esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calibration and Analysis of Rotation Axis Position Error
### Analysis of Average Position Error of A and C Axes  
Based on the error identification model of rotary axes established in Section 2 of this paper, the average position error of the two rotation axes was calibrated using multi-degree of freedom, step-by-step contact scheme and measurement data obtained in Section 4.1. As shown in Figure 6, five key position errors of the A-axis were identified. It was found that the value of \(\delta_{xA}\) was small and negligible, and that the average angular positioning error \(\varepsilon_{aA}\) of A-axis was the largest. The deviations of the five position errors were about 0.002 mm in both positive and negative directions. In this section, the average linear errors \(\delta_{xC}\) and \(\delta_{yC}\) along the X and Y directions of the C-axis were calibrated, and the average angular errors \(\varepsilon_{aC}\), \(\varepsilon_{BC}\) and \(\varepsilon_{yC}\) around the X, Y and Z directions were calibrated. As shown in Figure 7, the average positioning error \(\varepsilon_{yC}\) of C-axis was the largest among the five position errors, reaching 0.033 mm at A = 60, which was one of the key factors affecting rotation axis error. The five average position errors of C-axis were increased by increasing the rotation angle of the A-axis.
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 4 Comparison and Discussion  
Table 1 shows an overall summary of the various radiation-hardening SRAM cell designs covered in the previous section, in terms of their delay, power, stability, upset resilience, and control signal generation overhead. HSPICE simulations were performed with ASAP7 finFET technology [22] at 0.7 V. The dynamic power consumption was derived as the average power consumption for the read and write operations with 200 MHz in a 128 \(\times\) 128 bitcell array. The read and write stabilities were measured in terms of the read static noise margin (RSNM) and word-line write trip voltage (WWTV), respectively. The robustness to SEU was evaluated in terms of the critical charge (Q\({}_{\text{C}}\)), which is the minimum charge injected to the storage node to cause a data flip. The bitcell area was derived through the layout performed in the ASAP7 finFET design rule.  
In Table 1, the trade-offs between different performance aspects in the various designs are clearly shown. Therefore, if one of the SRAM cell designs is to be chosen for a certain application, the most suitable SRAM cell design can be chosen considering the constraints in the environment of the application. In this section, the trade-offs between different bitcell designs are analyzed in depth to provide guidance to design or select the appropriate radiation-hardened SRAM bitcell.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Design**} & **Dynamic** & **Read** & **Write** & **Read** & **Write** & **Area** & **Multiple** & **Control** \\  & **Power** & **Delay** & **Delay** & **(RSNM,** & **(WWTV,** & **(\(\mu\)m2)** & **QC (fC)** & **Node** & **Signal** \\  & **(\(\mu\)W)** & **(ps)** & **(ps)** & **(ps)** & **(ps)** & **(ps)** & **(ps)** & **(ps)** & **(ps)** \\ \hline DICE-12 T & 429.19 & 41.40 & 11.40 & 293 & 149 & 0.05832 & 52.34 & No & No \\ \hline Quatro-10 T & 363.52 & 46.58 & 16.78 & 223 & 45 & 0.046656 & 21.57 & No & No \\ \hline WE-Quatro-12 T & 444.16 & 42.30 & 14.20 & 285 & 156 & 0.052488 & 21.37 & No & No \\ \hline NS-10 T & 315.42 & 87.27 & 12.40 & 381 & 236 & 0.040824 & 8.21 & No & No \\ \hline PS-10 T & 319.74 & 47.92 & 14.70 & 312 & 189 & 0.046656 & 9.96 & No & No \\ \hline RHD-12 T & 505.15 & 52.09 & 19
# A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Turkiye
###### Abstract  
The recent expansion of the strong-motion observation network, along with the increase in data obtained during major earthquakes and efforts to create consistent metadata for source, path, and site effects for both old and new records, has significantly improved the quality of data and the level of modeling in Turkiye. The mainshock and aftershock records of the 6 February 2023 Kahramanmaras earthquake doublet (\(M_{W}\) 7.8 and 7.7), which are among the most destructive earthquakes in world history, constitute an up-to-date and important data source for this study. In this study, we present new ground-motion prediction models (GMPMs) for shallow crustal earthquakes using strong-motion data recorded in Turkiye. Our GMPMs are calibrated using 20,173 strong-motion records from 