esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### Security Analysis
#### 4.7.6 Limitations and Acknowledged Threats  
While our framework provides strong privacy guarantees, some information leakage remains possible, particularly in cases of misconfigurations. The server does learn what types of operations are being requested, which could potentially reveal something about the nature of the image processing tasks. Initially, we considered using homomorphic encryption to classify operations and hide this information, but we realized that, if the server offers only a limited set of services, it might deduce the operation type anyway. Furthermore, the server can observe timing patterns that might allow it to infer the complexity of the images being processed, since more complex images may take longer to process, even with FHE encryption. The server also observes access patterns, including the frequency and timing of client requests, which could potentially be analyzed to understand usage behavior.  
We are actively considering several approaches to address these remaining privacy concerns in future versions of our framework. Programmable bootstrapping could help hide operation types from the server, making it harder to determine what specific image processing operations are being performed. We could also implement padding and dummy operations to obscure access patterns, making it difficult for the server to distinguish between real requests and noise. Additionally, differential privacy techniques could be applied to obfuscate usage patterns, adding mathematical guarantees about the privacy of client behavior, even when the server observes aggregate statistics.
## 3 Results and Discussion
### Development of Seismic Microzonation Map
#### 3.1.1 Determination of Peak Surface Acceleration  
Peak surface acceleration (PSa) is the amount of vibration received by the soil surface; in this study, it refers to the seabed. It is the results of the ground response analysis conducted for the study area. As discussed in a previous section, a non-linear ground response analysis for the one-dimensional analysis, namely NERA, was adopted in this study. Six (6) seismic events were used, three events from Sumatera and three events from Sabah varies from 2004 to 2016. PSa were obtained for five (5) different return periods (100-year, 200-year, 500-year, 1000-year, and 2500-year return periods). In this sub-section, the sample of PSa results is presented for far-field soil type D. Thus, the effects of different sites can be viewed.  
The results presented in this section are based on GRa conducted where the value of PGa was obtained from the bottom point of the last layer of the soil in the analysis, which was considered as the bedrock, while PSa was the value of the vibration in the acceleration predicted on the first layer, which was the surface (known as the seabed in this study). The result for PSa was taken from the acceleration value on the first layer. In the sample, PGa and PSa were analyzed by providing the mean according to far-field. Figure 7 presents PGa and PSa for soil type D. Figure 7a represents the PGa and PSa values for site D1 where the minimum PGa was 0.02 g at the 100-year and 200-year return periods while the maximum value was 0.05 g at the 2500-year return period. Referring to Figure 7b, the results of PGa and PSa for site D2 can be seen. The PGa value is within 0.02 and 0.05 g while for the PSa value, a slight increment can be seen for all return periods except for the 100-year and 200-year return periods when compared with site D1. The predictions of PSa values range from 0.03 to 0.057 g. Lower PSa values can be seen when the site comprises stiffer soil.  
Overall, GRa was conducted in the prediction of vibration received by surface or seabed by using NERA where six seismic events were replicated to predict the amount of the vibration on the surface. Based on seismic events, PSa for far-field was lower compared to a combination of far-field and near-field events, and near-field events. A comparison  
Figure 7: PGa and PSa for D1 and D2 under far-field seismic events.  
of the soil types resulted in soil type D being lower compared to soil type E. Hence, the average PSa from a combination of far-field and near-field seismic events was used in developing a map for the Malay Basin under seismic microzonation. Results found in this study align with previous studies where seismic events from the far-field were found to produce smaller ground acceleration but in a longer period compared to near-field seismic events [20; 36; 37]. A higher value was found in higher return periods, such as 1000-year and 2500-year return periods. The same pattern was found by other researchers [20; 21; 38].
## 2 Background
### Requirements for the Calibration Data Management and Data Formats
#### 2.3.1 General Guidelines and Good Practices  
In addition to international standards and regional legislations, there also exist several non-obligatory recommendations in the form of good practices or guidelines by different organizations, which have become widely used and unofficially agreed de-facto standards for the industry. These de-facto standards can be guidelines by NMIs or regional metrology organizations (RMO) on calibration procedures for specific instrument types, guidelines by societies and associations of the experts in the field [50,51], or guides and whitepapers by companies, e.g., instrument manufacturers, which are typically based on the relevant legislative requirements.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 3 Propose Scheme
### Calculation of Competency Value Based on Fuzzy Logic
#### 3.4.1 Procedure  
For each one-hop neighbor node, a node calculates a competency value as follows.  
