esa-sceva/satcom-chunk-collection huggingface dataset:
# Food Processing with UHP Waterjets
## 3 Waterjet Food Systems
### Cutting Heads  
Cutting heads are the tools that are mounted on the system manipulator to perform the cutting by generating high-velocity cutting jets. The cutting head includes the on/off valve, the water body, and the orifice.  
On/Off valves, see Figure 7a, are either normally closed or normally open based on the design of the actuator which is mostly operated pneumatically. Standard shop air (\(\sim\)0.4 MPa) is used to actuate the valve in either case. The critical parts in UHP valves are the poppet and seat which are designed to accomplish at least 100,000 cycles and thus the seals, material selection, and material treatment are all of great importance to meet the severe UHP tribological demands. In normally closed valves, air acts upon the actuator piston against a spring to allow for the lifting of the poppet off the seat (containing a hole) and enables the flow to take place. It closes when the air pressure is released. In normally open valves, air pressure is used to close it. For food cutting, the on/off cycle time must be minimal for efficient operation. A typical opening time for these valves is about 48 ms and a close time of about 160 and 115 ms is normal for the normally closed and normally open valves, respectively. Due to the relatively high forces for opening and closing, solenoid-type valves are not suitable. The orifice in the cutting head is where the pressure potential energy is converted to kinetic energy and as described above, is of critical importance to the performance of the waterjet. Diamond (either natural, synthetic, or PCD) may be used with a lifetime of about 1000 h. Sapphire orifices are not preferred due to their relatively short lifetime. [PERSON] [6] developed a quick-change orifice mount, so an orifice could be replaced in seconds without having to disassemble the holding nut. This offers potential for automatic orifice replacement. Vacuum sensors have also been used in the aerospace industry, as stated by [PERSON] et al. [7], to detect changes in orifice health, but this has not been applied in food systems.  
Other types of cutting heads are related to the entrainment of different media into a high-velocity waterjet. These media could be air or solid particulate material to act as abrasives. Figure 7b shows a standard abrasive water jet (AWJ) cutting head that can be used for this purpose.  
Figure 7: Waterjet tools. (**a**) Pure waterjet nozzle and on/off valve (Curtesy KMT, Baxter Springs, KS USA). (**b**) Entrainment nozzle for creating high-velocity media waterjet.  
The air entrainment results in high-speed focused droplet jets which were found to be more effective for cutting some food products than standard coherent waterjets. Entrainment of solid particles included entrainment of sugar, salt, and ice.
## 5 Classification of Smart Grid Cyberattack
### Data Availability Attacks
#### 5.1.3 Flooding  
Attackers bombard a system with a large volume of traffic during a flood attack to prevent it from inspecting and allowing authorized network traffic. Energy depletion and message delays are two efficient effects of flooding attacks. In a power grid's electric substations, a lot of communication messages must be transmitted very fast. However, flooding attacks can increase the delay and affect how quickly these messages are delivered. In such a scenario, a network attacker (such as a compromised device) floods the system with false messages to consume the available CPU and network resources. This causes the delivery times of legitimate messages to increase, causing them to miss the delivery time. Therefore, flooding attacks have the potential to seriously harm smart grid applications' availability. Another effect of flooding attacks is energy depletion. The attacker may insert malware into a smart grid network to raise the transmission power and, consequently, the energy consumption of legitimate network devices [85].  
DoS and flooding attacks have many factors in common where the flooding attacks are sometimes put as one category of DoS. Application-layer DoS attacks try to deplete a system's resources, such as memory, CPU, or bandwidth, by flooding them with a large volume of requests. Attacks on the transport layer that target availability try to break up end-to-end connections by draining the sources, which eventually prevents the target device from receiving genuine traffic. Flooding attacks that targets TCP and UDP traffic are two typical examples [86].
