esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 2 Problem Definition  
After introducing the dynamic Cislunar space, there is a need to define the concerns and urgent needs of this significantly critical region. The main concern is the management of the Cislunar operations with due diligence and analyzing the space environment for both Earth and Moon orbits. For the Earth, the atmospheric drag, \(J_{2}\), and Solar Radiation Pressure (SRP) will be of prime importance to be studied. For the Moon, the SRP is the main factor affecting the mission operations; hence, it needs to be addressed through real-time simulations. For this, the small satellite constellations will remain centroid for this paper with strategies to be developed for attaining synergic autonomy in the given region.  
The autonomy defined will serve the Cislunar operations in the long term and will be crucial for all the missions to maintain safety and coordinate with other missions. The congestion and debris, which may cause in-orbit disasters with collisions and close proximities with debris and other missions, will be a primary problem in the coming years. This needs to be evaluated and extensively analyzed for collision and near-escape concerns in the orbit. There is a need for collectively addressing the space environment, and the congestion issues will be challenging but are highly needed for sustainable Cislunar operations in this NewSpace age. The balance of autonomy with small satellite constellation operations in these harsh and demanding space environments is the most challenging aspect at this point in time. This paper indicates these problems as the major cause of the Cislunar disaster in the near future if not addressed thoroughly both theoretically and practically. Figure 2 depicts this combination of the problems in a systematic approach, which is the ethos of this work that needs to find creative solutions.  
The sections below will be dedicated to these concerns to be solved through various analyses and find continuity in those solutions. The way Cislunar space is growing each month and year, there is an urgent need for addressing these concerns and avoiding bigger concerns in the orbits of the Earth and the Moon. The global need for data is growing exponentially as the concerns on Earth increase day-to-day. The commercial and non-commercial missions take their trials to make possible solutions for this demand, but still, there is no sizeable and optimal solution to it. Even for the Moon, the exploration needs post Chandrayaan-3 mission is a new rage globally and has seen an upward trend in launches.  
Figure 2: Cislunar-based process towards attaining autonomy [16].
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 4 Applications of MMP to Biology and Agriculture  
The considered examples of MMP implementation show that MMP is a powerful tool for non-invasive diagnostics of bioorganic systems, especially in combination with other optical techniques, significantly expanding their capabilities. The above review of MMP installations allows us to systematize optical methods supplemented by MM measurements into groups depending on:  
* Geometry of the sample--volumetric (MM scatterometry, MM spectrophotometry, MM fluorometry) and surface (MM-OCT and MM ellipsometry);
* Properties of incoming radiation--spectral (spectrophotometry and spectroscopic MM scatterometry, MM fluorometry and MM ellipsometry) and non-spectral (MM-OCT and single-wavelength MM scatterometry, MM fluorometry and MM ellipsometry);
* Received information--visual (MM-OCT and other methods of polarization-sensitive imaging) and structural (all methods that measure the MM with subsequent retrieval of the microphysical parameters of the sample using inverse analysis);
* Operating conditions--stationary and mobile.  
Table 1 contains brief information on MMP methods applicable to the characterization of the main types of biological and agricultural sample and the resulting properties.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Type** & **Methods** & **Sample Properties** & **References** \\ \hline \multirow{6}{*}{Organic materials and molecular systems} & MM ellipsometry and & Complex refractive index, & [93; 97] \\  & Transmission MMP & film thickness, & \\  & & Anisotropy, & \\  & & orientation of polymer molecules and fibers & [8; 94; 95; 107; 108; 109; 110; 111] \\  & MM scatterometry, & Structure and size of & \\  & Transmission MMP and & molecular aggregates & [64; 81] \\  & MM fluorometry & Chirality, molecular structure and orientation & [8; 12; 91; 112] \\  & MM lK spectroscopy & Spectral properties, composition & [97; 98; 99; 100] \\ \hline \multirow{6}{*}{Cell and microbiological systems} & \multirow{6}{*}{MM scatterometry} & Size and shape of & \\  & & cells and microorganisms, & \\  & & structural changes in DNA & [16; 63; 64] \\ \cline{1-1}  & MM imaging polarimetry & Distinguishing colonies & [31] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main types of biological and agricultural sample and MMP methods for their characterization.  
