esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Two-Stage Optimal Scheduling Model for Wind-PV-Thermal-Storage System
### Day-Ahead Optimal Scheduling Model
#### 4.1.3 Day-Ahead Dispatching Model Constraints  
* System Load Balancing Constraints At any time, the total output of the system generating and storing (i.e., thermal power plants, wind farms, PV power station and battery power stations) is equal to the system load, which has participated in the demand response. \[\sum_{i=1}^{N_{\text{G}}}P_{\text{G,}t,t}+\sum_{j=1}^{N_{\text{W}}}P_{\text{W,}j,t}+\sum_{k=1}^{N_{\text{PV}}}P_{\text{PV,}k,t}+\Delta P_{\text{BESS,}t}=P_ {\text{L,act,}t}\] (25) where \(P_{\text{L,act,}t}\) is the actual power of system load at time \(t\), details are in formula (3).
* Generator Constraints The output power of the thermal power unit is limited by its minimum and maximum output and the upward and downward climbing rate of the generator, and the start-stop state of units is restricted by the maximum and minimum start-stop time [26]. Constraints of wind turbine output power: \[0\leq P_{\text{W,}j}\leq P_{\text{W,}j,r}\] (26) where \(P_{\text{W,}j,r}\) is the rated output power of each wind turbine, which is provided by wind farms, and the actual output range of the wind turbine \(P_{\text{W,}j}\) is: \[0\leq P_{\text{W,}j}\leq\widetilde{P_{\text{W,}j,\text{max}}}\quad j=1,2, \ldots,N_{\text{w}}\] (27) where \(\widetilde{P_{\text{W,}j,\text{max}}}\) is the maximum output of the \(j\)-th wind turbine according to the predicted wind speed, which is variable and volatile. The output power constraint of a photovoltaic power station is the same as above.
* Battery Energy Storage Station Constraints The power of the BESS meets some constraints, which are shown in Appendix B.
## 3 Improved MPC-Based Trajectory Tracking Controller Design
### Linearized Error Model of BSR  
Because the lower control input is the PWM signal driving the thruster to generate the thrust, in the predictive model, the relationship between the robot states (the position \(x\), \(y\), and yaw angle \(\theta\)) and the driving force is considered. The dynamics of the robot reflect the relationship between the velocity and the driving force, and the kinematics describes the relationship between the robot states and velocity. Therefore, the predictive model for the BSR trajectory tracking is established by combining Equations (7) and (8):  
\[\dot{\mathbf{x}} =\left[\begin{array}{c}\mathbf{R}(\theta)\mathbf{v}\\ \mathbf{M}^{-1}(\boldsymbol{\tau}-\boldsymbol{D}\left(\mathbf{v}\right) \mathbf{v})\end{array}\right] \tag{12}\] \[=\mathbf{f}(\mathbf{x},\boldsymbol{\tau})\]  
Here, the state is defined as \(\mathbf{x}=\left[x,y,\theta,u,r\right]\), and the control input is defined as \(\boldsymbol{\tau}=\left[F_{u},F_{r}\right]^{T}\). It was determined that the above state-space equation is nonlinear. It is essential to linearize the Equation (12) in order to develop a state-space-based model predictive control algorithm. Each point on a referenced trajectory satisfies the above state-space equation, and \(d\) represents the reference. The general form is expressed, as follows:  
\[\dot{\mathbf{x}}_{d}=\mathbf{f}(\mathbf{x}_{d},\boldsymbol{\tau}_{d}) \tag{13}\]  
where, \(\mathbf{x}_{d}=\left[x_{d},y_{d},\theta_{d},u_{d},r_{d}\right]^{T}\) is a reference point and \(\boldsymbol{\tau}_{d}=\left[F_{ud},F_{rd}\right]^{T}\) is the corresponding control input. The Taylor series expansion on the reference point is applied to Equation (12), and high-order terms are ignored in order to obtain the following expression:  
\[\dot{\mathbf{x}}= \mathbf{f}(\mathbf{x}_{d},\boldsymbol{\tau}_{d}) \tag{14}\] \[+\left.\frac{\partial\mathbf{f}(\mathbf{x}_{\boldsymbol{\tau}})} {\partial\mathbf{x}}\right|_{\mathbf{x}=\mathbf{X}_{d},\boldsymbol{\tau}= \boldsymbol{\tau}_{d}}(\boldsymbol{\tau}-\boldsymbol{\tau}_{d})\]  
The continuous-time linearized error model of the BSR can be written through the following by subtracting Equation (14) from Equation (13).  
