esa-sceva/satcom-chunk-collection huggingface dataset:
# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 3 PoAIR Algorithm  
According to the rainstorm process semantics--i.e., one rainstorm includes one or more rainstorm sequences, and one sequence includes one or more instantaneous objects--PoAIR performs three stages to obtain a rainstorm process from a time-series of raster rainfall datasets, which is shown in Figure 2. The first stage identifies instantaneous rainstorm objects in each snapshot, the second stage generates a rainstorm sequence by matching and tracking the same rainstorm objects in successive snapshots, and the third stage reconstructs a rainstorm process by linking the rainstorm sequences which overlap in spatiotemporal neighborhoods.
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 5 Experimental Results and Comparative Analysis
### Experimental Datasets Description  
Our study used Rice Leaf Disease Image Samples from Mendeley Data (2018); it is an open-source dataset with 5932 images of rice leaf disease. The images are grouped into four rice leaf diseases as Bacterial blight (BB), Tungro (TG), Brown Spot (BS), and Rice Blast (RB). The category of BB, TG, BS, and RB have 1584, 1308, 1440, and 1600 images, respectively. The raw images come in different sizes ranging from 150 pixels to 300 pixels.
## 2 Bidirectional FSI Numerical Model
### Dynamic Modeling
#### 2.1.2 Drill String-Wellbore Wall Contact Force
The relationships among the penetration depth, contact radius, and contact pressure can be determined through integral relationships, as shown in Equation (12) [54]. \[\left\{ \begin{array}{l}R=\frac{R_{1}R_{2}}{R_{1}+R_{2}}\\ E=\frac{E_{1}E_{2}}{E_{2}(1-\mu_{1})+E_{1}(1-\mu_{2})}\end{array} \right. \tag{11}\]  
\[\left\{ \begin{array}{l}q_{\max}=\frac{3Q}{2\pi}(\frac{4}{3Q\pi(1+l_{1})R})^{ \frac{2}{3}}\\ a^{3}=\frac{3Q\pi(1+l_{2})R}{4}\\ \delta^{3}=\frac{9Q^{2}\pi^{2}(l_{1}+l_{2})^{2}}{16K}\end{array} \right. \tag{12}\]  
where \(q_{\max}\) is the maximum pressure at the contact center, and \(l_{1}\) and \(l_{2}\) can be represented by Equation (13) [55]. \[l_{1}=\frac{1-{\mu_{1}}^{2}}{\pi E_{1}},\;l_{2}=\frac{1-{\mu_{2}}^{2}}{\pi E_{ 2}} \tag{13}\]  
Ultimately, the general form of the contact force can be expressed as shown in Equation (14). \[F=K.\delta^{1.5} \tag{14}\]  
where \(K\) represents the stiffness coefficient, which is determined by the material properties of the contacting bodies and the equivalent contact radius, and can be expressed by Equation (15) [56]:  
\[K=\sqrt{\frac{16 RE^{2}}{9}} \tag{15}\]  
The normal contact force between the drill string and the wellbore wall consists of the stiffness force and the damping force. The normal contact force can be calculated by Equation (16) [57]. \[f_{n}=f_{ns}+f_{nd}=K\delta^{1.5}+C\frac{\dot{\delta}}{\left|\dot{\delta} \right|}{\left|\dot{\delta}\right|}^{m1}\delta^{m2} \tag{16}\]  
where \(K\) is the stiffness coefficient, \(C\) is the damping coefficient, \(\dot{\delta}\) is the time derivative of \(\delta\), \(m_{1}\) is the nonlinear contact force index, and the index \(m_{2}\) produces the indentation damping effect. In this study, the values of \(m_{1}\) and \(m_{2}\) are taken as 1 and 2, respectively.
