esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach
## 2 Advanced Sliding Mode Control Framework for UAV Security
### Adaptive and Fault-Tolerant Mechanisms
#### 2.3.4 Reconfiguration of Control Parameters  
Once a fault is detected and isolated, the control system adapts by reconfiguring its parameters to compensate for the fault. The control parameters \(\lambda_{\phi}\), \(\lambda_{\theta}\), and \(\lambda_{\psi}\) in the sliding surfaces are adjusted as follows:  
\[\lambda_{\phi} =\lambda_{\phi_{0}}+\Delta\lambda_{\phi}, \tag{18}\] \[\lambda_{\theta} =\lambda_{\theta_{0}}+\Delta\lambda_{\theta},\] \[\lambda_{\psi} =\lambda_{\psi_{0}}+\Delta\lambda_{\psi},\]  
where \(\lambda_{\phi_{0}}\), \(\lambda_{\theta_{0}}\), and \(\lambda_{\psi_{0}}\) are the nominal values of the parameters, and \(\Delta\lambda_{\phi}\), \(\Delta\lambda_{\theta}\), and \(\Delta\lambda_{\psi}\) are the adjustments based on the detected fault.  
These adjustments help maintain the desired system performance even in the presence of component degradation or failure.
# An Ultra-Low Temperature Transcritical R744 Refrigeration System for Future Detectors at CERN LHC
## 5 Discussion
### Over-Pressurisation at Stoppage  
The low pressure side of the first prototype has been designed for 60 bar, already anticipating uncontrolled over-pressurisation during commissioning of the unit. However, this shall be avoided as the future units will be designed to lower pressures. The main cause for this uncontrolled pressurisation was the open default position of flash gas control valve and bypass control valve of MP compressor, with the paths illustrated by the blue arrows in Figure 12. As baseline, the compressor slice was designed to be at equalised pressure everywhere once stopped while being drained via draining valve until pressure equalises return line pressure. As the shut-off valves are too slow and pressure in the receiver is high, the safety position of controlled valves had to be retubought. It has been proposed that the flash gas and bypass valve safety position is closed and that the system keeps relying on drain valve to equalise pressure everywhere. The low pressure side will be able to equalise the high pressure side through the MP compressor as it is not tight to reversed flow and the oil reservoir would be kept pressurised above slice. Moreover, one-way valve downstream the shut-off discharge valve has also been proposed to minimise impact in the process once the system is connected to discharge line and rely solely on flash gas injection to pressurise the whole slice.  
Figure 12: Flow path through flash gas controlled valve and MP compressor bypass valve (in blue) leading to over-pressurisation of low pressure side and draining path to return line through draining valve (in purple).
## 4 Experimental Tests  
### Test Conditions  
The proposed enhanced navigation approach is evaluated by field tests. In these tests, a visible light camera and an infrared camera are carried by the UAV (DJI M600 Pro made by DJI Technology Co., Ltd. in Shenzhen, China) to capture the real-time aerial image, as shown in Figure 8. All the data of the sensors are real-timely collected and processed by an onboard computer (Jetson Nano made by NVIDIA Corp. in Beijing, China), which has a CPU composed of 4-core ARM A57 @1.43 GHz and a GPU composed of 128-core Maxwell @912 Hz.  
The visible light and the infrared camera parameters are listed in Table 2, and the MEMS inertial sensor parameters are shown in Table 3.
# Advanced Metal Matrix Nanocomposites
## 3 Reinforcing Agents
The fourth electron is delocalized over the plane and accounts for the electrical conductivity of the graphene in the direction of the plane. Once a graphene layer is rolled up to a CNT, a three-dimensional structure ensues that bears resemblance to fullerenes. One distinguishes between single-wall and multi-wall CNTs, a major benefit of CNTs being their very high strength and high Young's modulus; tensile strengths between 13 and 52 GPa and Young's moduli between 320 and 1470 GPa could be measured at 1.36 nm thick single-wall CNTs [91]. In another study, measurements on multi-wall CNTs in which the outer shell carries the load showed tensile strengths between 11 and 63 GPa and a Young's modulus between 270 and 950 GPa [92]. This renders CNTs an ideal strengthening reinforcement material for metallic nanocomposites. [PERSON] et al. [93] studied pure magnesium, which was reinforced with 0.06, 0.18 and 0.3% by weight multi-wall CNTs of 20 nm diameter. A mixture of CNTs and magnesium chips were milled, compacted, and extruded. The thermal expansion coefficient was reduced by adding CNTs, as shown in Table 3; the yield strength of nanocomposites also increased with increasing CNT content (Table 3). [PERSON] et al. [94] produced an aluminum-based nanocomposite with 10 wt% multi-wall CNTs and compared the properties with the unreinforced Al-Si alloy, also produced by plasma spray forming. The Young's modulus more than doubled from 67.5 to 120.4 GPa by the addition of CNTs, but the tensile strength increased only slightly from 79.8 to 83.1 MPa and ductility decreased. Microstructure investigations have revealed that a SiC layer has formed on the CNTs which influences the failure in the tensile test; an overview of CNT reinforced metal matrix nanocomposites can be found in [95].
