esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental and Methods
### Fire Analysis  
#### 2.2.1 Fire Modeling  
In this study, the ignition source was a 0.2 m \(\times\) 0.2 m \(\times\) 0.2 m wooden block, and the simulation confirmed flame spread with the building finishing materials attached to the wall. Additionally, the fire room was configured as a closed space with no external air inflow. Polyurethane foam and MDF are commonly used building materials in Asia (including Korea) contributing to the high fire risk. To address this concern, we selected three different building finishing materials to highlight the importance of using flame-resistant materials and the need to evaluate the fire performance of newly developed building finishing materials. The building finishing materials selected for this study included polyurethane foam, MDF, and cellulose-based flame-resistant finishing materials [17; 21]. The cellulose-based flame-retardant building finishing material (CFBFM) comprises 100 g of cellulose,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **W (m)** & **L (m)** & **H (m)** \\ \hline Aspect ratio 1 & & 2 & \\ Aspect ratio 2 & & 4 & \\ Aspect ratio 3 & 2 & 6 & 2.2 \\ Aspect ratio 4 & & 8 & \\ Aspect ratio 5 & & 10 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fire room size.  
Figure 1: Floor area aspect ratio.  
30 g of expanded graphite, and 20 g of magnesium hydroxide. Figure 2 depicts the flowchart of specimen production.  
The specimen was manufactured with dimensions of 10 cm \(\times\) 10 cm \(\times\) 2 cm, conforming to the ISO 5660-1 standard [22] to confirm the flame-retardant performance. The manufacturing procedure was carried out by mixing water, expanded graphite, and magnesium hydroxide with finely pulverized waste paper, 3D printing it [23], and drying it.  
The physical property values of MDF and CFRBFM were measured through a cone calorimeter (FESTEC Co., Seoul, Republic of Korea) and LFA 1000 (LINSEIS, Selb, Germany) experiments. Data values provided by FDS were used for the polyurethane foam. Table 2 outlines the reaction parameters of the building finishing materials, while Table 3 specifies the boundary conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Polyurethane Foam** & **MDF** & **CFRBFM** \\ \hline Density (kg/m\({}^{3}\)) & 40.0 & 536.1 & 530.0 \\ \hline Specific heat (k/kg\(\cdot\)K) & 1.0 & 1.8 & 4.122 \\ \hline Conductivity (W/m\(\cdot\)K) & 0.05 & 0.2 & 0.835 \\ \hline Heat of combustion (k/kg) & 30,000.0 & 19,702.5 & 1246.7 \\ \hline CO yield (kg/kg) & 0.0310 & 0.0668 & 0.0284 \\ \hline Soot yield (kg/kg) & 0.10 & 0.009515 & 0.009861 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reaction parameter.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Condition** \\ \hline Heat Release Rate (HRR) & 200 kW \\ \hline Air Temperature & 20 \({}^{\circ}\)C \\ \hline Simulation Time & 400 s \\ \hline Interior Finishing Material Thickness & 2 cm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Boundary condition for fire simulation.  
Figure 2: Flowchart of the specimen production and experimental order [21; 22].
## 2 The MPwise System
### Communications  
The data transmission follows the real-time approach already developed in the SOSEWIN and is dependent on either single station or network topologies. The communications can be managed via LAN, mobile communication (GSM, UMTS, LTE (mobile broadband, [[https://en.wikipedia.org/wiki/Mobile_broadband](https://en.wikipedia.org/wiki/Mobile_broadband)]([https://en.wikipedia.org/wiki/Mobile_broadband](https://en.wikipedia.org/wiki/Mobile_broadband)))), or a self-organizing wireless mesh network. The most important reason for having a self-organizing network is to allow it the flexibility to adapt to dynamic environments which may lead to variable numbers of sensors. This can occur when, for example, a significant event occurs (e.g., earthquake) where several nodes of the system might be disabled due to damage or power outage. The system can therefore find alternative routing among the remaining nodes to restore a fully operational communications network.  
The communication of the results of the processing at each node throughout the network is based on the routing concept. The term routing refers to the procedure of selecting within a network the paths along which data can be sent from a source to a sink. Routing activities within a wireless network are made more complicated by the fact that all nodes act contemporarily as senders, transmitters and receivers of data. The main advantages of wireless mesh networks for seismic networks or arrays within urban environments are (1) the system is free from cable usage, thus, allowing improved array geometries and azimuthal coverage; and (2) in the case of large arrays, data can also be transferred to a user by multi-hop communications from those instruments that are not in a line-of-sight with the user itself, or are too distant (remote stations). As mentioned above, the OLSR that the MPwise employs is a proactive routing protocol, where every node has a map (routing table) of the complete network topology, allowing data to be immediately sent along the optimal path towards the users or gateways. It makes use of advanced metrics, i.e., measurement methods, for evaluating a multi-hop path within the network. The two wireless modules integrated in the MPwise system are following the 802.11 bgn wireless standard with a data rate for 802.11 n up to 150 Mbps. For this reason, no problems related to the data communication are expected for our applications (small size wireless arrays and building/structural health monitoring). In the past, various station configurations have been tested using up to 30 sensors simultaneously and exploiting real-time data communication without any problems.
