esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Plant Growth Promotion Potential
#### 2.4.1 Nutrient (N, P, S, Fe) Acquisition
In gram-negative bacteria, Fe-siderophore complexes are transported into the periplasm with the assistance of special receptor proteins called TonB-dependent receptors (TBDRs) [51]. The presence of the _tonB_ gene encoding periplasmic protein, which provides the energy required for the translocation of siderophore-iron complexes across the bacterial outer membrane, along with the _fuu_, _exbB_, _exbB_, _fepA_, and _pvdF_ genes encoding proteins involved in Fe-siderophore transport, was also detected in the ZCR6 genome (Table 4). Bacterial siderophores that can be efficiently used by plants have several functions in the rhizosphere [52; 53; 54]. Apart from increasing iron availability and thereby stimulating plant growth, siderophores can form stable complexes with other metals, such as Al, Cd, Cu, Pb, and Zn, and thus, can enhance their bioavailability. The resulting metal uptake by the plants caused by bacterial siderophores might enhance the effectiveness of the phytoremediation process [55].
## 4 Results and Discussion
### Detailed Analysis of Model Performance  
#### 4.7.1 Two Sensors with Non-Overlapping Data  
In this configuration, the TCN achieves a test accuracy of **0.9757**, outperforming other models. LSTM and GRU also perform well, with test accuracies of **0.9738** and **0.9748**, respectively. Traditional models like RF and SVC achieve lower accuracies of **0.9720** and **0.9726**, respectively, indicating challenges in capturing temporal patterns with limited sensor input.
## 5 Conclusions and Future Work  
This study proposed a task scheduling mechanism for properly prioritizing tasks and assigning them to various VMs before allocating them to PMs. The evaluation and task assignment process in the framework begins with three sets of inputs. All of the cloud-based SPs are included in the first set, along with a list of crucial attributes for assessing SP quality. Furthermore, the second set includes all available tasks and the criteria according to which the tasks should be evaluated by a cloud consumer. The final set  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tendency** & **T** & **SP/Resource** \\ \hline SP-Tendency & 3 & SP4 \\
2 & SP8 \\
13 & SP3 \\ \hline R-Tendency & 7 & R1 \\
1 & R1 \\
15 & R8 \\
12 & R8 \\
10 & R2 \\
9 & R2 \\
14 & R14 \\
5 & R11 \\
16 & R7 \\
6 & R4 \\
4 & R5 \\
11 & R15 \\
11 & R9 \\
11 & R6 \\
11 & R13 \\
11 & R3 \\
8 & R10 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Categorization and assignment of tasks (Experiment 4).  
of inputs includes all available cloud resources as well as the attributes used to assess them. Moreover, the proposed framework adopts an MCDM technique to prioritize different preferences and provide a prioritized list of SPs, tasks, and resources. Different MCDM techniques can be adopted in the presented framework. For example, in Experiments 1 and 2, the AHP was adopted as an MCDM, while the ANP and BWM were adopted in Experiments 3 and 4, respectively. The AHP allows decision makers to use a top-to-bottom structure, while the ANP structure depends on a network consisting of several nodes while considering elements' interdependencies. On the other hand, the BWM structure depends on identifying the most-desired criterion and the least-desired criterion and then making a pairwise comparison with respect to these two criteria with no need to compare all of the model's items. This reduces the time needed to evaluate each SP, resource, and task when using the BWM. The primary objective of having separately prioritized SPs and prioritized resources is to make it possible to execute tasks in accordance with their tendencies.  
Future work can be carried out to extend the current model so that it includes various types of inputs. For example, a broker can accommodate historical data about the performance and other criteria of SPs and their resources to give a realistic evaluation and prioritization of all available SPs. Furthermore, the model can utilize/combine other MCDM methods to expedite the task scheduling mechanism. Finally, the model should be supplemented with a robust mechanism that assures fairness when assigning tasks, and validating this supplement with real data is important.  
