esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Evolutionary Methods
### Quantitative Impact and Future Directions  
The quantitative impact of evolutionary methods in the food industry is profound. Studies have consistently shown that these methods can achieve significant cost savings, waste reductions, and energy efficiency improvements. For example, their application has led to cost reductions of 10-20%, waste reductions of 15-25%, and energy savings of 10-15% [66]. These improvements are not only economically significant but also critical for advancing the food industry's sustainability objectives [61].  
Looking to the future, the potential of evolutionary methods can be expanded through the development of hybrid optimization frameworks. By integrating these methods with neural networks, fuzzy logic, and reinforcement learning, researchers can address the limitations of individual algorithms and enhance their applicability to increasingly complex problems. For instance, combining Q-learning with cooperative evolutionary algorithms, as highlighted in recent research, allows for more effective decision-making in dynamic and distributed environments [75].  
Another promising direction is the integration of evolutionary methods with emerging technologies such as big data analytics and digital twin systems. These technologies enable more precise modeling and simulation of real-world systems, enhancing the ability of evolutionary methods to identify optimal solutions. For example, digital twins can provide real-time feedback on system performance, allowing evolutionary algorithms to adapt and refine their solutions dynamically [76].  
Evolutionary methods represent a cornerstone of modern optimization strategies, offering unparalleled capabilities for addressing the multifaceted challenges of the food industry. Their ability to handle complex, non-linear, and high-dimensional optimization problems makes them uniquely suited to advancing nutritional, economic, and sustainability goals. By enabling cost savings, waste reduction, and efficiency improvements, these methods contribute significantly to the Sustainable Development Goals (SDGs) outlined in the 2030 Agenda [75].  
As the food industry continues to evolve, the adoption of advanced multi-objective optimization techniques will be instrumental in building a sustainable and resilient food system. By integrating evolutionary methods with cutting-edge technologies such as IoT, machine learning, and digital twins, researchers and practitioners can unlock new levels of precision and efficiency in food system optimization. These advancements will not only enhance the industry's economic and environmental performance but also ensure its ability to meet the growing demands of a global population. The future of evolutionary optimization in the food industry is bright, promising transformative solutions that align with the goals of sustainability and resilience.
## 3 Problem Definition  
### Multi-Period Modular Capacitated Maximal Covering Location Problem (MMCMCLP)  
Suppose we are going to locate some facilities (like hospitals or disaster relief centers) in a city. Because of capital limitation, only a limited number of \(p\) facilities are going to be established that aim to cover the maximum number of demands. After defining the location of the facilities, according to the demand requests at each time period from each module, the assignment of modules and their proper size would be determined to maximize the total coverage amount minus the cost of establishing modules.  
The modules of the facility have four main properties:  
* Each module comes in different sizes. It can be chosen from different sizes to increase the service quality offered to demand points to overcome the service shortages or having idle units.
* The modules are portable and they can be transferred among the facilities when there is more request in another facility. The transferability is an important specification of modularity design that yields to flexibility in the system and reduces costs. The portability of most modules helps to provide a good level of service to demand points without having to provide more modules.
* As there are no constraints on the number of modules to be assigned to facilities, more of them can be established in the coming periods (if there is increasing demand) as expansion plans to have more coverage of demand points. The number and the location of the facilities after the decisions are made would be fixed for all time periods, but there is no such limitation for modules.
* Each size of the modules has a known lower and upper-level service capacity for which, the total number of allocated points should be between this lower and upper level capacity. On the other hand, the total number of demands allocated to all kinds of modules cannot exceed each facility's capacity.
