esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 4 Protocol Tunneling Detector
### Anomaly Detection  
During the test phase (see Figure 2), bit streams are analyzed by the trained ANN. In turn, the ANN flags three different cases as potential tunneling attacks and alerts security analysts when these cases occur: (i) the high confidence detection of more than one protocol in the same connection, (ii) the low confidence detection of one protocol for all the packets in the same connection, and (iii) the labeling, both with high and low confidence, of one or more protocols for the packets belonging to the same connection (as in the case of secure protocols over DNS). As later specified in Section 5, in the ANN, the high/low confidence threshold \(c\) can be dynamically set. In any case, the detection of encrypted packets into a clear-text connection generates alert notifications enriched with the information about the presence of encrypted protocol messages. Possibly, notified alerts can be filtered whitelisting source and/or destination IPs to reduce the false positives caused by well-known machines.  
Hence, if some packets of the connection are classified with low confidence, the corresponding bit stream's sequential features (refer to Section 4.2) are given in input to the compression/encryption detector. If all the packets contained in the connection are encrypted, then the connection and its corresponding metadata are given in input to the SSL/TLS analytics for further scrutiny [11]. On the contrary, if the connection contains some compressed/encrypted packets or none of them, depending on the protocol, the connection is considered anomalous. Indeed, it is worth noting that the combination of two different protocols is not always a signal of an occurring attack: as already discussed, SMB and HTTP connections can contain protocol-specific messages along with compressed data; however, DNS messages interleaved with other protocols are highly suspicious. Finally, since each single module of the proposed prototype has been trained only with legitimate traffic, it is potentially able to spot zero-day attacks having features which are different from the ones usually observed in the network.
## 6 Cost Function for Energy Consumption Estimation  
In this section, particular attention is given to the cost function, which is an essential aspect in choosing the driving mode for the omnidirectional mobile robot. The cost function is designed to consider multiple variables such as current consumption, velocity, trajectory tracking accuracy, or other performance criteria. By creating this cost function, the overall performance of the robot can be improved in terms of energy efficiency and motion precision. Thus, the analysis and adjustment of the cost function are crucial elements in the design and implementation process of the control system for the omnidirectional mobile robot.  
In this research, the significance of the cost function lies solely in making decisions regarding the operational mode of the mobile robot, with no emphasis on path planning algorithms. By employing the cost function, one can determine the relative efficiency of the robot's locomotion mode for a given type of movement, Figures 9 and 10.  
Figure 8: Electronic diagram for mobile robot components.  
Figure 9: Cost function evolution for 1 [m] square trajectory and differential drive.  
Here are the steps used to build the cost function:  
Defining Parameters:  
* Inputs:
* currentConsumption--Vector containing the current consumed by each wheel's motor (amps);
* wheelRotations--Vector containing the total number of rotations for each wheel;
* elapsedTime--Total time taken to complete the trajectory (seconds);
* wCurrent, wRotations, wTime--Weights for current consumption, wheel rotations, and time.  
- Output:  
* totalCost--Calculated total cost of the trajectory based on the inputs and weights;
* currentCost = sum(currentConsumption); the sum of all current consumed;
* rotationCost = sum(wheelRotations); the sum of all wheel rotations;
* timeCost = elapsedTime; the total time.  
Computing the weighted sum of costs:  
\[\text{C = wCurrent }\times\text{currentCost + wRotations }\times\text{rotationCost + wTime }\times\text{timeCost} \tag{26}\]  
Example for the cost function implemented into a MATLAB function block:  
function totalCost = calculateTrajectoryCost(currentConsumption, wheelRotations, elapsedTime, wCurrent, wRotations, wTime)  
totalCost = wCurrent \(\times\) currentCost + wRotations \(\times\) rotationCost + wTime \(\times\) timeCost;  
end  
The total value of the function for differential driving is totalCost = 19,411 and the total value of the function for omnidirectional driving is totalCost = 16,948. So, for this type of imposed trajectory, the omnidirectional driving mode is more efficient. These data will be used as input into the fuzzy logic decision block for a specific driving mode.  
This approach can be adjusted and refined based on the specific characteristics of the robot and the complexity of the environment in which it operates.
# Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks
## 5 Simulation Results
### Performance Comparison for Offloading Probability  
To evaluate our proposal, we compare it to the following two cache placement schemes for maximizing the offloading probability.  
* Most popular content (MPC): Both BSs and devices cache the most popular content [29].
