esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In order to improve the attitude control performance of the quadrotor, this paper first analyzes the dynamics model of the quadrotor and designs an attitude control system based on ADRC. Regarding the problem that ADRC has many parameters, it is difficult to adjust, and manual adjustment is difficult to achieve the optimal problem, this paper proposes the FOFPSO algorithm to optimize the ADRC parameters. The experimental results show that:  
Figure 14: The tracking curve of pitch angle with disturbance.  
Figure 13: The experiment platform of disturbance rejection.  
(1) The quadrotor UAV attitude control system based on ADRC theory has stronger control performance and disturbance rejection ability.  
(2) The FOFPSO proposed in this paper has the advantages of fast convergence speed, high solution accuracy, and not easy to fall into local optimal values; ADRC optimized by the FOFPSO algorithm has better performance in attitude tracking and disturbance rejection.  
(3) In order to verify the feasibility of the designed control system implemented on the aircraft experimental platform, this paper adopts a model-based design method and deploys the designed control system on the aircraft experimental platform for attitude tracking experiments and disturbance rejection experiments. The experimental results show that the designed control system has the advantages of fast tracking speed, small overshoot and strong robustness, which further demonstrates that ADRC has greater advantages in practical engineering applications.  
Conceptualization, Q.Z. and Y.W.; methodology, Y.W.; software, Y.W.; validation, Y.W., Q.Z. and X.L.; formal analysis, X.L.; investigation, Y.W.; resources, Q.Z.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Q.Z. and X.L.; supervision, Q.Z.; project administration, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the key research and development projects in Guangxi under Grant AB21076005, in part by the Project on Promoting Scientific Research Ability of Young and Middle-aged People in Guangxi under Grant 2018 KY1095, in part by the Specialized Talents in Guangxi under Grant AD18281018, in part by the Guangxi Natural Science Foundation of China under Grant 2018 GXNSFAA138092, in part by the Innovation Project of GUET Graduate Education under Grant 2021 YCXS121, in part by the College Students' Innovative Entrepreneurial Training under Grant C99 BXM00 BX87.  
The authors declare no conflict of interest.
# Internal Force Analysis of Buried-boring Piles in the Yuanzishan Landslide
## 3 Application of Buried-Boring Piles in the Yanzishan Landslide  
### Geological Background of the Yanzishan Landslide  
The Yanzishan landslide is located in Jiangnan Town, Langzhong City, Sichuan Province, China (31 deg34'05.0\" N, 105 deg56'20.0\" E). The slope elevation is approximately 480 m. The slope profile is divided into two levels of gentle platforms (Figure 3a). The elevation of the central platform is approximately 410 m, with an average gradient of 11 deg. The lower platform has an elevation of approximately 395 m, a width of approximately 70 m, and an average gradient of 9 deg. The Guangyuan-Nanchong Expressway passes through the front edge of the lower platform, and subgrade excavation during construction of the highway resulted in slope deformation (Figure 3b), which is a current threat to the safety of the highway.  
The slope body is mainly composed of quaternary slope deposits, with silty clay above the slip surface and muddy siltstone below the slip surface. The relevant geotechnical engineering parameters are shown in Table 2.  
The slip surface is buried at a depth of 10 m. The unbalanced thrust coefficient method was used to calculate the residual driving force of the landslide [31]. The residual driving force of the cross-section from A to A' of the Yanzishan landslide (Figure 4) was 300 kN/m. Emergency control of the landslide is needed to reduce slope deformation. Bored piles are proposed as an emergency control method.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Geotechnical Classification** & **Density** **(g/cm\({}^{3}\))** & **Internal Friction** **Angle \(\varphi\) (°)** & **Cohesion c (kPa)** & **Moisture Content** **(\%)** \\ \hline Soil & 2.00 & 30 & 13.8 & 28.9 \\ Muddy siltstone & 2.45 & - & - & - \\ \hline
**Geotechnical Classification** & **Poisson’s Ratio** & \begin{tabular}{c} **Saturated** \\ **Compressive** \\ **Strength (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Natural** \\ **Compressive** \\ **Strength (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Young’s Modulus** \\ **E (\(\times\) 10\({}^{4}\) MPa)** \\ \end{tabular} \\ \hline Soil & - & - & - & 0.000048 \\ Muddy siltstone & 0.25 & 3 & 5 & 5 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental parameters of geotechnical materials in the survey area.  
