esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 5 Measurement Results
### Comparison of Processing Time  
To verify the low complexity of the proposed channel estimation method, the duration of MLT and TIFA processing was evaluated and compared. Since the first stage, i.e., LS pilot estimation, is the same for both methods, it was excluded from the time measurement. Moreover, the pilot averaging phase in the MLT method was excluded as well due to it being an optional step. Consequently, only the processing times required for the interpolation of channel coefficients were compared. MLT uses MATLAB's inbuilt _griddata_ function to perform cubic interpolation. The source code of this function was customized so that all the unnecessary fragments, i.e., validation of input arguments and handling exceptions, could be removed to minimize the processing time.  
In order to evaluate the processing time, _tic to_ stopwatch functions were used. They counted the total time required to interpolate channel coefficients for 128 consecutive NB-IoT frames. For each channel estimation method, 80 iterations were conducted using waveforms with different SNR values and channel profiles. The platform used for the processing was equipped with an i7-10700 CPU. Table 2 presents the minimum, maximum and mean value of all the processing times. The results show that the linear interpolation and averaging procedure proposed in TIFA is about seven times faster than cubic interpolation used in the MLT method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Min** & **Max** & **Mean** \\ \hline MLT & 547 ms & 619 ms & 563 ms \\ TIFA & 69 ms & 95 ms & 74 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Duration of channel coefficient interpolation for consecutive 128 NB-IoT frames.  
Figure 11: Percentage of decoded MIB-NB ans SIB1-NB information blocks among trials with successful synchronization.
## 3 Methods
### Change Point Detection  
Analytically, one approach for the determination of change-points (CPs) is by Binary Segmentation. In addition, CPs can be determined as stochastic points of changing states using Hidden Markov Models (see Section 3.5 on Methods for HMM). We use both of these methods to assist the interpretation of the ozone spatiotemporal maps predicted from RFsp. Note, that HMMs can also provide a further complementary understanding of the temporal and the lagged effect between elevated aerosol levels induced by bushfires and depletion of ozone levels.  
Change-point detection is a pivotal area in the statistical analysis of time series. A single change-point detection problem is detailed in [54, 55], assuming the model is Gaussian. Various methods are available also for the detection of multiple change-points. Three of them are available in the R package changepoint. We briefly describe only one of the three methods here, namely Binary Segmentation. Binary Segmentation was first described in the paper [56] by [PERSON] and [PERSON] in 1965.  
Implementation of the Change Point Analysis  
We have used the R package changepoint in our study, refer to [37]. The changepoint package implements the methods described above for both single change-point and multiple change-point detection. A standard approach to such problems is a _generalized likelihood ratio statistic._ The method is described in [57]. This also serves as a reference for the R package changepoint ([[https://cran.r-project.org/web/packages/changepoint/index.html](https://cran.r-project.org/web/packages/changepoint/index.html)]([https://cran.r-project.org/web/packages/changepoint/index.html](https://cran.r-project.org/web/packages/changepoint/index.html)), accessed on 14 June 2022). The tests vary according to the families of distributions being analyzed and the specific parameters being tested.  
Several other R packages are available for change-point detection, including strucchange ([[https://cran.r-project.org/package=strucchange](https://cran.r-project.org/package=strucchange)]([https://cran.r-project.org/package=strucchange](https://cran.r-project.org/package=strucchange)), accessed on 25 June 2022), cumSeg ([[https://cran.r-project.org/package=cumSeg](https://cran.r-project.org/package=cumSeg)]([https://cran.r-project.org/package=cumSeg](https://cran.r-project.org/package=cumSeg)), accessed on 14 June 2022), and stepR ([[https://cran.r-project.org/package=stepR](https://cran.r-project.org/package=stepR)]([https://cran.r-project.org/package=stepR](https://cran.r-project.org/package=stepR)), accessed on 15 June 2022). These are discussed in detail and their methodologies are compared both algorithmically and in terms of results against specific problems in [58].
