esa-sceva/satcom-chunk-collection huggingface dataset:
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 4 Methodology
### Feature Selection
#### 4.1.2 Feature Importance Evaluation by Pearson Correlation Coefficients  
As an alternative approach to identifying influential features, Pearson correlation coefficients were computed between the features and the target variable _power_kw_. Such analysis can be concluded with a correlation matrix, which gives information about interpreting relationships between all predictor variables and the target variable. High correlation values indicate that features are strongly related either positively or negatively and can be prioritized for research [55].  
The results (see Table 3) confirmed the importance of three features: _predict_wind_, _predict_wind_speed_, and _predict_sea_level_pressure_, as well as identified an additional highly influential feature related to the strength of wind gusts (_predict_wind_gust_).  
The final selection of features was based on identifying predictors that appeared in both the Random Forest feature importance and Pearson correlation analysis lists, ensuring consistency across the methods. This overlap highlights features deemed influential by both  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Gini Importance Score** \\ \hline
**predict\_wind\_speed** & **0.511286** \\
**predict\_wind\_direction** & **0.083863** \\
**predict\_wind** & **0.074096** \\
**predict\_sea\_level\_pressure** & **0.065375** \\
**predict\_relative\_humidity** & 0.063730 \\
**predict\_air\_temperature** & 0.063255 \\
**predict\_feels\_like\_temperature** & 0.061680 \\
**predict\_cloud\_cover** & 0.037996 \\
**predict\_wind\_gust** & 0.029197 \\
**predict\_total\_precipitation** & 0.009522 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The most influential features for the _power_kw_ variable based on the Random Forest Regressor model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Correlation Coefficient** \\ \hline
**predict\_wind\_speed** & **0.751703** \\
**predict\_wind\_** & **0.739799** \\
**predict\_wind\_gust** & **0.719605** \\
**predict\_sea\_level\_pressure** & **0.350786** \\ predict\_feels\_like\_temperature & 0.200739 \\
**predict\_air\_temperature** & 0.174140 \\
**predict\_relative\_humidity** & 0.158937 \\
**predict\_cloud\_cover** & 0.125714 \\
**predict\_wind\_direction** & 0.120283 \\
**predict\_total\_precipitation** & 0.113998 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The most influential features for the _power_kw_ variable based on correlation analysis.  
linear and non-linear evaluation approaches, resulting in a robust and reliable selection. Based on the findings, further work was conducted using _predict_wind_speed_, _predict_wind_, _predict_wind_, _predict_wind_gust_, _predict_wind_direction_, _and_ predict_sea_level_pressure_ parameters.
# The Use of Rapeseed Husks to Remove Acidic and Basic Dyes from Aquatic Solutions
## 2 Materials and Methods  
### Rapeseed Husks  
The raw material used to prepare the sorbent consisted of rapeseed oil cake supplied by an oil extrusion plant located in the Warminsko-Mazurskie Province (Poland). The cake derived from industrial rape seeds from the local harvest of 2022.  
The sorbent preparation consisted in separating the husks from the crushed rape seed kernel (acc. to the procedure described in Section 2.5). Rape seed husks (RH) accounted for 31% of the oil cake weight. The content of individual components in the husk dry matter was as follows: lignin--25.0%; cellulose--13.7%; hemicellulose--19.0%; pectin--12.0%; proteins--18.1%; starch--0.4%; and other components (e.g., lipids, monosugars, ash)--12.2% [23; 24].
###### Abstract  
The deep integration of smart grid and Internet of Things technologies has made high-speed power line carrier communication a key communication technology in energy management, industrial monitoring, and smart home applications, owing to its advantages of requiring no additional wiring and offering wide coverage. However, the inherent characteristics of power line channels, such as strong noise, multipath fading, and time-varying properties, pose challenges to traditional encryption algorithms, including low key distribution efficiency and weak anti-interference capabilities. These issues become particularly pronounced in high-speed transmission scenarios, where the conflict between data security and communication reliability is more acute. To address this problem, a secure transmission method for high-speed power line carrier communication based on generalized two-dimensional polynomial chaotic mapping is proposed. A high-speed power line carrier communication network is established using a power line carrier routing algorithm based on the minimal connected dominating set. The autoregressive moving average model is employed to determine the degree of transmission fluctuation deviation in the high-speed power line carrier communication network. Leveraging the complex dynamic behavior and anti-decoding capability of generalized two-dimensional polynomial chaotic mapping, combined with the deviation, the communication key is generated. This process yields encrypted high-speed power line carrier communication ciphertext that can resist power line noise interference and signal attenuation, thereby enhancing communication confidentiality and stability. By applying reference modulation differential chaotic shift keying and integrating the ciphertext of high-speed power line carrier communication, a secure transmission scheme is designed to achieve secure transmission in high-speed power line carrier communication. The experimental results demonstrate that this method can effectively establish a high-speed power line carrier communication network and encrypt information. The maximum error rate obtained by this method is 0.051, and the minimum error rate is 0.010, confirming its ability to ensure secure transmission in high-speed power line carrier communication while improving communication confidentiality.  