* Coop. BS/D2D caching: Each BS tier and user tier applies cooperative content caching separately [30].  
We show that the offloading probability changes with an increment in the Zipf parameter for the three different cache placement schemes in Figure 4. We can see that the offloading probability of the proposed cache placement, MPC and Coop. BS/D2D caching schemes increases as the Zipf parameter \(\eta\) increases. The reason for this is that a large \(\eta\) leads to the storage of more copies of popular content because the Zipf parameter \(\eta\) indicates the skewness of the content access pattern; increasing \(\eta\) allows access to popular content with higher probability and access to unpopular content with lower popularity. However, this figure demonstrates that the proposed scheme outperforms both MPC and Coop. BS/D2D caching schemes in terms of the offloading probability. As the Zipf parameter increase, the performance gain becomes significant. This is because our scheme can adjust more concentrated content request to achieve more offloading probability.  
Figure 3: Comparison of offloading probability versus D2D distance \(R\) with different values of the Zipf parameter \(\eta\).  
We compare the offloading probabilities among the three different cache placement schemes by varying the cache storage size with the size of \(M\) contents as shown in Figure 5. We can see that the performances of all three schemes increase with an increase in the cache storage size, which provides the scope to cache more contents. Moreover, the offloading probability increases more with the proposed scheme compared to the other schemes, particularly with larger cache sizes. The offloading probability of the proposed scheme from 1 to 10 of the cache storage size improves averagely 13.5% and 23% than the Coop. BS/D2D caching and the MPC schemes, respectively. These results indicate that the proposed cache placement scheme leads to the highest reduction in content redundancy, which accordingly leads to the greatest increase in the cache capacity of the whole network to store more popular content. Further, that is a significant gain given the large volume of offloading content delivery traffic.  
Figure 4: Performance comparison among three different caching schemes with varying Zipf parameter \(\eta\).  
Figure 5: Performance comparison among three different caching schemes versus the cache storage size \(M\).
## 4 Results and Discussion
### Fault Characteristic Frequency Extraction and Fault Analysis
The spectrum of the current under different load conditions are shown in Figure 25.  
Figure 23: Amplitude change of the fault characteristic frequency under the time-varying load. Figure 24: The change of the BE under the time-varying load. In Figure 25, \(f_{e}\) represents the current fundamental frequency. It can be seen from Figure 25 that the characteristic frequencies of gear fault under different load conditions are seriously weakened by the current fundamental frequency and their frequency multiplication. Although the amplitude of the gear failure frequency increases as the load increases, the characteristic frequency of the failure is still weak relative to the current fundamental frequency, even at the highest load. In addition, the characteristic frequency of the gear faults is distributed at low frequencies in the current spectrums, but there are high-frequency shock components that are not related to the fault. These high-frequency signals also affect the extraction of gear failure frequencies. Therefore, the method based on the frequency spectrum analysis is not good for the diagnosis of the scraper conveyor gear. The method proposed in our paper can eliminate the interference of the fundamental frequency and the impact signal unrelated to the fault, and is more suitable for the diagnosis of the scraper conveyor gear fault.
## 2 Materials and Methods
### Dataset for Statistical Analysis  
Having the biomass concentration for each day of the GMS that makes up a season, the next step was to concatenate the daily production per day. Building the data set per day allows statistical analysis by calculating the probability distribution function (PDF)  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **References** \\ \hline Initial total nitrogen (\(N\)) & 224 mg/L & This study \\ Initial total phosphorus (\(P\)) & 16.1 mg/L & This study \\ Microalgae inoculum & 100 mg/L & This study \\ Minimum temperature & 8 \({}^{\circ}\)C & \\ Optimal temperature & 23.3 \({}^{\circ}\)C & [12; 36] \\ Maximum temperature & 35 \({}^{\circ}\)C & \\ Maximum-specific growth rate (\(\mu_{max}\)) & 0.948 day\({}^{-1}\) & [5] \\ Nitrogen removal coefficient \(Y_{N,\mu}\) & 0.71 & This study \\ Phosphorus removal coefficient \(Y_{P,\mu}\) & 0.035 & This study \\ Mortality rate (\(\mu_{D}\)) & 0.004 day\({}^{-1}\) & [27] \\ Inhibition constant (\(K_{i}\)) & 2400 \(\mu\)E m\({}^{-2}\)s\({}^{-1}\) & [11] \\ Half-saturation constant for light (\(K_{I}\)) & 124.115 \(\mu\)E m\({}^{-2}\)s\({}^{-1}\) & [37] \\ Half-saturation constant for Nitrogen (\(K_{N}\)) & 31.5 mg/L & [38] \\ Half-saturation constant for Phosphorus (\(K_{P}\)) & 10.5 mg/L & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values to perform the simulation.  
