esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 3 Results and Discussion
### Model Performance Comparison
#### 3.4.3 Comparison of Model Convergence  
The rate of model convergence can be judged by the degree of decline in the loss function, which in turn measures the efficiency of a model's learning. The change curves of the loss functions of the seven models are shown in Figure 10.  
As observed in Figure 10, the R50A3-LWBENet, SER34 AUNet, and MU-Net model losses are able to drop to near 0.1 in the first 100 iterations, and in the subsequent iterations, the losses flatten out, and the model saturates. Among them, R50A3-LWBENet has the lowest loss value. In contrast, the loss of the four models U-Net, AU-Net, RU-Net, and ARU-Net can only drop to near 0.2, which is much higher. In addition, through the number of 500 iterations, the R50A3-LWBENet model loss decreases faster than the rest of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Evaluation Metric**} \\ \cline{2-5}  & **PA** & **MPA** & **MIoU** & **Training Time per Epoch (s)** \\ \hline U-Net & 97.4 & 97.5 & 94.8 & 50 \\ AU-Net & 97.7 & 97.65 & 95.3 & 120 \\ RU-Net & 93.9 & 93.15 & 88 & 300 \\ ARU-Net & 98.3 & 98.4 & 96.5 & 280 \\ SER34 AUNet & 98.5 & 98.6 & 97 & 80 \\ MU-Net & 98.6 & 98.7 & 97.2 & 145 \\ R50A3-LWBENet & 98.8 & 98.85 & 97.6 & 82 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of model performance evaluation metrics.  
Figure 10: Loss function values for seven models.  
model losses and decreases to its lowest value faster, which in turn leads to a faster model convergence than the other six models.  
In summary, compared with U-Net, AU-Net, RU-Net, ARU-Net, SER34 AUNet, and MU-Net models, our proposed R50A3-LWBENet model is able to exclude the influence of complex terrain and various surrounding objects, and has the ability of higher accuracy, higher generalization ability, and faster learning of remote sensing images of lake water bodies.
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Application of Available Excuation Software to Wildfire Evacuation
#### 6.5.2 Lack of Wildfire-Specific Data  
While the software packages are versatile, they may not have access to wildfire-specific data necessary for accurate simulations. The availability of detailed and real-time wildfire data, such as fire progression maps, weather conditions, and fuel types, is crucial for accurately modeling the impact of wildfires on evacuation processes. The challenge lies in obtaining and integrating such data into the models, especially during rapidly evolving wildfire events.
# Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia
## 1 Introduction
The GOSIF dataset demonstrated better performance than MODIS GPP in evaluating GPP [42]. In addition, the SIF GPP captures changes in photosynthesis induced by hydrothermal stress better than the vegetation index, especially compared to GOME-2 SIF, and the finer spatial and temporal resolution of the GOSIF data allows for good monitoring of the crop's drought response [43]. Therefore, GOSIF GPP data were selected in this study to analyze the space and time changes in vegetation. Numerous outstanding drought indices have been developed and commercialized for drought surveillance, including the Standardized Precipitation Index (SPI), the Reclamation Drought Index (RDI), the Drought Severity Index (DSI), the Standardized Drought Index (SDI), and the Standardized Precipitation Evapotranspiration Index (SPEI), etc. [44; 45; 46; 47; 48]. However, only the SPEI and SPI are commonly utilized for drought expression on multiple time scales [49; 50; 51]. Compared to the SPI, the SPEI considers the key contribution of temperature change in drought monitoring and is inferred from precipitation and temperatures in the formalism of water balance [52]. Therefore, the SPEI was chosen to represent drought conditions in this study. There has never been a unique standard for the demarcation of the borders of Central Asia [53; 54]. Since Xinjiang, China, shares similar climatic characteristics with the five Central Asian countries, Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan, they are collectively referred to as Central Asia in this study [55; 56]. Central Asia is the biggest non-territorial drought area in the world, with temperatures rising higher than the global mean, a lack and uneven distribution of water resources, an extremely vulnerable ecological environment, and an extremely sensitive response to climate change [57; 58; 59]. Systematic evaluation of the lagged and cumulative effects of drought on vegetation GPP can contribute vital references for alleviating the impacts of drought on vegetation productivity in Central Asia. The objectives of this study were to (1) analyze the time and space patterns of vegetation GPP and the SPEI in Central Asia, (2) characterize the spatial and temporal responses of vegetation GPP to drought-lagged and cumulative effects in Central Asia, and (3) determine whether the main impacts of drought on vegetation are lagged or cumulative effects. In this study, the correlation coefficients of GPP and the SPEI were used as an essential parameter to analyze the response of vegetation GPP to drought between 2000 and 2018 in Central Asia.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 3 Methodology
### Establishment of AR Exhibition-Viewing System  
To create a natural exhibition-viewing experience for our study participants, we put the developed AR app on the Apple App Store and Google Play. Its primary features include scanning and positioning frames, two user modes, and the uploading of behavioral data to a cloud.  
