esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
With rapid socioeconomic development, accelerated industrialization, and continuously increasing energy consumption, particulates have become major urban air pollutants in China [1; 2; 3]. In particular, fine particulates PM\({}_{2.5}\) can not only reduce atmospheric visibility but also increase mortality and incidence of diseases such as respiratory diseases [4; 5; 6], and these particulates have aroused great public concern and attention. Studies have indicated that urban air pollution levels and their spatiotemporal distributions are not only associated with local emissions but also influenced by cross-regional transport from sources in surrounding areas [7; 8; 9]. Accurate identification of urban atmospheric pollution characteristics and transport mechanisms is critical to control and mitigate air pollution [10].  
Existing studies on the spatiotemporal characteristics of PM\({}_{2.5}\) and transport mechanisms have mostly focused on geo-statistics analysis [11], air quality models (e.g., large-scale Weather Research and Forecasting (WRF) model) [12], and back-trajectory clustering-based mechanism analysis. For back-trajectory clustering-based transport analysis, previous research has mostly focused on hotspots [13; 14], while there is limited research on small but seriously polluted cities in China.  
[PERSON] et al. (2018, 2019) indicated that PM\({}_{2.5}\) concentration and its influencing factors varied in different seasons, and they also preliminarily showed that the air pollution in Weifang City was affected by meteorological conditions and pollution of surrounding cities [6; 15]. The above-mentioned studies are based on qualitative analysis. To quantitatively analyze the cross-regional influences of Weifang in different seasons, this paper describes an in-depth study of the seasonal variations in internal and external potential sources and provides targeted pollution control measures for government over different seasons. In addition, to improve the credibility and accuracy of the results, different trajectory analysis methods are used to analyze the potential sources of Weifang PM\({}_{2.5}\) in this paper, which can be used to evaluate the results through mutual verification.  
Therefore, based on hourly high-frequency PM\({}_{2.5}\) data from 38 provincial monitoring stations in Weifang from 2015 to 2016, this paper examines the transport characteristics and potential sources of PM\({}_{2.5}\) in Weifang City in different seasons by using a back-trajectory clustering method. This research helps to better understand the causes of PM\({}_{2.5}\) pollution and the sources in Weifang as well as provides important scientific references for joint atmospheric pollution control in Weifang City.  
This paper includes five sections. Section 2 summarizes relevant studies on pollutant transport mechanisms and potential sources. Section 3 describes the data sources and methods used. The analytical results of the PM\({}_{2.5}\) pollution characteristics and potential source contributions in different seasons are presented in Section 4, followed by the conclusions in Section 5.
# A Video-Based Fire Detection Using Deep Learning Models
## 5 Conclusions  
We have proposed a deep learning-based fire detection method, called DTA, which imitates the human detection process. We assumed that the DTA process can greatly reduce erroneous fire detection. The proposed method uses the Faster R-CNN fire detection model to detect the SRoF based on its spatial features. Then, the features summarized from the SRoFs and non-fire regions in successive frames are accumulated by LSTM to classify whether there is a fire or not in a short-term period. The successive short-term decisions are then combined in the majority voting for the final decision in a long-term period. In addition, the areas of both flame and fire are calculated and their temporal changes are reported to interpret the dynamic behavior of the fire with the final fire decision.  
The proposed method has been experimentally proven to provide excellent fire detection accuracy, by reducing the false detections and misdetections, and to successfully interpret the temporal behavior of flame and smoke, which possibly reduces the false dispatch of firemen. In addition, we have constructed a large fire dataset which contains diverse still images and video clips that enhance the data from well-known public datasets. Not only is the dataset used for the training and testing of our experiment, but it also could be an asset for future fire research.  
Conceptualization, B.K. and J.L.; Data curation, B.K.; methodology, B.K. and J.L.; Project administration, B.K.; Resources, B.K.; Writing--original draft, B.K.; Writing--review & editing, J.L.  
For the research leading to these results, the authors would like to thank the Korean Ministry of Education and Brain Korea 21 PLUS Project for their funding.  
This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2019R1A6A1A09031717) and Brain Korea 21 PLUS Project, National Research Foundation of Korea.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, and in the decision to publish the results.  
Figure 19: Sample still shots of decreasing flame video clip.  
