esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optical Properties
A fitting numerical procedure according to semi-empirical models linking the output energy from the nanofluid, \(E_{out}\), to incident energy \(E_{in}\) allowed us to identify, for the samples showing monotonic curves (i.e., those at 355 and 532 nm wavelengths), the occurring mechanism, non-linear absorption (Equation (2)) or non-linear scattering (Equation (3)), which is responsible of the observed results [61; 62; 63]. \[E_{out}=\frac{T_{s}^{2}e^{-\mu L}E_{in}}{1+0.1E_{in}/E_{t}}, \tag{2}\]  
\[E_{out}=T_{s}^{2}E_{t}\ e^{-\mu L}\bigg{(}1+\frac{\mu^{e}}{\mu}\left(\frac{E_ {in}}{E_{t}}-1\right)\bigg{)}^{\frac{\mu}{2}} \tag{3}\]  
where \(T_{s}\) is a coefficient connected to the transmittance of the cuvette and \(L\) the optical path length (10 mm). The fitting parameters are \(E_{t}\), the energy threshold for each non-linear process and \(\mu_{e}\), the non-linear contribution to extinction coefficient due to the scattering by vapor bubbles created in the liquid. Non-linear absorption (Equation (2)) could be identified as the acting mechanism at 355 nm for both P- and S-type nanoadditives and at 532 nm for P-GnP, while both models (Equations (2) and (3)) could be acceptable for S-GnP at 532 nm. Therefore, non-linearity at a concentration of 0.005 wt% and at 355 and 532 nm is always of electronic origin in P-GnP samples, while for S-GnP samples a non-linear scattering contribution could appear at 532 nm. The non-monotonic and more markedly non-linear trends, which were evidenced at 1064 nm, suggest the coincidence of simultaneous non-linear mechanisms as the generation of bubbles. A quantitative assessment of the occurrence of bubbles was not possible due to limitations of the experimental apparatus. It can be pointed out that no significant temperature increases of the nanofluids were detected during the time of intermittent laser irradiation, about 20 min per test. The nanoparticles, with a much stronger light absorption than the surrounding liquid, could act as a localized source of heat, quickly heating the surrounding fluid and causing vapour transition. A deeper study of this effect has interesting prospects for further investigations.
## 2 Quantum Key Distribution
### The Decoy State Protocol  
In 2003, the decoy state protocol was introduced to detect PNS attacks [23]. It was quickly improved upon in a series of works [24; 25; 26; 27; 28]; and is now widely employed in commercially viable QKD systems such as Toshiba's record holding system [29] and the world's largest QKD network [30]. In particular, the decoy state protocol is advantageous as it is relatively easy to implement (low cost), increases the system's distributed secret key rate (high performance), and mitigates the PNS  
Figure 2: The eavesdropper ([PERSON] and [PERSON]\({}^{\prime}\)) is shown conducting a Photon Number Splitting (PNS) attack against the QKD system ([PERSON] and [PERSON]). Adapted with permission from [9], Copyright IEEE, 2016.  
attack (improves implementation security). Note, while the decoy state protocol is designed to detect PNS attacks, other QKD protocols are not vulnerable to the attack (e.g., Continuous Variable or Measurement-Device Independent [1]); however, despite recent advances of alternative protocols, the decoy state protocol continues to be the most cost effective and practical to implement.  
As described by [PERSON] et al., the decoy state protocol extends the BB84 protocol by configuring Alice to randomly transmit three types of pulses: (1) Signal; (2) Decoy; and (3) Vacuum, as described in Table 2[25]. Thus, [PERSON] randomly generates signal, decoy, and vacuum pulses according to their prescribed occurrence percentages and respective MPNs where the state of each pulse must be indistinguishable to [PERSON] (i.e., identical pulse shape, wavelength, duration, etc.) in order to maintain integrity of the security protocol. [PERSON] cannot know a priori the type of pulse received during quantum exchange, the only information available to her is each pulse's specific number of photons \(n~{}=~{}0,~{}1,~{}2,~{}3,\dots,~{}\mathrm{N}\) which she determines using her QND measurement.
