esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Irradiation Response and Linearity
After programing with a Vg= \(-80\) V bias for three seconds, the drain current was measured at Vd = \(-15\) V and Vg = \(-15\) V. Figure 7a demonstrates the response of the detector at the electric field at Vd = \(-15\) V and Vg = \(-15\) V, when the radiation beam was turned on and off with variable dose rates during the beam-on times of 600, 200, 300, 400, and 60 cGy/min for each step, respectively. Figure 7b represents the corresponding slope of the current measured when the radiation beam was on. The sensitivity of detectors during real-time readout was determined as the slope of the current-time plot when the radiation was on. The data are shown in Table 1. At this electric field, the drain current did not stabilize and continued to shift negatively as a result of charging of the electret (i.e., programming continued during the experiment). The slope values (in units of nA/s) reported in Table 1 were corrected with the average value of the slope 10 s before the beam was on and after the beam was turned off to determine the net change in slope caused by irradiation. All devices showed an approximately linear dependence on the dose rate range from 60 cGy/min to 600 cGy/min, with an average sensitivity of devices from 1.0 nA/s to 8.1 nA/s, respectively, at Vd = \(-15\) V and Vg = \(-15\) V.  
Figure 7: Real-time readout of a 6 MV photon beam after programing with a negative Vg = \(-80\) V bias for three seconds; (**a**) the drain current was measured at Vd = \(-15\) V and Vg = \(-15\) V with radiation on and off with dose rates of 600 cGy/min, 200 cGy/min, 300 cGy/min, 400 cGy/min, and 60 cGy/min. (**b**) The corrected slope values of the current when the radiation beam was on at corresponding dose rates.
## 3 Results and Discussion
### Quantitative Comparison  
This section employs quantitative analysis methods to compare the performance of the three networks, focusing primarily on network convergence speed and plate recognition accuracy. Network convergence is typically assessed by observing changes in network loss values. If the loss value decreases with the increase in epochs and stabilizes near a certain value, the network is considered to have reached convergence. Generally, faster network convergence indicates superior performance. The loss value trends for the three networks used in the system are shown in Figure 7, where the red line represents the loss function using the validation set, and the blue line represents the loss function using the training set.  
Overall, under the parameter setting of epoch = 60, all three networks reached convergence. Regarding training set loss values, the U-Net network started with the lowest initial loss (<0.7), PSPNet had the highest (>1.3), and DeepLab V3+ was in between. The training loss values of all three networks decreased with the increase in epochs. U-Net's loss value change was relatively gradual and remained almost constant after epoch = 40. PSPNet and DeepLab V3+ showed significant reductions in the first 10 epochs, then slowed down, similarly stabilizing after epoch = 40. The validation set loss value trends were similar, with U-Net having the lowest initial loss (<0.6), followed by PSPNet (>1.0), with DeepLab V3+ in between; all three networks' validation set loss values remained largely unchanged after epoch = 40.  
In summary, the training and validation loss values of all three networks stabilized after epoch = 40, indicating that each had reached convergence, and their convergence speeds were comparable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of the Network** & **Learning Rate** & **Batch Size** \\ \hline U-Net & \(1\times 10^{-4}\) & 32 \\ PSPNet & \(1\times 10^{-4}\) & 32 \\ DeepLabV3+ & \(5\times 10^{-5}\) & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter configuration for each model.  
Figure 7: Loss functions of different methods.  
The accuracy of plate recognition is the most direct reflection of network performance. Comparing the plate recognition accuracy of the three networks (Table 2), it was found that all three could effectively identify clean plates (MIOU > 60%, MPA > 75%). Among them, U-Net had the highest recognition accuracy, with MIOU and MPA values of 68.64% and 78.21%, respectively. PSPNet had the lowest accuracy, with a MIOU of 63.72% and MPA of 75.66%, and DeepLab V3+ fell in between. Overall, without any parameter optimization, the three networks achieved relatively high recognition accuracy (accuracy difference < 5%), demonstrating the suitability of deep learning technology for effective plate recognition.