Figure 7: Scheme of ATR-FTIR spectrometer for measuring the IR spectra of MM elements of the sample placed on the ATR surface.  
The prospects for the application of the MMP-supplemented methods described above to biology, biomedicine and agriculture are discussed below. Based on the material studied, we can point out the main research areas where MMP is increasingly used. Aiming to counter the lack of generalized information on using MMP in agriculture, we have schematically illustrated the main agricultural applications of MMP by a block diagram in Figure 8.
# The Impact of Intermittent Androgen Suppression Therapy in Prostate Cancer Modeling
## 2 Model and Method  
### The BK Model  
Baez and Kuang [8] introduced a two-subpopulation model for prostate cancer undergoing androgen suppression therapy. This model is a simplification from the model in [PERSON] et al. [7]. The model takes the form:  
\[\frac{dx_{1}}{dt}=\underbrace{\mu_{m}\left(1-\frac{q_{1}}{Q}\right)x_{1}}_{ \text{growth}}-\underbrace{\left(D_{1}(Q)+\delta_{1}x_{1}\right)x_{1}}_{ \text{death}}-\underbrace{\lambda(Q)x_{1}}_{\text{cell transformation}} \tag{1}\]  
\[\frac{dx_{2}}{dt}=\underbrace{\mu_{m}\left(1-\frac{q_{2}}{Q}\right)x_{2}}_{ \text{growth}}-\underbrace{\left(D_{2}(Q)+\delta_{2}x_{2}\right)x_{2}}_{ \text{death}}+\underbrace{\lambda(Q)x_{1}}_{\text{cell transformation}} \tag{2}\]  
\[\frac{dQ}{dt}=\underbrace{\gamma(Q_{m}-Q)}_{\text{diffusion: serum}\to\text{intracellular androgen}}-\underbrace{\frac{\mu(Q-q_{1})x_{1}+\mu(Q-q_{2})x_{2}}{x_{1}+x_{2}}}_{ \text{cell uptake of androgen}} \tag{3}\]  
\[\frac{dP}{dt}=\underbrace{bQ}_{\text{baseline PSA production}}+\underbrace{\sigma(Qx_{1}+Qx_{2})}_{ \text{tumor's PSA production}}-\underbrace{ep}_{\text{degradation}} \tag{4}\]  
\[D_{i}(Q)=d_{i}\frac{R_{i}}{Q+R_{i}},\quad\lambda(Q)=c\frac{K}{Q+K} \tag{5}\]\[\gamma=\gamma_{1}u(t)+\gamma_{2},\ \ \ u(t)=\begin{cases}0,\text{on treatment}\\ 1,\text{off treatment}\end{cases}\ . \tag{6}\]  
In this model, \(x_{1}\) and \(x_{2}\) represent the androgen-dependent (AD) and androgen-independent (AI) cells, respectively. \(P\) is the PSA level, and \(Q\) is the intracellular androgen level. In [8], the authors assumed that the level of intracellular androgen is the same as that of the serum androgen. Since only serum androgen data is available (and not intracellular androgen data), this assumption allows data fitting using clinical androgen data with the variable \(Q\), along with the data fitting of tumor biomarker PSA. The model assumes \(q_{1}>q_{2}\), which biologically means that the AI cell subpopulation can grow in an environment with lower androgen levels than AD cells can. A more thorough derivation of this model can be found in [8; 18]. As pointed out in [18], the mutation from AD to AI cells is sufficient to describe the transformation between these two cells, due to the lack of clinical data types.  
As mentioned in [18], the equation governing the dynamics of \(Q\) has a significant biological flaw. Since the diffusion is scaled with the difference between \(Q_{m}\) and the current value of \(Q\), it means that immediately after the patient is taken off treatment, the serum androgen level reaches its max (e.g., \(Q_{m}\)). Scaling the growth of intracellular androgen in this way is perhaps one of the causes for the sudden jump in PSA level between the on- and off-treatment intervals.
