esa-sceva/satcom-chunk-collection huggingface dataset:
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 3 Switched Auto-Regressive Neural Control (S-ANC)
### Hybrid MG Description  
This is a case study of a system constructed in Xanthi, Greece [74]. As depicted in Figure 3, the hybrid MG is comprised of a 15 kW PV array; a battery (BAT), a water tank (WT), and a fuel tank (FT) serving as energy storage systems (ESSs); an electrolyzer (EL); and a fuel cell (FC), as well as the utility grid (GR). The PV can be utilized in the hybrid MG as the primary energy source. If the PV cannot provide sufficient power, the BAT or the FC will meet the load. The GR will provide energy if the battery is depleted and no hydrogen is available. Alternatively, when the BAT is full and there is an excess, the EL will be utilized if there is space in the WT and the FT. The energy will then be sent to the GR.
## 4 Numerical Assessment of Operating Point \(OR=0.05\)/\(Pr=0.20\)
### Lateral Pressure Excitation at 2700 Hz  
For the second dominant frequency at Peak\({}_{HF}\), the distribution of pressure amplitudes illustrated in Figure 9 shows a totally different pattern. Especially in the lower half of the diffuser, the reconstruction at (2700 \(\pm\) 200) Hz shows a pure lateral excitation, which is also visible in the corresponding flow field in Figure 8b.  
Figure 8: Reconstructed flow field from the SPOD analysis (CFD, sensor positions: \(\circ\)): (a) \(M\) 1 + 2, \(\hat{t}\) = (600 \(\pm\) 100) Hz, T = 1/600 s; and (b) \(M\) 1 + 2, \(\hat{t}\) = (2700 \(\pm\) 200) Hz, T = 1/2700 s.  
Figure 7: Reconstructed pressure evolution at 600 Hz (CFD, \(M\) 1 + 2, \(\hat{t}\) = (600 \(\pm\) 100) Hz, T = 1/600 s).  
The analysis of the temporal propagation suggests that an interaction of the first lateral pressure mode and the shock structure causes a resonance. It is expected that the lateral displacement of the strong shock causes a compression of the recirculation zone and temporally increases the downward component of the near wall velocity. Along with a local increase of the pressure, the impingement onto the incoming jet at the diffuser entry causes a stronger deflection from the wall, as indicated by the arrows in Figure 8b. As the jets are further deflected towards the contrary side, the supersonic core gets constricted by strengthened oblique shocks as indicated by black dashed lines. As the lateral component is passed downstream due to the momentum of the incoming jets, the compression of the recirculation zone on the opposite side initiates the contrary deflection.  
Across multiple periods, temporal circumferential propagations of this disturbance occur as visible on the valve head in Figure 9. Similar to the axial excitation, dominating frequencies of the lateral force spectrum of the simulation match the SPOD results and may cause lateral excitations for elastic valve geometries. Even though amplitudes are small, experimental data confirm this assumption and show an increased vibration level at this frequency.
## 4 Discussion
### Combined Use and Analysis of GC-MS and E-Nose  
GC-MS and E-nose were used and analysed in combination to establish associations between sensors and specific flavour substances. Seven sensors, including W5S, W6S, W1S, W1W, W2S, W2W and W3S, were positively correlated with 10 key flavour compounds (1-nonanol, 3-methyl-1-butanol acetate, hexanoic acid ethyl ester, heptanoic acid ethyl ester, octanoic acid ethyl ester, decanoic acid ethyl ester, bencenepropanoic acid ethyl ester, fourful and 1-(2,6,6-trimethyl-1,3-cyclohexadien-1-yl)-2-buten-1-one), indicating that the seven sensors can effectively reflect the strong aroma of SW and DL.  
The association of GC-MS and E-nose needs to be an effective method to detect food flavour, considering that the detection accuracy of GC-MS is high, but it takes a long time, while E-nose detection is fast, but it cannot determine the specific substance. [PERSON] et al. [60] integrated the volatile compounds and electronic nose response values of sugarcane juice under different treatments, and took Euclidean distance as the metric standard to generate the cluster analysis heat map of each index, and found that the response value of S6 may be related to 1-amyl alcohol and 1-octene-3-alcohol, and the response value of S8 and S10 may be related to the content of ethanethiol to a certain extent. [PERSON] et al. [61] established the correlation between volatile compounds in the different dried _Chrysanthemun rankingense_ and the response value of the E-nose sensor. Sensor W1C had a good correlation with C62 (hexyl n-valerate), sensor W5C had a good correlation with C136 (phenanthrene), and sensor W3C had a close correlation with C109 (geranialene). The sensor W1W had high sensitivity to alkenes, and had good correlation with various terpenes, such as C117 (caryophyllene) and C130 (1-isopropyl-4, 7-dimethyl-1,2,3,5,6,8 a-hexahydronaphthalene).
