esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Baseline Case
#### 3.1.2 Baseline Case: Acoustics
[33] reported ranges of F1 = 644-658 Hz, F2 = 989-1059 Hz, F3 = 2530-2763 Hz and F4 = 3643-3715 Hz. Our predictions using the TF method are reasonably close to these values. In addition to formant frequencies, the spacing between formants is also important to quantify vocal tract acoustics. In our study, the distances between F1-F2, F2-F3, and F3-F4 are 308 Hz, 1368 Hz, and 1255 Hz, respectively. These values align closely with the experimental ranges of 331-415 Hz, 1471-1774 Hz, and 880-1185 Hz. The F1-F2 distance is notably smaller than the 690 Hz reported in the LES study. In summary, the formants prediction of the TF method, including both formant frequencies and spacing between them, show good agreement with the experimental study. To further characterize the acoustic output of our model and validate its physiological relevance, we calculated additional acoustic parameters including the sound pressure level and vocal efficiency. The sound pressure level is calculated using:  
\[SPL=20 log_{10}\bigg{(}\frac{RMS(p)}{p_{0}}\bigg{)} \tag{6}\]  
where \(p_{0}=2\times 10^{-5}\) Pa [35]. Based on the pressure-distance relationship, where acoustic pressure \(p\propto r^{-1}\)[14], we estimated the sound pressure level at a distance of 30 cm from the mouth to be approximately 73.78 dB, which aligns with typical human phonation ranges [35; 44; 45]. The vocal efficiency is calculated by:  
\[\eta_{v}=\frac{4\pi r^{2}10^{(SPL-120)/10}}{P_{\text{{\it pul}}}U_{g}} \tag{7}\]where \(P_{pull}\) is the pulmonary pressure, \(U_{g}\) is the mean glottal flow rate [35]. This results in a vocal efficiency of \(6.47\times 10^{-4}\%\) for the current case, which is in a typical range of human phonation [35].
## 2 Vision-Based Structural Displacement Measurement Methods  
### Digital Image Correlation Theory  
Digital Image Correlation (DIC) is one of the most commonly used dynamic displacements measurement method for small structures or components with speckles preinstalled on specimen surface. In DIC, correlation coefficients are used to characterize the approximation of two same size images, and a variety of correlation coefficients definitions have been proposed [15]. For two same size images \(f\) and \(g\) in an image sequence, indicating different deformed states of a component, the most commonly used correlation coefficient is the Normalized Sum of Squared Differences (\(C_{\text{NSSD}}\)) [5; 9], formulated as follows [15]:  
\[C_{NSSD}=\frac{\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}\left[f(i,j)-g(i,j)\right]^{2}} {\sqrt{\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}f(i,j)^{2}}\cdot\sum_{i=0}^{M-1}\sum_{ j=0}^{N-1}g(i,j)^{2}} \tag{1}\]  
where \(M\) and \(N\) are the image height and width, and \(i\) and \(j\) are the row and column pixel coordinates, respectively, all are in terms of pixels. In general, high precision motion tracking in DIC can be divided into two stages, pixel-level displacement location and subpixel-level displacement calculation, as shown in Figure 1.  
Moreover, to achieve high precision measurements, the huge workload in prefabrication and installation of a large number of high-reflectance artificial markers or speckles on structure surface is also a great challenge for engineers in full-field temporal-spatial deformation of large-scale structures. Generally, feature mismatching, computational redundancy, and huge time and economic consumption pose great difficulties to real-time full-field displacement monitoring of large-scale structures. Therefore, it is necessary to put forward a light-weight, full-field temporal-spatial deformation measurement or displacement monitoring method for large-scale structures.  
For a n-frame image sequence, the sub-image of tracked object is selected as mask and the \(C_{NSSD}\) matrices are calculated traversed in all frames of images. In the \(k\)th frame (\(k\) = 1, 2, 3, \(\ldots\), \(n\)), the pixel coordinate (\(x_{kp}\), \(y_{kp}\)), in accordance with the smallest value in \(C_{NSSD}\), is the pixel-level location of the mask in motion. Then, the sub-pixel coordinates (\(x_{k}\), \(y_{k}\)) of tracked target can be calculated using \(C_{NSSD}\) surface fitting, or gray moment conservation [19] in neighborhood of the pixel-level coordinates (\(x_{kp}\), \(y_{kp}\)). The motion of tracked object can be recorded with the initial coordinates (\(x_{0}\), \(y_{0}\)) and a series of dynamic coordinates (\(x_{k}\), \(y_{k}\)) (\(k\) = 1, 2, 3, \(\ldots\), \(n\)) at different times.
