esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Sensor Fabrication and Experimental Set-Up  
The SMS device was constructed following the principles outlined in Equation (4). These devices incorporate a length of Non-Core Multimode Fiber (NC-MMF) sandwiched between two Single-mode Fibers (SMFs). The spectral response of the device was tailored by simply modifying the length of the NC-MMF segment. The SMF used in this application is SMF-28 (Thorlabs, Newton, NJ, USA), characterized by a cladding diameter of 125 \(\upmu\)m and a core diameter of 8 \(\upmu\)m. The NC-MMF (FG125 LA, Thorlabs, Newton, NJ, USA), with a diameter \(D_{eff}\) of 125 \(\upmu\)m and a refractive index \(n_{eff}\) of 1.4445 at the operating wavelength of 1550 nm. The sensor assembly was created using a straightforward method involving the splicing of the SMF to the NC-MMF. Subsequently, the NC-MMF length was determined using a vernier caliper, with the splice serving as the zero-length reference point. After marking the NC-MMF at the desired length, it was cleaved at the marked point, followed by the splicing of the second SMF. All fiber segments were joined together using an arc fusion splicer (Fujikura, San Jose, CA, USA, model FSM-60S).  
Following the guidance provided by Equation (4) and opting for the fourth self-image \((p=4)\), the length of the NC-MMF section was computed to fabricate the sensor targeting a peak wavelength of \(\lambda_{peak}=1488\) nm. Consequently, the calculated length of the NC-MMF is approximately \(L_{MMF}=6.07\) cm. Subsequently, the sensor head was fitted with standard FC/PC connectors for the ease of handling and integration. The manufacturing process is simple and resilient: through a basic variational examination of Equation (4), it becomes evident that any deviation in the length of the MMF results in a change in the peak wavelength [51]:  
\[\delta\lambda_{peak}=\left(\frac{\partial\lambda_{peak}}{\partial L}\right) \delta L=-\left(p\frac{n_{eff}D_{eff}^{2}}{L_{MMF}^{2}}\right)\delta L \tag{5}\]  
Therefore, with \(p=4\), \(n_{eff}=1.4445\), \(D_{eff}=125\) um, \(L_{MMF}=6.07\) cm, and factoring in the standard resolution of a typical vernier caliper at \(\partial L=0.05\) mm, the deviation in the peak wavelength amounts to approximately \(\delta\lambda_{peak}\approx 1.4\) nm.  
The experimental set-up was carried out to evaluate the SMS structure (Figure 2). In this experimental set-up, a superluminescent diode (SLD, Thorlabs, Newton, NJ, USA, model SLD1550S-A1) is employed to generate a wide spectral range spanning from 1420 nm to 1650 nm. The generated signal is launched into the system through an FC/PC connection cable. Subsequently, the signal propagates through the SMS device and is gathered by a secondary patch cable; finally, its transmitted characteristics are assessed using an optical spectrum analyzer (OSA, Anritsu, Morgan Hill, CA, USA, model MS9740A).
## 2 Related Work and Background
### Related Work
#### 2.2.5 Summary and Extensions to Federated Learning  
Table 1 summarizes the taxonomy of existing schemes according to their access requirements (white-box vs. black-box), temporal characteristics (static vs. dynamic), embedding methodology (training vs. fine-tuning), and secret key parameters. Note that these algorithms have been developed primarily for centralized learning scenarios. Extensions to federated learning, where training is distributed among clients with private data, have also been proposed [42; 60; 61]. These federated schemes represent extensions of the above methods adapted to the unique challenges of distributed model training.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Existing Works**} & \multicolumn{2}{c}{**White-Box**} & \multicolumn{2}{c}{**Black-Box**} & \multicolumn{2}{c}{**Stages of Application**} & \multirow{2}{*}{**Secret Key**} \\ \cline{2-2} \cline{4-6}  & **Dynamic** & & & & **Training** & **Fine-Tuning** \\ \hline [PERSON] et al. [45] & & ✓ & & ✓ & ✓ & Matrix \\ \hline DeepSigns [51] & ✓ & & ✓ & & ✓ & TriggerSet, Matrix \\ \hline RIGA [57] & & ✓ & & ✓ & ✓ & DNN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Taxonomy of ML watermarking schemes. Classification criteria include access type (white-box vs. black-box), watermark type (static vs. dynamic), embedding stage (training vs. fine-tuning), and secret key components required for watermark extraction.
