esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Experimental Results
The most relevant results reported in Table 6 can be summarized as follows:  
* the ensembles strongly outperform (\(p\)-value 0.01) the stand-alone CNN in each topology;
* in MobileNetV2, DenseNet201, and ResNet50, Stoc_4 outperforms 15 ReLU (\(p\)-value 0.05);
* DarkNet53 behaved differently: on this network, 15 Leaky and Stoc_4 obtained similar performance. In Table 7, we report the performance on a few data sets obtained by ResNet50, choosing the optimal values of BS and LR for ReLU. Even with BS and LR optimized for ReLU, the performance of Sto_4 is higher than that obtained by ReLU and 15 ReLU. The hardware improvements reduce the inference time; there are several applications where it is not a problem to classify 100 images in just a few seconds. In Table 9, we report the four best AFs for each topology with both MI = 1 and MI = 255. If we consider the two larger data sets, CO and LAR, the best AF is always a learnable one:  
* CO--ResNet: the best is Swish Learnable;
* LAR--ResNet: the best is 2D MeLU;
* CO--VGG16: the best is MeLU + GaLU;
* LAR--VGG16: the best is MeLU (\(k\) = 4). It is clear that some of the best AFs are proposed here. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & CH & HE & MA & LAR \\ \cline{2-5} ResNet50 & ReLU & 98.15 & 95.93 & 95.83 & 94.77 \\
15 ReLU & 99.08 & 96.28 & 97.08 & 95.91 \\ Sto\_4 & 99.69 & 96.40 & 97.50 & 96.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance (accuracy) with optimized BS and LR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GPU** & **Year GPU** & **Single ResNet50** &
\begin{tabular}{c} **Ensemble 15** \\ **ResNet50** \\ \end{tabular} \\ \hline GTX 1080 & 2016 & 0.36 s & 5.58 s \\ Titan Xp & 2017 & 0.31 s & 4.12 s \\ Titan RTX & 2018 & 0.22 s & 2.71 s \\ Titan V100 & 2018 & 0.20 s & 2.42 s \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Inference time of a batch size of 100 images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Topology** & **MI** & **Top1r** & **Top2r** & **Top3r** & **Top4r** \\ \hline ResNet50 & 1 & MeLU + GaLU & SRS & PDELU & Soft Learnable v2 \\ ResNet50 & 255 & MeLU (\(k\) = 8) & Splash & MeLU (\(k\) = 4) & 2D MeLU \\ VGG16 & 1 & SReLU & MeLU + GaLU & MeLU (\(k\) = 4) & ReLU \\ VGG16 & 255 & GaLU & MeLU (\(k\) = 4) & SReLU & APLU \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The four best AFs are reported (TopXr means X-th position in the rank among the AFs).
# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 1 Introduction
[46] proposed a method for accurately monitoring corn growth and estimating the Leaf Area Index (LAI) based on a UAV multispectral sensor. [PERSON] et al. [47] proposed a novel complex vegetation index including critical physiological parameters of plants and chlorophyll fluorescence caused by the sun. Recently, interest in urban regeneration projects for disasters such as heat waves and the urban heat island phenomenon has been increasing in Korea. Overseas, there is an eco-friendly urban regeneration plan at South East Wedge in Edinburgh and Castle Value in Birmingham has an urban regeneration project with a pleasant and safe environment [48]. In order to plan and promote urban regeneration projects like this, data to support decision-making is needed. Vegetation provides a comfortable living environment from heat waves and plays an important role in environmentally friendly urban regeneration plans [49; 50]. The idea for this study was discovered by accident. NDVI was calculated using a UAV to obtain vegetation information of an apartment complex necessary for the urban regeneration project. However, it was confirmed that the NDVI value appeared higher in the basketball court and the waterproof coating roof coated with the urethane. Therefore, a method was sought to effectively remove areas that were incorrectly classified as vegetation. As a result, considering the fact that the surface temperature of the basketball court and the waterproof coating roof is very high compared to the vegetation areas such as trees and grass land, the temperature was analyzed from a UAV equipped with a thermal infrared camera. Additionally, by combining the analyzed temperature information with NDVI, a study was conducted to create a highly accurate vegetation map. This study aims to develop a technology to effectively extract vegetation information by analyzing UAV images captured with multispectral and thermal infrared cameras for urban areas including basketball courts and the waterproof coating roofs covered with urethane. Finally, vegetation information analyzed by combining NDVI and thermal infrared images was compared with vegetation information to which only NDVI was applied to evaluate accuracy.
