esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Three-Stage Incremental Cooperative Communication  
We consider a WBAN in which the on-body sensor nodes transmit their sensed data to the coordinator/sink attached to the body. As the distances between sensor nodes in WBAN are small, it is assumed that all of the nodes are within the transmission range of each other. Communication is considered to be half-duplex. Figure 1 explains the system model of the proposed scheme. Our proposed WBAN scheme consists of four communication phases. There are three available potential relays \(R_{1}\), \(R_{2}\) and \(R_{3}\) for a source node. The proposed scheme has a three-stage ARQ mechanism, as shown in Figure 1. In the first phase of cooperation, the source transmits the data packet to the destination, and all three relays overhear this packet. If the destination node successfully decodes the packet in the first phase, it sends a short feedback in the form of positive ACKnowledgement (ACK), which indicates that there is no need for relaying. However, if the destination node fails to correctly decode the data packet, a negative ACKnowledgement (NACK) is sent, which is also heard by all relays. After this, the three-stage relaying process is invoked. If relay \(R_{1}\) has correctly received and decoded the data packet in the first phase, it forwards that packet to the destination during the second phase. If the packet is decoded successfully at the destination, it transmits back the ACK (ACK 2), and hence, the first stage of cooperative relaying is successful. Otherwise, the destination node sends the NACK (NACK 2), implying the need for the second stage of cooperative relaying. Upon overhearing NACK 2, relay \(R_{2}\) forwards the data packet, which is correctly received in the first phase, to the destination in the third phase. If the destination node is able to decode the packet successfully, it sends back ACK 3; otherwise, it sends NACK 3, which indicates the failure of the second stage of cooperation, as well. It may be noted that even if \(R_{1}\) does not transmit in the second phase (due to decoding failure at \(R_{1}\)), \(R_{2}\) can forward the packet to the destination in the third phase, if it had received the packet correctly in the first phase. The same is the case with the third relay \(R_{3}\): if \(R_{2}\) is unable to decode the packet in the first phase or the destination fails to decode and receive the packet correctly from \(R_{2}\), \(R_{3}\) is responsible for transmitting that data packet to the destination. If the destination node is able to successfully decode the packet, the success of the third stage of relaying occurs; otherwise, the packet is considered dropped. Figure 1 shows the incremental cooperative communication model for three-stage relaying. Figure 1: Three-stage incremental cooperative communication.
## 1 Introduction
[PERSON] _et al_. [7] segmented the nucleus first, applied a gradient vector flow (GVF) snake to segment WBCs, and used the boundary of the nucleus as the initial contour of the snake. [PERSON] _et al_. [11] proposed a WBC segmentation method by using stepwise merging rules based on mean-shift clustering and boundary removal rules with GVF snake. [PERSON] _et al_. [12] presented a WBC segmentation framework that segments the cytoplasm by using GVF snake. This framework identifies the cytoplasm by applying Zack thresholding [13] to a gray image with the nucleus region removed. Active-contour-based methods are limited to the selection of initial contour, and overlapping objects will lead to poor performance of the methods. Metaheuristic-based methods include genetic algorithm (GA) and differential evolution, among others. [PERSON] _et al_. [14] presented a method that combines SVM and GA to recognize blood cells and used GA to select the features used by SVM. Metaheuristic-based methods regard segmentation problem as optimization problem, but the application of metaheuristic-based methods in WBC segmentation is very limited [8]. Saliency-based methods are based on visual saliency attention. [PERSON] _et al_. localized and segmented WBCs based on visual saliency attention [15]. [PERSON] _et al_. realized WBC segmentation based on simulated visual attention, and gained satisfactory results [16]. Saliency-based methods are effective in the segmentation of WBCs. For some particular and practical applications, however, there is still some work to do. Some methods combine more than one of the categories listed above and achieve better results, such as combination of learning-based method and visual-attention-based method [17] and combination of learning-based method and thresholding-based method [18]. Other segmentation methods for WBC aside from the above five categories are available [19; 20]. [PERSON] _et al_. surveyed the current situation of WBC segmentation and indicated that the results of WBC segmentation are still not acceptable and need to be improved. They pointed out that the presence of artifacts, shape variations in the WBCs, and overlapped cells are the major problems and must be focused on [8]. Thus, aiming at the first two major problems, we present a novel, effective technique with good performance and robustness for WBC segmentation.
