esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions and Future Work  
This work presented the use of Genetic Algorithms (GAs), Particle Swarm Optimization, and the Crow Search Algorithm (CSA) to adjust the parameters of the Proportional Integral-Derivative (PID) controller to control the movements of a quadcopter. The goal was a reliable scheme to control the movements and direction of a quadcopter. We adopted a multi-objective fitness function for GA, PSO and CSA to optimize the parameters of the PID controller by controlling the quadcopter's flight. The performance of these algorithms was studied in detail with respect to time and accuracy. Convergence curves were presented to demonstrate the ability of this algorithm to provide an accurate controlling process. A fitness function was used to evaluate the performance of the PID controller to control the movements of a quadcopter. Superb results and credible flight orientation of the quadcopter were obtained through the use of GA, PSO and the traditional Ziegler-Nichols (ZN) method. The gain of the developed PID controller was tested through a closed-loop step response system. Further study is needed to assess the suitability of this method to control a quadcopter in a range of directions. It would be beneficial to perform a comparison of alternative meta-heuristic algorithms to control the PID of a quadcopter. Further work is needed to generalize the proposed controlling method based on meta-heuristics to tune the parameters of the PID controller for different types of aerial vehicles.  
Conceptualization, A.S., D.R.M., M.B. and S.A.; Methodology, A.S., M.B., A.M. and D.R.M.; Data curation, A.S., D.R.M., S.A. and H.T.; implementation and experimental work, A.S., M.B., D.R.M. and H.T.; Validation, A.S., M.B., H.T. and D.R.M.; Writing original draft preparation, A.S., M.B., D.R.M., A.M. and H.T.; Writing review and editing, A.S., M.B., A.M. and S.A.; Proofreading, A.S. and A.M.; Supervision, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
[PERSON] acknowledge that this work was supported by Taif University Researchers Supporting Project Number (TURSP-2020/73), Taif University, Taif, Saudi Arabia.  
Conflicts of Interest: The authors declare that there is no conflict of interest regarding the publication of this paper.
## 2 Materials and Methods
### Methods
#### 2.3.3 Trends and Relationship Classification  
To check the validity, a significance test is required. The F-test was used to test for the significance of the linear trend and the correlation coefficient between vegetation and climatic factors. More specifically, changing trends were divided into five categories: Extremely significant increase (ESI), significant increase (SI), extremely significant decrease (ESD), significant decrease (SD), and insignificant change (IS), according to the slope and significance level of the F-test. A changing trend is considered to be an ESI if slope \(>\) 0 and \(p\)\(\leq\) 0.01, SI if slope \(>\) 0 and 0.01 \(<\)\(p\)\(\leq\) 0.05, ESD if slope \(<\) 0 and 0.01 \(<\)\(p\)\(\leq\) 0.05, and an IS if slope = 0 or \(p\)\(>\) 0.05. The relationships were then divided into five categories (extremely significant positive correlation (ESPC), significant positive correlation (SPC), extremely significant negative correlation (ESNC), significant negative correlation (SNC), and insignificant correlation (ISC)) according to the correlation coefficient and significance level of the F-test. It is considered an ESPC if the correlation coefficient \(>0\) and \(p\leq 0.01\), SPC if the correlation coefficient \(>0\) and \(0.01<p\leq 0.05\), ESNC if the correlation coefficient \(<0\) and \(p\leq 0.01\), SNC if the correlation coefficient \(<\)0 and \(0.01<p\leq 0.05\), and ISC if the correlation coefficient \(=0\) or \(p>0.05\).
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation
### GNU Radio Flowgraphs  
In addition to the original GNU Radio blocks, other blocks from different Out of Tree (OOT) modules are used to implement the flowgraphs. The following OOTs are used in the design and testing: gr-APRS, gr-ax.25, gr-bruninga, gr-limesdr, gr-osmosdr, gr-satellite, and gr-satnogs. Another simple OOT has been created to perform comparison tasks. Additionally, we have attempted to control the noise and the flowchart parameters during runtime using the XML-RPC protocol.