## 3 MLP Neural Network and Training
### Training for Fundamental Harmonic Detection  
In [32], the MLP networks were trained to estimate the amplitude and phase of the main harmonics in distorted waveforms, from order 3 up to order 13. From these variables, the distortion component waveform was generated. This could be employed as compensation current, thus eliminating a part of the distortion. Besides, the patterns and training were based on a fixed 50 Hz fundamental frequency. The use of only few harmonics, and a fixed electric frequency, does not ensure an efficient operation for the typical nonlinear distortions. The ANN showed some potential for this kind of objective, but with limited and poor results. In [33], a comparative analysis was conducted between the application of the Adaline network and the MLP to obtain the reference signals for a shunt APF. The conclusion in that work is that the MLP performance is much lower than that of the Adaline method. However, in our opinion, the problem is with the inappropriate application strategy and training method.  
In this work, the APF control method is based on the sampling of every waveform cycle of the current or voltage for the ANN input. A nominal 50 Hz electric frequency was considered. Nevertheless, the method should also work regardless of which electrical frequency is used. In the case of 50 Hz, after every time interval of 20 ms, the ANN outputs are the rectangular coefficients of the fundamental harmonic.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.9 Wildlife Observation Sensor Node  
The node presented by [28] was mainly developed for wildlife (chimpanzee) monitoring purposes. Regarding processor and memory specifications, a CY8C4248 LQI-BL583 module by Cypress that incorporates a 32-Bit Arm Cortex-M0 CPU with 256 kB of flash and 32 kB of SRAM was exploited by the authors. The module can be interfaced with a 32 GB SD card module for providing extra memory for measured data.  
The CY8C4248 LQI-BL583 module by Cypress that incorporates a Bluetooth low-energy (BLE) module was chosen for communication at 2.4 GHz. The RF output power ranges from \(-18\) dBm to \(+3\) dBm. No sensors were incorporated in the node. Instead, it relies on the received signal strength indicator (RSSI) value to compute the distance between the animals. A 2600 mAh, 3.6 V lithium thinony chloride battery is utilized to supply power to the node. At optimized performance, the BLE module draws a 1.9 mA current and consumes 4.99 mW of power. At 46 MHz, the CPU draws 27.7 mA. When the processor is inactive, it draws 1.7 mA and consumes 1.5 mA of current when it goes to the deep sleep mode.
## 4 Simulation Experiments and Result Analysis  
Network simulator 3 (ns-3) is an open-source network simulation emulator that draws extensively on the successful technologies and experiences of existing excellent open-source network simulators. In addition, ns-3 has an excellent development environment with rich modules and open source code. Therefore, ns-3 can provide a high-performance network simulation environment that is closer to real networks.  
For the paper, ns-3 was installed on a Linux Ubuntu 16.04 system as a simulation experiment environment to build a highly controllable and reusable simulation platform. The paper divides the experiment into two parts: OpenAI Gym and a heterogeneous V2X network model, as shown in Figure 5.  
OpenAI Gym is a toolkit for reinforcement learning (RL) research. The interface integrates the RL framework into the network simulator ns-3, and the package is available open-source under the GPL license [37].  
First, a heterogeneous V2X network topology is built in the network simulation platform ns-3. Then, the heterogeneous V2X network model is constructed as a Gym, and the ICCDRL algorithm is trained using the NS3-Gym interface, which is a toolkit for DRL-based network protocols. The algorithm ICCDRL proposed in the paper is developed in the OpenAI Gym section. In each control time step, OpenAI Gym passes _cwnd_ and _ssthresh_ to the V2X network model, which in turn feeds the updated network state to the OpenAI Gym module.  