* **Calculation of the three factors:** Calculate the vehicle velocity, the number of vehicles moving to the same direction, and the channel condition information mentioned before.
* **Fuzzification:** Use predefined linguistic variables and membership functions to convert the factors to the corresponding fuzzy values.
* **Mapping and IF/THEN rule-based evaluation:** Map the fuzzy values to the predefined IF/THEN rules and combine the rules to get the rank of the neighbor as a fuzzy value.
* **Defuzzification:** Use a predefined output membership function and defuzzification method to convert the fuzzy value to a numerical value.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### First Set of Tests
#### 5.2.1 Method One  
This method was only successful for tests 1, 2, and 4. Figure 11 shows the limits of the threshold values. Observing the trends with regards to the frequency in tests 1-3, the minimum values are  
Figure 10: Method three result (green signal) using the average value against the temporal signal (black) for testing a 2% CSA defect.  
Figure 9: Calculated threshold using method three on Test 5 (red) against the second version comparisonValue (black). In the case of method one, the threshold would be a constant line instead of changing based on the iteration.  
increasing, while the maximum values are decreasing up until the point where the minimum will result in an outlier in Test 3. The same trend is observable for the minimum values in tests 4-6, where, although the maximum values could not be detected, the minimum values are increasing with regard to the frequency. With regard to the defect CSA (and SNR), the algorithm can detect the 2% CSA defect using a 30-kHz excitation frequency (Test 4). Nonetheless, when the frequency is increased to 34 kHz in Test 5, although the defect's SNR is increased by 1.68 dB, this method resulted in a false alarm, even at the minimum limit. Thus, it can be concluded that this algorithm is more sensitive to the frequency and local amplitude of the defect region rather than its temporal SNR and the overall energy of the noises in the region. Since higher energy is associated with lower frequencies, lower frequencies tests would achieve better results using this method for Defect 1.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 4 Experimentation and Analysis
### ECA-MRANet Model Performance Analysis
#### 4.4.2 Model Performance Analysis across Working Conditions  
In real-world environments, the working condition parameters tend to change irregularly during the production process, so the collected signal samples are unstable. Therefore, in this section, we will investigate the fault recognition capability of the ECA-MRANet model across working conditions and also compare it with three alternative models, namely MRANet, RANet, and CNN. MRANet is the ECA-MRANet model without adding the ECA, which concatenates features learned from the three channels in parallel, connecting them to the fully connected layer for fault identification. RANet is a branch of the MRANet model, which combines the residual network structure with CBAM. CNN is a single-channel network model that contains only the residual structure. For the MRANet and CNN models, the inputs are 32 \(\times\) 32 CWT time-frequency diagram. Table 7 shows three cross-condition combination data sets X1, X2, and X3, each containing 7200 samples in the training set and 450 samples in the test set. Specifically, the three data sets in Table 5 are combined two by two to form a training set, and the remaining data set is used as a test set. Taking Data Set X1 as an example, the training sets of Data Sets A and B in Table 5 are used to train the model, and the test set of Data Set C is used to verify the generalization ability of the model.  
Table 8 shows the fault recognition accuracies of the four models, ECA-MRANet, MRANet, RANet, and CNN, under different working conditions. The diagnostic accuracy of the multi-channel residual attention network model combining ECA is significantly higher than that of the traditional CNN network model. This indicates that the multi-channel input can enrich the network's learning of fault information from different perspectives, thus improving the diagnostic performance of the network. Among the four network models in Table 8, the average accuracy of the ECA-MRANet model reaches 96.96%, which is 1.23%, 2.38%, and 4.17% higher compared to the MRANet, RANet, and CNN models, respectively, which indicates that the ECA-MRANet model has a strong generalization ability under different working conditions, and further illustrates that the feature by ECA fusion is superior.
