esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Applicability of DWCA-DCH in Post-Disaster Scenarios  
The DWCA-DCH algorithm proposed solution can be utilized in emergency rescue operations such as natural disasters or accidents, because UAVs can provide timely and dependable communication services for rescue teams and victims on the ground. Post-disaster scenarios often involve large scale damage to infrastructure, high demand for emergency services, and unpredictable user mobility. In such circumstances, UAVs can play a vital role in providing network resilience and coverage extension by serving as aerial base stations or relays. However, UAVs face limitations such as limited energy, high mobility, and dynamic topology. The proposed DWCA-DCH can mitigate the aforementioned problems. The dual cluster head mechanism ensures network stability and survivability in the event of UAV failures or energy depletion. Additionally, the dynamic clustering algorithm can adapt to the high mobility and frequent topology changes of UAVs. Moreover,the Markov process-based survivability analysis method can evaluate the performance and reliability of the UAV network under different conditions and parameters.  
An example of using DWCA-DCH in post-disaster scenarios is depicted as follows. Assume a disaster has occurred in a specific area and some ground users urgently require communication service. A group of UAVs is deployed to form a cluster ad hoc network based on DWCA-DCH. Each cluster comprises one prime cluster head, one backup cluster head, and several cluster members. The prime cluster head is responsible for communicating with ground users and other clusters, while the backup cluster head is ready to take over when required. According to the aforementioned criterion of cluster head selection, the selected pCH is required to ensure the post-disaster network services and, thus, higher values of \(w_{1}^{p}\), \(w_{2}^{p}\), and \(w_{3}^{p}\) are recommended. As for the selection of bCH, the values of \(w_{4}^{b}\) and \(w_{5}^{b}\) are larger in order to meet the requirement of network stability. The clusters are dynamically formed and updated based on the network status and user demands. Moreover, to further enhance the resilience of the system, it is recommended that the bCH in S state flies to the charging pile as soon as possible to restore the system to available more quickly. By utilizing the proposed SAM-MP, it is possible to derive optimal deployment strategies according to the system performance under different conditions, such as drone quantity and speed. Additionally, the proposed SAM-MP can provide guidance accordingly, such as improving fault repair rates and other related suggestions.
# A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty
## 5 Tests and Results  
The proposed model was written in the General Algebraic Modeling System (GAMS, version 24.8.5), using CPLEX (version 12.6.1) as an optimization engine. To illustrate the applicability and effectiveness of the proposed model, two power systems were used, namely: a didactic 6-bus power system [69] and the IEEE RTS 24-bus power system. A time horizon of 24 h was used for both systems. Experiments on the 6-bus power system were run in a desktop computer with an Intel core i5-5200 processor running at 2.20 GHz, 16 GB RAM, whereas experiments for the 24-bus power system were performed on a computing server with an Intel Xeon Platinum 8160 with 96 processors running at 2.10 Ghz.  
Two numerical experiments were performed. The first one captures the behavior of the objective function (OF) and running time based on known uncertainties of past time periods, considered in our AARO-SCUC model. The main idea of this numerical experiment is to determine the impact of known uncertain parameters of past time periods on the cost of the system. In this way, it is possible to perform a trade-off analysis between the quality and robustness of the solution and the model computing time. This would allow for determining computationally effective and reliable solutions when dealing with large-scale systems.  
The second experiment is a post-hoc analysis of the robustness of the solutions obtained with our AARO-SCUC formulation. In this case, Monte Carlo Simulations (MCS) were carried out considering 100 replications and 300 random scenarios, which were generated using uniform distribution for each random variable. In the second experiment, the level of uncertainty of the random variables was increased up to 200% of its original value, using steps of 2%. Figure 1 illustrates the increase in the level of uncertainty associated with the random variables (blue dots) with respect to the defined uncertainty set (red box, representing the maximum uncertainty level of the variable). In the first rightmost box, the uncertainty level goes up to 50% of the maximum uncertainty level. Later, in the second box, this level reaches up to 100% of the maximum level. From 0% to 100% of the maximum uncertainty level, our AARO-SCUC model must always yield feasible solutions. That is, the number of violated constraints must be zero. Finally, in the third rightmost set, there are values of the uncertainty outside the maximum level, resulting in a number of violated constraints produced by these values. As a robustness measure of the solution,in this experiment, when a given scenario produces an infeasible solution, the number of violated constraints per replication is registered.
