esa-sceva/satcom-chunk-collection huggingface dataset:
# Pixel Interaction Model for Contrast Enhancement: Bridging Social Science and Image Processing
## 4 Experimental Results
### Performance Evaluation
#### 4.2.2 Numerical Performance  
Only visual assessment might not be good enough to establish the efficacy of the proposed algorithm against its competitors. For this reason, numerical evaluation is necessary. Each contrast-enhancement method has been numerically evaluated in this experiment, which is performance-oriented, based on the six metrics that are described in the experimental setup section. This section aims to discuss numerically which algorithm provides the best contrast-enhanced images among reference algorithms and the proposed method in terms of \(RMS\) (root mean square), \(E\) (entropy), \(MSE\) (mean-squared error), \(AME\) (absolute measure of enhancement), \(SNR\) (signal-to-noise ratio), and \(WSNR\) (weighted signal-to-noise ratio). Table 1 shows the results of these metrics for each image \(I_{1}\) to \(I_{6}\) generated by the comparison algorithms and the proposed one. These metrics not only allow for a mathematical review of the quality of the output images produced by the contrast enhancement algorithms but also provide in-depth information on how far the original and enhanced images are. Figure 13: Results of the proposed technique for image \(I_{6}\) for (AVHEQ), (BBHE), (ESIHE), (MMBEBE), (RSSIHE), and (C-OPINION) algorithms. As depicted in Table 1, the performance metrics of the proposed C-OPINION technique significantly outperform those of the competing methods. The BBHE method exhibits the lowest performance in the majority of images for the \(RMS\), \(MSE\), \(SNR\), and \(WSNR\) indicators. Furthermore, the AVHEQ algorithm frequently displays subpar performance. However, unlike the BBHE method, the AVHEQ presents exceptionally low values in almost all metrics, except for \(E\), indicating that it is not a robust method for generating high-contrast images when assessed quantitatively. Concerning the MMBEBIE and RSESIHE methods, they have average performance scores. The MMBEBIE and RSESIHE have the lowest scores in \(RMS\), \(E\), and \(MSE\), and in \(E\) and \(AME\), respectively. This makes both
## 3 Methodology and Algorithms  
### Theoretical Foundation  
#### 3.1.1 Virtual Environments for Structured Simulation and Data Augmentation  
Virtual environments have emerged as an indispensable tool in data-centric artificial intelligence, enabling researchers to decouple learning-based model development from the limitations of physical data acquisition. By leveraging physically grounded simulation engines and programmable control interfaces, modern virtual platforms allow for the synthesis of perceptually realistic, structurally consistent, and semantically controllable datasets across a wide range of modalities. In high-stakes domains such as robotics, embodied AI, and human-centric vision, this synthetic-to-real pipeline has fundamentally redefined how datasets are generated, curated, and scaled.  
From a systems perspective, virtual environments offer a generative prior over interaction spaces. They permit precise specification of agent morphology, environmental constraints, temporal control, and multimodal sensory feedback. This level of control is particularly beneficial when designing data regimes that demand statistical diversity while retaining structural coherence, e.g., varying motion trajectories while preserving physical feasibility, or sampling camera perspectives without violating temporal causality. Such controllability is difficult to achieve in naturalistic data collection pipelines, where sensor noise, occlusion, and annotation ambiguity often introduce unquantifiable biases.  
Virtual data augmentation is not merely a proxy for \"more data\", but a mechanism for bias shaping and generalization conditioning; it enables targeted perturbations of scene structure, motion context, or agent-object relationships in ways that real-world sampling cannot support at scale. This property is increasingly central in domains where distribution shift, edge cases, or long-tail behaviors dominate downstream model performance.  
Virtual environments have transitioned from visualization tools to algorithmic infrastructure providing not only photorealistic assets but also structured, verifiable, and fully controllable simulation scaffolds that form the foundation of modern synthetic data pipelines.
## 5 Experiments
### Cascade Fuzzy Reward System  
To address the challenges of sparse rewards and signal delays in DRL, this study introduces a CFRS based on fuzzy logic. We conducted ablation experiments comparing the CFRS with both a unique reward system (URS) and a unique fuzzy reward system (UFRS).  
