esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Datasets and Experiments  
(1) Dataset IIa [98] from BCI Competition IV, which includes EEG signals from nine subjects, includes four classes of imagery: left hand, right hand, foot, and tongue motor imagery (MI). Twenty-two electrodes on the scalp were used to record EEG signals for this dataset. For our experiments, EEG signals of the left- and right-hand MI were considered. All training (72 trials) and testing (72 trials) sets are available for each subject. Our experiment used 280 trials. We performed 10-10-fold cross-validation in the experiments. This dataset was used for all four proposed models. Figure 17 shows the locations of the selected electrodes on the scalp over the brain in IIa from BCI Competition IV.  
Figure 16: Deep formula detection with root detection classifier structure.  
(2) Dataset IVa [99] from BCI Competition III [100], which includes EEG signals from five subjects, includes right hand and foot motor imagery (MI). One hundred and eighteen electrodes were used for recording. All training (140 trials) and testing (140 trials) sets were available for each participant.  
We applied 10-10-fold cross-validation for training. Integrated training and validation data were considered for training and testing. The entire integrated dataset was randomly divided into ten folds. Each fold was applied as test data once, and the rest were used as training data. The procedure was repeated ten times, and the averages of the results were considered the final results. We used a Butterworth filter of the 100 th order for filtering. We restricted our experiments to the data between 0.5 s before the visual cue and 3.0 s after the visual cue. Therefore, we used 3.5 s of data, and each second included 250 features (points). In total, all of the input features of each trial for one electrode amounted to 875 features.
## 3 A Prototype System for Pedestrian Positioning
### Electronic Module
#### 3.1.1 6 DOF Sensor
Any offset in acceleration readouts grows quadratically with the time. This quickly leads to aggregation of errors and renders the analytical approach ineffective after a dozen of seconds. Figure 2: A picture of the built electronic module. The black box is a 6 DOF sensor—3-axial gyroscope and 3-axial accelerometer. The orange cuboid is an antenna of a GPS receiver. The module is connected with the PC through a mini-USB socket.
## 2 Materials and Methods
### Datasets
#### 2.2.3 ERA5-Land (ERA-5)  
ERA5-Land (hereafter ERA-5) is a fifth-generation reanalysis dataset, which is developed through global high-resolution numerical integrations of the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model. The model incorporates historical meteorological records by data assimilation techniques to maximize the consistency of the model output with real-world measures. This reanalysis dataset is a specialized version of the ERA5 dataset focusing on land surface variables. The main advantage of ERA-5 relative to ERA-Interim and fifth-generation ERA5 is the horizontal resolution, which is enhanced to 9 km compared to ERA5 (31 km) and ERA-Interim (80 km), where the temporal resolution is the same [35]. ERA-5 produces a total of 50 variables that cover the water and energy cycles over land globally with the same higher spatial (9 km) and temporal resolution (hourly). It contains detailed data from 1950 onward, with a grid size of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\)[13]. The precipitation data comprise rain and snow that fall to the surface and it do not include dew, fog, and precipitation that evaporate before landing on the surface of the Earth. In the current study, the monthly per day mean precipitation was acquired at the 00:00 time from [[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)) (accessed on 20 July 2024) and converted to the total monthly precipitation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**S. No** & **Stations** & **Longitude** & **Latitude** & **Elevation (m)** & **S. No** & **Stations** & **Longitude** & **Latitude** & **Elevation (m)** \\ \hline S11. & D.I.Khan & 70.920 & 31.820 & 171 & S33. & Mithi & 69.800 & 24.750 & 30 \\ S12. & Kakul & 73.250 & 34.180 & 21 & S34. & Chhor & 69.776 & 25.518 & 5 \\ S13. & Islamabad & 73.080 & 33.700 & 55 & S35. & Lasbella & 66.170 & 26.270 & 87 \\ S14. & Jhelum & 73.730 & 32.930 & 56 & S36. & Pasni & 63.480 & 25.270 & 9 \\ S15. & Sargodha & 72.670 & 32.050 & 187 & S37. & Jiwani & 61.800 & 25.070 & 56 \\ S16. & Lahore & 74.400 & 31.520 & 283 & S38. & Pangirur & 64.100 & 26.970 & 968 \\ S17. & Bahawalnagar & 73.330 & 29.970 & 161 & S39. & Nokkundi & 62.750 & 28.820 & 2015 \\ S18. & Multan & 71.430 & 30.200 & 122 & S40. & Dalbandin & 64.400 & 28.880 & 848 \\ S19. & Khanpur & 70.680 & 28.650 & 88 & S41. & Kalat & 66.580 & 29.030 & 2015 \\ S20. & Sukkur & 69.450 & 28.133 & 68 & S42. & Quetta & 66.880 & 30.250 & 1719 \\ S21. & Jacobabad & 68.467 & 28.300 & 287 & S43. & Zhob & 69.470 & 31.350 & 1405 \\ S22. & Rohri & 68.900 & 27.667 & 66 & & & & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
