esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Purpose of the Present Study  
The present study aimed to assess the effectiveness of CCT for inducing changes in cognitive functions among administrative workers. To our knowledge, this is the second study to investigate trainer-guided CCT in an office setting. The study was structured as a randomized controlled intervention trial (RCT) with a wait list control group, incorporating pre- and post-CCT assessments. In addition to examining general training effects on cognitive functions, our focus extended to evaluating the differential impact of CCT on work productivity and well-being, encompassing self-evaluations of life and job satisfaction, mood, and general health.  
We hypothesize that CCT will: (i) enhance cognitive functioning, preferably evidenced by far-transfer effects, (ii) improve self-evaluation outcomes related to perceived life and job satisfaction, mood, and general health, and (iii) not impede work productivity even though the CCT sessions were conducted during regular work hours.
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
###### Abstract  
With the world's energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization.  
hybrid electric vehicles; energy management strategies; vehicle modeling; equivalent consumption minimization strategy (ECMS); model predictive control (MPC) +
Footnote †: journal: Applied Sciences  
engine. When the engine operates at the high-efficiency point [18; 19], its low-speed constant torque characteristics can effectively compensate for the lack of acceleration performance. However, to fully realize these benefits, it is essential to establish an energy management strategy (EMS) that coordinates power sources and maximizes the use of regenerative braking energy. By doing so, fuel efficiency can be optimized, and emissions can be reduced [20; 21]. Numerous studies on various elements of HEVs have been published in the literature so far [22]. Therefore, this work presents a thorough review of the literature, with an emphasis on contributions to hybrid vehicle modelling and energy management. At the same time, some outlooks on future research directions are given.  
The main work of this paper is elucidated as follows: First, the emergence of HEVs is investigated, with a focus on the process and advantages of their development. Then, different hybrid vehicle configurations and their detailed modelling methods are listed, depending on different scenarios and features. Afterward, energy management strategies are broadly classified into three categories, namely rule-based energy management strategies, optimization-based energy management strategies, and deep learning-based energy management strategies. Their advantages and disadvantages are then carefully reviewed and evaluated. The purpose is to emphasize these strategies' novelty and their contribution to satisfying several optimization objectives, including the reduction of fuel consumption and emissions, driving comfort, optimized braking energy regeneration, and improved driving performance of the vehicle. At the end, relevant research gaps within the study domain are discussed.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Iceberg
#### 2.1.5 HF Radar  
Although HF radar has been mostly employed for the RS of the ocean surface, it also has iceberg detection capability. However, the detection of icebergs in the Doppler spectrum received from the ocean surface might be challenging. This is because the backscattered fields from the ocean surface spread over a wide range of frequencies, particularly close to zero Doppler, where the iceberg returns occur [50]. In other words, the clutter can mask the iceberg return because both of them appear in a narrow frequency band around zero Doppler. In this regard, [PERSON]. [51] proposed an analytical method based on the generalized functions approach [52; 53; 54] to estimate the scattered field for mixed paths with discontinuities, which is an extension of [55] for the analysis of scattered fields over layered media. Moreover, [PERSON] and [PERSON] [56] developed the radar cross-section of icebergs with arbitrary size and shape in the presence of a vertical dipole antenna using the presented methods in [51; 55]. [PERSON]. [50] also compared the iceberg-measured spectrum parameters with modeled spectrum to show the validity of their developments in [56]. An experiment was conducted using an HF radar operating at 25.40 MHz between July and August 1984 at Byron Bay, Labrador, to test the accuracy of the proposed method.
## 2 Discrete-Time 2 DOF Robot Manipulator Model  
The model of a 2 DOF manipulator robot, presented in [21; 22], is described using a second-order nonlinear differential equation:  
\[\mathbf{M}(\mathbf{q})\dot{\mathbf{q}}+\mathbf{C}(\mathbf{q},\dot{\mathbf{q}}) \dot{\mathbf{q}}+g(\mathbf{q})+f(\dot{\mathbf{q}})=\boldsymbol{\tau}\, \tag{1}\]  
where \(\mathbf{q}\) is the \(2\times 1\) vector of joint positions, \(\dot{\mathbf{q}}\) is the \(2\times 1\) vector of joint velocities, \(\boldsymbol{\tau}\) is the \(2\times 1\) vector of applied torques, \(\mathbf{M}(\mathbf{q})\) is the \(2\times 2\) symmetric positive definite manipulator inertia matrix, \(\mathbf{C}(\mathbf{q},\dot{\mathbf{q}})\) is the \(2\times 2\) matrix of centripetal and Coriolis torques, and \(g(\mathbf{q})\) is the \(2\times 1\) vector of gravitational torques.  