###### Abstract  
The rarity of equipment failures results in a high level of imbalance between failure data and normal operation data, which makes the effective classification and prediction of such data difficult. Furthermore, many failure data are dominated by mixed data, which makes the model unable to adapt to this type of failure problem. Second, the replacement cycle of production equipment increases the difficulty of collecting failure data. In this paper, an equipment failure diagnosis method is proposed to solve the problem of poor prediction accuracy due to limited data. In this method, the synthetic minority oversampling technique is combined with a conditional tabular generative adversarial network. The proposed method can be used to predict limited data with a mixture of numerical and categorical data. Experimental results indicate that the proposed method can improve 6.45% compared to other similar methods when equipment failure data account for less than 1% of the total data.  
Mixed-Type Data; fault diagnosis; SmoteNC cGAN; limited failure
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 2 Background
### Jamming and Cooperative Jamming  
In the coding for secrecy schemes it is necessary for [PERSON] to have some advantage over [PERSON], often an uncontrollable factor. Cooperative jamming [14; 15; 16] was considered to overcome this challenge to help lower the signal-to-noise ratio (SNR) of the wiretap channel. It consists of having an external jammer to help [PERSON] to communicate with [PERSON] using several known schemes, namely cooperative jamming by Gaussian noise, by structured codes, and by alignment.  
All these schemes have some disadvantages, for instance: (1) the synchronization, that is, the helper needs to be in sync with [PERSON] to correctly help [PERSON] to have the necessary advantage; (2) the willingness of the helper to spend his own energy in order to emit the jamming signal to make other communications secure; and (3) there has to be a trusted relationship between the helper and [PERSON], because if the helper stops jamming, then [PERSON] can retrieve the signal. Using [PERSON] as a jammer can overcome most of these issues since it helps to solve the synchronization problem, the issue of the need for cooperation and it facilitates the removal of interference (the receiving device knows the characteristics of the interference being generated).
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 2 Materials and Methods
### Datasets
#### 2.2.4 Land Cover Type  
This study used the land cover data of the European Space Agency with a spatial resolution of 300 m ([[https://maps.elie.ucl.ac.be/CCI/viewer/download.php](https://maps.elie.ucl.ac.be/CCI/viewer/download.php)]([https://maps.elie.ucl.ac.be/CCI/viewer/download.php](https://maps.elie.ucl.ac.be/CCI/viewer/download.php)), accessed on 15 August 2021). According to the land cover data of the European Space Agency, the different vegetation cover is reclassified into eight types (Figure 2): cropland, grassland, shrubland, woodland, urban areas, bare areas, water, and permanent snow and ice. Farmland, grassland, and woodland are the main land-use types in YRB. The nearest neighbor method was used to resample the land cover type data to the same resolution as GOSIF.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Drought Class** & **SPEI Value** \\ \hline Normal & \(-0.5<\text{SPEI}\) \\ Middle drought & \(-1.0<\text{SPEI}\leq-0.5\) \\ Moderate drought & \(-1.5<\text{SPEI}\leq-1.0\) \\ Severe drought & \(-2.0<\text{SPEI}\leq-1.5\) \\ Extreme drought & SPEI \(\leq-2.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Five level classification of drought based on SPEI.  
Figure 2: Different vegetation types in the Yellow River Basin.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Paint Industry  
Kaolin is a critical component in the paint industry, albeit with a smaller market compared to its use in paper coating and filling. Annually, around 600,000 tons of kaolin are incorporated into paints worldwide as extender pigments [96]. It has extensive applications in water-based interior latex paints and is also utilized in oil-based exterior industrial primers. Calcined and delaminated kaolins are particularly favored in interior water-based paints, which are characterized by pigment volume concentrations (PVC) between 50% and 70%. In contrast, semi-gloss and high-gloss water-based systems employ fine particle size kaolins at PVCs below 50% [92].  
