esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Near Real Time Weather Prediction Applications, User Connectivity Initiatives  
### JPSS Data in Numeric Weather Prediction (NWP) Assimilations  
The S-NPP ATMS radiances and CrIS FSR data sets are routinely accessed and assimilated by NWP centers worldwide, and forecast experiments conducted with the assimilation of these radiances have shown a positive impact [5]. Following the operational systems development starting with AIRS near-real-time processing using NUCAPS [112], NWP centers worldwide have accessed several hyperspectral radiance products, such as the thinned radiance data sets, principal component scores, and cloud-cleared radiances, etc. The NUCAPS enterprise processing system that runs on Aqua-AIRS, MetOp-A/B-IASI, and S-NPP/NOAA-20 CrIS instruments provide the retrieval products to the forecast users. The NOAA NWS, the ECMWF, and many other operational agencies worldwide assimilate these radiances into their NWP models. Forecast Sensitivity Observation Impact (FSOI) experiments conducted by [PERSON] et. al. [44] with CrIS data assimilation into the Global Forecasting System (GFS) show that utilizing 104 FSR channels from the new channel selection and adjusting assimilation weights has improved the CrIS ranking from 9 th to 5 th of the total moist energy norm computed from the Global Data Assimilation System (GDAS) ensembles.
# In-Band Full-Duplex Relaying for SWIPT-Enabled Cognitive Radio Networks
## 2 System Model and Problem Formulation  
As illustrated in Figure 1, we consider a CRN consisting of a primary system with one PT and one PR and a secondary system with one ST and one SR. A decode-and-forward (DF)-based FDR equipped with \(N\)-receive and \(M\)-transmit antennas is primarily employed in the secondary system, under the assumption that there is no direct link from ST to SR due to path loss and shadowing. We denote the channel vectors from PT/ST to FDR by \(\mathbf{h}_{z}\in\mathbb{C}^{N\times 1},\ z\in\{p,s\}\), while \(\mathbf{g}_{z}\in\mathbb{C}^{1\times M}\) represents the channel vectors from FDR to PR/SR. \(f_{ps}\in\mathbb{C}\) and \(f_{pp}\in\mathbb{C}\) stand for the channel responses from PT to ST and to PR, respectively. FDR is assumed to be a multiple-input and multiple-output (MIMO) device while PT and ST are merely equipped with one antenna (without loss of generality, the proposed system model can be extended to MIMO scenarios for PT and/or ST to further improve the system performance). Hence, \(\mathbf{H}_{RR}\in\mathbb{C}^{N\times M}\) designates the self-interference (SI) channel from the transmit antennas to the receive antennas at FDR. In particular, the residual SI can be modeled as \(\mathbf{H}_{RR}=\sqrt{\rho}\mathbf{H}_{0}\), where the entries of \(\mathbf{H}_{0}\) follow a Rician distribution, and \(0\leq\rho\leq 1\) denotes the level of SI suppression after applying cancellation techniques as proposed in [25].  
**Remark 1**: _In this paper, we focus on the effect of the TSR protocol under the perfectly known channel state information (CSI). Therefore, the results of this paper will provide an upper bound of system performance. However, the following proposed model and method can be applied to a robust design under channel uncertainty by decomposing the perfect channels into the channel estimates and estimation errors [6]._
## 4 Encrypted SFC Architecture
### The Service Function Forwarder Controller  
In our previous work [3], we have suggested using BGP as a control plane mechanism in order to have a standardised method of exchanging NSH routes. However, we identified concerns related to scalability and security of using BGP. Applying route security in BGP includes that each route has to be authorised on a per-peer basis and all viable routes need to be pre-enumerated. In addition, with no route aggregation, route propagation and exponential growth of BGP routes for EFs, we question the scalability of BGP as an NSH control plane protocol. Hence, we changed the control plane component from our previous work [3] from BGP to distributed RESTconf. This opened up for more efficiently giving the exact routing instructions to the specific Compute Nodes only. RESTconf also enabled a more flexible authorisation of the NSH routes, by authenticating the RESTconf connection by the Secure Socket Layer (SSL).  
However, we also selected RESTconf as the control plane protocol due to interoperability reasons. In federated multi-tenant NFV environments, an overlay network is created with virtual forwarding devices such as an SFF. This opens up for customising the virtual network devices and the network controller. This enables network operators to deploy customised networking software in an agile and fast manner. By utilising P4, it is also possible to specify customised flow rules when configuring these network devices. Hence, we utilised the feature of SDN-based network programmability, by specifying customised network configuration by the use of P4 flow rules over RESTconf. The need for customised flow rules is reasoned by the new NSH header extension.  
