esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Model and Methods  
### Physical Model and Computational Domain  
According to the propagation characteristics of CRD waves in an annular premixed combustor, Figure 1a presents the simple three-dimensional geometry configuration of CRD combustors that were numerically analyzed in this study. The fully premixed methane-air, with an equivalence ratio of one, was directly injected into the annular combustor channel without the complex inlet structures. Additionally, to initiate detonation in the annular combustor channel, a virtual initiator was used to generate the Chapman-Jouquet (CJ) ignition kernel in the right direction. After a CRD wave was formed, it could be self-sustainingly propagated along the direction of the CJ ignition kernel.  
To study the application of CRD combustors in gas turbines, the basic sizes of CRD combustors were determined according to the performance parameters of a 100 kW micro gas turbine, with an outside diameter of 274.8 mm, inside diameter of 234.8 mm, and axial length of 200 mm. To consider the effects of axial length on CRD combustor performance, including propagation stability, wave characteristics and outlet parameters for the other different axial lengths (175 mm, 150 mm, 125 mm, and 100 mm) were also discussed in this study.  
To comprehensively consider the axisymmetric characteristics of CRD combustors and their numerical complexity, the two-dimensional rectangular model shown in Figure 1b was selected as the computational domain in this study and was derived by expanding the three-dimensional CRD combustors along the generatrix, in the circle of the middle diameter. [PERSON] et al. [26] and [PERSON] et al. [27] have verified the effectiveness of this simplified method in the numerical simulation of CRD waves.
###### Abstract
For example, [PERSON] and [PERSON] studied changes in the normalized difference vegetation index (NDVI) from 1982 to 2016 in the HKH region and found that grassland showed an overall greening trend in NDVI magnitude [21]. [PERSON] et al. analyzed the response of grassland phenology and productivity to climate change in the Upper Indus Basin and explained that grassland phenology and productivity differed in different bioclimate regions [20]. [PERSON] et al. found that grassland productivity varied with altitude in the Hindu Kush Karakoram region and that the growth conditions of grassland improved from 2001 to 2012 [22]. However, there have been few studies on the carbon cycle in the HKH region. Only part of the research has focused on the Qinghai-Tibet Plateau and mainly focused on site observations of eddy covariance [23; 24; 25]. There is lack of large-scale studies on the carbon cycle throughout the HKH region; thus, the role of grassland ecosystems in the HKH region in the global carbon cycle remains unclear. Hence, a systematic assessment is needed to understand the grassland carbon dynamics in the HKH region as a basis for devising appropriate management policies. In this context, we aimed to estimate the carbon cycle of grassland in the HKH region on a large scale based on remote sensing to help us understand the carbon source and sink situation in this area. Based on this, our study mainly focused on the following three aspects of research: (1) estimate the spatial distribution characteristics of grassland NEP based on remote sensing and climate data and analyze differences in spatial distribution between NEP and NPP; (2) study the interannual fluctuations of grassland NEP in the HKH region through statistical trend analysis; and (3) analyze the responses of grassland NEP and NPP to climate change.
## 4 Proposed Scheme
### Phase 2: Sensing and Reporting  
Each sensing node \(S_{i}\) measures the data that it monitors from the environment as \(D_{i}\) and then encrypts it as the following: \(E_{i}=\ Enc\big{(}D_{i}K_{cj}\big{)}\). After that, the node generates the watermark of the  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(CH_{j}\) & The cluster head node of the \(j\)th cluster in the network \\ \(S_{i}\) & The sensor node \(i\) \\ \(n\) & Number of sensor nodes per cluster \\ \(N\) & Number of sensor nodes in the whole network \\ \(CH_{j}ID\) & Cluster identifier of cluster \(CH_{j}\) \\ \(K_{cj}\) & The cluster key of the \(j\)th cluster in the network \\ \(K_{m}\) & The master key of the network \\ \(W_{i}\) & Watermark of the sensor node \(S_{i}\) \\ \(D_{i}\) & Sensing data of the sensor node \(S_{i}\) \\ \(E_{i}\) & Encrypted sensed data of the sensor node \(S_{i}\) \\ \(R_{i}\) & The complete report of the sensor node \(S_{i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: System notation.  