## 4 Discussion  
### Accuracy Evaluation on the Extraction of Walnut Planting Areas in the Research Area  
This study obtained the Sentinel-2 image data set of Ganquan Town from 2017 to 2021 by obtaining a large number of remote sensing image data stored on the GEE cloud platform, using Sentinel-2 image data with a spatial resolution of 10 m. The thesis of this study is to perform image pre-processing, such as cloud removal, inset, and clipping, in order to avoid disturbances such as image loss, cloud cover, etc., and to ensure the quality of remote sensing image data. Previous studies have shown that the random forest algorithm has strong anti-interference and anti-overfitting capabilities. Compared with other machine learning algorithms, it has high accuracy and good effects on crop classification [31; 32; 33]. This study extracts the walnut planting area in Ganquan Town from 2017 to 2021 using the random forest algorithm, and the characteristics of walnut planting are highlighted via the EVI time series curve in image synthesis. When building the feature data set, the thesis combines textural features, terrain features, and vegetation indices NDVI and EVI, reducing the number of mixed pixels of the remote sensing image.  
After adding the enhanced vegetation index (EVI), classification accuracy is improved compared with the accuracy of spectrum + terrain + texture + NDVI. Subdividing orchard types with NDVI alone is difficult, and NDVI can be easily saturated in areas with high vegetation coverage, while the addition of blue-light wavebands to EVI reduces the impact of soil and the atmosphere [34], which is more suitable for monitoring dense vegetation areas. Compared with the NDVI construction feature data of a single vegetation index, the EVI accuracy added to the vegetation index significantly improved; the average overall accuracy increased from 92.522% to 92.828%, and the average Kappa coefficient increased from 89.928% to 90.344%. Compared with the statistical data released in the yearbook, the overall relative error is also small. Although the overall extraction effect of this study is good, there are still years exhibiting large errors in the extracted area. For example, in 2018, the difference between the remote sensing extraction of the planting area in the research area and statistical data is 1800 ha, with an absolute error of 20.4%.
## 5 Evaluation
### Autoencoder Model Training  
#### 5.2.1 Parameter Setting  
As presented in Section 4.2, there are three groups of hyperparameters to be determined: (i) input sequence steps for the sequence-to-sequence model; (ii) a latent dimension for the RNN GRU cells; and (iii) the filter size for the convolutional layer. The number of time steps is set it to five, thus, the model includes measurements from past 5 s. We set such a relatively small number as we intend to make the context relatively stable within the period so that the resulted representation can be better characterized as a stable context. As the loss for our model is the reconstruction error, we evaluate the effect of these parametersthrough reconstruction error, as shown in Figure 5. The latent dimension directly affects the dimension of our representation; there is a trade-off in determining the kernel number. On one hand, a larger latent dimension allows representations to encode more information. On the other hand, a larger latent dimension brings additional storage costs, model training, and application, at later stage. To achieve a balance, we select 60 when the error begins to gradually drop. We set the convolutional filter size as (12,12) so that each patch can capture a sufficient image while achieving acceptable reconstruction performance. We apply an Adam Optimizer and a ReLU activation function during the training procedure.
## 3 Results
### Reading Time  
A two-way repeated measure ANOVA was conducted to investigate the effects of color combination and medium on the reading time. All participants were exposed to each condition, enabling the assessment of both main effects and the interaction between color combination and medium on reading time.  
The analysis revealed a significant main effect of color combination on the dependent variable (\(F(2,\,102)=4.65,\,p<0.05\)), indicating that different color combinations had a significant influence on the participants' reading time (Figure 2). To further investigate these differences, pairwise comparisons were conducted using paired \(t\)-tests, while applying a Bonferroni correction to adjust for multiple comparisons, yielding a \(p\)-value of 0.017. Table 2 presents the results of the post hoc analyses. The results indicated a significant difference in reading time between AA (\(M=28.83,\,SD=8.20\)) and AC (\(M=27.13,\,SD=6.74\)), as well as between AC (\(M=27.13,\,SD=6.74\)) and CC (\(M=28.76,\,SD=7.69\)). However, no statistically significant difference was found between AA and CC, suggesting that these two color combinations resulted in similar outcomes in terms of reading time. Figure 3 shows these results.  
