esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Flame Length, Height, and Tilt Angle
#### 3.3.1 Definition of Flame Length, Height, and Tilt Angle  
The geometrical characteristics of a flame govern its radiation heat feedback to the surrounding environment and the surface of the oil pool and thus affect the fire spread. When set in a crosswind environment, a flame is inclined by the crosswind, which enhances the radiation intensity to the downstream area. Therefore, the flame length and flame tilt are the basic behavioral parameters for analyzing a wind-blown fire, which have been defined by previous researchers. The flame length (L\({}_{\mathrm{f}}\)) under a crosswind is expressed as the linear distance from the center of the fuel surface to the tip of the flame [29]. The flame tilt angle \(\alpha\) is the angle at which the connecting line from the center of the pool to the inclined flame tip deviates from the vertical direction, as defined by [PERSON] et al. [30].  
Figure 7 depicts two typical flame features deduced from flame photos based on the experimental results. For the relatively lower baffle heights, the flame shape was similar to the wind-blown pool fire scenarios without a baffle (Figure 7a) and the flame will tilt towards the direction of the wind. As the baffle height exceeded 2 cm, the flame shape changes as per Figure 7b. The flame part below the baffle height was close to the baffle due to the existence of the recirculation zone, and the flame part over the baffle tilted downstream due to the crosswind. The flame length and tilt angle were redefined as a result of the bending of the flame in this article. The flame length was divided into bottom flame length L\({}_{\mathrm{f1}}\) and upper flame length L\({}_{\mathrm{f2}}\) based on the baffle height, as shown in Figure 7b. Hence, the total length of the flame can be expressed as L\({}_{\mathrm{f}}\) = L\({}_{\mathrm{f1}}\) + L\({}_{\mathrm{f2}}\). The flame height H\({}_{\mathrm{a}}\) is defined as the height from the pool surface to the flame tip.  
As for the flame tilt angle, the former definition by [PERSON] et al. was applicable to describe a flame without a baffle blockage or a flame with a baffle height less than 2 cm, as shown in Figure 7a. However, the flame shape changed when a baffle over 2 cm in height was set. In this case, the flame tilt angle \(\alpha\) was used to describe the angle of inclination of the upper flame above the baffle height relative to the vertical direction, as shown in Figure 7b.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Forwarding Packets via a Single Parent
#### 4.1.9 Congestion Control using Multiple Sinks
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Main Feature** & **Routing Metrics \& Constraints** & **Evaluation Tool** & **Evaluation Metrics** \\ \hline \multirow{4}{*}{GTCC [16]} & Designing the parent change procedure using a & & & \\  & potential game & & & \\  & Detecting congestion based on the difference of packet & Link quality indicator (LQI), & & & Packet losses, throughput, \\  & transmission rate and the packet service rate. & transmission rate & transmission rate & & average hop count \\  & Notifying congestion explicitly by sending a DIO & & & \\  & packet with the CN bit set. & & & \\ \hline \multirow{2}{*}{QU-RPL [17]} & Selecting a parent based on queue utilization, hop count, and EX & ETX, queue utilization, hop count & Real testbed experiment & PDR, routing overhead, \\  & Selecting a parent based on queue utilization, residual & ETX, queue utilization, residual & & & \\  & energy, and ETX & energy & & \\ \hline \multirow{4}{*}{PC-RPL [19]} & Addressing the hidden terminal problem and load & Hop count, ETX, queue losses, link & Real testbed experiment & PDR, hop count, end-to-end \\  & imbalance by jointly controlling transmission power & losses, RSSI & & & \\  & and routing topology & & & \\ \hline \multirow{2}{*}{CA-OF [28]} & Using a weighted sum of ETX and buffer occupancy & ETX, buffer occupancy & Coojs simulation & \begin{tabular}{c} PDR, number of slot packets, \\ throughput, energy \\ consumption \\ \end{tabular} \\ \hline \multirow{4}{*}{OHCA [20]} & Adopting both resource control (parent change if \\  & available) and traffic control (sending rate adaptation). & Buffer occupancy, ETX, queuing delay & Coojs simulation & \begin{tabular}{c} Throughput, weighed fairness \\ index, end-to-end delay, energy \\  & Detecting congestion by monitoring if the arrival rate & & & \\  & exceeds the service rate & & & \\ \hline \multirow{4}{*}{CoAR [21]} & Using TOPSIS to select the best alternative parent. & ETX, queue utilization, residual & & \\  & Adjusting the threshold for congestion detection according to the buffer occupancy and the & energy, neighborhood index & & \\  & incoming/outgoing traffic & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [29, 46]} & Proposing two metrics exploiting the information & & & \\  & from the MAC Layer: R-metric representing & & & \\  & end-to-end reliability and \(Q\)-metric representing the optimal traffic for balanced energy consumption & LQI, busy channel probability, traffic & Testbed experiment (using \\  & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [30]} & Extending the original RPL for a wireless sensor & \
\begin{tabular}{c} Hop count and tie-breaking metrics \\ (available bandwidth, delay, MAC \\ layer queue occupancy, ETX) \\ \end{tabular} & Coojs simulation &
\begin{tabular}{c} PDR, delay, total \\ retransmissions \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Protocols forwarding packets via a single parent.