Figure 3: The sequence diagram of the proposed scheme.  
encrypted measure as \(W_{l}\). The embedding position of the watermark \(W_{i}\) is randomly chosen and varies frequently. The position of each nibble (4 bits) of \(W_{i}\) is generated using a PRNG. The PRNG is used to generate three random numbers for three different positions. Finally, after embedding the watermark, the report is sent as the following format to \(CH_{j}\)  
\[R_{i}=(E_{i}\mid W_{i}\mid Time) \tag{5}\]  
The detailed procedure of Phase 2 is shown in Algorithm 1.  
```
Input: pseudo random number generator parameters \(m_{j,cj}\) and \(a_{j}\). Cluster key \(k_{cj}\) Output:\(R_{i}\)= \(E_{i}W_{i}\)\(\mid\)Timetestamp
1\(D_{i}\)= sensed data
2\(E_{i}\)= Enc(\(D_{i}\), \(k_{cj}\))
3\(W_{i}\)= HMAC(\(E_{i}\mid\mid\)S\({}_{i}\mid\)Timetestamp) \(\mid\)
4 forcount=1 to numberOfPositionsdo
5\(X_{n+1}\)= (\(a_{j}X_{n}+c_{j}\)) mod \(m_{j}\)
6\(pos[count]\)= \(X_{n+1}\)
7
8 end for
9\(E_{i}W_{i}\)= Embed(\(W_{i}\), \(E_{i}\), \(pos[]\)) \(\rhd\)Embed watermark \(W_{i}\)in \(E_{i}\)at positions \(pos[]\)
10\(R_{i}\)= \(E_{i}W_{i}\mid\)Timetestamp
11\(Send(R_{i}\), \(CH_{j}\))
```  
**Algorithm 1** Sense and report algorithm \((m_{j,cj}\)/\(a_{j}\). Cluster key \(k_{cj}\)
# Peripheral Giant Cell Granuloma in Pregnancy: A Case Report
## 1 Introduction  
Peripheral giant cell granuloma (PGCG) is the most common oral giant cell lesion. It appears as an extraosseous soft tissue nodule that originates from the connective tissue of the perisoteum or the periodontal ligament membrane, and is localized within the soft tissues [1; 2; 3; 4]. This pathology is identified by other names, such as giant cell reparative granuloma, osteoclastoma, giant cell epulis, and giant cell hyperplasia of the oral mucosa [5].  
The lesion has a predilection for the buccal gingiva rather than the lingual or palatal. It occurs more frequently in the lower jaw rather than the upper jaw, especially in the incisors and canines [3; 6]. It is often located in the interdental papilla, dentulous alveolar ridge, or marginal gingiva in completely or partially edentulous patients, resulting in gingival contour changes that can cause tooth mobility and displacement [3; 4; 7]. It is more frequent in women than in men, with a 2:1 incidence ratio, and the incidence is higher in the age group of 30-50 years [3; 5; 6; 8]. While rare, it may occur in pediatric patientsas well [3,5]. In most cases, PGCG is asymptomatic; however, the presence of the lesion can be bothersome when it interferes with occlusion or is associated with an infectious or ulcerative process [3, 4, 5]. The size of the lesion may vary from a small papule to a massive enlargement which does not typically exceed a diameter of 5 cm [3]. In certain cases, progressive growth may lead to significant tumors that compromise speech, chewing, or swallowing, with evolution time ranging from three months to four years [3,4].  
PGCG develops extraosseously; however, in edentulous areas, a cup-shaped superficial erosion of the underlying bone may be noted, termed bone flattening or \"saucerization\". However, in dentate areas widening of the periodontal ligament space, destruction of the alveolar crest, and tooth displacement may be noted [3,4].  
Although the etiology of PGCG remains uncertain, it may be related to various factors, including trauma, inflammatory foci, nutritional impact, maladjusted restorations, overextended fillings, occlusal trauma, genetic predisposition, dental calculus, and the use of orthodontic appliances [3,5,9]. Additionally, hormonal changes during pregnancy have been considered a possible contributing factor. However, due to the limited number of clinical cases described in the literature, the association between PGCG and pregnancy remains controversial [10, 11, 12]. The objective of this study was to describe the surgical management of a clinical case of upper jaw PGCG associated with pregnancy.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### Sequence-to-Sequence Speech Synthesis  
Sequence-to-sequence (seq2 seq) neural networks can transduce an input sequence into an output sequence that may have a different length and have been applied to various tasks such as machine translation [52], speech recognition [53] and image caption generation [54], and achieved promising results. Since speech synthesis is the reverse process of speech recognition, the seq2 seq modeling technique has also been applied to speech synthesis recently. For example, the authors in [55] employed the structure with content-based attention [56] to model the acoustic features for speech synthesis. [PERSON] [16] adopted location-based attention to build an encoder-decoder acoustic model. To tackle the instability problem of missing or repeating phones that current seq2 seq models still suffer from, the authors in [57] proposed a forward attention approach for the seq2 seq acoustic modeling of speech synthesis. Tacotron, which is also a seq2 seq model with an attention mechanism, has been proposed to map the input text to mel-spectrogram for speech synthesis.