## 3 Results and Discussion
### Physicochemical Properties
#### 3.1.4 Refractive Index
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**NADES Sample**} & **Molar Refraction,** & **Specific Refraction,** & **Electronic** & **Polarizability Constant,** \\  & \(Rm\) **(cm\({}^{3}\)/mol)** & \(R_{D}\) **(cm\({}^{3}\)/g)** & **Polarization,** \(E\) & \(\delta\)**-10\({}^{-23}\) **(cm\({}^{3}\)/mol)** \\ \hline NADES1 & 19.4 \(\pm\) 0.1 & 0.220 \(\pm\) 0.005 & 2.11 \(\pm\) 0.05 & 0.770 \(\pm\) 0.004 \\ NADES1 + 10\% H\({}_{2}\)O & 17.5 \(\pm\) 0.1 & 0.220 \(\pm\) 0.005 & 2.07 \(\pm\) 0.03 & 0.693 \(\pm\) 0.003 \\ NADES1 + 20\% H\({}_{2}\)O & 15.5 \(\pm\) 0.2 & 0.219 \(\pm\) 0.004 & 2.04 \(\pm\) 0.04 & 0.614 \(\pm\) 0.004 \\ NADES1 + 30\% H\({}_{2}\)O & 13.4 \(\pm\) 0.2 & 0.218 \(\pm\) 0.002 & 2.01 \(\pm\) 0.03 & 0.533 \(\pm\) 0.003 \\ NADES1 + 40\% H\({}_{2}\)O & 11.3 \(\pm\) 0.1 & 0.214 \(\pm\) 0.004 & 1.97 \(\pm\) 0.04 & 0.450 \(\pm\) 0.002 \\ \hline NADES2 & 28.8 \(\pm\) 0.4 & 0.187 \(\pm\) 0.011 & 2.22 \(\pm\) 0.09 & 1.144 \(\pm\) 0.013 \\ NADES2 + 10\% H\({}_{2}\)O & 27.3 \(\pm\) 0.2 & 0.196 \(\pm\) 0.012 & 2.19 \(\pm\) 0.04 & 1.081 \(\pm\) 0.012 \\ NADES2 + 20\% H\({}_{2}\)O & 24.6 \(\pm\) 0.1 & 0.199 \(\pm\) 0.008 & 2.16 \(\pm\) 0.03 & 0.977 \(\pm\) 0.008 \\ NADES2 + 30\% H\({}_{2}\)O & 21.3 \(\pm\) 0.3 & 0.197 \(\pm\) 0.005 & 2.10 \(\pm\) 0.05 & 0.846 \(\pm\) 0.004 \\ NADES2 + 40\% H\({}_{2}\)O & 18.3 \(\pm\) 0.3 & 0.198 \(\pm\) 0.006 & 2.04 \(\pm\) 0.04 & 0.728 \(\pm\) 0.007 \\ \hline NADES3 & 24.2 \(\pm\) 0.2 & 0.198 \(\pm\) 0.006 & 2.14 \(\pm\) 0.05 & 0.962 \(\pm\) 0.004 \\ NADES3 + 10\% H\({}_{2}\)O & 22.0 \(\pm\) 0.1 & 0.199 \(\pm\) 0.007 & 2.10 \(\pm\) 0.03 & 0.874 \(\pm\) 0.005 \\ NADES3 + 20\% H\({}_{2}\)O & 19.8 \(\pm\) 0.2 & 0.202 \(\pm\) 0.003 & 2.06 \(\pm\) 0.03 & 0.785 \(\pm\) 0.004 \\ NADES3 + 30\% H\({}_{2}\)O & 17.4 \(\pm\) 0.3 & 0.203 \(\pm\) 0.004 & 2.04 \(\pm\) 0.04 & 0.692 \(\pm\) 0.004 \\ NADES3 + 40\% H\({}_{2}\)O & 14.9 \(\pm\) 0.2 & 0.202 \(\pm\) 0.003 & 2.00 \(\pm\) 0.02 & 0.590 \(\pm\) 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculated molar refraction, specific refraction, electronic polarization, and polarizability constant of the tested NADES at room temperature (mean \(\pm\) sd). Figure 4: Refractive indices as a function of added water percentage in the tested NADES at room temperature. NADES1—LA:ChCl (3:1); NADES2—LA: SL (1:3); NADES3—LA:GL-W (3:5:1). The information on the RI and \(\delta\) for NADES used as a solvent or extragent helps to explain the behavior of NADES, such as dispersion forces. The high \(\delta\) constant is evidence of strong dispersion forces [53]. The \(\delta\) values for tested NADESs are shown in Table 1. It was established that the polarizability constants for NADESs decreased linearly with the increase in water content in NADES. Similar results were reported by [PERSON] et al. (2018) for DES analogues [26]. The solvents with a high \(\delta\) constant and large polarizability should be capable of enjoying powerful dispersion forces [54], being suitable solvents for compounds with elevated polarizabilities.
