esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper studies a second order generalized integrator-frequency locked loop (SOGI-FLL) control scheme applicable for 3-phase alternating current/direct current (AC/DC) pulse width modulation (PWM) converters used in DC distribution systems. The 3-phase AC/DC PWM converter is the most important power conversion system of DC distribution, since it can boost 380 Vrms 3-phase line-to-line AC voltage to 700 Vdc DC output with various DC load devices and grid voltages. The direct-quadrature (\(d\)-\(q\)) transformation, positive sequence voltage extraction, proportional integral (PI) voltage/current control, and phase locked loop (PLL) are necessary to control the 3-phase AC/DC PWM converter. Besides, a digital filter, such as low pass filter and all pass filter, are essential in the conventional synchronous reference frame-phase locked loop (SRF-PLL) method to eliminate the low order harmonics of input. However, they limit the bandwidth of the controller, which directly affects the output voltage and load of 3-phase AC/DC PWM converter when sever voltage fluctuation, such as sag, swell, etc. occurred in the grid. On the other hand, the proposed control method using SOGI-FLL is able to do phase angle detection, positive sequence voltage extraction, and harmonic filtering without additional digital filters, so that more stable and fast transient control is achieved in the DC distribution system. To verify the improvement of the characteristics in the unbalanced voltage and frequency fluctuation of the grid, a simulation and experiment are implemented with 50 kW 3-phase AC/DC PWM converter used in DC distribution.  
DC distribution; 3-phase AC/DC PWM converter; SOGI-FLL; phase detection +
Footnote †: journal: Applied Sciences
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 3 Datasets and Experimental Conditions
### Evaluation Index  
Semantic segmentation was performed as a pixel-level classification. To evaluate the performance of rebar segmentation algorithm intuitively and quantitatively, the mean Intersection over Union (mIoU), precision, recall and F1 score were used as evaluation metrics, which are defined with respect to the pixel evaluation category. The corresponding formulas are as follows:  
\[Precision=\frac{TP}{FP+TP}\, \tag{3}\]  
\[Recall=\frac{TP}{FN+TP}\, \tag{4}\]  
\[mIoU=\frac{1}{k}\sum_{i=0}^{k}\frac{TP}{FN+FP+TP}\, \tag{5}\]  
\[F1\_score=\frac{2 TP}{2 TP+FP+FN} \tag{6}\]  
where \(TP\) denotes the number of pixels of segmented rebar, \(FP\) denotes the number of pixels of segmented background, \(FN\) denotes the number of pixels of unsegmented rebar, and \(k\) represents the number of categories.
## 3 Results and Discussion
### Comparisons across PFTs  
Scatterplots of satellite data-driven GPP estimates (FluxSat-7, \(\mathrm{NIR}_{V}\)-based and FluxSat-N at the highest MCD43D resolution of 0.0083\({}^{\circ}\) for eight-day data) versus flux-tower estimates are displayed for different PFTs in Figures 12 and 13. All three models showed good performance and relatively low biases across PFTs. On the whole, FluxSat-7 outperformed the other two models with higher correlations and slopes generally closer to unity. \(\mathrm{NIR}_{V}\)-based had the most dynamic range for deciduous broadleaf forest (DBF) \(+\) mixed forest (MF), followed by FluxSat-7; FluxSat-N showed the smallest range for this type. However, FluxSat-7 and FluxSat-N better reproduced the very high values of GPP found in some cropland sites as shown above.  
Figures 14 and 15 similarly compare IAVs across PFTs. Again, all three models effectively capture IAVs across PFTs. The models perform particularly well in capturing IAVs for savannas and grasslands. Upon further investigation of the EBF anomalies, we found two distinct clusters of points. The first cluster was for the Australian sites. The anomalies of these sites were well captured by the satellite data-driven models. The second cluster was for two European EBF sites (FR-Pue and IT-Cpz); anomalies from these sites in FLUXNET 2015 were not well captured by our satellite data-driven models.  
