esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Security Architecture for SDN-Based 5G Networks
## 5 Performance Evaluation
### MITM Attack  
In Figures 10 and 11, we show the simulation results under MITM Attack. In an MITM attack, the attacker secretly relays and possibly alters the communication between two parties who believe they are directly communicating with each other. In our simulation, the attacker will hijack the communication channel of a device and then generate his own message to the SecE. Similar to the simulation in IP spoofing, we vary the number of attacks from 0 to 500 and record the associated disconnect rate. The overall simulation result is shown in Figure 10, as we can see that, when the number of attacks reaches 500, the disconnect rate is lower than 0.1%. Then, we compare the performance of _Robust_ with two state-of-the-art ones. Taking the case of _moderate_ as the example in Figure 10(b), _Robust_ still has a disconnect lower than 0.01%, which is much better than that from \(TFSv1\) and \(SDSecurity\). The performance of \(TFSv1\) decreases a lot as the number of attacks increases because the communication channel of \(TFSv1\) lacks authentication. For the \(SDSecurity\), by its performance, still decreases a lot when used in a _dense_ scene and therefore can not be used in the large scene. Both of the two observations indicate that our schemes can have better performance when used to detect MITM attacks.
## 2 Methods and Experiment  
### Methodology  
In order to accurately quantify the foreign pressure broadening coefficients of the HCl transition line in Mid-IR range, the widely used technique, direct tunable diode laser absorption spectroscopy (dTDLAS) [21; 22], has been employed. For dTDLAS, the intensity of a monochromatic continuously tunable laser source transmitted through a gaseous sample is given by the \"extended\" Beer-Lambert law [23]:  
\[I(\lambda)=E(t)+I_{0}(\lambda)\cdot T(t)\cdot exp[-S(T)\cdot g(\lambda-\lambda _{0})\cdot n\cdot L] \tag{1}\]  
with the background emission \(E(t)\) at time \(t\), initial laser intensity \(I_{0}(\lambda)\), and the spectrally broadband transmission losses \(T(t)\) which are synchronously derived from the individual raw signals and absorption profiles. The exponential term embraces the absorption line strength \(S(T)\) at gas temperature \(T\), the area normalized (integrated area = 1) line shape function \(g(\lambda-\lambda_{0})\) (centered at the wavelength \(\lambda_{0}\)), the absorber number density \(n\) and the optical path length \(L\). The absorbance spectrum can be computed using the following equation:  
\[\alpha(\lambda,t)=-\ln\left(\frac{I(\lambda)-E(t)}{I_{0}(\lambda)\cdot T(t)} \right)=S(T)\cdot g(\lambda-\lambda_{0})\cdot n\cdot L \tag{2}\]The Voigt function can be used to model the lineshape of the absorbance spectrum in Equation (2), which considers the combined effects of Doppler and collisional broadening on the spectral line. These effects are characterized by the Doppler broadening full width at half maximum (FWHM), \(\Delta v_{D}\), and the collisional-broadening FWHM, \(\Delta v_{L}\), given by  
\[\Delta v_{D}=v_{0}\sqrt{\frac{8k_{\text{B}}T\ln 2}{Mc^{2}}} \tag{3}\]  
\[\Delta v_{L}=2\cdotp\cdot\gamma_{\text{self}}\cdot x+2\cdotp\cdot\gamma_{\text {foreign}}\cdot(1-x) \tag{4}\]  
where, \(M\) is the molecular mass of the absorbing species, \(c\) is the speed of light, \(k_{\text{B}}\) is Boltzmann constant, \(p\) is the total pressure, \(x\) is the HCl amount fraction, the coefficients \(\gamma_{\text{self}}\) and \(\gamma_{\text{foreign}}\) are the self and foreign broadening coefficients, which are dependent also on the gas temperature \(T\). Normally, Doppler broadening is calculated directly using Equation (3), and thus, does not have to be fitted, as the gas temperature is measured in the experiment. Note that the broadening coefficients (\(\gamma_{\text{self}}\), \(\gamma_{\text{foreign}}\)) calculated using Equation (4) from the measurements are the corresponding values at an experimental temperature \(T\), and have not been converted to the reference temperature \(T_{0}\) = 296 K, which is used in HITRAN [24]. For converting the broadening coefficients to a certain temperature, the self- and foreign-temperature dependence coefficients (\(n_{\text{self}}\) and \(n_{\text{foreign}}\)) are needed, which are often determined with relatively high uncertainties in the 10 to 20% range. Typical values for \(n_{\text{self}}\) and \(n_{\text{foreign}}\) are between 0.4 and 0.9.