Figure 2: Comparison of monitoring results and our calculated values of internal forces for the bored pile under various driving force distribution coefficients: (**a**) shear force; and (**b**) bending moment.
# Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
## 2 Hybrid Techniques Based on the Use of Other Manufacturing Techniques with AM
### Laser Metal Deposition + Turning + Milling  
[PERSON] [44] developed a hybrid multi-tasking platform (Mazak Hybrid Multi-tasking), which incorporated turning and milling functionalities with an LMD process. The motivation of this study was to reduce the manufacturing and process times for part development. In their work, IN 718 was deposited onto a stainless steel alloy (316S31) using the Mazak Hybrid Multi-tasking machine. The application for this hybrid part was for the offshore oil industry. A material failure occurred on the 316S31 substrate, which had inferior mechanical properties to IN 718. According to the report, a high density was achieved (quantity not specified), and no defects were observed at the interface [44]. However, this is unlikely to occur in such processes, knowing that defects are inevitable [16]. [PERSON] et al. [45] modified a similar work by developing a strategy that utilizes a robotic arm with six degrees of freedom and with multi-changeable heads integrated into the system. The goal was to create a multi-axis AM. The optimized 6-DOF hybrid additive-subtractive manufacturing process had an estimated production time savings of above 50%. The proposed system was material-efficient and provided better surface finishes [45].  
For convenience, a summary of the selected hybrid solutions discussed in this section is presented in Table 1.
## 3 Solar Radiation Prediction Time Horizons
### Time Horizon  
The estimation of the output power of solar systems is necessary for the proper functioning of the electricity grid or for the optimal management of the energy flows that occur in the solar system. Before predicting the output of solar systems, it is essential to focus prediction on solar radiation. The prediction of components of solar radiation (global or diffuse) could be performed by several methods and the accuracy of a prediction model depends mainly on the time horizons [11].  
For the development of any solar prediction model, we must contemplate the timescale (counted from a certain moment in which we make the prediction), which determines the future moment for which we make the predictions. The biggest drawback is its linear character, which makes it difficult for all problems to be properly modeled [22; 24]. Models are very sensitive to unusual observations, forcing you to review the time series for detection and correcting before designing the prediction model [25].
# Parameter Conversion between Controlled Pass-By Method and Alternative Close Proximity Method
## 3 Results and Correlation Analysis
### Parameter Conversion of Noise Experiment Results between CPB and A-CPX
#### 4.3.2 Considering the Shielding Effect of the Vehicle Body on Noise Propagation
With these ratios, the sound pressure levels \(\mathrm{SPL_{CPB2}}\) and \(\mathrm{SPL_{CPB4}}\) at the S2 measurement point were predicted using the noise values \(\mathrm{SPL_{CPX1}}\) and \(\mathrm{SPL_{CPX3}}\) measured at the S1 measurement point as follows:  
\[\mathrm{SPL_{CPB2}}=10\mathrm{lg}\bigg{(}\frac{\beta}{\alpha}\bigg{)}+ \mathrm{SPL_{CPB1}}=10\mathrm{lg}\bigg{(}\frac{\beta}{\alpha}\bigg{)}+ \mathrm{SPL_{CPX1}}-\Delta\mathrm{L_{1}} \tag{14}\]  
\[\mathrm{SPL_{CPB4}}=10\mathrm{lg}\bigg{(}\frac{\beta_{1}}{\alpha_{1}}\bigg{)}+ \mathrm{SPL_{CPB3}}=10\mathrm{lg}\bigg{(}\frac{\beta_{1}}{\alpha_{1}}\bigg{)} +\mathrm{SPL_{CPX3}}-\Delta\mathrm{L_{3}} \tag{15}\]Equations (14) and (15) were used to obtain the predicted tire noise values at the far-field S2 measuring point. The predicted tire noise values at the S2 measuring point were superposed at the same frequency to obtain the spectral distribution of the predicted total tire noise values. Then, the prediction value of the total tire noise pressure level at the S2 measuring point was calculated using the noise superposition equation. When considering the shielding effect of the vehicle body on tire noise propagation, the difference between the measured and predicted values of tire noise using CPB is shown in Table 7.  
