esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Applications
### Analysis of Calculation Results
#### 4.4.1 Solving for Factor of Safety in Natural Operating Conditions  
In the numerical simulation under natural conditions, the boundary conditions used are as follows: the side boundaries are fixed in the corresponding directions, and the bottom is fixed in the x, y and z directions. The damage criterion of the slope is the Mohr-Coulomb criterion, and its mechanical properties are listed in Table 3. In the computational simulation software 3 DEC, the convergence criterion to determine whether the simulation has reached equilibrium is whether the deformation rate converges within the detection threshold. The value of the deformation rate in the calculation refers to Figure 4, where a deformation rate threshold of 10 mm/d, i.e., 1.158 \(\times\) 10\({}^{-8}\) m/s, is adopted.  
From Figures 8 and 9, it can be seen that under natural working conditions, the range of the discount factor is between 1.065 and 1.750, and the range of the deformation rate is between 7.857 \(\times\) 10\({}^{-8}\) m/s and 2.766 \(\times\) 10\({}^{-2}\) m/s. The greater the discount factor is, the more the safety factor is calculated, and the larger the discount factor, the larger the calculated safety factor. When the discount factor is 1.750, the maximum value of the deformation rate is 2.766 \(\times\) 10\({}^{-2}\) m/s; when the safety factor is between 1.090 and 1.750, the results of the deformation rate calculation do not converge, and when it is between 1.063 and 1.086, the results of the calculation of the discount factor converge, and they are less than the threshold value. The rate of deformation changes abruptly at the discount factor of 1.086, so the safety factor of 1.086 is obtained. The stability analysis of rocky slope is carried out, and it is found that the slope remains basically stable under natural working conditions, which is consistent with the results of the on-site monitoring.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Warning Level** & **Blue Warning** & **Yellow Alert** & **Orange Alert** & **Red Alert** \\ \hline
**Risk Status** & **Take Note of** & **Cautionary** & **Be on the Alert** & **Distress** \\ \hline Deformation rate (mm/d) & 20–40 & 40–60 & 60–100 & \textgreater{}100 \\ Improvement of tangent angle (\({}^{\circ}\)) & 55–60 & 60–80 & 80–85 & \textgreater{}85 \\ Deformation rate continuity requirements & / & Continuous 48 h deformation speed \textgreater{} 10 mm/d \\ \hline \end{tabular}
\end{table}
Table 3: Daily deformation threshold of slopes.  
Figure 8: Deformation rates corresponding to different discount factors under natural working conditions. (**a**) A discount factor of 1.750. (**b**) A discount factor of 1.500. (**c**) A discount factor of 1.250. (**d**) A discount factor of 1.109. (**e**) A discount factor of 1.090. (**f**) A discount factor of 1.086. (**g**) A discount factor of 1.078. (**h**) A discount factor of 1.063.
## 2 Materials and Methods
### Curve Fitting Algorithms
#### 2.3.2 Variable Projection Algorithm  
The Variable Projection Algorithm [23] is a method used to solve separable nonlinear least squares problems. The least squares problem is said to be separable when the model parameters can be separated into two sets of parameters: one that enter linearly into the model, \(\mathbf{c}=[c_{1},\ldots,c_{k}]\), and another set of parameters that enters the model non linearly, \(\mathbf{a}=[a_{1},\ldots,a_{l}]\), such that \(\mathbf{x}=[\mathbf{c},\mathbf{a}]\). For each observation \(y_{m}\) of a separable nonlinear least squares problem, the model consists of the following linear combination:  
\[\varepsilon_{\mathbf{r}}(\omega)=\sum_{j=1}^{k}c_{j}\,\phi_{j}(\omega;\mathbf{a}) \tag{9}\]  
where \(\phi_{j}(\omega;\mathbf{a})\) is a nonlinear function that depends on nonlinear parameters. Functional \(\Psi\) is written in terms of residual vector \(\mathbf{r}\) as follows:  
\[\Psi(\mathbf{a},\mathbf{c})=\frac{1}{2}\|\mathbf{y}-\mathbf{\Phi}(\mathbf{a})\mathbf{c}\|^{2} \tag{10}\]in which the \(j\)-th column of the matrix \(\mathbf{\Phi}\) is \(\phi_{j}(\omega;\mathbf{a})\). The linear parameters \(\mathbf{c}\) could be obtained from the knowledge of \(\mathbf{a}\) by solving the linear least squares problem:  