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
## 5 Measurements
### Single Detectors  
The Keithley source measurement unit (SMU) 2600B was used to perform high-resolution DC measurements for each structure and, under THz radiation, generate the required gate-source voltage and measure the drain-source current, referred to as _detector current_. A motorized moving table placed the individual detectors into the beam focus. A calibrated pyroelectric THz detector (SLT Sensor und Lasertechnik; THz 20 HS) measured the THz power at the detector location. Since the SMU has a strong low-pass behavior, a chopping frequency of 10 Hz was used. The detection current was determined from the digitized signal using a fast Fourier transform (FFT).
## 3 The Proposed Scheme
### Training Strategy  
The proposed scheme was trained by viewing the VLC channel as a black box. Fortunately, researchers have developed several reference channel models for indoor environments for VLC [26]. Therefore, the training data can be easily obtained by simulations [12]. As for collecting the training set, the receiving plane is divided into several grid unitswith equidistant spacing used as potential locations for the PD. After VLC transmission and optical-to-electrical conversion, the received signals are collected under different PD locations. With the skillful preprocessing of the received signal, every spatial pattern and one sample of the transmitted signals are combined as the training data. Practically, we should collect a diverse and abundant training set, including the potential PD locations, to enhance the parameters learning ability of the proposed scheme.  
The direct-current-biased optical (DCO)-OFDM, containing in total 512 sub-carriers with 16-QAM constellation mapping, is adopted as the training symbol, and only five symbols are randomly generated in each training epoch. Moreover, the NMSE between the raw **x** and the equalized \(\tilde{\textbf{x}}\) is employed as the training loss function, demonstrated by  
\[loss=\frac{\sum\left\|\textbf{x}-\tilde{\textbf{x}}\right\|_{2}^{2}}{\sum \left\|\textbf{x}\right\|_{2}^{2}}. \tag{18}\]  
Note that the DC gain is removed from the training set so that the training loss of the proposed scheme can be fairly evaluated. Furthermore, the training procedure was implemented using TensorFlow on a work station running with a graphics processing unit (GPU) of NVIDIA GeForce 2080 Ti; the adaptive moment estimation (Adam) was adopted as the optimizer, and the learning rate was fixed to 0.0001. As in the testing stage, only several special links were adopted to evaluate the system performance for the simplicity of the demonstration.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
###### Abstract  
The quality of weather radar affects the reliability and effectiveness of monitoring severe convective weather. Therefore, rigorous calibration and validation are the foundation for the quantitative application of weather radar. Among the available methods, radial velocity validation is of great significance for reducing the false alarm rate in the identification of tornadoes and thunderstorms. Based on the traditional method that utilizes internal and external instrument radar velocity measurements, we propose a weather radar radial velocity validation method that uses RTK UAV to simulate external targets. In addition, according to the characteristics of the UAV application scenarios, we introduce the evaluation parameter of optimal absolute accuracy to supplement the original parametric system. The experimental results show that the evaluation parameter of optimal absolute accuracy can effectively reduce the interference caused by the systematic deviation of the UAV due to the internal and external environment, which can affect the validation results. When the UAV velocity is not greater than 10 m/s, the optimal absolute accuracy of the radial velocity validation is less than 0.05 m/s, which is essentially consistent with the external instruments' measurement results. This method can be effectively applied to the procedural handling of weather radar radial velocity validation. It is significant for ensuring the accuracy and quality of weather radar radial velocity measurements and improving the effectiveness of radar velocity data applications.  
weather radar; X-band; radial velocity; RTK UAV; validation
# Development of Patient Specific Conformal 3D-Printed Devices for Dose Verification in Radiotherapy
## 2 Materials and Methods
### Conformity of 3D-Printed Boluses  
In order to evaluate the conformity of the 3D-printed bolus to the patient surface and presence of air gap between the bolus and the skin, CT images were acquired for two 3D boluses of different thicknesses (0.5 and 1 cm thickness) attached to the 3D-printed phantom. The air gaps were analyzed in 2D slices by slicing through the CT scans and measuring the thickness of these air gaps.