## 2 Results
### Cis-Acting Elements Analysis  
The cis-acting elements in the 1500 bp upstream promoter region of all SmCCO genes were analyzed by PlantCARE, as shown in Figure 2. Most elements are associated with hormonal and abiotic/biological responses. In plants, there are two types of cis-acting elements involved in transcriptional regulation in response to drought stress: ABRE and DRE [35]. Among them, ABRE is a homeotropic element that responds to abscisic acid and is possessed by ABA-dependent genes [36]. DRE is an ABA-independent gene that responds to dehydration and homeopathy [37]. According to the analysis, 17 out of 26 genes in the SmCCO gene family contain ABRE, and 2 genes contain DRE. Among them, SmCCD4 is an ABA-dependent gene and acts on drought stress.  
Figure 1: Phylogenetic trees, gene structures, and conserved motifs of SmCCO genes. (**A**) SmCCO proteins were clustered into 6 subgroups, designated as S1 to S6. (**B**) Yellow boxes and grey lines represent exons and introns. (**C**) Boxes represent motif distributions.  
Figure 2: The number of cis-acting elements in the SmCCO promoter. The graph was generated based on the presence of cis-acting elements in response to specific elicitors/conditions/processes (x-axis) in the SmCCO gene family members (y-axis).
# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 2 Materials and Methods
### Low-Pass Filtering Mono-Based Sentiment Classification Algorithm  
To enable application in a wider range of practical scenarios, a mono-channel-based emotion classification algorithm with a low-pass filter was proposed. AudioSegment Pydub was used to mix movie scene clips in 5.1 audio format into mono channel, and a low-pass filter (with an upper cutoff frequency of 120 Hz) was added to simulate the SW channel. For this experiment, 7 clips of scenes with a \"shock\" emotion and 8 clips of scenes with other emotion contexts were used as the training and validation sets, while 6 movie test clips (3 shock scenes and 3 other emotion scenes) in mono channel were used as the test set. The feature selection method followed the same steps described in Section 2.2.5, including analysis of variance, kernel density estimation (KDE) plot, and correlation heat map, to extract features from the low-pass filtered mono channel.
## 4 Experiments and Results
### Evaluation Experiments Using Representations Extracted from a Fine-Tuned Image Encoder
#### 4.4.4 Example of Rare Color Retrieval  
Some studies on rare colors do not give exact HEX or RGB values, and the names of these colors are originally derived from people's intuitive impressions of some actual objects in nature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & & \multicolumn{3}{c}{**Similarity Score**} \\ \hline  & Image 1–Image 2 & Image 2–Image 3 & Image 1–Image 3 & Image 3-ukiyo-e print with similar structures and different colors \\ Image pairs & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ Fine-tunned CLIP (ours) & 0.98256 & 0.987789 & 0.98914 & 0.897935 \\ CLIP (ViT-B/32) & 0.98254 & 0.987763 & 0.98917 & 0.898041 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Similarity score calculation on the same ukiyo-e print digitized at the different institutions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Query** & **Rank 1** & **Rank 2** & **Rank 3** & **Rank 4** \\ \hline CLIP (ViT-B/32) & & & & \\ Cross-Vison Transformer [36] & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 12: Similar image retrieval using the ukiyo-e print digitized at the different institutions.  
A fine-tuning process example is shown in Figure 18 that corresponds to the case mentioned in the study [6], and it can be seen that real-word data which is used for pre-training can help to catch more abstract information from a large visual database. We applied the learned knowledge to achieve color information retrieval for artwork collection.  
Table 13 shows the possible colors for 'dayflower Blue', which was extracted from an ukiyo-e print _Butterflies around a wine jar_[38] by colorgram [31]. It is difficult to find an abstract description of the color precisely on the palette, because people's sense of color perception is different.  
