esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Control Strategy for Maximum Power Extraction
### States Estimation via High Gain Differentiator  
State estimation is the process that deals with estimating the internal states of a real system with its perspective outputs. This process also involves the reduction of chattering effect from the input signal. The method used for states estimation in this paper is High Gain Differentiator (HGD). HGD is quite useful in estimating the derivatives of the states \((x_{1},x_{2},x_{3},I_{pv})\) of the given PV system. The equations for HGD [42] to estimate derivative of the states of the system are  
\[y_{1}=y_{2}+\frac{\alpha_{1}}{e}(x_{1}-y_{1}) \tag{44}\]  
\[\dot{y}_{2}=\frac{\alpha_{2}}{e^{2}}(x_{1}-y_{1}) \tag{45}\]  
where \(0<e<1\) and \(\alpha_{1}\), \(\alpha_{2}\) are gains of HGD.  
The HGD technique used for estimating the derivatives of the system states is used further by invoking flatness property for recovery of the unknown states.  
Definition 1: A flat system is defined as the system whose states/parameters/inputs can be expressed in terms of the flat outputs and their derivatives, For example  
\[x_{j}=\Phi(x_{i},\dot{x}_{i},\dots,x_{k},\dot{x}_{k},\dots,u,\dot{u},\dots,u_{ r}) \tag{46}\]  
where \(x_{i},\dot{x}_{i},x_{k},\dot{x}_{k},u,\dot{u},u_{r}\) are the states and the derivatives of the system's variables.  
In general, the measurement of \(x_{2}\) is not available, therefore, using HGD and the flatness property, one can estimate the value of \(x_{2}\) as  
\[x_{2(e)}=-\frac{C_{1}}{u}(x_{1}-\frac{I_{pv}}{C_{1}}) \tag{47}\]
# Portable Prototype of Hydrogen Fuel Cells for Educational Training
## 5 Conclusions  
This paper presents an experimental prototype suitable for the education of Electrical engineering students. The portable educational prototype is capable of a simple recording of V-I characteristics of different connections of fuel cells (serial and parallel). Furthermore, the prototype can help obtaining the analysis and operation procedure of the electrolyzer, and visualizing the operation of electric loads, such as motor and bulb. Experimental analysis of the electrolyzer illustrates that water electrolysis produces 60 mL hydrogen and 30 mL oxygen. The study proves that water composition (H\({}_{2}\)O) has two atoms of hydrogen and one atom of oxygen. The V-I characteristics show that the electrolyzer has a threshold voltage of 1.4 V. If the threshold voltage is not reached, no current will flow through the electrolyzer. Therefore, the water electrolysis process will not be performed. The electrolyzer, as a two-tank system, has losses in terms of hydrogen leakage. Thus, the experiment determined a hydrogen leakage constant of 0.8 mL/min, from which it can be concluded that high-quality equipment for hydrogen storage is needed.  
The paper also determined the efficiency of fuel cells in series and parallel modes of operation. In the series mode of operation, it is mathematically proven that the efficiency is higher at lower currents. Still, the fuel cell operating area is required where the power is the highest. According to the V-I characteristic of the series connection, the current value is between 0.6 and 0.8 A. According to the results, it obtained an efficiency of approximately 25%. On the other hand, in the parallel operation mode, the power constantly increases by increasing the current according to the V-I characteristic. According to the test results, the efficiency is approximately constant for the current between 0.4 and 1 A, which is about 50%. Thus, a parallel connection is recommended in the application of fuel cells.  
This work was funded by the Deanship of Scientific Research at Najran University grant code (NU/RG/SERC/11/2).  
Not applicable.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IEA & International Energy Agency \\ MCFC & Molten Carbonate Fuel Cell \\ SOFC & Solid oxide fuel cell \\ FCV & Fuel cell vehicles \\ CHP & Combined heat and power \\ PSO & Particle swarm optimization \\ AFC & Alkaline fuel cells \\ PAFC & Phosphoric acid fuel cells \\ \(ZrO_{2}\) & Zirconium dioxide \\ \end{tabular}
# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 2 Materials and Methods
### Multi-Scale Feature Extraction  
The primary goals of the initial phase involve establishing the spatiotemporal relationships among air quality monitoring stations and incorporating them into the model input matrix. The PM2.5 concentration at the appropriate location in space at time t, which is the matrix of WH, can be written as the input of our network as a matrix, \(\text{Xt}\in\text{RH}^{*}\text{W}\). Our model receives its input from a spatiotemporal matrix of contaminants that was created in chronological order. The time interval between two time points on the axis can be denoted as Ts, e, and l, where Ts is the starting point and e is the ending point, with e being greater than s. Additionally, l is the time granularity of the time granule, that is, the number of time points contained in Ts, e, l. Different lengths of PM2.5 data need to be generated prior to extracting PM2.5 temporal characteristics at different levels. We change the sliding window's size to produce PM2.5 data of various lengths.  
