esa-sceva/satcom-chunk-collection huggingface dataset:
# A Single-Launch Deployment Strategy for Lunar Constellations
## 5 Conclusions  
A strategy to deploy the satellites of a lunar constellation to multiple orbital planes from the same launch trajectory is investigated in this research.  
The strategy is developed by taking advantage of low-energy capture trajectories existing in the Sun-Earth-Moon system, which allows the targeting of the orbital elements of the capture orbit by providing small \(\Delta V\) in the neighborhood of libration point \(L_{1}\). The theoretical background on low-energy captures and their characterization in the dynamic framework of the ER4 BP are provided, and the strategy is derived from them.  
The analysis verifies the strategy: it is applied to a scenario of actual interest, namely the deployment of a constellation that can provide polar and global coverage. Results from the analysis prove its effectiveness and demonstrate its performance in terms of deployment time, which does not exceed one sidereal month (26 days), and \(\Delta V\), which is lower than 1 km/s for the worst case examined.  
Converting \(\Delta V\) to finite-time maneuvers, it can be verified that they can be successfully performed by the same chemical propulsion systems available to small satellites, requiring less than 1/3 of the mass for a 100 kg spacecraft.  
This research received no external funding.  
Not applicable.  
All data are reported in the manuscript.  
The author declares no conflict of interest.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 2 Experimental Measurement Setup and Methods
### Experimental Procedure  
As described in the literature, noise can be classified into five categories that affect NB-PLC signals [28; 29]. In particular, the perturbations depend on their duration, spectral occupation and amplitude. These five categories are named: colored background noise, narrowband noise, periodic impulsive noise asynchronous periodic impulsive noise synchronous, and aperiodic impulse noise. The first three categories have stationary characteristics, while the two last ones are impulse noises with a short duration, randomness, and high power spectral density (PSD). Their main features are given below:  
* Colored background noise corresponds to the superposition of several low signals that are present in the channel. This type of noise has a low decreasing PSD in the frequency domain. The dependence of the frequency-colored noise is very clear at low frequency, and its PSD is practically constant at high frequency.
* Narrowband noise is composed of sinusoids coupled with the grid by either broadcast signals or electrical devices. Broadcast signals are typically amplitude-modulated sinusoids.
* Periodic impulsive noise asynchronous to the mains is identified as an impulse train in the frequency band from 50 to 200 kHz. It is generated by the switched-mode power supplies.
* Periodic impulsive noise synchronous to the mains is caused by high power switching of semiconductor devices.
* Aperiodic impulsive noise is composed of impulses of short duration and high amplitude caused by switching transients.  
Figure 3: Five measurement sites in various locations of the city of Tours in France.  
Noise characteristics, such as PSD, amplitude, inter-arrival time, in time-frequency representation require the use of two specific measurement protocols.
## 2 Application of Thermography in Pig Breeding
### Neonatal Mortality Reduction  
Neonatal hypothermia is known as a frequent cause of mortality in pre-weaning piglets, particularly affecting those individuals who have lower temperatures during the first few hours after their birth [11]. As a rule, those individuals who weigh less at birth have a hard time recovering from the initial drop in body temperature, which occurs during the first hour after birth, and they subsequently struggle to maintain it within an adequate level [12]. The use of a digital thermometer for measuring rectal temperature (RT) is considered the gold standard, so it has been widely used in piglet viability research. Thermography allows faster and continuous identification of those piglets requiring supportive interventions in the immediate postpartum, thus enabling the aforementioned interventions (Figure 2).  
