esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Field Variation of ELF Near-Region Excited by HED in a Homogeneous Half-Space Model
###### Abstract  
Great attention has been paid to the propagation of electromagnetic (EM) waves across the sea surface due to its important applications. Most of the previous research, however, focuses on the half-space model illustrating the deep sea environment. In this paper, EM field distribution in the extremely low frequency (ELF) near-region under horizontal electric dipole (HED) excitation in homogeneous half-space seawater is analyzed based on the general expression of the Sommerfeld integral using the quasistatic approximation method. The focus is on deriving complete and effective solutions in air and seawater regions under the cylindrical coordinates for the EM near-field, which is generated by an HED in a shallow sea. The resulting formulas can be given by a few summands in closed form as the well-known Fourier-Bessel integrals. The analytical approximate expression of ELF Sommerfeld EM field integral excited by the HED in the homogeneous half-space seawater is deduced under the condition that the propagation distance \(\rho\) satisfies \(k\rho<<1\). To this end, the EM field distribution in the range close to the HED antenna in seawater is simulated, the results have shown that the minimum attenuation value of the vertical electric component \(E_{z}\) is about 15 dB, and that of the radical magnetic components \(H_{\varphi}\) is about 30 dB, and these values are found to be of greatest potential for the near-field region propagation among the electric and magnetic components. Finally, the correctness of the proposed method is verified by comparison with [PERSON]'s approximation method and [PERSON]'s exact expression approximation method, which demonstrated the correctness of the proposed method.  
extremely low frequency; quasistatic approximation; Sommerfeld integral; horizontal electric dipole +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
We succeeded in the transportation and quality control of the RBC solution for transfusion to a remote island using UHs. We believe that temperature is the most important factor affecting the quality of RBC solutions during transportation.  
The UH transport was able to transport blood under controlled temperatures, and the actual analysis of the blood showed that it was possible to meet the rapid demand for blood transfusions anywhere in Japan, including remote areas, which proved our hypothesis. Blood transported at an appropriate temperature can also be used before the expiration date if the blood was not used upon arrival.  
This trial is the first to transport packs of RBC solution for transfusion beyond a part of the western Pacific Ocean. Our success indicates novel transportation avenues for remote islands. In the long run, we want to ensure safe and secure transport to supply RBC solutions to remote Japanese islands.  
**Author Contributions:** Conceptualization, K.Y.; safety, M.M. and N.H.; demonstration, K.Y. and T.Y.; testing and certification of data, H.F. and M.N.; writing and administration, F.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by Japan Airlines Corporation.  
**Institutional Review Board Statement:** The ethics committee of The Tokyo Metropolitan Bokutoh Hospital approved this project at No. 30-082-3, 7 June 2019.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** A part of this study was written based on the results of Smart Island Projects promoted by the Ministry of Land, Infrastructure, Transport and Tourism in Japan.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Modulation Techniques
### On-Off Keying  
OOK can use either non-return-to-zero (NRZ) or return-to-zero (RZ) pulse formats. In NRZ-OOK, the absence of peak optical represents a digital symbol \"0\", whereas the transmission of an optical pulse of peak power \(\mathrm{P_{T}}\) represents a digital symbol \"1\".  
BER of OOK is given by:  
\[\mathrm{P_{OOK}}=\mathrm{Q}\left(\sqrt{\frac{E_{b}}{2N_{0}}}\right) \tag{8}\]  
where \(E_{b}/N_{0}\) is referred to as SNR per bit.  
The average energy per bit \(E_{b}\) calculated as \(E_{b}=2(RP_{r})^{2}T_{b}\), where R is photodetector responsivity, \(P_{r}\) is average received optical signal power, \(T_{b}\) is bit duration, and \(N_{0}/2\) is double-sided power spectral density determined as, \(N_{0}=2 qI_{B}\), where, \(q\) is the electron charge, and \(I_{B}\) is the average photocurrent generated by the background light.  
The performance of the OOK-modulated FSO link under different turbulence regimes can be determined by substituting P(I) from Equation (4) and bit error rate from Equation (8) into Equation (7). Using different values of the refractive index structure \(C_{n}^{2}\), Rytov variance (\(\sigma^{2}\)) for different channel conditions can be determined. The \(\sigma^{2}\) can be further used to calculate the impact of turbulence eddies represented by \(\alpha\) and \(\beta\) in Equations (4) and (6), respectively. The received irradiance (P(I)) can then be calculated and substituted further in Equation (7).  