## 2 Effect of Earthquake on Soil Slopes  
If a slope is situated in a region subject to earthquakes, the design must satisfy these adverse conditions. The effect of the shaking depends on whether the shear strength of the soil material remains adequate during cyclic loading or shaking results in a significant loss of strength. Since deformation is the result of shearing or sliding movement, slope stability analysis is necessary to ensure that the factor of safety is adequate to satisfy dynamic loading and minimize the resulting deformation. In the case of loose, saturated, cohesionless material, the total lack of strength due to cyclic loading might induce liquefaction, which is when a cohesionless saturated or partially saturated soil loses structural strength as a result of an applied stress (such as trembling during an earthquake or another abrupt change in stress condition), and a material that is normally a solid acts as a liquid. Liquefaction assessment requires a more complex analysis and additional data, such as pore water pressure measurements, and is beyond the scope of this paper.  
The susceptibility of a slope to failing due to a seismic event is also determined through the critical acceleration coefficient \(\mathrm{k_{y}}\). The coefficient of critical acceleration \(\mathrm{k_{y}}\) is an appropriate measure of a soil or rock mass' resistance to earthquake induced sliding. The value of the coefficient depends on the slope inclination \(\beta\). Essentially, \(\mathrm{k_{y}}\) is as important for the sliding block model method [55], as the static safety factor is for the limit equilibrium method; these two variables are linearly related [56]. According to [PERSON] and [PERSON] [57], \(\mathrm{k_{y}}\) is a measure of safety factor, and is the yield acceleration of the slope. [PERSON] and [PERSON] [57] proposed a method to relate these two coefficients which is independent of the assumed failure mechanism and the material properties. The coefficient of critical acceleration \(\mathrm{k_{y}}\) is unique for each slope and is calculated when the safety factor is equal to one.
# A Comprehensive Review of Multimodal Analysis in Education
## 4 Applications in Education
### Special Needs and Inclusive Education  
This category refers to applications that aim to support learners with diverse needs, including disabilities, language delays, or age-related limitations. These studies apply multimodal technologies to create more accessible, differentiated, and supportive learning environments. Examples include inclusive learning platforms, mobile systems designed for learners with cognitive or physical challenges, or approaches that value gestural and non-verbal communication as valid learning expressions.  
For instance, [PERSON] et al. [13] developed an IoT-based system for Applied Behaviour Analysis (ABA) therapies targeting students with special education needs (SENs). Their system integrates physiological sensors, environmental data, and motion capture to enhance the predictive accuracy of behaviour change in SEN students, achieving an impressive 98% accuracy and 97% precision in behaviour change prediction. Similarly, [PERSON] [18] explored the use of smartbands in primary education settings to monitor student stress levels and guide behavioural interventions. Their approach leverages HRV and EDA to provide real-time insights into student well-being, supporting more precise and contextually informed interventions for students with SEN.
## 3 Materials and Methods
### SPME-GC-MS Analysis of Aroma Compounds  
The determination of the free and liberated bound aroma compounds was carried out following the methodology described by [PERSON] et al. [4], with a slight modification. A 50/30 \(\upmu\)m polydimethylsiloxane/divinylbenzene/carboxozen (PDMS/DVB/CAR) solid-phase microextraction (SPME) extraction head (Supelco, Bellefonte, PA, USA) was inserted. For the GC\(-\)MS determination, GC\(-\)MS analysis was performed on a GC\(-\)MS/MS Quantum TSQ 9000/TRACE 1310 (Thermo Fisher Scientific, Waltham, MA, USA) using a J&W 122-4732 DB-17 ms (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column. After adsorption on SPME fiber of 50/30 \(\upmu\)m DVB/CAR/PDMS (Supelco, Bellefonte, PA, USA) on a magnetic stirrer (Corning, NY, USA) at 50 \({}^{\circ}\)C for 30 min, the extraction fiber was loaded into the GC inlet set at 220 \({}^{\circ}\)C and desorbed without splitting the helium stream for 2 min. The oven temperature program was as follows: 50 \({}^{\circ}\)C, held for 6 min, followed by 3 \({}^{\circ}\)C/min up to 120 \({}^{\circ}\)C, held for 2 min, 10 \({}^{\circ}\)C/min up to 250 \({}^{\circ}\)C, held for 2 min. The temperature of the ion source was set at 230 \({}^{\circ}\)C and the mass spectra were recorded at 70 eV in the electron impact (EI) ionization mode. The MS range was collected from 29 \(m\)z to 540 \(m\)/z and the scanning rate was 2.88 scan s\({}^{-1}\). The carrier gas was helium, and the flow rate was 1.0 mL/min. The corresponding peaks of individual compounds were identified based on the NIST/WILEY spectrum library and sample peak areas were identified and quantified relative to the RI and mass spectra of available authentic standards. Each standard was dissolved in methanol to obtain a stock solution. Stocks were diluted in the appropriate aqueous buffer for free volatiles (pH 3.2, citrate-phosphate buffer solution) and bound volatile compounds (pH 5, citrate-phosphate buffer solution), respectively to obtain individual standard curves from between 5 and 9 concentration levels (2.5, 5, 10, 20, 40, 80, 160, 320, 640 ng/g, Table S1 and Figure S1). The standards were extracted under the same conditions as used for the grape samples (Section 3.4).
