esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison between IFOA-LS-SVM Model and MR Model
#### 3.4.1 MR Model  
Multiple regression is a time-honored technique, which is used to predict the variance in an interval dependent according to linear combinations of dummy independent variables, dichotomous or interval [44]. Generally, multiple regression aims to learn about the relationship between a dependent variable and several independent variables. The general equation of multiple regression is expressed as follows [45; 46].  
\[y=\sum_{i=1}^{n}a_{i}X_{i}+c \tag{22}\]  
where \(n\) represents the number of dependent variables, \(a\) represents the regression coefficient, \(X\) represents the independent variable, and \(c\) is a constant which represents the regression line intercept.  
To establish the predictive model of multiple regression with the process parameters obtained from Section 3.1, the experimental data were tested and used as the basic statistics. For characterizing the variation of extraction yield of quercetin used as an independent value, the SPSS Version 22.0 (2015) package was used to calculate the descriptive statistics such as mode, median, standard deviation, minimum, maximum, mean, and so on.  
Multiple regression analysis was performed to correlate the measured extraction yield of quercetin to six extraction parameters, i.e., \(X_{1}\), extraction time; \(X_{2}\), extraction temperature; \(X_{3}\), ultrasound power; \(X_{4}\), ultrasound frequency; \(X_{5}\), solvent volume/sample ratio; and \(X_{6}\), ethanol solution concentration (Table 4). Multiple regression models to predict extraction yields of quercetin and rutin were given below.  
\[y=0.236X_{1}-0.685X_{2}-0.198X_{3}+0.702X_{4}+0.225X_{5}-0.663X_{6}-12.146 \tag{23}\]  
where \(y\) represents the extraction yield of quercetin.
## 3 Results and Discussion
### Grain Yield and Grain Lipid Content  
Grain yield peaked in 2016, when it was 57.9% higher than in the first year of the study (Table 3). This result was attributed to favorable temperature (especially in July) and precipitation, which promoted the mineralization of soil organic matter and increased the availability of nutrients for maize plants. In all years, the average grain yield was highest in mineral NPK treatments. The supply of nutrients, especially in organic form, should be accompanied by favorable weather conditions that stimulate the activity of microorganisms responsible for their mineralization [56, 57, 58, 59]. In 2015, low grain yields were accompanied by a low lipid content in maize grain, which was attributed to weather conditions (low precipitation levels) that inhibited plant growth. Higher rates of MBM (2.0 and 3.0 t ha\({}^{-1}\)) contributed to lipid content in maize grain.  
Figure 5: Residual levels of organochlorine pesticides in soil, \(\mu\)g kg\({}^{-1}\), interactions between years and treatments. (**a**) content of \(\gamma\)-HCH, (**b**) content of DDT, (**c**) content of DDD, (**d**) content of DDE. Values followed by the same letters do not differ significantly in Tukey’s (HSD) test (\(p<0.05\)). \(\gamma\)-HCH—\(\gamma\)-hexachlorocyclohexane, DDT—1,1,1-Trichloro-bis-2,2 [4-chlorophenyl]-ethane, DDD—dichlororoethane, DDE—dichlororoethylene.
## 3 Field Case Study of Pumping Test
### Mobara Test Site  
The pumping test site selected is located at Mobara city (Chiba Prefecture), north Boso Peninsula of the Kanto region of Japan, where there are many wells for agricultural use with depths of up to 200 m. The test site consisted of two pumping wells and two monitoring wells (Figure 2). A single-mode optical fiber cable was installed behind one of the monitoring wells (Well-a) and it was cemented in the annulus between well casing and the geological formation. The two pumping wells (Well-3 and Well-4) are located at a distance of 175 m and 280 m from the Well-a respectively. The other monitoring well (Well-b), located at a distance of 5.5 m from Well-a, was used to monitor the water level during the pumping test. Figure 3 shows a schematic view of geological cross-section between the wells. Sediments at the test site, from the surface downward, are of the Kasamori, Chonan, and Kakinodai Formations, all of which belong to the upper Kazusa Group [36].  
