esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Multimodal Sarcasm Detection  
Applications such as social networks and news websites have generated abundant multimodal data. As a result, people have conducted extensive multimodal investigations; for example, sentiment analysis [23; 24; 25; 26; 27], image and text retrieval [28], reason extraction [29; 30], and sarcasm detection [31]. Unlike text-modal-based sarcasm detection, multimodal sarcasm detection aims to identify sarcastic expressions implied in multimodal data. [PERSON] et al. [11] first process multimodal sarcasm detection tasks in social platforms through manually designed text and image data features; manual features require higher costs. [PERSON] et al. [32] introduce a multitasking framework to identify irony and emotions. Furthermore, [PERSON] et al. [12] create a new text-image sarcasm dataset collection from Twitter and propose an attention-based fusion model for multimodal sarcasm recognition which achieves a simple late fusion. [PERSON] et al. [33] exploit relational and decompositional networks to model semantic correlation in sarcastic recognition. These early studies using simple fusion networks make extracting mutual information between modalities difficult. The HKE model [34] proposes the joint use of cross-modal graphsand syntactic analysis. These studies employ adjective-noun pairs to enrich multimodal information. [PERSON] et al. [35] propose inter-modal and common attention mechanisms to complete sarcastic detection tasks. The DIP [36] framework recently proposed leverages a contrastive loss to capture sarcastic information from factual and emotional perspectives. [PERSON] et al. [13] focused on learning the incongruuous relationships through a cross-modal graph convolutional network by building intra-modal and cross-modal graphs for each multimodal sample. Furthermore, they [37] present a cross-modal graph neural network in which the edge weights come from SenticNet to capture the inter-modal inconsistency. [PERSON] et al. have embedded sentiment word into multimodal vectors [38]. These graph neural networks have achieved excellent performance but bring much computational trouble.  
These studies have achieved high classification accuracy by learning the inconsistencies-relationships between modalities through attention mechanisms or graph neural networks. Although the above deep learning-based methods avoid the high cost of manually designing features, there are still some areas for improvement in utilizing commonsense knowledge. Meanwhile, they focus on improving the model's classification performance while ignoring the model's size. This paper proposes a multimodal interaction model based on knowledge augmentation to utilize visual knowledge information. This model has fewer parameters, which is beneficial for deployment on mobile devices.
## 2 Materials and Methods
### Data Collection  
Data collection was conducted in a single session for each volunteer, lasting approximately 45 min, at the Laboratory of Technological Innovation, Strength, and Functional Assessment (LabIT) at TecnoCampus. Climatic conditions in the laboratory were consistent (20-25 \({}^{\circ}\)C and 60-65% humidity). One or two participants were assessed per data collection session. Each session began with a standardized warm-up and familiarization period lasting 10-15 min, during which the protocol was explained in detail, informed consent was obtained, and the procedure was practiced. The warm-up included general joint mobility exercises, 5 min of cycling on a cycloergometer, and 15 repetitions of bodyweight squats.  
The criterion measurement system was calibrated before each session. This was followed by the exercise protocol (lasting 30 min), in which participants performed the exercise with the tested load configurations (see Figure 2). The load order for each participant was randomized prior to the start of the experiment using a list of conditions (1-5, as shown in Figure 2), generated by the Randomizer mobile application [45]. The rope length was adjusted to match each participant's range of motion before each set, ensuring full extension while avoiding a phase without tension. For each load configuration, participants performed three initial half-squats to progressively accelerate the flywheel, followed by a set of 15 near-maximal half-squats. Participants were instructed to maintain a range of motion from 90\({}^{\circ}\) of knee flexion to full extension of the hips and knees, exerting maximal effort during each repetition while adhering to the specified range of motion. A 3-min recovery period was provided between sets. If a set did not meet the prescribed guidelines, it was discarded, followed by a 3-min recovery period, after which the set was repeated.  
Figure 2: Load configurations tested in the study. The arrow on the left side of the figure indicates the progression of load from lower to higher resistance across the tested configurations.
## 3 Operation and Models of the VANET Testbed
### Network Models
#### 3.2.2 Driving Video Model  
The driving video model is used to analyze and characterize the video trace of a live driving scene captured by a vehicle camera. The results are then used to generate models for use in our simulator for real-time driving video communication in a VANET environment. The implementation of this wireless driving video traffic model allows us to handle a wide range of burstiness in terms of packet inter-arrival and packet size arising from the unique nature of wireless video traffic during driving as mentioned above. A guideline to check the efficiency of a traffic model is, however, needed as to set a standard to be followed. Parsimony, analytic, relative accuracy, flexibility, implement ability and absolute accuracy are factors listed in [53] showing the engineering tradeoffs among different traffic models.  
