esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A Brief Review of the Shapiro Delay Formulae and the Aims of This Paper
### Higher-Order Elliptic Integrals--Basic Definitions  
It is necessary for this definition to be given because, further, when calculating the second part of the propagation time, we shall encounter elliptic integrals of the fourth order.  
According to the general definition, elliptic integrals are of the type  
\[\int R(y,\sqrt{G(y)})dy\;\;\text{or}\int R(x,\sqrt{P(x)})dx, \tag{48}\]  
where \(R(y,\vec{y})\) and \(R(x,\vec{x})\) are rational functions of the variables \(y\), \(\vec{y}\) or \(x\), \(\vec{x}\) and \(\vec{y}=\sqrt{G(y)}\), \(\vec{x}=\sqrt{P(x)}\) are square roots of the arbitrary polynomials \(G(y)\) and \(P(x)\) of the fourth or of the third degree, respectively. In accord with the notations in the monograph [34], the fourth-order and the third-order polynomials will be of the form  
\[\vec{y}^{2}=G(y)=a_{0}y^{4}+4a_{1}y^{3}+6a_{2}y^{2}+4a_{3}y+a_{4},\]  
\[\vec{x}^{2}=P(x)=ax^{3}+bx^{2}+cx+d. \tag{49}\]So, in the general case, elliptic integrals of the \(n\)-th order and of the first kind are defined as  
\[J_{n}^{(4)}(y)=\int\frac{y^{n}dy}{\sqrt{a_{0}y^{4}+4a_{1}y^{3}+6a_{2}y^{2}+4a_{3} y+a_{4}}},\]  
\[J_{n}^{(3)}(x)=\int\frac{x^{n}dx}{\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{50}\]  
Elliptic integrals of the \(n\)-th order and of the third kind are defined as  
\[H_{n}^{(4)}(y)=\int\frac{dy}{(y-c_{1})^{n}\sqrt{a_{0}y^{4}+4a_{1}y^{3}+6a_{2}y^ {2}+4a_{3}y+a_{4}}},\]  
\[H_{n}^{(3)}(x)=\int\frac{dx}{(x-c_{2})^{n}\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{51}\]  
In the above definitions, the upper indices \"\((3)\)\" or \"\((4)\)\" denote the order of the algebraic polynomial under the square root in the denominator, while the lower indice \"\(n\)\" denotes the order of the elliptic integral, related to the \"\(x^{n}\)\" or \"\(y^{n}\)\" terms in the nominator of (50) and in the denominator of (51) (for \(n\)-th order elliptic integrals of the third kind).
# Development of a Spatial Heterodyne Terahertz Raman Spectrometer with Echelle Gratings
## 4 Conclusions  
In this work, echelle gratings were introduced into the spatial heterodyne terahertz Raman spectroscopy technique. Two echelle gratings with a groove density of 36 gr/mm were used, and two prisms with a top angle of 2.665\({}^{\circ}\) were used to broaden the field of view.  
Based on the characteristics of a single level of the echelle grating with extremely high resolution and multi-level simultaneous diffraction, the system can meet the requirements for high spectral resolution and a wide spectral detection range simultaneously. In this work, samples of organic acids, food additives, medicines, and aqueous solutions were tested in the terahertz Raman band, and the terahertz Raman spectra of the samples were measured successfully. In addition, wide band spectra of organic solutions and other samples were also measured. The experiments described above prove that the system shows good performance in terms of both high spectral resolution and a wide detection band. Through experimental calculations, the spectral resolution of the system was determined to be 1.37 cm\({}^{-1}\), which is 3.8 times that of the SHTRS (shown in Table 1), and the spectral detection range for a single level is 701.61 cm\({}^{-1}\). Nine levels were used in this paper, and the total spectral detection range reached 6314 cm\({}^{-1}\), which is 2.5 times that of the SHTRS. The maximum SNR of the system can reach 837.  
