esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Two-Stage MG Operation Model Considering Interactive Power Deviation  
As illustrated in Figure 1, the proposed UFRs of MGs will be transmitted to ULG, replacing massive real-time data with concise low-dimensional boundary parameters. This approach significantly reduces communication and computational burdens on distribution dispatching centers while enabling global high-precision optimal dispatch.  
The operation of distribution-microgrid systems is carried out under the coordinated interaction of day-ahead and intra-day scheduling, aiming to achieve an effective combination of global optimal scheduling and real-time dynamic adjustments. The day-ahead scheduling is primarily based on forecasts of the load demand and renewable generation for the following day, from which the overall operational strategy and resource allocation are formulated. In contrast, the intra-day scheduling is based on real-time load and generation data, dynamically adjusting the operational scheduling to respond to unpredictable short-term fluctuations and uncertainties. This coordinated operational model will fully leverage the advantages of distribution-microgrid systems in flexible regulation and resource optimization. On the one hand, the day-ahead scheduling provides macro-level guidance for stable system operation, optimizing the operation of distributed resources, reducing operational costs, and enhancing energy efficiency. On the other hand, the intraday dispatch schedules dynamically characterize the self-balancing capability of microgrids under uncertainty. It is worth noting that, as illustrated in Figure 1, the MGs discussed in this study operate at average voltage levels of 10 kV or 400 V.
## 2 Experiment
### Color Image Complexity Measures
#### 2.4.1 The Color Entropy  
The Color Entropy measures the disorder in signals [3,48], relating to the variation of signal values, while for images, to the variation in texture image colors, with no information on pixels spatial arrangement. The same definition is considered in this paper, with an extension to the multidimensional color case, as described in [3].  
\[H=-\sum_{i=1}^{N}p_{i}\cdot\log_{2}(p_{i}) \tag{1}\]  
where \(p_{i}\) is the probability of appearance of pixel value \(i\) in the image, and \(N\) the amount of possible pixel values.  
Based on the color entropy measure, the natural images selected for the experiment are comprised between 7.33-16.37 and fractal images between 16.45-17.84. Frac images exhibit higher complexity in colors compared to Nat images, as shown in the color entropy distribution in Figure 2(a), meaning that the synthetic fractal images have higher variability in the color space.
## 3 Lactic Acid Bacteria (LAB): An Emerging Group of PGPR  
### Soil- and Plant-Associated LAB  
Diverse genera of beneficial rhizobacteria have already been proposed as PGPR, with _Bacillus_ and _Pseudomonas_ being the predominant genera [88]. Nevertheless, metagenomic analyses of plant and rhizosphere microbiomes have resulted in the identification of an emerging group of PGPR, namely, lactic acid bacteria (LAB), which are barely detectable in the plant-soil ecosystem due to their low abundance [18; 89]. LAB are known as microarophilic, Gram-positive, cytochrome-deficient, and nonsportulating bacteria that are also involved in food and silage fermentation as well as soil health; however, some of them are recognized as human pathogens [90; 91]. Despite intensive investigations into the conventional function of LAB in the food processing industry, too little attention has been given to their other functions, such as acting as biofertilizers, biocontrol agents, and biosolutulants in plant growth. Furthermore, little is known about LAB due to the difficulty of isolating them by plating serial dilutions of rhizosphere soil samples since enrichment methods using selective culture media have been largely ignored [18; 92; 93]. Despite their low relative abundances, LAB have been isolated from the rhizosphere in some studies, which has consequently led to their introduction as a crucial component of sustainable agricultural approaches as environmentally sustainable and efficient strategies to control pests and diseases and enhance crop yield [93; 94; 95].  
It has been stated that root exudates, including amino acids, carbohydrates, enzymes, organic acids, phenols, and flavonoids, account for a considerable proportion (5-21%) of photosynthetically fixed carbon in plants, which can change the soil environment and, consequently, shape microbial communities [96]. Although such a carbohydrate-rich rhizosphere is ideal for LAB, a quick breakdown of organic acids in the rhizosphere has been proposed as a limiting factor in the capability of LAB to acidify soil to their benefit, thus preventing LAB from being the predominant taxon in agricultural soils [97]. Moreover, recent research on the efficient transfer of LAB from the rhizosphere and phyllosphere to the plant endosphere [98] provided an interesting strategy to assess their roles in plant growth and production. In fact, LAB also constitute a small fraction of the epiphyte [99; 100] and endophyte populations of plant microbiota [100; 101]. Among the plant-associated LAB, there are some well-known generalist taxa, including _Laciplantibacillus plantarum_, _Lactococcus lactis_, _Leuconostoc_ spp., _Weissella_ spp., and _Enterococcus_ spp., and some specialist taxa, such as _Fructilactobacillus forum_ and _Fructobacillus_ spp., that have been discovered relatively recently [102]. However, the consequences of LAB on plant physiology still need to be fully deciphered. Overall, the genomic diversity in LAB is mainly due to the particular pressure applied by each plant niche [103; 104].
