esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 3 Design of Smart Collision Avoidance Control System for USVs
### Generation of Collision Advoidance Course
#### 3.3.2 Optimal Collision Avoidance Waypoint  
For determining the optimal collision avoidance waypoints, a schematic diagram is illustrated in Figure 12 based on the original designated initial waypoint (\(x_{S}\), \(y_{S}\)), the end waypoint (\(x_{E}\), \(y_{E}\)), \(z\) is the slope from initial and end waypoint, the center of the collision circle zone \(\left(x_{n}^{C},y_{n}^{C}\right)\), the radius of collision circle zone is \(b_{n}\), the area of collision circle zone is \(O_{n}\), and collision avoidance waypoint is \(\left(x_{n}^{cap},y_{n}^{cap}\right)\). Observing the collision avoidance waypoint in the first and second collision circle zones can determine that the second collision avoidance waypoint \(\left(x_{2}^{cap},y_{2}^{cap}\right)\) is within the collision circle zone and a new optimal collision avoidance waypoint needs to be determined that is outside the zone.  
Figure 10: Relationship of relative bearing and \(\zeta\).  
For solving the optimal searching problem, the famous oscillatory particle swarm optimizer (OSC-PSO) [19; 20] was adopted to determine the new optimal collision avoidance waypoint. If there are \(n\) number of collision avoidance waypoints, then the particle's variable parameter equals \(n\). The number of particles will affect the convergence rate  
Figure 11: Relationship of relative bearing and \(p\).  
Figure 12: Schematic diagram of collision circle zones, collision avoidance waypoints, linear equations, the position of the controlled USV, and waypoints.  
of searching for the optimal waypoint. The fitness function (_f_) is selected by applying OSC-PSO as follows:  
\[\begin{array}{c}f\qquad=\sqrt{\left(x_{S}-x_{1}^{cap}\right)^{2}+\left(y_{S}-y _{1}^{cap}\right)^{2}}\\ \qquad+\sum\limits_{i=1}^{n-1}\sqrt{\left(x_{i}^{cap}-x_{i+1}^{cap}\right)^{2}+ \left(y_{i}^{cap}-y_{i+1}^{cap}\right)^{2}}\\ \qquad\qquad+\sqrt{\left(x_{n}^{cap}-x_{E}\right)^{2}+\left(y_{n}^{cap}-y_{E} \right)^{2}}\\ \qquad\qquad\qquad+M\end{array} \tag{13}\]  
where  
\[\begin{array}{c}M=\sum\limits_{i=1}^{n-1}\left(b_{i}-\sqrt{\left(x_{i}^{cap }-x_{i}^{C}\right)^{2}+\left(y_{i}^{cap}-y_{i}^{C}\right)^{2}}\right),\\ if\quad\left(x_{i}^{cap},y_{i}^{cap}\right)\in O_{i}\end{array} \tag{14}\]  
Equation (14) is used to evaluate whether the collision avoidance waypoint enters the collision circle zone or not.  
If the following two conditions are true simultaneously, the iteration process of OSC-PSO will be terminated because non-collision trajectories that are not in the collision circle zone are determined.  
1. The number of iterations equals the maximum number of iterations;
2. \(M=0\), which means that the collision avoidance waypoint is outside the collision circle zone.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 4 Experimental Flow  
### Encryption Algorithm  
_Step 1:_ First, a text is taken for the encryptions, as shown in Figure 10.  
_Step 2:_ To convert text to binary, firstly, the plain text is divided into letters, and then, the ASCII of each letter is created, as shown in Figure 11.  
_Step 3:_ After converting plain text to ASCII values, each ASCII value is converted to binary values, as shown in Figure 12.  
_Step 4:_ All binary bits are reversed to make data more secure, as shown in Figure 13. Due to reversing mechanism, if an attacker tries to decrypt the data, the attacker will keep trying to decrypt the reversed data instead of obtaining the original data.  
_Step 5:_ After reversing the bits, salting is applied. The same salting value is applied at the start and end of the binary values, as shown in Figure 14.  