## 3 Results
### Linear Correlations between SST and SIF
Figure 4: Time series of basin-wise average SST anomalies over (**a**) Western Indian Ocean, North Indian Ocean and Central Indian Ocean and time series of (**b**) NINO3 SST anomalies, DMI and W-CIO index during monsoon season (2001–2018), which depicts a strong interannual variability. (Software used: Python version 3.7 (2019), [[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/)) (accessed on 12 September 2021) and R version 4.0.5 (2021), [[https://www.R-project.org/](https://www.R-project.org/)]([https://www.R-project.org/](https://www.R-project.org/)) (accessed on 12 September 2021)). Overall, the Pearson correlation coefficient reached a maximum value of \(-\)0.74 between CIO and CPH in June. Only the robust SST-SIF correlations, which are statistically significant (\(p<0.05\)), were considered here. The bolded values for \(p<0.05\) do not account for multiple testing. Broadly, the SIF anomalies of all the ACZs except EHR appear to be impacted by SST variations in at least one tropical Indian Ocean region during one of the monsoon months. The SST anomalies for June in WIO showed statistically significant correlations with the SIF variability of ECPH (\(-\)0.55), EPH (\(-\)0.61), SPH (\(-\)0.48), CPH (\(-\)0.55), TGP (\(-\)0.56), UGP (\(-\)0.59) and WHR (\(-\)0.53), while the correlations with the SIF variability in August in WCPG and LGP were +0.58 and \(-\)0.48, respectively. For the month of September, WIO exhibited a significant correlation with WCPG (+0.59), TGP (\(-\)0.47) and WDR (\(-\)0.51). The SST anomalies in NIO also displayed a considerable degree of correlation with the SIF anomalies of EPH (\(-\)0.53), CPH (\(-\)0.61), MGP (\(-\)0.56), UGP (\(-\)0.54), TGP (\(-\)0.48) and WHR (\(-\)0.50) in June. Considerable SST-SIF links were observed only between NIO and WCPG in August (+0.65) and September (+0.50). Similarly, at the onset of the monsoon, the CIO SST anomalies were linked to the SIF anomalies of EPH (\(-\)0.68), WPH (\(-\)0.66), CPH (\(-\)0.74), TGP (\(-\)0.50), UGP (\(-\)0.55), MGP (\(-\)0.55), GPH (\(-\)0.60) and WHR (\(-\)0.54). These ACZs cover the entire Deccan Plateau except for the southern parts, the full stretch of the Indo-Gangetic Plains and India's hot and cold deserts. When the monsoon peaks in August, the teleconnections with the CIO SST anomalies were confined to the arid regions of GPH (+0.59) and WDR (+0.51) on the western Indian boundary. CIO SST influence shifted to ECPH (+0.53) and EPH (+0.48) of eastern coastal India, corresponding to monsoon withdrawal. However, the significant SST-SIF correlations observed after June were very limited.
## 7 Simulation Results and Discussion  
Simulation results of proposed work after carrying out extensive simulations in Aqua-Sim [37] are presented against three existing state of the art schemes; GEADR [5], EnOR [8], RE-PBR [28] and AUV-CH [29]. The performance is evaluated based on PDR, fraction of local maximum nodes, energy consumption per packet per node, end-to-end delay and depth adjustment. Further the analysis of proposed methodologies is done by varying traffic load as well. The detailed discussion is presented as follows.
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 2 Fault Detection Method
### Feature Construction
#### 2.1.3 Norm-Based Statistical Features (U9-U14)  
To further enhance the fault detection accuracy, we introduce norm-based transformations of the raw current and voltage signals. These features are essential in capturing subtle waveform variations and ensuring robustness to noise.  
* **U9--**Represents the sum of the absolute values of voltage samples over a defined window, effectively capturing the total signal intensity.
* **U10--**Reflects the root mean square (RMS) value of the voltage signal, emphasizing the dominant frequency components.
* **U11--**Extracts the maximum absolute value in the voltage signal, highlighting peak disturbances.
* **U12--**Represents the sum of the absolute values of current samples, useful in quantifying the total current deviation.
* **U13--**Provides an RMS-like measurement for the current, emphasizing significant fluctuations.
* **U14--**Extracts the maximum absolute value in the current signal, useful in detecting extreme deviations due to faults.  
These statistical features enhance the robustness against noise and small signal variations, particularly in scenarios where traditional amplitude-based features fail to provide clear fault indicators.
