esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Soil Moisture Mapping Methodology
### Radar Backscattering Model  
In this study, the Water Could Model (WCM) was combined with the modified Integral Equation Model (IEM) and used to simulate the radar response for different conditions of vegetation and soil in an agricultural context. The WCM is the most commonly used model to estimate soil moisture in the presence of vegetation cover because it uses a simple formulation and consequently, it could be easily inverted [21; 29; 31; 32; 33; 45; 46; 47; 48]. In the WCM, the total backscattered signal is equal to the sum of vegetation contribution and soil contribution attenuated by the vegetation layer. The soil contribution is a function of the soil moisture and roughness. The vegetation contribution and the attenuation are computed using one or more vegetation parameters, such as the leaf area index, NDVI, and vegetation water content [25; 26; 27; 29; 30; 49; 50].  
The parametrization of the WCM used in this work was performed in a previous study [26] for the VV and VH polarizations (\(\theta\) between 18\({}^{\circ}\) and 40\({}^{\circ}\)) in using wide database of SAR C-band measurements, NDVI values derived from optical images, and in situ measurements of soil moisture (\(mv\)) and soil roughness (\(Hrms\)). In [PERSON] et al. [26], the WCM was parameterized in using data collected over a study site located in central Tunisia (Kairouan Plain: 35\({}^{\circ}\)1\({}^{\prime}\)-35\({}^{\circ}\)55\({}^{\prime}\) N-9\({}^{\circ}\)23\({}^{\prime}\)-10\({}^{\circ}\)17\({}^{\prime}\) E) that contains mainly agricultural fields of cereals. The NDVI values ranged between 0.1 and 0.86, the \(mv\) values were between 4.0 and 39.6 vol %, and the \(Hrms\) was between 0.7 cm and 4.6 cm. The vegetation contribution and the vegetation attenuation effects were computed using the NDVI as the vegetation descriptor. In addition, the soil contribution was simulated using the IEM modified by [PERSON] et al. [15; 17]. The results showed that the parameterized WCM simulates the backscattering coefficients in the VV and VH polarizations with an accuracy (Root Mean Square Error \"RMSE\") of approximately 1.4 dB (R\({}^{2}\) approximately 0.60), which is close to the S1 measurement accuracy [51; 52].
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 2 Materials and Methods
### DCCA Intercorrelation Analysis  
Detrended correlation analysis (DCCA) is an extension of the detrended fluctuation analysis (DFA) method and has become a standard technique for studying long-range power-law correlations between two nonstationary time series [28]. In this paper, the correlation between AQI series and meteorological series is analyzed using the detrended intercorrelation method in conjunction with the sliding window rule, which divides long-range series windows into three scales (short, medium, and long) and calculates the DCCA coefficient quantitative intercorrelation within each window. The particular measures are as follows:  
Suppose two distinct time series \(X\), \(Y\) exist:  
\[\left\{\begin{array}{l}X=x(i)|i=1,\ldots,N\\ Y=y(i)|i=1,\ldots,N\end{array}\right. \tag{3}\]  
1. Calculate separately the cumulative signal for sequences \(X\) and \(Y\):\[\begin{cases}R_{k}=\sum_{i=1}^{k}x_{i},k=1,\ldots,N\\ R_{k}=\sum_{i=1}^{k}y_{i},k=1,\ldots,N\end{cases} \tag{4}\]
2. The cumulative signal is partitioned into \(N\)-\(n\) overlapping windows containing \(n+1\) values, with values ranging from \(i\) to \(i+n\).
3. Using least squares to fit the local signal patterns inside a local window: \(\widetilde{R_{k,i}}\) and \(\widetilde{R_{k,i}^{\prime}}\).