## 2 Materials and Methods
### Experimental Case Study
#### 2.2.2 Data Analysis  
Angles extracted by Vicon PiG biomechanical model are compared with those predicted by the proposed RGB-MAS. To provide a better understanding, the considered angles respectively measured by the two systems are reported in Table 2.  
Figure 4: Experimental camera layout for RGB (those labeled in yellow) and Vicon system.  
Figure 5: Markers’ layout according to the PlugInGait full body model.  
Resulted angles measured through these two systems are also compared with those manually extracted by expert ergonomists.  
Shapiro-Wilk test is used to check the normality of the distribution of the error in all these analyses. Results evidence that the distributions follow a normal law for this experiment. Root mean square error (RMSE) is computed for the following condition:  
\[\text{RMSE}_{1}=\sqrt{\frac{\sum_{i=1}^{\text{N}}\left(\text{RGB}_{i}-\text{ MAN}_{i}\right)^{2}}{\text{N}}} \tag{6}\]  
\[\text{RMSE}_{2}=\sqrt{\frac{\sum_{i=1}^{\text{N}}\left(\text{VIC}_{i}-\text{ MAN}_{i}\right)^{2}}{\text{N}}} \tag{7}\]  
\[\text{RMSE}_{3}=\sqrt{\frac{\sum_{i=1}^{\text{N}}\left(\text{RGB}_{i}-\text{ VIC}_{i}\right)^{2}}{\text{N}}} \tag{8}\]  
where \(\text{RGB}_{i}\) is the ith angle measured by the RGB-MAS system, \(\text{VIC}_{i}\) the one measured by the Vicon system and, finally, \(\text{MAN}_{i}\) the angle measured manually.  
Based on the collected angles, a Rapid Upper Limb Assessment (RULA) is performed manually, according to the procedure described in [12]. Then, RMSE is computed to compare the resulting RULA scores estimated according to the angles respectively predicted by the RGB-MAS and Vicon with that performed considering the angles estimated from the video analysis by the experts themself.
# A Unified Denoising Framework for Restoring the LiDAR Point Cloud Geometry of Reflective Targets
## 2 Materials and Methods  
### Dataset  
The dataset used in this study was collected using the DJI Livox (Shenzhen, China) Tele-15 LiDAR sensor, which operates at a laser wavelength of 905 nm, with a range precision (1\(\sigma\) @ 20 m) of 2 cm and a laser beam divergence half-angle of 0.12\({}^{\circ}\) (vertical) \(\times\) 0.02\({}^{\circ}\) (horizontal). The reflective target used for data acquisition was a square traffic sign with dimensions of 0.6 m \(\times\) 0.6 m. Due to the non-repetitive scanning characteristics of the Tele15 sensor, the point cloud density varied across different positions within the sensor's scanning area. To better simulate the diversity of data encountered in autonomous driving scenarios, we considered several factors during data collection, including variations in lighting conditions, target distance, laser incidence angle, and point cloud density at different target locations. Data were collected at 14 distinct distances under varying lighting conditions. During data acquisition at each distance, the LiDAR sensor was mounted on a tripod, and its orientation was adjusted to change both the position of the target within the sensor's field of view and the laser incidence angle on the target surface. At each distance, 90 samples were acquired, resulting in 1260 samples in total in the dataset.  
The data acquisition scenario and point cloud are shown in Figure 1. The ground-truth point cloud of the target in each sample was obtained through manual editing and programmatic processing, incorporating prior knowledge of the target's geometric shape and dimensions.
