esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 6 Problem Formulation  
The wind power is forecasted for 24 h. The first objective is to maximize load factor for maximum utilization the power resource (RES generation from wind power plant). The second objective is to minimize the consumption cost. \[Obj1=maximize\;\;LF \tag{11}\]  
\[Obj2=minimize\;\;C \tag{12}\]  
where LF is the load factor (Equation (13)) and C (Equation (14)) is the total consumption cost. \[LF=\frac{\hat{L}}{L} \tag{13}\]  
\[C=\sum_{i=1}^{n}L_{i}\times P_{i} \tag{14}\]  
where \(\hat{L}\) is the sum of total load, \(\hat{L}\) is the average load, \(L\) is the load vector, \(P\) is the LMP vector and the unit of LMP is \(\$/\,MWh.\;n\) is the length of the load and LMP vectors. There are a few constraints of the system. The first constraint is that the demanded load must be equal to the load after applying the DSM scheme. The second constraint is that, after applying the DSM, the consumption cost should be less than the initial cost. The third constraint is that load factor must increase. The following are the constraints (Equations (15)-(17)):  
\[L=L_{new} \tag{15}\]  
\[C\leq C_{old} \tag{16}\]  
\[LF_{new}>LF \tag{17}\]  
where \(L\) is load before DSM and \(L_{new}\) is load after applying DSM. \(C_{old}\) is the consumption cost before DSM and \(C\) is cost after DSM. \(LF_{new}\) is the load factor after DSM. The purpose of the proposed DSM scheme is to bring the consumption as close to the normal distribution curve as possible. Let the input vectors contain 24 values: W = wind power forecast, L = day-ahead demand and P = day-ahead LMP. The other variables used in the algorithm are: C = consumption cost, S = subsidiary, DWD = demand-wind power difference, \(P_{new}\) = new adjusted price, and \(L_{new}\) = new normally distributed load after applying DSM scheme. Manage_Demand(-) is the proposed function for managing demand in an economical manner. This function will distribute the load in a normal form by shaving the peak periods and filling the valley periods (Algorithm 1). The resultant load profile achieved by this method will follow the normal distribution, approximately.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Single Node Upset Recovery Analysis
#### 2.5.5 Single Event Transient Occurs at Node S3  
S3 changes from '1' to '0'. This deactivates transistors N2 and N4 and activates P1. However, since node S1 firmly maintains the value '1', transistor N1 also remains activated, although both P1 and N1 are temporarily activated at node S0. However, since S1 remains unchanged and continues to hold a '1', transistor N1 remains continuously activated as well. This allowing S0 to recover to '0'. Consequently, S0 activates transistor P6, and S3 recovers to '1'. Through this process, all nodes are restored to their original states.
## 2 Experiment and Methods
### Prediction Method
#### 2.3.4 Hybrid BPNN Model Based on GA Optimization  
BPNN is a typical feedforward network. Through the method of forward propagation of network structure, BPNN uses training function to reverse modify the network weight matrix and threshold value. The model is built in MATLAB simulation and compilation environment, and the network model is trained by fully adjusting the number of hidden layer neurons.  
Since the optimal number of hidden neurons was not known during initial modeling, the number of hidden neurons was determined by empirical formula \(S=2n+1=13\) (\(n\) is the number of nodes in the input layer). If the number of hidden neurons is lower than the optimal value, there may be inadequate fitting, which may result in a high generalization error. If the number of hidden neurons is higher than the optimal value, overfitting and high variance may occur. Therefore, the initial weights and thresholds of the network are randomly obtained, and the number of hidden neurons is optimized by GA to determine the neural network topology. Each individual in GA population contains the number of hidden neurons in a network. The objective function is used to calculate the individual fitness value, and the number of hidden neurons corresponding to the optimal fitness value is found through selection, crossover and mutation operations. The objective function is to minimize the training data RMSE.
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 2 Shock Initiation Criterion  
The shock initiation of explosives is closely related to their safety and power. Scholars have carried out a lot of research on shock initiation of explosives and established critical initiation criteria. In 1961, [PERSON] and [PERSON] proposed the initiation criterion based on critical pressure [10]:  
\[P_{i}=P_{c}, \tag{1}\]where, \(P_{i}\) and \(P_{c}\) represent the input shock wave pressure and the critical initiation pressure, respectively. If \(P_{i}\geq P_{c}\) is reached, the detonation will be triggered.  
