esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Self-Driven Profiler with Buoyancy Adjusting System towards Ocean Thermal Energy
## 3 Dynamic Performance of the Buoyancy Regulation System  
The commonly used analysis methods of dynamic characteristics of the hydraulic systems mainly include transfer function and simulation method. With the development of computer technology, digital simulation has become a common method to study the dynamic characteristics of the hydraulic system. The power bonding diagram is a very effective simulation modeling tool for the dynamic features of the hydraulic system. It can clearly express the physical significance of the model, and there is a correspondence between the bonding graph and the system's mathematical model. The system's state equation can be easily derived according to the bonding diagram. In this paper, the dynamic characteristics of the buoyancy control system are simulated and analyzed by using the power bonding graph. The following assumptions are made in establishing the power bond diagram of the system:  
1. The initial energy of the profiler comes from the ocean thermal energy. The conversion of the ocean thermal energy to the potential energy of the high-pressure fluid in the accumulator is a slow process. It is assumed that the initial source of energy is the accumulator.
2. The solidification of the phase change material is a very slow process. It is assumed that the solidification shrinkage rate of the phase change material is the same as the oil discharge rate of the inner cylinder.
3. The melting of the phase change material is a slow change process. It can be assumed that the melting and expansion rate of the phase change material is the same as the oil charging rate of the accumulator.
4. The accumulator releases oil for a short time, which is regarded as an isothermal process.  
The power bonding diagram of this system is shown in Figure 2. It is mainly composed of four parts, which are float control, sink control, PCM solidification process and PCM melting process. The details are shown as follows:
# Behavioral Dynamics of Pedestrians Crossing between Two Moving Vehicles
## 2 Methods
### Bearing Angle  
Dynamics of interceptive movement are often described in terms of the bearing angle [7; 8], which refers to the angle between the velocity vector of the human subject and the line of sight between the subject and the object she/he hopes to intercept. In brief, this model asserts that people intercept a moving object by choosing such a trajectory that the bearing angle is kept constant.  
Our case of crossing a road may be cast into an interception task: The pedestrian must \"intercept\" the empty gap between the vehicles [17]. We may hence apply the bearing angle approach to our crossing experiment and model. One difficulty with this approach is that the gap is not a point but a moving area. As an obvious choice, we may simply use the gap center \(x_{g}(t)\), with respect to which the bearing angle is \(\theta_{g}(t)=\arctan\left[x_{g}(t)/y(t)\right]\). However, the pedestrian may not cross the gap center; it is thus more appropriate to examine the bearing angle with respect to the point within the moving gap that the pedestrian actually crosses. With \(t^{*}\) denoting the crossing time, we have \(y(t^{*})=0\) and let the position of the gap center at the crossing time be \(\Delta\ x\), i.e., \(x_{g}(t^{*})=\Delta\ x\). We then define the crossing point,  
\[x_{c}(t)=x_{g}(t)-\Delta\ x=x_{0}-\Delta\ x+v_{c}t=v_{c}(t-t^{*}), \tag{5}\]  
and consider the angle with respect to \(x_{c}\):  
\[\theta_{c}(t)=\arctan\left[\frac{x_{c}(t)}{y(t)}\right]. \tag{6}\]  
Taking the time derivative of Equation (6) results in  
\[\theta_{c}=\frac{x_{c}y}{x_{c}^{2}+y^{2}}\left(\frac{x_{c}}{x_{c}}-\frac{\dot {y}}{y}\right). \tag{7}\]  
Assuming that \(y\) follows the simple crossing model (i.e., Equation (2)), \(|\dot{y}|\) is small when \(t<t_{a}\). Considering the signs of variables (especially, \(x_{c}<0\) and \(\dot{x_{c}}>0\)), we thus have that \(\theta_{c}<0\), indicating a decreasing bearing angle. When \(t>t_{a}+2\tau\), the speed approaches the maximum: \(\dot{y}\approx v_{\rm max}\), so that we have  
\[\frac{\dot{x}_{c}}{x_{c}}-\frac{\dot{y}}{y}\approx\frac{v_{c}}{v_{c}(t-t^{*})} -\frac{v_{\rm max}}{v_{\rm max}(t-t^{*})}=0, \tag{8}\]  
which, upon substituting into Equation (7), yields \(\theta_{c}=0\) or a constant bearing angle. The model thus predicts that the bearing angle should decrease at the first stage of crossing and remain constant thereafter. The constant value \(\theta_{c}^{*}\) that the bearing angle approaches can be estimated by  
\[\lim_{\Delta\ t\to 0}\theta_{c}(t^{*}-\Delta\ t) = \lim_{\Delta\ t\to 0}\arctan\left(\frac{v_{c}\Delta\ t}{v_{\rm max} \Delta\ t}\right) \tag{9}\] \[= \arctan\left(\frac{v_{c}}{v_{\rm max}}\right).\]
## 2 Materials and Methods  
### Direct and Inverse Kinematics  
Robotic systems consist of links connected by joints that allow relative motion between adjacent links. The degrees of freedom (DOFs) of a robot defines the number of independent variables required to determine the position and orientation of all components in the mechanism. At the end of the chain of links is the end-effector, whose configuration depends on the robot's function. The end-effector may incorporate tools such as grippers, soldering irons, electromagnets, or other specialized devices. The position and orientation of the robot are typically described in terms of a tool reference frame attached to the end-effector, relative to the fixed base reference frame of the robot. The forward kinematic model calculates the position and orientation of the end-effector when provided with a specific set of joint angles relative to the robot's base frame. This model determined the spatial location of the end-effector based on the given joint coordinates and also facilitated velocity control of the end-effector.  
