esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The STAMP/STPA analysis yielded quantitative results, identifying 99 loss scenarios, 99 safety constraints, and 297 recommendations. The risk matrix approach categorized 12 scenarios as extreme risk, 7 as very high risk, 13 as high risk, 56 as medium risk, and 12 as very low risk. Furthermore, the analysis revealed that human factors accounted for 57.3% of causal factors, hardware for 18.6%, procedures for 12.4%, environment for 6.5%, and software for 5.1%. These quantitative findings provide valuable insights into the distribution of risk levels and the predominance of human factor-related risks in UAS integration for public safety applications.  
This study systematically applied the STAMP/STPA framework to analyze the potential risks associated with integrating UAS into existing public safety and law enforcement systems, a measure prompted by the challenges of rapid urbanization. The comprehensive analysis identified critical intersections between current UAS technology and public safety practices, detailing potential risks and safety constraints. STAMP/STPA proved to be a rational methodology, especially at the initial stages of implementation, where actual operational experience is scarce. By leveraging system theory and focusing on potentially unsafe control actions, STAMP/STPA offers a powerful framework for identifying hazards and suggesting mitigation measures early in the system development and deployment phases. The study also highlights the need for consistent guidelines and the importance of stakeholder engagement in the successful integration of UAS. Future research should explore emerging technologies, evolving regulatory frameworks, and the long-term effects of UAS deployment on public perception and effectiveness. By prioritizing safety, considering human factors, and actively engaging stakeholders, the integration of UAS into public safety and law enforcement operations can be achieved responsibly and effectively, ultimately enhancing public safety in rapidly urbanizing environments.  
Conceptualization, E.G.; Methodology, E.G.; Formal analysis, E.G. and J.-S.L.; Investigation, E.G.; Resources, J.-S.L.; Data curation, E.G.; Writing--original draft, E.G.; Writing--review & editing, H.-C.J. and J.-Y.L.; Supervision, H.-C.J. and J.-Y.L.; Project administration, H.-C.J. and J.-Y.L.; Funding acquisition, J.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT, Education) (No. 2018R1A5A7023490) and the Dongguk University Research Fund for 2022.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 4 Results
### Test Turbine 2
#### 4.2.2 Comparison of Different Blades (Same Turbine Type)  
Next, the effect of different turbine blades was evaluated by comparing sensors mounted at same positions but different blades. Each of Sensors \(S15\), \(S10\) and \(S29\) was mounted at one blade tip of Test Turbine 2 (see Figure 14, right). Fingerprints of Sensors \(S29\) and \(S15\) on Blades \(A\) and \(B\) overlap, i.e., all identified vibrations in the mid to high frequency range were detected by both sensors. In contrast, the fingerprint of Sensor \(S10\) (Blade \(C\)) slightly deviates and an additional vibration was detected (see Vibration 3 from the top for Sensor \(S10\)). A conclusion in favour of either a high sensitivity to measurement noise or different physical properties of Blade \(C\) is difficult due to the small size of the dataset. Besides the one additional vibration, characteristics of identified fingerprints overlap and indicate that there is no significant difference in blade properties at the blade tip for this test turbine. Again, joint vibrations in the lower part of spectrograms are not considered in detail due to measurement noise.
## 3 Results and Discussion
### Note on Toxicity and Interfering Substances  
The toxic response of Con A has been evaluated by [PERSON] [30], who showed that the systemic effects on the body would be dependent on the dose and route of administration. Converting results from rodents into an equivalent human dose suggests that subcutaneous injections (representative of a leaking biosensor) would result in a local inflammation (hemorrhagic skin lesions) of up to a concentration of 0.9 \(\times\) 10\({}^{-2}\) mg per kg. The equivalent amount released from an implantable biosensor used in the study would be 1.5 \(\times\) 10\({}^{-4}\) mg per kg (or 60 times smaller). In contrast, the intravenous LD\({}_{50}\) dose was reported to be 2.2 \(\times\) 10\({}^{-2}\) mg per kg. Given that some of the Con A could migrate from the implantation site and into the bloodstream, it is highly unlikely that a ruptured Con A based biosensor would cause any systemic toxicity. Furthermore, immobilisation of the Con A onto the sensor surface, as proposed in this study, should prevent any unwanted release of the lectin in the case of a compromised sensor.  