4. By deleting the local trend and calculating its covariance, the residual series within the window is obtained. \[f_{DCCA}^{2}(n,i)=\frac{1}{n+1}\sum_{k=i}^{i+n}\Bigl{[}\left(R_{k}-\widetilde {R_{k,i}}\right)\left(R_{k}^{\prime}-\widetilde{R_{k,i}^{\prime}}\right) \Bigr{]}\] (5)
5. For various window lengths, the associated detrended covariance is obtained. \[F_{DCCA}^{2}=\frac{1}{N-n}\sum_{i=1}^{N-n}f_{DCCA}^{2}(n,i)\] (6)
6. The introduction of coefficient \(\rho_{DCCA}\) to measure the interseries connection. \[\rho_{DCCA}=\frac{F_{DCCA}^{2}(n)}{F_{xDFA}(n)F_{yDFA}(n)}\] (7)  
where \(F_{xDFA}(n)\) and \(F_{yDFA}(n)\) are the \(DFA\) scaling parameters for the \(X\) and \(Y\) sequences. The calculations are as follows:  
\[\begin{cases}F_{xDFA}^{2}(n)=\frac{1}{(N-n)(n+1)}\sum_{s=1}^{N-n\dot{i}+n} \sum_{k=i}^{i+n}\left(R_{k}-\widetilde{R_{k,i}}\right)^{2}\\ F_{yDFA}^{2}(n)=\frac{1}{(N-n)(n+1)}\sum_{s=1}^{N-n\dot{i}+n}\left(R_{k}^{ \prime}-\widetilde{R_{k,i}^{\prime}}\right)^{2}\end{cases} \tag{8}\]  
where \(\rho_{DCCA}\) reflects the relationship between time series variables. A score between \([-0.2,\,0.2]\) shows that there is no correlation between the series, a value between \([0.2,\,1]\) indicates a positive correlation, and a value between \([-1,\,-0.2]\) indicates a negative correlation.
## 1 Introduction
### Diagnosis of Osteoporosis  
The diagnosis of osteoporosis relies on a comprehensive approach, including a detailed patient history, a thorough clinical examination, spinal X-rays, bone mineral density (BMD) measurements, and laboratory investigations [5]. The patient's history should focus on identifying risk factors such as family history, previous fractures, dietary habits, lifestyle factors (e.g., alcohol consumption), use of medications affecting bone metabolism, and age at menopause onset [6].  
Bone mass, expressed as bone mineral density (BMD), can be assessed using various techniques generally indicated as bone densitometry or computerized bone mineralometry (CBM). The most reliable procedure is the so-called DXA (dual-energy X-ray absorptiometry) which should be always measured at two levels: hip and spine.  
BMD values are essentially expressed with the T-score the value 0 is the average BMD value of healthy young adults (peak bone mass). The Z-score (0 is the average value of individuals of the same age and gender) can be a useful parameter, even if less used for defining the degree of bone loss. According to the WHO criteria, four categories of subjects are identified based on the measured T-score [7]:  
* T-score from +2.5 to \(-1.0\) SD (normal);
* T-score between \(-1.0\) and \(-2.5\) SD (osteopenia);
* T-score \(<-2.5\) SD (osteoporosis);
* T-score \(<-2.5\) SD in the presence of at least one fragility fracture (severe osteoporosis).  
It has been observed that the risk of fracture begins to increase exponentially with densitometric values of T-score \(<-2.5\) standard deviations (SD), which, according to WHO, represents the threshold for diagnosing osteoporosis. Bone mass is directly related to the mechanical strength of the bone and inversely related to its fragility.  
BMD measurements of the lumbar spine, femoral neck, total hip, and distal third of the radius are predictive of fragility fractures. Measurements on the distal radius are performed only when lumbar and/or femoral evaluation is imprecise or inaccurate, in severely obese patients, and in patients with hyperparathyroidism.
# URANS Analysis of a Launch Vehicle Aero-Acoustic Environment
## 3 Results and Discussion  
### Validation of the Results  
Experimental data to validate CFD models are scarce in the vicinity of the exhaust gases. Hence, two sets of acoustic data measured during the lift-off of the VEGA launch system were provided by ESA.  
The first set of experimental data were extracted from a phased array of microphones located at one of the masts surrounding the launch platform, as those shown in [18]. The second set consisted of the measurements of six microphones along the fairing surface. As the experimental data are confidential, the results were normalized. Figure 3 shows a comparison of the time-averaged value of total pressure at each microphone with the equivalent positions translated to simulations. The comparison was performed in terms of the mean value of pressure and its standard error. Hence, the vertical line represents the acceptable range calculated as the _mean value \(\pm\) the standard error_, and the marker represents the mean value. The blue results represent the experimental measures, and the red represents the simulation results. The comparison at the fairing surface in Figure 3a shows the average pressure for the six microphones. The mean pressure obtained at Microphones 2, 3, and 4 from the simulation was within the acceptable range of the experimental measurements, whereas none of the other microphones were. These microphones had less than a 3% error. Concerning the pressure along the microphone array placed at a mast, shown in Figure 3b,  
Figure 2: Mesh details. On the left picture, mesh domain cross-section representing each of the cells. On the right, representation of the mesh classified by the size of the cells.  
