esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 CCSDS 131.2-B Protocol for High-Rate Telemetry  
The CCSDS 131.2-B standard was defined by the CCSDS in 2012 as a flexible advanced coding and modulation scheme for high-rate telemetry [16]. The standard has been defined to address the major challenges encountered while operating a transmission above 10 GHz, in particular at the Ka-band (25.5 GHz to 27 GHz). It is specifically intended for low-Earth orbit (LEO) satellites, which are visible from Earth in small time windows (\(\sim\)10 min), in which all data must be transmitted to the ground stations. In fact, the standard is applied to create the transmission signal on-board satellite by encoding and modulating the generated data, to improve the quality of the transmission over the noisy satellite-ground station channel in a small amount of time. There are different ways of using the Modulation and Coding (ModCod) techniques on spacecrafts, of which two are usable with CCSDS:  
* Variable Coding and Modulation (VCM), where satellite elevation or time coordinates are taken into account to change the ModCod to maximize link efficiency;
* Adaptive Coding and Modulation (ACM), where both the satellite's position and environmental conditions are considered to maximize link occupancy.  
For signal coding, many current LEO satellites make use of RS encoders, along with trellis codes. For example, the satellites of the Sentinel program [17] use 4D-8 PSK Trellis Coded Modulation (4D-8 PSK-TCM), which exploits RS coding. CCSDS 131.2-B instead is based on a turbo-like coding and modulation scheme based on Serially Concatenated Convolutional Codes (SCCC). This scheme makes use of a set of different modulation techniques (e.g., QPSK, 8 PSK, 16 APSK, 32 APSK, and 64 APSK) and a wide range of coding rates [16]. The large variety of modulation schemes available, together with a properly selected coding rate, allows the overall system to efficiently handle the available bandwidth, adapting itself to the variable conditions of the link. Specifically, the CCSDS 131.2-B makes use of link adaption strategies based on 27 different ModCods, applied based on VCM or ACM to overcome major challenges, such as satellite visibility (i.e., due to the satellite orbit), channel impairments (i.e., due to the wireless channel), or to guarantee specific performance (i.e., in terms of data rate). Using different ModCods aims to maximize the transmission of data while dealing with varying channel conditions, where a worst-case sizing of the transmission parameters would result in unacceptable inefficiency, given the large dynamic of the possible attenuation during a satellite pass over the ground station.  
A brief description of the transmitter and receiver sides of CCSDS 131.2-B based communication is now given.
## 4 Adoption of the Remote Sensing Information by End Users
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Remote Sensing Data** & **Main Focus** & **End user/s** & **Country of Adoption** & **Economic Cost** & **Year** & **Inference** & **Reference** \\ \hline MODIS derived & Grassland & Policymakers & & & & & To manage the carrying capacity of sheep. & [194] \\ Enhanced vegetation index (EVI). & Grassland classification. & Policymakers and farmers & China & Free & 2018 & & & \\ \hline  & & & & & & & & \\ Satellite imagery & FORAGE system estimator. & The general public (emphasis on range) & Australia & Free & 2018 & & & \\  & & & & & & Queensland state & \\  & & & & & & & & \\ Above Net Primary Production from NDVI derived from MODIS. (Satellite data and GIS). & Forage productivity to manage stocking rate and the carrying capacity. & Policy makers and farmers & Argentina & Free & 2007 & & \\  & & & & & & & \\ NDVI derived from Landsat & Productivity by eliminating noxious weeks. Pasture conservation. & Farmers and range & USA & Free & 2006 & & \\  & & & & & & & \\ Landsat imagery & Land cover classification and pasture management. & Government, range manager. & China & Nil & 2004 & & \\  & & & & & & & \\ \hline  & & & & & & & \\ ERS satellite data & Estimating pasture biomass & Policymakers and national agency & Bolivia & Nil & 2003 & & \\  & & & & & & & \\ Landsat and SPOT imagery and GIS system. & Data to support pasture management framework. & Farmers & Mongolia with a cost. & Satellite imagery can with a cost. & 1999 & & [220] \\ Landsat and SPOT imagery and GIS system. & Pasture growth and productivity through fertilizer application. & Researchers, institution (CSIRO) and Agric company. & Satellite imagery was provided through a license. & & & \\ \hline  & & & & & & & \\ \end{tabular}
\end{table} 
Table 7: \(Cont.\)
## 2 System Model  
In this paper, we consider a multiple LEO satellites multiple UTs communication scenario, shown in Figure 1. The symbols used in the system are mainly summarized in Table 1.  
