esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Sources and Methods
### Ozone Precursors  
The formation of ozone is predominantly initiated by photochemical processes that involve the oxidation of VOCs and carbon monoxide (CO). These reactions are catalyzed by NOX and are further stimulated by UV radiation. Thus, the inclusion of these two key ozone precursors into surface ozone estimation models was deemed of paramount importance for elucidating the mechanisms of ozone production and for the prediction of air quality. This research utilized daily nitrogen dioxide (NO2) and HCHO data collected by the SSP satellite, along with a reanalysis dataset comprising NO2 profiles at 1000 hPa, HCHO total column densities (with a resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\)), and NOX emissions from the CAMS inventory spanning the years 2019 to 2021. These datasets were integrated to develop an enhanced model for estimating the surface ozone concentrations in the NCP. Furthermore, six VOC datasets (propane, cyclopentene, methane, hydrogen peroxide, hydroxyl radical, and peroxyacetyl nitrate) from the 2019-2021 CAMS emission inventory (0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\)) were also employed to enhance the optimization of the surface ozone estimation model. For the purpose of maintaining spatial uniformity, the datasets of ozone precursors were resampled to conform to a grid size of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\). Concurrently, the daily mean values were computed for the datasets of six VOCs, CAMS-HCHO, and CAMS-NO2 to align with the MDA8 standards. Notably, as the CAMS-NOX dataset represents monthly emission estimates, the inventory data for a given month were applied uniformly across all days of that month for consistency.
## 2 Study Area  
Hongliulin coal mine, which belongs to Shaanxi Coal and Chemical Industry Group Co., Ltd., is located in the north Shaanxi province, China. The surface elevation in the coal mine is 1086.40-1331.73 m and the maximum elevation difference is roughly 245 m. Therefore, there are many gullies in the mining area, and the surface is covered with loess (Q\({}_{2}\)_l_) and sand (Q\({}_{3}\)s, Q\({}_{4}\)_el_, Q\({}_{4}\)_el_). The strata below the loess, from new to old, are as follows: Baode Formaton (N\({}_{2}\)b), Zhiluo Formation (J\({}_{2}\)z), Yan'an Formation (J\({}_{2}\)y), Fuxian Formation (J\({}_{1}\)f) and Yongping Formation (T\({}_{3}\)y). The primary coal-bearing formation is the Yan'an Formation, and the mineable coal seams are 2\({}^{-2}\), 3\({}^{-1}\), 4\({}^{-2}\), 4\({}^{-3}\), 4\({}^{-4}\) and 5\({}^{-2}\) coal seams, in that order. Moreover, there are no geological structures such as faults and folds in the study area. Under the loess, an overburden generally consists of a red soil layer (aquitard), weathered rock layer (aquifer) and bedrock. The weathered rock layer (aquifer), with an abundance of water resources, has a depth of around 50 m and a thickness of 20 m. In addition to being the main source of incoming mine water, it provides a significant amount of freshwater for the coal mine. In this coal mine, the depth of coal seam is generally less than 200 m, which belongs to the shallow buried coal seam and the shallow multi-seam mining activities are becoming more frequent. Correspondingly, shallow multi-seam mining activities increase the probability of weathered rock layer (aquifer) and surface water inrush into the underground, leading to the deterioration of the surface ecological environment, Thus, revealing the characteristics of stratum damage caused by shallow multi-seam mining is of great significance for the prevention of water inrush disasters and environmental protection of the Hongliulin coal mine.  
