esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Core Insights and Critical Reflections
### Challenges Facing UAV-RS  
In terms of technology, UAV-RS still faces a series of challenges, which limit its performance in some specific application scenarios and wider promotion. The main technical bottlenecks include the real-time and accurate processing of data, the balance between sensor performance and cost, the endurance and stability of the flight platform, and the ability to operate in environments complex (Table 10).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technological Bottlenecks** & **Specific Manifestations** & **Impact and Challenges** \\ \hline \multirow{4}{*}{Real-time and accurate data processing} & Massive data volumes lead to time-consuming processing; complex algorithms (e.g., SfM, deep learning) involve high computational costs; edge computing capabilities are limited; and factors such as sensor calibration, illumination conditions, and algorithm robustness affect accuracy. & Meeting the requirements of emergency response and real-time monitoring remains challenging; data quality significantly impacts decision-making reliability; and problems such as variations in image orientation, high overlap ratios, and variable scales further complicate processing. \\ \hline \multirow{4}{*}{Sensor performance and cost} & High-performance sensors (hyperspectral, LiDAR) are expensive; miniaturization, lightweight, and low power consumption still have room for improvement; calibration and maintenance require high professionalism; consumer-grade sensors have limited performance. & Limits its popularity among low-cost platforms and small- and medium-sized users; affects data quality and application effects; some sensors are difficult to meet specific application requirements (such as detection inside the canopy). \\ \hline \multirow{2}{*}{Endurance and stability of flight platforms} & Battery technology bottlenecks lead to short flight time; poor flight stability under complex weather conditions; limited payload capacity; and impacted by electromagnetic interference. & Limit the scope and efficiency of operations; affect data acquisition quality and flight safety; and make it difficult to undertake large-area, long-term monitoring tasks. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Core bottlenecks of remote UAV sensing technology.
# Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
## 5 Use Case: Shiptec
### Real-World Counterpart  
The DT analyzed, in this case, represents a system with multiple hierarchy levels. A fleet of ships operated by a navigation company such as SGV represents the highest hierarchy level of the ecosystem in this case. Ships are often operated together, and therefore, Shiptec provides a dashboard to monitor them at the fleet level. However, for this case study, we focused on the value created by the DT on the next lower level, an instance of a single ship. Therefore, the counterpart seen in Figure 12 does not include higher ecosystem levels such as a fleet.  
The ship MS Diamant is regarded as a system of systems consisting of different subsystems. These are the propulsion system, the heating, ventilation, and air conditioning system (HVAC), the command bridge, and the kitchen. These systems interact in different ways, with the central subject being power distribution and consumption on the ship, since the ship generates its power completely self-sufficiently. The propulsion system plays a central role in this task. It can be seen as a small power station that supplies the other systems with power in addition to its primary task of moving the ship forward. Therefore, the remainder of the paper focuses on the propulsion system to outline the structure of the framework. Various connected products from different original equipment manufacturers (OEM) are installed within the propulsion system. In the case of MS Diamant, this includes two ship engines that drive the ship's propellers via shafts. One shaft-driven electomotor is installed on each of the two shafts, which can also serve as a generator for charging the ship's batteries. The batteries then provide power for the other systems on the ship, such as the kitchen. All these connected products are equipped with sensors that generate data flowing into the DT of the ship.
# Crop Land Change Detection with MC&N-PSPNet
## 2 Materials and Methods
### Data  
To validate the effectiveness of the proposed method, this paper selected the High-Resolution Semantic Change Detection (HRSCD) dataset captured by the French National Institute of Geographic and Forest Information (IGN). The HRSCD dataset, sourced from an open access publication on semantic change detection, is composed of aerial images stitched together. It is suitable for multi-label tasks such as semantic segmentation. The dataset comprises 291 pairs of RGB images stored in TIFF format, each with dimensions of 10,000 \(\times\) 10,000 pixels. Each image pair consists of an early image captured in either 2005 or 2006 and a second image captured in 2012. The label data is stored in a single-channel TIFF format with a resolution of 0.5 m per pixel. The imaging bands include the R, G, and B bands, covering six land cover classes: bare land, urban areas, agricultural areas, forests, wetlands, and water bodies.  