Based on the results above, E-SHTRS demonstrates a good spectral detection performance, and can realize spectral detection for many types of samples, thus giving the method relatively wide application prospects. This method can provide theoretical support for measurements in fields including biomedicine, environmental science, and food safety. The system is expected to broaden the development and application of terahertz Raman spectroscopy in a variety of important fields.  
\begin{table}
\begin{tabular}{c c c} \hline
**Structure** & **Spectral Resolution** & **Spectral Detection Range** \\ \hline SHTRS & 4.945 cm\({}^{-1}\) & 2532 cm\({}^{-1}\) \\ EMSHRS & 1.051 cm\({}^{-1}\) & 4287 cm\({}^{-1}\) \\ E-SHTRS & 1.37 cm\({}^{-1}\) & 6314 cm\({}^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison of various SHS.  
Figure 8: Sulfur SNR curves.  
**Author Contributions:** Conceptualization, X.L., Y.S. and N.S.; methodology, Y.S.; software, Q.C. and N.S.; validation, Y.S. and X.L.; formal analysis, F.L.; investigation, Q.N. and J.G.; resources, X.L. and N.S.; data curation, J.C.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S.; visualization, G.W.; supervision, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China (61975255, 52227810, U2006209, 62075216), Jilin Province Research Projects in China (20220201083 CX).  
**Institutional Review Board Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Potential Challenges in Underground Mines
#### 2.2.2 Rapid Time-Varying Channels and Propagation Velocity/Delay  
Environmental channel characteristics in mines change irregularly, which complicates the design of an efficient channel model. Frequent movement of convenient communication equipment along with variations in the prime channel of mines leads to the production of Doppler frequency shifts and fast signal strength fluctuations [18]. Signal propagation  
Figure 2: Pictorial presentation of conventional communication methods (**a**) through-earth, (**b**) through-wire, and (**c**) through-air communication methods.  
characteristics in a terrestrial environment are very much different from a compact reflected environment of underground mines; the signals face a lot of hindrance from the dielectric medium of mines. Variation in properties of such mediums and non-homogeneous materials can generate a propagation delay effect for transmitted signals [19]. Power cabling, electric machinery, and other mining appliances that are used by underground communication can create some noise in particular frequency bands, which corrupts data bits and adds interference during the signal propagation [20].
## 1 Introduction  
Altered dental eruption is represented by a clinical condition in which there is no eruption of the tooth in its correct location. One of the main objectives of interceptive orthodontics is to prevent the development of the permanent teeth problems, and among these, one of the most frequent is the inclusion of canines. The prevalence of maxillary canine impaction ranges from 1.0% to 2.5% [1; 2; 3]. Many studies have reported that the majority of the impacted maxillary canines are palatal (85%) when compared to labial (15%) [4].  
When the canine begins its eruptive path, it appears with mesial inclination and becomes more upright until it reaches the distal part of the lateral incisor root. If the canine does not follow this path, it becomes impacted, and this can lead to several problems [5; 6].  
A correct diagnosis together with clinical examinations and radiographs are fundamental before starting an orthodontic treatment. The most recommended kind of X-ray for the early prediction of impacted maxillary canine is the panoramic radiograph taken at age of 8 or more (>11 yr) if the canine's bulge is impalpable [7].  
Many orthodontic techniques, including specific devices, have been proposed to recover the impacted canine into the arch and manage the anchorage in the best way.  
The \"Canine First Technique\", already explained in the literature, is an orthodontic approach for the impacted canines [8; 9]. The aim of this technique consists of moving the crown away from the roots of adjacent incisors with cantilever spring and temporary anchorage devices Since the introduction of TADs as anchorage devices, the effectiveness and predictability of orthodontic treatment has improved [10]  
In the literature, there are many authors who discuss the treatment of impacted canines with conventional fixed appliances, but no studies have been carried out concerning clear aligners. The aim of this study is to show the advantages of combining TADs and aligners.  
Two orthodontic treatments of the palatal and buccal displaced canines will be reported. In both cases, the protocol includes two phases: the first one exposes the canine and moves it away from the roots of the lateral incisors, and the second aligns and levels the arches and finalizes the occlusion.
