esa-sceva/satcom-chunk-collection huggingface dataset:
# Grid-Friendly Active Demand Strategy on Air Conditioning Class Load
## 3 Harmonic Suppression Technology
### Current Hysteresis Tracking Control  
The traditional hysteresis control principle utilizes the difference between the compensation current and the command current through the loop width, that is, constructs a hysteresis loop, 0 as the center, H, and -H as the upper and lower limits to ensure the difference within the hysteresis range. Then, the switching action of the converter is achieved. The improved technique uses a voltage-space vector double hysteresis current tracking control method to reduce both switching frequency and current tracking error at the same time [21]. Higher harmonic components can be reduced while ensuring the current response speed. The voltage SVPWM (Space Vector Pulse Width Modulation) synthesizes the reference voltage vector accurately, which improves the DC voltage utilization rate while maintaining a constant switching frequency. The idea of double hysteresis current control based on the space voltage vector is shown as Formula (2).  
\[U_{k}=RI_{C}+L\frac{dI_{C}}{dt}+e \tag{2}\]  
Among them, \(U_{k}\) is the inverter output voltage vector, \(I_{C}\) is the corresponding output current vector, and \(e\) is the terminal voltage. Error current vector \(\Delta I\) is defined as the difference between the current reference vector \(I_{C}^{*}\) and actual output current vector \(I_{C}\), as Formula (3).  
\[\Delta I=I_{C}^{*}-I_{C} \tag{3}\]  
Substituting Formula (3) into Formula (2), and ignoring the effect of equivalent resistance, results in Formula (4).  
\[L\frac{d\Delta I}{dt}=\left(L\frac{dI_{C}^{*}}{dt}+e\right)-U_{k} \tag{4}\]  
Define reference voltage vector \(U^{*}\), as in Formula (5).  
\[U^{*}=L\frac{dI_{C}^{*}}{dt}+e \tag{5}\]  
Figure 3: \(i_{p}\)–\(i_{q}\) based harmonic detection method schematic.  
which can be simplified as Formula (6).  
\[L\frac{d\Delta I}{dt}=U^{*}-U_{k} \tag{6}\]  
When the current error vector is in a range other than the outer loop, the current error is large. Then, fast control is required, and only the partition of the error current vector is considered. To rapidly reduce the current error, the central axis of the range where the error current vector is located should be selected. At this time, regardless of the reference voltage vector \(U^{*}\), \(U^{*}-U_{k}\) will be a component that points to the origin of the coordinate along the central axis of the partition \(\Delta I\), and this component is the largest of all choices. The improved technique simulation diagram is shown in Figure 4.  
Waveforms before and after APF harmonic processing are shown in the middle of Figure 2a and the middle of Figure 2b. It can be seen in the figure that the distortion rate of the current has dropped significantly after APF, and is near 5.38%. Among them, the 5 th, 7 th, and 11 th harmonics are obviously reduced, and the harmonics can basically be controlled. However, the THD still does not meet the standard of 5% or less; there are still large spikes and burrs in the filtering cycle, and the waveform is not very perfect.
## 3 Material and Methods
### Training and Validation  
The entire dataset was mainly divided into two categories: 75% for training and 25% for validation. The training and validation were further categorized as COVID-19 and normal chest X-ray images. A set of eight hundred X-ray images of both COVID-19 and normal patients was used for training and validation purposes as shown in Table 3. Images were given to the model as input with a batch size of 32 for training. The batch size refers to the number of samples processed and it must be greater than one and less than or equal to the number of samples in the training dataset. After training, the dataset was provided to the model for validation to check its effectiveness. Shuffling was applied to the images for training, but the images used in validation of the data were not shuffled.  
