esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Fleet Transition Modeling for Sustainable Inland Waterways Transport
###### Abstract  
The transition to sustainable waterways transport is imperative in the face of environmental and climate challenges. Local lakes, often overlooked, play a significant role in regional transportation networks and ecosystems. This study focuses on Orta lake, Italy, and aims to facilitate its transition to sustainable inland waterways transport by substituting its diesel-based fleet with electric vessels. Firstly, a comprehensive market analysis was conducted to understand the available electric vessel models and their technical characteristics. This included parameters such as capacity, range, and charging time. Based on the market analysis, an optimization model was developed to determine the minimum number of electric vessels required to completely replace the existing diesel-based fleet. This model considers various constraints and objectives, such as meeting transport demand, minimizing the number of vessels, and reducing environmental impact. The developed model was then applied to the case study of Orta lake using the collected market data. The results indicate an optimal fleet configuration and provide insights into the feasibility and implications of the transition. This study contributes to the growing body of knowledge on sustainable inland waterways transport and offers a methodology that can be replicated and adapted for other local lakes or maritime settings.  
sustainable inland waterways transport; electric ships; fleet transition; optimization model +
Footnote †: journal: Applied sciences  
0023
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 4 Evaluations
### Computational Complexity Comparison  
Table 7 summarizes the computational complexity based on the CPU times using the above-mentioned algorithms. The best results are marked bold. They were all programmed via MATLAB R2019a and tested on a Core i7-6700 CPU (3.4 GHz) with 32 GB RAM. It is evident that for any image sizes ranging from \(320\times 240\) to \(4096\times 2160\), the proposed ICAP always possesses the fastest processing speed. DCP exhibits the poorest performance, due largely to the computationally intensive soft-matting. The processing time of DCP for a \(4096\times 2160\) image could not be even measured to due insufficient RAM. FFD is better than DCP in terms of processing speed, since filtering techniques are exploited in place of soft-matting. HPM is about twice as fast as FFD. Conversely, CAP is only faster than the slowest DCP in this comparison because it uses a very large guided image filter continuously after a large minimum filter. The proposed ICAP reduces the processing time of CAP by more than six times. The advantage of processing speed is attributed to the clever use of a quad-decomposition algorithm, mHMF, simple low-pass filter, and linear form of adaptive weight.  
Figure 16: Qualitative assessment of different algorithms on a real hazy outdoor scene: (**a**) hazy image, dehazed images and corresponding zoomed-up regions of (**b**) DCP, (**c**) FFD, (**d**) CAP, (**e**) HPM, and (**f**), the proposed ICAP.  
Figure 17: Qualitative assessment of different algorithms on real-world scenes: (**a**) hazy images; (**b**) results of DCP; (**c**) results of FFD; (**d**) results of CAP; (**e**) results of HPM; and (**f**) results of the proposed ICAP.
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 2 Related Work
### Attention Mechanisms  
Evidence from the human perception process [23] illustrates the importance of the attention mechanism. Attention is generally divided into two types: top-down conscious attention and bottom-up unconscious attention. It is worth noting that the mechanism uses top-down information to guide the bottom-up feed-forward process, which biases the distribution of the most informative feature expressions while suppressing the less useful [41; 42; 43]. Attention is the ability to focus perception on a stimulus while ignoring other potential stimuli. It has proven to be one of the most interesting areas of research in cognitive neuroscience. The benefits of the attention mechanism have been demonstrated in many tasks, including neuro-machine translation [44], image subtitles [45], and lip reading [46]. In order to enhance the performance of image classification, some researchers have introduced attention mechanisms into their network models [33; 47; 48]. In [PERSON]'s neurophysiological study of attention in macaque monkeys [49], all regions of the visual cortex showed a greater sensitivity to attention when the target stimulus was presented with competing stimuli, compared to the condition when the target stimulus was presented alone. Another study of macaques similarly found that attention regulated sensory responses in areas V2 and V4 primarily when two or more simultaneous stimuli competed for access to a neuron's receptive field; no such effect was observed when only a single stimulus was inside the receptive field [50]. An fMRI study found that selective attention in humans modulates neural activity in the visual cortex through top-down biasing signals [48]. When a monkey or a person is asked to pay attention to a particular area of space, corresponding increases in neural activity can be observed in V1, V2, and V4 [49; 51; 50]. Such activation enhancement is described as a \"gain\" of the sensory process [52] or as an increase in neuronal sensitivity. [29; 30; 31; 32] show that there is a small but significant interaction between feature-selective and spatial attention. Recent research [53] suggests that feature-based attention operates in a spatially global manner throughout the field of view, and that the spatial focus of attention should be carefully controlled to maximize the global feature-based attention effect.
