esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Description
### Physics-Based Model (PHYS) from NOAA for Off-Track Comparison  
It must be noted that, although our ML methods are trained and tested using collocated data on the CALIOP track, our ultimate goal is to apply the dust detection algorithm to the whole swath of VIIRS to achieve the best possible spatial sampling. One challenge facing us is how to evaluate the VIIRS dust detection results off the track of CALIOP when both the training and testing data, as described in the last section, are on track. To overcome this challenge, we introduce a physics-based VIIRS aerosol detection product (ADP) developed by a NOAA team [24; 41]. It will be referred to as the PHYS model for short. This product identifies dust based on the dust spectral signatures, especially its strong absorption at ultraviolet (UV) and blue channels and its thermal signals. The product flags out smoke and dust over global cloud free and snow/ice free surfaces at 0.75 km spatial resolution at nadir view. Absorbing aerosol index (AAI) and Dust Smoke Discrimination Index (DSDI) are generated using the VIIRS M1 (0.415 \(\upmu\)m), M2 (0.445 \(\upmu\)m), and M11 (2.25 \(\upmu\)m) bands following  
\[AAI=100*[log_{10}(R_{M1}/R_{M2})-log_{1}0(R^{\prime}_{M1}/R^{\prime}_{M2})] \tag{1}\]  
\[DSDI=10*[log_{10}(R_{M1}/R_{M11})] \tag{2}\]  
where \(R\) is the observed top of atmosphere (TOA) reflectance and \(R^{\prime}\) is the reflectance from Rayleigh scattering. Using empirical thresholds, predefined thresholds that are suitable over general conditions are used to identify dust for each VIIRS pixel. The estimated accuracy for dust detection based on this physics-based ADP is 80% over land and over ocean. In addition, the detection limit of the VIIRS ADP product is set as for only smoke/dust events with AOD > 0.2, by considering the inability to separate aerosol type for AOD < 0.2 and threshold development is based on events with AOD > 0.2 [24; 41]. It should be noted that the comparison of our ML based methods and the this PHYS model can be only considered as an evaluation rather than a validation because all the detection methods are designed based on different motivations for different purposes and they all have some inherent advantages and limitations.  
In summary, we will train our ML based VIIRS dust detection methods based on the collocated on-track CALIOP data. Then, we will validate the methods using the on-track testing data. Finally, we will apply our methods to the whole VIIRS swath (i.e., off-track) and compare the results with NOAA's PHYS model both on and off the CALIOP track.
# Cross-Domain Access Control Model in Industrial IoT Environment
## 3 Distributed Access Control Model Based on a Master-Slave Chain
### Dynamic Selection Strategy of Master Chain Nodes Based on Reputation Mechanism  
In this system, the master chain is composed and maintained by master chain nodes, and both the master chain and slave chain utilize Byzantine consensus (PBFT). In order to prevent the master chain nodes from accidents in the process of cross-domain access control and ensure the security and reliability of cross-domain access control, reliable and honest nodes were selected to become master chain nodes. Therefore, this paper proposes a dynamic selection mechanism of master chain nodes based on a reputation mechanism. The reputation value of a node is affected by many factors of the node and affects the final reputation value [22]. Specifically, different domains periodically select the 10% of edge nodes with the highest reputation value as master chain nodes. All notations in the text will be presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(En_{i}\) & Edge node \\ \(D_{j}^{X}\) & Device \(j\) in domain \(X\) \\ \(McN_{k}\) & Master chain node \\ \(Dom_{X}\) & Domain \(X\) \\ \(PK_{i}\) & Public key of device \(i\) or node \(i\) \\ \(SK_{i}\) & Private key of device \(i\) or node \(i\) \\ \(SC_{i}\) & Number of historical honest consensus behaviors of \(En_{i}\) \\ \(AC_{i}\) & Total number of historical consensus behaviors of \(En_{i}\) \\ \(MC_{i}\) & Number of consensus times of malicious behavior of \(En_{i}\) \\ \(OnlineT\) & Online hours of \(En_{i}\) \\ \(TotalT_{i}\) & Total time of \(En_{i}\) joining the network \\ \(R_{i}^{j}\) & Reputation value of \(En_{i}\) in the \(block_{i}\) \\ \(H_{1}\) & Hash function \\ \(1 MS\) & Identity management server \\ \(SC_{A}\) & Slave chain of domain \(A\) \\ IDSC & Intra-domain access control smart contract \\ CDSC & Cross-domain access control smart contract \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the notations.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 3 Proposed Method  
We propose an MDPN framework that contains an ISOL module and a GMDP module. Different from the attention mechanism method mentioned in Section 2.2, which does not eliminate noise completely, the ISOL module completely eliminates background and pseudo-objects by generating masks so as to obtain a clean and complete target area. Different from the GNN methods mentioned in Section 2.3, which all use GNNS alone or stacked in parallel, the GMDP module obtains richer features with deeper abstraction levels layer by layer through series-stacked GNNs. As shown in Figure 3, MDPN first sends the input images to the ISOL module, which is based on instance segmentation. For each input image, the ISOL module creates a new image that only contains the object region. Then, the new images are sent to the backbone to obtain the object features. After that, three levels of distribution features of the object features are extracted by the GMDP module and then used to update the object features. The updated object features go on to the next iteration. We calculate the class prediction using the updated object features at the end of each iteration.