## 3 Modeling and Control Strategy Design
### Driver Model  
The driver model generates the load information for any given driving cycle. First, based on the assumption of a road with zero slope, the dynamic equation of the vehicle can be written as:  
\[M\frac{dv}{dt}=\frac{T_{m}i\eta\gamma_{T}}{r}-Mgf-\frac{1}{2}\rho AC_{d}\
u^{2} \tag{1}\]  
Figure 3: Block diagram of the powertrain control system.  
On the right-hand side of the equation, the first term represents the traction force, entirely provided by the traction motor; the second term is the rolling resistant force, and the third term is the air drag force. \(M\) is the total mass of the vehicle, \(T_{m}\) is the output torque of the motor, \(i_{0}\) is the gear ratio of the final drive, \(\eta_{T}\) is the transmission efficiency, \(r\) is the radius of the tire, \(f\) is the rolling resistance coefficient, \(\rho\) is the air density, \(A\) is the frontal area of the vehicle, and \(C_{d}\) is the air drag coefficient. Since the traction force is only provided by the motor in SHEVs, the load power is equal to the output power of the motor and can be calculated by:  
\[p_{load}=\left\{\begin{aligned} & T_{m}\omega_{m}/\eta_{m},\ T_{m}\geq 0\\ & T_{m}\omega_{m}\eta_{m},\ T_{m}<0\end{aligned}\right. \tag{2}\]  
where \(\omega_{m}\) is the motor speed and \(\eta_{m}\) is the motor efficiency. Furthermore, the accessory load under normal driving conditions, such as from air conditioning and infotainment systems, should be considered. Then, the total required power is obtained by:  
\[p_{d}=p_{load}+p_{acc} \tag{3}\]  
Second, the drive model aims to simulate the actual driving actions; that is, the driver uses the accelerator/brake pedals to control the traction torque based on the vehicle speed. In this model, a PI controller is designed to generate the desired pedal position signal according to the vehicle's speed tracking error. Note that the accelerator pedal's position signal and brake pedal's position signal are combined together and denoted by _APP_, where the brake pedal's position is defined by a negative value for convenience. Then, a pre-calibrated map converts the pedal position to the desired torque at different vehicle speeds. The torque signal will be sent to the dynamic equations of the vehicle to obtain the instantaneous speed and complete the feedback loop. The structure of the closed-loop driver model is shown in Figure 4. The outputs of the driver model include the total required power, which will be used for the ECM decision, and the vehicle speed and APP information, which will be used as NVH constraints.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods  
To finish negotiation with limited communication resources, a multi-granularity negotiation method was developed. Firstly, the traditional multi-agent negotiation model is divided into three levels of granularity: low-level granularity, middle-level granularity, and high-level granularity. Mission selection algorithms were designed for each level of granularity. A fixed consistency algorithm is used to realize the decentralization of the satellite cluster. After that, we designed different negotiation processes and consistency mechanisms to simplify the negotiation process of different granularity. Secondly, different samples on different target numbers, bandwidths and transmit hop numbers were collected to train a neural network. Finally, the best neural network was established to select the best granularity onboard. The flowchart of the multi-granularity negotiation method proposed in this paper is illustrated in Figure 1.
# Design, Implementation and Power Analysis of
## 4 Discussion
### Characteristics of Energy Consumption Ratios  
Figure 9 is the graph plotted for the Energy Consumption Ratios of 60 W incandescent, 8 W LED and 0.5 W LED light bulbs.  
