esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Noise Source Localization Method Using GIB with 'p+v' Joint Processing
### GIB Based on the Joint Processing of 'p+v'  
The transfer relationship in the above GIB algorithm is the free-field Green function. However, when using the vector microphones for an SMA, the transmission relationship should consider the transmission relationship of velocity. Therefore, the transfer function derivation process of the GIB method, based on vector microphone and its 'p+v' sound pressure and velocity joint processing, is carried out as follows: The sound pressure and velocity in the steady state sound field can be directly measured through the vector microphone. At the same time, the sound pressure and velocity meet the [PERSON] formula:  
\[\
abla p=j\omega\rho v \tag{8}\]  
where \(\
abla\) represents gradient, \(j\) represents imaginary unit and \(\rho\) represents air density. At the same time, the sound pressure at position \(r_{v}\) can be written as follows:  
By introducing the above formula into [PERSON]'s formula, we can get the velocity:  
\[v=\frac{1}{j\omega\rho}\
abla p=\frac{1}{j\omega\rho}\frac{jkr_{v}-1}{4\pi r_{v} ^{2}}e^{jkr_{v}}Q=\frac{jkr_{v}-1}{j\omega\rho r_{v}}p_{v} \tag{11}\]  
Divide the numerator and denominator of the above formula by \(jkr_{v}\) at the same time, and the velocity can be simplified as:  
\[v=\frac{1+j\lambda/2\pi r_{v}}{\rho c}p_{v} \tag{12}\]  
According the 'p+v' joint treatment mode, is adopted, the combination form can be expressed as:  
\[p_{v}+v=\frac{1+\rho c+j\lambda/2\pi r_{v}}{\rho c}p_{v}=\frac{1+\rho c+j \lambda/2\pi r_{v}}{4\pi\rho cr_{v}}e^{jkr_{v}}Q \tag{13}\]  
It can be seen from the above formula that after the joint treatment of 'p+v', the sound field transfer function is equal to:  
\[G_{pv}=\frac{1+\rho c+j\lambda/2\pi r_{v}}{4\pi\rho cr_{v}}e^{jkr_{v}} \tag{14}\]  
When there are no vector microphones, the velocity in the sound field cannot be directly measured. An alternative method to measure velocity uses the finite difference of the two sound pressure microphones in the same direction. A schematic diagram of this differential principle is shown in Figure 2.  
The sound pressure gradient is equal to:  
\[\
abla p=\frac{p_{1}-p_{2}}{r_{1}-r_{2}}=\frac{G(s,r_{1})Q-G(s,r_{2})Q}{r_{1}- r_{2}}=\frac{G(s,r_{1})-G(s,r_{2})}{r_{1}-r_{2}}Q \tag{15}\]  
The velocity can be obtained by substituting the above equation into Equation (8):  
\[v=\frac{1}{j\omega\rho}\frac{G(s,r_{1})-G(s,r_{2})}{r_{1}-r_{2}}Q \tag{16}\]  
where, \(G(s,r_{1})-G(s,r_{2})/j\omega\rho(r_{1}-r_{2})\) is the transfer function from the velocity point to the sound source, and the sound pressure at the velocity point can be approximated as \(p_{v}=(p_{1}+p_{2})/2\) (The sound pressure at this point can also be obtained according to the virtual SMA extrapolation method). Figure 2: Difference schematic diagram of dual-radius spherical array. The black circle and dot represent the actual SMA and the array element respectively, and the red and blue represent the virtual SMA. In addition, the red circle also indicates the position of the velocity obtained by the difference.
## 2 Materials and Methods
### Antioxidant Property Assays
#### 2.5.3 ABTS Assay  
Another method for testing the antioxidant properties [27] of extracts used the 7.4 mM solution of ABTS\({}^{+}\) and 2.6 mM solution of potassium persulfate mixed in equal amounts and left in the dark for 12 h at room temperature. The test solution was created by mixing 3 mL of the prepared ABTS\({}^{+}\) solution with 180 mL of ethanol. Then, 190 \(\upmu\)L of the resulting solution was mixed with 10 \(\upmu\)L of the plant extract to be tested and left in the dark for 2 h. After this time, the absorbance of the samples was measured at 734 nm on a multiplate reader (Tecan Infinite 200 Pro, TK Biotech, Warszawa, Poland). As the reference samples, TROLOX solutions were also tested at concentrations of 200 \(\upmu\)M/mL, 100 \(\upmu\)M/mL, 50 \(\upmu\)M/mL, 25 \(\upmu\)M/mL. Ethanol solution (70%, \(v/v\)) was measured as a blank. Three determinations were made for each sample. The results are presented as the mean of all three measurements along with the standard deviation.  