_Step 6:_ The binary values obtained after the salting mechanism are split into eight bits and converted into decimal values, as shown in Figure 15.  
_Step 7:_ After obtaining decimal values, [PERSON]'s cipher encryption formula \"\(E=D+N\)\" is applied to each decimal value and obtains the following results, as shown in Figure 16.  
_Step 8:_ The decimal values obtained from the Caesar cipher formula are converted to ASCII, and the Caesar cipher text is obtained, as shown in Figure 17.  
_Step 9:_ After receiving the Caesar ciphertext, the _Cipher Matrix mechanism_ has been implemented, for which, first of all, the Cipher Matrix has been created, as shown in Figure 18.  
_Step 10:_ After creating the cipher matrix table, the rules of the cipher matrix algorithm are applied to the Caesar ciphertext, in which first the text is divided into pairs (\(P1\) to \(Pn\)), and according to the cipher matrix rule, the character that cannot be paired is paired with \"\(x\)\", as shown in Figure 19.  
_Step 11:_ After forming groups \(P1\) to \(Pn\), cipher matrix rules are implemented on each pair, as shown in Figures 20-24, and different c  
Figure 16: Caesar cipher encryption result.  
Figure 17: Decimal to ASCII.  
Figure 18: Cipher Matrix Table.  
Figure 15: Binary to Decimal.  
_Step 12_: A cipher matrix text is obtained after implementing the cipher matrix rules, as shown in Figure 25.  
_Step 13_: After obtaining the cipher matrix text, the user-defined key is taken. After that, the user-defined key and cipher matrix text are converted to ASCII, as shown in Figures 26 and 27.  
_Step 14_: After the ASCII conversion, ASCII values of cipher matrix text and key are converted to binary, and then, these binary values are XORed, as shown in Figure 28.  
_Step 15_: Pairs of binary values obtained from the XOR operation are formed and converted to decimal form, as shown in Figure 29.  
_Step 16_: After obtaining the decimal values, each decimal value is converted into ASCII values, as shown in Figure 30, and a ciphertext is obtained.  
A secure cipher text is shown in Figure 31.
## 3 Field Case Study of Pumping Test
### Geological and Numerical Models
#### 3.5.2 Three-dimensional Model for THM-coupled Simulation  
Figure 6: Water head changes estimated from the theoretical solution of a 2D radial flow in a homogeneous and isotropic infinite horizontal layer for pumping at a rate of \(Q=480\) L/min calculated at a distant of \(r=175\) m. (**a**) Results for different permeability (\(K\)) and hydraulic diffusivity (\(D\)).  
(**b**) Results for different permeability (\(K\)) and layer thickness (\(H\)).  
Figure 7: Water head changes estimated from the theoretical solution of a 2D radial flow in a homogeneous and isotropic infinite horizontal layer. **(a)** Results of a single test. (**b**) Results of multiple tests in two wells, water head data were fitted by a single model. (**c**) Same to (**b**), but water head data were fitted by a combination of two single models to count the effect of spatial inhomogeneities. The observed data (dashed line) were represented very well by the theoretical solution with the optimal values of the permeability \(K\) of the layer and the compressibility \(\beta_{p^{\circ}}\) of the pores.  
The geometry of the numerical model for THM-coupled simulation is shown in Figure 8. The far-field boundaries were placed at a distance of 5 km to approximate infinite boundaries. The in-site stress was applied in all zones and as loads acting on the far-field boundaries. The model has dimensions of 10,000 m \(\times\) 10,000 m \(\times\) 300 m centered at the fiber well. Roller boundary conditions were imposed on the four sides and bottom of the model. This model is divided into grid elements with dimensions in the horizontal plane ranging from 20 to 2500 m according to the distance from the pumping well and depth dimensions of 10 or 20 m. The total number of active elements (zones in FLAC3D) is 23,896. To count for the inhomogeneity in the Chonan Formation, two sub-models, model-A and model-B, were examined. In model-A, the Chonan Formation is divided into two parts, L-2s and L-2n, with different permeability. In model-B, the permeability was assumed to linearly decrease along the dip direction of the layers (Figure 8).