An essential aspect of robot control is solving for the end-effector position using measurements of the joint coordinates, which involves solving the system of equations defined by the forward kinematics. However, for parallel manipulators, this problem often leads to multiple solutions because there can be several valid configurations of the manipulator that correspond to the same joint coordinates [31]. The complexity of forward kinematics for parallel manipulators arises due to the closed-chain structure of these systems. Consequently, solving for the inverse kinematics--determining the joint angles from a desired position and orientation of the end-effector--becomes equally crucial. This process enables precise trajectory planning and control by converting the desired spatial position into corresponding joint movements.
## 4 Proposed Method
### Network Architecture  
The proposed model is a combination of CNN and LSTM approaches. Such an architecture is frequently referred to as a long-term recurrent convolutional network (LRCN) [32]. The model takes a sequence of samples, and then subsequent convolutional layers are used to extract features that are important for further classification. While the original work [32] worked with images, our model works with eye movement data samples containing preprocessed attributes such as velocity or acceleration. The features extracted from the input data by the convolutional layers are then passed as a sequence to the recurrent layers. Recurrent layers function as a memory-based network. Their subsequent outputs are generated based on the current input, and all inputs proceed in a sequence. In contrast to [21], only the last output of the network is used. Thanks to the combination of convolutional and recurrent layers, the network is able to utilize both spatial and temporal features of the signal.  
Therefore, the network takes a stream of samples as input. Each sample is a set of different eye movement features, such as direction, velocity, and their higher-order derivatives. The stream of samples is analyzed in windows of 50 samples (which gives a time span of 100 ms in our dataset). To obtain a prediction for each sample, the window moves over the sequence one by one, and the sample in the middle of the window is classified according to the neighboring samples. The network comprises different layers, precisely three convolutional layers with 32, 16, and 16 filters and a kernel size of 5. After the convolutional layers, a permute function reshapes the CONV2D output into an LSTM input shape, two LSTM layers with 32 and 16 units, batch normalization layers, and a dense output layer. A sequence of samples with a shape of \(50 xN\), where \(N\) is the number of features for each feature's combination, serves as the input to the network. The network architecture is shown in Figure 1.  
The model was evaluated using a leave-one-file-out cross-validation approach. In each fold, one file was reserved for testing, while the remaining files were used for training. Sequences of 50 samples were created from the raw data to form input windows for the model. The model was compiled using the RMSprop optimizer with the default learning rate of 0.001, categorical cross-entropy as the loss function, and categorical accuracy as the evaluation metric. During training, the model was trained for 25 epochs with a batch size of 50. For each training iteration, the labels were binarized using LabelBinarizer to encode the categorical labels into a one-hot format.