The thermoregulatory capacity of newborn piglets is related to their body energy and metabolism, which can be influenced by genetic differences among different existing breeds, such as average weight and metabolic efficiency. We also need to consider external factors, such as air temperature or the state of piglets' skin (wet or dry). Drying piglets at birth, placing them near a heat source, and providing them with some energetic supplementation improves their thermal status and prevents hypothermia, but it is necessary to pay attention before shooting with the thermal camera, because emissivity of the wet skin is different from that of dry skin and the presence of heat sources can affect data [12; 13]. [PERSON] found out that the skin temperature was not related to birth weight but was influenced by the huddling behavior of piglets and their location (near or far) from a heat source [13], and [PERSON] found out that the IRT data were not predictive of mortality risk, however, they could be used to predict shivering [14]. Note that although not all piglets suffering from hypothermia would shiver [15], IRT could be used to identify and help piglets in need in order to limit mortality.  
[PERSON] has carried out a study regarding the possibility of applying thermography to the assessment of thermoregulation differences among piglets belonging to two genetic lines, whose growth efficiency is lower or higher. Piglets belonging to a genetic line with higher efficiency showed a higher temperature at both the 15 th and 30 th minute postpartum, particularly the ear temperature, suggesting an effect of the genetic selection for feeding efficiency on neonatal thermoregulation abilities [16].  
Figure 2: Control of skin and environmental temperatures during farrowing with an infrared camera.
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 2 Overview of Generative Models
### Generative Adversarial Network  
GANs [29] are the deep generative models that consist of two neural networks: a generator \(G\) and a discriminator \(D\). The G learns to create an image by sampling from a noise distribution \(p_{z}\), i.e., transform the noise \(z\) into \(G(z)\), with the aim to make the ideal \(p_{z}\) converge to the real data distribution \(p_{data}\). Meanwhile, the \(D\) learns to distinguish whether the input sample \(x\) is real or fake. The optimization of a GAN can be viewed as a mini-max problem with value function V (\(G\), \(D\)) that is written as in Equation (2).  
\[\underset{G}{\text{minmax}}V(G,D)=\mathbb{E}_{p_{data}(x)}\log D(x)+\mathbb{E} _{p_{z}(z)}\log[1-D(G(z))] \tag{2}\]  
where \(\mathbb{E}\) is the empirical estimate of the expected value of the probability. In the optimization process, \(G\) and \(D\) compete with each other until reaching the Nash equilibrium [42]. At that point, \(G\) considers the generated samples to be realistic enough. GANs have been widely used in many fields, such as face recognition [43; 44], video prediction [45; 46], and natural language processing [47; 48], etc. In the field of defect inspection, GANs are the most popular generative model to address the issues of data limitation, where more related details can be found in the next section.
## 3 HGV Tracking with Multiple Model Algorithms
### IMM Tracking Algorithm
#### 3.2. AGIMM Tracking Algorithm  
In order to solve real-world problems the use of only a small number of models is not good enough but the use of more models does not necessarily improve the performance and increases the computational burden considerably. Thus, the concept of variable structure multiple model estimation is proposed and several VSMM algorithms are developed by researchers. An effective VSMM algorithm is to make adaptive the grid of the parameters that characterize the possible modes.  
In AGIMM tracking algorithm a coarse grid relating about attack angle is set up initially, and then the grid is adjusted recursively. HGV tracking using AGIMM algorithm can be demonstrated as follows:  
Because the real attack angle (unknown) is assumed to be within the continuous range [6; 12], we can consider a three-model IMM algorithm with time-varying supporting digraph \(D_{k}\) and the model set \(\left\{\alpha_{1,k},\alpha_{2,k},\alpha_{3,k}\right\}\) for grid values (vertices of \(D_{k}\)) \(\alpha_{1,k}\leq\alpha_{2,k}\leq\alpha_{3,k}\in[6,12]\).  
Initially, the AGIMM is started with the coarse grid  
\[D_{0}=\left\{\alpha_{1,0}=6,\alpha_{2,0}=9,\alpha_{3,0}=12\right\} \tag{27}\]  
At each recursive time-step \(\left(k\to k+1\right)\), the grid is adjusted as following two-step adaptation logic.
