esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Prospects  
In conclusion, we have investigated multicolor sideband generation based on CFWM both theoretically and experimentally. Analysis and computer simulation including the effect of phase-matching in FWM reasonably explain the reported experimental results.  
The main characteristics of multicolored sidebands obtained in our experiment can be summarized as follows.  
1. Tunability in a wide spectral region. Fifteen spectral up-shifted pulses and two spectral down-shifted emissions were obtained simultaneously in a spectra domain that spanned more than 1.8 octaves. The wavelengths of the sidebands could be tuned from near-ultraviolet to near-infrared by adjusting the crossing angle between the two input beams or by replacing the nonlinear bulk medium.
2. Ultrashort pulse width. The pulse width of the sidebands remained nearly unchanged for the Stokes and anti-Stokes pulses. Nearly transform-limited compressed pulses as short as 15 fs could be obtained when one of the two input beams was properly negatively chirped and the other was positively chirped.
3. High output energy. The pulse energy of the sideband could be increased to 1 \(\upmu\)J, a power stability better than 1% RMS. We expect that an even higher output power could be generated by increasing the pump energy and expanding the spot sizes of the two pump beams on the optical medium to avoid saturation.  
We have reported the generation of multicolor sidebands consisting of 2-D arrays, and provided some possible explanations. Beam breakup and CFWM are responsible for this interesting phenomenon. Careful investigation using both simulation and experiment are still needed for complete understanding of this new phenomenon.  
Future studies into multicolored sidebands extending in the visible and near-IR spectral regions, which are generated with pump lasers with MHz repetition rates, would be useful in numerous applications such as nonlinear microscopy. A pulse energy of 1 \(\upmu\)J for each pump pulse has been confirmed to be enough energy for multicolor sideband generation in a diamond plate. This energy can be reduced further, if the pump beams are tightly focused on a medium with higher third-order nonlinearity. For example, CdSSe-nanoparticle-doped glass has 5.6 times larger third-order susceptibility than a diamond plate, even at wavelengths far from its resonant frequency [73].  
This work was partly supported by the 21 st Century COE program on \"Coherent Optical Science\", and partly supported by the grant from the Ministry of Education (MOE) in Taiwan under the ATU Program at National Chiao Tung University. A part of this work was performed as a joint research project with the Laser Engineering group at Osaka University, under contract subject B1-27.  
[PERSON] performed most of the experiments; [PERSON] performed part of the experiments and wrote the paper; [PERSON] proposed some of the experiments, discussed with the coauthors, and also revised the paper.  
The authors declare no conflict of interest.
# An Image Segmentation Method Based on Improved Regularized Level Set Model
## 3 Proposed Method
### New Energy Functional  
The distance regularization term is employed to ensure the stability and the validity of the level set evolution [65]. To accurately segment an image, the energy functional should contain an external energy term and force the evolution curve to move toward the target edge.  
On the basis of the distance regularization term, by combining the internal and external energy terms, a new energy functional is given as  
\[E(\varphi)=\mu P(\varphi)+\lambda E_{\text{int}}(\varphi)+\
u E_{ext}(\varphi). \tag{10}\]  
To minimize the impact of noise in the process of segmenting images, the external energy term \(E_{ext}\) (\(\phi\)) is utilized, where the Laplacian of a Gaussian (LoG) filter is contained. The LoG filter is employed to maintain the sensitivity during the evolution of the level set and then drive the zero level set to move toward the target edge. Based on the above descriptions, assume that \(\phi\) is a level set function, and then the \(E_{ext}\) (\(\phi\)) is expressed as  
\[E_{ext}(\varphi)=\int_{\Omega}{(\Delta G_{\sigma}*I)(H(-\varphi))dxdy}, \tag{11}\]  
where \(\Delta\) is the Laplacian operator, \(G_{\sigma}\) denotes the Gaussian kernel function, \(G_{\sigma}*I\) describes the convolution operation of \(I\) with \(G_{\sigma}\), and the Heaviside function \(H(x)\) can be described as  
\[H(x)=\frac{1}{2}\bigg{[}1+\frac{2}{\pi}\text{arctan}\frac{x}{\varepsilon} \bigg{]}. \tag{12}\]  
Then, the internal energy term is expressed as  
\[E_{\text{int}}(\varphi)=\int_{\Omega}{\delta(\varphi)|\
abla\varphi|dxdy}, \tag{13}\]  
where \(|\
abla\varphi|\) is the gradient mode of \(\phi\), and \(\delta(x)\) is the Dirac delta function and can be written as  
\[\delta(x)=\frac{1}{\pi}\frac{\varepsilon}{\varepsilon^{2}+x^{2}}. \tag{14}\]  
Thus, a new energy functional is constructed by combining our proposed distance regularization term with the internal and external energy terms. The new energy functional is described as  
\[E(\varphi)= \begin{array}{ll}\mu\int_{\Omega}{(\frac{1}{2}(|\
abla(\varphi)|-3)^{ 2}+6\ln(|\
abla(\varphi)|+2)-(2+6\ln 3))dxdy}\\ +\lambda\int_{\Omega}{\delta(\varphi)|\
abla(\varphi)|dxdy}+v\int_{\Omega}{( \Delta G_{\sigma}*I)(H(-\varphi))dxdy},\end{array} \tag{15}\]  
where \(\mu\), \(\lambda\) and \(\
u\) are greater than zero.  