# Team-Communication Modeling Framework for Multi-Unit Nuclear Power Plant Accident Analysis
## 3 Results and Analysis
### Team-Communication Analysis Methodology
Then, for variability analysis, we modified the original simple version of the FRAM analysis by taking into account the nature of communication. We decided that the ultimate goal of team communication is to deliver the right information in the right way at the right time. The \"right information\" requirement leads to a new content variability, the \"right way\" requirement changes the definition of the existing precision variability, and the \"right time\" requirement is identical to the existing time variability. The time variability is manifested into too early, on time, too late, and not at all; the precision variability is manifested into precise, acceptable, and imprecise; and the newly added content variability is manifested into relevant, correct, irrelevant, and none. Figure 3 illustrates the addition to the original FRAM variability analysis.
###### Abstract  
This study investigates the vehicle-bridge coupling vibration performance of corrugated steel web box girder bridges under three-dimensional pavement roughness conditions. To effectively account for these roughness characteristics, a three-dimensional contact constraint method is proposed. The accuracy of the proposed method is first verified, followed by an analysis of a 30 m span corrugated steel web box girder bridge to evaluate the influence of vehicle speed, pavement grade, roughness dimensions, and box girder configurations on the impact factor. The results show that the impact factor does not consistently increase with vehicle speed. As pavement conditions worsen, the impact factor shows an upward trend, with each grade of road surface deterioration resulting in an average 19.1% increase in the impact factor. In most scenarios, three-dimensional pavement roughness results in smaller impact factors compared to two-dimensional pavement roughness, with average reductions of 2.4%, 7.3%, and 13.5% for grade A, B, and C roads, respectively. Replacing the corrugated steel web with a flat steel web leads to an average reduction of 4.2% in the mid-span dynamic deflection of the bridge, despite the impact factors of both configurations being relatively similar. Substituting the concrete bottom slab with an equivalent steel bottom slab increases the mid-span dynamic deflection by an average of 28.4% and nearly doubles the impact factor. The impact factors determined by most national standards generally fall within the range for grade A pavement, suggesting that the calculation methods in these standards are mainly suited for newly constructed bridges or those in good maintenance.  
corrugated steel web box girder bridge; vehicle-bridge coupling vibration; three-dimensional pavement roughness; contact constraint method; impact factor +
Footnote †: journal: Applied sciences
###### Abstract  
Future generation communication systems are exemplified by 5G and 6G wireless technologies, and the utilization of integrated air-to-sea (A2S) communication infrastructure is employed to extend network coverage and enhance data throughput to support data-driven maritime applications. These ground-breaking techniques have promoted the rapid development of the maritime internet of things (MIoT). In particular, the integration of air base stations (ABSs) in the MIoT can achieve broadband, low-delay, and reliable wireless transmissions. Considering the potential of ABS-enabled communications, this survey presents the state of the art in the A2S integrated MIoT. More specifically, relevant A2S integrated MIoT architectures are discussed together with the role of their building blocks. Next, we introduce the enabling technologies, including the sensor, communication, data processing and storage, and security and privacy protection techniques. Then, resource allocation, cloud/edge computing and caching, routing protocols, and spatial location optimization in the maritime environment are discussed and grouped based on their performance targets. Additionally, we also show the potential applications of the A2S integrated MIoT in marine environment monitoring, traffic, navigation safety, and resources management. Finally, several future challenges in the area of the A2S integrated MIoT are given, related to the technical security, reliability, and energy efficiency, etc.  
air base stations (ABSs); air-to-sea (A2S) integrated communication; future generation communication systems; maritime internet of things (MIoT) +
Footnote †: journal: Marine Science and Engineering  
0
Footnote 0: _J. Mar. Sci. Eng._**2024**, 12, 11. [[https://doi.org/10.3390/jmse12010011](https://doi.org/10.3390/jmse12010011)]([https://doi.org/10.3390/jmse12010011](https://doi.org/10.3390/jmse12010011))
## 2 Materials and Methods
### Materials
#### 2.2.4 Application Development and Smartwatch Control  
In collaboration with a software engineering expert, an application was developed to collect HR and affective data and another one to control smartwatches. The app installed on the smartphone for the measurement of HR and affect was to activate the HR sensor of smartwatch to initiate HR measurement and to collect data via participants' affective self-reports, which was designed to be stored automatically on Google Cloud. A watch control app was also developed on the Galaxy S9 with Android Pie (version 9.0) to ensure that the measurement app operated effectively on the smartwatch. The watch control app comprised four modes: watch connection, HR measurement, affect measurement, and data management. In the \"watch connection\" mode, the connection status of up to 30 smartwatches was monitored. The \"HR measurement\" mode was used to initialize, start, pause, or stop the smartwatch HR sensor. In the \"affect measurement\" mode, a vibration alarm was sent to the participants on their smartwatches, which cued to report current feelings and arousal states by selecting responses on the watch's touch screen. The HR and affective data were stored in the Google Cloud for real-time monitoring (Figure 1). All data acquired during the experiment were freely accessible to be downloaded or deleted via the \"data management mode\".