## 5 Challenges and Limitations in Carotenoids Analysis
### Challenges in Sample Preparation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Extraction** & **Advantages** & **Limitations** & **Extracted Carotenoids** & **Refs.** \\ \hline SE & Simple, widely used, effective for lipophilic carotenoids & High solvent consumption, environmental concerns, potential for oxidation and carotenoid degradation & \(\beta\)-carotene, lycopene, phytoteine, & [57, 58] \\ \hline UAE & Reduced time, lower solvent use, enhanced mass transfer & Ultrasound intensity and frequency must be optimized, risk of oxidation due to cavitation effects & \(\beta\)-carotene, lutein, zeaxanthinhin, atsaxanthin, lycopene, cathaxanthin, neocanthin & [5, 73-76] \\ \hline MAE & Highly selective, fast, efficient, improved extraction yields & Heat-sensitive carotenoids may degrade, potential formation of cis-isomers, solvent selection is critical & \(\beta\)-carotene, lutein, lycopene, violaxanthin, capsaicin, zeaxanthin, cathmaxanthin, capsanthin & [8, 70, 71, 81] \\ \hline SFE & Use of sustainable solvent (CO\({}_{2}\)), high purity, solvent-free extraction & Expensive equipment, requires specific pressure pressure and temperature control for optimal yield & Lycopene, \(\beta\)-carotene, phytoteine, photovoltaic, lutein, zeaxanthin, violaxanthin & [77-80] \\ \hline EAE & Mild conditions, environmentally friendly, preserves carotenoid bioactivity & Long extraction times, enzyme cost, enzyme specifically affects efficiency, limited applications & Lutetin, \(\beta\)-carotene, zeaxanthinhin, violaxanthin, instaxanthin, incovanthin, antheraxanthin & [80, 82, 83] \\ \hline PEF & Enhances cell permeability, facilitates solvent penetration, low energy input & Requires optimization for each matrix, may not be effective for all carotenoid-rich tissues & Lutetin, \(\beta\)-carotene, zeaxanthinhin, capsanthin, astaxanthinhin, lycopene, neocanthin & [84, 110, 120] \\ \hline HHPE & Effective for cell disruption, moderate temperatures prevent degradation, retains bioactivity & High-pressure equipment required, limited scalability, requires precise control of pressure conditions & Lutetin, \(\beta\)-carotene, astaxanthinhin, zeaxanthin, lycopene, phytoteine, phytoteine & [81, 82] \\ \hline SWE & Comparable to solvent efficiency, avoids organic solvents, effective for polar carotenoids & High temperatures can degrade carotenoids, may favor cis-isomerization, risk of oxidation if not properly controlled & Lutetin, \(\beta\)-carotene, violaxanthinhin, neocanthin, zeaxanthinhin, carboxanthin, antheraxanthin & [85, 121] \\ \hline OH & Uniform heating reduces oxidation risk, enhances mass transfer & Requires specialized equipment, temperature control is critical to prevent carotenoid isomerization & \(\beta\)-carotene, lycopene, astaaxanthin, lutein, zetaaxanthin, yoke, glucine, phytoplankton, cyclashutin & [86, 122] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Advantages and limitations of carotenoid extraction technologies.
