esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 6 Conclusions  
In this study, we employed five meta-heuristic approaches to obtain optimal paths for the IoD, conducting extensive simulations and analyzing the results for comparison. Our results showed that IPSO could generate smoother paths with minimum energy consumption, yielding best, worst, mean, and standard deviation values. These results signify a notable advancement in the operational efficiency and sustainability of UAV missions, highlighting the potential of IPSO in complex path-planning scenarios. Furthermore, theexploration of the straight line rate and relative percentage deviation provided crucial insights into the path length and energy consumption optimization capabilities of these algorithms. Despite close competition between IPSO and PSO in certain scenarios, our rigorous statistical analysis, encompassing non-parametric tests and ANOVA, affirmed IPSO's dominance, particularly in the overall IoD formation performance. This underlines the algorithm's robustness in maintaining high stability within the IoD formations. Finally, the execution time for each algorithm was evaluated and compared. The results indicated that PSO was more time-efficient in generating the IoD paths compared to the others, whereas GA took a longer time to accomplish the same task.  
In future research, we plan to enhance IPSO with adaptive learning mechanisms tailored for dynamic environments. Additionally, we aim to explore hybrid algorithms that strike a balance between path optimization and execution speed, as well as apply these algorithms in 3D path planning and real-world scenarios. These endeavors will further advance UAV navigation efficiency within the IoD context.  
Conceptualization, G.A. and T.S.; methodology and Software, M.G. and M.H.; writing--original draft preparation, G.A, T.S. and A.M. formal analysis, writing--review and editing A.Y.; investigation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Project Number INML2104 under the interdisciplinary 619 center of smart mobility and logistics at King Fahd University of Petroleum and Minerals.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to acknowledge the support of the Computer Engineering Department at King Fahd University of Petroleum and Minerals for the support of this work.  
The authors declare no conflict of interest.
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 2 Materials and Methods  
From the several methods proposed in the literature for pest detection, an acoustic-based detection method was chosen to be implemented on a wireless sensor. This type of solution was preferred because it did not require illumination conditions (_Lobesia botrana_ is known for its repuscular behavior [34]), image processing or the sending of a large amount of data to a server in order for the server to determine if there was detection or not. The acoustic detection proposed for this sensor was via the previous characterization of the flying acoustic spectral signature of the moth to generate a filter that matched this signature and to recognize if there was a flying _Lobesia botrana_ near to the sensor.  
The acoustic signal of flying insects has been a topic of several studies [35; 36]. Flying insects have characteristic sounds in the spectrum, most commonly described by a low frequency sound produced by the wing fluttering and a higher frequency sound produced by the contact of wings during the fluttering [37; 38]. These sounds are then specifically matched to each species in that the sound frequency, harmonic behavior and temporal duration depend on the physical characteristic of the flying insect (i.e., wing size, insect size). Nevertheless, this moth has not been previously characterized in the literature in terms of its acoustic signature. As the possible acoustic signature was unknown for the authors, it was necessary to obtain the sounds produced or emitted by the moth in a wide range spectrum.  
The proposed sensing method consisted of generating a specific filter to obtain the signal coming from the _Lobesia botrana_ moth. To characterize the filter band, many experiments were performed in a controlled laboratory environment, recording audio signals with different quantities of moths and comparing the resulting measured signals with a case without moths on the system as the control. Finally, temporal and spectral analyses were performed in order to obtain the acoustic signature.
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 3 Calibration of GNSS-R Land Reflections
### Distinguishing Coherent and Incoherent Returns  
It is widely known from surface reflection and scattering theory that the surface properties determine the physical mechanism of electromagnetic radiance scattered from the surface toward the instrument [11; 20]. Generally, if the surface is smooth with respect to the incident radiation (in this case the GPS L1 C/A code signal, at approximately 19 cm wavelength) the result will be a coherent reflection. Conversely, relatively rough surfaces with respect to the EM wavelength will result in diffuse scattering toward the instrument. Each of these cases result in distinctly different equations to estimate the surface parameter of interest: for coherent reflection the surface reflectivity, for diffuse scattering the NBRCS.  
Considering this duality of possible physical reflection/scattering mechanisms, for land GNSS-R observations it is advisable to calculate both the NBRCS and the surface reflectivity for every CYGNSS DDM. To assist the user in determining which is most appropriate for a given DDM, a flag for identifying the presence of coherence was developed. This flag is based on assessment of the degree of \"power spreading\" in a DDM, and is described in detail in [13]. It is acknowledged that this flag retains some uncertainty, motivating the reporting of both the NBRCS and reflectivity for all land observations.