## 4 Discussion
### Uncertainties, Errors, and Accuracies
In addition, remote sensing data is easily affected by cloudiness and aerosols; further work should be carried out to improve the data quality.
## 5 Case Study: Experimental Validation in a Representative Smart Building Scenario
### FML-Based Representation of the Fuzzy Rule-Based System
* **Rule Base:** It contains the set of fuzzy IF-THEN rules validated by the expert panel and summarized in Table 4. Each rule references linguistic terms from the knowledge base. * **FIS Configuration:** It specifies the type of inference engine (e.g., Mamdani), aggregation, and defuzzification methods used. An example excerpt of the FML encoding for the variable Room Temperature 1 is shown below (Listing 1):  
```
<KnowledgeBasename=\"EnergyKB\"> <FuzzyVariablename=\"RoomTemperature1\"domainLeft=\"0\"domainRight=\"40\" type=\"input\"> <FuzzyTermname=\"Low\"> <TriangularShapeparam1=\"0\"param2=\"0\"param3=\"20\"/> </FuzzyTerm> <FuzzyTermname=\"Medium\"> <TriangularShapeparam1=\"15\"param2=\"22.5\"param3=\"30\"/> </FuzzyTerm> <FuzzyTermname=\"High\"> <TriangularShapeparam1=\"25\"param2=\"40\"param3=\"40\"/> </FuzzyTerm> </FuzzyVariable> </KnowledgeBase>
```  
**Listing 1**. FML definition for input variable \"Room Temperature 1\". A corresponding rule using this variable and referencing the fuzzy output for HVAC control might look like (Listing 2):  
```
<RuleBasename=\"EnergyRuleBase\"activationMethod=\"MN\"andMethod=\"MN\" orMethod=\"MAX\" ruleConnectionMethod=\"AND\"> <Rulename=\"RI\"connector=\"and\"weight=\"1.0\"operator=\"MN\"> <Antecedent> <Clause> <Variable>RoomTemperature1</Variable> <Term>High</Term> </Clause> <Clause> <Variable>Occupancy1</Variable> <Term>True</Term> </Clause> </Antecedent> <Consequent> <Clause> <Variable>HVACControl</Variable>
```<Term>CoolingHigh</Term>  </Clause>  </Consequent>  </Rule>  </RuleBase> ```
All variables defined in Table 2 are encoded in the knowledge base with appropriate linguistic terms and membership functions, and all rules listed in Table 4 are translated into structured FML format. The model employs the minimum and maximum operators for the logical and and or operations, respectively, and utilizes the center of gravity method as the defuzzification operator to perform fuzzy inference [19]. This representation enables full compatibility with the JFML-IoT inference engine, ensuring that the proposed system can operate across several devices while maintaining transparency and compliance with the IEEE std 1855-2016. The complete FML file used in this case study is available for download from the official JFML website ([[https://www.jfml.es/](https://www.jfml.es/)]([https://www.jfml.es/](https://www.jfml.es/)), accessed on 16 July 2025) and the JFML-IoT GitHub repository ([[https://github.com/jfMRes/JFML_IoT](https://github.com/jfMRes/JFML_IoT)]([https://github.com/jfMRes/JFML_IoT](https://github.com/jfMRes/JFML_IoT)), accessed on 16 July 2025). These resources also provide additional illustrative examples, video demonstrations, and documentation to facilitate reproducibility and adoption by the research and development community.
## 2 Literature Review
### Asset Management Methodologies and Models Applied to Warranty Management
#### 2.2.1 Balanced Scorecard (BSC) Model  
The Balanced Scorecard (BSC) is a strategic management tool that allows organizations to align their organizational objectives with specific performance indicators [17]. In the context of warranty management, the BSC provides a comprehensive view of asset performance across four key dimensions: financial, customer, internal processes, and learning and growth. This balanced approach enables companies to proactively adapt their warranty policies, ensuring that they respond to both operational needs and customer expectations.  
