esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainable Energy Solutions: Utilising UGS for Hydrogen Production by Electrolysis
## 2 Methodology and Concept Description
### Exergy Process Description in Pressure Reduction Station at UGS Facility
The temperature of the gas at the outlet of the TE must be kept above the hydrocarbon and water dew point to avoid condensation and hydrate formation; therefore, a preheater was required to maintain the required outlet temperature of the gas. In the withdrawal cycle, the high pressure (19.6 MPa) of the gas produced was reduced via regulating valves before it entered the low-pressure gas transportation network. Every year, approximately 450 \(\times\) 10\({}^{6}\) Sm\({}^{3}\) of high-pressure gas passed through three reduction valves, and each reduction valve was located on a separate and parallel gas reduction line. Figure 2: Theoretical thermodynamic path through an expander.
## 2 Various Strategies for Enhancing the Production of Biofuel
### Engineering the Plant Biomass for Biofuel Production  
As discussed in the above Section 2.1, plant biomass is chiefly composed of lignin, hemicellulose, and cellulose. Cellulose fibrils are generally held together by lignin, thereby providing mechanical strength to the plant. The presence of lignin restricts the availability of cellulose for enzymatic hydrolysis. Hence, [PERSON] et al. engineered a plant _Pinus radiata_ to produce less lignin by downregulating the expression of 4-coumarate-CoA ligase gene. This gene is important for synthesizing lignin precursors [50]. Similar work was also reported by [PERSON] et al., where p-coumaroyl-CoA 3\({}^{\prime}\)-hydroxyase enzyme was downregulated in a hybrid plant obtained by crossing _Populus grandidentata_ with _Populus alba_ with the help of the RNA interference (RNAi) approach. This downregulation resulted in a 60% drop in the lignin content of the plant [51].
## 3 Proposed Non-Linear Current Control Scheme of Grid Connected Inverter with LCL Filter
### Output Current Control
#### 3.1.2 Proposed Reactive Current Control  
The method for control of \(i_{gq}\) is similar to that of \(i_{gd}\), with a difference that the process begins with Equation of \(d\epsilon_{6}/dt\) in (20) and (25). For the sake of brevity, the details of the controller design process are not elaborated here. By performing a similar procedure for \(i_{gq}\), the final input control variable is as given by (53).  
\[v_{Iq}=L_{1}n_{2}-L_{2}\omega I_{Id}+v_{cq} \tag{53}\]  
where,  
\[n_{2}=\beta_{1}e_{1}+\beta_{2}e_{2}+\beta_{3}e_{3}+\beta_{4}e_{4}+\beta_{5}e_ {5}+\beta_{6}e_{6} \tag{54}\]  
in which, the coefficients are as given in the following equations of (55)-(60).  
\[\beta_{1}=-2\;w \tag{55}\]  
\[\beta_{2}=-m_{1}-m_{2}-m_{3} \tag{56}\]  
\[\beta_{3}=-2c_{1}m_{1}w-2c_{1}m_{2}w-2c_{1}m_{3}w \tag{57}\]  
\[\beta_{4}=-2c_{1}+\frac{1}{L_{2}}+3c_{1}\;w^{2}-c_{1}m_{1}m_{2}-c_{1}m_{1}m_{3 }-c_{1}m_{2}m_{3} \tag{58}\]  
\[\beta_{5}=3\;w+L_{2}c_{1}\;w^{3}-2L_{2}c_{1}w-L_{2}c_{1}m_{1}m_{2}w-L_{2}c_{1} m_{1}m_{3}w-L_{2}c_{1}m_{2}m_{3}w \tag{59}\]  
\[\beta_{6}=m_{1}+m_{2}+m_{3}-L_{2}c_{1}m_{1}-L_{2}c_{1}m_{3}+L_{2}c_{1}m_{1}w^ {2}+L_{2}c_{1}m_{2}w^{2}+L_{2}c_{1}m_{3}w^{2}-L_{2}c_{1}m_{1}m_{2}m_{3} \tag{60}\]  
Using the control input of \(v_{Iq}\) in (53) leads to the derivative of the new defined positive definite function of (61)  
\[V_{\text{L2}}=\frac{1}{2}\epsilon_{6}^{2}+\frac{1}{2}Y_{1}^{2}+\frac{1}{2}Y_{2 }^{2} \tag{61}\]As follows,  