Using these factors on a scale of 1 to 3, 3 being the highest, Table 3 shows the settings of a few popular models including the FBM (Fractional Brownian Model) and the OPNET-emulated TCP traffic. By comparison, we adopt the Mini-Pareto model to be used later due to its overall score. In particular, we have taken a systematic approach in developing our mini-Pareto model. Driving video traffic traces were collected using the same camera used for a road test. In our testbed, we traced the frames from the receiver instead of the transmitter in order to correspond to video traveling from vehicle to vehicle, and through the RSU to the city's road monitoring center in a safety network. The traces were analyzed and stochastically represented and plugged into our simulation platform. A detailed discussion about these steps is presented in Section 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Physical Layer & IEEE 802.16e & NA \\ BS TX Power & 5 & W \\ Number of TX & SISO & NA \\ BS Antenna Gain & 15 & dBi \\ Minimum Power Density & \(-80\) & dBc \\ Maximum Power Density & \(-30\) & dBc \\ Link Bandwidth & 20 & MHz \\ Base Frequency & 5.8 & GHz \\ Physical Layer Profile & OFDM & NA \\ \hline \hline \end{tabular}
\end{table}
Table 2: RSU parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **FBM** & **TCP Model** & **Mini-Pareto** \\ \hline Parsimony & 3 & 1 & 2 \\ Analytical & 1 & 1 & 2 \\ Flexibility & 1 & 1 & 1 \\ Implemental & 2 & 1 & 3 \\ Accuracy & 2 & 3 & 2 \\ Mobility & 2 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Traffic model comparison.
## 2 Materials and Methods
### Downdraft Gasifier System  
This experiment utilized the downdraft gasifier in the gasification process to minimize tar production. The gasifier was a reactor connected to the continuous stirred tank reactor (CSTR) with continuous-process feeding of the wood pellet. The pellet holder was used to continuously fill the wood pellet feeder for ignition in the gasifier. During continuous operation, the biomass was moved into the gasifier using a screw in the feeder that was connected to a motor. The gasifier had dimensions of 120 mm for the inner diameter and 500 mm for length. The screw for mixing the wood pellet in the gasifier was installed and made the temperature uniform. The air was used as an oxidizer with a fixed air-flow rate of 80 L/min, and it was controlled by the gas-flow-rate meter (Azbil CMS200, made by Azbil Corporation, Tokyo, Japan). Additionally, the air supply was controlled by programmable logic control (PLC). The soot remover was adopted to reduce the tar formation because if the process only used the activated carbon as charcoal, it would not be enough to minimize tar production. The principle of separation is to use small-sized silica stone to filter the heavy tar in the syngas. The cooler with circulating water was used to reduce the temperature of the syngas from the gasifier. This syngas was sucked using a suction pump before being delivered to the buffer tank. The operating condition of these experiments included a uniform temperature between 400-600 \({}^{\circ}\)C. All data was saved in a data-logger product from National Instrument, USA. The yielded syngas from the biomass gasification process was quantified for the tar compound using gas chromatography (GC-MS, Agilent, 490 Micro GC, TDC, Ar carrier). The block diagram of the biomass gasification system is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ultimate Analysis (wt. \%, Dry Ash-Free) JIS M8813.** \\ \hline O (balance) & 43.37 \\ N & 0.09 \\ S & 0.09 \\ H & 6.43 \\ C (dry, ash-free) & 50.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ultimate Analysis (UA) of wood pellet. JIS: Japan industrial standard  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Proximate Analysis (wt. \%, Dry Basis) JIS M8812** \\ \hline Volatile matter (dry base) & 81.82 \\ Ash & 0.53 \\ Fixed carbon & 17.65 \\ Low heating value (LHV) & 15.37 MJ/kg—dry \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proximate Analysis (PA) of wood pellet.
## 6 Conclusions and Future Works
This necessitates the development of methods for dynamically adjusting BS group assignments to primary MECS to further improve MEC system energy efficiency. Thirdly, even though our simulations utilize real-world data, the variety and volume of available data are limited, which causes a sparse reward problem that can hinder the determination of an optimal \(\eta(t)\). To address this constraint, the development of novel augmentation techniques for multivariate time-series data is essential. In addition, the independent operation of the workload prediction module and the MECS state determination module may lead to suboptimal performance outcomes. To further enhance the performance, an integrated methodology that can cohesively optimize both prediction and state determination processes. Finally, the emergence of new services may alter user MEC service usage patterns, potentially degrading the inference performance of deep learning models. Therefore, it is crucial to develop techniques for continuously monitoring the similarity between the distribution of model training data and observed data, ensuring that deep learning models remain current and adaptive to evolving usage patterns. Conceptualization, J.P.; Methodology, M.K. and J.P.; Software, M.K. ; Supervision, J.P.; Validation, M.K. ; Visualization, M.K. ; Writing--original draft, J.P.; writing--review and editing, J.P. and M.K. ; visualization, M.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This paper was supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (RS-2024-00406796, HRD Program for Industrial Innovation). The present research has been conducted by the Research Grant of Kwangwoon University in 2024. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Conflicts of Interest:** The authors declare no conflicts of interest.