Table 7 shows that when considering the shielding effect of the vehicle body on noise propagation, the average difference value was 2.7 dB. Comparing Tables 5 and 7, we inferred that when considering the shielding effect of the vehicle body, the difference between the measured value and the predicted value using CPB is larger than when the shielding effect of the vehicle body is ignored.
## 2 Materials and Methods
### Productivity of Existing RES in Crete  
Solar and wind speed potential of Cretan Island is one of the greatest in EU, reaching up to 2100 kWh/m\({}^{2}\)/year solar radiation and above 8.5 m/s average wind speed in various spots on the island. The percentage of total output from RES contribution was increased for the year 2020 to reach 23.5%. The island is a single administrative region of Greece, which is divided into 4 individual prefectures, which in the context of this study are called areas 1, 2, 3, 4. As can be seen in Table 2, there are a total of 47 wind farms erected around the island, with a combined nominal power output of 215.12 MW [18].  
In addition, as indicated in Table 3, there are now 1204 small PV plants erected around the island, each with a nominal power output of 80 kW. The majority of these plants are attached to substations with voltages ranging from 0.4 kV to 20 kV, which is the standard practice worldwide [30].  
In Figure 4, the Cretan RES production for each month of 2023 is depicted as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S.L.** & **Prefecture of Wind Parks** & **Plants** & **Installed Power (MW)** \\ \hline
1 & Area 1 & 25 & 107.12 \\
2 & Area 2 & 8 & 45.40 \\
3 & Area 3 & 5 & 24.30 \\
4 & Area 4 & 9 & 38.30 \\ \hline  & TOTAL & 47 & 215.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wind power plant allocation.  
Figure 3: Total conventional production in Crete during 2023.  
The highest production was noted in July when the PV parks reached their peak due to prolonged sun exposure, accompanied by the production of WT parks. In addition to energy from the sun and the wind, the enormous olive tree farming that takes place on the island of Crete provides the island with a significant amount of solid biomass resources. In Crete, there are insufficient supplies of hydro energy and geothermal energy with a high enthalpy. It is anticipated that in the not-too-distant future, Crete will be host to not just one but many wind farms located offshore in addition to a solar thermal power plant. With the interconnection of Crete's electric grid, it is anticipated that the island would produce more environmentally friendly power that is derived from renewable energy sources. Tables 4 and 5 exhibit Crete's electricity production and carbon emissions from the island's different fossil fuels and energy sources, while Table 4 presents the island's installed capacities for those fuels and sources [31].  
Based on the information that is now available on the installed power and the output of energy for 2018, which is provided in Table 2, an estimate was made regarding the productivity of the solar-PV systems and the wind parks that are currently operating in Crete. Comparatively, the capacity factor of wind farms is 0.291, whereas the yearly productivity of solar photovoltaic systems is 1412 MWh/MWp.
## 9 Discussion  
Accurate positioning is the key to achieving the high-precision autonomous navigation of unmanned ships, and the positioning of unmanned ships based on multiple combined sensors has always been the focus of research by scholars. Data fusion based on two types of sensors is the current mainstream technology for unmanned-ship positioning, but there are shortcomings in affecting positioning accuracy and reliability due to the weakening or loss of certain sensor signals. In recent years, how to improve positioning accuracy by further fusing the data of three or more types of sensors has become a research hotspot. Adding sensors can help improve the reliability and accuracy of unmanned-ship positioning, but there is still a lack of research on how to further improve the accuracy and efficiency of data fusion through testing the confidence distance of data, as well as how to achieve effective localization under low or weak signals through data correction. In view of this, for the positioning of unmanned ships, in this paper, a multi-sensor positioning system incorporating ZigBee, GPS, and BDS was first built. Then, the particle-filter algorithm was introduced for denoising multi-sensor data. Next, the latest positioning data from multiple sensors were integrated into the proposed distribution using unscented transform to cause the proposed distribution to be close to the real distribution of the probability density function. Finally, the Gaussian mixture model was constructed, the adaptive threshold was set, and the data-confidence factor was associated with the hierarchical sampling to further improve the fusion filtering accuracy of the positioning data. The numerical test results of the three groups of models and the experimental test results of the navigation path all show that the proposed method not only realizes the mutual complementation, weighted correction, and confidence assignment of multi-sensor signals, but also significantly reduces their root-mean-square error and standard deviation compared with other filtering algorithms, which verifies that the proposed method has the advantages of high data reliability and a good filtering-fault-tolerance performance, and achieves the accuracy and reliable positioning of unmanned ships.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Index** & **TCPF-Z + G** & **TCPF-Z + B** & **BPF-Z + G/B** & **TCPF-Z + G/B** \\ \hline Average positioning error & 2.754 & 2.864 & 2.727 & 2.092 \\ Maximum positioning error & 4.561 & 4.875 & 4.945 & 3.631 \\ Positioning RMSE & 2.834 & 2.973 & 2.847 & 2.168 \\ Positioning Std & 0.572 & 0.717 & 0.751 & 0.506 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data positioning test results in section B.
