esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### SPME Equipment and HS-SPME Optimization  
The stock mixture of standard solutions was prepared by addition of 20 \(\upmu\)L of each of the VSCs to 96% _v/v_ ethanol in the total volume of 25 mL. The working mixture was prepared by 10-fold dilution of stock mixture in 96% _v/v_ ethanol. The internal standard (IS) solution (thiophene) was prepared in the concentration of 9.42 mg/L. Samples for the method development were prepared in 10 mL of total ethanol-water solution volume, and 10 \(\upmu\)L of working mixture along with 10 \(\upmu\)L of IS was added to each tested sample.  
To find the best SPME conditions, a number of variables were tested, i.e., fiber coating, ethanol concentration, addition of NaCl, addition of EDTA, and temperature and time of extraction.  
Five different fiber coatings from Supelco (Bellefonte, PA, USA) were tested: 100 \(\upmu\)m Polydimethylsiloxane (PDMS), 60 \(\upmu\)m Carbowax/Polyethylene Glycol (PEG), 85 \(\upmu\)m polyacrylate (PA), 85 \(\upmu\)m Carboxen/PDMS (CAR/PDMS), and 50/30 \(\upmu\)m DVB/Carboxen/PDMS (DVB/CAR/PDMS). All tested fibers were of 1 cm length. Before injection, fibers were conditioned in the hot injection port according to manufacturer's recommendations. Additionally, fibers were also conditioned, before each sample extraction, for 10 min at 200 \({}^{\circ}\)C (PEG) or 250 \({}^{\circ}\)C (PA, PDMS, CAR/PDMS, DVB/CAR/PDMS).  
To examine the effect of ethanol concentration on the extraction of VSCs, the mixtures of ethanol and water were prepared in the total volume of 10 mL with final ethanol concentration of 2.5%, 5%, and 10% _v/v_. The effect of NaCl addition was tested in the range of 0-3 g of NaCl per sample, which corresponds to a concentration of 0-30% _v/v_. The EDTA was added to selected samples in a dosage of 0.1 g. The time and temperature of extraction were tested in values of 15, 30, and 45 min, and 20, 35, 50, 65, and 75 \({}^{\circ}\)C, respectively.  
Each sample was prepared in a 20 mL glass screw-cap vial with magnetic cap and teflon/silicone septa and equilibrated for 15 min at the desired temperature. The agitation rate of 600 rpm was constant for all analyses. After extraction, the analytes were desorbed in the GC injection port from the fiber at 250 \({}^{\circ}\)C for 5 min. Samples were analyzed in triplicate.
# Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
## 7 Research Gaps in the Existing Literature  
The research gaps detailed below were identified through our thorough assessment of the literature.  
* Insufficiently large datasets: due to the potential loss of reputation or money, the majority of victim organizations are reluctant to disclose information regarding attacks undertaken against them. Furthermore, there are no complete databases in the public domain that include all traffic kinds, including genuine, low rate, high rate, and flash traffic [37; 39; 40; 42; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 781]. Therefore, experimental settings are necessary to provide extensive datasets for the thorough validation of DDoS detection methodologies.
* Access to skewed datasets: occurrences of DDoS attacks are typically highly skewed in comparison to genuine events in the datasets currently available [37; 39; 40; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 70; 72; 75; 77]. Therefore, a large number of cases in each class are required in order to execute deep learning techniques effectively. For effective study in this area, good enhancement strategies are needed to provide a large enough volume of all forms of traffic.
* Demand for high-quality preprocessed data: the caliber of the preprocessed data affects how accurate the resulting deep learning model is. As a result, effective preprocessing methods are needed for effective DL model training [61; 62; 63; 65; 66; 67; 68; 69; 70; 72; 75].
* Binary categorization: the majority of the currently available literature [37; 39; 40; 42; 53; 54; 57; 63; 64; 65; 67; 68; 69; 70; 73; 75; 76; 77; 78; 81] concentrates on binary categorization of DDoS assaults rather than multi-class classification.