## 5 Discussion of the Results
### The Buckling Coefficient \(K_{u}\)  
#### 5.3.1 Hole Position Varying Along \(x\)-Axis  
Figure 16a\(\sim\)d show the change in \(K_{u}\) as hole position parameter \(e_{x}/b\) is enlarged with the BCs of 4S, for different \(d/b\) from 0 to 0.7 and different plate aspect ratios \(\beta\) from 1 to 4. It can be seen that hole position parameter \(e_{x}/b\) has a little effect on \(K_{u}\) for \(d/b=0.1\), especially as the plate aspect ratios \(\beta\) enlarged. It also can be seen that all the curves are very similar in trends and values with the same \(d/b\) when \(e_{x}/b\) is less than 0.5. It means that when \(\beta\) is an integer greater than 1 and \(e_{x}/b\) is less than 0.5, the buckling coefficient \(K_{u}\) can be taken as a square plate with the same width \(b\), the same hole \(d/b\), and the same FRP pasting. It also can be seen from the figures that as \(e_{x}/b\) is enlarged from 0 to 0.5, \(K_{u}\) decreases but gradual slow for \(d/b\) is 0.1, and first increases and then decrease for \(d/b\) is 0.2 and 0.3, and drastically increases for \(d/b\) is from 0.4 to 0.7. It means that when \(d/b\) is bigger than 0.4 and \(e_{x}/b\) is less than 0.5, the buckling coefficient \(K_{u}\) is very unstable, so it should be avoided to place big hole too close to the loading edge. For \(e_{x}/b\) is bigger than 0.5, \(K_{u}\) shows little variation with \(d/b\) and \(e_{x}/b\), except when \(\beta=2\) and \(d/b\) is bigger than 0.4. Figure 16b shows that for \(\beta=2\) and \(e_{x}/b=1\), \(K_{u}\) increases from 4.48 to 5.99 as \(d/b\) is enlarged from 0.4 to 0.7, this also proves the necessity of placing the big hole in the middle area of the plate.  
Figure 17a\(\sim\)d show the change in \(K_{u}\) as hole position parameter \(e_{x}/b\) is enlarged with the BCs of 3S1F, for different \(d/b\) from 0 to 0.7 and different plate aspect ratios \(\beta\) from 1 to 4. In general, \(K_{u}\) of plates with BCs of 3S1F is very small, and the curves shows that \(K_{u}\) keeps almost constant irrespective of \(e_{x}/b\) when \(\beta=1\), 2, and \(\beta=3\) (or 4) with \(d/b\) less than 0.3. When \(\beta=3\) (or 4) and\(d/b\geq 0.3\), \(K_{u}\) increases as the hole moves away from the loading edge towards its center along \(x\)-axis. Take \(\beta=3\) and \(d/b=0.7\) for example, as \(e_{x}/b\) is enlarged from 0.375 to 1.5, \(K_{u}\) increases from 0.41 to 0.47. Therefore, it is also recommended to place the big hole close to the middle area of the plate in \(x\)-axis with the BCs of 3S1F when \(\beta\) is bigger than 3.  
Figure 16: \(K_{u}\) vs. \(e_{x}/b\) for perforated rectangular plate (\(\beta=1\), 2, 3, 4) with the BCs of 4S.  
Figure 17: _Cont._
## 4 Layered SDN-Based Optimization Framework
### Stochastic Optimization and Temporal Decomposition  
Since the different layers cannot communicate instantaneously, the parameters of the queues change dynamically underneath. Clearly, the objectives of the optimization must be defined in such a way that they stay constant while the bottom layer changes stochastically from one state to the next. The proposed framework can be seen as a special case of _stochastic gradient descent_ where the network dynamics, via the evolution of the queues, impose the sequence of training sample updates. In particular, the SDN orchestrator operates its optimization at every time instant \(t=mPL\), performing  
\[\begin{split}\max_{\boldsymbol{x}}\sum_{o=1}^{O}-\frac{\Theta_{o} [m]x_{o}}{V}+\frac{1}{P}\sum_{p=0}^{P-1}\mathbb{E}\left\{\mathcal{U}_{o}^{ \star}\left(x_{o};(mP+p)\,L\right)\right\}\\ \text{s.t.}\sum_{o=1}^{O}x_{o}\leq Z,\end{split} \tag{16}\]  
with \(\mathcal{U}_{o}^{\star}\left(x_{o};(mP+p)\,L\right)\) equal to the optimal value of the problem solved at the operator layer below:  
\[\begin{split}\max_{\boldsymbol{y}_{o}}\sum_{g\in\mathcal{G}_{o}} \frac{1}{L}\sum_{\ell=0}^{L-1}\mathbb{E}\left\{\mathcal{U}_{g}^{\star}\left(y _{g};(mP+p)L+\ell\right)\right\}\\ \text{s.t.}\sum_{g\in\mathcal{G}_{o}}y_{g}\leq x_{o},\end{split} \tag{17}\]  
and \(\mathcal{U}_{g}^{\star}\left(y_{g};(mP+p)L+\ell\right)\) being the optimal values of the optimization in (5) for \(t=(mP+p)L+\ell\). The updates derived in (11)-(14) will then be used to update the decisions \(\boldsymbol{x}\) every \(PL\) and the decisions \(\boldsymbol{y}\) every \(L\), as if convergence to the solution of a static problem has been achieved in the time horizonsof length \(PL\) and \(L\), respectively. By introducing \(K_{i}\) as the number of iterations of each update in layer \(i=1,\ldots,4\), respectively, starting from the bottom, we can derive the following relations:  