## 3 Design and Simulation
### Analysis of Preparation Tolerance  
During the production of optical waveguide devices, variations in the length and width of the coupling region can arise due to both the fabrication process and material characteristics. These discrepancies are often unavoidable because they stem from the inherent limitations and fluctuations in manufacturing technologies and the physical properties of the materials used. These deviations can have significant adverse effects on the performance of the device. Therefore, designing devices with good preparation tolerance is essential. Preparation tolerance refers to a device's ability to maintain high performance despite variations in its fabrication. Devices with significant tolerance margins can accommodate these variations without substantial decline in performance. This not only ensures consistent device operation but also leads to reduced manufacturing costs, as it minimizes the need for highly precise and expensive fabrication processes. Additionally, it minimizes performance fluctuations, thereby enhancing the reliability of the optical systems in which these devices are used [21]. In this study, we aim to analyze the device tolerance passes under the condition of constant coupling zone width. This approach allows us to systematically study how length variations affect the excess loss and imbalance of the device. This analysis helps to determine the optimal dimensions that provide the best performance while maintaining a high tolerance to fabrication variations.  
As depicted in Figure 7, when \(W_{0}\) is kept constant and \(L_{\text{MMI}}\) increased from 3.5 to 4.5 \(\upmu\)m, the excess loss and imbalance are less than 1 dB. These results demonstrate that the device is highly tolerant to variations in \(L_{\text{MMI}}\). To further investigate the resulting values of excess loss and imbalance, while keeping the length of the coupling region constant, the width of the coupling region was modified.  
Figure 7: Effect of length of coupling region on excess loss and imbalance.  
As depicted in Figure 8, when \(L_{\text{MMI}}\) is kept constant and \(W_{0}\) increased from 2 to 2.8 \(\upmu\)m, the excess loss and imbalance exhibited a strong variation. This can be attributed to the fact that the \(L_{\text{MMI}}\) is proportional to the square of \(W_{0}\). Thus, even a slight variation in \(W_{0}\) causes a more significant change in both excess loss and imbalance compared to that caused by variation in length. To maintain low levels of excess loss and imbalance, the width deviation of the coupling region should be kept within the range of \(\pm\)0.2 \(\upmu\)m.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 4 Vision-Aided PDR System Design
### Visual Place Recognition  
The main task of VPR is to find a similar image in the image dataset search by comparing the images with the stored database to find out where the image is located. The algorithm diagram is shown in Figure 5. The main steps of visual place recognition are:  
* Extract features from the location image in the original map library, and establish a feature vector dataset.
* Extract the feature vector of the image to be predicted in the same way.
* Compare the feature vectors in the dataset and the feature of the image to be predicted by the method and select the location corresponding to the most similar feature vector as the predicted value.  
To assist PDR system to get better positioning performance, in this paper, we train a VPR network for indoor scenes. The following subsections present the structure of our VPR network, training process, ranking search algorithm and the results of place recognition.
# New Security Improvements in Next-Generation Passive Optical Networks Stage 2 +
## 3 Security in NG-PON2
### Main Possible Security Weakness  
Currently, cryptography is confronted by many cyberattacks. The brute force attack [31] is the simplest and oldest. An attacker tries to guess a secret key in the brute force attack. This attack may act ineffectively, but due to the development of computers, this attack was used in many cases. The increase in computer performance caused an increase in the length of authentication and encryption keys used in cryptographic algorithms. If the length of the secret key is sufficiently long and does not allow performing the brute force attack in polynomial time, an attacker can find security or implementation weaknesses of the used cryptographic algorithm for the purpose of unauthorized authentication or obtaining the secret key. Another way that the attacker can obtain the authentication and encryption key is by tampering with the device. If the attacker obtains the secret key, then it is able to use it for authentication and decrypting data. The attacker can also create a clone of the device from which the attacker obtained the secret key. This problem can also occur in NG-PON2 in the case where the attacker somehow obtains the registration_ID of an ONU. The attacker can use the registration_ID for authentication and to compute the MSK and derived shared keys. The attacker can also clone the ONU by using its registration_ID. This is the main security weakness that we observe in the NG-PON2 standard. This security threat can be solved by physical unclonable functions [32].
## 4 Results and Discussion
### Model Validation from Experimental Data
#### 4.1.3 Purification Efficiency  
The simulated and measured purification efficiency of the CCKEF system on three test days is shown in Figure 9.  
In engineering applications, grease is deposited in the grease purifier, especially before system cleaning (the case kitchen was cleaned at the beginning of August). Grease accumulation reduces the purification efficiency of the electrostatic purifier. As a result, the measured purification efficiency was as low as 82.72% on April 30 and increased to about 85% on 30 August and 11 September, as shown in Figure 9. The measured purification efficiency of the CCKEF system was lower than the designed value. The simulated purification efficiency was slightly higher than the measured value, with a deviation between 4.86% and 9.28% but close to the designed value.  