generalized two-dimensional polynomials; chaotic mapping; high-speed power lines; carrier communication; secure transmission; communication key +
Footnote †: journal: Applied sciences  
Article
## 3 Results and Discussion
### Adsorption Kinetics
#### 3.4.2 Kinetic Modelling  
The rate of ARV and NSAID adsorption by MIP and NIP was measured as a function of time. If second-order kinetics is used, the plot of \(t/Q\) vs. \(t\) should provide a straight line. The values \(K_{2}\) and \(Q_{e}\) were computed from the intercept and slope of the linear plots of \(C_{e}/Q\) vs. \(C_{e}\), respectively; the data is shown in Table 4. The kinetic modeling results showed that the adsorption process followed pseudo-second-order kinetics as they better obeyed a straight line. The adsorption capacities (\(Q_{e}\)) were 0.90, 0.83, 0.06, 0.83, 0.07, and 0.13 mg\(\cdot\)g\({}^{-1}\) for EMI, TENO, NAP, DICLO, IBU, and EFV, respectively. These adsorption capacities were lower in the MIP than in the NIP. For instance, tenofovir disoproxi has an adsorption capacity of 3.58 mg\(\cdot\)g\({}^{-1}\) in the NIP compared to 0.83 mg\(\cdot\)g\({}^{-1}\) in the MIP. This could be due to the flexibility of the NIP, which has a smoother surface and larger pore size diameter than the MIP, as mentioned earlier in the paper. The pseudo-second-order kinetic model results fit well with the Freundlich isotherm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Polymer**} & \multirow{3}{*}{**Compounds**} & \multicolumn{2}{c}{**Pseudo-First-Order**} & \multicolumn{2}{c}{**Pseudo-Second-Order**} \\ \cline{3-6}  & & \(R^{2}\) & \(R^{2}\) & \(K_{2}\) (mg\(\cdot\)g\({}^{-1}\) min\({}^{-1}\)) & \(Q_{e}\) (mg\(\cdot\)g\({}^{-1}\)) \\ \hline \multirow{6}{*}{MIP} & Emtricitabine & 0.86 & 1.00 & 2.75 & 0.90 \\  & Tenofovir & 0.61 & 1.00 & 3.47 & 0.83 \\  & disoproxi & 0.50 & 0.90 & 3.19 & 0.06 \\  & Diclofenac & 0.88 & 1.00 & 1.16 & 0.83 \\  & Ibuprofen & 0.86 & 0.83 & 0.07 & 0.07 \\  & Efavirenz & 0.86 & 1.00 & 2.07 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated results of Kinetic Models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Polymer**} & \multicolumn{3}{c}{**Langmuir Isotherm (Equation (6))**} & \multicolumn{3}{c}{**Freundlich Isotherm (Equation (5))**} \\ \cline{2-6}  & **Compound** & \(R^{2}\) & **Intercept** & **1/n** & \(K_{f}\) & \(R^{2}\) \\ \hline \multirow{6}{*}{MIP} & Emtricitabine & 0.6124 & \(-\)0.1977 & 1.1968 & 0.6943 & 0.9869 \\  & Tenofovir & 0.9709 & \(-\)0.7240 & 0.302 & 5.3002 & 0.9150 \\  & Naproxen & 0.6227 & \(-\)0.0942 & 2.487 & 0.8050 & 0.8139 \\  & Diclofenac & 0.8222 & 0.6402 & 0.7003 & 4.3672 & 0.9311 \\  & Ibuprofen & 0.9840 & \(-\)1.4099 & 16.359 & 0.0389 & 0.7001 \\  & Efavirenz & 0.8525 & \(-\)1.2718 & 1.5028 & 0.0535 & 0.9313 \\ \hline \multirow{6}{*}{NIP} & Emtricitabine & 0.8854 & 1.1022 & 0.5594 & 15.929 & 0.9951 \\  & Tenofovir & 0.7598 & 1.0834 & 0.7247 & 12.117 & 0.7425 \\ \cline{1-1}  & disoproxi & 0.9856 & 0.277 & 0.4303 & 1.8958 & 0.9683 \\ \cline{1-1}  & Naproxen & 0.9592 & 0.6221 & 0.6456 & 4.1889 & 0.9840 \\ \cline{1-1}  & Ibuprofen & 0.9585 & 0.0919 & 0.4199 & 1.2356 & 0.6853 \\ \cline{1-1}  & Efavirenz & 0.8784 & 0.8577 & 0.7603 & 7.2061 & 0.9812 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adsorption Isotherms.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 3 Methodology
### Ad Hoc Routing Protocols  
Several routing protocols are implemented in the INET framework. The target of this research is to compare topology-based routing protocols [55] in certain traffic contexts. We chose the DSDV protocol as a proactive routing protocol. For the reactive routing protocol, we selected AODV and DYMO protocols for comparison.  