Kaolin's fine particle size, with 98% of particles being less than 2 um, significantly enhances paint properties such as suspension, viscosity, and leveling. As an economical exender for titanium dioxide (TiO\({}_{2}\)), the dominant pigment in paints, kaolin effectively reduces production costs [97; 98]. The high aspect ratio and thin plate-like structure of delaminated kaolins contribute to a smoother paint film surface and improved sheen. The incorporation of calcined kaolins in paint formulations improves the scrubability and mechanical strength of the paint films. These kaolins also enhance washability, facilitate stain removal, and improve enamel holdout by preventing enamel penetration into the paint structure. In flat paints, calcined kaolin increases hiding power, film toughness, and scrubability, although it may compromise stain resistance [99]. Paint formulations can be optimized for specific performance criteria by blending various extenders and pigments.  
In essence, kaolin's role in the paint industry is multifaceted, providing cost-efficient improvements to multiple paint properties. Its contributions to suspension, viscosity, and surface smoothness and its function as a TiO\({}_{2}\) extender underscore its value in both water-based and oil-based paint formulations. The _Krebs & Hiebert_ and _BASF_ companies utilize kaolin to enhance the performance characteristics of their coatings, such as opacity and durability.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 2 SDR Platform  
The SDR platform was developed based on that described in [21], the basic architecture of which is shown in Figure 1. This platform uses, as discussed in the introduction, the NI-USRP-2901R and NI-USRP-2944R hardware. The operating frequency of the USRPs ranges from 70 MHz to 6 GHz, they have two RF chains and support a sampling rate of up 56 MHz, which is sufficient to record an LTE signal (maximum 20 MHz bandwidth). The technical specifications of the USRPs used; their features in terms of the signal sampling, encoding, and decoding processes; the problems and the solution techniques implemented for the connection of the USRPs with computers; and the generation of LTE signals with MatlabTM and their processing by the USRPs are described below.  
The testbed structure consists of one evolved Node-B (eNB), which is achieved with the NI-USRP-2944R board and the MatlabTM software. The connection of a PC (Intel-I7-9700, CPU: 3 GHz, RAM: 16.0 GB, 64-bit operating system) to the NI-USRP-2944R is performed using a NI-IMAQdx GigE Vision High-Performance Driver, a 10 Gigabit E  
Figure 1: SDR Platform Architecture.  
and a 10 Gigabit Ethernet cable. On the other hand, the Decode-and-Forward Relay Node is implemented using a PC, the MatlabTM software, and the NI-USRP-2944R board, connected using a NI-IMAQdx GigE Vision High-Performance Driver, a 10 Gigabit Ethernet Card for Desktop, and a 10 Gigabit Ethernet cable. The UEs were developed using two PCs, the MatlabTM software, and two NI-USRP-2901R boards, where the connection from the PC to the USRP is performed through USB 3.0. The antennas are general-purpose for LTE and WiFi, which work on the 700-960 MHz and the 1710-2700 MHz bands. They have a gain of 5 dBi, an impedance of 50 \(\Omega\), and have SMA connectors which are compatible with the SDRs.
## 3 Results and Discussion
### Destructive Tests  
#### 3.3.1 Flexural Test  
In a four-support flexural test, the flexural strength is given by the following equation:  
\[f_{ct,fl}=F\,L/b\ t^{2} \tag{7}\]  
where \(f_{ct,fl}\) stands for the flexural strength (MPa), \(F\) for the maximum load (N), \(L\) for the distance between the support rollers (mm), \(b\) for the width of the bar (mm), and \(t\) for the thickness of the bar (mm). The specimens' maximum load and flexural strength values are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mortar Designation** & **E (GPa)** & **G (GPa)** & **\#** \\ \hline C & 2.629 & 1.111 & 0.18 \\ P & 1.286 & 0.526 & 0.23 \\ P.NMS & 1.253 & 0.512 & 0.22 \\ V & 2.817 & 1.164 & 0.22 \\ V.NMS & 2.691 & 1.117 & 0.20 \\ \hline \end{tabular}
\end{table}
Table 4: Average properties result from non-destructive tests.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mortar Designation** & **UPV’s E Ratios (\%)** & **IEV’s E Ratios (\%)** & **UPV’s G Ratios (\%)** & **IEV’s G Ratios (\%)** \\ \hline C & - & - & - & - \\ P & 44.9 & 53.1 & 43.2 & 51.7 \\ P.NMS & 45.6 & 49.7 & 43.5 & 48.9 \\ V & 109.7 & 104.6 & 107.7 & 101.8 \\ V.NMS & 100.7 & 104.1 & 98.8 & 102.5 \\ \hline \end{tabular}
\end{table}
Table 5: Developed mortars’ non-destructive test results to commercial composition ratios.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Channel Encoding Using Polar Codes  
The bitstream b is channel-encoded using rate 1/2 polar codes [44] to generate a codeword, where the input vector consists of both information and frozen bits. Polar codes are chosen for their low computational complexity and strong performance in achieving channel capacity under successive cancellation decoding. These characteristics make them highly suitable for both classical and quantum communication systems.  