## 2 The Learning Algorithm  
### Hypothesis Set  
In this subsection, we briefly illustrate the hypothesis set and training and testing algorithms. The flow chart is illustrated in Figure 1. The details are discussed in the following sections. Given the training data set \(\tau=\{(\vec{x}_{i},y_{i})|i=1,\ldots,l\}\), where \(y_{i}\in\{\pm 1\}\) are the labels for binary classification, the goal of a classification algorithm is to find a hypothesis \(f_{\theta}:(\vec{x}_{1},\ldots,\vec{x}_{n_{t}})\mapsto\{-1,+1\}^{n_{t}}\) with small prediction error, where \(n_{t}\) is the number of test data. The classification algorithm consists of two parts, training and prediction. A training algorithm finds a hypothesis by minimizing some loss function \(L(f_{\theta}(\vec{x}_{i}),y_{i})\). Our hypothesis set consists of the ground states of the Ising model. The hypothesis set is  
\[f_{\theta}(\vec{x}_{1},\ldots,\vec{x}_{n_{t}}) =(s_{1},\ldots,s_{n_{t}}),\,\vec{x}_{i}\in T\text{ for }1\leq i\leq n_{t}, \tag{1}\] \[(s_{1},\ldots,s_{n_{t}+n_{\alpha}}) =\arg\min_{\{s_{k}\}}[-\sum_{\begin{subarray}{c}i<j\\ \vec{x}_{i},\vec{x}_{j}\in\mathcal{S}\cup T\end{subarray}}J_{i,ij}(\vec{x}_{i },\vec{x}_{j})s_{i}s_{j}-\sum_{\begin{subarray}{c}\vec{x}_{i}\\ \vec{x}_{i}\in\mathcal{S}\cup T\end{subarray}}h_{i}s_{i}], \tag{2}\]  
where \(S=\{\text{support vectors}\}\) and \(T=\{\text{test data}\}\), \(n_{sv}=|S|\), \(n_{t}=|T|\), \(s_{i}\in\{-1,1\}\). \(h_{i}=y_{i}\) for \(\vec{x}_{i}\in S\) and \(h_{i}=0\) for \(\vec{x}_{i}\in T\). The free parameters of the model are, in general, \(\theta=(J_{ij})\). We use a physics-motivated ansatz to efficiently determine the support vectors and \(\theta\) from three free parameters, \(\theta^{\prime}=(\alpha,\beta,\epsilon)\), as explained in the following subsections. Finally, the training is performed by minimizing the loss function with respect to the remaining three free parameters, \(\theta=(\alpha,\beta,\epsilon)\), with particle swarm optimization (PSO). The loss function is the cross-entropy loss function  
\[L(f_{\theta}(\vec{x}_{i}),y_{i})=-(1-q(\vec{x}_{i}))\ln(p_{-1}(\vec{x}_{i}))- q(\vec{x}_{i})\ln(p_{+1}(\vec{x}_{i})), \tag{3}\]  
where \(q(\vec{x}_{i})=(1+y_{i})/2\); \(p_{\pm 1}(\vec{x}_{i})\) is the probability that \(s_{i}=\pm 1\) for the Ising ground state.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 3 Preliminaries and Problem Formulation
### Problem Formulation  
The LBS server divides the area covered by an RSU into \(I\times J\) cells as shown in Figure 2. _cell\({}_{ij}\)_ denotes the cell of row \(i\) and column \(j\), \(i=1,\)\(2,\)\(\ldots,\)\(I,j=1,\)\(2,\)\(\ldots,\)\(J.\) The location of _cell\({}_{ij}\)_ is denoted as \(\mathbf{r}_{ij}\), and \(\mathbf{r}_{ij}=(x_{i,j},y_{i,j})\). The request probability of _cell\({}_{ij}\)_ is \(q_{ij}\), the service semantics of _cell\({}_{ij}\)_ is \(e_{(i,j),\mu}\), and the information matrix \(Q(\mathbf{r},\)\(q,\mathbf{e})\) for each RSU can be set up.  
Figure 2 shows service request probability distribution, the area is divided into \(10\)\(\times\)\(10\) cells. The star represents the user's real location, and the triangle represents the dummy location, and the shade in each cell represents its request probability generated based on the Borlange data set [35]. The gray block represents the road, and \(\mathbf{R}\) represents the location area accessible by the road.  
