esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Impact of Illumination and Background on Differences between fAPAR and fIPAR  
By comparing the fAPAR and fIPAR values obtained with different backgrounds and at different illumination conditions, Figure 6, it can be observed that while the two fractions are closely correlated, they are far from equivalent. The fIPAR values are noticeably higher than fAPAR values. The average overall relative difference between fAPAR and fIPAR exceeded 9%, and it was around 14% in certain cases, e.g., when considering sunlit leaves with a concrete background and at SKYL = 0% (Table 2).  
The differences between fAPAR and fIPAR were further studied for different leaf chlorophyll contents. Figure 7 compares results for very low and high leaf chlorophyll contents (Cab = 10 vs. 100 \(\mu\)g/cm\({}^{2}\), respectively) for sunlit and shaded leaves. At high chlorophyll levels, there are small differences between fIPAR and fAPAR (typically less than 5-10%) for all background and illumination conditions considered, due to the high amount of foliar absorption. In comparison, fIPAR can be up to 20-25% higher than fAPAR for leaves with low chlorophyll content. The results also show that the highest fractions of absorbed and intercepted PAR occur during direct illumination (SKYL = 0%, SZA = 0\({}^{\circ}\)) of sunlit leaves, Figure 7c.  
Finally, differences between the effect of grass and concrete backgrounds on fAPAR and fIPAR are greatly influenced by the distributions of sunlit and shaded leaves, SKYL, and SZA. Figure 8 shows that the relative difference in absorbed and intercepted PAR fractions between the grass and concrete backgrounds is most pronounced for shaded leaves, especially for SKYL \(\geq\) 20%, at the higher solar zenith angle, SZA = 45\({}^{\circ}\). It follows that the background influence on both fAPAR and fIPAR will be enhanced by the diffuse illumination and oblique SZA.  
Figure 5: Density biplots of fAPAR (**a**) and fIPAR (**b**) of sunlit leaves modelled with grass and concrete backgrounds (pixel-wise data).  
Figure 6: The juxtaposition of the fAPAR and fIPAR values at different backgrounds (grass (a,c) and concrete (b,d)) and illumination conditions (sunlit and shaded leaves). The legend represents the density of points.  
Figure 7: Sample plots of average fAPAR and fIPAR based on a different background, illumination conditions, and C\({}_{\text{ab}}\) levels.
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 8 Discussions and Recommendations  
Predictive maintenance is a technique that uses tools and condition monitoring techniques to monitor the performance and health state of a structure or equipment during operation in a sustainable manner [83; 84; 85; 86]. However, it can be noticed in the literature that the application of maintenance 4.0 is mostly focused on a specific machine or industrial equipment. The idea of creating a user-friendly and multifunctional platform, as depicted in Figure 16, that integrates different types of sensors and enables in-situ PdM on different types of equipment in real-time can be considered revolutionary in the field [87]. This platform highlights support in maintenance management and decision-making based on acquired data and developed models of PdM. It allows users and the maintenance team, depending on their predefined access level to the platform, to manipulate and modify proposed decisions anytime and at any step of the maintenance planning process. The platform gives remote access via user authentication protected by a VPN (private virtual network). Thus, a cloud infrastructure is provided that allows mobile and web connections in order to receive notifications received by the alarms. Apart from remote connectivity, this platform will provide real-time on-site data monitoring collected by machines and equipment in the form of a dashboard that calculates key performance indicators and supports API (application programming interface) protocols for data customization and alarm notifications. This provides remote plant monitoring from any browser and also ensures high security against attacks by preserving the integrity and confidentiality of information.  
This platform will help operators work more efficiently, safely and save money by reducing downtime and costs. It acts as an HMI operator interface where all necessary information from the equipment can be viewed in real-time through the data acquisition and transfer system. Then it allows the supervision of all the installed machines and also allows following the operations remotely in addition to keeping the history and future predictions thanks to the application of AI.
