esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Basic Performance Evaluation
#### 5.1.3 Flow Completion Time  
In this section, we analyze the distribution of completion times for data flows of varying sizes across four scenarios, with the results presented in Figure 9 (Standard box plot format). As flow size increases, the completion times across all scenarios generally show an upward trend. The median flow completion time under CS is consistently the highest, with the widest distribution range, indicating that without buffer management, the limited buffer resources are not effectively allocated during congestion. This results in a significant number of packets being unable to enter the buffer for service, thereby degrading overall transmission performance. ST shows some improvement over CS, particularly in higher bandwidth scenarios (3 and 4), as CS lacks buffer management, making performance degradation more severe in such conditions. DT provides only slight improvements over ST, primarily for large flows (1-2 MB), with less noticeable gains in Scenarios 3 and 4. This suggests that relying solely on classic DT does not yield significant performance optimization in the scenarios discussed in this paper. To enhance the performance of the reordering buffer, more suitable threshold calculation methods must be designed to address the specific characteristics of ICN multipath transmission. Furthermore, applying RED in conjunction with DT outperforms DT alone in all four scenarios, indicating that finer-grained, flow-level management based on reserved buffer space further improves performance. In the RCBRB method, the distribution of flow completion times is relatively wide across all scenarios. This is primarily because RCBRB adopts an isolated buffer design for each flow, where its buffering mechanism does not enforce the eviction of packets with longer waiting times, as these packets do not directly impact the delivery of other flows. This characteristic results in a more dispersed distribution of flow completion times. A comparison of the median results reveals that, in all four scenarios, RCBRB performs significantly worse than other methods for large flows (1-2 MB). This further corroborates the conclusion in Section 5.1.2 regarding its limitations in data scale scalability.  
Figure 9: _Cont._  
RBMM demonstrates the best flow completion time performance across various traffic scales in all four scenarios, with the lowest degree of outliers. In the case of large flows (1-2 MB), its strong performance indicates that this method effectively reduces the long-term occupation of buffer resources by large flows, allowing for a more equitable use of buffer resources across different traffic scales. This ultimately lowers overall flow completion times and enhances the user experience.
## 3 Results and Discussion
With the assumption mentioned in Section 2.4, the analytical method can be used to accurately predict the mechanical properties if the lattice structures have a low relative density. In this study, the elastic modulus of the specimen with a lower relative density (50.48% and 50.68% relative densities) shows a good agreement between the results obtained from the analytical method and the experimental study. The discrepancy between the results obtained from both methods increases for the specimens with a higher relative density. For the elastic modulus, since some dimensional properties of unit-cell struts are included in the formula of [19], the results obtained from [19] are slightly closer to the experimental results than those obtained from [17]. The yield strength values obtained from the analytical method in the work by [17] are lower than those obtained from the experiment. With the considered relative densities in this study, the wall portion of all infill specimens reaches the yield point before the lattice portion. Thus, Equation (7) was used to compute the yield strength for this case. The discrepancy between the results obtained from Equation (7) and the experiment also increases, caused by the discrepancy in the elastic modulus results. Since, in the rule-of-mixtures model, the yield strength is a function of the elastic modulus, the prediction of the yield strength yields an accurate result for the specimens with a low relative density. The experimental results of the tensile test show that, when the relative density of the specimens increases, the mechanical properties rapidly increase. For example, when considering the relationship between the elastic modulus and the relative densities, the relation is non-linear for specimens with a higher relative density [23]. The 316L specimens were used in the fatigue test because this material is widely used in many applications with excellent corrosion resistance and ductility. The ultimate tensile strength values of the full-solid and infill specimens in Tables 3 and 4 were used to define the stress level of the specimen for each considered infill density, i.e., 16%, 20%, 24%, and 100% equivalent (full-solid). The geometry and dimensions of the samples in the fatigue test are the same as those in the tensile test. The fatigue results of eight to ten samples were collected for each infill density. The S-N data of the full-solid specimens and infill specimens plotted in both linear-log and log-log scales are shown in Figures 12 and 13, respectively. The stress values between 30% and 90% of the ultimate tensile strength were used to set the maximum stresses for each level in the fatigue test. It can be observed that the fatigue limits of the specimens with 16% and 20% infill densities show a slight difference, while that of the specimens with 24% infill density shows a considerably higher fatigue limit. It is implied that the relationship between the fatigue behavior and the relative density is also non-linear when the specimens have a higher relative density.