The innovation of our system lies not just in the application of the technology itself but in how these technologies are innovatively integrated into the interactive and participatory experiences of exhibitions. By leveraging AR technology, we offer visitors a novel way to interact, simultaneously investigating how such interactions influence visitor behavior patterns within the exhibition.  
Particularly, our AR system utilizes the precise positioning capabilities of the AR Foundation, but our innovation extends beyond this. We further developed two main modes for the system, a passive exhibition-viewing mode and an interactive message mode that allows visitors to leave messages in the 3D space of the exhibition. This application and expansion of existing technology enable deeper interaction with the artworks for visitors, introducing a new method of participation in the cultural exhibition domain. The specifics of these functions are outlined below.
## 2 Data and Methods
### Data and Data Preprocessing  
The GPP, ET, and meteorological data during 2003-2005 are from the FLUXNET2015 dataset ([[http://fluxnet.fluxdata.org](http://fluxnet.fluxdata.org)]([http://fluxnet.fluxdata.org](http://fluxnet.fluxdata.org)), accessed on 1 July 2022). Both sites have data from 2003 to 2005. Meteorological data include shortwave radiation (SR), air temperature (T\({}_{\mathrm{a}}\)), vapor pressure deficit (VPD), precipitation (P), and soil water content (SWC).  
To reduce the impact of noise in the data, we filtered the data as follows [2; 25]. First, only measured and high-quality gap-filled data were selected. Second, to reduce the influence of surface evapotranspiration on ET in the growing season and to minimize the impact of seasonality in leaf area index (LAI), only summer daytime data (6 a.m. to 6 p.m. in June, July, and August) were chosen, and the data on rainy days and the day after the rainy days were excluded. Since the LAI of the two forest stations both exceeded 3.5, the filtered ET value included a large portion of transpiration (T). Third, to avoid the boundary layer stability, we selected the data when the shortwave radiation was larger than 50 W m\({}^{-2}\) and the sensible heat flux was larger than 5 W m\({}^{-2}\).
## 4 Discussion
_ltrA_ serves as a stress response protease that helps in degrading proteins that are damaged due to environmental stresses, especially high temperature, and also helps against oxidative damage [68]. Furthermore, exopolysaccharideacharide-producing genes were represented in our study. _epsF_ had biological functions of exopolysaccharideacharide biosynthesis glycosyltransferase, while _exoF_ had the biological function of exopolysaccharide production protein precursor. These EPS genes have shown evidence that they are responsible for biofilm formation. These plant-associated biofilms have biological functions such as protection against numerous environmental stresses, which include pH changes, osmotic shock, desiccation, and salinity [69]. Other important functions include a reduction in microbial competition and protecting the host plant, thereby increasing the growth and yield of the crop [69]. In a study by [PERSON] et al. [70], it was shown that biofilm formation was increased with increasing salt concentration, which explains why it is most useful during salinity stress. Another notable gene represented in our study is the _plzF_ encoding gene, which is responsible for phenazine synthesis. The _plzF_ proteins are implicated in the production of phenazine derivative antibiotic and antifungal compounds against bacteria and fungi [71]. For instance, phenazines produced in _Pseudomonas_ are involved in the biocontrol of many diseases, including soil-borne pathogens [72]. In addition, we discovered genes responsible for stress alleviation from our metagenomic samples of sunflower rhizosphere microbiome origin. Genes _cspABCDEFG_, _groES_, and _groEL_, which are responsible for cold shock and heat shock protein production, were observed in our study. These results were in accordance with a study by [PERSON] and [PERSON] [61] where these genes were responsible for cold and heat shock response. These genes come into play during drought periods when water is a problem. Drought is a major limiting factor in agricultural production, and a plant is said to undergo drought stress either due to difficulty in the water supply to the root or when the transpiration rate becomes extremely high [73]. In addition, chaperones encoding the gene for heat shock are not only expressed when the temperature is high but also in response to other environmental stresses such as salinity and osmotic water, cold, and oxidative stress [74].
