esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Geophysical Flows through GPUFOW
## 2 Materials and Methods
### The Physical-Mathematical Model
#### 2.1.2 The Thermal Model  
The original thermal model implemented in MAGFLOW assumed a constant emissivity value both for the molten lava and for cooled crust (generally equal to 0.9). However, recent laboratory studies have demonstrated that the emissivity increases non-linearly with cooling, revealing considerably lower values than typically assumed. In particular,analysis of 'aa' lava samples from the 2001 Mt. Etna eruption, over a wide range of temperatures (773 to 1373 K) and wavelengths (2.17 \(\upmu\)m to 21.0 \(\upmu\)m) showed that emissivity is temperature-dependent, leading to differences up to 20% in the final emplacement of the simulated lava flows [42]. In light of this study, we found an empirical relationship that links the emissivity and temperature of lava:  
\[\varepsilon(T)\approx 0.976716+0.0000408881\ T-1.95062\cdot 10^{-7}\cdot T^{2} \tag{3}\]  
To take this into account, GPUFLOW introduces support for a variable emissivity model. To allow for experimentation, the coefficients can be set by the user, as with most parameters in the model.  
Another improvement in the thermal model of GPUFLOW is the inclusion of the so-called \"windchill\" parameter that controls cooling by atmospheric convection. This introduces a cooling component proportional to the temperature difference between lava and air, with a user-controlled parameter, expressed in \(\mathrm{W}/\mathrm{m}^{2}/\mathrm{K}\). The default value is a null coefficient, which corresponds to the assumption that heat loss due to air convection is negligible compared to radiation. In the absence of wind, a typical value for the coefficient would be \(\sim\)5 \(\mathrm{W}/\mathrm{m}^{2}/\mathrm{K}\), increasing proportionally to the square root of the wind speed [43].
## 3 AI Techniques and Their Applications
### Hybrid and Multimodal Approaches
#### 3.3.2 Sensor Fusion and Multimodal Learning  
Combining GPR with complementary data sources significantly enhances interpretive accuracy and mitigates ambiguity, particularly in complex subsurface investigations. Multimodal data fusion strategies frequently integrate GPR with technologies such as LiDAR or photogrammetry for high-resolution surface mapping, InSAR or SAR for detecting structural movements, and visual or infrared imaging for capturing material contrasts. These integrated approaches leverage the strengths of each modality to offer a more comprehensive understanding of the surveyed environment. Data fusion can occur at different stages of the processing pipeline: early fusion involves concatenating raw data or extracted feature maps from each modality before feeding them into a model; late fusion merges the decision outputs from independently trained models to achieve a final prediction; and intermediate fusion employs architectures that learn shared representations across modalities through joint training. Each of these fusion strategies offers distinct advantages depending on the application context, data quality, and computational resources available. Early fusion may preserve richer contextual information but can be sensitive to data alignment and noise, while late fusion offers modularity and ease of implementation. Intermediate fusion often provides a balance, enabling deep learning models to exploit complex inter-modal relationships. These multimodal fusion techniques are proving particularly effective in real-world applications such as infrastructure health monitoring--where assessing the integrity of bridges, tunnels, or roads requires both surface and subsurface data--and in archaeological or cultural heritage contexts, where accurately mapping buried structures with minimal intrusion is essential. By synergizing GPR with complementary sensing technologies and applying intelligent fusion strategies, researchers and practitioners can significantly improve the reliability and depth of subsurface interpretation, facilitating more informed decision-making in both engineering and environmental domains [55; 56]. The multimodal fusion framework integrating GPR, RGB, and thermal data for infrastructure assessment is shown in Figure 5 and Table 4.