## 4 Review: Slant Links Rain Fade Models
### Fade Slope Model  
Fade mitigation techniques require proper design and implementation by introducing the first- and second-order statistics such as fade slope, duration of fades, and duration between fades [29]. The time interval between two crossings is defined as the time interval between the same attenuated threshold, and the device length is defined as the time interval between two crossings beneath the same attenuating threshold [126]. Various estimation studies of fade slope properties in temperate climatic conditions are reported in [127; 128]. Since rain characteristics differ significantly over tropical regions, widely used ITU-R models are insufficient for predicting rain attenuation and modeling related propagation impairments [129]. Similar to the rain attenuation model, different variants of rain fade slope models are proposed for tropical climatic conditions [130]. By definition, the fade slope can be defined in [130] as Equation (52). The same technique is reported in [131] with consideration of the \(\Delta t\) to be 2 s. A typical fade slope diagram is shown in Figure 5.  
\[\zeta(t)=\frac{A(t+0.5\Delta t)-A(t-0.5\Delta t)}{\Delta t} \tag{52}\]
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 6 Data Analytics
### Utility Feedback and Other Applications  
Finally, HEMS can benefit from certain instructions offered by the utility, which can be determinant for appliance scheduling. Examples include price signals, offers, or alarms. This information is also important for demand-side management or demand response campaigns. The commands or instructions given by the control plane of the HEMS have a wide spectrum of applicability. For example, the gathered information can be used for energy savings, such as in [14]. Other applications may depend on the context in which the system is being deployed. This is the case of remote monitoring, activity recognition and other healthcare services. In [2], the authors presented broad applications and used the view of Appliance-Level Energy Characterization (ALEC) including feedback, recommendations and customized marketing, energy-efficient appliances and labeling standards, utility and grid operations, flexibility for renewable energy integration, energy management and conservation, equipment manufacturing and technology development, smart security, surveillance and healthcare, in addition to social sciences and economics benefits.
## 3 Master-Slave Optimization Framework
### Master Stage: Multi-Vorse Optimizer (MVO)
#### 3.1.1 Foundations of the Algorithm  
In the MVO algorithm, each potential solution to the optimization problem is conceptualized as a universe. A set of candidate solutions is, thus, referred to as a multi-verse. The quality or fitness of each universe is quantified by an objective function that the algorithm seeks to minimize or maximize. MVO simulates the dynamic evolution of these universes through three main operators derived from cosmological analogies:  
1. White holes: Universes with high fitness (inflation rate) probabilistically share their components with others, mimicking the emission of matter and energy;
2. Black holes: Universes with low fitness are more likely to absorb components from others, analogous to the gravitational absorption of black holes;Wormholes: Allow universes to perform local searches by perturbing their positions relative to the best-known solution, encouraging the exploitation of promising regions.  
These operators are integrated to balance the global exploration and local exploitation of the search space throughout the optimization process.
# Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling
## 5 Constraints Relaxation
### Resistance Function Convexity in the Polar Coordinate System
In general, it can be demonstrated that the function \(\text{B}(\theta)\) is convex when the following condition holds:  
\[RG=\frac{3}{8}\bigg{(}\Big{(}{vg_{i,j}}\times sin\theta_{i,j}\Big{)}^{2}\times \Big{(}4+2\sqrt{3}\Big{)}+\Big{(}{vg_{i,j}}\times cos\theta_{i,j}\Big{)}^{2} \times\Big{(}4-2\sqrt{3}\Big{)}-4\times\Big{(}{vg_{i,j}}\times cos\theta_{i,j }\Big{)}\times\Big{(}{vg_{i,j}}\times sin\theta_{i,j}\Big{)}\bigg{)}\]8For example, by adopting \(a,b=1\) the function \(\mathrm{B}(\theta)\) becomes a convex for \(c>1.5\) (see Figure 5b). The value \(c\) is determined by the value of the \(R\) parameter which depends (see (2)-(18)) on the parameters of the environment where the flight is carried out and on the technical parameters of the UAV:  
