esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Depthwise Dilated Separable Convolution  
Depthwise dilated separable convolution expands the effectiveness of dilated convolution by combining depthwise separability with a dilation rate, enhancing computational efficiency and receptive field coverage (as depicted in Figure 1). This method involves two steps: (a) performing depthwise dilated convolution for each input channel independently with a dilation rate of \(d\), increasing the receptive field to \(r_{d}\times r_{d}\), and (b) applying pointwise convolution to learn linear combinations of the depthwise convolution outputs [32]. Here, \(r\times r\) is the receptive field of a regular convolution for learning representation.  
Mathematically, we represent depthwise dilated separable convolution as  
\[Y(f,k)=\sum_{c=1}^{C}\left(\sum_{s=1}^{S}X_{c}(f+d\cdot s)\cdot W_{c,k}(s)\right) \tag{2}\]  
where \(X_{c}[f]\) is the input feature map in the channel \(c\), \(W_{c,k}(s)\) is the s-th weight in the depthwise filter for the channel \(c\) and output channel \(k\), \(S\) denotes the filter's size, and \(d\) denotes the dilation rate. The inner summation represents the depthwise dilated convolution, and the outer summation represents the pointwise convolution across all input channels, \(C\). \(Y(f,k)\) is the resultant output feature map for the k-th output channel.  
This convolution reduces computational complexity while maintaining an increased field of view for feature representation learning, making it a potentially efficient and powerful tool for road extraction from satellite imagery.
# Cloud-Enabled Deployment of 5G Core Network with Analytics Features
## 1 Introduction  
The rapid evolution of mobile communication technologies has driven an unprecedented demand for fast and reliable connectivity, making the deployment of mobile 5G Core Networks (5 GCN) crucial. To support the scalability and flexibility demanded by modern 5G networks, cloud infrastructure has emerged as a promising solution. Virtualization, in particular, enables operators to effectively meet user demands while reducing costs [1]. Furthermore, collaboration between network service providers and device manufacturers has accelerated the availability and adoption of 5G-capable devices [2].  
Global trends emphasize the development of cloud-based applications, data hosting, and complete network deployments as the new standard [3]. Mobile network providers and operators recognize the advantages of integrating cloud solutions with mobile telecommunications, reinforcing the synergy between 5G networks and the cloud [4, 5].  
The primary aim of this paper is to address the practical challenges and issues associated with the cloud-based deployment of virtualized 5G networks. Motivated by the need to advance cloud-based 5G core network deployments, our testbed serves as an experimental platform designed to facilitate future research in this area [6]. By integrating a previously missing network function into a widely used 5 GCN implementation, our work addresses a critical gap and provides significant benefits to the community. This contribution is especially important for the development of open-source 5G solutions, a timely and essential research topic [7].  
The contribution of our work can be summarized as follows:  
* We propose a testbed for 5G network deployment in a virtualized environment with support for network slicing (Section 4).  
* We conduct a verification process for the testbed, which reveals an error-prone scenario stated in Section 5.
* We propose an original solution to the identified problem (Section 5.2).
* Based on these findings, we design and develop a network function that mirrors the functionalities of the Network Exposure Function (NEF) and can be easily integrated with virtualized cloud-based deployments.  
The NEF enhances the flexibility and utility of 5G networks by enabling external applications to access monitoring, provisioning, and policy/charging capabilities. Thus, the proposed quasi-NEF component can significantly benefit mobile network administrators, as detailed in Section 6. By providing a structured and accessible framework for data collection and processing, our quasi-NEF component enables real-time analysis and decision-making. This capability allows for the optimization of network resources, predictive maintenance, and enhanced user experiences through AI-driven insights. Integrating this quasi-NEF not only improves the operational efficiency of the network but also supports the development of innovative applications and services that leverage data analytics and AI to deliver superior performance and reliability and improve the efficiency of cloud-based virtualized 5G network deployments [8].