Figure 3: Profile showing well locations and simplified geological columnar sections.  
Figure 2: Map of the test site and configuration of wells for pumping, fiber sensor monitoring, and water head measurement.  
**Kakinokidai Formation:** Within the site, the Kakinokidai Formation is formed of sandy siltstone. Indistinct stratification is present in the upper part of the formation. Over a wider region, the formation coarsens upward and fines eastward.  
**Chonan Formation** (approximately 120 m thick): The Chonan Formation is subdivided into two parts: the lower and upper parts. The lower part (approximately 40 m thick) is formed mostly of siltstone-dominated alternations with thin slumped beds. The upper part (approximately 80 m thick) is composed mostly of sand-dominated alternations with numerous slumped beds.  
**Kasamori Formation** (154 m thick): The Kasamori Formation is composed mostly of highly bioturbated deposits such as sandy siltstones and silty sandstones. The Kasamori Formation is characterized by low permeability and can be considered as a caprock layer in the site.
###### Abstract
The first one consists of sensor data of a fleet of aircraft engines until their failure and was obtained from a simulation software process by NASA [10]. This is a typical case where an RUL model can be developed since sensor data are available. The second use case is a friction welding process at BoschTermotechnology, where the welding tool should be replaced before failure to avoid damaging the working pieces. This case illustrates an opportunity where a dynamic preventive approach fits since the failure does not represent safety consequences, as in the case of the aircraft engines, and cost savings may be achieved even without sensor data. The primary contributions of this work are as follows:  
1. A PMD policy is proposed for cases where no sensor data are available. This proves effective in reducing maintenance costs since it can improve current PM policies by dynamically increasing or decreasing the time between interventions. The results indicate cost savings of up to 51.8% compared to static PM policies. 2. A novel predictive maintenance (PdM) approach for maintenance scheduling with imperfect RUL prognostics (PdMI) is proposed. This novel method leverages sensor data and the k-LSTM-GFT, an RUL prognostic method presented by [9], to optimize maintenance planning. In contrast to the existing literature, our approach dynamically calculates a safety factor to account for the uncertainty in RUL estimation by utilizing the failure probability estimated by the k-LSTM-GFT method. The results of this approach are very promising, being only 3-5% above the minimum cost that can be achieved. 3. A comprehensive comparative study of the proposed methods against traditional and idealistic maintenance scheduling policies is presented. 4. The proposed framework showcases versatility as it can be applied even when no sensor data are available. The PdM policy with imperfect RUL predictions (PdMI) is employed when sensor data are available, while the PMD strategy is used in cases where sensor data are limited. The remainder of this document is organized as follows: Section 2 presents the most recent works related to maintenance planning. Section 3 describes in detail the use cases adopted and formulates the problem. Section 4 presents the RUL prediction model that served as the base for one of the proposed maintenance scheduling approaches. Section 5 details the proposed framework for maintenance scheduling, while Section 6 describes other maintenance scheduling approaches used for comparison. Section 7 presents the main results and discusses them in detail. Finally, Section 8 summarizes the main findings of this work.
## 4 Discussion
### Limitations of the Study and Future Perspectives  
Biodiversity conservation has been one of the hot topics in ecological service research in recent years. Therefore, developing effective methods for measuring biodiversity has remained a crucial and challenging issue [56]. Changes in plant diversity represent one of the consequences of landscape fragmentation and habitat change and are closely linked to LUC changes [30]. This study combined the landscape structure index and HQI to form a comprehensive biodiversity index, facilitating the assessment of spatiotemporal changes in plant biodiversity on a regional scale [57]. However, it is important to note that certain issues, such as the assessment of animal biodiversity, have not been thoroughly explored. Future studies should comprehensively consider the distribution of animals in the study area, conduct a thorough assessment of animal diversity indices and changes in plant diversity characteristics, and engage in multi-scale, multi-level, and multi-faceted biodiversity research.  