## 2 Biodiesel Synthesis Processes
Catalytic transesterification involves interchange of alkyl groups (R) of esters with alkyl groups of alcohols. It results in production of new ester (biodiesel) and alcohol (glycerol) [1]. Following two basic steps are involved in transesterification process:  
1. Alcohol and the catalyst react to form alkoxide ion;
2. A reaction between an alkoxide ion and triglycerides in oil to produce fatty acid alkyl esters [9]  
In cases where methanol is used as a solvent, the first step includes the formation of a methoxide ion. Hydrogen ions are liberated from methanol and at the same time hydroxides are donated by the base, for example NaOH/KOH. The combination of proton (from alcohol) and hydroxyl (from alkali) results in the formation of water. Soap is formed when metal ions (released from base) react with free fatty acids. This unwanted reaction of saponification is due to excess water in the reaction mixture. To avoid saponification, a water-free medium is required. The second step is the synthesis of fatty esters and glycerol when there is an excess of alcohol, for example, three moles or above react with one mole of triglycerides in oil. In order to attain 100 lbs. of biodiesel, 100 lbs. of fat, 1 lb. of catalyst and 20 lbs. of alcohol are required [10]. Basic schematic steps required for biodiesel production via alcoholsis followed by its characterization by advanced analytical techniques are presented in Figure 2.  
The yield of biodiesel is directly influenced by optimization parameters, for instance feedstock oil, amount and type of catalyst, time, and temperature, oil to methanol ratio and agitation rate. The brief discussion on criteria to select feedstock, alcohol (solvent/cosolvant) and catalyst selection for esterification and transesterification is given below:
## 1 Introduction
### Summary of the Research Aim and the Assumptions  
The provided state of the art demonstrates that the scheduling of offshore operations during the installation constitutes a crucial but hard-to-achieve task. On the one hand, it involves expensive resources, which must be chartered long before the actual project starts. On the other hand, offshore operations subject to highly dynamic weather conditions, which render accurate long-term planning impossible. This article aims to provide a methodology that can act as a basis for a decision-support system for the scheduling of offshore operations. In contrast to the approaches found in the literature, the proposed approach combines Mixed-Integer Linear Programming to achieve optimal plans with a Model Predictive Control scheme to handle the involved dynamics. Current approaches found in the literature usually entirely rely on optimization, which, in practice, would require perfect data, or use reactive approaches in their simulation, which usually results in sub-optimal plans. Compared to previous work, this article extends the proposed methodology to handle several vessels and port-site resource restrictions. Moreover, it presents a novel approach to discretize weather dynamics based on Markov Chains.  
The overall installation consists of three distinct, consecutive stages. Due to the consecutiveness, this article considers these stages as disjunct scheduling problems. These stages only differ in their parametrization. Thus, the article chooses the installation of top structures as an example, as it contains the most complex sequence of operations. All experiments subject to the following assumptions:  
* Each scenario only considers the actual installation part of the given supply chain. Consequently, this article assumes that components are always available at the base port.
* As in practice, this article only considers the so-called season from April to October, as it provides suitable weather conditions to perform offshore operations.
* If a scenario applies several installation vessels, all installation vessels are considered to be equal.
* The number of installation vessels is fixed for each scenario.
* The number of loading bays is fixed for each scenario.
* This article assumes that vessels host several crews to assure the availability of personnel seven days a week in three shifts (24 h).
* This article does not include staff planning and thus neglects differences in labor costs for night shifts or weekend shifts.
