esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Hybrid power systems based on renewable energy sources and diesel generators are efficient solutions for supplying electricity to remote and off-grid locations. One of the most crucial problems in hybrid power systems is frequency regulation, which is established by balancing the supplied power with the load demand using the load frequency control approach. Since most feedback signals are analog and the control setups are digital, the resulting control system is a sampled-data system, which requires careful designs for both the control law and the sampling frequency to guarantee closed-loop stability. This paper is concerned with the state-feedback load frequency regulation for hybrid wind-diesel power systems under event-triggered implementation. It is assumed that the full state measurement is available for feedback and that sensors and controllers communicate over a shared digital network. To mitigate the communication load on the network, an event-triggering mechanism is constructed by emulation, based on the time-regularization principle in the sense that each consecutive triggering instant is speared by a specified minimum dwell time. The closed-loop system is described as a hybrid dynamical system to account for mixed dynamical behaviors naturally arising in networked control systems. By means of appropriate Lyapunov functions, the closed-loop stability is ensured under the proposed triggering rule. Moreover, the enforced dwell time between transmissions ensures that the accumulation of sampling times is prevented, which is crucial for the event-triggering condition to be implementable in practice. The required conditions to apply this technique are derived in terms of a linear matrix inequality. Numerical simulations on an isolated hybrid power system were implemented to demonstrate the efficiency of the proposed method. Comparative simulations with relevant techniques in the literature were carried out, which showed that the proposed approach can produce fewer transmission numbers over the network.  
eurm-triggered control; renewable energy; wind turbine; networked control systems Article
## 3 Case Study
### Optimization Results of a Typical Spring Day  
To validate the effectiveness of the proposed control strategy, a simulation analysis is conducted with a deviation factor of \(\beta=0.05\). Furthermore, a traditional robust optimization method is selected for comparison with the approach proposed in this paper and the simulation results are presented in Figures 4-6.  
Based on the observations from Figures 4 and 5, it is evident that during the initial 33 time intervals the industrial park primarily relies on purchasing electricity from the upper grid to meet its power deficit. This is because the time-of-use tariff during this period corresponds to the valley tariff, which is significantly lower than the costs associated with ES and MT operation. As the time-of-use tariff transitions to the peak tariff, the power deficit of the industrial park is primarily met through the utilization of ES and MT, resulting in a sharp drop in the state of charge (SOC) of the ES. During the flat time-of-use tariff period, the industrial park utilizes purchased electricity to charge the ES, enabling peak tariff reductions and cost reductions for the park.  
Comparing the optimization results of Figure 5a,b, it can be observed that the output of the unit utilizing the robust method is higher than that of the proposed approach but it is less economically efficient.  
Figure 4: Upper grid and MT output optimization results of a typical spring day.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\beta\) & **0.01** & **0.02** & **0.03** & **0.04** & **0.05** \\ \hline Distributed ES & 55,862,4533 & 56,415,6233 & 56,968,7177 & 57,521,8120 & 58,074,9064 \\ Centralized ES & 56,247,3610 & 56,803,1515 & 57,361,1702 & 57,918,0747 & 58,474,9794 \\ \hline \end{tabular}
\end{table}
Table 5: Different types of ES simulation on a typical summer day (CNY).  
From Figure 6, it can be concluded that in addition to purchasing electricity from the upper grid to meet their own power shortage, the three microgrids will also exchange power, thereby reducing operating costs. The power shortage of the third microgrid is large. When the time-of-use tariff is the peak-time tariff, the power shortage is shared by the grid and the other two interconnected microgrids which reflects the complementary characteristics of the interconnected microgrid energy.  
In summary, the method proposed in this paper demonstrates both economic and robust characteristics. The optimized ES operation strategy exhibits a smoother performance and better adaptability to the operational control of the industrial park. These findings validate the effectiveness of the proposed control strategy in achieving economic and reliable operation of the industrial park.
# Material Flow Optimization as a Tool for Improving Logistics Processes in the Company
## 3 Methodology
The proposed solution is based on an analysis of the current state and the identification of optimization opportunities within existing logistics constraints. While advanced technological solutions, such as automated warehouses, may offer long-term benefits, their implementation requires high investment costs and an extended adaptation period. In contrast, the proposed solution, which focuses on optimizing internal transport processes and improving material flow organization, offers quick and practically feasible improvements without requiring significant capital expenditures. Although alternative analytical and technological approaches may provide additional optimization possibilities, the proposed methodology represents a balanced solution between accuracy, applicability, and financial efficiency. Its key advantage lies in its ease of implementation, direct usability for business decision making, and the ability to quickly respond to identified inefficiencies in material flow.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 5 Experimental Results
### Experiment 3: Two Degrees and Variable Load  
This third experiment assessed the system's response in a scenario that includes three different loads: 76, 121, and 197 g. In the test, the user flexes the elbow and shoulder until both are approximately 45\({}^{\circ}\). We increase the load at each execution to identify the error caused by the progression of the loads. Figure 14 shows how the system responds to the different loads applied. The shoulder and elbow errors varied little, and the average error variation was 0.294 for the shoulder and 0.355 for the elbow. The small variation shows that the impedance control is efficient in compensating the loads in the POC.  