Being a sugar binding lectin, Con A, may not be able to discriminate between glucose, dextran and other carbohydrates in the surrounding medium since it has an affinity towards \(\alpha\)-mannosyl, \(\alpha\)-glycosl and \(\alpha\)-N-acetylglucosamineimyl groups. Carbohydrates are abundant in biology, and any competing ligand would play a detrimental role in the specificity of a Con A based glucose sensor. Studies performed by [PERSON] on key dietary components [48] showed that although competing carbohydrates exist in the human body, their concentration would generate error signals below the detection limit of conventional glucose sensors.
## 4 Experiment Results and Discussion
The one-hour output power stability of the laser in room temperature environment was within \(\pm\)1%. The experimental results above show that the thermal control system has a strong cooling capacity to keep the substrate temperature of the pump laser module at the setting value.The laser was able to work normally in both the room and high temperature environments, and when pump power of the laser changed, the temperature control circuit was able to adjust the output drive voltage on TEMs according to the measured temperature value, keeping the temperature of the pump laser at a stable and suitable value.
## 4 Evaluation over Use Cases
### ICT Gateway  
This scenario, developed in the context of the NET2 DG project [39], illustrates how the methodology can guide the security evaluation of complex systems composed of different subsystems.  
The ICT GW, depicted in Figure 6, is a critical component in a smart grid that serves as a bridge between the distribution system operator and a variety of data sources and  
Figure 5: Tools and mechanisms used for the specific scenario.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Maturity Level** & **Description** & **Key Characteristics** \\ \hline \multirow{2}{*}{Initial} & Basic assessments, no formal procedures & Manual assessments, minimal documentation \\ \hline \multirow{2}{*}{Defined} & Formal processes introduced, limited automation & Standard-aligned, structured but manual processes \\ \hline \multirow{2}{*}{Managed} & Automated tools for risk identification & Partial automation, basic metrics applied \\ \hline \multirow{2}{*}{Optimized} & Advanced metrics and continuous feedback & Iterative improvements, extensive metrics usage \\ \hline \multirow{2}{*}{Sustainable} & Fully automated and continuous evaluation & Real-time analysis, predictive metrics, automation \\ \hline \hline \end{tabular}
\end{table}
Table 8: Maturity levels for the security evaluation methodology.  
actuation subsystems, including smart meters, remote terminal units, and inverters (INVs). The ICT GW consolidates and standardizes the data collected from these heterogeneous subsystems, storing them in a central database for use by domain applications. These applications, operating within the DSO, aim to improve grid efficiency, maintain voltage quality, and support outage diagnosis. The ICT GW's architecture is organized into three layers, the adapters layer, the domain logic layer, and the service layer, each contributing distinct functions to manage data flow, ensure operational efficiency, and enable seamless integration within the grid.  
While the ICT GW enhances data interoperability and grid management, it also introduces potential security risks, particularly due to its integration with multiple external subsystems. Given that the ICT GW is commercialized, a robust security evaluation offers an additional assurance layer to DSOs. The ICT GW, including the three architectural layers, is considered the TOE.
## 2 MDD/GNSS Installations and Measurement Methods
### The Global Navigation Satellite System (GNSS) Measuring System  
The GNSS antenna at the rover station was positioned above the MDD using the Swift Navigation(r) (San Fracisco, USA) GNSS receiver (Piksi(r) multi evaluation kit model), while at the base station, it was placed above the column containing the MDD's main control unit (see Figures 4 and 5). The GNSS system provided coordinates based on the phase center of the antenna. The precise coordinates of the base station antennas were obtained through the virtual reference station (VRS) technique provided by the Korea National Geographic Information Institute (NGII).  
