esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Underwater Experiment
### Multi-Sensor Covert Underwater Communication  
The actual underwater experiment was conducted to analyze the performance of the multi-sensor covert underwater communication. The underwater experiment was conducted in September 2016 at the coastal waters near Korea Maritime and Ocean University located in Yeongdo-gu, Busan. The experimental environment and schematic diagram are presented in Table 3 and Figure 5. The signal is transmitted 10 times iteratively by placing a delay between data packets.  
The number of actual underwater data bits was set to 500 bits, the number of transmitters was set to two, the number of sensors was two, distance to transmitter and receiver was 41 m and 36 m, distance between transmitters was 5 m, and depth of the transceiver was set to 3 m from the surface of the water. The response characteristics of each channel are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Parameter** \\ \hline Source & 500 bits \\ Number of PN code bits \(\mathrm{N_{TC}}\) & 16 bits \\ Sensor identification code & 2 \\ The number of Transmitter (U) & 2 \\ Channel coding method & (2, 1, 7) Convolution \\ Modulation & BPSK \\ Decoding method & BCJR \\ Center frequency & 16 kHz \\ Sampling frequency & 192 kHz \\ Distance & 5 m \\ (from Transmitter 1 to Transmitter 2) & 5 m \\ Distance (from Transmitter to Receiver) & Transmitter 1: 41 m, Transmitter 2: 36 m \\ Water depth & 4.3 m \\ Depth & Transmitter 1 and Transmitter 2: 3 m, \\ Receiver: 3 m \\ \hline \end{tabular}
\end{table}
Table 3: Parameters for underwater acoustic communication experiment.  
Figure 5: Illustration of the sea trial using the multiple sensor.  
In the RAKE receiver, the threshold value and weighted coefficient proposed in this paper were applied, and the performance of the RAKE receiver was compared and analyzed using only packets whose data were all decoded, out of 10 packets which were performed 10 times iteratively by time.  
As shown in Table 4, as same as single user underwater communication, the result of this experiment verified that performance was improved more significantly when both the threshold value and the weighted coefficient were applied compared to when only the existing RAKE receiver was applied. When the threshold value and weighted coefficient proposed in this paper were applied, all data were decoded, with no errors.  
This result indicated that the transmission and receive structure proposed through the actual underwater experiment was well-suited to the communication model for covert multiple accesses.
## 4 Results and Discussion
### Spray Radius  
Over time the particles collide with the wall after being hit by the atomizing particles and the amount of displacement along the wall is increasing, so that the spray radius increases at the later stage of the spray. Spray radius increases after hitting the wall, the droplet and the wall have a larger contact area, easy to quickly evaporate, spread, which also has a certain impact on the spray height. Diesel spray from the nozzle hole hit the wall. Diesel mist spread out along the wall and its forward is influenced by the surrounding air resistance and wall friction. Some dripping adhesions to the wall, which forms a peripheral spray edge.  
As time goes by, the middle part is hollowed and the periphery rises. The droplet velocity, momentum and density in the main spray zone are larger. The wall jet area is located in the interior  
Figure 6: Spray height.  
of the spray, and the droplet tangential velocity, momentum and concentration are large in this area. The earlier part of the oil drop coming into contact with the wall due to the loss of kinetic energy will be squeezed by the subsequent droplet of oil, resulting in a gradual increase in the radius of the spray and a turbulent mixing of the oil mist with the surrounding air in the peripheral area.  
Figure 7 shows the change in spray radius. Compared to two curves can be seen, at the beginning of the spray, spray radius is the same, then the experimental value is always lower than the simulation value and the change tendency of the two curves are consistent. Mainly due to the spray volume absorb gas, causing air density, leading to the experimental value slightly smaller, does not take into account this in the simulation.