Since there exists large numbers of satellites in an LEO constellation system, the beam-edge area can be covered by several LEO nodes. The radius of the overlapping area, covered by \(M\) satellites, is assumed to be \(r\). Specifically, \(K\) UTs are allocated in the area. To flexibly adjust the beam direction, yz-plane uniform planar phased array (UPA) with half-wavelength spacing is applied on the satellite. The number of array elements is assumed to be \(N_{y}^{S}=N_{z}^{S}\). Let \(\phi\) and \(\theta\) denote the azimuth and elevation angles of departure at the satellite and \(\lambda\) is the wavelength. Then, the array response vector at the satellite can be derived as [21]  
\[\mathbf{a}(\phi,\theta)=\frac{1}{\sqrt{N_{y}^{S}N_{z}^{S}}}[1,\dots,e^{it\mu(m \sin(\phi)\sin(\theta)+m\cos(\theta))},\dots,e^{it\mu((N_{y}^{S}-1)\sin(\phi) \sin(\theta)+(N_{z}^{S}-1)\cos(\theta))}]^{\mathrm{T}}, \tag{1}\]  
where \(t=2\pi/\lambda\) and \(\mu=\lambda/2\).  
Since the space-ground links contain large-scale and small-scale fading in LEO SATCOM, the downlink channel from satellite \(m\) to UT \(k\) can be modeled as  
\[\mathbf{h}_{k,m}^{H}=\rho_{k,m}l(d_{k,m})\sqrt{E_{S}}\mathbf{a}^{H}(\phi_{k,m},\theta_{k,m}), \tag{2}\]  
where \(l(d_{k,m})=\sqrt{(\lambda/(4\pi d_{k,m}))^{2}}\), \(d_{k,m}\) represents the distance between the satellite and UT, \(\rho_{k,m}\sim\mathcal{CN}(0,1)\) denotes the small-scale fading channel coefficient, \(\phi_{k,m}\) and \(\theta_{k,m}\) denote  
\begin{table}
\begin{tabular}{l l} \hline The number of the visible satellites in the overlapping area & \(M\) \\ \hline The number of the UTs in the overlapping area & \(K\) \\ \hline The number of array elements along the \(y\) axis & \(N_{y}^{S}\) \\ \hline The number of array elements along the \(z\) axis & \(N_{z}^{S}\) \\ \hline The radius of the overlapping area & \(r\) \\ \hline \end{tabular}
\end{table}
Table 1: Symbols of the system.  
the azimuth and elevation angles of departure from satellite \(m\) to UT \(k\), respectively. In detail, \(E_{S}\) is the normal gain of transmit beams.
## 2 Materials and Methods
### Theory of Algorithms  
#### 2.2.1 Extreme Gradient Boosting  
XGB (Extreme Gradient Boosting) trains multiple decision trees in series. Each decision tree learns from the previous decision tree and generates the final prediction result by synthesizing the decision values of all weak learners. XGB expands the loss function using a second-order Taylor series and introduces the regular term to avoid overfitting of the model [25]. Figure 2 is the schematic diagram of XGB model.  
Each round of training in boosting will add a new function to the model. The objective function is shown as Equation (1).  
\[Obj^{(t)}=\sum_{i=1}^{n}l(y_{i},\hat{y}_{i}^{(t-1)}+f_{t}(x_{i}))+\Omega(f_{t}) +\mathit{constant} \tag{1}\]  
where \(t\) is the number of training rounds; \(f_{t}(x_{i})\) represents the \(t\)-th regression tree; \(\Omega(f_{t})\) is the penalty term and \(\mathit{constant}\) is the constant term.  
Expand the objective function with Taylor series, and the result is shown in Equation (2):  
\[Obj^{(t)}\dot{=}\sum_{i=1}^{n}\left[l\left(y_{i},\hat{y}_{i}^{(t-1)}\right)+g _{i}f_{t}(x_{i})+\frac{1}{2}h_{i}f_{t}^{2}(x_{i})\right]+\Omega(f_{t})+\mathit{ constant} \tag{2}\]  
where \(g_{i}\) is the first derivative, \(h_{i}\) is the second derivative.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instrumentation** & **Type** & **Deviation** \\ \hline Dynamometer & AVL INDY S22-4 & \(\pm\)0.3\% \\ Air flowmeter & ABB-0(40) \(\dots\) 1200 kg & \(\pm\)0.1\% \\ Cylinder pressure sensor & Kistler 6125C & \(\pm\)1\% \\ Combustion analyzer & Kistler DEWE & - \\ Fuel consumption meter & AVL 735S & \(\pm\)0.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test bench instrumentations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Speed** & **Load Rate** \\ \hline
2200 rpm & \\
1600 rpm & 10–100\% (interval 10\%) \\
1200 rpm & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test conditions of the engine.  