## 3 Results
### Spatial Distribution of Radiation
It can be concluded that the coupling effect of chirped parameters to space radiation can be enhanced with the increase in pulse width. At the same time, it can be seen from the comparison of the time spectrum in Figure 4 that the peak value of electron radiation energy increases with the increase in the absolute value of the chirped parameter. The change in radiation energy distribution in the time dimension is also observed. Figure 4: Time spectrum, complete spectral line, and peak spread spectral line of the peak of the electron radiated power.
## 4 Experimental Design and Results
### Experimental Setup and Data Description  
The data are sourced from public datasets and actual collected physiological signal data from drivers. The public datasets include the MIT-BIH Heart Rate Database, while the actual data comes from the heart rate and respiratory frequency collected using a smart cushion device. Additionally, for a comparison, skin conductance (EDA) devices were fitted to the volunteers. To ensure the accuracy and completeness of the data, the sampling frequency of each driver's physiological signals was set to 100 Hz. The entire dataset covers physiological data from 80 volunteer taxi drivers under various driving conditions (such as high-speed driving and urban road driving), with a total duration (limited to driving periods and not continuously collected) exceeding 1200 h.  
Due to considerable noise interference and signal loss in the raw signals, preprocessing was first conducted on the data, including denoising (applying low-pass filtering to heart rate and respiratory frequency signals to eliminate high-frequency noise, and using wavelet transform for smoothing on skin conductance signals, with a cutoff frequency of 5 Hz), normalization (since the dimensions of different modalities of physiological signals vary, Min-Max normalization was performed to ensure that all input feature values are within the range [0, 1]), and data segmentation (considering that the model input is sequential data, the physiological signals of each driver were sliced according to a fixed window length to form fixed-length input data, with a window size of 10 s and an overlap rate of 50% to enhance the diversity of model training).  
To evaluate the impact of multimodal fusion on model performance, the experiments were divided into multiple groups for a comparison. The grouping method included a single-modal input group (using only heart rate (HR) signal input), a dual-modal input group (using heart rate (HR) and respiratory frequency (BR) signal inputs), a tri-modal input group (using heart rate (HR), respiratory frequency (BR), and skin conductance (EDA) signal inputs), and a baseline model group (using a traditional MLP model with tri-modal signals for a comparison with the CNN-LSTM model).  
The experiments divided the data into three sets: 70% of the data was used for training, 15% for validation, and 15% for testing. In addition, control groups were set up for a comparison, including a single-modal signal input group (using only heart rate signals), a dual-modal input group (heart rate + respiratory frequency), and a multimodal input group (heart rate + respiratory frequency + skin conductance). By comparing the performance of these groups, the impact of multimodal fusion on model performance can be analyzed.
# Volatile Organic Compound from _Trichoderma asperelloides_ TSUI: Impact on Plant Pathogenic Fungi
## 3 Results
### Trichoderma asperrelloides TSU1 Released VOCs against Fungal Pathogens  
We tested the effect of VOCs released from _T. asperelloides_ TSU1 against five fungal pathogens, namely, _C. cassiicola, F. incarnatum, N. clavispora, N. cubana_, and _S. rofsii_, through the sealed plate method. The results show that _T. asperelloides_ TSU1 released VOCs with antifungal abilities for the inhibition of fungal pathogen growth (Figure 3). Fungal growth in the tested plates was significantly smaller than that in the control plates (_p_ < 0.05). The  
Figure 2: Phylogram derived from maximum-likelihood analysis of combined ITS, _rbp2_, and _tefl-a_ genes of 36 taxa of _Trichoderma_. Sequences of _Nectria aurantiaca_ and _N. estromatica_ used as the outgroup. Numbers above branches represent (**left**) maximum-likelihood bootstrap percentages and (**right**) Bayesian posterior probabilities. Bootstrap values \(\geq\)70% and Bayesian posterior probabilities \(\geq\)0.90 shown. Scale bar represents 10 substitutions per nucleotide position. Sequences of fungal species obtained in this study are in bold.  