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 2 Materials and Methods
### Network Structure  
We consider an \(L\times L\) area in the STIN network. There are in total \(N\), cells including \(N-1\) terrestrial cells, and one satellite hanging overhead. Terrestrial cells are modeled with reference to [24] and distributed sparsely in hexagonal cellular cell mode with the inter site distance (ISD) \(D\). The satellite is modeled as a geostationary Earth orbit (GEO) satellite [25]. The coverage diameter of a single beam of the satellite is usually 50 to 250 km, which is very large compared with TN coverage. Therefore, in the system we assume the central beam of the satellite covers the whole TN area. TN and NTN cells differ in bandwidth, defined as \(B_{g}\) and \(B_{s}\). \(M\) users are randomly distributed, requiring data transfer at a random rate with an average of \(R_{agg}\). Users move at a fixed speed \(v\) and with random angles.  
TN channel is modeled as a downlink channel with additive white Gaussian noise and Rayleigh fading. The maximum achievable rate is expressed as  
\[C_{m,n}=B\frac{n_{RB}}{N_{RB}}\log_{2}\left(1+\frac{p_{s}|h|^{2}}{\Gamma( \epsilon)d_{m,n}^{2}N_{0}}\right), \tag{1}\]  
where \(C_{m,n}\) is bit rate between user \(m\) and cell \(n\), \(n_{RB}\) is the number of assigned resource blocks (\(RB\)), \(B\) is the maximum usable bandwidth of the cell, \(N_{RB}\) is the total number of \(RB\), \(p_{s}\) is the transmission power and \(\epsilon\) is the target block error rate (BLER).  
\[\Gamma(\epsilon)=-\frac{2\lg\left(5\epsilon\right)}{3} \tag{2}\]  
represents the signal to noise ratio (SNR) margin to meet the desired target BLER with the QAM constellation [26].  
\[h=\frac{1}{\sqrt{2}}(N(\mu_{0},\sigma_{0})+j\cdot N(\mu_{0},\sigma_{0})) \tag{3}\]  
is the channel fading coefficient where \(N(\mu,\sigma)\) means a Gaussian random number in Equation (4). \(d_{m,n}\) is the distance between user \(m\) and cell \(n\). \(N_{0}\) is the noise power.  
\[X\sim N(\mu,\sigma):f(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-\mu)^{2}}{2 \sigma^{2}}} \tag{4}\]  
The altitude of GEO is 35,536 km. It has a typical reflector antenna with a circular aperture [25]. We consider the simulation area is in the coverage area of the central beam and that the elevation angle of the beam's center is 90\({}^{\circ}\). The NTN channel is modeled with reference to [27], and the inter-beam interference is modeled with reference to [28], and they are not described in detail due to our being limited in space.
## 2 Related Works  
In this section, we give an overview of related contributions to the application of VLC for IoT and other applications in the literature. While many papers have discussed the application of VLC for diverse wireless technologies, only a few have highlighted VLC for IoT in particular. In [1], the fundamentals and challenges of indoor VLC systems were investigated. Additionally, the authors identified the characteristics of channel models in indoor VLC as well as presented the theoretical details of channel modeling. In [5], the authors proposed a VLC system design for IoT by integrating VLC in the dark and utilized orthogonal frequency division modulation (OFDM) to overcome the limitations of traditional VLC for data transmission. In a similar contribution [9], VLC-over-UART systems were proposed and bit error rate analysis was used to evaluate the performance of the proposed system. In another contribution [18], an overview of the potentials of LiFi in transforming indoor lighting into the backbone of wireless communications was carried out. In [4], the concept of VLC was introduced and the application of VLC--as well as some of the challenges facing VLC--were discussed. Similarly, in [26], VLC opportunities for future 6G as well as other novel VLC concepts were discussed. The challenges and potentials of VLC for industrial applications were presented in [27] by examining the latest research trends in VLC systems. In their contribution, the authors in [28] highlighted how optical wireless communications (OWC) have contributed to 5G and IoT solutions. In [3], the concept of light-based internet of things (LIoT) was introduced and discussed. Lastly, in [10], as part of their contributions, the authors gave an overview of the challenges and applications of optical IoT (OIoT) in relation to VLC and optical camera communications (OCC) as part of 5G standards. In Table 1, we summarize the differences in the contributions in our paper in comparison to cited related works.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 6 PULSAR Applications in Forest and Wildfire Management
### Detection and Tracking of Elements  
UAVs equipped with advanced detection technologies, such as thermal cameras and computer vision systems, are at the forefront of wildfire management and prescribed fire operations. The proposed UAV is able to adopt these technologies, which are adept at detecting and tracking critical elements including people, animals, vehicles, and hotspots. Thermal cameras play a key role in identifying heat sources that may signal an impending wildfire and in monitoring the progression of controlled burns, while computer vision algorithms enable the identification and tracking of specific elements, enhancing situational awareness [45]. This is particularly beneficial for safeguarding personnel during firefighting efforts and for observing wildlife to minimize environmental impacts. The integration of these detection technologies into the proposed UAV bolsters the overall effectiveness of surveillance and response strategies in fire science, providing a vital tool for use in the preservation of natural landscapes and wildlife habitats.