In 1969, [PERSON] and [PERSON] conducted a series of experiments on shock initiation of LX-10, PBX-9404 and nitromethane [11]. Results show that shock initiation of the explosives depends not only on shock wave pressure \(P\), but also on pressure duration \(t\). They proposed the famous Walker-Wasley criterion based on critical energy \(E_{c}\), which is applicable to both solid and liquid explosives:  
\[P^{2}t=E_{c}. \tag{2}\]  
The typical initiation criteria based on critical velocity are Jacobs-Roslund empirical criterion and Held criterion. Jacobs-Roslund empirical criterion is as follows [12]:  
\[vd^{1/2}=(1+k)[a+bT/(d\cos\theta)], \tag{3}\]  
where, \(v\) is the impact velocity of projectile; \(d\) is the projectile diameter; \(K\) is the pointed coefficient; \(T\) is the shell thickness; \(\theta\) is the impact angle; \(a\) and \(b\) are the parameters fitted according to the test results.  
When \(T=0\), Equation (3) can be simplified as Held criterion [13]:  
\[v^{2}d=C_{e}, \tag{4}\]  
where, \(C_{e}\) is the initiation threshold determined by experiment. According to Equation (4), if the projectile diameter is large enough, the initiation threshold can be reached at a small impact velocity, which is inconsistent with the actual situation. Therefore, the applicability of the Held criterion is limited.  
A satellite tank filled with liquid hydrazine can be regarded as a covered liquid explosive. Since Walker-Wasley criterion is applicable to liquid explosives, it is used to predict the shock initiation of the satellite tank under hypervelocity impact.
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 2 Related Work
### Stale Synchronous Parallel Strategy  
The computing power of each worker node may be different in a real environment. For the most commonly used iterative-convergent algorithm in machine learning, the current mainstream distributed machine learning idea is that each worker node trains the model in one iteration and submits the local updates to the PS, then enters the synchronization barrier. When all worker nodes have submitted their local parameters and get the updated global model parameters, the synchronization barrier will be released and the next iteration will start. As shown in Figure 4, the strategy that ensures the global coherence of parameter updates is called the Bulk Synchronous Parallel (BSP) strategy. From the above description we can see that BSP has two obvious defects. The first one is that each iteration requires a lot of communication. [PERSON] [16] has shown that the time required for parameter communication is up to six times the time required for iterative computation in Linear Discriminant Analysis (LDA) theme modeling running on 32 machines. The second one is that all worker nodes that have completed an iteration must wait at the synchronization barrier for the slowest node to finish and then start the next iteration, so the cluster load must be well balanced. However, [PERSON] [14] has shown that even in the load-balanced cluster, some worker nodes will become randomly and unpredictably slower than other worker nodes.  
Facing with these disadvantages, [PERSON] proposed SSP [16], which utilizes the fault tolerance of the iterative-convergence algorithm instead of the synchronization barrier for each iteration. Each worker node can directly execute the next iteration after finishing the current iteration. Only when the worker node with the largest number of iterations is faster than the worker node with the least number of iterations s times, all nodes will enter synchronization barrier and be synchronized once, and the s is called the stale threshold. This strategy effectively accelerates the training efficiency of the distributed  
Figure 4: Bulk Synchronous Parallel Strategy (BSP).  
machine learning model. [PERSON] used this strategy to implement a parameter server system for distributed machine learning [32].  
However, the fault tolerance of the iterative-convergence algorithm is finite. SSP does not consider its finiteness. SSP does not limit the number of iterations which may influence the accuracy of the model. Besides, SSP does not consider the dynamic environment, so it cannot cope with external interferences in the training process.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 9 Smart Port Use Case
### Methodology
#### 9.5.1 Data Processing  
Data mining needs to be done at the physical layer due to the higher volume of data generated by the sensors. Data mining is the process of extracting useful data patterns from a real-time continuous dynamic data stream. It is hard to store all the generated data from the sensors so it needs to be processed earlier. Useful data can be extracted by using ML algorithm. There are various data mining techniques based on ML algorithms and can be applied according to the application requirements. In [49], an ML-based data mining framework of wireless sensor network is discussed.
