esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Security and Privacy Risk Assessment
### Security and Privacy Risk Assessment at the Requirements Analysis Phase
#### 8.3.2.1 Determine Impact  
Impact refers to the extent to which a threat event might affect the application. Impact assessment criteria may include:  
* [noitemsep,topsep=0 pt]
* Harm to user health and organization reputation.
* Operational impacts.
* Financial loss.
* Reputational harm.
* Loss of assets.  
The assessor team also needs to consider the asset's valuation while calculating the impact score of a threat. An asset's valuation will include the importance of that asset to fulfil the business objectives, the replacement value of the asset and the business consequences due to the asset being lost or compromised. For example, a physical attack on a sensor device or a database will have a different impact on business operations. A physical attack on a sensor will only compromise that particular sensor device. If the database is compromised and data are lost, then it will have a much larger impact on financial, reputation, regulatory consequences and the operation of the application. Table 5 outlines the assessment scale for calculating impact scores.  
Figure 8: Steps to conduct risk evaluation and risk treatment.  
Table 6 illustrates an example for identifying the impact level of a physical attack on a sensor node. During the calculation, the impact level value is assigned to each impact factor and then the average is calculated.
## 10 Conclusions  
The resilience of power systems must be considered with great concern, as power outages in contemporary economics lead to huge losses. Modern power systems face a wide range of risks related to cyberattacks or even non-technical factors such as geopolitical tensions. The rapid spread of generation from renewable power sources ensures the decreased inertia of the systems. The factors mentioned increase the chances for power systems to suffer serious and unfavorable events causing desynchronization, islanded mode operation, or even blackouts, and decrease their capabilities to withstand these. The issue must be handled comprehensively, from identifying a power system's capability to remain stable during these events and strengthening cyber-security, to the application of the technical means to increase its stability.  
The results of this research have proved that the analyzed system is capable of withstanding desynchronization and remaining stable during disturbances in the islanded mode.  
Nevertheless, the ongoing growth of generation from renewable power sources, particularly in real-world scenarios with restricted availability of some turbo- or hydropower units or HVDC links, requires the consideration of means for further reliability improvement and desynchronization mitigation. Improving the stability of the system could be achieved by increasing the inertia of the system and by decreasing of its impedance. This could be achieved by entering additional turbo- and hydropower units, synchronous compensators, and energy- and power-storage devices, and by strengthening links of the transmission networks. As all these means require substantial financial investments, the improvement of the system's reliability brings additional research tasks to identify the optimal mean, its optimal amount, and the optimal installation place.  
The reliability of the system during desynchronization and operation in islanded mode could be assured by proper preconceived analysis and planning and proper real-time situation handling. The presented sequence of actions (algorithm) reveals the main aspects which must be managed during non-planned EPS desynchronization and operation in islanded mode (and how they are to be managed) and could be easily adapted to any particular power system.  
Conceptualization, R.D. and M.K.-J.; Methodology, R.L. and R.B.; Software, R.D. and R.B.; Validation, R.L. and M.K.-J.; Formal analysis, R.D. and R.L.; Investigation, R.D., R.L., R.B. and M.K.-J.; Resources, R.L., R.D. and M.K.-J.; Data curation, R.L. and R.B.; Writing-Original draft preparation, R.D. and R.L.; Writing-Review and editing, R.D., R.L. and M.K.-J.; Visualization, R.D., R.L., R.B. and M.K.-J.; Supervision, R.D. and R.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Experimental Investigations on Laser Ablation of Aluminum in Sub-Picosecond Regimes
###### Abstract  
Due to high power and ultrashort pulses, femtosecond lasers excel at (but are not limited to) processing materials whose thicknesses are less than 500 microns. Numerous experiments and theoretical analyses testify to the fact that there are solid grounds for the applications of ultrafast laser micromachining. However, with high costs and complexity of these devices, a sub-picosecond laser that might be an alternative when it comes to various micromachining applications, such as patterns and masks in thin metal foils, micro-nozzles, thermo-detectors, MEMS (micro electro-mechanical systems), sensors, etc. Furthermore, the investigation of sub-picosecond laser interactions with matter could provide more knowledge on the ablation mechanisms and experimental verification of existing models for ultrashort pulse regimes. In this article, we present the research on sub-picosecond laser interactions with thin aluminum foil under various laser pulse parameters. Research was conducted with two types of ultrafast lasers: a prototype sub-picosecond Yb:KYW laser (650 fs) and a commercially available femtosecond Ti:S laser (35 fs). The results show how the variables such as pulse width, energy, frequency, wavelength and irradiation time affect the micromachining process.  
