esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 4 Blockchain Copyright Protection Model Based on Vector Map Unique Identification
### Copyright Registration Process
#### 4.1.2 Unique Identification Construction  
In this paper, we define the unique identifier as a fixed-length binary sequence composed of information values represented by 0 and 1. Utilizing the quantized index of geometric features from the vector map dataset and information values obtained through topological feature quantization identifiers, the precise procedure for constructing the unique identification unfolds as follows:  
Step 1: Retrieve the vector map file containing the watermark and iterate through all geographical features within the dataset. Employ the Douglas-Peucker (DP) algorithm to extract feature points of vector maps, thereby improving the robustness of subsequent unique identification construction processes.  
Step 2: For each geographical feature, construct the Delaunay triangulation \(T\) and obtain all circumcircles of triangles adjacent to the coordinate point \(P_{i}\). Record the smallest radius of the circumcircle corresponding to the angle \(\theta\) of the triangle and map this angle value to a hash value, thereby achieving a quantitative representation of geometric relationships. Consequently, abstract the relationships between features into indices of unique identifiers.  
\[FI_{i}=Hash(key\times AF_{i})\%FI_{len} \tag{15}\]  
where _Hash_() is the normalized hash function, _key_ is the encryption key, \(AF_{i}\) represents the most significant bit of the angle feature parameter of the \(i\)-th feature, \(FI_{len}\) is the length of the identification sequence, and \(FI_{i}\) is the unique identification mapping index obtained from quantizing the \(i\)-th feature.  
Step 3: For every geographical feature, determine the distance to the nearest dissimilar feature based on Equation (4), and establish a distance matrix accordingly. Compute the average of the distance matrix using Equation (7) to attain a comprehensive assessment of spatial correlation.  
Step 4: By contrasting the distance from each geographical feature to the closest dissimilar counterpart with the mean of the distance matrix, encode the correlation between geographical features in binary format as per Equation (8). Distances exceeding the mean value are denoted as 1, whereas those falling below the mean value are denoted as 0, thereby finalizing the generation of unique identifiers.  
Step 5: Leverage the unique identifiers, watermark information, user details, timestamps, and additional data as transaction inputs. Utilize smart contracts to record the information into the unique identification repository on the blockchain, thereby finalizing the copyright registration procedure.
## 2 Lateral-Line System and Artificial Lateral-Line System
### Physical Device of Artificial Lateral-Line System Suitable for the Underwater Robot
#### 2.2.2 Introduction of the Artificial Lateral-Line System of Underwater Robot  
In this paper we mainly focus on the estimation of the interference force and torque of the water environment to the robot by using the artificial lateral-line system. Specifically, the pressure-sensor array is used to measure the pressure at different positions of the underwater vehicle. The hydraulic information is then deduced and the external disturbance force of the robot is estimated, according to the value and position of the pressure at each point and the relationship between the pressure at each point.  
A micro-pressure sensor is chosen to collect the pressure at the key points of the robot body. Moreover, a total of 9 pressure sensors are installed on both sides and the nose tip of the robot to form a pressure-sensor array, in order to establish an artificial lateral-line  
Figure 3: Sketch of the underwater robot. The six thrusters are labeled as 1–6.  
system on the robot's body. Specifically, the model of the commercial pressure sensor is ms5803-01 ba from TE Connectivity Ltd. The resolution of the pressure sensor is 1.2 PA and it is sensitive to tiny hydrodynamic pressure variations in water.  
The nine pressure sensors are labeled as \(P_{0}\), \(P_{L1}\), \(P_{L2}\), \(P_{13}\), \(P_{14}\), \(P_{R1}\), \(P_{R2}\), \(P_{R3}\) and \(P_{R4}\). They represent the node of the nose, the four nodes on the left and the four nodes on the right. The position of the pressure sensors at the nose tip and both sides of the robot is shown in Figure 4. The pressure data are read to the MCU through the IIC bus, and then sent to the computer onshore for further storage and analysis through the wired RS485 serial port.
# The Role of Delays in the Performance of Blasting
## 4 Discussion  
The results of the experimental campaign were analyzed by observing operational KPIs. One of the most important parameters is specific priming (SP); that is, the density of delay per unit of blasted rock. SP was related to parameters associated with the quality of fragmentation resulting from the detonation.  