As seen in Figure 3, we create a network with numerous branches, each of which is capable of extracting temporal information at various sizes. Multi-scale features contain a greater amount of information compared to single-scale features. H*W is a matrix with dimensions of H and W, where * represents matrix multiplication. The multi-granularity feature method for predicting PM2.5 offers benefits in enhancing accuracy and overall performance.  
Figure 3: Multi-branch network architecture.  
Figure 2: Layout of 30 air quality monitoring stations in Jilin Province.  
Each branch in the multi-grain feature extractor has the same structure. The convolution method captures the characteristics of different regions while preserving spatial details of PM2.5. Various network branches can extract features of different sizes. One of the scale feature sequences can be written as Sn = [Sn + 1, Sn + 2,..., Sn + k] if the extraction process begins on day n.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 3 Data and Methods
### Forecasting with Long Short-Term Memory  
Once the data from Quinta de Santa Barbara between 2010 and 2022 had been estimated, LSTM networks were used to predict the variables under study for the short- to medium-term future.  
According to [36], LSTM neural networks consist of three key components: input gate \(i_{t}\), forgetting gate \(f_{t}\), and output gate \(o_{t}\). These components regulate the storage and removal of information within the network. This makes it possible to control when a memory unit retains previous information and maintains a constant error during the back-propagation process [27]. LSTMs have a cell state and a hidden state. The cell state propagates through the input and output sequences, while the temporal information is processed by the three gates responsible for incorporating, filtering, and selecting relevant information for output. The mathematical formulation outlining the LSTM structure can be represented by Equations (2)-(7) [32]:  
\[f_{t}=\sigma(W_{f}[h_{t-1},x_{t}]+b_{f}) \tag{2}\]  
\[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{3}\]  
\[o(t)=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{4}\]  
\[c^{\prime}_{t}=\tanh(W_{s}[h_{t-1},x_{t}]+b_{s}) \tag{5}\]  
\[c_{t}=c_{t-1}\odot f_{t}+g_{t}\odot i_{t} \tag{6}\]  
\[h_{t}=\tanh(c_{t})\odot i_{t} \tag{7}\]  
Figure 1: Location of the studied site.  
where \([h_{t-1},x_{t}]\) represents the input signal, which consists of the input neuron \(x_{t}\) at time \(t\) and the cell state \(c_{t-1}\) at time \(t-1\). \(W_{f}\), \(W_{i}\), \(W_{o}\), and \(W_{s}\) denote the weights that connect the forgetting gate, input gate, output gate, and cell state, respectively, to the input signal. \(b_{f}\), \(b_{i}\), \(b_{o}\), and \(b_{s}\) represent the biases, and \(\sigma\) denotes the activation function. Additionally, \(h_{t}\) corresponds to the output value at time \(t\). The operator \(\odot\) represents the element-wise multiplication.  
In this paper, instead of using tanh as the activation function, the rectified linear unit (ReLU) was considered. In brief, it returns the input value if it is positive, and zero if it is negative, introducing non-linearity to the model.  
Since the hyperparameters that characterize an LSTM affect the accuracy of forecasts, a GA was considered to improve the performance. The fitness function uses the mean square error (MSE) between the predicted and observed values to guide the GA in finding the best LSTM parameter combination.
## 2 PSs Siting and Timing: Technical Background and Problem Formulation  
The PS level must cover the entire utility service territory. The power delivered to each customer must come through some PSs. Thus, the utility service territory is divided into several PS service areas, as shown in Figure 1, where a river crosses the service territory.  
Each PS service area consists of the part of the service territory the PS serves. Its area is covered by the Medium Voltage (MV) feeder system emanating from its buses. Usually, the service area for a PS substation is: _contiguous_, not broken into two or more separate parts; _exclusive_, PS areas do not overlap; _roughly_ circular; centered upon the PS.  