Figure 9,b show a study of the characteristics of \(\mathrm{ECR_{BS}}\) and \(\mathrm{ECR_{MS}}\). The study shows the reduction in ratio of energy consumption with increase in the number of light bulbs. It depicts a decrease in \(\mathrm{ECR_{BS}}\) with an increase in the power of light bulbs and a decrease in \(\mathrm{ECR_{MS}}\) with decrease in the light bulb power. Consequently, the power consumption of light bulbs is indirectly proportional to \(\mathrm{ECR_{MS}}\) and \(\mathrm{ECR_{BS}}\), respectively. \(\mathrm{ECR_{BS}}\) and \(\mathrm{ECR_{MS}}\) parameters are scenario- based and it can be seen that as the number of light bulbs increases \(\mathrm{ECR_{BS}}\) and \(\mathrm{ECR_{MS}}\) maintain a typical straight line for 60 W incandescent and 8 W LED light bulbs, but they give a quite indirect line for 0.5 W LED light bulb up to eight light bulbs. This is because for up to eight very small power light bulbs, the PARSLAD device alone consumes 50% more power (0.3006 W) than the required power of a low value wattage bulb (0.5 W) to operate in ON state. This problem diminishes by deploying more than eight8 bulbs. We could see the linearity of \(\mathrm{ECR_{BS}}\) and \(\mathrm{ECR_{MS}}\) diminishes and an approximate straight line is observed for eight to 12 light bulbs. It can be stated that above 12 number of 0.5 W LED bulbs we observe a straight line similar to the 60 W incandescent and 8 W LED bulb. Thus, the energy consumption ratio parameter is resistant to scalability of light bulbs but in case of very low power bulb, the parameter becomes unaffected only after eight light bulbs.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 3 Methods
### Data Fusion Mechanism Based on Integer Wavelet Transform
#### 3.3.3 Data Fusion Based on Integer Wavelet Transform  
The data collected by a single node have strong temporal correlation due to the short interval, while the data generated by multiple nodes in the network have some spatial correlation due to their close distance. We used integer wavelet transform techniques to remove spatiotemporal dependent redundancy of these data.  
Time-dimensional data fusion of single node  
The operation process of time-dimensional data fusion using integer wavelet transform is as follows. Step 1: Data preprocessing. It mainly carries out data cleaning and data integer transformation on the raw data collected by the nodes. Step 2: Integer wavelet transform. The time-dimensional data series is decomposed into low- and high-frequency coefficients based on integer wavelet transform. Step 3: Coefficients quantification. The wavelet coefficients were quantified by using scalar quantification techniques. Step 4: Encoding. The wavelet coefficients are encoded using the encoding algorithm, such as the deflate algorithm.  
The operation process of spatial-dimensional data fusion using integer wavelet transform is as follows. Step 1: Integer wavelet transformation of data for the underlying node. The nodes underlying the network are divided into the even nodes \(s_{0,j}\) and the odd nodes \(d_{0,i}\) in the order, and then the even node \(s_{0,1}\) transmits its data to the adjacent odd node \(d_{0,1}\) and calculates the integer wavelet coefficients \(s_{1,1}\) and \(d_{1,1}\). Correspondingly, all the coefficients of the one-level wavelet decomposition (\(d_{1,1}\), \(d_{1,2}\), \(\ldots\), \(d_{1,n}\)) and (\(s_{1,1}\), \(s_{1,2}\), \(\ldots\), \(s_{1,n}\)) can be obtained. Step 2: Integer wavelet transform of data for the convergence node. The underlying odd nodes send the low-frequency coefficient (\(s_{1,1}\), \(s_{1,2}\), \(\ldots\), \(s_{1,n}\)) to the convergence nodes of the upper layer and form a new data sequences. By repeating the Step 1 operation, we can obtain the three-level wavelet transform coefficients (\(d_{2,1}\), \(d_{2,2}\), \(\ldots\), \(d_{2,n}\)) and (\(s_{2,1}\), \(s_{2,2}\), \(\ldots\), \(s_{2,n}\)). Step 3: Integer wavelet transform of data for the Sink node. The Sink node obtains the k-level wavelet transform coefficients (\(d_{k,1}\), \(d_{k,2}\), \(\ldots\), \(d_{k,n}\)) and (\(s_{k,1}\), \(s_{k,2}\), \(\ldots\), \(s_{k,n}\)). The diagram of spatial-dimensional data fusion of nodes is shown in Figure 6.  