# Multi-Body Model of Agricultural Tractor for Vibration Transmission Investigation
## 3 Results and Discussion
### Modal Analysis
Modifying stiffness and damping may help reduce vibration levels in these frequency bands. Resonance of the driver seat suspension is present in frequency bands of 1.25 Hz and from 2 Hz to 4 Hz. Based on Figures 15-18, it can be concluded that the first mode is dominant because, in higher frequencies, driver seat suspension effectively reduces vibration levels. In other words, driver seat suspension is soft enough to have a natural frequency lower than the highest acceleration frequency bands (1.6 Hz, 2 Hz, 2.5 Hz), enabling a transfer function lower than 1 for these bands. It is also helpful to analyse the damping ratios of eigenmodes. The highest damping (78.87%) is present in the first mode, where spring-dampers and the driver seat suspension are in resonance. By observing the damping of all modes, it can be concluded that modes with tyres and front bushing in resonance have generally lower damping ratios. In contrast, modes with spring-dampers and driver-seat suspension have higher damping ratios. Analysis of one-third octave frequency spectra at different locations and the modal analysis revealed three questions that need to be answered to fully understand the behaviour of the model in terms of driving comfort and transmission of vibrations from road excitations to the driver seat:  
1. Are the tractor tyre resonances at frequency bands from 1.6 Hz to 2.5 Hz the main contributor to vibration levels in these bands? 2. Are the front bushing resonances at frequency bands from 8 Hz to 12.5 Hz causing an increase in vibration not only at the front part of the cabin but also at the rear part, resulting in high vibration levels at the driver seat compared to the tractor body? 3. Is it possible to reduce vibration response in critical frequency bands by modifying the damping characteristic of rear spring-dampers? Three case studies were performed and analysed in the following chapters to answer these questions.
## 4 Discussion
### Flavonoid TLC Analysis  
#### 4.2.1 Fingerprint Detection  
Flavonoids are plant secondary metabolites that have a polyphenol structure. Flavonoids can be classified into several subgroups, including flavones, flavonoid, Flavonon, flavan-3-ol, anthocyanin, isoflav, and chalcone [36]. Flavonoids generally are colorless but can appear yellow (mainly flavones and chalcones) and blue, red, or purple for anthocyanin [37]. The selectivity of the TLC method in identifying flavonoid content in extract samples can be enhanced by choosing the right derivatization agent [38]. The appearance of 2 peaks at wavelengths 330 and 540 indicates that these peaks are acetylated anthocyanins. This is supported by previous research reporting that the ethanol extract of the sweet potato (_Ipomoea batatas_) contains anthocyanins in mono- or diacylated form [17]. The spectra of flavonoids show two main absorption maxima at 240-400 nm. These maxima are commonly referred to as Band I (300-380 nm) and Band II (240-280 nm) and are present in the extracts analyzed in this study. The characteristic wavelength range for Band I varies depending on flavonoid type; flavones is 304-350 nm, 3-hydroxyl substituted flavonoid is 328-357, and flavonoids (free 3-hydroxyl) is 352-385 nm [39]. Previous research has identified 29 types of flavonoids, including flavan-3-ols, flavones, flavonoids, flavanones, and anthocyanins, across 5 different cultivars [40]. Flavonols such as quercetin and kaempfer were also found in the ethanol extract of the leaves of Simon No. 1 [41]. Meanwhile, the anthocyanins potentially present in the sweet potato leaf extract in this study are penoidin, pelargonidin, and cyanidin glycosides [17].
## 2 Digital Traffic Environment
### Ship Domain  
The ship domain is considered a safe area that ensures navigation safety [15], which prevents other ships and stationary objects from entering a certain range around the OS. It has been half a century since the concept was proposed [34]. In the process of continuous improvement and development [30], the boundary smoothing model shown in Figure 2 is widely accepted.  
The aim of this study is to provide a collision avoidance decision-making method for ships navigating in the TSS waters. When the OS sails in TSS, if there is a collision risk with the TS, most of them are crossing situations. Therefore, the safety distance required for the port and starboard side is basically the same. However, the distances required for the fore and aft of the vessel are obviously different. When the collision risk comes from the aft, the other ship generally overtakes the OS, and the OS is a stand-on vessel. When the risk comes from the fore, there are many different encounter situations, and the avoidance requirements are more complicated correspondingly. Therefore, the safety distance of the fore should be greater than that of the aft. On the other hand, when the OS is moving ahead, the distance variation of the fore or aft is greater than that of the port and starboard sides. Therefore, the required safety distance of the fore and aft should also be greater than that of both sides.  
