esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Groundtrack Pattern of a Single Pair Mission on the Gravity Recovery Quality
## 1 Introduction
In order to have a clearer overall view of this paper, it is important to mention that the GRACE Follow-on (GFO) mission has already been realized; therefore, the result of this research does not directly address the performance of GFO in the course of its orbit. However, our study tries to address the general impact of the sampling pattern on gravity recovery, which is also of great importance for understanding the quality variations by GFO products in its future life, as it is expected that by different sampling (ground-track pattern change) within GFO at different time-interval solutions, its recovery quality is also going to vary significantly. Another important point to be mentioned is that, similar to the GRACE mission in the recent years, the science community expects to have short-interval gravity recovery (shorter than month) for GFO as well, particularly since GFO is benefiting from more advanced instrumentation, and hence, fewer measurement errors. That makes the weekly and shorter than weekly solutions more interesting which can be allowed by CNR and partially MCNR, and is the focus of this paper. For this purpose and to investigate the sampling impact on the recovery quality, a few examples are chosen for our study's scope. Here, we test some scenarios with different \"right ascension of ascending node\" (RAAN) values over a hydrological basin in central Africa. Indeed, in both theory and reality, RAAN changes with time, and unless implementing mission control by humans during the satellite lifetime (which is rare, and mainly for altitude and repeat-orbit correction), the satellite path is not continuously corrected in order to bring RAAN to its initial value. Therefore, the selected RAAN values and the choice of central Africa in this research work should be seen as examples to study the recovery quality variations in different sampling scenarios. In fact, in this study, we do not address orbit optimization in specifics (which is the function of many other parameters), but instead, we try to address the importance of sampling pattern on the quality of gravity recovery through few examples. The choice of central Africa in the paper refers to its location close to the equator, which means a bigger area on the Earth, and so less dense sampling in the spatial domain when compared with sampling the higher latitudes. This choice can therefore address the sampling impact in a clearer way. Furthermore, the particular choice of the RAAN values refers to different sampling behavior, i.e., to the case where sub-cycle happens and to the cases where it does not happen in that area (and at that specific time).
## 3 Methods
### GAM-MLP Information Fusion
#### 3.4.2 Activity Classification and Recognition  
The GAM-MLP model concludes with a fully connected layer followed by a SoftMax layer for classification purposes [32]. The diagram illustrating the classification and recognition process in the MLP of layer plus SoftMax layer is shown in Figure 7 below.  
The function of the SoftMax layer is to transform neural network output scores into a probability space, ensuring category probabilities range from 0 to 1, summing to 1. For neural network output scores \(z=(z_{1},z_{2},\dots,z_{n})\), the Softmax layer's output is given by  
\[y_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{n}e^{z_{j}}} \tag{21}\]  
Figure 7: Flow chart depicting the classification and recognition in the MLP of layer plus SoftMax layer.  
Here, \(e\) is the base of the natural logarithm, and \(y_{i}\) represents the probability for the ith class.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 2 Preliminary  
### Polynomial Response Regression (PRS)  
Suppose a deterministic function of \(m\) design variable has been evaluated at \(n\) sample points. The \(l\)th sample point is denoted as \(\mathbf{x}^{(l)}=\left[x_{1}^{(l)},x_{2}^{(l)},\ldots,x_{m}^{(l)}\right]^{T}\) and the associated response is \(y^{(l)},l=1,2,\ldots,n\). The polynomial regression approximation to the true response \(y(\mathbf{x})\) can be written as  
\[y(\mathbf{x})=\hat{y}(\mathbf{x})+\varepsilon, \tag{1}\]  
where \(\varepsilon\) is the error associated with the approximation, and \(\hat{y}(\mathbf{x})\) is the approximate function which is a sum of basis functions with their coefficients  