## 2 Methods
### Machine Learning Algorithms
#### 2.3.1 k-NN  
The k-NN algorithm stands out as one of the most straightforward machine learning approaches. When applied to classification tasks, it determines the category by considering the majority category among the k-nearest neighbors. In regression problems, it estimates  
Figure 2: The methodology’s flowchart.  
the value using a weighted mean function, represented by the formula \(\hat{f}(x)=\sum_{i=1}^{k}\frac{f(x_{i})}{k}\), where \(\hat{f}(x)\) denotes the estimated value. Alternatively, more advanced functions can be employed, such as weighting by the inverse of distances, as follows:  
\[\hat{f}(\vec{x})=\begin{cases}\sum_{i=1}^{k}\frac{w_{i}(x_{1},\ldots,x_{k})f(x_{ i})}{\sum_{i=1}^{k}w_{i}(x_{1},\ldots,x_{k})}&\text{if }d(\vec{x},\vec{x}_{i})\
eq 0\ \forall\ i\leq k\\ f(x_{i})&\text{otherwise},\end{cases} \tag{1}\]  
or using more mathematically complex distance functions, such as exponential weighting by distance or employing a Gaussian function (Gaussian kernel). Additionally, within the k-NN algorithm, we have the flexibility to change the distance function. A common approach involves exploring various values for the Minkowski distance (Equation (2)), as follows:  
\[d(\vec{x},\vec{y})=\left(\sum_{i=1}^{n}\lvert x_{i}-y_{i}\rvert\right)^{\frac {1}{p}} \tag{2}\]  
which allows for adapting the distance metric to better suit the characteristics of the dataset. In this study, we set the number of neighbors (k) to be the set \(\left\{1,2,3,4,5,6,7,8,9,10,15,20\right\}\), determining the quantity of nearest neighbors considered for predictions. Additionally, we adjusted two hyperparameters to enhance model performance. Firstly, for the distance function, we utilized the Minkowski distance with \(p\in\left\{1,2,3\right\}\). Secondly, for the weight function, we adopted two different approaches--the first was to assign equal weight to all points within each neighborhood and the second was to use the weighted by the inverse of their distance.
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 4 Experiments
### Performance Evaluation  
We also conducted experiments to evaluate a set of models, and present a performance comparison of all evaluated models in Table 4, using the best hyper-parameter settings and the fused loss function, as reported in the previous two subsections. We give the result analysis as follows.  
* The set of models CNN, BiLSTM, SentiBERT, BiLSTM-CNN, and SentiBERT-BiLSTM-CNN forms an ablation study, from which we can evaluate the performance of each individual module and the combined versions. It can be seen that the pure CNN model performs the worst since a single-layer CNN cannot learn any contextual information. Both BiLSTM (with attention) and SentiBERT present an obvious improvement. SentiBERT is on a par with BiLSTM-CNN in precision, but outperforms it in recall. Our final model, SentiBERT-BiLSTM-CNN tops every other model, showing its power to combine the strength of each individual building block.
* The set of models fastText-BiLSTM-CNN, word2 vec-BiLSTM-CNN, BERT-BiLSTM-CNN, and SentiBERT-BiLSTM-CNN are evaluated to compare the effect of word embeddings. FastText [52], word2 vec [53], BERT, and SentiBERT are used for the same purpose, i.e., to generate word embeddings. A model's ability to preserve contextual information determines its performance. From the results, we observe that by adding contextual embeddings, the models gain improvements to varying degrees. SentiBERT-BiLSTM-CNN, as the best-performing model, demonstrates superior capability in encoding contextual information.
* Another observation is that SentiBERT-BiLSTM-CNN outperforms BERT-BiLSTM-CNN by 1.23% in F1, meaning that sentiment in Tweets is a crucial factor that can help detect disaster Tweets, and a sentiment-enhanced BERT validates this hypothesis.
* Lastly, SentiBERT-BiLSTM-CNN outperforms BERT\({}_{\text{hyb}}\), i.e., the SOTA, by 0.77% in F1. Although BERT\({}_{\text{hyb}}\) presented the highest precision 0.9413, its precision-recall gap (4.21%) is large, compared to that of SentiBERT-BiLSTM-CNN (0.34%), meaning that BERT\({}_{\text{hyb}}\) focuses more on optimizing precision. On the other hand, SentiBERT-BiLSTM-CNN demonstrated a more balanced result in precision and recall.
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 2 Background and Related Work  
This section presents a literature review of relevant background concepts that were used for the design of the proposed solution. Initially, an overview of the 5G network architecture is presented, followed by a description of various types of infrastructure sharing and neutral host models. Finally, an analysis of other works of significant interest to this paper is conducted.