## 2 Descriptions of HEAGV with Series Hybrid Powertrain
### Air-Ground Vehicle Model  
When HEAGV is driving on the ground, according to the vehicle longitudinal dynamics, the power demand of each wheel can be calculated as follows.  
\[T_{w}=0.25[mgfcos\theta+0.5 AC_{d}\rho_{air}v^{2}+mgsin\theta+\delta m\frac{dv}{dt}]r \tag{1}\]  
where \(m\) denotes the air-ground vehicle mass, \(g\) refers to the gravity acceleration, \(f\) denotes the rolling resistance coefficient, \(\theta\) represents the slope of the road, \(A\) stands for the fronted area, \(C_{d}\) shows the air drag coefficient, \(\rho_{air}\) is the air density, \(v\) is the vehicular speed, \(\delta\) is the mass factor caused by the rotating inertia of wheels and powertrain rotating components, \(r\) is the wheel radius, \(v\) is the HEAGV's speed along the longitudinal direction. In addition, the hub motor, and mechanical brakes together provide the torque required by the wheels, and the powertrain torque balance can be expressed as  
\[T_{w}=\eta_{T}^{sign(T_{m})}T_{m}+T_{b} \tag{2}\]  
where \(\eta_{T}\) and \(T_{m}\) are transmission efficiency and hub motor torque, respectively. Since there is no reduction mechanism, the transmission efficiency is set to 1.  
When the HEAGV is flying in the air, according to flight aerodynamics [27], the force \(F_{ver}\) required for vertical flight is composed of the force \(G\), the acceleration resistance \(F_{acc}\), and the drag force \(F_{air}\), the formula of force in vertical flight is expressed as  
\[F_{ver} =G+(F_{acc}+F_{air})\times sign(V_{ver}) \tag{3}\] \[=mg+(\delta ma_{v}+\frac{1}{2}A_{u}C_{D}\rho_{a}V_{ver}^{2})\times sign (V_{ver})\]  
\[sign(V_{ver})=\left\{\begin{array}{cc}1,&\text{climb}\\ -1,&\text{descend}\right. \tag{4}\]  
where \(\delta\) is the mass factor caused by the rotational inertia of the rotating part, \(a_{v}\) is the vertical acceleration speed, \(A_{u}\) is the vertical area, \(C_{D}\) is the air drag coefficient, \(\rho_{a}\) is the density of air, \(V_{ver}\) is the vertical climb/landing velocity.  
When the HEAGV is flying horizontally in the air, the force is  
\[F_{ cru}=\sqrt{F_{hor}{}^{2}+F_{ver}{}^{2}} \tag{5}\]  
\[F_{hor}=\delta ma_{hor}+\frac{1}{2}A_{f}C_{D}\rho_{a}V_{hor}^{2} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Title 3** \\ \hline Powertrain system & Series Hybrid Electric & Air/Ground \\ Vehicle mass (kg) & 2000 & Air/Ground \\ Gravity acceleration (m/s\({}^{2}\)) & 9.81 & Air/Ground \\ Wheel radius (m) & 0.39 & Ground \\ Frontal area (m\({}^{2}\)) & 7.8 & Ground \\ Paddle radius (m) & 0.9 & Air \\ Vertical area while climbing/landing (m\({}^{2}\)) & 32 & Air \\ Equivalent frontal area in cruise (m\({}^{2}\)) & 12.4 & Air \\ Engine peak power (kW) & 110 & Air/Ground \\ Rotor-motor peak power (kW) & 50 & Air \\ Hub motor peak power (kW) & 60 & Ground \\ Battery-rated capacity (Ah) & 150 & Air/Ground \\ Battery-rated voltage (V) & 500 & Air/Ground \\ \hline \end{tabular}
\end{table}
Table 1: Basic HEAGV parameters.