## 2 Materials and Methods
### Experimental Setup  
The setup of the experimental study is the same as the system model shown at Figure 1, but we used a 128-element linear array probe with an element pitch of 0.375 mm that was manufactured by Japan Probe (Kanagawa, Japan). We selected a single transmitter and 28 receivers (\(N_{\text{R}}=28\)) with an element pitch of 0.75 mm. The distance between the transmitter and the first receiver was 18.75 mm. The center frequency of the transmitted wave was 1.0 MHz. We used two test specimens: (1) a 4-mm-thick copper plate (with a shear wave velocity of 2260 m/s and a longitudinal wave velocity of 4650 m/s) and (2) a 4-mm-thick transversely isotropic bone-mimicking plate (Sawbones, Vashon, WA, USA). The elastic parameters of the bone-mimicking plate have been determined in previous studies [20; 21]. The density, shear wave velocity, longitudinal wave velocity along fibers, and longitudinal wave velocity orthogonal to the fibers are 1.64 g\(\cdot\)cm\({}^{-3}\), 1620 m/s, 3570 m/s, and 2910 m/s, respectively [20; 21]. We put the probe parallel to the fibers.  
Figure 2: Proposed diagonal loading (DL) technique.  
In the proposed method, we set \(M_{\rm th}=N_{\rm sub}-1\), \(\omega_{\rm w}=0.5\) MHz, \(\eta_{2}=-40\) dB, and \(N_{\rm sub}=15\). We used \(\omega_{1}=4.9\) MHz and \(\omega_{2}=5.5\) MHz to select a frequency range that should include only noise.  
We prepared the DL factor using values of \(\eta_{1}\) within the \(-100\) dB to \(0\) dB range at intervals of \(10\) dB. \(M_{\rm th}=N_{\rm sub}-1\) represents the maximum value of \(M\) when using a sub-array size of \(N_{\rm sub}\).  
In the conventional SVD method, we used values of \(N=5\) and \(N_{\rm sub}=24\) to match those used in the conventional study [21]. In the previous study, multiple transmitters were used. The use of multiple transmitters and of sub-arrays is theoretically the same.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 4 Implementation Results
### Secret Key Identification and Prevention against DPA Attacks  
Once after the collection of power traces (as described previously in Section 4.1), we used the MATLAB script along with Algorithm 1 (implemented in MATLAB) to predict the key-bit values. Moreover, for each power trace, the prediction was made on the highest correlations calculated and plotted for the average square of the total amplitude of the differentials on each keybyte separately. The predicted bit values or correlation plot for the first-byte of the 128-bit secret key (in decimal form) are shown in Figure 4. Furthermore, the corresponding correlated values for each key byte are given in Table 2. For a first-byte of a 128-bit secret key, the power traces of DPA attack with the inclusion of our proposed countermeasure is shown in Figure 5.  
Figure 4: Retrieval of first-byte of a 128-bit secret key obtained after the execution of a DPA attack on DESIGN-I (AES without our proposed countermeasure).  
Figure 5: An erroneous retrieval of the first-byte of a 128-bit secret key obtained after the execution of a DPA attack on DESIGN-II (AES with inclusion of our proposed countermeasure).  
The achieved values after correlation, presented in the last column (i.e., column four) of Table 2 reveals that we have successfully applied the DPA attack on the selected AES algorithm. Moreover, it passes the Pearson correlation test, as all these values (see last column of Table 1) are in the range \(-1\) to \(1\). A larger peak in Figure 4 determines the identification of the first-byte (161 in a decimal) of a secret key. Including the first-byte, the identification of the remaining bytes of a 128-bit secret key is presented in Appendix A.  
Figure 5 reveals that there are \"ghost\" peaks, which lead to wrong keys corresponding to the target Sbox. We also test the attack by increasing the power traces up to 5000, but still it leads to a wrong key guess. Including the first byte, the prevention against the remaining bytes of a 128-bit secret key is shown in Appendix A. As a result, we believe that our proposed countermeasure over AES resists the DPA attack.
## 8 Performance Comparison with Related Protocols
The findings reveal that the AFB strategy exhibits a good performance in terms of the routing cost and network reliability compared to GPSR-FLDB [39], GPSR + Predict [40] and LDAB-GPSR protocols [47].
