esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Interval Analysis Method for Consideration of Uncertainties
\(\underline{\alpha}\) and \(\overline{\alpha}\) are the values of the variables of the optimization algorithm after its execution and represent the lower and upper bounds of the predicted ESPs of damage case c.  
After determining the lower and upper bounds of the ESPs, the possibility of damage existence (PoDE) is computed, and the damage severity is determined using the damage measure index (DMI). \[\phi_{i}^{E}=\phi_{i,0}^{E}\big{(}1+X_{\phi i}\big{)},\;i=1,2,\ldots,n_{m}, \tag{7}\]0  
\[\phi_{i}^{E}=\phi_{i,0}^{E}\big{(}1+X_{\phi i}\big{)},\;i=1,2,\ldots,n_{m}, \tag{7}\]1  
where \(\alpha_{u}^{I}\) shows the interval bound for the undamaged ESP \(\left(\left[\overline{\alpha_{uk}},\underline{\alpha_{uk}}\right]\right)\) and \(\alpha_{d}^{I}\) shows the interval bound for the damaged ESP \(\left(\left[\overline{\alpha_{dk}},\underline{\alpha_{dk}}\right]\right)\). A detailed explanation of these indexes is presented in the next section. The intersection of the intervals of the damaged and undamaged ESPs on the same axis is depicted in Figure 1, with the shaded region indicating the PoDE. The PoDE ranges from 0 to 100%, with 100% indicating a relatively high possibility of damage to that particular element and 0% indicating no damage. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Model Input** & **Output** \\ \hline ESMA 1 & \(\lambda_{ci}=\lambda_{ci}-\lambda_{ci}(\omega_{i})\) & \(\underline{\alpha_{ck}}\) \\ (Lower Bounds) & \(\phi_{cij}=\phi_{cij}-\phi_{cij}(\omega_{\phi})\) & \\ ESMA 2 & \(\overline{\lambda_{ci}}=\lambda_{ci}+\lambda_{ci}(\omega_{i})\) & \(\overline{\alpha_{ck}}\) \\ (Upper Bounds) & \(\phi_{cij}=\phi_{cij}+\phi_{cij}(\omega_{\phi})\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model input and output variables. Figure 2 depicts the regions of damaged and undamaged ESP on two different axes. A single rectangle depicts the probable damage region, with the failure plane for both states being equal. The shaded region reflects the ESP damage. Since the ESP damage is greater than that of the undamaged ESP, the PoDE is defined as the ratio of the area of damage region to the total area of the entire region. As a result, the quantitative measurement of the PoDE can be described as below [43]:  
\[PoDE=\frac{A_{damage}}{A_{total}}\times 100\%, \tag{17}\]  
The interval value of damaged and undamaged ESP will have large variances in reality. As a result, relying just on PoDE will not give an accurate estimation of the damage. As a result, the damage measure index (DMI) is developed [26]:  
\[DMI=SRF\times PoDE \tag{18}\]
## 4 Methodologies Used for the Analysis of VOCs in WBPs
### Other Methods
Although various studies showed that a fiber coated with DVB/CAR/PDMS achieved better results for VOC extraction in wood [47; 53; 126; 127],PDMS-coated fibers have also been used for the same purpose [69; 128]. In 2012, [PERSON] et al. [69] extracted 14 different compounds (most of which were terpenes) from Dahurian larch (_Larix gmelini_) particles using SPME with a PDMS-coated fiber, whereas [PERSON] et al. (2004) [128] performed SPME extractions on pine wood timber (_Pinus sylvestris_) and identified 12 VOCs by using a PDMS-coated fiber. Recently, an innovative and versatile sample preparation technique which allows the extraction of volatile and semi-volatile compounds was used to extract volatile carbonyl compounds from different WBPs. Developed in 2010, gas-diffusion microextraction (GDME) is a simple, affordable, and non-exhaustive sample preparation technique that merges the concepts of membrane assisted gas-diffusion and microextraction, with the possibility of derivatization being part in the extraction process [129]. Similar to how the use of different fiber coatings affects selectivity in SPME, the use of different acceptor solutions in GDME allows for the analysis of targeted groups of compounds [130; 131; 132; 133]. Through a GDME-HPLC-DAD-MS/MS methodology coupled with DNPH derivatization, [PERSON] et al. (2024) [98] identified 27 carbonyl compounds in commercial MDF samples, e.g., saturated aldehydes (C\({}_{1}\)-C\({}_{10}\)), unsaturated aldehydes (acrolein, crotonaldehyde), ketones (acetone, hydroxyheptanone, 2,3-pentanedione), dicarbonyl compounds (glyoxal, diacetyl), furfural, and benzaldehyde derivatives. Furthermore, a principal component analysis on volatile carbonyl compounds emitted from single-layered PBs produced with different particles (pine and recycled) and different resins (UF and UF fortified with melamine) showed that through the GDME technique it was possible to successfully distinguish PBs by their emissions, according to their differences in production [97]. Overall, alternative methodologies for VOCs determination offer faster and more affordable options compared to standard international methodologies. However, these are often performed under accelerated conditions, therefore not mimicking the indoor space at normal conditions. Among these alternative methodologies, classic extraction procedures are more complex and time-consuming, while miniaturized approaches like SPME and GDME provide simpler, faster, and more practical solutions for VOC analysis.