The calculus of variations and the steepest descent model [64] are introduced to minimize \(E(\phi)\), and the corresponding partial differential equation is expressed as  
\[\frac{\partial\varphi}{\partial t}=\mu div((1-\frac{3}{(|\
abla\varphi|+2)}) \
abla\varphi)+\lambda\delta(\varphi)div(\frac{\
abla\varphi}{|\
abla(\varphi )|})+\
u\delta(\varphi)(\Delta G_{\sigma}*I). \tag{16}\]
## 3 Materials and Methods
### The Proposed Model  
The tremendous feature extraction capacity of a convolutional neural network (CNN) [29] allows it to mine data extensively. A CNN is a two-dimensional image processing and financial signal processing algorithm [30]. To extract pixel information and achieve image identification and classification, a convolution kernel flows smoothly over an image. As the input data for predicting the remaining useful life of chemical properties are mostly one-dimensional, these features cannot be retrieved using a two-dimensional convolution kernel. As a result, a 1D-CNN [31] model is utilized to collect sensitive information and achieve the feature extraction of molecular properties in the feature extraction stage, based on the properties of the sequence data. In comparison to classic feature extraction approaches, CNN can extract data characteristics without requiring a lot of user effort or expert knowledge. Figure 2 shows the overview of the proposed one-dimensional convolutional gated recurrent unit neural network, and Table 2 shows the proposed model (1D-CNN-GRU) parameters.  
In this study, the molecular property dataset is adjusted before being input into the proposed one-dimensional convolutional gated recurrent neural network. The ID-CNN GRU architecture, dataset, training, and optimization algorithms will be detailed in depth in the next sections. All experiments were carried out using the Python programming language, which includes libraries such as numpy, pandas, seaborn, matplotlib, joyplot, sklearn, keras, and math.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Layer** & **Type** & **Units** & **Kernel** & **Filter** & **Other Parameters** \\ \hline
1 & Convolution1D & & 3 & 128 & Padding = same, activation = LeakyReLU, alpha = 0.001 \\ \hline
2 & Convolution1D & & 3 & 64 & Padding = same, activation = LeakyReLU, alpha = 0.001 \\ \hline
3 & Convolution1D & & 3 & 32 & Padding = same, activation = LeakyReLU, alpha = 0.001 \\ \hline
4 & MaxPooling1D & & & & Pool size = 3, strides-2, Padding = same \\ \hline
5 & GRU & 128 & & & Activation = LeakyReLU, alpha = 0.001,return sequence = True, activity regularizer = 12(0.001) \\ \hline
6 & Dropout & & & & Rate = 0.2 \\ \hline
7 & GRU & 64 & & & Activation = LeakyReLU, alpha = 0.001, kernel initializer = random uniform, seed = 42, activity regularizer = 12(0.001) \\ \hline
8 & Dropout & & & & Rate = 0.2 \\ \hline
9 & Flatten & & & & \\ \hline
10 & Dense & 512 & & Activation = LeakyReLU, alpha = 0.001 \\ \hline
11 & Dense & 256 & & Activation = LeakyReLU, alpha = 0.001 \\ \hline
12 & Dense & 1 & & Activation = Linear \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proposed Model (1D-CNN-GRU) Parameters.