To facilitate targeted detection of changes in agricultural zones, a binary classification was used in this study. Therefore, manual visual interpretation was performed using the ArcGIS 10.8 platform to categorize the images into agricultural and non-agricultural zones. Subsequently, the change detection labels were redrawn using semantic segmentation tags. Due to the large image data, the original images and labels were resized to 256 \(\times\) 256 image segments before model training. In total, 3,042 image segments were obtained. A subset of the dataset is shown in Figure 5.  
Figure 4: NAM structure: (**a**) Channel Attention Module, (**b**) Spatial Attention Module.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 6 Effect on Food Quality Characteristics
### Effects on Post-Harvest Physiology of Fresh Produces  
PL has been also shown to have impact on the physiological processes in the fresh produces. The results in this regard are varying with treated products, but overall show that there seems to be an increase in plant respiration rate and increase in CO\({}_{2}\) with increase in O\({}_{2}\) as a response to PL application. An increase of 80% in the respiration rate was noticed in shredded Iceberg lettuce [79]. There was an increase in CO\({}_{2}\) and ethanol due to respiration, but a reduction in O\({}_{2}\) and ethylene production in the fresh-cut avocado after PL application [43]. There was increase in respiration rate in PL treated watermelon [94] and mushrooms [54]. There was also increase in ethylene production and ethanol in the watermelons [94]. [PERSON] [108] observed slight increase in respiration rate in entive salad. [PERSON] et al. [109] demonstrated little increase in respiration in spinach. [PERSON] et al. [114] demonstrated increase in CO\({}_{2}\) and decrease in O\({}_{2}\) in PL treated tomatoes.  
Overall, the majority of these investigations tend to show that PL treatment has significant undesirable effects on the color and sensory attributes, especially in fresh produces like lettuce and mushrooms, meat products and fishes. which might be due to severe treatment conditions. The effects of PL on physico-chemical properties seem to vary product wise. Nevertheless, with respect to nutritional quality of foods, PL maintains the level of phytochemicals at over the period of post-harvest storage with demonstrated enhancement of phenolic compounds, carotenoids and antioxidant activity. Adding to the applications, PL has been shown to effectively mitigate the allergenicity and toxicity of several food products and inactivates select food quality degrading enzymes. The deleterious effect on sensory quality of food can be minimized by proper optimization of processing parameters of PL treatment like sample thickness, sample distance from lamp, fluence level, exposure time etc. The researchers [54; 80] have also suggested the use of chemicals like anti-browning agents or textural stabilizers for countering the observed decrease in firmness and browning of the food products, thereby enhancing their shelf life and maintaining the quality.
## 2 Materials and Methods
### Small-Signal Extraction Procedure Partially Assisted by EM Simulation  
In order to extract the model parameters of the modified small-signal equivalent circuit proposed in the previous section, the conventional extraction method is used along with the EM-based extraction method. The first step is to extract the model parameters of the conventional small-signal equivalent circuit [28,29]. First, pad model parameters can be extracted through measuring the pad test pattern. After de-embedding the pad model, extrinsic model parameters can be extracted through S-parameters measured in the cold bias region where the drain-source voltage (V\({}_{\text{ds}}\)) is 0 V. Figure 6 shows the small-signal equivalent circuit in the cold bias region. From the equivalent circuit of Figure 6, the Z parameters can be expressed as follows:  
\[Z_{11}=\text{R}_{\text{s}}+\text{R}_{\text{g}}+\frac{\text{R}_{\text{ch}}}{3}+ \text{R}_{\text{sdj}}/\frac{1}{\text{j}\omega\text{C}_{\text{sdj}}}+\text{j} \omega\left(\text{L}_{\text{s}}+\text{L}_{\text{g}}\right) \tag{1}\]  
