esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we propose a framework to derive a process tree from a configurable process tree based on the historical behavior of a process stored in a given event log. Through three different strategies, exhaustive, genetic and greedy, we allow deriving a process model that better conforms to the event log. The exhaustive strategy searches among all possible derived process models given by the Cartesian product of all possible configurations. This strategy finds an optimal configuration, which is used to derive the best possible process model, but not in a suitable time. The genetic strategy also finds a quasi-optimal configuration (sometimes even the optimal one) to derive a process model, but in less time than the exhaustive approach. Although in the general case, optimality is not guaranteed, in our experiments, it was always able to find an optimal configuration. Moreover, in our experiments, the genetic strategy did not have to iterate for many generations; it converges in less than 20 generations. Meanwhile, the greedy strategy finds a good approximate configuration to derive a process model in a very short time compared to the other two algorithms. We have validated the applicability of our framework using both realistic and real-life processes. The proposed strategies allow finding a very good derived process model in a short time, using the greedy strategy, or an optimal derived process model using the exhaustive or the genetic strategies.  
The main future works that can be performed to extend this research are the following. First of all, user-defined rules can be used to guide the configuration process [5]. These rules can constrain the search space, reducing the computing time required to derive a process model. Second, large configurable process models could be configured in successive stages through a decomposition approach [37]. When a configurable process model is large, it can be decomposed into sub-process models that can be configured independently, in order to reduce complexity and improve performance. Third, on the greedy strategy, we used a bottom-up approach. A top-down approach could also be explored. Fourth, including operator downgrading is another way to configure a node. By downgrading an operator, the behavior of the operator is restricted to a subset of the initially possible behavior [20]. Finally, when the conformance obtained by the derived process model is not good enough, you might think the event log contains knowledge about the process that is not considered in the configurable process model (e.g., activities that are observed in the event log, but do not exist in the configurable process model). In that case, you could use the event log to first enrich the configurable process model, before tackling the derivation task.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] designed the framework/experiments and also analyzed/described the results. [PERSON] implemented the strategies and performed the experiments. [PERSON]. [PERSON] contributed with the real-life experiments and related work section.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Statistical Analysis  
Shapiro-Wilks test was used to assess the normality of our data. No violations of normality in distribution was found (\(p\) > 0.05). All data are presented as mean and standard deviation (\(\pm\) SD). For the agreement analyses of the pre to post, training percentage changes (%) of VL, RF, VI, VM and T CSAs values were used. The decision of using the percentage changes and not the absolute values of the CSAs originates from the aim of the study which was to identify which of the four heads of quadriceps femoris is the best surrogate of quadriceps resistance training-induced hypertrophy (e.g., change in muscle size). Students' \(t\)-testswere used for the determination of the differences between pre and post-training absolute values of VL, RF, VI, VM and T CSAs separately. One-way analysis of variance (ANOVA) was used for the determination of differences between the percentage changes in VL, RF, VI, VM and T CSAs after training. Additionally, Bland & Altman 95% limits of agreements (LOA), standard error of the limits (SEL = \(\sqrt{3}\cdot\)_Standard Deviation of Difference_-_n_-1) and 95% confidence interval for the limits of agreement (CILOA = 95% LOA \(\pm\) (1.96\(\cdot\)_SEL_) were also calculated [36; 37; 38; 39; 40; 41; 42]. Values of means of the differences near to zero, as well as low values/ranges at LOAs and SELs are thought to be indicators of the absolute agreement of the measurements [36; 37; 38; 40; 41; 42] Statistical analysis was performed with SPSS Statistics Ver. 20 (IBM Corporation, Armonk, NY, USA). Statistical significance was accepted at \(p\leq\) 0.05 for all tests.
## 2 AUV Modeling and Simulation  
### Overview of Girona500 AUV  
This study utilizes the Girona500 AUV, a five-thruster configuration AUV that provides high configurability for various scientific instrumentation. This AUV has a multi-hull configuration, as it is composed of multiple streamlined hulls held together with a light frame. This approach incorporates the simplicity and stability of the open-frame platforms and also the low drag hydrodynamics of the torpedo-shaped vehicles [29]. Thus, the Girona500 AUV is perfectly suitable for survey and intervention capabilities.  