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 2 Related Works
### Three-Dimensional (3-D) Target Localization Methods  
The authors of [34] assisted an accurate indoor localization method based on the time- and angle-domains data of multipath transmissions with an existing 3-D map of the indoor conditions. The paper [35] employed the modified least-squares model to advance the accuracy of measuring the target coordinates on the \(Z\)-axis in 3-D environments. Furthermore, the selection of an optimal set of base stations according to their space distributions was considered as well. To construct a model of depicting the NLOS transmissions, the method of [36] was proposed to identify the obstructions in 3-D surroundings.
# A Framework for the Design of Privacy-Preserving Record Linkage Systems
## 3 System Design Framework for PPRL
### Key Roles in a PPRL System  
Table 1 describes the roles and responsibilities of different parties within a PPRL system. This table can be used to identify roles and establish responsibilities for different parties to a PPRL system. It can also help identify which roles have not been filled by existing parties so that either existing parties take up multiple roles or new parties can be engaged to take on those roles. All roles should be filled to ensure privacy protections in a PPRL system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Party** & **Roles and Responsibilities** \\ \hline \multirow{4}{*}{**Data Controllers**} & **Role**: Custodians of data subjects’ information \\ \cline{2-3}  & **Responsibility**: Has the legal obligation to protect the privacy rights of data subjects whose data they control. Must enact physical, administrative, and technical controls to protect data. Must ensure that any data processors who access and use data they control are able to meet the same standards they follow for privacy and security controls. \\ \hline \multirow{4}{*}{**Tokenizer**} & **Role**: Provides a technical solution for the creation of non-identifiable tokens to represent data subjects and facilitate linkages between datasets. \\ \cline{2-3}  & **Responsibility**: Provides a secure solution for the creation of data subject tokens. Said solution often involves deploying tokenization software on-site that does not require connection to the internet, cloud computing, or any external system to operate, requiring the solution to be modular and containerizable. \\ \hline \multirow{4}{*}{**Honest Broker**} & **Role**: Facilitates data linkages in a privacy-preserving manner. This can include performing de-identification and data encryption or just be limited to acting as an escrow service for encryption keys and patient token mappings. \\ \cline{2-3}  & **Responsibility**: Is a neutral third party that provides protections for data subject privacy within a data use and sharing system. Other responsibilities may be defined in an Honest Broker Agreement with contractual controls in place that specify their role in the system and what they can and cannot do with the data. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Roles and responsibilities of different parties in a PPRL system.
## 3 System Model and Preliminaries  
We assume that each node is equipped with a pressure sensor and an array of transducers to send and receive directional acoustic signals. The pressure sensor is used to estimate the node's depth while the array of transducers enables fine-grained directional transmission. Furthermore, AUNs are assumed to be deployed in deep-water scenario where the separation between the sea surface andbottom is large. In addition, we assume an obstacle free environment where transmitted rays may change their direction along the depth due to reflections and LEPs but on the average propagate away from the transmitter, i.e., no directional reversal in the horizontal axis. In this section, we discuss the assumed transducer configuration in detail. Then we review the adopted layering and SS estimation methodology. Finally, we discuss how to detect signal reflections and summarize the adopted ray tracing model in a layered environment.
## 4 Opportunities and Open Research Areas in VANET Multimedia
### Batch Transmission and Reconstruction of Audio-Visual Information
Slow decompression processes can result in visual artifacts, blurriness, or distorted images, thereby degrading the quality and user perception of the conveyed information. Faster decompression enables timely processing, enhancing the overall effectiveness and performance of such applications. From a network efficiency standpoint, faster image decompression reduces the time that network resources are occupied. This facilitates quicker access to the decompressed images by other vehicles or infrastructure units, enabling more efficient utilization of the network resources. Faster decompression contributes to reduced network congestion and ensures smoother data flow within the VANET environment.
## 4 Vehicle Dynamics Model
### Full-Car Dynamics Model  
Due to the complexity and uncertainty of practical vehicle driving and road conditions, front and rear coherence properties of vehicle and road can not be ignored. Steering wheel angle does not satisfy following relationship when compared with sharp turning: \(\sin\delta_{fl}=\delta_{fl}\), \(\cos\delta_{fl}=1\).  
