esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper introduced the RSoANU dataset, a collection of RIRs recorded in the Australian National University (ANU) Recording Studio. The dataset encompasses diverse acoustic scenarios featuring two wall panel environments, three source positions, and a planar grid of receiver locations. Recorded with both a 32-channel spherical microphone array and a B-format soundfield microphone array, the dataset includes 396 32-channel and 396 4-channel RIRs.  
We utilized the RSoANU dataset to analyze the acoustics within the ANU Recording Studio by examining key parameters such as reverberation time (\(T_{20}\)), clarity (\(C_{80}\)), definition (\(D_{50}\)), and center time (\(T_{s}\)). The results highlighted distinct differences between felt and wood panel environments, emphasizing the impact of panel material on acoustic properties. felt panels consistently exhibited faster sound decay, higher clarity, and better definition compared to wood panels, particularly in the mid-to-high frequency range. The variations in the parameter values across the receiver grid for different source positions revealed the influence of room environment and source-receiver positioning on acoustic features. Notably, \(C_{80}\) exhibited higher values directly in front of the source location, while \(T_{20}\) showed elevated values near the front and right walls.  
The RSoANU dataset holds the potential to contribute to the research and development of various audio applications, facilitating their validation and enhancement in authentic acoustic environments. It can aid in the designing of recording studios and guide optimal source and receiver placements across different recording scenarios. Future work will involve a more in-depth acoustic analysis, focusing on examining the trade-offs between information captured by a higher-order spherical microphone array and a commercially viable B-format soundfield microphone array.  
Conceptualization, G.C., A.B. and T.A.; data curation, G.C. and A.B.; formal analysis, G.C. and A.B.; investigation, G.C.; methodology, G.C. and A.B.; project administration, G.C., A.B. and T.A.; resources, A.B. and T.A.; supervision, A.B. and T.A.; validation, G.C., A.B. and T.A.; visualization, G.C. and A.B.; writing--original draft, G.C. and A.B.; writing--review and editing, G.C., A.B. and T.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement**: RSoANU dataset is openly available in Zenodo at [[https://doi.org/10.5](https://doi.org/10.5)]([https://doi.org/10.5](https://doi.org/10.5)) 281/zenodo.10720345 under the Creative Commons Attribution 4.0 International license.  
**Acknowledgments:** Authors would like to thank [PERSON] and [PERSON] of the ANU School of Music for providing access to the recording studio and granting permission to conduct the measurements.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 2 Related Works
The performances of different clustering protocols in WSNs are summarized in Table 1, from which the following can be concluded: (1) the Hybrid PSO-HSA [29] is more energy-efficient than the cuckoo [42], PSO [23], and HS [41] algorithms; (2) the cuckoo algorithm, in comparison with PSO and HSA, has poor cluster stability, and all of the algorithms suffer from an imbalance of exploration and exploitation; (3) none of the related works into WSNs consider the non-uniform distribution of events, and this is not suitable for disaster application. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Reference** & **Clustering Schema** & **Converge** & **Energy-Efficient** & **Cluster Stability** & **Event Awareness** & **Routing** & **Clustering Objective** \\ \hline LEACH [22] & Random Distributed & No & Low & Low & No & single-hop & Changing CH based Prob./Random \\ TEEN [40] & Probability Centralized & No & Medium & Low & No & single-hop & Changing CH based Threshold \\ \hline
[23] & PSO-based Centralized & Low balanced & Moderate & High & No & single-hop & Energy Efficiency + Min Distance + Node Hop count \\ \hline
[41] & HSA-based Centralized & Low balanced & Medium & Low & No & single-hop & Energy Efficiency + Min Distance \\ \hline TPSO-CR [27] & PSO-based Centralized & Moderate balanced & Moderate & High & No & multi-hop & Energy Efficiency + link Quality + Coverage \\ PSO-HSA [29] & PSO and HSA & Moderate balanced & High & High & No & single-hop & Energy Efficiency + Min Distance \\ \hline ICSHS [42] & Cuckoo-based Distributed & Moderate balanced & Moderate & Low & No & multi-hop & Energy Efficiency + Node degree + Intra Distance \\ \hline This Paper & PSO and HSA Centralized & High balanced & very High & High & Yes & multi-hop & Energy Efficiency + Cluster Closeness + Coverage \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparing different clustering protocols in Wireless Sensor Networks (WSNs) with our proposed method. LEACH: low-energy adaptive clustering hierarchy; TEEN: threshold-sensitive energy efficient sensor network protocol; PSO: Particle Swarm Optimization; HSA: Harmony Search Algorithm; CH: cluster head. TPSO-CR: Two-tier PSO protocol for Clustering and Routing. iCSHS: Integrated Clustering for Cuckoo Search and Harmony Search.