# Characterization of the Aroma of an Instant White Tea Dried by Freeze Drying
###### Abstract  
The aroma of an instant white tea (IWT) was extracted through simultaneous distillation-extraction (SDE) and analyzed by sensory evaluation, gas chromatography-mass spectrometry-olfactometry (GC-MS-O), aroma reconstruction, omission test and synergistic interaction analysis. Sensory evaluation showed the IWT was dominated with floral and sweet notes. The SDE extract had the aroma similar to the IWT. The main volatile components in the SDE extract were benzyl alcohol, linalool, hotrienol, geraniol, \(\alpha\)-terpineol, coumarin, camphene, benzeneacetaldehyde, 2-hexanone, _cis_-jasmin lactone and phenylethyl1 alcohol. GC-MS-O and aroma reconstruction experiments showed 16 aroma-active compounds. Linalool, _trans_-\(\beta\)-damascenone and camphene were the major contributors to floral, sweet and green notes based on flavor dilution analysis and omission test. Linalool and _trans_-\(\beta\)-damascenone had synergistic effect to promote floral and sweet notes. Camphene and _trans_-\(\beta\)-damascenone had synergistic effect to reduce green and sweet notes. The study helps to understand the aroma of IWT and antagonism interactions among aroma-active volatiles.  
Keywords:instant white tea; GC-MS; GC-MS-O; aroma reconstruction and omission test; synergistic interactions +
Footnote †: journal: _m molecules_
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 1 Introduction
### Motivation and Contribution  
The main contributions of this paper are as follows:  
(1) This study proposes a NOMA-based multi-UAV-assisted D2D communication model. In this model, multi-UAVs are deployed in 3D space as airborne base stations to provide services to ground cellular users and D2D clusters in which NOMA techniques are applied to D2D communications to improve spectrum utilization efficiency and system throughput. Based on the system model, a problem to maximize the system throughput is proposed: firstly, an in-depth study is carried out for the channel allocation problem, and subsequently, this study jointly optimizes the trajectory design and power control of the UAVs.  
(2) For the above channel allocation, trajectory design, and power control problems, this study proposes a joint optimization strategy aimed at maximizing the system throughput. To effectively solve this complex multivariate optimization problem, the overall problem is decoupled into two subproblems. First, for channel allocation, this study constructs dynamic simple edges and hyperedges using the dynamic hypergraph method and achieves efficient dynamic coloring by transforming the dynamic hypergraph into a directed graph, thus optimizing the allocation of channel resources. Secondly, for the trajectory design and power control problem, we propose the MDQN algorithm to intelligently optimize the flight trajectory and power control of the UAVs. Finally, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network algorithm, which can dynamically optimize channel allocation, trajectory design, and power control in real-time changing 3D environments, thus maximizing the system throughput.  
(3) This study conducts extensive simulations to compare the proposed algorithm with several benchmark algorithms. The results verify that our proposed joint dynamic hypergraph Multi-Agent Deep Q Network algorithm significantly improves the system throughput under different numbers of D2D clusters and different D2D cluster communication spacing schemes, UAV sizes, trajectory designs, and NOMA decoding orders. In addition, the trajectories of 3D UAVs are more reasonable when considering real scenarios.  
The rest of this paper is organized as follows. Section 2 introduces the system model. Section 5 establishes a formulation of the maximizing system throughput problem. Section 4 presents the solution to each subproblem. Section 5 discusses the simulation results to demonstrate the advantages of our proposed algorithm, and Section 6 concludes this paper.
## 6 Discussion  
### Strengths and Limitations of the DFDLOF Framework  
#### 6.1.1 Strengths  
The Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF) stands out for its innovative approach to personalized education through machine learning [51]. One of its primary strengths is the ability to dynamically adapt learning content and strategies to individual learners' needs. This adaptability ensures that each learner receives an educational experience tailored to their specific learning style, pace, and preferences, which maximizes engagement and learning outcomes.  
Another strength lies in DFDLOF's data-driven approach. By continuously collecting and analyzing data on learner interactions, the framework offers insights into learning patterns and behaviors that traditional educational models might overlook [52]. This capability enables educators and institutions to make informed decisions about curriculum design and instructional strategies.  
DFDLOF also provides real-time feedback to learners, a vital feature for keeping learners engaged and on track. The immediate responsiveness of the system to learner inputs ensures that misconceptions are corrected promptly and learners receive constant support throughout their educational journey.
