esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and Process
### Trial Control  
#### 2.5.1 Test Bench and Wind Tunnel  
To validate the accuracy of numerical simulation results, experimental research was conducted to investigate the relationship between the spherical coefficient and the deposition of aerosol particles on the surface of photovoltaic panels. The test setup comprises a fixed bracket holding a 40 cm \(\times\) 40 cm acrylic organic glass plate. The glass plate, depicted in Figure 4, functions as the substrate for particle deposition and is inclined at a 40\({}^{\circ}\) angle with respect to the ground. The deposition surface is divided into 25 equal parts, organized into 5 sampling layers, with each layer further divided into 5 equal parts. Each region is equipped with a 5 cm \(\times\) 5 cm adsorption film serving as the particle deposition surface. The adsorption film is securely fixed with transparent tape to minimize disturbances to the flow field near the deposition surface. To ensure stability during removal, a thin film is attached  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **Value** \\ \hline Inflow wind speed & 3.50 m/s \\ Air density & 1.239 kg/m\({}^{3}\) \\ Dynamic viscosity of air & 1.84 \(\times\) 10\({}^{-5}\) Pa-s \\ Aerosol particle density & 2650.00 kg/m\({}^{3}\) \\ \(d\) & 10.00, 20.00, 30.00 μm \\ \(\Psi\) & 0.73–0.91 \\ Volume fraction of aerosol particle & 5.00 \(\times\) 10\({}^{-3}\)\% \\ Inlet boundary & Velocity-inlet \\ Outlet boundary & Outflow \\ Wall boundary & Non-slip wall \\ \hline \end{tabular}
\end{table}
Table 1: Boundary conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & & & & \(\psi\) & & \\ \cline{3-8}  & **0.73** & **0.76** & **0.79** & **0.82** & **0.85** & **0.88** & **0.91** \\ \hline \multirow{3}{*}{\(d\) (um)} & 10.00 & Test-1 & Test-2 & Test-3 & Test-4 & Test-5 & Test-6 & Test-7 \\  & 20.00 & Test-8 & Test-9 & Test-10 & Test-11 & Test-12 & Test-13 & Test-14 \\  & 30.00 & Test-15 & Test-16 & Test-17 & Test-18 & Test-19 & Test-20 & Test-21 \\ \hline \end{tabular}
\end{table}
Table 2: Comprehensive test design table.  
to the adhesive surface of the transparent tape to prevent shaking of the adsorption film. The surface of the acrylic organic glass is coated with black spray to enhance visualization of particle distribution on the plate surface. The experiment is conducted in a \(B1\)/K2 low-speed wind tunnel, as illustrated in Figure 5. The \(B1\)/K2 low-speed wind tunnel comprises a power section, rectifier section, contraction section, closed section, expansion section, and open section. The power section and rectifier section house the fan and rectifier grid, respectively, while the contraction section and expansion section exhibit non-uniform flow fields. The open section, connected to the external environment and susceptible to environmental wind fields, cannot be used as a test section. The closed section provides a consistent and uniform wind field, making it suitable for testing. The test setup is placed within this section, which has a cross-sectional area of 92 cm \(\times\) 92 cm.
