esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Works  
Centralized and virtualized radio access networks have brought some new problems concerning optimized allocation of radio processing and transmission resources. Most optimization studies have focused on the placement of baseband processing units in the conventional centralized radio access network scenario, in which the entire BBU processing is performed at a central site. Among others, MILP formulations for the BBUs placement problem in the C-RAN, in which dedicated point-to-point (P2P) connections are established over an optical network, have been proposed in [12; 13; 14]. The authors of [15] extended the analysis to the network resiliency and energy efficiency context. A heuristic was proposed in [16] for solving the BBU placement problem in survivable C-RANs established over optical networks. A graph-based method as well as a genetic algorithm were proposed for the functional split selection and BBU placement problem [17]. The authors of [18] focused on joint functional split selection and data scheduling in BBUs in a fronthaul network with P2P connections and imposed latency constraints. The optimization problem was modeled as an MILP problem, in which optimization goal was to minimize latencies in the network. A literature survey related to resource allocation problems in C-RANs was presented in [19].  
There are not many works in the literature concerning optimization of both packet-based fronthaul networks and the C-RAN architectures with distributed DU and CU processing. MILP modeling was applied in [20; 21] for functional split selection in the C-RANs in which P2P links are used in the fronthaul and midhaul sections of the network. These works focused on minimization of the power consumption and bandwidth usage in midhaul. The authors of [22] formulated an MILP optimization problem concerning joint placement of DU and CU entities in the C-RANs connected using optical networks. The MILP model was used to analyze the advantages of distributed RU-DU-CU processing in comparison to the C-RAN architecture. In [23], a C-RAN scenario with flexible splitting of radio processing functions into a number of entities placed at different sites of the optical transport network was studied. To formulate the radio processing function placement problem, a MILP approach was used. The authors of [9] addressed the functional split selection problem jointly with the routing problem in a packet-based (RU-CU) C-RAN network. To solve the problem, two heuristic algorithms were developed. The latencies model applied in [9] does not account for packet buffering. In [24; 25],heuristic algorithms were proposed for the problem of routing with latency and flow scheduling constraints in a packet-switched C-RAN network. Recently, the problem of flow allocation with latency constraints in the NGFI network has been addressed in [26; 27]. To model the problem, the MILP approach was used, and to solve it an efficient meta-heuristic algorithm was developed.
# Interactions between Phase-Separated Liquids and Membrane Surfaces
## 5 Biomolecular Condensates Promoting Membrane Protein Clustering Influence Protein Function
### The Synapse Is Organized by Multiple Membrane-Mediated Biomolecular Condensates  
Several biomolecular condensates--including multiple membrane-associated ones--have recently been identified contributing to organization and protein function at the synapse (a recent review by [PERSON] et al. examines these in great detail [72]). In brief, multiple distinct postsynaptic and presynaptic membrane-associated biomolecular condensates have been characterized. Several protein components of the postsynaptic density (PSD) phase-separated in solution and formed membrane clusters through association with membrane-localized NR2B (a receptor fragment). These biomolecular condensates, like those already mentioned, could promote actin polymerization, and proteins associated with inhibitory synapse PSDs were actively excluded from excitatory PSD condensates [73; 74]. A set of proteins that compose inhibitory PSDs, including a scaffold and neurotransmitter receptors, have also recently been shown to phase separate on supported lipid bilayers [75]. Proteins from the presynaptic active zone have also phase-separated on the membrane by clustering calcium channels, and may keep these channels close to SNARE machinery for rapid signaling [76]. A cytoplasmic biomolecular condensate formed from synapsin was able to sequester small lipid vesicles [77], which is supported by evidence that in vivo synaptic vesicle clusters have liquid-like properties [78]. The sequestering of synaptic vesicles by synapsin is just one instance in which phase-separated condensates interact with small lipid vesicles; we will next discuss a variety of similar vesicle-biomolecular condensate systems.