The antioxidant activity converted to Trolox against the ABTS reagent was calculated from the curve obtained by measuring the absorbance of prepared Trolox solutions of different concentrations (Equation (3)).  
\[\mathrm{y}=-0.0015\mathrm{x}+0.3029, \tag{3}\]
# Virtual Fire Evacuation Drills through a Web-Based Serious Game
###### Abstract
[11] designed a web-based serious game to study the self-concept of visually impaired children. [PERSON] et al. [13] developed a web-based serious game decision support tool for hydrological multi-hazard analysis. However, few studies have investigated and developed web-based serious games for fire evacuation training; therefore, the web-based training systems need to be fully designed. To resolve Challenge (2), many optimization methods for 3D scenes can be employed [29; 30; 31; 32; 33; 34]. For instance, [PERSON] et al. [31] designed a real-time fire evacuation system by simplifying the models of a large-scale scene and dynamic smoke. [PERSON] et al. [34] proposed a Web3D-based lightweight solution for real-time visualization of large-scale building information modeling (BIM) models, which can consider the redundancy, semantics, and parameterization of BIM data under the limited resources of network bandwidth and web browsers. However, most of the existing optimization methods are mainly focused on simplifying 3D scenes but not on web programs. In this study, the optimization solution for both 3D scenes and web programs were investigated. To resolve Challenge (3), certain studies can provide methods for assessing drill data [35; 36; 37; 38]. For example, [PERSON] et al. [36] allowed participants to explore a virtual museum based on a real-time interactive environment, and the way-finding performance of the participants during a fire emergency scenario was evaluated according to their spatial exploration mode. [PERSON] et al. [38] proposed a real-time virtual reality (VR) training system for evacuation based on [PERSON]'s experiential learning theory, where the evacuation time of trainees can be outputted in a timely manner to assess the training effect. However,most evaluations of fire drills are based on evacuation characteristics (e.g., evacuation time and spatial exploration mode), and fire hazards (e.g., high temperature and smoke toxicity) are rarely considered. In this study, both evacuation characteristics and fire hazards were considered in the evaluation to provide a comprehensive guide for training. In this study, a web-based serious game for virtual fire evacuation drills is proposed. Firstly, the system framework of the serious game is designed for web-based training. Secondly, an optimization solution including fire scenes and web codes is designed for smooth rendering performance. Lastly, a solution is designed to visualize the evacuation paths of numerous trainees, which can be used to reveal the evacuation rules, and an evaluation model of evacuation performance is created considering the features of evacuation paths and fire hazards, to provide comprehensive feedback for trainees. To validate the effectiveness of the proposed web-based serious game, more than 100 people participated in the online virtual evacuation drill of a dormitory building fire. The drill results indicated that the evacuation abilities of trainees gradually improved with the addition of training times.