The potential impacts of LUC modeling on biodiversity depend heavily on the accuracy of the predicted LUC data [58]. In this study, the GeoSOS-FLUS model was employed to simulate future LUC changes under different scenarios. Although it combined natural and socioeconomic factors and had high simulation accuracy, this model relied on the prediction of LUC demand. Additionally, the Markov chain only provided a simple description of future change trends based on historical data [59]. Furthermore, the impact of model parameter settings, spatial scales, and social policy changes on the outcomes were not considered in the applications. Furthermore, the applications did not account for the impact of model parameter settings, spatial scales, and social policy changes on the outcomes. Therefore, the predicted results may not perfectly align with the actual situation in 2040, but they can reveal certain characteristics and change mechanisms in LUC, providing a reference basis for government decision making [60].  
For the HQ assessment using the InVEST model, parameters such as the maximum impact distance, threat source weight, and habitat sensitivity were primarily drawn from previous studies in similar regions. The setting of these parameters was somewhat subjective, potentially influencing the HQ evaluation results. Further research is needed to establish more reasonable guidelines for setting input parameters in the habitat quality model. In future studies, it is crucial to enhance continuous field monitoring to collect relevant data, thereby improving the capacity to interpret real-time habitat quality.
## 1 Introduction
The coupling between axial, bending and torsional effects that often arise in wings, especially when using composite materials, is added to the traditional Euler-Bernoulli beams in order to capture some of the most relevant characteristics of real wing structure. The mixed time-marching algorithm is proposed and applied into the implicit predictor-corrector integration scheme for the first time, in which the end-point algorithm is used in the predictor step for efficiency and mid-point algorithm with numerical damping in corrector step for energy conserving and computation accuracy and stability. By this means, the time step of nonlinear numerical integration can be extended to as long as 0.1 s (note that this time step is not a universal reference and it depends on the frequencies of the structure and the external forces, and is a function of wing size, mass, stiffness, etc.) and the Newton-Raphson correction will converge in no more than five iterations with a tolerance of displacement of \(1\times 10^{-6}\). To couple the flight dynamics with nonlinear aeroelasticity in a more intuitive and straightforward way, via the spatial locations and orientations of every element, the ground, body and airflow axes for flight dynamics are re-defined analogously to the global and elemental reference frames for structural dynamics at the local element level, followed by the redefinitions of local Euler angles (roll, pitch and yaw angles) and airflow angles (angles of attack and sideslip) of each element. With all the variables defined in local reference systems, the computational costs of coordinate transformations can be reduced, which improves the solution efficiency. The [PERSON] finite-state subsonic inflow model is coupled with the structural model to simulate the unsteady aerodynamics of the aircraft in flight, and it will be perfected by the ONERA dynamic stall model [33; 34] in the following work. As for the nonlinear trim of a complete flexible aircraft, the Newton-Raphson method is implemented with the forming of Jacobian matrix, which relates the resultant force and moment of the whole aircraft with the trim parameters. The presented framework can be used for the quick analyses of a complete flexible aircraft in its conceptual and preliminary design phases, including linear and nonlinear trim, aerodynamic load estimation, stability assessment, as well as linear and nonlinear time-domain simulations and flight performance evaluations. With the proposed framework, numerical studies based on two representative benchmarks were conducted and carefully compared with published literature. With excellent agreements reached, the accuracy, stability, efficiency and reliability of the presented framework and its corresponding computing codes are validated.