# Active Tunable Elastic Metasurface for Abnormal Flexural Wave Transmission
## 2 Mechanisms of Actively Tunable Elastic Metasurface  
### Description of Adaptive Unit Cells  
We used a negative proportional feedback control strategy to modulate the phase of the adaptive unit cells, which can control the flexural wave more efficiently and contribute to the fabrication of the metasurface. The proposed adaptive unit cell is shown in Figure 1, where Figure 1a shows the three-dimensional geometric model of the adaptive unit cell and Figure 1b shows the two-dimensional model. From Figure 1a, it can be seen that the adaptive unit cell contains two sub-cells. The gray and yellow regions indicate the base beam (aluminum) and the piezoelectric patch, respectively; the upper part of the piezoelectric patch is used as the piezoelectric sensor, and the lower part is applied as the piezoelectric actuator. Moreover, the negative proportional feedback control system is connected externally to the piezoelectric patch. The ratio of the sensed voltage generated by the deformation of the sensor to the external voltage applied by the control system is used to realize the positive active stiffness gain [40]. In the adaptive unit cell of length \(L=l_{1}+l_{2}\), the lengths of sub-cell 1 (\(l_{1}\)) and sub-cell 2 (\(l_{2}\)) are adjustable, and we define \(l_{1}/L\) as \(p\). The thickness of the base beam in sub-cell 1 is \(h_{\rm{s1}}\) and that in sub-cell 2 is \(h_{\rm{s2}}\), and the two substrates are covered with piezoelectric patches with a thickness of \(h_{\rm{p}}\). The width of the piezoelectric patch is the same as the width of the base beam. The total thickness of sub-cell 1 is \(h_{\rm{1}}=h_{\rm{s1}}+2h_{\rm{p}}\) and the total thickness of sub-cell 2 is \(h_{\rm{2}}=h_{\rm{s2}}+2h_{\rm{p}}\).
## 2 Materials and Methods  
### Experimental Analysis  
In this study, experimental data previously determined by [12; 13] were used to predict the \(\mathrm{q_{u}}\) of high-plasticity bentonite clay reinforced with basalt fiber. In the conducted studies, the \(\mathrm{q_{u}}\) values of both unreinforced and BF-reinforced samples were determined through unconfined compressive tests.  
In the study conducted by [PERSON] and [PERSON] [12], 6 mm long BF was used in various proportions (1%, 2%, 3%, 4%, and 5%) in bentonite clay. The \(\mathrm{q_{u}}\) of unreinforced bentonite clay was determined to be 206.93 kPa through unconfined compressive testing. It was found that the strength values of the bentonite samples reinforced with different proportions of BF ranged between 200.03 and 237.48 kPa. The findings of the study revealed that the highest strength was achieved with a 4% basalt fiber content in bentonite clay reinforced with 6 mm basalt fibers, and increasing the fiber ratio beyond this point led to a reduction in strength.  
In another study conducted by [PERSON] and [PERSON] [13], 12 mm long BF was used as a reinforcement material in various proportions (1%, 2%, 3%, 4%, and 5%). According to the unconfined compressive test results, the average \(\mathrm{q_{u}}\) values of basalt-fiber-reinforced samples ranged between 202.74 and 267.66 kPa. The study's results demonstrated that the peak strength was achieved with a 4% basalt fiber content when 12 mm long basalt fibers were used to reinforce bentonite clay. However, a decrease in strength was observed in the sample containing 5% basalt fiber.  
In both studies, the basalt fiber lengths varied, while the BF reinforcement ratios remained the same. Each mixture with 6 mm and 12 mm long fiber reinforcement in bentonite clay was compacted at the optimum water content of unreinforced bentonite, and cylindrical samples were prepared. The mixture ratios used in the experimental studies and the average q\({}_{\text{u}}\) values are presented in Table 1.  
In the first phase of the experimental studies, BF was separated with the help of a compressor and added to bentonite clay dried in an oven at 105 \({}^{\circ}\)C for 24 h, and then mixed. Subsequently, distilled water was added to these prepared mixtures by spraying at the optimum water content, and they were mixed both manually and with a mixer until a homogeneous mixture was obtained. This was carried out to prevent fiber clumping and aggregation. The mixing time was set to 10 min to ensure a homogeneous mixture. In these prepared mixtures, Proctor tests were performed using a fully automatic soil compact to compact the samples. Then, cylindrical samples with a height twice their diameter were taken from the Proctor mold, and unconfined compressive tests were performed. As a result of the experimental studies, a total of 60 different q\({}_{\text{u}}\) values were obtained, and the average q\({}_{\text{u}}\) values were determined for each mixture.