Using the procedure explained here, the link performance of the FSO link for the rest of the optical modulation formats explained in Equations (9)-(13) can also be determined by using them in Equation (7).
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Embeddings  
Labelling speech turns by speaker identity requires only a choice among a set of known subscribed speakers (case 1 in Section 4), or deciding whether the turn matches a speaker that was identified earlier in the data (case 2). In comparing a model of a known (trained) speaker with that of a speaker turn in SD, one may calculate an appropriate distance and compare this with a threshold. Since representation spaces are usually complex for SV (and many other pattern classification), simple distances (e.g., Euclidean) are often sub-par, even after normalization (e.g., of means, variances, or L2 length).  
A common method today to classify a set of data of variable duration (e.g., a speaker turn) is to (1) extract a _speaker embedding_ (a mapping from data of any duration to a fixed-length vector) with a model [37], and then (2) use a multi-class discriminative training stochastic classifier such as _probabilistic linear discriminant analysis_ (PLDA) [38]. Extraction of speaker embeddings is typically d-vectors or x-vectors, which are based on i-vectors (as noted in Section 5.4), and derived from inner layers of a neural network trained on speaker classification. Speaker embeddings assume that only one speaker is present in each segment, and thus, they do not handle overlapped speech.
# Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region
## 3 Methods
### Indicator Relative Density Calculation and Standardization  
Indicator relative density (IRD) refers to the ratio of each indicator value to its reference value, which is a key step in calculating the EQI. A method joining eco-geographical zones with ecosystem types was used for the calculation of the IRD. In this study, according to the natural geographical environment, the B&R region was divided into 10 eco-geographical zones including the West Asia zone, South Asia zone, East Asia zone, Southeast Asia zone, Central Asia zone, Russia zone, Oceania zone, Europe zone, North Africa zone and Southern Africa zone. Otherwise, the vegetation ecosystem type contained the cultivated land ecosystem, forest ecosystem, grassland ecosystem, shrub ecosystem, wetland ecosystem and tundra ecosystem according to the LULC distribution in the B&R region.  
Firstly, based on the eco-geographical zone and the ecosystem type of the B&R region, the indicators' maximum values in each eco-geographical zone and ecosystem type were obtained as the reference value. The final IRD of each eco-geographical zone and ecosystem type for each year was calculated using Equation (1).  
\[\text{IRD}_{i,j,k}=\frac{\text{F}_{i,j,k}}{\text{F}_{\text{max},i,j,k}}, \tag{1}\]  
where \(\text{F}_{i,j,k}\) and \(\text{F}_{\text{max},i,j,k}\) are the indicator value and its reference value for the \(i\)-th year, \(j\)-th eco-geographical zone and its \(k\)-th ecosystem type, respectively.  
Next, data standardization was conducted for all of the IRDs. The IRDs were calculated using different indicators that were distributed at different dimensions and dimensional units and would affect the data analysis. Therefore, the z-score method (Equation (2)) was used to normalize the IRDs to a uniform scale with mean values of 0 and a standard deviation of 1 which obeyed a standard normal distribution and is suitable for comprehensive comparative evaluation.  
\[Z=\frac{x-\overline{x}}{\sigma}, \tag{2}\]  
where \(Z\) is the standardized IRD, \(x\) is the input original IRD, \(\overline{x}\) is the arithmetic mean value of \(x\) and \(\sigma\) is the standard deviation of \(x\).
# One-Dimensional Consolidation Properties of Soft Clay under Multi-Stage Loading
## 6 Conclusions  
1. The total time-deformation law of multi-stage loading is the cyclic, nonlinear extension of that of single-stage loading. The final deformation of multi-stage loading and single-stage loading is approximately equal. However, it is important to note that the consolidation rate of single-stage loading is four times higher than that of multi-stage loading.
2. The consolidation coefficient of soft marine soil from northern China gradually decreases with an increase in consolidation stress. Follow the relationship \(C_{\
u}=\frac{2.33(1+e_{0})k_{0}}{\gamma_{m}C_{c}}\sigma^{\prime}e^{-a\sigma^{ \prime}}\).
3. The average degree of consolidation increases non-linearly with multi-stage loading. An increased number of multi-stage loading leads to a longer time of primary consolidation. Furthermore, the average degree of consolidation exhibits a logarithmic relationship with the load: (\(U_{t}=M\ln P+N\)).
4. In the calculation of consolidation for a soft soil foundation under multi-stage loading, the settlement predicted via the Terzaghi one-dimensional consolidation equation may have a 25% error compared to that predicted via the modified equation. The modified Terzaghi one-dimensional consolidation equation provides a more accurate representation of the actual consolidation behavior of soft soil.  