1565 shallow crustal earthquakes with depths of less than 35 km that occurred in Turkiye and its vicinity between 1976 and 2023. Our model is valid for magnitudes ranging from 4.0 to 7.8 (\(M_{W}\)), and for the time-averaged 30 m shear wave velocity (\(V_{S30}\)) values of 975 stations, which ranged from 131 to 1862 m/s. In the analyses performed, using the recently developed site amplification model, we calculated the model coefficients using the mixed-effects regression algorithms used by the GMPM developers. Additionally, a heteroscedastic model was created for aleatory variability as a function of \(M_{W}\). The closest distance to the surface projection of the fault plane (\(R_{IB}\)) is between 0 and 350 km. Using the metadata prepared according to these criteria, we derived up-to-date ground-motion prediction models for horizontal-component peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped pseudo-spectral acceleration (PSA) response spectra, at 36 periods ranging from 0.01 to 10 s. The variability in the predictions was decomposed into within-event, between-event, and site-to-site deviations to determine the total standard deviations (\(\sigma\)). Compared to previous models, the proposed GMPMs were developed using a much richer database with recent major earthquakes, and the consistent estimates and lower residuals in the comparisons support the reliability of the models.  
eurmable; acceleration; ground-motion prediction model; regression analysis; seismic hazard; Turkiye 2025  
2025 15 3442
## 4 Case Study
### Communication, Database, Servers, and Ancillary Equipment  
To execute the DT and optimizers in the cloud and in an autonomous way, additional systems are needed. Figure 5 exhibits a simplified schema of the architecture created for this case study, capable of enabling the flow of information between the cloud and the industry operators. The principal assets are as follows:  
Communication system: Responsible for ensuring the efficient transmission of data between the different systems participating. It collects the information from industrial Supervisory Control and Data Acquisition (SCADA) systems, as well as from sensors associated with PV generation, the SESS, EVs, chargers, and any other relevant facility's energy consumption. Its primary function is to establish a secure and coherent data flow between physical systems and the cloud while ensuring database integrity. For variables that were not previously digitized, such as electric vehicle monitoring, data loggers have been used.  
Database: It can be considered as the central component of the system, where all the relevant information is stored and acts as an intermediary between cloud-based applications, physical systems, and the user interface. It stores all measured variables from the plant, along with additional relevant data such as weather forecasts and energy prices. Also, the digital twins and optimization algorithms extract the necessary information for the algorithms and store the generated outputs. Subsequently, other applications, including the IoT platform and the user visualization interface, extract from this system the key insights to provide end users useful information about industry status and operation.  
Server and Cloud System: To enable the execution of the different digital twins and optimizers in the cloud, a computational infrastructure is required. In this case, a server, Application Programming Interfaces (API's), and other tools for orchestrating the different workflows were considered. The server is responsible for hosting and  
Figure 5: Simplified schema of the architecture designed to enable the communication between DTs, optimizers, the database, the industry, forecasts, and end user.  
executing the algorithms, while the API functions as a communication interface, facilitating various operations such as triggering DT and optimizers, interacting with the database, and processing data. Since each algorithm deployed in the cloud has distinct execution times, and some rely on the outputs of others, workflow orchestration tools like Apache Airflow [43] are employed to manage dependencies and ensure efficient task execution.
* IoT platform and user interface: The IoT platform enables the integration of different software tools, leveraging the key outcomes provided by DT and decision support systems. The user interface is responsible for the data visualization and user interaction with the monitored systems, providing access to outputs generated by the cloud-based applications.
# Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection
## 4 Experimental Results
### Discussion  
In this section, the proposed framework is tested on the test datasets (Bot-IoT, CI-CIDS2017, and UNSW_NB15) over numerous evaluation metrics, and the detailed ablation is conducted to compare with other SOTA methods. To evaluate and analyze the effectiveness of attack detection cases, we compared the proposed approach with recently published attack detection methods. To perform this task, we employed widely used estimation metrics (precision, recall, and F1), as detailed in these publications [56; 57; 58; 59; 60]. To classify the results obtained, the following cases were distinguished:  
True positives (TP): attack present and correct classification;  
True negatives (TN): attack not present and correct classification;  
False positives (FP): attack not present and incorrect classification;  
False negatives (FN): attack present and incorrect classification.  
A confusion matrix is a table used to estimate a classifier's goodness. There are the events considered in the rows, while in the columns, their classification is present. The data on the main diagonal represent correct classifications. From this table are also derived three merit factors that contribute to the analysis of a classifier's performance: the precision (P) (6) merit factor takes into account the number of correct attack identifications concerning the total number of detections. It is obtained with the following formula:  
\[\text{P}=\text{TP}/\text{TP}+\text{FP} \tag{6}\]  
The recall (R) (7) factor of merit takes into account the number of correct attack identifications compared to the total number of attacks made:  
\[\text{R}=\text{TP}/\text{TP}+\text{FN} \tag{7}\]  
Finally, the F1-score factor (F1) (8) is given by the harmonic average of precision and recall and measures the accuracy of the classification of events:  
\[\text{F1}=2\,\cdots\,\text{R}\,\cdots\,\text{P}/\text{P}+\text{R} \tag{8}\]
###### Abstract  
Selective laser melting process has already been developed for many metallic materials, including steel, aluminum, and titanium. The quasistatic properties of these materials have been found to be comparable or even better than their conventionally-manufactured counterparts; however, for their reliable applications in operational components, their fatigue behavior plays a critical role, which is dominated by several process-related features, like surface roughness, remnant porosity, microstructure, and residual stresses, which are controlled by the processing features, like imparted energy density to the material, its corresponding solidification behavior, the cooling rate in the process, as well as post-processing treatments. This study investigates the influence of these parameters on the cyclic deformation behavior of selective laser melted as well as hybrid-manufactured aluminum alloys. The corresponding microstructural features and porosity conditions are evaluated for developing correlations between the process conditions to microstructure, the deformation behavior, and the corresponding fatigue lives. From the numerical point of view, damage development with respect to process-induced cyclic deformation behavior is assessed by the phase-field method, which has been identified as an appropriate method for the determination of fatigue life at the respective applied stress levels. Fatigue strength of SLM-processed parts is found better than their cast counterparts, while hybridization has further increased fatigue strength. No effect of test frequency on the fatigue life could be established.  
selective laser melting; Al-alloys; porosity; residual stresses; deformation behavior; phase-field method +
Footnote †: journal: Applied Soft. 2018, 8, 1948; doi:10.3390/app8101948
# HS-SPME Gas Chromatography Approach for Underivatized Acrylamide Determination in Biscuits
## 2 Materials and Methods
### Optimization of HS-SPME Extraction Conditions  
Optimization of HS-SPME extraction conditions was carried out using Box-Behnken experimental plan with 3 experimental factors: temperature (T), propanol volume (V), and fiber exposure time (t), at 3 levels, in a total of 39 experiments (13 conditions, 3 replicates each) (Table 2). Acrylamide chromatographic peak area was used as response variable.  
Experimental factor levels were selected according to the HS-SPME specifications. Minimum and maximum temperatures were respectively set to warranty no dependence on laboratory temperature conditions (\(\geq\)30 \({}^{\circ}\)C) and avoid sample deterioration and formation of contaminants or artifacts (\(\leq\)60 \({}^{\circ}\)C) [31]. The total solution volume with different water to propanol ratios was always maintained at 40 mL in a 120 mL crew cap vial corresponding to a ratio of the volume of the liquid phase to the headspace volume (1/\(\beta\)) of 0.5. TheHS-SPME procedure, that included thermostatization and fiber exposure, was always 60 min.  