sub-picosecond regime; femtosecond pulses; precise micromachining; laser ablation; ablation threshold; micromachining of aluminum +
Footnote †: journal: Applied
## 4 Results and Discussion
### Analysis of the Friction Coefficient  
Figure 8 shows the friction coefficient variation of different surface friction pairs with the test time at different speeds. The friction coefficient of the smooth surface first plunges or fluctuates (from 0.06 to 0.013-0.025) during the initial 300 to 500 s, then gradually increases to 0.02-0.04 in the following 5000 s, and finally stabilizes until the end of the test. During the running-in process, the friction coefficients at 1000, 1250, and 1500 r/min fall directly to a stable value in the initial 300 s, and the friction coefficient at 1750 r/min oscillates to a peak value of 0.14 in the initial 500 s. The friction coefficient of the hemispherical-pit surface plunges or fluctuates (from 0.05 to 0.02-0.035) during the initial 200 to 1000 s, then slightly decreases and is stable at 0.01-0.032. The running-in period at 1000, 1250, and 1500 r/min is 200 s, and that at 1750 r/min is extended to 1000 s, with a peak value of 0.18. A higher sliding speed makes the micro-asperities on the upper and bottom specimen surface collide more fiercely during the running-in process under the same normal load, which produces the outstandingly high friction coefficient. The hemispherical-pit surface exhibited a lower friction coefficient, but experienced a more drastic relative motion at high speed.  
Next, we should calculate the average friction coefficient and standard error of different surface friction pairs at each speed. The curve relationship of the average friction coefficient with an error-bar with the speed is illustrated in Figure 9. As the rotation speed increases, the average friction coefficient of the smooth surface increases in a fluctuating manner, while that of the hemispherical-pit surface increases monotonically. According to the classic Stribeck curve (Figure 10), the smooth surface is in the transition state of mixed lubrication to hydrodynamic lubrication, while the hemispherical-pit surface is in  
Figure 8: Variation in the friction coefficient with the test time for different friction pairs: (**a**) smooth surface; (**b**) hemispherical-pit surface.  
Figure 7: Histogram of the bearing capacity for different types of friction pairs.  
the hydrodynamic lubrication state within the test speed range. This curve shape and development trend are consistent with those of the friction pairs--the upper specimen is made of 316L and the bottom specimen is made of CF/PEEK--under the same working conditions as those in [33], but the values decreased by 0.03-0.04 due to the specimen material interchange. The hemispherical-pit surface produces the hydrodynamic lubrication effect (as described in Section 4.1) which promotes the bearing capacity of the seawater film and increases the thickness of the seawater film, making both its average friction coefficient and standard error lower than that of the smooth surface. When the seawater film thickness exceeds the height of two surface roughness peeks, the full fluid lubrication is realized and the friction coefficient is decreased [32].