## 4 Model Implementation  
### Background  
In this study, the methodology that was developed was then validated using a project case study in Babil Governorate, Iraq. The network of the project is shown in Figure 6, which depicts activities as letters (A--excavation, B--foundation, C--brick construction, D--exterior plumbing, E--electrical works, F--roofing, G--interior installation, H--cement wall coating, I--interior painting, J--flooring, K--external wall construction, L--exterior painting, M--interior fixtures, and N--exterior fixtures). The critical activities in the project were A, B, C, D, G, H, I, and M. Each activity had five execution modes to choose from, as indicated in Tables 2 and 3. The time and cost of each option are represented using triangular fuzzy numbers. The first and third correspond to the maximum and minimum values, respectively, for time and cost, and the second is the most probable estimate for both parameters. In order to choose the best alternatives from a set of non-dominant solutions, the SUM is used as a decision-making tool to identify the optimal time-cost solution. We evaluate the capability and efficiency of the NSGA-II method developed in this research by comparing the obtained results with those of the actual project.  
Figure 6: Project network.  
The following modifications were implemented in the case study to address the problem in a resource-constrained environment:  
* The resources required for each activity were computed using Equation (13) and are shown in Table 4, with the resource cost approximated at USD 250 per day.
* The project parameters listed in Table 5 were used, with the costs of releasing and acquiring estimated based on project manager experience and prevailing market prices. Required resources = (cost of activity)/(duration activity \(\times\) USD 250 per day) (13)  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Activity** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** & **K** & **L** & **M** & **N** \\ \hline Mode 1 & 7 & 9 & 6 & 9 & 6 & 8 & 6 & 7 & 6 & 8 & 6 & 8 & 5 & 4 \\ Mode 2 & 4 & 8 & 6 & 8 & 5 & 7 & 6 & 7 & 5 & 6 & 5 & 5 & 3 & 3 \\ Mode 3 & 2 & 7 & 5 & 7 & 4 & 5 & 4 & 6 & 4 & 5 & 4 & 4 & 2 & 2 \\ Mode 4 & 1 & 7 & 4 & 6 & 3 & 4 & 3 & 4 & 4 & 4 & 3 & 2 & 1 & 1 \\ Mode 5 & 1 & 6 & 3 & 5 & 2 & 3 & 2 & 3 & 3 & 3 & 1 & 2 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Implementation modes for each activity (resources).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Duration (Optimistic, Most Likely, Pessimistic) (Days)** & \\ \hline
**Activity** & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** & **Mode 5** \\ \hline A & (8, 9, 10) & (11, 12, 13) & (15, 16, 17) & (18, 19, 20) & (21, 22, 23) \\ B & (41, 42, 43) & (44, 45, 46) & (48, 49, 50) & (52, 53, 5
## 2 Technical Design of the Device
### Continuously Variable Transmission (CVT) Efficiency  
The redesigned CVT (Figure 3a) is precisely able to produce a rotary acceleration in the output ring (\(\alpha_{\mathrm{out}}\)) in a seamless manner. This is because an input angular acceleration occurs in the input ring (\(\alpha_{\mathrm{in}}\)) due to the primary structure's deformation. Regarding the configuration of the CVT core, a stator, as well as an idler sun with the same centrality, are surrounded by a casted shell housing as depicted in Figure 3b. In this scheme, the idler sun is capable of rotating and translationally moving relative to the stator. Furthermore, through steel balls enclosed between the idler sun and a rotor with a two-way inward slope on its surface, the rotor can spin relative to the idler. The planets with a planetary arrangement are situated on the surface of the rotor while the shell housing surrounds all the planets. Moreover, two input rings and output rings were positioned in touch with the surfaces of the planets in a location where they are visible (see Figure 3a). In addition, a metal appendage was placed around CVT as shown in Figure 3a, which is able to spin by utilizing a motor shaft. This slight spin of the appendage leads the idler sun to linearly move relative to the stator. The idler sun's translational motion causes all axles of the planets to slant together, therefore, the planets' angles are adjustable. Consequently, bidirectional spinning of metal appendage causes the CVT ratio to adjust into overdrive (\(\phi>1\)) or underdrive (\(\phi<1\)) modes, as displayed in Figure 3c. An external battery can also be supplied to the motor shaft. A controller sends a control signal based on the received frequency of the excitation and then the motor begins to operate.  
