esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were employed to estimate energy output. Among these, RF exhibited the best performance with the lowest error metrics (MSE: 0.003, RMSE: 0.053) and the highest R\({}^{2}\) value (0.988). In the second stage, analysis of variance (ANOVA) was conducted to evaluate the statistical relationships between independent variables and the predicted dependent variable, identifying wind speed (\(p<0.001\)) and rotor speed (\(p<0.001\)) as the most influential factors. Furthermore, RF and GB models produced predictions most closely aligned with actual data, achieving R\({}^{2}\) values of 88.83% and 89.30% in the ANOVA validation phase. Integrating RF and GB models with statistical validation highlighted the robustness of the methodology. These findings demonstrate the robustness of integrating machine learning models with statistical verification methods.  
wind energy prediction; renewable energy; machine learning; ANOVA; statistical validation 2020 academic Editor: [PERSON]
###### Abstract
[28] proposed adding a learnable Gaussian bias G to the self-attention mechanism. The center location and window size of the Gaussian bias can be determined by the model autonomously, and the Gaussian bias is incorporated into the original attention distribution to form a new modified distribution, with which the attention mechanism can enhance its ability to learn local contextual information. [PERSON] et al. [29] proposed strengthening the self-attention mechanism by multiplying \(QK^{T}\) by a matrix of dependent syntactic information, calling the improved attention head PASCAL. Both articles aim to enhance the model's ability to learn local information, which may weaken the model's ability to capture long-range dependencies. This research also aims to improve the quality of translations by improving the attention mechanism. We propose a translation model for learning syntactic knowledge at both the encoder and decoder sides. The syntactic-assisted learning unit \(M\) is added to the encoder's self-attention mechanism to implicitly enhance the encoder's learning of Traditional Mongolian syntax, and the _Masked Multi-Head Dependency Parsing Attention_ (MMHDPA) layer is added to the front end of the decoder, which explicitly enhances the decoder's learning of Chinese syntax using the dependency syntax (\(DP\)) matrix extracted from the Chinese dependent syntax, A CNN is also integrated into the MMHDPA layer, making it easier for the attention mechanism to determine the weighting relationships between words. The proposed model has the following features. The source code and experimental results are publicly available at: [[https://github.com/CK-IMUT-501/applsci-1927936](https://github.com/CK-IMUT-501/applsci-1927936)]([https://github.com/CK-IMUT-501/applsci-1927936](https://github.com/CK-IMUT-501/applsci-1927936)) (accessed on 9 September 2022):  
* The syntactic-assisted learning unit \(M\) in the encoder can take into account the abilities of local and global information learning and is used in each encoder sublayer, expanding the hypothesis space of the model;
* The addition of the Chinese dependency syntax matrix (\(DP\)) to the decoder provides an intuitive, effective, and interpretable result;* The combination of CNNs and MMHDPA enhances translation performance with only a few additional parameters.
# Artificial Intelligence in Virtual Telemedicine Triage:
## 4 Results
### Performance of the Triage Measurement Device
#### 4.2.1 BPM (Heart Rate) Measurement  
In order to determine if the heart rate measurement is correct, a comparison of the measurements of the device built with respect to a commercially available device was carried out. We worked with a total of 20 people who attended the office of the doctor who assisted in the project. The results of the \"BPM\" measurements had a variation of 3.1 BPM with respect to a commercially available pulse oximeter, resulting in an average measurement error of 4.52%. This percentage error yields a reliability of 95.48%, which makes it functional when dealing with a patient as the healthcare professional emphasized that the vital sign status is checked for an abnormality.  