percentage inhibition of VOCs emitted by _T. asperelloides_ TSU1 against fungal pathogens was in the range of 38.88-68.33% (Figure 4). The percentage inhibition of VOCs against C. _cassicola_, _F. incarnatum_, _N. clavispora_, _N. cubana_, and _S. rolfsii_ was 68.33%, 38.88%, 54.22%, 42.22%, and 41.19%, respectively (Figure 4). VOCs emitted from _T. asperelloides_ TSU1 effectively inhibited the fungal growth of _C. cassicola_ causing lettuce leaf spot (68.33%), followed by _N. clavispora_ causing anthurium flower blight (54.22%).
## 3 Results
### The Emission of Volatile Organic Compounds from Plants for Plants Affected by NSAIDs  
The plants from the _Fabaceae_ family are not monoterpenes emitters in physiological conditions. There is a low emission of monoterpenes from plants under NSAID stress with no clear trend regarding drug type (Figure 3). The limonene is dominant in emission for all species, while camphene and \(\beta\)-ocimene show the lower emission.  
Figure 3: The emission rates of monoterpenes from leaves from _Pisum sativum_ (**a**), _Lens culinaris_ (**b**), _Vicia faba_ (**c**), and _Cicer arietinum_ (**d**). Plants in 0.5 L pots filled with commercial garden soil were treated by watering 100 mL of 0.5 mg L\({}^{-1}\) NSAIDs every second day over a total of 20 days. The values are averages of three independent measurements.  
Figure 2: Stomata conductance to vapor water from _Pisum sativum_ (**a**), _Lens culinaris_ (**b**), _Vicia faba_ (**c**), and _Cicer arietinum_ (**d**). Plants in 0.5 L pots filled with commercial garden soil were treated by watering 100 mL of 0.5 mg L\({}^{-1}\) NSAIDs every second day over a total of 20 days. The values are averages of four independent measurements. Data sharing different letters are significantly different (\(p<0.05\)), while data sharing the same letters are not significantly different (\(p>0.05\)).  
In contrast, 1-hexanol (a green leaf volatile) emission increased significantly (_p_ < 0.05) compared with the control for all plant species treated with NSAIDs (Figure 4). The higher emission of 1-hexanol was from plants treated with paracetamol 0.038 \(\pm\) 0.001 nmol m\({}^{-2}\) s\({}^{-1}\) with no statistical differences between the species (_p_ = 0.729).
## 6 Discussion
### Limitations and Future Work  
As a proof-of-concept study, our work has several limitations that should be addressed in future research. First, the sample size is relatively small and limited to a single clinical site. Although the results are promising, larger-scale studies across multiple NICU environments are required to assess the generalizability and robustness of the approach.  
Second, the behavioral annotations used as ground truth were based solely on human observation while adapting the NIDCAP framework for our purposes. While this provides a clinically relevant baseline, it remains inherently subjective. Future work will incorporate additional objective modalities such as EEG or polysomnography to refine and validate state definitions, especially for transitions and ambiguous cases.  
Third, although our system is capable of real-time inference, this study did not yet include full real-time deployment or integration into clinical workflows. Implementing the system in live clinical environments will require solving practical challenges such as robust camera stabilization, automatic recalibration, adaptive lighting management, and reliable data handling within hospital IT infrastructures.  
Finally, the social and human factors surrounding clinical adoption represent a non-trivial challenge. Successful implementation will depend on building trust among healthcare staff, designing intuitive user interfaces, and demonstrating the system's reliability in daily clinical operations. We are currently working with clinical stakeholders to address these aspects through usability testing, iterative design, and continued interdisciplinary collaboration.  