Based on the previous description, a symmetrical elliptical ship domain model with an offset center is adopted in this study. The imaginary ship is located at the center of the ellipse and at a distance of a*k before the OS. The model parameters are selected with the length of the OS (L) as the reference unit. The long and short axis of the ellipse field  
Figure 2: The boundary smoothing ship domain model.  
is set as a = 5 L and b = 2 L, respectively. The eccentricity coefficient k is 0.7. In future practical applications, the size of the parameters in the model can be adjusted by the captain according to the navigation environment and the ship's maneuverability.
## 3 Reservoir-Scale Simulations
### Seals and Interlayers  
Since the primary safety concern for CO\({}_{2}\) sequestration is effective sealing, a better understanding of the possible factors which play a vital role in affecting the sealing efficiency is crucial.  
Well logs suggest that the Sleipner field Utsira Sand is interspersed with a serial stack of shale inter-layers [85]. The thickest of these is 5 m and lies just below the seal, with deeper inter-layers having thicknesses below seismic resolution, ranging from \(\sim\)0.5 m to 5 m. Shale inter-layers may provide some storage capacity for CO\({}_{2}\). [PERSON] et al. [86] suggested that low-permeability zones can be stores for dissolved chemicals, since, in their study of mass transport and dissolution during CO\({}_{2}\) injection into a sandstone reservoir with multiple shale inter-layers, they found that the thicker and lower-permeability shale layers provided stable and long-term trapping, albeit with a smaller storage volume than that observed for sandstone. [PERSON] et al. [PERSON] et al. (2018) showed that a single, thin clay bed with a thickness of only 0.2 m could store a significant amount of dissolved trichloroethene for several years.  
The factors that may negatively influence the integrity of the sealing include geological heterogeneity, capillary entry pressure, geomechanics, and geochemistry. As a result, sealing capacity has received increasing attention ([PERSON] et al., 2018; [PERSON] et al., 2019). However, little consideration has been given to the combination of all these factors in one study. Several studies have been performed to evaluate just some of these factors. [PERSON] (2019) conducted a study which illustrated how vertical flow leakage through inter-layers was caused by overpressure build-up in more permeable regions of the reservoir for the Sleipner field, in a process known as the chimney effect. The study outcomes indicated that permeable fractures were caused by a pore pressure higher than the least compressive stress. Furthermore, [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) conducted experiments to investigate the sealing integrity and the leakage rate of CO\({}_{2}\) into the sealing sediment caused by capillary pressure breakthroughs. These studies led the authors to conclude that diffusive and advective CO\({}_{2}\) leakage via a non-fractured seal was minor and would not influence the storage capacity. In addition, [PERSON] et al. [PERSON] et al. (2019) performed a numerical investigation of the impact of capillary pressure on the CO\({}_{2}\)-rock-brine interaction in a reservoir, but neglecting the geomechanical risk. The obtained results showed that significant capillary forces would reduce the amount of gravity separation, resulting in a more homogenous CO\({}_{2}\) plume, which, in turn, would enhance the dissolution of CO\({}_{2}\) into brine.
## 3 Results
### Estimation of \(f^{esc}\) and \(SIF_{tot}\) Using Remotely Sensed Canopy Interceptance  
Canopy interceptance (\(i_{o}\)) was estimated using the method described in Section 2.3 with leaf area index (LAI) and clumping index (CI), which were then used to estimate SIF escape probability based on SIT. Figure 6 shows the comparison between estimated and simulated results (i.e.,'real' \(f^{esc}\)) of SIF escape probability.  
Figure 6a shows the estimated results of SIF escape probability using DART-simulated (i.e.,'real') LAI and CI as input for the estimation of canopy interceptance. We found that the estimation accuracy of SIF escape probability was markedly improved (maximum relative error less than 30%) when compared to the estimated results using simulated (i.e.,'real') canopy interceptance (maximum relative error up to 50%, see Figure 4b), except for the cases with a non-reflecting background where the SIF escape probabilities were generally underestimated.  