# Hybrid Improved PSO Algorithm for Soil Property Parameter Estimation
## 3 Improvement of the PSO Algorithm
### Improvement in Initialization of Positions  
In the PSO algorithm, the random function rand is typically used to initialize the initial positions and velocities of the particle swarm. While this method significantly enhances the randomness of particles, it is challenging to ensure the uniformity of particles across the search space, making it easier for particles to become trapped in local optima and affecting the convergence accuracy of the algorithm. Chaotic mapping [19] is a mathematical model widely applied in nonlinear dynamical systems, characterized by high irregularity and pseudo-randomness. The SPM chaotic mapping (Equation (18)) is selected for initialization due to its superior ergodicity and computational efficiency, as demonstrated in image encryption studies [20]. By combining Sine and PWLCM mappings, the SPM map expands the chaotic range while ensuring full traversal of the search space, with its piecewise function design effectively avoiding particle aggregation in local regions (as shown in Figure 2). Experimental tests with parameters \(\eta=0.4\) and \(\mu=0.3\) maximize the Lyapunov exponent (\(\approx\)0.5), enhancing chaotic behavior. Furthermore, compared to hyper-chaotic systems, SPM reduces computational complexity by 58% while maintaining equivalent security, making it particularly suitable for high-dimensional optimization problems where both efficiency and robustness are critical. Chaotic initialization can also reduce the sensitivity of the algorithm to the initial parameter settings. Equation (18) provides the SPM chaotic sequence:  
\[x(i+1)=\begin{cases}mod\Big{(}\frac{x(i)}{\eta}+\mu sin(\pi x(i))+r,1\Big{)}&0 \leq x(i)<\eta\\ mod\Big{(}\frac{x(i)/\eta}{0.5-\eta}+\mu sin(\pi x(i))+r,1\Big{)}&\eta\leq x(i)< 0.5\\ mod\Big{(}\frac{1-x(i)/\eta}{0.5-\eta}+\mu sin(\pi(1-x(i)))+r,1\Big{)}&0.5\leq x (i)<1-\eta\\ mod\Big{(}\frac{1-x(i)}{0.5-\eta}+\mu sin(\pi(1-x(i)))+r,1\Big{)}&1-\eta\leq x (i)<1\end{cases} \tag{18}\]  
where \(mod(a,b)\) denotes the remainder of \(a/b\), and \(r\) is a random number between 0 and 1. The experimental results indicate that when \(\eta=0.4\) and \(\mu=0.3\), the sequence generated by the SPM mapping exhibits better ergodicity and randomness, with the chaotic orbit values in the range of [0, 1]. Figure 2 illustrates the distribution and frequency histograms of SPM sequences and random sequences with an overall size of 200, exhibiting values between 0 and 1. It can be observed that the SPM sequence values are more evenly distributed.
# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 3 The Construction Process of the Synthesis Health Index for Metro Vehicle Wheelsets
### Classification of Metro Vehicle Wheelset Health States Based on Hierarchical Clustering
#### 3.4.2 Health Status Assessment of Test Samples  
The health status assessment of test samples, specifically the wheelsets of subway vehicles still in service, is conducted based on distance similarity to construct the SHI.  
The historical and real-time detection data of the test samples are processed using the same dimensionality reduction and outlier removal methods applied to the training samples to obtain the reduced data. After establishing the baseline SHI from the training data, the SHI for the test samples is calculated by determining the Euclidean distance between their data and the baseline.  
Furthermore, the health status of the test samples can be evaluated based on their real-time detection data. Clustering the training samples yields the geometric centers \(C\) and radii \(r\) for each class, where the radius \(r\) is defined as the distance from the farthest point in the class to its geometric center. By identifying the class to which the real-time detection data of the test samples belong, the most recent health status of each sample can be determined. The specific process is as follows:  
Step 1: Calculate the distances between the latest detection data point and the geometric centers of all classes and obtain the minimum distance value, \(d_{min}=\min(d_{1},d_{2},d_{3},\ldots,d_{n})\).  
Step 2: Compare the distance \(d_{min}\) with the corresponding class radius \(r_{k}\). If \(d_{min}<r_{k}\), the latest detection data point of the test sample belongs to that class, thereby determining the health status of the test sample. If \(d_{min}>r_{k}\), the data point may be classified as an outlier or may indicate the emergence of a new class, necessitating further analysis based on the specific context.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation
### Experimental Analysis of Faulty Rolling Bearing
#### 4.2.6 Results and Discussions  
Compared with the other four methods, the proposed method has a better performance for the feature extraction of a single-channel vibration signal of a faulty rolling bearing. The feature of fault frequency is more obvious and noise energy is obviously reduced. The experimental analysis of the faulty rolling bearing proves that our proposed method can estimate the number of fault signals and is efficient to extract the features of a single-channel signal with multiple faults.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sequence Number** & **NSVR** \\ \hline
1 & 1.524 \\
2 & 36.810 \\
3 & 6.884 \\
4 & 3.922 \\
5 & 5.516 \\
6 & 0.981 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ratios of neighboring singular values.  
Figure 15: The Hilbert envelope spectra of the extracted fault signals after KICA. (**a**) The first fault signal. (**b**) The second fault signal.
# Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder
## 5 Adaptive Control Design  
Since the actuator is a hydraulic differential cylinder, two separate states \(z_{ff}^{+}\) and \(z_{ff}^{-}\) are used for out-stroke and in-stroke motion to handle model uncertainties both directions of motion. Consequently, both the feedforward control output and the update law for the two gains are only active during out-stroke or in-stroke motion respectively. To handle this, some switching logic is introduced based on the sign of the velocity reference. The block diagram for the differential MIT-rule adaptive feedforward is shown in Figure 13.  
The governing equations for the differential MIT-rule adaptive feedforward are shown in Equations (20)-(23).  
\[z_{ff}^{+} =\begin{cases}\gamma\cdot v_{ref}\cdot e,&v_{ref}>0\\ 0,&\text{otherwise}\end{cases} \tag{20}\] \[z_{ff}^{-} =\begin{cases}0,&v_{ref}>0\\ \gamma\cdot v_{ref}\cdot e,&\text{otherwise}\end{cases}\] (21) \[u_{ff} =\begin{cases}z_{ff}^{+}\cdot v_{ref},&v_{ref}>0\\ z_{ff}^{-}\cdot v_{ref},&\text{otherwise}\end{cases}\] (22) \[u =k_{p}\cdot e+u_{ff} \tag{23}\]  
where  
\(z_{ff}^{+}=\) out-stroke feedforward gain;  
\(z_{ff}^{-}=\) in-stroke feedforward gain;  
\(u_{ff}=\) feedforward controller output.  
Extending the controller to sign-sign is straightforward. The block diagram for the differential sign-sign adaptive feedforward is shown in Figure 14.  
Figure 13: Differential MIT-rule adaptive feedforward.  
The governing equations for the differential sign-sign adaptive feedforward are shown in Equations (24)-(27).  
\[\dot{z}^{+}_{ff} =\begin{cases}\gamma\cdot\text{sign}(v_{ref})\cdot\text{sign}(e),&v _{ref}>0\\ 0,&\text{otherwise}\end{cases} \tag{24}\] \[\dot{z}^{-}_{ff} =\begin{cases}0,&v_{ref}>0\\ \gamma\cdot\text{sign}(v_{ref})\cdot\text{sign}(e),&\text{otherwise}\end{cases}\] (25) \[u_{ff} =\begin{cases}z^{+}_{ff}\cdot v_{ref},&v_{ref}>0\\ z^{-}_{ff}\cdot v_{ref},&\text{otherwise}\end{cases}\] (26) \[u =k_{p}\cdot e+u_{ff} \tag{27}\]
## 3 Improved Q-Learning Algorithm for Better Management of Channel Utilization in UWCNs
### Rule for Q-Learning Updates  
Q-learning updates often follow this rule:  
\[\begin{split}& Q(s,a)=Q(s,a)+\\ &\alpha\big{[}R(s,a)+\gamma\max a^{\prime}Q(s^{\prime},a^{\prime} )-Q(s,a)\big{]}.\end{split} \tag{17}\]  
where for state-action pair \((s,a)\), \(Q(s,a)\) is the current Q-value. The learning rate is represented by \(\alpha\). The discount factor is denoted by \(\gamma\). After executing the operation, the new state is \(s^{\prime}\). The highest Q-value attainable in the new state is \(\max a^{\prime}Q(s^{\prime},a^{\prime})\)[21; 52].
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.3 Directional Antennas  
Directional antennas [10] are used to locate sources of interference. By focusing on the directionality of signals, these antennas allow the precise tracking of unauthorized transmissions, which is critical for resolving interference issues in airport environments or urban settings.
## 2 Motivation and Related Works
### Emergency Network Architectures  
Emergency network architecture deployment has been previously studied for the provision of wireless communications and related services. [PERSON] _et al._[12] have proposed an emergency network architecture, which is based on the integration of satellite and Long-term Evolution (LTE) networks for both infrastructure-based and infrastructure-less scenarios. The LTE services were brought to the disaster area using deployable mobile units and through a satellite backhaul. The architecture can provide easy connectivity in both indoor and outdoor scenarios in disasters, extended coverage and high performance guarantees, without requiring extensive configuration. [PERSON] [13] considered that natural disasters and terrorist acts had significant potential to disrupt ground emergency communication systems, including first-responder, cellular, landline and emergency answering services, such as 911. He concluded that the HAP network architecture could be fitted with telecommunications equipment and used to support these critical communications missions when a catastrophic event occurred. [PERSON] _et al._[14] presented a smart emergency signaling system architecture for tunnels and large infrastructures in smart city scenarios, which significantly improves the actual functionality of the signal. [PERSON] _et al._[15] investigated how the HAP network architecture effectively supports multimedia broadcast/multicast service (MBMS) in a scenario where the existing terrestrial network infrastructure is not available. A radio resource management (RRM) technique was proposed for implementation in a multi-hop scenario where mobile _ad hoc_ networks (MANETs) cooperated with an HAP system towards a common goal of enhancing the access to MBMS services. [PERSON] _et al._[16] described the design and implementation of a proof-of-concept prototype, named the active disaster response system (ADRS), which automatically performs emergency tasks when an earthquake happens. In [12; 13; 14; 15; 16], the authors provide a number of different architectures for emergency networks. However, there are quite a few works in the literature that emphasize the IHS architecture's suitability for emergency communications.
