esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 4 Case Study
### BTB VSC HVDC Siting for Fault Current Reduction  
The site of the BTB VSC HVDC is selected to solve the fault current problem in the Korean metropolitan area and its effect is shown in Table 4. With the BTB VSC HVDC, there is only one bus, bus 1200, at which the fault current is above the 50 kA limit, whereas without the BTB VSC HVDC, there are multiple buses at which fault currents are above the 50 kA limit. The benefit of the BTB VSC HVDC for reducing the fault current is demonstrated.
## 5 Results and Analysis
### Hazard Computability Evaluation  
As described in Section 3.6, hazard computability assesses the likelihood of structural damage over varying years and evaluates the simplicity of computational procedures. Currently, PGA and \(S_{\text{a}}\) emerge as the IMs with superior hazard computability. The latest report from the United States' National Seismic Hazard Model (NSHM) [39] employs hazard curves to convey the 2%, 5%, and 10% exceedance probabilities for specific geographic regions over a 50-year span. The generation of these hazard curves relies on two pivotal factors: long-term seismic data and an abundant probability correction model. Within the United States Geological Survey (USGS) database [40], hazard calculations for PGA, PGV, peak ground displacement (PGD), and \(S_{\text{a}}\) are readily available and have been applied to various regions within the United States. For the area surrounding the Daikai station, which is the focus of this study, no hazard computation data are available. The absence of adequate earthquake data and a reliable probability correction model for this specific region necessitates the accumulation of such data for a nuanced understanding of hazard computability for different IMs.  
Moreover, the hazard computability of multiple IMs can be inferred through their relativity with PGA. Employing [PERSON]'s method [41], hazard curves for other IM types can be extrapolated from the existing IM hazard curve, as illustrated in Equation (8). As observed in Section 5.5, IMs such as A95, SMA, EDA, and \(I_{\text{a}}\) exhibit high relativity with PGA. The hazard curves for these correlated IMs can thus be derived through Equation (8) once a reliable PGA hazard curve is established.  
\[\lambda(IM_{2})=\int_{IM}P[IM_{2}\geq im_{2}|IM_{1}]\cdot|d\lambda(IM_{1})| \tag{8}\]  
Figure 17: Relativity ranking under different DMs.  
Figure 18: The value of \(R^{2}\) under each IM.
# SLLB-DEVS: An Approach for DEVS Based Modeling of Semiconductor Lithography Load Balance
## 2 Methodology
### Load Balancer Policy
#### 2.2.1 The Saturation Point Policy  
By analyzing the behavior of the existing E-beam cluster, it is possible to find a saturation core point that does not significantly change TAT, even if the number of allocations increases from a specific number of computation nodes independently of density. This means that when the software is operated under the existing FIFO policy, computing performance decreases in comparison to computational resources, from the saturation core point. The total amount of data that can be calculated in a single license can then be  
Figure 2: The structure of the legacy E-beam cluster load balancer.  
established. As a result, this leads to a cost increase due to purchasing additional licenses and hardware.  
To improve this problem, this study analyzed the saturation core point of the E-beam job according to the number of computation nodes, and designed a core model based on the results. This model prevents the allocation of unnecessary computing resources to process the E-beam job by calculating the saturation core point of the E-beam job, and allocating the appropriate number of computation nodes.
# Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
## 5 Conclusions  
In this study, we developed a validation rule that can be applied to OMOP CDM by selecting frequent values through a review of previous studies on the existing information system quality and healthcare quality dimensions. Additionally, we proposed a new DQ4 HEALTH model for OMOP CDM data quality management as a result of receiving expert advice based on the developed validation rule. The developed DQ4 HEALTH model was applied to three institutions with more than two million CDM data to conduct an empirical healthcare data quality evaluation study.  
As a result of analyzing the multicenter data quality error results with more than 2 million cohorts using the chi-square method, we confirmed that there is a difference in the quality of CDM data between hospitals. This means that even though the same OMOP CDM was applied, there was a difference in quality for each hospital. There was also a significant difference for each table. The types of errors presented in this study suggestthat the analysis results may be affected when conducting joint research using a common data model.  
In the future, it will be necessary to expand research to intuitively confirm the degree of data quality improvement through comparison before and after cleansing the error data derived from the data quality result. It is also necessary to expand the study on the effects of analysis results before and after comparison [35; 36; 37]. Finally, this study contributes to laying the foundation for the development of quality control tools using the developed quality control rules and results analysis method [38].  