\[\hat{y}(\mathbf{x})=\left[p_{1}(\mathbf{x}),p_{2}(\mathbf{x}),\ldots,p_{N_{b}} (\mathbf{x})\right]\beta, \tag{2}\]  
where \(N_{b}\) is the number of the basis functions. \(\beta=\left[\beta_{1},\beta_{2},\cdots,\beta_{N_{b}}\right]^{T}\) is the regression coefficients vector, and \(\left[p_{1}(\mathbf{x}),p_{2}(\mathbf{x}),\ldots,p_{N_{b}}(\mathbf{x})\right]\) denotes the basis functions vector. In this study, the second-order polynomials are used for \(\hat{y}(\mathbf{x})\), which is  
\[\hat{y}_{PRS}=\beta_{0}+\sum_{i=1}^{m}\beta_{i}x_{i}+\sum_{i=1}^{m-1}\sum_{j= 2,i<j}^{m}\beta_{ij}x_{i}x_{j}+\sum_{i=1}^{m}\beta_{ii}x_{i}^{2}, \tag{3}\]  
where \(\beta_{S}=\left[\beta_{0},\beta_{1},\cdots,\beta_{m},\beta_{11},\beta_{12}, \ldots,\beta_{mm}\right]^{T}\) is the regression coefficients vector of the second-order polynomials, and can be solved by the least-squares estimation method \[\mathbf{\beta}_{S}=\mathbf{\Phi}^{+}\mathbf{y} \tag{4}\] \[\mathbf{y}=\left[y^{(1)}\ y^{(2)}\ldots y^{(n)}\right]^{T}\] \[\mathbf{\Phi}=\left[\begin{array}{cccccccc}1&x_{1}^{(1)}&\cdots&x_{m }^{(1)}&{(x_{1}^{(1)})}^{2}&x_{1}^{(1)}x_{2}^{(1)}&\cdots&{(x_{m}^{(1)})}^{2} \\ 1&x_{1}^{(2)}&\cdots&x_{m}^{(2)}&{(x_{1}^{(2)})}^{2}&x_{1}^{(2)}x_{2}^{(2)}& \cdots&{(x_{m}^{(2)})}^{2}\\ \vdots&\vdots&\cdots&\vdots&\vdots&\vdots&\cdots&\vdots\\ 1&x_{1}^{(n)}&\cdots&x_{m}^{(n)}&{(x_{1}^{(n)})}^{2}&x_{1}^{(n)}x_{2}^{(n)}& \cdots&{(x_{m}^{(n)})}^{2}\end{array}\right],\]  
where \(\mathbf{\Phi}^{+}=\left(\mathbf{\Phi}^{T}\mathbf{\Phi}\right)^{-1}\mathbf{\Phi}^{T}\) is the Moore-Penrose pseudo-inverse of \(\mathbf{\Phi}\).
# Stimuli-Responsive Photonic Crystals
## 4 Active Tuning of the Photonic Band Gap
### Thermal and Magnetic Stimuli  
Photonic crystals fabricated using thermoresponsive materials like polymers or colloidal dispersions can be easily tuned through the application of a temperature gradient [48; 62; 63; 105; 106]. [PERSON] et al. fabricated a SiO\({}_{2}\) PhC and infiltrated the pores with a thermo-sensitive Poly (N-isopropylacrylamide) (PNIPAM) hydrogel. The thermal variation generates a blue shift of the photonic band gap and exhibits a reversible response in the range from 24 \({}^{\circ}\)C to 31 \({}^{\circ}\)C [64].  
Magnetically responsive PhCs are usually fabricated by integrating magnetic nanoparticles in the structure [16]. Herein, an external magnetic field interacts with the active material and changes its optical properties, orienting it according to the direction of the field [65; 66; 67; 68; 69; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109]. [PERSON] et al. synthesized some polyacrylate-capped superparamagnetic magnetite (Fe\({}_{3}\)O\({}_{4}\)) colloidal nanocrystal clusters (CNCs) with sizes from 30 to 180 nm. These clusters self-assembled into colloidal photonic crystals in solution. In Figure 2c,d, the change in the optical response is visible, as the magnetic field changes by controlling the distance between the sample and an NdFeB magnet [110].
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
###### Abstract  
Network layer multicast is a powerful method for transmitting data from sources to multiple group members. When joining a multicast group, a group member first sends a request to a designated router (DR). Then, the DR selects a node in the existing multicast tree (known as a multicast joining node, or MJN) to establish a multicast distribution path from the MJN to itself. The MJN selection method runs on the DR and has a significant impact on the distribution of the multicast tree, that directly affects the load distribution in the network. However, the current MJN selection method cannot effectively detect the load status of the downlink multicast path in the case of asymmetric routing, leading to network congestion and limiting the number of multicast groups that the network can accommodate (multicast capacity). To solve this problem, we propose an MJN selection method based on the reverse shortest path tree (RSPT). RSPT can effectively detect the load status of downlink multicast paths in case of routing asymmetry. Based on the detection results of RSPT, DR can select the MJN with the lowest path load to join the multicast tree. Our experimental results indicate that compared to existing multicast methods, our method has a lower cost and delay, and can effectively balance the network load in the case of asymmetric routing, increasing multicast capacity by more than two times.  