Recent research has addressed the topic of delays in blasting. Studies on crack growth Refs. [19; 20] suggest that delayed initiation allows enough timing for preconditioning the surrounding rock for neighboring blastholes. The study in [21] considered the initiation sequence of a row of holes, comparing one-directional initiation to initiating the row from the extremities toward the center. It was found that initiating blastholes from the center to both ends of the row can produce a more uniform size of fragmented rocks. Additionally, Ref. [22] discussed fracture creation, considering rock mass joints; for simultaneous holes, they suggested that cracks propagate along the lines between boreholes with larger filled joint strengths. Furthermore, Ref. [23] studied crack formation and damage to the surrounding rock, considering shaped charge form and short time delay blasting schemes. In the study by [24], which focused on no delay (simultaneous blasting), it was indicated that the number of simultaneous blastholes has a much smaller influence on vibrations than site constants.  
Figure 11: The higher the specific priming, the smaller the particle size and thus the lower the electricity costs at the primary crusher.  
Figure 12: Fewer holes detonating with the same or short delays between them distribute the explosive energy over time, resulting in reduced flyrock.  
The results of the present study suggest that simultaneous holes detonating at close distances, while not contour charges, still exhibited a cooperative effect along the line connecting the axes of the two holes. This cooperation caused the explosive energy to exert a shearing effect along the surface between the holes, resulting in the creation of fines and coarse fragments instead of effective fragmentation. Increasing the distance between simultaneous holes reduced the likelihood of creating a shearing effect, thereby directing the explosive energy to work along the line of least resistance (the burden) and promoting effective fragmentation. An increase in the density of simultaneous blastholes spaced more than 3 m apart resulted in better fragmentation, significantly reducing the need for secondary breaking by hydraulic hammer, bringing it close to zero. Transitioning from two to six simultaneous holes at a 2 m distance doubled the fines produced, increasing from approximately 10% to 20%.  
In general, increasing the specific priming enhanced the quality of fragmentation, reflected in lower values of fines, a smaller P80, and a lower impact of secondary breaking, along with a reduction in costs at the primary crusher. Notably, P80 was the most sensitive KPI, decreasing by 25% (from around 450 mm to approximately 360 mm) and doubling the specific priming (+220% from 0.0025 to 0.008).
## 2 Experimental Section
### Experimental Setup for Measuring Conductance  
The current (\(I\)) is proportional to the voltage (\(E\)) and inversely proportional to the resistance (\(R\)). The reciprocal of the resistance is called conductance (\(G\)). The conductivity (\(\kappa\)) can be obtained by multiplying the conductance by the distance between the electrodes (\(I\)) and dividing by the electrode area (\(A\)). Therefore, if the distance between the electrodes and area of the electrodes are the same under all the experimental conditions, the increase in conductance equals the increase in conductivity. In this study, the conductance was calculated using \(I\)-\(V\) curves, where the data can be easily measured using a potentiostat. The equipment used to measure the conductance is shown in Figure 1. It consists of four major components: a heating mantle, electrochemical cell, potentiostat, and data station. The temperature controller of the stirred heating mantle (Model MS-DMS; Misung Scientific Co., Ltd., Yangiu, Republic of Korea) was able to control the temperature with an accuracy of \(\pm\)1 \({}^{\circ}\)C; the temperature was double-checked using a K-type external temperature sensor to minimize errors. In the electrochemical cells, the additives were completely mixed with PCL polymer powder in glass vials (at an appropriate weight percentage for each additive) and thoroughly mixed on a digital hot plate stirrer before loading into a heating mantle for the conductance measurement. The heating mantle was preheated to 200 \({}^{\circ}\)C. After preheating, approximately 5 g of the polymer and additive mixture was added to the heating mantle. The conductance was measured by preparing a homogenous melt after achieving a stable state by maintaining the temperature in the heating mantle for 10 min. Measuring the conductivity of pure PCL and PCL with additives at high temperatures (approximately 200 \({}^{\circ}\)C, which is the actual temperature for melt electrospinning) using a commercially available conductivity meter has many limitations. Therefore, in this study, the conductance of the polymer solution was measured using a potentiostat. The \(I\)-\(V\) plot can be obtained from the linear sweep voltammetry (LSV) data. In the \(I\)-\(V\) plot, the slope of the line is equivalent to the conductance of the mixture. LSV analysis was performed on a CHI model 617E potentiostat (CH Instrument, Austin, TX, USA) equipped with two glassy carbon electrodes (GCE, a 2 mm diameter disk surface, purchased from Redox.me). GCE1 was used as the working electrode, and GCE2 was used as the counter and reference electrodes. The current flowing between the electrodes was measured by applying a voltage from 0 to 1 V. To obtain the conductance with the minimum error, the two GCEs were fixed at a distance of 5 mm using Teflon tape. All electrochemical tests were conducted in triplicate at 200 \({}^{\circ}\)C to ensure reproducibility.