## 5 Comparison and Discussion  
Considering the characteristics highlighted in the previous sections and summarized in Table 1, we want to highlight which technology could be considered a good choice in a social environment, such as Facebook. Principally, in a traditional blockchain network, every computer attempts to process every transaction, which can be very slow. The average confirmation time for a Bitcoin transaction is over ten minutes ([[https://www.statista.com/statistics/793539/bitcoin-transactionconfirmation-time/](https://www.statista.com/statistics/793539/bitcoin-transactionconfirmation-time/)]([https://www.statista.com/statistics/793539/bitcoin-transactionconfirmation-time/](https://www.statista.com/statistics/793539/bitcoin-transactionconfirmation-time/)), accessed on 1 September 2021), and it takes an average of 15 s for a transaction to be verified on the public Ethereum blockchain ([[https://ethgasstation.info/blog/ethereum-transaction-how-long/](https://ethgasstation.info/blog/ethereum-transaction-how-long/)]([https://ethgasstation.info/blog/ethereum-transaction-how-long/](https://ethgasstation.info/blog/ethereum-transaction-how-long/)), accessed on 1 September 2021). Our question is as follows: how can current blockchains be used in the case of Facebook, where over 50,000 Facebook 'Likes' happen every single second of the day? Every 'Like' is an individual data transaction that the system has to process. In order to be able to manage platforms such as Facebook, the main challenges to face in a social environment, and by taking into account our research question, are scalability and transaction fees. Scalability is the ability to allow more users to join the network while the chain grows in length. This is an ongoing issue with most blockchain models currently in use. A scalable blockchain solution needs to allow transactions to occur at a much higher speed. Another issue is the transaction fee on blockchain networks.  
The five analyzed blockchains have different characteristics. Steem and Hive are born to be social, instead of the other three, which are general blockchain with the possibility to build a DApp. Ethereum is the most popular blockchain in terms of social DApps, as shown in Figure 2. The alternative is now represented by EOSIO. At the time of writing, Klaytn did not provide any important characteristics, which can be considered in a real social scenario. For this reason, in the rest of the section, we analyze Steem and Hive as examples of social blockchains, and consider Ethereum and EOSIO, by taking into account both the scalability and transaction fees challenges, which are the main challenges considered before, to be a good choice.
## 3 Prediction of Manned Aircraft Trajectory in Terminal Areas
### Evaluation Metrics  
The average Euclidean distance error (EDE), which represents the distance between the actual and predicted position of the manned aircraft, is used to evaluate the performance of the constructed model. The actual positions are taken from the latitude, longitude, and altitude of the trajectories after coordinate transformation. The equation is shown as follows.  
\[EDE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\sqrt{{{{\left({{\overline{x_{i}}-{x_{i} }}\right)}^{2}}}}+{{\left({{\overline{y_{i}}}-{y_{i}}}\right)}^{2}}+{{\left({ {\overline{z_{i}}}-{z_{i}}}\right)}^{2}}}}} \tag{3}\]  
Among them, \(n\) is the number of predicted samples; \(\overline{x_{i}}\) and \(x_{i}\) are the predicted and the actual longitude of the aircraft at the time \(i\); \(\overline{y_{i}}\) and \(y_{i}\) are the predicted and the actual  
Figure 1: The GRU architecture for aircraft trajectory prediction.  
latitude of the aircraft at the time \(i\); \(\overline{z_{i}}\) and \(z_{i}\) are the predicted and the actual altitude of the aircraft at the time \(i\), respectively.
