esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 2 Study Area and Dataset  
### Study Area  
The SITS data for PASTIS are obtained from four distinct Sentinel-2 tiles situated in various regions within the French metropolitan territory, as illustrated in Figure 1a. These regions exhibit diverse climates and crop distributions. The Sentinel tiles cover an area of more than (4000 km\({}^{2}\)) with a spatial resolution of 10 m per pixel. Each pixel is characterized by 13 spectral bands. For the PASTIS dataset, we utilize all bands except the atmospheric bands B01, B09, and B10. Each of these tiles is divided into square patches measuring 1.28 km \(\times\) 1.28 km, resulting in a total of approximately 24,000 patches. Only 2433 patches were chosen, constituting 10% of all the patches. The 2433 selected patches were randomly divided into five splits, enabling us to conduct cross-validation, as illustrated in Figure 1b. The selection criteria prioritize patches that include rare crop types to mitigate the severe class imbalance present in the dataset. As shown in Figure 1.  
Figure 1: PASTIS data information. The spatial distribution of the four parcels corresponding to the PASTIS dataset (**a**). A patch distribution within a parcel (**b**). The patch retention principle (**c**); if most of the land features are located outside the patch boundary (red circle), they are labeled as “void”. If most of the land features are located inside the patch boundary (green circle), the patch is considered a valid parcel and retained. The legend refers to Figure 2 for reference.  
Figure 2: Land class codes and their corresponding colors, along with the number of parcels corresponding to each land class.
## 2 Methods and Background
### Model Settings and Validations
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \(\alpha\) & \(Q_{m}\) & \(Q_{b}\) & \(W\) & \(h_{0}\) & \(C_{0}\) & \(dt\) & \(\mathbf{Manning}\)’s & \multirow{2}{*}{\(M_{r}\)} & \multirow{2}{*}{\(\text{Fr}_{d}\)} \\  & **(\({}^{\circ}\))** & **(\(\mathbf{m}^{3}\)/s)** & **(\(\mathbf{m}^{3}\)/s)** & **(\(\mathbf{m}\))** & **(\(\mathbf{m}\))** & **(\(\mathbf{ppm}\))** & **(\(\mathbf{s}\))** & **(\(\mathbf{s}\))** & **(\(\mathbf{Roughness}\))** & \\ \hline C90 & 90 & & & & & & & & & \\ \hline C75 & 75 & & & & & & & & & \\ \hline C60 & 60 & 13.6 & 40.2 & 9.14 & 2.96 & 100 & 0.005 & 0.013 & 1.62 & 0.37 \\ \hline C45 & 45 & & & & & & & & & \\ \hline C30 & 30 & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation conditions for flow and solute mixing analysis in channel confluence. The EFDC model specifies the shear stress at the bottom cell layer for computation of the internal mode as  
\[\left(\tau_{x}^{b},\ \tau_{y}^{b}\right)=c_{b}\sqrt{u_{b}^{2}+v_{b}^{2}}(u_{b},\ v _{b}) \tag{15}\]  
where \(\tau_{x}^{b}\) and \(\tau_{y}^{b}\) are the bottom shear stress; \(u_{b}\) and \(v_{b}\) are the horizontal velocity component at the bottom cell layer; \(c_{b}=\kappa^{2}/[\ln(\Delta z/2\tau_{0}^{*})]^{2}\) is the bottom stress coefficient given assuming a logarithmic velocity profile where \(\Delta z\) is the bottom cell layer thickness, and \(z_{0}^{*}\) is the dimensional bottom roughness height [45]. In this study, \(z_{0}^{*}\) was set to 1 mm. For the solute mixing simulations, a scenario with an accidental spill was considered for the analysis of solute storage effects in the recirculation zone. Therefore, a fully mixed neutrally buoyant solute was introduced for 5 s at a concentration of 100 ppm from the tributary channel. The no-flux boundary condition was applied to all boundaries for the solute mixing simulations. To verify the accuracy of our simulations using grids following the Froude similitude, the experimental results from Shumate [51] were used for performing water depth and vertical velocity profile comparisons to the simulations, as shown in Figures 4 and 5, respectively. A comparison with the experimental data and simulation data for the water depth for the Case C90 at the points of \(y/W=0.17\), 0.50, and 0.82 revealed an MAPE of 2.7% to 3.3%. The vertical variations of velocity were properly reproduced compared to the measurements except for the result shown in Figure 5a. The discrepancy at \(x/W=-1.0\), \(y/W=0.17\) was caused by the hydrostatic assumption, which is insufficient to present the vertical pressure gradient in highly distorted velocity gradient due to the flow separation. Despite the discrepancies, the numerical simulation of the channel with scale enlargement following the Froude similitude adequately reproduced the experimental results. Figure 4: Comparisons of water depth for Case C90: (**a**) \(y/W=0.17\); (**b**) \(y/W=0.50\); (**c**) \(y/W=0.82\).