## Appendix D CDS-Hops: Detailed Derivation Steps
\begin{tabular}{l l} \hline \(G\) & Graph \\ \(V\) & Set of vertices \\ \(E\) & Set of edges \\ \(A_{DC}\) & Direct communication area \\ \(A_{MC}\) & Multi-hop communication area \\ \(A_{ellipse}\) & Area of ellipse \\ \(A_{FQ-ellipse}\) & Area of the first quadrant of the ellipse \\ \(n\) & Number of sensor nodes \\ \(D_{a}\) & Acceptable delay \\ \(V_{CDS}\) & Set of CDS nodes \\ \(V_{non-CDS}\) & Set of non-CDS nodes \\ \(E_{CDS}\) & Set of edges between CDS nodes \\ \(E_{non-CDS}\) & Set of edges between non-CDS nodes \\ \(E_{MST}\) & A subset of \(E_{CDS}\) that forms an MST \\ \(E_{non-MST}\) & A subset of \(E_{CDS}\) that does not form an MST \\ \(i\) and \(j\) & Node indices \\ \(S^{\prime}\) & Spanning tree \\ \(S^{\prime\prime}\) & A sub-graph of \(S^{\prime}\) that forms an MST \\ \(e(i,j)\) & Edge between \(i\) and \(j\) \\ \(a\) & Major axis of an ellipse \\ \(b\) & Minor axis of an ellipse \\ \(l(i,j)\) & Link between \(i\) and \(j\) \\ \(d\) & Euclidean distance \\ \(T_{GN}\) & Total number of GNs \\ \(\alpha\) & Offset factor that is used to compensate the distance margin \\ \(x_{i}\) & x-coordinate of node \(i\) \\ \(y_{i}\) & y-coordinate of node \(i\) \\ \(r\) & Radius of a circle \\ \(C\) & Circumference of a circle \\ \(A\) & Area of a circle \\ \(L_{p}\) & Length of AUV path \\ \(h_{CDS}\) & CDS hops \\ \(h_{non-CDS}\) & Non-CDS hops \\ \(N\) & Total number (of CDS or non-CDS edges) \\ \(P\) & Perimeter of the elliptical path traversed by an AUV \\ \(e\) & Eccentricity of an ellipse \\ \(\beta\) & A control variable for different trajectories of an AUV \\ \(dB\) & Decibels \\ \hline \end{tabular}   
**Table E4. ** List of symbols.
## 2 Zero Energy  
There are several definitions in the literature on zero energy. A zero energy building (ZEB) has greatly reduced energy loads such that renewable energy can supply the remaining energy needs of the building. Like sustainability, there are a number of definitions, and the devil is in the details when claims regarding a ZEB are made. There are no international agreed definitions of a ZEB yet. A ZEB can be considered to be a progression from passive sustainable building design, and the energy requirements of the building should be balanced between active and renewable technologies.  
The US Department of Energy was among the first to consider a ZEB. The EU (European Union) was among the first regions in the world to mandate its use. Article 9 (1) of the Energy Performance of Buildings Directive requires member states to \"ensure that: (a) By 31 December 2020, all new buildings are nearly zero-energy buildings; and (b) after 31 December 2018, new buildings occupied and owned by public authorities are nearly zero-energy buildings\" [5]. According to Article 2 (2) of the EPBD (Energy Performance Buildings Directive) a net zero energy building (NZEB) \"means a building that has a very high energy performance, as determined in accordance with Annex I. The nearly zero or very low amount of energy required should be covered to a very significant extent by energy from renewable sources, including energy from renewable sources produced on-site or nearby\" [5].  
According to [PERSON] [6], a ZEB is a building that has net zero energy consumption and zero carbon emissions annually. A true ZEB does not exist in reality. It is actually the concept of net zero where the sum of energy use and the energy generated in the building balance each other. Energy also has different meanings [7]:  
* Source energy
* Site energy
* Energy cost
* Emissions as a result of using energy  
Each of these are explained in some detail below.
## 4 Discussion
### WUE Responses to Phenology
In this case, summer ET may not increase if leaf transpiration and soil evaporation are limited by soil water availability [13], and so it exhibits a positive correlation with SOS. The decrease in GPP and ET due to earlier SOS linked with summer water deficit would imply Pattern III or Pattern VI. 3. Similar to spring, a longer growing season (later EOS) promotes autumn GPP, and might cause plants to close stomates and increase WUE because soil moisture deficit at this time of year is common. On the contrary, in some areas of United States, China and high latitudes (around 60\({}^{\circ}\) N), temperature and solar radiation during autumn tend to be negatively correlated, and warming would likely bring more cloudy weather and less insolation [60]. Hence, carbon assimilation may be limited by photosynthetically active radiation despite of an extended growing season [61], showing a negative relationship between autumn GPP and EOS. Besides, the offset between soil evaporation and leaf transpiration influenced by temperature and insolation determines the increase or decrease in autumn ET. This simultaneous decrease in GPP and ambiguous ET would imply Pattern I or Pattern II in autumn. [PERSON] et al. [4] have investigated variability of ecosystem-scale WUE at global scale using data-driven and process-oriented models and flux data, and showed that the seasonal variation of WUE trends is primarily attributable to effects of seasonal climatic variables. Here, our study further highlights that WUE during spring and autumn are extensively and significantly sensitive to phenology after statistically controlling for the climatic variables and CO\({}_{2}\) concentration. Even though during the summer WUE is also significantly affected by SOS in parts of regions with continental and subarctic climates. This demonstrates that phenology plays a crucial role in the regulation of ecosystem WUE, which should not be ignored in earth system models [13].