# \(H_{2}/H_{\infty}\) Output Tracking Control with a Performance Compensator for Aeroengines
## 4 Compensator Design
### Compensator Design for Bounded Reference  
The reference is usually given by the power level angle, which is controlled by the pilot in real time, and thereby \(h(t)<0\) does not always hold in practical application. When \(h(t)>0,t\in\left[t_{a},t_{b}\right]\), there may be no solution for Equations (29) and (31). The reference expressed in Equation (23) can be seen as a bounded subsystem, and thereby the instability is a power-bounded disturbance imposed on the following system  
\[\dot{x}=\left[\begin{array}{cc}A-BR^{-1}B^{T}P_{1}&A_{I}{+}K_{3}\\ 0&0\end{array}\right]x \tag{35}\]  
In order to ensure the solvability of Equations (29)-(31), for any \(t>0\), \(K3(t)\) can be calculated as follows:  
\[K_{3}(t)=\left\{\begin{array}{cc}R^{-1}B^{T}\left((A+BK)^{T} \right)^{-1}P_{1}A_{I}^{T},\ h(t)>0\\ -R^{-1}B^{T}P_{2}(t),&h(t)\leq 0\end{array}\right. \tag{36}\]  
where \(P_{2}(t)\) is calculated according to Equation (32).  
Figure 3: Diagram of output tracking control for a bounded reference.
## 3 Methods
### Burn-Type Differentiation
#### 3.1.2 Wildfire Detection Algorithm  
FINN data includes all fires detected by MODIS and VIIRS satellites, both for wildfires and prescribed, while permits are only for prescribed burns. Therefore, matching FINN data with permit data requires the removal of wildfires from FINN. In this research, we focus on detecting larger wildfires. Prescribed burns typically start and end on the same day, while wildfires can last multiple days, so we assume the fires that have more than a one-day duration are wildfires. The duration of the fires can be calculated by temporally tracking each fire in FINN. In other words, if FINN detects fires in the same region (the distance between detected fires in the region is less than the selected clustering distance) for consecutive days, the algorithm will mark all fires in that region during the period as wildfires. Since wildfires can also spread over long distances in one day, FINN may identify a wildfire as several fires. This segregation problem could lead to underestimations of the size of wildfires if we just track the wildfires temporally, so a spatial clustering algorithm is applied to cluster FINN fire records that are close to each other on the same day (Algorithm S1). In this spatial and temporal clustering algorithm,we use 1000 m as the spatial clustering distance and 800 m as the temporal clustering distance for clustering the different FINN records into a wildfire based using the elbow method [39]. The elbow method is widely used to determine the optimal number of clusters in a clustering algorithm, and the elbow point of the clustering parameters (spatial and/or temporal clustering distance) versus the number of clusters plot represents a suitable balance between clustering accuracy and model complexity. As a result, we have selected the elbow point as the optimal parameter setting for our clustering algorithm. It is recognized that wildfires can happen under similar meteorological conditions as prescribed burns, and some wildfires are small and extinguished in a short time, leading to potential misclassification.
## 2 Materials
### Landslide Influencing Factors
#### 2.3.3 Ecological Factors  
Erosion of the slope toe by fluvial activity, changes in pore water pressure, and runoff can lead to a decrease in slope stability [36; 41; 62]. Therefore, distance from the river is regarded as an important factor causing landslides in the mountains [58]. In general, the smaller the distance to the river, the lower the slope stability, resulting in an increased probability of landslide occurrence [63]. At present, most landslide susceptibility assessment studies have considered the influence of hydrological networks on landslides.  
However, rivers of different scales exert different impacts on slopes, which many studies have ignored, hindering the accurate analysis of the impacts of rivers on landslides. In this study, to accurately identify the relationship between the hydrological network and landslide occurrence, the river system was divided into two categories according to the tributary level: the distance from the main river and the distance from the stream. In this context, the main river refers to tributaries of Grade 6 and above, and the rest are classified as streams.  
Rainfall is a major triggering factor for landslides. High-intensity rainfall will lead to an increase in the pore water pressure inside the slope, increasing the sliding mass and decreasing the shear strength of the rock mass, which increases the chance of landslides [9, 25]. This study used the annual rainfall in the study area as a landslide influencing factor. Land cover is closely related to the occurrence of landslides [64], as it has a certain impact on the scale and type of landslides [33]. Different land cover types can lead to significant differences in vegetation types and frequency of human engineering activities, resulting in changes in landslide susceptibility [41].  
NDVI is an index reflecting vegetation growth within a given area. Vegetation coverage is closely related to runoff, infiltration, and weathering on the slope surfaces [30], affecting the occurrence of landslides. Therefore, NDVI can be used to quantify the impact of vegetation density on landslides [41]. The calculation formula is:  
\[NDVI=\left(NIR-R\right)/(NIR+R) \tag{3}\]  
where \(NIR\) is the reflection value of the near-infrared band, and \(R\) is the reflection value of the red band.  