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 5 Discussion
### Practical Implications and Ethical Considerations  
Detecting suicidal ideation on social media forums offers practical benefits. Automated detection tools could potentially identify at-risk individuals who might not otherwise seek help due to the fear of social stigma associated with mental health issues. Consequently, these online detection tools could enable more timely interventions. These systems could alleviate the burden on healthcare providers by flagging concerning posts for further review by a mental health professional, allowing for a more efficient allocation of resources. In addition to that, automated suicidal ideation detection tools could address the issue of fragmented care for existing patients, which entails long gaps between assessments. These tools could assist mental health professionals in monitoring patients between clinical interactions by analyzing patients' online activity and alerting if their mental health deteriorates.  
However, this approach also raises ethical concerns. First, the protection of privacy during model development is crucial. It is important to anonymize the data used for training suicidal ideation detection models to ensure no identifying information is included. Second, after deployment, users should be informed that their online data may be analyzed, ensuring transparency and fair practices. It is also essential to provide users with an option to opt out in case they do not want their activity monitored. There is also a risk of false positives when the user is incorrectly identified as experiencing suicidal ideations. This could lead to unnecessary interventions or stigmatization. To mitigate this, it is important to avoid overreliance on automated systems, and their outputs should always be verified by qualified professionals.  
In summary, the implementation of such online detection systems could add value, but the individual privacy, consent, and intervention guidelines should be carefully considered to ensure ethical use and prevent potential misuse.
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 3 RDTSN Model and Data Delivery Strategy
### Robot Delay-Tolerant Sensor Network (RDTSN) Model  
The Robot Delay-Tolerant Sensor Network (RDTSN) model is established, as shown in Figure 3. Several inspection robots, MMS and communication nodes deployed along the transmission line form an intermittently-connected mobile wireless sensor network. The communication nodes include static nodes (SNs) and wireless central nodes (WCNs). The WCN is connected to Optical Fiber Composite Overhead Ground Wire (OPGW). A data delivery strategy for RDTSN based on discrete time multi-path routing algorithm (DTMA) is proposed in this paper. RDTSN has the following properties.  
* The nodes have heterogeneous communication capabilities. In order to reduce energy consumption, the communication radius of robots and SNs are smaller than R (the maximum communication radius of robots and SNs), and a WCN has two communication radii of R and C (the maximum communication radii of WCN), 2 R \(<\) C.
* The robot has heterogeneous motion speed. The robot regulates the speed according to the line environment, operative mode and energy consumption, etc.
* The inspection data has heterogeneous data capacity and transmission delay requirements.  