Figure 11: Scatter diagrams comparing the mean annual GPP for each site for (**a**) FluxSat-7, (**b**) \(\mathrm{NIR}_{V}\)-based, and (**c**) FLUXCOM-RS, all computed at 0.0833\({}^{\circ}\) resolution versus FLUXNET 2015 GPP. Units of GPP are \(\mathrm{g\,C\,m^{-2}\,d^{-1}}\).  
Figure 12: Scatter diagrams of (**a**) FluxSat-7, (**b**) NIRV-based, and (**c**) FluxSat-N (columns) versus GPP from FLUXNET 2015 (all in g C m\({}^{-2}\) d\({}^{-1}\)) for different plant functional types (rows) with the 1:1 line (black line), linear fit (red dashed line and red fit) along with number of observations \(n\) and \(r^{2}\). All satellite-driven estimates use MCD43D reflectances at 0.0083\({}^{\circ}\) spatial and eight-day temporal resolutions.  
Figure 13: Similar to Figure 12, but for different plant functional types. All units are g C m\({}^{-2}\) d\({}^{-1}\).  
Figure 14: Similar to Figure 12, but showing interannual variations normalized by the FLUXNET 2015 climatological ranges (unitless) for different plant functional types.
# Recent Advances of Exciplex-Based White Organic Light-Emitting Diodes
## 3 Approaches for Exciplex-Based WOLEDs  
### Basic Aspects of Exciplex-Based WOLEDs  
According to the above concepts, two strategies have been proposed to apply exciplexes in WOLEDs, i.e., exciplexes are functioned as (i) fluorescent or TADF emitters; (ii) the hosts of fluorescent, phosphorescent and TADF dopants. Apart from the general issues to guarantee high-performance WOLEDs (e.g., the manipulation of charge injection, transport and balance, the management of exciton generation, harvesting, recombination, diffusion and quenching, the regulation of host (guest)-to-guest mutual energy transfer, and the enhancement of light outcoupling efficiency.) [146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156], some additional attentions are needed to be paid on the exciplex-based WOLEDs. On one hand, when using as the emitter, exciplexes give a new broad emission peak that appears at a long wavelength compared to the intrinsic donor and acceptor, which is beneficial to develop high-quality WOLEDs. For example, by using two exciplex emitters (merely complementary colors), [PERSON] et al. achieved a WOLED with a high CRI of 83 [157]. Besides, by utilizing the TADF mechanism and /or effective device engineering, all singlets/triplets generated in exciplex emitters could be harnessed, leading to highly efficient WOLEDs.  
On the other hand, when using exciplex as the hosts, the voltage could be lower than that of conventional hosts since the injection/transport of electrons and holes are via the LUMO of acceptors and the HOMO of donors, respectively. Additionally, the approximately equal S\({}_{1}\) and T\({}_{1}\) of exciplexes makes it easy to adjust the energy level of exciplexes to that of emitter dopants by selecting appropriate donors and acceptors, which renders the energy transfer from both S\({}_{1}\) and T\({}_{1}\) of exciplex to that of the guests more easily. Hence, by designing efficient exciplexes and enhancing device engineering,exciplex-based WOLEDs are very promising for the lighting and displays. In the following sections, the design strategy, device architecture, emission mechanism as well as EL procedure for exciplex-based WOLEDs have been highlighted, including WOLEDs based on exciplex emitters or hosts.