## 2 Related Work
By analyzing the bit-rate, packet size, and inter-packet times for upstream and downstream traffic, the authors found that upstream traffic had a lower bit-rate, smaller packet size, and shorter inter-packet times. Similarly, the authors in [24] examined the impact of various network conditions on the objective quality of games played on two cloud gaming platforms: StreamMyGame(SMG) and onLive. They found that using frame-rate as an objective metric, the objective quality of games decreased as the levels of delay and packet loss increased on both platforms. However, they found that onLive generally performed better than SMG. It is worth noting that the study's limitation is that they only tested traditional games with low frame rates (24, 30 fps) which is not representative of contemporary games that support higher frame rates and have more complex content. Similarly, the authors in [25] investigated the impact of varying levels of delay and packet loss on the subjective quality of cloud gaming. They utilized PlayStation 3 as an alternative for a high-spec cloud machine, and manipulated network parameters using a network emulator. They found that the performance of the game decreased as the levels of QoS parameters increased. They noted that when the packet loss rate (PLR) was greater than 1%, the perceived quality was unsatisfactory. The authors in [14] used a recently developed cloud gaming platform (Nvidia Geforce) and studied the effect of frame loss, jitter and latency on the subjective quality of gaming. Similarly, in [25], it was reported that the quality of the game decreased as the levels of QoS parameters increased. However, their research showed that games streamed on NVIDIA Geforce performed poorly as the delay increased, but showed better tolerance for packet loss. The authors in [15] also looked at the impact of packet loss on video and game QoE individually, and concluded that MOS distribution of game and video QoE were comparable. Recent studies have also examined the effects of a combination of delay and visual artifacts on QoE in cloud-based virtual reality (VR) applications [26]. Moreover, the authors in [27] examined the performance of cloud gaming applications on the Moonlight platform using objective measures. The researchers evaluated how different transport networks (LTE, Ethernet, and WiFi) affected Key Quality Indicators (KQIs) of cloud gaming. The metrics used were RTT, frame rate, rendering time, and rendering loss. The results showed that Ethernet was the best option for hosting games, outperforming WiFi and LTE. However, the study did not investigate the impact of varying Quality of Service parameters on game and platform performance. In all the aforementioned studies, the default settings of the network emulators were used, and, hence, they ignored any cross-correlation present between the network parameters and limited the ability to capture the impact on the end-user experience. This paper, however, implemented cross-correlation between network parameters and then captured its effect on both subjective and objective quality metrics for cloud gaming applications.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 3 Methodology
### Temporal Sparse Attention (TSA)
#### 3.1.1 Embedding Transformation  
The transformation is performed with an embedding to turn discrete inputs into a continuous vector space wherein inputs sharing similar properties will have similar representations.  
Input Representation: For each student, we use a sequence of standardized features (e.g., original, correct, attempt_count, position) to construct an interaction sequence.  
Embedding Transformation: These input features are passed through a linear layer to project them into a higher-dimensional space:  
\[x_{\text{embed}}=W_{e}x+b_{e} \tag{1}\]  
where  
* \(x\) is the input sequence of shape \((\text{seq\_len},d_{\text{in}})\);
* \(W_{e}\) and \(b_{e}\) are learnable parameters;
* \(x_{\text{embed}}\in\mathbb{R}^{\text{seq\_len}\times H}\) is the embedded sequence with \(H\) being the hidden dimension.
# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
## 3 AMR Framework
### Time Attention Mechanism  
In deep learning, the time attention mechanism is a method for handling sequential input. It allows the model to assign different importance or attention to the information at different time steps when processing sequence data. With the help of this approach, the model may more effectively understand the dependencies and significance of various sequence segments and change its attention as necessary. During initialization, this layer creates two sets of weights and is used for calculating attention scores. Attention scores are computed using Formula (14):  
\[e_{ij}=\tanh(Wx+b) \tag{14}\]  
where \(x\) represents the input. Softmax and the weighted inputs index and normalize the attention score to obtain the attention weight a. To emphasize the key components of the input sequence, these attention weights are applied to the input. Finally, the weighted inputs are summed along the sequence axis to produce the final output of the layer.  
By means of the aforementioned mechanism, the temporal attention mechanism is able to dynamically modify the weights in accordance with the significance of various segments of the input sequence. This allows the model to concentrate on the most pertinent information at every stage, thereby enhancing its processing efficiency and highlighting the salient features of the sequence data.  