## 3 Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Time Component**} & \multirow{2}{*}{**Level**} & \multicolumn{2}{c}{**Topology Processor**} \\ \cline{3-4}  & & **H-TNTP-PMU** & **SOTA TNTP** \\ \hline Sampling Delay (s) & - & 0.017 \(\pm\) 0.009 & 1.062 \(\pm\) 0.588 \\ \hline \multirow{3}{*}{Topology (\(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro s}\))} & Substation & 7.122 \(\pm\) 0.828 & - \\ \cline{2-4}  & Area & 9.514 \(\pm\) 0.783 & - \\ \cline{2-4}  & Network & 11.365 \(\pm\) 0.658 & 1.054 \(\pm\) 0.0274 \\ \hline \multirow{3}{*}{LSE (\(\mathrm{\SIUnitSymbolMicro s}\))} & Substation & 2.172 \(\pm\) 0.164 \\ \cline{2-4}  & Area & 82.776 \(\pm\) 3.857 \\ \cline{1-1} \cline{2-4}  & Network & 141.781 \(\pm\) 2.428 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computational time estimated for D-LSE at the substation level, area level, and network level for modified two-area four-machine power system model under steady state. Figure 14: (**a**) The PMU and SMRS sampling timeline. (**b**) The timeline for estimation utilizing H-TNTP-PMU as the topology processor. (**c**) The timeline for estimation utilizing SOTA TNTP as the topology processor.
# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 4 Results and Discussion
### Limitations and Future Directions  
The main limitations of the proposed method lie in data availability and scalability. Although the method is flexible to data availability that both (long) time series or bi-temporal data can be used, a longer time series produces more unbiased coherent-area estimation and subsequently achieves a better CPT. For satellite missions with irregular observation scenarios such as ALOS-2/PASAR-2 and TerraSAR-X, it can be hard to achieve a long time series of images with consistent acquisition parameters. Nevertheless, this does not mean that the method will fail to succeed with fewer multi-temporal data, as shown in the Joso case of this study. Promising results are achieved with less than 5 coherence sequence. This could benefit from the high spatial resolution of ALOS-2/PASAR-2 data (e.g., 3 m) so that the coherence of built-up areas can be estimated with more pure pixels thus showing low temporal variation. For data with a lower spatial resolution such as Sentinel-1 data (e.g., 20 m), a longer data sequence is preferable. Data acquisition is going to be a less crucial problem with the evolution of SAR missions. Satellite constellations such as the Sentinel-1 mission and the recently launched RADARSAT Constellation Mission (RCM) [61] with high temporal resolution can provide a long and dense observation sequence of an area of interest, and the upcoming missions such Tandem-L [62] and NASA-ISRO SAR (NISAR) [63] will increase the observation frequency of spaceborne SAR systems at the global scale. The computation bottleneck of the method is the GMM with the EM algorithm. The best-case run time for the EM algorithm is (_KNd_) at each iteration, where is the cluster number, \(N\) is the data point number and \(d\) is the data dimension. Therefore, it is challenging to scale for a massive dataset with high-dimension. One possible way to deal with the scalability problem is to leverage the advanced data summarization techniques such as the coreset-based GMM [64; 65], which guarantees that models fitting the \(M(M\ll N)\) coresets (weighted subsets of the original data) will also provide a good fit for the original dataset. Besides, the quality of SAR image segmentation also affects the calculation of CPT. Compared to the standard GMM used for segmentation in this paper,  
Figure 7: ALOS-2/PASAR-2 \(\sigma^{\circ}\) and \(\gamma\) temporal variation for several typical landscapes within the Joso study area: (**a**) \(\sigma^{\circ}\) temporal trends; (**b**) \(\gamma\) temporal trends.  