Figure 5: (**a**) Conventional and (**b**) proposed small-signal equivalent circuit of a GaN HEMT. Figure 6: Schematic of GaN HEMT small-signal equivalent circuit in the cold bias region. \[Z_{12}=Z_{21}=R_{\rm s}+\frac{R_{\rm ch}}{2}+j\omega L_{\rm s} \tag{2}\]  
\[Z_{22}=R_{\rm s}+R_{\rm d}+R_{\rm ch}+j\omega(L_{\rm s}+L_{\rm d}) \tag{3}\]  
where \(R_{\rm s}\), \(R_{\rm g}\), \(R_{\rm ch}\), and \(R_{\rm d}\) are the source, gate, channel, and drain resistance, respectively, \(L_{\rm s}\), \(L_{\rm g}\), and \(L_{\rm d}\) are the extrinsic source, gate, and drain inductance, respectively. \(R_{\rm sdj}\) and \(C_{\rm sdj}\) are the Schottky diode resistance and capacitance, respectively, and \(\omega\) is the angular frequency. Charges in the depletion region vary according to the gate bias, and as the charge increases, the channel resistance decreases. Using this relation, \(R_{\rm s}+R_{\rm d}\) can be extracted through the equation for the amount of charge and the real part of \(Z_{22}\) according to the gate biases. Then, using the \(R_{\rm s}+R_{\rm d}\) value obtained earlier, \(R_{\rm ch}\) at a specific bias can be found from the real part of \(Z_{22}\) of Equation (3), and \(R_{\rm s}\) can be extracted from the real part of \(Z_{12}\) of Equation (2). When \(R_{\rm s}\) is found, \(R_{\rm d}\) can also be extracted from the \(R_{\rm s}+R_{\rm d}\) value. \(L_{\rm s}\) and \(L_{\rm d}\) can be extracted using the imaginary parts of \(Z_{12}\) and \(Z_{22}\) in a similar manner. However, \(R_{\rm g}\) and \(L_{\rm g}\) cannot be extracted as simply as above due to \(R_{\rm sdj}\) and \(C_{\rm sdj}\). By applying the method used in [29] as it is, \(L_{\rm g}\) and \(R_{\rm g}\) can be extracted by approximating \(Z_{11}\) using the fact that \(\frac{1}{|\omega C_{\rm sdj}|}\) becomes very small compared to \(R_{\rm sdj}\) at high frequencies above 35 GHz. In Figure 5a, the Y-parameters of intrinsic parts that change according to the bias can be expressed by the following Equations (4)-(7).
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 3 Results
### NFC Potentiostat Performance  
#### 3.2.1 Cyclic Voltammetry  
NFC potentiostat performance was compared to a commercial potentiostat (CHI 611E, Texas Instruments Inc., Austin, TX, USA), which is a benchtop potentiostat designed to enable various electrochemical techniques to be performed in the laboratory. The cyclic voltammograms of 0.1, 0.5 and 1 mM potassium ferricyanide in 0.1 M KCl at a scan rate of 100 mV/s performed on the NFC potentiostat and the commercial potentiostat are shown in Figure 3. The cyclic voltammograms showed a characteristic duck shape for a fully reversible one-electron reaction of ferricyanide. The results showed good agreement of cyclic  
Figure 2: (**a**) Hardware components consisting of a planar antenna, near-field communication (NFC) microchip (SIC4341) and connector for electrode interface. (**b**) The integrated circuit of the NFC potentiostat, which includes (1) an analog part, (2) digital controller and memory unit and (3) the chemical sensor processing. voltammograms between the two devices. The higher the concentration of ferricamide, the greater the current signals observed. The average and relative standard deviation (RSD) values of cathodic peak current and anodic peak current obtained from both potentiostats are listed in Table 1. As shown in the table, the relative standard deviation was less than \(\pm\)5% (\(n=3\)), indicating that both potentiostats had a good reproducibility for conducting the experiments. To evaluate the performance of the potentiostats in cyclic voltammetry at various scan rates, cyclic voltammograms were recorded for 1 mM potassium ferricamide containing 0.1 M KCl by sweeping the potential from \(-\)300 to 600 mV at various scan rates of 5-200 mV/s. Cyclic voltammograms produced on the NFC potentiostat are shown in Figure 4a. When the potential is applied at higher scan rates, higher peak currents are observed because of the decrease in the diffusion layer [47]. Figure 4b shows the theoretical linear relationship between the redox peak current of ferricamide and the square root of the scan rate according to the Randles\(-\)Sevcik equation (Equation (1)), indicating that the reaction was a diffusion-controlled