# Smooth Information Criterion for Regularized Estimation of Item Response Models
## 2 Smooth Information Criterion  
In theory, a parameter estimate \(\widehat{\boldsymbol{\gamma}}\) for \(\boldsymbol{\gamma}\) of the IRT model may be obtained by directly minimizing an information criterion  
\[\widehat{\boldsymbol{\gamma}}=\operatorname*{arg\,min}_{\boldsymbol{\gamma}} \left\{2l(\boldsymbol{\gamma})+K_{N}\left(H_{0}+\sum_{h=1}^{H}u_{h}\boldsymbol {1}(\boldsymbol{\gamma}_{h}\
eq 0)\right)\right\}\,. \tag{10}\]  
The optimization function in (10) can be interpreted as a regularized log-likelihood function with an \(L_{0}\) penalty [28; 29]. Obviously, the indicator function \(\boldsymbol{1}\) in (10) counts the number of regularized parameters that differ from zero. Researchers [PERSON] and [PERSON] [26] proposed substituting the indicator function with a suitable differentiable approximation \(\mathcal{N}_{\varepsilon}\). To this end, a smooth information criterion, such as the SAIC and the SBIC, is obtained. In more detail, the differentiable approximation \(\mathcal{N}_{\varepsilon}\) for \(\boldsymbol{1}\) is defined as  
\[\mathcal{N}_{\varepsilon}(x)=\frac{x^{2}}{x^{2}+\varepsilon}\, \tag{11}\]  
where \(\varepsilon>0\) is a sufficiently small tuning parameter, such as \(\varepsilon=0.001\). The function \(\mathcal{N}_{\varepsilon}\) takes values close to zero for \(x\) arguments close to \(0\) and approaches \(1\) if \(|x|\) moves away from \(0\). A smoothed information criterion \(SIC(\boldsymbol{\gamma})\) (abbreviated as SIC) can be defined as  
\[SIC(\boldsymbol{\gamma})=2l(\boldsymbol{\gamma})+K_{N}\left(H_{0}+\sum_{h=1}^ {H}u_{h}\mathcal{N}_{\varepsilon}(\boldsymbol{\gamma}_{h})\right)\,. \tag{12}\]  
We obtain the SAIC for the choices of \(K_{N}\) in (12) of \(K_{N}=2\) and the SBIC for \(K_{N}=\log(N)\).  
Hence, the minimization problem (10) can be replaced by  
\[\widehat{\boldsymbol{\gamma}}=\operatorname*{arg\,min}_{\boldsymbol{\gamma}} SIC(\boldsymbol{\gamma})=\operatorname*{arg\,min}_{\boldsymbol{\gamma}} \left\{2l(\boldsymbol{\gamma})+K_{N}\left(H_{0}+\sum_{h=1}^{H}u_{h}\mathcal{N} _{\varepsilon}(\boldsymbol{\gamma}_{h})\right)\right\}\,. \tag{13}\]  
The optimization function in (13) directly minimizes a smoothed version of the information criterion.
## 4 Discussion  
The $tEWI package and data products are already being used for studies modeling chemical releases [33] and tracking hazardous industrial wastes [34]. $tEWI is also being used to develop \"rapid\" or automated life-cycle inventory models and create environmental accounts for environmentally extended input-output models. Point source releases of flows compiled by $tEWI are used to supplement emissions data for the U.S. Electricity $tCI Baseline [35] by providing emissions to air, water, and soil, as well as hazardous waste flows for all U.S. electricity-generating units. EIA data for electricity-generating units are readily available but typically only track a small subset of environmental flows preferred for life cycle modeling. Data from the NEL, TRI, and RCRAlInfo provide fuller coverage for a wider range of flows, while data from the eGRID provide an alternate data source for validation. The $tSEEIO model relies on $tEWI for point source emissions data to better characterize environmental impacts of U.S. industries [36]. The python package $tEWSA compiles facility emissions data from $tEWI and aggregates them based on the NAICS code assigned for each facility in _facilitymatcher_[37]. The resulting industry emissions totals are combined with emissions data from other sources to obtain broad environmental coverage for the $tSEEIO model. In both cases, _stewicombo_ helps prevent double counting of emissions flows for data reported across inventories.  