To further adapt vehicle driving and road conditions, compared to a 6-DOF half-car model, a full-car dynamics model with 9-DOF was developed based on the above assumptions [17; 18]. Vertical and lateral coupling dynamics of the vehicle were considered simultaneously as shown in Figure 7. The 9-DOF included three degrees of freedom for vertical, pitch, and roll movement of the vehicle body, 4-DOF for vertical movement of unsprung masses, and 2-DOF for lateral and yawing movement of the vehicle. Descriptions of the symbols used in Figure 7 are listed in Table 3. Parameters in Table 3 also come from CARSIM(r) (E-Class, SUV; version 8.02; Publisher: Mechanical Simulation Corporation, Detroit, MI, USA, 1996-2010).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Model Parameters** & **Symbol** & **Unit** \\ \hline Steering angle of front left side & \(\delta_{H}\) & deg (\({}^{\circ}\)) \\ Steering angle of front right side & \(\delta_{H}\) & deg (\({}^{\circ}\)) \\ Rear tire slip angle & \(\alpha_{r}\) & deg (\({}^{\circ}\)) \\ Displacement of the front/ rear axle & \(B\) & m \\ Longitudinal displacement of chassis & \(x\) & m \\ Lateral displacement of chassis & \(y\) & m \\ Lateral velocity of sprung mass C.G. & \(V_{y}\) & m/s \\ Longitudinal velocity of sprung mass C.G. & \(V_{x}\) & m/s \\ Suspension stiffness of left front side & \(k_{1}\) & N/m \\ Suspension stiffness of right front side & \(k_{s2}\) & N/m \\ Suspension stiffness of left rear side & \(k_{s3}\) & N/m \\ Suspension stiffness of right rear side & \(k_{s4}\) & N/m \\ Suspension damping coefficient of left front side & \(c_{p1}\) & Ns/m \\ Suspension damping coefficient of right front side & \(c_{p2}\) & Ns/m \\ Suspension damping coefficient of left rear side & \(c_{p3}\) & Ns/m \\ Suspension damping coefficient of right rear side & \(c_{p4}\) & Ns/m \\ Tire stiffness of left front side & \(k_{11}\) & N/m \\ Tire stiffness of right front side & \(k_{t2}\) & N/m \\ Tire stiffness of left rear side & \(k_{t3}\) & N/m \\ Tire stiffness of right rear side & \(k_{t4}\) & N/m \\ Vertical displacement of sprung mass C.G. & \(z_{b}\) & m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of symbols used for 9-DOF full-car dynamics model.  
Figure 7: 9-DOF vehicle dynamics model. **(a)** Full-car dynamics model. **(b)** Lateral dynamics model. **(c)** Roll dynamics model.
## 3 Materials and Proposed Method
### Performance Analysis of the Models  
The proposed models were evaluated using training and validation accuracies and losses, which are shown in Figure 19, while Figure 20 shows the training and validation losses of the models. VGG16 has the best training accuracy with the lowest training loss of 0.0011, while AlexNet + HPO has the second-best training accuracy of 99.9% with a training loss of 0.0043, and VGG 19 + HPO is the third-best with a training accuracy of 99.8% and a training loss of 0.0067. AlexNet + HPO performed best with a validation accuracy of 91.2%, and VGG16 + HPO had a VA of 90.2%, which made it the second-best model. The model with the lowest validation loss (0.5521) is VGG16 + IDA followed by AlexNet with a validation loss of 0.6788.  
Figure 19: Transfer learning models’ training and validation accuracies.  
Figure 20: Transfer learning models’ training and validation losses.  
Figure 18: Training and testing of MLP + HPO model: (**a**) training and validation accuracy, (**b**) training and validation loss.  
The training and validation accuracies of the transfer learning models are shown in Figure 21, while Figure 22 shows the training and validation losses of the models. CNN has the best training accuracy of 99.9% with the lowest training loss of 0.0040, while MLP has the second-best training accuracy of 99.3% with a training loss of 0.0386, and CNN + HPO is the third-best with a training accuracy of 98.7% and a training loss of 0.0530. MLP + HPO performed best with a validation accuracy of 89.9%, and CNN + HPO had a validation accuracy of 87.5%, which made it the second-best model. The model with the lowest validation loss (0.3604) is MLP + HPO, followed by MLP + IDA with a validation loss of 0.8379.  