# A Review on Decentralized Finance Ecosystems
## 2 Literature Review
### Blockchain Technology in the Financial Industry  
Blockchain technology has significantly contributed to modernizing finance, gaining recognition within the banking sector for its potential to bolster transaction security and foster financial market development [20; 22; 23; 24; 25]. Through decentralized operations, blockchain instills heightened confidence in previously centralized systems, establishing trustless networks where parties transact without relying on mutual trust [14; 26]. Immutable transaction records ensure both security and transparency, driving their widespread adoption across diverse industries.  
The decentralized and secure nature of blockchain has rendered it accessible and cost-effective, particularly benefiting unbanked and underbanked individuals, thereby promoting financial inclusion [2; 27; 28]. The advent of Bitcoin, coupled with blockchain, revolutionized financial transactions by eliminating the need for mutual trust. Moreover, blockchain's applications extend beyond finance to encompass distributed cloud storage, smart property, supply chain management, healthcare, and decentralized autonomous organizations (DAOs) [29; 30]. Blockchain adoption facilitates secure and transparent data storage, facilitating financial transactions among entities that lack trust without necessitating a central trusted third party [30]. In the realm of decentralized finance (DeFi), supported by blockchain and smart contracts, peer-to-peer transactions thrive. For instance, Ethereum, as a permissionless blockchain, facilitates decentralized and inclusive participation for all stakeholders [30].
## 3 Results
### Thickness Measurement of Wall and Bottom Part  
The wall thickness results for all experimental groups are presented in Table 3 and Figure 5. For all thickness conditions, the AO group and PO group exhibited the most accurate measurements, closely aligning with the intended thickness values with slight positive deviations. In contrast, the AL group demonstrated the closest measurements to the intended thickness values but with slight negative deviations. The ML group had the highest values relative to the original thickness for all thickness conditions (\(p<0.05\)).  
Figure 4: Representative visualization of the experimental groups based on RMS analysis, highlighting manufacturing accuracy variations.  
Figure 5: Boxplots displaying wall thickness measurements for all experimental groups under different merlon wall thickness conditions (0.3 mm, 0.4 mm, and 0.5 mm). The dashed line in the graph represents the original thickness. Columns sharing the same lowercase letter are not significantly different at \(p<0.05\) (one-way ANOVA followed by Games-Howell).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{3}{c}{**Thickness**} \\ \cline{2-4}  & **0.3 mm** & **0.4 mm** & **0.5 mm** \\ \hline AL & 0.293 \(\pm\) 0.013 & 0.396 \(\pm\) 0.010 & 0.495 \(\pm\) 0.007 \\ AO & 0.303 \(\pm\) 0.010 & 0.403 \(\pm\) 0.004 & 0.501 \(\pm\) 0.004 \\ PO & 0.304 \(\pm\) 0.006 & 0.401 \(\pm\) 0.003 & 0.501 \(\pm\) 0.002 \\ ML & 0.324 \(\pm\) 0.016 & 0.424 \(\pm\) 0.018 & 0.520 \(\pm\) 0.014 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured wall thickness (mean \(\pm\) standard deviation) of the experimental groups under different merlon wall thickness conditions. (Unit: mm).  
The bottom thickness results for all experimental groups are displayed in Table 4 and Figure 6. For all thickness conditions, the bottom thickness value of the AL group showed the lowest value among all experimental groups. In addition, the bottom thickness value of the ML group had the highest value for all wall thickness conditions.
###### Abstract  
This study aimed to analyse the effect of chlorhexidine digluconate (CHX DG) mouthwash on the adhesion of oral bacteria to orthodontic appliances. The interactions of four bacteria (_S. mutans, A. actinomycetemcomitans, S. oralis_, and _V. parvula_) with two alloys (stainless steel [SS] and nickel-titanium [NiTi]) and three CHX DG solutions (commercial products Curasept and Perio Plus, and pure CHX DG, all with 0.12% active substance) were tested. The adhesive effect on the orthodontic wires was evaluated after 24 h for _S. oralis_ and after 72 h for the other bacteria. The minimum bactericidal concentration of the solution for each bacterial strain was determined using the dilution method to test the antibacterial action. Salivary-pretreated orthodontic archives were exposed to minimal bactericidal concentrations of solution and bacteria. Commercial antiseptic products, especially Perio Plus, showed a better inhibition of bacterial adhesion to both alloys than pure CHX DG solution (_p_ < 0.05). _A. actinomycetemcomitans_ was most inhibited in the adhesion of all bacteria by the CHX DG products. A greater inhibition of streptococci adherence was observed on SS, while that of _A. actinomycetemcomitans_ was observed on NiTi. _V. parvula_ inhibition was product-dependent. Although there were differences between the strains and the tested agents, it can be concluded that Perio Plus most effectively inhibited the adhesion of all tested bacteria to the SS and NiTi alloys. _A. actinomycetemcomitans_ was most sensitive to all tested agents, while _S. mutans_ showed the highest resistance. The effectiveness of the tested agents was better on NiTi alloys.  
