esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data  
#### 2.2.1 Meteorological Data  
Daily meteorological data are input by BETHY and include maximum and minimum air temperatures, precipitation, and incoming shortwave radiation. Here, we used Climatic Research Unit--National Centers for Environmental Prediction (CRUNCEP) v7 [58] and re-gridded the data from the original 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) to the 8\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) CCDAS spatial resolution. The CRUNCEP is specifically designed to force the land surface models (CLMs) over a long period. The CRUNCEP has been successfully used to drive CLM for vegetation growth, evapotranspiration, and gross primary production and for the TRENDY (trends in net land-atmosphere carbon exchange over the period 1980-2010) project.
## 8 Limitations and Future Research  
Two major limitations in this study could be addressed in future research:  
1. Although the presented methodology can facilitate automation by reducing manual steps from the process, it can only focus on an individual process or step at a time. A method to identify multiple different processes with varying dataset to automate would be beneficial in terms of scalability in industries. However, controlling the number of the variables in a dynamic production environment for multiple processes will be challenging.
2. The methodology uses standardization and the elimination of manual steps to achieve automation. Therefore, it does not guarantee full automation of the process as some manual aspect might still need to be addressed in the process.  
The next phase of this research will monitor, calculate, and verify the tangible and intangible benefits such as cost savings and reduced labor hour, respectively, from the implemented process improvement by liaising with process engineers. This will add to the capability study, which proved that the CNC-CMM process under investigation was stable with a capability index greater than 1.67. The authors will also identify ways to automatically detect and prevent soft faults using the digitized data with the goal to fully automate the production process. It was observed from this study that the tools drift slowly during machining and it could lead to a part failure if not noticed. Further research will experiment with an ARMA tool drift model that should be able to identify a tool drifting outside the specification limits. The model will allow continuous monitoring of CNC tool data and aid the current ad hoc nature of manual interaction, or potentially take an automated correction step to further move the line towards full automation.  
Conceptualization, M.M.A. and K.B.; Data curation, M.M.A. and E.A.; Formal analysis, M.M.A.; Investigation, M.M.A. and E.A.; Methodology, M.M.A., A.B., and K.B.; Supervision, D.O. and K.B.; Validation, M.M.A. and E.A.; Writing--original draft, M.M.A.; Writing--review and editing, M.M.A., A.B., D.O., and K.B. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the DePuy Ireland Unlimited Company and Science Foundation Ireland on a co-funded grant agreement (SFI Targeted Project Agreement Reference No. 12/RC/2302).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the non-disclosure agreement with DePuy Ireland Unlimited Company.  
The authors declare no conflict of interest.
## 6 Results and Discussion
### Threats to Validity  
Using traditional evaluation metrics (e.g., accuracy, precision, and recall) to evaluate the performance on imbalanced datasets may lead to a biased performance. To mitigate this threat, we utilized MCC, which is a proper metric for imbalance classification problems. One threat to validity is related to the selection of the defect datasets. Our empirical results are based on the utilized defect datasets, which have different metrics (i.e., features).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **GAN+U** & **CWGANGP+U** & **CTGAN+U** \\ \hline CM1 & 0.0635 & 0.0343 & 0.0249 \\ JM1 & 0.1528 & 0.1817 & 0.2078 \\ MC1 & 0.0248 & 0.4015 & 0.1747 \\ MC2 & 0.1684 & 0.0487 & 0.0430 \\ PC2 & 0.0770 & 0.4280 & 0.1051 \\ Tomcat & 0.2123 & 0.2145 & 0.2379 \\ Lucene & 0.0872 & 0.0841 & 0.1271 \\ Eclipse-2.0 & 0.3599 & 0.3804 & 0.3454 \\ Zxing & 0.1630 & 0.1320 & 0.1908 \\ Prop-43 & 0.3466 & 0.3249 & 0.3075 \\ \hline \hline \end{tabular}
\end{table}
Table 7: MCC values of GAN-based methods with undersampling.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **GAN+U** & **CWGANGP+U** & **CTGAN+U** \\ \hline CM1 & Negligible & Negligible & Negligible \\ JM1 & Medium & Negligible & Medium \\ MC1 & Negligible & Large & Negligible \\ MC2 & Negligible & Negligible & Negligible \\ PC2 & Negligible & Negligible & Negligible \\ Tomcat & Negligible & Negligible & Negligible \\ Lucene & Negligible & Negligible & Negligible \\ Eclipse-2.0 & Large & Large & Negligible \\ Zxing & Negligible & Negligible \\ Prop-43 & Large & Negligible & Negligible \\ \hline \hline
**Total** & 7 Negligible, 1 Medium, 2 Large & 8 Negligible, 2 Large & 9 Negligible, 1 Medium \\ \hline \hline \end{tabular}
\end{table}
Table 8: Wilcoxon effect size of GAN-based methods with undersampling.  