## 4 Discussion
On the other hand, in this study, for all tested dyes, the maximum reflectance values were reached at the wavelength range of 640-730 nm, while the highest absorption of radiation energy, manifested by the lowest reflectance values, occurred in the spectral range of 400-500 nm. This confirms the choice of the Q-switched Nd:YAG laser (532 nm) as the most effective for red dye removal. Other lasers operating in the Q-switched mode: ruby (694 nm) and alexandrite (755 nm) show lower efficiency in removing this pigment. HSI was also used to assess the effectiveness of the Q-switched Nd:YAG laser in removing the five tested dyes from the skin. After a properly performed procedure, according to the assumption, the reflectance should decrease because less dye remains in the skin. The laser application caused a decrease in reflectance for all tested dyes except for white. White pigment is the most difficult to remove due to its high reflectivity. The greatest decrease in reflectance was observed for the B1 dye (red shade), which is the closest to red. The change in wavelength at which the reflectance maximum occurred for two dyes (B2 and B4), manifested by a visible change in their color, indicates that this type of treatment may cause discoloration of the pigment used in permanent makeup. Another determinant of the effectiveness of laser permanent makeup removal is the increase in homogeneity and decrease in GLCM contrast for all the analyzed colors except for white. These parameters change indicates a more uniform distribution of red pigments after laser treatment. Due to the fact that the amount of pigment in the skin has decreased, the skin has acquired a shade closer to natural. The increase in the homogeneity of the distribution of dyes after the laser treatment is also confirmed by the quadratic tree decomposition parameters. An increase in the percentage of larger areas and a decrease in small areas was observed after the laser treatment in all tested shades of red, which indicates an increase in image homogeneity. In conclusion, objective skin measurements are more reliable compared to subjective visual assessment. The hyperspectral imaging method is an effective and non-invasive technique of both optimizing the laser wavelength and assessing the effectiveness of removing permanent makeup pigments from the skin. The HSI method can contribute to the increase in the safety and effectiveness of laser treatments, which will reduce its number and enable the achievement of satisfactory results. Therefore, it is necessary to continue research in order to optimize other parameters of the laser, such as pulse width (msec) and energy density (\(\mathrm{J/cm^{2}}\)), to the individual characteristics of the skin. However, a significant limitation in the widespread use of this method is its high price.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm
### Generate the Policy for the \(i\)-th UAV  
For the optimization objective function of the \(i\)-th UAV in the \(t\)-th time slot, the method based on a random projection gradient descent (RPGD) could be applied to optimize the location of the UAV. The pseudocode of the algorithm is shown in Algorithm 1.  
\(\eta_{\max}\) and \(\varepsilon\) in Algorithm 1 represent the maximum step size and tolerance in the RPGD method, respectively. \(t^{\prime}\) and \(T^{\prime}\) represent the iteration variable and iteration number, respectively. \(T^{\prime}\) is determined by \(T^{\prime}=1/\eta^{2}\), where \(\eta\) is the step size of each iteration, which can be determined by \(\eta=\min\bigl{\{}\varepsilon^{2}/\log(1/\varepsilon),\eta_{\max}\bigr{\}}\). \(I_{\max}\) is the maximum step size of the motion within a time slot.  
When the UAV is in a disconnected state, it can adopt the following three policies. To balance these three motion policies, \(\varepsilon_{1},\varepsilon_{2}>0\) and \(\varepsilon_{1}+\varepsilon_{2}<1\) can be defined. When \(M_{i}=1\) for the \(i\)-th UAV, the system randomly generated a uniformly distributed random number \(\varepsilon\) between 0 and 1. At this point, the \(i\)-th UAV generated the next location policy based on the size of \(\varepsilon\) in the following three methods:  
Method 1: When \(\varepsilon\leq\varepsilon_{1}\), the \(i\)-th UAV generated \(\vec{\textbf{r}}_{i}^{t+1}\) based on the algorithm shown in Table 1.  