Compared to other classical error correction codes, such as low density parity check (LDPC) codes, turbo codes, or convolutional codes, polar codes offer a more structured and recursive design based on the concept of channel polarization. Polar codes are considered among the most advanced and strong error correction codes currently available. This design enables efficient encoding and decoding, especially in hardware-constrained environments. Moreover, many conventional error-correction codes are tailored to classical, memoryless channels. As a result, those models can lead to increased complexity or suboptimal performance. Polar codes, by contrast, provide a more adaptable and computationally efficient solution, making them a compelling choice in emerging quantum-enhanced communication frameworks.
## 4 Experimental Setup
### OPNET Simulation Setup  
Table 5 gives the values of the general parameters we used in our simulations. The terrain has an area of 1300 \(\times\) 1250 m. The relative position (x, y) on the terrain is used to integrate the VANET mobility model trajectories and obtain the initial positions of the vehicles. Vehicular environment for the path loss parameter is modeled according to the description in the \"Radio Tx Technologies for IMT2000\" white paper of the ITU [57]. The shadow fading standard deviation was set to 10 dB. The mobility pattern (trajectory) during the simulation is predefined using the _VanetMobiSim_ discussed in Section 3.2.1.1.  
We have investigated different scenarios such as highway and residential, and have studied the effect of varying traffic congestion (density), wireless interference and traffic speed limits. Due to space limitation, we shall present the highway scenario for this paper.  
In the highway scenario shown in Figure 14, we consider cars moving along the highway with a minimum speed of 60 km/h and a maximum speed of 100 km/h. There is one traffic light, just before the cars enter the highway. The car trajectories are represented by the white lines. We use the free space loss model for our pathloss. Unless otherwise stated, the following are assumptions taken throughout the paper for our simulation study:  
1. WiMAX BS is a \"stationary\" node. This is required due to the limitation of our OPNET model and we need it to act as an intermediate node for packet forwarding to the destination;
2. OFDMA is used by the RSU and there is always a slot available for each SS sending video traffic once the SS is within the communication range;
3. No disruption in a communication channel once the node is allocated a dedicated channel;
4. No obstacle along the LOS between the RSU and the mobile cars to allow the simulation to monitor the vehicles within the range of the RSU;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Note** \\ \hline Physical Layer & IEEE802.16e & WiMax \\ Data Rate & 10 & Mbps \\ BS Tx Power & 5 & W \\ MS Tx Power & 1 & W \\ Antenna Type & Omni-directional & Horizontal \\ BS Antenna Gain & 15 & dBi \\ MS Antenna Gain & 9 & dBi \\ Link Bandwidth & 20 & MHz \\ Modulation Scheme & 16-QAM & Model \\ Path Loss Parameter & Vehicular Environment & ITU \\ Number of Vehicles & 10 & Cars \\ Mobility Model & VanetMobiSim & Open \\ Number of RSUs & 2 & Base \\ Simulated Time & 3600 & secs \\ Seeds & 127 & random \\ Terrain Dimensions & \(1300\times 1250\) & m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.  
5. Finite buffer size for each transmitter in order to be more realistic so that the trade-off between buffer size and end-to-end delay can be determined.