Conceptualization, K.-H.K. and I.-YC.; methodology, K.-H.K. and I.-YC.; software, S.-H.C., K.-H.K. and S.-J.K.; validation, S.-J.K. and K.-H.K.; formal analysis, S.-H.C.; investigation, D.-J.K. and I.-YC.; resources, I.-YC. and D.-J.C.; data curation, I.-YC., D.-J.C. and Y.-W.C.; writing--original draft preparation, W.C. and K.-H.K.; writing--review and editing, I.-YC., J.-K.K. and W.C.; visualization, W.C. and K.-H.K.; supervision, I.-YC.C.; project administration, D.-J.K. and I.-YC. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technology Innovation Program (20004927, Upgrade of CDM-based Distributed Biohealth Data Platform and Development of Verification Technology) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the Catholic Medical Center (protocol code XC20 RNDI0161 and 6 July 2021).  
The requirement for written informed consent was waived by the Research Ethics Committee of the Catholic Medical Centre, and this study was conducted in accordance with relevant guidelines and regulations.  
Data Availability Statement: Data sharing was not applicable to this study. Data supporting the findings of this study are available from each hospital.  
The authors declare no conflict of interest.
# Bayesian Modeling of Traffic Accident Rates in Poland Based on Weather Conditions
## 2 Materials and Methods
### Models
#### 2.2.2 Model 2: County-Level Multifactorial Model  
The second yearly county-level model extends Model 1 by incorporating meteorological variables. This model includes annual averages of temperature, humidity, and total precipitation for each county, alongside the socioeconomic factors. This extension aims to capture the combined influence of weather conditions and socioeconomic characteristics on accident occurrence at the county level.  
The linear predictor for this model is expanded to:  
\[\log(\lambda_{i})=\alpha +\beta_{1}\cdot county\_population\_density_{i}+\beta_{2}\cdot county \_passenger\_cars_{i}+ \tag{5}\] \[+\beta_{3}\cdot county\_road\_density_{i}+\beta_{4}\cdot county \_mean\_temperature_{i}+\] \[+\beta_{5}\cdot county\_mean\_humidity_{i}+\beta_{6}\cdot county \_total\_precipitation_{i}\]
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 3 Protocol Specification
### Frame Format, Addressing, and Frame Length Calculation
Finally, \(L_{7}=L_{\text{M}}+7\), 7 being the length of the Modbus Application Protocol (MBAP) header [8]. Regarding RH transactions, requests have a fixed length, and the response length depends on the number \(N\) of registers being read; that is, \(L_{\text{M}}^{(\text{RH,S})}=5\) and \(L_{\text{M}}^{(\text{RH,R})}(N)=2+2N\). Symmetrically, WM transaction requests have lengths that depend on the number \(N\) of registers being written, while responses have a fixed length, leading to \(L_{\text{M}}^{(\text{WM,S})}(N)=6+2N\) and \(L_{\text{M}}^{(\text{WM,R})}=5\). Remembering that, according to [1], it must be \(L_{\text{M}}\leq 253\), the maximum number of registers that can be transferred in one RH transaction is \(N_{\max}^{(\text{RH})}=\lfloor(253-2)/2\rfloor=125\)  
Figure 3: Modbus-UDP frame format and its encapsulation into 100 BaseTX Ethernet packets (field width not to scale). Similarly, it is \(N_{\text{max}}^{\text{(WM)}}=\lfloor(253-6)/2\rfloor=123\). Combining the previous equations, the total length of the packets exchanged for an RH transaction involving \(N\) registers, without IPGs, can be calculated as  
\[ \begin{split} L_{1}^{\text{(RH)}}(N)&=L_{1}^{\text{( RHS)}}+L_{1}^{\text{(RH,R)}}(N)\\ &=(65+\max(5,7))+(65+\max(2+2N,7))\\ &=137+\max(2+2N,7)\enspace,\end{split}  \tag{2}\]  
taking into account that, in this case, it is \(L_{\text{M}}=L_{\text{M}}^{\text{(RHS)}}\) for the request and \(L_{\text{M}}=L_{\text{M}}^{\text{(RH,R)}}(N)\) for the response. Similarly, for a WM transaction, it is  
\[ \begin{split} L_{1}^{\text{(WM)}}(N)&=L_{1}^{\text{( WM,S)}}(N)+L_{1}^{\text{(WM,R)}}\\ &=(65+\max(6+2N,7))+(65+\max(5,7))\\ &=143+2N\enspace,\end{split}  \tag{3}\]  
since it is always \(N\geq 1\). As an example, Table 1 lists the packet lengths of RH and WM requests and responses, calculated from (2) and (3), for \(N=1\), 60, and 120 registers, the number of registers considered in the experiments described in Section 4. Another important quantity to be calculated is the L4 payload length, which corresponds to the L7 PDU length \(L_{7}\), for a given \(N\). This is because the iperf3 tool, used for the baseline performance assessment presented in Section 4.2, measures throughput at this level. The following relationships can easily be derived, leading to the lengths listed in Table 2.  