# Study on Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
## 3 Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
### Slope Characteristics of Landslide Body  
As shown in Figure 7, the average slope of the landslide deposits in the study area is small, and most of them belong to small-angle landslides. Statistics show that they are mainly concentrated between 15-40\({}^{\circ}\), especially 15-20\({}^{\circ}\) and 35-40\({}^{\circ}\). Field investigation found that most of the 15-20\({}^{\circ}\) landslides have been stabilized, and the possibility of overall sliding is very small. Another group of landslides between 35\({}^{\circ}\) and 40\({}^{\circ}\) is more unstable, and it is very likely to be revived under the action of heavy precipitation and continuous precipitation.  
In addition, it was found that the length of the landslide body in the study area has a certain relationship with the slope. The longer the landslide body is, the smaller the average slope is. On a slope, if the sliding force of the slope is greater than its own friction, the slope is very unstable and prone to landslides under the action of inducing factors. Assuming that the sliding force of the slope is \(F\), the friction force of the slope is \(f\), the friction coefficient is _\(\mu\)_, the component force of the gravity of the slope itself perpendicular to the slope is \(N\), the slope has a gradient of \(x\), the length of the slope is \(L\), and the height difference between the front and rear edges is \(H\), then there is:  
\[\mu=\frac{f}{N} \tag{1}\]  
\[\alpha=\frac{H}{L} \tag{2}\]  
According to the similar triangle principle, there are:  
\[\mu\propto\alpha \tag{3}\]  
For the same slope body, its \(N\) is a fixed value, then:  
\[\alpha\propto f \tag{4}\]  
According to the principle of mechanics, when \(F\) >_f_, the slope will decline, and vice versa, it is stable. Therefore, when \(x\) is greater than a certain angle, the landslide will occur, and vice versa, it will be stable.  
According to the above formula, it is deduced that the landslide with a large length has stabilized because of its small slope. For those landslides with smaller lengths, only a small number of them have stabilized. A large part of them are not stable because of their large sliding force, and their leading edge may be reactivated and disintegrated again; therefore, those landslides are above the fitting line, as shown in Figure 8.  
Figure 7: Landslide slope distribution map.
# Preparation Methods and Properties of CNT/CF/G Carbon-Based Nano-Conductive Silicone Rubber
###### Abstract  
Carbon-based nano-conductive silicone rubber is a kind of composite conductive polymer material that has good electrical and thermal conductivities and high magnetic flux. It has good application prospects for replacing most traditional conductive materials, but its mechanical and tensile strengths are poor, which limit its applications. In this study, carbon fiber (CF), graphene (G) and carbon nanotubes (CNT) are used as fillers to prepare carbon-based nano-conductive silicone rubber via solution blending, and the preparation methods and properties are analyzed. The results show that when the carbon fiber content is 7.5 wt%, the volume resistivity of carbon fiber conductive silicone rubber is 9.5 \(\times\) 10\({}^{4}\)\(\Omega\)-cm, the surface resistance is 2.88 \(\times\) 10\({}^{5}\)\(\Omega\), and the tensile strength reaches 2.12 Mpa. When the graphene content is 5.5 wt%, the volume resistivity of graphene conductive silicone rubber is 8.7 \(\times\) 10\({}^{4}\)\(\Omega\)-cm, and the surface resistance is 2.4 \(\times\) 10\({}^{6}\)\(\Omega\). When the carbon nanotube content is 1.25 wt%, the volume resistivity of carbon nanotube conductive silicone rubber is 1.34 \(\times\) 10\({}^{4}\)\(\Omega\)-cm, and the surface resistance is 1.0 \(\times\) 10\({}^{6}\)\(\Omega\). The three conductive nano-fillers in the blended carbon nano-conductive silicone rubber form a stable three-dimensional composite conductive network, which enhances the conductivity and stability. When the tensile rate is 520%, the resistance of the blended rubber increases from 2.69 \(\times\) 10\({}^{3}\) to 9.66 \(\times\) 10\({}^{4}\)\(\Omega\), and the rubber maintains good resilience and tensile sensitivity under repeated stretching. The results show that the proposed blended carbon nano-conductive silicone rubber has good properties and great application prospects, verifying the employed research method and showing the credibility of the research results.  
