esa-sceva/satcom-chunk-collection huggingface dataset:
# Similarity Model of a Rotary Lobe Expander Working with Various Fluids
## 1 Introduction  
There are several types of volumetric expanders. The most popular are the scroll expander [1], screw [2], reciprocating piston [3], rotary vane [4], and rolling piston [5]. In this paper, the rotary lobe expander (Roots expander) is taken into account. This type of expander is special because it has a built-in volume ratio equal to 1, which means that the gas pockets do not change their volume during the rotation [6].  
Volumetric expanders are used in many areas. Of special interest is their usage in waste heat recovery and in combined heat and power generation [7, 8].  
Recently, there have been more publications in which this type of expander is analyzed [6, 9, 10]. Numerical calculations of the rotary lobe expanders are presented in [11, 12]; similar numerical calculations, but for pumps of this type, are presented in [13, 14].  
In this paper, the simple analytical model of the power of these expanders is derived. It is based on the similarity theory presented in many books, e.g., in [15, 16]. The similarity in the derived models involves the similarity in pressure loss due to fluid viscosity, which decreases the power in case of no viscosity. This similarity allows the usage of knownpower from one working fluid in the calculation of the power of another. The construction of the expander is represented by one value of the volume transferred by the rotors during one shaft's revolution. The presented model allows easy calculation of the power for various fluids or for the other working conditions (pressure, rotational speed) before using the more precise but expensive numerical calculation. The authors did not find any studies on existing similarity models for the analyzed rotary lobe expander.  
The similarity model was tested on the exemplary rotary lobe expander, the GGP16 XA from Armak Motors (Hull, East Yorkshire, UK) [17], due to its higher allowed temperature compared with XGA16. This expander was disassembled and measured.
## 6 Conclusions  
A voice conversion method was proposed, based on a perceptually meaningful criterion. The objective function resides in the conventional MMSE and in the perceptual distance. The conversion rules for spectral envelop and spectral fine structure were jointly constructed in an iterative manner so that the perceptual distance was decreased incrementally. The effectiveness of the proposed method was confirmed through both objective and subjective evaluations. The experimental results also showed that the perceptual distance revealed a strong correlation with the PESQ. Moreover, it was confirmed that the results of the PESQ were consistent with the subjective listening test results. Currently, a simple conversion method is adopted for the spectral fine structure, which is based on PSOLA with a global pitch modification factor. More complicated conversion schemes for spectral fine structures will be considered in future study, and these will include pitch modification as well as LP-residual conversion.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea through the Ministry of Science and ICT under Grant S202003S00170.  
**Acknowledgments: This paper was written as part of Konkuk University's research support program for its faculty on sabbatical leave in 2017.**  
**Conflicts of Interest: The author declare no conflict of interest.**  
Figure 8: Subjective evaluation results with 95% confidence interval. Top: ABX test results, Bottom: Preference test results.
## 3 Ra-Lstm-Ann Model Building
### Model Validation  
In order to verify the accuracy of the LSTM-RA-ANN model constructed in this paper, the settlement monitoring data of F1 under the enclosure construction condition are used for the validation and comparative analysis of the performance of the constructed model and the traditional LSTM model in predicting the settlement. The hyperparameters of the model are selected as the initial learning rate, minimum iteration batch, maximum iteration period, and number of hidden units. After several training sessions, the hyperparameters of the model are as follows: initial learning rate 0.06, minimum iteration batch 20, maximum iteration period 400, and the number of hidden units 32. The Adam algorithm is used for loss transfer, the training step size is set to 5, and the data training mode of '5 + 1' is adopted (the settlement monitoring data of the first five days are used to predict the settlement value of the sixth day), and the output is in the form of a single-step output. Considering the consistency of the comparison conditions, these settings were still used for the basic parameters of the model for subsequent analyses. Using the proposed LSTM-RA-ANN model and the conventional LSTM model, the settlement prediction results for monitoring point F1 were obtained in Figure 6.  
