esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Microstructural Features  
The microstructural evolution of the CrCoNiTa\({}_{\mathrm{x}}\) alloy is demonstrated in Figure 2. Some white precipitates with lamellar morphology were observed in the Ta\({}_{0.1}\) alloy, and with increasing Ta addition, the volume fraction of the eutectic was significantly increased. Once the Ta addition reached 0.4, namely the Ta\({}_{0.4}\) alloy, fully eutectic morphology with typical lamellar structure was obtained. When the Ta addition exceeded 0.4 and reached 0.5 and 0.7, namely the Ta\({}_{0.5}\) alloy and Ta\({}_{0.7}\) alloys, in addition to the eutectic structure with lamellar, some bulk primary phase appeared to increase. Combining the results from Figure 1, the morphologies of the Ta\({}_{0.1}\) and Ta\({}_{0.3}\) exhibited hypoeutectic structure while Ta\({}_{0.5}\) and Ta\({}_{0.7}\) exhibited hypereuetectic structure. Ta content of eutectic point in CoCrNiTa\({}_{\mathrm{x}}\) alloys was 11.7 at.%. Compositions of the characteristic regions in alloys are shown in Table 1.  
Figure 1: The pseudo binary phase-diagram of CrCoNiTa\({}_{\mathrm{x}}\) alloys calculated from Thermo-Calc.  
Figure 2: SEM images of CrCoNiTa\({}_{\mathrm{x}}\) under as-cast microstructure; (**a**,**b**) = 0.1; (**c**,**d**) x = 0.3; (**e**,**f**) x = 0.4; (**g**,**h**) x = 0.5, (**i**,**j**) x = 0.7. Primary phase and eutectic under different compositions are marked.  
As given in Figures 1 and 2, the increase in Ta content in MEAs causes a transition in the primary phase and corresponding change in volume fraction. A complete transformation from hypoeutectic to hypereutectic is observed. The primary phase in hypoeutectic is \(\gamma\) phase while in hypereutectic is Laves phase, respectively. The microstructure formation and phase selection in Figure 2 can be explained through the solidification process.  
For the analysis combined with Figure 1, as for hypoeutectic eutectic Ta\({}_{0.1}\) and Ta\({}_{0.3}\) alloy, the \(\gamma\) phase is first nucleated during solidification process, the liquid and \(\gamma\) phase are in a mixed state until the eutectic temperature is reached. Subsequently, the temperature continues to decrease, the liquid phase switches to the eutectic point, and the eutectic reaction is triggered, forming \(\gamma\)/Laves lamellar eutectic microstructure. During the reaction, the \(\gamma\) phase and the Laves phase are coupled to grow and alternately arranged to produce lamellar structures. Finally, the structure consists of a mixture of primary \(\gamma\) phase and eutectic mixture of \(\gamma\) and Laves phases. In the CrCoNiTa0.4 alloy, once the temperature decreases down the point of eutectic reaction, the two phases nucleate at the same time and grow together. As for hypereutectic eutectic Ta\({}_{0.5}\) and Ta\({}_{0.7}\) alloys, the solidification process is similar to that of Ta\({}_{0.1}\) and Ta\({}_{0.3}\) alloys, but the first nucleated phase shifts to Laves phase. It possesses final microstructures of the primary Laves phase and the eutectic mixture of \(\gamma\) and Laves phases.
## 3 Dataset Description  
The RSoANU dataset comprises RIRs captured in the ANU Recording studio, considering two room configurations and three source positions (S1, S2, and S3). RIRs were recorded for 65, 67, and 66 receiver positions for S1, S2, and S3, respectively. Recordings conducted using both the em32 Eigenmike and Rode NT-SF1 microphone generated a total of 396 32-channel and 396 4-channel RIRs, respectively. Note that the RIRs recorded by Rode NT-SF1 microphones are in Ambisonic B-format with FuMa normalization. All RIRs are saved with a sampling frequency of 48 kHz and a duration of 1.5 s.  
The RIRs in the RSoANU dataset are organized in folders with the structure shown in Figure 4. Each multichannel RIR is saved in two file formats: MATLAB(tm) struct (*.mat) and 16-bit audio (*.wav). The filenames follow a convention based on the source code names and receiver code names outlined in Tables 15-16. For the em32 Eigenmike, the 32-channel RIR is saved under the \(IR\_Data\) field name in the (*.mat) struct file. For the Rode NT-SF1 microphone, the RIR is saved under the \(Bformat\) field name with separate child fields for \(W,X,Y,Z\) channels. The room temperature, sampling rate, microphone array type, source and receiver coordinates, excitation signal, and raw recording are recorded as metadata. The (*.mat) struct files for NT-SF1 also include the normalization standard under the \(Bformat\_standard\) metadata field.  