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 4 Simulation and Results Discussion
### WSN Coverage Optimization
#### 4.1.1 Application Method
In our work, the deployment of 40 sensors from a total number of 100 sensors was enough to fully cover an area of 100 m\({}^{2}\). Therefore, we optimized our deployment by 60%. It is obvious that the PSO algorithm successfully found the best deployment strategy for this application. The position and the number of deployed sensors were determined to fulfill the goal of maximum coverage of the monitoring area.
# CFD Simulation of Dry Pressure Drop in a Cross-Flow Rotating Packed Bed
## 2 Model
### CFD Model  
The single-phase flow model is adopted to simulate the gas flow in the cross-flow RPB. Considering the complexity of the packing structure, the porous media model is used to describe the packing in order to simplify the calculation, and three parameters (porosity, viscous resistance coefficient \(\alpha\) and inertial resistance coefficient \(\beta\)) that are determined experimentally are listed in Table 1. The sliding mesh model is used to simulate the rotating zone. As a complex flow characterized by expansion, contraction, free rotation and abrupt change in direction is formed in the RPB under the centrifugal force caused by the rotor, it is worth noting that no general model is available to describe the turbulence behaviors in a cross-flow RPB. In this paper, three turbulence models (RNG k-\(\epsilon\) model, Realizable k-\(\epsilon\) model and Reynolds stress model (RSM)) are used to obtain the turbulent flow characteristics in the cross-flow RPB for comparison. The conservation equation, momentum equation, turbulence model and porous media model are listed in Table 2.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 3 Proposed Multi-Color Channels Based GSR Model
### Construction of the Proposed Model
#### 3.1.2 Proposed Sparse Representation Model  
After constructing a multi-color channel image group \(Y_{i}^{C}\), a sparse representation model can be established based on the image group. By taking the traditional model Formula (5) and \(Y_{i}^{C}\) in Formula (9) in the regularization framework of Formula (2), the group sparse representation model based on multi-color channels of the proposed model in this paper is obtained.  
\[\hat{\beta}=\underset{\beta}{\text{argmin}}\frac{1}{2}\|y-D^{C}\beta^{C}\|+ \lambda\sum_{i=1}^{n}\|\beta_{i}^{C}\|_{1} \tag{11}\]  
Here, \(D^{C}\) denotes all the adaptive dictionaries obtained via performing singular value decomposition on \(\left\{Y_{i}^{C}\right\}_{i=1}^{n}\), i.e., \(D^{C}=\left[D_{1}^{C},D_{2}^{C},\ldots,D_{n}^{C}\right]\). \(\beta^{C}\) represents the sparse coefficient of all multi-color channel image groups \(\left\{Y_{i}^{C}\right\}_{i=1}^{n}\), i.e., \(\beta^{C}=\left[\beta_{1}^{C},\beta_{2}^{C},\ldots,\beta_{n}^{C}\right]\). It can be seen from Formula (11) that the proposed model simultaneously processes the image groups of multi-color channels.
## 3 Results and Discussion  
### Optimization of the Ag Nanowire Micro-Ring Structure Formation  
In order to make the Ag nanowire micro-ring structure, we employed an immiscible emulsion droplet formation method. Figure 1 shows that emulsion droplets could be continuously generated by tip sonication-induced vibration in DMF and DCB solution. During the process, Ag nanowires were captured in micro-scale bubbles (i.e., encapsulation) due to high surface tension force compared to the yield strength of Ag nanowires. We have previously shown that micro-rings are formed inside droplets by the force balance between the compressive force of the droplet and the restoring force of the high aspect ratio nanowire material [16]. Based on this mechanism, we investigated whether the micro-ring composed of Ag nanowire could be constructed in immiscible solution (Figure 2). The Ag nanowire micro-ring structure with a diameter in the sub-micron scale was successfully deposited on the substrate (Figure 2a,b). However, random generation of the emulsion droplets led to several entangled Ag nanowires in a single droplet. Figure 2c,d demonstrate that the entangled Ag nanowire micro-rings had a larger diameter (4-5 \(\upmu\)m) compared to the single Ag nanowire micro-rings (\(<\)4 \(\upmu\)m). This result led us to the next stage to sort the fabricated single and bundled Ag nanowire micro-rings with respect to droplet size using the PFF microchannel.  