The 25,214 working face situated in the north of Hongliulin coal mine has a strike length of 3070 m and an inclined length of 289-333 m, which was mined from 1 October 2023-20 July 2024. Furthermore, a goal with a length \(\times\) width of 260 \(\times\) 300 m has been found above the open-off cut of the 25,214 working face, which was caused by the mining of 4\({}^{-2}\) coal seam in the 24,205 working face. The mining conditions of the 25,214 working face and 24,205 working face are shown in Table 1. Figure 1 depicts the location of the working face and the stratigraphic structure of the A-A' of the 25,214 working face.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Working** & **Size/m** & **Coal Seams** & **Mining** & **Depth of** & **Thickness** & **Thickness** & **Dip Angle** \\
**Face** & **Size/m** & **Mined** & **Height/m** & **Coal** & **Seam/m** & **Bedrock/m** & **of Loose/m** & **of Coal** \\ \hline
25,214 & 3070 \(\times\) & & 5\({}^{-2}\) & 5.5 & 161–192 & 77–103 & 64–90 & 0\({}^{\circ}\)\(\sim\)3\({}^{\circ}\) \\
24,205 & 1215 \(\times\) 300 & 4\({}^{-2}\) & 3.5 & 92–110 & 30–48 & 44–80 & 0\({}^{\circ}\)\(\sim\)3\
## 4 Simulation Results
### Results of FS-CFELS  
The simulation results of FS-CFELS are given in Figure 10. The parameters considered constant for presenting the results are DC and TS as these parameters represent the application requirements, and as such, they differ only from application to application. Therefore, each subfigure represents practically a different set of applications that have similar requirements. Using this configuration we can see how LSD relates with the changing characteristics of the edge layer, which are represented by NNV and APCpNV.  
Figure 9: Simulation results for FS-ANVPC.  
The simulation results considering a set of non-complex applications that are delay tolerant are presented in Figure 10a. The results show that when _the vehicle_ is surrounded by many potentially helpful neighbors, the system selects the edge layer as the most appropriate layer for _the vehicle_ to run its applications. Although these applications do not require real-time processing, running them in the edge layer has two benefits: it exploits the high capacity of neighbors which at this point is being unused and it avoids unnecessary traffic being sent in the core network. On the other hand, in Figure 10b,c, we can see that the edge layer is hardly selected when the data complexity increases. Most of the data are sent in the fog or cloud layer, with the latter being used significantly more as the complexity increases. Using the cloud layer instead of fog for complex data frees the fog servers from unnecessary overload, considering the fact that these applications do not need to run in real-time.  
However, as we can see from the results shown in Figure 10d-f, the system decides that the time-sensitive applications will be processed only in the edge and fog layer and never in the cloud. This decision fulfills such a strong requirement like latency. When the applications are not too complex, _the vehicle_ is suggested to use mostly the resources of the neighboring vehicles, provided that there is a considerable number of them in its vicinity. When the number of neighboring vehicles is not very high or they are not prospective helpers, the system suggests the fog layer as the appropriate layer, especially for complex data. Fog servers have more powerful computing capabilities, and since they offer low latency as well, they can handle these data better while still ensuring real-time processing.
## 3 Methods
### Data Processing and Feature Extraction  
As a result of the recorded experiments, two raw data streams, video and EDA, were captured. Within the raw video data stream, both audio and visual information was recorded. In order to extract the video's audio and obtain a raw audio file, the open-source audio software Audacity (Muse Group, New York, NY, USA) was employed. In parallel, the video was processed using FaceReader 8 (Nolus, Wageningen, The Netherlands) software, resulting in a time series data stream for AFE-based valence. The output, or time points, from FaceReader 8 were aligned with the captured EDA, as the COBALT Bluebox's flashing light series confirmed the synchronization of data.  
Each physiological measure pertained to an interaction between the voice user interface and participant, starting from the moment the interface posed the question up until the participant's response. The participant's response was purposely excluded from the physiological measurement window in order to prioritize and observe the emotional build-up prior to a verbal response. Moreover, by observing this particular time window, the studied physiological measures focused on early indications of emotional responses. In contrast to the time windows chosen for physiological measures, the participant's verbal response was observed for the speech measure from the start of the participant's utterance to the end of his or her response.
## 4 Discussion  
The incorporation of washing fines into mortars offers significant environmental benefits but also presents technical challenges that need to be analyzed in depth. On the one hand, the results show that the addition of washing fines can considerably reduce the carbon footprint of mortars. In particular, a partial substitution of cement by these fines can reduce CO\({}_{2}\) emissions by up to 28% for a 30% substitution rate, which is in line with the construction industry's current objectives to minimize the environmental impact of construction materials [43]. This reduction is mainly due to a reduction in the use of cement, an energy-intensive material, which supports a circular economy approach by recovering industrial waste.  