## 2 Smart Grids System
### Power Supply
#### 2.2.1 Battery Management
In comparison to the standard charging method, the results showed that the multi-stage constant current charging technique could significantly reduce charging time by 56.8%, extend battery life by 21%, and enhance energy efficiency by roughly 0.4 percent of constant current and constant voltage [83]. Moreover, we used four cells for experiments to ensure the consistency of the results and to reduce the effect of the cell-to-cell variations [84]. The cells were new and uncycled and stored in a thermally managed storage chamber at 10 \({}^{\circ}\)C at 50% SoC before experiments to minimize their calendar aging [84]. In addition, different temperatures, charge-discharge rates, and the depth of discharge can give rise to the evolution of the dominant aging reactions that can offer guidance in selecting a reasonable factor range when designing accelerated aging tests [85]. However, the autoregressive recurrent Gaussian process regression (GPR), which considers current and historical voltage, current, and temperature measurements, as well as the prior SoC estimate, increased the estimation performance [86]. In addition, this battery management system (BMS) with FL controller method improved the battery's function and life [87]. Reducing energy costs is another subject in battery management, as many researchers considered reducing power consumption in their studies. The electricity needed to operate the system is not produced by the deployed MG system [88]. Therefore, a sizing method based on the system's consumption profile and the site's weather conditions was introduced to upgrade the MG system to produce the total electricity needed by the load [89]. Moreover, they found that integration of a photovoltaic system leads to the reduced economic viability of the battery by reducing the revenues generated by the battery while performing peak shaving [89]. In addition, we proposed a scheme that creates three clusters of various objective functions to coordinate charging and discharging cycles; the first cluster uses time of use tariffs to reduce grid-integrated energy storage batteries (GIESBs) power charging costs. The second cluster uses per-unit generation from photovoltaics (PVs) and wind  
Figure 4: State of charge (SoC). turbines (WTs) to reduce GIESBs charging power. The third cluster, however, reduces the GIE's discharge capacity [90].
## 6 Conclusions  
The PZTs installed on the beams effectively detected the initiation of cracks within their respective monitoring areas, with nearly all of them even anticipating the imminent development of these cracks.  
* The PZTs \"J\", positioned in the interfacial surface/zone, exhibit remarkable performance as in almost all the examined cases, they achieved to identify, even at early states, the changes in the structural integrity of the jacket.
* The RMSD index values proved to be effective indicators of damage severity and progression, allowing for a more precise assessment of the structural integrity of the beams.
* By comparing the symmetrical PZTs and their RMSD values, the severity of the jacket's degradation and the area of the forthcoming failure were also achieved.
* The voltage response curves obtained from the PZTs exhibited significant alterations at their resonant frequencies. These alterations signify a strong correlation between the voltage measurements and the structural integrity, affirming that voltage signals could serve as reliable indicators for early damage detection and monitoring.
* An in-depth examination is necessary to evaluate the qualitative information provided by the acquired signals, such as the shifting of voltage peaks and frequency resonant frequencies.  
The utilization of the proposed Structural Health Monitoring (SHM) method for real-time monitoring, aimed at detecting damages in retrofitted RC elements with U-shaped mortar jackets and predicting the jackets' slippage, represents a noteworthy contribution of this experimental work to RC structural applications, thereby optimizing their efficiency.  
Conceptualization, C.E.C. and N.A.P.; methodology, C.E.C. and N.A.P.; validation, N.A.P. and M.C.N.; formal analysis, N.A.P., M.C.N. and G.M.S.; investigation, N.A.P., M.C.N. and G.M.S.; data curation, N.A.P., M.C.N. and G.M.S.; writing--original draft preparation, N.A.P. and M.C.N.; writing--review and editing, C.E.C. and N.A.P.; visualization, N.A.P., G.M.S. and M.C.N.; supervision, C.E.C. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
The original contributions presented in the study are included in the article material; further inquiries can be directed to the corresponding author.  
The authors wish to express their sincere gratitude to Sika Hellas ABEE and especially to [PERSON], TMM Eng. Refurbishment & Building Finishing at Sika Hellas, for providing the grout for the jacket construction and the epoxy resin for the dowels' anchorage.  
The authors declare no conflicts of interest.
## 3 Results
### Introductory Findings Regarding G-QMS in Sectors of SoS Model
#### 3.1.2 Affinity Between G-QMS of G-Org. of SoS Model and G-QMS in SoS Model  
The relationship between the G-QMS of G-Org. of SoS model and the G-QMS in SoS model is defined by a complex network of interfaces, with several key elements emerging from the research findings:  
1. The G-QMS in SoS model is infrastructurally dependent on the G-QMS of G-Org. of SoS model, allowing for the support of the G-QMS in the SoS model.
2. The G-QMS of G-Org of SoS model inherently includes QMS entities of Quality Project Management for such and other systems (denoted by \(QMS_{pro_{x}}\)), while unlike them, the supra entity G-QMS in SoS requires an adapted, separate model. However, these entities share common elements in terms of content and structure, which are placed within each of the two models or in their external environments. These shared elements are linked by specific, unique interrelationships.