This nonlinearity generates distortions, often manifesting as higher-order harmonics, under high-frequency and high-energy ultrasonic excitation. The cumulative effect of these micro-disturbances from multiple defects leads to the overall distortion of the ultrasonic waveform, which can be quantitatively characterized by examining the amplitude of the higher harmonic components. Initially applied to metallic materials [15], nonlinear ultrasound techniques have expanded to nonmetallic materials in recent years. For example, studies on the epoxy curing process [16] have demonstrated that nonlinear ultrasound is sensitive to changes in the curing degree, with alterations in the second and third harmonic components reflecting changes in the material's mechanical properties. Additionally, research on physical and thermal aging in polymers indicates that nonlinear ultrasonic distortion can serve as an early indicator of material aging [17]. Previous studies have utilized the nonlinearity of ultrasonic wave propagation, specifically the generation of high-frequency harmonic parameters, in single-crystal and polycrystalline metals. This phenomenon is more sensitive to material fatigue life, tensile property degradation, creep damage, and interface bonding strength, among other factors, and has been applied to characterize the early-stage performance degradation of metallic materials. Domestic researchers have also introduced nonlinear acoustics into the performance evaluation of rubber materials. Some researchers have presented an efficient method for assessing the aging degradation of rubber using the quasi-static component (QSC) of ultrasonic longitudinal waves induced by acoustic radiation, and their experiments quantitatively observed the response of the QSC pulse to different levels of aging degradation [18]. Some researchers have conducted thermal aging experiments on silicone rubber, measuring the nonlinear coefficient using nonlinear ultrasonic methods during the aging process. They analyzed the changes in the nonlinear coefficient to investigate the thermal aging behavior and assessed the feasibility of using the nonlinear coefficient as an indicator of the aging of silicone rubber [19]. However, the presented characterizations struggle to establish a direct correlation with the mechanical property parameters [7]. In this paper, our work begins with the ultrasonic propagation phenomenon in fluororubber and constructs an appropriate viscoelastic constitutive model to describe the relationship between the mechanical properties and acoustic parameters. Based on this, a nondestructive ultrasonic evaluation method for mechanical property degradation is proposed, which involves the inversion of the constitutive model parameters.
## 2 Problem Statements
### Dynamic Model of the Robot Manipulator  
For an n-link rigid robotic manipulator, the dynamic model can be described as (refer to [45; 46])  
\[M(q)\ddot{q}+C_{m}(q,\dot{q})\dot{q}+G(q)+F_{r}\big{(}\dot{q})+\tau_{d}(t)= \tau(t), \tag{3}\]  
where \(q,\ \dot{q}\) and \(\ddot{q}\in\mathbb{R}^{n}\) correspond to the position, velocity, and acceleration of the robot manipulator, respectively. Additionally, \(M(q)\in\mathbb{R}^{n\times n}\) is the invertible inertia matrix, \(C_{m}\big{(}q,\dot{q}\big{)}\in\mathbb{R}^{n\times 1}\) is the matrix from the centrifugal force and Coriolis, \(G(q)\in\mathbb{R}^{n\times 1}\) is the gravitational force matrix, \(F_{r}\big{(}\dot{q}\big{)}\in\mathbb{R}^{n\times 1}\)  
Figure 1: Structure of radial basis function neural network.  
denotes the friction matrix, \(\tau(t)\in\mathbb{R}^{n\times 1}\) is the designed actuation input of actuators, and \(\tau_{d}(t)\in\mathbb{R}^{n\times 1}\) is a load disturbance matrix.  