# Advances in Quantitative Earth Remote Sensing: Past, Present and Future
## 2 Contributions
The authors used a spatial fusion downscaling model (SFDM) with Moderate Resolution Imaging Spectroradiometer (MODIS) data to overcome the coarse spatial resolution deficiency associated with passive microwave soil moisture products. They also developed long-term and multi-sensor soil moisture datasets using a time series reconstruction and difference decomposition (TSRDD) method for eliminating the inconsistencies in soil depth and time of measurements among different microwave soil moisture products from the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E), its successor AMSR2 and the Soil Moisture Ocean Salinity (SMOS). Overall, the downscaled soil moisture (SM) products were consistent with the in situ measurements (R \(>\) 0.78) and exhibited a low root-mean-square error (RMSE \(<\) 0.10 m\({}^{3}\)/m\({}^{3}\)). The downscaled SM data at a 1-km spatial resolution were used to analyze the spatiotemporal patterns and monitor abnormal conditions in the soil water content across North East China (NEC) between 2002 and 2018. The results showed that droughts frequently appeared in western part of Northeast China and southwest part of the Greater Khingan Range, while drought centers located in the central part of Northeast China. Waterlogging commonly appeared in low lying areas, such as the Songnen Plain. They observed that seasonal precipitation and temperature exhibit distinct interdecadal characteristics that were closely related to the occurrence of extreme climatic conditions and events. Abnormal SM levels were often accompanied by large meteorological and natural disasters (e.g., the droughts of 2008, 2015, and 2018 and the flooding events of 2003 and 2013). The spatial distribution of drought in this region during the growing season indicates that the drought-affected area was larger in the west than in the east, and the semiarid boundary extends eastward and southward. We have extended the deadline for submission of additional innovative papers to this special issue of _Sensors_ and will accommodate them until Spring 2020.
## 4 Discussion  
This paper proposes a new tag index generation algorithm named STIP, which combines tag index construction with data compression techniques. While maintaining high retrieval speed, it successfully reduces the storage cost of tag indexes by 76.2%. STIP uses a greedy algorithm to partition the tag index, optimizing the index construction process and providing a new technical approach for constructing the tag indexes in large-scale protein databases.  
Compared with mainstream protein data index construction algorithms such as OpenPFind, MODplus, TIIP, and PIPI2, STIP combines the index construction algorithm and the data compression algorithm, and constructs the index on datasets of ten common species with the lowest storage cost. In addition, STIP can construct a protein index and tag index for large-scale protein databases such as NCBI-nr, which is a task that other algorithms participating in the testing cannot complete, verifying the applicability of this algorithm in a large-scale protein database.  
Under the condition of not exceeding the set memory threshold, STIP, using greedy load-balanced partitioning, reduces the frequency of I/O by 52% and improves block saturation by 108.33% compared to ULP. The experimental validation on PXD009449 datasets demonstrates the superior search speed of STIP-Search. Tag indexing is widely used in the field of proteomics, and to address the issues of large index size and high memory demands, compressing tag index will be an important research direction.
# Smart Low-Cost Control System for Fish Farm Facilities
## 4 Proposal for Smart Low-Cost Control Systems for Aquaculture
### Proposal for Smart Low-Cost Control Systems for Aquaculture Facilities with Cages in the Sea
#### 4.1.2 Sensors and Actuators  
Most of the data can be obtained from sensors deployed in the cages and their surroundings. We highlight the water temperature and the DO sensor as the most important ones to predict fish performance. Commercial probes and sensors proposed in [110] are used for all the parameters of water quality and meteorological parameters. For fish and feed monitoring, two options are available, the use of cameras [111] and acoustic transducers [112]. The selection of the most suitable method depends on the conditions of the environment; cameras are preferred due to their lower cost and lower energy consumption. The location of deployed sensors and sensor nodes can be seen in Figure 12.  
The actuators in this network are the feeders for each cage. The system should be able to operate automatically, feeding the cages based on the measured temperature, wind, DO, and other relevant parameters, even if the connection with the DB is lost. This is one of the fault tolerance mechanisms for the connection with the DB. An additional mechanism is the inclusion of a 3G and 4G connection as a backup for the LoRa network.  
Figure 12: Deployment of aquaculture control system in cages. The deployment of sensor buoys, actuator buoys, gateways, and wireless technologies can be identified. The integration of remote sensing and WSN in the Database and Artificial Intelligence can be seen.