biofilm; dental plaque; orthodontic appliances; antiseptics +
Footnote †: journal: Applied Sciences
# A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems
## 2 The Sector Combination Optimization Algorithm to Obtain Initial Solutions
### Group the Customers  
Group the customers into two parts according to the nearest distance from the depot under the constraint (4), which is graphically like two concentric circles. \(\sum\limits_{i}^{i\in\text{{C}ir}}d_{i}\) represents the total customer demand of the inner circle of depot D, and \(\sum\limits_{i}^{i\in D}d_{i}\) represents the total customer demand of depot D.  
\[\sum\limits_{i}^{i\in\text{{C}ir}}d_{i}\leq(\sum\limits_{i}^{i\in D}d_{i})/2 \tag{4}\]  
The constraint (4) means that the total customer demand of the inner circle does not exceed half of the total supply of the depot D.  
For depot A, the total customer demand is 205, and half of the total demand is 205/2 = 102.5. Therefore, according to the nearest distance, under the constraint that the total demand does not exceed 102.5, the customers are allocated to the inner circle, and the remaining customers are assigned to the outer circle. The result of the grouping is shown in Figure 2. The customers belonging to the inner circle are: 1-3-5-7-8-10-12; the total customer demand of the inner circle is 86; the customers belonging to the outer circle are: 2-4-6-9-11-13, the total customer demand of the outer circle is 119.  
Figure 2: The sectors of depot A.
# Acoustic Parametric Signal Generation for Underwater Communication
###### Abstract  
This paper presents a study of different types of parametric signals with application to underwater acoustic communications. In all the signals, the carrier frequency is 200 kHz, which corresponds to the resonance frequency of the transducer under study and different modulations are presented and compared. In this sense, we study modulations with parametric sine sweeps (4 to 40 kHz) that represent binary codes (zeros and ones), getting closer to the application in acoustic communications. The different properties of the transmitting signals in terms of bit rate reconstruction, directivity, efficiency, and power needed are discussed as well.  
underwater acoustic communication; parametric technique; self-demodulation Article
###### Abstract  
As a trace gas involved in hydration during plant photosynthesis, carbonyl sulfide (COS) and its leaf relative uptake rate (LRU) is used to reduce the uncertainties in simulations of gross primary productivity (GPP). In this study, 101 independent observations were collected from 22 studies. We extracted the LRU, stomatal conductance (g\({}_{\rm s}\)), canopy COS and carbon dioxide (CO\({}_{2}\)) fluxes, and relevant environmental conditions (i.e., light, temperature, and humidity), as well as the atmospheric COS and CO\({}_{2}\) concentrations (C\({}_{\rm a,COS}\) and C\({}_{\rm a,CO2}\)). Although no evidence was found showing that g\({}_{\rm s}\) regulates LRU, they responded in opposite ways to diurnal variations of environmental conditions in both mixed forests (LRU: Hedges'd = \(-\)0.901, LnRR = \(-\)0.189; g\({}_{\rm s}\): Hedges'd = 0.785, LnRR = 0.739) and croplands dominated by C3 plants (Hedges'd = \(-\)0.491, LnRR = \(-\)0.371; g\({}_{\rm s}\): Hedges'd = 1.066, LnRR = 0.322). In this process, the stomata play an important role in COS assimilation (R\({}^{2}\) = 0.340, \(p\) = 0.020) and further influence the interrelationship of COS and CO\({}_{2}\) fluxes (R\({}^{2}\) = 0.650, \(p\) = 0.000). Slight increases in light intensity (R\({}^{2}\) = 1, \(p\) = 0.002) and atmospheric drought (R\({}^{2}\) = 0.885, \(p\) = 0.005) also decreased the LRU. The LRU saturation points of C\({}_{\rm a,COS}\) and C\({}_{\rm a,CO_{2}}\) were observed when \(\Delta\)C\({}_{\rm a,COS}\)\(\approx\) 13 ppm (R\({}^{2}\) = 0.580, \(p\) = 0.050) or \(\Delta\)C\({}_{\rm a,CO_{2}}\)\(\approx\)\(-\)18 ppm (R\({}^{2}\) = 0.970, \(p\) = 0.003). This study concluded that during plant photosynthesis and COS assimilation, light and water conditions co-regulated the stomata and LRU.  