\[PL \geq\max\left\{K_{4}\left(K_{3}\left(K_{2}K_{1}\tau_{N}^{G}+\tau_{G} ^{O}\right)+\tau_{O}^{S}\right),\;\underline{PL}\right\} \tag{18}\] \[L \geq\max\left\{K_{2}K_{1}\tau_{N}^{G}+\tau_{G}^{O},\;L\right\}, \tag{19}\]  
where \(\tau_{O}^{S}\), \(\tau_{G}^{O}\), and \(\tau_{N}^{G}\), are, respectively, the RTTs between the SDN orchestrator and the operators, between the operators and the GWs, as well as between the eNBs and the GWs (see also Figure 2, where \(\tau_{N}^{G}\) has been normalized to one). The inequalities in (18)-(19) indicate that if we want to act fast, e.g., reduce \(P\) and \(L\) (possibly to the minimum refresh times) we need to perform fewer iterations. Vice versa, if we want to perform more iterations, we must be willing to act slower in updating the decisions \(x\) and \(y\). If we view the static problem as a \"surrogate\" for the dynamic problem (up to the next decision), increasing the number of iterations and delaying future decisions can guarantee a better accuracy for a static scenario; however, the ability of the algorithm to incorporate new dynamic information is compromised. This trade-off creates another optimization issue which is an important future research direction.
# GNSS Applications to Assess Performance in Olympic Sailors: Laser Class
## 5 Conclusions  
This is the first study to analyze the VMG, distance, maneuvers, and time with GNSS devices in Laser class sailors and in a world championship regatta. The results of our study show that speed is the variable that determines the performance of a Laser class sailor on the upwind and downwind courses. Elite sailors have a greater command of the technique in the upwind and downwind courses, since they reach higher speeds on these courses. Elite male sailors are faster on upwind and downwind courses, while women are also faster on broad reach. Elite sailors travel a shorter distance on the upwind course. The maneuvers are not a variable that determines the performance of the sailor in any of the analyzed courses.  
Conceptualization, I.C. and J.C.-C.; methodology, L.P., B.M.; software, I.C., L.P., B.M.; validation, J.C.-C., J.C.-P. and I.C.; formal analysis, L.P. and B.M.; investigation, I.C.; resources, L.P. and B.M.; data curation, I.C.; writing--original draft preparation, I.C.; writing--review and editing, J.C.-C., J.C.-P., L.P. and B.M.; visualization, I.C.; supervision, J.C.-C. and J.C.-P.; project administration, L.P. and B.M.; funding acquisition, L.P. and B.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been supported by The European University of the Seas (SEA-EU) project(Erasmus+, European Universities; Ref 612468-EPP-1-2019-1-ES-EPPKA2-EUR-UNIV).  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to all the data used in this study are publicly accessible, and these are in World-Sailing\({}^{\text{\textregistered}}\) and SAP-Sailing\({}^{\text{\textregistered}}\).  
**Informed Consent Statement:** Patient consent was waived due to all the data used in this study are publicly accessible, and these are in World-Sailing\({}^{\text{\textregistered}}\) and SAP-Sailing\({}^{\text{\textregistered}}\).  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://site-isaf.soticcloud.net/sailors/sailor_search.php?includeref=18856&](https://site-isaf.soticcloud.net/sailors/sailor_search.php?includeref=18856&)]([https://site-isaf.soticcloud.net/sailors/sailor_search.php?includeref=18856&](https://site-isaf.soticcloud.net/sailors/sailor_search.php?includeref=18856&)) sailorid=&sailorsurname=[[https://www.sapsailing.com/gwt/Home.html#StartPlace:nul](https://www.sapsailing.com/gwt/Home.html#StartPlace:nul)]([https://www.sapsailing.com/gwt/Home.html#StartPlace:nul](https://www.sapsailing.com/gwt/Home.html#StartPlace:nul)).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 7 Economic Aspects  
As the applications of the advanced oxidation process seem to be in an early stage in the field of PFAS remediation, to the best of our knowledge, an in-depth analysis regarding the costs of such methodologies has not yet been published.  