process [47; 48]. Furthermore, the calibration plots obtained from the NFC potentiostat and the commercial potentiostat showed parallel results. A plot from the NFC potentiostat provided a linear slope for cathodic peak current of 1.242 A/(V/s)\({}^{1/2}\) (R\({}^{2}\) = 0.995) and an anodic peak current of \(-\)1.222 A/(V/s)\({}^{1/2}\) (R\({}^{2}\) = 0.996). In comparison, the linear slopes for cathodic and anodic peak currents recorded from the commercial potentiostat were 1.237 A/(V/s)\({}^{1/2}\) (R\({}^{2}\) = 0.999) and \(-\)1.242 A/(V/s)\({}^{1/2}\) (R\({}^{2}\) = 0.999), respectively. \[ip=\left[0.4463 nFAC^{0}\left(\frac{nFD}{RT}\right)^{1/2}\right]v^{1/2} \tag{1}\]
## 2 Dispersive Fourier Transformation Technique
### Implementation of DFT
#### 2.2.3 Operational Wavelength Bands of DFT  
Most of DFT-based systems are designed to operate in the 1550 nm band, to take advantage of available high-quality dispersive devices, e.g., DCFs, and the low-cost photonic devices developed for fiber optic communications in this wavelength band. However, 1550 nm band has limited utility in biomedical and imaging applications due to its strong water absorption and poor spatial resolution. While shorter wavelengths are desirable for those applications, the main challenge is the lack of efficient dispersive device outside the 1550 nm band. Recent study has shown that DFT can be implemented at shorter wavelengths for biomedical applications [75; 76].  
Particularly, the 800 nm wavelength band is useful for a broad range of industrial and biomedical applications, thanks to its large penetration depths in tissues, better spatial resolution, and the  
Figure 2: Group delay response of the linearly chirped fiber Bragg grating (LCFBG) operating at 800 nm. One-to-one wavelength-to-time mapping verifies the Dispersive Fourier transformation (DFT) at 800 nm band [77].  
availability of powerful Ti:Sapphire lasers in this spectral band. While a small-core SMF provides sufficient GVD for DFT at 800 nm [51], the propagation loss is large and the amount of dispersion is limited. Moreover, a smaller fiber core easily causes unwanted nonlinear signal distortion. As a promising solution, an LCFBG operating at 800 nm band has been successfully employed for DFT in this application-rich spectral band [77]. The use of LCFBG features large GVD, low-loss, low cost, compact footprint and significantly reduced nonlinear optical effects. Figure 2 shows the group delay response of the LCFBG, which is evaluated by comparing the shaped pulse spectrum and the stretched temporal waveform. The GVD of the 30 cm-long LCFBG is as large as 840 ps/nm.
## 3 Results and Discussion
### Experimental Results of Thermo-Physical Properties
#### 3.4.4 Thermal Diffusivity and Volumetric Thermal Capacity  
The calculated thermal diffusivity and volumetric thermal capacity are depicted in Table 6. Concerning the thermal diffusivity obtained from Equation (6), it decreased with addition of RWG by around 51% for both dry and saturated states as it dropped respectively from \(5.17\times 10^{-7}\) m\({}^{2}\).s\({}^{-1}\) to \(1.13\times 10^{-6}\) m\({}^{2}\).s\({}^{-1}\) and from \(1.23\times 10^{-6}\) m\({}^{2}\).s\({}^{-1}\) to \(6.04\times 10^{-7}\) m\({}^{2}\).s\({}^{-1}\). This decrease is due to the combined effects of thermal conductivity and thermal effusivity as the reduction in thermal conductivity of 36% exceeds the reduction in thermal effusivity of 8% for the highest RWG volume content.  
The volumetric thermal capacity indicates the amount of energy required to change the temperature of a given material's volume, and it expresses the amount of heat stored. The results of this parameter calculated from Equation (7) are presented in Table 6; it increased respectively by about 25% and 11% at dry and saturated states. This increase may be due to the increase in volume content of RWG in concrete with a volumetric thermal capacity value of \(\rho\)C\({}_{\rm P}\) = 2270 kJ.m\({}^{-3}\). K\({}^{-1}\) that is superior to the one of natural sand \(\rho\)C\({}_{\rm P}\) = 1600 kJ.m\({}^{-3}\). K\({}^{-1}\)[61].  
Figure 16: Thermal effusivity variation of modified concrete as function of RWG volume rate at dry and saturated states.