To simplify the approach and analysis, Equation (3) is given as  
\[\ddot{q}=\Xi\big{(}q,\dot{q}\big{)}+B(q)\tau(t)+\Delta_{u}\big{(}q,\dot{q},t \big{)}. \tag{4}\]  
where \(\Xi\big{(}q,\dot{q}\big{)}=M^{-1}(q)\big{[}-C_{m}\big{(}q,\dot{q}\big{)}\dot{q }-G(q)\big{]}\) is the nominal dynamic model of the robot manipulator without perturbations and uncertainties, \(\Delta_{u}\big{(}q,\dot{q},t\big{)}=M^{-1}(q)\big{[}-F_{r}\big{(}\dot{q} \big{)}-\tau_{d}(t)\big{]}\) represents the unknown perturbation and uncertainty terms, and \(B(q)=M^{-1}(q)\).  
The hypothesis here is that the control variables will follow the desired trajectory, with high performance, in finite-time under a robust control strategy. In this case, the proposed system does not need an exact robotic model.  
The following assumptions are crucial for the design approach.  
**Assumption 1**.: _The inertia matrix \(M(q)\), is an invertible, positive definite, and symmetric matrix that adheres to the bounded condition,_  
\[\theta_{1}\leq M(q)\leq\theta_{2}, \tag{5}\]  
_where \(\theta_{1}\) and \(\theta_{2}\) represent positive constants._  
**Assumption 2**.: _The unknown perturbations, uncertainties, and approximation errors of NN have an upper-bound satisfying the following relation,_  
\[\big{|}\Delta_{u}\big{(}q,\dot{q},t\big{)}\big{|}\leq\Omega, \tag{6}\]  
_where \(\Omega\) is an unknown positive constant._
## 2 Novel Pre-Whitening Method and S Transform
### Novel Pre-Whitening Method
Calculate the correlated kurtosis of the envelope signal using the following equation: \[Correlated\ kurtosis\ of\ M-shift=\] (5) \[y(t)\] is the input signal. \(\tau\) is the interesting period of the fault. \(N\) is the number of samples of input signal \(y(t)\). If \(\tau=0\) and \(M=1\), then it gives the traditional kurtosis and can be used to detect the specific PIS. For example, if the desired fault frequency is 50 Hz and the sampling frequency is 10,000 Hz, the value of \(\tau\) will be 200 samples. 4. Determine the optimal iteration number according to the CK value. A high CK value denotes the strong PIS. 5. When the optimal iteration number is acquired, then the squared envelope spectrum of the resulting envelope signal can be acquired as follows: \[\left|y(t)\right|^{2}=<\mathrm{H}^{k-1}[x(t)],\mathrm{H}^{k}[x(t)]>\] (6) \[F\Big{(}\left|y(t)\right|^{2}\Big{)}=F\Big{\{}<\mathrm{H}^{k-1}[x(t)], \mathrm{H}^{k}[x(t)]>\Big{\}}\] (7) where \(k\) denotes the optimal iteration number of the proposed method. In the original paper, the iteration number is judged by following two steps as a substitute of Steps (2) and (3). First, calculate the cross-correlation coefficient of the adjacent envelopes/signals using the following equation:  
\[y(t)=x(t)+j\hat{x}(t)=u(t)+f(t)+j\Big{[}\hat{u}(t)+\hat{f}(t)\Big{]} \tag{2}\]0  
where \(<>\) denotes the inner product operation. According to the Cauchy-Schwarz inequality, cross-correlation coefficient \(\mu_{k}\), \(k=1\), 2, 3,..., is in interval [0, 1]. When these two signals are everywhere equal, this indicator may be 1.0. Second, a hard threshold \(\lambda\) is set. If \(\mu_{k}\geq\lambda\), the iteration is terminated. Through this process, the deterministic component can be adaptively subtracted by cancellation of the DC offset from the envelope.