Eroded soil makes up most landslides, reflecting the long-term rainfall erosion damage of landslides [65]. Soil erosion has a non-negligible effect on the occurrence of landslides [66]. Soil erosion intensity reflects the intensity of destruction, denudation, transportation, and deposition of soil in a region under the effect of water, wind, freeze-thaw cycles, or gravity [67]. Soil erosion modulus is the primary index for soil erosion intensity classification. The soil erosion intensity classification standard in this study is based on the \"Classification Standard for Soil Erosion Classification\" (SL 190-2008) issued by the Ministry of Water Resources of China. There are 11 soil erosion intensity levels in the study area, of which levels 11-16 are hydraulic erosion and levels 31-35 are freeze-thaw erosion.
## 4 Catalytic Conversion of HMF on POPs
used this strategy in the preparation of a catalyst for HMF conversion. They used pyrrole and phenylaldehydes after distillation as precursors, and added ferric chloride to acetic acid mediated through hydrothermal synthesis to obtain an Fe\({}^{\text{III}}\)-POP-1 catalyst. In this reaction, 100% conversion of HMF and a 79% yield of FDCA could be obtained by using only water as a solvent and reacting in air at 100 \({}^{\circ}\)C for 10 h. The catalyst maintained activity in three reaction cycles [117]. No product appeared in the system after the subsequent reaction with a homogeneous Fe\({}^{\text{III}}\)-porphyrin complex, which indicates the importance of a large surface area and the multiple active sites produced by this simple and rapid synthesis method for catalytic reaction, and also reflects the interoperability of the two fields of adsorption and catalysis. Table 2 summarizes the thermal catalytic system of HMF with POPs as the carrier. All the cases listed achieved a high conversion rate of 95% or above under mild reaction conditions. In the case of the same oxidant, different solvents and substrates had large differences in the time required to convert to the target product. However, for the conversion of HMF, whether using oxidation, etherification, or other conversion routes, there have been relatively few studies on POPs as the catalyst. In terms of the current relatively mature thermal catalytic system of HMF, POPs still have a long way to go in terms of their overall catalytic efficiency compared to other porous materials (such as zeolite) with easier access. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Catalyst** & **Substrate** & \begin{tabular}{c} **Solvent** \\ **System** \\ \end{tabular} & **Ovidant** & \begin{tabular}{c} **Pressure** \\ **(Mpa)** \\ \end{tabular} & \begin{tabular}{c} **Time** \\ **(min)** \\ \end{tabular} & \begin{tabular}{c} **Temperature** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **X\({}_{\text{substrate}}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Yield** \\ **(\%)** \\ \end{tabular} & **Reference** \\ \hline HCP-x & Fructose & Ethanol/DMSO & Air & 0.1 & 480 & 104.85 & 99.8 & \begin{tabular}{c} 78.9 (EMF); \\ 15.4 (HMF) \\ \end{tabular} & [110] \\ \hline PDVTA-SO\({}_{3}\)H & HMF & Ethanol & Air & 0.1 & 30 & 110 & 99.8 & 87.5 (EMF) & [112] \\ \hline Cu-MnO\({}_{2}\) & HMF & \begin{tabular}{c} Terbutyl \\ alcohol \\ \end{tabular} & \
\begin{tabular}{c} TBHP \\ /Air \\ \end{tabular} & 0.1 & 720 & 80 & 95 &
\begin{tabular}{c} 96.8 (FDCA) \\ \end{tabular} & [113] \\ \hline Fe\({}^{\text{III}}\)-POP-1 & HMF & Water & Air & 1 & 600 & 100 & 79 (FDCA) & [117] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of reactive systems for HMF conversion using POPs as catalysts. Figure 10: Mechanism for the oxidation of HMF to FDCA catalyzed by Cu-MnO\({}_{2}\)@PDVTA [113].
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## 3 Results
### X.509 Based Key Management  
In this experiment, the ID of the administrator of the hospital was generated by using the OpenSSL library in python, and the X.509 certificate was generated and maintained in its own wallet. The public key associated with the generated private key was stored in the distributed ledger [36]. The following is part of the public key. The public key is used to verify the CDM delivered and is stored in IPFS.  