## 3 Results and Discussion
### The Spatial Distribution Patterns of Heavy Metals in the Twelve Cities
Figure 2: The spatial distribution patterns and concentrations of heavy metals in cropland soils of the cities in Shandong Province. Bars sharing the same color of each metal are not significant at \(\alpha=0.05\) (Duncan test). All the studied cities were polluted by Pb and Cd, except for Cd in Jinan and Liaocheng (Figure 2). Though the mean contents of Cd and Pb were 0.162 mg-kg\({}^{-1}\) and 31.77 mg-kg\({}^{-1}\), respectively, the concentrations of Cd in Yantai and Linyi reached 0.8 mg-kg\({}^{-1}\) and 0.6 mg-kg\({}^{-1}\), and the maximal Pb content was up to 73.9 mg-kg\({}^{-1}\). The high standard deviation also suggested that point-source pollution commonly existed in these cities. Deposition of fly ash and irrigation can transfer exhaust gas and industrial waste to surrounding cropland soils directly, which would also lead to the high accumulation of heavy metals [33; 34]. Thus, the point-source pollution and the similar spatial distributions of Cd and Pb may also attribute to the industrial activities [35; 36]. Cluster analysis to the metals showed that Cu, Ni, Pb and Cd were gathered together, and then clustered the Zn, while Cr had no significant association with other metals, indicating that sources of Cr may be very complex (Figure 4). [PERSON] et al. [37] reported that Cd, Pb and Zn in arable soils were mostly from the human factor, while [PERSON] et al. [19] indicated the lithogenic origin of Cr. The significantly higher contents of Cr in certain cities in this study suggest multiple sources from human activities, not domination by natural sources. The dendrogram based on the mean Euclidean distance between groups  
Figure 4: The cluster analysis of heavy metals among the studied cities. Figure 3: The cluster analysis of studied cities on the concentration of Cr.
## 3 System Model  
In this section, we briefly discuss our system model. In this regard, we state the assumptions made, the objectives and the setting. After these, we will discuss the network model and the AUV trajectories in the upcoming subsections.  
Assumptions: In this paper, all of the assumptions are based on a centralized solution to schedule transmissions, the knowledge of the AUV location, the knowledge of the nodes' energy levels, _etc._  
* Central power rests with the sink.
* Nodes always have data to send.
* All nodes are position aware.  
Objectives: This paper aims at network lifetime prolongation and throughput maximization (data loss minimization).  
Settings: These are as follows.  
* The network nodes are hierarchical, _i.e._, nodes are of four types; MNs, GNs, AUV and sink. The MNs and GNs are randomly deployed in the network field; the AUV moves on its defined trajectory; and the sink resides at the water surface.
* MNs gather the information of interest from their surroundings and send the gathered data to their respective GNs within their allocated time slots. The AUV moves on its defined path and gathers data from in-range GNs. Finally, the AUV transmits gathered data to the sink.
* Nodes, while sending data to other nodes, update them with their residual information via a field in the packet header.
* In order to match the changing GNs with the fixed route of AUV, the GNs are selected within the communication range of AUV, and a synchronization mechanism is defined between these, which will be discussed in the network model.
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
## 3 Results and Discussion
### Biodegradation of DR23 Dye
#### 3.3.2 Effect of Nanobiocatalyst Dosage  
DR23 dye degradation using the Fe\({}_{3}\)O\({}_{4}\) and CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized lac-cases was studied at different nanobiocatalyst dosages, ranging from 60.0 mg/mL to 300.0 mg/mL, as shown in Figure 4b. The study of dye degradation at different nanobio-catalyst dosages was performed at optimum conditions, including a pH of 5.0 and a temperature of 50.0 \({}^{\circ}\)C, using a dye concentration of 10.0 mg/mL. According to Figure 4b, the increased dosage of the nanobiocatalyst caused an increase in the degradation of DR23, which could be attributed to the increased active sites of the enzymes with the added amount of the nanobiocatalyst [40]. DR23 dye showed a high affinity towards both the nanobiocatalyst. The results indicated that the most efficient dosage was 300.0 mg/L for Fe\({}_{3}\)O\({}_{4}\) nanoparticles and CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase, with the resultant dye degradation equal to 83.0% and 89.0%, respectively. So, the expected results were obtained, in which the CuFe\({}_{2}\)O\({}_{4}\) nanoparticles that were used as support for the laccase enzyme enhanced its catalytic activity for dye degradation, due to the presence of copper ions.