## 2 Composition of Resource Scheduling Module  
To implement the proposed hybrid scheduling framework, it is essential to first understand how the radar resource scheduling system is structurally organized. This section outlines the key components of the phased array radar scheduling module, which form the basis for the method and algorithm proposed later.  
The resource scheduling function of phased array radar usually consists of three parts: \"task planning\", \"priority assignment\", and \"task scheduling\", as shown in Figure 1.
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods  
### Related Works  
In [7], the authors introduced an IoT application using a WSN distributed over a large geographic area in which the sensor nodes use LoRa technology. Their communication performance analysis was based on varying parameters related to the LoRa physical layer, such as the bandwidth (BW) and scattering factor (SF).  
Performance comparisons of different routing protocols have been presented in [8; 9; 10; 11; 12] using simulation tools and metrics such as the packet delivery rate, average latency, average jitter, and throughput. Simulations were carried out under various scenarios, such as different node densities and variations in terms of mobility.  
Routing protocols and LoRa networks have previously been detailed in [13; 14; 15]. In [13], a routing system protocol based on the AODV protocol was proposed for use in meshed LoRa networks. In [14], the development of a hybrid network based on a LoRa mesh topology and the LoRaWAN protocol was introduced. An emergency communication system using a mesh LoRa network and implementing a modified version of the AODV protocol was presented in [15] along with an evaluation of the system feasibility according to the package delivery rate.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
###### Abstract  
In regions where transportation and the economy are closely integrated, optimizing network structure and enhancing synergy are vital for regional integration. This paper constructs a dual-factor linkage network using enterprise investment and liner shipping data to analyze linkage strength and synergy effects among cities in the Greater Bay Area. The findings reveal that (1) a core-periphery structure exists, with core cities dominating resource flows while secondary cities remain weak. The logistics network is led by Hong Kong and Shenzhen, while the capital flow network showcases the dominance of Hong Kong, Shenzhen, and Guangzhou. (2) From 2016 to 2021, interactions between transportation and the economy deepened, showing strong correlations in logistics and capital flows among core cities and between core and edge cities, but weaker correlations with sub-core and edge cities. Core cities stabilize regional transportation and economy, fostering agglomeration, while sub-core cities are more reliant on them, indicating a need for better resource balance. (3) The spatio-temporal coupling analysis reveals significant heterogeneity in flows among cities, with many exhibiting antagonistic couplings outside core areas. This study enhances understanding of synergy mechanisms in transportation and economic networks, offering insights for optimizing layouts and improving capital flow efficiency.  
Guangdong; Hong Kong and Macao Greater Bay Area; logistics network; capital flow network; factor flow and allocation; spatial equilibrium 2020
# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
## 3 Proposed Method
### Proposed Retinex Model  
As mentioned in Section 3.2, the following alternating minimization method [8] is used to solve Equation (9). The basic pseudo-code of the proposed CSC Retinex model is as follows and it is represented by Algorithm 1.  