[PERSON], et al. (2020), 22 February 2022
###### Abstract  
The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net Primary Production (NPP) and the effects of meteorological factors on the NPP change on the Tibetan Plateau (TP) using version 5.0 of the Community Land Model. The results showed that the average NPP was 256 (g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)) over the past 40 years, with a continuously increasing trend of 2.38 (g C\(\cdot\)m\({}^{2}\).yr\({}^{-1}\)). Precipitation was the main factor affecting NPP changes, temperature had no significant effect on NPP changes, while radiation showed a negative trend. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively. Based on grass coverage, we categorized alpine grasslands into three types: high, medium, and low coverage. Our findings indicate the NPP change of the high-coverage grasslands was mainly affected by precipitation, and then the temperature and radiation. Comparatively, the precipitation change is the driving factor of the increased NPP of low-coverage grasslands, but the temperature increase is the negative factor. Our studies have implications for assessing and predicting vegetation responses to future climate change.  
NPP; CLM 5.0 model; Tibetan Plateau; spatio-temporal dynamic; climate factor Article
# A Baseline for General Music Object Detection with Deep Learning
## 4 Experimental Setup
### Object Detection Models
#### 4.1.2 RetinaNet  
The RetinaNet [39] belongs to the family of one-stage detectors that are built on convolutional neural networks. Other prominent representatives are OverFeat [40], Single Shot Detector (SSD) [41] or You Look Only Once (YOLO) [42]. These one-shot detectors create a dense set of proposals along a grid and directly classify and refine those proposals. As opposed to the two-stage detectors, they have to handle a large number of background samples, which potentially can dominate the learning signal.  
The RetinaNet [39] is an adaptation of a Residual Network [43] with lateral connections to create features on multiple scales [44]. Small convolutional subnetworks perform classification and bounding box regression on each output layer. RetinaNet was proposed along with the focal loss function, which tries to overcome the hard object-background imbalance issue by dynamically shifting weight to increase the contribution of hard negative examples and decreasing the contribution of easy positives.  
The configuration of the network model requires setting several hyperparameters. We specifically checked four different back-ends for feature extraction, namely: ResNet50 [43], MobileNet128 [45],DenseNet121 [46], and a highly simplified version of the DenseNet. Various anchor dimension settings were also examined: the ResNet50 feature extractor performed best in preliminary experiments and was subsequently chosen. The negative overlap threshold was set to 40%, so every box with lower Intersection over Union (IoU) counts as background; similarly, the positive overlap threshold was set to 50%, and every box with a higher IoU is treated as foreground; boxes in between are omitted from the training signal.  
Figure 1: Basic architectures of the considered types of object detectors.
