esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Discussions
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multicolumn{3}{c}{**Computation Time (in Seconds)**} \\ \cline{2-4}  & **DENSEA** & **NSGAII** & **MOCHC** \\ \hline \(mUAQMST\_Instance10\) & **1.05** & 1.94 & 1.27 \\ \(mUAQMST\_Instance20\) & **1.47** & 2.04 & 1.78 \\ \(mUAQMST\_Instance30\) & **3.38** & 4.87 & 4.32 \\ \(mUAQMST\_Instance40\) & **4.93** & 6.17 & 5.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computation time of the MOEAs on the four mUAQMSTP instances. The superior values are highlighted in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Uncertain Instance**} & \multicolumn{3}{c}{**Confidence Level**} \\ \cline{2-4}  & \multicolumn{2}{c}{**0.4**} & \multicolumn{2}{c}{**0.9**} \\ \cline{2-4}  & **HV** & **IGD** & **HV** & **IGD** \\ \cline{2-4}  & **DENSEA+NSGAII** & **DENSEA+NSGAII** & **DENSEA+NSGAII** & **DENSEA+NSGAII** \\ \hline \(mUAQMST\) & \(\sim_{p^{-}\to 4\to 314\times 10^{-3}}\) & \(\sim_{p^{-}\to 8.904\times 10^{-4}}\) & \(\sim_{p^{-}\to 5.360\times 10^{-3}}\) & \(\sim_{p^{-}\to 7.566\times 10^{-4}}\) \\ \(mUAQMST\) & \(\sim_{p^{-}\to 4.560\times 10^{-3}}\) & \(\sim_{p^{+}\to 3.476\times 10^{-3}}\) & \(\sim_{p^{-}\to 1.548\times 10^{-3}}\) & \(\sim_{p^{-}\to 6.273\times 10^{-4}}\) \\ \(mUAQMST\) & \(\sim_{p^{+}\to 2.223\times 10^{-3}}\) & \(\sim_{p^{-}\to 1.158\times 10^{-4}}\) & \(\sim_{p^{-}\to 5.217\times 10^{-4}}\) & \(\sim_{p^{-}\to 2.428\times 10^{-4}}\) \\ \(mUAQMST\) & \(\sim_{p^{-}\to 3.258\times 10^{-3}}\) & \(\sim_{p^{+}\to 3.753\times 10^{-4}}\) & \(\sim_{p^{-}\to 5.529\times 10^{-4}}\) & \(\sim_{p^{+}\to 4.779\times 10^{-4}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(p\)-value of hypothesis testing of HV and IGD for the four mUAQMSTP instances. Accordingly, it is observed from the reported \(p\)-values in Table 7, DENSEA is significantly better than NSGAII for HV and IGD at a 5% level of significance and at the corresponding confidence levels 0.4 and 0.9. Furthermore, here in Table 7, we observe that when the confidence level is set to 0.4, then for the instance, \(mUAQMST\_Instance30\), \(h_{0}\) is rejected for HV, and for the instances \(mUAQMST\_Instance20\) and \(mUAQMST\_Instance40\), \(h_{0}\) is rejected for IGD when two-sample parametric \(t\)-test is conducted. Similarly, by conducting the same hypothesis testing, for the instance, \(mUAQMST\_Instance40\), \(h_{0}\) is rejected for IGD at the 0.9 confidence level. However, at both the confidence levels of 0.4 and 0.9, for all the remaining instances, \(h_{0}\) is accepted for two sample parametric \(t\)-test and, therefore, by conducting the two-sided nonparametric Wilcoxon signed-rank test, the same \(h_{0}\) is rejected and subsequently, the corresponding \(p\)-values are calculated.