Figure 11 shows the behavior of the BPM measurements, where there is a trend of greater precision as the frequency range increases. There is a tendency for greater precision as long as the heart rate range is between 60 bpm and 65 bpm, since when the actual value is higher than 70 bpm the device presents a certain degree of difficulty when measuring. It is also important to mention that the MAX30100 sensor used is very sensitive to the position of the finger on the LEDs, so a bad positioning of the finger on the sensor will cause an erroneous measurement.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 3 Applications of Smart Drug Delivery Devices
### Wearable Smart Drug Delivery Devices
#### 3.1.3 Smart Inhalers  
Smart inhalers are a type of wearable smart drug delivery device that assists individuals with breathing disorders such as asthma and COPD to better manage their symptoms. These devices can monitor a patient's inhaler usage, provide reminders to take medication, and track medication adherence over time. Some smart inhalers are also able to measure lung function and provide feedback to patients and their healthcare providers. The information gathered by intelligent inhalers can be utilised to enhance treatment strategies, enhance patient results, and decrease healthcare expenditure. The smart inhalation devices for drug delivery, and their limitations, are listed in Table 3.
# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 2 Related Work
### Visual SLAM in Dynamic Environments  
As research progresses, the operating environments for VSLAM have evolved from simple, highly static or low-dynamic indoor settings to complex, highly dynamic real-world scenarios. Semantic SLAM employs deep learning networks for semantic segmentation and object recognition, effectively separating dynamic objects from the static environment, maintaining reliable recognition and segmentation even with certain dynamic changes, and thereby minimizing their negative impact on map construction and camera localization.  
Similar to DS-SLAM [12], proposed by [PERSON] et al., and DynaSLAM [13], proposed by [PERSON] et al., both methods use a combination of deep learning and geometric techniques to segment dynamic objects, employing semantic segmentation networks like SegNet [14] and Mask R-CNN [15] to segment and label dynamic targets, and then filtering out dynamic objects through motion consistency checks. However, semantic segmentation networks require complex pixel-level operations, including mask generation for each candidate box, which typically results in slower inference speeds. Detector-SLAM [16] proposes an object-oriented semantic structure method, using SSD [17] object detection for object detection and recognition, followed by segmentation of the target point cloud, and finally associating object data based on nearest neighbors. Techniques such as YOLO and SSD, which are single-stage object detection methods, are used to identify dynamic objects and eliminate them effectively. The object detection method can quickly and accurately detect dynamic targets in real time and generate dynamic target bounding boxes, achieving extremely high real-time performance. However, it can lead to excessive loss of static points, which negatively impacts the accuracy of the algorithm.  
These excellent open-source algorithms demonstrate the potential of integrating deep learning-based object detection and segmentation techniques into VSLAM systems. However, as shown in Table 1, most algorithms have their own advantages and disadvantages. Considering the shortcomings of the target detection algorithm, we combine effective target detection with simple geometric methods, as well as generate dynamic target mask using depth information, to minimize the loss of static feature points without affecting the running speed of the algorithm. By effectively handling dynamic objects and separating them from static elements, these methods aim to enhance the robustness and accuracy of SLAM in complex and dynamic environments.
## 4 Discussion
### Spatial Variability of Vegetation Dynamics Revealed by CVFZ  
Figure 10 depicts the spatial variability in vegetation dynamics captured by CVFZ, which reveals a large void in the central Sichuan region. The corresponding blank region in the global topographic map aligns well with the Chengdu Plain, which is predominantly an urban area [87]. The blank region's southwestern side corresponds to the Hengduan Mountains, where the CVFZ types are mainly 0 and 2, while the blank area's northeastern side corresponds to the Sichuan Basin, where the CVFZ types are primarily 3, 4, and 5.  
The vegetation types in the regions differ significantly based on MCD12Q1. Evergreen broadleaf forests cover only a small portion of the Chengdu Plain, while the Hengduan Mountains feature grasslands (represented by MCD-CVFZ types 10 and 12) with a greening speed of \(2.08\times 10^{-3}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) to \(4.72\times 10^{-3}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\), respectively. In contrast, the Sichuan Basin is predominantly savannas (represented by MCD-CVFZ types 43, 44, and 45) with a greening speed of \(1.34\times 10^{-2}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) to \(1.56\times 10^{-2}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\), which is nearly six times that of the Hengduan Mountains.  