Figure 12: Graph of the trajectory of the elbow and shoulder joint test. The solid blue line is the values read by the gyroscope, and the dashed orange line is the value computed by the control.  
Figure 13: Analysis of the absolute and normalized error of the control of two degrees of freedom.
## 2 Data and Methods
### Methods
#### 2.2.1 Model for Prediction
(1) Linear kernel function:  
\[K\left(x_{i},x_{j}\right)=x_{i}^{T}x_{j} \tag{12}\]  
(2) Sigmoid kernel function:  
\[K\left(x_{i},x_{j}\right)=\tanh\left(vx_{i}^{T}x_{j}+c\right) \tag{13}\]  
(3) Gaussian kernel function:  
\[K\left(x_{i},x_{j}\right)=\exp\left(-\frac{\left|x_{i}-x_{j}\right|^{2}}{2 \sigma^{2}}\right) \tag{14}\]  
(4) Polynomial kernel functions:\[K\big{(}x_{i},x_{j}\big{)}=\left(x_{i}^{T}x_{j}+c\right)^{d} \tag{15}\]  
The Gaussian kernel function performs best when compared to other kernels in terms of prediction error rate and rejection rate (i.e., correct prediction rate). Therefore, we chose the Gaussian kernel function as the kernel function of the SVM model in this study. The structure of the SVM model is schematically shown in Figure 3.  
According to [PERSON] et al. (2018), when Gaussian kernel function is chosen as the kernel function, the parameter \(g=1/2\sigma^{2}\) that comes with the function has an impact on the speed of training and prediction. Specifically, the smaller the value of \(g\), the more the number of support vectors. The franker the training period, the lower the training accuracy, and the worse the model prediction; the larger the value of \(g\), the fewer the number of support vectors, the easier the model is to over-train. The larger the value of \(C\), the lower the tolerance to error and the easier it is to overfit, while the smaller the value of \(C\), the higher the tolerance to error and the easier it is to underfit. Therefore, it is important to determine the appropriate penalty parameter \(C\) and kernel function \(g\). In this paper, the penalty factor and kernel function parameters in the support vector machine are optimally selected using a genetic algorithm, due to the advantages of simplicity, access, global parallelism, and good resistance to interference.
###### Abstract
Thirdly, the proposed model was applied to a case study to derive scheduling results, which were then analyzed. Lastly, the model was validated through in-depth discussions with experts to assess its excellence from various aspects. The proposed model was designed to yield detailed scheduling results for earthwork activities directly usable at the construction site by inputting the planned workloads per activity. Furthermore, the proposed model can minimize the additional work, extra labor input, rework, plan changes, and associated increases in costs and time owing to the gap between the site and the plan by providing appropriate scheduling at the planning stage.
## 4 Conclusions  
The successful deposition of Co-doped (CZO) and Co/Al-co-doped (CAZO) ZnO thin films using the electrospray method is demonstrated. All films have granular morphology with homogenous particle distribution on the surface, and no cracks are observed. Cobalt doping leads to a substantial decrease (more than 200%) in grain size from 143 nm in the case of pure ZnO to 61 nm and 71 nm for CZO and CAZO films, respectively, and an increase in crystallite size from 22 nm to 29 nm in the (101) direction. No additional peaks due to dopant impurities or dopant oxides are observed at a 5% doping concentration. All the films are transparent in the visible and near-infrared spectral ranges. The characteristic absorption peaks of Co\({}^{2+}\) ions assigned to charge transfers between donor and acceptor ionization levels within the band gap of the ZnO host are clearly distinguishable for both the CZO and CAZO films. The refractive index increases with doping from 1.86 for pure ZnO to 2.00 and 1.97 for CZO and CAZO films, respectively, at a wavelength of 600 nm.  
A widening of the optical band gap by 40-60 meV after doping is observed and attributed to the Burstein-Moss effect. The structural disorder judged from the calculated Urbach tails is greatest in the CZO films (236 meV), while the values for CAZO and pure ZnO are 138 meV and 119 meV, respectively. High-quality thin films of pure and doped ZnO films with comparable properties to these obtained by other means can be produced using the electrospray method without postdeposition annealing, using nontoxic and inexpensive precursors and solvents. This opens up the possibility of film applications in optoelectronics, spintronics and photocatalysis.  