_Article_  
**Mathematical Modeling of Bacteria Communication**  
**in Continuous Cultures**  
**[PERSON] 1,* and [PERSON] 2**  
1 Faculty for Mathematics and Informatics, Universitat Heidelberg, Im Neuenheimer Feld 205, D-69120 Heidelberg, Germany  
2 Faculty for Mathematics, Technische Universitat Munchen, Boltzmannstrasse 3, D-85748 Garching bei Munchen, Germany; [EMAIL_ADDRESS]  
* Correspondence: [PERSON]RESS]; Tel.: +49-6221-54-14134 Academic Editor: [PERSON]  
Received: 30 March 2016; Accepted: 3 May 2016; Published: 16 May 2016  
**Abstract:** Quorum sensing is a bacterial cell-to-cell communication mechanism and is based on gene regulatory networks, which control and regulate the production of signaling molecules in the environment. In the past years, mathematical modeling of quorum sensing has provided an understanding of key components of such networks, including several feedback loops involved. This paper presents a simple system of delay differential equations (DDEs) for quorum sensing of _Pseudomonas putida_ with one positive feedback plus one (delayed) negative feedback mechanism. Results are shown concerning fundamental properties of solutions, such as existence, uniqueness, and non-negativity; the last feature is crucial for mathematical models in biology and is often violated when working with DDEs. The qualitative behavior of solutions is investigated, especially the stationary states and their stability. It is shown that for a certain choice of parameter values, the system presents stability switches with respect to the delay. On the other hand, when the delay is set to zero, a Hopf bifurcation might occur with respect to one of the negative feedback parameters. Model parameters are fitted to experimental data, indicating that the delay system is sufficient to explain and predict the biological observations.  
**Keywords:** quorum sensing; chemostat; mathematical model; differential equations; delay; bifurcations; dynamical system; numerical simulation  
**MSC:** 92C40; 34K17; 34K60; 34C60
## 2 Model, Concept, Theory and Methods
### Model and Basic Theory
We close this subsection by stating some general results for the matrix representation of the evolution operator for symmetry breaking. The laser pulse for symmetry breaking transforms the initial state (Equation (7)) to the superposition (Equation (5)) of the ground (\(g\)) and excited (\(e\)) states. Let us list the molecular eigenstates in the order \(g,e,e^{\prime},e^{\prime\prime}...\) The corresponding order of the coefficients is \(\mathbf{c}(t)=(c_{g}(t),c_{e}(t),c_{e^{\prime}}(t),c_{e^{\prime\prime}}(t),...)^{ \intercal}\). Then, \(\mathbf{U}_{b}(t_{c},t_{i})\) takes block diagonal form  
\[\mathbf{U}_{b}(t_{c},t_{i})= \begin{pmatrix}\mathbf{U}_{bge}(t_{c},t_{i})&0\\ 0&\mathbf{U}_{bnge}(t_{c},t_{i})\end{pmatrix}  \tag{27}\]  
where  
\[\mathbf{U}_{bge}(t_{c},t_{i})= \begin{pmatrix}\cos\alpha\cdot e^{i\eta_{g}(t_{ c})}&-\sin\alpha\cdot e^{-i\eta_{g}(t_{c})}\\ \sin\alpha\cdot e^{i\eta_{e}(t_{c})}&\cos\alpha\cdot e^{-i\eta_{e}(t_{c})} \end{pmatrix}  \tag{28}\]  
is the \(2\times 2\) block matrix for propagating states \(g\) and \(e\) from initial time \(t_{i}\) to \(t_{c}\), and \(\mathbf{U}_{bnge}(t_{c},t_{i})\) is the block matrix for propagating all other (\"non-ge\") states. The first column of \(\mathbf{U}_{bge}(t_{c},t_{i})\) is imposed by Equation (17), while the second column is a consequence of the unitarity of \(\mathbf{U}_{bge}(t_{c},t_{i})\). The form of \(\mathbf{U}_{bnge}(t_{c},t_{i})\) is irrelevant for the present application because the first laser pulse does not transfer population from the initial ground state to any other excited states \(e^{\prime},e^{\prime\prime},...\) at the central time \(t_{c}\).
## 3 Model Formulation  
This section of the manuscript focuses on description of the nomenclature that will be used throughout this study and presents a mixed integer linear mathematical model for the discrete dynamic berth scheduling problem with spatial requirements (**BSPSR**). A detailed description of the **BSPSR** mathematical model components is presented in Table 1.  