## 5 Discussion  
### Cracking Deviation Due to Reservoirs  
Post-testing visual inspection of the crack planes validated the hypothesis that the 3D printed reservoirs crossed the cracked sections of the beam. As illustrated in Figure 14a, the reservoir system was fully de-bonded from the concrete along the crack plane. It is evident that the reservoir material was too stiff and the component was cast in such a large volume that it attracted the cracks and was responsible for the deviation of damage from the middle bending zone to the edges of the beam. Additionally, the cracking at the reservoirs instead of the middle section appeared to lower the ultimate load reached. The ultimate load measured for beams with a crack at the middle was about 4.71\(\pm\) 0.32 kN, whereas the load dropped to 3.94 \(\pm\) 0.77 kN. The reservoir design should be revised in future work and the possibility to replace the PET skeleton with a flexible, smaller in size  
Figure 12: (**a**) crack of an unhaeled beam (B9); (**b**) crack of a healed beam (B4); (**c**) detail view of crack branches after healing (B3).  
Figure 13: (**a**) fully filled with healing agent (B3); (**b**) air bubbles entrapped into a large crack (B2).  
component should be explored. It was shown that the flexible tube did not impact the crack formation and effectively delivered the healing agent.
## 3 Results
### Modeling \(R_{\rm s}\) Seasonal Variations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Models and Equations** & \(\mathbf{R^{2}}\) & **RSS** & **RMSE** & **AIC** \\ \hline \(R_{\rm s}=f(\text{LST-NDVI})\) & \((1)\)\(R_{\rm s}=a\text{LST}+b\text{NDVI}+c\) & 0.718 & **80.562** & 1.179 & 25.058 \\  & \((2)\)\(R_{\rm s}=a\text{exp}(\text{LST}+b\text{NDVI})\) & 0.782 & **6.537** & 0.336 & -120.612 \\ \(R_{\rm s}=f(\text{M},T_{\rm s}\text{NDVI})\) & \((1)\)\(R_{\rm s}=a\text{M}+\text{M}_{\rm s}+\text{M}_{\rm s}^{2}+cT_{\rm s}+b\text{ NDVI}+c\) & 0.817 & **52.419** & 0.951 & 2.132 \\  & \((2)\)\(R_{\rm s}=a\text{exp}(\text{M}_{\rm s}+c\text{M}_{\rm s}^{2}+d\text{Ts}+b\text{NDVI})\) & 0.854 & **4.364** & 0.274 & -142.049 \\ \(R_{\rm s}=f(\text{M},t\text{LST-NDVI})\) & \((1)\)\(R_{\rm s}=a\text{M}_{\rm s}+\text{M}_{\rm s}^{2}+d\text{ST}+b\text{NDVI}+c\) & 0.748 & **72.085** & 1.115 & 20.609 \\  & \((2)\)\(R_{\rm s}=a\text{exp}(\text{M}_{\rm s}+c\text{M}_{\rm s}^{2}+d\text{ST}+b\text{NDVI})\) & 0.819 & **5.431** & 0.306 & -129.363 \\ \hline \hline \end{tabular}  
* \(R^{2}\) is the coefficient of determination, RMSE (\(\mu\)m) CO\({}_{2}\) m\({}^{-2}\)s\({}^{-1}\)) is the root-mean-square error, and AIC is the Akaike’s information criterion. ** means the relationships are statistically significant at \(p<0.001\). \(R_{\rm s}\) is measured soil respiration (\(\mu\)m) CO\({}_{2}\) m\({}^{-2}\)s\({}^{-1}\)), \(T_{\rm s}\) is soil temperature (\({}^{\circ}\)C), LST is the nighttime land surface temperature observed by the Moderate Resolution Imaging Spectro radiometer (MODIS) onboard terra satellite (\({}^{\circ}\)C), and NDVI is the normalized difference vegetation index.  
\end{table} 
Table 2: _Cont._  
Figure 4: Relationships between soil temperature (\(T_{\rm s}\)) and land surface temperature (LST) in study site from 2007 to 2015 (n = 58). *** means the relationships are statistically significant at \(p<0.001\).
# Slug Regime Transitions in a Two-Phase Flow in Horizontal Round Pipe. CFD Simulations
## Materials and Methods
#### 2.1.4 Applied Turbulent Flow Models  
Simulation of fluid flow was based on the calculation of the Navier-Stokes equations. The Navier-Stokes equations are a set of partial differential equations which describe the motion of viscous fluid. Below are the equations to be solved for the fluid motion model.  