The relative positioning method, specifically the static base line method, has been identified as the most reliable and accurate approach for GPS positioning [19]. According to [PERSON] et al. [18], this method entails utilizing the carrier phase of the signal, at least two sets of antennas and receivers, data downloading software, baseline analysis (post-processing) software, and a computer. As a result, the relative positioning method was adapted for monitoring slab-track displacements in this study. Further details on the relative positioning method can be found in the works of [PERSON] and [PERSON] [25] and [PERSON] et al. [18].  
As can be seen in Figure 6, the processing of GNSS data involved the use of RTKLIB (V. 2.4.2), an open-source software developed by [PERSON] and [PERSON] [26]. This software  
Figure 4: Details of MDD modules and schematic after installation of MDD and GNSS receivers.  
comprises various programs such as RTKPOST, RTKNAVI, STRSVR, and RTKPLOT. RTKLIB includes tools for converting GNSS observation data to an RINEX format, allowing for the analysis of downloaded GNSS data on a computer. It also facilitates calculations for the Post-Processed Kinematic (PPK) technique, which provides precise calculation of coordinate changes.
###### Abstract  
The stability of global economies relies heavily on power systems (PS) that have sufficient operating reserves. When these reserves are insufficient, power systems become vulnerable to issues such as load shedding or complete blackouts. Maintaining grid stability becomes even more challenging with a high penetration of renewable energy sources (RES). However, RES, connected through power electronic devices, offer significant potential as ancillary service (AS) sources. Renewable energy-based microgrids (MG), which aggregate various RES resources and have substantial load control potential, further enhance the capability of AS provision from RES. The presence of diverse AS resources raises the question of how to dispatch ancillary service signals optimally to all resources. Most of the previous research work related to AS allocation relied on single-bus MG models. This paper proposes a detailed MG model for the optimal dispatching of ASs among the resources using Virtual Load, along with an optimization procedure to achieve the best results. The model incorporates voltage profiles and power losses for AS dispatching, and a comparative analysis is conducted to quantify the significance of grid modeling. The model and proposed procedure are tested using the CIGRE microgrid benchmark model. The results indicate that detailed modeling of MG can impact the results by 11%, compared to single-bus modeling, which qualifies detailed MG modeling for all future research work and shows the impact that modeling can have on technical and economic indicators of MG operation.  
ancillary service; microgrid modeling; virtual load; optimization Article Real-Time Optimization of Ancillary Service Allocation in Renewable Energy Microgrids Using Virtual Load. _Appl. Sci._**2024**, _14_, 8370. [[https://doi.org/10.3390/appl4188370](https://doi.org/10.3390/appl4188370)]([https://doi.org/10.3390/appl4188370](https://doi.org/10.3390/appl4188370))
## 3 Results and Discussion  
### Mean Corrosion Profiles  
Laser measurements were captured before dipping into the slag (i.e., after a holding time at 5 \({}^{\circ}\)C below the target temperature) and the MCP generated thereby was used as the reference curve to quantify all the dissolution parameters. Moreover, the laser measurements were also performed after each corrosion step and a corresponding MCP was generated. The MCP has two key benefits [15]: First, the nonconcentric rotation of the sample during laser measurements leads to a slight inclination of the individual profiles from the axis of rotation, and this inclination can be eliminated by averaging all the individual profiles. Second, the dimension analysis is ambiguous for all profiles because of non-axisymmetric corroded sample; the MCP provides an equivalent axially symmetric shape, which is the representative sample geometry, and the dissolution parameters can be easily extracted. Figure 2 shows the MCPs for the magnesia dissolution at 200 rpm and 1450, 1500, and 1550 \({}^{\circ}\)C. The Marangoni groove was present in all the cases. The experiment with all the planned corrosion steps could be completed at 1450 \({}^{\circ}\)C. At temperatures 1500 \({}^{\circ}\)C and 1550 \({}^{\circ}\)C, the experiments were aborted after seven and four corrosion steps, respectively, due to the higher corrosion depth at the Marangoni groove. The groove depth after a particular corrosion time was larger at higher temperatures. The Marangoni groove length increased with an increase in the corrosion time. The dissolution from the middle of the corroded part was lower than that of the other parts. The uncorroded portions of all the MCPs overlap with one another and with the MCP of the virgin sample. The radius and length of the corroded sample decreased with an increase in the corrosion time. For a given corrosion time, the corresponding reduction was higher with rising temperature. Figure 3 shows the virgin and corroded samples at the end of the experiment. The MCPs for the last corrosion steps accurately portray the corroded sample geometry. A slag droplet, which should be liquid and transparent at the target temperature, was present at the bottom tip of the corroded samples.