Figure 3: BiLSTM network model frame diagram.
# Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors
## 2 Adaptive Activity Recognition
### Proposed System Architecture
#### 2.1.2 Feature Extraction  
After gathering the data, they need to be transformed so as to extract the features that would provide all of the necessary information to the machine learning algorithm.  
It is necessary to transform the gathered data in order to extract features that include all the information needed by the machine learning algorithm. First, the sequences of inertial signal samples are divided into frames with overlapping and fixed-width sliding windows, calculating for each frame a feature vector, which is required in learning the internal characteristics of the inertial signals. These features are well-known statistics, namely, the mean, correlation, signal magnitude area (SMA), autoregression coefficients, and some more complex ones. These features, together with time domain signals, are selected according to prior work and importance analysis [25; 28; 32; 33].  
The time domain signals are as follows [33]:  
* Three accelerometer signals (XYZ axis).
* Three jerk signals given by the accelerometer signal data.
* One magnitude signal index computed as the vector-length of the three previous original accelerometer signals.
* The jerk magnitude index computed as the vector-length of these jerk signals.  
The features estimated from the time domain signals are as follows:  
* Mean value, computed usually as the average of readings per axis, where \(N\) is the number of readings for each sensor: \(\frac{1}{N}\sum_{i=1}^{N}x_{i}\).
* Median absolute deviation to evaluate the variation around the mean value, computed as (for each axis) \(\frac{1}{N}\sum_{i=1}^{N}|x_{i}-\mu|\).
* Standard deviation, quantifying the variation of readings from the mean value: \(\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_{i}-\mu}\).
* Average Resultant Acceleration, computed as the average of vector-lengths of each reading: \(\frac{1}{N}\sum_{i=1}^{N}\sqrt{x_{i}^{2}+y_{i}^{2}+z_{i}^{2}}\).
* Minimum and maximum in a frame.
* Inter-quartile range to measure the variability in a dataset.
* Energy measure computed as the average of squared samples in a frame.
* Signal magnitude area (SMA) computed through the normalized integral.  
Frequency domain signals are as follows [33]:  
* Three Fast Fourier transforms (FFTs) computed from three original accelerometer signals.
* Three FFTs computed from three jerk accelerometer signals.
* One FFT computed from a jerk magnitude signal.  
The features estimated from frequency domain signals (including similar features to those from the time domain) are as follows:  
* Frequency component index with the largest magnitude.
* Weighted average of the frequency components.
* Skewness and Kurtosis of the frequency domain signal.
* Histogram, which is constructed in a standard manner, dividing the range of minimum-maximum values of each axis into a specified number of equal-sized intervals, equipped with frequencies of hitting to these intervals: \(\frac{1}{N}\sum_{i=1}^{N}[(x_{i}\in b_{j})\to 1\), \(j=1...10]\).
## 4 Conclusions  
The novelty of this paper was that the DE algorithm can be used to modify APF and enhance the gain of position domain PDC-PID controller in the UR5 manipulator robot to minimize the contour tracking error and enhance the path of the UR5 robot. Three different fitness functions (ISE, IAE, and MSDAE) were used to determine optimization efficiency. The DE-ISE method resulted in minimum contour error in linear contour tracking. Minimum contour error in non-linear contour tracking is produced using the DE-IAE method. It has been concluded that the DE algorithm revealed good optimization in linear and nonlinear contours since the application of DE algorithm made it more flexible to avoid the local minimum. It is important to mention here that these optimization methods for gain tuning of the position domain controller can be extended to other controller designs made for contour tracking which suffered from manipulator problems. On the  
Figure 14: Trajectory of UR5 manipulator.  
Figure 15: Trajectory of UR5 manipulator in three cases (PDC-PID controller, APF and with PDC-APF modified by DE).  
other hand, the improved APF method with DE presented low computational cost allowing applications in dynamic environments for collision-free trajectories. The artificial potential field method requires adjustments of several parameters so that the generated path is satisfactory. As shown in this work, through the proper selection of the parameters ka, kr and ko it is possible to reach the position and optimize the path through a minimum distance. Therefore, we conclude that in all of the evaluations carried out, the DE algorithm presented good results regarding generation of a smooth contour trajectory and better computational cost. In future work, there are many ideas to explore. One of them would be to apply the fuzzy decision on the APF approach to avoid the obstacle in online working, and modify the APF approach by connecting it with the Internet.  