Although the five vegetation types share the same phenological cycle of 1, the LAI intra-annual fluctuation of the two grassland vegetation types smoothly rises before decreasing between \(-0.3\) m\({}^{2}\)m\({}^{-2}\) and \(1.1\) m\({}^{2}\)m\({}^{-2}\), peaking between Days 200 and 230. In contrast, the three savanna vegetation types have a volatility between \(-1.1\) m\({}^{2}\)m\({}^{-2}\) and \(1.9\) m\({}^{2}\)m\({}^{-2}\), with peaks around Day 210. The significant variability in potential vegetation dynamics between geographically similar regions illustrates the value of CVFZ land use management in China.
## 6 Test-Beds
Regarding the PRR and Delay values, the results show the average obtained after 10 repetitions of a one-hour run (for each indoor/outdoor scenario) together with their corresponding standard deviation (95% confidence interval, Batch Mean Method). They are represented in Figures 14 and 15.  
radio propagation and reduces the signal cancelation). Thus, it is easy to understand that this experiment shows greater similarities to the _Good Scenario_ simulation results. On the other hand, the substation indoor environment is affected by hard electromagnetic interferences, the absence of line-of-sight (in almost every pair of nodes) and the presence of many obstacles in the first Fresnel zone; finding the results obtained matching-enough to those obtained in the simulations under the _Harsh Environment_ label. It is worth mentioning that in order to also validate the results obtained in the simulation of the ZigBee-based network, we decided to carry out the outdoor and power-consumption experiments again, but this time, programming devices to form a ZigBee network. For the case of the power consumption analysis, we let them run another week, while testing the consumption of the seventh node: the results were 98.15% equal to the value obtained with the simulations--slightly higher in the test-bed than expected. The PRR obtained in such outdoor experiment was 89.15% (eighty-nine percent of the messages sent by nodes were received by the sink node); differing in just 1.13% from the simulation. These experiments clearly validate the results obtained in both, the simulation and the mathematical expressions and therefore, demonstrate the suitability of our SENSED-SG approach from a global perspective to provide a reliable control and monitoring means for the Smart Grid.
## 2 Design
RCWA expands the electromagnetic field and the dielectric constant of the material by a Fourier series, and solves [PERSON]'s equations by getting the eigenvalues and eigenvectors of the \(n\times n\) matrices, where \(n\) is the number of harmonics retained in the field expansion. In RCWA, the truncated order \(m\) can control the dimension of the matrices. The relationship between \(n\) and \(m\) can be expressed as: \(n=2\times(2m+1)\). Figure 2 shows the curve of the calculated diffraction efficiency variation with the truncated order. It can be clearly seen that when the truncated order was large enough (\(m>5\)), the diffraction efficiency tended to be stable. In this work, the truncated order was set to 20, which was sufficient to ensure the accuracy of the RCWA. Therefore, RCWA is an effective tool for dealing with periodic structures, especially diffraction gratings. In addition, SA algorithms were used to optimize grating parameters \(\{f,h_{1},h_{2},h_{3},\Lambda\}\). The  
Figure 1: (**a**) Schematic diagram of the grating and (**b**) the refractive indices of the material of the dielectric layers. SA algorithms could replace the task of optimizing grating parameters with the task of finding the minimum value of the merit function:  
\[MF=\{\frac{1}{N}\underset{\lambda_{i}}{\overset{\lambda_{N}}{\sum}}[(1-DE_{-1,TE} (\lambda_{i}))^{2}+(1-DE_{-1,TM}(\lambda_{i}))^{2}]\}^{1/2} \tag{3}\]  
where \(DE_{-1,TE}\) and \(DE_{-1,TM}\) are the diffraction efficiencies of \(-1\)st order for TE and TM polarizations, respectively. In the optimization process, the incident wavelength varied from 1000 nm to 1100 nm with an interval of 5 nm (\(N=21\)) and the incident angle was Littrow angle at 1053 nm. Furthermore, the grating parameters were confined in the ranges of: \(0.2<f<0.8\), \(500\) nm \(<\Lambda<1650\) nm, \(100\) nm \(<h_{1}<1000\) nm, and \(100\) nm \(<h_{2}<1000\) nm. Finally, the optimized grating had a duty cycle of 0.423, a period of 727.6 nm, a first layer thickness of 256.0 nm, a second layer thickness of 139.2 nm and a match layer of 231.6 nm.