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 4 Analysis of Test Results
### Regression Analysis  
By combining the development trend of the internal force curve in the tunnel and the change trend of the safety factor, it can be seen that the internal force of the tunnel structure was still increasing, and the safety factor of the structure was still changing. Therefore, in the later stage of tunnel structure operation, it was impossible to determine whether the safety factor of the secondary lining structure was still within the limit range specified by the regulations. Further research and analysis on the long-term safety of tunnel structures are needed. The reliability of the regression analysis of loess tunnel surrounding rock deformation prediction in reference [17] was combined with the development trend of the safety factor scatter plot. The fitting function selected for safety factor \(K\) is:  
\[K=A+Be^{Ct}+De^{Et} \tag{11}\]  
where: \(K\) is the safety factor; \(t\) is the time; \(A\), \(B\), \(C\), \(D\), \(E\) are coefficients.  
This article used Origin software for the regression analysis and prediction. Regression analysis was performed on the safety factor data of the seven selected measurement points. The calculation results of the safety factor fitting function of each measurement point are shown in Table 4.  
Figure 17: Time-varying safety factor of the secondary lining.  
From the regression analysis results, it can be seen that the exponential function fit well with the results of different measurement points, and the correlation coefficients were all above 0.99, which proves that the change law of the safety factor conforms to the exponential function law. Since the exponential function is a bounded function, it is possible to make a reasonable prediction of the final value of the safety factor based on this function, and then evaluate the future safety of the tunnel based on whether the final predicted value is less than the critical safety factor value.  
The two dangerous areas mentioned above were the vault measurement point 1 and the arch foot measurement point 7, and the final predicted values of their corresponding regression equations were 2.83421 and 2.36814, respectively. Their final predicted values were all greater than the critical safety factor value 2 of the corresponding area, which meets the tunnel safety standards. Therefore, we can reasonably predict that the Yulinzi Tunnel will still be in a safe state in the future.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 6 Results
### Open Issues and Future Trends  
#### 6.2.1 Explainable Artificial Intelligence (XAI)  
One of the important trends and directions for study is using explainable artificial intelligence. In XAI the outcomes and the decisions of an AI solution are understandable by humans, so the administrator can understand why the AI solution considered certain traffic as malicious or benign, in contrast to the existing blackbox AI solutions. This will give useful feedback for understanding the reasons behind the AI decisions and give the administrator the ability to either reject or accept the decisions related to his system. Consequently, this will reduce the number of false positive alerts and improve the system.
# A Channel Allocation Mechanism for Cellular Networks
## 3 Channel Allocation Mechanism in Cellular Networks
### The Number of Handover In (\(l_{i}\))  
When a communicating MS (i.e., (a) in Figure 6) in the car moves from the source cell to the target cell, the handover procedure can be performed if the target cell has a free channel. There is a road covered by a set of cells and a communicating MS in a car on the road. For instance, Figures 6 and 7 illustrate the scenario and timing diagrams for Handover In the specific Cell\({}_{i}\). A call is performed at time \(t_{0}\) by a MS (in Figure 7). Furthermore, the MS moves from Cell\({}_{i-1}\) to Cell\({}_{i}\) at \(t_{1}\) (in Figure 7) and a free channel is allocated for the handover procedure of the MS by the BSC or RNC (i.e., (b) in Figure 6).  
For estimating the number of Handover In (\(l_{i}\)), the call holding time (\(\tau\)) is assumed to be exponentially distributed with the mean \(1/\mu\)[26; 27; 28; 29; 30; 31; 32; 33]. In this study, the car flow and vehicle speed are assumed as \(F_{i}\) and \(V_{i}\). Moreover, the distance of the cell coverage is \(l_{i}\), so the time difference between entering Cell\({}_{i}\) (i.e., \(t_{1}\)) and leaving Cell\({}_{i}\) (i.e., \(t_{2}\)) is measured as \(l_{i}/V_{i}\). The variable \(x\) is the time difference between call arrival (i.e., \(t_{0}\)) and entering Cell\({}_{i}\) (i.e., \(t_{1}\)). When the call holding time (\(\tau\)) is longer than the variable \(x\), the handover procedure will be performed. Therefore, the number of Handover In (\(l_{i}\)) of Cell\({}_{i}\) can be expressed as Formula (4):  
\[I_{i}=F_{i}\times\int_{x=0}^{\infty}\Pr(\tau>x)dx=F_{i}\times\int_{x=0}^{ \infty}\int_{\tau=x}^{\infty}\mu e^{-\mu\tau}d\tau dx=\frac{F_{i}}{\mu}. \tag{4}\]  
Figure 6: The scenario diagram for vehicle movement and Handover In the specific Cell\({}_{i}\)[27; 28; 29; 30; 31; 32; 33].  