Generally speaking, there is an optimum PS location from an economic perspective, and an optimum location regarding electrical performance, reliability, and service. The optimal location for a PS from the cost standpoint is rarely the lowest cost site, i.e., resulting in an overall compromise between all the costs involved in the PS deployment: land cost,  
Figure 1: Example of several PS service areas. Red squares are PS service areas, while green lines are the service area boundaries.  
site preparation cost, cost of getting transmission in and feeders out, and proximity to the loads it is intended to serve. Moreover, operational issues must be taken into account, i.e., along with any particular PS site, there is an optimal service area, which is the area, and load, around that site that is best served by it rather than by any other PS substation.  
Generally, every customer in a utility system should be served from the nearest PS. Serving each customer from the nearest PS ensures that the distribution delivery distance is as short as possible, reducing feeder costs, electric loss costs, and service interruption exposure. Therefore, valuable guidelines for optimizing the site, size, and service area are to serve every customer from the nearest PS, locating PSs close to as many customers as possible.  
Following the aforementioned technical background, the proposed methodology for siting and timing PSs looks for the optimal service areas while locating the PSs as close as possible to the loads. The approach is mainly based on the three following features:  
1. A GIS for capturing, elaborating, and displaying spatial input data.
2. A PSO algorithm to optimally locate the new PSs.
3. A Voronoi diagram-based approach assesses the PSO solutions quality and finds the PS service areas.  
In the following, a brief introduction to the mentioned features is reported.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 3 How Can Dynamic Allocation Flows Be Realized in Mobility Support?
### Late-Binding Node Selection Problem Statement
#### 3.3.2 Problem Statement
The MMKP's mathematical formula can be expressed as:  
\[max\ V(x)=\sum_{i=1}^{g}\sum_{j=1}^{l_{i}}v_{ij}x_{ij} \tag{8}\]  
Subject to  
\[\sum_{i=1}^{g}\sum_{j=1}^{l_{i}}c_{ijk}x_{ij}\leq r_{k},\forall k\in[1,m] \tag{9}\]  
\[\sum_{j=1}^{l_{i}}x_{ij}=1,\forall i\in[1,g] \tag{10}\]  
\[\overline{D}=\sum_{i=1}^{S}\sum_{j=1}^{F}D_{i}^{j}Z_{i}^{j}/F \tag{2}\]0  
By mapping the knapsack set to the router set, the project set is regarded as the data flow set, and the resources on the knapsack are regarded as the bandwidth of the switching path; \(D_{i}^{j}\) and \(B_{i}^{j}\) corresponds to the profit of the item and the weight of the required resources. Thus, the late-binding node selection problem can be mapped to an MMKP without considering the space resource constraints of the Mobility Flow Table (MFT). Similarly, the resources in the knapsack can also be regarded as the space resources of the Mobility Flow Table (MFT) in the router. In this situation, without considering the bandwidth capacity limit of the switching path, the late-binding node selection problem can also be mapped to an MMKP. Therefore, we believe that the late-binding node selection problem is a joint problem of two MMKPs. There is no solution for this special MMKP at present. In Section 4, the author provides a solution.
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
## 4 Design of Energy-Efficient and Obstacle-Avoiding Routing Protocol
### Candidate Forwarding Relay Set Determination
For example, when the current node \(n_{s}\) wants to send a packet to destination node \(n_{D}\), \(n_{s}\) first broadcasts the packet to its neighbor nodes \(n_{s}^{j}\). Each neighbor node of the source node calculates the angle \(\theta\left(n_{s}^{j}\right)\) using the cosine theorem based on the positions of the \(n_{s}^{j}\), \(n_{s}\) and \(n_{D}\). The nodes \(n_{s}^{j}\) with angle \(\theta\left(n_{s}^{j}\right)<90^{\circ}\) meet the constraint. The neighbor nodes of the current node that satisfy the above three constraints will be recorded in the candidate forwarding relay set. Algorithm 1 details a method of determining a candidate forwarding relay set in the situation that the current node perceives marine animal. Algorithm will be repeated in each transmission hop until the data packet finally reaches the destination node. ```
Input: the positions of the \(n_{s}\), \(n_{s}^{j}\) and \(n_{D}\) Output: \(N(i)=\{n_{s}^{1}\), \(n_{s}^{2}\),..., \(n_{s}^{j}\}\) // is the number of the candidate forwarding relays Initialization: \(N(i)=\emptyset\);
1: M = \(N_{n}\) ; // Number of neighbor nodes
2:for\(j=1\): M do
3: Calculate SNR, \(d_{s}^{j}\) and \(\angle n_{s}^{j}n_{s}n_{D}\);
4:if\(SNR\geq SNR_{0}\), \(R_{5}<d_{s}^{j}\leq R_{6}\) and \(\theta\left(n_{s}^{j}\right)<90^{\circ}\)then
5:\(N(i)=N(i)+\{n_{s}^{j}\}\);
6:endif
7:endfor
```  
**Algorithm 1** Candidate Forwarding Relay Set Determination  
If the current node cannot perceive the presence of the marine animal, then there is no interference from marine animal, thus the constraints are reduced to two, including the signal-to-noise ratio (SNR) and the included angle between the current node and its neighbor nodes. Similarly, the neighbor nodes  
Figure 3: Various radius of animal and sensor. of the current node that satisfy the above two constraints will be recorded in the candidate forwarding relay set.