The objective function (1) of the **BSPSR** mathematical model, denoted as \(\mathbf{Z}\), aims to minimize the total weighted turnaround time of vessels (which is composed of the total weighted vessel waiting and handling times) and the total weighted vessel late departures. \[min\mathbf{Z}=[\sum_{i\in I}\left(W_{i}\mathbf{T}_{i}^{\text{WT}}\right)+\sum_{i\in I} \sum_{j\in I\in K}\left(W_{i}T_{ij}^{HT}\mathbf{x}_{ijk}\right)+\sum_{i\in I}\left( W_{i}\mathbf{T}_{i}^{L}\right)] \tag{1}\]  
Constraint set (2) indicates that no more than one vessel can be served at each berthing position at the time. \[\sum_{i\in I}\mathbf{x}_{ijk}\leq 1\ \forall j\in J,\ k\in K \tag{2}\]  
Constraint set (3) guarantees that each vessel will be assigned to one of the available berthing positions in any service order. \[\sum_{j\in J}\sum_{k\in K}\mathbf{x}_{ijk}=1\ \forall i\in I \tag{3}\]  
Constraint sets (4) and (5) ensure that the horizontal and vertical clearance requirements will not be violated for each vessel moored at one of the MCT berthing positions. \[(L_{i}^{V}+D_{i}^{H})\mathbf{x}_{ijk} \leq L_{j}^{B}\ \forall i\in I,\ j\in J,\ k\in K \tag{4}\] \[(H_{i}^{V}+D_{i}^{V})\mathbf{x}_{ijk} \leq H_{j}^{B}\ \forall i\in I,\ j\in J,\ k\in K \tag{5}\]
# Performance Improvement for PMSM Driven by DTC Based on Discrete Duty Ratio Determination Method
## 5 Experimental Analysis
### Steady-State Performance  
The steady-state performances of FS-DTC and FS-DDTC are compared under the same operating condition. The PMSM is operated at 500 rpm and the reference values of torque and flux linkage are 0.8 N\(\cdot\)m and 0.3 Wb, respectively. The torque and flux linkage waveforms of the PMSM driven by different control strategies are shown in Figure 7, and the stator currents of the PMSM driven by two control strategies are shown in Figure 8.  
From these experimental results it can be found that, the torque ripples and the flux ripples of FS-DTC are 0.32 N\(\cdot\)m and 0.045 Wb, respectively. The torque ripples and the flux linkage ripples of FS-DDTC are 0.35 N\(\cdot\)m and 0.05 Wb, respectively. The total harmonic distortions of the input current \(I_{\rm THD}\) of the PMSM driven by FS-DTC and FS-DDTC are 6.11% and 6.26%, respectively. Therefore,  
Figure 8: The waveforms of stator current when using: (**a**) FS-DTC; (**b**) FS-DDTC.  
Figure 7: Experimental torque and flux linkage of the PMSM when using: (**a**) FS-DTC; (**b**) FS-DDTC.  
although the proposed discrete duty ratio determination method simplifies the complexity of FS-DTC, the required steady-state performance in FS-DTC will not be affected.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 3 Trajectory Planning for the MR-SPS Considering Dynamic Constraints
### Initial Value Selection  
The selection of the initial value of the variables has a great impact on the result of the optimization. The initial value close to the optimal solution can avoid local optimization and improve the operation speed. Section 2.2 has given the ideal angular trajectory, namely the analytical angular trajectory without dynamic constraints which can be used as a reference for the selection of initial value. At most time, by calculating ideal \(\gamma\) and \(\theta_{j}\) at discrete time, the compact matrix notation forms \(\left[\gamma_{initial}\right]_{m\times 1}\) and \(\left[\theta_{initial}\right]_{m\times 1}\) are obtained. The initial values \(\left[X_{\gamma initial}\right]_{(n_{\gamma}-3)\times 1}\) and \(\left[X_{\theta_{initial}}\right]_{(n_{\theta}-3)\times 1}(j=1,2)\) can be calculated by solving the following equation to get the least square solution:  
\[\left[\gamma_{initial}\right]_{m\times 1} =\left[B_{\gamma}\right]_{m\times(n_{\gamma}+1)}\left[P_{\gamma,0 }\quad P_{\gamma,1}\quad\left[X_{\gamma initial}\right]_{(n_{\gamma}-3)\times 1 }^{T}\quad P_{\gamma,n_{\gamma}-1}\quad P_{\gamma,n_{\gamma}}\right]^{T} \tag{41}\] \[\left[\theta_{initial}\right]_{m\times 1} =\left[B_{\theta}\right]_{m\times(n_{\theta}+1)}\left[P_{\theta_{ j},0}\quad P_{\theta_{j},1}\quad\left[X_{\theta_{initial}}\right]_{(n_{\theta}-3) \times 1}^{T}\quad P_{\theta_{j},n_{\theta}-1}\quad P_{\theta_{j},n_{\theta} }\right]^{T} \tag{42}\]  
However, around equinox, the rate of change of the ideal angular trajectory is large when the right ascension between the sun and the SPS is about \(0^{\circ}\) or \(180^{\circ}\), so the inversely calculated initial value violates the dynamic constraints a lot. Nevertheless, at this moment, the altitude angle of the sun is small, and quite high energy receiving efficiency is guaranteed as long as the \(y_{\theta}\)-axis is perpendicular to the orbital plane. \(\left[\gamma_{initial}\right]_{m\times 1}\) and \(\left[\theta_{initial}\right]_{m\times 1}\) are given by: \(\gamma=0\) and \(\theta_{j}=-\pi/2+2\pi\tau\). By solving Equations (41) and (42), the initial value \(\left[X_{\gamma initial}\right]_{(n_{\gamma}-3)\times 1}\) and \(\left[X_{\theta_{initial}}\right]_{(n_{\theta}-3)\times 1}(j=1,2)\) can be acquired.  