```
1:Initialize \(j=0\), and let \(l^{0}=s\) be the initial illumination function
2:At the \(k\)th iteration:
3:Given \(l^{j}\), compute \(r^{j+\frac{1}{2}}\) by solving \(\min_{r,d,z}E_{1}(r,d,z)=\frac{1}{2}||r-\sum_{k=1}^{K}d_{k}*z_{k}||_{2}^{2}+ \beta\sum_{k=1}^{K}||z_{k}||_{1}+\frac{\eta}{2}||l^{j}-s-r||_{2}^{2}\)
4:And update \(r^{j+1}\) by using \(r^{j+1}=\max\{r^{j+\frac{1}{2}},\ 0\}\)
5:Given \(r^{j+1}\), compute \(l^{j+\frac{1}{2}}\) by solving \(\min_{l}E_{2}(l)=\frac{\alpha}{2}||\
abla l||_{2}^{2}+\frac{\eta}{2}||l-s-r^{ j}||_{2}^{2}\)
6:And update \(l^{j+1}\) by using \(l^{j+1}=\max\{l^{j+\frac{1}{2}},\ s\}\)
7:Go back to step 2 until \(\frac{||l^{j+1}-r||}{||l^{j+1}||}\leq\epsilon_{l}\) and \(\frac{||r^{j+1}-r^{j}||}{||r^{j+1}||}\leq\epsilon_{r}\)
```  
**Algorithm 1**Basic Pseudo-Code for Proposed CSC Retinex Model.
## 5 Experiment Results
### Comparative Algorithms  
To address the multi-satellite collaboration and network resource optimization problem, this study selects five comparative algorithms for performance evaluation. These algorithms include Adaptive Batch Inductive Optimization (ABIO) [38], Mutation Quantum Particle Swarm Optimization (QPSO) [39], Differential Evolution with Local Search (DE) [40], Adaptive Genetic Algorithm (AGA-ES) [41], and Adaptive Temperature Simulated Annealing (TSA) [42]. Specifically, ABIO has a time complexity of \(O(T\cdot B\cdot D)\), QPSO has a time complexity of \(O(T\times M\times D)\), DE has a time complexity of \(O(T\cdot N\cdot D)\), AGA-ES has a time complexity of \(O(P\cdot G\cdot N)+O(N^{2})\), and TSA has a time complexity of \(O(K\cdot nt)\) when the number of iterations per time scale is approximately equal, and \(O(K\cdot n_{0}\cdot m^{T})\) when the batch size increases exponentially with the time scale. These algorithms demonstrate excellent convergence and adaptability in solving network optimization problems, effectively balancing global and local searches in dynamic environments.  
Specifically, ABIO [38] was selected for its efficient handling of batch processing and inductive reasoning capabilities, enabling effective resource allocation under fluctuating network conditions with a time complexity of \(O(T\cdot B\cdot D)\), where \(T\) is the number of iterations, \(B\) is the adaptive batch size, and \(D\) is the problem dimension (i.e., the number of variables in the optimization problem). QPSO [39] was chosen due to its enhanced exploration capabilities through quantum mutation strategies, which help to avoid premature convergence in complex optimization landscapes, with a time complexity of \(O(T\times M\times D)\), where \(T\) is the number of iterations, \(M\) is the number of particles, and \(D\) is the problem dimension. DE [40] employs a hybrid approach that combines Differential Evolution with local search, ensuring a balanced exploration and exploitation of the solution space, and has a time complexity of \(O(T\cdot N\cdot D)\), where \(T\) is the number of iterations, \(N\) is the population size, and \(D\) is the problem dimension. AGA-ES [41] exhibits high adaptability by dynamically adjusting mutation and crossover rates, maintaining high performance in heterogeneous satellite environments, with a time complexity of \(O(P\cdot G\cdot N)+O(N^{2})\), where \(P\) is the population size, \(G\) is the number of generations, and \(N\) is the population size. Finally, TSA [42] improves the balance between exploration and exploitation through an adaptive temperature schedule, enhancing task offloading decisions in dynamic network scenarios. Its time complexity is \(O(K\cdot nt)\) when the number of iterations per time scale is approximately equal, and \(O(K\cdot n_{0}\cdot m^{T})\) when the batch size increases exponentially with the time scale, where \(K\) is the number of time scales, \(n_{0}\) is the initial batch size, \(m\) is the batch size growth factor, and \(T\) is the total number of external time scales.