# Influence of Construction Joint and Bridge Geometry on Integral Abument Bridges
## 3 Development of 3D Numerical Models  
The 3D FE models in Figures 4 and 5 were developed to predict the response of instrumented IABs to thermal loads; analysis was performed using ANSYS. For the superstructure, shell and beam elements represent deck slab and girders, respectively, as shown in Figure 4. For the substructure, shell elements represent the abutment and backwall and beam elements represent the piles, as shown in Figure 5.  
Figure 4: Cross-section of superstructure.  
Figure 5: Schematics of 3D numerical model: (**a**) bridge modeling and (**b**) integral abutment modeling.  
Bridge loads include backfill pressure, time-dependent effects, temperature load, and temperature gradient load. Backfill pressure applied to abutments is the traditional, linearly varying distribution with depth.  
The girder boundary conditions at the abutment were rigidly connected to the back-wall without bearings. At the internal piers, the superstructure was built continuously and supported by elastomeric bearings. The piles were rigidly connected to the bottom of the abutment and fixed at the pile tips.  
The FE models were implemented in a two-step analysis. In the first step, earth pressure behind the abutment was initially applied to consider an initial abutment displacement before backwall placement. Then, in the second analysis step, temperature- and time-dependent loads were applied after nonlinear springs of backfill-abutment interaction curves were set. Detailed analysis procedure can be found in the literature [15; 29; 41].
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 6 Risk Analysis
### High-Risk-Level Threats and Mitigation Strategies
#### 6.2.3 Tampering with Sensorfusion_to_Cluster_CAN  
**Security Risk**. Tampering with sensor fusion-to-cluster CAN communication is a security risk, since attackers with physical access to the vehicle could manipulate data,usually through the OBD. While the main intent of these data is to provide information for the display of the AEB system, malicious manipulation may mislead the driver into unsafe driving conditions. The financial and privacy impacts are negligible, but the operational impact is high, as it disrupts the function of the vehicle. Medium feasibility and moderate safety concerns set the overall risk as medium. **Security Measures**. To mitigate the risk of manipulation in sensor fusion-to-cluster CAN communication, secure communication protocols like TLS should be implemented to ensure data's integrity and confidentiality. The regular monitoring and auditing of communication channels can help detect anomalies or unauthorized access, maintaining the accuracy and reliability of the cluster display and ensuring the safe operation of the vehicle.
## 3 Identification of Bridge Damage Location under the Action of Mobile Vehicles
### Bridge Damage Conditions  
In this paper, the bridge damage unit is simulated by using the stiffness reduction in the bridge damage area, and the damage degree is expressed by \(\theta_{d}\). Six working conditions  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Symbol** & **Unit** & **Value** \\ \hline Body mass & \(m_{p}\) & kg & 2000 \\ Car suspension and wheel mass & \(m_{wi}\) (\(i=1.2\)) & kg & 100 \\ Stiffness coefficient of vehicle suspension & \(k_{wi}\) (\(i=1.2\)) & N/m & \(4.07\times 10^{5}\) \\ Vehicle suspension damping coefficient & \(c_{wi}\) (\(i=1.2\)) & N-s/m & 7509 \\ Wheel stiffness coefficient & \(k_{wi}\) (\(i=1.2\)) & N/m & \(1.56\times 10^{5}\) \\ Wheel damping coefficient & \(c_{wi}\) (\(i=1.2\)) & N-s/m & 1000 \\ The lateral distance between the wheel & \(d_{i}\) (\(i=1.2\)) & \(m\) & \(1\) \\ and the center of mass of the car body & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle parameters of the test vehicle.  
Figure 5: Simplified schematic diagram of tractor-testing vehicle (unit: m).  