Data from Figure 10 reveals that models employing URS converge more slowly, achieving convergence around 250 k episodes, indicating limitations in global optimization. In contrast, UFRS models converge within approximately 120 k episodes, demonstrating faster optimization speeds. Most notably, CFRS models converge within just 100 k episodes, illustrating the effectiveness of cascaded fuzzy rewards in efficient signal propagation and rapid global optimization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Obstacles** & **3** & **4** & **5** & **6** & **7** \\
**Number** & & & & & \\ \hline Baseline & 0.892 & 0.765 & 0.643 & 0.562 & 0.513 \\ \
\begin{tabular}{c} End-to-end \\
\begin{tabular}{c} success \\ \end{tabular} & 0.940 & 0.931 & 0.924 & 0.901 & 0.876 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Strategy success rate when changing complexity.  
Figure 9: Performance comparison between end-to-end DDPG and baseline DDPG.  
Figure 11 focuses on the trends in collision rates under different reward systems. The URS model initially experiences a rapid decrease in collision rates but later stabilizes around 20%, possibly due to settling at local optima induced by its simplistic reward mechanism. UFRS shows a significant initial decrease in collision rates, with fluctuations within the 15-20% range, indicating some robustness in dynamic environments. CFRS, on the other hand, exhibits a continual decrease in collision rates, eventually stabilizing at a low level of around 5%, further proving its robustness and efficiency in practical operations.  
In summary, the introduction of the CFRS significantly enhances the model in terms of convergence speed, stability, and reduced collision rates. This design not only promotes rapid global optimization of the algorithm but also enhances the model's robustness and reliability in complex environments. Therefore, the CFRS provides an efficient and robust reward mechanism for DRL in complex tasks such as robotic arm path planning.
## 1 Introduction
### Research Objectives  
Image acquisition parameters and processing methods vary significantly across other similar studies. Subsequently, the differences in UAS image quality and the resulting land cover classifications between these studies are vast. However, to better understand the factors influencing the quality of UAS imagery, diligent testing of data collection parameters and associated results are needed to integrate UASs into forestry applications effectively [PERSON] et al. (2019). Therefore, the first research objective was to establish effective data procurement methods with UASs for both before and after planned disturbances. This first objective was also intended to optimize the processing parameters for this application and to examine to what extent the parameters can be modified to enhance data collected with poor quality. While collecting and processing quality data are important to any forest inventory, equally important are the quality and usefulness of the resulting data products [PERSON] et al. (2019).  
Data classification parameters and results also vary significantly across other pertinent studies, yet are crucial for the replicability and effective implementation of this method [PERSON] et al. (2019). Therefore, the second research objective was to establish effective data classification methods with UAS imagery to quantify the land cover from both before and after planned disturbances. By developing sequential maps of the land cover at every stage surrounding the disturbance treatment, quantifiable areas of vegetation loss, retention, and regeneration can be determined [3].  
Because this study also involved validating the resulting classifications through visual interpretation, these results can be used to compare the output quality among land cover types [37]. Therefore, the third research objective was to determine _if_ there was a significant difference in the output classification quality between the prescribed burn and the selective harvest treatment land covers. By comparing the quality of the classified map series between the two disturbance types, the reliability of this inventory method between open and closed canopy land covers can be examined. Completing these objectives established an effective methodology for the efficient inventorying of land cover change both _before_ and _after planned_ disturbance treatments.