The proposed customized CNN model was trained on 100 epochs and reached up to 98% accuracy and loss decrease up to 99%, as visualized in Figure 8. The reason to choose 100 epochs for training was preferred after several iterations of trial and error. An epoch is a term used in DL that refers to the number of passes the DL algorithm has made over the entire dataset [34]. Execution time for 100 epochs was calculated to provide detailed information of results. The Figure 9 shows the graph which indicates the time taken for all epochs to run and train the model. The calculated average time for 100 epochs turned out to be 8.37 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Parameters** \\ \hline Conv2D & (None, 222, 222, 32) & 896 \\ \hline Conv2D & (None, 220, 220, 64) & 18,496 \\ \hline MaxPooling2D & (None, 110, 110, 64) & 0 \\ \hline Dropout & (None, 110, 110, 64) & 0 \\ \hline Conv2D & (None, 108, 108, 64) & 36,928 \\ \hline MaxPooling2D & (None, 54, 54, 64) & 0 \\ \hline Dropout & (None, 54, 54, 64) & 0 \\ \hline Conv2D & (None, 52, 52, 128) & 73,856 \\ \hline MaxPooling2D & (None, 26, 26, 128) & 0 \\ \hline Dropout & (None, 26, 26, 128) & 0 \\ \hline Flatten & (None, 86528) & 0 \\ \hline Dense & (None, 64) & 5,537,856 \\ \hline Dropout & (None, 64) & 0 \\ \hline Dense & (None, 1) & 65 \\ \hline \hline \end{tabular} Total: 5,668,097  
\end{table}
Table 2: Summary of proposed CNN model showing layer names, output shapes and parameters.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Class** & **Training** & **Validation** \\ \hline COVID-19 & 300 & 100 \\ Normal & 300 & 100 \\ Total & 600 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data distribution.
## 4 Experimental Results and Discussion
### Experimental Results and Analyses
#### 4.4.2 The Performance of the Fuzzy Matching Module  
Next, we input the entities incorrectly recognized by the NER model into the fuzzy matching module for an experiment. The experimental results are shown in Table 8. For the NLPCC2016 database, out of 7690 pieces of test data, a total of 225 entities are not correctly recognized. After inputting these entities into the module for fuzzy matching, 135 of them can be modified to the correct entities which can be successfully retrieved in the knowledge base. After processing by this module, the accuracy of the named entity recognition will be further improved to about 99%. Subsequently, we also use the power grid dataset to perform the similar experiments. For the power grid dataset, out of 1023 pieces of test data, a total of 77 entities are not correctly recognized. After inputting them into the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** &
\begin{tabular}{c} **Expand** \\ **Lexicon** \\ \end{tabular} & **P** & **R** & **F1** \\ \hline SoftLexicon+Bi-LSTM-CRF & YES & 91.24\% & 93.65\% & 92.43\% \\ \hline (BERT) & NO & 90.48\% & 92.86\% & 91.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of two models on the power grid dataset. “Expand Lexicon” denotes whether the model uses the expanded lexicon.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **P** & **R** & **F1** \\ \hline
**Bi-LSTM-CRF** & & & & \\ with random embedding & **80.45\%** & **78.15\%** & **79.28\%** \\ with pre-trained embedding & **81.34\%** & **78.63\%** & **79.96\%** \\ with SoftLexicon & **86.51\%** & **84.45\%** & **85.47\%** \\
**LGN**[49] & **87.72\%** & **87.98\%** & **87.85\%** \\
**Bi-LSTM-CNNs-CRF**[50] & **81.30\%** & **82.16\%** & **81.73\%** \\
**Lattice-LSTM** & **86.20\%** & **84.64\%** & **85.41\%** \\ \hline
**BERT+SoftLexicon** & & & \\ +Bi-LSTM-CRF & **90.48\%** & **92.86\%** & **91.65\%** \\ +Bi-LSTM-CRF (Expand Lexicon) & **91.24\%** & **93.65\%** & **92.43\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results of eight different models on the power grid dataset. The upper part is the model without using BERT, and the lower part is the model using BERT. The result of all models is from our experiments.  
fuzzy matching module, 68 of them can be modified to the correct entities which can be retrieved in the knowledge base. The accuracy of the named entity recognition will be further improved to about 99.12%. Therefore, the experiment results show this module will improve the performance of Chinese NER in the KBQA application scenario. Relying on the information stored in the knowledge base, this module can achieve efficient fuzzy matching. Moreover, the fuzzy matching module has strong portability and is easy to deploy. It can be easily deployed into existing NER models to form an NER system, which can help construct the KBQA system with better performance.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 5 Constraints  
Dataset scale: The size of the Turkish financial tweets' dataset is comparatively modest, which may constrain the applicability and reliability of the developed models.  