Figure 2: Block diagram model of microalgae growth in batch culture.  
of the biomass concentration of each data group per day (D2, D3, D4, D5, D6, or D7). For example, the biomass concentration is put together for all days 2 (D2) of those GMSs, and the same for days 3 (D3), etc. Day 1 (D1) was not taken as it corresponds to initialization. The idea was to calculate the PDF of the microalgae concentration for each of these datasets, grouped by day. The final step of the methodology consists of observing whether the biomass concentration on a given day (D2, D3, D4, D5, D6, or D7) of the TMS selected in [19] is, for the same day, in the bin with the largest PDF.  
For a given season, the determination of the average daily biomass concentration was carried out for all GMS generated. The model outputs for identical days (D2, D3, D4, D5, D6 or D7) were used to calculate their daily PDFs. The PDFs of biomass concentrations obtained from all D2 of the GMS for the representative summer TMS used as input data are plotted and discussed in Section 3.2. The same was applied to the other TMS days. Beyond showing the range of probable results for a given day in terms of their frequency of occurrence, this graphical representation includes the graphic concordance of the biomass concentration for the corresponding day of the same day in the TMS. Therefore, it was possible to compare whether this biomass concentration position belonged to the concentration bin with the highest PDF value. For greater precision, the length of the biomass concentration bin is reduced to 2 mg/L.
# Monitoring of [PERSON]'s Disease Based on Wireless Sensing Technology
## 5 The Data Processing
### Model Training  
We used SVM and RF to train the model to ensure the accuracy of data classification and to determine which algorithm has a better effect in practical applications. At the same time, in order to make the training model reliable, we used the four-fold cross validation method to divide the data set.  
We selected the radial basis function (RBF) as the kernel function of SVM, and the RF contained 500 decision trees.
## 4 Excursions Towards Higher and Lower Gravitational Potential Energy
### Moving Long Distances Towards Higher and Lower Locations  
So far, we have been considering relatively short-range upward and downward excursions that require driver action, and which often result in additional fuel consumption. Considering journeys between two towns, \(A\) and \(B\), separated by lateral distances of say \(L_{AB}=100\) km and a constant upward or downward slope in between, the dominating effect in generating transport value and in determining fuel consumption is air friction.  
Moving along the connecting road between towns \(A\) and \(B\), the trip is slightly elongated due to the road sloping (Figure 11a). The downward-dragging gravitational forces, on the other hand, need to be compensated by upward-dragging engine forces (Figure 11b). With both forces being compensated, the vehicle is effectively moving on a flat road, thus generating a transport value of  
\[W_{fr\_AB}(M,L_{AB},H_{AB},\ v_{AB})=\frac{1}{4}\gamma_{fr}v_{0}S_{AB}^{2}, \tag{22}\]  
where \(\gamma_{fr}\) is a shortcut for \(\gamma_{fr}=\frac{1}{4}\varrho_{air}\ A\ C_{d}\). As the engine itself is left with the task of overcoming the effect of air friction and of lifting the vehicle to the higher location of \(B\), the total energy demand is as follows:  
\[E_{fr\_AB}(M,L_{AB},H_{AB},\ v_{AB})=\frac{1}{2}\gamma_{fr}v_{AB}^{2}S_{AB}+MgH _{AB} \tag{23}\]  
and the associated energy efficiency or energy relaxation time:  
\[\eta_{E_{fr}}(M,L_{AB},H_{AB},\ v_{AB})=\frac{\frac{1}{4}\gamma_{fr}v_{0}S_{AB }^{2}}{\frac{1}{2}\gamma_{fr}v_{AB}^{2}S_{AB}+MgH_{AB}}. \tag{24}\]  
In the limit of small height differences \(H_{AB}\), the ecologically rated transport value for travelling from \(A\) to \(B\) become  
\[\eta_{E\_{fr}}(M,L_{AB},H_{AB},\ v_{AB})\cong\frac{1}{2}\frac{S_{AB}}{v_{AB}}+ \frac{MgH_{AB}}{\gamma_{fr}v_{AB}^{3}}, \tag{25}\]  
and  
\[\eta_{E_{fr}}(M,L_{AB},H_{AB},\ v_{AB})\cong\frac{1}{2}\frac{S_{AB}}{v_{AB}}- \frac{MgH_{AB}}{\gamma_{fr}v_{AB}^{3}} \tag{26}\]  
for the downhill travel from \(B\) back to \(A\). The sloping of the road obviously results in an asymmetry of the energetic cost for upward and downward traveling along the same road.  