# A Branch-and-Price Algorithm for the Online Scheduling of Valet Drivers
## 4 Column Generation-Based Method
### Label Setting Algorithm
#### 4.3.3 Dominance Rule  
Considering two labels \(L_{k}\) and \(L_{k^{\prime}}\) at the same node, e.g., \(\eta_{k}=\eta_{k^{\prime}}\), \(L_{k}\) dominates \(L_{k^{\prime}}\) if the following inequalities hold, and at least one is strict:  
* \(c^{\prime}_{k}\leq c^{\prime}_{k^{\prime}}\);
* \(A_{k}\leq A_{k^{\prime}}\);
* \(p_{k}\geq p_{k^{\prime}}\);
* \(C_{k}\subseteq C_{k^{\prime}}\).  
Applying this dominance rule will effectively reduce the number of labels, increase the efficiency of the algorithm, and ensure that the labels with respect to the optimal path are not discarded in the process. The validity and the correctness of the dominance rule is demonstrated in the following lemma.  
Lemma 1: _Let label \(L_{1}\) dominate label \(L_{2}\), and let \(L_{4}\) be an extended label of \(L_{2}\). Then, there exists a label \(L_{3}\) extended from \(L_{1}\) that dominates label \(L_{4}\)._  
Proof: Suppose a label \(L_{1}=\{\eta_{1},c^{\prime}_{1},T_{1},p_{1},C_{1},L^{*}_{1}\}\) and a label \(L_{2}=\{\eta_{2},c^{\prime}_{2},T_{2},p_{2},C_{2},L^{*}_{2}\}\). Let \(L_{4}\) be an extended label from \(L_{2}\) to a node \(k\). Without loss of generality, let us assume that \(L_{1}\) and \(L_{2}\) are associated with the delivery node \(d\), i.e., \(\eta_{1}=\eta_{2}=d\), and node \(k\) is a pickup node. According to the dominance rule, we have \(c^{\prime}_{1}\leq c^{\prime}_{2}\), \(T_{1}\leq T_{2}\), \(p_{1}\geq p_{2}\), and \(C_{1}\subseteq C_{2}\). Then, considering a label \(L_{3}\) extended from \(L_{1}\) to node \(k\), we have \(c^{\prime}_{3}=c^{\prime}_{1}+c_{dk}-\lambda_{h}\), \(c^{\prime}_{4}=c^{\prime}_{2}+c_{dk}-\lambda_{h}\), hence we have \(c^{\prime}_{3}\leq c^{\prime}_{4^{\prime}}\); \(T_{3}=T_{1}+t_{dk}\), \(T_{4}=T_{2}+t_{dk}\), thus \(T_{3}\leq T_{4}\); \(p_{3}=p_{1}-h_{dk}\), \(p_{4}=p_{2}-h_{dk}  
the heuristic pricing algorithm cannot find any negative column, the label setting algorithm will be initiated to search for all the negative columns.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 2 Machine Learning Techniques for Time Series Analysis
### Ensemble Methods  
Ensemble methods like Random Forests and Extra Trees Classifiers combine multiple models to enhance predictive accuracy. In finance, these methods have demonstrated superior performance in forecasting stock price changes by capturing complex feature interactions [4]. Ensemble learning has also been applied to healthcare time series data for early disease detection [28].  