It can be seen from the wavelet variation coefficients in Figures 8 and 9 that both wavelet coefficients have obvious sudden changes near the damage location. However, there is some interference near the damage location, which is not conducive to the accurate identification of the damage location and is easy to cause misjudgment. However, through the maximum successful approximation approach (MSAA) processing on the wavelet transform coefficients, the damage location can be accurately identified and misjudgment can be reduced. Figures 8 and 9 shows the wavelet coefficients after the maximum successive approximation processing.  
Figure 8: Bior4.4-d1 coefficient after MASS treatment.  
Figure 7: Wavelet transform coefficient (db20–d5) diagram.  
It can be seen from Figures 8 and 9 that the wavelet transform coefficient of the acceleration response difference in the detection vehicle has a large mutation at the damage position, and the damage position can be determined. Using MSAA method to process wavelet coefficients can accurately locate the damage location and reduce misjudgment. Under the finite element simulation calculation, the bridge damage detection method based on health data can accurately locate the single damage and multiple damage of a continuous beam bridge. In condition 6, it can be seen that the peak value of wavelet transform coefficient increases with the increase in damage degree.
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.4.3 Combining the Spatial and Channel Attention Gate  
We investigate the combined strategy of sAG and cAG. We present results on the CVC-ClinicDB dataset for polyp segmentation. Table 6 reported the performance of separate attention gates and combined attention gates and the number of parameters in the models. Remarkably, by integrating our proposed method to the baseline U-Net, the model complexity increases a very small fraction with only 0.22% of the parameters more. We observe that all attention gate methods boost the segmentation performance when integrated into the standard U-Net. The inclusion of sAG improves the performance over the baseline U-Net model, and integrating cAG is even more effective. The aggregation of both produces the best results when combining both sAG and cAG (i.e., our scAG).  
Figure 7 shows empirical results to identify the contribution of sAG and cAG. We can observe that sAG helps the model to better localize and refine the structure of the target object, but it still lacks semantic information. In contrast, cAG boosts the semantic concepts, but fails to refine the shape of the object. However, combining both provides the advantages of these behaviors and produces superior results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **No. of Parameters** & **Dice Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline U-Net & \(8637\times 10^{6}\) & \(65.93\pm 6.34\) & \(61.49\pm 7.30\) & \(70.00\pm 6.62\) \\ U-Net + sAG & \(8639\times 10^{6}\) (+0.025\%) & \(67.55\pm 6.97\) & \(63.65\pm 3.96\) & \(70.16\pm 8.01\) \\ U-Net + cAG & \(8654\times 10^{6}\) (+0.195\%) & \(70.49\pm 5.40\) & \(67.43\pm 9.38\) & \(\mathbf{76.07\pm 6.13}\) \\ U-Net + scAG & \(8656\times 10^{6}\) (+0.22\%) & \(\mathbf{71.72\pm 6.81}\) & \(\mathbf{69.81\pm 8.97}\) & \(74.21\pm 4.56\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance on the CVC-ClinicDB dataset by using separate attention gates (best results marked bold).  
Figure 6: Representative results from three datasets by using different U-Net architectures.
# Electrochemical Detection of Penicillin G Using Molecularly Imprinted Conductive Co-Polymer Sensor
## 2 Materials and Methods
### Chemicals  
The chemicals used are mentioned below with their reference:  
* Deionized water as solvent and rinsing liquid.
* Penicillin G (PenG) from Merck(tm): CAS 69-57-8 (C\({}_{16}\)H\({}_{17}\)N\({}_{2}\)NaO\({}_{4}\)S), (>96 wt.%), 356.38 g/mol.
* Pyrrole-3-carboxylate acid (PyCOOH) from Merck(tm): CAS 931-03-3 (C\({}_{5}\)H\({}_{5}\)NO\({}_{2}\)) (96 wt.%), 111.10 g/mol.
* Pyrrole (Py) from Merck(tm): CAS 109-97-7 (C\({}_{4}\)H\({}_{5}\)N) (98 wt.%), 67.09 g/mol.
* Sulfuric Acid from Merck(tm): CAS 7664-93-9 (H\({}_{2}\)SO\({}_{4}\)) (95 wt.%), 98.08 g/mol.
* Ammonium persulfate (APS) from Merck(tm): CAS 7727-54-0 ((NH\({}_{4}\))\({}_{2}\)S\({}_{2}\)O\({}_{8}\)) (>98 wt.%), 228.2 g/mol.