## 4 Analysis of Single Objective Algorithms Overview
### Learner Performance-Based Behavior
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Single** & \multirow{2}{*}{**Advantage**} & \multirow{2}{*}{**Disadvantage**} & \multirow{2}{*}{**Applications**} \\
**Algorithms** & & & & & \\ \hline EO & - & It provides an EO with parallel & Slow convergence speeds & - & It is used for solving a very large number of real-world optimization tasks [8]. \\  & & processing power that achieves & Premature convergence & - & - \\  & & computationally fast overall & & & - \\  & - & It provides an EO to find several & & & \\  & - & Optimal solutions, thus leading to & & & \\  & & the facilitation of MOO and & & & \\  & - & multi-modal problem solution & & & \\  & - & It presents the EO with the & & & \\  & - & capability of normalizing decision & & & \\  & & variables (in addition to & & \\  & & constraint and objective functions) & & & \\  & & within the evolving population & & & \\  & & with the use of population-best & & & \\  & & min. and max. values. & & & \\ \hline DE & - & Efficiency, simplicity, and real & - & Slow convergence speeds & - & It is used for solving a very large number of real-world optimization problems \\  & - & Works with 2 populations, which & Instability performance & & & \\  & & are new generation and old & & & [16,18]. \\  & - & generation of same population. & & & \\  & - & The population size is adjusted by & & & \\  & - & NP parameter. & & & \\  & - & Local searching property and & & & \\  & & speediness & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The advantages, disadvantages, and applications of the single algorithms. ## 5 Analysis of Multi-Objective Algorithms

This discussion will only address MOO among the several kinds of optimizations [25]. In MOO, the best values for several desirable objectives are found. Given that many real-world problems lend themselves to modeling with many competing aims, it has great practical value ([PERSON], 2019; [PERSON], 2004; [PERSON] and 2019, [PERSON]) [60; 61; 62]. MOO is used because it simplifies the optimization process by doing away with the necessity for intricate formulae. The capability to come to a compromise or trade-off on opposing issues is a difficult decision-making task in MOOs. Numerous difficult applications of multiple objective problems exist, including staff scheduling ([PERSON], 2015) [63], workshop scheduling ([PERSON] et al., 2018) [64], transportation challenges ([PERSON] and [PERSON], 2021) [65], energy efficiency, and other applications [66]. A thorough examination of the current multi-criteria decision-making models and their real-world applications is given in the paper by [PERSON]
# Cryobiotechnology of Plants: A Hot Topic Not Only for Gene Banks
## 2 Cryopreservation of Plants
### Plant Cryopreservation Methods
#### 2.1.4 Cryo-Plate Methods  
The newest cryogenic procedures use cryo-plates. Two main cryo-plate methods, known as the V and D cryo-plate methods, can be distinguished [22] based on the dehydration process. Using the former method, explants are dehydrated on cryo-plates using PVS2 vitrification [27], whereas the latter method uses air dehydration of explants [28]. In both methods, shoot tips are placed in small wells of an aluminum cryo-plate with alginate beads and dehydrated there with PVS2 solution or with air flow in a laminar flow cabinet, depending on the particular method. Afterwards, the cryo-plate is directly immersed into liquid nitrogen. The main advantage of cryo-plate techniques is their user-friendliness, mainly due to the easy handling of samples on the aluminum plates [27; 28]. Successful cryopreservation using cryo-plates methods has been reported for many plant species including strawberry [29], sugarcane [30], date palm [31], mat rush [32], and potato [33].
## 2 Methods  
For the purposes of reproducing qualitatively the relationships described in the experiment by [13], consider a 2-dimensional model that describes the dynamics of serotonin in the gut \(S_{S}(t)\) and its impact on the development of the enteric nervous system \(N(t)\). Assume that there exists a normal gut serotonin synthesis rate \(S_{0}\), and that \(S_{S}\) is cleared from the gut at a SERT-receptor-independent rate \(s_{out}\); clearance rate can also be increased as a function of SERT receptor and is accounted for by parameter _sert_.  
The enteric nervous system \(N(t)\) is assumed to increase proportionally to the \(S_{S}\) that remains in the gut proportionally to the amount of available serotonin per volume of \(N\) according to saturating function \(k_{n}N(t)\frac{S_{S}(t)/N(t)}{\xi+S_{S}(t)/N(t)}=k_{n}\frac{N(t)S_{S}(t)}{\xi N (t)+S_{S}(t)}\), where parameter \(k_{n}\) represents the rate of conversion of gut serotonin into ENS volume, capturing the overall effect of gut serotonin as a growth factor, as indicated by the experiments of [13]. Assume also that the ENS can become pruned over time at some rate \(n_{out}\), since it cannot grow indefinitely.  
The resulting system of equations becomes  
\[\begin{split} S_{S}(t)^{\prime}&=S_{0}-s_{out}S_{S} (t)(1+sert),\\ N(t)^{\prime}&=kn\frac{S_{S}(t)N(t)}{\xi N(t)+S_{S }(t)}-n_{out}N(t)\end{split} \tag{1}\]  
Parameters used in the simulations are summarized in Table 1. Values were chosen to qualitatively capture the experimentally observed behavior and do not necessarily represent physiological values.