For Proof-of-Concept purposes, we created a very simple RESTconf based network controller with a set of predefined P4 flow-entries. It consists of a simple HTTP client that distributes flow rules over RESTconf by using Linux shell scripts.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 2 Method
### CAD Models  
Figure 1 shows the CAD models used for simulation. Two tower models were used as shown in Figure 1a,b with different constructive parameters as presented in Table 2. The truncated cone shape is the most common shape, while exponentially tapered towers are noteworthy exceptions.  
The top and bottom sides of the investigated wind-turbine rotor blades are shown in Figure 1c,d; Blade v1 is in yellow, Blade v2 is in green, and Blade v3 is in red. Blade v1 is the simplest model, made from a single aerodynamic profile, tapered towards the wing tip, and with a straight leading edge. Blade v2 also features a straight leading edge but is composed of different aerodynamic profiles along the span towards the end and also wing twist (\"washout\" in context of aircraft), i.e., a decreasing angle of attack from root to tip. The profiles are taken from the NACA and DU Series catalogs [35; 36; 37]. Blade v3 was originally created as an experimental platform for textile-based sensors by TU Dresden's Institute of Textile Machinery and High Performance Material Technology [38]. It also features different airfoil profiles, and in addition to blade v2, it has a curved leading edge. Blade v1 is 58 m in length; blades v2 and v3 are 63 m. This corresponds to the size of current wind turbines with 3-4 MW nominal power output and rotor diameters of about 125 m.
# HISEA-1: The First C-Band SAR Miniaturized Satellite for Ocean and Coastal Observation
## 3 Conclusions  
As the first C-band SAR ocean remote-sensing small satellite, HISEA-1 can obtain images with a spatial resolution of up to 1 m and a large imaging width of 100 km. The  
Figure 5: Image of Delaware Bay obtained by striping mode. (**a**) Whole SAR image of Delaware Bay whose gridding size is 13,996 \(\times\) 44,480 (horizontal \(\times\) vertical) and spatial resolution is 3 m. (**b**) Enlarged view in the top rectangle area in Figure 1a as the arrow points. (**c**) Enlarged view in the middle rectangle area in Figure 1a as the arrow points. (**d**) Enlarged view in the bottom rectangle area in Figure 1a as the arrow points.  
first batch of images from HISEA-1 provided considerable information about the land and seas. Images of land could identify different landcover, including buildings, arable lands, highways, rivers, and lakes. It could also provide 3D features of high rises. On the sea, sea ice, some vortex phenomena, oil film, rip currents, etc., could be identified in the images. Some wind and wave parameters could also be retrieved from the SAR images of the ocean.  
This paper has confirmed the great potential of HISEA-1 in ocean and coastal observations. Given the relatively low weight and cost, it is more convenient to construct the satellite constellation with more small satellites such as this, which is what we are planning and will be carried out in the foreseeable future. However, HISEA-1 is still in the commissioning stage and the calibration has not yet been finally finished; more relevant analysis and products will be generated as the calibration and track data are completed.  
Conceptualization, X.G. and S.X.; writing--original draft preparation, S.X.; writing--review and editing, X.G., L.M., X.-H.Y. and L.H.; visualization, S.X. and T.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2019 YFA0606702), the SOA Global Change and Air-Sea Interaction Project (grant numbers GASI-02-PAC-YGST2-02) and the National Natural Science Foundation of China (grant numbers 41630963, 41776003 and 91858202).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available because HISEA-1 is still in commissioning stage.  
The authors would like to thank Spacety and China Electronics Technology Group Corporation (CETC) 38 th Institute for developing satellite platform and SAR payload, and thank all participants and collaborators involved in the HISEA-1 C-band SAR small satellite project.  
The authors declare no conflict of interest.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 5 Result and Discussion
### Analysis of Training and Validation Data  
The _noncontextual_ model started converging after about 60 epochs as indicated in the accuracy and loss plots in Figure 7a,b. Compared to the _noncontextual_, the _contextual_ model as shown in Figure 8a, b converged much earlier at about 30 epochs.  
Although it took the _contextual model_ about 161 s to be interrupted by the early-stopping call-back, the _noncontextual_ model ran for another 100 s more before being interrupted. This is, however, surprising given that the _contextual model_ is exposed to more input signals and is expected to take a longer time to train. This suggests that the _contextual_ model was faster at discovering the temporal relatedness between the signals using the additional ambient  
Figure 8: Line graph showing (**a**) accuracy plot and (**b**) loss plot as a function of number of epochs for the _contextual_ model.  