## 2 Experimental Methodology
### Recording of Wave Evolution in Solid Waveguides
#### 2.2.2 Digital Holography
Therefore iterative relative shifting of the two processed images along with calculation of their difference, representing misalignment error \(E_{\text{misalignment}}=\sum_{i\in image}|I_{1}(i)-I_{2}(i)|\), may not be implemented in this case. Instead of the analysis of two phase images themselves in the data processing workflow, one should use a gradient-based approach by considering \(E_{\text{misalignment}}^{phase}=\sum_{i\in image}|\
abla I_{1}(i)-\
abla I_{2}( i)|\) as a misalignment error. The latter criterion is insensitive to an additive constant applied to either of the phase images and therefore allows for correct stitching of phase images, although it is more sensitive to phase image deviations resulting from shot and coherent noise. Therefore, low-pass filtering should be applied prior to the stitching of phase images in order to decrease noise impact and to obtain correct coordinates needed for correct alignment of two phase images. Note that before numerical processing of phase images, the phase unwrapping algorithm should be applied. Due to the fact that gradient-based approach is applied for image stitching, it is crucial to perform a complete phase-image unwrapping and eliminate possible singularities on the phase image. After determination of the proper shifting parameter between the two images we directly overlapped the two 2D arrays onto each other and stitched them by adding a proper constant to the second image, aimed at the subtraction of the offset due to the relativity of the phase distribution. The wave parameters were determined in the same way as described above for the analysis of holographic interferograms. The wave width corresponded to the length of the phase disturbance, and the wave amplitude was calculated using a formula similar to (1) with \(\Delta K\) replaced by \(\Delta\phi/2\pi\):  
\[A=\frac{\Delta\phi\lambda}{2\pi h(n-1)(1-v)} \tag{2}\]  
where \(\Delta\phi\) is the maximal phase shift. It should be noted that digital recording imposes certain restrictions on waveguide parameters, which should not introduce too strong distortions to the recording wavefront. Any deviations from sidewall flatness cause deformation of the interference fringes, which may not be resolved by the digital camera. Note that, due to significant differences in realization of classical holographic interferometry and digital holography, it is quite difficult to implement these two methods  
Figure 2: Reconstruction of elastic wave profile by stitching three digital holograms obtained at different delays between the moments of shock wave generation and hologram recording. Waves travel from left to right. at the same optical setup. This is due to different interference angles and magnification coefficients required for their implementation. Moreover, in the digital holography, we utilized CW laser radiation instead of the pulsed laser used in holographic interferometry.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 1 Introduction
### Communication Scenario  
With the evolution of new paradigms in computing, including cloud computing and internet of things (IoT), the private information of users is transmitted through the open public communication network. Therefore, it is important to ensure that the user's confidential information is protected from unauthorized disclosure. Due to this reason, a user as well as the server must verify the validity of each other before any data transmission happens. Generally, smart cards are used in a user-server environment where a legitimate user wishes to access the facilities offered by a server. A user, along with their smart card, has to authenticate themself with the server to access its services. Furthermore, the user has to confirm that they are interacting with a legitimate server and not with an adversary. This communication model has been demonstrated in Figure 3.
# Potential of Supercritical _Acrocomia aculeata_ Oil and Its Technology Trends
## 4 Phytochemical Composition
### Phenolics Compounds  
Phenolic compounds are substances that have a benzene nucleus and one or more hydroxyl groups. They can be divided into several classes (phenolic acids, flavonoids, tannins, lignins, and stilbenes). These compounds are known to have several biological activities,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Pulp** & **Kernel** \\ \hline Carpilic Acid (C8:0) \% & & 6.28 \\ Carpilic Acid (C10:0) \% & 0.15 & 4.78 \\ Lauric Acid (C12:0) \% & - & 45.4 \\ Mrisistic Acid (C14:0) \% & 0.22 & 9.5 \\ Palmitic Acid (C16:0) \% & 15.8 & 6.88 \\ Palmitoleic Acid (C16:1) \% & 3 & - \\ Esterasic Acid (C18:0) \% & 1.51 & 2.65 \\ Oleic Acid (C18:1) \% & 71.76 & 24.05 \\ Linoleic Acid (C18:2) \% & 1.79 & 3.02 \\ Linolenic Acid (C18:3) \% & 1.01 & - \\ Cis-11-Eicosenic Acid (C20:1) \% & 0.18 & 0.12 \\ Saturate Acids \% & 18 & 75.63 \\ MCFA (cadeia media) \% & 82 & 56.46 \\ MUFA (monoisaturads) \% & 74.94 & 24.17 \\ PUFA (polinsaturads) \% & 2.80 & 3.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pulp and kernel fatty acids composition of _Acrocomia aculeata_[10; 12; 14].  
Figure 2: Molecular structure of \(\beta\)-carotene.  
including antioxidant, anti-inflammatory, antimicrobial, and anticancer effects [12; 21], in addition to being secondary metabolites most commonly found in plants.  