\[\frac{dV_{12}}{dt}=-m_{1}e_{6}^{2}-m_{2}\gamma_{1}^{2}-m_{3}Y_{2}^{2} \tag{62}\]  
where,  
\[Y_{1}=\omega e_{5}+\frac{e_{4}}{L_{2}}+m_{1}e_{6} \tag{63}\]  
\[Y_{2}=-e_{6}-m_{2}Y_{1}-(-\omega^{2}e_{6}+\omega\frac{e_{3}}{L_{2}}+\frac{ \left(\omega e_{3}+\frac{e_{2}-e_{6}}{C_{f}}\right)}{L_{2}}+m_{1}\omega e_{5}+ m_{1}\frac{e_{4}}{L_{2}}) \tag{64}\]  
Based on the Lyapunov theorem and considering (61) and (62), all variables of \(e_{6}\), \(Y_{1}\), and \(Y_{2}\) move exponentially to zero. Deriving \(e_{6}\) to zero exponentially means the independent tracking of the reference value of \(i_{gq}^{ref}\) from other variables, especially without any interaction with the active current dynamics. Also, the resonance mode is dampened properly.  
Moreover, the poles of the control system, related to reactive current \(\left(i_{gq}\right)\) control channel, are equal to \(m_{1}\), \(m_{2}\), and \(m_{3}\) which are the control parameters in (54) and (55). The poles are pure real with zero imaginary part which means complete damping of the resonance mode.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 3 Chlorophyll Fluorescence Detection near the Ground
### Passive Chlorophyll Fluorescence Measurement
#### 3.2.3 Radiative Transfer Model Inversion  
In addition, [PERSON] et al. proposed a novel approach to explore the information of SIF and vegetation biochemical and biophysical parameters from the canopy-level high-resolution apparent reflectance data using the numerical inversion of the SCOPE model [88]. In the SCOPE model, the radiative transfer modules are used to simulate the reflectance and fluorescence [89]. Based on the radiative transfer modules of the SCOPE model, the apparent reflectance can be expressed as follows [88]:  
\[\rho^{\text{-RTM}}=\frac{\frac{r_{\text{so}}(\pi l_{\text{-sun}}^{\text{mass}} )+r_{\text{d}}(\pi l_{\text{-sky}}^{\text{mass}})}{\pi}+F_{out}^{RTM}}{L_{sun}^ {\text{meas}}+L_{sky}^{\text{mass}}} \tag{7}\]  
in which, the first term of numerator is the modeled reflected radiance, the denominator is the incoming radiance. In fact, \(L_{sun}^{\text{mass}}\) and \(L_{sky}^{\text{mass}}\) are obtained by MODerate resolution atmospheric TRANsmission (MODTRAN). The cost function \(f\) is defined, and the least squares algorithm is used to obtain the full spectrum of canopy SIF [88].  
\[\begin{array}{l}f=\text{ER1}^{T}\text{ER1}+\omega\ast\text{ER2}^{T}\text{ER2} \\ \text{ER1}=\left\{\rho^{\text{*,RTM}}(\lambda)-\rho^{\text{*,meas}}(\lambda), \quad\lambda\in\lambda_{\text{{\em abs}}}\\ \left((\rho^{\text{*,RTM}}_{0}(\lambda)-\rho^{\text{*,RTM}}_{\text{BL}}( \lambda)\,)-(\rho^{\text{*,meas}}(\lambda)-\rho^{\text{*,meas}}_{\text{BL}}( \lambda)\,),\quad\lambda\in\lambda_{\text{abs}}\\ \text{ER2}=\frac{p-p_{0}}{\alpha_{p0}}\end{array}\right. \tag{8}\]  
In Equation (8), the first term shows the two residuals, one is between the model reflectance and the measured apparent reflectance of the absorption bands, and the other one is residual of the height of the spikes due to the filling of the SIF between the modeled data and the measured data. The second term shows the priory knowledge and expected deviation. This method uses the radiative transfer modules of SCOPE to model the apparent reflectance, and then computes the residual between the modeled data and measured data to obtain the full spectrum of SIF. [PERSON] et al. also used radiative transfer modeling to retrieve the SIF and other biophysical parameters [90]. Parameters used in Equations (7) and (8) are explained in Table 2.