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 1 Introduction
### The Significant Advantages of Deep Learning Techniques  
Traditional ship detection methods [5; 6; 7; 8] based on artificial feature modeling rely on manually designed algorithms to extract shallow features. These algorithms often focus on specific scenes and exhibit limited generalization abilities. They still face challenges in terms of accuracy, computational efficiency, and robustness in complex environments, making them less applicable [9; 10; 11].  
In recent years, significant progress has been made in the field of object detection in natural images, largely attributed to the rapid development of deep learning technology [12]. The introduction of deep learning models has greatly improved the accuracy of object localization and recognition in images. Convolutional neural networks (CNNs) have emerged as powerful tools for feature extraction, enabling automatic learning and extraction of features from images. The renowned Faster R-CNN model [13] has become a representative model in natural image object detection, opening possibilities for applying deep learning-based object detection models to remote sensing images.
## 3 Experimental Verification
### Experimental Principles and Platform Construction
#### 3.2.1 Demagnetization Signal Generation  
The first step of the experiment to measure the magnetic hysteresis line of grain-oriented silicon steel is demagnetization. The role of demagnetization is to ensure that the starting working point of the oriented silicon steel is located in the origin; if the starting working point is not fixed to the origin, it will lead to a large experimental measurement of the random error. Therefore, the sample should be demagnetized before performing the magnetic property measurement experiment. The demagnetization signal is generated by the host computer, which generates a sinusoidal attenuation signal and outputs it through  
Figure 10: Power amplifier input and output signals: (**a**) \(f=50\) HZ; (**b**) \(f=1.55\) kHZ.  
Figure 9: Experimental device.  
the data acquisition card, so that the starting point of the magnetic property measurement is near the origin. The numerical expression for the demagnetization signal is \(\sin(2\pi f_{d}t)\)\(\times\)\(e^{-mt}\), where \(f_{d}\) is the frequency of the demagnetization signal, \(t\) is the time, and \(m\) is the attenuation factor. The waveform of the demagnetization signal output is shown in Figure 11. During the experiment, it was found that a demagnetization effect less than 0.3 is effectively sufficient and greater than 0.3 is not effectively sufficient, so the attenuation coefficient can be set to within 0.3, as needed.
_applied_  
_sciences_  
_Article_  
**Two-Wavelength Computational Holography for**  
**Aberration-Corrected Simultaneous Optogenetic Stimulation**  
**and Inhibition of In Vitro Biological Samples**  
**[PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 1, [PERSON] 3(c) and [PERSON] 1,2,4,5,*(c)**  
1 Laboratory of Measurement and Sensor System Technique, Faculty of Electrical and Computer Engineering, TU Dresden, Helmholtzstrasse 18, 01069 Dresden, Germany; [PERSON]S] (L.B.);  
[EMAIL_ADDRESS] (T.H.)  
2 Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, 01069 Dresden, Germany  
3 Universitats-Augenklinik Bonn, Department of Ophthalmology, University of Bonn, Ernst-Abbe-Strasse 2, D-53127 Bonn, Germany; [EMAIL_ADDRESS]  
4 Excellence Cluster Physics of Life, TU Dresden, 01307 Dresden, Germany  
5 Faculty of Physics, School of Science, TU Dresden, 01069 Dresden, Germany  
* Correspondence: [EMAIL_ADDRESS] (F.S.); [EMAIL_ADDRESS] (J.W.C.)  