\[\mathbf{c}=\mathbf{\Phi}(\mathbf{a})^{\mathsf{t}}\mathbf{y} \tag{11}\]  
which stands for the minimum-norm solution of the linear least squares problem for fixed \(\mathbf{a}\), where \(\mathbf{\Phi}(\mathbf{a})^{\mathsf{t}}\) is the Moore-Penrose generalized inverse of \(\mathbf{\Phi}(\mathbf{a})\). By replacing this in Equation (10), we obtain the Variable Projection functional:  
\[\mathbf{\Psi}_{\mathrm{VP}}(\mathbf{a})=\frac{1}{2}\left\|\mathbf{y}-\mathbf{\Phi}(\mathbf{a})\mathbf{ \Phi}(\mathbf{a})^{\mathsf{t}}\mathbf{y}\right\|^{2} \tag{12}\]  
The Variable Projection algorithm consists of two steps: first minimizing Equation (12) with an iterative nonlinear method and then using the optimal value found for \(\mathbf{a}\) to solve for \(\mathbf{c}\) in Equation (11) [33]. The principal advantage is that the iterative nonlinear algorithm used to solve the first minimization problem works in a reduced space, and less initial guesses are necessary. A robust implementation in Matlab, called VARPRO[34], has been adapted and used to deal with complex-value problems, choosing the Levenberg-Marquardt option for the solution of Equation (12).
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 3 Metal-Organic Frameworks
### MOFs as SPME Coatings for Environmental Applications
#### 3.3.6 Extraction of Polychlorinated Biphenyls  
Polychlorinated biphenyls (PCBs) are a class of persistent organic pollutants accounting for more than 200 synthetic organic compounds. PCBs are listed among the top five priority hazardous substances by the US EPA due to their toxicity, persistency, ubiquitous presence, and bioaccumulation [81]. Despite their production being banned in the 1970s, they have been detected in all the environmental compartments and ecosystems, including air, water, soil, and in wildlife, plants, animal tissues, and food products.  
Considering that PCBs are listed by the IARC in group 1 (carcinogenic to humans) and are recognized endocrine disruptors, their monitoring in the environment is of pivotal importance: to this aim, different MOF-based SPME coatings have been devised [82; 83; 84].  
A coating with nitrogen-doped carbon nanotube cages (N-CNTCs), derived from ZIF-67 obtained by calcination of the MOF and deposited onto SS wires by means of an epoxy resin, was successfully proposed for PCB determination [83]. The MOF morphology was preserved, obtaining a thin N-doped carbon nanotube assembled structure with uniform hollow cages. The material showed better extraction performance than that achieved using either commercial coatings (75 \(\upmu\)m CAR/PDMS and 65 \(\upmu\)m PDMS/DVB fibers) or the non-hollow material, which also required prolonged time to reach equilibrium. The extraction capability of the N-CNTCs was attributed to \(\uppi\)-\(\uppi\) interactions between the analytes and the interconnected graphitic framework, as well as to the N doping. The DI-SPME-GC-MS method was characterized by LOQs in the 0.33-0.72 ng/L range, a four order of magnitude linear range, and good precision. The method was applied for the determination of PCBs in six surface water samples, quantifying PCBs in the 1.9-26.2 ng/L range. Similarly, hollow carbon nanobubbles (HCNBs) were obtained from a ZIF-8 MOF [84]. The developed material maintained the morphology of the framework, also featuring an internal hollow structure able to provide short diffusion distances and increased extraction capability. The SPME coating was tested for the extraction of different classes of persistent pollutants, namely BTEX, PAHs, and PCBs, always showing higher extraction capabilities than commercial PDMS and PDMS/DVB fibers. As in [PERSON] et al. [83], the presence of a high content of nitrogen in the structure increased the adsorption sites and surface polarity, resulting in the efficient extraction of PCBs. The HS-SPME-GC-MS method was optimized and validated, obtaining LOQs in the 0.0017-0.0042 ng/L range, a five order of magnitude linear range, and RSDs \(\leq\) 14%. Finally, the method was tested for the extraction of five model PCBs from rainwater, pond, and river water samples, obtaining \(\Sigma\)PCBs of 37.1, 64.1, and 100.2 ng/L, respectively.