## 3 Establishment of Numerical Analysis Models
### Selection of Constitutive Model  
According to the research of [PERSON] et al. (2018), the small strain hardening model (HSS model) could reasonably consider the nonlinearity, stress correlation and other characteristics of the modulus of elasticity in the elastic (small strain) stage of the soil; at the same time, the unloading deformation path corresponding to the unloading elastic modulus could no longer be applied after unloading. Therefore, this study adopted the HSS model as the constitutive model of soil.  
The HSS model contained 13 parameters, including four strength-related parameters (effective cohesion of soil \(c^{\prime}\), effective internal friction angle \(\varphi^{\prime}\) of soil, dilation angle of soil \(\psi\), and failure ratio determined by triaxial drained shear test \(R_{\text{f}}\)), seven stiffness-related parameters (reference stress \(p^{\text{ref}}\), tangent modulus determined by standard consolidation test \(E_{\text{ood}}^{\text{ref}}\), reference secant modulus determined by triaxial consolidation drained shear test \(E_{\text{50}}^{\text{ref}}\), loading and unloading modulus determined by triaxial consolidation drained loading and unloading test \(E_{\text{ur}}^{\text{ref}}\), loading and unloading Poisson's ratio \(v_{\text{ur}}\), coefficient of static lateral pressure \(K_{0}\), modulus stress level index \(m\)), and two small strain characteristic parameters (actual initial shear modulus \(G_{0}\) and shear strain value \(\gamma_{0.7}\) corresponding to shear modulus attenuation to 70% of the initial shear modulus). The parameters of the HSS model used were determined based on the survey report and triaxial consolidation undrained test (see Table 3).
# Facile Method by Bentonite Treated with Heat and Acid to Enhance Pesticide Adsorption
## 3 Results and Discussion
### Characterisation of Bentonite and Modified Bentonite
#### 3.1.4 FT-IR Analysis  
The middle-IR and near-IR spectra of adsorbents are shown in Figures 2 and 3, respectively. A characteristic bentonite stretching band at 3626 cm\({}^{-1}\) is attributed to OH-dioctahedral smectite. These spectra confirm that the modified bentonite samples are still characteristic of bentonite. Bands at 3420 cm\({}^{-1}\) and 1636 cm\({}^{-1}\) correspond to the stretching and bending vibrations of hydrogen-bonded water molecules [35]. After calcination, the spectra show a decrease in the intensities of the Al-Al-OH band at 913-920 cm\({}^{-1}\) and the Al-Mg-OH band at 840 cm\({}^{-1}\) for BC\({}_{500}\) and BA\({}_{0.5}\)C\({}_{500}\) samples, respectively, suggesting the occurrence of dehydroxylation during calcination and the partial leaching of octahedral Al cations in acid-treated samples [19; 28]. Changes in the tetrahedral sheets are reflected in the position and shape of the Si-O stretching band [35]. There is a slight decrease in the tetrahedral Si-O band intensity at 1039 cm\({}^{-1}\) for modified samples. We assigned this to Si-O vibrations within a three-dimensional amorphous phase formed during the acid and thermal treatments [35]. Two bands at 520 and 469 cm\({}^{-1}\) correspond to bending vibrations of Al-O-Si and Si-O-Si, respectively. The band at 520 cm\({}^{-1}\) is the most sensitive band in the presence of any remaining Al\({}^{3+}\) species in the octahedral sheets and two samples of BA\({}_{0.5}\) and BA\({}_{0.5}\)C\({}_{500}\) obviously show those with a broader band. The band at 622 cm\({}^{-1}\) results from coupled Al-O and Si-O out-of-plane vibrations [36].  