By comparing the simulated and measured values of air temperature, air velocity at different measurement points and the purification efficiency of the CCKEF system, it was found that the simulated and measured deviations were all within 10%, indicating that the established numerical model can better reflect the actual operation of the system and can be used for performance prediction of CCKEF system in engineering practice.
## 3 Results and Discussion  
### Analysis of Raw Material  
The composition of REMES is displayed in Tables 1 and 2. In this slag, Nd, Pr, and Gd are the major REEs. Additionally, according to Figure 1, neodymium fluoride was the major phase of the REEs and neodymium oxyfluoride was also present, with some oxide impurities, including Fe\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\). As shown in Figure 2, the XPS analysis illustrated that neodymium compounds were mainly composed of NdF\({}_{3}\) and that gadolinium was combined with both O and F, while praseodymium was combined with O. Notably, the XRD pattern shows no peak of Al, while the XPS result displays a peak of Al\({}_{2}\)O\({}_{3}\), which might be caused by the detection limit of XRD.  
To determine the distillation temperature, the raw material was analyzed by TG-DSC. The results are shown in Figure 3a. The DSC curve showed an endothermic peak at approximately 950 K, and some substance might have melted at this temperature. Additionally, the weight change of REMES had no obvious difference until approximately 1100 K. Figure 3b shows the saturated vapor pressure of LiF calculated by the Antoine equation [33], which reaches 10 Pa before 1180 K, indicating that the evaporation of some substance might start at 1100 K. Therefore, the initial distillation temperature for experiments with different temperatures was selected as 1173 K.  
In a previous study, the distillation temperature of NdF\({}_{3}\) was estimated [34]. NdF\({}_{3}\) could evaporate from the slag after 1473 K at 1 Pa. Compared with the saturated vapor pressure of LiF, NdF\({}_{3}\) and LiF could condensate at different places.  
Figure 3: (**a**) The TG-DSC results of the raw material; (**b**) the saturated vapor pressure of LiF.  
Figure 2: XPS results of the raw material: (**a**) Nd; (**b**) Gd; (**c**) Pr; (**d**) Al.  
In summary, the REMES was mainly composed of REEs, fluorine, carbon, lithium, and some oxide impurities. Rare earth elements primarily existed as fluorides, with some forms of oxyfluorides and oxides. Lithium was mainly lithium fluoride. The main components of the oxide impurities were silicon, iron, and aluminum oxides. Combined with the TG-DSC results and the thermodynamic analysis of NdF\({}_{3}\) and LiF, the vacuum distillation method could be used to recycle this slag.
# Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece
## 4 Discussion
### Study Limitations  
During the conduct of the research, certain limitations were presented. Specifically, the analysis is based on a sample that consists of 1347 trips. The clusters represent the prevailing mobility patterns observed in 2022 and do not capture long-term variations or trends. The representation of all socio-demographic groups was not equal in this set. Young people are highly represented and that is why a distinct cluster that describes their trips was formed. Another limitation of this research was the lack of real-life trip distances, i.e., network distances. Network distances deviate from the Euclidean ones; a uniform multiplier was used in all distances to describe these deviations. Nevertheless, this assumption may not be realistic in all cases. A more detailed breakdown of the zones inside the study area seems useful to achieve smaller extents and thus greater accuracy in distances. Additionally, the trip duration could be an additional dimension in the estimation process of clusters. This would allow the consideration of traffic congestion, public transport frequencies and network coverage revealing interesting spatial patterns about mode choice. Furthermore, there was no differentiation of public transport beyond the categories of buses and train services. This research did not consider multimodal trips since survey respondents reported only the main transport mode. Lastly, all respondents included in the dataset described at most five trips. This constrained the ability for someone to report a (more) complex travel schedule. Nevertheless, it questionable and requires further research how spectral clustering can treat complex itinerates. In addition, there were no plans with zero trips; this case would correspond to remote workers who have emerged after the COVID-19 pandemic. Remote workers may also be enclosed in some clusters that are associated with intra-zonal trips, namely: clusters 1 and 6.