Figure 1: A schematic of the coiling nanowire inside a water-in-oil emulsion droplet.
## 5 Discussion
### Practical Implications  
From a practical standpoint, this study provides actionable insights for data scientists, business analysts, and e-commerce system designers. For large, high-volume transactional environments--such as real-time recommendation systems or dynamic pricing engines--FP-Growth is clearly the preferred algorithm, offering both computational efficiency and deeper rule extraction capability.  
However, Apriori may still be advantageous in environments where interpretability, resource constraints, or didactic simplicity are prioritized, such as academic settings, small business analytics, or systems with limited computing power.  
Moreover, the integration of RapidMiner and SPSS in the analysis showcases the feasibility of conducting statistically grounded rule-mining experiments using accessible, GUI-based platforms, which is especially valuable for non-programmer practitioners.
## 3 Results  
### Environmental Conditions  
The vertical profiles of the environmental variables closely agreed between the stations (Figure 2). Thermal and salinity stratification was observed at depths greater than about 55 m and chlorophyll fluorescence also peaked at around 55 m depth at all the stations. The vertical profile of water temperature was consistent among the stations, ranging from 7.5 to 20 \({}^{\circ}\)C. In particular, the influence of artificial reefs on hydrography was not seen. The temporal changes in water temperature near the seabed at A2 and A5 alsoexhibited a similar trend, ranging from 7.0 to 7.9 \({}^{\circ}\)C and from 7.1 to 8.0 \({}^{\circ}\)C, respectively. The light intensity near the seafloor was generally consistent at the two stations, ranging between 0.10 and 1.2 umol photons m\({}^{-2}\) s\({}^{-1}\) at A2, and between 0.10 and 0.89 umol photons m\({}^{-2}\) s\({}^{-1}\) at A5, respectively. The measured average flow speed near the seafloor at A5 was 0.06 m s\({}^{-1}\), and the maximum flow speed was 0.16 m s\({}^{-1}\) during the study period, and north-northwestward flows were dominant (31.4%) (Figure 3). The particle size in the bottom sediment was the coarsest at A1 among the stations; the fraction of 4.75-19 mm contributed 44.4% and the median diameter (D\({}_{50}\)) was 3.70 mm (Table 2). The particle size distribution at A2 showed a higher proportion of particles smaller than 0.85 mm than at A1 (A1: 35.8%, A2: 49.7%), although the fraction of particles of 4.75-19 mm was also relatively high (29.5%), while the D\({}_{50}\) showed a middle value (0.89 mm) among the sites. The particle size distribution of A5 was dominated by the fraction of \(<\)0.005 mm (28.3%) and the D\({}_{50}\) was the smallest among the three stations (0.12 mm). Overall, the particle sizes of the sediment at the AR stations (A1, A2) were larger than at the non-reef station (A5). The LOI of the sediment sample was 4.1% at A2 and A5 and was relatively low at A1 (3.5%). The TOC concentration in the sediment of A1, A2, and A5 was 5.2, 5.9, and 5.4 mg g\({}^{-1}\), respectively, while the content of TON was under the limit of quantification (\(<\)1.0 mg g\({}^{-1}\)) at all the stations. The LOI and TOC concentrations at A2 showed the highest values among the three stations. In contrast, those at A1 located in the AR area also showed the lowest values among the stations.  
Figure 2: Vertical profiles of the environmental variables (water temperature, salinity, density (\(\sigma_{\rm T}\)), and chlorophyll fluorescence) at A1, A2, and A5.