# Mobile Robots: Trajectory Analysis, Positioning and Control
## 2 The Present Issue
The authors conducted experiments to analyze the effects of network delays on collaborative operations, finding that the average and maximum force applied to objects increases as delays grow. However, they also observed that when the total network delay between the two users remains constant, the applied force remains nearly identical, regardless of individual delay distributions. This highlights the supportive role of the user with the lower delay in mitigating the challenges faced by the user with the higher delay. To address these issues, they proposed an enhanced robot position control method that adjusts the contribution ratio of each user's input based on their respective network delays. The experimental results demonstrated that this approach effectively reduced the applied force compared to conventional methods, particularly in scenarios with high network delay disparities. The findings emphasize the importance of incorporating adaptive delay-based control strategies to improve operability and accuracy in dual-user remote robot systems, making the proposed method applicable in scenarios such as remote medical surgery and deep-sea exploration. The authors of Contribution 11 provide a comprehensive review of foot-terrain interaction mechanics for heavy-duty legged robots, which are critical for navigating and performing tasks in complex and harsh environments. The authors focus on analyzing the mechanical behavior of foot-terrain interactions to address challenges such as the instability caused by dynamic interactions with uneven terrains. They examine various foot-supporting structures, including cylindrical, semi-cylindrical, and spherical configurations, and evaluate their impact on the robots' mobility and stability. The review also discusses the development of mechanical models for foot-terrain interactions, including pressure-sinkage and tangential force models, which are essential for predicting and optimizing robot performance on soft or slippery surfaces. The authors highlight key technologies, such as biomimetic foot designs inspired by natural systems, and the integration of multimodal information fusion to enhance terrain recognition and adaptability. The study identifies unresolved issues, such as improving foot design for diverse terrains and refining control strategies to reduce sinkage and slipping. The authors conclude by emphasizing the importance of advancing foot-terrain interaction research to enhance the mobility and functionality of heavy-duty legged robots in applications ranging from planetary exploration to industrial operations.
## 4 Data Storage and Processing: Automated Data Processing and Storage
### Irrigation Decision Making with Machine Learning Models
#### 4.3.2 Automated Application of Irrigation Insights Using Irrigation Systems
Furrow and flood irrigation, while simple and low-cost, have traditionally had fewer implementations of automation and precision management technologies, which can affect water use efficiency and potential environmental impacts. However, the recent research has demonstrated progress in integrating IoT and ML technologies to enhance automation in various irrigation systems. For instance, [PERSON] et al. [123] developed a fog-based intelligent irrigation system for furrow irrigation that utilizes LoRaWAN for data transmission and machine learning for rainfall prediction, demonstrating potential water savings of approximately 60%. The environmental impacts of flood irrigation pose significant challenges globally, driving regulatory responses and affecting technology adoption patterns. Studies have shown that flood irrigation can lead to soil salinization, reduced water infiltration, and degradation of groundwater quality through salt loading and nutrient leaching [124]. These environmental concerns have prompted increasing regulatory restrictions on flood irrigation in many regions. For example, in Brazil, environmental agencies are implementing strict controls on flood irrigation due to its low efficiency and excessive water consumption [125]. Despite these challenges and the resulting regulatory pressures, research on the application of both IoT and ML technologies specifically to flood irrigation systems appears to be limited in the current literature. The relative difficulty of deploying fully automated control still varies across irrigation technologies, with sprinkler and drip systems generally being more amenable to automation than surface irrigation methods like furrow and flood irrigation. Key factors influencing the feasibility of automation include the complexity of the system design, the availability of compatible sensors and actuators, the variability in water distribution, and the potential for water and energy savings. Studies have shown that improved management practices and automation can significantly increase flood irrigation efficiency, with some systems achieving efficiency rates of up to 80% [126], suggesting potential for technological solutions to address both environmental and efficiency concerns. Further research is needed to explore the full potential of IoT and ML applications in automating both furrow and flood irrigation systems.