```
-BEGIN PUBLIC KEY--- -MIBIJANBkgkqhkiG9w0 BAQEFAAOCAQ8 AMIIBCgKCAQEAsZhhPfQsP3 MTrCnn7 wsJ wLX/VWxg1/Qf4 USv/ulfJ6 ta2 hcg2 hg+TzSqv3 bfHjFssKBCSp7 QnxC1 dITt1 QbAK oifDT0 dEOOoyYZ7o1 xTZBfJUZBa6w7 vAnMmOnKPdf/QXgOZjHXr+c4 mPJOB5 OBVA wlaiO3 ASSACkdeGZ2 IZxrZe3 tXYeX3 Ng8a/nJ8 OSdAkwuoMKTQ3 roooi8I4 iC2 SXU 5 sgb9 TaxF/nnm8q4 vEiMPBi8 dYfqsVPfh4 tA2 PoESqO/4g4c295 Awfm2 FT7 ut8Z5 n1 YyS3 PZP7 AcGnInWcZ6K5 NAsUzxsA60+WAgXluUb4 lzuCX9 ZhhHM+/utu50 inQZd xQIDAQAB ---END PUBLIC KEY---
```
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 3 Audio Datasets
### Data Analysis
#### 3.4.1 Phonetic Frequency  
One of the key aspects of developing a corpus for voice-cloning applications, especially in a TTS setup, is that it should be phonetically balanced in order to contain a representative number of samples of each phonetic unit [38,41,42]. Following this approach, we measured the phonetic content of both corpora in terms of the frequency of phones and diphones.  
Accounting for 26 Spanish and 38 English phones, a total of 386 diphone combinations for _XRey_, 446 for _Tux_, and 1255 for _Hi-Fi TTS 92_ were found. It should be noted that not all of the \(26^{2}=626\) and \(38^{2}=1444\) diphone combinations are phonotactically possible in Spanish and English, respectively. The distribution of diphones in both datasets is illustrated in Figure 1.  
The obtained diphone distribution curves are similar to those computed in our previous work [3]. These findings confirm that a significantly large text corpus results in the frequencies of phones and diphones conforming to the same numerical distributions.
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
## 2 Quaternion Orthogonal Designs
In the subsections, this way of COD generation is used to form different generalized QOD constructions. In Figure 1, we briefly explain the basic nomenclature, which describes the main working in quaternion domain. We start with two CODs that form a symmetric-pair such that they give rise to a QOD. In earlier works, this was not directly used to describe communication among dual-polarized antennas. Rather, an STBC was constructed from a QOD and its even columns represent signals being sent through one polarization while entries in odd columns are signals transmitted through an orthogonal polarization plane. However, the simultaneous transmission through a dual-polarized antenna is being modeled by quaternions in this paper. Although, we have an enriched theory of quasi-orthogonal designs yet our main focus is to work on an approach which algorithmically identify among them, those designs that have decoders with two main characteristics, that is, linear and decoupled. In this work, we notice that the departure from the complex to quaternion domain serve this purpose. The dotted line connecting CODs \(\mathbf{A},\mathbf{B}\) with quasi-STBCs \(\mathbf{C}_{Q}\) or \(\mathbf{C}_{q}\) indicates a vivid difference between their working which distinguishes them in terms of code rates, decoding delays at both ends, zero vs non-zero entries, linear and decoupled decoders. The generalized construction techniques employ (2), which provides us the symmetric-paired square CODs that act as seeds to generate three classes of square and non-square QODs.
## 6 Security Analysis
### Computational and Communication Costs
#### 7.2.2 Computational Cost
This disparity in performance is largely due to the fact that both SA and Versa rely on an elliptic curve Diffie-Hellman key exchange to construct \(n-1\) encryption keys for secure communication and compute \(n-1\) pseudorandom number seeds for generating cryptographic parameters used to mask local model gradients. Moreover, Versa requires two masking procedures to enable verification. Notably, in our scheme, each user's computational load does not scale with the number of other participants. Hence, as the number of users grows, the superiority of our proposed scheme over others becomes increasingly pronounced.
## 1 Introduction
### Intersatellite Communication Description  
To achieve the primary mission objective, the AztechSat-1 nanosatellite utilized GlobalStar's Satellite Transmitter Integrated Global Positioning System Receiver (STINGR). This system incorporates a radio transmitter, a GPS receiver, and a patch antenna within a single integrated unit. Operating in simplex transmission mode, the STINGR system can communicate with the GlobalStar constellation. It automatically sends the data to the company's gateways for transmission to the Internet.  
Figure 2: AztechSat-1 mission operation diagram.  
For the main parameters of the STINGR communication module, please refer to Table 1. Any microcontroller with serial communication capabilities can use the modem.