Figure 20: Changes in areas of flame and smoke with final decisions by majority voting for the video clip of Figure 19.
## 5 Conclusions  
In this work, we present an elbow interaction force estimation algorithm to establish the relationships between the sEMG signal and the elbow interaction forces. Considering the advantages of deep learning technology in nonlinear regression, a ResNet-BiLSTM algorithm is proposed. In the experiment, we used the sEMG signals processed by decoupling and window algorithm as the input of the ResNet-BiLSTM network. The experiment used four subjects to complete a task three times at four angles. The results show that the ResNet-BiLSTM method can accurately estimate the elbow interaction force. The method improves the force required for the motion control of the wearable assisted robot. At the same time, it provides effective input information for human motion intention recognition.
## 4 Simulation Experiments and Discussion
### Experiment 2: Analysis of the Reflector TDE Impacts on ABP Accuracy
#### 4.4.2 Analysis for Thermal Translation Errors
On the other hand, once the thermal translation error level was specified, most thermal translation errors (except for \(y_{\text{PSR},ma}\), \(y_{\text{MR},ma}\), and \(z_{\text{MR},ma}\)) led to sinusoidal BPEs over a scan period of the ECF. The fluctuation range of the BPEs was generally 1\(\sim\)2 \(\upmu\)rad with a small thermal translation error; however, as the thermal translation error increased, the fluctuation of the BPEs would gradually become pronounced, and the fluctuation range could reach a maximum of 10 \(\upmu\)rad.
# Heterogeneously Integrated Multicore Fibers for Smart Oilfield Applications
For smart oilfields, compared with normal single-mode fibers, a unique characteristic of multicore fiber is that bending would generate local tangential strain in off-center cores, and the strain is angular-position-dependent. The cores in off-center positions lead to high bending sensitivity [24]. However, although multicore fiber design has been developed for a long time, it is still challenging to propose an integrated multicore fiber for smart oilfields applications due to the design complexity, which should take into account the performance of each core itself and the crosstalk between the adjacent cores. For digital transmission cores, the main goal is to realize low differential mode group delay (DMGD) because the computational complexity of digital signal processing (DSP) increases with that, and low DSP complexity is the basis of real-time transmission, which is limited by the processing capability of the widely used chips. As for analog transmission cores, the effective area (A\({}_{\text{eff}}\)) should be enlarged to depress the nonlinear effect due to the watt-scale input power [25]. To maintain the accuracy and the sensitivity of sensing, the arrangement of different types of cores should be considered and the core pitches between neighboring cores should be optimized to keep the stability of the signal transmission. However, to date, there are few reports about multicore fiber design especially for smart oilfields, which could support real-time data transmission, device-to-device networking, and variables monitoring at the same time. In this paper, we propose a heterogeneously integrated seven-core fiber for interconnection and awareness applications in smart oilfields, which could not only support digital and analog signal transmission but could also measure temperature and vibration through Raman effect and phase-sensitive optical time-domain reflectometry. The core arranged in the center is used for digital transmission to maintain communication stability, while others are arranged around the center for analog transmission and sensing, with equal pitches to increase sensing sensitivity. The core for digital signal transmission has a low differential mode group delay of 10 ps/km over the C-band and the crosstalk between adjacent cores is lower than \(-\)55 dB/km at the pitch of 50 \(\upmu\)m. A 25-Gbaud transmission over 50 km is simulated. Each core for analog signal transmission has a large effective area of 172 \(\upmu\)m\({}^{2}\) to suppress the nonlinear effect due to the watt-scale input power. In all, the proposed heterogeneously integrated multicore fiber exhibits great potential to be applied in smart oilfields, meeting the demand for efficient and cost-effective oil production.