In contrast to the effect of color, there was no significant main effect of the medium on the dependent variable (\(F(1,\,51)=0.45,\,p=0.51\)), suggesting that both the digital (\(M=28.46,\,SD=7.86\)) and print (\(M=28.02,\,SD=7.32\)) presentation modes resulted in similar reading time. Furthermore, the analysis showed no significant interaction effect between color combination and medium (\(F(2,\,102)=0.14,\,p=0.87\)). This lack of interaction indicates that the reading time remained consistent across the print and digital medium  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & \(t\) & \(df\) & **Sig. (2-Tailed)** \\ \hline AA–AC & 2.44 & 103 & 0.016 * \\ AA–CC & 0.13 & 103 & 0.900 \\ AC–CC & \(-3.04\) & 103 & 0.003 * \\ \hline \hline \end{tabular}  
* Represent statistically significant results at the 0.017 level.  
\end{table}
Table 2: Results of paired samples’ \(t\)-Test.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 5 Conclusions  
This paper describes a novel redesign of the Modbus-UDP protocol. It addresses the main shortcomings of previous proposals that layered Modbus on top of a UDP transport. Namely, it reinstates the broadcast communication capability, which was lost as the Modbus protocol evolved towards Modbus-TCP, and introduces an automatic datagram loss detection and recovery mechanism that counteracts the inherently unreliable delivery of UDP datagrams.  
The correct implementation of broadcast communication was confirmed by porting a broadcast-based protocol originally proposed for Modbus-RTU and Modbus-CAN [13] and verifying that it passed its test suite. In addition, the performance of the newly proposed Modbus-UDP design, in terms of throughput and resilience to data link errors, was compared against Modbus-TCP. To guarantee a fair comparison, the two protocols were tested within the same Modbus master-slave protocol stacks [28, 29], on the same testbed, and with the same application test programs.  
The experimental results, validated by suitable statistical hypothesis tests, show that Modbus-UDP achieves a mean round-trip time of only 38% with respect to Modbus-TCP and exhibits 50% of its standard deviation when subject to a 1% two-way IP datagram loss probability. Those results, combined with the lower overhead of UDP versus TCP well documented in literature, make the redesigned Modbus-UDP protocol better suited for a variety of IIoT systems with limited computing and communication resources.  
Possible areas of future work include the design and implementation of a Modbus-UDP \(\leftrightarrow\) Modbus-RTU gateway along the lines described in [23] and making use of the version of Modbus-UDP proposed here, as well as the adaptation of the security enhancement techniques originally designed for CAN to Modbus-UDP. Moreover, a pilot study aimed at replacing Modbus-CAN with Modbus-UDP in a real-world industrial application is currently ongoing.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The author declares no conflicts of interest.  
Figure 11: Modbus-UDP transmit (**a**) and receive (**b**) path overhead in the Modbus Master protocol stack as a function of the kind of transaction and number of registers \(N\), \(n\) = 10,000 samples.