ICCDRL was set with a time step of 0.1 s, and a total of 800,000 steps were trained. When the training reached 80,000 steps, ICCDRL had converged, and the reward value obtained at this time tended to be stable.  
Thereafter, the paper designed different simulation experiments to compare the performance of ICCDRL with traditional algorithms such as NewReno, Cubic, and Westwood DRL-based DCC [35] in terms of packet loss rate, throughput, link utilization, delay, and jitter. Furthermore, the convergence, fairness and friendliness tests of ICCDRL are conducted to verify that the intelligent congestion control algorithm based on deep reinforcement learning proposed in the paper has superior performance than NewReno, Cubic and Westwood.
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## 3 Results and Discussion
### Evaluation of In Vivo Hepatoprotective Activity of GPPs
#### 3.5.2 Effects of GPPs on Serum ALT and AST Activities  
Serum ALT and AST enzymatic activities have been regarded as evaluation indicators of liver injury. The levels of serum ALT and AST activities were measured in this work [38]. As shown in Figure 4C,D, serum hepatic enzymes (ALT and AST) were significantly increased in CCl\({}_{4}\)-induced liver injury compared with the control group, whereas compared with the model group, the silymarin group had significantly decreased contents of ALT and AST enzymes. Moreover, it was observed that the 100 mg/mL and 200 mg/mL GPP groups effectively prevented the CCl\({}_{4}\)-induced elevation in serum ALT and AST in a dose-dependent manner. GPP pretreatment at high dose (200 mg/kg body weight) decreased the serum ALT and AST levels to 9.75 \(\pm\) 0.53% and 41.09 \(\pm\) 3.48%, respectively, suggesting that GPPs could ameliorate the effects on liver injury induced by CCl\({}_{4}\).
## 3 Results and Discussion
### Photoelectric Properties Test of the ZnO-Ag-ZnO Interconnection Structure  
Considering that ZnO nanowires have good ultraviolet (UV) photoelectric response characteristics, we tested the photoelectric properties of the ZnO-Ag interconnection structure after soldering to further demonstrate its potential application. Figure 6a illustrates the schematic image of the UV detector based on the ZnO-Ag-ZnO interconnection structure. The I-V test results are shown in Figure 6b. It can be seen that the detection current is small when the UV light is off, and the detection resistance is around 13.5 M\(\Omega\). When the UV light is turned on, the current of both ends is significantly increased, and it increases to 1.47 \(\mu\)A when a bias voltage is 5 V. The detection resistance is around 3.4 M\(\Omega\), and the resistance of the structure is reduced to one-fourth of the original when the lights were off. The results demonstrate that an interconnected structure provides good photoelectric enhancement under 350 nm UV light irradiation. In order to judge the photoelectric performance of the device in a better way, Figure 6c further illustrates the current response of the interconnected ZnO-Ag device upon 350 nm UV light on-off cycles. The test is carried out with a bias voltage of 5 V at both ends. It can be discovered that the dark current is about 0.2 \(\mu\)A, and the photocurrent jumps to about 1.0 \(\mu\)A-1.5 \(\mu\)A. During continuous UV light on-off for 70 s, the photoresponse changes periodically, proving a good reproducibility and stability for interconnection structure. Figure 6d shows the photoelectric response curve of a single cycle from 25 s to 45 s in Figure 6a. The response time of the device to 350 nm light detection (the photocurrent increases from 10% of the peak value to 90%) is 8 s, and the recovery time (the photocurrent changes from 90% of the peak value to 10%) is 10 s. These results show that the ZnO-Ag heterogeneous interconnection structure has a good UV photoelectric response, which provides a novel method for constructing various photoelectric devices through the soldering of nanowires.  