## 4 Results
### Model Accuracy and Importance Evaluation of Quantitative Remote Sensing Products
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier Type** & **Input** & **Overall Accuracy** & **Mean FI-Score** \\ \hline Bi-LSTM & GPP only & 81.86\% & 0.819 \\  & NDVI only & 79.66\% & 0.801 \\  & GPP + ET & 83.61\% & 0.835 \\  & GPP + ET + NDVI & 84.93\% & 0.848 \\  & GPP + ET + NDVI + LAI & 86.34\% & 0.864 \\  & GPP + ET + NDVI + LAI + FAPAR & 87.21\% & 0.872 \\  & GPP + ET + LAI + NDVI + FAPAR + Albedo & 88.04\% & 0.878 \\  & GPP + ET + LAI + FAPAR + Albedo & 87.39\% & 0.872 \\ \hline LSTM & GPP only & 79.13\% & 0.793 \\  & NDVI only & 75.23\% & 0.758 \\  & GPP + ET + LAI + FAPAR + Albedo & 84.37\% & 0.833 \\ \hline CNN & GPP only & 56.40\% & 0.553 \\  & NDVI only & 53.10\% & 0.513 \\  & GPP + ET + LAI + FAPAR + Albedo & 72.35\% & 0.716 \\ \hline SVM & GPP only & 63.73\% & 0.611 \\  & NDVI only & 60.58\% & 0.601 \\  & GPP + ET + LAI + FAPAR + Albedo & 73.90\% & 0.727 \\ \hline Random Forest & GPP only & 78.30\% & 0.773 \\  & NDVI only & 72.84\% & 0.72 \\  & GPP + ET + LAI + FAPAR + Albedo & 79.83\% & 0.793 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the classification accuracies between different models and different quantitative remote sensing products. plays a leading role in this land cover classification model. Quantitative remote sensing products can be used as a new reliable factor for land cover classification research.
# Best Practices Evidenced for Software Development Based on DevOps and Scrum: A Literature Review
## 4 Discussion
### Context of the Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Benefits** & **Improvement Observed** & **Supporting Studies** \\ \hline \multirow{4}{*}{Scrum adoption} & \multicolumn{2}{c}{Actively involved stakeholders.} \\  & Transparent communication channels. & \\ \multirow{2}{*}{Scrum adoption} & Increased team collaboration and improved predictability. & [4] \\  & Creation of a collaborative culture. & \\ \multicolumn{2}{c}{Continuous improvement.} & \multicolumn{2}{c}{Constant quality measurement or concurrent testing.} \\ \multirow{2}{*}{DevOps adoption} & \multicolumn{2}{c}{Early and continuous feedback.} & \\  & Productivity increased by 20\%, and deployment time decreased & [45] \\ \multirow{2}{*}{Faster release cycles} & \multicolumn{2}{c}{Time to market decreased by 25\%, and incident resolution time} \\  & \multicolumn{2}{c}{decreased by 40\%.} & [29; 48] \\  & \multicolumn{2}{c}{Quality deliverable.} & \\ \multirow{2}{*}{Continuous integration} & \multicolumn{2}{c}{Early and continuous feedback.} & \\  & \multicolumn{2}{c}{Quality deliverable.} & \\ \multirow{2}{*}{Continuous integration} & \multicolumn{2}{c}{Time to market decreased by 25\%, and incident resolution time} & \\  & \multicolumn{2}{c}{decreased by 40\%.} & \\ \multirow{2}{*}{Automated testing} & \multicolumn{2}{c}{Transparent communication channels.} & \\  & Test execution speed increased by 35\%, and defect detection & [111] \\ \multirow{2}{*}{Shift-left testing} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{increased by 18\%.} \\ \multirow{2}{*}{Security automation} & \multicolumn{2}{c}{Early defect detection increased by 40\%, and bug-fix costs} & \\  & \multicolumn{2}{c}{increased by 25\%.} & \\ \multirow{2}{*}{Security automation} & \multicolumn{2}{c}{Security vulnerabilities decreased by 30\%, and response time} & \\  & \multicolumn{2}{c}{decreased by 50\%.} & \\ \multirow{2}{*}{Agile transformation} & \multicolumn{2}{c}{The development cycle decreased by 25\%, and project success} & [20; 30] \\  & \multicolumn{2}{c}{rates increased by 18\%.} & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Scrum and DevOps implementation benefits. Finally, the analysis reveals a growing trend in the literature towards DevOps research, driven by its ability to optimize Agile frameworks and its emphasis on automation. This trend suggests an opportunity to explore innovative combinations of practices between both frameworks, intending to provide more comprehensive solutions to the specific challenges of VSEs.