## 5 Applications
### Transistors  
For electronic device fabrication, carrier mobility is considered the main part of materials applications. By providing an externally applied field, the high mobility material's carriers respond quickly and this type of fast response is considered best for high-field effect devices [152]. Computational studies show that the SC behavior of GR can be changed by doping, and the band gap is controllable. It can be used in various transistor applications depending on the band gap, for example, on-off ratio. It is also suggested that by adding GR in conventional SC at the time of fabrication, the various properties of the conventional SCs can be modified, such as on-off ratio, high low voltage control, etc. The numerous properties of GR SCs are dependent on the fabrication size.
## 2 Static Properties Analysis
### Effect of Material on Static Properties  
The properties of sensitive materials are also closely related to the static properties of the sensor. In this section, the theoretical analysis of the MXene-based capacitive humidity sensor is carried out, and the effects of the change of the dielectric coefficient on the static properties of the sensor are analyzed. The Clausius-Mossotti equation [37, 38] can describe the relationship between the dielectric constant of the sensitive layer and the humidity, which is formulated as,  
\[\frac{N\alpha}{3\epsilon_{0}}=\frac{\epsilon_{r}(RH)-1}{\epsilon_{r}(RH)+2}- \frac{\epsilon_{r}(0)-1}{\epsilon_{r}(0)+2} \tag{13}\]  
where \(N\) denotes the concentration of water vapor adsorbed in the sensitive layer, \(\alpha\) denotes molecular polarizability, \(\epsilon_{r}(RH)\) denotes the dielectric coefficient at a certain RH, \(\epsilon_{r}(0)\) denotes the dielectric coefficient at room temperature without RH, and \(\epsilon_{0}\) denotes the vacuum permittivity. In this model, \(\alpha\) is \(3\times 10^{-12}\), \(\epsilon_{r}(0)\) of MXene without humidity is 8.69, and \(\epsilon_{0}\) is 8.85 \(\times\)\(10^{-12}\) F/m.  
The concentration \(N\) is obtained from the ideal gas law, as shown in Equation (14),  
\[N=\frac{RH\cdot P}{R\cdot T} \tag{14}\]  
where \(P\) denotes saturated vapor pressure, \(R\) denotes the molar gas constant, and \(T\) denotes the temperature. In this model, \(R\) is 8.314. The saturated vapor pressure is related to temperature, and the empirical equation is summarized in the following equation,  
\[\ln P=b_{0}+\frac{b_{1}}{T}+b_{2}\ln T \tag{15}\]  
All the coefficients are normalized and set as \(b_{0}=49.34\), \(b_{1}=-6651.95\), and \(b_{2}=-4.54\). Based on Equations (14) and (15), the relationship between the model dielectric constant versus humidity and temperature can be obtained by simulation, as shown in Figure 5a.  
The dielectric coefficient is input into the model, and the relationship between normalized capacitance versus temperature and humidity can be obtained by simulation, as shown in Figure 5b.  
As shown in Figure 5b, the capacitance of the MXene-based capacitive humidity sensor shows a non-linear ascending trend with humidity at a certain temperature. In the low humidity state, the capacitance increases slowly, and the growth rate increases with the  
Figure 5: (**a**) Dielectric coefficient and (**b**) normalized capacitance under different \(T\) and \(RH\).  
humidity. The growth rate of capacitance with humidity also increases with temperature. This behavior can be explained by the change of \(N\). When the temperature increases, the concentration of water molecules increases under the same humidity, which leads to the surge of water molecules absorbed by the sensitive layer and finally results in the high level of the capacitance.
## 4 Radiometric Processing Workflow
### Sensor-Related Calibration
#### 4.2.2 Spectral Calibration  
Spectral response gives the system's radiometric response as a function of wavelength for each band and spatial pixel [120; 136; 137; 123]. Monochromators or line emission lamps are usually used for determining the spectral calibration. Either complete measured spectral response or some functional form is used in calculations; typically, a Gaussian with the central wavelength and the full width of half maximum (FWHM) is used as the model spectral response. In addition, the smile effect, which causes a shift of the central wavelength as the function of the position of the pixel in the focal plane, and the keystone, which causes a bending of spatial lines across the spectral axis [123] needs to be corrected. Thus, the spectral response must be measured in the spectral as well as in the spatial detector dimension. The smile and keystone characterization are necessary, in particular with hyperspectral sensors [123; 137].  