## 3 Data and Methods
### Method
#### 3.2.3 POT-Stacking  
The POT-stacking technique assumes that the surface deformation in the study area is mainly linear [28]. The advantage is that the calculation is efficient and the influence of random errors can be effectively reduced. The POT-stacking technique obtains multiple POT deformation results by controlling the spatiotemporal baseline with multiple master images and accumulates the deformation results to effectively solve the temporal and spatial decoherence problems [29]. It is one of the methods that can be used to effectively improve the accuracy of the POT deformation solution. It is easy to portray the morphological characteristics of significant deformation areas in the POT deformation fields. In this paper, the LS is used to average the POT deformation fields, and the POT maps are weighted by time intervals to obtain the LOS-oriented deformation rate for the study area, which is calculated as  
\[\varphi_{rate}=\sum_{i=1}^{N}\Delta t_{i}\varphi_{i}/\sum_{i=1}^{N}\Delta t_{i} ^{2} \tag{8}\]  
where \(\Delta t_{i}\) is the time interval for the POT results of a group \(i\); \(\varphi_{i}\) is the POT deformation for a pair \(i\); \(\Delta t\) is the time baseline for the POT pairs; and \(N\) is the number of POT image pairs.
## 4 Discussion
### Implications  
The findings of this study present substantial practical implications for the construction industry. The proposed method not only addresses the unquantifiable and non-real-time monitoring issues inherent in human judgment, but also facilitates the quantitative evaluation and automatic recognition of an HBM's working conditions. By identifying the various working states of the HBM, the study enables the implementation of timely corrective actions, reducing the potential negative impacts on construction safety and the quality of the final structure. In practical engineering projects, this study uses machine learning to track and classify HBM workflows in real-time, improving the safety and efficiency of working at heights and ensuring that projects are completed on schedule. Notably, the method of this study is not only applicable to HBMs but also extends to other climbing equipment, such as auto-climbing formwork systems [58] and slip formwork systems [59]. It can be utilized in a wide range of construction projects, including high-rise buildings and towering structures.  
Moreover, this study provides valuable insights for stakeholders and policymakers. By using accelerometers to accurately identify the critical working condition of the HBM, specialized subcontractors can grasp the performance status of the HBM and undertake timely maintenance work. This proactive approach helps reduce both human and financial costs associated with workplace accidents, and ensures the smooth operation of the HBM. The general contractors can use the information to adjust the construction progress plan promptly, coordinate the work of labor teams with different construction equipment, minimize the construction process conflicts, reduce the project downtime, and enhance the overall efficiency of the project. The project owners can leverage the HBM state data to monitor the construction progress of the building's main structure. With this knowledge, they can make more informed decisions about construction operations and allocate resources effectively.
## 8 Conclusions
The workshop findings inform the design of assistive technologies that can be easily retrofitted into existing homes, with usability being a key factor for user adoption. Designers should consider the potential limitations (cognitive, physical) of older adults to ensure that the technology is accessible and easy to use. To address R2a, we explored the learning preferences of older adults for new technology. Although the co-creation workshop did not directly address preferred learning methods, it highlighted the importance of user-centred design and education. Future research can explore how older adults prefer to learn about new technologies, such as through hands-on training, written materials, or video tutorials. This information can be used to develop educational mechanisms that enhance technology adoption and improve the quality of life for older adults. R2b focused on the impact of learning preferences on the design and implementation of assistive technologies, including educational mechanisms like chatbots. The workshop highlighted a need for educational mechanisms to improve technology adoption, and participants discussed options like chatbots. Future research can explore user preferences for educational tools alongside user needs identified in the workshop. By understanding the learning preferences of older adults, we can create educational mechanisms that are tailored to their needs, ultimately enhancing technology adoption and improving their quality of life. In conclusion, this study provides valuable insights into the barriers and facilitators to smart home technology adoption among older adults living independently. The systematic review and co-creation workshop results highlight the importance of user-centric design, consideration of socio-emotional impactors on technology acceptance, and the need to focus on actionable design insights. The proof-of-concept development presents a compelling case that the available technology is sufficiently usable and fit for purpose to create such systems, while being cost-effective and easy to deploy. Future research should focus on longitudinal studies, exploration of learning preferences, performance expectancy, social aspects of technology adoption, and quantifying user needs to refine the newly developed model.