# Impact of Environmental Factors on Stilbene Biosynthesis
## 3 Impact of Environmental Factors on the Biosynthesis of Stilbenes
### UV Radiation
Regarding UV-B, several studies showed that these wavelengths can stimulate the biosynthesis of polyphenols, including stilbenes [184; 199; 200; 258]. UV-B triggers a radiation-specific signaling pathway in grape skin, which activates the biosynthesis and accumulation of secondary metabolites [259]. A UV-B treatment was found to elicit a significant increase in stilbene production, although less marked than that achieved with UV-C [260]. In 2008, [PERSON] and co-workers [258] analyzed the stilbene content in berry skins collected from plants of _V. vinifera_ cv. Malbec cultivated under sunlight with full UV-B (+UV-B) or filteredUV-B (\(-\)UV-B) in three different locations at 500, 1000, and 1500 m above sea level (asl) and observed that different solar UV-B levels affect the accumulation of _trans_-resveratrol. The highest resveratrol content was detected in the berry skins from the +UV-B treatment at 1500 m asl, where the difference between +UV-B and \(-\)UV-B was statistically significant. Studies on ozone-treated Scots pine also showed a slight increase in stilbene synthase mRNA, as well as in pinosylvin and pinosylvin methyl ether contents under exposure to UV-B light [261; 262]. Post-harvest treatment with UV-B and UV-C has been widely used in fruit and vegetable storage [199; 263], where it delays fruit ripening and senescence [264], and activates the defenses against pathogens [265]. Post-harvest UV treatment has been exploited in grapes to increase the content in phenolic compounds including stilbenes in berries and wines [199] (Table 1). As previously mentioned, the impact of UV-A on stilbene biosynthesis has been underestimated, however recent studies suggest that this radiation deserves more attention. An increase in stilbene content related to UV-A irradiation has been observed in _Eucalyptus nitten_ (H. Deane and Maiden) [PERSON] by [PERSON] and colleagues [266]. The authors postulated that, since stilbenes have absorbance properties consistent with a function as UV-A screens, they could be part of an active UV-A response. This conclusion is consistent with the observation that adult retinal pigment epithelial cells treated with resveratrol show higher viability when exposed to UV-A [267]. A role of UV-A in stilbene biosynthesis was observed in leaves of grey alter (_Alnus incana_ (L.) [PERSON]) and white birch (_Betula pubescens_ [PERSON]) trees under field conditions [268]. It was recently observed that _trans_-resveratrol and _trans_-pterostilbene biosynthesis in leaves of O'Neal high bush blueberries (_Vaccinium corymbosum_ L.) can be enhanced by irradiation with both UV-A and UV-C [269]. The comparison of the two UV-treatments showed that UV-A is more effective in promoting _trans_-resveratrol production, while UV-C is better in enhancing _trans_-pterostilbene production.
# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 4 Simulation and Analysis
### Simulation of Fast Planning
Compared with the RRT and Bias-RRT algorithms, the APS-RRT planner has a significant improvement in terms of the running time, reducing 81.9% and 79.7%, respectively; the APS-RRT planner also reduces the average number of nodes by 82.6% and 76.1%, respectively. In Map3, the RRT algorithm has a success rate of 96%, the Bias-RRT algorithm has a success rate of 88%, and the APS-RRT planner maintains a success rate of 100%. Map4 represents the environment of narrow passages and contains many obstacles. The narrow passage environment poses a problem for sampling-based algorithms. This is because the probability of the samples falling within the passage is very low. The planning success rate of the RRT algorithm and the Bias-RRT algorithm drop significantly in Map4. In comparison, the APS-RRT planner maintains a high planning success rate, reflecting the performance of the proposed algorithm in the specific environment. Compared with the RRT and Bias-RRT algorithms, the APS-RRT planner reduces the average time cost by 50% and 46.9%, respectively, and the average number of nodes is reduced by 64% and 53.4%, respectively. In addition, the proposed APS-RRT has lower standard deviations. It is clear that the proposed algorithm can maintain stability and efficiency in different environments. Figure 7: From left to right in each row are the path planning results of the RRT, Bias-RRT, RRT* and APSD-RRT algorithms. The blue lines reflect the growth status of the search tree, and the red lines represent the generated path. The dark blue lines in (**d**) illustrate the optimized path.