As can be seen in Figure 6, for monitoring point F1, the coefficient of determination of the LSTM-RA-ANN model proposed in this paper is much higher than that of the traditional LSTM model, which is 4.5 times that of the traditional model; the RMSE and MAE are 0.54 times and 0.57 times that of the traditional LSTM model, respectively. In terms of the overall agreement, the predicted settlement values from the LSTM-RA-ANN model in this paper are in better agreement with the monitoring values than those from the traditional LSTM model.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 2 Multicarrier Modulations and FIR Filter
### FIR Filter
Thus the transfer function between input and output signals is given by  
\[Y(z^{N})=T_{\text{TMUX}}(z^{N})\cdot X(z^{N}), \tag{11}\]  
where  
\[X(z^{N})=[X_{0}(z)\quad X_{1}(z)\quad\cdots\quad X_{N-1}(z)]^{T}, \tag{12}\]  
\[Y(z^{N})=[Y_{0}(z)\quad Y_{1}(z)\quad\cdots\quad Y_{N-1}(z)]^{T}. \tag{13}\]
# Progress of Capillary Flow-Related Hydrovoltaic Technology: Mechanisms and Device Applications
## 2 Current Understandings of Capillary Flow-Related Electricity Generation Mechanisms  
Capillary flow-related hydrovoltaic energy originates from either the chemical or physical potential of water, i.e., from its change in enthalpy or mechanical flow energy [15; 25; 29]. Since the origin of this energy source involves a combination of complex elements such as flow energy, chemical potential, and even surface electrification of the liquid [32; 33], elucidating the exact mechanism behind electricity generation is a daunting task. In the following sections, we review the current popular mechanisms for capillary flow-related hydrovoltaic electricity generation.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 4 Discussion
### Legal and Epistemological Considerations on 3D Cheloscopy
#### 4.6.4 Study Limitations  
While this study provides valuable insights into the operator dependency and reliability of analog and digital methods for counting lip wrinkles, it has some limitations:  
* Small sample size: The study was conducted on a limited number of lip imprints, which may affect the generalizability of the results. A larger sample size would improve the statistical power and reliability of the findings.
* Exploratory nature: This study was exploratory and primarily descriptive. Advanced statistical analyses, such as intraclass correlation coefficient (ICC) or Bland-Altman analysis, were not performed due to the preliminary nature of the investigation.
* Operator variability: Only two operators were involved in the study, with different levels of experience. Including more operators with varying expertise would provide a more comprehensive evaluation of operator-dependency.
* Methodological constraints: The analog method relies heavily on subjective interpretation, which may introduce bias. Although the digital method showed greater consistency, its implementation requires specialized equipment and software, which may not be universally accessible.
* Lack of ground truth: The study did not include a \"ground truth\" or reference standard for the exact number of lip wrinkles in each imprint. This limited our ability to assess the absolute accuracy of the methods.  
Despite these limitations, our findings highlight important differences between the analog and digital methods and underscore the need for further research with larger sample sizes and more robust statistical analyses.
## 6 Discussion
### Differences and Similarities between GPP Datasets  
In the study, correlation analysis and the Kruskal-Wallis test were applied to analyze the similarities and differences among the datasets. Initially, scatter plots were used to determine at which levels the datasets were more similar or different. It was generally observed that, while the similarity was high at lower values, it decreased with higher values, indicating the presence of increasing variance. Correlation values confirmed high positive relationships across all regions, but since these values were linear and not statistically powerful enough to compare the entire group together, the KW test was additionally applied. The results of the KW test and subsequent pairwise comparisons indicated that, in each region, at least one dataset possessed a distinct structure (Figure 6). The most significant difference among the GPP datasets was observed in the PMLV2 dataset. Its emphasis on following physical processes could be the reason for this divergence and, given its explicit coupling of the carbon and water cycles, one may argue that it has advantages for the temperate, semi-arid, and arid climate zones of Turkiye. A notable finding was that,in arid regions like Konya and Sanliurfa, almost all datasets exhibited differences (Figure 6), further emphasizing the importance of water limitation on GPP, GPP products, and their trends. Zhang and Aizhong Ye [107] reported similar results. Basically, the differences in various remote sensing-based GPP products can be attributed to different algorithms using different input data and having different parameter values when describing environmental mechanisms over climate zones [108]. In addition, stomatal closure when drought occurs in terrestrial ecosystems has been shown to affect photosynthesis due to limited leaf and canopy water content or high VPD in the atmosphere. Different GPP models using different model parameters, such as the maximum LUE and optimum temperature for different ecosystems, are the second major contributor to the possible differences in arid regions. Moreover, different GPP datasets in the literature have used different flux tower data to calibrate LUE parameters, which may also contribute to differences in simulated GPP [109]. The use of different leaf area index data and land cover data [50] can also cause significant differences. Additionally, the number of outliers in all datasets for these regions was higher than in other regions. These analyses suggest that variations in the algorithms of the datasets significantly impact the calculation of values in arid regions. Therefore, it is recommended that, in developing new GPP prediction methods, one should consider these variations and devise algorithms that account for the distinct characteristics of arid regions.