Not applicable. Not applicable.  
Data availability StatementData are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
MCDM Multi-Criteria Decision Making  
AHP Analytic Hierarchy Process  
ANP Analytic Network Process  
BWM Best Worst Method  
CSP Cloud Service Provider  
VM Virtual Machine
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 2 Background of Technologies behind SDVC
### Vehicle-to-Everything Communication  
The Third Generation Partnership Project (3 GPP) through mobile broadband standardization has published LTE-V2X technology as a new standard for deploying V2X applications [7]. The proposed LTE-V2X technology is a derivative of the cellular uplink technology that maintains similarity with the current LTE systems. In fact, the LTE-V2X includes two working modes: cellular communication (LTE-Uu) and direct communication (PC5) [23]. The LTE-Uu connection uses the traditional licensed  
Figure 1: High level of cloud-based vehicular application network.  
cellular network to implement Vehicle-to-Vehicle (V2V) and vehicle-to-pedestrian (V2P) communication by forwarding (as shown in Figure 2). The LTE-Uu uploads V2X messages to the application server through the stationary User Equipment (UE)-type RSU. Moreover, PC5 enables direct communication between two vehicles through a Device-to-Device (D2D) protocol. In fact, the D2D communication protocol maintains direct communication between nearby UE-capable devices. Currently, D2D protocol enables direct communication for up to 1000 devices in proximity range of 500 m. Thus, D2D devices operate in peer-to-peer network topology mode [23]. In fact, the RSUs forward V2X messages in the target area by establishing direct communication between vehicles over PC5 (as shown in Figure 2). Therefore, the advantages of D2D are the technical aspects of direct device-to-device discovery and battery life efficiency inter-operable with each device [23].
## 1 Introduction
GMSCs offer other advantages: they are easy to isolate without requiring invasive procedures, proliferate fast and homogenously, maintaining stable morphology, normal karyotype and properties in time [29]. However, another advantage in using MSCs for bone engineering is linked to their capacity to release different factors, including growth factors, soluble proteins and free nucleic acids [30], specifically, the conditioned medium (CM) contains different growth factors secreted by MSCs, possibly very useful for regenerative purposes, as recently shown [31] with special reference to bone regeneration [32; 33; 34; 35]. In addition, the extracellular vesicles (EVs) released by MSCs, in particular the exosomes, were proved to offer great potential in regenerative medicine [36; 37], including a positive regulation of osteoblastic differentiation [38]. Indeed, several studies reported the MSC-derived EV capacity to increase both bone-defect healing and osteochondral regeneration [39; 40]. Moreover, in recent studies, we also tested polyethylenimine (PEI)-engineered EVs. PEI is a synthetic polymer that has been shown to possess low toxicity and high biological activity, forming non-convalent complexes with the DNA, which can be efficiently taken up by cells through endocytosis [41]. Moreover, PEI is able to induce the intracellular release of PEI-nucleic acid complexes from endosomes. Thus, PEI-EVs, obtained by non-covalently coating vesicles with PEI, were demonstrated to be internalized at the cytoplasmic level with higher efficiency when compared to EVs alone [16; 20] probably by promoting the release of EV content into cells, and to favor fracture healing. Indeed, PEI behaves as a proton-sponge thus activating osmotic swelling that causes a burst of endosomes without the need for an additional endosymolytic agent. The aim of this study was the investigation of the bone density distribution in-vivo, on rat calvaria defects, grafted with two alternative constructs: (a) COL scaffold seeded with human PDLSCs (hPDLSCs) and hPDLSCs-derived CM or hPDLSCs-derived EVs or hPDLSCs-derived PEI-EVs; (b) PLA scaffold enriched with human GMSCs (hGMSCs) and hGMSCs-derived CM or hGMSCs-derived EVs or hGMSCs-derived PEI-EVs. We investigated whether the enrichment of the defect site with CM, EVs and PEI-EVs could substantially modify the bone remodeling kinetics during the defect healing, as these products were reported to favor bone regeneration. In particular, we focused the study, performed by synchrotron radiation-based high-resolution tomography, on the analysis of bone mass density distribution: this investigation, to our knowledge, has never been performed in these constructs.