Then, the model in Equation (1) can be represented in state space as:  
\[\begin{split}\mathbf{x}_{1}&=\mathbf{x}_{2}\\ \mathbf{x}_{2}&=-\mathbf{M}^{-1}[\mathbf{C}\mathbf{x }_{2}+g(\mathbf{x}_{1})+f(\mathbf{x}_{2})]+\mathbf{M}^{-1}\boldsymbol{\tau}\,\\ \mathbf{y}&=\mathbf{x}_{1}\,\end{split} \tag{2}\]  
where \(\mathbf{x}_{1}=[q_{1}\ q_{2}]^{\top}\) is the output of the system, \(\mathbf{x}_{2}=[q_{1}\dot{q}_{2}]^{\top}\), \(\mathbf{M}=\mathbf{M}(\mathbf{q},\dot{\mathbf{q}})\in R^{2}\), \(\mathbf{C}=\mathbf{C}(\mathbf{q},\dot{\mathbf{q}})\in R^{2}\), \(g(q_{1},q_{2})\in R^{2\times 1}\), \(f(\dot{q}_{1},\dot{q}_{2})\in R^{2\times 1}\), and \(\boldsymbol{\tau}=[\tau_{1}\ \tau_{2}]^{\top}\) is the torque vector as the input signal. Using the Euler method, the model in Equation (2) can be represented in discrete-time as:  
\[\begin{split}\mathbf{x}_{1}(k+1)&=\mathbf{x}_{1}(k)+T_{ s}\mathbf{x}_{2}(k)\\ \mathbf{x}_{2}(k+1)&=\mathbf{x}_{2}(k)-T_{s}\mathbf{ M}^{-1}[\mathbf{Cx}_{2}(k)\\ &\quad+\mathbf{g}(\mathbf{x}_{1}(k))+\mathbf{f}(\mathbf{x}_{2}(k))]+ \mathbf{M}^{-1}\mathbf{\tau}(\mathbf{k})\\ \mathbf{y}(k)&=\mathbf{x}_{1}(k)\,\end{split} \tag{3}\]  
with \(\mathbf{x}_{1}(0)=[0\ \ 0]^{\top}\) and \(\mathbf{x}_{2}(0)=[0\ \ 0]^{\top}\), where \(k\in Z\cup 0\) is the time index with \(Z\) as the set of non-negative numbers and \(T_{s}\) is the sampling time. It must be noted that the Euler method represents a good alternative for the nonlinear discrete plant when the sampling rate is very low [23].
## 3 Distributed PV Storage Virtual Synchronous Generator System
### Bidirectional PV Storage Converter
#### 3.4.4 Complete Control Scheme  
Improved PQ control strategy enables the inverter with active frequency regulation capability. However, inverters lack the moment of inertia, which are less friendly to the power system. In order to make the output of the PV storage system reflect the nature of the synchronous generator under the premise of frequency regulation capability, a new control of PV storage converter was constructed by combining the virtual synchronous generator technology with the improved PQ control described above (see Figure 15).  
When the power grid is stable, the PV system acts as a constant power source according to the command value. When the frequency fluctuates, it actively participates in the frequency regulation of the power grid to maintain system stability with a similar inertia characteristic of the synchronous machine for which the friendship of the PV storage system to the grid is greatly improved.  
The grid frequency \(f\) and voltage \(U\) can be obtained by using the phase-locked loop and calculating. Then the two can generate the power correction amount \(\Delta P\), \(\Delta Q\) by the droop part, \(\Delta P\), \(\Delta Q\) are added to the original power reference value (dispatching value) to get the final power reference value \(P_{\mathrm{ref}}\), \(Q_{\mathrm{ref}}\). Afterwards, they enter the power outer loop control shown in Figure 12 and the outputs of the power outer loop control are taken as the input power of VSG \(P_{\mathrm{m}}\), \(Q_{\mathrm{m}}\). After the VSG control shown in Figure 14, the reference voltage vector amplitude \(E\) and phase angle \(\theta\) are produced. Then the reference value of d, q axis voltage \(U_{\mathrm{d}}\)\({}^{*}\), \(U_{\mathrm{q}}\)\({}^{*}\) can be obtained by voltage synthesis and  
Figure 14: Control block diagram of VSG.  
coordinate transformation. Then control signals enter the voltage and current double closed loop controlling method. The result of the voltage outer loop are identified as given values for the current inner loop \(I_{\text{dref}}\)\({}^{*}\), \(I_{\text{qref}}\)\({}^{*}\). The current inner loop is compensated by the feed forward and decoupled to obtain the voltage reference values \(U_{\text{dref}}\), \(U_{\text{qref}}\). After the coordinate transformation and PWM modulation, the inverter switching signal is generated and the system completes the closed loop.