# Research on the Reliability of Bridge Structure Construction Process System Based on Copula Theory
## 2 Basic Theory of the Copula Function  
### Definition of Copula Function  
Copula theory was first proposed by [PERSON] [16]. [PERSON]'s theory states that any multivariate joint distribution function can be decomposed into a corresponding marginal distribution and a Copula function, which uniquely determines the correlation between variables [28]. The Copula function is theoretically a joint distribution function, and [PERSON]'s theorem is the basis for the practical application of the Copula function. According to [PERSON]'s theorem, there exists only one unique Copula function satisfying the following Equation [29]:  
\[F(x_{1},x_{2},\cdots x_{n})=C(F_{1}(x_{1}),F_{2}(x_{2}),\cdots F_{n}(x_{n}); \ \theta) \tag{2}\]  
where \(C\) is the Copula function describing the structure of the correlation between the variables \((x_{1},x_{2},\cdots x_{n})\), which is independent of the distribution with each marginal distribution \(F_{n}(x_{n})\); \(\theta\) is the correlation parameter of the Copula function.  
The correlation between the failure modes of the structural system is constructed on the basis of Copula theory in two main steps. Step 1 is to determine the failure function of each failure mode and to specify the distribution of each random variable in the failure function. Step 2 is to select the optimal Copula function to characterize the correlation structure between the failure modes. The two abovementioned steps are carried out independently without interfering with each other. It can be seen that the joint distribution function established when analyzing the reliability of the bridge construction period system based on Copula theory is not affected by the type and distribution of the failure function. The joint distribution function with any distribution type and correlation can be constructed in practical engineering. For the binary joint distribution, the joint cumulative distribution function \(F(x_{1},x_{2})\) and the joint probability density function \(f(x_{1},x_{2})\) are  
\[F(x_{1},x_{2})=C(F_{1}(x_{1}),F_{2}(x_{2});\ \theta)=C(u,v;\ \theta) \tag{3}\]  
\[f(x_{1},x_{2})=D(u,v;\ \theta)f_{1}(x_{1})f_{2}(x_{2}) \tag{4}\]  
where \(u=F_{1}(x_{1})\) and \(v=F_{2}(x_{2})\) are the marginal cumulative distribution functions of the variables \(X_{1}\) and \(X_{2}\), respectively; \(C(u,v;\ \theta)\) is the two-dimensional Copula function; \(D(u,v;\ \theta)\) is the density function of the two-dimensional Copula function; \(\theta\) is the correlation parameter of the Copula function, which is used to characterize the correlation between random variables.
# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 2 Dataset
### Dataset Partition and Data Augmentation  
(1) Dataset Partition  
The dataset was divided into 3 subsets with the ratio of 19:1:4 for training, validation, and testing, respectively. We repeated the above data set division method 5 times, and trained each network 5 times, respectively. For each network, the average accuracy and average \(F_{1}\) score of these 5 experiments were taken as indicators to measure the network performance.  
(2) Data Augmentation  
In order to increase the diversity of the dataset, alleviate over-fitting in the training stage, reduce the dependence of the model on certain attributes, and enhance the anti-interference ability of the model under complex conditions, four data enhancement methods were adopted for the images of the training set [12; 34]: (a) Introduce angle interference: 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) rotation and horizontal and vertical mirror transformation were used; (b) Introduce light interference: adjust the brightness value of the image (set the enhancement factor as 1.5 and 0.8 respectively, where the enhancement factor of 1 is the original image, and 0 for the complete black image) and the contrast value (set the enhancement factor as 3 and 0.8, respectively, where the enhancement factor of 1 is the original image); (c) Introduce noise interference and add Gaussian noise (the expectation of Gaussian distribution is 0 and the variance is 0.01); (d) Compress and enhance images through Principal Component Analysis. The example pairs of the original image and the enhanced image are shown in Figure 2. After data augmentation, one image can be extended to 14 images.  
Figure 1: The examples of ATLDD dataset: (**a**) Alternaria leaf spot; (**b**) Rust disease; (**c**) Brown spot disease; (**d**) Gray spot disease; (**e**) Mosaic disease; (**f**) Healthy leaves.
## 2 Mathematical Model  
### Principles of GLV Model Simulation  
The GLV gas lift model in OLGA 2022.1.0 software is used to simulate the flow behavior of gas-liquid-viscous multiphase flows (e.g., containing highly viscous crude oil, non-Newtonian fluids, etc.). The model injects gas from the annulus or casing into the tubing, defines the corresponding inflow-dynamic relationships according to different reservoirs, and calculates the mass flow rate between the reservoir and the wellbore using a binomial capacity equation [16]. At the same time, the model can simulate the production effect of a gas well by adjusting the downstream depth of the tubing at a constant injection pressure (formation pressure). Therefore, a gas lift model is selected in this paper to simulate the production effect of gas wells. The mass conservation equation of gas-liquid two-phase flow is shown in Equation (1), the energy conservation equation of gas-liquid two-phase mixing is shown in Equation (2), and the mass flow rate calculation equation is shown in Equations (3) and (4). The descriptions of each parameter are shown in Table 1.  