The RSoANU dataset is publicly accessible online at [[https://doi.org/10.5281/zenodo.10720345](https://doi.org/10.5281/zenodo.10720345)]([https://doi.org/10.5281/zenodo.10720345](https://doi.org/10.5281/zenodo.10720345)) [30] under the Creative Commons Attribution 4.0 International license.
## 2 Background  
### Virtual Reality  
VR and its extension (XR) is an immersive technology that allows users to interact with digital environments in real time. Devices such as head-mounted displays (HMDs), motion controllers, tracking systems, sensors (e.g., Gyroscopes, Accelerometers and Magnetometers) and sensory feedback systems are used to create simulations that replicate real-world or fantastical scenarios [20, 21]. This technology has found applications in diverse fields, including healthcare, gaming, education, industry, business, and many more [22, 23]. For example, VR is extensively used for surgical simulations, enabling medical professionals to perform complex procedures in a risk-free environment [24, 25]. Similarly, in education, VR provides interactive learning experiences, fostering deeper engagement and comprehension among students [4, 26]. VR has also garnered increased interest from the general public due to advancements in VR gaming and the availability of commercially accessible devices such as the Meta Quest [27, 28]. Despite its growing adoption, challenges persist, particularly regarding motion sickness, latency, and the high cost of hardware, which limit accessibility and scalability [29, 30, 31]. Even though standalone VR headsets such as the Meta Quest have come a long way, insufficient computational power, resolution limitations, restricted fields of view, and tracking errors affect immersion and user experience negatively [32]. The overall graphical fidelity, physical simulations, and user interactions within VR environments often lack realism, breaking the sense of immersion [33]. Additionally, there is a lack of standardization across platforms and the development of immersive VR environments has multiple barriers including the difficulty to forecast and simulate motion as well as testing and evaluating these systems with real users [34]. The current state of VR is characterized by rapid advancements in hardware and software, driven by innovations in fields such as computer graphics and sensor technologies. The development of standalone VR devices, such as the Meta Quest series, has made VR more accessible by eliminating the need for tethered connections to powerful computers. The integration of advanced computer graphics, sensor technologies, and innovative software continues to drive VR forward and attempts to eliminate known issues, making it a transformative tool in many domains as it enables the creation of highly immersive experiences.
## 8 Conclusions  
This work presented a new approach for enhancing OLSR protocol security for mobile ad-hoc networks (MANETs) through secure multipoint relay (MPRs) Selection. This approach is based on the principles of <<Trust no One>> and a <<Decentralized Verification System>>. Furthermore, its main goal is to build a trusted topology with the ability to detect malicious nodes.  
The proposed solution is a trust-based model structured around 03 steps. The first one is where network nodes will exchange information about their neighbors with all other nodes. This step is crucial for building knowledge about network topology in general. The second step is based on the broadcast of all messages by the node to other nodes, and therefore each node is going to have a copy of the topology received from each other node. The last step focuses on the detection of dishonest nodes through the verification and comparison of all the topologies. If the topologies are the same, we can conclude that all the nodes are honest. Thus, we can build our secure topology database, which is the main pillar of the routing process. Otherwise, in the opposite case, we would detect exactly where the anomaly originated. This will lead to the detection and isolation of the dishonest node, followed by the creation of the network topology database. Our model will help choose an honest MPR based on the topology it creates. The topology is then decentralized across all nodes in the network. The findings clearly indicated that our proposed algorithm outperformed the widely used UM-OLSR implementation. Our MPR election algorithm not only ensured a higher packet delivery ratio but also exhibited exceptional performance  
Figure 18: Packet drop percentage by number of malicious nodes.  
in detecting and isolating malicious nodes, significantly enhancing the security of the OLSR protocol control plane.  
Conceptualization, N.I., S.M. and C.M.A.; Methodology, N.I., S.M., C.M.A. and H.Z.; Software, S.M. and H.Z.; Validation, N.I., S.M. and C.M.A.; Formal analysis, N.I., S.M. and C.M.A.; Investigation, N.I., S.M. and C.M.A.; Writing--original draft, N.I., S.M., C.M.A. and H.Z.; Writing--review & editing, N.I.; Project administration, N.I. 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.