* Financial Perspective: The BSC helps monitor and optimize the financial impact of warranty management through predictive maintenance. The ability to anticipate and avoid failures minimizes downtime and reduces the operational costs associated with claims, increasing asset profitability and its contribution to the company's bottom line.
* Customer Perspective: In this dimension, the BSC focuses on customer satisfaction by evaluating both the speed and quality of claims resolution. Through AI and data analysis, warranty policies can be tailored to individual customer needs, offering agile afterscales services aligned with each user's expectations, increasing their loyalty and perception of value.
* Internal Process Perspective: Integrating technologies such as AI and automation enables the optimization of internal warranty management processes. Continuous asset monitoring facilitates the early detection of potential failures, allowing intervention before significant issues occur. This proactive approach maximizes asset availability and reliability, ensuring efficient afterscales services.
* Learning and Growth Perspective: Constant training of staff in the use of digital technologies is essential to maintain advanced and sustainable warranty management. Through the BSC, companies can measure and strengthen their team's ability to adapt to new technologies and real-time analysis systems, ensuring that human talent is prepared to respond to the demands of a digitalized environment.  
Broadly speaking, the BSC provides a structured and balanced framework that allows monitoring the performance and effectiveness of warranty policies, maintaining constant alignment with the strategic objectives of the organization and with customer expectations [18]. In addition to being a measurement tool, the BSC facilitates the alignment of digitalization and AI initiatives with the company's strategic objectives. This is especially valuable in sectors such as the automotive or aeronautics industries, where assets represent significant investments and must be kept in optimal operation to justify the cost. Continuous feedback through KPIs allows areas for improvement to be identified--adjusting warranty policies according to the evolution of market demands--and promotes sustainability and technological innovation, essential factors in modern warranty management [18].
###### Abstract  
Monitoring the spatio-temporal dynamics of desertification is critical for desertification control. Using the Urat front flag as the study area, Landsat remote sensing images between 2010 and 2020 were selected as data sources, along with MODI7A3H as auxiliary data. Additionally, RS and GIS theories and methods were used to establish an Albedo-NDVI feature space based on the normalized difference vegetation index (NDVI) and land surface albedo. The desertification difference index (DDI) was developed to investigate the dynamic change and factors contributing to desertification in the Urat front banner. The results show that: 1 the Albedo-NDVI feature space method is effective and precise at extracting and classifying desertification information, which is beneficial for quantitative analysis and monitoring of desertification; 2 from 2010 to 2020, the spatial distribution of desertification degree in the Urat front banner gradually decreased from south to north; 3 throughout the study period, the area of moderate desertification land increased the most, at an annual rate of 8.2%, while the area of extremely serious desertification land decreased significantly, at an annual rate of 9.2%, indicating that desertification degree improved during the study period; 4 the transformation of desertification types in Urat former banner is mainly from very severe to moderate, from severe to undeserted, and from mild to undeserted, with respective areas of 22.5045 km\({}^{2}\), 44.0478 km\({}^{2}\), and 319.2160 km\({}^{2}\). Over a 10-year period, the desertification restoration areas in the study area ranged from extremely serious desertification to moderate desertification, from serious desertification to non-desertification, and from weak desertification to non-desertification, while the desertification aggravation areas ranged mainly from serious desertification to moderate desertification; 5 NPP dynamic changes in vegetation demonstrated a zonal increase in distribution from west to east, and significant progress was made in desertification control. The change in desertification has accelerated significantly over the last decade. Climate change and irresponsible human activities have exacerbated desertification in the eastern part of the study area.  
url  
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## 4 Results
### Hypothesis Testing  
#### 4.3.1 Data Normality and the General Hypothesis Test  
To determine the appropriate statistical method for analyzing the relationship between omnichannel service perception and customer satisfaction, a normality test was conducted on the data sets for both variables.  
**Null Hypothesis (H\({}_{0}\)).**: _The data for the omnichannel variable and the satisfaction variable are normally distributed._
**Alternative Hypothesis (H\({}_{1}\)).**: _The data for the omnichannel variable and the satisfaction variable are not normally distributed._  
The Kolmogorov-Smirnov test, appropriate for samples larger than 50 participants, was applied (Table 10). The results yielded a \(p\)-value of 0.000, indicating a significant deviation from normality. Consequently, the null hypothesis (H\({}_{0}\)) was rejected, and the alternative hypothesis (H\({}_{1}\)) was accepted. Given the non-normal distribution of the data and the ordinal nature of the variables (both classified into three levels: low, regular, and high), the Spearman rank-order correlation test was selected for subsequent inferential analysis.  