\[L_{7}^{\text{(RHS)}}=12,\quad L_{7}^{\text{(RH,R)}}(N)=9+2N,\quad L_{7}^{\text {(WM,S)}}(N)=13+2N,\quad L_{7}^{\text{(WM,R)}}=12. \tag{4}\]
## 4 Experiments and Discussion
This step is repeated two more times from 44,100 Hz to 11,025 Hz and 8000 Hz, then back to 44,100 Hz. The BER during the resampling attack is shown in Table 4. BER information shows that the proposed embedding method leads to lower BER and better robustness. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Domain** &
\begin{tabular}{c} **Cut-Off Frequency** \\ **(Hz)** \\ \end{tabular} & **BER (\%)** \\ \hline Reference [9] & Time domain & 3000 & 27.1 \\  & & 6000 & 26.5 \\ Reference [22] & DWT & 3000 & 26.3 \\ Reference [22] & DWT & 6000 & 25.4 \\ Reference [23] & DWT & 3000 & 26.5 \\ Reference [28] & Time domain & 6000 & 24.1 \\ Proposed & DWT & 3000 & 29.7 \\  & & 6000 & 28.5 \\ \multirow{2}{*}{Proposed} & \multirow{2}{*}{DWT} & 3000 & 5.62 \\  & & 6000 & 2.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BER (%) IN THE LOW-PASS FILTERING ATTACK. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Domain** & **Amplitude of Noise in dB** & **BER (\%)** \\ \hline \multirow{3}{*}{Reference [9]} & \multirow{3}{*}{Time domain} & \(-40\) & 18.25 \\  & & \(-30\) & 20.34 \\  & & \(-20\) & 32.67 \\  & & \(-40\) & 10.09 \\ Reference [22] & DWT & \(-30\) & 13.23 \\  & & \(-20\) & 31.57 \\  & & \(-40\) & 9.65 \\ Reference [23] & DWT & \(-30\) & 12.76 \\  & & \(-20\) & 32.94 \\  & & \(-40\) & 21.83 \\ Reference [28] & Time domain & \(-30\) & 24.95 \\  & & \(-20\) & 32.76 \\  & & \(-40\) & 1.89 \\ Proposed & DWT & \(-30\) & 2.03 \\  & & \(-20\) & 6.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BER (%) IN THE GAUSSIAN NOISE ATTACK. We performed all experiments above using patients' Electrocardiogram (ECG) data from the website in [33; 34]. With the exception of a few patients with damaged ECGs, most of the ECGs were successfully tested by our proposed method.
## 3 Future Directions of MES Development
### Development Directions for MES
#### Cloud-based MES  
With respect to the cloud-based MES, the main benefit for companies in choosing a cloud-based solution is that almost no local IT resource investment is required [46]. Moreover, a cloud solution can handle the weaknesses of their current system regarding redundancy and high upgrade cost because the cloud is a virtualization of resources that maintains and manages itself [47].  
In addition, [PERSON] [48] raised the concept of smart manufacturing with cloud computing, which is the adoption of cloud computing in the manufacturing sector. It is interesting to know that with a cloud-based solution, manufacturing companies will be able to eliminate the IT resources and outside support and maintenance, but in the meantime, companies are able to develop better-integrated and more efficient processes [49].  