# Exercise Suppresses Appetite in Obesity: A Biochemical, Metabolic, and Molecular Approach
## 2 Exercise and Appetite-Regulatory Hormones
The levels of CCK increase soon after acute exercise in non-obese individuals and are maintained for up to 2 h after exercise [157; 158], likely as a mechanism to suppress hunger after exercise [36]. The findings related to exercise training on CCK levels are variable, with reports of increases after intensive or hypoxia training [158; 159] and a reduction in runners after high-intensity training [160], and even no changes following 12-week training in individuals with obesity [161]. More extensive studies are needed on the effects of acute and chronic exercise CCK levels in subjects with obesity. Peptide YY (PYX), a short peptide secreted by L-cells in the ileum and colon, exhibits postprandial elevation (particularly following fatty meal consumption) and declines during fasting periods. The direct injection of PYY into the central nervous system reduces appetite [162; 163]. PYY is produced by neurons in the gigantocellular reticular nucleus of the medulla oblongata [164] and exerts its actions by binding to NPY receptors NPY1R, NPY2R, NPY4R, and NPY5R [165; 166]. The hippocampus and hypothalamus have a high density of these receptors, with three subtypes [NPY1R, NPY4R, and NPY5R] regulating food intake [167]. Two hours after the infusion of PYY led to 30% and 31% reductions in caloric intake in individuals with obesity and lean subjects, respectively [168]. Low postprandial concentrations of PYY are measured in individuals with obesity and may aid in unchecked food intake [169; 170] (Figure 1). However, moderate-intensity aerobic training for 12 weeks increased postprandial PYY levels in men and women with obesity [40] (Figure 2). Changes in PYY concentrations after chronic exercise are variable in persons with obesity, with some studies reporting no changes [30; 65; 171], and other studies measuring increases in PPY levels after a long-term exercise period of more than 12 weeks [172; 173]. Several studies reported increased levels in the PYY hormone in non-obese females and males after acute exercise [174; 175; 176]. Increased PYY levels following exercise induces a feeling of satiety and reduces food intake.
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 2 Experiment and the Procedure  
### Experimental System and Data Acquisition  
In order to verify the effectiveness of the proposed kurtosis criterion VMD and SOM neural network method in gear fault diagnosis, a spiral bevel gear test rig was established and tested. The experimental system is shown in Figure 1. The test rig includes a three-phase asynchronous motor (7) for driving, a speed reducer (5), and an electromagnetic speed control motor controller (8) for motor speed regulation. The reducer (5) is connected to the output shaft by a coupling (3), and the speed of the three-phase asynchronous motor (7) is controlled by a speed controller, which can operate the reducer (5) to be tested at various speeds. The reducer (5) to be diagnosed is Shanghai Nini reducer, the model is T6-3/1-LR (Shanghai Nini reducer Co., Ltd, Shanghai, China), which is driven by three-phase asynchronous motor (7) through coupling (6). The rated power output of three-phase asynchronous motor (7) (Shanghai Bengxin electric motor Co., Ltd, Shanghai, China) is 1.1 kW. Among them, the electromagnetic speed-regulating motor controller (8) (Shanghai Shanchuang instrument & meter Co., Ltd, Shanghai, China) allows manual adjustment of load torque. The vibration signal of the reducer (5) is collected by the acceleration sensor (4), which adopts 4514b-001 acceleration sensor (4) produced by B&K company in Denmark. Among them, the acceleration sensor (4) output is sent to laptop (1) through data acquisition analyzer (B&K, Type 3053-b120, B&K company, Denmark) (2). The acceleration sensor (4) layout position is at the output shaft of gear reducer (5). The B&K data acquisition analyzer (2) is used to collect the vibration signals of the gears when it is, so as to simulate the deterioration process of gear fault. The failure parts of normal gear, gear with tooth wear, gear with tooth crack, and gear with tooth break in the spiral bevel gear test rig is shown in Figure 2.  
Figure 1: Experimental system: (1) laptop, (2) B&K data acquisition analyzer, (3) coupling, (4) acceleration sensor, (5) reducer, (6) coupling, (7) three-phase induction motor, and (8) electromagnetic speed control motor controller.  
In this paper, normal gear, gear with tooth wear, gear with tooth crack, and gear with tooth break under four conditions were analyzed. Among them, the sampling frequency of gear vibration signal is 8192 Hz, the acquisition duration of each signal is 0.25s, and a total of 16 segments of data are collected.
## 4 Numerical Results
### Channel Utilization
(**a**) Assume all the collided slots are only caused by two packets; (**b**) Collided slots are caused by two or more packets in practice. Figure 6: Actual channel utilization when \(z=2,4,10\).