* Insufficient effort on unknown data or zero-day attacks: when the instruction and assessment datasets contain the same traits or patterns, ML models are able to function well. However, ML-based algorithms are unable to accurately detect unknown threats in real-life situations, where attacks may be launched using novel patterns. As a result, these models must be frequently updated in order to account for novel and untested assaults [53].
* Using an offline dataset for evaluation: the majority of the research we reviewed used offline datasets to assess deep learning models [37; 39; 42; 55; 56; 57; 58; 59; 61; 62; 63; 64; 65; 66; 67; 69; 70; 72; 73; 75; 77; 78; 81]. The implementation of these models in actual networks remains a work in progress. Real-time evaluation of models would be highly beneficial for adequate verification.
* No deployment of automated real-time defense models: most DDoS assaults overwhelm the target site in a relatively short amount of time, and network managers are often unable to automatically identify and fight back against these attacks. The primary cause of this is that defense strategies themselves become susceptible to DDoS assaults based on floods. Therefore, high-speed and computationally efficient DDoS solutions are needed in order to automatically stop those attacks.
# Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models
## 3 Methods
### Generic Epidemic Spread Model  
The SIR compartment model is adapted to capture the progression of the epidemic for each agent. Thereby the model is kept very generic to allow further extensions (see Section 5). At the beginning of the simulation, the population is divided into two mutually exclusive states: (1) susceptible agents which can contract influenza and (2) infected agents which are able to transmit the virus. Infected agents are randomly sampled from the whole population \(N\), given the probability of initially infected individuals \(\mathbb{P}_{I}\). Once an infectious individual arrives at a fixed location to perform any stationary activity (e.g., study, work, shopping,...) every susceptible individual in this location has probability \(\mathbb{P}_{\beta}\) to get infected too. This is the same for the susceptible agent which arrive later throughout the day. At the end of every day, an infected agent recovers with probability \(\mathbb{P}_{\gamma}\). For simplicity reasons, the model is developed in such a way that all recovered individuals cannot become infected again and they will remain immune. Figure 3 illustrates the scheme of the generic epidemic spread model.  
Figure 3: Scheme of the generic epidemic spread model. Each agent can be assigned to a disease state (S, I, R). If a susceptible agent is in contact with an infected agent, with (transition) probability \(\mathbb{P}_{\beta}\), the agent becomes infected. At the end of the day each infected agent has a (transition) probability \(\mathbb{P}_{\gamma}\) to recover.  
The transmission of influenza from an infected individual to a susceptible individual is a complex process dependent upon a variety of factors. The following paragraphs discuss some of the key simplifying assumptions.  
* A constant and closed population is assumed meaning that although the number of individuals in each disease state changes over time, the sum of all states is always equal to the population size. Demographic turnovers and fatalities due to the virus are not considered.
* Every agent has the same probability \(\mathbb{P}_{\beta}\) to get infected, even though children are much more infectious than adults and shed virus from just before they develop symptoms until two weeks after infection. Additionally, every susceptible agent in a facility has the probability of getting infected, even if there is no direct contact with an infected agent.
* It is assumed that infected agents do not change their daily activities, i.e., they continue with their original plans.
* Every agent has the same probability \(\mathbb{P}_{\gamma}\) of getting recovered.