network layer multicast; multicast joining node; asymmetric routing;multicast capacity +
Footnote †: journal:  
00 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 2 Materials and Methods
### Infrastructure and Functional Components
#### 2.3.4 Manufacturing Execution System  
MESs are used in manufacturing to track the evolution of raw materials until they become finished products. In this way, it can help the decision makers to analyze in real time the current scenario on the shop floor and act to improve the production process.  
With new technologies emerging, MES allows the development of the Industrial Internet of Things (IIoT) environment, where all components may be connected and may interact with each other, from the low level within the factory to the higher level of analysis tools. It builds the bridge between the planning system (ERP, LCA, etc.) and the control system (PLC, SCADA, etc.), as shown in Figure 9. However, it needs to be designed to facilitate the integration of legacy systems to be compliant with Industry 4.0 technologies.  
The inclusion of MES in the platform will allow better access to information about the production, improve quickness in decision making, reduce both production time and costs, detect failures in time to correct them, improve the quality of the final product, increase consumer satisfaction, and more. This can be achieved through digitization, which allows adapting the manufacturing systems to I4.0 and new technologies such as IoT and AI.  
The functional layers of MES should be as follows:  
Production parameterization: includes version control, production rules, bills of material, and resources, focused on defining how to manufacture the product.  
Management of resources: focuses on registration and analysis of resource information, aiming to prepare and execute production orders with the right and available resources.  
Production planning: determines the production schedule to meet the production requirements received from the ERP (or a specialized scheduling system).  
Production execution: checks resources to inform other sub-systems regarding the production progress.  
Production traceability: tracks the process information to have a complete sequence of lots, orders, and equipment.
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 2 Optical Packaging
### Fiber-to-PIC Coupling
#### 2.1.3. Evanescent-Coupling  
A new approach to Fiber-to-PIC coupling, termed 'evanescent coupling' was first applied to Si-Photonics by IBM-Zurich in 2015 [36]. In this scheme, as in edge-coupling, an inverted-taper is used to efficiently extract the mode from the PIC waveguide. However, instead of being captured by an on-PIC SSC, the mode evanescently couples into a second waveguide on a different optical-chip, that is in very close face-to-face proximity with the PIC--see Figure 7. The refractive index and MFD on the optical-chip can then be easily matched to that of standard SMF fibers, allowing for a two-step (Fiber-Chip-PIC) coupling process. Currently, chip-to-chip evanescent coupling has only been demonstrated between a Si-PIC and a polymer-based optical-chip, but there is no clear reason why this approach cannot be transferred to glass- or SiON-based optical-chips for more robust photonic devices.  
Evanescent coupling is attractive, because it offers all the characteristic advantages of edge-coupling, such as low insertion-loss, broadband coupling, and low sensitivity to polarization, as well as the relaxed alignment tolerances typical of grating-coupling [36]. Additionally, unlike edge-coupling, the inverted-taper of the evanescent coupler does not need to be located at the edge of the PIC, and does not depend on precision dicing and polishing of the PIC edges, further reducing its cost to implement at volume.
## 3 Elastographic Methods
### Two-Dimensional Shear Wave Elastography (2D-SWE)
#### 3.3.4 Number of Measurements Needed  
2D-SWE enables evaluation of the velocity of several shear wave fronts in real-time. There are no specific manufacturer recommendations on how many measurements are sufficient to obtain reliable results. In addition, repeating procedures to obtain 10 measurements is challenging in children. The mean LSMs obtained with three, five and seven acquisitions demonstrated almost perfect agreement with the reference obtained with 15 acquisitions in both free-breathing and breath-holding status. Three acquisitions can be enough for hepatic LSMs in children older than 6 years regardless of breathing status or hepatic pathology. More acquisitions are recommended for children under the age of 5 years during free breathing [72]. To reach an acceptable liver stiffness error range below 5%, the use of the SI to identify unreliable measurements was found to reduce the number of acquisitions required from 8 to 5 in the free-breathing group, and from 6 to 2 in the breath-hold group [74].