###### Abstract  
Micro-grid (MG) deployment has dramatically become more popular with the high penetration of renewable energy resources (RER). This trend brings with it the merits of independent power grid clean energy resource-based systems, and simultaneously the demerits of an unstable grid due to the intermittent nature of RER. Control and monitoring of MG through robust and ubiquitous communication system infrastructure is the solution to overcoming this intermittency. There has been an increasing focus in recent years on using wireless communication technologies as a prominent candidate in holistic proposal for the micro-grid management system (MGMS). The MGMS has been developed using the multi-agent system (MAS), multi-micro-grid (multi-MG), Internet of things (IoT) integration, and cloud concepts requiring new communication specifications, which can be satisfied by next-generation wireless technologies. There is, however, a lack of comprehensive corresponding investigation into management levels of MG interaction requirements and applied communication technologies, as well as a roadmap for wireless communication deployment, especially for the next generation. In this paper, we investigate communication technology applications in the MG and focus on their classification in a way that determines standard gaps when applying wireless for MG control levels. We also explore and categorize the literature that has applied wireless technologies to MG. Moreover, since MGMS has been revolutionized by attaching new technologies and applications to make it an active element of the power system, we address future trends for MGMS and offer a roadmap for applying new enhancements in wireless technologies, especially the fifth generation (5G) of wireless networks with its exclusive characteristics, to implement this novel approach.  
micro-grid; energy management system; IEC 61850; 5G; LoRa; multi-agent system +
Footnote †: journal: _env
## 5 Results
### Simulation Experiment Capture Detection  
The UR5's 3D model was imported into the ROS control system \"Moveit\" to establish a robotic arm control system, with configuration parameters automatically generated by algorithms. Subsequently, the Gazebo simulation environment incorporated 3D models of objects and cameras to create a grasping simulation test platform. Additionally, the grasp detection model was integrated into the ROS system for conducting simulation experiments. Figure 12 illustrates simulated capture and detection scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Method** & **Input** & **Accuracy (\%)** & **Times (ms)** \\ \hline Depierre [24] & Jacquard & RGD & 93.6 & - \\ [PERSON] [5] & GG-CNN2 & D & 84 & 19 \\ [PERSON] [6] & GRCNN & RGB & 91.8 & 19 \\ \hline Ours & LGAR-Net2 & RGB & 94.4 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Jacquard dataset experimental results.  
Figure 11: (1)–(2) are the comparison results on the jacquard dataset, (3)–(4) are the comparison results on the Cornell dataset.  
The method for robot grasping detection proposed in this paper is utilized for the purpose of grasping and detecting objects within a simulated environment. The simulation results for the detection of various objects are presented in Table 3.  
The analysis of the experimental results indicates that the pose detection accuracy of the network in this paper is up to 93.3%, which is slightly lower than the maximum accuracy of the model training, but it also demonstrates good detection performance. Utilizing the grasping pose detection algorithm designed in this paper, the robot can basically complete the grasping task in the simulated scene. Across various target objects and diverse object placements, the machine consistently achieves successful grasping actions, underscoring the algorithm's robust adaptability to novel scenes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Object** &
\begin{tabular}{c} **Grasp Pose Detection** \\ **Accuracy (\%)** \\ \end{tabular} & **Success/Attempt** & **Accuracy (\%)** \\ \hline Coke Bottle & 87.5 & 13/15 & 86.7 \\ Wood Block & 88.1 & 13/15 & 86.7 \\ Screwdriver & 93.5 & 14/15 & 93.3 \\ Remote & 90.1 & 13/15 & 86.7 \\ Hexagon Nut & 93.2 & 13/15 & 86.7 \\ Weight & 88.7 & 13/15 & 86.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of simulated grasping experiments.  
Figure 12: The simulated grasping scene in ROS system; the first row is the lateral and above view, and the second row is the depth image and RGB image taken by the depth camera.