# Dependence of Dispersion on Metamaterial Structural Parameters and Dispersion Management
## 2 Theory of Light Pulse Propagations in MMs
According to the definition of the index of refraction \(n=\sqrt{\varepsilon_{r}\mu_{r}}\), we can deduce the \(i\)th order dispersion as follows [19]:  
\[\beta_{1}=\frac{dk}{d\omega}=\frac{1}{c}\Bigg{[}n+\frac{1}{n}\Bigg{(}\frac{1+ \overline{\omega}_{p}{}^{2}}{\overline{\omega}^{2}}-\frac{2\overline{\omega}_ {p}{}^{2}}{\overline{\omega}^{4}}\Bigg{)}\Bigg{]}, \tag{6}\]  
\[\beta_{2}=\frac{d\beta_{1}}{d\omega}=\frac{1}{c\omega_{pe}}\Bigg{[}\frac{1}{n \overline{\omega}}\Bigg{(}-\frac{1}{\overline{\omega}^{2}}-\frac{\overline{ \omega}_{p}{}^{2}}{\overline{\omega}^{2}}+6\frac{\overline{\omega}_{p}{}^{2} }{\overline{\omega}^{4}}\Bigg{)}-\frac{1}{n^{3}\overline{\omega}}\Bigg{(} \frac{1+\overline{\omega}_{p}{}^{2}}{\overline{\omega}^{2}}-\frac{2\overline {\omega}_{p}{}^{2}}{\overline{\omega}^{4}}\Bigg{)}^{2}\Bigg{]}, \tag{7}\]  
\[\beta_{3}=\frac{d\beta_{2}}{d\omega}=\frac{1}{c\omega_{pe}}\Bigg{\{}\begin{array}[ ]{l}3\Bigg{[}\frac{1}{n}\Bigg{(}-3\frac{1}{\overline{\omega}^{4}}-3\frac{ \overline{\omega}_{p}{}^{2}}{\overline{\omega}^{4}}+10\frac{\overline{\omega} _{p}{}^{2}}{\overline{\omega}^{0}}\Bigg{)}-\frac{1}{n^{3}}\Bigg{(}\frac{1+ \overline{\omega}_{p}{}^{2}}{\overline{\omega}^{3}}-\frac{2\overline{\omega} _{p}{}^{2}}{\overline{\omega}^{0}}\Bigg{)}^{2}\Bigg{]}\\ +\overline{\omega}\Bigg{[} \begin{array}{l}\frac{1}{n}\frac{12\overline{\omega }_{p}{}^{2}\overline{\omega}^{2}-60\overline{\omega}_{p}{}^{2}+12\overline{ \omega}^{2}}{\overline{\omega}^{0}}+\frac{3}{n^{5}}\Bigg{(}\frac{\overline{ \omega}_{p}{}^{2}\overline{\omega}^{2}-2\overline{\omega}_{p}{}^{2}+\overline{ \omega}^{2}}{\overline{\omega}^{0}}\Bigg{)}^{3}\\ +\frac{3}{n^{3}}\Bigg{(}\frac{\overline{\omega}_{p}{}^{2}\overline{\omega}^{2} -2\overline{\omega}_{p}{}^{2}+\overline{\omega}^{2}}{\overline{\omega}^{0}} \Bigg{)}\Bigg{(}\frac{3\overline{\omega}_{p}{}^{2}\overline{\omega}^{2}-10 \overline{\omega}_{p}{}^{2}+3\overline{\omega}^{2}}{\overline{\omega}^{0}} \Bigg{)}\end{array} \Bigg{]}\Bigg{\}}, \tag{8}\]  
\[\beta_{4}=\frac{d\beta_{3}}{d\omega}=\frac{1}{c\omega_{pe}}^{3} \tag{9}\]  
where \(\overline{\omega}=\frac{\omega}{\omega_{pe}}\) and \(\overline{\omega}_{p}=\frac{\omega_{pm}}{\omega_{pe}}\) are the normalized light frequency and plasma frequency. The third-order nonlinear coefficient can also be simplified as follows:  
\[\Gamma=\frac{\chi^{(3)}\overline{\omega}\omega_{pe}(1-\frac{\overline{\omega }_{p}{}^{2}}{\overline{\omega}^{2}})}{2 nc}. \tag{10}\]
# Machine Learning-Assisted Secure Random Communication System
## 5 Reception
### Legitimate Receiver 'LR'
#### 5.2.1 DT-Based Classifier  
A cart-based classification is performed by utilizing a pretrained DT that has been trained on an \(\alpha\)-stable noise signal dataset (\(\alpha\)-SNS\(\mathcal{D}\)) solely known to the T and LR. The \(\alpha\)-SNS\(\mathcal{D}\) used for training the DT consists of a random carrier signal and channel settings. The target variable to be predicted is the skewness parameter of the \(\alpha\)-stable noise carrier. The utilized \(\alpha\)-SNS\(\mathcal{D}\) is expressed as  
\[\mathcal{D}=\{(\alpha,\;\beta,\;\text{N},\;\text{MSNR}_{\text{dB}},\;\text{y} \;)\} \tag{9}\]  
where \(\alpha\in\{0.6,\;0.8,\;1.1\}\), \(\beta\in\{-1,\;-0.9,\;-0.8,\;0.8,\;0.9,\;1\}\), and N \(\in\{15,\;25,\;35\}\), as defined in Table 2. The MSNR\({}_{\text{dB}}\in[-10,\;5]\) is defined in Table 3. The '\(y\)' is the \(\alpha\)-stable noise sample generated via the specified combination of \(\alpha\), \(\beta\), N, and MSNR\({}_{\text{dB}}\). Each unique combination in (9) produces a different '\(y\)' each time, so 100 unique '\(y\)' values are taken to train the DT for a single combination. The DT-based classifier is trained on an \(\alpha\)-SNS\(\mathcal{D}\) to predict the target parameter \(\hat{\beta}\). The classifier learns to predict the output as follows:  
\[\hat{\beta}_{t}=\;f(\alpha,\;\text{N},\;\text{MSNR}_{\text{dB}},\;y_{t}) \tag{10}\]
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 1 Introduction  
### From Communication System Layer to Artificial Neural Network Layer  
Communication systems have been rapidly evolving over the last two decades. While the provided data rates are in the order of 10 kbps in the second-generation Global System for Mobile Communication (2G/GSM), the new standard 5G is supporting more than 10 Gbps, and the expected rate is increasing towards 1 Tbps in beyond 5G and 6G-era.  