# High Imperceptible Data Hiding Method Based on Combination Theory for AMBTC Compressed Images
## 4 Experimental Results
### Results and Discussion
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Image** & **Metrics** & **[PERSON] et al.’s Scheme** & **[PERSON] et al.’s Scheme** & **[PERSON] et al.’s Scheme** & **Proposed Scheme** \\ \hline \multirow{3}{*}{Airplane} & PSNR & 33.63 & 31.65 & 31.95 & **38.25** \\ \cline{2-5}  & MSE & 28.22 & 44.47 & 41.50 & **9.72** \\ \cline{2-5}  & Capacity & 95,255 & 114,666 & 81,097 & **195,928** \\ \hline \multirow{3}{*}{Baboon} & PSNR & 30.88 & 26.39 & 26.00 & **37.97** \\ \cline{2-5}  & MSE & 53.12 & 149.30 & 163.33 & **10.36** \\ \cline{2-5}  & Capacity & 83,060 & 126,600 & 64,882 & **104,284** \\ \hline \multirow{3}{*}{Boat} & PSNR & 31.68 & 30.59 & 31.15 & **36.27** \\ \cline{2-5}  & MSE & 44.11 & 56.76 & 49.89 & **15.34** \\ \cline{2-5}  & Capacity & 83,240 & 115,448 & 78,178 & **158,440** \\ \hline \multirow{3}{*}{Lena} & PSNR & 33.48 & 31.42 & 33.97 & **37.42** \\ \cline{2-5}  & MSE & 29.15 & 46.78 & 26.06 & **11.76** \\ \cline{2-5}  & Capacity & 91,190 & 124,877 & 75,128 & **201,364** \\ \hline \multirow{3}{*}{Pepper} & PSNR & 32.67 & 31.72 & 33.40 & **35.89** \\ \cline{2-5}  & MSE & 35.18 & 43.76 & 29.72 & **16.72** \\ \cline{1-1} \cline{2-5}  & Capacity & 83,600 & 125,657 & 78,799 & **197,632** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison with related works. The last comparison is to [PERSON] et al. 's scheme. As seen in Table 2, the pattern of [PERSON] et al. 's scheme is generally similar to [PERSON] et al. 's scheme. However, regarding image quality, [PERSON] et al. 's scheme is better than [PERSON] et al.'s. Conversely, [PERSON] et al. 's scheme can embed more secret bits than [PERSON] et al.'s. Finally, from the above discussion, it can be concluded that the proposed scheme is more accurate than the proposed scheme. Figure 10: Graphic of the performance comparison: (**a**) image quality comparison and (**b**) hiding capacity comparison [22; 23; 24]. proven that the proposed scheme performs better than the three other schemes for handling the complex block. The proposed scheme is highly invisible and has the most excellent hiding capacity.
## 4 Practical Experiments
### Shisper Glacier
#### 4.1.1 Processing--Shisper Glacier  
_Planet4 Stereo_ was run using the NIR band of the PSS considering image pairs with a minimum overlap of 10% and a convergence angle of at least 4\({}^{\circ}\). The GLO-30 DEM served as the approximation, while glacier regions were masked using RGI outlines during the alignment of pairwise-calculated point clouds to mitigate potential shifts resulting from bundle block adjustment without the use of GCPs. Table 2 and Figure 5 present the resulting DEMs and corresponding statistics.  
Figure 4: Shisper glacier study area with Ali Abad and Karimabad village. PSS images used for DEM calculation in 2019 are marked, as well as areas that are further analyzed in Section 4.1.2.  
Mean residuals at the camera positions from bundle-block adjustment and for triangulated image points (detected in at least two images) were approximately 0.3 pixels for both years. These values align with the literature expectations and closely match the results reported by [16]. The intersection errors in the error maps are randomly distributed, with higher errors observed in snow-covered regions and areas with steep terrain. The average intersection errors are 1.4 m in 2017 and 1.6 m in 2019, corresponding to 0.3 pixels and 0.4 pixels, respectively, relative to the mean GSD of the PSS, which is 3.9 m. Note that the DEM generated in 2019 covers the ROI of Shisper glacier well (as in 2017) but with some gaps in the upper mountain regions. This is attributed to the fact that the image coverage focused on a better representation of the ROI, which was at the expense of the image coverage of the higher areas. No ascending scenes were usable covering the ROI.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{3}{c}{**PlanetScope DEM 2017**} & **PlanetScope DEM 2019** \\ \hline Number of images & 16 (NIR) & 14 (NIR) \\ Minimum overlap (\%) & 10 & \\ Minimum convergence angle (\({}^{\circ}\)) & 4 & \\ Ortho workflow & yes (GLO-30 DEM) & \\ \hline \multicolumn{3}{l}{**Bundle-block adjustment**} \\ \hline Number of triangulated points (sparse) & 7762 & 4160 \\ Mean residuals of images (px) & 0.3 & 0.3 \\ Mean residuals of triangulated points (px) & 0.3 & 0.3 \\ \hline \multicolumn{3}{l}{**Stereo reconstruction**} \\ \hline Mean intersection error (m) & 1.4 (max: 11.8) & 1.6 (max: 13.7) \\ \hline \multicolumn{3}{l}{**DEM sampling**} \\ \hline DEM coverage (km\({}^{2}\)) & 600.8 & 444.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shisper glacier DEM processing settings.  