The sensors and instrumentation included for the AUV navigation are mainly the attitude and heading reference system (AHRS), depth sensor, and doppler velocity log (DVL), as well as the GPS and ultra-short baseline (USBL). Figure 1 shows the block diagram with each navigation sensor connected with the localization filter, employing the Robot Operating System (ROS) framework [30]. This study uses the default navigation system from the COLA2 software architecture [31] and replaces its control system with the one developed during this article. The AUV navigator takes the data from the onboard sensors and merges them through a constant velocity model using an EKF. Furthermore, to initialize the vehicle position and bound dead-reckoning errors, the GPS receiver measures the vehicle position while, being at the surface, the pressure sensor calculates the vehicle's depth, and the USBL system measures the vehicle position when submerged [32].  
Figure 2 illustrates the simplified AUV model and includes its position and velocities. Moreover, the five-thruster configuration of the Girona500 AUV (see Figure 3) provides thrust forces in the surge, sway, heave maneuverings, or performing turns. The six DOFs AUV general motion uses the north-east-down (NED) local coordinate system. The following vectors \(\eta_{\text{GDOFs}}\) and \(\
u_{\text{GDOFs}}\) consider the AUV positions and velocities in all six DOFs, respectively [16]  
Figure 1: Implemented filter in the localization and mapping system for the Girona500 AUV.  
\[\eta_{6\text{DOFs}}=[N,E,D,\phi,\theta,\psi]^{\top},\ \
u_{6\text{DOFs}}=[u,v,w,p,q,r]^{\top}, \tag{1}\]  
where  
\(N,E,D\) indicate to the NED positions in earth-fixed coordinates,  
\(\phi,\theta,\psi\) define the Euler angles in earth-fixed coordinates,  
\(u,v,w\) are the linear velocities based on the body-fixed reference frame,  
\(p,q,r\) denote the angular velocities based on the body-fixed reference frame.
## 1 Introduction
In addition, the hydrophobic interactions between the aliphatic chains and hydrophobic basal planes are essential in the stabilization of individual RGO sheets in water [14]. [PERSON] et al. [15] employed a series of ionic surfactants to stabilize the GO sheets during the reduction process. They found that the surfactants successfully intercalated in both the GO and the RGO sheets, preventing the restacking phenomena. In addition, their results indicated that the aqueous electrolytes had a higher capacitance response than the ILs electrolytes for the same surfactant intercalated electrodes. This may be due to the fact that aqueous electrolytes usually have small ions that, therefore, facilitated ion transport in small pores [16]. In contrast, for example, the average ion diameter of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, (EMI-TFSI) is D-0.7 nm [17], which is usually larger than the reported interlayer distance of surfactant intercalated RGO (\(\sim\)0.4 nm). Thus, the small interlayer distance may have prohibited the IL to access the surface area in between the RGO sheets. Here, we propose a facile approach to enlarge the interlayer distance with the assistance of an anionic surfactant, Sodium dodecyl sulfate (SDS), or a cationic surfactant, Cetyltrimethylammonium bromide (CTAB), shown in Figure 1. SDS consists of a 12-carbon tail with a negatively-charged head group (SO\({}_{4}\)\({}^{-}\)) and a sodium counter ion (Na\({}^{+}\)). In contrast, CTAB consists of a 16-carbon tail