## 2 Design of the State Vector
### Error of the State Vector
\begin{array}{l}\delta\ddot{\overline{\mathbf{q}}}_{ic}=\hat{\mathbf{q}}_{ic}^{*} \otimes\left(\frac{\mu_{c}}{2}\overline{\mathbf{q}}_{i}^{\mu_{\omega}}\otimes \overline{\mathbf{\omega}}_{i}\otimes\overline{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}+ \frac{1-\mu_{c}}{2}\overline{\mathbf{q}}_{ic}\otimes\overline{\mathbf{\omega}}_{c}- \frac{\mu_{\omega}}{2}\overline{\mathbf{q}}_{i}^{\mu_{\omega}}\otimes\hat{\mathbf{ \omega}}_{i}\otimes\hat{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\otimes\overline{\mathbf{q }}_{ic}-\frac{1-\mu_{\omega}}{2}\hat{\mathbf{q}}_{ic}\otimes\hat{\mathbf{\omega}}_{c} \otimes\overline{\mathbf{q}}_{ic}\right)\\ =\left[\frac{\mu_{c}}{2}\delta\overline{\mathbf{q}}_{ic}\otimes\overline{\mathbf{q}}_{ ic}{}^{*}\otimes\overline{\mathbf{q}}_{i}^{\,\,\mu_{\omega}}\otimes(\hat{\mathbf{ \omega}}_{i}+\Delta\overline{\mathbf{\omega}}_{i})\otimes\overline{\mathbf{q}}_{c}^{\, \,1-\mu_{\omega}}-\frac{\mu_{\omega}}{2}\left(\hat{\mathbf{q}}_{c}^{\,\,1-\mu_{ \omega}}\right)^{*}\otimes\left(\hat{\mathbf{q}}_{i}^{\,\,\mu_{\omega}}\right)^{* }\otimes\hat{\mathbf{q}}_{i}^{\,\,\mu_{\omega}}\otimes\hat{\mathbf{\omega}}_{i} \otimes\hat{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\otimes\delta\overline{\mathbf{q}}_{ ic}\right)\\ +\left[\frac{1-\mu_{\omega}}{2}\delta\overline{\mathbf{q}}_{ic}\otimes\left( \overline{\mathbf{q}}_{c}+\Delta\overline{\mathbf{\omega}}_{c}\right)-\frac{1-\mu_{ \omega}}{2}\hat{\mathbf{\omega}}_{c}\otimes\delta\overline{\mathbf{q}}_{ic}\right]\\ \left[\begin{array}{l}\frac{\mu_{\omega}}{2}\delta\overline{\mathbf{q}}_{ic} \otimes\left(\overline{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\right)^{*}\otimes \overline{\mathbf{\omega}}_{i}\otimes\overline{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\\ +\frac{1-\mu_{\omega}}{2}\delta\overline{\mathbf{q}}_{ic}\otimes(\overline{\mathbf{q}} _{c}^{\,\,1-\mu_{\omega}})^{*}\otimes\Delta\overline{\mathbf{\omega}}_{i}\otimes \overline{\mathbf{q}}_{ic}^{\,\,1-\mu_{\omega}}\\ -\frac{\mu_{\omega}}{2}\left(\hat{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\right)^{*} \otimes\overline{\mathbf{\omega}}_{i}\otimes\overline{\mathbf{q}}_{ic}^{\,\,1-\mu_{ \omega}}\otimes\delta\overline{\mathbf{q}}_{ic}\end{array}\right]+\left[\begin{array} []{l}\frac{1-\mu_{\omega}}{2}\delta\overline{\mathbf{q}}_{ic}\otimes\hat{\mathbf{ \omega}}_{c}\\ +\frac{1-\mu_{\omega}}{2}\delta\overline{\mathbf{q}}_{ic}\otimes\Delta\overline{\mathbf{ \omega}}_{c}\\ -\frac{1-\mu_{\omega}}{2}\hat{\mathbf{\omega}}_{c}\otimes\delta\overline{\mathbf{q}}_{ic} \end{array}\right]\\ \end{array} , \tag{11}\]  
According to Appendix A, the rotation of \(\mathbf{p}_{ic}\) with respect to \(\overline{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\) is written as follows:  
\[\left(\hat{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\right)^{*}\otimes\overline{\mathbf{p}}_{ ic}\otimes\hat{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\triangleq\hat{\mathbf{R}}\left(\hat{\mathbf{q}}_{c}^{\,\,1-\mu_{ \omega}}\right)^{T}\mathbf{p}_{ic}=\hat{\mathbf{R}}_{\mu_{\omega}c}^{\,\,T}\mathbf{p}_{ ic}, \tag{12}\]  
where \(\mathbf{R}_{\mu_{\omega}c}^{\,\,1}\) is the rotation matrix converted from \(\overline{\mathbf{q}}_{c}^{\,\,1-\mu_{\omega}}\).