## 6 Space-Segment Optimization Aspects
### Optimization Metrics Related to the Space Segment
However, the trade-off between constellation altitude and coverage depends on application. With a lower altitude, the satellite signal reaches the receiver at a higher SNR, but more satellites are needed to provide the coverage. As for a mathematical explanation, we have already presented a cost model from [123] in Equation (7). Another significantly more complex metric that is required for the optimization process of most advanced methods, such as the Machine Learning (ML)-based approaches, is the application-specific objective function. This metric has different names and serves different purposes depending on the literature area; some examples are the 'fitness function' (for evolutionary algorithms), 'the reward function' (for RL-based methods), and 'the cost function' (for the dynamic optimization methods). Such functions are typically designed to include a high number of non-linearities that best represent the important system characteristics based on the application. With regards to the space segment, an objective function can simply be any of the already mentioned metrics (i.e., \(C/N_{0}\) for GNSS or LEO-PNT applications as well as for high communication throughputs), some combination of them (i.e., \(C/N_{0}\) and GDOP), or completely custom crafted as in [20; 74]. As an example, we present Equation (13), which defines an objective function \(f(x)\) that combines the \(C/N_{0}\) and the launch cost \(C_{launch}\) using some weights \(w_{1}\) and \(w_{2}\), where \(x\) is the vector of the required parameters for the calculations. Note that in a typical ML-based method, these weights are fine-tuned by the optimization process, but they can also be tuned manually, which is typical for evolutionary algorithms. \[f(x)=w_{1}C/N_{0}+w_{2}C_{launch} \tag{13}\]
## 1 Introduction
Network security has become a prominent theme for both industry and academia, with a surge of interest in its implementation within SDN [8] and NFV [9] architectures. Some of these implementations take advantage of the programmability of the network by applying solutions based on machine learning to detect anomaly flows [10] or implement a softwarized IDS in the SDN network [11] or develop NFV functions as firewalls to be deployed at the edge [12]. SDNFV technologies embody a novel and sophisticated paradigm to address security issues linked with new service deployments. Varied applications can be devised to manage security concerns of services or applications within the network using the benefits of SDNFV architecture. In this work, a threat detection and mitigation algorithm is presented that takes the advantages provided by SNDFV-based networks. The algorithm employed leverages information entropy, a fundamental concept introduced by [PERSON] [13], to quantify uncertainty within network traffic patterns. Information entropy has demonstrated its efficacy in characterizing information content across a multitude of domains and applications like big data, fuzzy logic, or medicine [14; 15; 16; 17]. To measure uncertainty within the network, the algorithm calculates the entropy of four key packet parameters: source IP address, destination IP address, source port, and destination port. These entropy values subsequently serve as the foundation for initiating appropriate mitigation techniques within the network infrastructure. The remainder of this article is organized as follows: Section 2 introduces Network Function Virtualization, Software-Defined Networks, and their relationship. Section 3 details the SDNFV network architecture used in this work, along with its design and protocol development. Section 4 presents threat detection and mitigation in an SDNFV-based network, describing the algorithm for detecting potential security issues and the mechanisms to mitigate them using virtualized network functions in a real scenario. Finally, Section 5 provides the concluding remarks of this study.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 2 Theoretical Background
### Order Spectrum Correlated Kurtosis  
In [18], [PERSON] found that the kurtosis value of a signal with a single impulse is always higher than a signal containing consecutive periodicity of impulses. To solve the effect of random transients on kurtosis, [PERSON] proposed Correlated Kurtosis (CK) to detect signal cyclic transients by using the periodicity of the fault nature, and verified that CK can decrease the inference of single transient impulses. The CK of a vibration signal \(x\) is defined as [14]:  
\[CK(x)=\frac{\sum\limits_{n=1}^{N}\left(\prod\limits_{m=0}^{M}x_{n-mT}\right)^{ 2}}{\left(\sum\limits_{n=1}^{N}x_{n}^{2}\right)^{M+1}} \tag{4}\]  
where \(N\) is the length of \(x\), \(T\) is the period of interest impulses, and \(M\) is the CK shift.  