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 2 Background
### Benefits and Profits  
According to [10; 11; 12], the benefits of SSI include:  
* Owner-centric: The owner is the root of trust for their devices. Once a user is identified as the owner and can verify their identity, the next step is only to create a network of devices where the common parameter among all is the same owner. This way no third parties are needed to control the device. These devices can also have their own identities that allow them to create trust relationships, for example, devices that have the same owner automatically trust each other.
* Privacy: Users' identities are stored locally, whereas before they were stored at a service provider. Thus, it is located in a place closer to the user, usually in his wallet, and over which they have full control. Furthermore, encrypted identities could also be stored with the user's private keys.
* Decentralization: With the use of SSI, the user is the one who decides how and when to use their IoT devices without the intervention of any third party. It also provides an additional layer of security as the device information is not hosted anywhere outside.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Feige-Fiat-Shamir Protocol** \\ \hline
1. [PERSON] & Publishes her public key \([n,v,x]\), where \(x=r^{v}\) for some random number \(r\), with \(1<r<n-1\) \\
2. [PERSON] & Generates a random number e such that \(1<e<v-1\) \\  & Sends \(e\) to [PERSON] \\
3. [PERSON] & Computes \(y=r\cdot s^{e}\) and sends \(y\) to [PERSON] \\
4. [PERSON] & Verifies that \(y^{v}\cdot y^{e}\ mod\ n=x\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Guillou-Quisquater Protocol. Reprinted/adapted with permission from Ref. [9]. 2015, [PERSON].  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Schnorr Protocol** \\ \hline
1. [PERSON] & Computes \(x=a^{r}\ mod\ p\) for a random \(r\) \\  & Sends \(x\) to [PERSON] \\
2. [PERSON] & Generates a random number \(e\) such that \(1<e<2^{i}-1\) \\  & Sends \(e\) to [PERSON] \\
3. [PERSON] & Computes \(y=(r+s\cdot e)\ mod\ q\) \\  & Sends \(y\) to [PERSON] \\
4. [PERSON] & Verifies that \(a^{q}\cdot v^{e}=x\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Schnorr Protocol. Reprinted/adapted with permission from Ref. [9]. 2015, [PERSON].  
* End-to-end security: By exchanging decentralized identifiers applying asymmetric cryptography, devices authenticate and exchange messages securely.
* Standardization and open source: Decentralized identifiers are being developed according to W3C specifications, where companies as well as independent researchers are free to suggest new approaches.
# Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain
## 5 Experiment and Numerical Results  
### Experiment Configuration  
In this section, we present our experimental evaluations, which mainly focus on the training time required for using reinforcement learning to obtain a near-optimal solution. We use the hardware configuration of the experimental environment with a CPU, 2.4 GHz Intel Core i5, and an 8 GB memory at 1600 MHz DDR3. Our simulator program was developed in the PyTorch framework [41] and the Ipot optimization library [42]. Due to the difficulty of accessing real databases in a hospital, which contain very sensitive information, we refer to the data used in [43] with a list of 70 drugs. The setting range of the parameters in our work is shown in Table 2.  
In order to evaluate the system, we compare our work with the following baseline approaches:  
* Optimal: We use the Ipopt solver [42] to solve the DR2O problem with the assumption that the demand during \(T\) is given. Hence, the optimal result of DR2O, in this case, can be considered as the offline optimal solution to compare with other approaches.