Based on the Bellman-Ford algorithm [56; 57], DSDV is a loop-free proactive routing protocol that maintains the routing table containing sequence numbers for each route in the network. A node will update its routing table when it receives a route update with a higher sequence number. If the route update has the same sequence number, a route with a better metric is selected. A periodic update of the routing tables is necessary for the DSDV protocol. Full dump or incremental update methods are used to perform the routing updates. In the full dump method, nodes transmit the whole routing table, whereas, in the incremental update, the node only transmits entries that have changed. Routing updates are broadcasted by either transmitting infrequent full dumps or frequent incremental updates. In a high dynamic topology, routing information needs to be updated more frequently, consuming more power.  
Reactive protocols do not maintain routing information about all the nodes but rather only keep the information of the nodes that are present in the route. AODV is a loop-free reactive protocol where routes are generated based on demand from the source node. When a source node finds no route to the destination node, it initiates the route discovery process by flooding [58] the network with route request (RREQ) messages. After discovering the destination node, a unicast message is sent back to the source node in the form of a route reply (RREP) message. A route is established and kept in the routing table of the source node until the link is expired. If any node is unreachable due to broken links, a route error (RERR) message is broadcasted. Like AODV, DYMO is a reactive protocol in which routes are calculated on demand. However, DYMO does not support unnecessary HELLO messages and relies on the sequence numbers assigned to all the messages. Table 1 shows the characteristic comparison between Ad hoc routing protocols.
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 3 Overview of Self Organized Fast Routing Protocol (SOFRP)  
This section provides an overview of the proposed protocol, prior to the detailed description in Section 4. Our proposal is based on the automatic formation of predefined routing paths to forward the data packet from each node to the sink, without the use of additional measurement devices, such as pressure sensors. However, it is assumed that nodes are placed in a specific formation rather than deployed randomly. For easier reference of the acronyms used, they can be looked up in Section 5, where Table 1 lists the acronyms used for names of messages and Table 2 contains the acronyms used to denote mathematical parameters.  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Packet Name** & **Description** & **Code** \\ \hline BEACON & Beacon & 1100011 \\ CF\_RR & Clear to Forward the RR & 0101000 \\ CFRR\_ACK & CFR RR ACKnowledgement & 0100001 \\ CFRR\_BACK & CFRR from intermediate node to the side nodes & 1011100 \\ CFRRBACK\_ACK & CFRR\_BACK ACKnowledgement & 1000111 \\ CNCL & CaNCel. & 010001 \\ CNCL\_ACK & CaNCel. ACKnowledgement & 010000 \\ ENQ & ENQuiry & 0111100 \\ ENQ\_ACK & ENQuiry ACKnowledgement & 0111100 \\ INPO\_REQ & INPO\_REQ & INPO\_REQ & 0111010 \\ INFOREG\_ACK & INPO\_REQ & ACKnowledgement & 0111010 \\ RR & Route Request & 0000111 \\ RR\_ACK & Route Request & 0100101 \\ RR\_RSP & RRResponse & 0101101 \\ RRSP\_ACK & RR\_ReFonse & 0001101 \\ SGN & Search Gateway Neighbor & 0101010 \\ SGN\_RSP & SGN\_RSPonse & 0101001 \\ SGNRSP\_ACK & SGN\_RepsPonse & 001100 \\ SHN & Search Horizontal Neighbor & 0001011 \\ SHN\_ACK & SHN ACKnowledgement & 0100000 \\ ST\_RSP & Start sending the RR ReFSponse & 0110001 \\ STRSP\_ACK & ST\_RSP ACKnowledgement & 0110101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Packet Descriptions.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 4 Simulation of S1 Data with Backscattering Models
### Bare Soil Backscattering Model  
In the present study, two empirical expressions were used to model the radar backscattering over bare soil, for the VV and VH polarizations. These can be written as:  
\[\sigma_{vv}^{soil} =\alpha_{vv}\:mv+\beta_{vv}\:\log(Hrms)+\delta_{vv} \tag{2}\] \[\sigma_{vh}^{soil} =\alpha_{vh}\:mv+\beta_{vh}\:\log(Hrms)+\delta_{vh} \tag{3}\]  
The observations describing the bare soil reference fields are separated into two datasets. The first of these includes 45 fields and is used for the calibration of the proposed expression, whereas the second set of (45) fields is used to validate the results. Table 3 lists the calibrated model parameters for the VV and VH polarizations and corresponding statistical parameters (RMSE and R\({}^{2}\)). The two datasets correspond to multi-temporal measurements made in two sets of distinctly different reference fields.  