Figure 5: Diagram of hierarchical network structure in a WSN.
## 5 Conclusions  
This study carried out a combined experimental and theoretical investigation of the flexural behavior of a HPFRCC. A numerical approach for modeling the flexural and cracking behaviors of HPFRCC was proposed:  
1. Compressive and tensile constitutive relations were proposed based on the experimental results. A bi-linear relation was assumed for the compression. Regarding the tensile behavior, the total strain was assumed to be a sum of the elastic and crack strains.
2. In order to develop a realistic model that could account for sequential multiple cracking and localization behavior of the HPFRCC, the pure bending region was divided into several  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Classifications**} & **Moment (k\(\mathbf{\cdot}\mathbf{m}\))** & **Deflection (mm)** & **Crack Width (mm)** \\ \hline \multicolumn{2}{c}{Results with the homogeneity} & 1.754 & 1.662 & 0.266 \\ Monte Carlo & Average & 1.661 & 1.284 & 0.203 \\ simulation & Standard Deviation & 0.046 & 0.164 & 0.010 \\  & Specimen 1 & 1.529 & 1.176 & 0.258 \\ Test results & Specimen 2 & 1.687 & 1.273 & 0.421 \\  & Specimen 3 & 1.635 & 1.337 & not available \\  & Average & 1.617 & 1.262 & 0.340 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis and test results at a maximum moment.  
Figure 13: Region of peak moment and deflection obtained with 99% certainty: (**a**) peak moment and deflection region; (**b**) probabilistic distribution on peak moment and deflection.  
segments with random material properties. The properties of the segments are assigned based on the probabilistic distribution of the material parameters.
* A Monte Carlo simulation had been conducted in order to consider the uncertainty for the material properties of HPFRCC. Results of the Monte Carlo simulation showed that the test results are within the region of prediction with 99% certainty.  
With this proposed method, the complete flexural behavior of the HPFRCC beam, such as load-deflection and load-crack width responses including localization of cracks, could be rationally predicted.
## 6 Conclusions  
The collected GPS and BDS2 observations of TH-2 satellites provide a good platform for analyzing the PROD based on multi-GNSS. The onboard GPS and BDS2 observations were analyzed; the amount of GPS observations was far greater than that of BDS2, but the signal quality of the BDS2 was comparable with that of GPS.  
The RMS of difference between the NDT and CHS solutions in 3D was smaller than 1.5 mm outside the maneuver periods. Different weights of GEOs observations were adopted to discuss the contribution of GEOs. The result showed that the average value of the daily RMS of difference between the GPS-based and BDS2-based PROD increased with the decrease in the GEOs' weight. When assigning the same weight to MEO, IGSO, and GEO satellites, the RMS of difference between the GPS-based and BDS2-based PROD in 3D components was 7.08 mm, 2.89 mm, and 7.96 mm in the global, Asia-Pacific region and non-Asia-Pacific region, respectively.  
After adding BDS2 to GPS, the number of visible satellites from TH-2A and TH-2B increased by 21.1%, and the PDOP improved by 19.8%. The DD ionospheric-free OC phase residual showed that the applied models fit the GPS and BDS2 observations well. The average of the daily RMS of the 6 h orbit overlapping differences in the R, T, and N were all within 1 mm. The average of GPS ADOP reduced from 0.160 cycle to 0.153 cycle; BDS2 combined with GPS can further enhance the accuracy of the float ambiguity. This study can be used as a reference for PROD based on multi-GNSS.  
Conceptualization, B.Y. and D.G.; methodology, B.Y. and B.J.; software, B.Y., K.S. and X.Q.; validation, B.Y., H.Z. and Z.H.; formal analysis, B.Y.; writing--original draft preparation, B.Y.; writing--review and editing, D.G., K.S., B.J., H.Z. and Z.H.; visualization, B.Y.; supervision, X.D.; project administration, D.G.; funding acquisition, D.G., B.J. and X.D. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Key R&D program of China (Nos. 2020 YFA0713502) and the National Natural Science Foundation of China (Nos. 41874028 and 61803018).  