NIR spectra were recorded to determine the Si-OH groups in the adsorbent (Figure 3). The bands at 4533 cm\({}^{-1}\) and 7078 cm\({}^{-1}\) are assigned to the combination mode and first overtones (2\(\mathrm{v}_{\mathrm{OH}}\)) of structural OH groups, respectively, while the band at 5240 cm\({}^{-1}\) corresponds to the combination mode of water molecules [26; 28]. BA\({}_{0.5}\), BA\({}_{0.5}\)C\({}_{500}\), and BC\({}_{500}\)A\({}_{0.5}\) shows a new band at 7319 cm\({}^{-1}\) assigned to the first overtone for silanol (2\(\mathrm{v}_{\mathrm{SI}}\)-OH), confirming the formation of Si-OH groups during treatment with acid [35].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Adsorbents** & **Weight Loss (\%)** & \\ \hline  & **50–150 \({}^{\circ}\)C** & **150–400 \({}^{\circ}\)C** \\ \hline B & 7.83 & 2.50 \\ BA\({}_{0.5}\) & 5.56 & 2.01 \\ BC\({}_{500}\) & 1.03 & 1.41 \\ BA\({}_{0.5}\)C\({}_{500}\) & 1.18 & 1.21 \\ BC\({}_{500}\)A\({}_{0.5}\) & 4.59 & 2.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Water content (%) in bentonite and modified bentonite.
# Electrospinning of Essential Oils
## 3 Electrospun Fibres Containing EOs and Their Applications
### Thyme Essential Oil  
Herbaceous perennials and subshrubs of the genus _Thymus_ L. (Lamiaceae family) grow in the Mediterranean region, northern Europe, Asia, South America and Australia [97]. Among the _Thymus_ species, _Thymus vulgaris_ L. (commonly known as thyme) is widely used as aromatic and medicinal plant in food, agriculture, pharmaceutical, and cosmetic industries. Thyme EO possesses strong antibacterial and fungicidal activities, being rich in oxygenated monoterenes and hydrocarbon monoterenes: thymol, carvacrol, p-cymene and \(\gamma\)-terpinene [11; 98; 99; 100].  
Recently, thyme EO has been encapsulated into fibres of poly(vinylpyrrolidone (PVP) and gelatine (average diameter between 200 and 400 nm) by performing the electrospinning process at a voltage of 25 kV, a flow rate of 0.5 mL/hour and electrodes distance of 17 cm [61]. PVP/gelatine fibrous mats containing 3% w/w of thyme EO were tested against _S. aureus_, _E. coli_, _P. aeruginosa_, and _E. faecalis_, and inhibition zones of 16 mm were recorded for both _S. aureus_ and _E. coli_; smaller zones (11-12 cm) were instead measured for the other microorganisms. The encapsulated EO maintained the antibacterial activity even when the electrospun mats were stored at 24 and 37 \({}^{\circ}\)C, and inhibition activity against _S. aureus_ and _E. coli_ was visible after 8 days of incubation. In another study, zein/PEO fibres (average diameter of 6 um) containing thyme EO (6% w/v) were electrospun from aqueous solutions using a portable and hand-held electrospinninging device operating at 15 kV, feeding rate of 30 uL/minute and working distance of 12 cm [58]. The resulting zein/PEO/thyme EO fibres showed antibacterial activity against _E. coli_ and _S. aureus_, with inhibition zones in the range of 4-6 cm; whereas fibres without EO were ineffective. Animal tests were performed to assess the healing properties of the zein/PEO/thyme EO fibres when they were deposited in situ on skin wounds. The healing process was accelerated when the animals were treated with zein/PEO/thyme EO dressings, achieving almost complete wound closure after 11 days; differently from wounded animals without dressings that experienced a longer healing time. One of the main advantages of miniaturised and portable electrospinninging apparatuses is the deposition of networks of bioactive fibres directly at the wound site to ensure excellent coverage of the injured area [31]. In-situ electrospinninging of polymeric solutions containing EOs can better preserve the chemical composition of the therapeutic oils by avoiding the storage of the composite fibres before use. This would limit the evaporation of the most volatile components of the EOs.