Following the confirmation of non-normality, the study proceeded to test the general hypothesis regarding the relationship between omnichannel perception and customer satisfaction at the service station (Table 11, Figure 2).  
**Null Hypothesis (H\({}_{0}\)).** _There is no direct and significant relationship between omnichannel service perception and customer satisfaction._  
**Alternative Hypothesis (H\({}_{1}\)).** _There is a direct and significant relationship between omnichannel service perception and customer satisfaction._  
The [PERSON]'s rho test, a non-parametric correlation method, was applied. The analysis produced a \(p\)-value of 0.000, supporting the rejection of the null hypothesis (H\({}_{0}\)) and acceptance of the alternative hypothesis (H\({}_{1}\)). Thus, the findings confirm that a direct and statistically significant relationship exists between the perception of omnichannel service quality and customer satisfaction at the service station (\(p<0.05\)). This result underscores the critical role of integrated digital service strategies in enhancing the subjective experience of users, particularly in digitally emerging environments.
# Feasibility Assessment of Acid Gas Injection in an Iranian Offshore Aquifer
## 7 Main Uncertainties  
Based on the simulation results and analysis, several crucial reservoir properties and factors have been identified as main uncertainties for the Surmeh formation. These include:  
1. Porosity and permeability: they play a significant role in controlling fluid flow and pressure behavior. Accurate analysis of these properties is crucial and can be obtained through coring and laboratory testing.
2. Formation temperature: it affects the solubility of gas in water and impacts the behavior of fluids within the reservoir. Understanding the formation temperature is important for accurate modeling and prediction.
3. Injectivity and fall-off test: conducting injectivity tests and fall-off tests provides valuable information about the reservoir's ability to accept injected fluids and the behavior of pressure response. These tests help to determine the formation injectivity and generate Vertical Flow Performance (VFP) curves, which are necessary for compressor design and calculation of well-head pressure.  
Figure 19: BHP for 3 cases with an injection rate of 70 MMSCFD and porosity of 12%. (Red: Compositional; Green: Black oil without CO\({}_{2}\) solubility; Brown: Black oil with CO\({}_{2}\) solubility).  
Figure 18: BHP for the same case (Surmeh formation) with porosity values of 6% and 12% and permeability of 100 md.  
4. Relative permeability and capillary pressure curves: understanding relative permeability and capillary pressure relationships is essential for accurate reservoir modeling and simulation. These curves provide insights into multiphase flow behavior and fluid displacement within the reservoir.
5. Fracture pressure: determining the fracture pressure of the reservoir is important for well design and drilling operations. It helps to ensure that the pressure exerted during the injection or production operations does not exceed the integrity of the reservoir.
6. Caprock integrity: it acts as a seal for the reservoir and is crucial to prevent fluid migration and to maintain the reservoir pressure. Analyzing the caprock's integrity helps assess the risk of potential leaks or breaches.
7. Gas solubility in water: the temperature of the reservoir influences the solubility of gas in water. Understanding the gas solubility is vital for accurately modeling gas-water interactions and predicting fluid behavior during injection and production processes.  
Addressing and reducing these uncertainties through comprehensive analysis and testing will improve the understanding of the Surmeh formation and enhance decision-making in reservoir management and development strategies.
## 2 Theoretical Background
### Effect of Temperature  
Temperature variations can produce effects on dielectric permittivity measures. The dielectric constant of water follows a simple inverse proportionality to temperature, ranging from 88 at 0 \({}^{\circ}\)C to 75 at 35 \({}^{\circ}\)C, but the temperature calibration to soil moisture measurement is highly soil dependent. Sometimes, the real dielectric can trend upward with increasing temperature, and sometimes downward with increasing temperature [26].  
For electrical conductivity a standard temperature calibration can be performed according to the relationship:  
\[\sigma_{25\ ^{\circ}C}\ =\ \frac{\sigma_{T}}{\left[1\ +\ 0.02(T\ -\ 25)\right]} \tag{12}\]
# Enlarged Head Pressure-Dispersed Anchor Cable for Foundation Pit Engineering Purposes
## 4 Numerical Simulation Analysis of the Anchor-Cable Support System
### Selection of Soil Constitutive Model and Calculation Parameters  
As the Mohr-Coulomb elastic-plastic constitutive model is both practical and accurate, it is widely used in numerical simulations of foundation pit support. Therefore, this model was employed in the simulation calculation, considering the effect of pore water pressure on soil mass. According to the step-by-step excavation process of the actual support case, seepage calculation and mechanical calculation were used alternately in model calculation until the excavation was completed. The model drawing from the excavation of the foundation pit to the bottom of the pit is shown in Figure 15. In Figure 15, the model is 60 m long and 30 m high. The coordinates of the points in Figure 16 are A (35, 0.6, 0); B (35, \(-\)0.6, 0); C (5.9, 0.6, 0); D (5.9, \(-\)0.6, 0); E (\(-\)0.4, 0.6, \(-\)3); F (\(-\)0.4, \(-\)0.6, \(-\)3); G (\(-\)25, 0.6, \(-\)9.52); and H (\(-\)25, \(-\)0.6, \(-\)9.52). The parameters of the contact surface between soil, anchor cable material, grout, and soil are shown in Tables 2-4.  