## 4 Indoor Dynamic Beam Deflection Measurement Experiment and Validation  
To validate the proposed method for structural full-field dynamic deflection measurement, an indoor cantilever vertical beam vibration experiment was conducted, and results were compared with those of the Digital Image Correlation (DIC) method.
# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 4 Materials
### Data Analysis
#### 4.3.1 Pitch  
Mean values of all F0 measurements for English and Korean speakers are shown in Figures 2 and 3, respectively. In both TORGO and QoLT datasets, speakers with dysarthria generally have higher F0 values. Even English speakers with mild dysarthria tend to have a higher F0, except in the case of maximum F0. Furthermore, English speakers with mild dysarthria have a lower standard deviation (25.35 Hz) compared to healthy speakers (35.5 Hz), supporting the findings from [30] that speakers with mild dysarthria tend to be more mono-pitch. However, the opposite is apparent in Korean speakers where healthy speakers have a slightly lower standard deviation (30.2 Hz) compared to the mild group (35.2 Hz).  
A two-way mixed ANOVA revealed no significant two-way interaction between the severity of the group and pitch measure for English, where F (5.33, 19.56) = 0.59, \(p>0.05\), but a significant interaction existed with Korean speakers, where F (6.31, 69.38) = 2.90, \(p<0.05\). Note when calculating F-values, the values in brackets refer to the degree of freedom for between-subject and within-subject variables. Furthermore, a post-hoc test with a Bonferroni adjusted \(p\)-value revealed that the simple main effect is significant for max F0, where F (3, 33) = 2.02, \(p<0.05\).
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
###### Abstract  
In recent decades, wireless sensor networks (WSNs) have become a popular ambient sensing and model-based solution for various applications. WSNs are now achievable due to the developments of micro electro mechanical and semiconductors logic circuits with rising computational power and wireless communication technology. The most difficult issues concerning WSNs are related to their energy consumption. Since communication typically requires a significant amount of energy, there are some techniques/ways to reduce energy consumption during the operation of the sensor's communication systems. The topology control technique is one such effective method for reducing WSNs' energy usage. A cluster head (CH) is usually selected using a topology control technique known as clustering to control the entire network. A single factor is inadequate for CH selection. Additionally, with the traditional clustering method, each round exhibits a new batch of head nodes. As a result, when using conventional techniques, nodes decay faster and require more energy. Furthermore, the inceptive energy of nodes, the range between sensor nodes and base stations, the size of data packets, voltage and transmission energy measurements, and other factors linked to sensor nodes are also completely unexpected due to irregular or hazardous natural circumstances. Here, unpredictability represented by Triangular Fuzzy Numbers (TFNs). The associated parameters of nodes were converted into crisp ones via the defuzzification of fuzzy numbers. The fuzzy number has been defuzzified using the well-known signed distance approach. Here, we have employed a multi-criteria decision-making (MCDM) approach to choosing the CHs depending on a bunch of characteristics of each node (i) residual energy, (ii) the number of neighbors, (iii) distance from the sink, (iv) average distance of cluster node, (v) distance ratio, and (vi) reliability. This study used the entropy-weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) approach to select the CH in WSNs. For experiments, we have used the NSG2.1 simulator, and based on six characteristics comprising residual energy, number of neighbor nodes, distance from the sink or base station (BS), average distance of cluster nodes, distance ratio, and reliability, optimal CHs have been selected. Finally, experimental results have been presented and compared graphically with the existing literature. A statistical hypothesis test has also been conducted to verify the results that have been provided.  