Figure 7: Line graph showing (**a**) accuracy plot and (**b**) loss plot as a function of number of epochs for the _noncontextual_ model.  
signals. Although given the stochastic nature of deep learning algorithms, convergence as a function of number of epochs sometimes varies [24].  
Moreover, the training accuracy and loss plots (Figure 9) show how the two models compare during training. Figure 9 clearly shows that the _contextual_ model, for most of the training process, continues to outperform the _noncontextual_ model. Although the two models seemed to match one another in performance toward the tail end, at the point in time the _noncontextual_ model trained better. In Figure 10, we compare the two models' performance with the validation data. Once more, the _contextual_ model outperforms the _noncontextual_ model in the first 10 epochs and even throughout the validation process confirming that the additional input signals from the context sensors produced superior performance to the noncontextual model.  
While neural networks are known to be robust to noise, model accuracy often improves when time-series data are preprocessed for noise reduction and smoothing using median filters [32].
# Multi-Classifier Pipeline for Olive Groves Detection
## 3 Results
### Performance Evaluation  
The evaluation of performance intends to check if there is an improvement in the olive trees detection by the multi-classifier pipeline compared to the baseline pipeline. Table 4 shows the TPR of the baseline pipeline and the multi-classifier pipelines. The multi-classifier pipeline using MLP raised the TPR to 83.5% while the pipeline using OCSVM reached 87.1%. The improvement is also observed in FPR, which is described in Table 5. The multi-classifier pipeline decreased the FPR by approximately 40% and 13% using MLP and OCSVM respectively, compared to the FPR calculated in the baseline pipeline.
## 2 Literature Review
### Research Objectives and Significance  
The studies in Sections 2.1-2.3 have thoroughly discussed the impact of outdoor environmental performance on elderly individuals, emphasizing the critical role of outdoor thermal environments and sunlight hours in promoting both the physical and mental health of the elderly. Additionally, these studies highlight the positive effects of optimizing block spatial morphology on improving outdoor environmental performance. From a methodological perspective, performance-driven design workflows based on parametric design platforms such as Rhino/Grasshopper have been widely recognized for their significant potential in multi-objective optimization tasks for outdoor environmental performance. These existing studies have facilitated the precise identification of spatial morphological indicators that may influence outdoor environmental performance, enabling architects to uncover the intrinsic relationships between \"morphology and environment\". This, in turn, provides valuable design decision-making support for a range of applications, including urban design, architectural design, and urban renewal.  
This study focuses on the outdoor environmental performance of elderly-oriented blocks. It applies the performance-driven workflow of \"morphology generation-performance evaluation-morphology optimization\" to enhance outdoor environmental performance for elderly groups. Using the combinations of various architectural forms within different prototypical blocks as decision variables determined by multi-objective optimization, this study aims to maximize the winter pedestrian UTCI (UTCI-W), minimize the summer pedestrian UTCI (UTCI-S), and maximize sunlight hours (SHs) as objective functions. It explores block morphology layouts under optimized outdoor environmental performance conditions, providing design references for elderly-friendly block layout schemes.  
Furthermore, considering the advantages of machine learning over traditional regression methods in offering more reliable and accurate predictions, this study introduces the interpretable machine learning algorithm XGBoost (eXtreme Gradient Boosting) to uncover the nonlinear relationships between block spatial morphology and outdoor environmental performance. By identifying spatial morphological indicators that may affect the outdoor environmental experience of elderly individuals, this research combines parametric modeling and machine learning approaches to optimize elderly-friendly block morphology and achieve the high-precision identification of block morphology indicators influencing outdoor environmental performance.  
The outcomes provide early-stage design decisions for block layout, reducing costs associated with later-stage environmental performance optimization and retrofitting. This research offers valuable references for the planning and layout of elderly-friendly blocks, improving the outdoor environment for elderly residents, enhancing their quality of life, and promoting the physical and mental health of the elderly population.