## 2 Background and Related Work  
The following sections describe the state of the art in three primary components of the methodology, but all are aimed at a single objective--GNSS shadow prediction. An innovative methodology with alternatives to all three components is proposed in this paper and therefore the structure of the discussion must focus on three things required to accurately predict GNSS shadows : information on the obstructions in the area, a method for calculating satellite locations and a method for assessing whether the satellite signal has been obstructed.
# Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
## 4 Selection of Collapsibility Evaluation Indicators  
Previous collapsibility evaluations of loess suggest that the selection of evaluation indicators should be based on factors that affect collapsibility [25, 32, 47]. According to previous studies, the factors most closely related to the collapsibility of aeolian sand are the water content and the density [40, 48]. The related physical parameters are the water content, the saturation, the dry density, and the void ratio. Among them, the water content affects the thickness of the bound water film between particles [39] and the size of the  
Figure 8: Particle size gradation curve of soil samples in each test pit.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Pit** & **Density** & **Water Content** & **Specific** & **Dry Density** & **Maximum Dry** & **Minimum Dry** \\
**Number** & **(g/cm\({}^{3}\))** & **(\%)** & **Gravity** & **(g/cm\({}^{3}\))** & **Density (g/cm\({}^{3}\))** & **Density (g/cm\({}^{3}\))** \\ \hline JSZH 1-1 & 1.567 & 3.4 & 2.65 & 1.51 & 1.73 & 1.38 \\ JSZH 1-2 & 1.572 & 4.0 & 2.66 & 1.51 & 1.72 & 1.38 \\ JSZH 2-1 & 1.600 & 3.9 & 2.66 & 1.54 & 1.77 & 1.37 \\ JSZH 2-2 & 1.614 & 4.0 & 2.64 & 1.55 & 1.78 & 1.37 \\ JSZH 3-1 & 1.621 & 3.4 & 2.66 & 1.57 & 1.74 & 1.39 \\ JSZH 3-2 & 1.619 & 3.3 & 2.64 & 1.57 & 1.72 & 1.39 \\ JSZH 4-1 & 1.587 & 4.5 & 2.62 & 1.52 & 1.77 & 1.38 \\ JSZH 5-1 & 1.588 & 4.3 & 2.67 & 1.52 & 1.79 & 1.39 \\ JSZH 5-2 & 1.601 & 4.2 & 2.65 & 1.54 & 1.80 & 1.39 \\ JSZH 5-3 & 1.649 & 4.4 & 2.65 & 1.58 & 1.81 & 1.39 \\ JSZH 6-1 & 1.660 & 5.4 & 2.68 & 1.57 & 1.71 & 1.39 \\ JSZH 6-2 & 1.660 & 5.3 & 2.66 & 1.58 & 1.71 & 1.39 \\ JSZH 6-3 & 1.670 & 5.3 & 2.64 & 1.59 & 1.71 & 1.39 \\ JSZH 6-4 & 1.670 & 5.1 & 2.64 & 1.59 & 1.72 & 1.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the indoor physical property tests.  
matrix suction [43]. This indicates the degree to which the pores are filled with water. The dry density reflects the compactness of the soil [48]. The void ratio represents the relative content of pores in the soil and is related to the collapse space of the aeolian sand [38,39]. Thus, we tried to use four physical parameters, the water content, dry density, void ratio, and saturation, as collapsibility evaluation indicators.