the spatial constraint GMM [66] which imposes the contextual information in the mixing coefficient can achieve a better segmentation quality.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 4 Design of Joint Positioning Algorithm
### Complexity Analysis  
Based on the above description, it can be established that the main computation complexity is from IFFT operation, peak search and eigenvalue decomposition. For comparison, Let \(\Xi_{\text{TDOA}}\), \(\Xi_{\text{AoA}}\) and \(\Xi_{\text{joint}}\) be the signal processing complexity at each TRP under UL-TDOA, UL-AoA, and proposed joint UL-AoA+TDOA; the corresponding expression can be modeled as  
\[\left\{\begin{array}{l}\Xi_{\text{TDOA}}=\mathcal{O}(N_{\text{R}}N_{\text{FFT }}),\\ \Xi_{\text{AoA}}=\mathcal{O}(N_{\text{R}}N_{\text{FFT}})+\mathcal{O}\big{(}N_{ \text{R}}^{3}\big{)}+\mathcal{O}\big{(}N_{\theta}N_{\varphi}\big{)},\\ \Xi_{\text{joint}}=\mathcal{O}(2N_{\text{R}}N_{\text{FFT}})+\mathcal{O}\big{(}N_ {\text{R}}^{3}\big{)}+\mathcal{O}\big{(}N_{\theta}N_{\varphi}\big{)},\end{array}\right. \tag{15}\]  
where \(N_{\theta}\) and \(N_{\varphi}\) is the quantitative space of each \(\theta_{i}\) and \(\varphi_{i}\). As shown in Figure 5, \(\theta_{i}\) ranges over \(\{-\pi,\pi\}\), and \(\varphi_{i}\) is within \(\{-\pi/2,\pi/2\}\). It should be noted that a common complexity introduced by \(N_{\text{symb}}^{\text{SERS}}\)\(N_{\text{RE}}^{\text{SERS}}\) is ignored in the above expression, since the basic signal processing is required under each algorithm. In Section 5.2, we also quantified the time complexity.
# Virtual Zirconia Implants Replace Titanium Implants?
## 8 Conclusions  
To conclude, answering the question posed in the title of this review, it is unlikely that zirconia implants will replace the titanium implants' use entirely owing to the high success rates, long-term stability, and satisfactory osseointegration of the titanium implants found on the market nowadays. However, based on the literature, it seems like zirconia implants are reliable substitutes for cases in which titanium is not an option or in cases where aesthetics is in high demand. Moreover, it is essential for the clinician to be aware of the advantages and drawbacks of this implant option to make the best decision for the treatment of the edentulous area. Furthermore, possibly, in the future, zirconia implants can be the first choice for a patient with a history of periodontitis/peri-implantitis, but more studies with longer follow-up are necessary to prove that zirconia has better performance against bacterial challenges.  
Based on the findings of this review, it is possible to conclude that  
* Titanium implants are still the standard material for the replacement of a missing tooth;
* Titanium particles are potentially harmful to the peri-implant tissue; however, more studies are necessary for definitive conclusions;
* Osteointegration of zirconia and titanium implants are similar and are influenced by microstructure and the treatment of the surface;
* Some clinical studies show better soft tissue response with zirconia implants, suggesting that this material can provide a protective effect against inflammation;
* Some studies suggest that zirconia implant seems to be less susceptive to peri-implantitis than titanium implants;
* There is a lack of studies with a long-term follow up of zirconia implants.  
[PERSON], [PERSON] and [PERSON] contributed to the conception, acquisition, and interpretation of the review data. [PERSON] was responsible for initial draft preparation. [PERSON] and [PERSON] were responsible for the final review and supervision. 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 conflict of interest.
# Multi-AGV Scheduling under Limited Buffer Capacity and Battery Charging Using Simulation Techniques
## 3 Multi-AGV Scheduling Rules for Practical Shopf floors
### AGV Scheduling Rule  
AGV scheduling involves a comprehensive set of decision-making mechanisms that determine the assignment of jobs to AGVs, the selection of AGVs for specific transport tasks, and the utilization of charging stations. Figure 2 illustrates the overall procedure of multi-AGV scheduling in this paper, which utilizes two kinds of decision rules: the job selection rule and the AGV selection rule. Let \(job_{k}\) denote a transportation job associated with a single transportation batch to be moved from one of the production lines to an arbitrary drop-off location. The job selection rules are used to choose one of the existing transportation jobs, considering factors such as job urgency, priority, pickup location, and drop-off location. The chosen transportation job is assigned to an AGV chosen by applying AGV selection rules. In other words, AGV selection rules are used to choose one of the available AGVs, and factors such as battery level and the current location of AGVs can be considered in identifying the best AGV for a specific job. Note that an AGV is available for a new transportation job if and only if it does not satisfy any of the following sub-conditions:  
Sub-condition 1 for an unavailable AGV: The AGV is carrying a previous transportation batch.  
Sub-condition 2 for an unavailable AGV: The AGV is empty, and it is heading to a charging station since it satisfies the start-charge condition after unloading the previous transportation batch.  
Sub-condition 3 for an unavailable AGV: The AGV is empty and staying at a charging station; however, it does not satisfy the stop-charge condition yet.  