Figure 5: Electrical characterizations of ZnO nanowires and Ag nano-solder interconnection. (**a**) Test electrode of nanowire interconnection structure; (**b**) I\(-\)V electrical test curve before and after soldering.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 5 Future Challenges
### Scalability of AI-Based Methods  
In AI-based methods, while generic multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) have been widely used for wireless resource allocation, there are two well-known technical challenges. First, MLPs and CNNs are more difficult to train in large-scale settings than in small-scale counterparts. For example, as demonstrated in the beamforming problem [59], although the performance of CNNs is near-optimal when trained and tested under a two-user setting, there exists an 18% performance gap to the classic algorithm when trained and tested under a 10-user setting. Secondly, MLPs are designed for a pre-defined problem size with fixed input and output dimensions. In the context of an RIS problem, this means that a well-trained MLP for a particular RIS dimension is not applicable to other settings when the numbers of reflecting elements differ.  
Recent studies have shown that incorporating a permutation equivariance property into the neural network architecture can reduce the parameter space, avoid a large number of unnecessary permuted training samples, and most importantly make the neural network generalizable to different problem scales [60; 61; 62; 63]. In particular, graph neural networks (GNNs) [60; 61] and attention-based transformers [62; 63] have been shown to possess the permutation equivariance property and have demonstrated superior performance, scalability, and generalization ability in wireless resource allocation problems. For instance, in the beamforming problem, a GNN trained with data generated in a setting of 50 users was shown to achieve near optimal testing performance under a much larger setting of 1000 users [60].  
This result simultaneously solves the two challenges mentioned above (difficulty of training in large-scale settings and generalizability to different settings). Interestingly, permutation equivariance also exists in RIS phase shift design problems since exchanging the channels of two reflecting elements should result in a corresponding permutation of the optimized phase shift design. Therefore, it is expected that GNNs and attention-based transformers would be effective neural network architectures for the RIS design problems as well.
# Research on Tracking Technique Based on BPSK-CSK Signals
## 4 Discussion  
A high-precision service has gradually become the standard of GNSS, and high information rate has also become the design requirement of future satellite navigation signals. CSK-modulated signals, which have the advantage of a high information rate, have attracted a lot of attention from scholars, but its BER is higher than that of BPSK-modulated signals and the number of related signals required is more than that of BPSK, whereas BPSK-modulated signals have a low BER, but their low information transmission rate is not enough to meet the high-precision service of GNSS. In addition, in complex urban environments, CSK modulation will also provide better performance than BPSK, which is an orthogonal \(M\)-propagation modulation that allows for non-coherent demodulation. Non-coherent modulation allows the demodulation of CSK signals in harsh environments such as urban or indoor environments, whereas for BPSK signals, demodulation is not possible unless the PLL is locked.  
The BPSK-CSK signal tracking method proposed in this article combines the high information transmission rate of the CSK modulation signal and the low BER of the BPSK signal, and gives a compromise solution, which provides a solid foundation for a high-precision GNSS service. CSK modulation has the advantages of a high transmission rate, flexible bit rate, and non-coherent demodulation, but there are the advantages of the difficult-to-achieve synchronisation process, and the complexity of the receiver is high. The number of correlators is much larger than that of BPSK and so on. Considering the above problems, this paper uses it in the design of GNSS signals. Considering the above problems, it is not feasible to use CSK modulation to completely replace BPSK in a GNSS system, but CSK modulation can be gradually introduced into GNSS systems to achieve the purpose of improving the positioning accuracy and quality of service, and optimising the system performance.  
