CSL is 1.58. The S-shaped trend in the ESPs under different test conditions is similar. At the beginning of the test, due to the poor permeability of this soft soil, the induced excess pore water pressure is small. The stress applied to the specimen is almost entirely borne by the soil skeleton, and the deviator stress applied to the specimen increases rapidly within a small strain range. With continuous shearing, the excess pore water pressure begins to grow rapidly, and the increase of deviator stress decelerates, leading to the ESPs of the specimen being deflected to the left. When the axial strain reaches a certain value, the rate of increase in excess pore water pressure decreases, and the ESP deflects again. Moreover, the ESPs no longer develop after reaching the undrained shear strength, whereupon they cross the CSL.  
Figure 12 shows the static and post-cyclic shear ESPs with different confining pressures, for \(U_{r}=100\%\) and CSR = 0.25; the ESPs of the post-cyclic specimens do not completely coincide with those without cyclic loading history ESPs for the specimens after cyclic loading have a greater degree of deflection to the left compared with the ESPs of specimens not subjected to cyclic loading.  
The ESPs of static and post-cyclic undrained shearing with different CSRs are shown in Figure 13. Due to the full reconsolidation of the post-cyclic specimen, the starting points of the ESPs overlap and are equal to the confining pressure of 60 kPa. The deviator stress at the inflection point of each ESP increases with the increase of CSR. The failure points of specimen lie on the CSL.  
Figure 12: Effective stress paths of the soft soil at different confining pressures (\(n=1000\), \(CSR=0.25\), \(U_{r}=100\%\)).  
Figure 14 shows the ESPs of static and post-cyclic undrained shearing with different degrees of reconsolidation. Since the degree of reconsolidation is determined by the dissipation of accumulated excess pore water pressure, the \(p^{\prime}\) value at the beginning of shearing increases with the increase of \(U_{r}\). Compared with Figures 12 and 13, the shape of ESPs with different degrees of reconsolidation is not S-shaped; similarly, the failure points cross or lie on, the CSL.
## 3 Results
### Determination of the Modified TSM
This result is similar to that of [23], in which only the wind speed dependence of \(\zeta\) is considered. Figure 6 shows an example of comparison of the NRCSs predicted by the modified TSM (solid curves) and the conventional TSM (dashed curves) using different wave spectra and the Cosine spreading function, and their comparisons with CMOD5.n at a wind speed of 16 m/s and an incidence angle of 50\({}^{\circ}\). As can be seen, whichever wave spectrum is used, the modified TSM can well simulate the upwind-downwind asymmetry, which cannot be described by the conventional TSM. It achieves this by introducing a negative correction in the downwind direction (180\({}^{\circ}\)) and a positive correction in the upwind direction (0\({}^{\circ}\)) using the additional bispectral term. Moreover, the minimum of the modified-TSM-simulated NRCS deviates from the crosswind direction (90\({}^{\circ}\)/270\({}^{\circ}\)) and moves to the downwind direction. All these changes diminish the gap between the predictions of model and CMOD5.n [47]. Figure 4: Estimations of the \(\zeta\) parameter with the use of different directional wave spectra of D + Cosine, D + Sech, D + Gaussian, A + Cosine, A + Sech, A + Gaussian, E + Cosine, E + Sech, E + Gaussian, H18 + Cosine, H18 + Sech, and H18 + Gaussian as a function of incidence angle (**a–c**) at wind speed of 3 m/s, (**d–f**) at wind speed of 9 m/s, (**g–i**) at wind speed of 16 m/s.
# Enhanced Beacons Dynamic Transmission over TSCH
## 4 Proposed Enhanced Beacons Dynamic Transmission Mechanism
### Theoretical Network Formation Expected Time  
The analytical model developed in [31] proves that in a network with \(m\) channels, the probability for a join-seeker to get associated with a synchronizer after exactly \(j\) EB transmissions can be computed following [31] (Equation (5)):  
\[P_{j}=P_{n}(1-P_{n})^{j-1}, \tag{2}\]  
where \(P_{n}=m^{-1}\) is the probability of the synchronizer sending an EB on the channel scanned by the join-seeker.  
In the following, we develop an analytical model for the proposed EBDT-TSCH (the code used to validate the mathematical model as well as to obtain the figures associated with it is available at [[https://github.com/erikortiz84/EBDT-TSCH.git](https://github.com/erikortiz84/EBDT-TSCH.git)]([https://github.com/erikortiz84/EBDT-TSCH.git](https://github.com/erikortiz84/EBDT-TSCH.git))). The maximum time between successive EBs changes dynamically in EBDT-TSCH. In the intensive phase (\(j\leq u\)), the average elapsed time to send the \(j\) EBs is \(jE(t_{i})\). Meanwhile, in the predetermined phase, the average elapsed time to send \(j\) EBs, \(j>u\) EB can be computed as the time needed to send the first \(u\) EBs plus the time to send the rest of the \(j-u\) EBs. Therefore, we can formalize the average elapsed time to send the \(j^{th}\) EB at any phase as the following:  
\[E(t_{j})=\begin{cases}jE(t_{i}),&j\leq u\\ uE(t_{i})+(j-u)E(t_{p}),&j>u,\end{cases} \tag{3}\]  
where \(E(x)\) is the expected value of \(x\). As both, \(t_{i}\) and \(t_{p}\) follow a uniform distribution:  
\[E(t_{i})=\frac{\rho+1}{2}Tebi; \tag{4}\]  
Figure 3: EBDT-TSCH with \(\alpha=0.5\), \(\rho=0.75\), and \(\beta=1\) for 4 available network channels.  
\[E(t_{p})=\frac{\rho+1}{2}\textit{Teb}. \tag{5}\]  
Using (2), (3), and the expected value definition, we derive the average time needed for a join-seeker to get associated with a synchronizer as the following:  
\[E(t) =\sum_{j=1}^{\infty}P_{j}E(t_{j}),\] \[=\sum_{j=1}^{u}P_{n}(1-P_{n})^{j-1}jE(t_{i})+\sum_{j=u+1}^{\infty }P_{n}(1-P_{n})^{j-1}\big{(}uE(t_{i})+(j-u)E(t_{p})\big{)}. \tag{6}\]  
Working on (6) and using (4) and (5) for the proposed EBDT-TSCH, the expected time needed for a join-seeker to get associated with the network is as follows (see proof in Appendix A):  
\[E(t)=\frac{\textit{Teb}(1+\rho)m}{2}\Bigg{(}\alpha-(\alpha-1)\bigg{(}1-\frac{ 1}{m}\bigg{)}^{\lfloor\beta m\rfloor}\Bigg{)}. \tag{7}\]