* Over-provisioning: This is a simple strategy that is often carried out in the inventory system. Based on the average utilization (gathered from the log files of history), the expired period, and the current remaining volume of drugs, a refilling decision is considered. To prevent a shortage problem, the amount of drugs to be refilled is often provided with an additional volume, which results in a higher cost of operation.
* Ski-rental: As presented in the formulation, DR2O can be an instance of the ski-rental problem. Therefore, to evaluate the performance of the DRLD, we implement an online algorithm with the \(c\)-competitive value where \(c=(2-1/T)\).
* Max-min [12]: This refilling strategy is one of the most useful mechanisms for inventory management. By using the min level, a trigger will be active to make a refilling decision to obtain the maximum target quantity. To prevent the shortage problem, the min value is often set with a high volume (we use 45% in our work); therefore, the max-min baseline also results in a high total cost.
# Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE
## 5 Discussions
### Validation of InSAR Subsidence with GNSS Data
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **BARK** & **TEDD** & **STRA** \\ \hline Mean (UP) & \(-5.707\) & \(-1.978\) & 3.318 \\ Mean (INSAR Sub) & \(-5.753\) & \(-1.696\) & 4.400 \\ Variance (Up) & 6.788 & 0.673 & 3.018 \\ Variance (InSAR Sub) & 10.687 & 0.933 & 3.372 \\ Observations & 48 & 23 & 20 \\ Pearson Correlation & 0.903 & 0.033 & 0.218 \\ Hypothesized Mean Difference & 0 & 0 & 0 \\ Df (degree of freedom) & 47 & 22 & 19 \\ t Stat & 0.221 & \(-1.083\) & \(-1.287\) \\ \(p\) (T \(<=\) t) two-tail & 0.825 & 0.290 & 0.043 \\ t Critical two-tail & 2.011 & \(-0.281\) & 2.093 \\ Mean difference & 0.046 & \(-0.281\) & \(-1.082\) \\ St. Deviation of difference & 1.444 & 1.246 & 2.234 \\ Standard error of difference & 0.208 & 0.259 & 0.499 \\ T Alpha half 95\% CI & 2.011 & \(-1.083\) & 0.111 \\ Lower confidence limit & \(-0.373\) & \(-0.563\) & \(-1.138\) \\ Upper confidence limit & 0.465 & 0 & \(-1.026\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Two-paired \(t\)-test for GNSS and InSAR land movement. at \(p\)-value (0.825 for Bark) and alpha (0.05 for 95% confidence limit). For the stations, a \(p\)-value greater than alpha null hypothesis is accepted. Thus, acceptance of null hypothesis shows that subsidence indicated by InSAR and GNSS is consistent at Bark. Similarly, \(t\)-value for Tedd and Stra are \(-\)1.08 and \(-\)1.28, respectively, while critical \(t\)-values for Tedd and Stra are \(-\)0.28 and 2.09, respectively. Additionally, the \(p\)-value for Tedd and Stra are 0.0337 and 0.2188, respectively, which are both higher than alpha (0.05). Therefore, we have enough evidence that subsidence indicated by PSInSAR are consistent and agree very well with observed GNSS up values for all the three observed stations.
# Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors
###### Abstract  
Aimed at the problems of high redundancy of trajectory and susceptibility to background interference in traditional dense trajectory behavior recognition methods, a human action recognition method based on foreground trajectory and motion difference descriptors is proposed. First, the motion magnitude of each frame is estimated by optical flow, and the foreground region is determined according to each motion magnitude of the pixels; the trajectories are only extracted from behavior-related foreground regions. Second, in order to better describe the relative temporal information between different actions, a motion difference descriptor is introduced to describe the foreground trajectory, and the direction histogram of the motion difference is constructed by calculating the direction information of the motion difference per unit time of the trajectory point. Finally, a Fisher vector (FV) is used to encode histogram features to obtain video-level action features, and a support vector machine (SVM) is utilized to classify the action category. Experimental results show that this method can better extract the action-related trajectory, and it can improve the recognition accuracy by 7% compared to the traditional dense trajectory method.  
Action recognition; foreground trajectory; motion difference; SVM +
Footnote †: journal: Applied Sciences