State agency studies report an average cost of USD 2.00/gallon for PFAS treatment when considering the combined activity of GAC and AIX (anion exchange resins)) for infrastructure capacities from 2 to 50 million gallons per day (MGD) and with an initial investment from USD 3 to 120 million [145].  
For budgeting PFAS management, the United States Environmental Protection Agency proposed a sequence of several necessary activities in the following order: Baseline monitoring > Public engagement > Regulatory engagement > Bench or pilot testing > Capital investment in full-scale treatment > Operation and maintenance costs of full-scale treatment > Long-term monitoring [146].  
The same institution has a \"Drinking water treatment technology unit cost model\" and emphasizes the need for updating the model to be capable of addressing PFAS issues. The updating appears necessary because of the lack of consensus throughout the world regarding the acceptable levels of the diverse PFASs present in drinking water.  
[PERSON] et al. [147] undertook an economical evaluation on the applications of AOPs for degradation of PFAS substances, taking into consideration the stage of development of the technology, energy consumption, the time required to achieve 90% degradation of the substance, and the total volume of contaminated solution. Table 3 summarizes the results obtained with initial budgeting costs. The reported data show still an advantage in costs related to the use of direct photolysis, with the highest costs attributed to photocatalysis. However, the need to use radiation at a 185 nm wavelength imposes limitations to such applications in terms of lamp durability and safety. In the other hand, investments in photocatalysis research could lead to materials capable of efficiently degrading PFAS compounds with the positive feature of working under near UV-visible light.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 3 System Model
### UAV Application Model  
Our application model divides the disaster area into \(n\) subareas \(\mathbb{A}=\{\mathbb{A}_{1},\mathbb{A}_{2},\ldots,\mathbb{A}_{n}\}\). Each subarea is monitored by the number of air drones \(D=\{D_{1},D_{2},\ldots,D_{m}\}\), where \(m\) is the total number of drones in a specific subarea. The monitoring drones in each subarea are divided by the central substation \(CS\) into separate groups \(G=\{G_{1},G_{2},\ldots,G_{k}\}\), where \(k\) is the number of the groups in a certain subarea; \(k\) is varied according to the size and importance of the subarea. The number of drones per group \(G_{i}\) is varied as the drones can enter/leave the group anytime; \(|G_{1}|+|G_{2}|+\cdots+|G_{k}|\leq|D|\); where \(|D|=m\). The drones collect information about the assigned subarea and forward their readings to \(CS\) via \(\beta\) cluster heads; selecting the cluster heads depends on various parameters, such as the current number of the drones in the group, the quality of the transmission channels, and the surrounding environment. The cluster heads are regular drones chosen to forward the readings of the whole group to \(CS\). The readings are transmitted from cluster heads to \(CS\) via the drones that are flying back to the station to recharge their batteries; these drones work as relay nodes. \(R=\{R_{1},R_{2},\ldots,R_{j}\}\), where \(j\) is the current number of relays in the subarea. Finally, the model has a trusted authority (TA) that is providing the keying parameters for different parties in the connection. Figure 1 shows the network model.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Climate Characteristics of Rainfall
#### 5.3.3 Distributions of Convection  
Several researchers studied common properties of convection from a global tropical perspective. They found that the intense convection or convective rainfall was more frequent over land than over oceans, and some of them occurred near the great mountain ranges (Figure 8) [142; 143; 111; 144]. The continental mesoscale systems often had more intense embedded convection than oceanic systems, which could form very wide stratiform regions [143].
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 3 Biocompatible 3D Printing Materials
### Biomaterial Inks
#### 3.2.2 Thermoplastics and Resins  
Thermoplastics are common materials for 3D printing across many technical industries, and are also used by hobbyists. In bioprinting, the key advantage is that they can be processed and undergo multiple thermal cycles for the incorporation of factors, and to form filaments for extrusion, resins for photolithography or polymer melts for electrospinning. We have previously shown that off-the-shelf materials can be used in tissue engineering applications [11; 12]. Thermoplastics such as polycaprolactone (PCL), polyvinyl alcohol (PVA) and polylactic acid (PLA) have been bioprinted for use as both supports for cell-seeded hydrogels that require mechanical reinforcement, and for direct implantation in vivo [55; 72]. They are printed using extrusion from filaments or polymer melts so they can produce structures with high resolution and very good shape fidelity, giving excellent control over porosity, which can in turn influence the mechanical properties of the scaffold [73].  