## 6 Performance Evaluation
### Convergence and Parameter Analysis  
In this subsection, we demonstrate the impact of the discount factor and learning rate on the performance of the algorithm proposed in this paper to help us choose appropriate parameter values.  
(1) Impact of the Discount Factor: Figure 4 illustrates the impact of the discount factor on the performance of the algorithm proposed in this paper. We selected four typical discount factors: 0.95, 0.75, 0.55, and 0.35. From this figure, it can be observed that all curves converge after approximately 1500 iterations. Among all the curves, the curve corresponding to a discount factor of 0.75 achieved the highest reward, and its convergence speed was only slightly slower than the curve with a discount factor of 0.35. Therefore, in all subsequent simulations, we set the discount factor to 0.75.  
(2) Impact of the Learning Rate: Figure 5 illustrates the impact of different learning rates on the performance of the algorithm proposed in this paper. In the simulation, we compared four different learning rates: 0.0001, 0.001, 0.01, and 0.1. The learning rates for both the actor and critic networks are the same. From each curve, it can be observed that the curves corresponding to learning rates of 0.0001, 0.001, and 0.01 converge at around 1000 iterations. The curve corresponding to a learning rate of 0.1 does not converge because when the learning rate is too large, the algorithm skips over local or global optimal solutions, resulting in poor performance of the model. Among the four curves, the curve corresponding to a learning rate of 0.01 converges the fastest and exhibits the best performance. Therefore, we chose a learning rate of 0.01 as the parameter for subsequent simulations.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Batch size & 64 \\ \hline Replay buffer size & 100,000 \\ \hline Learning rate of actor network & 0.001 \\ \hline Learning rate of critic network & 0.001 \\ \hline Soft update factor of actor network & 0.0001 \\ \hline Soft update factor of critic network & 0.0001 \\ \hline Discount factor & 0.75 \\ \hline \end{tabular}
\end{table}
Table 2: Algorithm parameter settings.  
Figure 4: Impact of discount factor.
## 6 Conclusions  
This study proposes a spatial information measurement method for area maps that considers the diversity of nodes and edges and Gestalt cognitive principles. Firstly, the method uses areal objects in area maps as the basic unit for information measurement and constructs an adjacency graph structure representing the spatial distribution characteristics of area objects by utilizing the Voronoi diagram adjacency relationships as the adjacent edges. This abstraction transforms the spatial information of the area map into a graph structure. Secondly, four types of feature indicators are designed to represent spatial information in the graph structure, measuring the diversity of spatial information. Finally, the graph structure is used as the basic computational unit to integrate the four types of spatial information-representing features to comprehensively calculate the spatial information of the area map. Given the regular arrangement characteristics of real map data, this study introduces Gestalt cognitive principles and the theory of the limited growth of visually repeated information to eliminate redundant information, making the measurement results more consistent with visual perception. Two experiments are designed to test the proposed method, and the results indicate that the method effectively distinguishes the degree of disorder in spatial distribution while demonstrating better discrimination for changes in information compared to existing methods. Additionally, the information measurement values obtained using this method are comparable and unaffected by subjective factors such as dimensions and weights, making it a more efficient method for measuring spatial information in area maps.  