## 3 Primary Controller Design
### Power Sharing Strategy  
Figure 6 illustrates the control diagram for the power sharing strategy involving power injection in a grid-tied operation and voltage regulation in case of an islanded operation. In both modes, the power sharing control operates in the same way. Namely, the SESS is in charge of regulating the voltage in the dc bus (yellow region with controller \(C_{dcs}(s)\)) or delivering a power reference to the bus in case of a power injection (orange region with controller \(C_{dcs}(s)\) and reference generated by the secondary control). In the meantime, the BESS is responsible for cancelling the SESS current in a steady state through controller  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transfer Function** & **Nomenclature** & **Description** \\ \hline \(\frac{L_{s}(s)}{L_{o}(s)}\) & \(G_{ccs}(s)\) & \(1\) \\ \hline \(\frac{V_{s}(s)}{L_{o}(s)}\) & \(G_{ccs}(s)\) & \(-\frac{\frac{1}{s}}{s+\frac{1}{C_{dcs}}}\) \\ \hline \(\frac{h_{fcs}(s)}{L_{o}(s)}\) & \(G_{dcs}(s)\) & \(B_{s}\) \\ \hline \(\frac{V_{m}(s)}{L_{o}(s)}\) & \(G_{dcs}(s)\) & \(\frac{-A_{s}L_{o}s^{2}+(B_{s}-A_{s}EL_{s})s^{2}+(B_{s}-A_{s}F_{s}-\frac{A_{s}} {s})s+\big{(}B_{s}F-\frac{A_{s}D}{C_{s}}\big{)}}{(C_{km}s+A_{s}B_{s})(s^{2}+E_{ s}+f)}\) \\ \hline \(\frac{L_{s}(s)}{L_{o}(s)}\) & \(G_{ccb}(s)\) & \(1\) \\ \hline \(\frac{V_{s}(s)}{L_{o}(s)}\) & \(G_{ccb}(s)\) & \(-\frac{\frac{1}{s}}{s+\frac{1}{C_{dcs}}}\) \\ \hline \(\frac{L_{s}(s)}{L_{o}(s)}\) & \(G_{dcb}(s)\) & \(-\frac{B_{s}}{B_{s}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transfer functions of the linearized sliding dynamics.  
Figure 5: Charging control loop implementing the CC-CV protocol.  
\(C_{dcb}(s)\). Thus, in terms of energy, it is the BESS that supplies the desired power to the bus, while the peaks and disturbances are absorbed by the SESS. Therefore, for the design of the controllers of current injection and voltage regulation modes, it must be taken into account that the SESS dynamics has to be faster than that of the BESS. The resulting dynamics from the interaction between the SESS and the BESS modules decouples the power demand of the ChPs into low-frequency (BESS current) and high-frequency (SESS current) components without resorting to filtering or estimating [11].  
The binary variable \(m_{sc}\) and its opposite \(\hat{m}_{sc}\) are used to change between the two operating modes of the SESS. With that in mind, \(m_{sc}=0\) corresponds to power injection and \(m_{sc}=1\) to voltage regulation. Moreover, both variables also reset the controller to preclude saturation in their outputs. The auxiliary memory variables \(mem_{s}\) and \(mem_{v}\) store the output value of the voltage regulation and power injection controllers, respectively, and operate in the same way as \(mem_{j}\) does in the charging control case. The controller \(C_{dcb}(s)\) does not require a secondary memory because it works continuously. A low-pass filter with a 10 kHz cutoff frequency has been applied to the \(i_{HESS}\) and \(i_{s}\) currents to obtain their respective dc components and a continuous-time error signal for the controllers.
# A Real-Time Numerical Decoupling Method for Multi-DoF Magnetic Levitation Rotary Table
## 4 Experiment Results  
### Experiment Setup  
The numerical current decoupling unit was implemented on the Xilinx(r) Virtex-7 VC707 Evaluation Board as given in Figure 6a. In FPGA implementation, except for the necessary aforementioned IP cores, the DAC (Digital to analog converter)board interface module and the SDIO (Secure digital input and output) bus module were also designed. The two modules can display and store a real-time solution on the oscilloscope and SD card, respectively. In the test, the moving part of the maglev system was emulated to move along the trajectory, given as Equation (16), in which the units of translations and rotations are millimeters and radians, respectively, so the system works in a large translational and rotational stroke. Thus, the trajectory module was also inserted into the implementation, as shown in Figure 6b.  
The real-time solution contains elements in the current-wrench matrix and the obtained current values for a desired force and torque vector. The data are displayed as the waveform on the oscilloscope via the DAC board. As there are only four channels on the oscilloscope, the displayed solutions are selected by a dual in-line pin (DIP) package switch. Additionally, these displayed dates should be transformed from a single floating-point format to a fixed-point format, then adjusted to LVDS (Low-Voltage differential signaling) format and sent in DDR (Double data rate)mode.  