The penalty term is defined as Equation (3):  
\[\Omega(f_{t})=\gamma T+\frac{1}{2}\lambda\sum_{j=1}^{T}{w_{j}}^{2} \tag{3}\]  
where \(T\) is the number of leaf nodes and \(w_{j}\) represents the weight of the \(j\)-th leaf node.  
The objective function can be reduced to Equation (4):  
\[\begin{split} Obj^{(t)}=\sum\limits_{j=1}^{T}\left[\left(\sum_{i \in I_{j}}{g_{i}}\right)w_{j}+\frac{1}{2}\left(\sum_{i\in I_{j}}{h_{i}}+\lambda \right){w_{j}}^{2}\right]+\gamma T\\ =\sum\limits_{j=1}^{T}\left[G_{j}w_{j}+\frac{1}{2}\left(H_{j}+ \lambda\right){w_{j}}^{2}\right]+\gamma T\end{split} \tag{4}\]
## 3 Research Methodology
### Data Analysis  
The procedural scheme of the proposed optimization algorithm is based on a number of fundamental sections. First, data from hybrid sources were acquired and stored using fingerprinting and RSS principles. Data were kept as they were recorded, without any particular preprocessing, any further analysis, or suggestion of a limited database.  
The second step consisted of creating a mathematical model for both the distribution of the signal data and a mainframe for the tessellation of the research space. As the behavior of the measured signal intensities along the 2D space of the experimental area proved to be inherently non-linear, a Multilayer Perceptron (MLP) neural network was developed and trained thanks to its versatility in modeling inherently non-linear data. In consideration of the supervised training features of any MLP, the target was identified as the preprocessed position of the 96 anchor points. Given a tessellation configuration with a predetermined tiling position and corner point, each anchor point would be assigned to the tile to which it belonged geometrically. The MLP target would then become a set of discrete, integer pairs of coordinates.  
Finally, an optimization algorithm was developed to address the main subject of this research, i.e., to investigate the possibility of reducing the positioning error through the MLP by means of an appropriate choice of the tile size and collocation of the tessellation corner point. Taking into account the number of anchor points and the possible variability of the tessellation geometry, a heuristic approach to the optimization process was selected.  
Figure 1: A sketch of the research area, with depictions of irregular boundaries, signal sources, and the grid of anchor points where signal intensities were recorded.  
A QPSO algorithm was chosen due to its features of rapid convergence to a global optimal solution and its relative ease of programming when compared to other available optimization algorithms [35].
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacterial Toxins in Cancer Therapy
#### 3.3.3 Exotoxin A  
Exotoxin A is the 66 kDa peptide derived from _Pseudomonas aeruginosa_, which inhibits the synthesis of protein via ADP-ribosylation of elongation factor-2 (EF-2) [85]. This toxin is usually used as an immunotoxin with different ligands. _Pseudomonas_ exotoxin A (PE), in conjugation with breast tumor selective antibodies (MAB), formed the immunotoxins with cytotoxicity effect on human breast tumor cell lines [72]. Another immunotoxin containing _Pseudomonas_ exotoxin A, in conjugation with Hereeptin antibody, can improve the efficiency of the Hereeptin drug and improves the efficiency for treating breast cancer via overexpression of the HER2-neu receptor. Hereeptin is a monoclonal antibody against Her2-neu receptor, which is overexpressed in some breast cancer cell lines and is used in targeted therapy [71]. Furthermore, this exotoxin induces the cytotoxicity and leads to death in cancer cell line by apoptosis. The anticancer effect of this toxin has been examined on different breast cancer cell lines including MCF-7, BT-20, CAMA-1, and SKBR-3 [71; 72].