## 3 Results and Discussion
### Antioxidant/Antimedical Activity
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Extraction Techniques**} & \multirow{2}{*}{**Ratio (g/mL)**} & **Extraction Yield (\%)** & **TPC (mg GAE/g DW)** & **FRAP (\(\mu\)mol FSE/g DW)** & **ABTS (mg AAE/g DW)** & **DPPH (mg TE/g DW)** \\ \hline \multirow{2}{*}{MAE} & 1/20 (condition 1) & 23.62 \(\pm\) 0.02 & 12.63 \(\pm\) 1.01 b  & 71.09 \(\pm\) 1.07 a  & 13.29 \(\pm\) 0.69 & 4.93 \(\pm\) 0.93 \\  & 2.5/20 (condition 2) & 16.30 \(\pm\) 0.07 & 16.89 \(\pm\) 1.06 a  & 59.75 \(\pm\) 1.09 b  & 12.42 \(\pm\) 0.85 & 5.08 \(\pm\) 0.84 \\ \hline \multirow{2}{*}{UAE} & 1/20 (condition 1) & 25.19 \(\pm\) 0.04 & 12.17 \(\pm\) 0.72 b  & 52.32 \(\pm\) 1.95 c  & 12.87 \(\pm\) 1.76 & 4.35 \(\pm\) 0.73 \\  & 2.5/20 (condition 2) & 28.41 \(\pm\) 0.10 & 12.40 \(\pm\) 0.91 b  & 45.80 \(\pm\) 1.37 d  & 11.38 \(\pm\) 3.54 & 4.54 \(\pm\) 1.40 \\ \hline \end{tabular} Different letters (a, b, c, d) in the same column indicate significant differences between extracts (\(p<0.05\)). TPC: total phenolic content; FRAP: ferric reducing antioxidant power; FSE: ferrous sulphate equivalents; AAE: ascorbic acid equivalents; TE: trolox equivalents; DW: dry weight.  
\end{table} 
Table 1: Extraction yield, total phenolic content (TPC), in vitro antioxidant/antiradical activities (evaluated by ABTS, FRAP and DPPH assays) of _C. pepo_ extracts prepared by MAE and UAE. Values are expressed as mean \(\pm\) standard deviation (\(n=3\)). [PERSON] et al. obtained an ABTS value of 79.30 mg Trolox/100 g DW for _C. maxima_ seed extracts using an ultrasonic water bath for 1h at 30 \({}^{\circ}\)C, employing water as an extraction solvent [80]. Using ethanol and methanol as an extraction solvent, [PERSON] et al. obtained ABTS values of 7.92 and 1.95 uM Trolox/g fresh weight (FW), respectively, using _C. pepo_ (Miranda cultivar) seed extracts and the UAE technique [81]. Nevertheless, the results obtained in the present study could not be compared with these ones since the units are different.
# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 1 Introduction
### Application of Neural Network in Geological Feature Identification  
Subsurface modeling and seismic interpretation technology have rapidly evolved over the past twenty years in order to obtain the most accurate extraction of such features as unconformities, channels, faults, carbonate (specifically karst-related), and even to delineate  
Figure 2: Schematic map of main reservoirs in A Field.  
salt bodies. The applicability of approximation theorem to mimic a non-linear operator for a deep neural network-based platform was introduced by [17]. Conventional Machine Learning (ML) method to build multi-layer perceptron (MLP) and K-means clustering to aid seismic feature classification [18]. A combination of extensive image processing and ML methods by emphasizing a structural tracking algorithm was introduced by [19] to enhance salt domes and faults, with detailed reservoir characterization. Reassuring approach inferring 3D labeled synthetics for channelized body prediction in 3D field datasets utilizing minimum training data being fed into the neural network was demonstrated by [20]. Salt boundary estimation for velocity model building and detailed understanding on salt tectonics has been successful in an experiment using U-Net whereby the proposed method replaced the encoder-decoder network architecture with 3D operators to automate salt probability volumes [21].