# A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios
## 5 Results and Discussion
### Distribution of Errors  
Since the papers in books on camera-based realistic scenarios tend to have a great impact on the curvature of staff lines, we manually divide the test set of the CPMS dataset into 359 flat scores and 241 curved scores based on the degree of curvature.  
The pitch accuracy comparison in Figure 10 shows that our method performs similarly for flat and curved scores, whereas the CRNN-CTC performs significantly better on flat scores than curved scores. This indicates that the effect of stave curvature on our method is much less than that on CRNN-CTC, which means that our stave-aware method effectively handles staff distortion and is more robust.  
The comparison of type accuracy is presented in Figure 11. Both methods achieve similar type accuracy for both sets of scores. Moreover, the type accuracy of both methods is slightly higher for straight music scores than for curved music scores, suggesting that the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Training Set**} & \multicolumn{3}{c}{**Accuracy on CPMS(\%)**} \\ \cline{3-5}  & & **Pitch** & **Type** & **Note** \\ \hline CRNN-CTC & & 44.23 & 51.58 & 37.42 \\ \hline Ours & & 89.77 & 94.46 & 85.17 \\ \hline CRNN-CTC & & 95.07 & 96.74 & 91.95 \\ \hline Ours & & 97.10 & 97.15 & 94.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy comparison between our method and the CRNN-CTC model.  
Figure 10: Pitch accuracy comparison histogram for different bending levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Training Set**} & \multicolumn{3}{c}{**Accuracy on CPMS(\%)**} \\ \cline{3-5}  & & **Pitch** & **Type** & **Note** \\ \hline \multirow{3}{*}{Ours} & PrIMuS & 89.77 & 94.46 & 85.17 \\ \cline{2-5}  & PrIMuS + CPMS training set & 97.10 & 97.15 & 94.40 \\ \cline{2-5}  & DeepScoresV2 & 99.23 & 96.87 & 96.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy comparison of our method on different datasets.  
effect of music score curvature on type accuracy is minimal. This is because the curvature of staves primarily affects the position (pitch) of the notes rather than their shape (type).
## 3 SAgic-IoT Platform Description
### General Scenario  
Figure 1 shows the IoT on-site scenario for the SAgic-IoT platform. The architecture consists of four main nodes: the sensor nodes, the camera nodes, the gateway node, and the processing and storage node. Sensor nodes gather data from all sensors and periodically send it to the gateway node via other sensor nodes or directly, depending on the node's distance from the gateway node. They are deployed on the greenhouses and establish a wireless network (based on 802.15.4) to communicate among themselves using radio-frequency links. The camera nodes send the images via Wi-Fi to the gateway node, where the trained CNN model for plant disease detection is stored. When the model identifies a possible disease situation in the crop area, a notification indicates the camera's location and the detection image. A gateway node is also deployed along with the sensor nodes to enable a connection between the SAgic-IoT platform and the Internet (cloud server). The gateway node supports three communication interfaces: ZigBee to maintain communication with the sensor nodes, Wi-Fi to connect to the camera nodes, and a cellular interface to transmit data to the storage and processing node. A farmer has remote access to the gathering data to know the greenhouse conditions and control the different sensor and actuator devices and alert notifications when the CNN model detects a crop health problem situation. The gateway node includes a cellular network interface to transmit all gathered data to the cloud server. A web and mobile application were created as a cloud service to consult data information, receive alert notifications, and decide about control systems.  
