esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
###### Abstract  
High-precision direction of arrival (DOA) of wideband signals is a very important technology in the field of radar and communication. In this work, we propose an efficient support vector regression (SVR) architecture via a genetic algorithm (GA) for wideband DOA estimation, which exhibits high estimation performance and generalization performance. By adopting the two-sided correlation transformation (TCT) algorithm, the network is trained only from reference frequency data to increase the training efficiency. In order to reduce the redundant information in the array covariance matrix and lower the dimensionality of the input features, the array covariance matrix at a single frequency point is preprocessed according to its conjugate symmetry and elemental characteristics, and the dimensionality-reduced input features are obtained. Specifically, the dimensionality of the input features does not increase with the number of sub-bands when dealing with broadband signals or ultra-broadband signals, which can significantly reduce the training time of the model and the storage capacity of the system. The increased performance of the proposed algorithm is highly desirable in resource-constrained scenarios, and the experimental results demonstrate the efficiency and superiority of the proposed network compared with existing methods.  
DOA estimation; broadband signals; machine learning; support vector regression; genetic algorithm +
Footnote †: journal:  
Article
## 3 Optimization Model
### Renewable Energy Generation  
The powers from wind and PV systems depend on the weather. Figure 4 shows a typical power curve of wind and PV [39]. During the daytime (10:00-17:00), wind power fluctuates sharply, while PV generates at its peak. PV stops working during the night because of the unavailability of the irradiance from the sun, while wind power generates its peak output energy. These two curves are used as estimated (forecasted) curves for wind and PV power in this model and mean to peak ratios of the values in these curves are 53.16%, 26.46% respectively.  
Figure 5 presents the IHEMS. Its left section shows three main parts: forecast, optimization, and online control. The forecast model uses external forecasting data and foretells the generations from PV and wind, and predicts the electrical and thermal demands. The optimization model uses these predicted values and produces the optimized schedule for these devices to minimize the operational costs. The control model gets input from the online measurements, and balances the generation and load because of the inherent difference between the forecasted data and the actual requirement.  
However, the forecasting of loads and renewable energy resources is a complex task. The data obtained from the forecast has a direct influence on the scheduling of system devices performed in the optimization model. The prediction modeling of renewable power generation has been a hot issue and it requires numerous factors to be considered. Researchers have developed a variety of prediction models. A wind-prediction framework based on chaotic time series and singular spectrum analysis is presented in [40]. [PERSON] et al. presented the use of retraining machine learning algorithms to improve wind power prediction [41]. Results suggest a 17% increase in the prediction accuracy compared to persistence approach. A hybrid model for renewable energy and load prediction based on data mining is proposed in [42]. The outputs of these models (and others like them) enable us to use the optimization model to work out the power generations of energy resources for the forthcoming day. The main focus of this paper is to develop the optimization model for the SH. Therefore, it neglects the prediction and control models.  
The right section of Figure 5 presents the flowchart for the RCGA-based optimal scheduling process. The details of these modules are presented in the next section.  
Figure 4: Wind and PV Power Curve.
## 5 Simulation Results
### Performance of Algorithm
In Figure 8, the performance comparison of the residual energy maximization problem achieved by three methods varies from 1.6 to 3.2 s. Figure 8a shows that the RSB algorithm provides a larger residual energy than the other two algorithms. As the time block length \(T\) increases, the residual energy of three algorithms increases. This is because the larger the time block length \(T\), the more energy each mobile device harvests, and the energy consumption of local computing decreases. Accordingly, the residual energy shows an upward trend. When the time block length \(T\) is smaller, the energy efficiency of the proposed RSB algorithm outperforms NSGA2 and CONMIN. As the time block length \(T\) increases, the performance difference between the three algorithms tends to become small, indicating that our algorithm is more suitable for shorter time block length \(T\). It can also be observed that the CONMIN algorithm consumes more energy to solve the objective function, indicating that efficiency of the algorithm is lower. Figure 6: Performance vs. AP Transmission Power. (**a**) Residual Energy; (**b**) Time Cost. From Figures 6b, 7b and 8b, we can see that the execution time of the RSB algorithm and CONMIN algorithm is significantly lower than the NSGA2 algorithm. This is because the nonconvex problem solved by the NSGA2 algorithm has high computation complexity and long latency. The convex problem solved by our RSB algorithm and CONMIN algorithm has higher execution efficiency. Furthermore, it can also be observed that the residual energy of our algorithm to solve the problem in this paper is much larger than the CONMIN algorithm. The above figures verify that the proposed RSB algorithm can achieve a better solution and can guarantee lower time overhead.