## 2 Working Principle Qualitative Explanation  
Figure 1a depicts the block diagram of the directly modulated RSOA followed by a serially connected BFL. The purpose of directly modulating the RSOA is to imprint the digital information contained in an RF signal applied to the RSOA electrical interface on an optical signal that constantly drives the RSOA. However, the faster the digital content of the RF excitation modifies the RSOA bias current, the more the data equivalent bandwidth exceeds the RSOA nominal modulation bandwidth, \(B_{mod}\), which is finite [27], and hence the more is the process far from being ideal. As a consequence, the performance of the directly modulated RSOA becomes pattern-dependent and eventually unacceptable. Nevertheless, this performance limitation can be overcome and the RSOA be directly modulated at an extended data rate than that allowed by its limited modulation bandwidth by means of optical notch filtering. The goal pursued through the exploitation of this technique is to produce a filter characteristic which is the opposite to that of the RSOA, as shown in Figure 1b [28]. This equalizes the total frequency response and makes it independent of the electrical modulation frequency for a wider range than for the RSOA only. Moreover, by properly tailoring the frequency of occurrence of the repetitive lobes and the points of their extinction with respect to the spectral position of the encoded pulses, the spectral content of the latter can be acted upon by the filter's transmission according to their exact binary content [21; 27]. In this manner, the impairments associated with these components due to the irregular RSOA gain variation by the electrical excitation are converted by the filter's slope into amplitude changes which cancel those after the RSOA [29]. Consequently, the uniformity between output pulses of same binary content and the distinction between output pulses of different binary content can be significantly enhanced. Thus the performance degradation of the encoded optical signal at the RSOA output can be sufficiently suppressed so that RSOAs can efficiently support the target applications both as external modulators and amplification elements. The desired spectral response of the compensating frequency discriminator can be technologically implemented in the polarization-based domain using a BFL, as described in the following.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 6 VLC Application  
VLC provides many applications from high-speed internet chains with LED bulbs to interplanetary communication and quantum communication. VLC also brings a new perspective to computing that is considered ubiquitous. In this section, we discuss the potential of visible light communications, focusing on these applications: indoor VLC [2; 10; 12; 88], ID and position systems [6; 126], in-vehicle communications [127], and underwater communication [6; 128].
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 2 Reinforcement Learning
### Actions, Policies, and Value Functions
The fixed-point equation in (5) reduces this complexity, but not to the extent that direct computations are feasible. Deep RL finds approximate solutions to (3). The value function \(V_{\pi}(s)\) is modeled by a neural network. Instead of directly computing (3), RL samples from the next reachable subset of states and observes the accrued reward. If the neural network is sufficiently deep and the subset of sampled states is sufficiently large, there is a decent chance the estimate \(\hat{V}_{\pi}(s)\) will converge to the actual \(V_{\pi}(s)\). There is no formal proof of this statement, only compelling experimental results and simulations [11; 12]. The fixed-point Equation (5) still plays a key role in deep RL, albeit indirectly through the \"update rule\". Each new observation feeds into a running estimate \(\hat{V}_{\pi}(s)\) of \(V_{\pi}(s)\):  
\[\hat{V}_{\pi}(S_{t})+\alpha(R_{t+1}+\gamma\hat{V}_{\pi}(S_{t+1})-\hat{V}_{\pi} (S_{t}))\rightarrow\hat{V}_{\pi}(S_{t}). \tag{6}\]  
The parameter \(\alpha\) refers to the sensitivity of the estimate to the newly acquired data (the observed reward \(R_{t+1}\)). The update rule in (6) is called the \"Bellman update\". In deep RL,the difference between the update and the previous estimate (the loss function) drives the parameters of the neural net in the direction of the update via stochastic gradient descent.
## 2 Instrument Design  
MERITXELL is a ground-based multisensor instrument which includes a multiband dual-polarization microwave radiometer with several additional features that make it unique as compared to other instruments. Its name stands for Multifrequency Experimental Radiometer with Interference Tracking for eXpEriments over Land and Littoral. MERITXELL has been designed to fulfill the following requirements:  
* to include all passive radiometric bands up to 100 GHz used for Earth observation from a satellite (this excludes the 50-60 GHz bands ([1], Chapter 1)).