Figure 5: Comparison of the total emitted SIF (\(SIF^{SIT}_{tot}\)) using different SIF escape probabilities with DART reference values (\(SIF^{DART}_{tot}\)): (**a**) plots using \(f^{esc}_{SIT}\) estimated at varying solar zenith angles from 20\({}^{\circ}\) to 70\({}^{\circ}\); (**b**,**c**) plots using \(f^{esc}_{SIT}\) estimated at constant solar zenith angle (i.e., solar zenith angle (SZA) = 70\({}^{\circ}\) for (**b**); SZA = 20\({}^{\circ}\) for (**c**).  
Additional errors can be introduced into the estimation of SIF escape probability when remotely sensed data of LAI and CI were used as input compared to real LAI and CI values. To explore this impact, artificial errors (\(-50\%\) and \(+50\%\) constant value error of LAI\({}^{*}\)CI) were generated and then were used to estimate SIF escape probability (Figure 6b,c). Introducing a \(-50\%\) error of LAI\({}^{*}\)CI, SIF escape probabilities were overestimated in most simulations, especially for the cases with high FVC and background mostly covered by vegetation (Ref-2). We noted that the introduction of a \(+50\%\) error on LAI\({}^{*}\)CI has no apparent impact on the estimation of SIF escape probability, with only a slight underestimation per simulation.  
To further evaluate the influence of remotely sensed canopy interceptance on the estimation of SIF escape probability, we calculated \(SIF_{tot}^{SIT}\) (Figure 7) using the estimated SIF escape probabilities (i.e., \(SIF_{tot}^{SIT}=SIF_{obs}/f_{SIT}^{esc}\)) mentioned in Figure 6. When compared with the DART-simulated reference values (\(SIF_{tot}^{DART}\)), additional errors of LAI\({}^{*}\)CI reduce the estimation accuracy of \(SIF_{tot}^{SIT}\) (and \(f_{SIT}^{esc}\)) (R\({}^{2}\) reduced from 0.91 to 0.66 and 0.88). Further, underestimated LAI\({}^{*}\)CI produces a larger influence on the estimation accuracy (R\({}^{2}\) = 0.66) of \(SIF_{tot}^{SIT}\) (and \(f_{SIT}^{esc}\)) than overestimated LAI\({}^{*}\)CI (R\({}^{2}\) = 0.88).
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 5 Integrating Cognitive Radio in LPWAN for the Internet of Things
### Network Architecture to Support CR-LPWAN
#### 5.3.3 CR at both LPWAN End Node and Gateway  
In CR at both end node and gateway (CRENGW) scheme, all devices in the network are embedded with CR technologies. In this case, all devices, including end nodes and GWs, perform all CR functions including sensing white spaces, making decisions, and accessing the network. An additional function that could be performed in CRENGW is cooperative sensing, wherein IoT end nodes interact to decide pertaining to the presence/absence of PU signals. CRENGW is more complex in its design as against other schemes, however, it provides the highest communication reliability use-case, which makes it suitable for sensitive applications in medical and military domains.  
Following the above schemes, and similar to [76], we prescribe the CRGW scheme in the architecture of Figure 5. Deploying CR functions at the GW instead of at the end-nodes may be appropriate since it minimizes design complexities at the LPWAN end nodes, which are often resource constrained. It is convenient to deploy CR technologies at the GW since most GW infrastructures are designed to be robust both computationally and memory wise. Furthermore, GWs are often connected to power supply utility outlets and to backup power supply units, thereby assuaging the increased energy demands of integrating CR in LPWAN.  
When in operation, the CR-LPWAN GW scans the electromagnetic spectra to detect white spaces. In other designs, the geolocation database technology was used at the GW [85]. However, apart from being motivated by the IEEE 802.22 standard for CR [86], we suggest that SS be deployed in CR-LPWAN systems, because a number of IoT applications/devices are designed to be deployed in remote locations wherein internet services may be unavailable. In such a case, it becomes ineffective to rely on geolocation technologies.  