This may impact the validity of the results. To mitigate this threat, we selected different imbalanced defect datasets from different projects with different sizes and imbalance ratios. Moreover, these defect datasets are widely used and open sources, making our study results replicable and verifiable. GAN-based methods generate different samples of different qualities at each run of the experiment. To mitigate this threat, each experiment was repeated ten times, and the final result was the average of all runs. This step can validate the performance and avoid randomness of GAN outcomes.
# Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC
## 2 Background Knowledge
### Probabilistic Hosting Capacity  
Hosting capacity refers to the possible maximum penetration capacity of DERs that a renewable energy resource can accommodate in a grid without any serious negative phenomenon and operate satisfactorily [21]. If the penetration capacity of DERs gradually increases and the penetration reaches a certain level, reliability violation occurs, and the total penetration capacity at that time can be chosen as the hosting limit. Even under the same operating conditions, the estimated hosting capacity may vary depending on the selected criteria for determining the hosting limit. The possible criteria include the following [22]:  
* Voltage and current limit;
* Equipment thermal overloading;
* Protection scheme malfunctioning.  
There are additional criteria for the transmission system cases. As the output of the DERs fluctuates, the scheduling of the generator changes, and the reserve needs to be dealt with dynamically [23]. In addition, the allowable range of the voltage or current may be different from the grid code according to each voltage level. In the Ireland system operator, EirGrid, the proportion of the DER generation was managed using an indicator called system nonsynchronous penetration [24]. Some of the criteria include:  
* Supply and demand balance;
* Frequency and transient stability;
* Adequate reserve procurement.  
Further, if the deterministic approach is chosen, instead of the probabilistic hosting capacity, the smallest amount among the determined capacities based on each criterion is the hosting capacity of the system. This is illustrated in Figure 1. In this case, the hosting capacity (HC) number 1 is the hosting limit. In contrast, if the probabilistic approach is chosen, the result is also accepted probabilistically; moreover, when operating near the point with the highest probability, it is operated with a most likely to violate criteria and is called the average probabilistic hosting capacity. In addition, it is possible to obtain the cumulative distribution function (CDF) of the results for operating decision-making.  
Figure 1: Concept of deterministic and probabilistic hosting capacity.
## 3 Results and Discussion
### Antimicrobial Activity of PLA-(Co-cit) Films against L. monocytogenes on Food Samples
The lower water activity of foods compared to culture broth may also reduce the antimicrobial diffusion rate into the aqueous phase of the food [34]. Other authors have observed mild antibacterial activity in PLA films. In one study, a decrease in the count of 0.55 log CFU/g and 0.17 log CFU/g for _Staphylococcus aureus_ and _Escherichia coli_ was observed, respectively [35]. It is also possible that the PLA films altered the redox potential on the surface of the foods. However, as they are highly permeable to gases, this seems unlikely, and even if the oxygen availability was reduced, _Listeria monocytogenes_ is facultatively anaerobic. Figure 3: Aerobic plate counts (**A**) and presumptive _Listeria monocytogenes_ counts (**B**) on turkey ham with high background count. Treatments: inoculated with _L. monocytogenes_, covered by PLA film with 23.3% \(w\)/\(w\) Co-cit (red, circles); inoculated with _L. monocytogenes_, covered by PLA film without Co-cit (blue, squares); inoculated with _L. monocytogenes_, no film (green, triangles); uninoculated, no film (yellow, Xs). Different letters within the same time point indicate significant differences (\(p<0.05\)) between inoculated samples (uninoculated samples not included in the calculation); where there are no letters, there were no significant differences (\(p>0.05\)). Error bars represent standard deviation. The straight line on the lower graph represents the detection limit—samples with no colonies were assigned a value of one-half of this value. Figure 4: Aerobic plate counts (**A**) and presumptive _Listeria monocytogenes_ counts (**B**) on turkey ham with low background count. Treatments: inoculated with _L. monocytogenes_, covered by PLA film with 23.3% _w/w_ Co-cit (red, circles); inoculated with _L. monocytogenes_, covered by PLA film without Co-cit (blue, squares); inoculated with _L. monocytogenes_, no film (green, triangles); uninoculated, no film (yellow, Xs). Different letters within the same time point indicate significant differences (_p_ < 0.05) between inoculated samples (uninoculated samples not included in the calculation); where there are no letters, there were no significant differences (_p_ > 0.05). Error bars represent standard deviation. The straight line on the upper graph represents the detection limit—samples with no colonies were assigned a value of one-half of this value. Figure 5: _Cont._
# Investigation on Hydroplaning Behaviors of a Patterned Tire on a Steel Bridge Deck Pavement
## 3 Tire-Fluid-Pavement Interaction Model
### Hydroplaning Analysis  
The tire model, fluid model, and pavement model were combined for hydroplaning analysis based on the CEL algorithm [12; 13; 31; 14]. The tire model and pavement model were assembled, and the contact conditions were defined by the tire-SBDP interaction. Then, the tire deformation and grounding analysis, tire braking/rolling/cornering analysis were conducted through steady state analysis of 3D tire rolling on the pavement. The stable stress-strain field at the given rolling speed was obtained for the subsequent transient analysis. After that, the fluid model was imported and the contact among tire, water film, and pavement was defined as general contact condition. The stable stress-strain field obtained in the previous step was transferred as the initial condition of hydroplaning analysis. The rolling tire on the wet pavement was simulated using a relative motion method. The same translation velocity was applied to the fluid and pavement model, and the corresponding angular velocity was applied to the tire model. Accordingly, a hydroplaning model with a complex tire pattern was developed, as shown in Figure 13(a). In addition, a hydroplaning model with simple longitudinal pattern (Figure 13(b)) was developed to compare the hydroplaning behaviors with different tire patterns. The hydroplaning analysis procedures can be summarized in Figure 15.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \(\rho_{0}/\text{kg}\cdot\text{m}^{-3}\) & 1000 \\ \(c_{0}/\text{m}\cdot\text{s}^{-1}\) & 1484 \\ \(S\) & 1.979 \\ \(\Gamma_{0}\) & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter values of Mie–Grüneisen state equation.  
