esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Augmented Reality in Industry
### AR in the Secondary Industry  
Based on the data presented in Figure 7 regarding augmented reality bibliometrics in industry, a distribution of the number of publications across various manufacturing industry sectors can be observed. The automotive sector has the most publications, amounting to 41 articles. Subsequently, the textile sector demonstrates a comparatively lower number with only 5 publications, followed by the food, chemical, and pharmaceutical sectors, which have 10, 4, and 3 publications, respectively. These results prove that the application of augmented reality has been most explored and researched in the automotive sector, followed by sectors such as textile, food, chemical, and pharmaceutical. In the automotive industry, augmented reality can be applied in many areas, such as vehicle design, manufacturing, maintenance, and operator training. It can help visualize projects, optimize production processes, detect defects, and support decision making [20; 21; 22].  
Although the textile, food, chemical, and pharmaceutical sectors have fewer publications, there are still possibilities for the application of augmented reality in these areas. For example, in the textile sector, the main aspects of Industry 4.0 identified focus on implementing technologies aimed at computerizing and automating processes, whose main objectives are to increase productivity and reduce costs. Projects to implement augmented reality and 3D simulation technologies in the textile and clothing industry are still in their infancy, usually implemented through tools to create and develop new models of processes, products, and commerce [23].  
Figure 6: Augmented reality in industrial sectors.  
In the food sector, it can be applied in the commercial sector (boosting the positive effect on the desirability and probability of purchase) in the instruction of cooking techniques and the presentation of nutritional information [24; 25; 26; 27; 28]. In the pharmaceutical sector, augmented reality can improve production efficiency, provide employee training, improve workplace safety, and provide real-time information about production processes. However, implementing AR in industrial applications is challenging and requires customized solutions to meet the specific needs of the sector [29]. AR in the chemical industry will be highlighted in Section 3.3 below.  
In summary, this data analysis suggests that augmented reality has significant application potential in several manufacturing industry sectors. The automotive sector is the most explored so far, but there are still opportunities for research and development of augmented reality in the textile, food, chemical, and pharmaceutical sectors.
## 2 Materials and Methods
### Statistical Analysis  
We applied the statistics and metrics below to determine the classification models' performance and compare clinical and imaging variables between different cohorts. The details of each metric are further described in the Supplementary Material.  
* The confusion matrix [25; 39] provides a summary of the prediction results on classification and the true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) for each class. The metrics obtained from the confusion matrix include accuracy, sensitivity, specificity, and F1 Score [40; 41; 42].
* The ROC curve [34] plots the true positive rate (sensitivity/recall) versus the false positive rate (1--specificity) for different threshold values. The AUC (Area Under the Curve) [43] is the area under the ROC curve and provides a measure of the aggregate performance across all classification thresholds. An AUC of 0.5 indicates random prediction, while a value close to 1 indicates strong discrimination;
* The confidence interval (CI) for a performance metric [44] provides a range within which the metric falls with a specified probability (for example, 95% CI);
* Cross-validation (e.g., stratified k-fold cross-validation) splits the data into multiple training and validation sets to obtain a reliable estimate of performance and reduce the risk of overfitting;
* The Chi-Square test [45] is used to examine the relationship between categorical variables or to test the degree of fit between observed data and an expected distribution;
* The independent samples \(t\)-test [46] is a statistical hypothesis test used to determine whether there is a significant difference between the means of two groups, assuming the data follow a normal distribution,
## 2 Materials and Methods
### Random Patch Extraction  
We introduce RPE to enrich patch diversity within each farmland parcel, thereby improving generalization and reducing overfitting. This design is inspired by previous findings that exposure to multiple spatial offsets enhances model robustness [47; 48]. The conventional fixed-offset tiling-based scheme--hereafter referred to as fixed patch extraction (FPE)--extracts patches at the same coordinates across epochs, often leading to redundant information. In contrast, RPE samples each patch using a random spatial offset such that the resulting patch includes a sufficient portion of the parcel. Consequently, even within a single parcel, RPE exposes the model to multiple views that differ in background clutter, illumination, and planting layout. This diversity mitigates duplication in training inputs, improves generalization, and reduces the risk of overfitting. As shown in Figure 6, RPE provides significantly more spatial variation across training iterations compared to FPE. Throughout the experiments, we quantitatively assessed the benefits of RPE against the fixed-baseline FPE.  