With several active applications, $tEWI is expected to be further expanded to support more advanced emissions tracking and life cycle modeling. For example, data provided in some inventories will enable more refined processing of emission compartments. Stack height for air emissions is relevant for air quality modeling and life cycle impact assessment. Additionally, most inventories provide geographic coordinates for facilities, which can be compared to maps of population density and better characterize the human health  
Figure 5: National contribution of Life Cycle Impacts by Flow in 2018. GWP, global warming potential; AP, acidification potential; EP, eutrophication potential; PM, particulate matter formation; SF, smog formation; ETX, ecotoxicity; HCAN, human-health cancer; HNON, human-health noncancer; ODP, ozone depletion potential.  
impacts of releases. Furthermore, while StEWI currently only supports U.S.-based inventory sources from the USEPA, the framework and data structures could also be adapted to inventories from other countries. These updates, as well as other expansions to emissions compartments, are expected in future releases of StEWI.
# Transfer Learning Model for Crack Detection in Side SlopesBased on Crack-Net
###### Abstract  
Accurate detection of slope cracks plays a crucial role in early landslide disaster warning; however, traditional approaches often struggle to identify fine and irregular cracks. This study introduces a novel deep learning model, Crack-Net, which leverages a multi-modal feature fusion mechanism and is developed using transfer learning. To resolve the blurred representation of small-scale cracks, a nonlinear frequency-domain mapping module is employed to decouple amplitude and phase information, while a cross-domain attention mechanism facilitates adaptive feature fusion. In addition, a deep feature fusion module integrating deformable convolution and a dual attention mechanism is embedded within the encoder-decoder architecture to enhance multi-scale feature interactions and preserve crack topology. The model is pre-trained on the CrackVision12K dataset and fine-tuned on a custom dataset of slope cracks, effectively addressing performance degradation in small-sample scenarios. Experimental results show that Crack-Net achieves an average accuracy of 92.1%, outperforming existing models such as DeepLabV3 and CrackFormer by 9.4% and 5.4%, respectively. Furthermore, the use of transfer learning improves the average precision by 1.6%, highlighting the model's strong generalization capability and practical effectiveness in real-world slope crack detection.  
c 2025156951
## 3 Results and Discussion
### Compositional Classification, Manufacturer, and Commercial Brand  
Figure 5 illustrates the distribution of studies based on compositional classification, manufacturer, and commercial brand. Most studies (50%) primarily considered CAD-CAM RCBs with a \"nano ceramic\" composition, characterized by a blend of nanoparticles agglomerated into clusters, embedded in a highly cross-linked polymer matrix. This composition integrates nanotechnology with ceramics, featuring approximately 80% ceramic load [24]. The second most prevalent classification (30%) pertained to \"polymer-infiltrated ceramic network (PICN)\". A further 20% of studies focused on simplified compositions denoted as \"resin composite blocks\", which incorporate ceramics, glass ceramics, glasses, or a blended composition as reinforcement fillers [23; 25].  
Figure 4: The categorization graph of the study design type and the evidence level represented by these studies. (**A**). Schematic diagram of the percentage of studies considering the study design. The study reporting a clinical trial is further described in detail. (**B**). Schematic diagram of the type of studies selected and the level of evidence according to the levels of evidence pyramid.  
CAD-CAM resin blocks comprise a resin matrix and various fillers designed to optimize their mechanical and aesthetic properties for dental restorations. The resin matrix typically includes monomers such as Urethane Dimethacrylate (UDMA) and Triethylene Glycol Dimethacrylate (TEGDMA), which provide flexibility and ease of handling. The filler composition often consists of inorganic materials like silica and zirconia, which enhance the strength and wear resistance of the blocks. These fillers can vary in content, usually from 70% to 80% by weight, and in size, from micro to nano particles, contributing to the material's overall durability and performance. Additionally, silane coupling agents improve the bond between the resin matrix and the fillers, with typical ratios around 50% to 60%. This combination of resin monomers, filler composition, and silane coupling agents results in a material that is not only strong and durable but also capable of being precisely milled and easily repaired, making CAD-CAM resin blocks a versatile choice for various dental applications.  
Notably, 3M ESPE emerged as the most prominent manufacturer, with their product 3M\({}^{\text{TM}}\) Lava\({}^{\text{TM}}\) Ultimate Restorative being the predominant focus in the selected investigations. GC America was the second most prevalent manufacturer, with Vita Enamic as the second most investigated commercial brand. Vita Enamic, classified as PICN, emerged as a comparable material in the studies due to its availability in the market.  