However, the incorporation of washing fines has negative effects on certain technical properties of the mortar, in particular, an increase in porosity and a decrease in mechanical and heat resistance. Tests have shown that the higher the rate of substitution of cement by washing fines, the greater the increase in porosity, resulting in a loss of density and a reduction in flexural and compressive strengths. For example, at a 30% substitution rate, compressive strength falls by almost 50% compared to the reference mortar without the addition of fines. This behavior is consistent with other studies on the use of secondary  
Figure 5: Embodied carbon of mortar incorporating washing fines.  
cementitious materials (SCM), which show that increased porosity due to poor compaction of fine particles affects mechanical strength [22; 44].  
Wash fines, mainly composed of silica and calcium particles, have limited chemical reactivity compared to traditional Portland cement, which reduces their ability to form a dense network of hydration products. In addition, increased porosity promotes water absorption, increasing susceptibility to shrinkage and reducing durability [45]. These results suggest that simply replacing cement with washing fines is not enough to guarantee acceptable mechanical performance in demanding applications. It would therefore be appropriate to explore combinations with other SCMs, such as fly ash or blast furnace slag, to offset these negative effects while maintaining the environmental benefits [46; 47].  
Finally, although the environmental benefits are undeniable, further research is needed to improve the formulation of mortars incorporating washing fines. Pre-treatment of the fines, such as thermal or chemical activation, could increase their reactivity and thus improve their performance. In addition, the addition of specific additives could improve compaction and limit the increase in porosity. Future studies should also assess the long-term impact of these formulations on the durability of the structures, in particular, their fire resistance, which showed a significant drop at high temperatures in this study.
## 2 Materials and Methods
### Butler Matrix  
The performance of the Butler matrix depends on the performance of the individual parameters of each component such as bandwidth, S-parameter, isolation and insertion loss, and phase difference between the output ports. All the components were assembled to complete the Butler matrix with 4 branch line couplers, 2 phase shifts, and 2 crossovers to provide maximum isolation according to the block diagram as shown in Figure 2. The total width and length of the substrate is 159.50 mm \(\times\) 130.56 mm. The array elements of the square patch antenna were attached to the output ports of the Butler matrix, as shown in Figure 9. The distance between the adjacent elements of the antenna array was kept at 0.5\(\lambda\)  
Figure 8: Structure of a square patch antenna array of a 4 \(\times\) 1 element.  
Figure 7: Schematic of the single-element square patch antenna with dimensions and (**a**) front and (**b**) back views.  
The antenna array was simulated using CST Studio Suite within an idealized underground mining environment. Key parameters, including substrate characteristics, beam angles, and the spacing between antenna elements, were systematically adjusted to enhance performance. The system's beam steering capabilities and gain were evaluated across four distinct beams, with iterative refinements made to reduce signal reflection and maximize coverage. The simulation was conducted at an operating frequency of 5.5 GHz, selected for its lower interference and enhanced data transmission capabilities. The Rogers RT5880 substrate, with a permittivity of 2.2 and a thickness of 0.787 mm, was utilized for the antenna design. The underground mining environment was modeled as a rectangular tunnel with reflective walls, accounting for multipath signal interference. To achieve optimal gain and minimize side lobes, the antenna elements were spaced at 0.5\(\lambda\) within the array. The key materials and components used in the design and simulation of the antenna system, along with their physical properties and sources, are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Type** & **Physical Properties** & **Source/Location** \\ \hline Rogers RT5880 Substrate & Relative Permittivity: 2.2, Thickness: 0.787 mm, Copper cladding: 0.035 mm & Manufacturer: Rogers Corporation \\ Antenna Array Elements & Circular Polarization, Operating & Designed for underground mining environments \\  & Frequency: 5.5 GHz & Simulated using CST \\ Branch Line Coupler & Length: \(\lambda\_8/4\), Impedances: \(Z_{0}/\sqrt{2}\) & Software \\  & and \(Z_{0}\), Phase Shift: 90\({}^{\circ}\) & Simulated in CST software \\ Crossover & Impedance of branches: similar to BLC, & Simulated in CST software \\  & Extra line length for phase shift & Simulated in CST software \\ Phase Shifter & Phase Delay: \(-45^{\circ}\), Extra Line Length: 10.60 mm & Simulated in CST software \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of sample types, physical properties, and sources used in antenna design and simulation.  
Figure 9: Butler matrix network with four elements of the antenna array.