Conceptualization, [PERSON] and J.W.; methodology, W.Y.; validation, X.Y. and X.L.; investigation, W.Y.; resources, J.W.; data curation, X.Y.; writing--original draft preparation, W.Y.; writing--review and editing, J.W.; supervision, J.W.; project administration, W.Y.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42107169.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: Relationship between settlement and time under multi-stage loading.
## 3 Results
### PLS-DA Classification Models  
To further investigate predictive relationships between spectral inputs and class labels, PLS-DA was carried out. This approach enabled an efficient classification of spectral data derived from both cranial and extracranial sites. The K-S algorithm was used to select representative samples that captured the variability within the dataset and to define the calibration subset for model development. The dataset was partitioned into training (70%) and testing (30%) subsets for model calibration (training) and validation (test), respectively (as reported in Table 2). By adopting this approach, the risk of possible overfitting was mitigated. Figure 5: Principal Component Analysis (PCA) score plots of the first two principal components (**a**) based on spectra collected from the cranial/cortical site in both patients (MS) and healthy subjects (Normal). Panel (**b**) shows the loadings plot for the first principal component. Figure 6: Principal Component Analysis (PCA) score plots of the first two principal components (**a**) based on spectra collected from the cranial/brainstem site in both patients (MS) and healthy subjects (Normal). Panel (**b**) presents the loadings plot for the first principal component. All four classification models showed outstanding classification performance. As reported in Table 2, each model achieved perfect scores across all key performance indicators (including sensitivity, specificity, precision, and accuracy) during calibration (C), cross-validation (CV), and external prediction (P), with an error rate of zero in every case. Although the models achieved perfect classification performance, the lack of an external validation dataset remains a limitation, and future studies will aim to assess generalizability using independent test sets. Variables with a VIP score exceeding 2 played an important role in distinguishing tissue types. The VIP score plots for the \"MS Biceps/Normal Biceps\" and \"MS Triceps/Normal Triceps\" comparisons are shown in Figure 7, while those for \"MS Cortical/Normal Cortical\" and \"MS Brainstem/Normal Brainstem\" are shown in Figure 8.
## 3 The LOBIN Prototype: A Practical Study Case
### System Description
* The Location Subsystem consists of a set of Beacon Points (BPs), which are deployed in well-known positions, and a set of end devices (so-called Location Wireless Transmission Boards--LWTBs), which are carried by targeted users (e.g., patients or any other personnel from the hospital). The BPs, as their name suggests, send beacons periodically with well-known transmission power. The LWTBs collect signal strength information received from different BPs and send it wirelessly. * The Wireless Communications Infrastructure Subsystem (WCIS) is placed in between the Location and Healthcare-Monitoring Subsystems and the Management Subsystem. It is responsible for carrying data from the former to the latter and commands from the latter to the former. It consists of a set of devices (so-called Distribution Points--DPs) that transmit ad hoc data up to a Gateway, which forwards them to the Management Subsystem. Thus, the Gateway  
Figure 3: System Architecture. interfaces with the WCIS (non-IP-based) and with the wired communications infrastructure (IP-based) connected to the Management Subsystem. * The Management Subsystem represents the Information Technology (IT) infrastructure that handles the information associated with every single patient. It consists of a Management Server, which processes and stores all the data associated with the patients, and a Graphical User Interface (GUI), which allows the hospital staff to monitor the status of the patients. This subsystem can be integrated into commercial hospital management systems. Next, each subsystem is explained in detail.