conductive silicone rubber; carbon fiber; graphene; carbon nanotubes; blended filler +
Footnote †: journal: Applied Sciences
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications
### Soil Leves and Embankments Monitoring
#### 3.1.1 Distributed Temperature Sensing for Soil Levees and Embankments
Nonetheless, the authors suggested that there may be more sophisticated methods for analysing heating and cooling phases to circumvent these problems. Likely, the issues highlighted in this work may affect the active method for seepage detection. Regarding the location of fibre-optic cables inside the levees, the state-of-the-art requires that it must be defined according to the ultimate objective of the monitoring. It should be installed lengthwise the levee, at the upstream face for water tightness control, or at the downstream toe or face for seepage monitoring [106]. Installation under the embankment body is also possible for flow velocity measurements. Retrofitting of existing levees has also been proposed by burying the cable in the soil, in a narrow trench dug at the toe (see Figure 10) or under the surface of the downstream slope. Specific depths and precise locations must also be defined after careful site characterisation, in order to maximise the chance of capturing potential leakages [139]. Figure 10: A trench dug at the downstream toe of a levee for the installation of the optical fibre cable (visible at the right) for a Raman-DTS monitoring campaign. Figure 9: **Left**: Setup used to investigate the variations of the temperature field generated by the internal erosion in a sand box model. The optical fibre was deployed at three depths, each crossed by five fibre sections orthogonal to the flow direction and interrogated using an OFDR interrogator. Additional conventional sensors were used for comparison and pressure measurements (reproduced with permission from [136]). **Right**: the sand box instrumented with the optical fibre (visible on the right lateral face of the box). The use of engineered geotextiles and geogrids has been extensively addressed by several authors and tested in physical models of embankments toward the aim of making the installation easier [138; 141; 142; 143; 144; 145]. The main advantage with the use of geotextiles for levee monitoring is the ease and homogeneity of installation in new artificial earthworks, but retrofitting of existing structures may require invasive interventions. For new and existent structures, particular care should be given to avoid preferential filtration flows alongside the geotextile. Other installation configurations were also investigated: for example, by installing optical fibre vertically by exploiting existing boreholes [146].
## 4 Composite Performance Analysis and Adaptive Performance Tuning
### Adaptive Parameter Tuning  
In power systems, the disturbances are mainly caused by the loss of the load, and large disturbances, which are represented by the step changes of the load, range from 0.03 to 0.05 pu [11; 22]. In some renewable-rich countries (such as Germany, the United States, and the UK), the system operators have made grid codes for renewables to provide ancillary services [35]. For example, the prescribed limit for ROCOF is 0.125 Hz/s [36]. In addition, in this material, the maximum ROCOF and PFR delivery time in its case study are set to be 0.5 Hz/s and 10 s, respectively. In another research, the maximum frequency deviation \(\Delta f_{max}\) is set as 0.2 Hz [30]. It is noted that if the ROCOF exceeds 1 Hz/s, the frequency relay would be tripped and incur a large disturbance to a power system [37]. Generally, the delivery of PFR is required to be within 10-30 s, and the PFR capacity should always limit the frequency between 49.5 and 50.5 Hz. The conventional standards (when the nominal frequency is 50 Hz) are summarized in Table 3.  
Figure 12: Energy provided by varying \(H\) and \(R\) at the settling time.  
It is noted that the stages of IR and PFR activate in chronological order. Therefore, based on the composite effects and the preset standards, a flowchart of parameter design for VSG-PEIs is shown in Figure 13. In step A, the standards for ROCOF and the frequency nadir are input. In step B, the satisfying range of \(H\) is obtained according to the standard of the ROCOF. In step C, the satisfying range of \(R\) is obtained by the nadir requirement. In step D, the desired \(H\) and \(R\) are determined by checking the settling time and frequency.