Figure 1: General scenario for SAgic-IoT.
## 4 Results and Discussion
### Free-Decay Test
However, the damping ratios in all DOFs except that of yaw maintain almost the same values with no effect from the SPM layout. Moreover, in the yaw decay test, the yaw displacement does not return to the equilibrium position with the increase of time. This means that the platform does not possess any mooring restoring stiffness in the yaw DOF of the SPM turbine, which coincides with our expectation for the SPM layout. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Load Case**} & \multirow{2}{*}{**Description**} & **Initial** & **Enabled** & **Simulation** & **Wind** & **Wave** \\  & & **Conditions** & **DOFs** & **Length** & **Condition** & **Condition** \\ \hline \multirow{2}{*}{I} & Free decay, & \multirow{2}{*}{Surge} & Platform and & \multirow{2}{*}{20 min} & \multirow{2}{*}{No wind} & \multirow{2}{*}{Still water} \\  & & surge & & & & & \\ \multirow{2}{*}{II} & Free decay, & \multirow{2}{*}{Sway} & Platform and & \multirow{2}{*}{20 min} & \multirow{2}{*}{No wind} & \multirow{2}{*}{Still water} \\  & & sway & +22 m & & & & \\ \multirow{2}{*}{III} & Free decay, & \multirow{2}{*}{Heave} & Platform and & \multirow{2}{*}{5 min} & \multirow{2}{*}{No wind} & \multirow{2}{*}{Still water} \\  & & heave & & & & \\ \multirow{2}{*}{IV} & Free decay, & \multirow{2}{*}{Roll} & \multirow{2}{*}{+8 deg} & Platform and & \multirow{2}{*}{5 min} & \multirow{2}{*}{No wind} & \multirow{2}{*}{Still water} \\  & roll & & & & & & \\ \multirow{2}{*}{V} & Free decay, & \multirow{2}{*}{Pitch} & Platform and & \multirow{2}{*}{5 min} & \multirow{2}{*}{No wind} & \multirow{2}{*}{Still water} \\  & pitch & & & & & & \\ \multirow{2}{*}{VI} & Free decay, & \multirow{2}{*}{Yaw} & Platform and & \multirow{2}{*}{15 min} & \multirow{2}{*}{No wind} & \multirow{2}{*}{Still water} \\  & yaw & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Properties of load cases in the free-decay test; DOFs: Degrees of Freedom. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Terms**} & \multirow{2}{*}{**Surge**} & \multirow{2}{*}{**Sway**} & \multirow{2}{*}{**Heave**} & \multicolumn{3}{c}{**Roll**} & \multicolumn{3}{c}{**Pitch**} & \multicolumn{3}{c}{**Yaw**} \\ \cline{4-10}  & & & & **SPM** & **MPM** & **SPM** & **MPM** & **SPM** & **MPM** \\ \hline \multirow{2}{*}{MAP++} & Natural period (s) & 112.3 & 112.5 & 17.4 & 26.3 & 25.6 & 26.2 & 25.6 & Infinite & 79.0 \\  & Damping ratio (\%) & 4.32 & 4.26 & 3.65 & 3.12 & 3.20 & 3.13 & 3.30 & 0.00 & 1.83 \\ \multirow{2}{*}{MoorDyn} & Natural period (s) & 113.4 & 113.5 & 17.4 & 26.3 & 25.8 & 26.2 & 25.8 & Infinite & 79.3 \\  & Damping ratio (\%) & 4.87 & 4.84 & 3.65 & 3.12 & 3.34 & 3.13 & 3.35 & 0.00 & 2.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis results of the free-decay test. Figure 3: Cont. Figure 3: Time histories of the free-decay test: (**a**) surge decay; (**b**) sway decay; (**c**) heave decay; (**d**) roll decay; (**e**) pitch decay; (**f**) yaw decay; SPM: Single-Point Mooring, MPM: Multiple-Point Mooring.