## 2 Materials and Methods
### Study Design  
Participants came to the laboratory on four occasions, spaced 48-72 h apart. The initial visit involved measuring anthropometrics and establishing the one-repetition maximum (1 RM) for the parallel back-squat on a Smith machine, which was the exercise used in all following sessions. During the second and third sessions, for familiarization, participants performed a set of repetitions to failure at the maximum intended velocity with three different loads (70%1 RM, 80%1 RM, and 90%1 RM). The fourth visit was the main experimental session, consisting of four sets of repetitions to failure at the 75%1 RM load, with each set followed by a 2 min rest period. The 75%1 RM was selected because it is a relative load commonly used in RT programs and it has been previously considered in studies that have explored the properties of %Rep-%VL relationships [5; 6; 19]. In this session, both the generalized and individualized %Rep-%VL equations derived from the data of the first set were employed to predict %Rep at a 20%VL during the remaining sets. The %VL was calculated using two different reference velocities: the fastest mean velocity (MV\({}_{\text{fastest}}\)) specific to each set and the MV\({}_{\text{fastest}}\) from the first set. The 20%VL was chosen because it has been found to effectively enhance strength adaptations from a back-squat training regimen while reducing the fatigue associated with higher %VL [20]. Data from all participants were aggregated to establish the generalized %Rep-%VL relationship, whereas individualized %Rep-%VL relationships were calculated for each participant separately.
## 4 Performance Evaluation
### Experimental Method and Environment  
This paper mainly evaluates the influence of the number of drones and the number of blockchain nodes on the public key request time and identity authentication time. In this experiment, the RSA algorithm is used to generate public and private keys for the drone, blockchain, and government management departments. Four Alibaba Cloud servers are used to build the blockchain system. A notebook is used to simulate the UAV owners. The quadrotor UAV model is used in MATLAB for digital UAV simulations. The blockchain system run in Ubuntu 16.0 with 2 GB RAM, and the UAV owners and digital drones run on Windows 10 with a 1.8 GHz AMD Ryzen 7 4800U CPU and 16.0 GB RAM. In the experiment, the Hyperledger Fabric platform developed by IBM is adopted as the underlying development platform of the blockchain system, and the go language is used to develop smart contracts.
# On the Octonion Cross Wigner Distribution of 3-D Signals
## 3 Octonion Wigner Distribution
### Basic Properties
From the fact that OWD is the octonion Fourier transform of the octonion correlation product, it results that: * for every \((\mathbf{x},\mathbf{\omega})\in\mathbb{R}^{3}\) we have \[\int_{\mathbb{R}^{3}}\int_{\mathbb{R}^{3}}W^{R}_{f}(\mathbf{x},\boldsymbol{ \omega})\,\mathrm{d}\boldsymbol{\omega}\,\mathrm{d}\mathbf{x}=\left\|f\right\| _{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{2}.\]2 hence \(W^{R}_{f,g}\in L^{\infty}(\mathbb{R}^{3}\times\mathbb{R}^{3};\mathbb{O})\) for all functions \(f,g\in L^{2}(\mathbb{R}^{3};\mathbb{O})\);* for every \(f,g\in L^{2}(\mathbb{R}^{3};\mathbb{O})\) and \(\mathbf{x}\in\mathbb{R}^{3}\), \(W^{R}_{f,g}(\mathbf{x},\cdot)\) is _uniformly continuous_;