with a positively-charged head group (N-(CH\({}_{3}\))\({}_{3}\)\({}^{+}\)) and a bromide counterion (Br\({}^{-}\)) [15]. The anionic surfactant, SDS, was intercalated in between the TRG sheets, forming a composite named TRGS. Afterward, these intercalated TRGS sheets were rinsed with an EMI-TFSI solution during filtration and was named as TRGSE (Thermally reduced graphene oxide intercalated with SDS and EMI-TFSI). Following the same procedures and using the cationic surfactant CTAB, TRGCE (Thermally reduced graphene oxide intercalated with CTAB and EMI-TFSI) composites could be obtained. The Coulombic force between the ILs and the intercalated ionic surfactants may have led to large-sized ionic aggregates or micelles and, thus, increased the interlayer distance. Additionally, the asymmetric EDLC cell, which is composed of TRGCE on one side and TRGSE on the other side, allowed us to investigate the intercalated ion species on the performance of the cell under different polarities of charging voltages. For example, the cell with TRGSE as the cathode and TRGCE as an anode was named S+C\(-\), while the cell with TRGSE as the anode and TREGCE as the cathode was named S\(-\)C+, as shown in Figure 2. Additionally, an EDLC cell with neat TRG as the electrode was prepared for comparison.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 3 Methodology
### MGSP for Hash Code Learning
#### 3.5.2 Local Similarity Interaction  
Capturing rich similarity structure information among each image-text pair \((\mathbf{v}_{i},\mathbf{t}_{i})\) is another desiderata to further enhance cross-modal similarity preserving. This kind of information is mainly reflected in the latent local feature relationships between modalities. To this end, we develop a local similarity interaction (LSI) sub-module to improve similarity consistency preserving within each image-text pair. Specifically, a key-value cross-attention mechanism is used on the top of GSA sub-module to learn fine-grained cross-modal similarity associations. We obtain _query_\(\mathbf{Q}_{l}^{v}\) and \(\mathbf{Q}_{l}^{t}\), _key_\(\mathbf{K}_{l}^{v}\) and \(\mathbf{K}_{l}^{t}\), as well as _value_\(\mathbf{V}_{l}^{v}\) and \(\mathbf{V}_{l}^{t}\) through linear transformation to obtain the visual and textual attention map \(\mathbf{M}_{l}^{v}\) and \(\mathbf{M}_{l}^{t}\):  
\[\begin{split}\mathbf{Q}_{l}^{v}=\mathbf{Z}^{v}\mathbf{W}_{l,\mathbf{Q}^{v}}^{v}, \quad\mathbf{K}_{l}^{v}=\mathbf{Z}^{v}\mathbf{W}_{l,K}^{v},\quad\mathbf{V}_{l}^{v}=\mathbf{Z}^{v} \mathbf{W}_{l,V}^{v},\\ \mathbf{Q}_{l}^{t}=\mathbf{Z}^{t}\mathbf{W}_{l,\mathbf{Q}}^{t},\quad\mathbf{K}_{l}^{t}= \mathbf{Z}^{t}\mathbf{W}_{l,K}^{t},\quad\mathbf{V}_{l}^{t}=\mathbf{Z}^{t}\mathbf{W}_{l,V}^{t}.\end{split} \tag{11}\]  
\[\begin{split}\mathbf{M}_{l}^{v}=softmax\Bigg{(}\frac{\mathbf{Q}_{l}^{v}( \mathbf{K}_{l}^{t})^{T}}{\sqrt{d}}\Bigg{)},\\ \mathbf{M}_{l}^{t}=softmax\Bigg{(}\frac{\mathbf{Q}_{l}^{t}(\mathbf{K}_{l}^{v} )^{T}}{\sqrt{d}}\Bigg{)}.\end{split} \tag{12}\]  
Thus, the attention-weighted representations are \(\mathbf{X}_{l}^{v}=\mathbf{M}_{l}^{v}\mathbf{V}_{l}^{v}\) and \(\mathbf{X}_{l}^{t}=\mathbf{M}_{l}^{t}\mathbf{V}_{l}^{t}\). Similar to the above self-attention, the local are calculated as follows:  
\[\begin{split}\mathbf{P}^{v}=\sigma(norm(\mathbf{X}_{l}^{v}+\mathbf{Z}^{v})), \\ \mathbf{P}^{t}=\sigma\big{(}norm(\mathbf{X}_{l}^{t}+\mathbf{Z}^{t})\big{)}, \end{split} \tag{13}\]  
where \(\sigma(\cdot)\) is still \(ReLU(\cdot)\).