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 2 Microbial Polymers
### Poly (D/L-\(\gamma\)-Gitumatic Acid) (\(\gamma\)-PGA)  
Poly (\(\gamma\)-glutamic acid) is considered one of the smartest microbial biopolymers due to its distinctive structural characteristics and applications. \(\gamma\)-PGA is an optically active polyamide that is structurally composed of D-L glutamic acid adjoined by a peptide bond. \(\gamma\)-PGA Poly (\(\gamma\)-glutamic acid) and Poly (\(\alpha\)-glutamic acid) are two isomeric arrangements usually extracted extracellularly through a reaction of glutamic ester monomers with an appropriate producer bacteria under optimum fermentation conditions [23; 24] The availability of free carboxyl and amine groups in \(\gamma\)-PGA at specific pH ranges makes it a suitable biopolymer to capture cations, especially heavy metals from noxious environments [159]. In the realm of environmental research, its emerging directive is to make sustainable material from the waste stream. \(\gamma\)-PGA can be used for multifarious environmental goals: in bioremediation [160; 161]; as a bio-flocculant agent [65; 162]; for wastewater treatment [163] or soil sedimentation [164]; as an anti-freezing agent [165] or antifungal agent [166]; for the construction of filter membranes [167; 168]; and as biodegradable green plastic [169]. Although the microbial fermentation of \(\gamma\)-PGA has been extensively researched, costs related to manufacturing, particularly those associated with substrates and processes, continue to be high [170].
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 1 Introduction
Third, the planning path often contains redundant turning points, which affect the motion efficiency of the mobile robot. For example, some algorithms do not effectively optimize the path in the path-planning process, which makes unnecessary turns in the path and increases the robot's running time and energy consumption. Similar to some of the above studies, this study also tries to improve the ACO algorithm to enhance the path-planning performance, and proposes an AGV PP algorithm based on the artificial potential ACO technique, but compared to the similar algorithms mentioned above, it makes the following improvements to solve the bottleneck problem. An initial HF based on potential field, including the gravitational effect, is developed to improve the algorithm's capability of diverting target spots. Secondly, it develops an iterative and adaptive pheromone volatilization coefficient to enhance global search efficiency through a dynamic parameter update mechanism. The pruning optimization is subsequently invoked to eliminate unnecessary points from the path in order to make it smoother and more efficient. The simulation results show that this enhanced algorithm performs significantly better than the conventional method with respect to path length, convergence speed, and other important parameters, especially in environments with a high density of obstacles.
## 3 Methodology
### [PERSON]'s Approach to Yield Estimation
#### 3.1.3 Soil Quality Index
Satellite RS data provide real-time information regarding these soils, proving to be a powerful analytical tool for predicting salt-affected regions and crops affected by salinity. Slope Gradient Index  
DEM is a digital cartography dataset representing a continuous topographic elevation surface through a sequence of cells. Each cell indicates the \(Z\) height of a feature at certain X and Y locations. DEMs are a representation of the \"bare earth\" since they incorporate elevation data for geological ground features such as valleys, mountains, and landslides, to mention a few. They lack elevation information for non-ground elements such as flora and structures [40]. DEMs are typically derived using RS-collected radar data, although they can also be derived by field research. DEMs are the most popular base for digitally created relief maps and are frequently utilised in GIS. Using DEM and GIS, this article intends to construct a method to derive and map the Slope Gradient (SG) index. \"Slope\" characterizes the change rate of DEM in every pixel. The spatial distributions of SG collected reveal regions with abnormally high SG levels [40]. Several algorithms exist to calculate SGI from DEM. QGIS uses a planar method for this reason. In this approach, the rate of change (delta) of the surface in the horizontal (dz/dx) and vertical (dz/dy) directions between each neighbouring cell and the centre cell is used to calculate the slope.
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 3 Leveraging Software Engineering for Sustainable Smart Cities with Electric Vehicles
### The Vital Role of Software Engineers in the Development of Smart Cities  
Building smart cities that fully support electric vehicles (EVs) requires collaboration across various engineering and technology disciplines. Effective interdisciplinary collaboration enables experts from fields such as mechanical engineering, electrical engineering, urban planning, and software engineering to work together in designing and implementing solutions tailored to the unique needs of EVs.  