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 5 Localized Trigger Identification Algorithm
### Partitioning Wireless Devices Based on the Algorithm of Hexagon tiling and coloring
Similarly, when \(\alpha>3\), \(D_{h}=r\), \(\max\{d(v_{t1},v_{t2})\}=\max\{d(v_{t1},j_{1})\}=\max\{d(v_{t2  
**Lemma 4**: _Given \(d=(R+r)\), the number of colors \(c\) generated by Algorithm 1 to paint the entire hexagon tiling \(h\) is:_  
* \(\left[(\frac{2}{\sqrt{3}}(\alpha+1)+1)\right]^{2}\)_, when_ \(D_{h}=r\)_._
* \(\left[(\frac{2}{\sqrt{3}}(\frac{\alpha+1}{\alpha-2})+1)\right]^{2}\)_, when_ \(D_{h}=R-2r\)_._  
In general, if a hexagon tiling \(H\) has hexagons of diameter \(D_{h}\), then considering a distance \(d\in\Re^{+}\) the \(k^{2}\)-coloring algorithm needs \(\left\lceil\frac{2d}{D_{h}\sqrt{3}}+1\right\rceil^{2}\) colors, hence, it is straightforward to show that when:  
* \(D_{h}=r\) and \(d=R+r=(\alpha+1)r\), \(k^{2}\)-coloring algorithm needs \(c=\left[\frac{2}{\sqrt{3}}(\alpha+1)+1\right]^{2}\) colors
* \(D_{h}=R-2r=(\alpha-2)r\) and \(d=R+r=(\alpha+1)r\), \(k^{2}\)-coloring algorithm needs \(c=\left[(\frac{2}{\sqrt{3}}(\frac{\alpha+1}{\alpha-2})+1)\right]^{2}\) colors
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 4 Discussion
The assumption corresponds to the worst case of successful transmission with four times of repeated carrier sensing with a (2\({}^{8}\) - 1) \(\times\) 0.32 ms backoff time [35], 0.128 ms carrier sensing, and 18 ms latencies from hardware settings. For a 10-second measurement interval, the cost of packet collision and carrier sensing is considered non-negligible if the number of transmitters approaches 25. If legal limitations for the use of wireless communication are not a concern, low-frequency wireless communication for drones [25] could reduce the number of required drones. In contrast, higher-frequency communication, such as millimeter-wave communication, provides higher throughput and enables a large number of drone swarms, provided that the receiver gain is maintained by novel technologies such as massive Multi-Input Multi-Output (MIMO) drone swarms [36] with future advancements in beamforming speed, antenna size, etc. For future applications of this system to water quality monitoring, the controllable range of the amphibious drones can be extended with tracking technology (e.g., 'follow me' technology [37]) and a power generator (Figure 22). Similar systems have been proposed in earlier studies [38; 39]. The vertical flight ability of amphibious drones can be advantageous for locations with dense aquatic vegetation. Combined use of the system with a high-sensitivity LoRaTM module would enable water quality monitoring in large-scale water bodies (e.g., seas, lakes, reservoirs) with a greater number of amphibious drones. **Author Contributions:** Conceptualization, O.K. ; methodology, O.K., A.S., H.M. and T.N. ; software, A.S.; validation, A.S., O.K., H.M., T.N., M.I., S.W., M.Y., H.O. and N.K. ; formal analysis, O.K. ; investigation, A.S., O.K., T.N., H.M., S.W. and T.K. ; resources, O.K. ; data curation, A.S. and O.K. ; writing--original draft preparation, A.S.; writing--review and editing, A.S., O.K., H.M., T.N. and M.I. ; visualization, A.S.; supervision, O.K. ; project administration, O.K. ; funding acquisition, O.K. and H.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research and the APC were funded by Akita Prefectural University, the Grant for Promotion of Academic-Industrial Collaboration (2021-2023 fiscal year). Figure 22: Example of a wireless data relay application to amphibious drones. A tracking (‘follow me’) technology (3D Robotics Inc., Berkeley, CA, USA) enables large-scale water surveys with amphibious drones. Data Availability Statement: Datasets, movies/photos, programs, and 3D-printer files described as a result of this study are available upon request to the corresponding author. We thank [PERSON] for his patience and support during field tests. The field tests were realized through the courtesy of the staff in Akita Prefectural Koizumigata Park, Akita, Japan. We sincerely appreciate [PERSON] of Iwate Prefectural University for her invaluable suggestions related to this manuscript. The authors declare that they have no conflicts of interest.