\[R=\left( \begin{array}{c}\frac{CAP_{i,j}}{t_{i,j}}-\frac{(W\times g)^{\frac{3}{ 2}}}{\sqrt{2\times D\times A}}\\ \frac{0}{0.5\times C_{D}\times A\times D}\end{array} \right)^{\frac{2}{3}}-{vg_{ i,j}}^{2} \tag{55}\]  
The fact that function \(Y_{k,l}(\theta)\) is convex implies the previously adopted findings, e.g., (22) and the following assumptions \(a=VG=vg_{i,j}\times cos\theta_{i,j}\), \(b=VS=vg_{i,j}\times sin\theta_{i,j}\):  
\[ \begin{array}{c}\left(\frac{CAP_{i,j}}{t_{i,j}}-\frac{(W\times g)^{\frac{3}{ 2}}}{\sqrt{2\times D\times A}}\right)^{\frac{2}{3}}\\ \frac{0}{0.5\times C_{D}\times A\times D}\end{array} \right)^{\frac{2}{3}}-{vg_ {i,j}}^{2}\\ >\frac{3}{8}\bigg{(}\Big{(}({g_{i,j}}\times sin\theta_{i,j})^{2} \times\Big{(}4+2\sqrt{3}\Big{)}+\Big{(}{vg_{i,j}}\times cos\theta_{i,j}\Big{)} ^{2}\times\Big{(}4-2\sqrt{3}\Big{)}-4\\ \times\Big{(}{vg_{i,j}}\times cos\theta_{i,j}\Big{)}\times\Big{(}{vg_{i,j}} \times sin\theta_{i,j}\Big{)}\Big{)}\]  
In simple terms:  
\[R>RG \tag{57}\]  
where \(R\)--parameter specified by (23), \(RG\)--parameter specified by the formula: \(RG=\frac{3}{8}\Big{(}({vg_{i,j}}\times sin\theta_{i,j})^{2}\times(4+2\sqrt{3} )+({vg_{i,j}}\times cos\theta_{i,j})^{2}\times(4-2\sqrt{3})-4\times({vg_{i,j}} \times cos\theta_{i,j})\Big{)}\). Figure 6 shows a graph of the \(R\) values for the sample technical parameters and a graph of the \(RG\) values above which (\(R>RG\)) the function \(\mathrm{B}(\theta)\) becomes a convex function. Therefore, because the R-values encountered in practice are very high (i.e., \(R>>\) 100), it is assumed that functions \(\mathrm{A}(\theta)\) and \(\mathrm{B}(\theta)\) are convex. Consequently, since the sum of the convex functions is also a convex function, the resistance function \(Y_{k,l}(\theta)\), described by (51) is also convex. Figure 6: Examples of UAVs technical parameters (**a**) and their corresponding values \(R\) and \(RG\) (**b**).
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 4 Case Studies, Results and Discussion
### Effect of Installing Static Capacitors on System Transient Stability  
The transient stability of a system can be controlled by installing capacitor banks at the generation bus.
## 4 Experiments
### Data Preprocessing
#### 4.3.2 Time Series Feature Construction  
It is recognized that power load data exhibit significant time series characteristics and interdependence between neighboring data points. Therefore, in addition to selecting environmental factors such as weather and humidity as features, we employ a sliding window method to construct features from the power load data. Specifically, we define a sliding window of 48 time units [28] that moves step by step along the time series. Consequently, the feature vector at each time point includes data from the previous 48 time units, capturing the dynamic changes in the historical data at that point. This process is illustrated in Figure 8. For instance, the feature vector for the nth data point is \(\alpha_{n}=[Load_{n-48},Load_{n-47},\ldots,Load_{n-1}]\). Using the sliding window method to construct features enhances the model's ability to capture temporal dependencies and reveals potential patterns and trends in the data. This approach provides richer and more accurate feature information for electricity load forecasting. The feature vectors for the first 48 data points are incomplete due to missing values within the sliding window. Therefore, we remove the first 48 records from the dataset after feature construction to prevent any potential impact of these missing values on subsequent data analysis and model training. The 48 load features we constructed, combined with factors such as weather and humidity, result in a total of 55 features for each electricity load value.
## 2 Energy Efficiency Landscape
### Energy-Efficient Network Model  
This study abstracts and introduces a model for energy-efficient routing in edge-AI-driven SONs. Considering the high dynamics of satellite network topologies, the network is segmented using equidistant time snapshots, with topological connections assumed static within each snapshot to facilitate the analysis of network states in specific intervals \([t_{1},t_{2}]\).  
The input conditions include \(G(V,E)\), which captures all node and link information within the snapshot; \(V\) includes all network nodes such as satellites and ground gateways, with noted remaining battery capacities, while \(E\) encompasses all inter-satellite and ground-to-satellite link details, including bandwidth usage. \(T\), the traffic matrix, shows the volume of network flows between any two global regions, with Equations (1)-(10) detailing the changes in power levels and lifespan loss during the snapshot.  