###### Abstract  
The efficient scheduling of carrier aircraft support operations in the flight deck is important for battle performances. The supporting operations and maintenance processes involve multiple support resources, complex scheduling process, and multiple constraints; the efficient coordination of these processes can be considered a multi-resource constrained multi-project scheduling problem (MRCMPSP), which is a complex non-deterministic polynomial-time hard (NP-hard) problem. The renewable resources include the operational crews, resource stations, and operational spaces, and the non-renewable resources include oil, gas, weapons, and electric power. An integer programming mathematical model is established to solve this problem. A periodic and event-driven rolling horizon (RH) scheduling strategy inspired by the RH optimization method from predictive control technology is presented for the dynamic scheduling environment. The periodic horizon scheduling strategy can track the changes of the carrier aircraft supporting system, and the improved event-driven mechanism can avoid unnecessary scheduling with effective resource allocation under uncertain conditions. The dual population genetic algorithm (DPGA) is designed to solve the large-scalescheduling problem. The activity list encoding method is proposed, and a new adaptive crossover and mutation strategy is designed to improve the global exploration ability. The double schedule for leftward and rightward populations is integrated into the genetic process of alternating iterations to improve the convergence speed and decrease the computation amount. The computational results show that our approach is effective at solving the scheduling problem in the dynamic environment, as well as making better decisions regarding disruption on a real-time basis.  
carrier aircraft; dynamic scheduling; support operation; rolling horizon strategy; dual population genetic algorithm
## 2 Materials and Methods
### Volatile Compounds  
Untargeted volatile analysis of CS was performed using the headspace solid-phase micro-extraction technique (HS-SPME) and gas chromatography-quadrupole mass spectrometry (GC-\(q\)MS), according to a previously described method on coffee matrices [37]. The SPME fibre, a Carboxen/Polydimethylsiloxane/Divinylbenzene (CAR/PDMS/DVB), 2 cm \(\times\) 50/30 \(\upmu\)m film thickness (Supelco, Bellafonte, PA, USA), was exposed to the headspace of the sample using an SPME autosampler (PAL System, Combi PAL, Zwingen, Switzerland). Two millilitres of ultrapure water, 50 mg of CS, and 5 \(\upmu\)L of \(\alpha\)-thujone (internal standard, 5.15 mg/L) were placed in a 20 mL screw-cap glass vial fitted with a silicone-PTFE septum (Supelco, Milan, Italy) [38]. The vials were stirred at 10\(\times\)\(g\) and 30 \({}^{\circ}\)C for 5 min, then the fibre was exposed to the headspace for 20 min at 30 \({}^{\circ}\)C. The fibre was then removed and inserted into the injector in splitless mode at 260 \({}^{\circ}\)C for 5 min. GC-\(q\)MS analysis was performed using a Shimadzu GC-2010 gas chromatograph equipped with a Shimadzu QP-2010 Plus quadrupole mass spectrometer (Shimadzu Corporation, Kyoto, Japan) and a DB-WAXETR capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness, J&W Scientific Inc., Folsom, CA, USA). The temperature programme started at 40 \({}^{\circ}\)C and was maintained for 1 min, then increased at a rate of 3 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C, and finally increased at a rate of 10 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C (held for 5 min). The flow rate of the carrier gas (He) was 1 mL/min. The injection port temperature was 260 \({}^{\circ}\)C, and the ion source temperature and the interface temperatures were 250 \({}^{\circ}\)C. Detection was performed using electron ionisation at 70 eV with a scan range of 30-330 \(m/z\). Tentative identification was performed using mass spectral matching against the NIST 05 MS library, combined with linear temperature-programmed retention indices. Analyte identification was confirmed using analytical standards, when available. The semi-quantitative concentrations of each volatile compound detected were calculated as the area of each volatile marker ion quantifier divided by the response factor of the internal standard using the ion quantifier (\(m/z\) 81) of the internal standard \(\alpha\)-thujone as micrograms of \(\alpha\)-thujone equivalents per kilogram of CS sample. All experiments were performed in triplicate.