In [26], it was also observed that the kurtosis value is considerably affected by shaft rotational frequency. To eliminate the influence of speed on kurtosis and the frequency smear, COT is employed to convert the non-stationary filtered envelope time signal into the stationary vibration in the angular domain. Based on the key-phase signal, which is used to obtain the sampling-time marks of the even-angle sampling, an interpolation scheme is employed for resampling the original time-domain signal into the angular domain. Here, we use cubic spline interpolation. After that, the envelope order spectrum is utilized to expose the order structure in the signal, and the fault characteristic order (FCO) can be indicated clearly.  
Previous research found that kurtosis is sensitive to external interference, especially in varying speed conditions, and the components contained in the signal have a single transient characteristic. Thus, it is difficult to detect fault sensitive components. To address the above problem, the order spectrum analysis is combined with correlated kurtosis to form a new feature, OSCK, to detect the fault-sensitive frequency band under varying speed conditions.  
The OSCK can be defined as follows:  
\[\mathcal{O}\mathcal{S}\mathcal{C}\mathcal{K}(A,T)=\frac{\sum\limits_{n=1}^{N} \left(\prod\limits_{m=0}^{M}A_{n-mT_{o}}\right)^{2}}{\left(\sum\limits_{n=1}^{ N}A_{n}^{2}\right)^{M+1}} \tag{5}\]  
where \(A\) is the envelope order spectrum amplitudes, and \(T_{o}\) is the period of impulses.  
Figure 1: The paving of the Fast Kurtogram.
## 3 Results
### Additional Validation for the Retrieved Model  
For more precision inthe model, the four samples (RG2-RG5), which werenot included in the analysis, wereused to validate the model (Table 6). The first value in March and the last value in November weretoo high, because we tried to collect water samples where the water condition was rather uniform. However, it is still possible that the water samples capture locally high turbidity, while the reflectance of the satellite is on average about 900 m\({}^{2}\).  
The final turbidity maps, after applying the generated models, are presented in Figures 8 and 9, Figures S1a-c and S2a-c. For March, the estimated concentrations ranged from 2.329 to 3.023 NTU (Figure 8 and Figure S1a-c) in relation to the in situ concentration turbidity, which ranged from 1.31 to 2.049 NTU. For November, the estimated concentrations ranged from 0.337 to 1.33 NTU, compared to 0.362 to 1.18 NTU for the measured values (Figure 9 and Figure S2a-c).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Observed (NTU)** & **Predicted (NTU)** & **Square Residual** & **RMSE** \\ \hline \multirow{4}{*}{March 2014} & 5.600 & 2.28803 & 10.969 & \multirow{4}{*}{2.2} \\  & 2.820 & 2.14487 & 0.456 & \\ \cline{1-1}  & 0.888 & 1.45088 & 0.317 & \\ \cline{1-1}  & 5.270 & 5.45901 & 0.036 & \\ \hline \multirow{4}{*}{November 2014} & 1.2 & 2.204 & 1.008 & \multirow{4}{*}{1.39044} \\  & 0.5 & 0.854 & 0.125 & \\ \cline{1-1}  & 0.6 & 1.331 & 0.535 & \\ \cline{1-1}  & 3.5 & 1.3641 & 4.562 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation of the retrieved model in March and November 2014.  