The ratio between the rotary motion of the flywheel and the screw's translational motion due to the inter-story drift can be adjusted through the transmission ratio of the CVT (\(\phi=\frac{\alpha_{\mathrm{out}}}{\alpha_{\mathrm{in}}}\)). The CVT core possesses the maximum ratio in the underdrive status (\(\phi<1\)) at 0.45 while the ratio in the overdrive status (\(\phi>1\)) is 2.2. In the overdrive status, the distance between the surface of the output ring and the planet's axle is more than that of the input ring (\(\mathrm{r_{in}}<\mathrm{r_{out}}\)). The CVT ratio range (0.45-2.2) can be broadened by modifying the design of the CVT core and its constituents. Therefore, the performance of the AID can be enhanced. Eventually, the operation of the CVT in overdrive or underdrive mode  
Figure 2: Symbolic representation and principal components of adaptive inertance damper (AID).  
enables the device to attain the variable inertia resistance force. It happens due to the alteration of the CVT ratio within its range (0.45 \(<\)\(\phi\) \(<\) 2.2).
## 3 Distributed Mission Scheduling Model
### Convergence Analysis for DMSA  
In this section, we present theoretical analysis on convergence for DMSA. The analysis is conducted in two steps. Firstly, the existence of Nash Equilibrium should be demonstrated. It is well known that every game with a finite number of players and action profiles has at least one Nash equilibrium [39]. In a game model of DEOS, it is clear that the numbers of satellites and scheduling plans are finite, so a Nash Equilibrium must exist.  
Secondly, we should prove that DMSA will stably converge to an optimal solution from any initial plan. According to the distributed optimization strategy proposed in this paper, when the inter-satellite communication is unblocked, a consensus can be reached among different satellites, and the optimization results of satellites in each round of the game are independent of each other. Therefore, after \(t\) rounds of gaming, the payoff \(F\) of the entire system is  
\[F=\sum\limits_{i=1}^{N}U_{i}^{t}(a_{i}) \tag{17}\]  
From the perspective of formula (17) alone, it seems that it only needs to ensure that each individual's payoff \(U_{i}^{t}\) is the current optimal to guarantee the overall global optimal performance. In fact, due to the \"short-sighted\" characteristics, each player can only obtain a local optimal solution based on the current partial information, while the accumulation of the local optimal does not necessarily represent the system-level optimal. Therefore, this article adopts the greedy rule, so that after rounds of gaming, each player will compare its \(U_{i}^{t}\) with previous action \(a_{i}^{t-1^{\prime}}\)s payoff and always select one with larger payoff as new action \(a_{i}^{t}\). Consequently, the payoff of each player must be \"non-decreasing\", namely,  
\[\begin{split}&\forall i\in\{1,\ \ldots,n\},\forall t\\ & U_{i}^{t}(a_{i}^{t})\geq U_{i}^{t-1}(a_{i}^{t-1})\end{split} \tag{18}\]  
From the definition of scheduling payoff, it can be seen that the total payoff in a scenario is upper bounded. Along with the existence of Nash equilibrium, through each iteration of \"non-decreasing\" execution, as long as the number of iterations can be guaranteed, the distributed optimization will be stabilized. In other words,'stability' means that all the individual payoffs will remain unchanged for the remaining rounds of gaming, therefore leading to a system-level balance. This is also the inspiration for setting a memory table for each satellite; the length of \(L\) should be set based on the overall requirements of scheduling: when \(L\) is small, coordination can be reached with shorter computation time, but scheduling effects rely heavily on the initial targets allocation; when \(L\) is large, a more satisfactory solution can be obtained with higher computation cost; when \(L\) is infinitely large, a Nash Equilibrium must be reached. To conclude, by setting a proper \(L\) in real world application, DMSA can be balanced between efficiency and effectiveness, the scheduling solution can stably converge to a local optimum, or even the global optimum.