PORO-LAY are a group of PVA-polyurethane-elastomer blends that can be extrusion printed from filament before rinsing with water, to wash out the PVA component, leaving the nanoporous elastomer. We have shown the entrapment and subsequent controlled release of doxorubicin (a common anti-cancer drug) or zoeldronate (anti-resorptive), over a period of seven days [6; 7] in 3D-printed scaffolds. Using different variations of PORO-LAY with increasing levels of porosity, the total release of doxorubicin was also increased, inducing the largest decrease in metabolic activity in a prostate cancer cell line [6]. Delivery of cancer drugs to specific tissues is often achieved intravenously, but there is little currently done to prevent the venous spreading of excess drug to healthy tissues. A thermoplastic mesh-like filter was printed using continuous liquid interface production (CLIP) before being coated with a polystyrene-sulphonate absorber. The cylindrical filter device was printed and implanted in the iliac vein of a swine. Doxorubicin was then injected up-stream of the device, and the levels of doxorubicin in the blood were shown to be significantly lower after blood passed through the coated filter compared with the uncoated control [74]. This offers a solution by which a device can be implanted in large veins leading from diseased tissues to \"mop-up\" excess chemotherapeutics.
## 3 Illustrative Examples  
### Example I: A Two-Layered Cohesive Slope  
In this section, a two-layered cohesive slope, which has been used to investigate the slope system reliability by [PERSON] [17], is taken as an illustrative example to further examine the effects of autocorrelation structures on the reliability analysis of the slope. It should be noted that the same ACFs were assumed for the two layers. The geometry of the slope and LEM results are schematically plotted in Figure 1. The slope comprises two undrained clay layers, where the substratum is denoted by clay 1 and the upper layer clay 2. The undrained shear strengths of the two clays, which are spatially varied here, are subjected to lognormal distributions. The statistics of the undrained shear strengths are given in Table 2. With the mean values of the soil properties, the FS was calculated as 1.390 using the [PERSON]'s simplified method and the associated CSS tends to be a toe failure, which is schematically shown in Figure 1. These results are consistent with those reported by [PERSON] [17], showing that the constructed LEM model is accurate enough and can be used with MCS for the subsequent slope reliability analysis. Note that to be consistent with the previous study [17], the unit weight was considered constant.  
To further consider the spatial variability of the undrained shear strengths in the established LEM model, the slope domain was then discretized into 1210 random field elements with 1281 nodes, as shown in Figure 1. Based on the midpoints of these elements, the stochastic nature of the undrained shear strengths was simulated by random field theory using the Cholesky decomposition method, as described in Section 2.2. It should be noted that the 1210 elements mean that there are a total of 1210 random variables in each MCS realization. For each of the MCS realizations, the above LEM model was invoked to evaluate the slope stability results. The whole execution of the MCS realizations is termedas the probabilistic slope stability analysis. Since the autocorrelation structure is the key input to the random field simulation, it is, therefore, easy to investigate the effect of the autocorrelation structure on the reliability analysis of the slope with varying ACF types and SOFs by using the probabilistic slope stability analysis. The effects of ACFs and SOFs are studied in the following subsections.
## 3 Methodology
### Post-Processing Methods
#### 3.1.2 The GPP Method  
For the generator-based method, GPP, calibration of the precipitation PDF is executed individually across distinct seasons or magnitudes and is aligned with relevant observations. The post-processed ensemble precipitation forecasts are then conditionally resampled from this calibrated PDF, using the forecast information in EPFs. More details can be found in [PERSON], [PERSON] [18].  
Based on the EPFs and observed precipitation data from the summers from 2014-2018, this study adopted a cross-validation approach to optimize its post-processing methods. When forecasting for a specific year within this range, data from the remaining years served as the training dataset for calibration. For a comprehensive representation of the calibrated PDF, an ensemble size of 1000 was chosen. Two post-processing methods were, respectively, applied to each EPF; therefore, post-processed EPFs, i.e., BMA-EPFs and GPP-EPFs, were obtained. As an example, the term BMA-CMA refers to CMA forecasts that have undergone post-processing via the BMA method. The Matlab software was used for precipitation post-processing.