The information of the spectral response functions of the lightweight spectral sensors is still rather limited. To date, mostly monochromators [22; 128; 129] or HgAr, Xe, and Ne gas emission lamps [38; 40; 126] have been used for spectral calibration. [PERSON] et al. [25] characterized their Ocean optics USB 4000 point spectrometer to measure fluorescence that requires a rigorous calibration. They also used emission lamps while also integrating vibration tests that simulated real flight situations, and found that the system had a good spectral stability.  
A different approach can use Fraunhofer and absorption lines of the atmosphere for spectral calibration [138], if the spectral resolution of the sensor is good enough. [PERSON] et al. [139] published software that estimates the spectral shift of a given data set to moderate resolution atmospheric transmission (MODTRAN) simulations [140]. This approach is particularly important, since it can be used during the flight campaign, and the spectral performance can be different in laboratory and actual flight environments.
## 4 Case Study
### Results
#### 4.2.3 Second Instance-Combining Results  
In the previous instance, the results from ET and AM are entirely independent, and now they are gathered in the proposed ANFIS to achieve more accuracy when their results diverge. The 690 simulations based on the first scenario were split into two sets, one with 483 (70%) for training and the other with 207 (30%) for testing, which is from where the results in this subsection came from.  
It is remarkable in Figure A7 through both confusion matrices, how the misclassification errors are complementary. The AM performed significantly better in general, even though its false negatives are 10% higher to classify state A2 than the ET technique, which achieved 27.7% of false negatives to classify state A4.  
The ANFIS model managed the first results, increasing general accuracy prediction, as seen in Figure A8. It is not possible to describe the process of decision making performed as much as it is challenging to provide a complete set of rules that could explain all expected behavior. However, for the given results, it is possible to state that the proposed ANFIS model has learned those rules adequately.
## 2 SMART 1 Array Records' Analysis  
The SMART 1 array is located on the east coast of Taiwan Island. Figure 1 presents the layout of the SMART 1 array. The array consists of 37 stations, including one central station, and three loops formed by 36 stations, with each loop containing 12 stations. The radii of these three loops were 200 m, 1 km, and 2 km, respectively. The value of \(V_{5,30}\), which is the average shear velocity of the top 30 m, is denoted by different colored circles in Figure 1. The \(V_{5,30}\) of these stations ranges from 267.67 to 357.43 m/s and belongs to soil category C, as stipulated in the European Code [27]. Additionally, the triangle symbols in Figure 1 indicate the incident direction of the earthquakes relative to the central station. Further information regarding the SMART 1 array is available in previous publications [5; 28].  
Four sets of reverse-mechanism waveforms recorded by the SMART 1 array were acquired from the PEER NGA-WEST2 database. Table 1 lists the metadata of the four events. \(R_{jb}\) is the Joyner-Boore distance to the rupture plane. Similar to previous studies [29; 30], we used the data in the S-wave window, which is assumed to be stationary. Earthquake records were aligned before further processing. The aligning process considers both the time and frequency-domain features of the records, i.e., the autocorrelation peaks of the S-wave window data and the group delay of their Fourier spectra. The S-wave window lengths were determined based on both the time-domain features of the aligned records and the results from previous studies [29; 31; 32]. Periodograms cannot be affected by the alignment process. Therefore, the preprocessing does not affect the amplitude variation features of the periodogram.  
In each of the four sets of ground motions, unavailable records exist. Illustrations in Figure 2 present five conditions of the unavailable records, including zero, one, two, and six records being unavailable. The unavailable records impose a nonnegligible influence on the estimation of the statistical value and will be introduced in the next section.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Event No.** & **Name** & **Year** & **Magnitude** & **Mechanism** & \(R_{jb}\) (km)** &
\begin{tabular}{c} **S-Wave** \\ **Window** \\ \end{tabular} \\ \hline
1 & SMART 1 (5) & 1981 & 5.9 & Reverse & 25.50 & 7–11 \\
2 & SMART 1 (25) & 1983 & 6.5 & Reverse & 95.57 & 3–8 \\
3 & SMART 1 (40) & 1986 & 6.32 & Reverse & 58.69 & 6–11 \\
4 & SMART 1 (45) & 1986 & 7.3 & Reverse & 56.01 & 5–18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Metadata of earthquakes.  