## 4 Numeric Analysis
### Effects of Sauwater Turbulence and Beam Parameters on Channel Capacity
According to the turbulence theory, with the increase in the inner scale of turbulence, the uniform area in the seawater increases when the beam passes through the channel, that is, the influence of the channel on the transmitted beam decreases. We can also find that the channel capacity decreases with the increase in the outer scale, but the range of change is small. The smaller the outer scale of turbulence is, the less wave front distortion will be generated, so the crosstalk probability will be smaller. The reasons for this phenomenon are as follows: When the turbulent outer scale increases, the deflection of light ray in the beam is growing, and the frequency of light rays going through different paths also increases. As a result, the optical path difference between the light rays increases, and the wave front distortion of the beam increases accordingly. However, because the  
Figure 7: Channel capacity of the link with the random double-position power Gaussian vortex carrier and wavelength \(\lambda=561\) nm versus pure seawater, clear seawater, costal seawater, and turbid harbor seawater. refractive index difference is small in the outer scale of turbulence, the wave front distortion of the beam increases but not much. In Figure 10, we investigate the effects of dissipation rate of kinetic energy per unit mass of fluid and dissipation rate of the mean-squared temperature \(\chi_{t}\) on the channel capacity of the RCDPPGV carrier. It is clear from Figure 6 that the magnitude of the channel capacity decreases with increasing \(\chi_{t}\) and decreasing \(\varepsilon\). The reason for this result comes from that with the increase in \(\chi_{t}\) and the decrease in \(\varepsilon\), the turbulence of seawater increases, and the influence of turbulence on the beam increases [31]. Figure 8: Channel capacity of the link with the random double-position power Gaussian vortex carrier communication system versus temperature–salinity contribution ratios and different values of wavelength \(\lambda\). Figure 9: Channel capacity of the link with the random double-position power Gaussian vortex carrier optics communication system versus the inner scale of turbulence \(\eta\) and the outer scale of turbulence \(L_{y}\).
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 6 Results and Evaluation
### Latency and Jitter  
In this subsection, we present and discuss the results of the latency and jitter of the network models (Figure 5). While GNS3 and the hybrid model showed comparable results, it should be noted that the values for the SEED Internet Emulator were so small that can barely be noticed in the chart.
## 2 Review Conclusion  
In SWIPT systems, TS and PS are the two most commonly used methods in receivers because of their ease of implementation. In [20; 27; 28], the PS based receiver divides the received signal stream into two parts by the power splitter, thereby realizing the synchronization of information transmission and energy harvesting. In [21; 23; 31], the TS based receiver uses the time splitter to set the energy harvesting and information transmission operations to be performed at different timeslots. In this paper, we utilize the characteristics of OFDM through different subcarriers to implement SWIPT without splitters, thus, there is no need to optimize PS/TS ratios, which is necessary in the aforementioned literature for better system performance.  
As mentioned in the introduction section, researchers have proposed many advanced strategies to improve energy efficiency, such as the three-step design in [22], the transmission mode adaptation in [29], and the multi-dimensional resource optimization framework in [30]. However, these do not have applicability in the case of multiple relays having the ability to improve the system capacity and diversity gain [32]. Our proposed algorithm can solve the energy limitation problem of multi-relay IoT system effectively.  
The rest of the paper is organized as follows. In Section 3, we introduce the system model and present the problem formulation. The proposed joint resource allocation solution is illustrated in Section 4. Section 5 provides simulation results and discussions. Finally, conclusions are drawn in Section 6.
# Analysis of Underlapped Symmetrically Ported Valve-Controlled Asymmetric Cylinder Drive
## 1 Introduction  
Hydraulic power systems [1] are widely used in industrial manufacture, building construction, port transportation, funfairs, etc. They use fluid to achieve energy transmission without a rigid joint. Hydraulic power is able to produce massive force and torque within a compact and lightweight package, and conventional rigid components like gears and joints can be avoided.  