## 5 Empirical Study
### Machine Learning Concepts and Techniques
The gradient boosting algorithm computes the loss function at each step to compare the actual and predicted values and then optimizes the algorithm during training to achieve the minimum loss, which represents the optimal generalization. It can handle numerical and categorical data and is useful for non-linear relationships between data features. * _Bagging Regressor_: The bagging regressor is a model generated based on ensemble learning that combines base models to improve model stability and reduce variance [82]. In this study, we applied a bagging regressor with Linear Regression as the base model to enhance the model performance in forecasting. The model was configured with ten estimators (ten individual Linear Regression models), every single one trained on different subsets of training data samples using the bootstrapping technique for sampling with replacement. Each independent model fitted a Linear Regression model to a unique subset of data. The final prediction was obtained by measuring the average of all the outputs. This approach reduces model overfitting and leverages the diversity of the predictions from diverse models. * _Ensemble Techniques_: Ensemble learning combines the predictions of base models to build a more robust and accurate model [83]. Ensemble techniques include several methods such as Bagging, Boosting, and Stacking. These methods improve predictive performance by averaging the prediction values or by using metamodels to combine the predictions of the base models. In our empirical study, we propose the following two ensemble techniques. (1) The regressor models were then combined using a stacking ensemble. Stacking involves training a new model that combines a list of base models with the final estimator. The base model list includes an extra tree regressor, AdaBoost Regressor, and the Linear Regression and then feeds these models into the final estimator Linear Regression model, which learns how to combine them by learning how to weigh the predictions of the base models. Stacking leverages the strengths of each base model and achieves better performance. (2) The second ensemble technique combines the prediction models with Voting Regressors. The Voting method aggregates the predictions of the multiple individual models. A voting regressor was used to enhance the effort estimation. The predictions of the three individual models (extra-tree, linear, and AdaBoost regressors) were aggregated. The Voting Regressor combines predictions using an averaging mechanism. The final prediction was the mean of each model's predictions. This approach leverages the strength of the individual models and reduces the impact of errors on each model.
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 5 Implementation, Results, and Analysis
### SD-WAN Design Based on the Proposed OT WAN Risk Assessment  
In this section, we propose an SD-WAN based design to secure OT infrastructure following our proposed RS approach. Aligning to the obtained results, SD-WAN has been selected to be deployed on the IT side of the network where it is part of the layered security design. SD-WAN is integrated into the OT Purdue model (The Purdue Enterprise Reference Architecture (PERA) model is a hierarchical framework for designing and securing industrial control systems by organizing operations from physical processes to business planning [91] (pp. 12-44)) as depicted in Figures 7 and 8. The design is based on NIST CSF and ISA/IEC 62443 standards, which incorporate a zero-trust architecture to segregate the IT network from the OT network. In the proposed design, the zero-trust next-generation firewall is implemented with IPS/IDS modules installed and perimeter DMZ firewalls to isolate OT from IT network. Traditionally, OT environments are designed as air-gapped facilities which necessitates a robust segregation method on the extended OT network to replicate it. Based on the proposed setup (see Figure 7), policies must be implemented on both SD-WAN and firewalls ensuring that proper logging and threat intelligence are available both locally and remotely. This setup enhances security by monitoring and responding to potential threats in real time. It is important to note that traffic management is one of the key features of SD-WAN, which allows it to monitor and apply policing in either inbound or outbound traffic, allowing only one-way traffic, which is OT to IT and not bidirectional and should also be imposed on the firewalls.  
Figure 6: Heatmap of security measures across technologies (sorted by highest mitigation).  
Figure 7: OT branch site low-level design.  
Figure 8: OT remote site low-level design.  
Figure 8 illustrates how SD-WAN can be configured as a medium for out-of-band connectivity, a management connection utilized in both IT and OT network designs. Focusing on OT, the design shows that SD-WAN can be placed in the DMZ, dedicated to managing traffic, which enhances security for remote patching of OT servers, out-of-band maintenance connections, and hardens third-party remote access for ICS and CPS. This setup is also applicable to smaller OT infrastructures, where IT networks are connected remotely over WAN.  