## 1 Introduction
Machine learning algorithms, in tandem with image segmentation techniques, have become indispensable for distinguishing infected cells from healthy ones, greatly enhancing the reliability of viral assays [24; 25]. However, despite the success of these technologies in other viral research, they remain underutilized in chikungunya virus studies. Traditional methods, such as immunofluorescence assays, still rely on manual counting, which is both time-consuming and prone to error [26; 27]. Recent advancements in AI-driven imaging, such as viral plaque counting [28] and ELISPOT assays [29], have demonstrated the potential for automated analysis in virology. While these studies focus on generalized virus detection or plaque formation, the current study specifically targets the Chikungunya virus, addressing unique challenges associated with detecting infected cells and viral particles in high-throughput workflows. By focusing on this specific pathogen, the algorithm offers a tailored approach that builds on and advances the capabilities of existing methodologies. Automated systems that integrate machine learning into image analysis workflows have the potential to accelerate antiviral compound screening while improving accuracy. Yet, there remains a notable research gap in applying these technologies specifically to chikungunya virus studies [30]. This study addresses this gap by developing an automated image processing methodology designed to detect and classify infected and uninfected cells in high-throughput chikungunya virus assays. By utilizing MvTec Halcon software, an automated algorithm was created to optimize cell counting, segmentation, and feature extraction in viral assays. Manual screening of viral assays requires significant time and concentration, particularly when processing large numbers of cells and viruses. This workload can cause fatigue, increasing the likelihood of human error and reducing the reliability of results. Furthermore, manual methods are inherently limited in scalability, making them unsuitable for high-throughput workflows. These challenges highlight the critical need for automated solutions to improve accuracy, consistency, and efficiency in virology research, particularly for vector-borne diseases like Chikungunya. To validate the accuracy of this approach, the algorithm's performance was rigorously compared against manual analyses conducted by three virology experts. Following validation, the algorithm was applied to screen antiviral compounds in chikungunya virus experiments, demonstrating its potential to transform drug discovery workflows. This research highlights the importance of integrating advanced image processing technologies into biomedical applications, particularly in addressing emerging infectious diseases. By improving the speed and accuracy of antiviral screening, this study represents a significant advancement in chikungunya virus research and emphasizes the critical role of technological innovation in modern virology.
## 3 Results and Discussion
### Volatile Compounds Identified by GC-MS
The (E,E)-2,4-heptadienal, with nutty and green odors, increased significantly in GT, from 1.52% to 13.46%, and increased considerably during drying [1]. This result was consistent with the GC-IMS result. However, hexanal decreased in BT and GT, inconsistent with the GC-IMS results. This finding shows some differences between the results of the two experimental methods. The high content of compounds was detected mainly by GC-MS [13], and the low content of compounds were detected mainly by GC-IMS [11]. (E,E)-2,4-Decadienal, with cucumber and melon odors, was not detected in FTL or BT, evidently it was accumulated during GT producing. Among alcohols, linalool and geraniol were abundant in BT and GT, similar to famous Chinese spring teas (like Keemun black tea [26] and Xihu Longjing tea [33]). The linalool oxides (I, II, III) formed during BT and GT production; increasing more significantly in BT during fermentation. These compounds are important sources of sweet and floral fragrances in BT. The content of phenylethyl alcohol, which has floral and rose-like odors, increased slightly in BT but significantly decreased in GT. Benzyl alcohol, with sweet and floral odors, and \(\alpha\)-terpineol, with a floral odor, were detected only during GT production [17]. Ketones are generally considered to present talllow and burnt aromas, and also present floral aromas that are enhanced as the carbon chain grows [29]. In BT and GT, the content of \(\beta\)-Ionone increased significantly, and even more in GT, mainly because of the enzymatic oxidation of \(\beta\)-carotene during fermentation in BT processing or thermal degradation during GT processing [31]. The content of geranyl acetone increased in GT and decreased in BT. Sulcatoire, with green and fruity odors, was detected in BT and GT, and 3, 5-octaiden-2-one, with a rose-like odor, was only detected in GT. These ketones were probably produced by the oxidation and condensation of carotenoids, such as phytoalkenes and phytofluidroalkenes [29].
# Smart Grid for Industry Using Multi-Agent Reinforcement Learning
## 4 Proposed MARL Approach
### Environment
#### 4.2.1 Electricity Market  
For industrial companies, several markets exist for potential participation (see Figure 2). Since the presented system focuses on the short-term control of a production site, the possible market participation is limited to the intraday market due to its lead time of only five minutes. In the other markets, electricity can be traded only at least one day in advance, so these markets are out of scope for the system in question. However, power which has already been purchased in advance on any of the aforementioned markets (hereinafter referred to as \"prior purchased power\") is an input into the system and must be consumed.  