# Influence of Cutter Head on Cavitation of Non-Jammed Submerged Grinder Pump
## 5 Experimental Verification  
### Cavitation Phenomenon  
The grinding effect test of the grinding pump was carried out in a pool with a transparent window. The test pool is shown in Figure 20a. During the test, the prototype was positioned in the pool, and the water in the pool was deep enough to cover the motor. The camera recorded the process of grinding debris through the transparent window. The outlet of the grinding pump was covered with a flange plate with a small hole. The hole diameter was determined via calculations, and the small hole diameter was used to control the flow rate during the prototype test. The back of the flange plate was wrapped with a filter screen to collect the sundries discharged from the grinding pump. After the grinding pump was started and ran stably, the grinding test objects were placed in the inlet area. The sundries were sucked into the submersible grinding pump for grinding and discharge.  
In order to limit the size of the particles passing through the submersible grinding pump, the flow area where the cutter head and the static cutter head cooperate was composed of several semicircular flow channels and radial gaps between the dynamic and static cutter heads. The moving cutterhead rotated at high speed with the pump shaft, and the liquid velocity near the cutter head was relatively large. Hence, cavitation was prone to occur near the cutter head; it had a bad influence on the inlet flow state of the grinding pump, thereby affecting the performance of the grinding pump. This phenomenon could be clearly seen in the process of the grinding effect test, as shown in Figure 20b, which was the cavitation phenomenon observed during the test. It was accompanied by a \"buzz\" sound.  
Cavitation near the cutter head can cause cavitation damage to the cutter head and affect the normal operation of the grinding pump. The impact of this is multi-faceted. Firstly, cavitation can interfere with the exchange of liquid energy in the grinding pump, causing a decrease in the external performance of the pump. The characteristic performance is decreased, or the flow may even be cut off. Secondly, cavitation will lead to large pressure  
Figure 20: Cutter cavitation test. (**a**) test water tank, (**b**) Cutter cavitation.  
pulsation, which is likely to induce vibration and noise in the grinding pump. Moreover, the bubbles generated by cavitation could suddenly collapse, and the liquids collide with each other in an instant. The metal surface of the cavitation part would be destroyed if this occurred.
## 2 Materials and Methods
### Sample Preparation and Metabolomics Analysis  
Extraction and derivatization of metabolites were performed according to the method previously described in [12], with slight modifications. Briefly, 50 mg of freeze-dried samples were weighed into a centrifuge tube and 1 mL of precooled 100% methanol was added. Twenty microliters of 4-chloro-L-phenylalanine (1.5 mg/mL in water) were added as an internal standard (IS). After vortexing and homogenization, the mixture was ultrasonically extracted for 30 min and then centrifuged at 11,000\(\times\)\(g\) for 10 min. The supernatant was pipetted into another centrifuge tube, then 300 \(\mu\)L of chloroform and 600 \(\mu\)L of deionized water were added. After centrifugation at 2200\(\times\)\(g\) for 15 min, the supernatant was collected and dried using a vacuum concentrator without heating. The methoxime-derivatization process was performed by adding 40 \(\mu\)L of methoxamine hydrochloride (20 mg/mL in pyridine), followed by shaking at 37 \({}^{\circ}\)C for 2 h using a thermomixer. Then, 70 \(\mu\)L of N-Methyl-N-trimethylsilyl trifluoroacetamide (MSTFA) was added and the mixture was incubated at 37 \({}^{\circ}\)C for 30 min. Afterward, the prepared samples were transferred to glass vials. For each sample, six biological replicates were prepared.  
Meanwhile, a quality control (QC) sample was prepared by pooling the same amount (10 g) of all the investigated samples. The QC sample was regularly inserted into the analytical sequence of the actual samples to monitor the reproducibility of the analytical method and check the potential contaminations in the laboratory.  
Gas chromatograph-mass spectrometer (GC-MS) analyses were performed using a GCMS-TQ8040 (Shimadzu, Kyoto, Japan) coupled with a DB-5 fused silica capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m; Agilent Technologies, Santa Clara CA, USA), and the instrument parameters were set, as previously described [12].