## Materials and Methods
### Vegetation Indices  
We used four VIs in this study: the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the abovementioned GNDVI and NDVI. These four VIs, as many others, are based on the ratio of solar reflection at the visible bands (RGB) to that at the near infrared (NIR) waveband (Table 1). The four VIs used here are regularly used in studies for tracking changes in plant-related traits, such as leaf growth (e.g., leaf area index), transpiration and evapotranspiration, as well as CO\({}_{2}\) uptake and aboveground biomass from optical sensors mounted on unmanned aerial vehicles and/or satellites [9; 17; 39; 40; 41; 42] (see also Box 1 in [32]).  
Of the four VIs, NDVI is the most widely used [32]. Although NDVI has some saturation issues over dense vegetation [43], it was shown to be very reliable in monitoring vegetation dynamics, particularly in natural [8] and agricultural [16] Mediterranean vegetation systems. GNDVI, which is quite similar to NDVI, with the difference being that it uses the green instead of the red band (Table 1), is considered to be more sensitive to changes in chlorophyll-a concentrations compared to NDVI [44; 45]. EVI and SAVI were both designed to overcome the saturation drawbacks in NDVI and GNDVI [43; 46]. With a canopy background adjustment factor, both indices are considered to be efficient in terms of reducing soil background effects, with SAVI apparently being the most effective in that sense [43].  
The four VIs were derived using the four spectral bands provided by Planet's images following the formulations given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Formulation 1** & **Reference** \\ \hline GNDVI & GNDVI & \(\frac{\rho(\lambda_{\text{NIR}})-\rho(\lambda_{\text{Comm}})}{\rho(\lambda_{ \text{NIR}})+\rho(\lambda_{\text{Comm}})}\) & [45] \\ NDVI & NDVI & \(\frac{\rho(\lambda_{\text{NIR}})-\rho(\lambda_{\text{Rad}})}{\rho(\lambda_{ \text{NIR}})+\rho(\lambda_{\text{Rad}})}\) & [47] \\ EVI 2  & [43] \\ SAVI 3  & [48] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Formulation and source of the four vegetation indices used in this study.
## 3 Results  
### Data Description and Analysis  
#### 3.1.1 Descriptive Statistics of Measured SOC Content  
The measured SOC contents of the 58 sampling points that were in bare soil, ranged from 15.2-49.4 g kg\({}^{-1}\), with a mean of 22.3 g kg\({}^{-1}\) (Table 3). They lay within the range of SOC content usually observed in the Naizin watershed and were consistent with those measured in a previous study (2010). The descriptive statistics of the calibration and validation datasets generated for the three iterations using the S2 time series are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**It.** & **Dataset** & **n** & **Mean** & **St. Dev.** & **Median** & **Min** & **Max** & **Skewness** \\ \hline \multirow{2}{*}{1} & Cal & 358 & 22.2 & 3.5 & 21.9 & 15.2 & 49.4 & 3.4 \\  & Val & 154 & 22.3 & 3.9 & 21.9 & 17.1 & 49.4 & 4.2 \\ \hline \multirow{2}{*}{2} & Cal & 359 & 22.3 & 3.5 & 22.0 & 15.2 & 49.4 & 3.6 \\  & Val & 153 & 22.2 & 4.0 & 21.8 & 17.1 & 49.4 & 3.8 \\ \hline \multirow{2}{*}{3} & Cal & 358 & 22.5 & 4.0 & 22.0 & 15.2 & 49.4 & 3.8 \\  & Val & 154 & 21.9 & 2.5 & 21.8 & 15.2 & 30.1 & 0.4 \\ \hline \end{tabular}  
* It: Iteration, Cal: Calibration, Val: Validation, n: Number of individuals, St. Dev: Standard deviation.  
\end{table}
Table 4: Descriptive statistics of soil organic carbon content of the calibration and validation datasets generated for the three iterations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**n** & **Mean** & **St. Dev.** & **Median** & **Min** & **Max** & **Skewness** \\ \hline
58 & 22.3 & 3.6 & 21.9 & 15.2 & 49.4 & 3.7 \\ \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics of soil organic carbon (g\(\cdot\)kg\({}^{-1}\)) content measured at the 58 sampling points that were in bare soil in the study area in 2020.