# A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method
## 3 Adaptive Model Predictive Current Control for Inverters
### Selection of Switching States  
The overall control scheme proposed in this paper can be divided into three steps: (i) Select the optimal switching state, (ii) apply the modulation, and (iii) adaptively modify the parameters. In this method, the mathematical model expression in \(dq\) coordinate system is employed. In order to calculate the optimal switching state, the cost function of MPC is given as  
\[J=\left|i_{dref}-i_{d}(k+1)\right|+\left|i_{qref}-i_{q}(k+1)\right| \tag{6}\]  
where \(i_{dref}\) and \(i_{qref}\) are the references of inverter output currents in two-phase rotating coordinate system. The output variables (\(y_{1}\) and \(y_{2}\)) and control variables (\(u_{1}\) and \(u_{2}\)) are defined as follows:  
\[y=\left[\begin{array}{c}i_{d}\\ i_{q}\end{array}\right]\quad\quad u=\left[\begin{array}{c}S_{d}\\ S_{q}\end{array}\right] \tag{7}\]  
The mathematical model expression shown in the Equation (5) can be rewritten as follows after the discretization.  
\[\left\{\begin{array}{ll}L\frac{i_{d}(k+1)-i_{d}(k)}{T_{s}}&+Ri_{d}(k+1)= \omega Li_{q}(k+1)\\ &-e_{d}(k+1)+\frac{2}{3}U_{DC}(k+1)S_{d}(k+1)\\ L\frac{i_{q}(k+1)-i_{q}(k)}{T_{s}}&+Ri_{q}(k+1)=-\omega Li_{d}(k+1)\\ &-e_{q}(k+1)+\frac{2}{3}U_{DC}(k+1)S_{q}(k+1)\end{array}\right. \tag{8}\]  
where \(T_{s}\) is the length of control period, and \(\omega\) is the angular frequency. In this expression, all of the variables, including the switching state variables, are of DC system and will not change greatly between successive two steps. Therefore, the following expression can be obtained.  
\[\left\{\begin{array}{c}U_{DC}(k+1)\approx U_{DC}(k)\\ e_{d}(k+1)\approx e_{d}(k)\\ e_{q}(k+1)\approx e_{q}(k)\end{array}\right. \tag{9}\]  
Obviously, the minimum value of cost function \(J\) can be obtained when satisfying following conditions  
Figure 3: Main process of A-MPC current control method.  
\[\left\{\begin{array}{l}i_{d}(k+1)=i_{\textit{dref}}\\ i_{q}(k+1)=i_{\textit{qref}}\end{array}\right. \tag{10}\]  
Taking above values into the model expression, the switching state variables can be calculated as  
\[\left\{\begin{array}{l}S_{d}(k+1)=\frac{\epsilon_{d}(k)+R_{p}i_{\textit{dref} }(k)-\omega_{L}i_{\textit{qref}}(k)+l_{p}\frac{i_{\textit{dref}}(k)}{T_{S}}}{ \frac{1}{3}U_{DC}(k)}\\ S_{q}(k+1)=\frac{\epsilon_{q}(k)+R_{p}i_{\textit{qref}}(k)+\omega_{L}i_{ \textit{qref}}(k)+L_{p}\frac{i_{\textit{qref}}(k)}{T_{S}}}{\frac{1}{3}U_{DC}(k) }\end{array}\right. \tag{11}\]  
This is the expression to calculate the optimal switching states of the next period. In this expression, the model parameters \(R_{p}\) and \(L_{p}\) are of predicted values and will be modified online. During every control period, the measured variables of currents, voltage are used to calculate the switching state variables. In this method, only above expressions are needed to obtain the optimal switching states. As a result, the computation is reduced comparing with other FCS-MPC inverter control methods.
## 3 An Instance-Based Learning (IBL) Approach
### RUL Estimation
#### 3.4.2 Similarity Evaluation  
If we can determine \"a segment\" in the learning instance feature sequence that is \"similar\" to that of the test instance, we can conclude its RUL without much difficulty. This segment may be original or synthetic as described above. We now describe how to assess similarity. In this study, we use the root mean squared (RMS) difference measure to evaluate similarity between the test and learning instances, as described below.  