By solving the optimization using two sets of initial value, the better result is selected as the optimal solution. The following criterion can help choose the initial value:  
\[\sigma=\int_{0}^{1}\tilde{\gamma}^{2}d\tau \tag{43}\]  
where \(\sigma\) represents the smoothness of the curve.
# Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units
## 6 Summary  
In this study, with reference to the time-delay problem of slurry density control, a mathematical model of the controlled object was established, the Smith fuzzy PID control scheme optimized by the genetic algorithm was innovatively proposed, and the simulation analysis and experimental verification of the control scheme were performed. We were able to confirm through simulation verification that the system with Smith fuzzy PID control optimized by the genetic algorithm demonstrated almost no occurrence of overshoot and that it has the best steady-state performance and the shortest rise time. We built a slurry mixing experimental platform, and the experimental results show that when the system set density was changed, the control time to reach the steady state of the Smith fuzzy PID control optimized by the genetic algorithm was shorter than that of the conventional PID control. Moreover, the valve openness records prove that the valve openness of the system with Smith fuzzy PID control optimized by the genetic algorithm changed more steadily to reach the steady state faster than that of the conventional PID control. Therefore, the experimental results show that the proposed optimization algorithm has an excellent control effect in actual application scenarios. The design of the mixing density control system described herein is based on theoretical and simulation calculations and experiments in a laboratory setting; however, because of the set conditions, it was not taken to an industrial site for testing. Actual engineering conditions are more complex, there are many interference factors present, and there also exists the subsequent opportunity to visit the actual site to carry out further testing.  
Figure 17: Experimental curve of valve openness.  
**Author Contributions:** Conceptualization, J.C.; methodology, X.G. and H.Y.; software, G.H. and H.Y.; validation, C.H. and G.H.; formal analysis, X.G.; investigation, J.C.; resources, W.X.; data curation, G.H.; writing--original draft preparation, [PERSON] and G.H.; writing--review and editing, J.C.; visualization, [PERSON]; supervision, W.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
**Acknowledgments:** We sincerely acknowledge [PERSON] for his experimental facility support.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Wanhua Chemical Group Co., Ltd. and author [PERSON] was employed by the company Cosco Shipping Heavy Industry Co., Ltd. The remaining authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Varying Noise Level--Speech Command Results
### Speech Command Performance Comparison  
In order to compare the performance of each of the 12 speech commands using their trendline characteristic, each weighted trendline was normalised so they can all be plotted on to the same \(y\)-axis, overlaid on the same graph and visually compared. This was done by dividing the weighted trendline \(y(x)\) values previously computed by the number of words in the speech command. Mathematically, normalised  
\[Y_{N}(x)=\frac{Y(x)}{n}=\frac{1}{n}\left[y(55)\quad y(60)\quad y(65)\quad y(70 )\quad y(75)\quad y(80)\quad y(85).\right] \tag{13}\]  
where \(n\) is number of words in the specific speech command being normalised. The resulting comparison plot is as shown in Figure 7. Note that the poorest performance was observed in the speech recognition of SC5, 'Hover', followed by SC1 'Go Forward' and SC2 'Go Backward' as indicated in the combined trendline in Figure 7. The best speech recognition performance was observed in SC4 'Step Right', SC6 'Land', SC11 'Step Right One Metre', and SC12 'Stop'. Both single-word and multi-word speech commands were found in each performance category. In addition, the fact that significant fail safe commands, such as SC6 'land' and SC12'stop', were among the most resilient to noise corruption, having a high recognition success rate at a higher noise level, is very important in UAV applications, where fail safe commands are expected to be very reliable; otherwise, it may be impractical to use.