Figure 13: Fluid model (**a**) geometric model; (**b**) FE model.  
Figure 14: Hydroplaning model (**a**) complex tire pattern; (**b**) simple longitudinal tire pattern.  
Figure 15: Hydroplaning analysis flow chart.  
To validate the developed hydroplaning model, the modeling method was utilized to simulate the hydroplaning on the road pavement. The simulation results were compared with the calculation results by the NASA hydroplaning equation and Gallaway hydroplaning equation, as shown in Figure 16. As the NASA equation is applicable to hydroplaning assessment for smooth tires or tires with simple patterns, the hydroplaning speed simulation results of tires with longitudinal patterns and complex patterns are larger than the calculation results by NASA equation. The Gallaway equation is developed based on the hydroplaning test of complex patterned tires. It is observed that the simulation results of complex patterned tires and calculation results by Gallaway equation are approximate each other. Accordingly, it is regarded that the hydroplaning analysis method developed in this study is valid.
## 4 Discussion
### FWD External Load  
FWD in the present study covered a shorter total distance than CMF but did not differ significantly from other positions. Similarly, a recent study observed that FWDs in a 1-4-3-3 formation covered a significantly shorter TD than CMFs; however, no differences were noted compared to other positions [25]. Another study reported that FWDs covered the least TD compared to all other positions in the 1-4-3-3 formation [24]. It has also been reported that FWDs covered the second shortest TD in the 1-4-4-2, 1-4-3-3, and 1-3-5-2 formations [16]. Lastly, [PERSON] et al. (2024) [11] observed that FWDs covered the second least TD in the 1-3-4-3, 1-3-5-2, 1-4-1-4-1, and 1-4-2-3-1 formations. From the above, it appears that regardless of the formation, FWDs cover less distance than players in other positions--a fact determined by the tactical role of the position and the specific areas of the pitch these players move in.  
In distances covered at high speed, there were no significant differences between positions, whereas in sprints, FWDs covered a greater distance than CDFs. Additionally, FWDs performed fewer accelerations than CMFs and fewer decelerations than both CMFs and WDFs. These findings are partially confirmed by previous studies that indicate FWDs in the 1-4-3-3 formation covered a greater distance than CDFs at speeds exceeding 19.8 and 24 km/h [24]. However, [PERSON] et al. (2025) reported that in the 1-4-3-3 formation, FWDs covered greater distances at maximum speed compared to CMF [25]. [PERSON] et al. (2020) [16] also noted that FWDs covered the second greatest distance at high speeds in the 1-4-4-2, 1-4-3-3, and 1-3-5-2 formations. A recent study found that FWD covered the greatest distance at maximum speed in the 1-3-5-2 and 1-4-3-3 formations [11]. Previous studies have reported that FWDs lag behind in the number of accelerations and decelerations compared to wide-position players [33; 34]. Generally, the actions of forwards are characterized by high intensity, as they often operate under pressure from one or more opponents. However, their performance may also be influenced by team formation and the number of nearby teammates.