Continuity equation:  
\[\frac{\partial\rho}{\partial t}+\overrightarrow{\
abla}\cdot(\rho V)=0 \tag{1}\]  
Momentum equation (according to Navier-Stokes equation):  
\[\frac{\partial(\rho V)}{\partial t}+\overrightarrow{\
abla}\cdot(\rho V\times V )=\overrightarrow{\
abla}\cdot\left(-P\hat{\tau}_{eff}\right)+\rho F \tag{2}\]  
where:  
\[\hat{\tau}_{eff}=\mu\left(2\hat{\varepsilon}-\frac{2}{3}\left(\overrightarrow {\
abla}\cdot V\right)\hat{\xi}\right) \tag{3}\]  
\[e_{ij}=\frac{1}{2}\left(\
abla_{j}V_{i}+\
abla_{i}V_{j}\right) \tag{4}\]  
Energy equation:  
\[\frac{\partial(\rho h)}{\partial t}+\overrightarrow{\
abla}\cdot(\rho Vh)= \overrightarrow{\
abla}J_{q,eff}+\frac{dP}{dt}+\hat{\tau}:\hat{e}+\rho\varepsilon \tag{5}\]  
\[J_{q,eff}=-\frac{\lambda}{C_{p}}\overrightarrow{\
abla}h \tag{6}\]  
Since the velocities of water and air were high enough, the flow was assumed to be turbulent. Fluid motion was described by the Reynolds-averaged Navier-Stokes (RANS) equations [33].  
The \(k\)-\(\varepsilon\) model with scalable wall functions [34] was used to close the RANS equations calculation scheme. The boundary conditions for the turbulence model were standard: 5% turbulence intensity, and the ratio of turbulent viscosity to molecular viscosity equal to 10 at the inlet and outlet boundaries. The standard constants were used in the \(k\)-\(\varepsilon\) model that Fluent offers by default.  
Since there was a need to monitor the interface to determine the multiphase flow regime accurately, it was decided to use the volume of fluid (VOF) model. This model supplements the Navier-Stokes equations with the concentration equation for each of the phases in the cell of the computational grid. Thus, in each of the cells, there was one of either the phase or an interface. The VOF method was not very demanding on resources, and was one of the frequently used methods for this class of tasks [35]. For more accurate tracking of the behavior of the interface, the VOF method was improved, taking into account the surface tension between the phases. Besides, the calculation of the flow was carried out under the action of a gravitational force along the Y-axis.  
Since the interface changes dynamically, the problem was unsteady in the time domain. The used time step range was from 10 ms to 1 ms. The time step was adaptive and generally close to the minimum value of 1 ms.  
The time step was adjusted based on the condition that the solution was independent of the time step size.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 3 Vulnerabilities
### Energy Attacks  
Another class of DoS attacks against battery-powered LoRaWAN devices involves those aiming at maximizing energy consumption [11]. As explained in Section 2.3, for Class-A signaling, the highest energy consumption takes place during packet transmission, during which the RF transmitter and the RF receiver are activated. In order to spare energy, the downlink reception windows are limited in duration and, given that no packets are received during these time windows, the modern is set back to sleep mode. However, if an attacker is able to synchronize to the LoRaWAN-device communication, the LoRa modem can be forced into downlink packet reception mode, which increases the energy consumption significantly. More precisely, an attacker might target the second reception window, for which the reception parameters are typically unchanged from transmission to transmission. Experimental validation of such an attack revealed an increase in energy consumption from 36% to 576%, which, in other words, would reduce a battery-driven  
Figure 6: Bidirectional wormhole attack in LoRaWAN.  
device lifetime by several years. As noted in [11], such attacks are more difficult to detect in comparison to, e.g., another kind of energy attack, by forcing a LoRaWAN device to re-transmit packets by direct jamming. Nevertheless, worm-hole attacks in a LoRaWAN network's supporting-adaptive rate might cause even worse energy consumption, as the attacker is able to force a higher SF setting by manipulating LoRaWAN metadata via replays and packet drops.
## 3 Methodology
### Image Threshold Segmentation  
Threshold selection is a key step in defining [PERSON]'s or Xu's NDWIs. The threshold values for [PERSON]'s and [PERSON]'s NDWIs were set to zero [32,34], but threshold adjustment in individual situations can achieve a more accurate delineation of water bodies [38]. Hence, dynamic or variational thresholds are needed when different regions or different phases of remote sensing data are employed to detect water body information [11]. The maximum between-class variance method (the Otsu method) is one such dynamic threshold method [44]. In this study, the Otsu method was used to determine the threshold for separating water bodies from the background features.  