Neither RPE or CBS is tied to any specific network architecture; they are broadly applicable to any classification pipeline that operates on fixed-size patches, a common paradigm in tiling-based parcel classification. Our approach focuses on dataset extraction and sampling strategies rather than on model design itself, making it applicable to a wide range of systems that employ fixed-size patch-based training. To assess the effectiveness of our training-stage techniques in isolation, we conducted experiments on a simplified parcel-level framework originally proposed as an early form of tiling-based parcel classification [42; 43]. This baseline system serves as a foundational structure that  
Figure 5: Sampling strategy comparison between NCB and CBS.  
remains representative and widely applicable, providing a neutral ground for evaluating patch-level learning strategies. By avoiding the complexity of modern architectures, we minimized confounding factors, ensuring that the improvements stem directly from the proposed training methods.
## 3 Numerical Calculation
### Differential Equation of Seepage Analysis  
Many researchers [29; 30; 31] have studied groundwater seepage in tunnel engineering. The differential equation that governs three-dimensional seepage in a rock mass is expressed as:  
\[\text{Basic equation of seepage}:\ \frac{\partial}{\partial x}\bigg{(}k_{x}\frac{ \partial H}{\partial x}\bigg{)}+\frac{\partial}{\partial y}\bigg{(}k_{y}\frac {\partial H}{\partial y}\bigg{)}+\frac{\partial}{\partial z}\bigg{(}k_{z}\frac {\partial H}{\partial z}\bigg{)}+Q=\frac{\partial\theta}{\partial t}, \tag{1}\]  
\[\text{Steady flow equation}:\ \frac{\partial}{\partial x}\bigg{(}k_{x}\frac{ \partial H}{\partial x}\bigg{)}+\frac{\partial}{\partial y}\bigg{(}k_{y}\frac {\partial H}{\partial y}\bigg{)}+\frac{\partial}{\partial z}\bigg{(}k_{z}\frac {\partial H}{\partial z}\bigg{)}+Q=0, \tag{2}\]  
\[\text{and}\]  
\[\text{Unsteady flow equation}:\ \frac{\partial}{\partial x}\bigg{(}k_{x}\frac{ \partial H}{\partial x}\bigg{)}+\frac{\partial}{\partial y}\bigg{(}k_{y}\frac {\partial H}{\partial y}\bigg{)}+\frac{\partial}{\partial z}\bigg{(}k_{z}\frac {\partial H}{\partial z}\bigg{)}+Q=m_{w}\gamma_{w}\frac{\partial H}{\partial t}, \tag{3}\]  
where \(H\) is the total water head; \(k_{x}\), \(k_{y}\), and \(k_{z}\) are the permeability coefficients of the \(x\), \(y\), and \(z\) directions, respectively; \(t\) is time; \(Q\) is the source sink term, which is generally a function of position and time, and \(\theta\) is the water content per unit volume.
## 3 Motivation
### Color Correction  
Color cast correction aims to restore the color histogram of sand-dust images to its original distribution. Since the original distribution is unknown, statistical features serve as references for channel compensation and normalization. However, extreme cases may result in residual color casts due to inappropriate reference channels or limited parameter adaptability. In recent advancements in underwater image enhancement, channel compensation-based color correction methods have demonstrated considerable effectiveness [26; 27; 28; 29], prompting our interest in adapting these techniques for sand-dust image restoration. Typically, the color channel with the highest mean value is considered the best-preserved channel and is selected as a reference channel. The mean difference and the reversion of the attenuated channel can serve as adaptive parameters for channel compensation. However, this assumption does not hold for extreme red-dust images, where the blue and red channels suffer severe degradation. In such cases, the pixel values of the blue channel are concentrated around 0, while those of the red channel are concentrated around 255, as shown in Figure 1f. If the lost information is not effectively restored, color bias will remain. Therefore, based on the histogram distribution characteristics of sand-dust images, we propose a new compensation strategy that applies appropriate adjustments to all three RGB channels, effectively recovering the lost color information.  