In _Acrocomia aculeata_, the most common compounds are tannins, flavonoids, catechins, steroids and/or triterpenoids, and saponins [11]. Among the phenolic products, caffeic acid, p-coumaric acid, and ferulic acid stand out, which are mainly present in the seeds and their oil. The flavonoids most often found in the species are catechins, quercetin, and luteolin, present in the leaves and fruits and responsible for the bitter taste of these parts of the plant [6].  
Different authors found levels that varied between 1.4 and 2.69 mg GAEg\({}^{-1}\) oil (equivalent gallic acid) of total phenolic compounds in macauba pulp oil [4].
## 3 Results  
### Slab Phantom Tests  
The fluence reconstruction accuracy was tested in the slab phantom. The results of the irradiations with the slab phantom show that the fluence reconstruction is accurate (within 2%) in all slab configurations (Figure 3). In the homogeneous configuration, the fluence reconstructed by the deconvolution kernel was within 1% accuracy in most of the tested field's size: only the 3 \(\times\) 3, the 4 \(\times\) 4, and the 15 \(\times\) 15 beams show discrepancies between 1% and 1.5%. The slab phantom with the air insert showed the worst result for the 3 \(\times\) 3 field size, with a discrepancy of 1.9%; however, the overall accuracy is high, as all the other field sizes, except the 15 \(\times\) 15, are below the 1% accuracy. With the bone inhomogeneity, the accuracy of fluence reconstruction is similar to the homogeneous phantom: only the 3 \(\times\) 3 field size exhibits deviation greater than 1%, and all other field sizes are below the 1% threshold. As the new deconvolution kernel of the 5.8 DC version accurately produced the fluence in all phantom configurations, the final dose reconstruction accuracy of the DC 5.8, in the presence of inhomogeneities, is mainly dependent on the dose computation algorithm. In fact, as shown in Figure 4, the PB algorithm obtained the typical dose overestimates in the presence of air, and the CC algorithm obtained good results compared to the Monte Carlo TPS algorithm.
## 4 Conclusions  
The eutectic microstructure obtained in SLM-processed Al4047 can be controlled as per requirement using the appropriate SLM processing conditions where cooling rate plays the most important role. Ultra-fine grains are obtained which can be coarsened by pre-heating the base plate in SLM chamber. Pre-heating the base plate also resulted in reducing the remnant porosity from 0.38% to 0.29% where more time is available to the relatively stable melt pool, such that the gas bubbles could escape through the melt reducing the gas porosity.  
Quasistatic properties obtained in SLM-processed Al4047 are higher as compared to their cast counterparts due to fine grains as well as the solubility of Si in the matrix where less free Si has reduced the brittleness, which is evident in the simultaneous change in tensile strength as well as fracture strain. Interrupted quasistatic testing inside computed tomography chamber has shown the phenomenon of stress relaxation in this alloy, which further increased the ductility. Hybrid specimens resulted in slightly reduced strength but significantly increased ductility which can be employed for manufacturing hybrid structures reducing the costs.  
Fatigue strength of SLM structures has been found dependent not only on the microstructure but also on the remnant porosity. The interaction of the two mechanisms has changed the fatigue strength depending on the applied stresses where base plate heating has been found, resulting in less strength at high-stress amplitude, and higher strength than its non-base plate heated counterpart at lower stress amplitudes. Hybrid structures have resulted in higher fatigue strength as compared to pure Al4047 over the whole range of applied stress amplitude due to the low level of cyclic softening in hybrid specimens. Influence of test frequency has not been found to be significant for the investigated Al alloys. The phase-field method was applied as a tool to numerically calculate the onset of damage, crack initiation, and ultimate failure. An extension of the model in order to incorporate the kinematic hardening and influence of the damage on material constants will be part of the future work.  
[PERSON], [PERSON], and [PERSON] contributed to the methodology, investigation, and writing of the original draft. [PERSON] and [PERSON] supervised the investigations and reviewed and edited the manuscript. This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant number: WA 1672/25-1. The authors thank the DFG for its support within the research project \"Mechanism-based understanding of functional grading focused on fatigue behavior of additively processed Ti-6 Al-4V and Al-12 Si alloys\". The authors declare no conflict of interest.