# A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
## 2 Materials and Methods
### Satellite Data Acquisition  
The study area covers two Sentinel-2 granules: 34 UFE and 34 UEE available from orbits no. 36, 136, and 79 allowing image acquisition every 5-6 days. Satellite images from Sentinel-2A and Sentinel-2B at processing level-2A were retrieved automatically using Google Earth Engine (GEE), i.e., cloud-based platform that provides geospatial data, tools, and computing power for developers to analyze and visualize the world's satellite imagery and other geospatial data. Users can access and analyze using various programming languages such as Python and JavaScript. It also provides a suite of tools for processing and analyzing the data, including machine learning algorithms for image classification and time-series analysis [35]. For the current study, JavaScript API was used in Earth Engine Code Editor. The average cirrus and cloud cover for the granules in 2022 was 52.15% (Figure 3), whilst the climate in the region is moderate with continental elements, which means that the growing season is short, snow and ice last for a long time, and there is a significant amount of cloud cover. As a result, to create comprehensive images of the area, mosaics were created using images captured by the Sentinel-2 satellite. To combine Sentinel-2 (S-2) data into a mosaic, Google Earth Engine (GEE) uses an algorithm that calculates a high-dimensional weighted geometric median. This approach ensures that the spectral relationships across all the S-2 bands are maintained in the resulting mosaic. Cloudless satellite image mosaics were run spanning around ten days before and after  
Figure 1: Major types of wetland vegetation with predominance of sedges (**left**), sedge mosses (**middle**), and reeds (**right**). Photo credit: [PERSON].  
ground measurements. Full list of periods selected for temporal mosaicking of Sentinel-2 images in 2022 over study area is presented in Table 1.
## 2 Development of an Advanced SR-JOINT (SR-II)
### Optimal SR-Joint Shape Design
#### 2.2.2 Optimal Design through SVM  
It was discovered through sensitivity analysis that each variable affected the objective function. Accordingly, the relationship between the objective function and design variables was predicted through machine learning. An SVM was applied during machine learning to optimize the shape for load minimization. SVM is actively used in pattern recognition and data analysis; moreover, it can be used for classification and prediction simultaneously. However, the SVM requires a significant amount of time to build the model, and it explains the results poorly. However, it has a low overfitting risk and high prediction accuracy while being easy to use [22]. Assuming that the decision boundary to be discovered in the SVM is \(w^{T}x+b=0\) in Figure 11, the vector \(w\) is a normal vector perpendicular to the decision boundary, and the classification and prediction methods of the SVM involve maximizing the margin surrounding the hyperplane of the decision region accordingly. The SVM is often used for classification; however, it is also actively used in regression analysis. The regression SVM was constructed based on a value obtained by converting the array in which the object was stored, and the optimal hyperplane value that minimized the margin was obtained by changing the gamma value of the kernel function built in the SVM library.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Curling Length [mm]** & **Distance [mm]** & **Jig R [mm]** & **Load [kN]** \\ \hline
5.7 & 6 & 5 & 159.76 \\
5.7 & 15 & 4.7 & 196.35 \\
5.3 & 15 & 5 & 190.23 \\
5.7 & 15 & 5 & 197.91 \\
5.3 & 6 & 5 & 156.66 \\
5.3 & 6 & 4.7 & 159.40 \\
5.7 & 6 & 4.7 & 161.44 \\
5.3 & 15 & 4.7 & 198.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset for sensitivity analysis.  
Figure 10: Result of sensitivity analysis.  