## 2 Materials and Methods
### Fabrication of GI-MMF FBGs  
The GI-MMF BGs were fabricated on common OM4 GI-MMF from Corning(c), using a KrF excimer laser (ATL Laser, UV inscription at 248 nm) and the phase mask technique. The OM4 GI-MMF was acrylate coated and had core and cladding diameters of 50 \(\upmu\)m and 125 \(\upmu\)m, respectively. To enable the inscription of BGs in the Germanium (Ge)-doped silica core, the OM4 GI-MMF was hydrogen loaded at a pressure of 200 bar for two weeks at room temperature (23 \({}^{\circ}\)C). Immediately after the hydrogen loading, the acrylate fiber coating was removed and 300 mm long fiber samples were exposed to the UV light from the KrF excimer laser with a repetition rate of 15 Hz, a pulse energy of 5 mJ and an inscription time of 2 min. The BG inscription length was 7 mm and the period of the applied phase mask was 1070 nm. The transmission spectrum of the inscribed BG was carefully monitored using a broadband light source (Thorlabs SLS201L/M) and an optical spectrum analyzer (Ando AQ6317B). An example of a transmission spectrum of a BG inscribed in the hydrogen loaded OM4 GI-MMF is shown in Figure 2. In total, 21 absorption dips of the fiber BG transmission spectrum were measured, where each absorption dip represents the Bragg reflection of an individual mode group and adjacent mode groups at the grating structure inside the GI-MMF [6] and the wavelength of the measured Bragg reflection peak decreases with increasing order of the mode groups, i.e., Bragg reflections peaks of the higher order mode groups are in the blue wavelength region.  
Following the inscription of the BG, the cladding of the modified GI-MMF was removed over a length of 55 mm using hydrogen fluoride (HF) acid using the etching technique described elsewhere [2]. The final diameter of the etched fiber part was approximately 43 \(\upmu\)m. In addition, after the etching process was complete, one end of the fiber containing the BG was spliced to the OM4 GI-MMF pigtail and the other end was cut close to the spatial position of the BG in order to obtain a single ended sensor probe, i.e., for RI and temperature measurement.
# Fragile Watermarking of 3D Models in a Transformed Domain
## 5 The Proposed Algorithm
### Watermark Extraction and Verification  
The extraction of the watermark starts with building the EU for every vertex. Using the KLT basis from the key generator module, 28 coefficients are extracted from every EU: from the subset of coefficients used for watermark embedding, the watermark bits are computed using Equation (7) and stored in a string \(w_{e}\). Note that this operation requires the computation of a forward KLT; the inverse KLT is not used in the proposed algorithm.  
After that, the watermark generator module is called for every EU, producing a watermark string \(w\) for each of them.  
At this point, the verifier module compares the two strings \(w_{e}\) and \(w\) for every vertex: if the strings are equal, then the vertex is marked as authentic; otherwise, it is flagged as potentially forged.  
The possible forging of a 3D model represented as a set of vertices connected to form polygons may be done by altering or modifying:  
* at least one of the coordinates of a vertex, or
* removing, replacing, or adding a vertex to a polygon, or
* shuffling the order of the vertices on the perimeter of a polygon.  
According to the integrity protection provided by the proposed algorithm, a vertex can be detected as tampered if:  
1. at least one of the coordinates \(x,y,z\) has been altered: in this case, if only the lower part (the vertical striped bytes in Figure 2) of one of the values is modified, then the vertex alone will be flagged as tampered; if the higher part is altered, then the vertex itself and all the vertices belonging to the polygons containing the vertex will be detected as tampered.
2. one of the polygons containing it is altered, that is: * one of its vertices has been modified in the higher part of the coordinates, or * at least one of its vertices has been substituted by another vertex, or * the order of the vertices on the perimeter has been changed (comprising the clockwise or anti-clockwise order), or * the number of vertices has been changed;
3. it has been removed from one of the polygons it belongs to, or
4. it has been added to a polygon (even duplicating a polygon).
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
## 2 Materials and Methods
### Data and Model
#### 2.1.2 WACCM-X  
WACCM-X is one of the atmosphere components of the Community Earth System Model (CESM) (detailed information can be seen at [[http://www.cesm.ucar/edu/](http://www.cesm.ucar/edu/)]([http://www.cesm.ucar/edu/](http://www.cesm.ucar/edu/)), accessed on 10 May 2021), developed by the National Center for Atmosphere Research (NCAR). The altitude range of WACCM-X is from the ground to the upper thermosphere (\(\sim\)600 km). The horizontal resolutions of WACCM-X are 1.9\({}^{\circ}\) and 2.5\({}^{\circ}\) in the meridional and zonal directions, respectively. The model has 126 vertical levels in free-run mode and 145 levels in specified dynamics (SD) mode. The WACCM-X includes the physical processes of WACCM developed by NCAR, a neutral chemistry model for the middle atmosphere, an tion chemistry model for the mesosphere and lower thermosphere regions, ion drag, and auroral processes, parameterizations of extreme ultraviolet heating and infrared transfer under nonlocal thermodynamic equilibrium, and modified parameterizations of gravity wave effects and vertical diffusion. An electrodynamics module, which can self-consistently solve the interactions between electric fields, plasma motions, and neutral winds, was implemented in WACCM-X 2.0. [PERSON] et al. (2010) and [PERSON] et al. (2018) gave detailed descriptions of WACCM-X and its upgrade [27; 28].  