## 1 Introduction
Each sensor node within the network can be outfitted with an RFID reader, allowing them to effectively detect the assets carrying these RFID tags. Subsequently, the gathered information can be transmitted from the base station to IoT devices connected to the internet for more in-depth analysis. Specifically, this work addresses and mitigates issues associated with cyber-poaching [32], such as resourceful smart hunters equipped with advanced internet-related devices, capable of detecting incoming signals, who may attempt to locate the source of these incoming signals by using a backtracking method that commences at the base station (BS). Another common significant challenge that this work tries to handle is related to the potential degradation of network lifetime seen in existing SLP techniques. Wireless sensor networks typically rely on battery power, and the introduction of privacy measures can lead to increased energy consumption. Striking a delicate balance between the imperative for source location privacy and the critical necessity of prolonging the network's operational lifespan proves to be a challenging task, particularly within resource-constrained environments. The proposed protocol (SLP-E) is designed carefully by taking into account various tactics that can be employed by a backtracking passive attacker who tries to uncover the origin of data packets and by trying to balance the energy consumption used by each node in the network with the aim of enhancing NLT. Generally, SLP techniques can be implemented in real-world scenarios for wireless sensor networks; however, they can present several key challenges. These include the complexity of key management in large dynamic networks [33], the delicate balance required between maintaining location accuracy essential for WSN applications and preserving source privacy [34], the added complexity of dynamic network topologies due to sensor mobility and potential failures [24], etc. Addressing these challenges necessitates advanced solutions that can effectively harmonize data accuracy, security, and adaptability. In SLP-E, there are different assumptions made and potential scopes considered. The comprehensive information about the considered assumptions is given in the network model and attacker (hunter) models that have been considered in this study (see Section 3), and the details of the proposed protocol are given in Section 4.
# Design and Research of Photonic Reservoir Computing for Optical Channel Equalization
## 3 Results and Discussion
### Parameter Optimization of Reservoir Photonic Devices
Therefore, we only discuss cases where the waveguide delay coefficient _rid_ falls within the range of 0.1 to 1.0, incremented by 0.1. For a fair comparison, the \(R_{dc}\) of DCs in all reservoir layouts is set to 0.5. Processing errors in long waveguides can introduce random phases to the optical signals transmitted through them. For a fair comparison, we average the BER values of the equalized signals generated from 50 sets of random phases. The equalization results are shown in Figure 4b. The BER of all four reservoir layouts with varying node counts exhibits an increasing trend as _rid_ increases. Furthermore, none of the layouts demonstrate equalization effectiveness when _rid_ exceeds 0.3. Except for the 12-node layout, the performance of the other layouts with different node counts is highly consistent across the various _rid_ values, with the curves appearing nearly overlapping. This indicates that the equalization performance of the 24-node, 36-node, and 48-node layouts is relatively uniform across different delay line lengths. The 12-node layout achieves the best equalization performance under conditions where _rid_ ranges from 0.2 to 1.0. This is attributed to the fewer nodes, resulting in better output power compared to layouts with more nodes. Although the 12-node layout yields better BER results, they remain within the same order of magnitude. The 24-node layout achieves the best result of BER = 3.073 \(\times\) 10\({}^{-4}\) at _rid_ = 0.1, which is one order of magnitude lower than the BER of the distorted signal. The characteristics of the 36-node and 48-node layouts are very similar, both losing their equalization capability when _rid_ > 0.2. The results indicate that, for the same equalization task, reservoir layouts with different node counts converge towards a preference for shorter waveguide delay lines. The optimal result for the waveguide delay line length coefficient is 0.1. Figure 4: The equalization effect of PhRC is analyzed considering (**a**) various DC splitting ratio coefficients and (**b**) different waveguide length coefficients.