Figure 5: Schematic diagrams of the percentage of studies considering the (**A**). compositional classification, (**B**). manufacturer, and (**C**). commercial brands.
# Phonons and Thermal Transport in Si/SiO\({}_{2}\) Multishell Nanotubes: Atomistic Study
## 3 Results and Discussion
The latter fact confirms the importance of our theoretical findings for an accurate prediction of thermal transport phenomena in Si/SiO\({}_{2}\) nanotubes in a wide range of lateral cross sections, numbers and thicknesses of the shells. However, the phonon dispersion and transport properties in multishell nanostructures with sizes of the order of a few hundred nm require further analysis because they are in the transitional region between the areas of applicability of elastodynamics and the atomistic approaches. Usually, TC in nanostructures strongly depends on surface roughness and/or roughness of interfaces between layers in the case of multilayered nanostructures. Depending on the model, different values of the specularity parameter \(p\) were employed for the theoretical predictions and the interpretation of experimental results [28; 29; 52; 59; 60; 61; 62]. To elucidate the dependence of TC in MNTs on \(p\), we plot in Figure 5a, the TC of a single-shell Si/SiO\({}_{2}\) NT (5 ML/6 ML/4 ML) as a function of temperature for different values of \(p\). A decrease of the specularity parameter suppresses TC over the entire considered range of temperatures. Thus, the boundary scattering plays an important role in limiting thermal transport even for \(T>150\) K, where the three-phonon Umklapp scattering becomes relatively strong. This results in decreasing TC with \(T\) for \(T>150\) K. In Figure 5b, we show how different values of \(p\) affect the drop of TC in MNTs as compared with NWs. Figure 4: Temperature dependence of the TC (thermal conductivity) (**a,b,d,e**) and phonon DOS (density of states) (**c,f**) in Si/SiO\({}_{2}\) MNTs. The TC is calculated considering the phonon Umklapp and boundary scatterings (**a,d**) as well as the phonon Umklapp scattering and “effective scattering rate” due to diffusion of vibrational excitations in SiO\({}_{2}\) (**b,e**). The TC curves in Figure 5b were calculated taking into account both the phonon-boundary scattering and the diffusion of vibrational excitations in SiO\({}_{2}\) layers. Coexistence of the phonon-boundary scattering and the \"effective scattering\" due to the diffusion of vibrational excitations in SiO\({}_{2}\) layers results in lower values of TC in comparison with the cases of their separate action. At the same time, the TC reduction with rising number of shells remains manifested for different values of \(p\) (see Figure 5b). It is also worth noticing that for \(p=0.9\) the ratios between thermal conductivities in MNTs with different numbers of shells \(\kappa_{ph}(N)/\kappa_{ph}(1)\) for \(N=2\) and \(5\) (see Figure 5c) are close to experimental values [43] despite the fact that experimental multishell NTs possess much larger sizes.
# Defacement Detection with Passive Adversaries
## 4 System Architecture and Implementation  
In the implementation of our defacement response system, we have taken into consideration the following:  
* Application and web page analysis can lead to a high computational cost, and to some bandwidth constraints--this is often underestimated, but our experiments have shown that for enterprise-level Web Sites even just the scanning of all content with reasonable frequencies can be a demanding requirement.
* The analysis of a complex application or Web Site is suited for parallel processing, as there is little or no correlation between pages or site sub-directories when defacement detection is our purpose (at the state of the art [1; 26], even though attacks can be massive and affect most Web Site pages, each page is individually targeted, and defaced content is not spread over multiple pages). Different pages (URLs) in the target Web Site will normally have different importance, and will thus be assigned a _weight_--for example, the home page will normally be assigned the highest weight.
* The implementation should be suited for installation with multiple clients with different and difficult to predict IP addresses, so as to avoid differential defacement [14], i.e., sophisticated defacement instances where normal page content is returned to clients that are suspected monitors, whilst defaced content is returned to normal end-user clients.  