Python 3.7 and Scikit-learn, which is a machine learning library, were used in this study. The regression method and prediction accuracy varied depending on the kernel in SVM regression. The kernels used in the SVM included linear, polynomial, and radial basis functions. The prediction accuracy analysis of each kernel based on 50 randomly selected data indicated that prediction accuracies of the radial basis function, linear, polynomial kernels were 0.93, 0.91, and 0.54, respectively; subsequently, the radial basis kernel that yielded the highest value was used for this optimal design. The parameters used in the kernel did not exhibit a significant difference in accuracy; hence, 0.1 for gamma and 100 for C were applied. As for the training dataset, 61 datasets excluding the defect data were partitioned into ratios of 25%, 50%, 75%, and 100% among the 100 data analyzed earlier. Meanwhile, for the test dataset, a dataset for each design variable was created, as shown in Table 4.
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 5 Simulation Results
### BER Performances  
In this subsection, we present the BER performances analysis of the proposed method. Firstly, we set the smoothness parameter value to 0.5 and then to 5, with variable CR and modulation types. The results depicted in Figure 16 (4-QAM), Figure 17 (16-QAM), and Figure 18 (64-QAM) were achieved by varying the Signal-to-Noise Ratio (SNR).  
Figure 16 shows that the BER performances of our method are similar compared to the normal OFDM BER curve, especially for CR values greater than 4. On the other hand, for CR values lower than 4 dB, the signal degradation in terms of BER becomes more and more significant. In addition, those large CR values (i.e., higher than 4 dB) offer good performances in terms of PAPR reduction (see Table 3) without degradation of the BER performance, as was previously mentioned. We observe in Figure 17 that the BER performance with \(\beta=5\) is better than that recorded with \(\beta=0.5\) for all CR rates. Regarding signal degradation, it starts at CR rates below 6 dB.  
Figure 17: BER performance of the PC method with variable CR, N = 64, and the 16-QAM modulation over the AWGN channel.  
Figure 18: BER performance of the PC method with variable CR, N = 64, and the 64-QAM modulation over the AWGN channel.  
Figure 18 presents the performance curves in terms of BER in the case of the 64-QAM modulation. As shown in this figure, the signal degradation starts a CR value of 7 dB, and it becomes worse and worse from 6 dB and lower.  
We conclude that for the three considered modulations, namely 4-QAM, 16-QAM, and 64-QAM, the performance of the proposed method is better as long as the CR increases. In addition, it is shown that the BER degradation is reduced when the smoothness parameter beta is equal to 5 for all the modulation types. Table 3 summarizes the obtained gain in terms of the PAPR at CCDF = \(10^{-4}\) and BER loss at \(10^{-3}\) for each modulation type by varying the Clipping Ratio when \(\beta\) is equal to 5.
## 6 Performance Analysis
### Energy  
In this section, we analyze the energy consumption of the ZigBee devices in smart home applications. Here, the energy consumption is defined as the average energy consumption by a ZigBee device to successfully transmit one slot amount of data. Let us consider \(P_{rx}\) and \(P_{tx}\) to be the energy consumption to receive and to transmit a packet, respectively. ZigBee devices are assumed to be in the power saving state during the backoff procedure. Taking \(T_{plt}\), \(T_{A}\), \(T_{cca}\) as the time duration of transmitting a packet, receiving an acknowledgment and CCA duration, respectively, the average energy consumption per device can be analyzed as follows.  
\[E=\frac{\alpha T_{cca}P_{rx}+(1-\alpha)\{(1-P_{s})E_{c}+P_{s}E_{s}\}}{(1- \alpha)P_{s}T_{plt}} \tag{37}\]  
where \(E_{s}\) is the energy consumption of successful transmission and \(E_{c}\) is energy consumption due to collision, which can be calculated as:  
\[\left.\begin{array}{lcl}E_{s}&=T_{cca}P_{rx}+T_{plt}P_{tx}+T_{A}P_{rx}\\ E_{c}&=T_{cca}P_{rx}+T_{plt}P_{tx}\end{array}\right\} \tag{38}\]