## 6 Validation of Sensors for the Measurement of Concrete Pore Pressure  
The new measurement technique was validated by means of hydrostatic test performed on a reference concrete (named R30A7), deeply investigated under high confinement pressure [5; 6; 7; 8; 9]. The mix properties and mechanical characteristics are given in [23] and in Table 5. These specimens were kept for more than five months in water and considered as fully saturated. Figure 14 depicts the evolution of the interstitial pore pressure as function of the confinement pressure using both pressure sensors. Since these tests aimed to validate the concept of each sensor, the confining pressure was limited to 400 and 300 MPa on the HS and MS sensors, respectively. The sealing of the sensor was checked after each test by controlling that no confinement fluid (DOZ) was present inside the water collecting cap.  
It is clear from the experimental results that both sensors had a very close response to the concrete interstitial pore pressure despite the difference in their concept. The concrete pore pressure measured by the HS and MS sensors remained at a quasi-null value until 40 MPa of confinement pressure. This behavior could be due to two possibilities. The possibility is that the cap saturation procedure was not perfect and a small amount of air was trapped inside it. In this case, the pore pressure could not increase before the free air was purged outside the cap micro-holes. The second possibility was due to the saturation of the concrete sample being lower than \(S_{r}\leq 100\%\). The pores not filled with water could delay the increase of pore pressure. Both reasons might explain what was observed for both tests where the pore pressure started to increases when all free pores were closed. After this stage, a rapid increase of the pore pressure was noticed for both tests, reaching 350 and 200 MPa for the HS and MS sensors, respectively. The observed gradual increase of the pore pressure for both tests could be explained by the progressive collapse of the concrete pores exhibited under high confinement pressure.  
Figure 13: Comparison between the HS and MS deformations: applied pressure vs. strain.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Concrete Mix Properties** & **Kg/m3** & **Mechanical Properties** & **Values** \\ \hline Cement CEM 1 52.5N & 263 & Compressive strength (MPa) & 30 \\ Sand ’D’ 1.8 mm & 838 & Porosity accessible to water (\%) & 12 \\ Gravel ’D’ 0.5–8 mm & 1007 & Slump (cm) & 7 \\ Water & 169 & W/C ratio & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compositions and mechanical properties of the R30A7 ordinary concrete.
# Application of Tooth Gear Impact-Echo System for Repeated and Rapid Data Acquisition
## 5 Conclusions  
This study demonstrated that tooth gears have great potential to produce impact sources of IE testing and generate IE vibration modes including thickness and flexural modes where the results are comparable with conventional steel ball impactors. To perform a rapid scanning test, tooth gears were used as impactors and their performance was evaluated. The contact duration of each impactor was theoretically and experimentally analyzed and a tooth gear IE system was developed. The developed system was evaluated using a testing slab, which included both shallow and deep delaminations. A further signal-processing scheme was used to visualize the signal set obtained by the rapid scanning test. The findings and conclusions of the research are summarized as follows:  
Figure 11: Results of spectrogram from the deep delamination; (**a**) steel ball hammer, (**b**) 11-tooth gear, (**c**) 30-tooth gear.  
Figure 10: Results of spectrogram from the shallow delamination; (**a**) steel ball hammer, (**b**) 11-tooth gear, (**c**) 30-tooth gear.  
* the developed tooth gear IE system is possible to apply to an impact-echo test with low acoustic noise and provides fast data collection with continuous matter. A signal set of 100 data points was measurable within one min. Using the system, the thickness mode frequencies are measurable beyond flexural mode frequencies, and measured thickness mode frequencies corresponded well with the depth of the deep delamination;
* by analyzing different contact mechanism from conventional hammer-type impact, reducing the pitch or increasing the number of teeth decreased the contact duration and provided higher frequency excitation. Based on our experimental results, the 11-tooth gear was limited at generating the thickness mode from the 200 mm depth delamination, while the 30-tooth gear was successful;
* based on the contact mechanism, the contact duration of a conventional steel ball and two tooth gears was analyzed. The theoretical contact duration corresponded well with the actual contact duration measured using a high-speed camera, implying an excited frequency range;
* data visualization using spectrogram showed a band of dominant frequencies (repeated frequency modes) from the signal set, which helps operators to make decisions based on repeatable vibration modes.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON], [PERSON], and [PERSON] contributed device/analysis tools; [PERSON], [PERSON], and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Incheon National University Research Grant in 2018.  