Figure 10 plots the results obtained through validation of the proposed model, by comparing the signal strengths predicted by the model with the real data derived from the bare soil surfaces described in the second dataset. This figure shows that there is a strong agreement between the simulations and measurements, with an RMSE equal to 1.27 and 1.23 dB for the VV and VH data, respectively, and a bias equal to 0.1 and 0.2 dB for the VV and VH data, respectively. These results highlight the potential of this model for the analysis and inversion of radar data over bare soils.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Polarisation** & \(\alpha_{pq}\) & \(\beta_{pq}\) & \(\delta_{pq}\) & **RMSE (dB)** & \(\mathbf{R^{2}}\) \\ \hline pq = VV & 0.17 & 3.25 & \(-\)15.06 & 1.47 & 0.61 \\ pq = VH & 0.13 & 1.88 & \(-\)23.01 & 1.29 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calibration parameters and statistical precision coefficients computed with the proposed empirical models, for the VV and VH polarizations, relative mean square error (RMSE) and correlation coefficient R\({}^{2}\).  
Figure 10: Empirical model validation: (**a**) VV polarization; (**b**) VH polarization.
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 6 Simulation Verification
### Formation Reconfiguration under Leader Disengagement
#### 6.3.2. Performance Assessment Model Construction
For instance, the number of the original leader 'UAV 0' in the swarm communication topology is 0, whereas after its disengagement, 'UAV 2' turns to be the new leader of the swarm, which means at the moment, the UAV identified by 'UAV 2' corresponds to the node numbered 0 in the swarm communication topology. At the same time, the numbers of other followers in the swarm communication topology can change correspondingly. On the other hand, as aforementioned, the desired distance for each follower with respect to the leader is associated with the number in the swarm communication topology rather than the number contained in the identifier. Therefore, as demonstrated in Figure 18, under the leadership of 'UAV 2', obvious changes occur for the movement of the followers whose numbers in the swarm communication topology change, including 'UAV 1', 'UAV 3', 'UAV 5', 'UAV 7', 'UAV 9' and 'UAV 11', while the movement trends of the followers 'UAV 4', 'UAV 6', 'UAV 8', and 'UAV 10' remain unchanged for their unaltered numbers in the swarm communication topology._  
Figure 17: Comparison of the performance scores for each follower. Figure 18: The flight trajectories of the leader–follower multi-UAVs when \(t\in[120,180)\). Figure 19: The trajectory trace error for each follower when \(t\in[120,180)\). Figure 20: The linear velocity for each follower when \(t\in[120,180)\).
## 3 Results and Discussion
### Physicochemical Properties of Sausages
#### 3.1.2 pH, Salt, Water-Holding Capacity (WHC), and Cook Loss  
The pH of SS and IW seaweeds was 5.55 and 6.29, respectively [19]. The pH values of SS and IW sausage treatments ranged from 5.71 to 5.93 and 5.82 to 5.89, respectively (Table 3). The overall pH of SS and IW sausages was significantly influenced by the fat and salt levels, and a fat \(\times\) salt interaction was observed. In this study, the addition of seaweeds to reduced-fat and -salt sausages did not affect (\(p>0.05\)) the pH of the sausage treatments compared to the control. However, within each fat treatment, the pH value of SS sausage treatments decreased with increasing salt levels. [PERSON] et al. [37] also observed a reduction in pH values as the amount of salt applied increased in pork sausage batter. The impact of salt on the pH of meat is mostly attributed to the stronger affinity of the Cl\(-\) ion to proteins, such as myosin, compared to the Na+ ion. The Cl\(-\) ions are adsorbed by the positively charged groups of myosin, causing a change in the isoelectric point and thereby reducing the pH [38,39]. The overall salt content of SS sausage treatments was significantly influenced by the fat and salt levels, and a fat \(\times\) salt interaction was observed, while fat and salt levels had a significant effect on the salt content of IW sausages. Fat and salt may interact, affecting water retention and the distribution and solubility of salt within the sausage, which may impact the salt content of the sausages [38]. This may also be due to the varying fat and salt levels in their formulations (Table 1). Seaweed sausages (SS and IW treatments) containing 1.5% salt had salt levels that were comparable (\(p>0.05\)) to the control. The salt content of SS and IW sausages was higher than the projected levels for each formulation (0.5%, 1.0%, and 1.5% salt) (Table 1), presumably due to the salt levels present in both brown seaweed species (12-15%) [19]. The overall WHC of SS sausage treatments was significantly influenced by fat level, and IW samples were affected by fat and salt levels. The WHC of SS (10f/1.5s, 10f/1.0s, and 10f/0.5s) and IW (10f/1.5s and 10f/0.5s) treatments was significantly higher than the control. This increased WHC may be attributed to the increased pork meat content in sausages (Table 1). The overall cook loss of IW sausages was significantly impacted by the fat and salt levels.