Figure 5: Calculated DEMs of the glacier area from PSS images in 2017 and 2019.
# RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation
## 2 Related Work
### Entity Relation Extraction  
Relation extraction, as an important level of information extraction, has been widely researched. These proposed methods can be classified into three categories: supervised learning, semi-supervised learning and unsupervised learning. The most typical methods of supervised learning are kernel-based methods [10; 11; 12; 13; 14; 15]. Although supervised learning-based methods perform very well, their performance much relies on the availability of a large amounts of manually labeled data. So, many researchers begin to focus on the semi-supervised learning [16; 17; 18; 19], which can make full use of unlabeled data given a small amount of labeled data.  
Above works are often limited to extracting the pre-defined types, which make it difficult for open domain applications. Recently, many unsupervised learning-based methods [20; 21; 22; 23; 24; 25] of open domain relation extraction have been proposed to reduce the heavy manual labor. TextRunner [5] was the first Open IE (OIE) system, where a large set of relational entity tuples were extracted without requiring any human labor and then these tuples were assigned a probability. [PERSON], [PERSON] and [PERSON] [7] developed another Open IE system of REVERB by introducing two syntactic and lexical constraints on verb-centered relations. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [8] presented the first Chinese Open IE system (CORE) which can extract entity relation triples from Chinese texts by combining many NLP techniques. [PERSON] and [PERSON] [6] described entity-relation analysis in IBM Watson, which aimed to detect noun-centric-phrases in the text. Distributional relation representation is similar with unsupervised learning-based methods. Both of them aim to extract entity relations without requiring any specific relation types. Compared with previous works on Open IE, distributional relation representation focuses on the training of entity vectors instead of extracting more abundant features by introducing a series of NLP tools.
# Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection
## 6 Conclusions  
Our approach combines the strengths of contrastive learning and auto-encoders for robust OOD detection, showcasing superior performance compared to existing methods. The synergistic relationship between contrastive learning and an auto-encoder contributes to improved end-to-end training. The experimental results highlight the efficacy of our model in detecting outliers during testing. Specifically, when trained on CIFAR-10, our model achieves a remarkable average 93.7% detection rate for OOD data, and when trained on CIFAR-100, it achieves an average 87.8% detection rate for OOD instances. Our method proves competitive with or surpasses other OOD detectors, demonstrating significant performance gains, especially in scenarios with only unlabeled data. By combining contrastive learning with an auto-encoder, our model achieves a more discriminative and robust feature representation. This improvement is reflected in its ability to accurately distinguish between in-distribution and OOD samples, outperforming existing methods in terms of detection accuracy. The refined latent space, learned through the synergy of contrastive learning and an auto-encoder, enables the model to generalize effectively to diverse and unfamiliar datasets. This adaptability is a crucial advancement in OOD detection, especially in real-world scenarios where data distributions may evolve. We validated the efficacy of our approach through extensive experiments. In summary, our work stands as a noteworthy contribution to OOD detection, introducing a novel approach that elevates detection performance, enhances robustness to unseen distributions, and undergoes thorough empirical validation, establishing its practical significance in real-world applications. The potential industrial and commercial applications of our method are diverse, including applications in OOD detection in industrial processes, fraud detection in financial transactions, quality control in manufacturing, and healthcare diagnostics. Our method exhibits several advantages, including enhanced efficiency in handling diverse datasets, scalability for adapting to different domains, and ease of implementation into existing systems and workflows. These features make our proposed OOD detection method a promising solution for real-world applications, offering practical advantages in terms of accuracy, adaptability, and seamless integration into various industries. Conceptualization, D.L., J.B. and H.Z. ; methodology, D.L. and B.Y. ; software, H.Z. and D.L. ; writing--original draft preparation, D.L. and H.Z. ; writing--review and editing, D.L. and J.B.; visualization, D.L. ; supervision, J.B.; project administration, J.B. All authors have read and agreed to the published version of the manuscript. This study was supported by the MOTIE funding program, \"Advanced Graduate Education for Management of Convergence Technology\". Data Availability Statement: Publicly available datasets were analyzed in this study.