The main idea of the strategy is to deploy a certain number of SNs and WCNs in RDTSN, so that the robot can communicate with the MMS, SNs and WCNs in a wireless way. As the robot is walking along the ground, a message can be delivered to the connectable WCN directly or forwarded via other  
Figure 3: Robot Delay-Tolerant Sensor Network (RDTSN). OPGW, Optical Fiber Composite Overhead Ground Wire.  
nodes. Then, the message can be forwarded to the CMP by WCNs via optical fiber. At different times, each node can decide whether it sends a message to the neighbors or store the message in the local buffer until a better delivery path is obtained, according to its own information.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 2 Background: DTNs and Issues in FANETs  
In particular scenarios, infrastructure-based services access might not always be available or convenient. Examples are disaster scenarios, where the infrastructure networks could have fallen down, or in search and rescue operations that could need the deployment of a network in areas not covered by infrastructure. On the other hand, cellular networks could become a not-optimal solution for several reasons, like coverage gaps, interoperability issues between carriers, and the service model. Classic ad-hoc networks seem to be a potential solution, but in many cases the dynamic nature of such context could affect the routing performance, due to frequent link failures, delays, packet route errors, etc. [5].  
An ideal solution comes through the integration of DTNs into vehicular networks, enabling and maintaining network connectivity under high delays and intermittent links. Recently, the combination of UAVs in DTNs is starting to become popular, providing a more close approximation of a realistic case, due to the high dynamicity of UAVs in terms of speed, distances and autonomy. In Ref. [6], a detailed overview of DTN technology applied to the FANET infrastructure is provided, with special focus on tactical scenarios and DoD (Department of Defence) programs. UAVs as DTN nodes used for carrying data from and to isolated buildings is considered in Ref. [7], where a smart city is the context scenario. Other works propose the implementation of an UAV architecture to carry the data within a building, creating an indoor DTN system [8].  
Several application scenarios could rely on airborne devices to perform several tasks. A particularly interesting example is represented by Nowadays; Urban/environment monitoring is another example in which UAVs can perform tasks in a distributed way. Realistic and synthetic mobility models for the scenarios described above are discussed in Refs. [1; 9]. Most of these applications require an already planned route schedule, where the UAVs' path is defined in advance. This aspect can be exploited to the advantage of the routing process.  
DTN routing takes place on a time-varying topology, where links come and go, sometimes predictably. Many studies focused on the fact that mobile nodes can have a fixed path scheme: buses, satellites, etc. In this case, if we assume that a node will reach a destination, a DTN routing protocol is easily designed and effective. If the nodes are numerous and with an unpredictable, randomized mobility, then a probabilistic heuristic may be adopted.  
On the other hand, when we consider a FANET, the conditions are different. First of all, UAVs do not move with a fixed path scheme; this is also due to the mission dynamicity, since the tasks could change frequently. Hence, a DTN routing protocol based on a certain type of predictability is not possible. Secondly, the number of UAVs of a particular application scenario may be small, resulting in lower density as well as lower contact chances.
## 3 Datasets and Evaluation Metrics
### Datasets
#### 3.1.5 Data Enhancement for HDR  
Data augmentation in the high dynamic range images is an essential technique for increasing the variability of training datasets, especially in image processing and computer vision tasks. HDR images are a particularly costly type of data to obtain, as they require multiple exposures captured in different lighting conditions, as well as specific equipment to ensure high-quality capture. Therefore, data augmentation techniques can be of great importance in increasing the robustness and the performance of deep learning models, compensating for the scarcity of available data.  
The following topics present some recent techniques used to augment HDR datasets.  
Neural Augmentation for Saturation Restoration  
An approach to restoring saturated regions in low dynamic range images derived from HDR scenes combines model-based methods with data-driven approaches. In this process, synthetic LDR images with different exposures are generated and refined by a neural network before being combined using exposure fusion algorithms. The aim is to improve the recovery of details in shadow and highlight areas by using a neural network to correct the exposures and then synthesize an enhanced HDR image. This technique, presented in [88], has been shown to be effective in recovering saturated regions and increasing the overall quality of the images; however, these synthetic images generated in the process could be a useful resource in data augmentation for HDR.  
Local-Area-Based Mixed Data Augmentation  
In order to improve HDR video reconstruction, a technique called HDR-LMDA applies localized exposure increases and RGB channel permutation to LDR images. By changing exposures in specific regions and shuffling the color channels of small blocks of the image, the model is forced to learn how to deal with the recovery of incorrect exposures and localized color correction. This method, proposed in [89], has been shown to be effective in increasing performance metrics such as the PSNR (Peak Signal-to-Noise Ratio), offering significant improvements over traditional techniques.  