Figure 14: Monitoring of the horizontal displacement of the pile top.  
Figure 16: FLAC3D mesh model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Soil 1** & **Soil 2** & **Soil 3** & **Soil 4** & **Soil 5** & **Soil 6** \\ \hline Thickness/m & 1.2 & 4.7 & 2.5 & 2.6 & 4.7 & 14.3 \\ Dry density/Kg/m\({}^{3}\) & \(1.47\times 10^{3}\) & \(1.47\times 10^{3}\) & \(1.37\times 10^{3}\) & \(1.32\times 10^{3}\) & \(1.55\times 10^{3}\) & \(1.55\times 10^{3}\) \\ Volume modulus/kPa & \(2.92\times 10^{4}\) & \(3.33\times 10^{4}\) & \(1.87\times 10^{4}\) & \(3.50\times 10^{4}\) & \(7.25\times 10^{4}\) & \(7.67\times 10^{4}\) \\ Tangential modulus/kPa & \(1.35\times 10^{4}\) & \(1.54\times 10^{4}\) & \(8.62\times 10^{3}\) & \(1.62\times 10^{4}\) & \(3.94\times 10^{4}\) & \(4.60\times 10^{4}\) \\ Cohesion/kPa & 12.0 & 2.00 & 8.70 & 24.0 & 0.50 & 0.50 \\ Friction angle/\({}^{\circ}\) & 15 & 30 & 7.1 & 12.3 & 35 & 35 \\ Dilation angle/\({}^{\circ}\) & 3 & 5 & 1 & 1 & 5 & 5 \\ Permeability & \(6.12\times 10^{-8}\) & \(6.12\times 10^{-6}\) & \(6.12\times 10^{-8}\) & \(6.12\times 10^{-8}\) & \(2.45\times 10^{-5}\) & \(6.12\times 10^{-5}\) \\ coefficient/m\({}^{2}\)/kPa\(\cdot\)s & \(0.7\) & 0.6 & 0.907 & 0.846 & 0.65 & 0.7 \\ P Porosity & 0.41 & 0.38 & 0.48 & 0.46 & 0.39 & 0.41 \\ Water content & 25\% & 25\% & 35\% & 40\% & 25\% & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Soil parameters.  
Figure 15: Model diagram of the excavation of the foundation pit to the bottom of the pit.
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 3 System Modeling
### Dynamic Model  
In this study, a 3-DOF dynamic model is built to describe the vehicle dynamic behavior, as shown in Figure 2.  
The coordinate is the same as the coordinate in the kinetic model in Figure 2. The 3-DOF dynamic model is set up as (8):  
Figure 2: The force analysis for a 3-DOF dynamic model for a 6-wheeled skid-steered vehicle.  
\[\left\{\begin{array}{l}m(\ddot{x}-\dot{y}\dot{\phi})=\sum\limits_{i=f,m,r}\sum \limits_{j=l,r}F_{x,ij}\\ m(\ddot{y}+\dot{x}\dot{\phi})=\sum\limits_{i=f,m,r}\sum\limits_{j=l,r}F_{y,ij} \\ I_{\ddot{z}}\dot{\phi}=\frac{\beta}{2}\sum\limits_{i=f,m,r}\left(F_{x,i\prime}-F _{x,il}\right)+\sum\limits_{j=l,r}\left(aF_{y,f}+bF_{y,mj}+cF_{y,rj}\right)\\ I_{w}\dot{\omega}_{ij}=T_{ij}-RF_{x,ij}-R\omega_{ij}\ i=f,m,r;j=l,r\end{array}\right. \tag{8}\]  
where \(F_{x,ij}\) and \(F_{y,ij}\) denote the longitudinal and lateral force, respectively, on each tire; \(I_{z}\) denotes the moment of inertia about the \(z\)-axis of the vehicle; and \(I_{w}\) denotes the moment of inertia of the wheel. The tire force is calculated in the next section.