Constraints include the fact that total data flow on a link must not exceed its capacity, as defined in Equation (11), where \(F_{l}\) and \(C_{l}\) represent the total bandwidth of data flow and maximum capacity for link \(l\), respectively; satellite battery levels must remain above zero and not exceed maximum capacity, as shown in Equation (12), where \(C(t_{1})\) and \(C(t_{2})\) represent the battery levels at the start and end, and \(P_{s}(t)\) and \(P_{l}(t)\) represent charging and consumption power, respectively.  
\[F_{l}\leq C_{l} \tag{11}\]  
\[C(t_{2})=C(t_{1})+\int_{t_{1}}^{t_{2}}P_{s}(t)-P_{i}(t)dt,0\leq C(t_{2})\leq C _{max} \tag{12}\]  
Performance metrics include network throughput and average service delay to measure network performance, with the goal of energy-efficient routing to optimize network lifespan consumption, service blockage rate, and average delay \((L_{EE},B_{EE},D_{EE})\), as specified in Equation (13).  
\[Min(L_{EE},B_{EE},D_{EE}) \tag{13}\]  
Outputs include the set of service paths and corresponding network performance indicators \((L_{EE},B_{EE},D_{EE})\), along with a set of dormant payloads as part of the topology planning scheme.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
###### Abstract  
In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes. In contrast, the cluster routingtechnique utilizes a non-iterative process and can outperform state-of-the-art models with fewer parameters, while preserving the part-whole relationship and demonstrating robust generalization to novel viewpoints. This paper aims to further analyze and clarify this concept, providing insights that allow users to implement the cluster routing technique efficiently. Additionally, we expand the technique and propose variations based on the routing principle of achieving consensus among votes in distinct clusters. In some cases, these variations have the potential to enhance and boost the cluster routing performance while utilizing similar memory and computing resources.  
Keywords:data-dependent routing; capsule; capsule network +
Footnote †: journal: Applied
## 4 Electromagnetic Performance Analysis  
The electromagnetic performance of the optimal multi-tooth HE-DSDPM was investigated by using the 2-D FEA. The \(I_{dc}\) excitations of \(+5\) and \(-5\) A were considered for the expansion of the flux-regulated behaviour in the machine. The magnetic field distribution, flux linkage, back-EMF, THD and cogging torque were investigated in the open-circuit condition, which is \(I_{ac}\) of 0 A. The production of electromagnetic torque and ripple torque were examined under load conditions, such as the \(I_{ac}\) of 8 A and current density of 2 A/mm\({}^{2}\). The analytical result is compared with the conventional HE-DSDPM based on the design specifications in Table 1.
## 4 Simulation and Experiment Validation
### Envelope Spectrum Analysis of Simulated and Measured Signals  
To verify the effectiveness of the proposed model, the envelope spectrum analysis method was used to compare and analyze simulation and experimental data. The formula for calculating the characteristic frequency of bearing inner ring faults is:  
\[f_{i}=\frac{1}{2}\cdot Z\cdot\omega_{c}\bigg{(}1+\frac{d_{b}}{D_{W}}\bigg{)} \tag{21}\]  
where, z is the number of rolling elements; \(\omega_{c}\) is the angular velocity of the rolling element around the bearing circle center; \(\mathrm{d_{b}}\) is the diameter of the rolling element; \(\mathrm{D_{w}}\) is the pitch diameter.  
Simulation and experimental datasets with a load of 50 N and a speed of 600 r/min were used for comparative analysis, in which the sampling frequency (fs) was 1024, and  
Figure 13: Time domain diagram of measured signal.  
when the rotating shaft frequency was 10 Hz, the characteristic vibration frequency of spalling failure of the bearing inner ring was calculated by Formula (21) as 54.388 Hz. Envelope analysis was carried out on the simulation and measured data of bearings, as shown in Figure 14. Figure 14a shows the envelope diagram of the non-center position simulation signal, and it can be clearly observed that the peak frequency conforms to the fundamental frequency of 10 Hz and the characteristic frequency of 54 Hz and their harmonics. Figure 14b shows the envelope diagram of the non-center position measured signal, and its peak frequency may appear around the characteristic frequency due to other factors, but it is also very close. There are many side frequencies around each characteristic frequency; this is due to the modulation phenomenon caused by the inner ring rotating with the axis when the fault occurs. Through comparative analysis, the calculated results of the model in this paper are basically consistent with the characteristic frequencies of the experimental results, which proves that the model in this paper is effective.