# A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
## 6 Benchmark Datasets  
Benchmark datasets are commonly utilized to evaluate the performance of cross-modal retrieval. Table 4 displays the analysis and explanation of classical cross-modal datasets, including the names of the datasets, the numbers of images and texts, and descriptions.  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Name** & **Number** & **Description** \\ \hline NUS-WIDE [92] & 269,648 & Every image includes 2 to 5 label claims on average. \\ \hline [[https://www.image-net.org/](https://www.image-net.org/)]([https://www.image-net.org/](https://www.image-net.org/)) (accessed on 1 January 2022) & 14,197,122 & ImageNet aims at classification, positioning, and detection task evaluation. \\ \hline Pascal VOC 2007 [93] & 9963 & It includes training, validation, and test and marks 24,640 objects. \\ \hline Pascal VOC 2012 [94] & 11,530 & Each image is tagged with 20 categories of objects, including people, animals, vehicles, and furniture. Each image has an average of 2.4 objects. \\ \hline Wikipedia [95] & 2866 & The most-often used dataset for retrieval study is Wikipedia, which consists of entries with relevant picture–text pairs. \\ \hline SBU Caption [96] & 1,000,000 & Image captions are a retrieval task containing 1 million image URLs and title pairs. Humans write the captions, which are then filtered to leave only those with at least two nouns, noun–verb pairs, or verb–adjective pairs. \\ \hline Flickr 8k [97] & 8092 & Each image is accompanied by five human-generated subtitles that focus on humans or animals performing an action. \\ \hline Wiki-CMR [98] & 74,961 & A web cross-modality dataset includes written paragraphs, photos, and hyperlinks. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of representative datasets facilitating cross-modal retrieval.
## 4 Results of Parameter Identification and Optimization
### Results of Parameter Identification  
In this article, only the simpler method was used to identify the parameters of the mathematical model and optimize the drive of cylinders with large dimensions and power, which proved to be sufficiently stable and accurate, and the applicability of other methods was not examined. In order to apply the minimization method to the identification of mathematical model parameters and the optimization of the hydrodynamic processes, a computer program named OPTIA was created. The block diagram of the basic structure of the program is shown in Figure 6. The program consists of two modules that perform a specific set of functions. Each module consists of one or more subroutines. Some modules perform the following functions:  
* OPTIA module--loading of input data (start and limit values of parameters, experimental pressure values, etc. ), creation of output data file, minimization of functionals. The OPTIA module is an integral part of the ADS 2000 measurement and regulation system program library;
* Module AKSIP--calculation of pressure values in the cylinder and other hydrodynamic quantities according to the mathematical model. In the OPTIA module, the maximum number of parameters for optimization is NMAX = 17. Using the developed OPTIA program for parameter identification and optimization, seven characteristic parameters of the piston axial pump were optimized. An overview of the parameters on which the optimization was performed is given in the file OPTIA.DAT (Table 1). The following parameters have been optimized: suction pressure pu, volume of the suction chamber Vs, volume of the pressure chamber Vv, central radius of the suction opening of the distribution plate R2, the angle of the beginning of the suction phase 2, the stiffness of the pressure valve spring cv and the length of the pressure pipeline Lc (Table 2). The volume of the pressure chamber, the volume of the suction chamber as well as the length of the pressure pipeline have no significant effect on the maximum degree of delivery of the cylinder, so in the further analysis, attention is focused on the parameters shown in Table 3.  
The results in this paper refer to the analysis of the energy efficiency of large-sized cylinders and the power from pressure pulsations when operating with axial piston pumps. Axial piston pumps are widely used in hydraulic systems due to their high efficiency and reliability. However, pressure pulsations can significantly impact the energy efficiency and power output of these pumps, especially in large-sized cylinders. Pressure pulsations are rapid changes in pressure within the hydraulic system, typically caused by the periodic motion of the pistons in axial piston pumps. The causes include the cyclic nature of piston movement, pump design and configuration, load variations and fluid compressibility. Figure 6: Block diagram of the OPTIA program.
## 2 The System Description  
Consider the following multi-input OEAR system:  
\[x_{j}(t) + a_{j1}x_{j}(t-1)+a_{j2}x_{j}(t-2)+\cdots+a_{jn_{j}}x_{j}(t-n_{j}) \tag{1}\] \[= b_{j1}u_{j}(t-1)+b_{j2}u_{j}(t-2)+\cdots+b_{jn_{j}}u_{j}(t-n_{j}),\] \[x(t) = \sum_{j=1}^{r}x_{j}(t),\] (2) \[w(t) + c_{1}w(t-1)+c_{2}w(t-2)+\cdots+c_{n_{c}}w(t-n_{c})=v(t),\] (3) \[y(t) = x(t)+w(t), \tag{4}\]  
where \(u_{j}(t)\in\mathbb{R}\), \(j=1,2,\cdots,r\) are the inputs, \(y(t)\in\mathbb{R}\) is the output, \(x(t)\in\mathbb{R}\) represents the noise-free output, \(v(t)\in\mathbb{R}\) is random white noise with zero mean, and \(w(t)\in\mathbb{R}\) is random colored noise. Assume that the orders \(n_{j}\) and \(n_{c}\) are known, \(y(t)=0\), \(u_{j}(t)=0\) and \(v(t)=0\) for \(t\leq 0\). The parameters \(a_{ji}\), \(b_{ji}\) and \(c_{i}\) are to be identified from input-output data \(\{u_{j}(t),y(t),j=1,2,\cdots,r\}\).  