Table 14 shows top 3 of search results for query 'Blue' and 'Dayflower blue'. From the results, it can be seen that if only 'blue' is entered, the system is biased towards searching for rare colors associated with light blue. When dayflower blue is entered, the system can search for some blues that are closer to the colors indicated in Table 13.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 5 Architectures
### All-Optical Operations  
In a whole optical network, the greatest part of operative costs is related to optical transponders, i.e., tunable lasers and photo-receivers. For example, in the Italian network, their cost is about 60%-70% of the whole network cost [70]. In order to reduce costs, the number of transponders (so, the number of optical-electro-optical (O-E-O) conversions) has to be reduced. Reducing the power consumption is a critical issue not only for cost reduction but also for a greener environment. It has been estimated that, in 2007, 37% of the total carbon emissions of the whole ICT industry was due to the network infrastructure and devices. By 2020, ICT is expected to account for around 2% of the global carbon emissions [78]. In 2007, metro and access networks consumed the 60% of the total power amount used in the entire telecommunication network [79]. In 2013, the total contribution to energy consumption due to legacy fixed access, fiber-to-the-X (FTTx) new generation access, aggregation, and transmission networks, is about 40% of the total energy consumption in the telecommunication system [78], as shown in Figure 11.  
In order to reduce the power consumption also in the metro and access segment, a different approach is needed. All-optical networks (AONs), where O-E-O transponders are used only at the network edge, have been proposed a quarter of a century ago and, despite the progress made in the last decades, their acceptance has encountered skepticism regarding optical impairments [80]. It can be envisioned that AONs could get new emphasis from the possible application in re-configurable metro and access networks, where launch power is lower and distances are shorter, so optical impairments are less impactful. While O-E-O regenerations in opaque networks do not allow 'analog transparency' since they reshape optical waveforms, AONs allow transparency for both digital content of signals ('digital transparency') and their analog waveform. AONs also allow'spectral transparency', i.e., flexibility in the placement of wavelengths within the optical spectrum [80]. Analog, digital, and spectral transparency allowed by AONs appears particularly attractive for the deployment of flexible transceivers, flexible grids, and optical switches for future software-defined metro and networks.  
Figure 11: Distribution of power consumption across different network segments [78].
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Lane Detection Dataset
#### 5.1.3 Random Forests  
For this classifier, the parameters used to maximize the output accuracy were criterion, class_weight, max_depth and min_samples_leaf. The model random forest classifier from the Anaconda distribution of Python was run over a range of values for each parameter in individual loops. A plot was made in each case to observe the values for each parameter that provided a high accuracy without overfitting the data. The final set of values used for the parameters of the classifier was as follows: criterion is \"Entropy\", class_weights are 0 to 0.5 and 1 to 0.5, max_depth is 5 and min_samples_leaf is 5. The corresponding results are presented in Figure 5 and Table 3.  
Similar to the precision-recall curve of gradient boosting, there is a continuous relationship for the metrics of all three classes, and the curves seem to approach linearity. The steep drop in precision with increase in recall is one characteristic that stands out as unfavorable. The instability should preferably be avoided. As can be observed in Figure 5a, the precision-recall curves for all classes individually as well as for the whole dataset show a poor trade-off between precision and recall. An encouraging score for one metric provides a poor score for the other metric.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
**Class 0 (normal)** & 0.56 & 0.65 & 0.60 & 40,320 \\
**Class 1 (aggressive)** & 0.57 & 0.40 & 0.47 & 35,752 \\
**Class 2 (drowsy)** & 0.65 & 0.71 & 0.68 & 42,206 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-class evaluation metrics with gradient boosting classifier for lane detection.  
Figure 5: Random forests simulation results: (**a**) precision–recall curve; (**b**) ROC curve.  
The random forest model also provides a consistent set of precision, recall and F1-scores, leading to a decrease in the possibility of both under-fitting and over-fitting.  
The class-wise scores are also encouraging. However, it can be observed that there is a significant variation in the metrics across the different classes. There is also a significant drop in recall and F1-scores for class 1 (aggressive).
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
###### Abstract  
Hydrophilic phytochemicals, such as flavonoids and phenolic acids, possess important biological activities, including antioxidant, anti-inflammatory, and anticancer effects. However, their application is hindered by low membrane permeability, poor chemical stability, and limited skin penetration. This review provides a comprehensive analysis of advanced delivery strategies aimed at enhancing the solubility, bioavailability, and therapeutic efficacy of selected hydrophilic compounds. Specifically, it focuses on the encapsulation of flavonoids such as quercetin, luteolin, and apigenin, as well as phenolic acids including ferulic acid, caffeic acid, and chlorogenic acid. The review discusses various nanocarriers: liposomes, niosomes, exosomes, and polymeric nanoparticles (e.g., nanocapsules, nanospheres) and compares their structural characteristics, preparation methods, and functional benefits. These delivery systems improve the physicochemical stability of active compounds, enable controlled and targeted release, and enhance skin and cellular absorption. Despite certain challenges related to large-scale production and regulatory constraints, such approaches offer promising solutions for the pharmaceutical and cosmetic application of hydrophilic plant-derived compounds.  