Sometimes, a transportation job must wait for an available AGV when all AGVs are unavailable, which can cause a long waiting time for the job and poor service in the multi-AGV system. Thus, job selection and AGV selection rules should be carefully  
Figure 2: Multi-AGV scheduling procedure.  
designed. Examples of the job selection rules and the AGV selection rules are listed in Tables 2 and 3, respectively.
## 3 Results and Discussion
### Selecting the Configuration of ANNs  
According to the literature, the dataset was divided into testing and training portions, comprising 15% and 85% of the entire experimental dataset, respectively; this is done to confirm the consistency \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Transformation**} & \multicolumn{5}{c}{**Pearson’s Coefficient**} & **AAPC** \\ \hline
**Function** & **Number** & **Input1** & **Input2** & **Input3** & **Input4** & **Input5** & **Input6** & \\ \hline  
xp(Output) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\
26 & Log(Output) & \(-\)0.7178 & 0.3671 & 0.5563 & \(-\)0.2200 & \(-\)0.1397 & 0.2387 & 0.3733 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: [PERSON]’s coefficient values for different transformations. of expected models. Determination of weights and topology of a network is called the optimization process, and results in the optimum function when specific performance equations are utilized. The determination of the number of optimal neurons contained in the hidden layer is of great importance. The number of inputs, outputs, the architecture of the network, and the algorithm used for training can all affect the optimum number of neurons in a hidden layer. As expressed by [PERSON] and [PERSON] [39], determining the optimum number of hidden neurons is not simple, and cannot be performed without training, and solely by examining different numbers of them to estimate each generalization error. [PERSON] and [PERSON] [40] demonstrated that a single hidden layer having enough number of processing units would have an acceptable performance. As such, we chose a single hidden layer with multiple processing neurons in this study. The trial and error method was used to develop a successful model. The dominant statistical parameters, such as the average absolute relative error, correlation coefficient, and root mean squared errors, were examined for all of the topologies. This provided the opportunity for careful inspection and visualization of the cross-validation or generalization error of each network design. As mentioned before, the optimization of the MLP was done by changing the neuron content of the hidden layer while the statistical indexes were monitored. The minimum number of data needed for training is 2-11 times of weight and bias. Hence, for an MLP that has one dependent and six independent variables, the number of hidden neurons were computed as follows:  
\[3\times(8N+1)\ \leq\ 279\ (training)\ N\ \leq\ 11 \tag{7}\]  
The best outcomes of the MLP network for the number of hidden neurons are presented in Table 4.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 2 Overview of RPL
### DODAG Construction for Multipoint-to-Point Traffic Flows  
RPL constructs a topology as a directed acyclic graph (DAG) which has one or more roots. In RPL, a DAG root is a node within the DAG that has no outgoing edge, as shown in Figure 1. The DAG can be partitioned into one or more destination-oriented DAGs (DODAGs), with one DODAG per sink. A DODAG root may act as a border router for a DODAG, may aggregate routes in the DODAG, and may redistribute DODAG routes into other routing protocols. Since RPL was designed for lossy network environments, each node in a DODAG maintains a set of candidate parents for fault tolerance purposes. From the set, each node selects one or multiple preferred parents as the primary relay node(s) for packet delivery.  
Each node in the DODAG is assigned a rank. The rank of a node indicates the node's individual position relative to other nodes with respect to a DODAG root. The rank strictly increases in the Down direction, (i.e., from DODAG roots toward the leaf nodes) and decreases in the Up direction. The rank is used to avoid and detect loops created due to topology changes. The rank is not a path cost, but can be derived from path metrics based on the DAG's objective function (OF).  
DODAG construction begins with the root node broadcasting a DAG information object (DIO) message, which includes RPLInstanceID, the DODAG identifier, a monotonically increased version number, rank, and other fields. The nodes closest to the root will first hear this message, and decide whether to join this DODAG. If they want to join, they compute their own rank values independently, and transmit DIO messages containing their own rank values to their neighbors. If a node learns that it has already received a copy of the DIO message, it remains silent. Through DIO messages, RPL nodes select and maintain upward routes toward the DODAG root according to an objective function defining how to select and optimize routes. Note that a DODAG is constructed to optimize performance for multipoint-to-point traffic.  
A node transmits a DIO message using a Trickle timer (RFC6206) [10]. The Trickle algorithm quickly propagates updated information when inconsistency is detected among nodes, whereas it slows the sending rate down exponentially when nodes agree and therefore information is not changed. Through this adaptive transmission interval adjustment and the suppression mechanism, the Trickle algorithm achieves scalability and energy efficiency.