Hydraulic linear actuators are mainly of the symmetric cylinder or asymmetric cylinder type. The symmetric cylinder type has equal piston areas and the asymmetric type (also called single rod) cylinder comes with different piston areas. The symmetric cylinder is usually utilised for high-performance applications, due to its linear performance with a symmetric ported control valve. This type of cylinder is widely applied in aerospace and military contexts for its high performance and dynamic response [2]. However, the asymmetric cylinder [3] drive constitutes 80% of contemporary hydraulic applications [4], such as cranes, injection moulding systems, hydraulic press machines, etc. The asymmetric cylinder drive is usually controlled by a symmetric ported valve, meaning an unequal flow rate will be delivered into the cylinder chambers with the same valve opening in the extending or retracting state, which means system parameters do not remain the same after the valve crosses its null position [5]. [PERSON] [6] studied these nonlinearities, but how the system performs when the valve is in the underlap region is not explicitly explained. [PERSON] [7] indicates that pressure jumps will occur when the valve spool is around its null position, causing 'implosion' or 'explosion' of oil owing to its compressibility, so that smooth operation around the valve's null position is impossible. Therefore, this article conducted a nonlinear study on valves in an underlap state and proposed an analytical solution for \(X\) factor when the valve spool is in the underlap region to improve computational efficiency.
## 2 Related Works  
Given the scenarios of multiple potential secondary relays, the primary sensor network tends to choose an optimal secondary network to cooperate for obtaining the maximum possible payoffs. In [32], the authors proposed to select the relay based on the knowledge of the instantaneous signal-to-noise ratios (SNRs) of the satellite-relay and relay-destination links. For practical application of such a complex scheme, CSI of all relay-destination links and satellite-relay is required at the satellite, which is provided by using feedback from the destination to the satellite. Due to the high latency in satellite systems, it is very difficult to provide CSI of all links at the satellite. For an alternative relay selection scheme, the satellite only requires the CSI feedback of the satellite-relay links from the relays [33], where the total capacity maximization can not be guaranteed. Besides, for the secondary networks, it is possible to increase network throughput through cooperative spectrum sharing. However, there exist required price paid, including the cost of booting energy consumption, hardware price, time-window value, etc. Besides, both primary and secondary networks are with incomplete information. Without considering the fairness or QoS assurance in cooperative overlay spectrum sharing networks, the secondary relay has no willingness to cooperate with the primary networks.  
Market-driven mechanism consisted of auction-based or pricing-based approach can efficiently redeploy the scarce resource, balance the demands and even attract potential participants [34; 35]. Auction theory received considerable attention for solving the problem of maximizing utilization in the case of resource scarcity, which is with uncertainty about the peculiar information and achieving competitive fairness [36]. The authors in [37; 38] proposed auction-based resource allocation and relay selection in cooperative communications. However, to our knowledge, few related work is available for the satellite systems. Due to the long-time delay attributes of satellite communications, sequential auction mechanisms, such as English Auction and Dutch Auction, with uncertain bidding episodes, are improper for this scenario. Besides, since the satellite computational resource is limited, it is arduous to process excessive information in a centric manner. The sealed-bid second-price auctions, also named Vickery auctions, in which the bidder who submitted the highest bid is awarded the object being sold and pays a price equal to the second-highest amount bid, is that bidding one's true value is a dominant strategy, which gives unique Nash equilibrium (NE) [39; 40]. Hence, this mechanism can achieve efficient and effective decision by one shot in a distributed manner, which reduces considerable overhead including the decision time and satellite side computational complexity. The distributed manner here means that all potential relays make decision themselves by evaluating the true value of the cooperative spectrum sharing. Hence, we study auction-based relay selection on cooperation spectrum sharing in HSTSNs.
# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
###### Abstract
In most of the reviewed studies, dataset details are either missing or not diverse enough, leading to concerns about overfitting and reduced real-world applicability. Additionally, while deep learning models have demonstrated high accuracy, many rely heavily on computational power and large datasets, making them challenging to deploy in practical, resource-constrained environments, thus hampering their scale and reliability [9; 10]. Image-based recognition techniques also have inherent problems, such as limited accuracy due to variations in lighting, background and disease manifestation. Furthermore, current models are reactive and typically do not discover cotton diseases before considerable damage has been caused. This motivated the development of a new approach to cotton disease detection. The application of Neural Networks (NNs), especially Convolutional Neural Networks (CNNs), has rapidly advanced the capabilities of automation, precision monitoring, and intelligent decision-making across both agricultural and industrial sectors. In agriculture, CNNs are extensively used for image-based tasks, such as:  
* Crop disease identification using leaf images
* Weed detection for precision spraying
* Fruit ripeness estimation using color and texture features
* Soil pattern analysis and satellite-based crop mapping  
These networks excel due to their ability to automatically extract and learn hierarchical spatial features without manual pre-processing. CNNs outperform traditional machine learning methods in visual pattern recognition, which is especially valuable for noisy, real-world agricultural environments. In industrial settings, CNNs and deep NNs are deployed in:  
* Defect detection in manufacturing via surface image inspection
* Predictive maintenance using time-series sensor data
* Automated visual inspection systems for quality assurance
* Object tracking and robotic control in assembly lines  
Industries benefit from CNNs' robustness to distortion and their scalability to large datasets with high variability in lighting, orientation, or noise. When combined with reinforcement learning or optimization techniques (e.g., PSO, GA), they enable adaptive process control and real-time decision support. This paper presents the development of a novel deep-learning approach based on a hybrid BERT-ResNet-PSO model for the detection and classification of cotton leaf diseases. Finally, disease detection with our proposed model is robust and efficient due to leveraging the best of Bidirectional Encoder Representations from Transformers (BERT) for image segmentation, ResNet for feature extraction, and PSO for parameter tuning. In what follows, Section 2 provides a detailed review of the related work on cotton leaf disease detection. Section 3 presents the proposed hybrid deep learning approach for dealing with plant disease detection. Section 4 discusses the experimental setup and the evaluation metrics with the utilized datasets, implementation details and a comparison with the existing models. Section 5 provides the conclusion of the study.
## 3 Results and Discussion
### Nutritional Screening and Morphofunctional Assessment  
#### 3.2.1 In Which Patients under Follow-Up for COPD Should Nutritional Screening Be Performed?  
**Recommendation 5**.: _GARIN suggests performing nutritional screening in all patients diagnosed with COPD, regardless of the degree of severity and stage of the disease._  
Grade of recommendation GPP, (98%) Strong consensus.  
Comment:  
Extrapulmonary manifestations in COPD patients due to their chronic inflammatory state, including nutritional status, body composition, and changes in muscle mass, fat mass, and bone mineral density, have been shown to be determinants in the prognosis and severity of the disease, so nutritional screening is recommended for all patients diagnosed with COPD [11; 15; 57; 58; 59; 60].
## 2 Materials and Methods
### Proposed Masked Area Colorization Approach
#### 2.2.3 Noise Removal  
Noise reduction improves and enhances image quality. In this technique, image filtering technologies are utilized to improve picture quality. When there is noise in an image, colorization performance suffers much more. Pixel values were changed using a weighted average to match the weighted average for the whole area or cluster of pixels. After the noise is reduced, the image quality is improved. The mean weighted average is a filtering method that is intuitive and straightforward to implement. As a consequence of this procedure, cluster density variation is decreased, and the cluster mean value is corrected. Furthermore, employing the pixel definition of each image pixel reduces noise. The value of each missing pixel is replaced by an average weighted value, which is then modified concerning its neighbors.
# Compressed Nonlinear Equalizers for 112-Gbps Optical Interconnects: Efficiency and Stability
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} VE & Volterra series-based equalization \\ NNE & Neural network-based equalization \\ VCSEL & Vertical cavity surface emitting laser \\ BER & Bit error rate \\ IM-DD & Intensity modulation and direct detection \\ PAM & Pulse amplitude modulation \\ \end{tabular}  
Figure 7: BER versus VCSEL DC bias for (**a**) VE and NNE (**b**) Pruned VE and NNE.