## 2 Materials and Methods
### Particle Swarm Optimization (PSO) Algorithm  
Particle Swarm Optimization (PSO) is an evolutionary computation technique inspired by the study of bird flock foraging behavior. The core idea of PSO is to utilize the information sharing among individuals within a group to produce an evolutionary process from disorder to order in the problem-solving space, thereby obtaining feasible solutions to the problem.  
In the PSO algorithm, each particle in the search space can be regarded as a solution to the actual problem. The quality of the solution is reflected by the fitness value calculated using a fitness function; a higher fitness value indicates a closer proximity to the optimal solution. Each particle has a random initial position and velocity. Based on its individual historical best and the group's historical best information, the particle searches for the optimal position in the space. Through continuous iteration and updates, the particle swarm is able to converge towards the global optimal position, thereby obtaining the optimal solution to the problem (for references, see acoustic emission research). The particles update their velocities and positions according to specific formulas.  
\[v_{i}^{k+1}=\omega\times v_{i}^{k}+c_{1}\times r_{1}\times\left[x_{best^{(i)}} ^{k}-x_{i}^{k}\right]+c_{2}\times r_{2}\times\left[x_{best}^{k}-x_{i}^{k}\right] \tag{2}\]  
\[x_{i}^{k+1}=x_{i}^{k}+v_{i}^{k+1} \tag{3}\]  
In the formula provided, \(v_{i}^{k}\) and \(x_{i}^{k}\) represent the velocity and position of the i-th particle during the \(k\)-th iteration, respectively. Additionally, \(pi(k)\) and \(g(k)\) denote the historical best position of the \(i\)-th particle and the global best position of the entire swarm during the \(k\)-th iteration, respectively. The inertia weight is represented by w, while \(c1\) and \(c2\) are the individual and social learning factors, typically ranging between [0, 2]. Both \(r1\) and \(r2\) are random numbers generated within the range [0, 1]. To ensure that particles conduct effective searches within a certain space during the search process, the particles' positions and velocities need to be constrained within specific ranges, namely [\(-x_{max}\), \(x_{max}\)] and [\(-v_{max}\), \(v_{max}\)], respectively. These constraints help to prevent particles from straying too far from promising regions of the search space and ensure that the search remains focused and efficient.  
The main implementation steps of the Particle Swarm Optimization (PSO) algorithm are outlined in the following Figure 5:
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Network Architecture  
Another way to categorize routing protocols in WSNs is to classify them as data-centric or position-centric (geo-centric) routing protocol based on the network architecture. In the following, we detail those two classes.
## 6 Simulation Analyses  
We carried out simulation analyses of scenarios in which the number of satellite orbital elements is changed under continuous low thrust, given a satellite with an initial orbital altitude of 380 km and a continuous low thrust applied at 12:00 on 25 January 2024. The thrust parameters are set with reference to Starlink V2.0 mini. The thruster specific impulse is 2500 s, the electric thrust magnitude is 170 mN, and the thrust acceleration is 2.26 \(\times\) 10\({}^{-4}\) m/s\({}^{2}\). The initial orbital elements of the satellite ignition point are shown in Table 2, and other simulation parameters were set as in Table 1.  
Assuming that radar equipment located at different locations can acquire satellite measurements, information on the distribution location of radar equipment is shown in Table 3. It is assumed that the radar distance measurement accuracy is 30 m (1\(\sigma\)), the angle measurement accuracy is 0.01\({}^{\circ}\) (1\(\sigma\)), and the minimum elevation angle is 5\({}^{\circ}\). The simulation gives radar measurements of the satellite (azimuth, elevation, and range).  