Pre-processing complexity: Recognizing the intricacies in handling Turkish tweets, the authors concede the challenges arising from ambiguity and informal language during pre-processing. This may result in potential inaccuracies or biases in sentiment classification.  
Binary versus multi-class classification: The discernible performance difference between binary and multi-class classifications underlines the complexities in effectively capturing more refined sentiment categories.  
Domain specificity: Given that the models are specifically trained on financial tweets, there is a possibility that their effectiveness might not extend seamlessly to other domains or diverse sentiment analysis tasks.
# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
###### Abstract  
The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R\({}^{2}\)), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R\({}^{2}\), MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R\({}^{2}\), MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.  
energy-efficient buildings; smart buildings; machine learning; random forest; optimization +
Footnote †: journal: Applied Sciences
## 4 Modulation of Phytoalexin Accumulation through Engineering of Plant Hormones
syringae_ pv _tabaci_ was addressed through the overexpression of the _isopentenyl transferase_ (_ipt_) gene from _Agrobacteriumum umefaciens_ driven by the synthetic pathogen-inducible _4 xJERE_ promoter in the _4 xJERE:ipt_ plasmid. Isopentenyl transferase indeed catalyzes the rate-limiting reaction in the route to CKs. In _ipt_-overexpressing tissues challenged with _P. syringae_, CK production was enhanced and this correlated well with increased resistance to this pathogen. As the protocol used for pathogen inoculation included pre-infiltration of leaves with _A. tumefaciens_ which can interfere with symptom development, experiments were completed by two additional genetic approaches leading to ectopic production of CKs independent of prior infiltration with _A. tumefaciens_. The _ipt_ gene was successively expressed under the control of a senescence-induced promoter (SAG12:ipt) and under the control of a chemically inducible tetracycline-dependent promoter (TET:ipt). In both transformed tobacco lines, there was a strong CK accumulation well correlating with disease symptom reduction. Changes in endogenous CK concentrations enhanced the production of capsidiol and scopoletin by respectively 4.6 and 6.4-fold compared with the controls. Ectopic phytoalexin synthesis resulting from overexpression of the _4 xJERE:ipt_ construct was associated with the CK-mediated resistance against _P. syringae_. Interestingly, there was an induced expression of 5-_epi_-aristolochene synthase, catalyzing a rate-limiting step in the route to capsidiol, together with an induction of the cinnamate-4-hydroxylase which is implied in the phenylpropanol pathway leading, namely, to the coumarin scopoletin. Conversely, expression of other enzymes related to phytoalexin biosynthesis such as the glucosyltransferase TOGT involved in the sorage of scopoletin (see section 3), phenylalanine ammonia lyase, the key enzyme of the phenylpropanol pathway and flavonoid synthase 1, were respectively moderate, not significantly affected or even repressed upon CK-mediated resistance of tobacco to _P. syringae_. Importantly, this CK-mediated resistance was also shown to be SA- and JA- independent.
# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 4 DDQN-MTGPSR Protocol Design
### DDQN-MTGPSR Routing Decision  
DDQN-MTGPSR optimizes the original forwarding mode of GPSR by not only considering the distance between neighbor nodes and destination nodes but by extracting the set of neighbor state information of the current node where the packet is located and then inputting it into the DDQN for the next hop decision. The operation of the routing decision system can be divided into two processes: 1) the routing table creation and maintenance and 2) the routing decision process for the DDQN-MTGPSR protocol. This study summarizes the DDQN-MTGPSR routing decision process in Algorithm 1.  