Figure 6 illustrates the similarity matching by using  
In the above equation, \(\Delta\) as indicated in Figure 6a, is constant for all synthetic feature sequences because it is excluded from scaling. In the example shown in Figure 6, the prognostic segments are chosen between 300- and 1800-time cycles from the test instance and between 20- and 2750-time cycles from the learning instance. Therefore, \(T_{p}\) = 1500, \(T_{l}\) = 2730. The learning instance Bearing1-1 is recorded as failing at 2785 cycles, hence \(\Delta\) = 53. At the best matching, \(T_{g\_min}\) =\(-\)34, \(\alpha\) = 0.75. Thus, \(RUL\) (0.75) = 0.75 \(\times\) 2730 \(-\) (1500 \(-\) 34) + 53 = 635 (time cycles).  
Note that in the above computation each comparison pair of feature sequences, \(L_{k}\) and \(P_{k}\), are normalized with the mean \(\mu\) and standard deviation \(\sigma\) of the feature segment \(P_{k}\), using Equations (14) and (15), respectively. In this way, the similarity metrics \(d_{min}\) for all different pairs of test and learning sequences are in the consistent measurement scale [0, 1], where smaller \(d_{min}\) indicates a more similar pair of comparison sequences.  
\[(L_{k}-\mu(P_{k}))/\sigma(P_{k}) \tag{14}\]  
\[(P_{k}-\mu(P_{k}))/\sigma(P_{k}) \tag{15}\]
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 3 Satellite Sensors for SUHI Studies
### Landsat  
As shown in Table 1, almost 53% of researchers have used either one or multiple Landsat images in their SUHI studies, which emphasized the importance of the Landsat constellation to the SUHI knowledge. Since the launch of Landsat in 1972, the satellite constellation has carried four generations of sensors including: (1) Multispectral Scanner (MSS) sensors, optical instruments, and video cameras aboard Landsat 1, 2, and 3, (2) MSS and Thematic Mapper (TM) sensors aboard Landsat 4 and 5, (3) Enhanced Thematic Mapper Plus (ETM+) aboard Landsat 7, and (4) Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) aboard Landsat 8 [70,71]. From the TM sensor onward since 1982, the ability to measure thermal radiance and, thereby, the LST of the Earth's surface has made a breakthrough in investigating SUHI.  
There are three main reasons for Landsat's popularity in SUHI studies. First, Landsat \"is the longest running uninterrupted Earth observation program\" [72]. Second, researchers, as a result of a policy change in 2008, can freely obtain Landsat images. These features, therefore, provide a consistent, reliable, and voluminous archive of data for researchers to explore different aspects of SUHI. It is noteworthy to mention that, until 2008, scientists had to pay for Landsat data and the U.S. Government has again been considering charging users for access to the Landsat archive [73]. Third, Landsat series 5, 7, and 8 capture the Earth's surface in a 16-day repeat cycle with swath coverage of 185 km \(\times\) 185 km. The temporal resolution provides a reasonable period to reflect the changes in SUHI or land cover/use of a city and the swath coverage is big enough that it allows scientists to process a single image to investigate an entire urban environment. These both increase time efficiency and accuracy of the SUHI studies. Note that the atmospherically corrected Landsat thermal data was not previously available as a standard product, and researchers either used top-of-atmosphere temperature values or employed various methods to atmospherically correct the data themselves [39]. Currently, there is no operational Landsat LST product that combines data from the various Landsat sensors. Recently, [PERSON] et al. [74] developed an operational algorithm for retrieving Landsat LST consistently for all sensors. The U.S. Geological Survey and the LST dataset will implement the algorithm, which is expected to be available at the Land Processes Distributed Active Archive Center.
## 3 Results and Discussion  
### Membrane Characterization with Pure Water  
The flux for clean water as a function of time was determined under different thermal and hydrodynamic conditions. The experiments with different feed temperatures showed clear difference in the flux (Figure 2a). The flux increased with temperature due to increases in the corresponding driving force i.e., the vapor pressure difference between the feed and permeate sides. The average fluxes for 38 \({}^{\circ}\)C, 47 \({}^{\circ}\)C, and 56 \({}^{\circ}\)C at a flow rate of 21.6 L/h were measured to be 1.29 L/m\({}^{2}\)-h, 2.36 L/m\({}^{2}\)-h, and 3.86 L/m\({}^{2}\)-h, respectively. The flux was also measured at a feed temperature of 56 \({}^{\circ}\)C, and flow rates of 14.8 L/h and 21.6 L/h were used to compare the effect of flow rates on water flux (Figure 2b). The average flux at 14.8 L/h was measured to be 3 L/m\({}^{2}\)-h, which is significantly lower than for the flux at 21.6 L/h. A high flow rate decreases the temperature polarization and temperature drop along the membrane module, which contributes to the observed corresponding high flux, as explained in the literature [21].  