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 2 Experimental Materials and Basic Parameters
### Basic Parameters of Suppressant Materials  
#### 2.2.1 Surface Tension and Viscosity of Dust Suppressant Solutions  
The surface tension and viscosity of these suppressants were measured using an automatic interfacial tension meter and a Shanghai viscosimeter, with the test conditions maintained at a temperature of 25 \({}^{\circ}\)C and a humidity of 43% RH. The results are shown in Figure 2.  
Figure 1(a) shows the surface tension values of the three dust suppressants--DA, OP-10, and SDS--at varying mass concentrations. At a concentration of 1.0 \(\times\) 10\({}^{-3}\)%, DA had the lowest surface tension (24.6 mN/m), followed by OP-10 (31.9 mN/m) and SDS (55.45 mN/m). The results indicate that DA exhibited the most substantial interfacial activity, significantly reducing surface tension in comparison to OP-10 and SDS. As the mass concentration increased to 1.0%, all suppressants showed reduced surface tension: DA (23.75 mN/m), OP-10 (31.92 mN/m), and SDS (31.8 mN/m). The convergence of these values at higher concentrations indicates that each suppressant reached CMC, where surfactant molecules aggregate into micelles, limiting further reduction in surface tension [20]. DA's ability to significantly reduce surface tension at both low and high concentrations reflects its strong interfacial activity [21]. In contrast, OP-10's moderate reduction in surface tension is likely due to the stabilizing effect of its hydrophilic Polyoxyethylene chain [22], while SDS showed a gradual reduction due to slower adsorption dynamics at low concentrations and a more significant reduction at higher concentrations as micelles formed [23].  
Figure 1(b) illustrates the viscosity of the three dust suppressants at varying mass concentrations. Viscosity reflects a fluid's internal resistance to flow, playing a key role in dust-mist particle interactions. The experimental results indicate that the viscosity of the suppressants exhibited a variable, increasing trend with increasing mass concentrations for all suppressants. The results also indicate that the suppressants' effect on bulk viscosity is minimal at low concentrations, at higher concentrations, as micelles formed in the dust suppressant solutions, the viscosity increased due to the interactions between micelles [24]. The trend of the increase in viscosity is as follows: DA > OP-10 > SDS > water. Overall, the experimental results highlight the primary role of surface tension and viscosity in dust suppression efficiency. These findings align with theoretical predictions that focus on dust suppressants at the solid-liquid interface and its effects on spray dynamics and dust-mist interactions [25].  
Figure 2: Surface tension and viscosity of dust suppressant solutions at different concentrations.
# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
## 2 Background and Related Work
### SDN-Based Routing Algorithms
As well, The roads' construction uses information collected by dedicated equipment, which presents extra processing time to accomplish the routing task. Besides, the suggested protocols used the cellular network in exchanging the packets between the data plane and the control plane, which represents a limited option given the exponential growth of the equipment deployed in future cities, particularly with the scope of the IoT. The wireless devices spread will lead to a scarcity of resources in terms of medium allocation. As well, the arrival of 5G will overthrow the current cellular networks, with the deployment of innovative decentralized architectures based on hybrid clouds and supporting IoT applications. Such advancement will revolutionize existing solutions by requiring their update to provide a high quality of service (QoS) for users, and high quality of experience (QoE) [21]. In contrast, our protocol anticipates the destination discovery step and avoid it by using a prediction mechanism to reduce the transmission delay. Besides, we are adopting the WiFi 802.11P technology in network communication, as this type of technology is most suitable in high mobility communications.
# Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
## 4 Open Issues and Discussions
### Trust Layers for Edge Intelligence  
Blockchain-enabled trust could be considered in different layers for researchers and developers from different research areas. We further extend the description of blockchain trust layers [117] for blockchain-enabled edge intelligence as follows:  
* _Social Layer_: the layer at which task publishers (e.g., IoT devices or human users), workers (e.g., edge nodes or servers), and blockchain platforms (e.g., smart contracts, decentralized applications) could interact with each other and make transactions on resources and information. In this layer, interactions among entities could include reputation establishment [118], resources marketing [119], paid collaboration, identity management, and the regulation of training strategies.