\[\dot{\widetilde{\mathbf{x}}}=\mathbf{A}(t)\widetilde{\mathbf{x}}+\mathbf{B}(t )\widetilde{\boldsymbol{\tau}} \tag{15}\]  
Figure 5: Diagram of the proposed model predictive control (MPC) controller for BSR trajectory tracking.  
where, \(\widetilde{\mathbf{x}}=\left[x-x_{d},y-y_{d},\theta-\theta_{d},u-u_{d},r-r_{d}\right]\), and \(\widetilde{\mathbf{\tau}}=\left[F_{u}-F_{ud},F_{r}-F_{rd}\right]^{T}\). The matrix \(A(t)\) and \(B(t)\) are described in the Appendix A.  
By using the approximately discrete method, the discrete form of Equation (15) can be expressed, as follows:  
\[\widetilde{\mathbf{x}}(k+1)=\widetilde{\mathbf{A}}\widetilde{\mathbf{x}}(k)+ \widetilde{\mathbf{B}}\widetilde{\mathbf{\tau}}(k) \tag{16}\]  
where, \(\widetilde{\mathbf{A}}=\mathbf{I}+T\mathbf{A}(t)\), \(\widetilde{\mathbf{B}}=T\mathbf{B}(t)\), \(\mathbf{I}\) is an identity matrix.
## 4 Discussion  
### Major Finding and Result Comparison  
In this study, the PLUS model was used to forecast changes in land use in the forest farming subregion of the Loess Plateau from 2025 to 2050. The land usage prediction indicates that the soil erosion in 2025, 2030, and 2050 will be 48.85, 49.84, and 47.61 t/ha per year, respectively, and minor erosion will continue to be the main erosion degree. The results showed that the study area had abundant LUCC, and the NDVI regression slope values were >0, generally consistent with previous studies [52]. The findings also indicate that soil erosion would worsen as a result of future precipitation's large rise. As a consequence, shrubs may be managed as a priority in the Loess Plateau's ecological system going forward, and at the same time, afforestation and grass planting, afforestation and natural closure, and grass planting and natural closure are effective methods for conserving soil and water [53].  
The primary land types in the Loess Plateau were cropland and woodland. All land types showed a decrease in erosion in 2020; the decrease in cultivated land erosion was the most obvious. Reclamation in the study area resulted in a decrease in the cropland area [12]. Many scholars have studied the spatial distribution pattern and temporal variation characteristics of vegetation coverage in the Loess Plateau, and the results all show that the average annual vegetation coverage in the Loess Plateau shows an upward trend from 2000 to 2020. Overall, the vegetation cover of the Loess Plateau is higher in the north and lower in the south [54; 55]. [PERSON] et al. showed that runoff and erosion show clear differences under different rainfall and land use patterns, and this study further supports that conclusion [56].
# Space Weather Effects on Satellites
## 2 SW Drivers and Their Effects on Spacecraft Technology
### Magnetized Plasma Structures  
Incoming fast-speed plasma structures with magnetic field orientation opposite to the terrestrial dipole are the drivers of geomagnetic sub-storms (due to CIRs/SIRs) and storms (due to ICMEs) in the magnetosphere. These are complex events, often preceded by increased EM emission and accompanied by fluxes of energetic particles. Their combined effects lead to an expansion of the upper atmosphere. The related frequent collisions with neutral atoms cause an increased atmospheric drag, affecting mostly LEO satellites [48; 49] as well as the Starlink constellations of spacecraft at very low Earth orbit (VLEO), e.g., [50]. As a result, the altitude decreases gradually, which requires re-entry boosts in order to avoid tracking loss. That leads to premature fuel exhaustion and shortens the lifetime of the mission. Their increased fuel requirements mean heavier spacecraft and thus larger costs for the spacecraft owners.  
Another major consequence of the GSs is the ground-level effects, namely GICs, that carry significant risk for ground-based, marine, and underground infrastructure, e.g., [2].