In addition to these more recent approaches, traditional data augmentation techniques, such as geometric transformations, modifications to the color space (brightness, contrast, and saturation), and the use of generative adversarial networks (GANs), continue to be widely used. These techniques are still an efficient way to avoid overfitting and help improve model performance, especially in scenarios with limited or unbalanced data. Some recent studies, including those of the datasets cited in this section [9; 31], demonstrate how these approaches can be combined to improve HDR image reconstruction and increase model robustness.
# Design and Analysis of Systematic Batched Network Codes
## 2 Ordinary Batched Network Coding  
We briefly introduce ordinary (non-systematic) batched coding to assist the further discussion of the systematic design. A batched network code is formed by an outer code and an inner code. Here, we focus on a specific outer code called the BATS outer code, which was originally introduced by the BATS code. Readers are referred to [23] for more information about the BATS code.
### Atomic and Electronic Structure of [M(Salen)] Complexes and Their Polymers
#### 3.2.3 NEXAFS and EXAFS
A detailed comparison of the C 1\(s\) NEXAFS spectra of H\({}_{2}\)(Salen) and [Ni(Salen)] made it possible to identify the absorption bands caused by the C 1\(s\) electron transitions for the ethylenediamine fragment (the ethylene molecule C\({}_{2}\)H\({}_{4}\) and the mine functional group -N=C-) designated in Figure 16b by capital dashed letters as well as those for the phenolic C\({}_{6}\)H\({}_{5}\)OH fragment designated in Figure 16b by capital letters with asterisks. In addition, from a combined analysis of the N 1\(s\) and C 1\(s\) spectra, it follows that the ethylenediamine bridge does not change upon passing from free salen to the [Ni(Salen)] complex. However, the O 1\(s\) NEXAFS spectrum (Figure 16c), in contrast to the N 1\(s\) and C 1\(s\) spectra, has undergone significant changes in [Ni(Salen)] compared to H\({}_{2}\)(Salen). Narrow low-energy absorption resonances in the [Ni(Salen)] spectrum are associated with transitions of O 1\(s\) electrons to two vacant antibonding \(\pi^{\star}\)-MOs, reflecting the presence of a \(\pi\)-bond with the Ni atom (\(\pi\)-back-donation) and \(\pi\)-conjugation between oxygen atoms and phenolic rings, respectively. Thus, the NEXAFS and EXAFS spectroscopies are highly informative experimental techniques that provide valuable knowledge about the electronic structure of molecular complexes [M(Salen)] and their polymers, as well as about the structure of the local environment of the atoms under study in these systems. However, from the works cited above, it follows that NEXAFS and EXAFS data are available only for a small number of monomers, ([Co(Salen)], [Cu(Salen)], [Ni(3-CH\({}_{3}\)saltMe)], and [Pd(3-CH\({}_{3}\)Salen)]), and some polymers, (poly-[Ni(3-CH\({}_{3}\)saltMe)] and poly-[Pd(3-CH\({}_{3}\)Salen)]). Moreover, NEXAFS data for the ligand atoms (nitrogen, oxygen, and carbon) are known only for the H\({}_{2}\)(Salen) molecule and the monomer [Ni(Salen)] complex.