## 3 Results
### Convergence--Learning Curves  
Learning curves are graphical tools that monitor a classification model's diagnostic performance during training by plotting accuracy (or loss) metrics against increasing training data. In clinical ML applications like retinal OCT analysis, these curves compare a model's evolving performance on training data (its learning progress) against validation data (its  
Figure 2: Confusion matrices comparing classification results from a 3-way split (train/validate/test) dataset with and without SMOTE: The left matrix (without SMOTE) shows 7.34% false-abnormal classifications with 5.02% false-normal cases, while the right matrix (with SMOTE) demonstrates improved normal detection with reduced false-abnormal cases (5.39%) at the expense of slightly increased false-normal classifications (5.94%). The background colors in the matrices: pink indicates True Positives and True Negatives, while blue represents False Positives and False Negatives. (a) Without optimization. (b) With optimization.  
generalizability to new cases), visually depicting whether the algorithm improves with experience or becomes overfit to specific training patterns. By tracking how learning stabilizes across retinal pathologies and healthy scans, these curves help ensure models achieve reliable, consistent diagnostic capabilities. Learning curves are critical for confirming that reported performance metrics reflect clinically trustworthy decision-making.  
Figure 3 presents learning curves for an ML model developed for the classification of retinal OCT images into normal and abnormal categories. Four distinct curves are displayed: training and validation accuracies both with and without the application of SMOTE (Equation (9)). Both sets of curves exhibit a pattern where the training and validation accuracies converge toward similar values and then plateau together, which is a hallmark of well-trained ML models that have reached an optimal balance in their learning progression. The observed gap between training and validation curves represents the model's generalization capability, i.e., a natural phenomenon where models typically perform better on training data than on unseen validation data. This gap stems from the fundamental challenge of generalization in ML and reflects the balance between underfitting and overfitting. Notably, the implementation of SMOTE results in a reduced gap between training and validation accuracies compared to the non-SMOTE approach. This narrower separation suggests enhanced generalization capabilities and reduced overfitting when SMOTE is applied. The improvement indicates that SMOTE effectively addresses class imbalance issues in the dataset by generating synthetic examples of minority classes, which is particularly valuable in medical imaging applications such as retinal OCT classification where pathological conditions may be underrepresented in training data. The learning curves thus demonstrate that SMOTE contributes to a more robust and reliable model for distinguishing between normal and abnormal retinal conditions.
## 3 Optimal Solution to Problem (7)  
Since \(E_{c}\) in (3), \(R_{c}\) in (4), \(E_{d}\) in (5), and \(R_{d}\) in (6) are not concave functions, problem (7) is not convex. Therefore, it is challenging to solve problem (7). However, in first optimizing for \(\rho_{c}\) and \(\rho_{d}\), followed by optimization with respect to \(P_{d,\text{J}}\) and \(P_{d,\text{E}}\), it is possible to obtain the optimal solution. To this end, problem (7) can be rewritten as follows:  
\[\max_{P_{d,\text{J}},P_{d,\text{E}}} f(P_{d,\text{J}},P_{d,\text{E}}) \tag{8a}\] \[\text{s.t.}\quad P_{d,\text{J}}+P_{d,\text{E}}\leq P_{d}^{\text{ max}}, \tag{8b}\]  
Figure 1: SWIPT in D2D-enabled cellular networks.  
\[P_{d,\mathrm{E}}^{\mathrm{opt}}=P_{d}^{\mathrm{max}}-P_{d,\mathrm{I}}^{\mathrm{opt}}. \tag{16}\]  
Then, through substituting \(P_{d,\mathrm{I}}^{\mathrm{opt}}\) in (15) and \(P_{d,\mathrm{E}}^{\mathrm{opt}}\) in (16) into (12) and (13), the optimal solutions for \(\rho_{c}\) and \(\rho_{d}\) can be obtained as follows:  
\[\rho_{c}^{\mathrm{opt}}\in\Bigg{[}\frac{\rho_{c}^{\mathrm{ND}}P_{b}|h_{ \mathrm{bc}}|^{2}}{P_{b}|h_{\mathrm{bc}}|^{2}+P_{d}^{\mathrm{max}}|h_{dc}|^{2} },1-\frac{(1-\rho_{c}^{\mathrm{ND}})\sigma_{c,\mathrm{cov}}^{2}}{\sigma_{c, \mathrm{cov}}^{2}-(1-\rho_{c}^{\mathrm{ND}})P_{d,\mathrm{I}}^{\mathrm{opt}}|h_ {dc}|^{2}}\Bigg{]}, \tag{17}\] \[\rho_{d}^{\mathrm{opt}}=\frac{Q_{d}}{\zeta T(P_{b}|h_{bd}|^{2}+P_ {d}^{\mathrm{max}}|h_{dd}|^{2})}. \tag{18}\]  
Since the BS coordinates the entire network, it calculates and forwards the solution in (15)-(18) to the CU and D2D pair.  