###### Abstract  
Three nanoporous alumina structures (NPASs) obtained by the two-step anodization method were optically and electrochemically characterized. Two of the structures were symmetric (NPAS-Sf and NPAS-Ph) and one was asymmetric (NPAS-And); pore size ranged from 10 nm to 100 nm and porosity was 12% in the case of the symmetrical NPAS and 23% and 30% for each surface of the asymmetric structure NPAS-And(A) and (B), respectively. Optical parameters of the studied samples (refraction index and extinction coefficient) were obtained from ellypsometric spectroscopy measurements carried out for wavelengths ranging between 250 nm and 1700 nm (visible and near infrared regions), with the total average refraction indices being 1.54, 1.52, 1.14, and 1.05 for NPAS-Sf, NPAS-Ph, NPAS-And(A), and NPAS-And(B), respectively, which indicates porosity control of refraction index values. Electrochemical characterizations (concentration potential and impedance spectroscopy measurements) were performed with NaCl solutions, and they allowed us to estimate samples of effective fixed charge concentration (\(1.22\times 10^{-2}\) M, \(1.13\times 10^{-3}\) M, and 1.15 \(\times\) 10\({}^{-3}\) M), ion transport numbers, permselectivity (33.0%, 3.1%, and 9.6%), and the electrical resistance of each solution/sample system as well as the interfacial effects associated to solution concentration-polarization, which seems to be mainly controlled by pore size and sample symmetry.  
nanoporous alumina structures; ellypsometric spectroscopy; concentration potential; impedance spectroscopy; sample structure +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Comparison of Spectral Separability for Different Fire Severities  
From Figure 8, the spectral separability between different fire severities exhibits a similar trend in both the post-fire image and the pre- and post-fire difference image. Both datasets exhibited higher spectral separability in the near-infrared to short-wave infrared range (705 nm-1341 nm), followed by the short-wave infrared range (1964 nm-2468 nm). Other spectral ranges showed relatively lower separability. Overall, the spectral separability in the post-fire image was higher than in the pre- and post-fire difference image. Both the post-fire image and the pre- and post-fire difference image, the spectral separability between unburned and burned areas of different fire severities was notably higher in the near-infrared to short-wave infrared range (705 nm-1341 nm) and the short-wave infrared range (1964 nm-2468 nm), but the spectral separability between different fire severities was relatively lower. Among the severities, moderate- and high-severity areas showed higher spectral separability in the near-infrared to short-wave infrared range (705 nm-1341 nm) in the post-fire image, and in the short-wave infrared range (1964 nm-2468 nm) in the pre- and post-fire difference image.  
Figure 7: Comparison results of spectral curves for different fire severities in the pre- and post-fire images. (**a**) Unburned; (**b**) Light severity; (**c**) Moderate severity; (**d**) High severity.
## 5 Simulation
### Simulation of Energy Harvesting  
In agriculture, sensor nodes are deployed in an outdoor environment. Moreover, the sensor node's battery life represents a challenge because the life of the battery drains due to distinct environmental conditions. Thus, the optimal solution for implementing energy harvesting for sensor nodes involves renewable energy sources. Energy harvesting is the optimal mechanism used to power the activities of the sensor nodes, including sensing, preprocessing, and transferring data. The evaluation of sensor node battery life using solar panels and wind turbines was performed using a Cisco packet tracer. The Cisco packet tracer is a visualized-based simulation tool that encourages the user to implement distinct network-based simulations. The simulation model for evaluating the sensor node's energy through solar panels and wind turbine was implemented in the Cisco packet tracer environment and is shown in Figure 8.  
Figure 7: Flow chart for execution of localization of algorithms.  
The lifetime of the nodes is presented in the simulation, as shown in Figure 10. Data transmission occurs from end nodes to the hub, the hub to a central server, and from mobile phones via cell towers. The simulation panel in Figure 9 presents the number of IoT devices connected to the network. Each renewable resource is connected using a power meter for calculating the power consumption. Timestamp details of the IoT devices are represented in the time column; the IoT 2 device is connected to the network with a time of 0.129 s. Devices IoT0 and IoT1 are currently connected within 1.011 s. Total device connection time is 77.660 s with a specific time gap or delay. Further power is transferred to the battery for storage purposes. A sample scenario of power of 11 kWh in the case of a turbine and 78 watts with 82 Wh was considered.  
The establishment of LoRa-based communication from the sensor node to the mobile phone is presented in Figure 10. The simulation model shown in Figure 8 signifies the  
Figure 8: Sensor node communication establishment using LoRa link.  
Figure 9: Sensor node energy conservation using renewable resources in an agriculture field.  
communication link established among sensor end nodes and establishes a connection with the gateway to transmit the data to the central server. In the simulation panel, the data transmission time in seconds, from the router to the hub and the hub to smartphones, is displayed. The simulation signifies the on-air data transmission. Three events are noted as having a time of 218.952 s. Each of the three devices, i.e., wireless router, central office, and another wireless router, connected to the network at the same timestamp. This signifies, in LoRa communication, that the device synchronization time gap is extremely small, which is an additional advantage. As soon as the authentication key matches, the devices are synchronized and begin communicating.