While channel compensation can restore the information in the color channels, it does not eliminate color shift but instead limits it to a controlled range. We can remove the residual color shift via normalization. However, it is crucial to maintain the consistency of the mean and variance across the color channels during this process; otherwise, color bias may persist. For example, [PERSON]'s normalization method [5], which retains the mean of the green channel, ensures variance consistency through initial color correction but inevitably causes damage during normalization, leading to color artifacts. Drawing inspiration from color transfer techniques [30; 31], we propose an approach where channel compensation first constrains the color shifts to a controllable range, followed by color transfer to eliminate these residual biases.
## 4 Materials and Methods
### MALDI-ToF Mass Spectrometry  
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF-MS) analysis was performed using a Shimadzu Axima Performance MALDI-ToF/ToF spectrometer from Shimadzu Group Company. In order to measure MS and collision-induced dissociation (CID--tandem mass spectrometry) spectra of the peptides, the instrument was operated in the positive reflectron mode. The samples were co-crystallized with an organic matrix (a saturated suspension of \(\alpha\)-CHCA in a solvent mixture 2:1--AcCN:0.1% TFA in water type 1) in a ratio of 1:1 and further prepared with the dry drop method. Consequently, the sample was transferred to a solid surface plate (TO-431R00 Polished Steel). To acquire the MS spectra, the following parameters were used: nitrogen LASER 337.1 nm (LTBMLN100), positive ion mode, low mass gate of 400 Da and mass range of 500 to 1500 Da. For MALDI-ToF spectrum calibration, a standard peptide mixture (BSA, ACTH 18-39 for linear mode, Cytochrome C, ACTH 7-38 for reflectron mode) was used. The final mass spectrum was obtained as a result of 200 profiles (with two shots per profile) per each acquisition.  
The synthesized peptides were also investigated using tandem mass spectrometry through CID of the protonated structure. The mass range was set from 100 to 600 Da, with 20 shots per profile and 40 profiles per spectra and CID with collision energy of 20 keV with the helium mode. The ion gate was set within 5 Da of the observed mass by ToF/ToF experiments, and the pulsed extraction was optimized for each peptide mass. All values obtained following MS/MS analysis were well-correlated with the theoretical data obtained with GPMAW 6.11 software.
## 4 Discussion  
### Simultaneous Operation of Mobile Radio and Transponder  
In flight operations, there was strong interference in the LTE module after the transponder was activated, and as a consequence, the connection to the UTM network was interrupted. A reduction of the transponder transmission power from the standard setting of up to \(200\,\mathrm{W}\) to about \(75\,\mathrm{W}\) as well as a 90 degrees twisted orientation of the LTE and transponder antennas to each other enabled a stable connection to the LTE base station regardless of the operating status of the transponder.  
An investigation revealed that the amplifier in the antenna receive path is saturated due to the high input power of the ADS-B transponder, thus, resulting in connection failures. The LTE modem supports three LTE frequency ranges at band 20 (\(800\,\mathrm{MHz}\)), band 3 (\(1800\,\mathrm{MHz}\)) and band 7 (\(2600\,\mathrm{MHz}\)). The amplifiers in the receive path are thus designed to operate at least from \(700\,\mathrm{MHz}\) to \(2700\,\mathrm{MHz}\). The filtering before the amplifier  
Figure 13: Reported altitude information of uas1 (**green stars** = altitude information based on FMS data, **red stars** = altitude information based on the barometer of MMT, **red dots** = altitude information from a reference transponder located on the ground).  
does not allow sufficient attenuation of the 1090 MHz ADS-B frequency band, which leads to saturation of the amplifier in the receive path of the LTE modem if the ADS-B power is too high.
## 2 Method for Measuring Rail Surface Roughness
### Chord-Based Measurement with a Single Sensor  
As depicted in Figure 2, where \((r_{x},r_{y})\) and \((f_{x},f_{y})\) are coordinate of contacting points between rail surface and rear and front wheel, respectively, \(d\) is the distance from rear wheel (\(r_{x}\)) to the sensor, and \(d^{\prime}\) is the \(x\)-axis projection of \(d\), chord-based measurement assumes that the measurement system with the chord length of \(L\) positioned at a distance of d from the edge drives on a rail of sinusoidal form(\((y(x)=Ysin(kx)=Ysin(2\pi x/\lambda))\)) with an arbitrary wavelength. In this case, the coordinates of the point where the two wheels contact the rail can be expressed as Equations (1) and (2).  