## 2 Kinematic Modeling
### Kinematic Model with Errors  
The actual and nominal forward kinematics of the parallel robot are inconsistent due to the manufacturing and installation errors. The pose of the parallel robot considering the manufacturing and installation errors can be denoted by position vector \(\mathbf{t}^{\prime}\) (\(\mathbf{t}^{\prime}=[x^{\prime}\ y^{\prime}\ z^{\prime}]\)) and orientation vector \(\mathbf{\omega}^{\prime}\) (\(\mathbf{\omega}^{\prime}=[\mathbf{\omega}^{\prime}\ \beta^{\prime}\ \gamma^{\prime}]\)). Thus, the rotation transformation matrix of the mobile platform relative to the fixed base can be expressed as follows:  
\[\mathbf{R}^{\prime}=\begin{bmatrix}cos\beta^{\prime}cos\gamma^{\prime}&\dot{s}ina^ {\prime}sin\beta^{\prime}cos\gamma^{\prime}-cos\dot{s}in\gamma^{\prime}&cos^{ \prime}sin\beta^{\prime}cos\gamma^{\prime}+\dot{s}ina^{\prime}sin\gamma^{ \prime}\\ cos\beta^{\prime}sin\gamma^{\prime}&\dot{s}ina^{\prime}sin\beta^{\prime}sin \gamma^{\prime}+cosa^{\prime}cos\gamma^{\prime}&cosa^{\prime}sin\beta^{\prime }sin\gamma^{\prime}-\dot{s}ina^{\prime}cos\gamma^{\prime}\\ -sin\beta^{\prime}&\dot{s}ina^{\prime}cos\beta^{\prime}&cosa^{\prime}cos\beta ^{\prime}\end{bmatrix} \tag{12}\]  
Therefore, an actual forward kinematics including the manufacturing and installation errors is constructed. By substituting Equation (12) and position vector \(\mathbf{t}^{\prime}\) into Equation (6), the actual forward kinematics of the parallel robot can be expressed as follows:  
\[\begin{cases}f_{1}=\left\|\mathbf{R}^{\prime}\mathbf{a}^{\prime}_{1}+\mathbf{t}^{\prime \mathrm{T}}-\mathbf{b}^{\prime\mathrm{T}}_{1}\right\|-l^{\prime}_{01}-l_{m1}=0\\ f_{2}=\left\|\mathbf{R}^{\prime}\mathbf{a}^{\prime}_{2}+\mathbf{t}^{\prime\mathrm{T}}-\mathbf{b }^{\prime\mathrm{T}}_{2}\right\|-l^{\prime}_{02}-l_{m2}=0\\ f_{3}=\left\|\mathbf{R}^{\prime}\mathbf{a}^{\prime}_{3}+\mathbf{t}^{\prime\mathrm{T}}-\mathbf{b }^{\prime\mathrm{T}}_{3}\right\|-l^{\prime}_{03}-l_{m3}=0\\ f_{4}=\left\|\mathbf{R}^{\prime}\mathbf{a}^{\prime}_{4}+\mathbf{t}^{\prime\mathrm{T}}-\mathbf{b }^{\prime\mathrm{T}}_{4}\right\|-l^{\prime}_{04}-l_{m4}=0\\ f_{5}=\left\|\mathbf{R}^{\prime}\mathbf{a}^{\prime}_{5}+\mathbf{t}^{\prime\mathrm{T}}-\mathbf{ b}^{\prime\mathrm{T}}_{5}\right\|-l^{\prime}_{05}-l_{m5}=0\\ f_{6}=\left\|\mathbf{R}^{\prime}\mathbf{a}^{\prime}_{6}+\mathbf{t}^{\prime\mathrm{T}}-\mathbf{b }^{\prime\mathrm{T}}_{6}\right\|-l^{\prime}_{06}-l_{m6}=0\end{cases} \tag{13}\]  
The global Newton-Raphson algorithm, as introduced in Section 2.2, can also be utilized to solve the actual forward kinematics of the parallel robot. Accordingly, the Jacobi matrix to solve the actual forward kinematics is expressed as follows:  
\[\mathbf{J}=\begin{bmatrix}\frac{\partial f_{1}}{\partial x}&\frac{\partial f_{1} }{\partial y}&\frac{\partial f_{2}}{\partial y}&\frac{\partial f_{3}}{ \partial y}&\frac{\partial f_{1}}{\partial y}&\frac{\partial f_{1}}{\partial y }\\ \frac{\partial f_{2}}{\partial x}&\frac{\partial f_{2}}{\partial x}&\frac{ \partial f_{2}}{\partial x}&\frac{\partial f_{
# Fiber-Optic System for Monitoring Pit Collapse Prevention
## 3 Results  
The experiments were carried out by two methods. The first method controlled changing the intensity of the light spot and changing the pixel pattern when a displacement occurred.  
The number of the pixels increased as the OF diverged (Figure 8).  
The plot of the number of pixels dependence on displacements is shown in Figure 9.  
To improve reliability of the results, they were verified by the second method, which consists of controlling additional losses using an optical power meter. When a displacement occurred, increasing additional losses was recorded. In both experiments, an almost linear  
Figure 8: Changing the number of spots with displacement of the optical fiber ends: (**a**) position 1; (**b**) position 2.  
Figure 7: Plot obtained from the developed software.  
Figure 9: Number of pixels dependence on displacement.  
dependence was obtained, which made it possible to construct a sufficiently accurate sensor for measuring displacements depending on the change in the properties of the light wave passing through the fiber core when their ends diverged inside the optical ferrule.  