the mean pressure obtained by the simulation was not within the experimentally acceptable range. However, the experimentally acceptable range was within the mean minus the simulation's standard error. In this region, the performance of the CFD model was not ideal. Factors such as the simplification of the exhaust plume input data or the movement of the rocket led to different sources of error in both locations. However, the parameters obtained at the plume development region and the flow behavior agreed with the literature [13], and the results at the fairing presented a better accuracy.
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 3 NB-IoT Downlink System Model
### NRS Generation and Mapping  
Narrowband reference signal (NRS) carries out the duty of channel estimation and signal repair, respectively. NRS is injected inside a subframe consisting of one shared or control channel. At the receiver side, transmitted NRSs are calculated from the provided synchronization signal. NRS sequence \(\mathbf{r}_{l,n_{k}}(m)\) is generated according to the sequence below [38]:  
\[\mathbf{r}_{l,n_{k}}(m)=\frac{1}{\sqrt{2}}(1-2.c(2m))+j\frac{1}{\sqrt{2}}(1-2. c(2m+1)),m=0,1,\ldots,2N_{RB}^{\max,DL} \tag{1}\]  
where the radio frame slot number is denoted by \(n_{s}\), \(l\) which represents the OFDM symbol number within the slot and \(N_{RB}^{\max,DL}\) is the largest bandwidth by the multiplication of the number of subcarriers in NB-IoT downlink, which is set to the value 110 for legacy LTE. \(\mathbf{C}_{i}\) is the length-31 gold sequence defined pseudo-random sequence for the \(i^{th}\) element which is initiated for each OFDM symbol at its starting with  
\[\mathbf{c}_{init}=2^{10}\cdot(7\cdot(n^{\prime}{}_{s}+1)+l+1)\cdot(2\cdot N_{ ID}^{cell}+1)+2.N_{ID}^{cell}+N_{CP} \tag{2}\]  
Figure 3: Block diagram of NB-IoT downlink transmission scheme.  
where \(N_{CP}=1\) because of only normal CP application in NB-IoT and  
\[n^{\prime}{}_{s}=\left\{\begin{array}{ll}10\lfloor n_{s}/10\rfloor+n_{s}{\rm mod }(2)&\text{for frame structure type 3}\\ n_{s}&\text{otherwise}\end{array}\right.\]  
Different resource elements are occupied by NRS in a subframe. The elements of a subframe are denoted by \(k\) in the frequency domain, which denotes the subcarrier number, and \(l\) in the time domain, which represents the symbol number [36]. The resource mapping formula for NRS is  
\[a_{k,l}^{(p)}=r_{l,n_{s}}(m^{\prime}) \tag{3}\]  
where \(a_{k,l}^{(p)}\) is the complex-valued modulation symbol, \(p\) is the antenna port, and \(n_{s}\) represents the slot number. \(k\) and \(l\) are defined by the following formulae:  
\[k=6m+(v+v_{shift}){\rm mod}6 \tag{4}\] \[l=N_{symb}^{DL}-2,N_{symb}^{DL}-1\] \[m=0,1\] \[m^{\prime}=m+N_{RB}^{\max,DL}-1\text{ and }\] \[v_{shift}=N_{ID}^{Ncell}{\rm mod}6\]  
where \(v_{shift}\) and \(v\) represent different frequency domain positions for different reference signals.  
Here, two different values of \(l\) and \(m\) vindicate that in each slot, four resource elements will be assigned to the corresponding NRS, and eight resource elements in each subframe. The resource element mapping for a single antenna port is shown in Figure 4. NRS is transmitted with data symbols at the transmitter and is used for estimating the channel performance in the downlink NB-IoT system at the receiver. NRS symbols are inserted into the specifically assigned subcarriers at every NB-IoT downlink slot. On each content of the resource grid which consists of NRS, an inverse discrete Fourier transform (IDFT) process is executed for converting it into a time-domain reference sequence with the addition of CP.  