## 5 Conclusion  
We compare LAI from eight different uncoupled LSMs against satellite data over the Northern Hemisphere, during the 1986-2005 period. This was achieved by calculating the mean LAI, seasonal amplitude and growing season variables (onset, dormancy an length). Our results show that all models overestimate LAI by 2-3 units, particularly over the boreal forest, relative to the satellite data and literature estimates. Models that include a high number of plant functional types (10-16) compare more favorably to the satellite data than those that only have a few (4-5). Models that calculate their phenology based on temperature perform better than those with complex photosynthetic modules. Likewise, models with prescribed vegetation more closely match observations than those that simulate it dynamically. Finally, all models overestimate the length of the by 4-40 days based on LAI compared with the observations, largely due to the dormancy date occurring 20-60 days later. This is inconsistent with the photosynthetic active period calculated by GPP, which was on average 3 months smaller. This highlights the need to improve the deciduous phenology in all models, particularly leaf shedding.  
While vegetation models have developed a great deal, there is still a need for improvement. LAI is a key variable in all models and its correct representation, both temporarily and spatially, is key to predicting correct carbon fluxes. As the literature suggests, any overestimate in the length of the growing season and its trend is likely to affect albedo and have important effects on the radiation budget of the area. The satellite data represents a unique opportunity to test models against observational data and to determine where improvements can be made. Moreover, additional variables can be validated, allowing the identification of possible problems within the models.  
We acknowledge the TRENDY-DGVM project that is responsible for all the data on DGVMs used in this work. We also thank [PERSON] for his valuable contribution and comments on the development of the paper and [PERSON] for is help on the methodology. The corresponding author also thanks the CONACYT-CECTI and the University of Exeter for their funding during the PhD studies. The National Center for Atmospheric Research is sponsored by the National Science Foundation.
## 1 Introduction
A more recent development for image quality assessment is the deep BIQA methods. Unlike previous methods they do not rely on handcrafted features to determine the quality of an image by capturing vital information from an image. Recently, the advancements in deep learning techniques have allowed the solving of many research problems such as image segmentation [17; 18; 19], problems associated with bio-informatics [20; 21] and others [22]. Deep BIQA methods employ deep neural network (DNN) which is capable of extracting features in more depth than traditional BIQA approaches [23]. Various BIQA methods deploying VS along with machine learning methods are utilized to obtain image quality. A deep BIQA method that uses a convolutional neural network to apply VS to the images to extract the specific regions in an image and evaluates their quality to compute the quality of the entire image [24]. There is a lack of effective BIQA methods which deploy deep neural networks to evaluate the quality of images which includes the assessment of screen content and natural scene images and real-life images captured by digital imaging equipment, and images are embedded with synthetic type distortions [13]. To solve the complexity of the learning mechanism of NR-IQA, we have proposed using spinal-net on the feature maps attained using pre-trained architecture. The spinal-net learning mechanism improves the monotonicity relationship between objective and subjective ratings. It is also important that the feature maps are highly efficient and have high-quality learning. Therefore, a deep architecture with an adaptive receptive field (Inception-ResNet-V2) is used for the purpose.
## 4 Implementation of Security Mechanisms into SDNFV-Based Networks
### SDNFV Controller/Orchestration  
The architecture incorporates a RYU-based controller, which is a component-based SDN controller, featuring a set of predefined components essential for its functionality. These components are customizable within the controller application to adapt its behavior to specific problem requirements.  
To support fundamental BEBA functionalities, various new messages, actions, and match fields must be integrated. The BEBA-supported controller extends the basic controller implementation, enabling the parsing of user-defined applications. It utilizes experimental messages to construct the application's logical model and a user-defined payload to convey essential information for traffic switching, flow decisions, or network rule execution.  
The BEBA implementation has been adapted to incorporate the NFV orchestrator. It acquires information from the NFVI (in this scenario, the Docker cluster), deploys virtualized functions, and redirects traffic to the NFV. Figure 6 illustrates the comprehensive architecture of the controller/orchestrator.  
The RYU SDN framework is expanded through BEBA implementation utilizing the OpenFlow 1.3 protocol [33] and external libraries integrated into the original framework [34]. Notably, the SDNFV network is heterogeneous, demanding the controller to manage switches and implement a switching protocol for flow routing. OpenFlow switches with BEBA support execute normal applications (like predefined L2 switches).  
The developed application built upon this framework leverages BEBA implementation to analyze flows and detect suspicious ones. Subsequently, the NFV orchestration module selects the optimal Docker server to deploy the container housing the NFV functionality.  
Figure 6: Architecture of the controller/orchestrator.  
It establishes a secure channel, executes deployment commands for the NFV, and upon successful deployment, reconfigures the flow tables to divert the identified flow to the NFV container, thus mitigating the attack.