# Semantic Communication on Digital Wireless Communication Systems
###### Abstract  
Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure compatibility with existing wireless systems. Specifically, a series of physical layer processing modules in end-to-end transmission are designed. Additionally, we develop a semantic communication simulator to implement and evaluate this framework. To optimize the performance of this framework, we introduce a novel physical layer metric, termed Integer Error Rate (IER), which provides a more suitable evaluation criterion for semantic communication compared to the conventional bit error rate (BER). On the basis of the IER, a minimum Manhattan distance constellation mapping scheme is proposed, which can improve the transmission quality of semantic communication under the same BER condition. Furthermore, we propose a hybrid joint source-channel coding (JSCC) and separate source-channel coding (SSCC) transmission scheme. This scheme decouples the semantic quantization output from the modulation order by segmenting the bits to be transmitted. Simulation results demonstrate that the hybrid JSCC/SSCC transmission scheme can improve the semantic performance, such as the Peak Signal-to-Noise Ratio (PSNR), in low Signal-to-Noise Ratio (SNR) environments while reducing bandwidth usage by up to 50% compared to the benchmark scheme.  
s 2025  
16 January 2025  
Revised: 25 February 2025  
Accepted: 25 February 2025  
Published: 27 February 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Semantic Communication on Digital Wireless Communication Systems. _Electrois_**2025, 14, 956. [[https://doi.org/10.3390/electronics1480596](https://doi.org/10.3390/electronics1480596)]([https://doi.org/10.3390/electronics1480596](https://doi.org/10.3390/electronics1480596))
## 3 Controller Design
### Transition Peocess Based on TD  
We hope the output speed \(\omega\) of system to track the step reference signal \(\omega\)' as fast as possible, so a large error proportional gain is generally used. However, increasing the error proportional gain will make the closed-loop system become an under-damped and overshoot occurs, which is a contradiction between rapidity and overshoot. In fact, any system has inertia, the output speed \(\omega\) can only change from zero, but the reference signal \(\omega\)' is a step signal. Therefore, the initial value of error \(e\) = \(\omega\)' at \(t\) = 0 s. Large error proportional gain can cause a large initial impact on the system, which make the system easy overshoot. If the initial error can be reduced, a large error proportional can be used to speed up the transition process. Therefore, a suitable transition process is arranged according to the reference signal \(\omega\)', and the output \(\omega\) tracks the transition process to reach the control target. In this paper a tracking-differentiator (TD) from ADRC is used as the transition process.  
\[\left\{\begin{array}{l}H_{1}(k+1)=H_{1}(k)+hH_{2}(k)\\ H_{2}(k+1)=H_{2}(k)+h(fst(H_{1}(k),H_{2}(k),\omega^{*},r,h))\end{array}\right. \tag{24}\]  
\[\left\{\begin{array}{l}d=rh\\ d_{0}=hd\\ y=H_{1}+hH_{2}\\ a_{0}=\sqrt{d^{2}+8r|y|}\\ a=\left\{\begin{array}{l}H_{2}+\frac{(a_{0}-d)}{2}sign(y),\\ H_{2}+\frac{y}{r},\\ fst=-\left\{\begin{array}{l}rsign(a),\\ r\frac{d}{d},\end{array}\right.\end{array}\right.\begin{array}{l}|y|>d_{0} \\ |y|\leq d_{0}\end{array}\right. \tag{25}\]  
where \(h\) is the integration time step, \(r\) is the speed factor that determines the tracking speed, \(sign()\) is symbolic function, \(H_{1}\) is the transition process of reference signal, \(H_{2}\) is the differential of transition process.  
Finally, the feedforward-based PMSM speed regulation system is shown in Figure 3.