Conceptualization, G.M. and T.B.; methodology, G.M. and T.B.; software, T.B. and B.G.; validation, G.M., B.G. and T.B.; formal analysis, B.G. and M.V.; investigation, M.V., B.G. and G.M.; resources, G.M. and T.B.; data curation, G.M. and T.B.; writing--original draft preparation, G.M., B.G. and T.B.; writing--review and editing, T.B.; visualization, T.B., B.G. and G.M.; supervision, T.B.; project administration, T.B.; funding acquisition, T.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Bulgarian National Science Fund under the project KII-06-H38/7 (05.12.2019). T.B. and M.V. acknowledge the partial support of the National Center of Mechatronics and Clean Technologies, BG05M2 OP001-1.001-0008-C01, supported by the European Regional Development Fund within the Operational Programme \"Science and Education for Smart Growth 2014-2020\".  
Not applicable.  
Acknowledgements. Research equipment of the Distributed Research Infrastructure INFRAMAT, part of the Bulgarian National Roadmap for Research Infrastructures supported by the Bulgarian Ministry of Education and Science, was used in this investigation. The authors declare no conflict of interest. References
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 3 Methodology  
The Long Short-Term Memory (LSTM) model is a common method for passenger flow prediction. The [PERSON] model has good performance for time series prediction. However, URT passenger flow has temporal and spatial characteristics, and external factors will also affect the prediction accuracy. Therefore, the TCN model can be used to effectively capture the temporal and spatial information of passenger flow while maintaining the causal convolution characteristics. Based on this, this methodology combines TCN and [PERSON] to construct a TCN-LSTM combined model.
## 2 Study Area, Data and Methodology
### Grid Processing  
The grid processing mode divided the study area into a regular grid matrix of the same size and used the grid as a statistical unit in the geospatial analysis. The mode replaced the regular square grid as the spatial statistical unit of the ESEV and ESV instead of irregular land use patches, ensuring capacity invariance in the unit, not only highlighting the spatial position distribution characteristics of the ESV but also facilitating the spatialized quantitative statistics of the ESV.  
In this work, the study area was divided into 200 \(\times\) 200 m, 500 \(\times\) 500 m, 1 km \(\times\) 1 km, 2 km \(\times\) 1 km and 4 km \(\times\) 4 km square grids by the equal space sampling method. Spatial overlap computing was carried out by using the grid matrix and land use data for each study year to calculate the area of various land use types in each grid in each study year and calculate the total ESEV of each grid. The spatial distribution of ESEV and the ecosystem service capacity for each study year were evaluated from the spatial overlap analysis.
# FASTSET: A Fast Data Structure for the Representation of Sets of Integers
## 4 Computational Experiments  
In this section, we report on some computational experiments (performed on Intel(r) CoreTM i3 CPU M 350 @ 2.27 GHz with 2.8 GB of RAM) in which we have compared the performance of FASTSETs to that of other set data structures included in the standard library of the Java distribution. In particular, Java provides three classes implementing sets via different data structures, namely (i) BitSet[8] implementing the Bitmap data structure; (ii) TreeSet[9] implementing a set as a self-balancing tree with logarithmic cost for all main operations; (iii) HashSet[10] implementing a set by a hash table. We have coded the class FASTSET in Java and have run a first set of experiments in which we have performed a large number of random operations (such as insertions and removals of random elements) for various values of \(N\). The results are listed in Table 2. From the table it appears that FASTSET and BitSet perform very similarly with respect to single-element operations, and they are both better that the other two data structures. It should be remarked that in actual implementations such as this one, Bitmaps are very effective at these type of operations, since they can exploit the speed of some low-level instructions (such as logical operators) for accessing the individual bits of a word. When we turn to listing all elements, however, FASTSETs outperform the other implementations. In particular, a GetAll after 50,000 random insertion on a FASTSET is from 10 up to 30 times faster than for the other data structures. In a second run of experiments, we have considered the two simple combinatorial algorithms described in Section 3, which make heavy use of sets during their execution. The pseudocode listed in Algorithms 1 and 2 refers to an implementation using FASTSETs. The algorithms were translated in equivalent algorithms using BitSet, TreeSet and HashSet in place of FASTSET. The translation was made so as to be as fair and accurate as possible. This was easy for most operations, since they translate directly into equivalent operations on the other data structures. Other operations, such as using GetElem() in a loop to access all the elements of a FASTSET, were realized by calling the _iterator_ methods that the Java classes provide, and which are optimized to make sequential access to all the elements of each structure. For both Vertex Cover and Max-Cut the tests were run on random graphs of \(n\) vertices and an average of \(p(\frac{n}{2})\) edges, where each edge has probability \(p\) of being in the graph. We have used \(n\in\{1000,3000,5000\}\) and \(p\in\{0.001,0.005,0.01\}\), so that the largest graphs have 5000 nodes and about \(125,000\) edges. For each pair \((n,p)\) we have generated 5 instances and computed the algorithms average running time. The results are reported in Table 3 for Vertex Cover and Table 4 for Max-Cut. For both problems, the implementation using FASTSETs was the fastest. In particular, on the Vertex
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
###### Abstract  
The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the automatic identification system (AIS). However, small vessels can be challenging targets for spaceborne sensors without relatively high resolution. Moreover, when faced with non-cooperative targets, hull detection alone is insufficient for obtaining critical information like target speed and heading. The wakes generated by the movement of ships can be used to solve both of these issues. Several interesting solutions have been developed over the years, based on both traditional and learning-based methodologies. This review aims to provide the first thorough overview of ship wake detection solutions, highlighting the key ideas behind traditional applications, then covering more innovative applications based on deep learning (DL), to serve as a solid starting point for present and future researchers interested in the field.  