* _Data Layer_: the layer at which information records in blockchain are managed by self-governance. The recorded data could include learning model parameters, IoT data, reputation records, published tasks, transaction records, and the history of global learning models. This layer concerns with learning quality, data integrity, privacy, protection, the architecture of blockchain transactions, and the lifecycle of records.
* _Technical Layer_: the layer at which strategies are implemented to realize the functions of edge intelligence. Platforms (e.g., Hyperledger Fabric) and mathematical foundations are included in this layer. Such strategies could include learning algorithms, consensus mechanisms, incentive mechanisms, zero-knowledge proofs, secure multi-party computation, contribution evaluation frameworks, optimization algorithms, etc.
# Annotated-VocalSet: A Singing Voice Dataset
## 2 Steps to Generate the Dataset
### Extracting Notes Features  
After annotating the pitch contours with the onset, offset, and transitions, as explained above, the following formulas were used to calculate each note's features.  
1. _Start time_: is the onset time.
2. _End time_: the time of the offset.
3. _Duration_: calculated by subtracting the _\"Start time\"_ from the _\"End time\"_.
4. _Type_: can be a rest, sound, or transition according to Equation (1).  
\[\textit{Type}=\left\{\begin{array}{cl}\textit{Rest}&\textit{if the estimated pitches between}\\ &\textit{start and end times are0}\\ \textit{Sound}&\textit{if the estimated pitches between}\\ &\textit{start and end times are not0}\\ \textit{Transition}&\textit{if the start and end times}\\ &\textit{are related to a transition}\end{array}\right. \tag{1}\]
5. _Average F0_: contains the average of the F0s of the note.  
Figure 3: Points’ statuses on a pitch contour. There are two notes, E4 and F4, sung by a professional female singer. **(a)** Showing offset, the start of a transition, the end of a transition, and onset events in order. **(b)** The transition was not considered, and the onset started immediately after the offset point according to **(a)**. **(c)** Similarly, the transition was not considered, but the offset was annotated to lie immediately before the onset point in **(a)**. **(d)** Likewise, the transition was not considered, but the middle points between the onset and offset points in **(a)** are annotated as offset and onset.  
_Median F0_: includes the median of the estimated F0s for the current note.
* _Min F0_: contains the minimum estimated F0 in the current note.
* _Max F0_: shows the maximum estimated F0 in the current note.
* _The standard deviation of F0s (STD)_: this column shows the standard deviation of the estimated F0s of the current note.
* _Average F0s in the range of STD (AverageStd)_: This column includes the average of the only estimated F0s that satisfy the following condition: \[(Average\ F0)-(2*STD)\leq F0_{i}\leq(Average\ F0)+(2*STD)\] (2) This metric calculates the average by omitting the pitches that were determined to be outliers. Since, in some cases, especially when the note's duration was very short, none of the estimated pitches were within one standard deviation distance, and thus two standard deviation distances were considered instead.
* _Estimated MIDI code_: includes the MIDI codes associated with the calculated AverageStd, as shown in Equation (3).  
\[12\,*\,\log_{2}((AverageStd)/\ 440)+69 \tag{3}\]
## 3 Sensitivity Analysis
### Effect of Clay Sensitivity  
As the underlying clay will be softened due to the large deformation induced by deep penetration of spudcan, the effect of the sensitivity of underlying clay on the resistance profile is studied. Three cases of spudcan penetration into sand overlying clay with different clay sensitivities (\(S_{t}=1\), \(3\), \(5\)) are compared in Figure 8. The resistance at the sand-clay interface decreases about 10% from \(S_{t}=1\) to \(3\), while the resistance profiles are nearly identical for \(S_{t}=3\) and \(5\). For most offshore soft to medium soft clays, the typical range of soil sensitivity is between \(2\) and \(5\)[11]. The clay sensitivity shows little influence on the peak resistance in the sand layer. However, it has a significant effect on the post-peak resistance profile.  
Figure 7: Penetration resistance profiles with different critical thresholds.