## 3 Validations  
### The Developing Flow of Non-Newtonian Power-Law Fluid in a Channel  
The developing flow through a confined channel has been extensively used as a benchmark validation for non-Newtonian fluid. In this section, the two-dimensional steady laminar developing flow is considered to validate the in-house code. In the numerical simulation, the flow of power-law fluid develops with a uniform inlet velocity (\(U_{0}\)) through a rectangular channel of \(h\) in height and \(L\) in length, as shown in Figure 1. The macro density and velocities on the boundaries are set as \(\mathbf{u}=(U_{0},0)\) at \(x=0\), \(\mathbf{u}=(0,0)\) at \(y=0\) and \(h\) for the channel wall, and \(\rho=\rho_{0}\) at \(x=L\). The non-equilibrium extrapolation presented by [PERSON] et al. (2018) is adopted to calculate the velocity distribution functions on the boundaries.  
The numerical simulation is performed in a computational domain with the size of \(15h\times 1h\) and the uniform mesh size is \(h/50\). The dimensionless time step \(\Delta t^{*}=\Delta tU_{0}/h=0.001\). The Reynolds number (Re) that controls this problem is defined as \(\text{Re}=\rho_{0}h^{n}U_{0}^{2-n}/m\). The Reynolds number of 10 is used in the current steady simulation, and five power-law indexes \(n\) ranging from 0.3 to 1.5 (including shear-thinning \(n<1\), shear-thickening \(n>1\), and Newtonian fluid \(n=1\)) are considered. The analytical solution of the velocity profiles of the fully developed flow can be expressed as ([PERSON] and [PERSON], 2016)  
\[\frac{u_{f}(y,n)}{U_{0}}=\frac{2n+1}{n+1}(1-|1-\frac{2y}{h}|^{(n+1)/n}). \tag{25}\]  
The fully developed velocity profiles from present numerical simulations and that predicted by Equation (25) are presented in Figure 2. According to the comparisons in Figure 2, excellent agreements (discrepancies \(<2\%\)) are achieved by the present numerical method. The velocity peaks presented in Table 1 also show the good accuracy of the current method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sources** & \(n=0.3\) & \(n=0.5\) & \(n=0.7\) & \(n=1.0\) & \(n=1.5\) \\ \hline Present & 1.242 & 1.333 & 1.411 & 1.501 & 1.591 \\ Analytical & 1.231 & 1.333 & 1.412 & 1.500 & 1.600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The velocity peaks at \(\text{Re}=10\).  
Figure 1: Schematic of power-law fluid flow in a channel.
###### Abstract  
This paper proposes a high-precision interventional pressure measurement catheter with a compact structure for in vivo pressure monitoring. This catheter uses a minimally invasive testing method, and the proposed compact structure reduces pressure loss caused by fluid movement inside the catheter, ensuring accuracy in dynamically varying pressure measurements. The paper conducts a theoretical analysis of the pressure transmission process inside the catheter, fabricates a prototype, and establishes both dynamic and static pressure testing systems. Through experimental research on the effects of the catheter's dimensions on the accuracy of dynamically varying pressure measurements, it demonstrates that the compact pressure measurement catheter can effectively enhance the precision of dynamically varying pressure measurements. The catheter offers high precision, small size, and simple manufacturing processes. It holds promising applications in fields such as in-body pressure measurement and microfluidic system monitoring.  