A plot of optical loss versus displacement is shown in Figure 10.  
An automatic approximation of the experimental results was carried out, and it was found that the best mathematical model for numerical optimization is a linear approximation of the measurement results. As a result, the laboratory sample of the fiber-optic sensor showed a fairly high degree of linearity. An absolute error of 2.31, a relative error of 6.21% and a Student's coefficient of 2.120 with a confidence interval of 0.94 were calculated based on the measurement results. The hardware and software complex was designed to monitor rock displacements and crack expansions by such a parameter as an increase in additional losses in FOS, which is converted into a change in the number of pixels. The larger the offset is, the more white pixels are generated, so the calculated accuracy satisfies the production requirements. It is important to control not only the exact numerical parameters of its expansion but also the growth of the crack. Measures to improve the accuracy of measurements with modification of designs to reduce the influence of external interference on their operation will be taken in further experiments.  
A fiber-optic quarry collapse prevention monitoring system was installed at the Kenzhem quarry of AK Altynalmas JSC (Kazakhstan) for testing (Figure 11).  
Currently, this enterprise uses only mechanical monitoring and visual inspection methods with the participation of surveyors without the use of expensive equipment. At the moment, the sensors and control systems developed by the authors are undergoing comprehensive industrial tests, based on the results of which detailed schemes for the  
Figure 11: Implementation of a monitoring system in production.  
Figure 10: Optical loss dependence on displacements.  
expanded implementation of data from fiber-optic rock condition monitoring systems will be developed, which is the subject of future research.
## 2 Laryngeal Image Modalities
### Laryngeal High-Speed Videoendoscopy
#### 2.2.1 Advantages  
* It captures the true intra-cycle vibratory behavior of the vocal folds, providing a more reliable and accurate objective quantification of vocal-fold vibrations. Therefore, it is possible to study aperiodic movements of vocal folds observed in some voice disorders [4; 37].
* The combination of HSV with acoustics and other physiological signals may provide complementary, high-precision measures that can improve the clinical practice [38; 39].
* It is used to examine the basic physiology of different singing styles, such as extremely high-pitched singing, throat singing, or different pop and rock styles [40], and to study the oscillatory characteristics of the vocal folds across classical soprano and tenors singers [41; 42].
* It is useful to get insights into tissue vibratory characteristics, influence of aerodynamical forces and muscular tension, vocal length, phonatory breaks, laryngeal spams, vocal folds contact and evaluation of normal laryngeal functioning in situation of rapid pitch change such as onset and offset of voicing or glides [5; 43].
## 1 Introduction
The combination of the Hill cipher, chaotic map, and Kronecker xor product is expected to provide robust security for images, as each technique brings its own strengths to the table; it should also be able to overcome the problem of plain text attacks due to having more than one secret key. Hill cipher is a well-known and widely-used technique for symmetric key encryption, the chaotic map is a powerful tool for generating randomness and unpredictability, and the Kronecker product is a mathematical operation that can be used to create complex, nonlinear transformations. By leveraging the strengths of these techniques, this study's proposed image encryption method is expected to be highly effective in protecting the confidentiality and integrity of images.
## 2 Materials and Methods
### Apparatus and Chromatographic Conditions
#### 2.2.2 Nano-LC  
Chromatographic analysis was performed using a Agilent 1260 Infinity nanoLC chromatograph equipped with a quaternary nano-pump, a six port micro-scale manual injector (Rheodyne), and a UV-Vis diode array detector with 80 nL nanoflow cell (Agilent, Waldbrunn, Germany). The detector was coupled to a data system (Agilent, ChemStation) for data acquisition and treatment. The analytical signal was recorded between 190 nm and 400 nm and monitored at 254 nm. A Zorbax 300 SB C18 (50 mm \(\times\) 0.075 mm i. d., 3.5 \(\upmu\)m particle size) analytical column (Agilent) was used for separation.  
The mobile phase was a mixture of water-acetonitrile in gradient elution mode. The percentage of acetonitrile in the mobile phase was linearly increased from 30% at 0-2 min, to 100% at 8 min, and then kept constant until 13 min; finally, the acetonitrile percentage was linearly decreased to reach a percentage of 30% at 18 min, and maintained constant until the end of the run; the run time was 30 min. The flow rate was 0.5 \(\upmu\)L/min. All solvents were filtered through 0.22 \(\upmu\)m nylon membranes (Teknokroma) before use.