## 4 Results and Discussion
### Analysis of Influencing Factors on Resistivity
#### 4.1.1 Filler and Raster Angle Orientation  
Figure 8 shows an overview of the batches with the lowest resistivity of each material and raster angle orientation. It can be seen that the standard deviation is quite low so that the repeatability of the manufacturing process is ensured. However, it has to be noted that only three specimens per batch were manufactured. In general, the resistivity is lowest for the raster angle orientation 0\({}^{\circ}\), followed by \(\pm\)45\({}^{\circ}\) and 90\({}^{\circ}\). These results are to be expected due to the higher interlayer resistance according to the  
Figure 7: Influence of the silver paste on resistivity of the feedstock material.  
resistance model developed by [PERSON] et al. (2017) [4]. Consequently, the raster angle provides a vast potential for adjusting the resistivity of additively manufactured electrically conductive components. Further, the resulting resistivity strongly depends on the filler. For example, for a raster angle of 0\({}^{\circ}\), the resistivity of the copper-based material M3D is about 450 times less than of 3 dk, is approximately 240 times less than PPC, and about 30 times less than FFE. In addition, the anisotropy of M3D's and FFE's resistivity regarding the raster angle of 0\({}^{\circ}\) compared to \(\pm\)45\({}^{\circ}\) or 90\({}^{\circ}\) is higher than that of the CB filled materials PPC and 3 dk. This fact probably results from an orientation of the filler particles in the extrusion direction. This presumption was supported by [PERSON] et al. (2014) [31] who found a high fiber orientation in the extrusion direction for carbon fiber-polymer composites and [PERSON] et al. (2016) [20] who observed an increase of the conductivity during the extrusion process.  
The resistivity of the selected additively manufactured specimens made of PPC and FFE are higher compared to their feedstock materials, whereas for 3 dk and a raster angle of 0\({}^{\circ}\) the resistivity is almost identical. Compared to that, the specimen's resistivity made of M3D is significantly higher than of the feedstock material in all raster angle orientations (cf. Figure 7).
## 5 Conclusions  
We have investigated a power split ratio and optical delay phase dependent asymmetric dual-loop optical feedback to study suppression of frequency-fluctuations induced due to longer feedback cavities. Self-mode-locked (SML) two-section quantum-dash (QDash) laser operating at \(\sim\)21 GHz and emitting at \(\sim\)1.55 \(\upmu\)m was device under investigation. This study revealed that efficiency and performance of dual-loop optical feedback including better side-band suppression can be achieved by controlling three key parameters: lengths of each external feedback cavities, coupling strengths and optical delay phase settings of both feedback loop. Measured experimental results further demonstrates that dual-loop optical feedback with power split ratio (\(\sim\)\(-\)22 dB: \(\sim\)\(-\)22 dB) including longer feedback cavity at integer resonance and fine-tuning of shorter feedback cavity yield better side-band suppression (\(>\)33 dB) at integer resonance as well as on full delay range tuning (0-84 ps) (13-33 dB). This study suggest that asymmetric dual-loop optical feedback is a cost-effective and inexpensive solution to eliminate external cavity induced noise-fluctuations and offers compact and efficient OEOs for fully monolithic integration.  
Supervision, J.G.M.; Writing--original draft, H.A. All authors have read and agreed to the published version of the manuscript.  
European Office of Aerospace Research and Development (FA9550-14-1-0204); Science Foundation Ireland (SFI) (12/IP/1658).  
The authors declare no conflict of interest.
# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
## 3 Results and Analysis
### Evaluation of Prediction Model  
#### 3.1.1 Experimental Details of Prediction Model  
The NGSIM dataset is applied to evaluate the model, in which two different road conditions, US101 and Lankershim, are selected for training, and the original data volume is too large and the data are not preprocessed to be used directly for neural network training. For this reason, the dataset needs to be preprocessed. Firstly, the vehicle data with a low total number of frames collected are excluded, only four data columns, Local_X, Local_Y, v_Vel, and v_Acc, which are highly relevant to vehicle trajectories, are retained for network training. Secondly, different vehicles are successively distinguished according to different IDs, and at the same time, the trajectory information of each vehicle is sorted in chronological order, and the surrounding vehicles within the range of interest of the ego-vehicle are screened out under the same moment. Finally, considering that the final trained data are to be used in simulation and real environments, the standard unit of length in these environments is m, while the standard unit of length in the dataset is ft, so it is necessary to convert the unit of the dataset. Finally, in order to improve the training accuracy, the column data are normalized, and the data are smoothed using the Savitzky-Golay filter to obtain the sample data. The above sample data is divided into the training set and test set with a ratio of 8:2, and the hyperparameters are set for the prediction model of this paper as shown in Table 2.