# Compton Scattering Imaging of Liquid Water in Porous Carbon-Based Materials  
[PERSON]  
1 Japan Synchrotron Radiation Research Institute (JASRI), Hyogo 679-5198, Japan; [EMAIL_ADDRESS]  
[PERSON]  
2 Fuel Cell Cutting-Edge Research Center Technology Research Association (FC-Cubic TRA), Tokyo 135-0064, Japan; y-tsuji@fc-cubic.orip  
[PERSON]  
3 Graduate School of Human and Environmental Studies, Kyoto University, Kyoto 606-8501, Japan;  
[EMAIL_ADDRESS]  
[PERSON]  
4 Nissan Analysis and Research Center (NISSAN ARC), Kanagawa 237-0061, Japan; [EMAIL_ADDRESS]  
[PERSON]  
1 Japan Synchrotron Radiation Research Institute (JASRI), Hyogo 679-5198, Japan; [EMAIL_ADDRESS]  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/article/pdf](https://www.mdpi.com/journals/article/pdf)]([https://www.mdpi.com/journals/article/pdf](https://www.mdpi.com/journals/article/pdf))  
in a commercial battery under discharge and the structural change due to the volume expansion [16]. In addition, a new analytical technique was developed using the line shapes of Compton scattered X-rays (S-parameter) [17]. The S-parameter analysis has successfully revealed the Li concentration in commercial batteries [18; 19; 20; 21]. Recently, the CSI technique has achieved two-dimensional imaging with a combination of a pinhole and a two-dimensional X-ray detector, which enables high-speed imaging of a two-dimensional cross section of an object [22]. The advantage of CSI over X-ray CT is that CSI has direct access to such two-dimensional cross sections without sample rotation. This is beneficial to the experiments of commercial electrochemical cells in operation. High-energy X-ray radiography is not sensitive to light element substances, such as liquid water and carbon materials, since the X-ray absorption is substantially reduced for such light elements at high energies. On the other hand, X-ray Compton scattering is sensitive to light-element materials [23; 24]. It is also noted here that the radiation damage is small since the photoelectric absorption is substantially reduced in high-energy X-rays.  
A drawback of the CSI technique is its low data-accumulation efficiency due to the small aperture size of the pinhole. This will be overcome by adopting a system of coded aperture masks [25].  
In this study, we have performed a feasibility test of synchrotron-based CSI for liquid water observation in fuel cells. This technique is applied to a material of gas diffusion layer (GDL) in dry or wet, to probe the effect of water penetration into the GDL material.
## 3 Materials and Methods
### The Virtually Enhanced Senses System
#### 3.1.1 User Requirements  
In this section we consider the navigation system interface design from the perspective of a total loss of visual information. The information provided can afterwards be adapted for specific user profiles, as both needs and means of assistance mainly vary according to the type and degree of visual loss.  
Required data for navigation are summarized below [28]:  
1. \"The presence, location, and preferably the nature of obstacles immediately ahead of the traveler.\" This relates to obstacle avoidance support;
2. Data on the \"path or surface on which the traveler is walking, such as texture, gradient, upcoming steps,\" etc.;
3. \"The position and nature of objects to the sides of the travel path,\" i.e., hedges, fences, doorways, etc.;
4. Information that helps users to \"maintain a straight course, notably the presence of some type of aiming point in the distance,\" e.g., distant traffic sounds;
5. \"Landmark location and identification,\" including those previously seen, particularly in (3);
6. Information that \"allows the traveler to build up a mental map, image, or schema for the chosen route to be followed.\" (see \"Cognitive Mapping\" in [29]).  
Even if such information is contained in the device, it must also be conveyed to the user within a time window, e.g., when triggering obstacle detection warnings. This leads to the study of the bandwidth of the remaining sensory modalities, and how to make use of it without masking natural stimuli useful for navigation, e.g., listening to traffic sounds. Furthermore, it is necessary to adapt the output data to higher-nature cognitive processes [17]. Therefore, and given the larger bandwidth of vision, among other sensory modalities, task-oriented interfaces are required to minimize the amount of data provided.  
However, the theoretical basis which sustains the design of these interfaces is a currently active research area. For instance, there is still no consensus regarding the capabilities of the targeted public to acquire spatial knowledge, e.g., how the absence of vision degrades mental representations of the environment in terms of performance in subsequent navigation tasks [5]; the role of landmarks in the development of such representations [29]; etc.  
Nevertheless, previous works provide several useful cues for efficient tactile and acoustic spatial data encoding, such as the importance of the motor-sensing loop or the role of synesthesia, cortical plasticity and even rote learning. In addition, recent work in neuroscience provides new insights into the perceptual and cognitive processes involved [17; 30]. The contributions range from studies on the visual acuity of sensory substitution devices [31; 32], to the analysis of correlated neural activity, e.g., activation of the Visual Word Form Area (VWFA) and Fusiform Face Area (FFA) of the visual cortex through tactile/acoustic stimuli [30].
# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
## 3 Related Works
### Research on MR-OFDM Compensation  
The MR-OFDM-compliant research from [20] proposed a complementary-metal-oxide-semiconductor architecture for the IEEE 802.15.4g SUN receiver and transceiver with a zero-IF architecture, low-frequency noise, and circuit-based dc offset control for energy efficiency in smart grids. The measured sensitivity for the data rate of 100 kbps was \(-\)103 dBm with a PER of 1%. However, the compensation for the mobile scenario topology was not considered. The SNR estimation for IEEE  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Option 1** & **Option 2** & **Option 3** & **Option 4** \\ \hline Nominal bandwidth (kHz) & 1094 & 552 & 281 & 156 \\ Channel spacing (kHz) & 1200 & 800 & 400 & 200 \\ DFT size & 128 & 64 & 32 & 16 \\ Active tones & 104 & 52 & 26 & 14 \\ \#Pilot tones & 8 & 4 & 2 & 2 \\ \#Data tones & 96 & 48 & 24 & 12 \\ Subcarrier & 10.416 & 10.416 & 10.416 & 10.416 \\ Data rate (kbps) & 100–800 & 50–800 & 50–600 & 50–300 \\ Power sensitivity (dBm), PER \(<\) 10\% & \(-\)103 & \(-\)105 & \(-\)105 & \(-\)105 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data rates for IEEE 802.15.4g SUN MR-OFDM [10].  
Figure 2: Structure of a packet.  
802.15.4g MR-OFDM from [21] used the preamble signal over the frequency selective fading channels. From the structure of the IEEE 802.15.4 specification with the STF and the LTF, we calculated the estimate by using the preamble signal on which the inverse fast Fourier transform, the CP, and the Rayleigh fading were used.  
In [6], the authors used STF, which was made by a pseudo noise sequence, to estimate the integer frequency offset, whereas [7] proposed algorithms that used the LTF in the preamble to estimate the IFO by using the LTF format in advance, as show in Figure 2. They estimated the CFO accurately for each packet. However, with the dynamic channel, the CFO can be changed in each OFDM symbol. The conventional algorithms that use the preamble part to estimate the CFO seem no longer suitable, especially with the environments that have fast change-over times. In this paper, we propose a scheme that can estimate and compensate for the CFO in real time for changing environments.
## 2 Data
### Data Preprocessing  
Data preprocessing is a pivotal aspect of the research process. In the initial step, outliers were identified by employing the interquartile range method and incorporating domain knowledge from meteorology. This approach merged quantitative and qualitative methods to guarantee precise outlier detection. Interpolation techniques were subsequently applied to manage missing values within the dataset. Post outlier detection, a forward-filling method was implemented for handling the outliers, wherein outliers were replacedwith data from the preceding time point. This approach preserved the coherence and temporal trend of the time series data, thereby minimizing their potential distortion of the subsequent analyses.  
The hourly PM2.5 and PM10 data were then transformed into daily averages by computing the mean across each 24 h interval, aligning with the daily mean data collected over the prior eight years. An in-depth analysis on wind speed and direction was also executed, with the establishment of the east-west wind vector as the U wind component (orientated at 90 degrees for due east and 270 degrees for due west), and the north-south wind vector as the V wind component (orientated at 0 degrees for due north and 180 degrees for due south). This bifurcation aimed to dissect the nuanced influences of wind on pollutant dispersion more thoroughly.  
In meteorological research, the Pearson correlation coefficient is frequently harnessed to evaluate inter-relations among variables. Accordingly, this study will utilize the Pearson correlation coefficient to elucidate the association between PM2.5 and PM10 concentrations and meteorological parameters. The formula applied for this purpose is delineated below:  
\[r_{xy}=\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)\left(y_{i}-\overline {y}\right)}{\sqrt{\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}( y_{i}-\overline{y})^{2}}} \tag{1}\]  
The Pearson correlation coefficient, denoted as \(r_{xy}\), quantifies the degree of linear correlation between variables X and Y. The variable \(n\) represents the sample size, which is the number of data points. The notation \(x_{i}\) denotes the value of variable X for the \(i\)-th sample, and \(y_{i}\) denotes the value of variable Y for the \(i\)-th sample.