Authors thank [PERSON] for valuable discussion of the tooth-gear application on the air-coupled impact-echo.  
The authors declare no conflict of interest.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis  
To check the performance of the proposed scheme, extensive simulations were performed using Visual Studio 2019 with C-Sharp as a programming language using framework 4.7, where the healthcare values are taken from the SQL Server 2015 database. A comma () was used as a delimiter for the separation of health readings and their addition to the database. At the server level, C sharp was used to create a setup, using the ASP.net and WPF services for deployment on the Azure cloud to execute the deduplication functionality of the DICA and base schemes. The proposed scheme, the DICA, was compared with some other robust techniques, including DSW [23], RAM [34], CWCA [35], and WCA-BF + ASE [36]. Afterward, the healthcare-related data readings were used to examine the number of chunks and average chunk sizes for different numbers of chunks.  
The simulation in this study utilized real-time values within the typical ranges for various health parameters, ensuring a realistic evaluation of the proposed technique. The data included body temperature and blood pressure readings reflecting normal and abnormal conditions, electrocardiogram (ECG) data capturing the heart's electrical activity, and oxygen saturation levels within the physiological range. By employing these real-time values, the simulation accurately mirrored real-world scenarios, enabling a reliable evaluation of the technique's performance in a healthcare IoT environment. The simulation parameters and their corresponding values are presented in Table 2.  
Besides the server side, an Android application was developed at the client-side level. The mobile app facilitates the addition of further parameters based on readings taken from patients. It was ensured that the healthcare data do not exceed the minimum and maximum data range; for example, the temperature must not lie beyond 106 \({}^{\circ}\)F. To improve the accuracy, we used the simulation tool NS-2.35 on the Ubuntu operating system. It was employed to perform low-level calculations for the exchange of messages between sensor devices, as well as to examine energy utilization and residual energy levels. For the sensor nodes, collector devices, and sink nodes, separate classes are created to handle the node arrangement and corresponding functions. TCL files were utilized to set up the parameters for healthcare and transmit messages; this was managed using the C code, which performs the message transmission and receiving functions, along with setting the data packet parameters. Besides this, in the TCL files, the setdest(\(\cdot\)) function was specified to observe patients' movements. To examine the residual energy after the data collection, data transmission, and deduplication operations, an energy model was employed. Trace files record the residual energy of all types of nodes, including the sensor nodes and collector devices, which were further analyzed using an AWK script. Several evaluation metrics were employed to analyze the proposed scheme, DICA, and the former techniques.
## 2 Materials and Methods
### Measurements of Fucoxanthin Fluorescent Properties  
To obtain the excitation point, fucoxanthin stock in DMSO (40 mM) was diluted to 2 mM, 1 mM, 500 \(\upmu\)M, and 250 \(\upmu\)M. From each solution, 150 \(\upmu\)L was loaded into a 96-well clear bottom plate (Nunc, Denmark), and absorbance was recorded at 2 nm increments from 230 nm to 850 nm. The peak through absorbance scan was specified as a possible excitation wavelength. Next, 150 \(\upmu\)L of each solution was transferred to a 96-well black-bottom plate (Costar, WA, USA) to acquire an emission wavelength. By applying the excitation wavelength for fucoxanthin, the emission scans were measured in 2 nm increments up to 850 nm from the maximum absorption scan added 20 nm. The narrowest peak displayed through emission scans was determined as its emission point. Using the excitation and emission point, as obtained above, the fluorescence intensities of fucoxanthin when mixed with A\(\beta\) were scanned to monitor whether fucoxanthin shows a fluorescence shift in the presence of A\(\beta\) monomers or aggregates. A\(\beta\) (1-40) peptides in this study were synthesized by Fmoc solid phase peptide synthesis (SPPS) protocols as previously reported [17]. A monomers (20 \(\upmu\)M) were prepared by diluting A\(\beta\) stock (10 mM) in DMSO stored at \(-\)80 \({}^{\circ}\)C. By incubating the same A\(\beta\) (20 \(\upmu\)M) at 37 \({}^{\circ}\)C for 6 days, A\(\beta\) aggregates were also prepared. Fucoxanthin was diluted to the same concentration as above (2 mM, 1 mM, 500 \(\upmu\)M, and 250 \(\upmu\)M). Then, 25 \(\upmu\)L of either A\(\beta\) monomers or A\(\beta\) aggregates (20 \(\upmu\)M) and 75 \(\upmu\)L of each fucoxanthin solutions were mixed in a 96-well black-bottom plate. The fluorescence spectrum of fucoxanthin in the presence or absence of A\(\beta\) was recorded at 2 nm increments at varying spectra range. Every fluorescent spectral scan was done under the microplate reader (Infinite 200 PRO, Tecan).