\[r_{y}=y(r_{x})=Ysin(kr_{x}) \tag{1}\]  
\[f_{y}=y(f_{x})=Ysin(kf_{x}) \tag{2}\]  
where \(Y\) is the amplitude of the sinusoidal rail, and \(k\) is \(2\pi/\lambda\). Since the value \(z(x)\) measured by the sensor is the distance from the platform to the rail surface at sensory position \(x\), the vertical rail waveform can be expressed as follows:  
\[\begin{split} z(r_{x}+d^{\prime})&=\overline{PQ}= y((1-\alpha)r_{x}+\alpha f_{x})-\{(1-\alpha)y(f_{x})+\alpha y(f_{x})\}\\ &=Ysin(k(1-\alpha)r_{x}+\alpha kr_{x})-(1-\alpha)Ysin(kr_{x})- \alpha Ysin(kf_{x})\end{split} \tag{3}\]  
where \(\overline{PQ}\) is a line segment between the sensor position on the platform and the rail surface, and \(\alpha=d/L=d^{\prime}/(f_{x}-r_{x})\).  
Therefore, the amplitude \((Z=|z(x)|)\) and phase \((\Phi=phase(z(x)))\) measured by the sensor on the platform can be calculated numerically by using the magnitude and phase of trigonometrical function from Equation (3), while the transfer function between the measured value and the rail surface roughness can be defined as follows [14]:  
\[H(\lambda)=Z(\lambda)/Y(\lambda) \tag{4}\]  
The transfer function of Equation (4) can be used to calculate the amplification ratio of the measured sensor value to the actual value of the rail surface and the change to phase in accordance with sensor position. Measuring rail surface roughness with a sensor may amplify or attenuate the measured value depending on sensor position (\(d\)), rail surface wavelength (\(\lambda\)), and device length (\(L\)). In other words, it is necessary to change sensor position (\(d\)) value for repeat measurements to verify the measured value when using a sensor to measure rail with corrugation of a specific wavelength since it is difficult to predict measurement distortion. However, it is often impossible to adjust sensor position on some measurement platforms, making it difficult to trust measurement accuracy.  
Figure 2: Chord offset measurement model with single sensor (adapted and modified from [14]).
## 2 Materials and Methods  
### Remote Conductivity Control System for Water and Fertilizer Integration Machine  
The remote conductivity control system for a water and fertilizer integration machine is shown in Figure 1. The system consists of a control section and a monitoring section. The control section includes water supply components and fertilization components, equipped with response facilities such as filters, fertilizer storage tanks, pumps, pipelines, drip irrigation belts, and wireless electric control valves. The monitoring section includes remote conductivity sensors, pressure sensors, flow sensors, and others. Data transmission between the control and monitoring sections is realized through the LoRa wireless communication protocol, which provides long-distance, low-power, and stable data exchange capabilities, suitable for agricultural field environments.  
The water and fertilizer integration system consists of multiple coordinated subsystems. The pumping unit uses a hose pump with a starting torque of 230 N-m, a maximum flow rate of 5310 L/h, and a maximum operating pressure of 0.8 MPa. The pump is driven by a frequency converter, enabling variable flow control according to real-time demands.  
The fertilizer supply unit is designed for the proportional mixing and delivery of fertilizers such as urea, monoammonium phosphate, and potassium phosphate. It features a paddle stirring mechanism, driven by a three-phase asynchronous motor with a rated power of 0.75 kW. The stirring rod has a shaft diameter of 1 m and is equipped with two straight blades, ensuring an adequate mixing efficiency. Fertilizer is transported using a single-phase screw self-priming pump rated at 1.8 kW, with a suction range of 10-25 m and a flow rate of 9 m\({}^{3}\)/h. The unit supports a maximum stirring volume of 1.5 m\({}^{3}\).  
For flow measurement, an electromagnetic flow sensor with a measurement accuracy of \(\pm\)0.5% is integrated into the system, ensuring precise fertilizer dosing.  
The power supply unit converts on-site three-phase power into 220 V, 24 V, and 12 V outputs, providing stable power to the control, monitoring, and actuation components throughout the system.  