A common approach to ensemble modeling for time series is stacking, where predictions from multiple base models are combined using a meta-learner. This technique integrates outputs from diverse forecasting methods such as ARIMA (statistical modeling), Prophet (decomposition-based forecasting), and LSTMs (deep learning). In this framework, each base model independently processes the input data to generate its prediction. A meta-learner then takes these individual predictions as inputs and produces a final forecast that typically outperforms any single constituent model. This methodology creates a powerful synergy by combining traditional statistical approaches with advanced deep learning techniques. Statistical models often excel at capturing seasonal patterns and trends, while deep learning models can identify complex non-linear relationships in the data. The meta-learner weighs the strengths of each model appropriately, compensating for individual weaknesses and producing more robust and accurate forecasts. The stacking approach has proven particularly valuable in domains with complex time-dependent patterns where no single modeling technique consistently dominates, such as financial forecasting, energy demand prediction, and retail sale forecasting.
## 4 Architecture Design Overview
### Design Principles for Trust Adaptation  
We proposed the following trust model for IDaaS [8]. Figure 3a shows three security domains: Telekom, Salesforce, and Amazon. Each domain has an IDaaS. A user Bob registers at his _home IDaaS_ (e.g., Telekom) for authentication. The IDaaS in the other domains (e.g., Salesforce) federate with the home IDaaS. We call them _a visitor IDaaS_. In a domain, all SPs trust its IDaaS as an authoritative resource to handle authentication requests (e.g., SP1 trusts IDaaS1, SP2 trusts IDaaS2). This trust model reduces the complexity for a user and an SP to establish trust with each partner individually.  
To adapt the security infrastructure for cloud services, our approach considers separation of duties between a _security architect_, an IDaaS, and application developers. We allow the security architect to describe the security infrastructure and express his requirements (the \"what\"). We let the IDaaS provision the security infrastructure according to the requirements (the \"how\"). In the end, application developers only need to care about what user attributes are available to them from the environment so  
Figure 3: (**a**) Trust model; (**b**) Trust adaptation process.  
they can use them in their applications. To achieve this goal, we also collected the most frequently used security design patterns [33] and modelled the security components.  
In Figure 3b, the adaptation process starts by registering the security topologies of cloud services in a modelling engine (i.e., an orchestration engine). In the topology, we describe all trust capabilities (and requirements) of AAI components. When two cloud services sign a B2B contract, the orchestrator matches their trust capabilities and requirements (step 1), then adapts their AAI implementations in a target cloud hosting (step 2). When a cloud service (e.g., SP2) migrates to another cloud provider (step 3), the adaptation process starts again with input from the new runtime environment (step 1). In general, we do not define a static trust between services, but let the orchestrator establish a dynamic trust between them.
## 5 Review of Related Articles
### The Potential of the Tactile Internet  
In a characteristic advancement to various Internet encapsulations, thoughts of the TI [4] are rising in which ultra-responsivity and ultra-reliability will empower the remotely operated real-time control and physical haptic communications. The TI will include a new area of measurement of human and machine interaction through constructing interactive and intelligent frameworks. The TI will facilitate a genuine change in outlook from content delivery to skillset-delivery networks and, subsequently, change every part of society. As of now, the Institute of Electrical and Electronics Engineers (IEEE) and European Telecommunications Standards Institute (ETSI) [109] standards are also incorporating standardised activities for the TI.