Figure 10: Road scenario as in Figure 9 above. Pink arrows denote profile forces of gravitational origin acting within the hill and valley sections. Broken red arrows denote engine-controlled, gravity-compensating control forces. Filled circles denote momentary positions of the transported mass. Speed indications denote associated momentary speeds.  
Figure 11c shows that this asymmetry is large when travel speeds are relatively slow but decreases as average speeds are being increased.
## 4 Discussion
### Limitation  
This study has some limitations that should be acknowledged and addressed in future research. Firstly, this study only used kaolin clay as the testing material, which may not represent the behaviour of other types of clay or soil mixtures. Future studies could consider different types of clay mixed with sand or recycled materials [21; 22] to investigate the effect of soil composition on interfacial shear adhesion. Secondly, this study only performed the test at one displacement rate of 5.0 mm/min, which may not capture the dynamic loading conditions encountered in real-world applications. Future studies could consider different displacement rates and check the impact of loading rate on the adhesion behaviour. Thirdly, this study conducted the test at a constant room temperature of 23 \({}^{\circ}\)C, which may not reflect the environmental variations affecting soil properties and adhesion mechanisms. Future studies could consider the impacts of different temperatures [23] on the interfacial shear adhesion between kaolin and brass. This study focused on the impacts of dry density and moisture content on the adhesion of kaolin clay, which are important factors influencing soil performance and stability. However, other factors such as contact area, surface roughness, and contact time could also affect the interfacial adhesion behaviour and should be explored in future research.
## 2 Related Work
### Beamforming  
Several studies have been proposed to optimize beamforming vectors and power configurations [15; 16; 17; 18] for improvement of the information and energy transfer. However, they do not take WuRs into account. The most relevant work is by [PERSON] _et al_. [19]. They take advantage of multiple ultra-wideband impulse radios (UWB-IR) statically mounted on the wall forming a circle that cover all semi-active sensor nodes inside. Multiple UWB-IRs create the beamforming to trigger the wake-up detector of a semi-active sensor node. However, they do not consider the scenario of mobile UWB-IRs and sensor nodes.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 6 Simulation Examples
### Channel Estimation Techniques for the IEEE 802.11p Standard  
Channel estimation and equalization techniques are widely used in wireless communications to recover the signal that propagated through the channel. In the IEEE 802.11p standard, channel estimation is performed using the LTSs \(T_{1}\) and \(T_{2}\) that are inserted in the preamble of each frame. For this standard, the signal model shown in (1) comprises both training and information data, in such a way that \(\mathbf{Y}=[\mathbf{Y}^{\mathrm{e}}\ \mathbf{Y}^{\mathrm{d}}]\), \(\mathbf{H}=[\mathbf{H}^{\mathrm{e}}\ \mathbf{H}^{\mathrm{d}}]\), \(\mathbf{X}=[\mathbf{X}^{\mathrm{e}}\ \mathbf{X}^{\mathrm{d}}]\), and \(\mathbf{N}=[\mathbf{N}^{\mathrm{e}}\ \mathbf{N}^{\mathrm{d}}]\), where the superscripts \((\cdot)^{\mathrm{e}}\) and \((\cdot)^{\mathrm{d}}\) denote training data blocks and information data blocks, respectively; \(\mathbf{Y}^{\mathrm{e}}\), \(\mathbf{H}^{\mathrm{e}}\), \(\mathbf{X}^{\mathrm{e}}\), and \(\mathbf{N}^{\mathrm{e}}\) are \(N_{p}\times 2\) complex-valued matrices; \(\mathbf{Y}^{\mathrm{d}}\), \(\mathbf{H}^{\mathrm{d}}\), \(\mathbf{X}^{\mathrm{d}}\), and \(\mathbf{N}^{\mathrm{d}}\) are \(N_{p}\times F\) complex-valued matrices; and \(F=K-2\) corresponds to the transmitted OFDM information data symbols on each frame. The OFDM training symbols are identical, so that \(\mathbf{x}_{1}^{\mathrm{e}}=\mathbf{x}_{2}^{\mathrm{e}}\), where \(\mathbf{x}_{k}^{\mathrm{e}}\in\mathbb{C}^{N_{p}\times 1}\) is the \(k\)-th column of the matrix \(\mathbf{X}^{\mathrm{e}}\) for \(k=1,2\).