Figure 7: (a) Scatter plots and (b) line graphs shows the comparison of observed and satellite-retrieved turbidity values at nine sampling sites from the Ramganga River in November 2014.  
Figure 8: Turbidity map of the Ramganga River on 11 March 2014.
# Nonlinear Wave-Induced Uplift Force onto Pipelines Buried in Sloping Seabeds
## 2 Numerical Model
### Wave Sub-Model  
As for the wave sub-model, the Reynolds-Averaged Navier-Stokes (RANS) equation and Renormalization-Group (RNG) \(k-\varepsilon\) turbulence model are adopted for simulating the  
Figure 1: Sketch of the numerical model for wave-seabed-pipeline interaction.  
fluid motions; at the same time, the Volume of Fluid (VOF) method is adopted for tracking the free water surface:  
\[\frac{\partial}{\partial x_{i}}(u_{i}A_{i})=0 \tag{1}\]  
\[\frac{\partial u_{i}}{\partial t}+\frac{u_{j}A_{j}}{V_{F}}\frac{\partial u_{i}} {\partial x_{j}}=-\frac{1}{\rho}\frac{\partial p}{\partial x_{i}}+G_{i}+f_{i} \tag{2}\]  
where \(x_{i}\) is the Cartesian coordinates, \(i,j\) are the directions; \(A_{i}\) is the fractional area; \(u_{i}\) is the velocity; \(G_{i}\) is the body acceleration; \(\rho\) is the fluid density; \(V_{F}\) is the fractional volume open to flow; \(f_{i}\) is the viscous acceleration, which can be expressed as:  
\[\rho V_{F}f_{i}=-\frac{\partial}{\partial x_{j}}\big{(}A_{j}\tau_{ij}\big{)} \tag{3}\]  
\[\tau_{ij}=-2\mu\Bigg{(}\frac{\partial u_{i}}{\partial x_{i}}-\frac{1}{3}\frac {\partial u_{j}}{\partial x_{j}}\Bigg{)},i=j \tag{4}\]  
\[\tau_{ij}=-\mu\Bigg{(}\frac{\partial u_{j}}{\partial x_{i}}+\frac{\partial u _{i}}{\partial x_{j}}\Bigg{)},i\
eq j \tag{5}\]  
where \(\mu\) is the dynamic viscosity and \(\tau_{ij}\) is the stress tensor.  
The RNG \(k-\varepsilon\) turbulence model is defined as:  
\[\frac{\partial k_{T}}{\partial t}+\frac{u_{j}A_{j}}{V_{F}}\frac{\partial k_{T }}{\partial x_{j}}=P_{T}+Diff_{k}-\varepsilon_{T} \tag{6}\]  
\[\frac{\partial\varepsilon_{T}}{\partial t}+\frac{u_{j}A_{j}}{V_{F}}\frac{ \partial\varepsilon_{T}}{\partial x_{j}}=\frac{CDIS1\cdot\varepsilon_{T}\cdot P _{T}}{k_{T}}+Diff_{\varepsilon}-\frac{CDIS2\cdot\varepsilon_{T}^{2}}{k_{T}} \tag{7}\]  
where \(k_{T}\) is the turbulent kinetic energy; \(P_{T}\) is the turbulent kinetic energy production; \(\varepsilon_{T}\) is the rate of turbulent energy dissipation; \(Diff_{k}\) is the diffusion term of \(k_{T}\); \(Diff_{\varepsilon}\) is the diffusion term of \(\varepsilon_{T}\).  
The governing equation for the VOF method is as follows:  
\[\frac{\partial F}{\partial t}+\frac{1}{V_{F}}\frac{\partial}{\partial x_{i}} (FA_{i}u_{i})=0 \tag{8}\]  
where \(F\) is the fractional volume of fluid.  
In actual marine environments, the wave height can reach tens of meters, and the wave exhibits a steep crest and flat trough. In this case, the linear wave theory is no longer applicable. Therefore, this paper focuses on the nonlinear wave-induced uplift force onto the pipeline buried in sloping seabed.