The thrust acceleration results are derived from the inverse solution of two radar observations separated by a certain length of time. Let the initial moments of the two radar observation arcs be \(t_{10}\), \(t_{20}\), and the ending moments be \(t_{1k}\), \(t_{2k}\), and the observation durations be \(\Delta t_{1}\), \(\Delta t_{2}\). The time intervals between them are denoted as \(\Delta t\). The length of the visible arc segment at a single station varies from 3 to 9 min depending on the different positions of the satellite in relation to the station. In order to ensure the accuracy of the semi-major axis solution, only arc segments with observed arc segments greater than 6 min were selected for orbit determination.
## 3 Results and Discussion
### Cyclic Stability  
To test the reusability of the GO/P (AA-co-AM) hydrogels, the four metal ions were adsorbed using the hydrogels, and the adsorbed hydrogels were desorbed using 0.1 mol\(\cdot\)L\({}^{-1}\) of HCl solution. The adsorption-desorption experiment was repeated three times. The adsorption capacities of the desorbed hydrogels were obtained, as shown in Figure 10. From Figure 10, it can be determined that the adsorption capacity of the GO/P(AA-co-AM) hydrogels for the four metal ions decreases to different degrees with the increase in the number of repeated adsorption-desorption cycles. This is because the individual metal ions are more strongly bound to the GO/P(AA-co-AM) hydrogel during the adsorption-desorption process, and this active site is occupied and not resolved during the desorption process. Therefore, when the metal ions adsorbed with the GO/P(AA-co-AM) hydrogel are reused, the previously occupied active site cannot absorb the metal ions again. Therefore, the adsorption capacity of the GO/P(AA-co-AM) hydrogels was reduced. In addition, the GO/P(AA-co-AM) hydrogels were filtered and dried in repeated adsorption-resolution experiments, so the graphene oxide hydrogels were always in the swollen and dried states. The frequent switching of these two states caused a certain degree of damage to the three-dimensional mesh structure inside the hydrogel, which also led to the degradation of the adsorption performance of the graphene oxide hydrogel. After three  
The internal structure mechanism of GO/P(AA-AM) hydrogel is shown in Figure 11. By cross-linking acrylamide hydrogels and doping graphene oxide in the hydrogels, the monosubstituted olefins of graphene oxide can form rings with acrylic acid and acrylamide, thus achieving a three-dimensional mesh structure, while graphene oxide can be successfully cross-linked in the hydrogels. The free water in the hydrogel can adsorb metal ions during the hydrogel swelling process. The carboxylic acid functional group and hydroxyl functional group on graphene oxide can adsorb metal ions through ion exchange.
## 3 Results
### Atomic Force Microscopy  
Three-dimensional images obtained with AFM in the apical area show the homogenous surface structure of both experimental materials (Figure 4).  
E1-DCR samples present a complex topography with smooth dentin and the sealing layer (Figure 4a). A high number of dentimal tubules are completely covered by the sealer with a maximum penetration depth of 2.8 um. The profile line shows how the sealer appropriately moistens the edges of the dentimal tubules, reducing their diameter and forming a sealing foil.  
In E2-BC samples the dentimal tubules are better highlighted and the sealing material structures are less visible (Figure 4b). This indicates a better flow of the pellicle, ensuring the surface homogeneity and also an improved penetration into the dentimal canals, up to 4.2 um.  
Figure 3: Representative SEM images of commercial sealers (magnification 1000X): Real Seal (**a**,**b**); TotalFill BC (**c**,**d**); RS- RealSeal, TF-BC- TotalFill BC, HL-hybrid layer, D-dentin.  
Figure 3c shows results in case of RS samples, where the most significant canal diameter reduction was encountered. This can be correlated to a higher viscosity of the sealer, allowing the formation of a thicker layer and ensuring a better seal. The depth of penetration in this case had a mean value of 2.5 \(\upmu\)m.  
On tridimensional images in case of TF-BC samples, a topography with highlighted dentinal tubules surrounded by sealer was observed. The sealing was obtained with very low diameter reduction and higher penetration (3.8 \(\upmu\)m).  
A significant statistical association was observed between the diameter of the dentinal tubules and the sealer width (Table 1). In case of resin-based endodontic materials (both experimental and commercial), a direct proportion was observed between the diameter of the dentinal tubules and the sealer width. In case of bioceramic-based materials, a reduced sealer width was observed, meaning a decreased penetration into the dentinal tubules.