Method 2: When \(\varepsilon_{1}<\varepsilon\leq\varepsilon_{2}\), the \(i\)-th UAV moved by \(I_{\max}\) in the direction of the geometric mean of the stored information of the location;  
Method 3: When \(\varepsilon>\varepsilon_{2}\), the UAV moved by \(I_{\max}\) in a random direction (with a uniform distribution at intervals \([-\pi,\pi]\)).
# Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability
## 3 Test and Results  
### Input Data of Distribution System  
The network topology of the distribution system consisting of approximately 40% residential and 60% commercial load is depicted in Figure 1. In this modified framework, daily load curves were obtained from Bosphorus Electric Distribution Corporation (BEDAS) in which the feeder included nine load-points each having different consumption and transformers as 34.5/0.4 kV. There were 3503 consumers included in the feeder. The connection between utility grid and feeder was also provided by 154/34.5 kV transformer. In order to make realistic assumptions and observe the effect of PV power plant on the reliability improvement, a prosumer (factory with on-site PV generation) was taken into account as in LP9. The used real radiation data for PV generation were obtained from the National Renewable Energy Laboratory (NREL) [31] by considering the latitude of the region and were normalized in accordance with the ambient temperature. The evaluated radiation data are demonstrated in Figure 2 and validated by comparison of those data with the relevant literature in [32; 33]. It should be noted that the radiation data may have changed according to the weather condition based on whether it was a cloudy day or a sunny day. The power demand of load points was procured by both utility grid and PV energy supply unit for providing a reliable and sustainable network.  
The daily load curve of the distribution feeder was obtained by using provided one year hourly energy consumption data from the distribution company. According to these raw data, the loading pattern of every season on hourly basis was acquired. The peak values of the load curves were 3.52 MVA, 2.93 MVA, and 2.40 MVA for winter, spring, and summer, respectively. The highest amount of consumption occurred in winter periods, and its characteristic was accordingly used in this study for modeling the loads, as shown in Figure 3.  
A detailed study was carried out on the above mentioned radial distribution feeder for investigating the impacts of both incorporated PV plant and DR programs, including smart households oriented concepts on system reliability improvement. The system reliability indices were calculated in DigSILENT PowerFactory and MATLAB environments by using the given parameters of distribution system components, as indicated in Table 1. In addition, load point interruption frequency (LPIF), load point interruption time (LPID), and load point energy not supplied (LPENS) criteria were  
Figure 1: General overview of the distribution system model for the test cases.  
Figure 2: The used radiation data taken from National Renewable Energy Laboratory (NREL).  
also analyzed to evaluate the effects of PV based generation unit and DR on reliability from end-user standpoint.
## 4 Results
### OMNeT++ Indication  
To evaluate the performance of vehicular communication and security management, OMNeT++, NSL-KDD, NS-2/3, and UNSW simulators were discussed in Section 2 for introduction purposes [52]. In addition to all of these tools, OMNeT++ is one of the backbone simulators for vehicular communications. To handle advanced-level vehicular communication systems, OMNeT++ is introduced. In the simulation environment, we introduce mobile devices, SUMO, and Veins to be experts in handling such simulation experiments. In OMNeT++, SUMO is considered to control the traffic simulations to generate the normal vehicles and mobility inside the vehicles. As SUMO and OMNeT++ work together to generate full traffic control, Veins is used as the glue to connect both and make a complete vehicular communication system for the proposed methodology. In this thesis, the OMNeT++ is designed to enhance 1-Hop broadcasting among roadside units (RSU) and vehicles [53]. This is performed to distribute the messages to each node in the range of the sender vehicle.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 3 Target Tracking with IMM Filtering