## 3 Results
And the retrieval OAM PDs after the compensation of GS algorithm corresponding to \(C_{n}^{2}=1\times 10^{-15}\)m\({}^{-2/3}\) and \(C_{n}^{2}=1\times 10^{-14}\)m\({}^{-2/3}\) can reach 97.51% and 19.32% respectively. It Obviously indicates that, under the weak turbulence strength level of \(C_{n}^{2}=1\times 10^{-15}\)m\({}^{-2/3}\), the retrieval of the pre-compensation based on the GS algorithm is effective for enhancing the OAM's detecting resolution. In contrast, for the case of turbulence strength level of \(C_{n}^{2}=1\times 10^{-14}\)m\({}^{-2/3}\) with phase screen number of \(N=150\), it obviously closes to strong turbulence strength level; thus, the GS algorithm cannot effectively retrieve the distorted LG beams, which is also the same as the obtained conclusion earlier. \begin{table}
\begin{tabular}{c l l c c c} \hline \hline \(C_{n}^{2}\)**Value** & **OAM Mode** & **W/o or W/ GS** & **N = 20** & **N = 50** & **N = 100** \\ \hline \multirow{4}{*}{\(1\times 10^{-15}\)m\({}^{-2/3}\)} & \(l=2\) & \multirow{2}{*}{W/o GS} & 65.10\% & 58.70\% & 46.88\% \\  & \(l=6\) & & 28.43\% & 25.03\% & 20.80\% \\  & \(l=2\) & & 72.29\% & 70.65\% & 70.60\% \\  & \(l=6\) & & 27.67\% & 29.03\% & 29.01\% \\ \hline \multirow{4}{*}{\(1\times 10^{-14}\)m\({}^{-2/3}\)} & \(l=2\) & \multirow{2}{*}{W/o GS} & 46.58\% & 26.60\% & 17.02\% \\  & \(l=6\) & & 19.82\% & 6.08\% & 6.13\% \\ \cline{1-1}  & \(l=2\) & & 60.69\% & 59.07\% & 52.63\% \\ \cline{1-1}  & \(l=6\) & & 19.63\% & 20.03\% & 16.58\% \\ \hline \multirow{4}{*}{\(1\times 10^{-13}\)m\({}^{-2/3}\)} & \(l=2\) & \multirow{2}{*}{W/o GS} & 12.57\% & 11.84\% & 11.97\% \\  & \(l=6\) & & 3.04\% & 7.10\% & 5.57\% \\ \cline{1-1}  & \(l=2\) & & 15.56\% & 13.95\% & 11.32\% \\ \cline{1-1}  & \(l=6\) & & 5.93\% & 5.27\% & 8.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The PD values of OAM beams without (W/o) and with (W) GS algorithm corresponding to Figure 6.
## 4 Evaluation
### Results of the Operation Characteristic Estimation  
Figure 11 shows the result for a driver of the adjacent lane from the ego vehicle. The X-axis depicts the time, and the Y-axis represents the estimated values of operation characteristic variables. The blue line indicates \(\alpha\), the green one is \(l\), and the black line shows \(m\). These parameters do not have units. It is confirmed that stable values were estimated for all parameters. In addition, the red line indicates the reaction time (in seconds). In the figure, although the reaction time is slightly unstable, the value is distributed around 1.5 s. This value is in the range between 0.92 and 1.94 s, as reported in the previous study [8]. As mentioned in Section 3.2, the operation characteristics may not drastically change in a short time period including the reaction time. Therefore, the moving average window was set with the constant size of 1 s. However, the value was manually determined, and the large value may be more appropriately from this result. The specified parameter setting needs to be investigated as a part of the future work.  
The estimated value at each time is applied to the trajectory prediction; then, the acceleration towards adjacent vehicles can be calculated based on the real-time estimation. As there is no ground truth for the operation characteristic variables, it is impossible to evaluate the performance of the proposed method in this case. The effectiveness of the real-time estimation is discussed in Section 4.4.  
Figure 10: Example of the driving intention estimation: In the figure, \(\tau_{j}\) indicates the time of detecting the lane-changing of adjacent vehicle, and \(\tau_{c}\) represents the time of crossing the lane marking by the vehicle. It is confirmed that the lane-changing was successfully detected in advance before crossing the lane marking.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 2 Fundamentals of the Classification Algorithms Applied  
In this research, several classification algorithms were used, whose fundamentals are described below.