ship wakes; wake detection; maritime surveillance; multi-spectral; SAR; deep learning 2024 rightsrej
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 5 Performance Evaluation
### Visual Analysis  
To provide a more intuitive comparison of performance differences in emergency communication network deployment between the proposed model and the greedy strategy, we conducted a visual comparative analysis of deployment results generated by both algorithms. To ensure experimental comparability, both groups adopted identical parameters, including microwave direction, disaster center, optical cable access point, on-site command center, and deployment terrain.  
As illustrated in Figure 6, the two strategies demonstrate significant differences. The greedy strategy focuses solely on optimizing individual deployment elements, leading to a fragmented distribution pattern of deployed components. This over-dispersed layout not only increases inter-node connectivity costs but also substantially raises the complexity of network topology construction. In contrast, reinforcement learning demonstrates advantages in holistic network performance optimization. By sacrificing partial local optimality of 5G and microwave nodes, it achieves lower overall deployment difficulty and a more rational topology structure. This trade-off strategy effectively balances conflicting requirements between network efficiency and deployment feasibility. The comparative analysis validates the systematic optimization capability of the proposed model in complex scenarios, with its decision-making pattern better aligning with the practical emergency communication network deployment principle of \"global optimality prioritized over local optimality\".  
In summary, based on all experimental results, the combination of the DDQN algorithm and ResNet architecture demonstrates superior decision-making performance in this task. This hybrid approach enables more efficient and stable completion of dynamic emergency communication resource deployment tasks while exhibiting stronger practical application potential.  
Figure 5: Training results of different methods: (**a**) Comparison results of loss functions. (**b**) Comparison results of reward functions.
# Research on Vehicle-to-Vehicle MIMO Wireless Channels in Various Tunnels
## 1 Introduction
### Main Contributions  
To solve the aforementioned research problems, we propose a geometry-based channel model for describing the V2V communications in practical tunnel scenarios, as shown in Figure 1. It is worth mentioning that the proposed channel model is for sub 6 GHz, whichis mainly for V2V communications [25; 26]. The contributions of this article are summarized as follows:  
* We propose a 3D geometry-based stochastic scattering channel model for V2V communications in tunnel scenarios. The impacts of the typical physical properties of the propagation scenarios on the V2V channel characteristics in the time, space, and frequency domains are derived and investigated. The simulation results show that the proposed channel model has the ability to describe the practical V2V communication scenarios.
* In the proposed channel model, the time-varying parameters of the propagation paths and angles are derived to characterize the channel non-stationarity in the time domain. Furthermore, we consider the impacts of the road sections and moving time/directions/speeds of the transmitter and receiver on the correlation properties of different propagation links.
* We derive the correlation property functions of the proposed V2V channel model in the time, space, and frequency domains, which are, respectively, the space cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency CCFs, as well as the Doppler power spectrum distributions (PSDs). Furthermore, we provide the numerous simulation results of the propagation characteristic for different physical properties of the tunnel, as well as the different motion properties of the transmitter and receiver.
* The proposed algorithm for V2V channel modeling considers the balance between generality and complexity. To be specific, when we properly adjust the model parameters, such as the Rician factor and motion time/velocities/directions of the transmitter and receiver, the proposed channel model has the ability to characterize the main propagation characteristics for V2V communications in tunnel scenarios, which shows the generality of the proposed V2V channel model.  
The rest of this paper is organized as follows: In Section 2, we describe the system channel model for V2V communications in tunnel scenarios. In Section 3, the physical properties of the proposed propagation channel model for V2V tunnel communication scenarios are discussed. In Section 4, we derive the correlation property functions of the proposed V2V channel model in the time, space, and frequency domains, such as the space CCFs, temporal ACFs, frequency CCFs, as well as the Doppler PSDs. In Section 5, we give the simulation and theoretical results of the propagation characteristics of the proposed V2V channel model for tunnel scenarios. Finally, a summary and outlook are given in Section 6.