# Resazurin-Based Assay for Quantifying Living Cells during Alkaline Phosphatase (ALP) Release
## 3 Results and Discussion
### Determining Cell Number and Comparison to Hemocytometer Assay  
The cell culture growth curve represents the number of live cells over a period of time and covering the four phases [37]. In the first two growth phases, cells are more likely to be consistent, while in the stationary phase, cells may be more differentiated. Morphology may become polarized, and has more spreading for the extracellular matrix. Therefore, determination of the viability of cells in using quantitative analysis was required. Resazurin reduction of each cell obtained from the previous experiment was used to estimate viable cells using the linear equation obtained by the calibration curve. For comparison, the number of cells was also compared to manual accounting using the hemocytometer assay. Figure 7 shows the comparison of the estimating cell number and accounted cell number. Estimating cell number had the same trend as the resazurin reduction assay previously described in Figure 5. In contrast, the results given by the hemocytometer displayed slight differences. However, both assays displayed the growth curve. Values of the estimated cell numbers in the Balb/c 3T3 and Ht-29 cells were higher than those manually accounted in all phases. A549 cells also had higher estimated values in all phases except for in the stationary phase. On the other hand, MCF-7 demonstrated a different trend, as it showed a J-shape in the estimated cells and an s-shape in the accounted cells. However, the difference between these values on the day of control was high in the Ht-29 cells (\(84.96\times 10^{3}\) cells/mL) and MCF-7 cells (\(70.51\times 10^{3}\) cells/mL) compared to the difference in Balb/c 3T3 (\(58.18\times 10^{3}\) cells/mL) and A549 cells (\(11.13\times 10^{3}\) cells/mL). Overall, the growth curves given by the hemocytometer were sharper than the estimated ones. Notably, manual accounting had multiple steps, which increased the chance of losing cells, whereas cell number given by the resazurin assays were estimated theoretically based on a calibration curve, where the possibility of losing cells during assays was low.  
Figure 7: Logistic growth curves of (**A**) Balbç/3T3 cell lines; (**B**) A549 cell lines; (**C**) MCF-7 cell lines; and (**D**) Ht-29 cell lines, over five days. (\(\square\)) estimated cell number using linear equation of standard curve and, (\(\bigcirc\)) accounted cell number using hemocytometer; all compared versus incubation time over five days. Data are expressed as the mean \(\pm\) SD of measurements of three independent experiments.
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 3 Methodology
### Pynetdicom: A Python Implementation of the DICOM Networking Protocol  
_Pynetdicom_ is a Python library that implements the DICOM network protocol and enables communication between DICOM applications. It provides functionalities to establish associations between DICOM nodes, and facilitates the sending and receiving of DICOM data. In the context of the DICOM simulator, _Pynetdicom_ is utilized to establish a connection with the PACS server and transmit DICOM images. The library offers an AE class, which is employed to create an Application Entity representing the local node or device in the simulator. To define the supported presentation contexts, which determine the combination of SOP Classes and Transfer Syntaxes that the AE can handle, the _supported_contexts_ attribute of the AE class is set. This attribute specifies the supported DICOM services and protocols.  
The _add_requested_context()_ method is used to specify additional requested contexts, allowing the AE to indicate the SOP Classes and Transfer Syntaxes that it wants to use for communication with the remote DICOM entity. The _associate()_ method establishes an association with the PACS server, enabling subsequent data exchange. This method initiates the DICOM association negotiation process, which involves negotiating supportedpresentation contexts, transferring the DICOM application context, and verifying the compatibility between the local and remote DICOM entities.  
To transmit DICOM datasets to the connected PACS server, the _send_c_store()_ method is utilized. This method ensures proper encoding and delivery of the DICOM data. It handles the DICOM C-STORE service, which is responsible for storing DICOM datasets on the PACS server. The _send_c_store()_ method provides status codes to indicate the success or failure of the storage request. Finally, the _release()_ method is used to terminate the association between DICOM entities. This method initiates a graceful closure of the communication session by sending release request messages to both the local and remote entities.