Where \(\varepsilon\) takes the value of 0.1, \(\alpha\) takes the value of 0.001 and \(\gamma\) takes the value of 0.95. Until the simulation is terminated, all the nodes within the network will keep repeating the route establishment and maintenance phase and the DDQN-MTGPSR based routing decision making phase as described above, to assist in forwarding the packets within the network from the source node to the destination node.  
Figure 2: _DDQN_ model interacting with the network environment.
# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 3 Experiment and Results
### Comparative Analysis of Clustering Results  
To verify the robustness of the proposed model, the clustering results of the GW-DC were compared with those of five other clustering models, including Stacked Autoencoder (SAE) +K-Means, Autoencoder (AE) +K-Means, AE_Conv +K-Means, DEC, and DEC_FCN. AE_Conv +K-Means was the structure that replaced the fully connected layer (FCL) in AE with the convolutional layer. DEC_FCN referred to the structure that replaced the convolutional layer in DEC models with FCLS. The index NMI (%) and ACC (%) were employed to measure the clustering effect. The comparative results of different models are shown in Table 3.  
Compared with the AE +K-Means model, the NMI and clustering accuracy of the AE_Conv +K-Means model on five datasets were improved, and the maximum difference in NMI and accuracy were 13.15% and 24.17%, respectively. Comparing the results of the DEC and the DEC_FCN, the DEC model with convolutional layers displayed a better clustering effect. Besides, the SonyAIBORobotSurface2 dataset had a higher clustering effect on the DEC model with NMI and accuracy of 30.17% and 79.08%, respectively. In the other five datasets, the NMI index of the GW-DC model was significantly higher than that of DEC, and the maximum difference reached 44.49%. Through comparative analysis of clustering indexes, the GW-DC model showed a better clustering effect than that of other deep clustering models.
###### Abstract  
The FeCoCrNiB\({}_{\mathbf{x}}\) high-entropy alloy (HEA) coatings with three different boron (B) contents were synthesized on Q245R steel (American grade: SA515 Gr60) by laser cladding deposition technology. Effects of B content on the microstructure and wear properties of FeCoCrNiB\({}_{\mathbf{x}}\) HEA coating were investigated. In this study, the phase composition, microstructure, micro-hardness, and wear resistance (rolling friction) were investigated by X-ray diffraction (XRD), a scanning electron microscope (SEM), a micro hardness tester, and a roller friction wear tester, respectively. The FeCoCrNiB\({}_{\mathbf{x}}\) coatings exhibited a typical dendritic and interdendritic structure, and the microstructure was refined with the increase of B content. Additionally, the coatings were found to be a simple face-centered cubic (FCC) solid solution with borides. In terms of mechanical properties, the hardness and wear resistance ability of the coating can be enhanced with the increase of the B content, and the maximum hardness value of three HEA coatings reached around 1025 HV\({}_{0.2}\), which is higher than the hardness of the substrate material. It is suggested that the present fabricated HEA coatings possess potentials in application of wear resistance structures for Q245R steel.  
high-entropy alloy; coating; laser cladding; microstructure; wear resistance Article
## 3 Precision Sowing Conditions of consistent Sowing-Depth Regulation
### Future Development Issues and Dynamics  
#### 3.3.1 Soil Covering and Compacting Technology Developmental Issues  
A soil-covering and compacting operation directly affects the inconsistency of sowing depth during the precision seeding operation. Due to the complex operating environment and uneven surface height [70], the current soil-covering device needs to effectively realize the adjustment of the soil-covering amount. The soil-covering operation is mainly realized by squeezing and convergent reflux. Most of the research focuses on the soil-compacting operation, but there needs to be research into the quantitative soil covering. The actual effect on the sowing depth is mainly the combined operational effect of soil covering and compacting, so it is essential to realize active quantitative soil covering to control the sowing depth. According to the current research on soil covering and compacting, the following problems are mainly concentrated upon:  
1. Precision-seeder soil-covering and compacting components for different operating environments lack the corresponding adaptability to soil covering, and basically, all companies are a product applied to various scenarios; at the same time, soil-compacting devices on different soils also have interoperability, which affects the lack of data accumulation and research.