# Distributed Edge Computing to Assist Ultra-Low-Latency VANET Applications
###### Abstract  
Vehicular ad hoc networks (VANETs) are a recent class of peer-to-peer wireless networks that are used to organize the communication and interaction between cars (V2V), between cars and infrastructure (V2I), and between cars and other types of nodes (V2X). These networks are based on the dedicated short-range communication (DSRC) IEEE 802.11 standards and are mainly intended to organize the exchange of various types of messages, mainly emergency ones, to prevent road accidents, alert when a road accident occurs, or control the priority of the roadway. Initially, it was assumed that cars would only interact with each other, but later, with the advent of the concept of the Internet of things (IoT), interactions with surrounding devices became a demand. However, there are many challenges associated with the interaction of vehicles and the interaction with the road infrastructure. Among the main challenge is the high density and the dramatic increase of the vehicles' traffic. To this end, this work provides a novel system based on mobile edge computing (MEC) to solve the problem of high traffic density and provides and offloading path to vehicle's traffic. The proposed system also reduces the total latency of data communicated between vehicles and stationary roadside units (RSUs). Moreover, a latency-aware offloading algorithm is developed for managing and controlling data offloading from vehicles to edge servers. The system was simulated over a reliable environment for performance evaluation, and a real experiment was conducted to validate the proposed system and the developed offloading method.  
VANET; mobile edge computing; SDN; offloading; latency; 5G +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 128; doi:10.3390/f11060128
# Enabling Trustworthy Multicast Wireless Services through D2D Communications in 5G Networks
## 5 Performance Evaluation
This significant degradation of the goodput/good ADR is due to the high amount of data loss, as confirmed by Figure 5. Thanks to the implementation of the security mechanisms foreseen in eCMS-sD2D, data loss can be reduced by about 70%. In particular, when the maliciousness threshold is set to zero, thanks to the better selection of RNs, eCMS-sD2D shows the best results. Nevertheless, data loss is not zero, due to the fact that malicious RN can be identified only after the eNodeB detects an incorrect behavior. As the threshold value increases, the efficiency of eCMS-sD2D in terms of selection of RNs is always lower, so the results are increasingly similar to D2D-SF. Figure 4: Comparison between ADR D2D and Good ADR D2D for eCMS-sD2D, with three different settings of the maliciousness threshold, and D2D-SF, with 75% of malicious nodes. Figure 3: Mean (**a**) throughput D2D and (**b**) goodput D2D for eCMS-sD2D, with three different settings of the maliciousness threshold, and D2D-SF, under increasing percentage of malicious nodes. Because of security, as shown in Figure 6, the mean number of malicious D2D UE that gains service is reduced (see the first bar with respect to the fourth bar). However, despite this metric showing an improvement of about 10%, it takes into account both malicious users served by the CMS transmission (on which the security mechanisms do not have impact) and selected malicious RNs. This is the reason why the performance improvement of eCMS-sD2D with respect to eCMS-D2D is limited. Going more in depth in the analysis, we show in Figure 7 the mean number of selected malicious RNs under increasing percentage of malicious users. We can appreciate that the reduction in the number of selected malicious RNs is of more than the 50% also in case of a high number of malicious nodes (75% of malicious nodes). Figure 5: Data loss for eCMS-sD2D, with three different settings of the maliciousness threshold, and D2D-SF, under increasing percentage of malicious nodes. Figure 6: Mean number of malicious D2D nodes for eCMS-sD2D, with three different settings of the maliciousness threshold, and D2D-SF, under increasing percentage of malicious nodes.