# Indole-3-Acetic Acid Action in Outdoor and Indoor Cultures of _Spirulina_ in Open Raceway Reactors
## 2 Materials and Methods
### Culture Conditions  
Both indoor and outdoor experiments were conducted in duplicate for 30 days, utilizing open reactors of the 6 L raceway type (with a usable volume of 5 L). Continuous agitation was achieved by paddle wheels rotating at 20 rpm. The initial biomass concentration of the cultures was set at 0.2 g L\({}^{-1}\)[28]. The microalgae were cultivated in Zarrouk medium [29] employing a batch-fed system, with IAA supplementation introduced at a concentration of 0.1 mg L\({}^{-1}\) on the 15 th day of cultivation, according to the results obtained in previous research [15; 16]. In addition, control experiments were performed. These were conducted in the same conditions as the supplemented experiments but without IAA treatment.  
Indoor cultures were situated within a thermostat-controlled chamber, maintaining a consistent temperature of 30 \({}^{\circ}\)C, a 12 h light/dark photoperiod, and an illuminance of 80 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\) provided by fluorescent lamps. The outdoor experiments were carried out under unregulated environmental conditions during the winter period (August 2022) in the city of Rio Grande, RS, Brazil (latitude 32\({}^{\circ}\)4/23.028\({}^{\prime\prime}\) S, longitude 52\({}^{\circ}\)10\({}^{\prime}\)2.079\({}^{\prime\prime}\) W). The outdoor bioreactors were situated within a greenhouse covered by a transparent film, offering protection against UV rays [30]. Monitoring occurred daily in the early afternoon (2 p.m.) to record the cultivation and ambient temperatures. A digital thermometer (SH 102, [PERSON], Sao Paulo, SP, Brazil) was employed, and solar radiation levels were measured with a universal light meter (ULM- 500, Walz, Effeltrich, BY, Germany).  
The indoor experiments were designated C-I and IAA-I for the control and supplemented groups, respectively. The designations C-O and IAA-O were assigned to the control and supplemented experiments, respectively, conducted in the outdoor environment.
# Watermelon Rind and Flesh Volatile Profiles and Cultivar Difference
## 3 Results
### Watermelon Rind Major Volatiles and Cultivar Difference  
A total of 132 volatiles were identified in eight samples (Table 1). There were 105 volatiles present in four rind samples, while only 75 volatiles were found in common between all four cultivars of rind. The most dominant (at least 2% peak area in all samples) were (Z)-3-nonen-1-ol (12.4-23.9%), (Z,Z)-3,6-nonadien-1-ol (7.6-21.4%), (E,Z)-2,6-nonadien (5.6-13.6%), hexanal (7.4-11.5%), (E)-2-nonenal (3.3-10.8%), and acetaldehyde (2.5-4.1%). They were significantly different (\(p\leq 0.05\)) in abundance between rind samples. Excursion had the greatest abundance of (Z)-3-nonen-1-ol and (Z,Z)-3,6-nonadien-1-ol, while Exclamation and Fascination had the lowest abundance of each, respectively. Captivation had the greatest abundance of (E,Z)-2,6-nonadien and (E)-2-nonenal, while Excursion had the lowest in both. Exclamation had the greatest abundance of hexanal, Fascination had the greatest abundance of acetaldehyde, and Captivation had the lowest abundance of both.  
A number of volatiles were present in all samples, yet only dominant in one particular cultivar while having a low abundance (less than 2% peak area) in others, \(p\leq 0.05\). The rind of Captivation exhibited a high abundance of (E)-2-hexanal (10.6%) particularly compared to Fascination (0.9%), while Fascination was more abundant in nonanal (12.6%) compared to Excursion (0.7%). Fascination find also contained abundant (Z)-6-nonenal (12.1%) compared to no detection in Exclamation. Excursion was more abundant in 1-hexanol (4.3%), (Z)-3-hexen-1-ol (5.7%), and (Z)-6-nonenol (2.6%) compared to all remaining three rind samples (<2%).  