A transient historical simulation with WACCM-X in the free-running mode for 2014 was conducted. This simulation applied the real value of the Kp index, the Ap index, and the F10.7 solar radio flux. Climate Model Intercomparison Project 6 (CMIP6) provided lower boundary forcings, chemical emissions, and solar radiative and particle [29; 30]. The main magnetic field was specified according to the 12 th International Geomagnetic Reference Field (IGRF-12) [31]. The thermosphere is a strongly forced system in which small changes in the initial state do not significantly affect the calculated climate of the thermosphere [32], so a single transient historical simulation was used in our investigation. The period of the input data set is from January 2014 to December 2014.
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 3 The Cut-In Vehicle Safety Distance Model
### The Safe Distance Model of the Cut-In Vehicle for a Straight Lane
#### 3.1.2 The Minimum Longitudinal Safety Distance between Cut-In Vehicle L and Host Vehicle H  
As shown in Figure 15, considering the critical collision scenario, assuming that cut-in vehicle L collides with host vehicle H at point c, S represents the initial lateral distance between the upper edge of cut-in vehicle L and the lower edge of host vehicle H. D represents the lane width.  
The lateral acceleration of host vehicle H is 0 and the lateral position is constant. Assuming that the moment of collision, point c, is \(t_{c}+t_{adj}\), in order to avoid a collision between cut-in vehicle L and host vehicle H, the following equation needs to be satisfied:  
\[x_{H}(t)<x_{L}(t)-l_{0}\times\cos(\theta(t))\quad\forall t\in\left[t_{c}+t_{ adj},T\right] \tag{16}\]  
where \(x_{P}(t)\) is the abscissa of the host vehicle and \(l_{0}\) is the length of vehicle.  
Ignoring the influence of vehicle yaw, the above formula can be simplified to the following formula:  
\[x_{H}(t)<x_{L}(t)-l_{0}\quad\forall t\in\left[t_{c}+t_{adj},T\right] \tag{17}\]  
Figure 15: The position of cut-in vehicle L and host vehicle H.  
Use \(S_{r}(t)\) to represent the longitudinal distance between cut-in vehicle L and host vehicle H during the lane change:  
\[S_{r}(t)=x_{L}(t)-L-x_{H}(t)\quad\forall t\in\left[t_{c}+t_{adj},T\right] \tag{18}\]  
In order to prevent cut-in vehicle L from colliding with host vehicle H during the lane change process, the following equation needs to be satisfied:  
\[S_{r}(t)=\left(\begin{array}{c}S_{r}(0)+\int_{0}^{t}\int_{0}^{\lambda}(a_{L} (\tau)-a_{H}(\tau))d\tau d\lambda+\\ (v_{L}(0)-v_{H}(0))\times t\end{array}\right)>0\quad\forall t\in\left[t_{c}+t _{adj},T\right] \tag{19}\]  
where \(S_{r}(0)=x_{L}(0)-l_{0}-x_{H}(0)\). The minimum safe distance, \(MSD(L,H)\), at which cut-in vehicle L will not collide with host vehicle P during the lane change process is obtained  
\[MSD(L,H)=M_{t}ax\left(\begin{array}{c}\int_{0}^{t}\int_{0}^{\lambda}(a_{H} (\tau)-a_{L}(\tau))d\tau d\lambda\\ +(v_{H}(0)-v_{L}(0))\times t+l_{0}\end{array}\right)\quad\forall t\in\left[t _{c}+t_{adj},T\right] \tag{20}\]  
where \(a_{H}\) is the longitudinal acceleration of the host vehicle and \(v_{H}\) is the longitudinal velocity of the host vehicle.
# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 7 Discussion
### Edge-Based Analytics and Fog-Based Analytics  
Transmission data streams between edge-side equipment and cloud-side communications play an essential role in IIoT advancements [2]. This study represents the use of data analytics in the manufacturing process. However, model deployment is a significant stage in the data analytics procedure. The trained model can be deployed as API, which allows a user to send a service request with an API key, and the server side can respond to the request with a prediction. The API can be used in an edge-side platform to form edge-based analytics. Edge-based analytics provides computing resources for applications with networking close to the edge-side (e.g., factory floors and end-users). In contrast, the fog-based framework lies between the edge and cloud. Fog-based analytics facilitates computing operations, providing connectivity between the edge-side and cloud-based platforms. Related studies have used fog computing in the predictive maintenance problem [44]. The sustainable development of IIoT-based PdM applications is crucial for smart manufacturing; thus, configurable API use may co-create the value for manufacturing maintenance. Herein, the trained model is deployed as an API that can be used in edge-, fog-, and cloud-based analytics for flexible use (see Figure 10).  
Figure 10: Predictive maintenance analytics from the edge to cloud.
## 3 Electromagnetic Simulation
### Electromagnetic Simulation Setup  
The considered power dissipation is related only to copper traces losses. According to this, the device (die) is shorted, and it is modelled with conductive copper elements in this simulation. Hence, no Si and GaN die contributions have been considered in this simulation because the focus is on the interconnections. In Table 1, the material data, retrieved from suppliers, used in the electromagnetic simulation, have been reported.  
Figure 4 shows the considered package's ports. In yellow, depicted are the seven ground ports in parallel, and in green the switch ports in parallel. As previously mentioned,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Simulated Objects** & **Relative Permitivity** & **Relative Permeability** & **Bulk Conductivity [S/m]** & **Dielectric Loss Tangent** \\ \hline Copper & Traces, vias, die & 1 & 0.999991 & \(58\times 10^{6}\) & 0.02 \\ Copper-CuFe\({}_{2}\)P & Lead-frame & 1 & 0.999991 & \(39\times 10^{6}\) & 0.02 \\ Sinter paste & Solder & 1 & 0.99998 & \(3.3\times 10^{6}\) & 0 \\ Encapsulant & Case & 5 & 1 & 0 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties used for electromagnetic simulation.  
the simulation is performed in DC at a frequency equal to 500 kHz and fixing current at 90 A. As imposed by boundary conditions, the current travels from ground to switch ports. This condition, which is very stressful in terms of current, will be considered as the case study for the import of power losses in the thermal simulation in the next section. The copper trace thickness is equal to 32 \(\upmu\)m. According to the DC regime, as explained in Section 3.1, a three-dimensional tetrahedral mesh with adaptive refinement has been implemented.
## 3 Extraction Techniques
### Emerging Extraction Technologies
#### 3.2.2 Microwave-Assisted Extraction (MAE)  
In the last decade, the use of microwave-assisted extraction (MAE) to recover active molecules from several natural sources, including hops, has also been addressed. This extraction technique is based on the application of microwave energy, which is converted into heat mainly through two mechanisms, which are ionic conduction and dipole rotation [48]. This process increases the pressure and temperature inside the cell matrix, which causes the rupture of the cell structure, improving the release of the desired compounds. MAE has been recognized as a promising green technique over conventional extraction methods since it presents various advantages, including (1) shorter extraction time, (2) minor energy input, (3) decreased solvent consumption, (4) ease of operation, and (5) a minimum degradation of bioactive compounds [28; 48].  
[PERSON] et al. [50] explored the use of microwave-assisted hydrodistillation (MAHD) to extract essential oils from hops scCO\({}_{2}\) (sc: supercritical) extract, and the results were compared with those obtained using conventional hydrodistillation (HD). Under optimized conditions (335 W microwave power at LSR of 8:3), MAHD yielded 3.77% of essential oils in an extraction time of 30 min, while with conventional HD, only 1.90% was reached using a longer time (276 min). The results of quantitative analyses of \(\beta\)-myrcene and \(\alpha\)-humulene of the obtained oil by MAHD were 77.36% and 9.47%, respectively.