* Methanol (MeOH) from M Supelco(tm): CAS 67-56-1 (CH3 OH) (>99 wt.%), 32.04 g/mol.
* Hydrochloric acid from VWR Chemicals(tm): CAS 7647-01-0 (HCI) (>37 wt.%), 36.46 g/mol.
## 4 Materials and Methods
### Three-Dimensional RP-SAX-RP Systems  
Utilizing an isocratic pump, a range of ion pairing agents were administered to a 150 \(\upmu\)m I.D. capillary filled with 5 \(\upmu\)m of C18 resin. The running buffer was consistently held at a pH of 10.0 across the initial analytical column in the first dimension. Due to the adjustments made to the trapping and vent valves, the flow was channeled toward the secondary dimension pre-column. At the same time, an isocratic pump supplied a 1.2 \(\upmu\)L/min flow of pH10 buffer to support the transfer of peptides between the two-dimensional columns. A first-dimension reversed-phase (RP) column outlet had an anion exchange column connected to it. This column was composed of 150 \(\upmu\)m I.D. capillaries packed with 5 cm of SAX-NP5 resin, which was used for peptide elution with acetonitrile fraction injections maintained at a pH of 10.0. The peptides were then eluted from the column using varying quantities of ammonium formate and potassium chloride, also injected at a pH of 10.0. A trapping and vent valve directed the flow to the pre-column in the third-dimension column to transfer peptides between the second- and third-dimension columns. For 35.5 min, the isocratic pump provided 1.2 \(\upmu\)L/min of ammonium formate buffer at pH10.0 and a binary pump provided 10 \(\upmu\)L/min of 0.2 M acetic acid (to dilute the organic content and acidify the column effluent). Peptides were eluted by injecting acetonitrile fractions at pH 10.0, with or without ion-pairing agents. Both valve positions were altered post-trapping to create a split in the column, allowing the gradient elution (0.5-40% B in 60 min, A = 0.2 M acetic acid, B = acetonitrile with 0.2 M acetic acid) of peptides to a column (25 m I.D. capillary containing 50 cm of 5 mm RB C18 resin (Sepax, Newark, DE, USA) with an integrated 1 mm emitter tip), and into a mass spectrometer at a flow rate of 3-5 nL/min. The experimental design ensured the transfer of equivalent peptide complexities to the final column with each injection. Consequently, the organicluents used to elute peptides from the first to the second dimension typically contained low salt concentrations (70 mM ammonium formate or 10 mM KCl, pH 10), so that a subset of peptides could be simultaneously eluted to the final dimension column. During gradient elution, a binary pump supplied 10 \(\upmu\)L/min of 0.1% formic acid to dilute the organic content and acidify the effluent from the previous two dimensions. Furthermore, at a flow rate of 4-5 nL/min, the binary pump delivered acetonitrile and formic acid to the final column to elute the peptides. The fractionation experiment was completed, and the columns were regenerated with 90% acetonitrile, 1 M KCl in 10% acetic acid, 50% acetonitrile with 500 mM ammonium formate, and 900 mM ammonium formate in 10% acetic acid. In addition, three rapid organic gradients of 5 min were applied to the final dimension column.
## 3 Influence of Structural Parameters of the Magnetic Circuit on Magnetic Induction Intensity
### Effect of Cylinder Thickness on Magnetic Induction Strength  
The cylinder thickness was varied in the range of 5 to 9 mm, with intervals of 0.5 mm, while all other structural parameters remained constant. The simulation results are presented in Figures 15 and 16.  
Figures 15 and 16 illustrate that variations in the cylinder shell thickness have a minimal impact on the magnetic field distribution and magnetic induction intensity in each  
Figure 16: Plot of magnetic induction intensity clouds corresponding to different lengths of cylinder thickness.  
Figure 15: Simulation results of cylinder thickness with different lengths. (**a**) Plot of magnetic induction intensity versus distance; (**b**) plot of average magnetic induction intensity versus distance.  
region. Although the cylinder thickness shows a limited effect on the magnetic induction intensity of the damper, the magnetic induction intensity cloud diagram reveals that it does influence the distribution of the magnetic induction intensity at the cylinder barrel and magnetic core. An incremental adjustment in the wall thickness can lead to a more uniform distribution in the magnetic circuit of other structures. Considering the working environment demands on the roller cage shoe, where it must withstand substantial stress, an approximate thickness of 8 mm is deemed suitable.