Define the parameter vectors:  
\[\boldsymbol{\theta} := [\boldsymbol{\theta}^{\mathrm{r}},\boldsymbol{c}^{\mathrm{r}}] ^{\mathrm{r}}\in\mathbb{R}^{n},\quad n:=n_{0}+n_{c},\] \[\boldsymbol{\vartheta} := [\boldsymbol{\theta}_{1}^{\mathrm{r}},\boldsymbol{\theta}_{2}^{ \mathrm{r}},\cdots,\boldsymbol{\theta}_{r}^{\mathrm{r}}]^{\mathrm{r}}\in \mathbb{R}^{n_{0}},\quad n_{0}:=2n_{1}+2n_{2}+\cdots+2n_{r},\] \[and the information vectors as  
\[\boldsymbol{\varphi}(t) := [\boldsymbol{\phi}^{\mathrm{ T}}(t),\boldsymbol{\psi}^{\mathrm{ T}}(t)]^{\mathrm{ T}}\in\mathbb{R}^{n},\] \[\boldsymbol{\phi}(t) := [\boldsymbol{\phi}_{1}^{\mathrm{ T}}(t),\boldsymbol{\phi}_{2}^{\mathrm{ T}}(t),\cdots,\boldsymbol{\phi}_{r}^{\mathrm{ T}}(t)]^{\mathrm{ T}}\in\mathbb{R}^{n_{0}},\] \[\boldsymbol{\phi}_{j}(t) := [-x_{j}(t-1),-x_{j}(t-2),\cdots,-x_{j}(t-n_{j}),u_{j}(t-1),u_{j}(t -2),\cdots,u_{j}(t-n_{j})]^{\mathrm{ T}}\in\mathbb{R}^{2n_{j}},\] \[\boldsymbol{\psi}(t) := [-w(t-1),-w(t-2),\cdots,-w(t-n_{c})]^{\mathrm{ T}}\in\mathbb{R}^{n_{c}}.\]  
The information vector \(\boldsymbol{\varphi}(t)\) is unknown due to the unmeasured variables \(x_{j}(t-i)\) and \(w(t-i)\). By means of the above definitions, Equations (2)-(4) can be expressed as  
\[x(t) = \sum_{j=1}^{r}x_{j}(t)=\sum_{j=1}^{r}\boldsymbol{\phi}_{j}^{ \mathrm{ T}}(t)\boldsymbol{\phi}_{j}, \tag{5}\] \[w(t) = \boldsymbol{\psi}^{\mathrm{ T}}(t)\boldsymbol{c}+v(t)=y(t)- \boldsymbol{\phi}^{\mathrm{ T}}(t)\boldsymbol{\phi},\] (6) \[y(t) = \sum_{j=1}^{r}\boldsymbol{\phi}_{j}^{\mathrm{ T}}(t)\boldsymbol{\phi}_{j}+\boldsymbol{\psi}^{\mathrm{ T}}(t)\boldsymbol{c}+v(t)\] (7) \[= \boldsymbol{\phi}^{\mathrm{ T}}(t)\boldsymbol{\phi}+\boldsymbol{\psi}^{ \mathrm{ T}}(t)\boldsymbol{c}+v(t)\] \[= \boldsymbol{\varphi}^{\mathrm{ T}}(t)\boldsymbol{\theta}+v(t).\]  
Equation (7) is the identification model of the multi-input OEAR system, and the parameter vector \(\boldsymbol{\theta}\) contains all the parameters to be estimated.
## 3 Results and Discussion
### Thermogravimetric Analysis (TGA)  
Figure 6 shows the TGA curves of the neat PCMs and composite samples as the residual mass and derivative of weight loss. The main results are summarized in Table 2.  