# The Combined Compact Difference Scheme Applied to Shear-Wave Reverse-Time Migration
## 2 Principle of RTM and Combined Compact Difference Scheme  
### Principle of RTM  
The technique of reverse-time migration imaging (RTM) is composed of the following three steps [34]:  
1. The source wavefield is obtained by using the source constructed manually or extracted from actual data, and the corresponding model is numerically simulated to obtain the source wavefield \(S(x,z,t)\), where \(x,z\) is the space vector.
2. Using the seismic data obtained at the receiver, the reverse continuation propagation passes through the same velocity model, and the corresponding receiver wavefield \(R(x,z,t)\) is obtained, where the position of the receiver is \(x_{r},z_{r}\).
3. Applying appropriate imaging conditions, such as cross-correlation, we obtain \[I(x,z)=\int_{0}^{T}S(x,z,t)R(x,z,t)dt\] (1)where \(S(x,z,t)\) is the source wavefield obtained via forward modeling, \(R(x,z,t)\) is the receiver wavefield obtained at the same time via reverse continuation simulation under the same velocity model, and \(t\) is the total propagation time.  
It can be seen from Equation (1) that the final result of reverse-time migration is affected by the accuracy of the source wavefield \(S(x,z,t)\) and the receiver wavefield \(R(x,z,t)\). Notably, the method to improve the accuracy of the source wavefield \(S(x,z,t)\) can also be applied to the receiver wavefield \(R(x,z,t)\). Thus, a high-precision finite difference scheme is applied to generate the source and receiver wavefields \(S(x,z,t)\) and \(R(x,z,t)\), which will give an ideal result of reverse-time migration. In this paper, we adopted cross-correlation imaging conditions; in addition, the method in this paper can also be applied to some imaging conditions developed by researchers in recent years [35; 36; 37].
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 1 Introduction
When applied to the satellite-based search of marine mobile targets, track prediction can reduce target location uncertainty and guide satellite-based search tasks, thus improving target search efficiency. The rest of this paper is organized as follows. In Section 2, we review related work. We overview our method and detail the main steps in Section 3. The results of the experimental evaluation are presented in Section 4 and discussed in Section 5. Finally, conclusions are outlined in Section 6.
## 2 Materials and Methods
### Methodology  
The methodology used in this study was divided into several stages, which included data extraction, exploratory data analysis, feature selection, and the construction of classification models. This can be observed in Figure 1, which follows a CRISP-DM model [23].  
* Stage 1: Analysis-Design of the Prototype An analysis of the different processes was carried out to obtain an overview of the most common variables, such as temperature, time, and pressure, among others. In addition, the type of communication to be implemented was evaluated, and the WiFi protocol was chosen in this case. With this information, a data acquisition system was designed for input and output, using sensors and an embedded system that would help maintain stable connectivity without data loss.
* Stage 2: Construction-Review of the Prototype Work was carried out in the instrumentation of a data acquisition (DAQ) card, selecting appropriate sensors to condition electrical signals for digital reading. Additionally, an IoT board was programmed to process, send, and store data in the cloud and within an interface capable of handling large amounts of data.
* Stage 3: Prototype Testing Once the DAQ was available, tests were conducted to observe the data flow, both for storage and real-time visualization on a dynamic interface.
* Stage 4: Measurement and Data Acquisition with DAQ The results obtained from testing the prototypes were evaluated, and feedback was gathered for improvements, leading back to Stage 1 of development.
* Stage 5: Data Analysis with Machine Learning Algorithms Software tools using Machine Learning algorithms and AI models were employed. Time series and classification algorithms were used to study relationships between variables. Machine Learning models were selected using Python, along with a web platform that provided guidance for data analysis and model selection.
* Stage 6: Interpretation of Results with Reactive Agent After obtaining a model that generated optimal classification, a reactive agent based on expert process knowledge was developed. The agent was fed expert recommendations, which provided relevant information to generate solutions for queries regarding the process status.
* Stage 7: Feedback and Evaluation of Results  
Figure 1: CRISP-DM methodology.  
The integration of DAQ with classification models and the reactive agent was evaluated, providing clear information about the process status.
# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 2 Materials and Methods
### Data Standardization and Data Augmentation  
The collected data varied in length due to measurement errors from the recording device. To ensure consistency, all samples were standardized to a fixed length of 150 using zero-padding. If a sample had fewer than 150 values, the existing data remained unchanged, and the missing values were filled with zeros to reach 150. Zero-padding was applied to enable consistent input dimensions for convolutional layers, while dropout layers were used to mitigate potential overfitting or artifacts caused by the added padding signals.  