The time taken for execution was also used for the evaluation of the models. It was discovered that those transfer learning algorithms that used HPO were executed at the lowest time. AlexNet + HPO executed at 28 min, while VGG16 + HPO executed at 33 min. It was also discovered that the next models with a lower time of execution are the ones that used image data augmentation at the preprocessing stage of their datasets. AlexNet + IDA executed at 50 min, while VGG16 + IDA executed at 78 min as seen in Table 10.  
Figure 21: Deep learning models’ training and validation accuracies.  
Figure 22: Deep learning models’ training and validation losses.
## 2 Experimental Program
### Test Specimens  
Tests to find strength in compression were carried out on specimens (cylinders) of diameter 100 mm and height 200 mm. In this study, direct pull-out tests were performed on a cubic (200 mm \(\times\) 200 mm \(\times\) 200 mm) specimen. A deformed steel bar of 13 mm in diameter was embedded in the center of each pull-out test sample. The steel bars were prepared to ensure an embedded length of 5 d\({}_{\text{b}}\)[63; 64] (d\({}_{\text{b}}\) is the diameter of the steel bar) at the center of the specimen, as presented in Figure 2. To avoid bonding the steel bar with concrete at both ends (unbonded length), the steel bar was passed through the plastic pipes, as shown in Figure 2.  
The inner end of each plastic pipe was sealed with the help of silicon to avoid the penetration of cement slurry inside the pipe. For each concrete mix given in Table 1, two specimens were cast. To study the flexure response of all concrete mixes prepared in this study, RC beams of cross-section 100 mm \(\times\) 200 mm and length 1650 mm were prepared and tested under four-point bending. The testing span for each RC beam was kept at 1500 mm. Each RC beam was reinforced with 2 longitudinal bars of 10 mm diameter at the bottom, while 2 longitudinal steel rebars of 6 mm diameter were used as hanger bars. To avoid premature failure in shear, 2-legged shear reinforcement was provided using 6 mm diameter steel bars at 85 mm center to center. The shear reinforcement was provided up to a span/3 distance from both ends. The reinforcement detail of each RC beam is shown in Figure 3. In order to study the tensile strain development in the tension steel, strain gages were pasted on both bottom steel bars at the midspan section of each RC beam, as shown in Figure 4.  
Based on the fact that the water absorption of RAs and Nas was different, as indicated in Table 1, both coarse RCAs and RBAs were used in the saturated surface dry (SSD) condition. For this purpose, the aggregates were dipped in water for 24 h and then placed in an open environment to dry their surfaces before their use in making concrete. For all concrete mixes, the w/c ratio (0.55) was kept constant. After 24 h of casting, all prepared test samples were placed in a controlled environment of 25 \({}^{\circ}\)C and a relative humidity of 95% until the day of testing, which was carried out at 28 days of age.  
Figure 2: Sample preparation for the pull-out test.
# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 2 Materials and Methods  
### Dataset and Channels  
The DEAP database is used in this study. It is a multimodal database for emotion analysis. The EEG and peripheral physiological signals of 32 participants (16 males and 16 females) aged between 19 and 37 (mean 26.9) and facial videos of 22 of the participants were recorded while they were watching 40 music videos, each of which was 1 min long. Self-assessments were also performed by the participants in addition to signal recordings. The participants rated each video according to their arousal, valence, liking, dominance, and familiarity levels (with continuous 1-9 scaled self-assessment manikins, except for familiarity, rated with a discrete 1-5 scaled manikin). The database contains both preprocessed and unprocessed data. EEG data were down-sampled to 128 Hz, EOG artifacts were removed, and a band-pass frequency filter of 4.0-45.0 Hz was applied. The data were also down-sampled to 128 Hz as a preprocessing measure for the other channels (EOG, EMG, GS, respiratory belt, plethysmography, and temperature) [17]. In the present study, preprocessed DEAP data were used. The 32 EEG channels used in this study are given in Figure 2.  
Figure 2: 32 Channel placement of EEG according to the 10–20 electrode placement system (numbers indicate the channel numbers of EEG signals from the DEAP dataset).