The HS-SPME conditions were initially determined using a GC with a flame ionization detector (FID) with a high AA concentration, stock solution A (5 g/L). To approach the range of AA known to exist in biscuits, the amount used in the following trials was reduced using AA stock solution B (0.01 g/L) and using a GC-qMS for quantification. Maintaining HS-SPME temperature at 60 \({}^{\circ}\)C, and thermostatization and fiber exposure times of 15 and 45 min respectively, a full factorial design for the propanol (V) volume factor was performed using 0, 10, 20, 30, and 40 mL of propanol for a total volume of 40 mL, assuming additive volumes. The limiting conditions, namely 0 and 40 mL of propanol, previously excluded from the Box-Behnken design were also included in this new data set. The glass vial was placed in a thermostatized bath adjusted to 60 \({}^{\circ}\)C \(\pm\) 0.1\({}^{\circ}\)C for 15 min, and then the CAR/PDMS fiber (selected fiber) was inserted in the headspace for 45 min. All measurements were performed at least in three independent replicates.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 5 Conclusions  
In this paper, an approach for crop classification for HSI images is proposed. Firstly, the bands are selected based on agricultural phenology, where biophysical properties of plants are also taken into consideration. Then, a two-dimensional CNN is trained with the extracted bands. The proposed method is tested and validated on two benchmark datasets. The average accuracy of the crops corn and soya from Indian Pines is 96.81% and 97.75%. For the Salinas dataset, the average accuracy of the crops fallow and lettuce romaine is 97.93% and 98.01%, respectively. The results clearly show the effectiveness of the proposed band selection in selecting the required features necessary for crop classification. In the future, we can incorporate spatial information along with spectral information and extend it to more crops from the datasets.  
Conceptualization, L.A.; Data curation, L.A.; Funding acquisition, L.A. and A.F.; Methodology, V.R. and K.L.N.B.P.; Project administration, M.P.; Resources, A.F.; Software, A.F.; Supervision, M.P.; Validation, V.R. and K.L.N.B.P.; Writing--original draft, V.R. and K.L.N.B.P.; Writing--review & editing, M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors thank the Vellore Institute of Technology, Vellore for providing a VIT seed grant for carrying out this research work.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class No** & **Class Name** & **Accuracy in \%** \\ \hline
1 & Red roof & 99.63 \\
2 & Road & 94.76 \\
3 & Bare soil & 99.43 \\
4 & **Cotton** & **99.82** \\
5 & Cotton firewood & 96.85 \\
6 & **Rape** & **99.62** \\
7 & **Chinese cabbage** & **97.81** \\
8 & Packchoi & **97.93** \\
9 & Cabbage & **99.59** \\
**10** & **Tuber mustard** & **98.45** \\
**11** & **Brassica parachinesis** & 97.75 \\
**12** & **Brassica chinesis** & 96.38 \\
**13** & **Small brassica chinesis** & **97.81** \\
**14** & **Latuca sativa** & **99.02** \\
15 & Celuce & 97.21 \\
**16** & **Film covered lettuce** & **99.72** \\
**17** & **Romaine lettuce** & **98.41** \\
**18** & **Carrot** & **99.63** \\
**19** & **White radish** & **97.70** \\
**20** & **Garlic sprout** & **98.82** \\
21 & Broad bean & 94.88 \\
22 & Tree & 99.01 \\ \hline \end{tabular}
\end{table}
Table 8: Ablation experiments of the proposed network.
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 4 Results
### Stationarity Analysis  
The ADF test was employed in order to analyse stationarity of the signals based on the Akaike Information Criterion. Table 7 shows the \(p\)-values, for all of the period under analysis (R2), of the three pumps. As the table shows, all the ADF statistic test values are strongly negative, indicating strong evidence against the null hypothesis of non-stationarity. In other words, the time series are proven to be stationary in this period, based on the visual evidence seen when looking at Figures 1-4 and also the information that comes from  
Figure 4: Seasonal decomposition of variable VCP 3.  
the factory: the pumps receive periodic maintenance interventions so that their vibrations are kept at safe levels over time.  