2. In the soil-covering operation, the soil covering is mainly achieved by convergent reflux and other rough forms of soil covering, which is prone to uneven soil covering in the complex field environment, and which brings specific difficulties to the subsequent soil-compacting operation.
3. The contact soil components of domestic enterprises are mostly imported imitations, lacking core competitiveness in material technology. At the same time, there is a shorter service life than abroad, and the parts are not strong enough to cause damage in the complex field operating environment, easily affecting the operation, and so on.
4. At present, there is a lack of research into soil-covering devices, while the real-time regulation of soil compacting is in its initial stage. The development of intelligent soil-contact components for sowing depth control is a weak point.
5. With the popularization of high-speed precision-seeding technology, the technical problem of the corresponding domestic soil-covering and compacting devices effectively matching the performance requirements under high-speed working conditions has yet to be solved.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
###### Abstract  
Non-orthogonal multiple access (NOMA) plays an important role in achieving high capacity for fifth-generation (5G) networks. Efficient resource allocation is vital for NOMA system performance to maximize the sum rate and energy efficiency. In this context, this paper proposes optimal solutions for user pairing and power allocation to maximize the system sum rate and energy efficiency performance. We identify the power allocation problem as a nonconvex constrained problem for energy efficiency maximization. The closed-form solutions are derived using Karush-Kuhn-Tucker (KKT) conditions for maximizing the system sum rate and the Dinkelbach (DKL) algorithm for maximizing system energy efficiency. Moreover, the Hungarian (HNG) algorithm is utilized for pairing two users with different channel condition circumstances. The results show that with 20 users, the sum rate of the proposed NOMA with optimal power allocation using KKT conditions and HNG (NOMA-PKKT-HNG) is 6.7% higher than that of NOMA with difference of convex programming (NOMA-DC). The energy efficiency with optimal power allocation using DKL and HNG (NOMA-PDKL-HNG) is 66% higher than when using NOMA-DC.  
n _Article_
## 2 Materials and Methods
### PTCs Curve  
Different from API tubing connections, the unique metal-to-metal seal design and torque shoulder of PTCs notably improve the gas sealing performance of the tubing, effectively eliminating the possibility of gas leakage under complex loads [32; 33]. The make-up torque curve of PTCs, also referred to as the PTCs curve, serves as a crucial indicator of the proper connection of PTCs. It is generated by logging and analyzing the torque and turns data during the premium threaded connections tubing make-up process. As the threaded tubing is progressively tightened, the applied force and torque gradually increase until a particular resistance is met. Figure 1 visualizes the structural features of PTCs and the torque curve demonstrated at each stage of the make-up process.  
As depicted in Figure 1, in the initial make-up stage, the primary resistance originates from the friction among the threads, leading to a PTCs curve shown by the solid red line in Figure 1a, called the \"thread interference section\". Once all thread segments make contact, the metal-to-metal sealing phase commences, causing a notable rise in the torque curve, as demonstrated by the red solid line in Figure 1b, termed the \"metal-to-metal section.\" When the metal-to-metal section achieves a certain level, the torque shoulder activates to prevent excessive metal contact. Consequently, the torque curve quickly rises until the complete make-up process concludes, as depicted by the solid red line in Figure 1c, dubbed the \"torque shouldering section.\" Hence, a typical make-up torque curve exhibits three phases: gradual increase, significant increase, and rapid increase, representing the thread interference, metal-to-metal sealing, and torque shouldering sections, respectively [8]. However, during real make-up processes, abnormalities in the PTCs curve may appear due to misalignment or thread damage, as demonstrated in Figure 2b,c. This paper aims to intelligently evaluate the connection quality of PTCs by automatically recognizing these three types of PTCs curves.  
Figure 1: PTCs and corresponding torque curves for different make-up stages: (**a**) thread interference; (**b**) metal-to-metal section; (**c**) torque shouldering section.