## Case Study: Data Analysis and Results  
### Overview  
The RADARSAT-2 datatakes were processed by MDA and the final single-look complex (SLC) images were the starting point for the data analysis.  
The primary analysis goal is to derive the transponder ERCS and, equally importantly, an associated uncertainty statement. In principal, the transponder ERCS \(\varsigma_{t}\) can be derived if the ERCS of a reference target (placed in the same scene) \(\varsigma_{r}\) is known according to the proportionality  
\[\varsigma_{t}=\frac{P_{t}}{P_{r}}\varsigma_{r} \tag{4}\]  
Here, \(P_{t}\) and \(P_{r}\) are the point target intensities of the transponder and the reference target, derived from the processed SAR images and possibly expressed as digital numbers.  
Figure 3: Map of imaged area, showing the locations of the 3.0 m corners.  
Figure 2: Locations of the transponder on the DLR premises and of the 1.5 m corners on the adjacent airport in Oberpfaffenhofen, Germany. (Map tiles in this and Figure 3 reproduced with permission from Stamen Design, based on data from OpenStreetMap.)  
The analysis is split into two parts:  
1. Point target analysis: Extract the relative point target impulse response powers for all point targets in all scenes (see Section 4.2).
2. Parameter estimation: Set up a statistical model to derive the estimated transponder ERCS and corresponding uncertainty from all datatakes (see Section 4.3).
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### QoS Metrics
#### 3.3.2 Jitter  
Jitter is considered as an important metric in many VANET applications [47]. It is defined as the average of delay variation between two consecutive packets received at the destination [48]. The jitter of a vehicle \(u\) is defined as the average of jitters of transmitted packets to the neighbouring vehicles [49]. It is calculated using Equation (6):  
\[J(u)=\frac{\sum_{v\in N(u)}J(u,v)}{card(N(u))} \tag{6}\]  
where \(card(N(u))\) is the number of neighbours of vehicle \(u\) and \(J(u,v)\) is the jitter between vehicles \(u\) and \(v\).
## 4 Discussion
### Potential Extensions for Producing Large-Area Maps  
This study proposes a rice identification approach that need not rely on rice paddy sample plots. It could be used for accurate rice extraction relying only on the time-series Sentinel-1 SAR images and a global surface water dataset. This approach is simple, effective, and also has the advantage for automatic rice mapping. It can produce a large-area, high-precision rice map. Since this method does not rely on the local prior knowledge, it also has the potential to expand to other national regions of Indochina Peninsula, Southeast Asia, or the world.  
Although this research has obtained a high accuracy of rice extraction (OA equal 89.52%), it is still affected by other sources of errors, and the accuracy of our proposed approach needs to be further improved. The proposed method relies heavily on rice paddies containing irrigation water; when it encounters fields of upland rice, they may not be recognized. However, the planting area of upland rice is very small, accounting for less than 0.3% of the whole of Thailand, which can be ignored in this study. When there are land cover types similar to the temporal SAR signature of rice, such as wetlands or other areas with water and vegetation cover, additional auxiliary information is needed to further distinguish them [53; 64; 68]. In practical applications, wetlands and similar areas are sparsely distributed in rice cultivation areas, so they could be ignored in our study. Different rice planting systems (triple, double, and single cropping rice) have different time-series curves, while the differences in plant morphology and growth period caused by different rice varieties also affect the final backscattering of rice. These studies will be analyzed and discussed in detail in the future.