The cloud plays a role as a platform in the evolution of MES. As cloud computing is already practical at the business application level, it is very reasonable to build an MES based on web services and provide a standard for information sharing/transferring environments. Cloud technology will be adopted to support monitoring, information exchange, and also other real-time interactions [31].  
However, there are still many challenges connected with bringing MES to the cloud. MES tends to be highly industry- and process-specific, which means it is highly customized for running a specific process at specific plants. It needs to be able to change quickly when processes or requirements change [50]. Despite its many advantages, the cloud-based MES construction should be developed in consideration of the characteristics of \"Scalability and reconfigurability\".
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 2 Materials and Methods
### Simulation Data  
To estimate the overall power consumption of different design solutions, the network topology used in [6] was considered (Figure 5a). In detail, the network consisted of 24 nodes and 28 links, for each of which 2 directions will be considered, interconnecting the nodes, and forming a unified network. The network map was divided into 35 regions, calculating the probability of an occurrence of a future earthquake that could damage the links in each region.  
On the map of Figure 5a, the letter code beside every node signifies the name of the town or island and is utilized to identify each individual node on the map. On the second map, depicted in Figure 5b, the numbers displayed next to each link represent the distance between two nodes that each link is spanning through. Additionally, Figure 5b shows the geographical regions used in the simulation, with each region specified with a unique number code in red.  
It also offers clarity regarding which links pass through each of these geographical regions. Using real earthquake catalogs from the Geodynamic Institute of the National Observatory of Athens for the period from 1 January 1964 to 1 January 2023, all earthquakes with a magnitude greater than 4 on the Richter scale, deemed as strong, were included in the data considered by the simulation. For the purposes of this research, the country was divided into 36 distinct districts/regions. For each region, all earthquakes of magnitude 4 and above were studied. Then, the average recurrence period of a 5 Richter earthquake (\(T_{m}\)5) was calculated, as well as the probability that such an earthquake (5 Richter) would occur in the next five years (\(P_{t}\)). The areas considered dangerous, and taken into account, were those where the probability of a magnitude 5 earthquake in the next 5 years is greater than 20%, 30%, 40%, and 50%. The choice of a magnitude 5 earthquake was made based on the work in [38; 39; 40], which indicates that a 5 Richter earthquake can affect a network link that crosses this 5 km radius earthquake epicentral (damaging) area.  
The methodology developed in this work is independent of the method or reliability of the seismicity quantities calculated and applied in this examination. That is, alternative zones, catalogs, and/or measures of seismicity could be used for the study area (mainland Greece), without changing the applicability of the proposed approach. This paper essentially provides a \"proof of concept\" on how seismicity measures, regardless of how they are evaluated, can be effectively used to design WDM networks to anticipate and take into account earthquake-induced problems.
## 1 Introduction
In addition, another practical utility is to facilitate the transmission of information, materials, or other goods in quarantined regions due to the presence of an outbreak of infections during a pandemic, as is the case of the Coronavirus pandemic. The proposed UAV networks are independent of the Internet and they are of the DTN type. Multiple simulations based on experimental preliminary flight tests are performed to analyze the efficiency of the data transmission in the case of protocols based on the transmission of a single copy or multiple message copies.
## 4 Results and Discussion
### V\({}_{\text{S}30}\) Predictive Equation for the Middle-Chelf Basin  
The wavelength corresponding to the V\({}_{\text{S}30}\) value was estimated for each average dispersion curve. The average wavelength found is \(\lambda\) = 41 m \(\pm\) 3. After that, the V\({}_{\text{S}30}\) values were correlated with \(V_{R41}\) values (Rayleigh wave velocity at \(\lambda\) = 41 m), and the best linear fitting was obtained (Equation (9)):  
\[V_{\text{S}30}\ =\ 10171\ *\ V_{R41}-6719 \tag{9}\]  
The regression plot, along with the residuals, is shown in Figure 10. The correlation degree is R\({}^{2}\) = 0.9472. Equation (9) was applied to dispersion curves obtained in two other cities of the Middle-Chelif Basin from a previous study [21] (Table 3). The aim was to  
Figure 9: V\({}_{\text{S}30}\) map and soil classification in the El-Abadia city [21].  
evaluate the reliability of the V\({}_{\rm s30}\) predictive equation. The V\({}_{\rm s30}\) values were predicted within a maximum error of 5.6%, and the site classifications were correct. For the dispersion curves obtained in the present study, the V\({}_{\rm s30}\) values were predicted within a maximum error of 7.6%, and the site classifications were correct, except for ATF1 site.  