It is evident that the full thermal degradation of the PCMs occurs in the temperature range of 200-300 \({}^{\circ}\)C, with faster mass loss for paraffins with respect to the fatty acid mixture. The residues at 700 \({}^{\circ}\)C are consistent with the EG content (12.3 wt.%), in fact, EG is stable in the considered temperature interval [39]. Discrepancies from the nominal value can be attributed to local inhomogeneities in the PCM-to-EG ratio.  
Figure 6: TGA curves: (**a**) residual mass and (**b**) derivative of mass loss of neat PCMs and samples.
## 3 5G RCS Message Filtering Models and Methods
### The CNN Model for RCS Message Filtering
#### 3.3.4 Optimizations for CNN  
In order to increase filtering accuracy, four optimization methods are applied.  
* Batch normalization--Batch normalization is used to standardize the extracted relevant features between CNNs. It not only speeds up learning but also decreases the internal covariate shift of a CNN.
* Dropout--Before the last fully connected layer, a dropout function runs on the output matrix of CNNs to reduce computational complexity and redundant information. RCS message filtering is a dropout-suitable scenario in which the card data of most messages are insufficient.
* Optimizer--Adam optimizer is applied to optimize parameters, in this case to increase accuracy.
* Dense--Dense, also known as the fully connected layer, runs an activation function to determine the property of an RCS message. Many activation functions, such as Sigmoid, Softmax and Linear, are tested. The Linear function is the present setting as it produces the best results.  
The proposed CNN is shown in Figure 3.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 2 Related Work  
The problem of controller placement has been a long-term concern in SDN. In this section, we discuss the existing research on controller placement strategies in terrestrial networks and STINs.
# Spatial Variability of Grouting Layer of Shield Tunnel and Its Effect on Ground Settlement
## 6 Conclusions  
In this study, a random finite element model of tunnel excavation was established. The effect of spatial variability of soil parameters and variability of grouting-layer thickness on the ground-surface settlement was studied, respectively. The major conclusions are summarized as follows:  
The grouting layer of a shield tunnel shows a great spatial randomness along the circumferential direction. According to the statistical analysis, the thickness of the grouting layer follows a normal distribution and its coefficient of variation ranges from 0.1 to 0.4.  
In terms of soil parameters, the COV of the soil strength parameters, friction Angle and cohesion, has little effect on the PDF of the maximum surface settlement, while the COV of the soil Young's modulus shows highly sensitive to the maximum surface settlement. Both the horizontal SOF (\(s_{h}\)) and the vertical SOF (\(s_{v}\)) nearly have no influence on the PDF of maximum surface settlement.  
The spatial variability of the grouting layer shows a significant influence on the ground settlement. The mean value and standard deviation of maximum surface settlement increase with the decrease of the grouting-layer thickness. The ground settlement will be underestimated without considering the spatial variability of the grouting layer of the shield tunnel.  
In order to consider the influence of the spatial randomness of grouting layers, a practical prediction approach for ground settlement was given based on 95% fractile. It is worth noting that the value predicted by the theoretical formula is a little higher than the results from the Random Finite Element method.  
[PERSON] proposed the idea of the paper, [PERSON] conducted the numerical simulations, and [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Project NO. 51778636).  
The authors declare no conflict of interest.
# Efficient Video Frame Interpolation Using Generative Adversarial Networks
###### Abstract  
Frame interpolation, which generates an intermediate frame given adjacent ones, finds various applications such as frame rate up-conversion, video compression, and video streaming. Instead of using complex network models and additional data involved in the state-of-the-art frame interpolation methods, this paper proposes an approach based on an end-to-end generative adversarial network. A combined loss function is employed, which jointly considers the adversarial loss (difference between data models), reconstruction loss, and motion blur degradation. The objective image quality metric values reach a PSNR of 29.22 dB and SSIM of 0.835 on the UCF101 dataset, similar to those of the state-of-the-art approach. The good visual quality is notably achieved by approximately one-fifth computational time, which entails possible real-time frame rate up-conversion. The interpolated output can be further improved by a GAN based refinement network that better maintains motion and color by image-to-image translation.  
deep learning; generative adversarial network (GAN); frame interpolation +
Footnote †: journal: Medical Image Computing  
**2020**020, 10, 6245; doi:10.3390/app10186245