## 2 Materials and Methods  
### Study Area  
The Qilian Mountains serve as a crucial ecological security barrier in western China, playing a significant role as the water source for the Yellow River basin and several inland river basins. The six major river basins in the Qilian Mountains include the Shule River, Heihe River, Shiyang River, Qinghai Lake, Datong-Huangshui River, and part of the Qaidam Basin. Predominantly located in the northeast of the Qinghai-Tibet Plateau, the Qilian Mountains are bordered by the Hexi Corridor to the north and the Qaidam Basin to the south, marking the boundary between the Hexi Corridor, Loess Plateau, and Qinghai-Tibet Plateau. The geographical coordinates are between 89.3\({}^{\circ}\)E-107.0\({}^{\circ}\)E and 34.2\({}^{\circ}\)N-45.0\({}^{\circ}\)N, and it covers an area of approximately 1.7663 million km\({}^{2}\) (Figure 1).  
The region comprises multiple northwest-to-southeast parallel mountain ranges and wide valleys, with peaks ranging from 4500-5500 m and valley floors at elevations of 3000-4000 m. The primary vegetation distribution includes desert-steppe zones between 2000 and 2300 m, grassland zones between 2300 and 2600 m, forest-steppe zones between 2600 and 3200 m, shrub-grassland zones above 3200 m, alpine meadow zones, and glacier zones above 4100 m. Rivers such as the Datong River, Shiyang River, and Heihe River in the six major basins originate from the forest-steppe and shrub-grassland water-conserving forests. The Qilian Mountains have complicated landscapes and served as an experimental site for integrated watershed studies, land surface measurements, and hydrological observations for a very long time [20]. Producing a long-term, fine resolution historical land cover dataset for this region, especially for data before 1990, addresses substantial  
Figure 1: The location of major river basins in the Qilian Mountains (**left**) and the visualization of geographical characteristics including color composite from remote sensing image (**right**).  
gaps in early observations. This advancement will enhance the integrated monitoring capabilities of the \"mountain, water, forest, field, lake, grass, and sand\" system, supporting the integrated analysis and assessment of the ecological environment and water resources in the Qilian Mountains. It will also provide scientific support for ecological protection and restoration efforts, as well as the development of national parks in the area.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 2 Tissue Sources for Preparation of ECM-Based Biomaterials for Skin Repair
### Acellular Dermal Matrix (ADM)
#### 2.1.2 AlloPatch(r)  
Pliable is an aseptically processed human reticular (HR)-ADM, intended for use in chronic or acute wound coverage [34]. It requires no rehydration or refrigeration prior to use and can be stored at ambient temperature according to the manufacturer's recommendations. This dermis differs from many other ADMs in that it does not contain a papillary portion, resulting in a homogeneous matrix that retains its original architecture and its key ECM components (Figure 2). The absence of asymmetry or orientation is considered beneficial in the clinical setting, having a positive impact on cell infiltration and native tissue remodeling [38]. In a prospective, randomized, controlled, multicenter study, this HR-ADM was shown to be superior in promoting DFU healing as compared to standard of care involving daily dressing changes with a collagen alginate matrix [39].