## 1 Introduction
Initially, existing state-of-the-art ML and deep learning (DL) models are surveyed for their performance on classification tasks. This analysis evaluates recent advancements within space object categorization, assessing relative merits alongside limitations inherent in each method. Subsequently, theoretical backgrounds pertinent to selected analytical methodologies are provided. This segment addresses fundamental principles influencing method selection, articulating reasoning aligned with identified research gaps. Motivation underlying the study originates from unresolved classification and localization challenges present in prior scholarly research, which justify novel exploration. Next, the development of experimental procedures is outlined, encompassing dataset preprocessing, feature engineering, model selection, hyperparameter tuning, validation strategies, and performance evaluation metrics. Afterward, the results derived from implementing these procedures upon the Galaxy Zoo dataset are systematically interpreted in relation to predefined re search objectives, offering comparative analyses against prior benchmarks. This section articulates implications emerging from empirical evidence while identifying constraints encountered throughout methodological applications. Limitations discovered during experimentation receive careful attention, facilitating consideration for subsequent research avenues within astronomical object classification employing ML methodologies.
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
## 3 Results and Discussion
### Comparison  
The AAOD and AAE\({}_{388-500}\) exhibit regular annual, seasonal, and monthly variations. The optical properties of aerosols obtained by different instruments, testing methods, and measuring wavelengths are inconsistent. The AAODs in the six regions except the TB observed in our study are approximately close to northeast China, where the maximum AAOD occurred in Liaoning Province in the range of 0.02 to 0.03 based on MERRA-2 data [5]. The hot summer produces a relatively lower AAOD, while the cold season reveals the highest absorptive aerosol loading. The seasonal trend changes of the AAOD agree quite well with the results of [PERSON] et al.'s analysis over hyper-arid regions [29]. The winter AAOD\({}_{388}\) over the desert region is higher than those seen in urban areas of China, e.g., Hangzhou (AAOD\({}_{440}\): 0.06) [69], and Hefei (AAOD\({}_{5500\text{nm}}\): 0.05 [70], but similar to that obtained in Shenyang (AAOD\({}_{440}\): \(\sim\)0.1) [71] on the basis of CE-318 sun photometer measurements. Similarly, [PERSON] et al. observed the AAOD variation trend in the United States from 2005 to 2015, and pointed out that the increase in AAOD was mainly attributed to an increase in dust concentration rather than BC [24]. In contrast, the AAOD in our study presents an obvious upward trend in winter and spring, which has a bearing on the concentrations of carbonaceous aerosols and dust.  
As for the AAE\({}_{388-500}\), the values in the TB in the four seasons herein are significantly larger than those for Lhasa (AAE\({}_{370-950}\): 1.04), Jiaozuo (AAE\({}_{440-870}\): 1.09), and Nanjing (AAE\({}_{470-660}\): 1.56) with the seven-channel Aethalometer model AE33 [72], ground-based sun photometer measurement [73], and seven-channel Aethalometer model AE31 [74], respectively. [PERSON] et al. estimated that the average AAE\({}_{440-675}\) is 2.2 with the observational analysis method [75]. The monthly average AAE shows its lowest value in cold months and the maximum in June; [PERSON] et al. investigated and found that Singapore had a lower AAE\({}_{365-700}\) in February and a higher value in June [76], while their AAEs were lower than those seen in our study. The AAE\({}_{388-500}\) values acquired on the OMI usually surpass other approaches, and one reason may be that the AAE was obtained at shorter wavelengths in our study. The conclusion can be confirmed that the OMI AAE\({}_{388-500}\) (Xianghe: 2.766, Taihu: 2.826, Hongkong: 2.850, Sacol: 2.793) with short wavelengths is almost twice that of the AERONET AAE\({}_{440-870}\) (Xianghe: 1.288, Taihu: 1.310, Hongkong: 1.272, Sacol: 1.553) with a longer wavelength range (Table 1). This study provides a brief discussion, but more comprehensive and detailed research is needed to offer a detailed explanation, which is beyond our research scope.