Figure 7: The timing diagram for Handover In the specific Cell\({}_{i}\)[27; 28; 29; 30; 31; 32; 33].
# Automatic Age and Gender Recognition Using Ensemble Learning
## 1 Introduction
Artificial neural networks, support vector machines, k-nearest neighbors, convolutional neural networks, logistic regression, naive Bayes, random forest, recurrent neural networks, decision tree, and long short-term memory are some of the widely used machine learning algorithms [10; 11; 18]. Recently, two approaches have become dominant in machine learning: deep learning and ensemble learning [19]. Deep learning is a branch of machine learning based on a neural network structure inspired by biological models of computation and cognition in the human brain. Deep learning models can predict very complex non-linear relationships by automatically learning distinctive features from raw input data and can solve the most challenging problems [20]. However, they require large amounts of data and computational resources. Also, complex deep-learning models face the risk of overfitting. On the other hand, the ensemble learning approach is based on the idea of creating a stronger model by combining multiple individual models [19]. Additionally, the diversity provided by the base-level models that form the ensemble model also helps reduce the risk of overfitting. Ensemble learning has been successfully applied in various fields, and superior performances have been achieved compared to single models [21]. This research aims to develop a new model based on ensemble learning to recognize age and gender from the speech signal. For this purpose, eight different ensemble models were created by combining five base-level classifiers in different orders, and the optimum ensemble model was determined by evaluating the performances of each. The results obtained in the experiments reveal that the ensemble model proposed in this research provides superior age and gender recognition accuracy compared to the existing methods in the literature. The main contributions of the study can be summarized as follows:  
* This is the first study, to our knowledge, that uses ensemble learning for classification tasks while considering both the speaker's age and gender. * The classification in the study is based on a more detailed class definition, where speakers are categorized as male and female with 10-year age intervals. * Even though the proposed ensemble model takes into account a higher number of classes, it demonstrates superior performance compared to state-of-the-art methods.
[10] emphasize how challenging it is to characterize defects due to unknown topology, geometry, material properties, and non-linear deformation. They present a way of identifying unknown geometric parameters and materials with neural networks. They use a meshless method, parameterizing the geometry of the material using a differentiable and trainable method that can identify multiple structural features. The defined framework can be applied in cases involving unknown material properties and highly deformable geometries, aimed at material characterization, quality assurance, and structural design. [PERSON] et al. [11] review articles related to machine learning in material studies. Studying the mechanics of materials using these new techniques provides a better understanding of the materials used in the construction industry. [PERSON] et al. [12] show the use of a lightweight convolutional neural network, WearNet, which can automatically detect scratches for components in contact sliding, such as those in metal forming. The authors claim that the result was excellent, with a classification accuracy of 94.16%, a much smaller model size, and faster detection speed. The methodology presented in this work has also been developed from [4], with the use of neural networks to detect and classify pathologies in concrete structures. In addition to the theory, new practical experiments were also presented to validate the proposed methodology. The present work seeks to differentiate the identification of pathologies using neural networks in relation to the researched works. This work proposes a method for visually inspecting concrete structures using neural networks, understanding the importance of improving the classification of pathologies identified by detection. The proposal uses the classification capacity of detection to carry out a specific classification in the sequence under particular conditions. This article puts forth the idea of carrying out both processes with the same technology to allow the sequence, detection, and classification to be in the same scope.