Figure 4: NRS mapping for a single antenna port.
## 2 Literature Review  
[PERSON] et al. [11] introduce a segmentation approach relevant to the compilation of K-means and semantic segmentation methods for segmenting weeds and crops in color images. The two distinct databases of agronomic images are employed for segmenting methods. Everything except the plants has been eliminated from the images with the use of the threshold method. Then, utilizing U-net and the K-means subtractive approach for the segmentation of weeds and crops, semantic segmentation has been implemented. A U-net for the segmentation of weeds and wheat on images was given in [12]. An image classifier task has been employed for choosing the backbone network for the encoding part. The abovementioned task on a similar dataset will be exploited for pretraining and choosing the decoding network. The training process implemented TL. [PERSON] et al. [13] formulates a weed segmentation and mapping structure that processes multispectral images received from drones by utilizing deep neural networks (DNNs). Many researchers are increasingly making an effort in weed or crop semantic segmentation just by considering single images for classification and processing.  
A weed species and density evaluation approach rely upon an image semantic segmentation NN that has been modeled in [14]. To train the network, an amalgamation of fine-tuning and pretraining training approaches has been employed. The pretraining datasets are the images that just have a single weed species in a single image. The weeds will be labeled mechanically by an image segmentation approach related to the minimum error threshold and Excess Green (ExG). The fine-tuning data are real imageries comprising many crops and weeds and are manually labeled. In [15], an AI-oriented method has been modeled to find weeds' unintentional growth on agricultural fields and rise the control rate, concerning technological advances. This presented technique will train the dataset by utilizing pretrained DL techniques and has been optimized through metaheuristic optimization approaches through the selection of the species-related activations that can be sent to Softmax, which was in the final layer of DL techniques.  
In [16], a technique related to DL has been modeled for weed segmentation from the images. This approach may segment weeds from crops and soil in the images. This semantic segmentation approach has been advanced using a simplified U-net. An image augmentation method has been devised because of the difficulty of image labeling for the semantic segmentation of weeds. The semantic segmentation network is trained by a two-stage training technique made up of fine-tuning and pretraining. [PERSON] et al. [17] intended to assess three TL approaches with the use of a VGG16-oriented encoder net for segmenting the oilseed rape images in a field including high-density weeds. Three TL methods utilizing a VGG16-based encoder method have been modeled, and their performances will be compared with a VGG19-related encoder net.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Core Network
#### 3.1.4. Vendor Trust
\begin{table}
\begin{tabular}{c c} \hline \hline
**Security Consideration** & **Explanation** \\ \hline \multirow{6}{*}{Confidentiality and Integrity} & Confidentiality and integrity are two of the most basic security considerations for any public network. Data integrity is vital to maintaining a trusted connection between two users. If the data are altered in any way or cannot be verified, trust is broken and the network can no longer be considered safe. Figure 6 is a good example of a potential decrease in confidentiality and data integrity with information being passed to an untrusted network link. \\ \hline \multirow{6}{*}{Mutual Authentication} & One way to verify that all components within a network are legitimate is to utilize Mutual Authentication (MA). One example of MA is the 5G-AKA protocol, which authenticates the UE with the CN and vice versa in a quick and efficient manner. Node authentication is discussed in [41], which allows for the verification of different network components. This ensures all devices in the backhaul are authorized and will deny access to rogue actors. \\ \hline \multirow{6}{*}{Access Control} & In order for a user to securely connect to a 5G network, there need to be methods in place to only allow access to legitimate users. This is standard practice for any network, but protecting against illegitimate user access is always a ‘cat and mouse game’ between researchers and hackers. \\ \hline \multirow{6}{*}{Availability} & In any 5G network, availability is paramount, especially for the URLLC slice of 5G, guaranteeing an ultra-reliable and low-latency Quality of Service (QoS). User authentication has a direct impact on the availability of a network. If a large number of illegitimate users are able to gain access to a network, they would not only degrade network integrity but also severely deteriorate availability by consuming resources that are then no longer available to legitimate users of the network. Availability is not only dependent on the resources of the network but also how usable, effective, and efficient the network can be [42]. This can range from how the user accesses the network to provisioned authentication and connection methods and more. \\ \hline \multirow{6}{*}{Perfect Forward Secrecy} & These methods of keeping user data private are crucial when storing information within a core network. The author suggests the use of session keys to allow for future recovery if a private key is exposed. \\ \cline{1-1}  & \multicolumn{1}{c}{} \\ \cline{1-1}  & \multicolumn{1}{c}{} \\ \cline{1-1}  & \multicolumn{1}{c}{} \\ \cline{1-1}  & \multicolumn{1}{c}{} \\ \cline{1-1}  & \multicolumn{1}{c}{} \\ \cline{1-1}  & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 3: 5G backhaul network security requirements and considerations.