* _Riemann-Lebesgue lemma_ holds with respect to \(\boldsymbol{\omega}\), i.e., for every \(f,g\in L^{2}(\mathbb{R}^{3};\mathbb{O})\) and \(\mathbf{x}\in\mathbb{R}^{3}\) \[\int_{\mathbb{R}^{3}}\int_{\mathbb{R}^{3}}W^{R}_{f}(\mathbf{x},\boldsymbol{ \omega})\,\mathrm{d}\boldsymbol{\omega}\,\mathrm{d}\mathbf{x}=\left\|f\right\| _{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{2}.\]3
* for every \(f,g\in L^{2}(\mathbb{R}^{3};\mathbb{O})\) and \(\mathbf{x}\in\mathbb{R}^{3}\) such that \(h(\cdot;\mathbf{x})\in L^{2}(\mathbb{R}^{3};\mathbb{O})\) we have \(W^{R}_{f,g}(\mathbf{x},\cdot)\in L^{2}(\mathbb{R}^{3};\mathbb{O})\). The last fact characterizes the OWD class for every \(\mathbf{x}\). We can also look at the characterization with respect to \(\boldsymbol{\omega}\) using the properties of convolution of \(\mathbb{R}\)-valued functions. It is known (see [54], Corollary 1.21) that if \(F\in L^{p}(\mathbb{R}^{n};\mathbb{R})\) and \(G\in L^{1}(\mathbb{R}^{n};\mathbb{R})\) for some \(p\geq 1\), then \(F*G\in L^{p}(\mathbb{R}^{n};\mathbb{R})\), where \(*\) denotes classical convolution, and  
\[\int_{\mathbb{R}^{3}}\int_{\mathbb{R}^{3}}W^{R}_{f}(\mathbf{x},\boldsymbol{ \omega})\,\mathrm{d}\boldsymbol{\omega}\,\mathrm{d}\mathbf{x}=\left\|f\right\| _{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{2}.\]4  
Assume that \(f,g\in L^{2}(\mathbb{R}^{3};\mathbb{O})\cap L^{1}(\mathbb{R}^{3};\mathbb{O})\) and \((\mathbf{x},\boldsymbol{\omega})\in\mathbb{R}^{3}\times\mathbb{R}^{3}\). Then  
Notice that \(H(\mathbf{x})=\int_{\mathbb{R}^{3}}\lvert f(\mathbf{t})\rvert\lvert g( \mathbf{x}-\mathbf{t})\rvert\,\mathrm{d}\mathbf{t}=(\lvert f\rvert*\lvert g \rvert)(\mathbf{x})\) is the convolution of \(\mathbb{R}\)-valued functions \(\lvert f\rvert\) and \(\lvert g\rvert\). We have that \(H\in L^{2}(\mathbb{R}^{3};\mathbb{R})\cap L^{1}(\mathbb{R}^{3};\mathbb{R})\) and  
\[\int_{\mathbb{R}^{3}}\int_{\mathbb{R}^{3}}W^{R}_{f}(\mathbf{x},\boldsymbol{ \omega})\,\mathrm{d}\boldsymbol{\omega}\,\mathrm{d}\mathbf{x}=\left\|f\right\| _{L^{2}(\mathbb{R}^{3};\mathbb{O})}^{2}.\]5  
So we proved that for every \(f,g\in L^{2}(\mathbb{R}^{3};\mathbb{O})\cap L^{1}(\mathbb{R}^{3};\mathbb{O})\) and \(\boldsymbol{\omega}\in\mathbb{R}^{3}\) we have that \(W^{R}_{f,g}(\cdot,\boldsymbol{\omega})\in L^{2}(\mathbb{R}^{3};\mathbb{O}) \cap L^{1}(\mathbb{R}^{3};\mathbb{O})\).
## 4 Results and Analysis
### Impact of Drought Events on WUE  
Based on the annual precipitation and air temperature raster data of the Li River Basin from 2001 to 2020, the SPEI values at the spatial scale were calculated by using the Climta_Indices database in Spyder (Python 3.11) [39]. Figure 11 shows the mean values of the annual-scale SPEI in the Li River Basin for the last 20 years. The analysis shows that the linear tendency rate of the annual SPEI was 0.0326/a, showing a weak upward trend, i.e., the basin tends to become wetter. In terms of drought type and frequency, the total number of years with annual SPEI values lower than \(-0.5\) in the basin was 4 years, which were 2003, 2009, 2011, and 2013; among them, the index value of 2011 was \(-1.3906\), which was a moderate drought within the interval of \(-1.5\) to \(-1.0\). As a result, the total number of annual-scale drought events in the basin from 2001 to 2020 was four, including three mild droughts and one moderate drought.  