wireless sensor networks; base station; TOPSIS; entropy; multi-criteria-decision-making; NSG2.1 simulator; NS2 simulator
## 3 Numerical Formulations for the Normalized Fokker-Planck Equation
### Discretization of the Normalized Fokker-Planck Equation  
The modified FP equation is a one-dimensional non-linear hyperbolic parabolic PDE in which both convection and diffusion terms are involved. The numerical algorithms for the FP equation can be obtained based on the procedures discussed in Section 3.1. The equation to be solved is Equation (16), which is analogous to the model equation, Equation (25), with the relations:  
\[\
u=\sin\varphi-\gamma;\ \rho=\frac{1}{\alpha} \tag{34}\]  
The implicit finite difference methods are computationally advantageous methods with respect to the explicit ones since they are unconditionally stable. In addition to the [PERSON]'s BTCS implicit method, the CN method is second-order accurate both in time and space domains.  
Applying the [PERSON]'s BTCS implicit scheme for the FP equation, we have the following formulation for numerical implementation  
\[\frac{g_{j}^{n+1}-g_{j}^{n}}{\Delta\tau}=(\gamma-\sin\phi)\frac{g_{j+1}^{n+1}- g_{j-1}^{n+1}}{2\Delta\phi}+\frac{1}{\alpha}\frac{g_{j+1}^{n+1}-2g_{j}^{n+1}+g_{ j-1}^{n+1}}{(\Delta\phi)^{2}} \tag{35}\]  
and  
\[g_{j}^{n+1}-g_{j}^{n}=(\gamma-\sin\phi)\frac{\Delta\tau}{2\Delta\phi}(g_{j+1} ^{n+1}-g_{j-1}^{n+1})+\frac{\Delta\tau}{\alpha\left(\Delta\phi\right)^{2}}(g_{ j+1}^{n+1}-2g_{j}^{n+1}+g_{j-1}^{n+1}) \tag{36}\]  
which can be rearranged  
\[\left[(\gamma-\sin\phi)\frac{\Delta\tau}{2\Delta\phi}-\frac{\Delta\tau}{a \left(\Delta\phi\right)^{2}}\right]g_{j-1}^{n+1}+\left[1+\frac{2\Delta\tau}{a \left(\Delta\phi\right)^{2}}\right]g_{j}^{n+1}-\left[(\gamma-\sin\phi)\frac{ \Delta\tau}{2\Delta\phi}+\frac{\Delta\tau}{\alpha\left(\Delta\phi\right)^{2} }\right]g_{j+1}^{n+1}=g_{j}^{n} \tag{37}\]  
Equation (37) can be represented as  
\[-(\eta+\frac{\xi}{2})g_{j-1}^{n+1}+(1+2\eta)g_{j}^{n+1}-(\eta-\frac{\xi}{2})g _{j+1}^{n+1}=g_{j}^{n} \tag{38}\]  
where \(\xi=(\sin\varphi-\gamma)\frac{\Delta\tau}{\Delta\phi}\), representing the Courant number, and \(\eta=\frac{\Delta\tau}{\alpha\left(\Delta\phi\right)^{2}}\), representing the diffusion number.  