## Appendix B
### MISTiC Airborne Observation with IASI over Ocean  
A comparison of MISTiC Airborne observations with an IASI-B observation in an adjacent spatial location (the closest cloud-free IASI footprint to the MISTiC observation) approximately one hour earlier is shown in Figure A5. It shows a reasonable correlation of the spectral feature positions in the MISTiC airborne observations with those of IASI. MISTiC radiances in higher brightness-temperature surface-dominated spectral channels above 2150 cm\({}^{-1}\) are somewhat higher than those of IASI, most likely due to modest ocean surface warming between the IASI and MISTiC overpass times. However, the apparent spectral resolving power of the MISTiC airborne instrument, during the ER2 flight, is somewhat less than expected when compared with IASI-B spectral observation. The IASI-B data in Figure A5 have been apodized to provide a spectral resolving power (\(>\)700:1) for which MISTiC was designed. The laboratory tests of the MISTiC instrument spectral resolving power, with all of the optics operated at the intended temperature range (cryogenic spectrometer, fore-optics near room temperature) shows spectral resolving power measurements matching design projections, so we conclude that the cause of the lower spectral resolving power during the airborne observations lies outside of the spectrometer. One important difference for the MISTiC airborne test conditions is that the fore-optics and dewar assembly were both far colder during the ER2 flight (\(-5\)\({}^{\circ}\)C) than expected, and much colder than experienced during the laboratory monochrometer testing. These more extreme temperature conditions outside the spectrometer most likely led to a focus error between the spectrometer and the other elements, resulting in the lower spectral resolving power during the airborne tests. Funding constraints did not allow for adjustments to correct this problem during the IIP, but the situation may be remedied in future flights. An additional difference with IASI is that, at very low radiance levels, the airborne MISTiC observes some additional radiance than IASI. This is most likely due to modest errors in the ER2 window emission correction in the radiometric calibration procedure for MISTiC airborne instrument.
## 2 System Model
### Hybrid Encryption Approach with CBC  
In Section 2.1, we described a framework for tracking the correct delivery of goods using RFID. Thus, suitable logs of the transactions made are recorded. However, these  
Figure 2: Tracking goods delivery.  
logs need to be secured, as malicious users who might access the drone during one of its stops could tamper with this information. Even if this information is encrypted, the key used would have to be stored somewhere in the UAV's memory, which also puts it at risk. Therefore, the main contribution of this paper is to address this problem by using an enhanced cipher block chaining (CBC) approach to secure the transaction logs, as illustrated in Figure 3.  
By encrypting each transaction data block and feeding it as input to the next block, corresponding to the next transaction (related to the next delivery operation), a malicious user accessing the UAV's data (e.g., at one of its stops) would only see the encrypted CBC data. Moreover, if the first block was generated at the starting station or Control Center (e.g., a train station as seen in Figure 1), using a timestamp and/or nonce to encode the start of the journey, it will be very hard for a malicious user capturing the drone to decrypt the whole chain. Added security can also be achieved if encryption is added to that first block using a key and/or initialization vector stored at the station, where it is secure from external malicious access. When the UAV returns to the station, this information can be properly decrypted and stored, and the logs of the transactions are duly recorded on a central server. A new chain can be started when the UAV departs for its next trip, which keeps the size of the chains manageable and makes this approach scalable. When satellite communications are available, even if intermittently during the UAV's trip, the encrypted chain could be transmitted periodically over the UAV-satellite link. This would save the records in case the whole UAV was compromised or shot down before its return.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 2 Complete Machine Structure and Working Principle
### Platform Structure and Details
#### 2.1.3 Underwater Vision Module  
The underwater vision system is an important part of the UBP to realize accurate feeding. Traditional underwater monitoring methods usually rely on manual observation and specialized equipment, which is costly and inefficient. The unmanned boat is equipped with an underwater vision module, enabling it to capture real-time photos of the underwater environment during cruising. Through AI algorithms, it identifies and monitors farmed objects, providing visualization data of the farming area to help farmers better manage the farming process. The underwater vision module is mainly developed by the NeZha development board, equipped with an underwater camera and an underwater flashlight for enhancing the underwater shooting effect. The underwater vision module is shown in Figure 5.