**Abstract:** Optogenetics is a versatile toolset for the functional investigation of excitable cells such as neurons and cardiomyocytes in vivo and in vitro. While monochromatic illumination of these cells for either stimulation or inhibition already enables a wide range of studies, the combination of activation and silencing in one setup facilitates new experimental interrogation protocols. In this work, we present a setup for the simultaneous holographic stimulation and inhibition of multiple cells in vitro. The system is based on two fast ferroelectric liquid crystal spatial light modulators with frame rates of up to 1.7 kHz. Thereby, we are able to illuminate up to about 50 single spots with better than cellular resolution and without crosstalk, perfectly suited for refined network analysis schemes. System-inherent aberrations are corrected by applying an iterative optimization scheme based on Zernike polynomials. These are superposed on the same spatial light modulators that display the pattern-generating holograms, hence no further adaptive optical elements are needed for aberration correction. A near-diffraction-limited spatial resolution is achieved over the whole field of view, enabling subcellular optogenetic experiments by just choosing an appropriate microscope objective. The setup can pave the way for a multitude of optogenetic experiments, in particular with cardiomyocytes and neural networks.  
**Keywords:** optogenetics; computer-generated holograms; ferroelectric spatial light modulator; stimulation; inhibition; cardiomyocytes
## 1 Introduction  
Alternating Current (AC) Signal analysis plays a key role in signal processing, filtering, and system identification. It is also very important for impedance spectroscopy, including human body tissue diagnosis [1], battery diagnosis [2], and cable diagnosis [3]. In this method, the impedance of a device-under-test (DUT) is measured at different frequencies. For this purpose, an excitation signal consists of a single frequency voltage sinewave or a current sinewave. The typical response of a linear time-invariant DUT is a current sinewave, respectively, a voltage sinewave, sharing the same frequency but having a different amplitude and phase. The impedance is calculated based on the amplitudes and phases of both voltage and the current. Conventionally, a sine frequency sweep and a magnitude-phase detector are used to calculate the impedance spectrum. However, this can be very time-consuming. Alternative excitation signals such as a multisine signal, i.e., the sum of multiple sinewaves, are used instead. It enables a shorter measurement time at the expense of more complex signal analysis [4]. Thereby, identifying the amplitude and phases of all the signals within the excitation and response signals can become challenging.  
To determine the amplitude and phase spectrum, Fourier-Transform-based methods, such as the Discrete Fourier Transform (DFT), are typically used [4; 5; 6; 7; 8]. Fast-Fourier Transform (FFT) [4; 9] is implemented to accelerate calculation of DFT [10]. Other approaches include Ordinary Least Squares, commonly known as Sinewave fit [11; 12; 13], Discrete-Time Fourier Transform (DTFT) [14], cross-correlation [15], non-linear data fitting. However, these approaches are computationally complex, and their efficiency for use and performance in nowadays' embedded systems for impedance spectroscopy is still questionable as these devices come with limited resources, i.e., memory and processing capacity.  
This paper compares AC signal analysis methods based on their processing time, memory consumption, precision, and accuracy against signal interference and signal incompleteness, i.e., spectral leakage, for embedded implementation. The second section presents the theoretical part and implementation details of the methods. The third section features a theoretical comparison of the processing time, memory usage, accuracy, and amplitude and phase determination precision. The signals are implemented in a reference Windows PC system for validation, using MATLAB, and C program using Visual C++ compiler. These results are compared with the STM32H743 implementation featuring Arm Cortex-M7 microprocessor running a native C program, a hardware-accelerated STM32 program, and a \"Native\" Teensyduino C code on Teensy 3.6. Although this work targeted two particular STM32 and Teensy microcontrollers models, the results can be generalized to single-board computers and microcontrollers with similar specifications.