Future directions will also explore semi-supervised or self-supervised learning techniques to reduce the burden of manual annotation and the incorporation of additional physiological signals to further enhance state classification accuracy.
## 1 Introduction
### Objectives and Main Contributions  
The fundamental objective of this study is to critically assess the potential of WebXR Metaverse platforms for providing touristic services and cultural promotion. Specifically, the study aims to evaluate the potential of virtual tourist information services developed in the Metaverse to enhance the overall user experience, compare the experiences and capabilities offered by desktops and VR systems for accessing these services, assess the value of a professional-assisted information services in the Metaverse to enhance user interaction and engagement and investigate the impact of device choice (desktop vs. VR headsets) on user experience during a virtual tour.  
The study contributes to the literature in several ways. Firstly, it provides an empirical investigation into the use of Metaverse platforms for touristic services and cultural promotion, which is a relatively new area of study. Additionally, it adds to the existing body of knowledge by contrasting the user experiences on desktops and VR systems for virtual tourism services. Furthermore, the study explores the added value of professional-assisted services in the Metaverse, contributing to the understanding of how such services can enhance the user experience. Lastly, the research has real-world implications for tourism and cultural promotion organizations, as it offers insights into how they can leverage the Metaverse to engage more effectively with their audiences.  
In comparison to previous works, this study expands on existing research by showcasing the effectiveness of professional-assisted services in the Metaverse, an area that has been less explored in previous studies. It differentiates itself from research focusing on chatbots and virtual assistants for tourism information by using real guides whose avatars enhance the sense of enjoyment and immersion within the Metaverse. The disruptive potential of the Metaverse in revolutionizing tourism management and marketing, as discussed by other researchers, is also evident in this work.  
The study demonstrates several advantages of using Metaverse platforms for touristic services. Users can quickly and easily adapt to virtual environments and perform tasks without distractions. The proposed interfaces have achieved a good level of perceived usability for both PC-based and VR-based interfaces. The use of a professional-assisted information service in the Metaverse enhances user interaction and engagement. Additionally, using video, audio and animated 3D elements creates a more immersive experience, allowing users to feel as if they are taking a trip back in time.
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 3 Network Model and Problem Formulation
### Problem Formulation
#### 3.3.1 Minimization of the Total Network Deployment Cost  
The first objective is to minimize \(\mathbb{C}_{net}\), the total deployment cost of the network, i.e., the sum deployment cost of ARNs, ECNs, SSNs, and URNs. The MPs are predeployed based on the marine areas of interest, while other types of nodes are deployed to collect and forward the information from MPs to the Internet servers. Denote the unit deployment cost of ARN, ECN, SSU, and URN as \(\mathbb{C}^{\text{ARN}},\mathbb{C}^{\text{ECN}},\mathbb{C}^{\text{SSN}}\), and \(\mathbb{C}^{\text{URN}}\). Thus, we have  
\[\mathbb{C}_{net}=\mathbb{C}^{\text{URN}}\sum_{m\in\mathbb{V}_{\text{URN}}}a_{m}+\mathbb{C}^{\text{SSN}}\sum_{n\in\mathbb{V}_{ \text{SSN}}}b_{n}+\mathbb{C}^{\text{ECN}}\sum_{l\in\mathbb{V}_{\text{ECN}}}h_{ l}+\mathbb{C}^{\text{ARN}}\sum_{t\in\mathbb{V}_{\text{ARN}}}z_{t}, \tag{5}\]  
where \(a_{m}\), \(b_{n}\), \(h_{l}\), \(z_{t}\) are binary variables of Candidate Locations (CLs) of URNs, SSNs, ECNs, and ARNs, respectively. The value of 1 indicates that the CL is selected to place a corresponding node, and vice versa.