## 2 Materials and Methods  
### Fabrication of Si-Rod-Array Metasurfaces  
The metasurfaces in this article were fabricated using silicon-on-insulator (SOI) substrates. We employed two types of SOI substrates; one had a SOI layer of 200 nm thickness and a buried oxide (BOX) layer of 400 nm thickness on a bulk Si wafer; the other had a 340 nm SOI layer and a 1000 nm BOX layer on a bulk Si wafer. Note that the SOI is crystalline Si, and that the atomic element of the BOX is SiO\({}_{2}\). Electron-beam lithography (EBL) was performed on the EBL substrates and produced a resist mask for the BOSCH process, which is able to etch only the SOI layer in a very anisotropic and selective way, and produces Si rods of the same height to the SOI-layer thickness. The etching procedure was previously reported [54]. After the BOSCH process, the remaining resist mask was removed using O\({}_{2}\) plasma.  
Figure 2 shows scanning electron microscope (SEM) images of the representative metasurfaces, which were fabricated using the substrates of a 200 nm SOI layer. Figure 2a,b shows the top-view SEM images of the metasurfaces of periodic lengths \(P\) = 330 and 400 nm, respectively. The corner-round squares are the Si rods. We note that exact squares are extremely difficult to fabricate using the standard EBL. The gaps between the Si rods are approximate 110 and 108 nm in Figure 2a,b, respectively. The white bars in Figure 2a,b represent a distance of 500 nm. Figure 2c shows an oblique-view SEM image of the metasurface in Figure 3a, suggesting that the etching of the SOI layer successfully produced rectangular Si rods as intended.  
We mention the reasons why we chose SOI substrates in this study. One is practical; we employed SOI substrates in the previous experiments [29; 48; 49; 50], and therefore, could use them at once. The other is that we preferred the relatively smaller optical loss in crystalline SOI than in amorphous Si films grown on transparent substrates such as SiO\({}_{2}\). A disadvantage of using SOI substrates is that light absorption is relatively difficult to evaluate, compared to the configuration using Si films on SiO\({}_{2}\) substrates. However, light absorption is not a key issue in this study.  
Also, we note that the difference in \(R\) spectra of Si-rod-array metasurfaces based on SOI substrates, and of the metasurfaces in Figure 1, is small in the wavelength range of interest. The \(R\) spectra are shown in the Supplementary Materials (Figure S5).  
Figure 2: SEM images of silicon-on-insulator (SOI)-rod-array metasurfaces. (**a**,**b**) top-view SEM images of periodic length \(P\) = 330 and 400 nm, respectively. The height of the Si rods was 200 nm. White scale bars represent 500 nm. (**c**) An oblique-view SEM image of (**a**). White scale bar represents 1000 nm.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Sleep/Wake-Up Schemes
#### 2.3.1 Energy-Efficient MAC Protocols  
Due to its position in the protocol stack, MAC entity is ultimately the responsible for introducing and extracting packets in/from the wireless medium; and so, it is the one who commands the radio transceiver, which works in half-duplex regimen switching between transmitting and receiving modes as commanded.  
From the point of view of energy efficiency, MAC protocols for WSN must minimize wasting energy in processes such as idle listening (checking actively packet arrivals), overhearing (receiving packets not intended for this node), collisions (frames lost causedby overlapping transmissions), overtransmitting (transmitting when receiver is not ready) and protocol overhead (transmitting/receiving protocol control packets/bytes). In Energy-Harvesting (EH) aware MAC protocols; however, some of these basic principles may be dynamically relaxed to achieve some kind of QoS if the external energy contribution allows it.  
Almost by consensus, WSN MAC protocols are classified as Contention Free (TDMA/FDMA/CDMA based), Contention-Based (Synchronous and Asynchronous) and Hybrid schemes (CDMA/TDMA + CSMA). However, from the point of view of energy efficiency the key concepts are Duty-cycling and Wake-up Radios.