To further understand the effect of drought events on WUE, correlation analyses were used to obtain the spatial distribution of correlation coefficients between WUE and the SPEI, as shown in Figure 12a,b. The correlation coefficients between them ranged from \(-0.7333\) to \(0.6264\), and the proportions of positively and negatively correlated areas were \(31.69\%\) and \(68.31\%\), respectively, while the proportions of significantly positively and negatively correlated areas were \(2.19\%\) and \(2.59\%\). That is, WUE and the SPEI in the basin were mostly significantly negatively correlated, and the WUE value tended to increase with a decrease in the SPEI, and a decrease in the SPEI value showed the occurrence of drought events or the enhancement in the degree of drought, which is in line with the results of the research of some scholars [40; 41]. Spatially, the correlation coefficients showed an increasing-decreasing-increasing trend from north to south, with positive correlation areas mainly distributed in the north and south, while the Guilin urban area and its surroundings in the central part of the county were the main concentration areas with negative correlation coefficients.  
For different land cover types, the relationship between the SPEI and WUE value points based on the annual scale was plotted and linearly fitted, as shown in Figure 13. According to the analysis of the graph, the SPEI and WUE of different land cover types were negatively correlated, in which grassland WUE showed the strongest negative correlation with the SPEI, with a coefficient of about \(-0.30\), followed by cropland WUE, with a correlation coefficient of \(-0.23\); forest and shrubland WUE decreased with an increase in the SPEI,  
Figure 11: Time series of SPEI in Li River Basin 2001–2020.  
but the correlation was not significant, and it showed a weak negative correlation, with a coefficient of \(-0.04\).
# Three-Phase PV CHB Inverter for a Distributed Power Generation System
###### Abstract  
This work deals with the design of a three-phase grid-tied photovoltaic (PV) cascade H-bridge inverter for distributed power conversion. The power balancing among the phases must be properly addressed. In fact, an intra-phase power imbalance--arising from uneven irradiance and temperature conditions--generates a per-phase power imbalance. This latter can be compensated by the injection of a proper zero-sequence voltage, while the intra-phase balance is ensured by means of a hybrid modulation method which is able to guarantee the handling of unequal DC (Direct Current) sources, stable circuit operation, and maximization of PV power production. The digital controller is developed and tested in Matlab/Simulink environment integrated with XSG (Xilinx System Generator), thus allowing an easy transfer on a field-programmable gate array (FPGA) platform and accurately describing the behavior of a real hardware implementation. Thus, numerical results have been considered to prove the effectiveness of the proposed approach.  
distributed energy conversion; grid-tied multilevel inverter; modulation technique; photovoltaic power systems Article
## 4 Design and Results of the Experiments  
### Content and Purpose of the Experiments  
Three experiments are carried out to verify the validity of the proposed method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Number of Parameters (Thousand)** & **Size of Model (M)** \\ \hline Xception [18] & 20,902.5 & 239 \\ MobileNet [19] & 3235.5 & 37.1 \\ ShuffleNet [20] & 1947.1 & 23.8 \\ Deep separable CNN [17] & 203.4 & 2.53 \\ CNN [17] + CBAM & 203.7 & 2.55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scale tests of some lightweight network.  
Figure 5: Diagram of model structure.  
* Experiment 1: In order to prove the effect of confidence learning on the noise detection effect of facial expression dataset and the effect of the proposed hyper-parameters, a group of hyperparameters were used to evaluate datasets with different noise ratios;
* Experiment 2: In order to prove the effectiveness of the proposed method in facial expression recognition using the model above, four public facial expression datasets are used as training sets for optimization and comparison experiments;
* Experiment 3: In order to prove the function of the proposed method in the practical lightweight recognition system, the system is built and a real-time recognition experiment of real samples is carried out. The overall accuracy rate, the accuracy rate of various expressions, and specific frames are compared intuitively.