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 2 Methodology
### Risk Evaluation  
The expected risk associated with earthquake and flood hazards concerning the bridge is delineated through risk curves. These curves present the annual probabilities of exceedance across different types of bridge rehabilitation expenses within the multiple impacts of seismic action and local scour induced by flood context. In the multi-hazard scenario denoted as \(m\), the bridge rehabilitation cost for repair or replacement, designated as \(C_{\text{RPm}}\), is explicitly defined as follows [19]:  
\[C_{\text{RPm}}=\sum_{1}^{4}p_{\text{m}}(\text{DS}=k|a_{\text{m}},d_{\text{m}} )C_{\text{n}}r_{\text{k}} \tag{6}\]  
The probability \(p_{\text{m}}(\text{DS}\)=\(k\,|\,a_{\text{m}}\), \(d_{\text{m}})\) signifies the likelihood for which the examined structure can endure DS \(k\) given seismic level \(a_{\text{m}}\) (PGA), derived from the bridge's fragility data, occurring alongside local scour \(d_{\text{m}}\). \(C_{\text{n}}\) denotes the replacement cost of the bridge. The values of damage ratios, \(r_{\text{k}}\), associated with damage \(k\), as outlined in Table 2 and recommended in HAZUS (2022) [20], are employed.  
\begin{table}
\begin{tabular}{c c} \hline
**Definition of Damage State** & **Optimal Damage Ratio** \\ \hline DS1 (Slight) & 0.03 \\ DS2 (Moderate) & 0.08 \\ DS3 (Extensive) & 0.25 \\ DS4 (Complete) & 0.33 * \\ \hline \end{tabular}  
* When the number of spans surpasses two, the optimal estimate for complete damage ratio is determined as [2 divided by the total number of spans]. For the specific bridge under examination, this equates to \(2/6\) spans, resulting in 0.33.  
\end{table}
Table 2: Different damage ratios of bridge element.  
In this research, the cost of repairing the bridge is determined in relation to its replacement value, with the damage ratio serving as the proportionality factor.
# Malicious Powershell Detection Using Graph Convolution Network
## 4 Experimental Results
### Results
#### 4.2.5 Comparison with Other Research  
Figure 13 shows a comparison of GCN with CNN. While the malicious powershell scripts were detected using powershell sequence data and the CNN model in [3,24], the powershell frequency data was used in GCN. Moreover, when the number of adjacent nodes in GCN was 0, the powershell frequency data in CNN was utilized and labeled as CNN-freq, as seen in Figure 13.  
Figure 11: Adjacency matrix generation time.  
Figure 12: GCN training time.  
Figure 13: Comparison of GCN with Convolution Neural Network (CNN).  
We randomly selected 500, 1000, and 1500 powershell scripts from the ETRI powershell dataset. For the 1500 powershell scripts, the accuracy of CNN-seq, CNN-freq, and GCN were 96%, 93.8%, and 96.6%, respectively. The accuracy of GCN using powershell frequency data was similar to that of CNN using powershell sequence data, with the former being slightly higher than the latter. Moreover, the accuracy of GCN was 2.84% higher than that of CNN-freq. [PERSON] et al. demonstrated the malicious powershell detection using word embedding [25]. However, we cannot compare the accuracy to our study owing to the different datasets utilized in both studies.  
In this study, we modified GCN to use powershell frequency data. However, for future research, we are considering modifying GCN to use powershell sequence data to achieve higher detection accuracy. Additionally, while we tried using 2000 powershell scripts, the Keras-GCN [20] modified for detecting malicious powershell caused an error. Therefore, in future research, we will modify Keras-GCN to process 2000 or more powershell scripts.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
## 2 Existing Algorithms
### 2-Opt and 3-Opt  
Optimising the problem using smaller moves is also a very popular technique that has yielded promising results. The 2-Opt and 3-Opt algorithms are branches of local search algorithms, which are commonly used by the theoretical computer science community for the solution of the TSP [16]. The 2-Opt algorithm removes two edges from the graph and then reconstructs the graph to complete the cycle. There is always only one possibility in adding two unique edges to the graph for the completion of the cycle. If the new tour length is less than the previous one, it is kept; otherwise, it is rejected. On the other hand, 3-Opt removes three edges from the tour, resulting in the creation of three sub-tours and eight possibilities for the addition of new edges to complete the cycle again. The time complexity in 3-Opt is O(n\({}^{3}\)) for a single iteration, which is higher than for the 2-Opt algorithm. Figure 3 shows the moves of the 2-Opt technique.