Data augmentation is a widely used technique in machine learning to artificially expand the training dataset by applying various transformations to the existing data [33; 34; 35]. This approach enhances model generalization by introducing variability and reducing overfitting, which is particularly beneficial in time-series data, where data collection can be costly and time-consuming. Several augmentation techniques--such as Gaussian Noise [33], Cut-out [34], Time Shift [35], and Window Warp [36]--have been utilized for time-series data to enhance its diversity. In the context of this study, data augmentation is expected to improve the model's robustness in recognizing both target and non-target movements under varying conditions.  
The dataset consists of six classes; five classes correspond to the predefined target movements, while the sixth class includes all non-target movements. This classification enables the model to distinguish specific gestures from a broad range of non-target movements.
## 3 Results and Discussion
### The Effect of Humidity  
Figure 8a shows variations in COP with the expansion ratio for the CT cycles with different indoor wet bulb temperatures. Figure 8b shows the p-H diagrams for the air cycles at the respective initial optimal COP. The isentropic efficiencies of the expander and compressor are 80% and 75%, respectively. In the legend, for example, 300.15 K/292.15 K denotes the dry/wet bulb temperatures. When the indoor humidity is high, there is a turning point in the curve of the COP that varies with the expansion ratio. In the zone after the turning point, the COP increases rapidly with the increase of the expansion ratio, and the temperature of after-cooler outlet is lower than the dew point. The latent heat of phase change allows the working fluid to absorb more cooling capacity in the after-cooler, which results in a decrease of absolute moisture in the air and a lower enthalpy at the expander inlet. Although the mass flow decreases slightly, the decrease in enthalpy has a greater impact on the cooling capacity of the system. Therefore, when the outlet temperature of the after-cooler is lower than the dew point, the cooling capacity of the system is greater. In addition, with the humidity increases, both the COP and the optimal expansion ratio of the cycle increase. Before the turning point, the optimal expansion ratios are 2.17, 2.20, 2.20,  
Figure 7: Comparison of COP and supplied-air temperature when the isentropic efficiency of expander and compressor is 85% and 80%, respectively.  
2.22 and 2.24, respectively, and the corresponding maximum COPs are 0.747, 0.756, 0.766, 0.776 and 0.785, respectively.
## 2 Materials and Methods
### Biophysical Parameters Estimation  
Leaf Area Index (LAI) was estimated from surface reflectance data using the biophysical processors [10] available in SNAP software version 8, based on trained neural networks, recently updated to version 3.0 with newly calibrated coefficients. Apart from re-calibrated coefficients, the newly introduced features are the support for L8 OLI sensor, the optional use of S2 spectral bands at a 10 m spatial resolution only (specifically those at green (B3), red (B4), and near-infrared (B8) wavelengths), and the computation of the biophysical parameters by the use of the spectral response of each satellite sensor.
## Case Studies
### Case Study 2: Duolingo
#### 5.3.3 Analysis and Findings  
Implementing the Data-Driven Personalized Learning Model (DDPLM) within Duolingo produced compelling results that highlight the potential of personalized learning systems powered by big data. Analysis of the post-implementation data revealed significant improvements in user engagement and learning efficiency, validated through enhanced performance metrics and user feedback.  
Key findings demonstrated that learners using the DDPLM-enhanced platform achieved faster mastery of language concepts than the traditional Duolingo experience. Specifically, the time taken to reach proficiency in complex grammatical structures and vocabulary was reduced by an average of 20%. This improvement is attributed to the DDPLM's ability to dynamically tailor learning paths and content to meet individual learning needs and styles.  
User retention rates increased, with more learners completing courses and continuing beyond initial levels. The personalized feedback and adaptive challenges provided by DDPLM were critical factors in maintaining user interest and motivation over longer periods.  
Feedback from users also indicated a higher level of satisfaction with the learning experience, citing the personalized approach and the responsiveness of the system to their learning progress as key enhancements. This user feedback underscores the value of integrating advanced data analytics into educational platforms to create more engaging and effective learning environments.  
These findings underscore the efficacy of DDPLM in transforming standard educational tools into more adaptive, responsive, and ultimately more effective educational experiences that can significantly improve learning outcomes in language education.  
Table 5 provides a comparative analysis of the outcomes from the case studies conducted on Edmodo and Duolingo, illustrating the impact of DDPLM.