As a consequence, our implementation addresses one page or application component at a time, in parallel and from different IP sources.
## 9 Research Gaps and Future Directions  
This section presents our findings, which identify the current research needs and potential future directions of STLF. This information is derived from synthesizing findings across multiple studies. Its inclusion is intended to highlight areas where further research is warranted based on the identified gaps in the current state of knowledge. The methodology employed to identify these research gaps is based on a comprehensive examination of the existing literature. The summarized view of research gaps and future direction is presented in Table 14.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Gap** & **Future Direction** \\ \hline Time-of-use considerations & Explore the incorporation of time-of-use factors into STLF models, with a particular focus on the impact of varying electricity demand patterns during different times of the day. \\ \hline Edge computing applications & The potential of edge computing in STLF to enhance local processing capabilities, reduce dependencies on centralized systems, and improve forecasting accuracy should be explored. \\ \hline Consistency in LF models & Incorporate meteorological, economic, and regional data for correlational analysis in future STLF models. \\ \hline LF in isolated grid sectors & Conduct additional research and design custom parameters to address the prediction challenges in isolated grid sectors. \\ \hline Model adaptability concerns & Develop adaptability STLF models capable of adjusting to diverse generation modalities and varying circumstances. \\ \hline \hline \end{tabular}
\end{table}
Table 14: Summarized view of research gaps and future direction.
# Application of Ultrafiltration for Recovery of Bioactive Phenolic Compounds from Rose Wastewater
## 3 Results and Discussion
In our study, the highest value of energy demand was established at a VRR of 8, a transmembrane pressure of 0.5 MPa, and with the 1 kDa membrane, (38.11 kW/m\({}^{3}\)); the lowest was at a VRR of 0 (beginning of the process), and a transmembrane pressure of 0.2 MPa--5.90 kW/m\({}^{3}\) with the 10 kDa membrane and 6.01 kW/m\({}^{3}\) with the 25 kDa membrane (\(p>0.05\)). The content of total phenolic compounds, phenolic acids, and flavonoid phenolic compounds in the initial rose wastewater, retentates, and permeates with the three membranes investigated is presented in Table 1. All the measured phenolic compounds increased in retentates during ultrafiltration from a VRR of 2 to 8 (\(p<0.05\)) in all the membranes tested. The lowest values were obtained in the permeates in all the membranes studied. After comparing the phenolic compounds and the three membranes, it can be seen that the highest values were observed when the 1 kDa membrane was used for ultrafiltration. Figure 3 presents the rejection of total phenolic compounds, phenolic acids, and flavonoid phenolic compounds depending on the VRR. An increase in the rejection of all the studied phenolic compounds was observed with the increase in the VRR from 2 to 8 (\(p<0.05\)). Comparing the studied membranes, it can be seen that the 1 kDa membrane, at a VRR of 8, offered the highest rejection value of total phenolic compounds (42.10%), phenolic acids (42.94%), and flavonoid phenolic compounds (41.58%), followed by the 10 kDa and 25 kDa membranes. Similar results were obtained by [PERSON] et al. [43] in blood orange juice, who found that polyphenic concentration depended on the molecular weight cut-off of the membrane. [PERSON] et al. [35] established higher rejections of total  
Figure 1: Permeate flux compared to the volume reduction ratio during the ultrafiltration of rose wastewater. polyphenols during the ultrafiltration of Valencia orange juice when a 20 kDa membrane was used than a 30 kDa membrane. According to [PERSON] et al. [44], the rejection of total phenols and flavonoids increased when the molecular weight cut-off of the membranes decreased. [PERSON] et al. [45] studied the ultrafiltration of apple juice and found a rise in the concentration factor and rejection of total polyphenols during ultrafiltration. According to [PERSON] et al. [46], a logarithmic decrease in the rejection of anthocyanins with a rise in the molecular weight cut-off of the membranes was observed during the ultrafiltration of roselle extract (_Hibiscus sabdariffa_ L.). [PERSON] et al. [47] declared that the rejection rate of jujube juice compounds depended on the molecular weight cut-off of the membrane, probably due to the different sieving effect of the membranes.