\[(\rho_{l}\alpha_{l}v_{l}A_{t}\frac{x_{i}M_{l}}{M_{l}}+\rho_{g}\alpha_{g}v_{g}A _{t}\frac{y_{i}M_{l}}{M_{g}})+N_{l}M_{l}A_{t}dz=\frac{\partial}{\partial t}( \rho_{l}\alpha_{l}A_{t}\frac{x_{i}M_{l}}{M_{l}}+\rho_{g}\alpha_{g}A_{t}\frac{y _{i}M_{l}}{M_{g}}) \tag{1}\]  
\[\begin{array}{l}\frac{\partial}{\partial t}[m_{g}(E_{g}+\frac{1}{2}v_{g}^{2 }+gh)+m_{l}(E_{l}+\frac{1}{2}v_{l}^{2}+gh)+m_{D}(E_{D}+\frac{1}{2}v_{D}^{2}+ gh)]\\ =-\frac{\partial}{\partial z}[m_{g}v_{g}(H_{g}+\frac{1}{2}v_{g}^{2}+gh)+m_{l}v _{l}(H_{l}+\frac{1}{2}v_{l}^{2}+gh)+m_{D}v_{D}(H_{D}+\frac{1}{2}v_{D}^{2}+gh) ]+H_{s}+U\end{array} \tag{2}\]  
The total mass flow rate is expressed as  
\[W_{TOT}=W_{G}\sqrt{\frac{\rho_{mix}}{\rho_{G}}} \tag{3}\]  
The mass flow rate of each phase flow (oil, gas, water, etc.) is expressed as  
\[W_{P}=\chi_{P}\cdot W_{TOT} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Abbreviation** & **Abbreviation Meaning** & **Unit** \\ \hline GLV & Gas lift valve & \\ \(\rho_{g}\) & Gas phase density & kg/m\({}^{3}\) \\ \(\alpha_{g}\) & The volume fraction of the gas & \\ \(v_{g}\) & Gas phase velocity & m/s \\ \(M_{g}\) & The mass flow rate of the gas phase & kg/s \\ \(\rho_{l}\) & Liquid phase density & kg/m\({}^{3}\) \\ \(\alpha_{l}\) & Liquid volume fraction & \\ \(v_{l}\) & Liquid phase velocity & m/s \\ \(M_{l}\) & The mass flow rate of the liquid phase & kg/s \\ \hline \end{tabular}
\end{table}
Table 1: Meaning of each equation abbreviation.
## 6 Detection Methods: Optical Frequency Comb Spectroscopy
### Frequency Comb Fourier Transform Spectroscopy  
FC-FTS is the measurement of the Fourier transform of the interferogram based on frequency combs source, which offers excellent spectral brightness and spatial coherence. There are two implementations of FC-FTS: Michelson interferometer-based Fourier transform spectroscopy and dual-comb spectroscopy (DCS) [168]. Each presents its own distinct advantages, but both rely on the same physical principle.  
**Michelson interferometer-based FC-FTS**. In FC-FTS, OFC works as the light source for Fourier transform spectrometers (FTS). The high spectral brightness, together with the spatial and temporal coherence of the combs, enable acquisition times orders of magnitude shorter than in conventional FTIR spectroscopy. Thus, FC-FTS could be promising in standoff chemical sensing of transient, non-repeatable phenomenal like combustion, plasmas, and explosions [169]. Presently, the applications of FC-FTS is still limited by spectral width, lower comb intensities, mechanically scanned mirrors to record the interferogram, placing limits on the acquisition of spectra [170]. The interferometer records the interference pattern between two combs as slightly different because the light reflected by the moving mirror of the interferometer is Doppler shifted [171; 172].  
**Dual comb spectroscopy.** DCS uses two frequency combs of slightly differing line spacing, one for reference and the other for sample detection [168]. From each pair of optical lines, one from each comb, a radio frequency beat note is generated on a detector. In this way, optical frequencies are converted into radio frequencies such that the amplitude and phase changes caused by the interaction of one of the combs with a sample can be detected. DCS has an advantage similar to a Michelson interferometer but without moving parts and employing a single point detector, in which case a minimum detectable absorption \(\sim\)1 \(\times\) 10\({}^{-8}\) cm\({}^{-1}\) has been demonstrated [173].  