## 3 Data and Method
### Research Method
#### 3.2.1 Three-Stage DEA Model
In this formula: \(\rho\) is the efficiency value, N is the number of inputs, M is the number of expected outputs, \(\left(S_{n}^{x},S_{m}^{y},S_{i}^{y}\right)\) represent the slack variables of input and output, \(\left(x_{kn}^{t},y_{km}^{t},b_{ki}^{t}\right)\) is the input-output value of the k-th production unit within the t-th period, and \(z_{k}^{t}\) represents the input-output weight, with \(0<\rho\leq 1\). When \(\rho=1\), the production unit is fully effective, but when \(\rho<1\), there is an efficiency loss in the production unit. The second stage: an SFA regression model is developed to decompose the slack variables into three independent variables, namely, environmental factors, random factors, and management inefficiency. The function model of SFA is as follows:  
\[ \begin{array}{l}S_{ni}=f^{n}(Z_{i},\beta^{n})+V_{ni}+U_{ni}\\ n=1,2,\ldots\ldots\ldots N;i=1,2,\ldots I\end{array}  \tag{2}\]  
In this formula: the slack variable, \(S_{ni}\), is the i-th decision-making unit of the n-th input. \(Z_{i}=\left(Z_{li},Z_{2i},\ldots\ldots,Z_{ki}\right)\) represent K environmental variables. \(f^{n}(Z_{i},\beta^{n})\) reflect the influence of environmental variables on the slack variable \(S_{ni}\); \(\left(V_{ni}+U_{ni}\right)\) represent mixed error  
Figure 1: Technology Roadmap. terms, and V\({}_{\rm ni}\) represents a random interference term. U\({}_{\rm ni}\) means that the management has no performance term, and V\({}_{\rm ni}\), U\({}_{\rm ni}\) means they are independent of each other. The new input value is quantified in a homogeneous environment:  
\[ \begin{array}{c}\chi_{\rm ni}^{*}=X_{\rm ni}+[\max(Z_{\rm i}\beta^{\rm n})-Z_ {\rm i}\beta^{\rm n}]+[\max(V_{\rm ni})-V_{\rm ni}]\\ n=1,2,\ldots\ldots\rm N;i=1,2,\ldots\ldots\rm I\end{array}  \tag{3}\]  
In this formula: \(\chi_{\rm ni}^{*}\) is the new input value, \(X_{\rm ni}\) is the original input value, and \([\max(Z_{\rm i}\beta^{\rm n})-Z_{\rm i}\beta^{\rm n}]\), \([\max(V_{\rm ni})-V_{\rm ni}]\) are the adjustment items. The third stage: the original input data are replaced by the adjusted input data from the second stage, with the operation process of the first stage repeated.
## 5 Conclusions  
In this study, a series of experiments were conducted to study the use of MICP technology for reinforcing coastal clay. The physical and mechanical indexes of the MICP-treated soil at different ages were obtained. Additionally, microscopic analyses of the  
Figure 9: Content of Ca\({}^{2+}\) in unit mass soil.  
treated clay were performed using SEM and XRD experiments. The main conclusions obtained from the analysis are as follows:  
1. The unconfined compressive strength of the soil strengthened using MICP increased by about 25%, and the permeability coefficient decreased by 76%. The shear strength of the soil treated using MICP increased, with its internal friction angle remaining unchanged at 22\({}^{\circ}\), and the cohesive force increased from 40 kPa to 60 kPa. The soil treated using MICP was less compressible, with its compression index decreasing from 0.225 to 0.172;
2. The results of the mechanical and XRD experiments show that the process of microbial-induced calcium carbonate formation, which reinforces the soil, mainly occurred in the first 14 days;
3. The calcium carbonate crystals between reinforced clay aggregates were relatively small, primarily serving a bonding role and having little effect on the internal friction angle of the soil. The XRD experiment results also show that microorganisms not only induce the Ca\({}^{2+}\) provided in the consolidating fluid to produce calcium carbonate but also promote the production of calcium carbonate from the Ca\({}^{2+}\) originally contained in the soil.  
Writing--review and editing, H.W. and C.X.; investigation, J.L. and L.Z.; validation, C.X. and J.L.; supervision, X.X., X.Z. and L.Z.; project administration, H.W., X.X. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52378372), the Zhejiang Provincial Natural Science Foundation (No. LHZ23E080002), and the Zhejiang Water Science and Technology Project (No. RB2117). We gratefully acknowledge the financial support we received.  
Not applicable.  
The data presented in this study are only available from the corresponding author upon request due to (specify the reason for the restriction).  
Author [PERSON] was employed by the company China Aerospace Planning and Design Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.