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
###### Abstract  
Wireless technologies are being introduced in industrial applications since they provide certain benefits, such as the flexibility to modify the layout of the nodes, improving connectivity with monitoring and decision nodes, adapting to mobile devices and reducing or eliminating cabling. However, companies are still reluctant to use them in time-critical applications, and consequently, more research is needed in order to be massively deployed in industrial environments. This paper goes in this direction by presenting a novel wireless acquisition system aimed at industrial applications. This system embeds a low-cost technology, such as XBee, not frequently considered for deterministic applications, for deploying industrial applications that must fulfill certain QoS requirements. The use of XBee 900 MHz modules allows for the use of the 2.4 GHz band for other purposes, such as connecting to cloud services, without causing interferences with critical applications. The system implements a time-slotted media access (TDMA) approach with a timely transmission scheduling of the messages on top of the XBee 900 MHz technology. The paper discusses the details of the acquisition system, including the topology, the nodes involved, the so-called coordinator node and smart measuring nodes, and the design of the frames. Smart measuring nodes are implemented by an original PCB which were specifically designed and manufactured. This board eases the connection of the sensors to the acquisition system. Experimental tests were carried out to validate the presented wireless acquisition system. Its applicability is shown in an industrial scenario for monitoring the positioning of an aeronautical reconfigurable tooling prototype. Both wired and wireless technologies were used to compare the variables monitored. The results proved that the followed approach may be an alternative for monitoring big machinery in indoor industrial environments, becoming especially suitable for acquiring values from sensors located in mobile parts or difficult-to-reach places.  
XBee; industrial internet of things (IIoT); monitoring; industrial WSN; Industry 4.0 +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
As highlighted in the literature, molecular imprinting functions like a miniature artificial \"lock\" designed to recognize a specific molecule, which acts as the corresponding \"key\" [68]. This technology enables researchers to create custom \"locks\" tailored to particular compounds. By treating the material, they form unique cavities that precisely match the target molecule in terms of the size, shape, and functional groups. A major advantage of these artificial receptors over natural ones is the flexibility in terms of the molecular design. Unlike natural receptors, which are typically limited to protein-based structures, artificial receptors can utilize a variety of frameworks, such as carbon chains or condensed aromatic rings, offering greater versatility.  
The composition of MIP consists of some main reagents, such as monomers, initiators, cross-linkers, and porogens. The reason for choosing monomers for this research was based on work [69] where three types of sorbents were studied: using 2-VP (sorbent A), MAA (sorbent B) and both monomers taken in a molar ratio of 1:1 (sorbent C). During the synthesis of these sorbents in the presence of Boc-L-tryptophan (as a molecular template) and their subsequent use for the chromatographic separation of Boc-D,L-tryptophan racemate, it turned out that the highest values of the separation parameters were achieved on sorbent C, slightly lower--on sorbent A, and the lowest--on sorbent B. The authors believe that the presence of both monomers in the reaction mixture is necessary for the formation of a pre-polymerization complex that promotes the formation of selective adsorption centers. That is why MAA and 2-VP were chosen for the synthesis of MIP. Other components necessary were the following: initiator was AIBN, porogens were toluene and HEC, and cross-linker was DVB.  
The use of AIBN as an initiator has several advantages; for example, it has a high reactivity and can initiate polymerization at low temperatures, which allows the production of polymers with a high degree of polymerization and molecular weight.  
Toluene and HEC are used in the creation of MIP, because they have a certain structure that contributes to the formation of the desired porosity of the material during its synthesis, as well as the ease of removal from the resulting material [67].  
In this work, MIP was obtained for the sorption of rare earth metals. To begin with, the synthesis scheme was worked out on lead, since rare earth metals are expensive.
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Therapeutic Effect on Different Types of Cancer
#### 3.6.3 Renal Cancer  
It has been noted that the antidiabetic use of pioglitazone is not associated with elevation in the risk of renal cancer [78]. Pioglitazone itself induced apoptosis in Caki cells, a model used for the study of clear cell renal cell carcinoma, as shown in Table 1[54]. Such effects are expected, considering the aforementioned, as well as the elevated presence of PPAR-\(\gamma\) receptors in renal carcinoma cells. Another cell line, 769-P cells, was heavily affected in a dose-dependent manner with imminent apoptosis as the application of the molecule led to significant morphological alterations, including cell density. The suggested mechanism of apoptosis, with limited proof, in this case, was mitochondrial with membrane irregularities and release of cytochrome c. This cell line also exhibited increased sensitivity to methotrexate after treatment with pioglitazone, something that was not evident in the Vero cell line [79]. Finally, there is a lot of interest in the combination of pioglitazone with cisplatin, as it showed nephprotective effects in rodent models HK-2 cells by preventing tissue damage and oxidative stress and it potentiated the latter's effects on renal adenocarcinoma cells inhibiting NF-\(\kappa\)B-mediated inflammatory response and activation of Nrf-mediated antioxidative stress signaling [80].