* to be built using a flexible back-end system to allow custom signal processing techniques for RFI detection, localization and mitigation, and data fusion algorithms.
* to include several optical sensors such as a thermal infrared and a multispectral camera with visible and near-infrared bands. These optical sensors are able to retrieve the complementary measurements used by data fusion algorithms.
* to include a GNSS reflectometer to retrieve GNSS-R data for data fusion algorithms.
* to be mounted in a ground-based mobile platform that allows to transport it, and to point it to any desired position in azimuth and elevation.  
An overall diagram of all constituent blocks of MERITXELL is shown in Figure 1. Preliminary versions of the instrument and its sub-systems were already introduced in [21; 22; 23; 24]. Nevertheless, the aim of this section is to present the final version of each sub-system, as well as the final integrated instrument.  
The following subsections describe each one of the different parts of the instrument, including the multiband radiometer, the temperature stabilization system, the additional sensors, and the enclosure and mobile unit.
## 3 Analysis and Design of the Force Control System
### Disturbance Rejection  
Consider the uneven characteristics of the surface, a trapezoid with slopes of magnitude \(M\) (see Figure 4a). Then, the disturbance \(x_{d}\) is modeled using a ramp signal \(X_{d}(s)=\frac{M}{s^{2}}\) with magnitude \(M\), and \(s\) is the Laplace variable. Note that the ramp magnitude \(M\) is proportional to the velocity \(v_{y}\) at which the task is executed. For example, when the robot  
Figure 3: Block diagram of the force control system.  
executes a linear movement from point A to point B, the bigger the velocity \(v_{y}\), the bigger the magnitude \(M\) of the ramp in the \(x\) direction.  
The performance of the force control system in Figure 3 is analyzed using the steady-state error \(e_{ss}=\lim_{s\to 0}sE(s)\), where \(E(s)\) is the force error \(e\) in the Laplace domain; then  
\[e_{ss}=\lim_{s\to 0}s\frac{KG(s)}{1+G_{c}(s)KG(s)G_{LP}(s)G_{T}(s)}X_{d}(s) \tag{5}\]  
where \(G_{c}=K_{p}+K_{d}s+\frac{K_{t}}{s}\) is the controller, \(G(s)=\frac{1}{s(t\approx 1)}\) is the transfer function of the robot, \(G_{LP}(s)\) is the filter, \(G_{T}\) is the transport delay, \(X_{D}(s)\) is the external disturbance, and \(K\) is the stiffness.  
Considering the ramp disturbance \(X_{d}(s)=\frac{M}{s^{2}}\), the control \(G_{c}(s)=K_{p}+K_{d}s+\frac{K_{t}}{s}\) ensures zero steady-state error \(e_{ss}\)[18]. However, the integral term \(\frac{K_{t}}{s}\) has a drawback since it produces a sluggish and oscillatory response.  
On the other hand, when the controller is \(G_{c}(s)=K_{p}+K_{d}s\), the steady-state error is \(e_{ss}=\frac{M}{K_{p}}\), but it can be reduced by incrementing the control gain \(K_{p}\) (or the control magnitude \(|G_{c}(s)|\)). In Figure 5, the curves for different values of \(e_{ss}\) are presented. These curves are obtained from \(e_{ss}=\frac{M}{K_{p}}\) using different ramp values and gains \(K_{p}\).  
The curves in Figure 5 represent a design tool considering the disturbance ramp magnitude \(M\) (proportional to the velocity \(v_{y}\) at which the task is executed) and force error  
Figure 4: The uneven characteristics of the surface. (**a**) Ramp shape and (**b**) sinusoidal shape.  
Figure 5: Curves of steady-state error \(e_{ss}\) in terms of control gain \(K_{p}\) and ramp value \(M\).  
via steady-state error. If the magnitude \(M\) is known/estimated, one can obtain the control gain \(K_{p}\) that produces the steady-state error \(e_{ss}\) presented in the curves, and vice versa, if a certain steady-state error \(e_{ss}\) is desired, one can choose the gain \(K_{p}\) producing this error.  
Note than the stiffness value \(K\) does not appear in disturbance analysis presented in this section. However, the value of \(K\) matters when it is big since the stability of the force control system may be compromised. Furthermore, one should be careful when the gain \(K_{p}\) is selected, since a big \(K_{p}\) value may affect the stability too.