### Mode Matched Kalman Filtering  
The Kalman filter was performed for each IMM mode. The first step is to predict the state of each target of which the dynamic state is modeled as:  
\[\hat{\mathbf{x}}^{t}_{j}(k|k-1)=F\hat{\mathbf{x}}^{t}_{j}(k-1|k-1),\ \ t=1,\ldots,n(k),\ j=1,\ldots,M, \tag{13}\]  
\[P^{t}_{j}(k|k-1)=FP^{t}_{j}(k-1|k-1)F^{T}+Q_{j}. \tag{14}\]  
Next, the residual covariance \(S^{t}_{j}(k)\) and the filter gain \(W^{t}_{j}(k)\) are, respectively, obtained as:  
\[S^{t}_{j}(k)=HP^{t}_{j}(k|k-1)H^{T}+R, \tag{15}\]  
\[W^{t}_{j}(k)=P^{t}_{j}(k|k-1)H^{T}S^{t}_{j}(k)^{-1}. \tag{16}\]
###### Abstract  
_Fistulina antarctica_ and _Ramaria patagonica_ are wild edible fungi from Patagonia, which produce mushrooms that have been consumed since ancient times by those in local communities. Both species possess high protein and low fat contents and other bioactive compounds with remarkable antioxidant activity. Drying is a widely and commonly practiced preservation technique that is cost-effective. However, the process of drying can have an impact on the levels of bioactive compounds. In this study, the effects of drying methods on the total phenolic content (TPC) and antioxidant activity in the frutification of _F. antarctica_ and _R. patagonia_ were estimated. The analysis was conducted using frutifications that were fresh-frozen, dried at 50, 60, and 70 \({}^{\circ}\)C, and freeze-dried. The TPC was significantly higher for the frutifications that were fresh-frozen and dried at 60 \({}^{\circ}\)C in _R. patagonia_ (with values of 14.78 and 13.67 mg GAE/mg of extract, respectively). Also, the fresh-freezing, freeze-drying, and 60 \({}^{\circ}\)C methods exhibited the highest inhibition of free radicals. In addition, _R. patagonia_ had a notably higher concentration of phenols and showed higher antioxidant capacity than _F. antarctica_. The greatest losses concerning phenols occur in _F. antarctica_ in the three evaluated air-drying temperatures (36.57, 29.76, 39.47% in 50, 60 and 70 \({}^{\circ}\)C respectively, compared with fresh-frozen). TPC and antioxidant activity were higher in fresh-frozen and freeze-dried samples. A drying temperature of 60 \({}^{\circ}\)C is advisable for both species, considering drying time and bioactivity. Overall, the frutifications from different processing methods exhibited unique functional properties. This information can be utilized to optimize the postharvest preservation and maximize the potential applications of these mushrooms.  
Keywords:antioxidant activity; DPPH; edible mushrooms; Patagonia; postharvest preservation +
Footnote †: journal: Applied
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### What Are the Roles of Artificial Intelligence in FANETs?  
Artificial intelligence (AI) approaches, including artificial neural network [95], fuzzy logic [96], reinforcement learning [97], and particle swarm optimization [98], has been adopted to improve the performance of complex systems in a diverse range of applications, such as FANETs. In FANETs, AI has been applied in routing [99] so that a decision maker observes the state of the operating environment, selects the best possible action (e.g., a stable and reliable route), receives reward, and learns about the appropriateness of the action under the state based on the reward. For instance, a flying node (decision maker) observes the mobility pattern of neighboring nodes (state), selects a stable and reliable route (action), receives throughput performance (reward), and learns the best possible route as time goes by [100]. In Reference [99], AI is used to predict the geographical location of flying nodes in the selection of next-hop node in FANETs. In Reference [101], AI is used to assist the nodes in VANETs and MANETs. In Reference [102], AI is used to form clusters and improve energy efficiency. In Reference [103], a swarm-based approach is applied to routing in a multi-UAV swarm scenario.
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
## 1 Introduction
Section 3 describes the characteristics of atmospheric turbulence, and Section 4 presents the mathematical analysis. Sections 5 and 6 describe the channel models and study area, respectively. Section 7 presents the results and their discussion. The concluding remarks are presented in Section 8.