The traditional principle for designing such a digital baseband wireless system can be divided into two steps:  
1. Partitioning of the processing blocks at the transmitter and receiver into different subcomponents, e.g., source coding, channel coding, and modulation;
2. Optimizing each subcomponent individually for a channel model while considering the application requirements.  
Although it is known how to optimally configure each component separately, this configuration does not assure the ideal communication performance of the joint system [1]. In addition, the channel model is not able to perfectly emulate all properties of a real-world channel due to model imperfections and hardware impairments. Therefore, a degradation of the performance in real channel conditions is witnessed. To mitigate this effect, ANN-based methods have gained a lot of attention in the field of communication in recent years [2; 3; 4; 5]. The challenge is to have highly functional communication systems competing with the system-level performance of the conventional ones and overcoming the need for prior mathematical modeling of the channel.  
The AE approach, where parts of the classical transmitter and receiver are replaced by artificial neural networks (ANNs), has delivered promising results [6; 7; 8; 9; 10]. Since the AE is designed as an end-to-end system, all components can be jointly trained to optimize the global system instead of adjusting each block individually; thus, the sub-optimal performance of the classical approach introduced by (1) can be resolved. Furthermore, ANNs provide the functionality to compensate for imperfections of the channel model and hardware impairments by retraining the network during runtime to adapt for the actual channel conditions, which tackles the drawbacks of (2) in the conventional system design.
## 2 Materials and Methods
### Flux Tower Observation Data  
We collected GPP dataset from three flux stations to detect its relationship with SIF, NDVI, and kNDVI as a benchmark for their response to vegetation production under water stress condition.  
1. Dinghushan Subtropical Evergreen Broadleaf Forest Flux Observation Station (112\({}^{\circ}\)32\({}^{\circ}\)3.8\({}^{\prime\prime}\)E, 23\({}^{\circ}\)10\({}^{\prime}\)24\({}^{\prime\prime}\)N), located within the CERN Dinghushan Forest Ecosystem Reaction in the low mountainous hills of Zhaoqing, Guangdong. It is situated at an elevation range of 14-1000 m, offering unique insights into subtropical evergreen broadleaf forest ecosystems.
2. Qianyanzhou Artificial Forest Flux Observation Station (115\({}^{\circ}\)03\({}^{\prime}\)29.2\({}^{\prime\prime}\)E, 26\({}^{\circ}\)44\({}^{\prime}\)29.1\({}^{\prime\prime}\)N), is part of the Qianyanzhou Red Soil Hilly Agriculture Comprehensive Development Experimental Station that is a member station of the Chinese Ecological Research Network. The station is located on a slope ranging from 2.8\({}^{\circ}\) to 13.5\({}^{\circ}\) and is surrounded by forest cover over 90%. It provides valuable data on artificial forest ecosystems.
3. Xishuangbanna Tropical Rainforest Flux Observation Station (101\({}^{\circ}\)15\({}^{\circ}\)55\({}^{\prime}\)E, 21\({}^{\circ}\)55\({}^{\prime}\)39\({}^{\prime}\)N), is located in the southern part of Yunnan Province, in the Xishuangbanna Dai Autonomous Prefecture. In this station, we collected the flux data from the flux observation system within a 'one-hectare sample plot' in a tropical rainforest.  
The three flux tower sites provide a comprehensive and diverse range of data. They are pivotal for the study as they cover a broad spectrum of ecosystems, from subtropical broadleaf forests to tropical rainforests. This diverse range allows for a more nuanced understanding of carbon dynamics across different ecosystems in southern China.