## 4 Discussion  
The strong performance of MSFNet can be attributed to its architecture, which was specifically designed for multi-source fusion and hierarchical spectral feature modeling. Unlike traditional CNN- or Transformer-based approaches that process single-modality inputs, MSFNet adopts a dual-branch structure to jointly leverage RGB and MS-VI data. Within the MSFM, the spatial semantic enhancement branch captures detailed texture and structural patterns from RGB images, while the spectral enhancement branch focuses on the physiological variations captured by MS-VIs. In addition, the proposed HSSFM incorporates a pyramid structure and attention mechanism to integrate spectral semantics across multiple levels. This design enhances the model's ability to capture fine-grained, scale-aware spectral variations, contributing to more accurate disease localization and classification.  
Compared to existing methods, MSFNet exhibits significant advantages in all metrics. RGB-based models are prone to background interference due to the absence of spectral information, whereas MS-VI-based models, despite their spectral sensitivity, lack a sufficient spatial resolution to effectively capture structural details. By combining both modalities, MSFNet strikes an effective balance between spatial detail and spectral sensitivity. In particular, the introduction of the HSSFM enables deeper spectral reasoning across scales, a capability rarely addressed in previous works.  
Despite its promising performance, the model has several limitations. First, all samples used in this study were collected during disease stages with visible symptoms, and the dataset lacks early-stage samples in which wheat plants exhibit no apparent signs of infection. This may hinder the model's effectiveness in early disease detection, which is crucial for timely intervention in real-world agricultural scenarios. Additionally, the current model supports only the diagnosis of stripe rust and yellow dwarf diseases, along with the classification of healthy wheat and background regions. The number of supported disease categories remains narrow.  
In future work, we aim to extend the model's capability to recognize a broader range of wheat diseases and improve its diagnostic accuracy during the early stages of infection. We will also focus on enhancing the model's robustness and generalization across various disease types, growth stages, and regional environments.
## 2 Materials and Methods  
### Study Areas and Available Data Sets: Greece/Turkey and China  
Besides the study area in Namibia described in [8], two further study areas with different vegetation types were used for the impact analysis of time series features for the extraction of TFV. Compared to [8] and to each other, both study areas have different vegetation types, which are described in this section. One of the study areas is part of the Evros catchment, located at the border between Greece and Turkey (Figure 1a) is one of the study areas. With a length of 515 km and a basin of about 52,900 km\({}^{2}\), the Evros river represents the second largest river in Eastern Europe. The period of highest discharge usually occurs between December and April. Several severe, large-scale floods frequently hit the catchment area, particularly in the southern part. The focus of the study lies on a flood event in spring 2015. The flood-affected areas consisted of farmland and forested areas where the water remained for over several weeks. For the analysis and classification, a time series of 60 dual-polarized S-1 scenes was used, which were acquired under the same orbital conditions between October 2014 and December 2016 (Table 1). Five S-1 images covered the flood event. Figure 1b shows the extent of the northern and southern areas of interest, where two  
Figure 1: Overview map with the location of the study area (red rectangle) in Greece/Turkey (**a**). Study area in Greece  
scenes, acquired on March 12, 2015 and April 5, 2015 were considered for classification as flood event images due to their temporal proximity to reference data for the two different areas. The northern area is dominated by TFV consisting of deciduous forest, whereby the majority of agricultural fields are entirely inundated during the flood event. In the southern part of the study area, TFV occurs mostly in high grassland areas. Validation of the classification was performed based on two reference masks (Figure 1c,d). The generation of the reference data was carried out by visual interpretation and manual digitalization of high-resolution optical WorldView-2 (Figure 2) and RapidEye (Figure 3) images, acquired on March 11, 2015 and April 4, 2015, respectively. Although the radar data and the optical image have a temporal shift of one and two days respectively, no changes in the flood extent could be observed.
## 4 Enabling Technologies for FSO Communication
### Adaptive Optics  
AO is a crucial technology in FSO communication systems, designed to counteract the negative impacts of AT [8; 379]. Turbulence can cause phase distortions in the optical wavefront, leading to beam spreading and signal degradation. AO systems utilize real-time feedback to adjust the shape of the optical wavefront, compensating for these distortions and thereby enhancing signal quality [277]. Consequently, through advanced wavefront sensing, real-time correction, and continuous innovation, the reliability and quality of FSO communication links can be improved significantly by AO systems, enabling more widespread and efficient deployment of this promising communication technology. This section explores the principles of AO, its implementation in FSO systems, and the current advancements in AO technology, highlighting its significance in enhancing signal quality and reliability.