Nonetheless, when the analysis is performed in smaller time windows, stationarity may not be guaranteed, according to the initial research question of the present research. The most important question, however, is what window sizes will work to show the relevant non-stationarity of the time series, if there is any. Considering thsi project's objectives, we decided to experiment with two distinct sizes of rolling windows: weekly and daily. This segmentation aims to obtain information that could lead to a more holistic approach.  
The weekly variation of the \(p\)-value throughout the time series is illustrated in Figure 5. The figure shows that the \(p\)-value varies from approximately zero to values above 0.75 in VCP 2 and 0.6 in VCP 1 and VCP 3. This indicates that there are weeks when the vibration follows a non-stationary path, which is the pattern being sought after in the present research.  
An example of the daily variation of the \(p\)-value throughout the time series is illustrated in Figure 6. As this figure shows, daily variations exhibit constant fluctuations, which make any analysis or monitoring very difficult to perform. The weekly signals produce more stable results, but even so there is certainly a lot of noise, and a more stable signal is required for more confidence in the results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Analysed** & **VCP1** & **VCP2** & **VCP3** \\ \hline ADF Statistic test & \(-18.90\) & \(-11.43\) & \(-6.16\) \\ \(p\)-value & 0.0 & \(6.53\times 10^{-21}\) & \(7.18\times 10^{-08}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the ADF test.  
Figure 5: Evolution of the \(p\)-value over time, using weekly moving windows, from February to October.
## 4 Conclusions  
In this study, the performance of a modified RO plant was evaluated by introducing an electric field into the system. The effects of different voltage levels on the RO desalination performance and membrane fouling were investigated. The results of the experiment illustrated that the use of an electric field beneficially impacted permeate flux, reduced the energy expenditure per unit of water produced, and abated membrane fouling compared to having no electric field, thus lengthening the membrane life.  
The results of the experiments demonstrated that the impact of the electric field on solutions with concentrations of 1000, 2000, and 3000 mg/L became more pronounced with an increasing voltage magnitude. The highest voltage (25 V) increased the permeate flux by 1.02%, 1.23%, and 1.37%, respectively. Furthermore, by applying an electric field of 25 V to solutions with concentrations of 2000 and 3000 mg/L, the _SEC_ was reduced by 31% and 59%, respectively. However, the solute rejection was not significantly improved by the use of the electric field.  
Regarding membrane fouling, the electric field inhibited the decrease in permeate flux due to Ca\({}^{2+}\) contamination. Nevertheless, the final permeate flux reduction was similar to that without the use of the electric field. On the other hand, the effect of HA fouling on permeate flux was more pronounced, with the permeate flux decreasing from 5.49 to 4.81 L/(m\({}^{2}\cdot\)h) after 120 h of operation. However, without an electric field, the permeate flux decreased to 4.23 L/(m\({}^{2}\cdot\)h). Further studies are needed to determine the exact mechanism behind this phenomenon.  
In conclusion, the results demonstrate that the use of an electric field can enhance the efficiency of RO treatment for brackish water and mitigate membrane fouling. This method of reducing the fouling of membranes and improving the efficiency of RO by incorporating electric fields has great potential for desalination and purification applications.  
Moving forward, our research in this field will be directed toward treating high-salinity water while also considering the economic feasibility, thus establishing a foundation for the utilization of electric fields in RO membrane engineering.  
Conceptualization, C.F. and X.Y.; methodology, C.F. and X.Y.; formal analysis, C.F. and X.Y.; investigation, C.F.; resources, X.Y.; data curation, C.F.; writing--original draft preparation, C.F.; writing--review and editing, C.F. and Y.G.; visualization, C.F.; supervision, X.Y.; project administration, C.F.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by a grant from [PERSON].  
Not applicable.  
The research data will be available upon request.  
The authors are grateful to [PERSON] for his financial support.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.