## 2 Load Profile Development
### Harmonic Distortion Evaluation
The assessments, which were performed on seventy-four different 13.2 kV feeders, led to the identification of four substation power transformers that serve feeders possessing low (<3%), medium (3-5%), and high (>5%) levels of voltage total harmonic distortion (THDV). Moving forward these transformers are referred to as XFMR 1, XFMR 2, XFMR 3, and XFMR 4. Because transformer aging is a function of current harmonic distortion, the harmonic current spectrum of the load on each substation transformer was monitored for ten consecutive days. The results indicate that even at today's low levels of DER penetration, current total harmonic distortion (THD\({}_{\text{I}}\)) at substation transformers can fluctuate between 3 and 9% during peak load conditions even if THD\({}_{\text{V}}\) is below the IEEE Std. 519 limit of 5% [15]. These results suggest that increased penetrations of PV, ES applications, and EV charging may easily push the average THD\({}_{\text{I}}\) above 10%. Tables 1 and 2 list the results of the substation transformer monitoring analysis. Figure 4 illustrates the harmonic spectrum of XFMR 4. For the capacity limit analysis of the 50 MVA SPX transformer, the harmonic distortion was varied throughout the 24 h period to mimic the typical distortion experienced by the evaluated units. The distortion consisted of the 3 rd, 5 th, and 7 th harmonic frequencies and matched the harmonic composite illustrated on XFMR 4. As THD\({}_{\text{I}}\) is increased between 0 and 35%, the contributions of the 3 rd, 5 th, and 7 th harmonics were increased equally to maintain the percent contributions. For example, at 4 am the contribution from the 3 rd harmonic distortion is always 3.75 times greater than the contribution from the 5 th harmonic distortion.
## 2 Islanding Detection Methods
### Hybrid Methods
#### 2.3.1 Voltage Unbalance and Frequency Set-Point Method  
Voltage imbalance is used as a passive approach and positive feedback as an active method in the design of this method. It is common practice to monitor the inverter's output voltages in order to gauge voltage unbalance. Each time an event occurs, DGs are subjected to disturbances, and the voltage unbalance increases. Islanding will be recognized if this jump exceeds the set values. Using this technique, it is possible to distinguish between load-switching events and islanding circumstances [29].
## 2 Literature Review: Context and Theoretical Framework
### COVID-19 Pandemic Context in Tecnologico De Monterrey and the Hyperflex Model  
Confinement by COVID-19 began suddenly on 16 March 2020. The author's institution suspended classes for one week to prepare the distance learning strategy, probably the fastest reaction in the country and due to its experience using the necessary tools to deliver classes in a hybrid format. Other Higher Education institutions delayed the academic continuity for two weeks or even months. Nevertheless, the Mexican response was recognized as notable in the Latin American region [2]. As a result, that semester period finished on time. Summer courses and the subsequent semester were offered under the same model, called the Hyperflex model or Digital Flexible Model (DFM) [24]. This hybrid model used video-classes through the Zoom video-conference system and allowed some face-to-face interactions when local measures dictated by the National Health Council permitted this.  
ML and the mobile revolution have been discussed and highlighted widely during the last decade [25]. Nevertheless, new aspects and considerations must be considered when an extended contingency experience is sustained by teachers and students who are unfamiliar with a full approach. However, some courses had already been delivered online under a similar model for a small number of students and faculty (as an example [26]). It has been remarked that the social behavior under the COVID-19 learning experience should also be considered in the success [27]. This experience involved the fear of the COVID-19 crisis, the depressive episodes due to the confinement, changes in the family dynamics, the accessibility of services (computer, electricity, and internet) [2], and the extended time attending or delivering online courses, generating a reshaping of the ML approach [28]. One of the most challenging issues was the video-class: the media used, the materials and content generated that had to be developed suddenly, changed the Online Learning (OL) environment for students and teachers imperatively, forever reshaping the Education perspective, as remarked in [29].  
The DFM considered in its design the appropriate tools for each course to be provided to each teacher (as recommended by [30]). These included a personal Zoom account for video-conferencing and software, tablets, communication apps for collaboration, and others commonly integrated with the Canvas LMS. In the learning block depicted in our study, classes were broadcast on the original timetable of the face-to-face courses. However, they had a didactic design more flexible in terms of attendance (broadcasted classes were recorded) and evaluation (differentiated by an extended bank of exams), which remained open for students several days and allowed a couple of attempts.