## 4 Results and Discussions
### Effect of Falling Height under Different Submergence Conditions
#### 4.1.3 Cases of Submarine Landslides  
The falling heights (h) are set as \(-0.3\), \(-0.35\), \(-0.4\), \(-0.45\), \(-0.5\) and \(-0.55\), respectively, and the submergence conditions of the landslides are submarine landslides. When the falling heights (h) are \(-0.35\), \(-0.45\) and \(-0.55\) m, the temporal evolution of water elevation in the G\({}_{1}\) and G\({}_{2}\) points are shown in Figure 8. With the decrease in the falling height, the mode of tsunami wave train caused by the submarine landslide becomes more and more complicated and is no longer a regular oscillation mode, and when h = \(-0.55\) m, the wave height increases unexpectedly at 4.2 s. The overall tsunami amplitude caused by the submarine landslides is smaller than that of the subaerial and partial subaerial landslides, of course, in the case of that the landslide volume is the same. In reality, the scope and volume of the submarine landslides are often larger than that of the subaerial and partial subaerial landslides, which leads to the fact that submarine landslides can also cause extremely harmful tsunamis. Submarine landslides usually occur with earthquakes, so in theory, they can occur anywhere in the ocean, rather than just near the coast. When submarine landslides occur on continental slopes or continental bases, the depth of the landslide is large, but the initial wave amplitude is not large, and it is difficult to detect on the sea surface. The large ratio of the wave length to wave height, the extremely fast propagation speed and the imperceptibility of such tsunamis often lead to huge disasters.  
Figure 8: The temporal evolution of the water elevation in G\({}_{1}\) and G\({}_{2}\) with submarine landslide: (**a**) h = 0.25 m; (**b**) h = 0.15 m; and (**c**) h = 0.05 m.  
Figure 7: The temporal evolution of the water elevation in G\({}_{1}\) and G\({}_{2}\) with partial subaerial landslide: (**a**) h = 0.25 m; (**b**) h = 0.15 m; and (**c**) h = \(-0.05\) m.
## 4 WSN Platforms and Energy Harvesting Techniques Investigations
### Energy Harvesting Techniques
#### 4.2.3 Wind Energy Harvesting Systems  
Wind is the second most widely used renewable energy source for generating large-scale power. Large-scale wind power generation technologies have been well-developed and studied for many years. However, research on wind energy harvesting in a small-scale area, specifically for SHM systems, has only emerged in recent years. Wind energy can be accessed during the day and at night, and even under rainy and cloudy conditions as compared to solar energy. Since many bridges are in windy regions, some researchers have paid more attention to wind energy harvesting and regard it as a feasible energy source for wireless sensor nodes in SHM applications [81].  
Table 13 presents an overview of the selected research studies for wind energy harvesting techniques. The second column shows the output voltage. The diameter of the wind  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Voltage (V)**} & \multirow{2}{*}{\
\begin{tabular}{c} **PV Module** \\ **Size (cm\({}^{2}\))** \\ \end{tabular} } & \multicolumn{4}{c}{**Performance**} & \multirow{2}{*}{**Rectification**} & \multirow{2}{*}{**Integration**} \\ \cline{4-4} \cline{6-7}  & & & **Max. Power** & & & \\  & & & **(mW)** & & & \\ \hline \multirow{3}{*}{[38]} & \multirow{3}{*}{5} & \multirow{3}{*}{112} & \multirow{3}{*}{450} & 82 (rechargeable & \multirow{3}{*}{DC-DC converter} & \multirow{3}{*}{DC-DC converter} & \multirow{3}{*}{
\begin{tabular}{c} Rechargeable (Li-Ion) battery and 50 F supercapacitors \\ \end{tabular} } \\  & & & & 75 (supercapacitors) & & supercapacitors \\ \hline
[48] & 4 & - & 3000 & - & - & \\ \hline
[53] & 5.5 & 38.5 & 500 & - & DC-DC converter & Rechargeable battery \\ \hline
[68] & 3.3 & 100 & 360 & 96 &
\begin{tabular}{c} Voltage regulators \\ and Boost-up converters \\ \end{tabular} & 250 F supercapacitors \\ \hline
[69] & 29 & 140 & 28,000.4 & - &
\begin{tabular}{c} Boost-up converters \\ \end{tabular} & Battery \\ \hline
[78] & 3.7 & 78 & 3000 & - & - &
\begin{tabular}{c} Rechargeable (Li-poly) battery \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 12: Solar energy harvesting systems.  
turbine is expressed in the third column. The fourth column presents the performance of the conversion process in terms of maximum output power and conversion efficiency. The fifth and sixth columns express the rectification and the integration processes, respectively. As can be seen in Table 13, the output power and voltage are directly proportional to the diameter of the wind turbine used in the systems. For example, [PERSON] et al. [69] proposed a wind energy harvesting system with maximum output power and an output voltage of 22.4 W and 29 V, respectively. A wind turbine with a diameter of 1.14 m has been used during this process. On the other hand, the system proposed by [PERSON] et al. [38] is capable of generating only 10 mW and 5 V with a 6.3 cm wind turbine.