## 5 Conclusions  
This paper presents a computationally effective NR-PSS synchronization scheme for 5G NR communication systems. By exploiting the autocorrelation feature of the differential NR-PSS sequence, we formulate a simplified variation of the proposed joint ICFO and CIDS synchronization method. In order to confirm the viability of the proposed NR-PSS synchronization scheme, we analytically compute the probability of detection failure, and further evaluate its accuracy by comparing it with simulation results. Numerical examples show that the proposed ICFO and CIDS detection method considerably reduces arithmetic operations while achieving the same level of detection performance as benchmark detection methods. Interestingly, the number of NR-PSS symbols used for the average estimate decreases significantly with the increase in the frequency selectivity of the channel, while still maintaining the complexity-performance trade-off. Upon implementing our proposed synchronization receiver in 5G NR systems, the arithmetic operation can be decreased by more than 90% irrespective of the channel model and SNR condition, which in turn leads to reduced power consumption and eventually contributes to improving the battery lifetime of UE devices.  
Conceptualization, Y.-H.Y. and Y.-A.J.; methodology, Y.-H.Y. and Y.-A.J.; software, Y.-A.J. and I.H.; writing--original draft preparation, Y.-H.Y. and Y.-A.J.; writing--review and editing, Y.-H.Y., S.-H.L. and I.H.; visualization, Y.-H.Y. and Y.-A.J.; supervision, Y.-H.Y. and S.-C.C. All authors have read and agreed to the published version of the manuscript.  
This work was in part supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIT) (No. RS-2023-00219051), in part supported by R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2020M3C1C1A02086428), and in part supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2022-0-00686, Development of High-Resolution Vector Network Analyzer HW Platform Supporting Sub-THz Frequency Band).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: The detection probability of the benchmark and proposed NR-PSS synchronization schemes versus \(M_{0}\): (**a**) SNR \(=-5\) dB (**b**) SNR \(=-10\) dB.
## 3 Results and Discussion
### Statistical Analysis
This supports the notion that intertidal classification can be improved in an adaptive manner when considering that tidal flats comprise a wide variety of tidal flat sedimentary facies,  
Figure 4: Temporal variability of the radar backscattering coefficient gamma naught value versus tide height (**a**) ocean, (**b**) salt pond, (**c**) land, and (**d–f**) tidal flats with the best fitting logistic function as red solid lines. textures, and exposure times. The error term from the best fitting logistic function may be explained by environmental factors, such as drainage rate, exposure time, and soil porosity [30]. Additionally, our method reduces the problem of choosing an appropriate fixed threshold to distinguish tidal flats from ocean and land in the Sentinel-1 SAR images.