Figure 1: Layout of the SMART 1 array and the azimuth directions of different events relative to the central station.
## 3 Materials and Methods
### Data Preprocessing
#### 3.3.2 Normalization  
To ensure consistency across features and models, all incidence rate and case count values were normalized using min-max scaling to a range between 0 and 1. This was especially important for neural network models (e.g., LSTM), which are sensitive to the scale of input features. Normalization helped stabilize the training process and improve model convergence.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 5 Projection Circular Orbits  
When the master satellite moves in a circular Earth orbit, its angular acceleration is equal to zero [1]. In this case, it is valid for (1) that \(\dot{\theta}y=\ddot{\theta}x=0\), \(r_{c}=\text{const}\), \(\dot{\theta}=\text{const}=n\), where \(n\) is the average angular velocity of the master satellite, and Equations (1) and (2) are of the following form [35]:  
\[\ddot{x}-2n\dot{y}-n^{2}x=-\frac{\mu(r_{c}+x)}{\gamma}+\frac{\mu} {r_{c}^{2}}+a_{x}, \tag{26}\] \[\ddot{y}+2n\dot{x}-n^{2}y=-\frac{\mu y}{\gamma}+a_{y},\] \[\dot{z}=-\frac{\mu z}{\gamma}+a_{z}.\]  
Linearized equations of (26) are called the Hill-Clohessy-Wiltshire (HCW) equations [1; 36]:  
\[\ddot{x}-2n\dot{y}-3n^{2}x=a_{x}, \tag{27}\] \[\ddot{y}+2n\dot{x}=a_{y},\] \[\ddot{z}+n^{2}z=a_{z}.\]  
The HCW equations have the following limited periodic solutions [9]:  
\[\begin{bmatrix}x(t)\\ y(t)\\ z(t)\end{bmatrix}=\begin{bmatrix}(c_{1}/2)\sin(nt+\phi)\\ c_{1}\cos(nt+\phi)+c_{3}\\ c_{2}\sin(nt+\phi)\end{bmatrix}, \tag{28}\]  
where \(x\), \(y\) and \(z\) are the coordinates of the relative motion, \(\phi\) is the phase angle in the plane between the master satellite and the slave satellite (the initial phase angle is determined when the equator of the l master satellite's in the locally horizontal plane \(yz\)) (the initial phase angle is defined, at the time of the equator crossing of the master satellite, in the local horizon \(yz\) plane). Constants \(c_{1}\), \(c_{2}\), \(c_{3}\), \(a_{0}\) are determined by the initial conditions as follows:  
* The choice \(c_{1}=c_{2}=\rho\), \(c_{3}=0\) sets the configuration in which the slave satellite moves around the master satellite so that the projection of its movement on the local horizontal plane is the circle \(y^{2}+z^{2}=\rho^{2}\), where \(\rho\) is a constant. Such an orbit is called a projection circular orbit (see Figure 2a).
* Selection \(c_{1}=\rho\), \(c_{2}=\dfrac{\sqrt{3}}{2}\), \(c_{3}=0\) sets the configuration in which the slave satellite moves around the master satellite in circles in the axes \(x,y\) and \(z\) of the local coordinate system: \(x^{2}+y^{2}+z^{2}=\rho^{2}\). This orbit is called the general circular orbit (GCO).
* Choice \(c_{1}=c_{2}=0\), \(c_{3}=\rho\) sets the master-slave or along-track orbit (ATO) configuration, where the slave satellite follows the master satellite along its orbit with a constant offset \(\rho\) (see Figure 2b).  
Substituting the values \(c_{1}=c_{2}=r_{dpc}\)\(c_{3}=0\) into equations (28), one obtains the following PCO equation:  
\[\begin{bmatrix}x_{d}(t)\\ y_{d}(t)\\ z_{d}(t)\end{bmatrix}=\begin{bmatrix}\left(r_{\text{dpc}}/2\right)\sin(nt+\phi) \\ r_{\text{dpc}}\cos(nt+\phi)\\ r_{\text{dpc}}\sin(nt+\phi)\end{bmatrix}, \tag{29}\]  
where \(r_{\text{dpc}}\) is the projected circular formation size, \(\phi\) is the in-plane phase angle between the master and the slave satellites, and \(n\) is the mean angular velocity, \(n=\sqrt{\mu/a_{c}^{3}}\), where \(a_{c}\) is the semi-major axis of the master satellite [1].