## 5 The Verification of the Modified MSRS Method
### Comparison of the MSRS Method with the CQC Method  
Figure 7a,b show the peak displacements and bending moments at the individual points along the length of the main beam calculated by the MSRS and CQC method in the case that the site is the Category IV and the ground movement intensity is the Level 9. The response spectrum of the case is shown in Figure 7a and the correlation coefficient, \(\rho_{ij}\) between mode \(i\) and \(j\) for the CQC method is calculated by Equation (27)  
\[\rho_{ij}=\frac{8\sqrt{\xi_{i}\xi_{j}}\Big{(}\xi_{i}+r\xi_{j}\Big{)}r^{3/2}}{ \big{(}1-r^{2}\big{)}^{2}+4\xi_{i}\xi_{j}r(1+r^{2})+4\Big{(}\xi_{i}^{2}+\xi_{j} ^{2}\Big{)}r^{2}}, \tag{27}\]  
where r is calculated by Equation (28)  
\[r=\frac{\omega_{i}}{\omega_{j}} \tag{28}\]  
where \(\omega_{i}\) is the frequency for mode \(i\).  
It is shown that the structural response calculated using the MSRS method is less than that obtained from the CQC method (Figure 7), because the MSRS method considers the wave passage effect and the CQC method does not involve this effect instead. This implies that the result calculated using the CQC method becomes conservative. Besides, the passage effect, which leads to the different phases of the seismic wave at individual supports, at the same time, might reduce the structural response in some cases. It can be seen that the results calculated by the two methods are obviously different at the middle point of the main beam. The bending moment and the biggest displacement at this point calculated by the CQC method are zero under coherent ground motion, while the corresponding values calculated by the MSRS method are 322 kN\(\cdot\)m and 4.075 mm, respectively. This is because the MSRS method includes the pseudo static effect derived from the displacement differences of individual supports and the CQC method does not introduce this pseudo static displacement instead. This further indicates that the MSRS method can more accurately reflect the structural response than the CQC method does.  
Figure 6: The analysis flowchart of the modified multi-support response spectrum (MSRS) method.
# Survey of Smart Parking Systems
## 4 Vehicle Detection Techniques (VDT) Implemented in SPS
### Prediction Models  
This type of detection is based on algorithms that estimate or predict the occupancy of parking places. These predictions can be generalized for a group of parking slots or for each space in a particular way. PMs offer the option of detecting vehicles and processing this information without physical validation, that is, without the use of physical sensors, cameras, smartphones, etc.  
One of the most widely used techniques for prediction is artificial intelligence using neural networks. These implementations are based on forming neural networks with the historical data collected on the availability of parking places in order to make a prediction.  
The real-time occupation prediction scheme based on recurrent artificial neural networks applies simple but flexible memory mechanisms to replicate the temporal dynamics of parking occupancy [216].  
Some implementations, such as convolutional neural networks (CNN) [251] for detecting vehicles in open car parks, were recently proven to be effective models, especially in analyzing image and video content from security cameras in closed circuit TV (CCTV) or similar [212]. The keys to these applications are techniques for scaling networks to tens of millions of parameters and massively labeled datasets used in the learning process. For the recognition of actions, CNN could be considered as a large-scale video classifier of the information of interest. In this case, it is about the availability of a parking slot [293].  
Other PMs use algorithms that predict the availability of parking facilities in real time using current (online) and combined historical information [294]. In this case, we can establish a hybrid model that uses other means of detection to obtain the current information, but whose processing determines a prediction regarding occupation and not the strict use of this information online.  
Some implementations used decision trees and classifiers such as the naive Bayes model to predict the level of parking occupancy for a block in the next hour [280]. Another model with an extremely low error level implemented regression trees according to the simulations [271] to predict parking occupancy in a specific area. In both cases, as mentioned above, the parking availability was predicted in a group manner.  
All of these types of solutions for predicting the availability of the parking places have a strong theoretical background on the operation and fidelity of each prediction model. Unlike the other VDTs that need an online infrastructure, they can work offline or in a hybrid way. However, a disadvantage is, although they may have high levels of approach, they do not ensure the availability of parking slots in real time.