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
###### Abstract  
The contact profile of a train wheel has a key role in its operation performance. Rolling smoothly and with reduced resistance results in an increase in the efficiency and safety of rail transport. The original shape and dimensions of the profile of the wheel are altered under operation of the train, especially due to braking events and the presence of external objects between the wheel and the railway. With the purpose of recovering the optimum contact profile, train wheels are periodically machined using special lathes. This repair operation is particularly critical in freight trains, which are only reshaped a few times throughout their service life and, therefore, high depths of cut are required to recover the wheel in a productive way. As the presence of chatter vibrations limits the productivity of these operations, a hybrid edge-cloud computing approach has been developed for chatter vibration suppression. An expert system based on automatic chatter detection and suppression has been developed in the edge. The expert system is based on continuous real-time vibration monitoring and combines continuous spindle speed variation (CSSV) and cutting speed reduction to suppress chatter. Cloud computing is used to extract wheel profile machining fingerprints and obtain insights from multiple aggregated machined wheels. An industrial implementation of the system is described in the present work.  
chatter; train wheel; smart system; turning; edge computing Article
## 2 Material and Methodology
### Permeability and Fractal Dimensions by NMR
#### 2.5.2 Fractal Dimensions by NMR  
Rock materials have good self-similar characteristic. The complexity of the pore structure can be quantitatively described by fractal theory, and it has been used to analyze the micro structures of porous materials [30; 31]. According to the fractal theory, the proportion of pores and fractals has a relation that can be expressed as in Equation (2) [32; 33].  
\[S_{V}=(\frac{r}{r_{\max}})^{3-D} \tag{2}\]  
where \(S_{V}\) is the proportion of pores with radii smaller than \(r\), \(r\) is pore radii, \(r_{\max}\) is the max pore radii of samples and \(D\) is the fractal dimension of samples.  
As \(r\) is linear, correlated with T\({}_{2}\) of NMR, Equation (2) can be transformed into Equation (3).  
\[S_{V}=(\frac{T_{2}}{T_{2\max}})^{3-D} \tag{3}\]  
Therefore, fractal dimension \(D\) can be calculated as Equation (4).  
\[\lg(S_{V})=(3-D)\lg T_{2}-(3-D)\lg T_{2\max} \tag{4}\]  
where \(T_{2\max}\) is the maximal value of the NMR T\({}_{2}\) distribution; its unit is ms.  
According to Equation (4), the fractal dimension of different pores can be obtained by adjusting the range of T\({}_{2}\). A typical plot shows the segmented fractal dimensions of sandstone used in this paper, shown as Figure 1.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters
### Configurations  
Quadcopters have four rotors, \"1, 2, 3, 4\", positioned relative to the body coordinate system based on two configurations: the \"Plus, +\" and the \"Cross, x\" configurations, as illustrated in Figure 10.  
The cross configuration is more commonly used in both the literature and the market than the plus configuration because it is more stable. Additionally, users can slightly adjust the speed of each propeller rather than only varying the speeds of two propellers [15; 117]. In cross configurations, the pair of diagonal rotors 1 and 3 rotate clockwise, and the opposite pair rotate counter-clockwise.
## 2 Theoretical Aspects  
As the creation of electron-hole pairs via photons is the fundamental principal of PV technology, the opposite mechanism, i.e., recombination, can cause a reduction in voltage (\(V_{oc}\)). If the following formula (1) is examined, the effects of the recombination mechanism on \(V_{oc}\) can be seen [7],  
\[V_{oc}=\frac{nkT}{q}\ln\biggl{(}\frac{I_{L}}{I_{0}}+1\biggr{)} \tag{1}\]  
where \(n\) is the ideality factor, \(kT/q\) is the thermal voltage, \(I_{L}\) is the light generated current, and \(I_{0}\) is the saturation current, which depends on the recombination velocities [7]. The rate of the surface  
Figure 1: Detailed structure of conventional Cu(In,Ga)Se\({}_{2}\) (CIGS) solar cell with examples of the most commonly used materials, adapted from [4].  
recombination (\(U_{s}\)), on the other hand, can be expressed as a function of interface defect density, \(N_{it}\)[8]. Its description is derived from the Shockley-Read-Hall (SRH) formalism:  
\[U_{s}=\frac{\big{(}n_{s}p_{s}-n_{i}^{2}\big{)}v_{th}N_{it}}{\frac{n_{s}+n_{1}}{ \sigma_{p}}+\frac{p_{s}+p_{1}}{\sigma_{n}}} \tag{2}\]  
where \(\sigma_{p/n}\) is the hole and electron capture cross section, \(p_{s}\) and \(n_{s}\) are the hole and electron densities, \(v_{th}\) is the thermal velocity of the electrons, \(n_{1}\) and \(p_{1}\) are statistical factors, and \(n_{i}\) is the intrinsic carrier concentration [8]. Slight variations in the CIGS structure and bond angle cause surface defects, and therefore, formula (2) should be corrected. Changing the \(N_{it}\) term to the \(D_{it}\) (interface trap density) at the extended SRH formalism with an integral over the bandgap energies is a way to correct the formula (2) [8].  