To prevent loss of availability in ICS and CPS, data backups (i.e., M0953 as outlined in MITRE | ATT&CK) can be secured over the WAN via SD-WAN, which also enables logging and monitoring of OT infrastructure. This can be achieved through a management connection or out-of-band communication (i.e., M0810 in MITRE | ATT&CK) using SD-WAN's ability to encrypt and segregate network segments with multi-VPNs. Another mitigation for availability loss is service redundancy (i.e., M0811 as presented in MITRE | ATT&CK), which can be implemented over WAN for ICS and CPS. SD-WAN's compatibility with various WAN technologies allows for secure and redundant options.
# Compensation Method for Polarization Mixing in the Homodyne Interferometer
## 1 Introduction
[PERSON] has proposed a method of adjusting the gains of the electrical detection circuit to reduce the nonlinear error caused by the PBSs [17, 18]. Although this simple and noteworthy method can remove the offset and different amplitudes induced by the PBSs in the detection part, it cannot eliminate the polarization mixing caused by the PBS in the interferometer part. [PERSON] has proposed a passive compensation method by realigning the axes of wave plates at specific angles according to the characteristics of the PBSs, instead of the conventional angles of wave plates [19]. The difficulty of this method lies in the high demand for an accurate test to determine the performance of the PBSs prior to setting up the interferometer. In addition, gain and offset correction should be performed on raw interference signals before the angles of the wave plates are optimized. [PERSON] recently designed an enhanced homodyne interferometer without DC offsets [16]. Two PBSs in the detection part are replaced with WPs with a high extinction ratio, and then, the gain and phase correction methods are combined to reduce the nonlinear error. However, the compensation for the polarization mixing of the PBS in the interferometer part remains lacking. Although several studies have been conducted to reduce the nonlinearity of a homodyne interferometer, the nonlinear error caused by the imperfection of the PBS in the interferometer part has not yet been compensated. The primary reason is the difficulty in eliminating the polarization mixing caused by the PBS in the interferometer part using gain correction or other methods. In the current study, an optical layout that can compensate for the polarization mixing caused by the PBS in the interferometer part is proposed. By combining it with the gain-adjustment method, it can reduce the nonlinear error by two orders of magnitude compared with using only gain correction.
## 4 Simulation Results
### Simulation Tests Conducted
#### Benefits of IQV controller compared to TSMC algorithm  
* The TSMC method can give satisfactory results but for a diagonal inertia matrix. For a symmetric matrix, the problem of tracking the desired trajectory, as shown by the tests performed, was not solved at all. This means that if the model errors are significant, the algorithm will be ineffective.
* For the proposed IQV algorithm, no such significant numerical problems arise as in the TSMC method.
* The applicability of the proposed IQV algorithm is greater than that of the TSMC scheme, because the former is suitable for both a model with a diagonal inertia matrix and a symmetric matrix, and it also takes into account functions representing friction.
* The selection of the control parameters in the case of the offered algorithm is intuitive, while the controller parameters in the TSMC method must be selected with a lot of time unless another selection method is added. Even then, when a set of parameters is selected correctly, a small change in them can lead to a loss of performance of the algorithm. Such an inconvenience is not present in the control scheme proposed in this paper.
# Analysis of Vulnerabilities That Can Occur When Generating One-Time Password
## 2 Background
### Random Number
#### 2.1.3 Pseudo Random Number Generator (PRNG)  
PRNG generates a large number of pseudo random sequences through one or more inputs. Its input value is called seed, and it is generated through a true random number generator (TRNG) because it must provide unpredictability and randomness. Therefore, PRNG is usually used together with TRNG because it uses the output of TRNG as seed [11; 12].  
Since the PRNG mitigates the bias, statistical characteristics, and correlations of the seeds, the pseudo random number sequence is more random than the true random number sequence. This generally uses cryptographic algorithms and is deterministic.