In Figure 2a, a vehicle user randomly generates \(k-1\) dummy locations in order to protect the location privacy. Then vehicle user uses the dummy locations and real location to send service request to the LBS server. In theory, the probability of exposing the user's real location can be \(1/k\). However, using some auxiliary information, the LBS server can deduce the real location with a probability of \(1/(k-k_{\text{d}})\), where \(k_{\text{d}}\) is the number of dummy locations be filtered out through the auxiliary information.  
In IoV, since the location of vehicles is restricted by the road, and the service request probability is used as auxiliary information, the validity of dummy locations generated with random dummy location selection algorithm in Figure 2a, E-DLS algorithm in [18] and Dest-ex algorithm in [25] is affected. The dummy locations filtered out by the LBS server, \(k_{\text{d}}\), increases. For example, in Figure 2a, \(k=4\), and \(k_{\text{d}}=3\). Hence, the effect of privacy protection is degraded.  
Therefore, to protect the location privacy of vehicles, it is necessary to ensure the validity of dummy locations generated. When road information, service request probability and service semantics are used as auxiliary information, set \(\mathcal{G}\) is set up for minimizing \(k_{\text{d}}\). The optimization problem can be defined as  
\[\begin{array}{c}\underset{\mathcal{G}}{\text{min}}k_{d}\\ \text{s.t.}G(r,q,e)\subset Q(r,q,e)\\ \forall r_{i,j}\in\mathcal{G},r_{i,j}\in\mathcal{C},r_{i,j}\in R\\ |\mathcal{G}|=k,\end{array} \tag{3}\]  
where \(G(r,q,e)\) is the information matrix corresponding to set \(\mathcal{G}\) which consists of vehicle user's location and \(k-1\) dummy locations, and set \(\mathcal{C}\) is the set of all locations of cells in the area covered by the RSU.
# Preliminary Biomechanical Evaluation of a Novel Exoskeleton Robotic System to Assist Stair Climbing
## 2 Materials and Methods
### Analyses  
For the evaluation of exoskeletons, muscle activities, joint moments, and joint reaction forces are often analyzed as simulation outputs [30; 31]. A comprehensive evaluation of the exoskeleton should consider the effects of the system on the entire human body. For this reason, all of the angular curves of the lower extremities in sagittal and frontal planes, the moments of the hip, knee and foot, as well as the assistive moments that were applied by the exoskeleton in the knee are evaluated on a subject specific basis. Since the eBUS is designed to assist knee extension, the measured sEMG signals from VAL and VAM during the stance phase were also analyzed. The muscle activity is indicated as a percentage of the within-subject maximum. Furthermore, the joint reaction forces in the knee with and without eBUS were compared. The joint moments and the joint reaction force were normalized to the respective body weights.  
All curves of angles, moments, and joint reaction forces are presented as a cycle for ascent and descent on a per-subject basis. The start of the cycle (0% cycle time) is defined by the first foot contact on the force plate with a threshold value of 20 N. The stance phase ends when the contact is released, and the swing phase begins at the same time. The end of the swing phase (100% cycle time) was determined by the marker trajectories. Each trial was trimmed, resampled, and linearly normalized to the duration of the stance respectively swing phase. The biomechanical results of each trial setting were averaged, standard deviated and plotted on the same graph.  
The data that are summarized in Table 3 were simulated successfully and could be used for the analysis. Due to large gaps in the trajectories and incomplete data from the exoskeleton, not all six trials could be simulated for subjects 5 and 6 and were excluded from the analysis.  
Figure 4: (**a**) Marker placement on the human and exoskeleton [29]. Markers on the human: two at the shoulders, four at the pelvis, four at the knees, six at the shanks, four at the ankles, and six at the feet. Markers on each exoskeleton leg: four at the thigh segment, three at the thigh rail, three at the shank segment, three at the shank lower rail, one at the foot rail, and four at the foot segment; (**b**) Human-exoskeleton-model during the inverse calculation of a stair ascent [4].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Movement**} & \multicolumn{2}{c}{**Subject 1**} & \multicolumn{2}{c}{**Subject 2**} & \multicolumn{2}{c}{**Subject 3**} \\ \cline{2-5}  & **With eBUS** & **Without eBUS** & **With eBUS** & **Without eBUS** & **With eBUS** & **Without eBUS** \\ \hline Ascent & 6 & 6 & 5 & 6 & 4 & 6 \\ \hline Descent & 6 & 6 & 5 & 6 & 3 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analyizable data per subject (S1, S2, S3), movement (ascent, descent), and setting (with/without eBUS).