With the proposed scheme in (15)-(18), the CU's harvested energy and the data rate can be ensured not to be degraded by the D2D signals, i.e., (7b) and (7c) can be satisfied with \(P_{d,\mathrm{I}}>0\). This is achievable because adjusting \(\rho_{c}\) as in (17) enables the CU to offset the rate loss caused by \(P_{d,\mathrm{I}}|h_{dc}|^{2}\) in (4) using the harvested energy obtained from \(P_{d,\mathrm{I}}|h_{dc}|^{2}+P_{d,\mathrm{E}}|h_{dc}|^{2}\) in (3). In other words, the CU utilizes the energy harvesting benefits from the received energy signals to compensate for its data rate loss caused by the information signals transmitted by the DT. Accordingly, the performance degradation of the CU can be prevented through this energy cooperation between the DT and the CU, for which the DT transmits energy signals along with information signals.
## 3 Results and Discussion
### Methylene Blue Adsorption
#### 3.2.2 Adsorption Isotherms
Figure 2a,b also presents the adjustment of the experimental data regarding H-180 and H-215, respectively, to Langmuir, Freundlich, and Sips isotherm models, for which the parameter values are reported in Table 3. The Langmuir isotherm model assumes \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Raw Material** & **Hydrochar Activation** & \(q_{max}\) **(mg\(\cdot\)g\({}^{-1}\))** & **Reference** \\ \hline Sawdust and sewage sludge & No & 70 & This study \\ Citrus waste & No & 31–66 & [42] \\ Winery waste & No & 2–37 & [42] \\ Sewage sludge & No & 38–71 & [43] \\ Corn straw & Yes & 20–60 & [47] \\ Pomergnate peels & Yes & 50 & [48] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maximum adsorption capacity (q\({}_{max}\)) of different hydrochars for methylene blue. Figure 2: Isotherm models of methylene blue adsorption by H-180 (**a**) and H-215 (**b**) obtained at hydrochar load of 3 g\(\cdot\)L\({}^{-1}\) at pH 10, 30 \({}^{\circ}\)C, 100 rpm for 24 h.  
monolayer adsorption, whereby the adsorption process occurs exclusively on a homogeneous adsorbent surface with identical adsorption sites, forming a single layer of adsorbate molecules. Once a site is occupied, no further adsorption can occur at that site. Furthermore, the model assumes that the adsorption-desorption process is reversible. On the other hand, the Freundlich isotherm model assumes multilayer adsorption, which occurs on a heterogeneous surface with adsorption sites of varying energy levels. The molecules can adsorb onto each other after the initial layer has formed. Regarding the Sips isotherm model, it is a hybrid model obtained from the combination of both the models aforementioned [50; 51]. Considering the \(R^{2}\) and MRD values in Table 3, the Sips isotherm model is suggested for describing the methylene blue adsorption by both H-180 and H-215. This model indicated a \(q_{max}\) value of around 80 mg\(\cdot\)g\({}^{-1}\). Additionally, no significant difference was noticed for both hydrocarbons regarding their adsorption performance since the 95% confidence intervals for \(q_{max}\) values of H-180 and H-215 overlapped. Thus, only H-180 was used in the following tests considering energy-saving reasons. It is preferable to conduct Co-HTC at 180 \({}^{\circ}\)C rather than at 215 \({}^{\circ}\)C.