## 5 Conclusions and Future Works  
1. For the three-segment, five-segment, and double-profile extended reach wells, taking into account the formation wellbore stability, formation strength, and deflection tool work to determine the well deflection angle, deflection point location, and target hitting range and other constraints; the optimization objective function of the shortest total length of the borehole; the smallest error of the second target hitting; the lowest cost; and the smallest friction resistance of the pipe string, the smallest rotary drilling torque is established, and the objective function of the longest open-hole extension limit in the horizontal section is established for extended reach wells.
2. Taking the extended section well as an example, two objective functions are used to solve the nondominated solution, whose results verify that the friction objective functions of lifting and lowering strings are highly correlated, and one of the minimum friction conditions is taken as the objective function.
3. Taking the optimization model of dual profile extended reach well in block 14-8 of the Lufeng Oilfield in the east of the South China Sea as an example and comparing  
Figure 14: Comparison of track length and drilling costs.  
Figure 13: Part of borehole trajectory.  
Figure 15: Comparison of friction and torque.  
it with the original well trajectory, the well trajectory optimized by multi-objective function can avoid the shortcomings of large target error and poor expansion ability; reduce the track length, friction, and torque; reduce the drilling cost; and achieve the goal of cost reduction and efficiency increase. It is proved that the multi-objective function optimization is practical in application and can provide a certain reference value for the design of well trajectory in field application.
* In the optimization design of well trajectory, in order to reduce costs and more effectively improve productivity, it is also necessary to establish an optimization model based on the three-dimensional trajectory model, optimize the well trajectory by considering more factors, and optimize the well trajectory by using more advanced and richer computer systems and optimization procedures so that the optimization design of well trajectory is closer to reality.  
Conceptualization, L.L., J.Q. and X.C.; Data curation, J.Q., L.L. and L.X.; Formal analysis, L.L.; Funding acquisition, J.Q. and L.X.; Investigation, C.W. and L.L.; Methodology, L.L. and X.C.; Resources, J.Q. and L.X.; Software, L.L. and J.Q.; Supervision, X.C.; Visualization, L.L.; Writing--original draft, L.L., X.C. and J.Q.; Writing--review & editing, X.C. and C.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Single Trace Side Channel Analysis on NTRU Implementation
## 2 Background
### Previous Side Channel Analysis on NTRU  
The first studied SCA on the NTRU was timing attacks in 2007 [29]. In 2010, [PERSON] et al. introduced a SPA and CPA on NTRU and proposed a countermeasure against the attack [7]. The idea behind the proposed SPA in [7] is that there exists a difference in the power consumption when adding non-zero with zero values and non-zero with non-zero values. They also performed CPA on the multiplication using 1000 traces. Also, a second order CPA is proposed in [7] using 10,000 traces. For the description of the attack, please refer to [7]. To prevent the SPA, they proposed to initialize the temporary buffer with a non-zero value and to randomize the order of computation and data. They also provided countermeasures against CPA such as masking and shuffling.  
In 2013, a first-order collision attack was proposed in [8] with the purpose of incapacitating the countermeasure proposed in [7]. Their attack against the first-order countermeasure is an improvement in [8] since the attack is performed with 5,000 traces. The target of the attack was when the same registers are loaded during the multiplication. Overall, the decryption code used for the analysis in [7] and [8] was not an official implementation. Although the proposed attacks can be applied to official implementation, the attack environment is restricted to the case where multiple executions of NTRU with the same key is possible.