If formula (2) is taken into consideration, it can be clearly seen that reducing \(U_{s}\) is possible by introducing a passivation layer. Two possible mechanisms will occur due to the implementation of this layer; these are chemical passivation and field-effect passivation. The total number of electrically active defects, i.e., dangling bonds, is decreased after the addition of the passivation layer. It is called the chemical passivation [9]. An implemented passivation layer, which has a high density of fixed charges (\(Q_{f}\)), repels the minority carriers, i.e., reduces \(U_{s}\), by creating an electric field [10]. It is called field-effect passivation. Besides the reduction of the recombination rate, the dielectric layer also increases reflection at the rear surface for certain thicknesses [11]. In this way, the optical path length of the light is increased.
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 2 Coupled CFD-DEM Mathematical Equations
### Flow Region and Model Parameters  
The flow region was 10 m \(\times\) 3 m \(\times\) 2 m and the size of the solar panel was 0.6 m \(\times\) 0.5 m \(\times\) 0.02 m, its installing height was 0.75 m and its inclination was 30\({}^{\circ}\). For the coordinate systems, the \(x\)-axis was along the wind flow direction, the \(y\)-axis was perpendicular to the ground and the \(z\)-axis was towards the span direction, and the origin of the coordinate systems was at the center point of the ground. The grid of the fluid area was tetrahedral with a size of 0.1 m. The grid of the solar panel surface was triangular with a size of 0.005 m, which was small enough to model the solar panel wall flow. The calculation area and grid are shown in Figure 2. A logarithmic velocity profile was used for the inlet boundary condition, which can be expressed as:  
\[u(y)=\frac{u_{*}}{k}\ln\frac{y}{y_{0}} \tag{11}\]  
where \(u_{*}\) is the friction wind velocity and \(y_{0}\) is the roughness height.
# An Embedding-Based Approach to Repairing OWL Ontologies
## 5 Experiments
### Results of Preparation  
For the preparation phase, the consumption time of each task is discussed below. These tasks can be seen as offline ones. Because translating axioms in an ontology to sentences is very efficient, we will not provide the details about the time of this task. Usually, it took no more than 2 s to finish this task. For ontologies km1500-3500 and dbpedia_2014 with many axioms, it spent about 4 s and 7 s, respectively.  
During the preparation process, computing embeddings for the obtained sentences is the most time-consuming task. Figure 2 presents the time to compute the embeddings of the sentences transformed from an ontology by applying the four pre-trained models mentioned in Section 5.2. Obviously, m1 is the most efficient model, and others perform similarly. Take the ontology dbpedia_2014 as an example. m1 spent 128 s while around 340 s for the other three models. The amount of time spent is determined by the size of an ontology. The ontology dbpedia_2014 has the most axioms (i.e., 5764 axioms) among all selected ontologies, and thus each model spent the most time computing embeddings for this ontology. The ontology koala contains no more than 50 axioms, and thus, less than 4 s were spent for each model.  
When computing similarities for sentence pairs, it is much more efficient than computing embeddings. Figure 3 shows the time to compute similarities for each ontology by using Cosine Distance or Euclidean Distance. From this figure and Figure 2 we can see that computing similarities for an ontology often took no more than 10 s while more than 10 s (even more than 100 s) for computing embeddings. In addition, it is obvious that no big difference can be observed between the two distance metrics. The amount of time consumed here is also determined by the size of an ontology.