## 2 System Model
\(\mathbf{h}_{sb}\in\mathbb{C}^{N\times 1}\), \(\mathbf{h}_{rb}\in\mathbb{C}^{M\times 1}\), and \(\mathbf{H}_{sr}=\mathbf{h}_{rs}\mathbf{h}_{sr}^{H}\in\mathbb{C}^{M\times N}\) represent the BS-to-Bob, IRS-to-Bob, and BS-to-IRS channels, respectively. Let us define channel \(\mathbf{h}_{tr}=\mathbf{h}(\theta_{tr},\varphi_{tr})\); normalized steering vector \(\mathbf{h}(\theta,\varphi)\) is expressed as follows:  
\[\mathbf{h}(\theta,\varphi)\overset{\Delta}{=}\frac{1}{\sqrt{N}}[e^{\beta \pi\Phi_{\theta,\varphi}(1)},\ldots,e^{\beta\pi\Phi_{\theta,\varphi}(n)}, \ldots,e^{\beta\pi\Phi_{\theta,\varphi}(N)}]^{T}, \tag{7}\]  
where  
\[\Phi_{\theta,\varphi}(n)\overset{\Delta}{=}-\left(n-\frac{N+1}{2}\right)\frac {d\cos\varphi\cos\theta}{\lambda},n=1,\ldots,N, \tag{8}\]  
\(n\) stands for the antenna index, \(d\) represents the spacing of adjacent transmitting antennas, \(\theta\) means the directional angle of departure or arrival, \(\varphi\) is the pitch angle, and \(\lambda\) denotes the wavelength. Figure 1: System diagram of hybrid IRS and UAV-aided directional modulation network. In accordance to (6), the achievable rate at Bob can be formulated as follows:  
\[R_{b}=\text{log}_{2}(1+\text{SNR}), \tag{9}\]  
where  
\[\mathbf{\Psi}=\mathbf{E}_{M_{a}}\mathbf{\Theta},\ \mathbf{\Phi}=\mathbf{E}_{M_{p}}\mathbf{\Theta}, \tag{3}\]0  
The transmission power of all active elements at the hybrid IRS is given by  
\[\mathbf{\Psi}=\mathbf{E}_{M_{a}}\mathbf{\Theta},\ \mathbf{\Phi}=\mathbf{E}_{M_{p}}\mathbf{\Theta}, \tag{3}\]1  
which satisfies \(P_{r}\leq P_{r}^{\text{max}}\), where \(P_{r}^{\text{max}}\) represents the maximal transmission power of \(M_{a}\) active elements. In this work, we maximize the achievable rate by jointly optimizing beamforming vector \(\mathbf{v}\), passive IRS PSM \(\mathbf{\Phi}\), and active IRS PSM \(\mathbf{\Psi}\). Since the logarithmic function is monotonically increasing, the problem of maximizing the achievable rate can be degraded to to that of maximizing the SNR, which is formulated as follows:  
\[\mathbf{\Psi}=\mathbf{E}_{M_{a}}\mathbf{\Theta},\ \mathbf{\Phi}=\mathbf{E}_{M_{p}}\mathbf{\Theta}, \tag{3}\]2 \[\mathbf{\Psi}=\mathbf{E}_{M_{a}}\mathbf{\Theta},\ \mathbf{\Phi}=\mathbf{E}_{M_{p}}\mathbf{\Theta}, \tag{3}\]3 (12b) \[\mathbf{\Psi}=\mathbf{E}_{M_{a}}\mathbf{\Theta},\ \mathbf{\Phi}=\mathbf{E}_{M_{p}}\mathbf{\Theta}, \tag{3}\]4 (12c) \[\mathbf{\Psi}=\mathbf{E}_{M_{a}}\mathbf{\Theta},\ \mathbf{\Phi}=\mathbf{E}_{M_{p}}\mathbf{\Theta}, \tag{3}\]5 (12d) \[\mathbf{\Psi}=\mathbf{E}_{M_{a}}\mathbf{\Theta},\ \mathbf{\Phi}=\mathbf{E}_{M_{p}}\mathbf{\Theta}, \tag{3}\]6 (12e) \[\mathbf{\Psi}=\mathbf{E}_{M_{a}}\mathbf{\Theta},\ \mathbf{\Phi}=\mathbf{E}_{M_{p}}\mathbf{\Theta}, \tag{3}\]7  
where \(\beta_{\text{max}}\) is the amplitude budget. Considering that this optimization problem is non-convex with a constant modulus constraint, it is generally challenging to tackle it directly. In what follows, an alternating optimization algorithm is proposed to compute the beamforming vector and hybrid IRS PSM.