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 4 Results
### Sobol Sensitivity Analysis  
Figures 5 and 6 illustrate the predictive accuracy of the LIBSVM model in identifying optimal vegetation indices related to maize phosphorus concentration and aboveground biomass. For phosphorus concentration, the model performed well in the training set (Figure 5a), achieving an R\({}^{2}\) of 0.834 and an RMSE of 0.048, indicating high fitting accuracy. However, in the test set (Figure 5b), the R\({}^{2}\) decreased to 0.615, explaining 61.5% of the variance, while a higher RMSE of 0.073 suggested greater prediction errors and lower accuracy and reliability during testing. For aboveground biomass prediction, the model exhibited superior performance in the training set (Figure 6a), with an R\({}^{2}\) of 0.838 and an RMSE of 0.322, explaining 83.8% of the variance. In the test set (Figure 6b), the R\({}^{2}\) remained relatively high at 0.770, explaining 77.0% of the variance, but the RMSE increased to 0.368, indicating larger prediction errors and reduced accuracy during testing. While the predictive performance for both variables was moderate during the testing phase, the model's strong performance in the training phase and overall robustness highlight its reliability and potential for sensitivity analysis and vegetation index selection.  
Figure 4: P dilution curves model.  
Using the prediction results, the first-order impact index (S) and total effect index (ST) were computed (Figure 7). Figure 7a illustrates the impact of different vegetation indices on predicting plant phosphorus concentration, while Figure 7b shows the corresponding indices for predicting aboveground biomass. Among the indices evaluated, MNVI exhibited the highest S and ST values in Figure 7a, indicating its significant direct and overall impact on predicting plant phosphorus concentrations, making it the most effective index for this parameter. In contrast, GSAVI showed the largest total effect and highest first-order index in Figure 7b, emphasizing its key role in the sensitivity analysis and its suitability as the optimal index for predicting the aboveground biomass. The vertical axes in both Figures represent the values of the S and ST for each vegetation index, providing a clear comparison of their contributions to the model predictions.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### Times Series of GEO SARs and LEO SARs  
For one observation, the GEO SAR may keep working for several hours (or dozens of minutes) with a quite low PRF (hundreds of Hertz) and the LEO SAR may only work for several minutes with thousands of Hertz. As shown in Figure 2, \(\Delta t\) is the time interval between the first pulse of the LEO SAR and the first pulse of the GEO SAR; the green envelope is from GEO SAR and the red envelope is from LEO SAR. Obviously, the RFI from GEO SAR may be received every few LEO SAR pulses, while the RFI from LEO SAR may be received several times in one GEO SAR pulse. In fact, the RFI far away from the echo signal in fast-time domain cannot affect the imaging result, so Figure 2 only shows the RFI from LEO SAR, which is close to the GEO SAR pulses.
## 3 Proposed Method
### Risk Minimization  
When there is no cut-in vehicle, the ego vehicle considers the collision risks to both the preceding and following vehicles. On the other hand, if the lane-changing is detected, the ego vehicle references the cut-in vehicle based on the predicted lane-changing maneuver. The proposed method finds the optimal position between the two vehicles to minimize the collision risk as shown in Figure 9. To avoid a collision with the cut-in vehicle, if the ego vehicle conducts inconsistent or excessive acceleration (deceleration), it can conversely cause a crash, as it is unexpected for the following driver. Thus, the acceleration (deceleration) is limited within \(\pm 0.5\) m/s\({}^{2}\). The optimal position to minimize the risk index is derived by  
\[(x^{*},y^{*})=\operatorname*{arg\,min}_{x,y}R(x,y), \tag{15}\]  
where  
\[x_{f}<x<x_{p}. \tag{16}\]  
In Equation (16), \(x_{f}\) indicates the position of the following vehicle and \(x_{p}\) represents that of the preceding vehicle. Finally, the control value is derived, as the ego vehicle arrives at the position \((x^{*},y^{*})\) two seconds in advance.  
Figure 8: Definition of the assessment space: (**a**) When there is no lane-changing vehicles around the ego, the assessment space is defined between the preceding and following vehicles in the same lane. (**b**) If the adjacent vehicle is predicted to change a lane to the front space of the ego vehicle, the assessment space is determined between the predicted position of the lane-changing vehicle and that of the following vehicle.  
Figure 7: The distribution of potential field: (**a**) The ego and the vehicle \(i\) drive with the same speed, (**b**) the ego is faster than the vehicle \(i\), and (**c**) the ego is slower than the vehicle \(i\). As shown, the potential field is distributed depending on the relative speed between the two vehicles.