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 5 Conclusions  
Owing to the multi-tooth contact that occurs in an RV reducer, the results calculated using a traditional contact model with a single contact stiffness will be inaccurate. Therefore, a new multi-tooth contact model and a transmission error model of an RV reducer were proposed to analyze the influence of the tooth profile modification and load on the transmission accuracy, mechanical properties, and transmission efficiency. The following conclusions were drawn:  
Figure 14: Effect of case 3 modification method on the transmission performance of RV reducer: (**a**) contact ratio, (**b**) transmission efficiency, (**c**) max contact force, (**d**) max friction, (**e**) mean TE value, and (**f**) TE amplitude.  
1. The accuracies of the contact force model and the transmission error model were validated by comparing the calculation results of the FEM and the test results of the RV reducer prototype, respectively.
2. In general, a tooth profile modification and load have a comprehensive effect on an RV reducer. Both the modification and load increase the mean value of the transmission error, although the appropriate modification amount can be selected to minimize the amplitude of the transmission error under different loads. With modification method 2, the contact force and friction can be minimized under the optimal modification amount, which increases linearly with the load. When the load is large, increasing the modification amount will increase the contact ratio for modification methods 2 and 3.
3. Modification method 1 will significantly reduce the contact ratio and increase the contact force, although the friction torque will be reduced, thus improving the transmission efficiency. In general, modification method 2 achieves a good performance and is suitable for common modification occasions. The transmission efficiency and contact ratio of modification method 3 are almost unaffected by the modification, and thus the method is suitable when a large clearance and load are needed.  
The multi-tooth contact model presented here can also be applied to various profile modification methods. Moreover, this research can serve as a fundamental for both the analyses of the transmission characteristics of the RV reducers, and further investigations of the rigidity and strength of the RV reducers.  
Conceptualization, Z.-Y.S. and H.W.; methodology, Z.-Y.S. and H.W.; validation, B.Y. and H.W.; formal analysis, H.W.; data curation, H.X.; writing--original draft preparation, H.W.; writing--review and editing, H.W. and B.Y.; funding acquisition, Z.-Y.S. and B.Y.  
This research was supported by the National Natural Science Foundation of China (No. 51805011, 51635001), and the National High Technology Research and Development Program 863 of China (No. 2015 AA043002).  
The authors declare no conflict of interest.
## 1 Introduction
In this sense, and based on the papers presented, some aspects can be highlighted: (1) To the best knowledge of the authors' knowledge, it is necessary to propose other methodologies that can analyze multiple failures in the electromechanical system, mainly in the IM and GB with IRT; (2) when deep learning approaches are used, the increase in data is not considered, nor is the optimization in the design of the algorithms; in addition to this, (3) the presence of noise or other factors that can undergo thermal imaging needs to be considered. Therefore, the contribution of this paper lies in its proposal of a non-invasive automatic methodology based on the convolutional neural network (CNN) and IRT for the detection and classification of different failures in electromechanical systems; certainly, this study is focused on the identification of faults in the IM and GB. This article presents an experimental study of multi-faults in the electromechanical system from thermal images obtained under different scenarios. On the one hand, several conditions are analyzed in the IM, such as healthy (HLT) IM, the outer bearing (OB), a broken bar (BB), and misalignment (MAL). The GB is also studied through healthy GB and wear in the gears by 50% and 75%. Finally, a combination of failures between the IM and GB is made with the proposed conditions to conduct a study of multi-faults in the electromechanical system. In addition, the increase in data from the presence of noise in the thermographic images is proposed, modifying pixel intensity levels to increase the number of thermal images, as well as the addition of different levels of salt and pepper noise to the original images. It should be noted that the design of specific-purpose CNN architecture, which can treat images corrupted by noise, is presented, obtaining very acceptable results with an accuracy of 98.5% for the classification of multi-faults in electromechanical systems. In this way, this proposal will contribute to the non-invasive and non-contact monitoring and timely diagnosis of electromechanical systems in harsh environments. This will have a direct or indirect positive impact on the problems associated with the operation of a failed electromechanical system, such as reduced operational efficiency, the risk of progressive damage to other parts of the system, increased safety risk, reduced process quality and the increased risk of downtime for the electromechanical system, which will undoubtedly affect the processes associated with the system. On the other hand, it will allow the identification of internal faults that may be difficult to detect using traditional methods.