in vivo; high precision; pressure monitoring; pressure loss; catheter structure
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 3 Methods
### Data Processing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Satellite Platform**} & \multicolumn{3}{c}{**Used Bands**} & \multicolumn{2}{c}{**The Number of**} & \multirow{2}{*}{**Investigated Period**} \\ \cline{2-2} \cline{5-6}  & **Band Name** & **Wavelength (\(\mu\)m)** & **Resolution (m)** & & **Images** & \\ \hline \multirow{8}{*}{ Landsat 5 TM} & Band1-Blue & 0.45–0.52 & 30 & & \\  & Band2-Green & 0.52–0.60 & 30 & & \\  & Band3-Red & 0.63–0.69 & 30 & & \\  & Band4-NIR & 0.76–0.90 & 30 & 207 & 1988–2011 \\  & Band5-SWIR 1 & 1.55–1.75 & 30 & & \\  & Band6-Thermal & 10.40–12.50 & 120 & & \\  & Band7-SWIR 2 & 2.08–2.35 & 30 & & \\ \hline \multirow{8}{*}{ Landsat 7 ETM+} & Band1-Blue & 0.45–0.52 & 30 & & \\  & Band2-Green & 0.52–0.60 & 30 & & \\  & Band3-Red & 0.63–0.69 & 30 & & \\  & Band4-NIR & 0.77–0.90 & 30 & 6 & 2012 \\  & Band5-SWIR 1 & 1.55–1.75 & 30 & & \\  & Band6-Thermal & 10.40–12.50 & 60 & & \\  & Band7-SWIR 7 & 2.09–2.35 & 30 & & \\ \hline \multirow{8}{*}{ Landsat 8 OLI} & Band2-Blue & 0.452–0.512 & 30 & & \\  & Band3-Green & 0.533–0.590 & 30 & & \\  & Band4-Red & 0.636–0.673 & 30 & & \\ \cline{1-1}  & Band5-NIR & 0.851–0.879 & 30 & 67 & 2013–2019 \\ \cline{1-1}  & Band6-SWIR 1 & 1.566–1.651 & 30 & & \\ \cline{1-1}  & Band7-SWIR 2 & 2.107–2.294 & 40 & & \\ \cline{1-1}  & Band10- & 10.6–11.19 & 100 & & \\ \hline \multirow{2}{*}{ Sentinel 2} & Band2-Blue & 0.46–0.53 & 10 & & \\ \cline{1-1}  & Band4-Red & 0.65–0.68 & 10 & 1 & 2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the investigated remote sensing data. \[\text{term}_{2}=\frac{\rho_{blue}+\rho_{green}+\rho_{red}-0.2}{0.6} \tag{3}\] \[\text{term}_{3}=\frac{\rho_{NIR}+\rho_{SWIR}\frac{1}{1-\rho_{SWIR} 2-0.3}}{0.5}\] (4) \[\text{term}_{4}=\frac{30-T_{b}}{10}\] (5) \[\text{term}_{5}=\frac{0.8-\frac{\rho_{green}-\rho_{SWIR}\frac{1}{ 1}}{\rho_{green}+\rho_{SWIR}\frac{1}{1}}}{0.2} \tag{6}\]  
where the cloud_score represents the cloud likelihood. A larger value of cloud_score indicates a greater likelihood of a pixel being cloudy. \(\rho_{x}\) is the top-of-atmosphere (TOA) reflectance, as shown in Table 1. \(T_{b}\) denotes the brightness temperature. A threshold of 0.3 was used to mask clouds in this study.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### Software Defined Networking, Programmable Data Plane, and Devices  
#### 3.1.1 Software-Defined Networking  
Software-defined networking (SDN) is an emerging networking paradigm that enables network administrators to oversee and govern the network infrastructure using software applications, instead of depending solely on conventional networking hardware [1]. SDN works by decoupling the control plane from the data plane, allowing the network to be centrally managed and dynamically configured. This is accomplished through the use of a centralized controller that communicates with switches and routers in the network using a standardized protocol, such as OpenFlow [16]. The principles of SDN can be summarized as follows:  
* Separation of control plane and data plane: The control plane, responsible for governing forwarding behavior, is separated from the data plane, which performs the actual traffic forwarding based on instructions from the control plane. This decoupling enhances the network architecture's flexibility and scalability, allowing for more efficient management.
* Centralized Control: The so-called SDN controller is responsible for handling control logic, being a high-level software program that can run on commodity servers.
* Programmability: The network can be programmed using software applications that run on top of the SDN controller, enabling dynamic configuration and automation of network functions.
* Virtualization: The network can be virtualized, enabling multiple virtual networks to run on a single physical network infrastructure.
* Open Standards: SDN uses open standards and protocols, enabling interoperability between different vendors' products and facilitating new developments.  
By adopting SDN, network administrators can create a more agile, flexible, and scalable network infrastructure providing better control and visibility over network traffic.  
Figure 1 illustrates a general architecture of SDN. The network is managed through network applications, which can include load balancing, firewall management, and network monitoring. The controller platform integrates the SDN controller, which manages network devices and enforces network policies. Communication between the controller and network devices is facilitated through protocols such as OpenFlow or vendor-specific alternatives. Network devices, such as switches and routers, are responsible for forwarding data packets between network nodes, constituting the data forwarding elements. To define the data plane behavior of SDN devices, P4 can be employed, specifying packet parsing, processing, and forwarding operations.