###### Abstract  
The design of the wellbore trajectory directly affects the construction quality and efficiency of drilling. A good wellbore trajectory is conducive to guiding on-site construction, which can effectively reduce costs and increase productivity. Therefore, further optimization of the wellbore trajectory is inevitable and necessary. Based on this, aiming at the three-segment, five-segment, double-increase-profile extended reach wells, this paper considered the constraints of formation wellbore stability; formation strength; and the determination of the deviation angle, deviation point position, and target range by the work of deflecting tools. In addition, the optimization objective function of the shortest total length of the wellbore, minimum error of the second target, lowest cost, minimum friction of the lifting and lowering string, and minimum torque of rotary drilling were proposed and established. The objective function of the longest extension limit of the horizontal section of the extended reach well is established. Taking the 14-8 block of the Lufeng Oilfield in the eastern South China Sea as an example, the actual data of the field were modeled, and the independence of the objective function was verified by comparing the number of non-inferior solutions of the two objective functions. By normalizing simplified to double-, three-, and four-objective functions, using a genetic algorithm and particle swarm optimization results, it can be found that the new method of optimization design established in this paper has an obvious optimization effect compared with the original design.  
directional well; well trajectory design; multi-objective optimization; friction; torque +
Footnote †: journal: Applied Sciences
## 2 Background and Related Work
### Related Works
Their main findings were: (1) The causes of RCs can be divided into trigger events and uncertainties, and in five different areas. This classification can lead to better planning that will ensure a better success rate for the project;(2) The key processes of RCM are: change identification, change analysis and change effort estimation; (3) Taxonomies and classifications are frequently used to change identification; (4) Decision making can be contradictory at different levels of the organization. This can cause a contradictory understanding of the change between the business and IT counterparts. Their general conclusion was that RCM is an elusive target to achieve and that there are many ways to tackle it. [PERSON] et al. [11] conducted an SLR in 2018 focused on analyzing the maturity of techniques used for requirements elicitation. The goal of their work was to collect and assess the available evidence of requirements elicitation techniques to aid the software analysts in picking the suitable technique for eliciting software requirements. The search strategy consisted of an automated search only, ran in seven electronic databases and covering a period from 1993 to 2015. The data for the SLR were extracted from 140 selected primary studies to answer two research questions. They concluded that the most widely used elicitation techniques identified were: traditional techniques, e.g., interviews, task analysis and questionnaires (27%). Similarly, modelling techniques, e.g., goal-based, scenarios, and business process models (21%) and collaborative techniques, including workshops focus groups and brainstorming (9%). Moreover, cognitive techniques, e.g., ontology, card sorting and repertory grid (7%), and agile techniques, e.g., user stories, mind mapping and group storytelling (7%) among others. Nevertheless, the selection and suitability of these techniques depend upon the stakeholders' characteristics, resources available, context and the problem at hand to be solved. The findings from the selected studies came from a well-balanced context (55.8% academic, 38.8% industrial, and 5.4% mixed settings). The authors followed a sound research method, and, because of this, their work shows a high level of quality. Unlike the works cited above, our study was not limited to any pre-established CV or specific RE activity. On the other hand, to ensure wider coverage of potential sources of evidence, we included two complementary search strategies: automatic search in electronic data sources (EDS) and a backward (based on references) and forward (based on citations) snowballing [33]. The period considered covered until June 2019 (searches were conducted in July 2019). Regarding the number of primary papers included, our study considered a similar amount to the average support level provided by the related works (99 primary works), but 36% more than the average (57 primary papers) of those works considering all the RE activities. A summary of these facts is shown in Table 1.