The authors declare no conflict of interest.
# Utilizing Energy Storage for Operational Adequacy of Wind-Integrated Bulk Power Systems
## 3 Operational Adequacy Evaluation Framework  
The proposed framework incorporates the conventional generation, wind generation, and ESS capacity into operational procedures of unit commitment and dispatch. Figure 2 demonstrates the relationship between the wind generation model, ESS model, and the load model within the unit commitment decision process to formulate an evaluation framework consisting of state enumeration, dynamic system state probability estimation, and optimal power flow methods to collectively assess the operational adequacy of the bulk power system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Charging (MW)**} & \multicolumn{2}{c}{**Discharging (MW)**} & \\ \hline \(AW_{k,w}^{\rm P}\) & \(ES_{k,w}^{\rm P}\) & \(AW_{k,w}^{\rm P}\) & \(ES_{k,w}^{\rm P}\) & **Probability** \\ \hline \(W_{k,1}^{\rm P}-{\rm C}{\rm F}^{\rm e}_{k,\ 1}\) & 0 & \(W_{k,1}^{\rm P}\) & \(ES_{k,1}^{\rm P}\) & \(\pi_{1,ki}\) \\ \(W_{k,2}^{\rm P}-{\rm C}{\rm F}^{\rm e}_{k,\ 2}\) & 0 & \(W_{k,2}^{\rm P}\) & \(ES_{k,2}^{\rm P}\  
The framework takes into account the continuous changes in the committed generating units in response to the anticipated load from one UCP to the next. It also recognizes the dynamic behavior of the wind generation, the ESS \(SOC\), and the load at each node of the grid network at each time interval within each UCP. Such changes are reflected on the system risk, represented by an operational adequacy index that is continuously evaluated at each time interval. The evaluation takes into account the committed generating units (\(C^{u}\)) and their capacity, the wind generation at the time, the integrated load (IL) that includes the ESS charging load, and the ESS capacity during charging and discharging modes obtained from the IPPT. The variation in these parameters and the associated risk curves within the evaluation period are presented in Figure 3. The unit commitment decisions for each UCP within the evaluation period are made considering the integrated generation and load models discussed in Section 2.3, followed by the development of the state enumeration-based bulk power system model described in Section 2.4.  
Figure 3: Risk area curves of a wind and energy storage system (ESS) integrated bulk power system within the evaluation period.  
Figure 2: Overview of the proposed operational adequacy evaluation framework.  
The operational adequacy index evaluation process requires the estimation of the system state probability and the load curtailment associated with each system state. The system state probability is a dynamic parameter and is estimated using the Dynamic System State Probability Estimation Approach (DSSPE) described in Section 3.1. A linearized optimal power flow (OPF) is used to estimate the load curtailments associated with each system state.
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 2 Basic Concepts of the Wind Power and Turbines  
The wind power process converts the kinetic energy of the wind into mechanical energy. This is converted into electrical energy through a wind turbine. The central physical element of this process is the wind turbine. The conversion process depends on the dynamics of wind flow and the advanced technology involved in the conversion process [1]. For this reason, the turbine's location directly influences wind power generation. The technology generally refers to optimizing output power by integrating parameters such as air density and turbine blade's swept area [2]. Wind turbines generate power at a specific wind speed, called the cut-in speed. The maximum output rate is reached at another speed named the rated speed [3,4]. The study by [PERSON] [5] mentions environmental and geographical factors as central elements for wind power scaling. Traditional power distribution faces challenges in the grid context. To overcome this, the measurements of ramp events need to be precise for forecasting and operational strategies adopted in the wind energy system [6,7]. Managing uncertainties in wind forecasting is debated in the literature, with the primary goal of achieving output stability. If the wind source energy penetration exceeds 30% of demand on an electric grid, the management should already be informed by the forecasting mechanism [8]. This management must timely identify meteorological disturbances such as storms or cold fronts [9]. These events directly affect the system's output [10,11].