Despite an intriguing potential for the measurement of molecular spectra spanning tens of nanometers within tens of microseconds at Doppler-limited resolution, the development of dual-comb spectroscopy is hindered by the demanding stability requirements of the laser combs [174; 168; 175]. For an ideal, the interference sampled waveform can be Fourier transformed to display the signal spectrum. In reality, the main difficulty comes from the time and phase fluctuations of the frequency comb. However, we experimentally demonstrate that the means of real-time dual-comb spectroscopy can be used to overcome this problem [176]. The true value of DCS for sensitive molecular detection lies in the MIR [177; 178], and compact design will be obtained using the semiconductor laser comb technology in the future [179; 180; 181].
# Experimental and Theoretical Study of the Influence of Saline Soils on Frozen Wall Formation
## 2 Experimental Study on the Effect of Salinity on Soil Thermophysical Properties  
### Sample Preparation  
Here, we describe the experimental studies that were implemented to identify the dependence of the thermophysical properties of soils on negative temperatures and levels of salt concentration in porewater. They are important for the subsequent analysis of the characteristic features of the effects of salinity on heat transfer in soils during freezing. Porewater containing a solution of NaCl salt was considered. Argillite-like clay, chalk, and sand were studied. The choices of the soils and salt types were based on the fact that they are the most common for the above-salt strata of potash salt deposits.  
This study was conducted according to a well-known technique [26].  
To elucidate the patterns of salt influence on the phase composition of water and the thermophysical characteristics of soils, the samples' initial compositions needed to be as identical as possible. Consequently, investigations were carried out on samples with a compromised structure (pastes) at a water content and density that matched those of samples with a natural composition.  
To achieve this, the chosen material was initially crushed using a laboratory mill and then sieved through a mesh with a diameter of 5 mm. Subsequently, it was meticulously mixed, placed within a sealed container, and left to age for a full calendar week. Preliminary experiments indicated that this duration was adequate for ensuring a consistent distribution of moisture throughout the sample's volume. Only after this period was the material's moisture content determined, which was regarded as its initial state.  
Each type of soil was represented by nine samples. These samples were divided into three groups of three samples each. Each group of samples was associated with a certain soil water content. Additionally, the samples in each group were divided as follows: (1) samples with distilled water and the standard freezing temperature of the porewater; (2) samples with a NaCl content for which the freezing point of the porewater was 2 \({}^{\circ}\)C lower than the standard freezing temperature; (3) samples with a NaCl content for which the freezing temperature of the porewater was 4 \({}^{\circ}\)C lower than the standard freezing temperature. It is worth noting that, in the case of clay, studies were conducted for three values of initial water content. Chalk and sand studies were performed with only a single value for initial water content.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 3 Satellite Networks Vulnerability Risk Assessment Method
### Satellite Networks Vulnerability Risk Assessment Formula  
To comprehensively evaluate the systemic risks of satellite networks with multi-layered architecture and nonlinear threat propagation, this study introduces a composite risk scoring formula:  
\[R=\left[\left(I^{\alpha}\cdot L^{\beta}\cdot M^{\gamma}\right)\cdot\frac{D+T}{2} \cdot\delta(t)\cdot C\right]\cdot\omega_{s} \tag{1}\]  
The proposed composite risk scoring formula integrates nonlinear amplification, dynamic decay, and segment-specific weighting to address the unique risk propagation characteristics of satellite networks. To ensure clarity and reproducibility, Table 3 defines all parameters in Formula (1), while subsequent subsections elaborate on their empirical or theoretical justifications.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 6 Uncertainties of Reconstructed Wind Characteristics
### Example for a Two-Beam Nacelle Lidar
#### 6.2.1 Reconstruction Algorithm
In real-world applications, the lidar tilt will follow closely the nacelle's motion. The two reconstructed wind characteristics are thus valid at the \(H_{m}\) height agl:  
\[H_{m}=H_{lid}+D_{conf}\sin\left(\varphi\right) \tag{25}\]  
where \(H_{lid}\) is the lidar height agl. and \(D_{conf}\) the configured measurement range. In the case where the aim is to estimate the wind speed at hub height, as it usually is in power performance testing, a correction of the wind speed reconstructed at \(H_{m}\) should be made, if possible. The issue of nacelle lidars probing the wind at a single height implies an extra uncertainty, characterising the wind speed uncertainty caused by the motion of the turbine's nacelle.