On the intraday market, the prices change dynamically over time based on the relevant supply and demand. The result is a high level of price uncertainty while trading. In addition, electricity can be purchased during every 15 min time interval, which is more than five minutes in the future. In order to reduce the complexity and to avoid modelling of price uncertainty, market trading is only limited to the next billing interval. This means that the market agent can only buy or sell electricity in the consecutive billing interval and at a defined time of nine minutes before the actual beginning of the corresponding billing interval. There thus exists a time buffer of four minutes in order to ensure that the trading is transacted within the required five minutes of lead time. A proportional order fee is modelled for every trade.
# Monitoring Forest Phenology in a Changing World
## 3 Emerging Technological Advancements
### Automating the Process of Phenological Monitoring  
Autonomous monitoring methods that alleviate the need for human labour are in development and will allow phenology to be monitored in near real-time. Satellites require little user input and satellite imagery has been used a lot for automated detection of events such as fires [185] and volcanic activity [186]. Similar, automated methods for monitoring phenology are becoming available. For example, the Harmonized Landsat Sentinel-2 gridded product, that provides radiometric and geometric corrected, near-daily, 30 m resolution data [148], has the potential to be incorporated into autonomous phenology monitor frameworks [187; 165].  
Automated analysis of phenological data from smaller devices is also feasible given recent developments allowing fixed devices such as digital phenological cameras to continuously record and send data wirelessly over mobile networks or upload links [188]. Developments in autonomous software for flying UAVs also means they can be flown over vegetative areas with little to no user input [189], and it is only a matter of time before the image data from such flights are fed back for automated analysis. The parallel developments in image processing techniques such as Structure-from-Motion (SfM) [190; 191; 192] and tree crown detection and delineation algorithms [193; 194], allow imagery to be autonomously stitched and ROIs to be determined [195; 196; 197; 198; 199]. Similar approaches have been used to autonomously detect and identify species [200; 201], but have yet to be applied to imagery collected for the purpose of monitoring phenology.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 5 Algorithm Design and Implementation
### Genetic Operations
#### 5.3.4 Knowledge Learning  
In this paper, a historical archive set is established to record the well-performing individuals during the iteration process, allowing their superior genetic information to be utilized in subsequent evolution. The decision of whether an individual needs to learn is based on a learning probability. The learning procedure is illustrated in Figure 7. First, a well-performing individual is randomly selected from the historical archive. Then, two genes \(i\), \(j\) are randomly chosen, and a learning segment \(s_{tu}=[i,j]\) is determined. The  
Figure 6: Mutation operator. (**a**) illustrates the mutation operation in the early stage of the iteration, while (**b**) illustrates the mutation operation in the later stage of the iteration.  
genes in the learning segment are replaced with the corresponding segments from the superior individual. The deduplication operation is performed at the end to obtain the final individual.  
The learning rate is dynamically adjusted based on fitness and the number of iterations, as shown in Equation (47), where, \(i_{\max}\), \(i_{c}\), \(f_{avg}\), \(f_{i}\), \(k\), and \(B\) represent the maximum number of iterations, the current iteration, the average fitness of the current population, the fitness of the current individual, the adjustment factor, and the base learning rate, respectively.  
\[l_{rat}=b_{rat}+k\cdot\left(1-\frac{i_{c}}{i_{\max}}\right)\cdot\left(\frac{ f_{avg}-f_{i}}{f_{avg}}\right) \tag{47}\]  
The algorithm utilizes three neighborhood structures: insertion, swap, and 2-opt. In each iteration, one neighborhood structure is randomly selected. If no improvement is found after applying the current structure for three consecutive iterations, the algorithm switches to the next neighborhood structure, cycling through them in order.  
(a) Insertion: A customer point is randomly selected from the chromosome, and it is inserted in front of the customer point that has the smallest spatiotemporal distance to it, generating a new chromosome.  
(b) Swap: Two customer points are randomly selected from the chromosome, and their positions are swapped to generate a new chromosome.  
(c) 2-opt: Two customer points are randomly selected from the chromosome, and all customer points between them are reversed in order, generating a new chromosome.