## 3 Results and Discussion  
### Effects of BGLSP on Color of Flour Blends  
Color is a crucial attribute affecting the quality of flour blends, significantly impacting the visual appeal of CSB [17]. Table 2 illustrates the impact of BGLSP on the color parameters of flour blends. The yellowness (\(b^{\star}\)) values initially decreased and then increased with the rise in BGLSP content. The color of CSB primarily originates from the inherent pigmentation of BGLSP, which is additionally influenced by oxidation reactions that may transpire during the steaming process [7; 10]. With the incremental addition of BGLSP, there was a statistically significant increase (\(p<0.05\)) in the redness (\(a^{\star}\)) values of the flour blends. Conversely, the lightness (\(L^{\star}\)) values exhibited a significant decrease (\(p<0.05\)) with the incorporation of BGLSP. The \(\Delta E\) value quantifies the extent of color deviation from standard wheat flour, with \(\Delta E>3\) denoting a perceptible color difference to the human eye [18]. The \(\Delta E\) values for  
\begin{table}
\begin{tabular}{l l l} \hline
**Quality Parameter** & **Score** & **Sensory Assessment Standard** \\ \hline Specific volume (mL/g) & 20 & Specific volume \(\geq\) 2.3: 20. \\  & & 1.7 \(\leq\) Specific volume \(\leq\) 2.3: minus 1 score per 0.1 mL/g reduction. \\  & & Specific volume \(\leq\) 1.7: 3–6. \\ Appearance & 15 & Smooth: 12.1–15. \\  & & Uneven, pliable, air bubble, concave or large burns: 1–12. \\ Color & 10 & White, milky, milky white: 8.1–10. \\  & & Light yellow, yellow: 6.1–8. \\  & & Gloom, dark: 1–6. \\ Internal structure & 15 & Longitudinal section with small pores evenly: 12.1–15. \\  & & Close pores, uniform or separated between the edges and the epidermis: 9.1–12. \\  & & Large pores, rough structure and uneven: 1–9. \\  & & Rebound quickly, can be compressed more than 1/2 after finger press: 16.1–20. \\  & & Rebound slowly or does not rebound after finger press: 12.1–16. \\  & & Difficulty pressing fingers, very hard: 1–12. \\ Chewing performance & 15 & Great chewing without teeth stickness: 12.1–15. \\  & & Chewing is not refreshing, slightly sticky or sticky: 1–12. \\ Flavor & 5 & Wheat fragrance, no smell: 4.1–5. \\  & & Bland flavor: 3.1–4. \\  & & Unpleasant smell: 1–3. \\ \hline \end{tabular} Note: Scoring system: poor \(\leq\) 70, average or general or acceptable = 70–79, good = 80–89, excellent = 90–100. Abbreviations: CSB, Chinese steamed bread.  
\end{table}
Table 1: Sensory quality scoring system for CSB.  
four blends containing BGLSP exceeded 3, indicating a marked color difference from the control group. Figure 3 presents the appearance and scanning results of CSB, demonstrating a trend towards darker coloration with increased BGLSP content. This darkening effect is likely attributable to the inherent dark brown hue of BGLSP, which influences the overall color of the CSB [10].
# Design of Surface Plasmon Nanolaser Based on MoS2
## 1 Introduction  
As one of the most important inventions in the twentieth century, laser plays an important role in our lives. After more than half a century of development, laser size has become smaller. Traditional semiconductor lasers can't breach the diffraction limit, so their spatial size and mode size are larger than half a wavelength, and the miniaturization of lasers is hindered [1; 2; 3]. Nanolasers based on surface plasma can achieve deep subwavelength radiance, which makes laser miniaturization possible [4; 5; 6; 7; 8; 9].  
However, designing high-performance surface plasmon waveguide-based nanolasers has paradoxical problems of mode locality and propagation loss. In recent years, the proposed hybrid surface plasmon waveguide structure has achieved the coexistence of high local mode distribution and low loss transmission to some extent. In hybrid surface plasmon waveguides, surface plasmon mode and dielectric waveguide mode coupling with each other in the low refractive index gap allow the layer of gaps to function to store energy, which promotes the reduction of transmission loss while enhancing the locality of the electric field [10; 11; 12; 13; 14]. The structure has been studied by researchers and achieved a lot of good results. In addition, the propose of the graphene [15; 16; 17; 18; 19; 20] and MoS2[21; 22; 23; 24; 25; 26; 27; 28] make nanolasers acquire a better performance.  
The nanolaser, which has a waveguide structure based on MoS2, has been put forward in this paper. By comparing the simulation results with SiO2 and MoS2 and MgF2 materials in the modified structure, it was found that MoS2 has a better performance. The electric field distribution, modecharacteristics, and laser threshold have been regarded as the criteria to analyze the effect of different materials [29; 30; 31; 32]. The influence of geometry on nanolaser based on MoS\({}_{2}\) is also discussed.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 2 Machine-Learning-Based Path Loss Prediction
### Model Selection  
Different models can be used for the path loss prediction, and the model selection should consider about requirements of both accuracy and complexity. As examples, we will introduce ANN, SVR, and decision tree in Section III. It has been reported that these algorithms have good performance in predicting path loss values [5; 20; 23].