## 4 Discussion
### Advancing Measurement Technology for Monitoring Wet Circuits in Closed Channels  
To ensure accurate results, it is imperative to have a properly sized strap. Our recommended width of 100 mm allows for secure attachment of the measurement electrodes, minimizing any potential disruptions during the measurement process. A key feature of our measurement strap is the incorporation of a phase on the side facing the flowing water or sewage. This ingenious design element facilitates a seamless flow, effectively preventing any disturbances or interferences that might compromise the accuracy of the measurements. Through careful consideration of the hydraulic dynamics within the channel, the strap guarantees reliable and consistent data collection (Table 3). Efficient installation is essential for widespread adoption and practical implementation of our measurement strap. To address this, we propose the use of a Roman screw with opposing threads. This innovative fastening mechanism streamlines the installation process, eliminating the need for additional tools. Alternatively, an expanding mechanism utilizing a fixed regular screw or a silicone cone inserted between the angle brackets of the thin-walled PVC strap offers easy and secure installation options.  
Incorporating special openings in the strap design further facilitates the installation of the expanding mechanism for the measurement strap housing. These purpose-built features enhance the usability and functionality of the strap, ensuring seamless integration into existing channel infrastructures. The unique shape of our measurement strap allows for collision-free mounting of the measurement electrodes. Applied on a flexible PCB, the electrodes are meticulously positioned to capture precise measurements without interference. To ensure their secure placement, we recommend utilizing additional straps with snaps for proper fixation. This robust fixation system guarantees the electrodes remain firmly in place throughout the monitoring process. Rainwater ingress in sewer channels is a persistent challenge faced by urban wastewater management systems worldwide [39]. The infiltration of rainwater into sewer networks not only imposes significant strain on treatment plants but also raises concerns regarding environmental contamination and system capacity overload. Detecting and addressing rainwater ingress in a timely manner is crucial to ensure the optimal functioning and resilience of urban wastewater management systems [40]. In addition to rainwater ingress detection, the device enables proactive maintenance strategies to mitigate the adverse impacts of infiltration. Early detection of rainwater ingress allows wastewater management authorities to implement immediate corrective actions, such as targeted cleaning, optimized pump operations, and diversion of excess flows to alternative treatment facilities. These proactive measures contribute to improving system efficiency, reducing treatment costs, and minimizing the risk of overflows and environmental contamination.
# Condenser Design for On-Board ORC Recovery System
## 3 Thermodynamic Model of the Condenser
### LMTD Model  
This method is used to define the condenser when the inlet and outlet temperatures are known. The LMTD (Logarithmic Mean Temperature Difference) is defined as:  
\[LMTD=\Delta\text{Tlm}=\frac{\Delta T_{1}-\Delta T_{2}}{ln\frac{\Delta T_{1}}{ \Delta T_{2}}} \tag{1}\]  
where \(\Delta T_{1}\) is the difference between the inlet temperature of the hot source and the outlet temperature of the cold source and \(\Delta T_{2}\) is the difference between the hot outlet temperature and the cold inlet temperature.  
\[\Delta T_{1}=T_{\text{h,i}}\ -\ T_{\text{c,o}} \tag{2}\]  
\[\Delta T_{2}=T_{\text{h,i}}\ -\ T_{\text{c,o}} \tag{3}\]  
The condenser surface can be determined by the following equation:  
\[A=\frac{Q}{U\ LMTD} \tag{4}\]  
The formula shows that U and A are inversely proportional. That means that it is necessary to maximize U in order to obtain a minimum exchange surface. The overall heat transfer, given by combined conduction and convection, is frequently expressed in terms of an overall heat-transfer coefficient \"U\". For a cylinder, the geometry is given by:  
\[U=\frac{1}{\left(\frac{1}{h_{i}}\right)+\frac{d_{1}\ln\left(\frac{\theta_{0}}{ \lambda}\right)}{2\lambda}+\left(R_{\text{A}}\frac{1}{R_{\text{water}}}\right)} \tag{5}\]  
The terms \"d\({}_{i}\)\" and \"d\({}_{o}\)\" represent the diameter of the inside and outside areas of the inner tube. To decrease the number of unknowns, the calculation of heat transfer coefficient \"U\" is only limited to the inner area \"A\({}_{i}\)\", and the term \"A\({}_{i}\)/A\({}_{o}\)\" is replaced by the equation:  
\[A_{i}=\pi d_{i}L_{1}\]  
Figure 3: Detail of the condenser software representation taken from Figure 2.