Now, assume that the NDWI pixels range from [\(a\), \(\cdots\), \(b\)], where \(-1\leq a<b\leq 1\). Using the Otsu method, the pixels can be divided into two classes: a non-water class ranging from [\(a\), \(\cdots\), \(t\)] and a water class ranging from [\(t\), \(\cdots\), \(b\)], where \(t\) is the threshold value. Then, the probabilities of a pixel being in the non-water class (\(P_{mw}\)) and the water class (\(P_{w}\)) and the mean pixel values of the non-water class (\(M_{mw}\)) and the water class (\(M_{w}\)) can be obtained by statistical analysis. The between-class variance of the non-water class and water class can be obtained by  
\[\begin{cases}\sigma^{2}=P_{mw}\cdot\left(M_{mw}-M\right)^{2}+P_{w}\cdot\left( M_{w}-M\right)^{2}\\ M=P_{mw}\cdot M_{mw}+P_{w}\cdot M_{w}\\ P_{mw}+P_{w}=1\end{cases} \tag{5}\]  
where \(\sigma\) is the between-class variance of the non-water class and the water class, and \(M\) is the mean value of the NDWI image.  
Obviously, the parameters in the Equation (5) are functions of the threshold value \(t\). The optimal threshold \(t^{*}\) can be determined by the between-class variance of the non-water class and the water class using the following equation:  
\[t^{*}=Arg\underset{a\leq t\leq b}{Max}\left\{P_{mw}\cdot\left(M_{mw}-M\right) ^{2}+P_{w}\cdot\left(M_{w}-M\right)^{2}\right\} \tag{6}\]  
Using Equation (6), the Otsu method selects the threshold by using the rule of maximum between-class variance of the background features (e.g., vegetation, soil, _etc._) and water body features. When part of the water body feature is mistakenly classified as a background feature or part of the background feature is wrongly classified as a water body feature, the between-class variance decreases. Equivalently, the greater the variance, the more different the background features and the water body features. Therefore, maximizing the variance between water body features and background features minimizes the probability of misclassification. In other words, a spectral water index model is more suitable for enhancing and extracting water bodies when the NDWI image has high between-class variance.
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 3 Scheduling Optimization by Genetic Algorithms
### Crossover and Mutation  
After selection, crossover procedures are applied to the chosen parent solutions to produce offspring solutions for subsequent generations. This process involves combining parent solutions to yield new solutions, known as children, thereby facilitating the enhancement of the population over time. In our genetic algorithm, a widely recognized single-point crossover technique is employed to generate fresh offspring solutions. In a single-point crossover, each parent possesses a single cutting point, and the offspring are derived by replicating distinct segments of each parent's solution based on this point. In the context of our scheduling, offspring solutions are created by interchanging combinations of machines and task instances through the single-point crossover operation, as illustrated in Figure 7a.  
In genetic algorithms, a crossover operation can result in invalid solutions. In our case, as solutions are composed of items \([m_{j},t_{i}]\), where \(m_{j}\) is a machine and \(t_{i}\) is a task instance, some task instances might be duplicated, while others might disappear after crossover. This issue can be resolved by imposing a constraint on the encoding process to ensure that task numbers in a solution are in increasing order. Specifically, instead of task instances being placed in any order within the solution, we assume that they appear sequentially from \(t_{1}\) to \(t_{n}\). This assumption ensures that after a crossover, every task instance appears exactly once in the solution.  
Figure 6: Tournament selection used in our genetic algorithm.  
Figure 7: Crossover and mutation operations used in our genetic algorithms.  
To assess the effectiveness of our single-point crossover operation, we investigate its fitness value in comparison with two other popular crossover operations: uniform crossover and scattered crossover. As shown in Figure 8, the three crossover methods exhibit very similar results. We chose the single-point crossover as it is the simplest among the three considered, yet it still exhibits competitive results.  
The mutation operation is employed to alter particular components of solutions produced through crossover operations, in order to explore the problem space more extensively. These mutation operations are recognized for their role in preserving diversity among the population and preventing premature convergence towards a local optimum. Within our genetic algorithm framework, an adaptive mutation operation is implemented. This strategy involves initially computing the average fitness value across the population and establishing this value as a threshold. Solutions surpassing this threshold are subjected to a reduced mutation probability of 0.25, while those with lower fitness values are as  
Figure 8: Fitness value as crossover operation is varied.  
signed a higher mutation probability of 0.1, thereby facilitating mutation operations. This methodology is illustrated in Figure 6(b).