Y.C.: conceptualization, methodology, software, investigation, formal analysis, writing--original draft; J.C.: data curation, writing--original draft; X.L.: visualization, investigation; X.B.: resources, supervision; Z.N.: conceptualization, resources, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
The datasets used in this study are managed by the Beijing Research Institute of Telemetry, Beijing, China and can be available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Models for PM\({}_{2.5}\) Estimation
Similar to the current results, [154] also found that the parameters with the highest importance in predicting PM\({}_{2.5}\) concentrations are CO, NO\({}_{2}\), SO\({}_{2}\) and AOD. Inclusion of the pollutant gases improved the performance of their RF model from R\({}^{2}\) = 0.69, _RMSE_ = 41.63 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\) to R\({}^{2}\) = 0.81, _RMSE_ = 32.74 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\), in a similar way as in the present study. These results were also in agreement with [65] who found that CO was the most important variable that explained 20.65% of the variation in estimated PM\({}_{2.5}\) concentrations in Xi'an, China, exhibiting a strong correlation with AOD. Furthermore, [81] studied the PM\({}_{2.5}\) composition in Klang Valley, Malaysia, and concluded that CO, NO\({}_{2}\), NO and SO\({}_{2}\) mostly affected the PM\({}_{2.5}\) concentrations. This is the first study conducted in Malaysia aiming to estimate the PM\({}_{2.5}\) concentrations based on machine-learning techniques. The satisfactory accuracy of the estimates, despite the biases and challenges in representing PM\({}_{2.5}\) pollution episodes, is especially important for the development of models aiming to systematically monitor PM\({}_{2.5}\) over the country, especially at remote areas with unavailability of measurements. However, the only 65 operational stations are still insufficient to cover the whole Malaysian territory with an area of 330,290 \(\mathrm{km^{2}}\). Establishing more air quality monitoring stations is very costly, and a certain station is only capable to satisfactorily represent the pollutant concentrations within a radius of about 15 \(\mathrm{km}\)[155]. Alternatively, remote sensing data encourages more studies to be conducted on atmospheric particulates and air quality, since satellite technology considers AOD as a key predictor of PM over a large area [156]. This would also help in evaluating the influence of the local/regional emissions from anthropogenic activities against those attributed to natural causes or long-range transported aerosols, mostly smoke, from Indonesia and other parts in northern Indochina.
## 5 Discussion
### Distribution of the Lobe Complex  
A submarine lobe system is a vital constituent of deep-marine fans. These lobes are characterized by geometries which are quite useful for interpreting the geological processes related to fan deposition [15]. Tectonically active regions are generally characterized by coarse sand units, representing the development of deep-marine fans having less than 10 km radial exposure on relatively higher slope angles, where a fan lobe system is frequently surrounded by shale cover [39].  
These submarine fans are composed of numerous lobe complexes. The lobe architecture consists of a composite hierarchy from a smaller unit of a lobe element to a larger unit, which is termed as a composite lobe system or lobe complex set [22; 24]. The lobe element is essentially comprised of one or more beds, with the thickness extending from a few decimeters to more than a meter [39]. The individual lithologies or beds collectively form an element of a lobe, while the group of lobe elements constitutes an individual lobe [15; 24]. These lobes are characterized by interbedded sandstone and shale, with a collective range in size of several meters in thickness, combined to form a lobe complex (Figure 10) or also termed as stacked composite lobes [15; 24]. Lobe components or sand-rich lobe complexes are separated from each other by a thick to massive hemipelagic to pelagic shale unit [22]. It is pertinent to mention here that each lobe component consists of one or more sandstone or shale beds, which enabled us to characterize the deep-marine deposits at a meter scale level. These high-resolution lithological observations could not be achieved by using seismic data. Hence, the study of lobe architecture at a lobe element scale caters to the idea of subseismic reservoir heterogeneity in submarine lobe-fan systems.  
Figure 9: Thinning and thickening patterns (**a**) marked by three cycles—two thinning and one thickening—each of about a 2 m vertical thickness in the Jalan UMS behind KFC outcrop. (**b**) Massive sand unit overlain by two thickening trends in the Jalan UMS behind KFC outcrop. (**c**) One cycle of thinning with a vertical thickness of only 1 m in the Jalan Sulaman outcrop.  
The results presented in this study reveal that there are multiple feeder channels in West Sabah's deposition, where the number of feeder channels and lobe complexes increased toward northwest Sabah. Multiple feeder channels resulted in three to four lobe complexes (LC), each of which was classified into individual lobes (L) and further into lobe elements (LE) (Figure 11). The thick to massive shale separated the individual lobe complexes.  
Figure 11: Distribution of lobe elements, individual lobes and lobe complexes in the studied outcrop sections [17; 24].  
Figure 10: The distribution of facies, facies associations and thickening and thinning cycles in the exposed sections from Kota Kinabalu to Tuaran in northwest Sabah.