An implementation diagram of the remote conductivity control system for the water and fertilizer integration machine is shown in Figure 2. The lower computer, based on a programmable logic controller (PLC), is responsible for monitoring sensor signals such as flow rate, pressure, and pipeline EC values, as well as controlling the water pump, fertilizer pump, and solenoid valves. The upper computer, developed using a combination of Python 3.8 and LabVIEW 2020 64-bit, communicates wirelessly with the cloud platform via the LoRa protocol, sends control signals to the lower computer, and uploads sensor data and component status in real time. The communication between the upper and lower computers adopts the Modbus RTU protocol, ensuring reliable and standardized data exchange.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Predictive Control of Fermentation
#### 4.2.2 Dynamic Control Systems  
AI-driven dynamic control systems go beyond static monitoring by actively managing the fermentation process. These systems use predictive models to adjust fermentation parameters continuously, based on real-time data inputs. For example, if the AI predicts an undesirable rise in temperature, it can automatically trigger cooling mechanisms to maintain optimal conditions [87].  
[PERSON] developed intelligent dynamic simulation models for fed-batch fermentation processes, which predict critical variables such as dissolved oxygen concentration and carbon dioxide levels. These models facilitate real-time adjustments to maintain optimal fermentation conditions, enhancing the overall efficiency and consistency of the process. This comprehensive control ensures a smooth and efficient fermentation process, leading to consistent high-quality wine production [87].  
Dynamic control systems can also manage nutrient additions and oxygenation schedules. By continuously analyzing the fermentation kinetics, AI systems can determine the precise timing and quantity of nutrient additions needed to support yeast activity, ensuring a robust fermentation process. This level of control minimizes the risk of stuck fermentations and other common issues [38].  
[PERSON] et al. demonstrated that machine learning algorithms, when integrated into dynamic control systems, could optimize nutrient management by predicting the precise needs of the fermentation process. These systems analyzed real-time data to ensure timely nutrient additions, thus preventing nutrient deficiencies or excesses that could disrupt yeast metabolism. This proactive management of nutrient levels contributes significantly to the efficiency and success of the fermentation process [38].
## 2 Topology Optimization under Single- and Multiple-Loading Conditions
### SIMP Approach  
Originally, TO was first formulated to determine which points in the design space should be filled with material and which points should remain void. In other words, the optimization problem was interpreted using discrete variables, where a variable value of 0 represented a void and a value of 1 represented a solid material. Nevertheless, dealing with a discrete 0-1 problem causes many difficulties, especially for complex problems. It is worth noting that mathematical methods that use information about the derivatives of cost functions cannot be employed for discrete problems. Therefore, other approaches are commonly used to replace the integer discrete variables with continuous variables, including the SIMP approach [27] and the \(\theta\)-type variables approach, where the density is given by a sigmoid function [60].  
The SIMP approach is well-known as a simple and commonly used approach for solving TO problems. In the SIMP approach, it is assumed that the density of each element in the design space can have a value between 0 and 1, turning the original problem into a continuous one. However, only using continuous variables between 0 and 1, instead of the discrete integer 0 or 1 often, results in unreasonable structures containing many \"gray\" elements with intermediate densities. Consequently, there is a need to use a so-called power-law interpolation to interpret material properties. This power-law interpolation acts as a penalty, where elements with intermediate densities exhibit poor performance, i.e., lower stiffness. Thus, the algorithm is forced to yield a nearly black-and-white solution. Following this interpolation law, the stiffness tensor of a given isotropic material is expressed as follows:  
\[E(x)=x^{p}E_{0},\,p>1, \tag{1}\]  
where \(x\) is the density of the considered element; \(E_{0}\) is the stiffness tensor of the element fulfilled with a solid material; and \(p\) is the penalty power in the SIMP approach. Usually, when using Equation (1) in the problem formulation, a lower bound on all design variables (densities), which is a sufficiently small positive value, is considered to prevent the singularity issue.  
There is also another way to avoid singularity, that is, assuming  
\[E(x)=E_{min}+x^{p}(E_{0}-E_{min}),\,\,p>1, \tag{2}\]  
where \(E_{min}\) is a small positive value, which serves as the lower bound of material stiffness tensor \(E(x)\). For simplicity, Equation (1) is used for the examples in this study.