## 6 Prediction of Concrete Shrinkage & Creep Effects
### Internal Force  
The predicted long-time change percentages of hanger forces are shown in Figure 15. The hanger forces will decrease continually. The reduction percentages of side-span hangers are bigger than the ones of mid-span hangers, and the ones of side-span hangers close to the towers are the most significant, with the value of \(-9\%\) after 50 years.  
The predicted changes of the hanger and cable forces at middle mid-span DS19 are shown in Figure 16. The internal forces of hanger and cable at DS19 will decrease by 4.2% and 4.6% after 50 years, respectively. The change rates will tend to be 0 approximatively, namely the cable system becomes more and more stable.  
Figure 16: Predicted changes of hanger and cable forces at DS19.  
Figure 14: Predicted long-time changes of girder alignments.  
Figure 15: Predicted change percentages of hanger forces.  
The prediction results that were obtained by the proposed method can be used to provide appropriate feedback to the construction control process for the self-anchored suspension bridge. Reasonable amounts of the pre-lift of mid-span girder and deviations of towers towards side-span can be determined to offset the adverse concrete S&C effects during long-term service life. A parametric study on the influence of the final girder alignment in the stress and geometry evolution of the girder was conducted. This study was accomplished by changing the hanger forces to provide different final geometries to the main girder. The change percentages of the global hanger forces were set as 1%-5%. The completion state of bridge after equilibrium analysis and the girder state evolution at mid-span section CS5 after 50 years are shown in Table 6, where the longitudinal stress values of the plate regions close to the first outer web were extracted to compare. The results show that appropriate pre-lift of girder at mid-span can effectively allocate the girder compressive stress reserves, the compressive stress reserves after hanger force adjustments of the top plate decreased, and the ones of bottom plate increased. Then, the compressive stresses after 50 years of the top plate will decrease in a safe range, and the ones of bottom plate will increase. The adverse influences brought by the concrete S&C in structural safety were reduced by the appropriate hanger force adjustment. In addition, the adjustment of the curvature of main girder should be conducted in a reasonable range to avoid the excessive bending moment in main girder.
## 4 Performance Evaluation
The DNN-IoT-BA performance was  
Figure 4: Network Connectivity vs. network density. (**a**) 30 kph; (**b**) 40 kph; (**c**) 50 kph. Figure 5: Cumulative distribution function (CDF) of successful connection (100–300 Vehicle units (VU)). efficient overall, due to the presence of IoT-BA, compared with the DNN and ANN, as the average minimum latency rate showed a lower failure rate. The results of the delivery ratio of packages with different car speeds and densities are given in Figure 7. As the transmitters failed to transfer the packets to the neighbouring VU, the packet delivery ratio was affected by a link failure due to increasing speed. Therefore, the connection breaks and the link with increasing network density exists. The break in connection had a significant influence on the packet loss stimulus rate, and thus on performance. The collision of the vehicle packets increased the packet loss rate with an increased vehicle density and resulted in a decreased functionality in the overhead network. The simulation results show that the DNN-IoT-BA was delivered with higher feasibility than DNN and ANN. The results of the data with different data rates are displayed in Figure 8. The data rates vary according to the density of the network and the speeds of the vehicle. As a result of the simulation, the maximum output was achieved at the borders of roads,  
Figure 6: Impacts on average latency with variable velocity. Figure 7: Impacts on packet delivery rate with variable velocity. as the vehicle moved in the direct and curved road sections with minimum speed and minimum throughput. The DNN-IoT-BA also enhanced the network performance compared to other methods with the optimal choice of vehicles in road segments. The optimal selection reduced the packet loss rate, and the selection of the optimally adjacent vehicle hops effectively maintained the transmission connection. Due to the absence of an IoT-BA--in which vehicles can be used for determining a connection, and thus strictly prevent energy consumption by linking reliability--a throughput decrease was found in DNN and ANN. The determination of neural network parametric values by the BA assisted in improving the DNN optimization by finding the routes for the packets to traverse across the network.