When a free channel is detected, the CR-LPWAN GW sends such free channels to all LPWAN end nodes in the network, which will be used by end nodes for onward communication to the GW and subsequent IoT devices. The GW may be linked to an IoT network server (NS) connected to different application servers (AS) based on the application area through a firewall. To improve access, most GWs may connect either via wireless or wired links to the internet. Although simple, the architecture of Figure 5 facilitates the concept of CR-LPWANs for IoT applications. Future ideas can be developed in this regard.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Proposed Method for Satellite
#### 5.2.2 Covariance Matrix of TDOA Result  
Define the TDOA estimation error as \(\Delta\eta_{t}=\hat{\mathbf{\eta}}_{t}-\eta_{t}^{o}\). Therefore, \(\Delta\eta_{t}\) is the optimal solution to the following problem:  
\[\left\{\begin{array}{l}\min_{\Delta\eta_{t}}\left(\mathbf{G}_{t} \Delta\eta_{t}-\varepsilon_{t}\right)^{\mathrm{T}}\mathbf{W}_{t}(\mathbf{G}_{t}\Delta \eta_{t}-\varepsilon_{t})\\ \mathrm{s.t.}\left(\Delta\eta_{t}\right)^{\mathrm{T}}\tilde{\mathbf{\Lambda}}_{t} \eta_{t}^{o}=0\\ \left(\Delta\eta_{t}\right)^{\mathrm{T}}\left(2\tilde{\mathbf{\Lambda}}_{3}\eta_{t }^{o}+2\left[-\left(\mathbf{s}_{N_{1}+1}^{o}\right)^{\mathrm{T}},0\right]\right)= 0\end{array}\right. \tag{80}\]  
\(\Delta\eta_{t}\) is defined as  
\[\Delta\eta_{t}=\left(\mathbf{I}_{4}-\tilde{\mathbf{G}}_{t}^{-1}\mathbf{\Psi}_{t}\left(\bm {\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \bmbm{ }}}}}}}}}}}}}}} \right)^{-1}\mathbf{\Psi}_{t}^{\mathrm{T}}\right)\tilde{\mathbf{G}}_{t}^{-1}\mathbf{G}_{ t}^{\mathrm{T}}\mathbf{W}_{t}\varepsilon_{t} \tag{81}\]  
where \(\mathbf{\Psi}_{t}=\left[\mathbf{\Psi}_{t1},\mathbf{\psi}_{t2}\right]=\left[\tilde{\mathbf{ \Lambda}}_{1}\eta_{t}^{o},2\tilde{\mathbf{\Lambda}}_{3}\eta_{t}^{o}+2\left[-\left( \mathbf{s}_{N_{1}+1}^{o}\right)^{\mathrm{T}},0\right]\right]\).  
The covariance matrix of \(\Delta\eta_{t}\) can be given as  
\[\mathbf{cov}(\Delta\eta_{t})=\tilde{\mathbf{G}}_{t}^{-1/2}\left(\Pi^{\perp}\left[ \tilde{\mathbf{G}}_{t}^{-1/2}\mathbf{\Psi}_{t}\right]\right)\tilde{\mathbf{G}}_{t}^{-1/2} \tag{82}\]  
Therefore, the covariance matrix of the source estimate is  
\[\mathbf{cov}(\hat{\mathbf{u}}_{t})=\left[\mathbf{I}_{3},\mathbf{0}_{3\times 1}\right]\tilde{\mathbf{G }}_{t}^{-1/2}\left(\Pi^{\perp}\left[\tilde{\mathbf{G}}_{t}^{-1/2}\mathbf{\Psi}_{t} \right]\right)\tilde{\mathbf{G}}_{t}^{-1/2}[\mathbf{I}_{3},\mathbf{0}_{3\times 1}]^{ \mathrm{T}} \tag{83}\]
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 3 Adaptive Bandwidth Tracking Strategy
### Adaptive Four-State KF-Based Algorithm
When the signal turns weaker, the C/N\({}_{0}\) becomes lower, so that the measurement noise \(\mathbf{R}_{k}\) becomes greater, \(\mathbf{K}_{k}\) decreases further and, finally, the LNBW is narrowed;
2. When the LOS dynamic increases, the process noise \(\mathbf{Q}_{k}\) becomes greater, \(\mathbf{K}_{k}\) increases further and, finally, the LNBW is widened.