Among these disciplines, software engineering plays an especially critical role as software engineers have the skills and knowledge to develop advanced software systems that will power smart cities for EVs. Software engineers can leverage emerging technologies and advancements in their field to create an intelligent and efficient EV charging infrastructure that enables the sustainable future of transportation. For instance, software engineers can develop software platforms that allow EV charging stations to monitor energy usage in real-time and communicate with other city systems [73]. They can also design software that optimizes the charging of EVs to reduce charging times and increase the number of vehicles that can be charged concurrently [74].  
By applying software engineering best practices, smart cities for EVs can be developed to be not only sustainable but also efficient, safe, convenient, and beneficial for all stakeholders. As EVs continue to gain mainstream popularity, the onus is on software engineers to rise to the challenges and opportunities presented by EVs and new technologies. Software engineers must pursue active collaboration with experts in other domains to address the complex needs of building smarter cities that support EVs while serving the environment and society.  
Building a sustainable infrastructure for EVs requires a diverse range of skills and areas of expertise. Software engineers are uniquely positioned to make significant contributions by developing advanced software systems that enable smart energy and transportation solutions. However, interdisciplinary collaboration is key; software engineering solutions must be designed through cooperation with mechanical engineers, electrical engineers, urban planners, and policymakers to ensure the holistic success of smart cities for EVs. Overall, nurturing a collaborative ecosystem of experts across fields is a call to action that will drive innovation in smart city development to support the growth of electric mobility.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 3 General Assumptions
### SiPM Modeling  
An SiPM is an array of APDs biased at the Geiger mode with the ability to detect a single photon arriving on its surface. These \"pixels\" are also commonly called single-photon avalanche diodes (SPADs). The photon counting process can be modeled by a Poisson distribution. Denoting the average number of received photons on the SiPM surface by \(\mu\), and the number of the counted photons by \(C_{\mathrm{ph}}\), the photon count probability \(Pr(C_{\mathrm{ph}}=k)\) is given by [7; 23]\[Pr(C_{\rm ph}=k)=\exp(-\mu)\frac{\mu^{k}}{k!}, \tag{3}\]  
where \(C_{\rm ph}=\sum_{i=1}^{N_{\rm SPAD}}c_{\rm ph}(i)\), with \(c_{\rm ph}(i)\) the photon count of the \(i^{\rm th}\) SPAD, and \(N_{\rm SPAD}\) the number of SPADs. The average photon count \(\mu\) can be expressed as a function of the received optical power as [6; 7]:  
\[\mu=\left(\frac{{\rm Y}_{\rm PDE}}{E_{p}}P_{\rm Rx}+f_{\rm DCR}\right)(1+P_{\rm AP }+P_{\rm CT})\,T_{s}, \tag{4}\]  
where \({\rm Y}_{\rm PDE}\) is the photon detection efficiency (which includes the SiPM fill factor), \(f_{\rm DCR}\) denotes the dark count rate, \(P_{\rm AP}\) is the probability of after pulsing, and \(P_{\rm CT}\) stands for the probability of cross talk. \(E_{p}\) denotes the photon energy, and \(T_{s}\) is the average counting period. Another important parameter of an SiPM is its dead time \(\tau_{d}\), which is the time required for each SPAD to recharge after detecting a photon. This causes the \"saturation\" of the SiPM at relatively high received powers (i.e., short ranges) [23], resulting in a nonlinear distortion (NLD) on the received signals. The dead time depends on the quenching device used in the SiPM design. Here, we consider passive quenching (PQ) devices for which the average output photon count, denoted here by \(\mu_{\rm PQ}\), is given by [7]  
\[\mu_{\rm PQ}=\mu\,\,\exp\Big{(}-\frac{\mu\,\tau_{d}}{T_{s}\,N_{\rm SPAD}} \Big{)}. \tag{5}\]