hydrophilic phytochemicals; delivery systems; liposomes; niosomes; exosomes; polymeric nanoparticles; controlled release; bioavailability; skin penetration; antioxidant and anti-inflammatory potential
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
###### Abstract  
Enhancing speech captured by distant microphones is a challenging task. In this study, we investigate the multichannel signal properties of the single acoustic vector sensor (AVS) to obtain the inter-sensor data ratio (ISDR) model in the time-frequency (TF) domain. Then, the monotone functions describing the relationship between the ISDRs and the direction of arrival (DOA) of the target speaker are derived. For the target speech enhancement (SE) task, the DOA of the target speaker is given, and the ISDRs are calculated. Hence, the TF components dominated by the target speech are extracted with high probability using the established monotone functions, and then, a nonlinear soft mask of the target speech is generated. As a result, a masking-based speech enhancement method is developed, which is termed the AVS-SMASK method. Extensive experiments with simulated data and recorded data have been carried out to validate the effectiveness of our proposed AVS-SMASK method in terms of suppressing spatial speech interferences and reducing the adverse impact of the additive background noise while maintaining less speech distortion. Moreover, our AVS-SMASK method is computationally inexpensive, and the AVS is of a small physical size. These merits are favorable to many applications, such as robot auditory systems.  
Direction of Arrival (DOA); time-frequency (TF) mask; speech sparsity; speech enhancement (SE); acoustic vector sensor (AVS); intelligent service robot +
Footnote †: journal: Accepted: 25 July 2018; Accepted: 25 July 2018; Published: 23 August 2018
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## Appendix A RiB First-Order Cumulative Distribution Functions
### Gaussian Approximation  
On the other hand, when \(\rho\) is large (\(\rho>3\)), we find a high signal-to-noise ratio scenario, for which it holds standard approximation  
\[F_{X}\left(x|\varepsilon,\sigma\right)\approx\int_{-\infty}^{x}\frac{1}{\sigma \sqrt{2\pi}}\exp\left[-\frac{\left(y-\varepsilon\right)^{2}}{2\sigma^{2}} \right]\ \mathrm{d}y \tag{10}\]  
In this case,  
\[\kappa\cdot F_{X}\left(x|\varepsilon,\sigma\right)\approx\int_{-\infty}^{x} \frac{1}{\sigma\sqrt{2\pi}}\exp\left[-\left(\log\frac{1}{\kappa}+\frac{\left(y -\varepsilon\right)^{2}}{2\sigma^{2}}\right)\right]\ \mathrm{d}y \tag{11}\]  
and \(0<\kappa<1\), then \(\log\frac{1}{\kappa}>0\) and finite. Following the same procedure as for the Rayleigh approximation. Figure 11,\(c\) depicts penalties corresponding to RiB in this case, varying both \(\kappa\) (left) and SNR (right). Once again, whenever RiB input lays below \(\sigma\) (noise level), RiB behaves as an expander with linear detrend \(\log\frac{1}{\kappa}\) affecting only when input and SNR are low.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Cloud Access Security Broker (CASB)
#### 3.3.2 Advantages of CASB  
CASBs are essential in enhancing security and preventing data breaches in cloud computing, especially within MCC environments [54]. They strengthen the security of cloud environments by offering visibility, threat protection, and access control, and ensuring compliance [49; 55]. Acting as security enforcement points between cloud service users and providers, CASBs help maintain a secure link between corporate networks and cloud service providers, ensuring that sensitive data remains protected and that security policies are consistently enforced across cloud applications and services [49; 56].
## 2 Materials and Methods
### Definition of Surface Profile Parameters of Interest and Fractal Dimension
#### 2.3.2 Roughness Prediction Model and Geometry Estimation  
The calculated \(Pa\)-values for each of the investigated channel sizes were used to generate a simple roughness prediction model as a function of the local orientation (\(\beta\)) and the channel diameter (\(D\)), namely \(Pa\) (\(\beta\), \(D\)). The model was created by collecting all \(Pa\)-values in the same data plot and using channel 6 mm A as the representative for the 6 mm channels. While the resolution of the estimated \(Pa\)-values was well over 1 per degree along \(\beta\), the \(Pa\)-values were only calculated directly for the corresponding channel sizes. Therefore, linear interpolation was used to fill the gaps between the analyzed channel diameters leading to the generation of a simple \(Pa\) (\(\beta\), \(D\)) prediction model, which is shown in Section 3.3.1.  
As an application relevant validation of the used surface texture characterization methodology, the model was used to modify an ideal (cylindrical) CAD geometry of a channel with \(D_{nom}\) = 6 mm following a point cloud manipulation approach first presented by [26]. The approach is aimed at utilizing roughness prediction models to modify the geometry of nominal channel designs based on their expected outcome when manufactured using LPBF.