## 2 Theory and Calculation  
The ANN [26] method requires a large set of data for network training. It then quickly calculates the required output data with significant accuracy and precision, in comparison with classical methods and neuro-fuzzy systems [27]. The SVM method has been derived from the statistical theory of learning [28], which applies structural risk minimization, while the ANN only applies the minimization of the mean square error in the data set. As a result, a better prediction is obtained by the SVM model. The BRT [29] method is a supervised learning method that contrasts with ANN and SVM. Other methods process ML data numerically and result in one \"best\" model. However, the BRT theory processes them independently of their assumptions and uses the boosting method to combine a large number of relatively simple models for the optimization of predictive performance.
# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
###### Abstract  
In order to adapt to the local brightness and contrast of input image sequences, we propose a new weighted average adaptive factor well-exposure weight evaluation scheme. The exposure weights of brighter and darker pixels are determined according to the local average brightness and expected brightness. We find that in the traditional multi-exposure image fusion scheme, the brighter and darker regions of the scene lose many details. To solve this problem, we first propose a standard to determine the brighter and darker regions and then use a fast local Laplacian filter to enhance the image in the region. This paper selects 16 multi-exposure images of different scenes for subjective and objective analysis and compares them with eight existing multi-exposure fusion schemes. The experimental results show that the proposed method can enhance the details appropriately while preserving the details in static scenes and adapting to the input image brightness.  
high dynamic range; image fusion; detail enhancement; fast local Laplacian filter +
Footnote †: journal: Applied  
ulti-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement  
122868
## 2 Related Work
Current simulation techniques exhibit a high technological development that enables the simulation of all phases of the life cycle of an industrial process with high precision and complexity, where the algorithms proposed are executed to analyze their performance in the presence of different events. A computer simulation of discrete events through a DT is carried out in [35]; such a simulation consists of three stages: definition of future scenarios, periodic executions, and decision-making based on the integration with data in a real environment of a logistic process in the aeronautical industry. Results showed the possibility of optimizing decision-making related to supply routes and the integration of the simulation model with the ERP. A system for identifying, modeling, monitoring, and optimizing dynamic changes of small objects is proposed in [5]. A hybrid neural network model with a learning algorithm was built with the data simulated, for synchronizing the physical and virtual systems. Results show the effectiveness of the proposed method, with a higher detection precision for a DT in intelligent manufacturing. A cloud-based reference model for a CPS with DT is presented in [36]. A hybrid neural network model with a learning algorithm was built with the data simulated, for synchronizing the physical and virtual systems. Results show the effectiveness of the proposed method, with a higher detection precision for a DT in intelligent manufacturing. A cloud-based reference model for a CPS with DT has been presented in [37]. The authors present a methodology to reduce the number of messages through the DT between the CPS components, and in this way maintain a uniform communication interface and carry out tasks securely through a classification of events for a uniform distribution and to guarantee architecture scalability. Specific aspects and behaviors of the system are modeled in [38] to obtain information about the state or indicators through the aggregation of black-box modules to the simulation model. Results showed that it may be used to observe various aspects of the process and for updating the real system with improvements that may be obtained in the digital model. An IIoT architecture with DT is proposed in [39; 40] to acquire data and features using cluster-based deep learning for anomaly detection. Results showed an improvement in learning, convergence, and energy-saving compared with various last-generation anomaly detection algorithms. Among the different proposals presented by the authors, it is evident that one of the greatest challenges is creating reliable digital models with acceptable computational costs, low latency, and deep analysis methods. On the other hand, the opportunities open the possibility of bidirectional information transfer, diagnosis, optimization, replication of digital processes, development of intelligent systems, and predictive analysis using intelligent control techniques.
## 4 A Real Case Study  
A factory continuously produces 17 product types \((j=1,2,\ldots,17)\). The demand for each product is continuous, and the production is made in batches. The setup time for each product is \(TS_{j}=0.0288\) months, \(\forall j=1,2,\ldots,17\) (about 5.76 h). The setup time is fixed for each type of product and does not depend on the production schedule.  
The monthly production rate for each type of product manufactured \(\psi_{j},\ j=1,2,\ldots,17\) was calculated by dividing the number of working hours per machine in 1 cycle (200 working hours in 1 month) by the production time needed to produce one unit in hours, obtained by work-study methods. The demand rate monthly for each type of product \(\lambda_{j}\) is determined by long-range customers' orders. Table 1 presents the monthly production rate and the monthly demand rate for each of the 17 types of products that should be produced. The monthly production time required to produce a batch of each type of product is \(T\psi_{j}=\frac{Q_{j}}{\psi_{j}}\) (see Figure 1). In our case, \(Q_{j}=\lambda_{j}\), so \(T\psi_{j}=\frac{\lambda_{j}}{\psi_{j}}\).