Formal analysis I.J.K., LQ.; Investigation, I.J.K.; Methodology, I.J.K. and L.Q.; Supervision Y.T., and L.Q.; Writing--original draft, I.J.K. and Y.T.; Writing--review L.Q. editing, Y.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Open Reserch Foundation of Advanced Innovation Center for Intelligent Robotics and Systems under Grant Number 2019 IRS18, and the Fundamental Research Funds for the Central Universities of China under Grant Number 2021 IVA019.  
Not applicable.  
To my husband [PERSON], my mother, my family, and to [PERSON] who have helped me in all my research studies. We also thank all the anonymous reviewers for their insightful comments.  
The author declares no conflict of interest.
# Non-Invasive and Accurate Blood Glucose Detection Based on an Equivalent Bioimpedance Spectrum
## 8 A Non-Invasive Blood Glucose Detection Experiment
### Multi-Person Blood Glucose Test  
The proposed blood glucose detection model is applied to different subjects in order to further verify its accuracy and generalization ability. For multi-person blood glucose testing experiments, the testing process is basically the same as that of single-person testing. The external experimental conditions, such as the test posture and the test environment, are consistent with the single-person blood glucose test, including keeping the subject's body stable and emotionally stable during the complete test process, drinking no water within 20 min of the test, etc., and controlling the ambient temperature during the test to be stable at room temperature. The subjects selected for this experiment consist of 16 males and 4 females, aged from 20 to 26 years old. Their physical types include thin, standard, overweight, etc. Their BMI values range from 13.7 to 31.8. The results are shown in Table 3 and Figure 18.  
Figure 18: Comparison of blood glucose test results. In Table 3, the K-proximity classification model identified five hyperglycame samples, including those from three males and two females. The biochemical blood glucose values of male hyperglycemia samples are 7.7, 7.8, and 8.5, respectively. The proposed classification algorithm can identify these samples accurately. For these three samples, the absolute error of blood glucose estimation obtained by the higher order multiple regression equation is greater than 0.5 (range from 0.7 to 1.03). On the other hand, for female hyperglycemia samples, the biochemical blood glucose values are 7.3 and 6.1, respectively. The former sample meets the characteristics of hyperglycemia according to the physiological diagnosis standard, and it has been correctly classified by the proposed model. However, the latter sample with a biochemical blood glucose value of 6.1 is not a hyperglycemia sample according to the physiological diagnostic criteria, so the proposed model makes an incorrect identification for it. The main reason is that the accuracy of the K-proximity classification algorithm depends on the value of k and the sample size of the known class. When the capacity of one sample is too large and the capacity of another sample is small, the test sample cannot be close to the target sample. For the above sample with incorrect classification, the corresponding information of the tested person is as follows: female, age 25, height 162, weight 43, and a BMI value of 13.7. This sample has the lowest BMI in the experiment,
# Investigation of the Scattering Noise in Underwater Optical Wireless Communications
## 3 UOWC Channel Model  
Channel modeling in a communications system is critical since it plays an important role in the design, implementation, and evaluation of the link under investigation. A practical and robust method for modelling the underwater channel is MC simulation, where a statistical method is adopted to evaluate the channel characteristics by generating \(N\) photons and then tracking the interactions of each photon with the medium and its trajectory. Note, \(N\) photons are transmitted simultaneously through the water, and each emitted photon is assigned with four basic features such as photon's position, propagation time, transmission direction, and weight, which is also known as the intensity. The trajectory of photons can be tracked through the channel from the Tx to the Rx. Compared with the analytical solutions of radiative transfer equation (RTE), the MC approach is more flexible for various link geometries without the restrictions on the scattering angles and therefore is widely employed in simulation of light propagation in a dispersive medium, e.g., beam propagation in the seawater [26; 27]. MC numerical method with respect to the absorption and scattering effects has been reported in several recent works [11; 28; 29]. In [30], they evaluated the continuous-variable quantum key distribution method over an underwater link by using MC simulation. They showed that the performance evaluated by MC simulation is more accurate due to considering the contribution of scattered photons and the system transceiver parameters compared to Beer's law which depended on wavelength and distance [31]. In [19], the channel capacity for various link distances, water types, and transceiver parameters were evaluated using MC simulation, and it was shown that the UOWC bandwidth for the clear, coastal, and harbor waters are in the order of hundreds of MHz, tens of MHz and MHz, respectively. The scattering will cause the coherent wavefront to be attenuated and dispersed, which leads to the reduced SNR and lower transmission data rates due to inter symbol interference.  
Note, during optical signal propagation, some non-scattered and scattered photons will randomly arrive at the optical Rx, which will result in scattering-induced noise, which is introduced in the next section. In this work, the UOWC channel is modelled using MC simulation, which is used to obtain the parameters for determining the scattering noise coefficient.  
Figure 1: System model.