There were 57 volatiles not present in all four cultivars, and eight of those were unique to certain cultivars. Those unique volatiles included (E)-2-nonenol (0.06%), 2-ethylhexanoic acid (0.02%), and ethyl cinnamate (0.01%) present in Captivation, and ethanol (0.3%), 2-heptanone (0.03%), and 1-heptanol (0.2%) in Excursion.  
There were several volatiles found in all of the rind samples that have never been reported in watermelon. The most dominant of those volatiles, which ranged from 0.01 to 0.8% abundance, included (E,E)-2,6-nonadien (0.06-0.17%), methanol (0.15-0.80%), dihydromycenol (0.07-0.23%), and isobornyl acetate (0.03-0.11%). The remaining volatiles had an abundance of less than 0.1% in the samples, including (Z)-3-hepten-1-ol (0.02-0.08%), 2-octanone (0.02-0.06%), 2-undecanone (0.02-0.07%), tetrahydrolinalool (0.02-0.08%), heptanoic acid (0.04-0.09%), and dimethyl disulfide (0.01-0.07%).
## 4 Results and Discussion
### Limitations and Future Work  
Despite the superior performance demonstrated by the proposed strategy-generation and prediction framework across various large-scale behavior data scenarios, several limitations remain that warrant further investigation and refinement. First, the current approach still relies on a minimum amount of historical behavior data to support state modeling and value estimation, which may pose challenges during cold-start phases or in cases involving newly deployed platforms and short-lived product categories. Under such conditions, the stability of early-stage policy training may be insufficient. Second, the model's training and inference procedures are currently executed within a centralized framework that aggregates data and generates policies in a unified manner. This architecture does not account for data silos and heterogeneity that are common across different platforms. In practical e-commerce environments, significant structural and access discrepancies exist among platforms with respect to data sharing. Future extensions could incorporate federated learning or multi-agent coordination mechanisms to enable cross-platform policy alignment and knowledge sharing without exposing raw data, thereby enhancing deployment flexibility and scalability in real-world settings. Future work will also consider the integration of multimodal information fusion mechanisms. By leveraging graph neural networks or joint language-behavior encoding strategies, diverse modalities--such as textual content, imagery, news feeds, and sequential user behavior--can be modeled collectively. This enhancement is expected to improve the model's adaptability to complex and dynamic market conditions while increasing its sensitivity to latent strategic signals. Ultimately, such developments would facilitate the construction of an interpretable and practically valuable intelligent optimization system for strategic decision-making.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Acoustic-Based Detection
#### 2.2.3 Datasets and Metrics for Acoustic-Based Detection  
To evaluate the performance of acoustic-based detection, the following two datasets and metrics are usually used, i.e., DroneAudioDataset [58] (can be found from GitHub repository: [[https://github.com/saraelemadi/DroneAudioDataset](https://github.com/saraelemadi/DroneAudioDataset)]([https://github.com/saraelemadi/DroneAudioDataset](https://github.com/saraelemadi/DroneAudioDataset)), accessed on 1 June 2023) and the Casabianca's Dataset [59] (can be found from GitHub repository: [[https://github.com/pcasabianca/Acoustic-UAV-Identification](https://github.com/pcasabianca/Acoustic-UAV-Identification)]([https://github.com/pcasabianca/Acoustic-UAV-Identification](https://github.com/pcasabianca/Acoustic-UAV-Identification)), accessed on 1 June 2023). The DroneAudioDataset contains recordings of UAV propeller noise in an indoor environment, with the audio clips categorized as either 'Drone' or 'Unknown'. The 'Unknown' category consists of white noise and silence audio clips that do not contain any UAV signals. In contrast, the Casabianca's Dataset contains recordings of a variety of UAVs in a range of background sound situations, including airplanes, helicopters, traffic, thunderstorms, wind, and quiet environments. These datasets provide a valuable resource for researchers to develop and evaluate audio-based UAV detection methods under different acoustic conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Accuracy** \\ \hline [PERSON]. [PERSON] et al. [27] & 96.38\% \\ [PERSON] et al. [38] & 99\% \\ [PERSON] et al. [39] & 98.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of acoustic-based detection using deep learning.  