## 3 Digital Image Acquisition, Processing and Modelling Reconstruction
### Digital Image Acquisition Devices
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Imaging Techniques**} & **Camera Setup** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{Early Imaging method} & Digitizer with & & \\  & microcomputer & Measures shape. & No angularity or texture addressed. \\ \hline \multirow{5}{*}{Dynamic imaging techniques} & VDG-40 & & & Assume idealized ellipsoid is a particle shape; cannot measure angularity and texture; use one camera magnification to take images of all aggregate sizes. \\ \cline{2-6}  & CPA & Measures shape of & Assume idealized ellipsoid is a particle shape; cannot measure angularity and texture; use one camera magnification to take images of all aggregate sizes. \\ \cline{2-6}  & PSDA & One line-scan & quantity. & Separate vibratory feed systems and backlights required to scan fine and coarse samples. \\ \cline{2-6}  & VIS & & Measures shape of aggregates. & Two-dimensional shape information; no angularity or texture addressed. \\ \cline{2-6}  & PSSDA & & & \\ \cline{2-6}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Advantages and Disadvantages of Imaging Techniques. At present, various technological means, such as high-definition digital cameras, electron microscopes, laser scanners and X-ray CT, are being applied in the field of particle morphological characterization, among which the relatively mature aggregate testing systems is the Aggregate Image Measurement System (AIMS) testing system created by the University of Illinois, USA [79]. [PERSON] et al. [82] developed AIMS and analyzed the stability of 3D dimensional measurements of coarse aggregates using X-ray CT scans for comparison. To verify the accuracy of the AIMS measurements, they used a wide selection of angular and textural indicators. Simultaneously, the true 3D dimensions of the aggregates were obtained using the X-ray scanning technique. The improved AIMS measurement method not only provides a fresh classification of aggregate particles in a sample but also automates the analysis process of aggregates, which is a positive point. Subsequent studies can incorporate statistical methods to further improve the technique. [PERSON] et al. [83]
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Methods and Approaches for Scheduling in TSN
#### 4.1.2 Network Performance  
The performance of a network in terms of providing bounded latency, jitter, quality of control (QoC), and the mutual influence between different traffic types plays a critical role in delivering the network's QoS objectives. Consequently, numerous research works have focused on optimizing network performance, with minimizing latency being the primary objective in this category.  
For instance, Studies [37, 40, 76, 77] focused on minimizing latency for HTS streams. The authors of [37, 40] employed exact methods based on ILP, while the authors of [76] developed a two-steps mechanism utilizing a hybrid genetic algorithm to find the schedule, which is subsequently compressed in the second step. Many works combine latency minimization with other optimization objectives. For example, works [78, 79] aimed to minimize both latency and jitter. [PERSON] et al. [78] adopted a first-order logic approach for incremental scheduling and utilized, in [78, 79], an SMT solver to find the optimal solution. Similarly, studies [80, 81] considered latency and jitter while aiming to minimize bandwidth utilization. They devised a genetic algorithm to rapidly calculate a good solution.  
Other approaches strive to minimize latency alongside the number of guard-bands using deep reinforcement learning [82], balance latency and traffic loss using machine learning and genetic algorithms (e.g., NSGA-II) [83], minimize latency and application response time through application-level scheduling [54], and minimize path length using an ant colony optimization approach combined with a binary multi-knapsack formulation [53]. In addition to focusing on HTS traffic, some works consider LTS traffic as well. For example, work [50] aimed to minimize the worst-case scenario for AVB traffic by managing the HTS queue count and routing of AVB using a GRASP metaheuristic.  
Furthermore, various approaches proposed to maximize the quality of control. For instance, authors of [84] deployed a constraint and logic programming approach to systematically search for optimal solutions, while in [85], a codesign optimization problem was formulated to find the optimal period that minimizes the settling time of the configuration. On the other hand, authors of [86] utilized a Tabu-search metaheuristic to increase diversification in a fog environment.