Overall, the proposed method is robust, coupling geometric information with spatial distribution information, and effectively accounting for the regularity of area objects. However, the method involves numerous parameter indicators and has a high computational complexity. Future research directions include improving the efficiency of spatial information measurement, reducing computational costs and time, and enabling real-time calculations in response to user interfaces.  
Conceptualization, Q.K. and X.Z.; methodology, Q.K. and D.H.; validation, X.Z. and D.H.; data curation, Q.K.; writing--original draft preparation, Q.K.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was jointly funded by the National Key Technology R&D Program of China under grant No. 2022 YFB390420501, National Natural Science Foundation of China (41971360)and the Fundamental Research Funds for the Central Universities of Central South University (1053320211416).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Methods and Materials  
### Vehicle Model  
The vehicle model is a one-dimensional model with three degrees of freedom which only includes vertical motions (Figure 2). The simulation model considers four basic subsystems of the vehicle: the chassis, the suspension systems, the unsprung mass and the tyres. The chassis is considered as a rigid body of mass (\(m_{\text{s}}\)) and is connected with the unsprung mass (\(m_{u}\)) through the suspension system, which is modelled as a spring and a damper (\(K_{u}\) and \(C_{u}\)). In this work, the damper is considered both passive and semi-active. Regarding the tyres, a more advanced modelling is applied compared to the common quarter car model. Firstly, the tyre sidewall is modelled as a spring and a damper (\(K_{T}\) and \(C_{T}\)) and connects the unsprung mass with the tyre tread (\(m_{t}\)), whose mass is varying dynamically according to analytical equations presented later in details. Finally, the tread element consists of linear springs and dampers (\(K_{t}\) and \(C_{t}\)), which receive the unevenness of the road profile (\(z_{R}\)) as an excitation.  
The governing equations of motion of the vehicle model are the following (Equations (1)-(3)):  
\[Sprung\ mass:m_{\text{s}}z_{\text{s}}+C_{u}(z_{\text{s}}-z_{u})+K_{u}(z_{\text{ s}}-z_{u})=0 \tag{1}\]  
Figure 2: Quarter car model.  
\[Unsprung\ mass:m_{u}\dot{z}_{u} -C_{u}(\dot{z}_{s}-\dot{z}_{u})-K_{u}(z_{s}-z_{u}) \tag{2}\] \[+C_{T}(\dot{z}_{u}-\dot{z}_{t})+K_{T}(z_{u}-z_{t})=0\] \[\text{{T}ead\ mass:m_{t}\dot{z}_{t}-C_{T}(\dot{z}_{u}-\dot{z}_{t})- K_{T}(z_{u}-z_{t})\] (3) \[+C_{t}(\dot{z}_{t}-\dot{z}_{R})+K_{t}(z_{t}-z_{R})=0\]  
while the parameters used are selected according to IPG/CarMaker 8.0 to generate a digital twin of the software demo vehicle. The parameter values are presented in Table 1.
## 4 Dynamic Assessment of Movement Performance
### Wearable Strain Gauges
#### 4.1.2 Sensing Knee Bondages  
Ref. [73] emphasizes the practicality of using a knee bandage as a wearable solution for capturing bio-signals and monitoring human activities and introduces the use of a knee bandage in the context of Human Activity Recognition (HAR) research. The introduced knee bandage (GenuTrain knee bandage provided by Bauerfeind AG), is utilized as a wearable carrier of sensors for the development of an end-to-end HAR system. The study goal is to assist in the early treatment of Gonarthrosis (known as knee osteoarthritis, which is a degenerative joint disease that affects the knee). The knee bandage incorporates sensors and bio-signal acquisition devices, such as electromyography electrodes, to capture data related to the user's movements such as walking, sitting to stand, or standing to sit. Throughout the research, the knee bandage serves as a platform for the implementation of the HAR-based mobile technology system. The integration of bio-signal acquisition devices and sensors into the knee bandage and the placement of electromyography electrodes on the right leg are specifically explained in the article.  
Additionally, in [74], the placement of body-worn sensors, including on the knees, is emphasized for sensor-based HAR, particularly in the context of gait analysis. Recognizing the pivotal role of the lower body, the article introduces a modeling scheme for gait-based activities, breaking down gait into two phases and five states. The chosen sensor positions below the waist are informed by the importance of understanding movements related to walking and other activities involving leg motion. This approach enables a detailed analysis of phases and states during activities like walking, emphasizing the significance of lower body sensor data for accurate and comprehensive HAR.