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 4 Methodology
### Testing  
The testing dataset is composed of the augmented data points at the third hour, which is at a slightly higher loading point than the training dataset. This is to also be able to test the amount of generalization the LSTM networks have learned. Since the WLS-SE assumes that the system state variables are the voltage magnitudes and angles, the neural network outputs are single columns in order of increasing bus number, with the magnitudes in the first half and the angles at the second half. Voltage magnitudes are measured in per-unit while the angles are normalized to 90\({}^{\circ}\). The error between them is measured with the mean absolute error (MAE) and the root mean square error (RMSE) metrics. These metrics are shown in Equations (19) and (20), respectively.  
\[C=\frac{1}{N}\sum_{i}^{N}(|\hat{y}_{i}-y_{i}|) \tag{19}\]  
\[C=\sqrt{\frac{1}{N}\sum_{i}^{N}(\hat{y}_{i}-y_{i})^{2}} \tag{20}\]
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 2 Target Floating Wind Turbine
### Verification of FOWT Numerical Modeling  
OpenFAST was developed to allow the integration of WAMIT analysis results, a hydrodynamic analysis tool, to model the complex interactions among the floating platform,  
Figure 1: Numerical model and schematic depictions of the target floating offshore wind turbine established in OpenFAST.  
Figure 2: Comparison of the steady-state simulation results.  
moorings, and hydrodynamic forces of floating offshore wind turbines [21]. In this study, hydrodynamics associated with the floating motion were implemented in OpenFAST by applying WAMIT analysis results, which were matched to the experimental results from model testing, including free decay and response amplitude operator (RAO). Figure 3 shows the results of the free decay analysis comparing the experimental and simulation data.  
Through the free decay calibration and the irregular wave calibration, the simulation results were found to have very similar frequencies and amplitudes to the experimental results. The RAO is suitable for evaluating whether the dynamic response of the floating platform is accurately modeled numerically, as it allows for the identification of the dynamic characteristics of the floater in the frequency domain, including the natural frequencies. Figure 4 presents the RAO analysis results comparing experimental and simulation data. The numerical model showed a close match with the experimental results in terms of the natural frequency responses of the surge, heave, and pitch motions observed in the low-frequency range.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 6 Attacks that Require Advanced Detection
### Interception Attacks  
The strategic impact of interception attacks on routing decisions and data flows is significant, involving the manipulation of BGP attributes and paths to intercept traffic. This manipulation is strategic and requires a nuanced understanding of the BGP's decision-making processes for its detection and mitigation.  
Interception attacks, particularly those executed as adversary-in-the-middle (AitM) operations within the BGP ecosystem, involve diverting and subsequently forwarding traffic to ensure it reaches its intended destination via the attacker's network. This dual action allows attackers to remain undetected while monitoring or manipulating data. The use of multiple vantage points can enhance the early detection of such attacks by identifying unusual routing patterns or discrepancies in BGP announcements that single-point observations might miss. This criterion supports the inclusion of interception attacks due to the potential benefits of early detection through comprehensive analyses. While interception attacks do not inherently involve collaboration between ASs, their impact and execution can be distributed across the Internet, affecting multiple routing paths and ASs. Our understanding of the distributed nature of these attacks and their propagation could benefit from a group-level analysis, making this criterion relevant for inclusion.  
Interception attacks involve complex interactions across ASs, as attackers manipulate BGP routes to insert themselves into the communication path between a source and its intended destination. Detecting these attacks requires an understanding of the dynamics of AS relationships and the ability to analyze routing behavior across multiple points, aligning well with this inclusion criterion.  