In the case of the ATF1 site, the estimated and predicted V\({}_{\rm s30}\) values are close to the limit between class C and D (according to the NEHRP classification [57]).
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Future Directions  
Future research work needed in this area includes implementation of blockchain, mobile edge computing (MEC), and cloud computing technologies for secure data transmission. Resource allocation and stable all-the-time connectivity using vertical handoff techniques and efficient media compression methods is another viable research area in emergency telemedicine applications. Deployment of telemedicine designs must take into consideration the issues of availability, scalability, and standardization of communication technologies by studying available open standards and the use of gateways and open application programming interfaces (API). In addition, it is important to consider miniaturization of wearable devices by reducing design complexity and improving power efficiency through deployment of optimization techniques such as multi-cast energy-aware routing protocols and dynamic access scheduling techniques. The concept of RF energy harvesting is a development that could greatly help to address a number of energy problems involved in the future progression of telemedicine.  
Prospective researchers might focus on combining existing telemedicine technologies highlighted in this study and incorporating emerging concepts such as the Internet of Medical Things (IoMT), Wearable Internet of Things (W-IoT), and Health Internet of things (H-IoT). This could improve rapid roll-out of remote health monitoring schemes and enhance the interoperability of devices and networks in telemedicine systems. Future surveys could explore areas such as medical data integration of conventional Electronic Health Records (EHRs) for wireless telemedicine and remote medical diagnosis, making the use of manual records and the usual face-to-face doctor-to-patient consultations less necessary. Future telemedicine research should concentrate on the advancement of telecommunication technologies, the integration of telemedicine with EHRs, and the development of new technologies such as artificial intelligence and wearable devices. Finally, researchers should concentrate on addressing challenges such as legal and regulatory issues, reimbursement, and access to technology. Researchers can help to ensure that telemedicine remains a safe, effective, and widely accepted method of healthcare delivery by addressing these challenges.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 4 Discussion
### Proposed Conv1D-WGAN-GP Model  
Across all datasets, Conv1D-WGAN-GP demonstrated the best balance between data fidelity and indistinguishability. Its ability to maintain low divergence scores and classifier accuracy across different-sized datasets illustrates strong generalization and robustness. While a traditional architecture like Vanilla-GAN sometimes performs reasonably on RMSE, its Real-vs-Synthetic Distinguishability Score is often high, and t-SNE plots show poor overlap, indicating it generates less realistic and distinguishable data. WGAN-GP tends to overfit or overshoot on some datasets, especially evident in its higher classifier accuracy and visual artifacts in distribution coverage. Conv1D-GAN, despite the benefits of convolution, frequently exhibits the highest errors and visual mismatches, particularly with the small Pig Farm dataset. These results validate the proposed architecture's suitability for practical applications in smart grid analytics, especially when real data are limited or privacy-constrained. Additionally, these findings highlight the efficacy of combining Wasserstein loss with gradient penalty and convolutional architectures, particularly for both small-scale and large-scale load profile generation.
## Abbreviations  
V2X Vehicle to Everything V2V Vehicle to Vehicle V2I Vehicle to Infrastructure V2P Vehicle to Pedestrian V2N Vehicle to Network DRL Deep Reinforcement Learning QoS Quality of Service DSRC Dedicated Short Range Communication C-V2X Cellular Vehicle to Everything 3 GPP 3 rd Generation Partnership Project C-V2X Cellular Vehicle-to-Everything CSMA Carrier Sense Multiple Access TCP Transmission Control Protocol ICCDRL Intelligent Congestion Control Strategy Based on Deep Reinforcement Learning VANET Vehicular Ad-Hoc Network IoV Internet of Vehicles UBRCC Utility-Based Rate Congestion Control HSR Hierarchical State Routing UAVs Unmanned Aerial Vehicle NDNs Named Data Networking DRL-CCP Deep Reinforcement Learning Congestion Control Protocol BDP Bandwidth-delay product DCC Dial Control Center SUMO Simulation of Urban Mobility FTP File Transfer Protocol BER Bit Error Rate DPO2 Proximal Policy Optimization DQN Deep Q-Network SDN Software Defined Network