Since the number of acoustic UAV datasets is limited, researchers often have to create their own datasets by recording UAV acoustic signals from publicly available videos or by conducting their own experiments. This involves using appropriate recording equipment, such as microphones, and selecting suitable recording locations and environmental conditions to capture a diverse range of UAV acoustic signatures. Despite the challenges associated with creating and using self-collected datasets, this approach allows researchers to tailor their experiments to specific application scenarios and to evaluate the performance of their proposed methods under realistic conditions. However, it is important to ensure that the datasets are properly annotated and validated to ensure the accuracy and reliability of the results.
## 2 Overview
### Application Areas  
WSN-based marine environment monitoring has a broad coverage including a number of application areas: water quality monitoring, ocean sensing and monitoring, coral reef monitoring, and marine fish farm monitoring. Different application areas require different WSN system architectures, communication technologies, and sensing technologies.  
A water quality monitoring system is usually developed to monitor water conditions and qualities including temperature, pH, turbidity, conductivity and dissolved oxygen (DO) for ocean bays, lakes, rivers and other water bodies. An ocean sensing and monitoring system is used to monitor ocean water conditions and other environmental parameters. A coral reef monitoring system is normally installed to monitor coral reef habitats using an autonomous, real-time and _in-situ_ wireless sensor network. A marine fish farm monitoring system is developed to monitor water conditions and qualities including temperature and pH, and accurately quantify the amount of fecal waste and uneaten feed for a fish farm.
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 2 Literature Review
### Interaction between Local and Overall Buckling  
To assess the interaction behavior between local and global stability failure modes, compression tests were made considering both centric and eccentric loading situations to ensure pure axial compression and/or the interaction between compression force and the bending moment. In most experiments, pinned-pinned support conditions were analyzed. The studies conducted by different researchers in this context are as follows:  
* [PERSON] et al. [9] conducted tests on 20 welded box section columns to validate the applicability of empirical equations.
* In the research carried out by [PERSON] et al. [10], 24 specimens were tested to verify an enhancement of an equation published by the same authors.
* [PERSON] et al. [11] examined eight specimens to verify theoretical values concerning elastic-plastic buckling in plates.
* The study by [PERSON] [6] aimed to investigate the interaction buckling phenomenon for welded box section columns made of different steel grades involving tests on 34 specimens.
* [PERSON] and [PERSON] [12] conducted experiments on 28 large-scale square and rectangular sections with yield stresses ranging between 235 and 355 MPa. The research considered residual stresses and local and overall imperfections, and aimed to propose a load resistance equation.
## 4 Experimental Methodology
### Network Condition Simulation and Analysis  
To robustly assess system adaptability to varying network environments, three distinct and carefully controlled network scenarios were implemented:  
* High-bandwidth, low packet-loss environment (20 Mbps bandwidth, 0.1% packet loss).
* Moderate-bandwidth, moderate-loss environment (10 Mbps bandwidth, 1% packet loss).
* Low-bandwidth, high-loss environment (2 Mbps bandwidth, 5% packet loss).  
These scenarios were methodically designed to represent common real-world conditions ranging from stable corporate LAN environments to challenging WAN scenarios with high network congestion. The control over network parameters was achieved using openWRT routers, which allowed precise emulation of bandwidth and packet loss characteristics. The latencies are captured using the method described in Figure 4. Video quality at each relay node was systematically captured using the video player, with Video Multi-Resolution Fidelity (VMAF) scores computed real-time to objectively quantify visual fidelity degradation under each network condition. The data collection procedure adhered strictly to standardized practices to guarantee accuracy and reliability. Each scenario was executed multiple times to ensure statistical significance and to account for potential variability in network performance, and the recorded VMAF scores were aggregated for comprehensive analysis.