These attacks exemplify the sophisticated manipulation of routing information, requiring an in-depth knowledge of BGP operations and network topology to execute successfully. The strategic use of BGP announcements to intercept traffic without detection highlights the complexity and sophistication involved, meeting this criterion for inclusion. Interception attacks are characterized by their stealthiness, as they aim to preserve the connectivity and functionality of the network while clandestinely monitoring or manipulating data.  
This stealthiness, coupled with the ability to evade detection by conventional monitoring systems, aligns with the criteria for stealthiness and evasive techniques.
## 5 Results and Discussions
### Modelling Insights and Discussions
In the Gurevich theory, the expression of SWS is as follows:  
\[SWS=\frac{C_{55}^{sat}-C_{44}^{sat}}{2C_{44}^{sat}} \tag{32}\]  
where:  
\[C_{55}^{sat}=\mu \tag{33}\]  
\[C_{44}^{sat}=\mu(1-\Delta_{T}) \tag{34}\]  
\[\Delta_{T}=\frac{\mu Z_{T}}{1+\mu Z_{T}} \tag{35}\]  
\[Z_{T}=\frac{16(\lambda+2\mu)e}{3\mu(3\lambda+4\mu)} \tag{36}\]  
In combining Equations (32) and (36), the following can be obtained:  
\[SWS=\frac{4e}{3}\bigg{(}1-\frac{\lambda}{3\lambda+4\mu}\bigg{)} \tag{37}\]  
where \(e\) is the fracture density. The term \(\frac{\lambda}{3\lambda+4\mu}\) was extremely small in the four fractured samples (less than 0.077); therefore, \(\gamma^{\text{sat}}\approx\frac{4}{5}e\). Compared with P-wave anisotropy, the \(SWS\) prediction was relatively close to the measured results. Since the fracture aspect ratio is 0.02 in the fractured samples, we assumed that no partial or complete closure of the fractures occurred during the passage of ultrasonic waves. The above analysis is based on this assumption. For cases with smaller aspect ratios, we believe the partial or complete closure of fractures should be considered.
# Dynamic Analysis of CRTS II Ballastless Track-Vehicle System Based on Moving Unit Method
## 4 Analysis of the Effect of Travel Speed on the Dynamic Response of the System
### Comparative Analysis of Vertical Acceleration of Ballastless Track  
#### 4.1.1 Vertical Acceleration of Rail  
The peak vertical acceleration of the rail under different speed conditions with discontinuous track plates is extracted and compared with the peak vertical acceleration of the rail under the same speed with continuous track plates. The comparison results are shown in Figure 5.  
It is understandable that the vertical acceleration of the steel rails increases with the increase in the travel speed, but not proportionally. The reason for this is that the short-wave discontinuity has a significant excitation effect on the steel rails' acceleration, which is also evidenced in the literature [6]. In addition, the vertical acceleration of the steel rails in the track plate discontinuity condition is greater and significantly different than in the track plate continuity condition, with an average difference of about 132 m/s\({}^{2}\).
## 3 Communication and Channel Modeling
### Channel Modeling
#### 3.1.6 Codebook Design  
Utilizing codebooks, with pre-designed codewords, is considered an effective and promising technique for handling channel estimation problems in dynamic environments including IoVs [62]. On top of IoVs' motion, the estimation of the end-to-end \(\rm{T_{x}}-RISeUAV-R_{x}\) cascaded channel is more challenging due to the passive nature of RIS and limited onboard resources for the UAV's overhead controller. Therefore, the beamforming design is complicated in practice. However, beam training based on a predefined codebook can be used. Codebook design is based on offline beam training for beam alignment between two end nodes. However, an exhaustive search along the large conventional codebook, to find the optimal UPA phase shift matrix configuration, will cause extremely high system overhead and power consumption. Therefore, as pointed out in Remark 6, a positioning-based codebook with an affordable size has been utilized [63]. The proposed predesigned codebook uses the estimated positions as initial points for determining the main reflecting pattern. Then, depending on the standard deviation of the localization error, sets of sampled points are determined for steering the beam. Also, optimization-based codebook design techniques have been considered utilizing the Karush-Kuhn-Tucker (KKT) condition to solve the problem with low-precision non-uniform codebooks [64] and the steepest-descent method (SDM) [65].