# Multiple Elimination Based on Mode Decomposition in the Elastic Half Norm Constrained Radon Domain
###### Abstract  
Multiple reflection is a common interference wave in offshore petroleum and gas exploration, and the Radon-based filtering method is a frequently used approach for multiple removal. However, the filtering parameter setting is crucial in multiple suppression and relies heavily on the experience of processors. To reduce the dependence on human intervention, we introduce the geometric mode decomposition (GMD) and develop a novel processing flow that can automatically separate primaries and multiples, and then accomplish the suppression of multiples. GMD leverages the principle of the Wiener filtering to iteratively decompose the data into modes with varying curvature and intercept. By exploiting the differences in curvature, GMD can separate primary modes and multiple modes. Then, we propose a novel sparse Radon transform (RT) constrained with the elastic half (EH) norm. The EH norm contains a \(l_{1/2}\) norm and a scaled \(l_{2}\) norm, which is added to overcome the numerical oscillation problem of the \(l_{1/2}\) norm. With the help of the EH norm, the estimated Radon model can reach a remarkable level of sparsity. To solve the optimization problem of the proposed sparse RT, an efficient alternating multiplier iteration algorithm is employed. Leveraging the high sparsity of the Radon model obtained from the proposed transform, we improve the GMD-based multiple removal framework. The high-sparsity Radon model obtained from the proposed Radon transform can not only simplify the separation of primary and multiple modes but also accelerate the convergence of GMD, thus improving the processing efficiency of the GMD method. The performance of the proposed GMD-based framework in multiple elimination is validated through synthetic and field data tests.  
multiple removal; sparse radon transform; mode decomposition; sparse inversion Article
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 3 Problem Formulation
### Simulation Setup  
We employed tabular Q-learning [46] rather than a deep function approximator because, after a lossless discretization, the state space remained small enough for an exact table. Node location is inherently discrete (\(|X|=4327\) nodes), the remaining data \(d\) are bucketed into 20 equal-width bins (0-5 MB,..., 95-100 MB), and both bandwidth and traffic density on outgoing links are mapped to four quartile-based levels. At the start of every  
Figure 4: Bandwidth availability in the environment.  
episode, all link speeds and bandwidths are resampled from their empirical distributions, so the agent faces a different traffic and wireless landscape each run.  
This yields:  
\[|S|\ =\ 4327\times 20\times 4\times 4\ =\ 1.38\times 10^{6}\]  
Distinct state tuples--an \(\sim\) 8 MB Q-table when stored sparsely via a collision-resistant hash map--are well within commodity GPU memory.  
Given this tractable size, tabular Q-learning offers exact value estimates with guaranteed convergence in finite MDPs, thus maximizing reproducibility and algorithmic transparency. Unvisited state-action pairs are initialized optimistically, and an \(\varepsilon\)-greedy schedule decays linearly from 0.9 to 0.05 over the first 200,000 interaction steps, speeding up convergence without resorting to deep neural networks.  
Q-values are updated with the learning rate \(\alpha=0.10\) and discount factor \(\gamma=0.99\). Each episode terminates upon arrival at the destination or after 4000 simulation steps. \(\varepsilon\) in the \(\varepsilon\)-greedy policy decays linearly from 0.90 to 0.05 over the first \(2\times 10^{5}\) steps. Optimistic initial Q-values were set to \(+50\) s to encourage exploration.  
Figure 5 depicts the key parameters that influence the performance of our agent, measured by trip completion time. These include the data transfer requirement, which specifies the volume of data to be transferred prior to the agent reaching its destination; the start and end points, which set the trip's length; the map size, which reflects the extent of the state space; and the reward function, which shapes the agent's behavior in this environment. To explore the impact of a specific parameter, we vary one parameter at a time, keeping the others constant.