Figure 5: Finite-state machine of current decoupling based on numerical wrench model and matrix operation.  
\[{}^{s}\mathbf{p}=(^{s}x,^{s}y,^{s}z,^{s}\alpha,^{s}\beta,^{s}\gamma)^{\text{T}}= \left(15\text{cos}\left(4\pi t\right),10\text{sin}\left(4\pi t\right),0.5+2t,0,0,10 \pi t\right)^{\text{T}} \tag{16}\]  
The design parameters given in Table 2 are employed in the layout of the RTL current decoupling module on FPGA. As analyzed in (13), the amount of the computation unit is \(14,336\). The latency of the **FT** and **MO** IP cores is \(254\) and \(3756\), respectively, referring to the HLS report, while the latency of the **SUM** IP core is \(4\) and the idle time between each coil solution is \(10\) clocks in the layout. Therefore, the time period is \(184.2\) us with the \(100\) MHz main frequency on the FPGA evaluation board. In this case, the sampling frequency would be \(5\) kHz for the real-time decoupling unit.  
The maglev system was also modeled with a program package running on Mathematica(r) named RadiaTM. This program package is a boundary element analysis tool, and solutions are used to evaluate the validation of the real-time solutions. Next, the current-wrench transformation matrix and the obtained current solutions are shown and compared.
## 2 System Description  
The hyperchaotic Chen system [27] is described as follows:  
\[\begin{split}\dot{u}&=a(v-u)+p,\\ \dot{v}&=du+cv-uw,\\ \dot{w}&=-bw+uv,\\ \dot{p}&=rp+vw,\end{split} \tag{1}\]  
where \(u,v,w\) and \(p\) are state variables and \(a,b,c,d\) and \(r\) are real parameters. Assume that \((u_{0},v_{0},w_{0},p_{0})\) is one equilibrium point in system (1). For convenience, by moving the equilibrium to the origin, system (1) can be written as  
\[\begin{split}\dot{x}_{1}&=-ax_{1}+ax_{2}+x_{4},\\ \dot{x}_{2}&=\alpha x_{1}+cx_{2}-u_{0}x_{3}-x_{1}x_{ 3},\\ \dot{x}_{3}&=v_{0}x_{1}+u_{0}x_{2}-bx_{3}+x_{1}x_{ 2},\\ \dot{x}_{4}&=w_{0}x_{2}+v_{0}x_{3}+rx_{4}+x_{2}x_{ 3},\end{split} \tag{2}\]  
where \(x_{1}=u-u_{0}\), \(x_{2}=v-v_{0}\), \(x_{3}=w-w_{0}\), \(x_{4}=p-p_{0}\), and \(\alpha=d-w_{0}\). System (2) is chosen as the drive system. The response system is given as:  
\[\begin{split}\dot{y}_{1}&=-ay_{1}+ay_{2}+y_{4}+k_{ 1}(x_{1}(t-\tau)-y_{1}),\\ \dot{y}_{2}&=ay_{1}+cy_{2}-u_{0}y_{3}-y_{1}y_{3}+k_{ 2}(x_{2}(t-\tau)-y_{2}),\\ \dot{y}_{3}&=v_{0}y_{1}+u_{0}y_{2}-by_{3}+y_{1}y_{2} +k_{3}(x_{3}(t-\tau)-y_{3}),\\ \dot{y}_{4}&=w_{0}y_{2}+v_{0}y_{3}+ry_{4}+y_{2}y_{3} +k_{4}(x_{4}(t-\tau)-y_{4}),\end{split} \tag{3}\]  
where \(y_{1,2,3,4}\) are state variables, \(k_{1,2,3,4}\) are coupling strengths and \(\tau\) is the time delay in signal transmission between systems (2) and (3). LS in systems (2) and (3) is reached if  
\[||y_{i}-x_{i}(t-\tau)||\to 0,\quad i=1,2,3,4,\quad for\quad t\to\infty. \tag{4}\]
# FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets
## 6 Mathematical Model
### Genetic Algorithm  
#### 6.2.1 Encoding  
The length of the chromosome is equal to \((m+1)*n\). The first \(n\) genes present the jobs' sequence, and the last \((n*m)\) ones are binary corresponding to the job position in relation to the maintenance period on each machine (just before or after the maintenance task).  
Figure 5 shows an example of the solution encoding where a set of two jobs and three machines is considered. The job number '2' will be initiated as the first job, followed by the job number '1' (two first genes). For the job number '1' (three next genes), the gene values indicate that it will be operated just before the maintenance period, in the first and the second machine, and after the maintenance period in the third one.