## 2 Materials and Methods
### GC-MS Analysis  
The volatile components were carried out using an 7890B/7000C GC-MS analytical instrument (Agilent Technologies, Palo Alto, CA, USA). In brief, homogenized tea samples (0.5 g) were transferred into a 20 mL headspace vial. Subsequently, 6 mL of boiling water along with 5 \(\upmu\)L of ethyl decanoate (with a concentration of 100 mg/L serving as the internal standard) were added, strictly adhering to our prior methodology [23]. The headspace vial was immediately sealed with a screw cap. The samples were incubated using metal-bathing at 60 \({}^{\circ}\)C, and a manual DVB/CAR/PDMS fiber (50/30 \(\upmu\)m, Supelco, Bellefonte, PA, USA) was subsequently introduced into the aforementioned headspace vial and subjected to exposure for a period of 60 min. Post-extraction, the DVB/CAR/PDMS fiber was transferred directly into the injector port and subjected to thermal desorption (lasting 5 min) at a temperature of 250 \({}^{\circ}\)C. The HP-5 ms capillary column, manufactured by Agilent Technologies Inc., was employed to facilitate the separation of volatile compounds, with dimensions of 60 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m. Highly purified helium (99.9995%) was employed as the carrier gas under a consistent flow rate of 1 mL/min. The split-less mode was implemented. The programmed temperature profile was set as follows: beginning at 40 \({}^{\circ}\)C for 5 min, steadily increasing to 100 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min (holding for 2 min), and finally, ramping up to 270 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min (holding for 4 min). The mass spectrometer was operational in the mode of electron ionization (EI), exhibiting a scanning extension from 33 to 550 \(m/z\). The ion source temperature and transfer line temperature were set at 230 \({}^{\circ}\)C and 270 \({}^{\circ}\)C, respectively.  
The qualitative assessment was performed on the MassHunter Workstation Software Unknowns Analysis B.07.01 platform for insightful interpretation. The compounds were identified through meticulous comparison of acquired spectra against those from the prestigious National Institute of Standards and Technology (NIST11) and well-establishedretention indices derived through the linear calculation formula of \(n\)-alkanes (C7-C40). The volatile compounds were semi-quantitatively analyzed by equating the peak intensities of each volatile to that of the internal standard (ethyl decanoate). Each sample underwent triplicate analysis.
# Identifying Misbehaving Greedy Nodes in IoT Networks
## 3 Selfish Behaviour in IEEE 802.15.4  
### IEEE 802.15.4 Characteristics  
The IEEE 802.15.4 standard defines the Physical Layer (PHY) and Medium Access Control (MAC) specifications for Low Rate-Wireless Personal Area Networks (LR-WPAN) [3].  
This standard is responsible for managing communications that are limited in terms of cost, power consumption, and speed. It is associated in the majority of implementations with the use of ZigBee [21] and 6 LoWPAN protocols in the ISM 2.4 GHz band [3]. It mainly uses two physical devices: FFD (Full Function Device) and RFD (Reduce Function Device). A FFD can communicate with any device on the network, while a RFD can only communicate with a FFD [3]. Two topologies are supported by the IEEE 802.15.4 standard: the star topology, and the peer-to-peer topology. Cluster tree topology is considered as a special case of peer-to-peer topology imposing hierarchical topological specifications on network formation, and using more FFDs than RFDs. The star topology is a centralized topology where communications are carried out only between the PAN coordinator and the nodes within its radio range. This topology uses the beacon enable mode using slotted CSMA/CA; it is a so-called synchronized mode, which uses a superframe whose role is to synchronize the nodes with the coordinator [3].  
In the peer-to-peer topology, communications are bidirectional between nodes, and without any restriction, since the intermediate nodes manage to relay the packets to the destination. This topology is more advantageous in terms of flexibility and scalability of the WSN network. The peer-to-peer topology uses the non-beacon enable mode. It is an asynchronous mode based on periodic wake-ups (duty cycle) of the nodes either to send or to check if they have messages which are intended for them. The channel access mechanism used in this mode is unslotted CSMA/CA [3]. We consider in our study a peer-to-peer topology of a wireless sensor network.