# A Weighted Turbo Equalized Multi-Band Underwater Wireless Acoustic Communications
## 2 The Multi-Band Underwater Wireless Acoustic Sensor (UWAS) Communication
### Threshold Detection Algorithm  
In some channel environments, a multi-band configuration shows a performance inferior to that of a single-band one. This is because the performance degradation in a particular band affects the output from all the bands, which is then input into an encoder, thereby, decreasing the overall performance. This problem can be solved by using a receiving end that analyzes the error rates of each band, sets the threshold values, and allocates the lower weights to the inferior bands.  
There are two conventional methods of setting the threshold values [20; 21]. The first is the ESNR (Effective Signal to Noise Ratio) estimation. This method uses a pilot signal or preamble data, which are already known between the receiving and transmitting ends, to estimate a receiving SNR. The relation between the estimated SNR (Signal to Noise Ratio) and the un-coded error rate of a channel coding/decoding algorithm is utilized to set a threshold value that satisfies the QEF (Quasi-Error-Free) condition of the channel-coding algorithm.  
The second one is the PES (Post-Equalization SNR) estimation. Like the ESNR method, a threshold value is set which can satisfy the QEF condition of a channel-coding algorithm. To set such a threshold value, the PES method measures an error element (which is the difference of the output between an equalizer and a received signal) at the equalizer, which applies LMS (Least Mean Square) or RLS (Recursive Least Square) types to the receiving end [22; 23]. However, since the threshold values, determined by this method, do not depend on noise power but on interference due to multi-path, they may be inaccurate in a real system with various multi-paths.  
The threshold-setting algorithm, proposed in this study, utilizes the preamble data that are already known to both the transmitter and the receiver. This algorithm sets a threshold value by measuring the error rate of the preamble data and predicting the data performance, at the payload section. The conventional underwater communication methods use the preamble data as the training symbol of an equalizer, to remove the effects of multi-path and synchronous acquisition of frequency, and the phase of the payload. However, since the performance of payload data can also be predicted based on the error rate of the preamble data, information can be provided to the decoding unit of payload data. In a poor-channel environment like that of a multi-path, numerous preamble data are allocated, and the error rates of these data are also related to that of the payload. Accordingly, the error rate of the un-coded preamble is used to predict the error rate of the payload, and to set a threshold value that can satisfy the QEF condition, after decoding the payload.
## 2 Methodology  
### The M2S Multi-Task FCRN and GRU Hybrid Network (M2S-MFCRGRU) Framework  
Given the focus of the efficient deep learning architecture, FCRN, on local information extraction, the incorporation of GRU into FCRN allows the hybrid network to more effectively capture time-related information. The framework of the proposed M2S-MFCRGRU is presented in Figure 1. Through the adoption of the hard parameter sharing, the single-task model can be extended to the multi-task model. The M2S-MFCRGRU network is illustrated in Figure 1a. In contrast to the traditional 1D trace-by-trace inversion method, the M2S training strategy implicitly exploits the spatial correlation between adjacent seismic data traces. Specifically, the M2S-MFCRGRU network takes \(2k+1\) seismic data traces as input and generates predictions for Vp and density for the middle trace. For this work, the value of \(k\) is set to 2.  
The M2S-MFCRGRU network consists of two partitions: the Encoder section and the Decoder section for different tasks. The Encoder section includes a convolutional layer with 16 kernels of size 299 \(\times\) 3, which effectively captures information from the seismic data. It also includes three stacked residual blocks, as depicted in Figure 1b. Each residual block consists of two convolutional layers: the first layer uses 16 kernels of size 299 \(\times\) 3 and the second layer features 16 kernels of size 3 \(\times\) 3; these two convolution kernels extract local information at different scales. As for each task in the Decoder section, the structure remains identical but performs different tasks. The task part comprises a 3 \(\times\) 3 convolutional layer with 16 kernels, followed by a GRU network and a 3 \(\times\) 3 convolutional layer with 1 kernel.  
In addition, batch normalization (BN) is applied after each convolutional layer, except for the last one, to improve the generalization ability and reduce training time. The rectified linear unit (ReLU) activation function is employed to enhance the nonlinearity to capture the complex patterns. The residual block further reduces computational complexity and the number of parameters by employing skip connections, while also mitigating the potential issue of gradient dispersion.