Employing the CN scheme, we have  
\[\frac{g_{j}^{n+1}-g_{j}^{n}}{\Delta\tau}=(\gamma-\sin\phi)\frac{\overline{ \delta}_{\phi}g_{j}^{n}+\overline{\delta}_{\phi}g_{j}^{n+1}}{4\Delta\phi}+ \frac{1}{\alpha}\frac{\delta_{\phi}^{2}g_{j}^{n}+\delta_{\phi}^{2}g_{j}^{n+1}} {2(\Delta\phi)^{2}} \tag{39}\]  
With the finite-difference operators defined as  
\[\overline{\delta}_{\phi}g_{j}^{n}=g_{j+1}^{n}-g_{j-1}^{n};\delta_{\phi}^{2}g_ {j}^{n}=g_{j+1}^{n}-2g_{j}^{n}+g_{j-1}^{n} \tag{40}\]the CN implicit formulation, after rearrangement, is given by  
\[-(\eta+\frac{\xi}{2})g_{j-1}^{n+1}+2(1+\eta)g_{j}^{n+1}-(\eta-\frac{\xi}{2})g_{j+ 1}^{n+1}=(\eta+\frac{\xi}{2})g_{j-1}^{n}+2(1-\eta)g_{j}^{n}+(\eta-\frac{\xi}{2} )g_{j+1}^{n} \tag{41}\]
## 1 Introduction
When the incident and reflected light are described using Jones vectors, the pBRDF becomes a Mueller matrix characterizing the polarization transmission of the optical element, according to the transformation relationship between the Jones matrix and the Mueller matrix [3]. Based on the principle that different physical properties of the surface defects of optical elements lead to a different modulation of the polarization state of the reflected light, this paper proposes a new method for the optical identification of surface roughness [14; 16], pockmarks, dirty particles above the surface, and SSBDs [17] in optical elements using RMMS. In this method, firstly, the combination of the BRDF and the Rayleigh-Rice vector scattering theory is innovatively proposed to construct the pBRDF model for various types of defects on the surface of optical elements, and then the BRDF is numerically analyzed to obtain the optimal detection conditions for defects on the surface of optical elements. On this basis, surface roughness and pockmarks on the surface of optical elements made of borosilicate at the nanometer level were simulated and analyzed via RMMS, and the accuracy of the simulation results was verified through spectroscopic ellipsometry experiments. Then, dirty particles and pockmarks, as well as SSBD, on the surface of the optical element were simulated and analyzed via RMMS [13]. Based on the RMMS of various types of defects on the surface of optical elements, the detection and classification of pockmarks, roughness, dirty particles, and SSBD on the surface of optical elements were realized [10; 18; 19]. Finally, the analysis of the one-to-one correspondence between the RMMS measurements and the size of various types of defects allowed the calculation of the dimensions of various types of defects utilizing Muller's matrix elements [17].
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 2 Materials and Methods
### Pre-Processing RGB-D Data
#### 2.4.4 The Ground-Truth  
After post-processing, the next step for the evaluation of the reconstruction is to generate a cuboid of reference points, in relation to the robot's base, to be used as ground-truth data. This was performed based on the first camera data. The charuco board was identified and the markers' corner was extracted from the image, as shown in Figure 6. Based on the cuboid geometry, the top reference was generated by rotating \(-90^{\circ}\) around the \(x\)-axis, followed by a rotation of \(180^{\circ}\) around the \(y\)-axis. Then, a translation of \(280\) mm, \(187\) mm and \(-190\) mm in the \(x\), \(y\) and \(z\) directions, respectively, was carried out, as shown in Equation (12). The backside was generated by rotating \(180^{\circ}\) around the \(y\)-axis and translating \(280\) mm and \(-200\) mm in the \(x\) and \(z\) directions, respectively, as shown in Equation (13).  
\[{}_{top}T^{b}=T^{0}*_{cub}T^{cam0}*trans_{a}*rot_{x^{-}90}*rot_{y^{180}}*(_{cub }T^{cam0})^{-1} \tag{12}\]  
where \({}_{top}T^{b}\) is the transformation from the top of the charuco origin to the robot's base, \(T^{0}\) is the transformation from TCP to the base, \({}_{cub}T^{cam0}\) is the transformation from the first camera to the charuco, \(trans_{a}\) is the translation, \(rot_{x^{-}90}\) is the rotation in the \(x\)-axis, \(rot_{y^{180}}\) is the rotation in the \(y\)-axis, and \((_{cub}T^{cam0})^{-1}\) is the inverse transformation from the first camera to the charuco.  
\[{}_{r}T^{b}=T^{0}*_{cub}T^{cam0}*trans_{b}*rot_{y^{180}}*(_{cub}T^{cam0})^{-1} \tag{13}\]  
where \({}_{top}T^{b}\) is the transformation from the top of the charuco origin to the robot's base, and the other terms have been explained in the previous paragraph.  
The final reference cuboid can be seen in Figure 10 with the coordinate system of every side shown as a visual aid.  
Figure 9: Use of a sharpening filter to improve ArUco recognition. (**a**) No filter applied. (**b**) Filter applied.  
The camera positions make it impossible to see only one charuco board at a time. To solve this challenge, the board can be rotated when it can be seen in at least two camera positions, or two (or more) boards can be used with known geometries.  
In this work, two reconstructions were performed using the charuco board and one using a robotic arm's position.