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Numerical Methods and Tools for Simulating STL  
As discussed in the previous sections, a good number of researchers have used analytical tools to predict the STL of sandwich and composite double wall structures. Analytical solutions give the exact solution of the physical problem. However, representation of complex configurations using analytical approach may be intractable. Motivated by this challenge, numerical methods such as finite element methods are better suited to model complex scenarios. The numerical solutions are approximation solutions while the exact solutions can be used to verify these approximations. In addition, the numerical approach not only produces interesting results despite the complexity of the structure, it can also be used to virtually validate analytical and experimental results. A number of numerical tools have been utilized to obtain solutions of sound transmission in different vibro-acoustic media. Typical of these tools are the Comsol Multiphysics [126; 127; 128; 129], Ansys Acoustic (ACT) [70; 130; 131; 132] and Abaqus [133; 134]. However, the use of Abaqus software for STL solution has been restricted to lower range of frequencies. This is because; it is computationally burdensome especially in the high frequency range [135]. The following sections discuss a combination of STL solutions undertaken by researchers for verification and validation of their research findings.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method (Instruments)** & **Goal** & **Results/Remarks** \\ \hline
[4] & Simulation & Attitude determination (AD) based & Converges from initial attitude errors of maximum 60\({}^{\circ}\) and with an attitude accuracy of 1\({}^{\circ}\) (1\(\sigma\)) or better \\ \hline
[5] & Observation (Rossi X-ray Timing & & \\  & Explorer satellite calibration & & \\  & maneuvers, Terra and Wide-Field & On-orbit calibration of satellite & \\  & Infrared Explorer mission, Upper & & gyroscopes & \\  & Atmosphere Research Satellite & & \\  & (UARS)) & & \\ \hline
[6] & Simulation & Absolute alignment calibration of a system comprising two star trackers, an inertial sensor assembly & AKF is effective to estimate absolute misalignments and gyro calibration parameters \\  & & & (ISA) of three fiber-optic gyros, and an imaging instrument based on Alignment Kalman Filter (AKF) & \\ \hline
[7] & Simulation & & AD using an Extended Kalman & \\  & & Filter (EKF), which applies the & \\  & & albedo model with a magnetometer & \\  & & and sun sensor & \\ \hline
[8] & Simulation and Observation (Total & Modeling the albedo for Sun/Earth & Albedo compensation in attitude \\  & Ozone Mapping Spectrometer & sensor used in attitude & estimation, improves the maximum \\  & (TOMS)) & determination & error from 9.9\({}^{\circ}\) to 1.9\({}^{\circ}\) \\ \hline
[9] & Simulation & AD using Unscented Kalman Filter (UKF) based only on magnetometer & The attitude estimation accuracies are below 0.5\({}^{\circ}\) after convergence \\ \hline
[10] & Simulation (PROBA-2 Spacecraft & Navigation system for & \\  & scenarios) & magnetic-only orbit and attitude estimation using the square-root & RSS attitude error of less than 1.4\({}^{\circ}\) and a time of convergence of less than 2 orbits \\  & & Unscented filter (MAGSURF) & \\ \hline
[11] & Simulation & Attitude and rate estimation & Filter converges within the +/\(-\)8\({}^{\circ}\) \\  & & algorithm using EKF based only on geomagnetic field data & range for any initial attitude error \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies related to attitude determination (simulation and in-orbit observations). This manuscript presents two methods to determine the attitude of the UVSQ-SAT CubeSat. This is an important step in the implementation process of the scientific results of the UVSQ-SAT space-based mission. Section 2 describes the inputs from the satellite along with the models and geometrical considerations of the two methods. Section 3 presents the implementation of the two different methods and how they differ. Section 4 shows the results obtained from the different methods and how the MEKF method has improved the accuracy of the determination of the UVSQ-SAT CubeSat attitude of the TRIAD method. Finally, Section 5 is dedicated to the presentation of conclusions and perspectives.