Figure 2: Flux as a function of time for clean water. (**a**) At different feed temperatures, all with flow rate of 21.6 L/h, (**b**) at different flow rates for a feed temperatures of 56.9 \(\pm\) 0.1 \({}^{\circ}\)C and 56.4 \(\pm\) 0.2 \({}^{\circ}\)C for 14.8 L/h and 21.6 L/h, respectively.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 3 Thruster Control Recovery Evaluation Based on Proposed Fault Detection
2 for(\(K_{p}=n_{1}\) to \(n_{2}\))do
3 for(\(t=0\) to \(t_{r}\))do
4 RK45 output iterative assignment if\(0\leq t\leq t_{r}\)then
5\(q_{cmd}=\left(\alpha_{cmd}-\alpha\right)/\left(t_{r}-t\right)\)
6\(e_{q}=q_{cmd}-q\)
7\(\dot{q}_{cmd}=e_{q}\) / \(K_{p}\)
8\(M_{cmd}=\left(\dot{q}_{cmd}-\dot{q}\right)\cdot I_{y}\)
9 Determining the thruster control number based on \(M_{cmd}\)
10 end if
11 Calculate operating moment input \(M\) to RK45
12 end for
13\(K_{p}\) is determined according to the principle of minimum \(\alpha_{cmd}-\alpha\) deviation when \(t_{r}\) arrives
14 end
```  
**Algorithm 1**\(K_{p}\) gain numerical calculation for rise time requirement  
where \(q_{cmd}\) is the pitch rate reference command, \(\alpha_{cmd}\) is then given command of the AoA, \(e_{q}\) is the deviation between the pitch rate reference command and the feedback, \(K_{p}\) is the adjustment gain in the limit range \(\left[n_{1}\,n_{2}\right]\) of the RLV's pitch acceleration, \(\dot{q}_{cmd}\) is the reference pitch acceleration, and \(M_{cmd}\) is the expected control moment. RK45 is the Runge-Kutta method including Equation (1). The second step is to calculate the control moment and identify the thruster control allocation scheme satisfying the steady-state error requirement based on Algorithm 1. The specific algorithm is as follows:where \(e_{\alpha}\) is the deviation between the AoA command and the feedback, \(\mathit{Thrst}_{num}\) is the remaining fault-free thruster working number, \(M_{0}\) is the control moment from a single thruster, \(\hat{M}\) is the control moment for each control period, and \(\hat{M}_{1}\) is an assumed control moment. The condition of \(|e_{\alpha}|\leq e_{\alpha}\), the algorithm used a circular prediction calculation by the Runge-Kutta method (RK45) to match it with \(\hat{M}_{1}\) input and the flight dynamics model; \(\hat{M}_{1}\) was replaced in turn from rest fault-free thruster \([-n\ n]\) at each cycle to find the optimized control moment. The third step is to evaluate whether the remaining fault-free thruster that can track the attitude control response performance under the failure of the thruster valve using the nonlinear model-based numerical control prediction. The prediction flow chart is as following Figure 6:In conclusion, the multi-thruster, from a working equivalence point of view, can be regarded as a single thruster. The upper limit of the thruster can be set, and fault detection and isolation can be conducted based on the three types of faults, with the isolation state either turned on or off for all of the types. A numerical calculation method based on a nonlinear model was used to evaluate control recovery and determine if the fault-free thruster could track the attitude control response performance following failure of a thruster valve.
###### Abstract  
The rapid development of information and communication technologies and wireless sensor networks has transformed agriculture practices. New tools and methods are used to support farmers in their activities. This paper presents a context-aware system that automates irrigation decisions based on sensor measurements. Automatic irrigation overcomes the water shortage problem, and automatic sensor measurements reduce the observational work of farmers. This paper focuses on a method for developing context-aware systems using ontologies. Ontologies are used to solve heterogeneity issues in sensor measurements. Their main goal is to propose a shared data schema that precisely describes measurements to ease their interpretations. These descriptions are reusable by any machine and understandable by humans. The context-aware system also contains a decision support system based on a rules inference engine. We propose two new ontologies: The Context-Aware System Ontology addresses the development of the context-aware system in general. The Irrigation ontology automates a manual irrigation method named IRRINOV(r). These ontologies reuse well-known ontologies such as the Semantic Sensor Network (SSN) and Smart Appliance REFerence (SAREF). The decision support system uses a set of rules with ontologies to infer daily irrigation decisions for farmers. This project uses real experimental data to evaluate the implementation of the decision support system.  
agriculture; smart irrigation; context-aware system; ontology; rules +
Footnote †: journal: Applied sciences