3. The diverse structural nature of both the G-QMS of G-Org. of SoS and the G-QMS in SoS models--particularly influenced by the specific structure of the SoS project--results in a variety of interrelationship forms between the two models. The G-QMS in Sectors of SoS model must, therefore, be flexible enough to accommodate such variability.
4. SoS projects are realized within G-organizations capable of accommodating such projects and capabilities. These G-organizations typically maintain a G-QMS in Sectors of SoS that encompasses these two supra-entities. The G-QMS of G-Org. of SoS not only interacts with the G-QMS in SoS but also plays a vital role in its formation and operational support, ensuring its successful integration and functionality.
# Perceptual Similarities between Artificial Reverberation Algorithms and Real Reverberation
## 4 Results
### Mean Value Analysis  
Box plots were used to detect the outliers from the raw data, defined as the values outside the upper and lower quartiles plus 1.5 times the interquartile range [58]. Figures 15-18 show box plots of the scores for the seven reverberation algorithms, reference (Ref), and anchor applied to female singing, male speech, cello piece, and drumbeat with 0.266 s, 0.95 s, and 2.34 s reverberation times, respectively. As these outliers were not identified as input or measurement errors and the listening test was based on a subjective evaluation, these outliers were retained and analysed using non-parametric hypothesis tests to ensure robustness.  
The data provided by these 20 experienced participants were then averaged to determine which algorithm was perceived as having reverberation most similar to the real reverberation. Figure 19a-c present the mean score with standard error of the female singing simulated by reverberation algorithms under short (0.266 s), medium (0.95 s), and long (2.34 s) reverberation times. Similarly, Figure 20a-c, Figure 21a-c, and Figure 22a-c present the same data for the male speech, cello piece, and drumbeat, respectively.  
Figure 16: Box plots of the scores of the seven reverberation algorithms, reference, and anchor simulating male speech with 0.266 s, 0.95 s, and 2.34 s reverberation time: (**a**) 0.266 s, (**b**) 0.95 s, (**c**) 2.34 s (‘+’ in figures presents outliers).  
Figure 17: Box plots of the scores of the seven reverberation algorithms, reference, and anchor simulating cello piece with 0.266 s, 0.95 s, and 2.34 s reverberation time: (**a**) 0.266 s, (**b**) 0.95 s, (**c**) 2.34 s (‘+’ in figures presents outliers).  
Figure 15: Box plots of the scores of seven reverberation algorithms, reference and anchor simulating female singing, male speech, cello piece, and drumbeat with 0.266 s, 0.95 s, and 2.34 s reverberation time: (**a**) 0.266 s, (**b**) 0.95 s, (**c**) 2.34 s (‘+’ in figures presents outliers).  
Figure 19: The error bars of the mean score with standard error of the female singing simulated by the reverberation algorithms under short (0.266 s), medium (0.95 s), and long (2.34 s) reverberation times: (**a**) 0.266 s, (**b**) 0.95 s, (**c**) 2.34 s.  
Figure 20: The error bars of the mean score with standard error of the male speech simulated by the reverberation algorithms under short (0.266 s), medium (0.95 s), and long (2.34 s) reverberation times: (**a**) 0.266 s, (**b**) 0.95 s, (**c**) 2.34 s.  
The mean scores of the above reverberation algorithms are listed in Table 11. The highest scores are shown in bold and are concentrated among the Gardner and HMS reverberation algorithms. In general, the Gardner reverberation algorithm's simulation of long reverberation is perceived as closer to the real reverberation. The HMS reverberation algorithm generally performs better in the simulation of medium and short reverberation times, except for the female singing in the short reverberation time, where the Gardner reverberation algorithm outperforms the HMS algorithm.
## 3 Routing for Data Centric Storage
### Hierarchical Voronoi Graph Routing (HVGR)
Instead of this, each node knows the path to a set of landmarks and route the messages towards them. This routing is demonstrated to be more efficient that GPSR. The disadvantage is the cost related to the hierarchical infrastructure creation. Choosing the landmarks as well as notifying their position to the nodes implies to use quite a lot of messages, in particular for large WSNs.
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 5 Wireless Technologies
### Self-Powered System  
The source of power is one of the key factors for the long-term, stable operation of wearable sensors. Lithium-ion batteries are used most commonly in wearable electronics. However, their bulkiness and rigidity could limit the wearability of the whole integrated system despite other components being flexible and stretchable. To address this issue, various approaches have been used to implement structural modifications, such as the island-bridge, serpentine structures, and origami folding [71; 141]. However, lithium-ion batteries still have toxicity issues, and they require intermittent recharging or replacement, which limits the sustainable use of the devices that use them. Therefore, there has been significant interests in developing self-powered systems and using them in wearable electronics. Wireless power transfer and energy harvesting technologies are the two main technologies that are being used in self-powered systems.