## 3 Results and Discussion
### Textural and Structural Characterization
In addition, the lattice parameters of mixed oxides remain practically unaffected, as compared to the pure oxides, revealing well-dispersed phases of the different constituent oxides. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{4}{c}{**XRD Analysis**} \\ \cline{2-5}  & \multirow{2}{*}{**Phase Detected**} & \multicolumn{2}{c}{**Average Crystallite Size,**} & \multirow{2}{*}{**Lattice Parameters (nm)**} \\  & & & \(\mathbf{D_{XRD}}\) **(nm)** & \\ \cline{3-5}  & & **CeO\({}_{2}\)** & **ZnO** & \\ \hline CeO\({}_{2}\) & Cerium (IV) oxide & 12.99 \(\pm\) 0.01 & - & a = b = c = 0.5430 \(\pm\) 0.0001 \\ CeO\({}_{2}\)/ZnO–0.2 & Cerium (IV) oxide & 12.14 \(\pm\) 0.01 & 44.41 \(\pm\) 0.05 & a = b = c = 0.5439 \(\pm\) 0.0001 \\  & & & & a = b = 0.3180 \(\pm\) 0.0001; c = 0.5232 \(\pm\) 0.0001 \\ CeO\({}_{2}\)/ZnO–0.4 & Cerium (IV) oxide & 11.92 \(\pm\) 0.01 & 44.65 \(\pm\) 0.05 & a = b = c = 0.5430 \(\pm\) 0.0001 \\  & & & & a = b = 0.3262 \(\pm\) 0.0001; c = 0.5225 \(\pm\) 0.0001 \\ CeO\({}_{2}\)/ZnO–0.6 & Cerium (IV) oxide & 11.56 \(\pm\) 0.01 & 39.32 \(\pm\) 0.05 & a = b = c = 0.5439 \(\pm\) 0.0001 \\  & & & & a = b = 0.3267 \(\pm\) 0.0001; c = 0.523 \(\pm\) 0.0001 \\ ZnO & Zincite & - & 34.50 \(\pm\) 0.05 & a = b = 0.3272 \(\pm\) 0.0001; c = 0.5233 \(\pm\) 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structural characteristics of CeO\({}_{2}\), ZnO and the CeO\({}_{2}\)/ZnO samples. Figure 2: XRD patterns of CeO\({}_{2}\), ZnO and the CeO\({}_{2}\)/ZnO samples.
# Optimal Fleet Transition Modeling for Sustainable Inland Waterways Transport
## 4 Results  
This section presents the results obtained from applying the optimization model to the case study of Orta lake. The results are visualized in Figure 4, which shows four subplots of  
Figure 3: Routes considered for the Orta lake case study with intermediate stops and distance.  
the state of charge of all necessary boats in four scenarios. These scenarios are characterized by four different charging powers of the charging stations that are assumed to be built in each port: 1000 kW, 500 kW, 200 kW, and 100 kW.  
In the scenario with 1000 kW of charging power, the results show that the entire fleet of boats (3) can be substituted with the same number of electric boats without any change to the routes or to the timetable. Furthermore, the state of charge of the entire fleet never goes lower than 50%, indicating a sufficient buffer for unexpected events or fluctuations in energy consumption.  
The scenario with 500 kW of charging power also allows for the substitution of the entire fleet with three electric boats. However, in this case, the state of charge of these boats goes under 50%, reaching very low values. This represents a risk for the safe management of the fleet, as it leaves less room for unexpected events or fluctuations in energy consumption.  
In the scenarios with lower charging power (200 kW and 100 kW), the minimum number of vessels increases to four. These scenarios illustrate the impact of the charging rate on the fleet configuration and operation. The lower charging rate of the electric batteries results in less steep charging segments compared to the other charts, indicating slower charging times.  
Figure 4 also shows scatter points marked whenever the model adds a new electric vessel to the fleet. These points appear on all four charging power scenario subplots. The reason is that the model initializes with only a single electric boat, then incrementally expands the fleet over the day as needed to meet scheduled departures. Whenever there is a scheduled route departure from a port but no electric vessel with adequate charge is available, a scatter point indicates the model assigning a new electric boat. The scatter points  
Figure 4: Number of boats and their state of charge over 24 h for each charging power scenario (1000 kW, 500 kW, 200 kW, 100 kW).  
visually demonstrate the discrete events of new vessel addition across all the scenarios. Since the charging power limitations make the existing vessels insufficient to cover the timetable, the model progressively supplements the fleet at points during the day.  
These results provide insights into the feasibility and implications of the transition to sustainable inland waterways transport in Orta lake. They highlight the importance of the charging infrastructure, specifically the charging power of the charging stations, in determining the optimal fleet configuration and operation.