esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 4 The Proposed Protocol
### Problem Description  
The majority of vehicular networking applications rely on broadcasting to disseminate safety and convenience data, which can easily lead to the broadcast storm problem under high-density scenarios. The broadcast storm problem occurs when many vehicles in the same vicinity broadcast nearly simultaneously. This phenomenon leads to a high overhead of redundant data, in addition to data loss or message corruption due to collisions. Therefore, simple broadcast does not scale in dense networks due to the excessive dissemination of the same data, which wastes the limited available channel bandwidth. In a recent work [37], the authors proposed interest-aware data dissemination protocol for delay-tolerant vehicular networks, which combines probabilistic broadcast and timer-based broadcast. Their simulation results showed that the proposed protocol costs less than simple schemes in obtaining a high data delivery, however, the overhead performance was not measured.  
To eliminate unnecessary transmissions for the purpose of redundancy overhead reduction, existing data dissemination protocols often provide a mean of broadcast control, by selecting a subset of vehicles to relay data further. The majority of these protocols rely on a fully distributed V2V architecture for estimating the number of vehicles nearby [10; 11; 12; 13; 14; 15; 16; 17; 18; 19], which we refer to as _traffic density_. This estimation is utilized to select relays at each hop based on the traffic condition until the area of interest is fully covered. To do so, existing protocols exploit beaconing with a fixed period, which has several drawbacks on the networking performance, such as wasted bandwidth and increased network congestion. For instance, when vehicles send a 200-byte beacon every 100 milliseconds, the channel would be 80% loaded at the range of 300 m [20]. Other protocols which do not rely on beacons usually ignore the condition of the traffic [21; 22; 23], and therefore, they do not scale well under high-density scenarios.  
In this work, we aim to propose and evaluate an efficient data dissemination protocol that addresses scalability in vehicular networks without relying on beacons. Efficiency in this work refers to providing full coverage without extra communication overhead, whereas scalability refers to maintaining low data redundancy and delay as density increases. To this end, we rely on an edge-based traffic condition estimation by utilizing an EC architecture. This estimation enables our protocol to scale well under high-density scenarios. Due to the potential of the edge in detecting traffic conditions in all possible directions in urban areas, data dissemination can be effectively suppressed for a minimized data redundancy overhead, by setting the broadcast criteria according to the condition of traffic in each direction.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 5 Adversarial Defense Methods in IoT Networks
### Network Optimization
#### 5.1.3 Gradient Regularization  
The concept of Gradient Regularization was introduced for the first time by [84]. It is a method that seeks to enhance the generalization ability of the ML Model by penalizing large changes in the output of the network, using regularization components within the cost function. [PERSON] et al. [76] use this concept to propose a promising defense method against adversarial examples. The authors found that training differentiable models of DNNs with gradient regularization enhances their resilience against adversarial perturbations. Likewise. [PERSON] et al. [80], and [PERSON] and [PERSON] [85] applied a regularization technique to bolster the algorithm's robustness, yielding favorable outcomes in its ability to withstand adversarial attacks. [PERSON] et al. [86] introduced a combined approach involving gradient phase and magnitude regularization to improve the robustness of ensemble models. [PERSON] et al. [87] introduced a new regularization technique called Bit Plane Feature Consistency (BPFC); this method utilizes information from higher bit planes to form a preliminary understanding, and then refines predictions using only the lower bit planes. [PERSON] et al. [88] proposed a regularization framework called Second-Order Adversarial Regularizer (SOAR) to improve the network's resilience to \(L_{\infty}\) and \(L_{2}\) limit-bound perturbations produced by PGD [51].  
As an adversarial defense method, the Gradient Regularization requires no prior knowledge of an adversarial attack. However, the main drawback is that it doubles the complexity of the training process. [PERSON] et al. [89] proposed a Complex-Valued Neural Network (CVNN) framework to improve gradient regularization.
# Parameter Optimisation in Selective Laser Melting on C300 Steel
## 2 Materials  
The alloy selected for this research is a C300 steel alloy, classified as managing steel. This steel has also other designations, such as 1.2709, M300 and tool steel. It is a low-carbon alloy with high nickel and cobalt contents. It has a superior mechanical strength due to intermetallic precipitates formed microscopically during the ageing treatment, such as Ni\({}_{3}\)Ti and Fe\({}_{2}\)Mo [16]. These compounds result in a higher hardness than conventional low-carbon martensitic steels. In addition, it has a high corrosion resistance, even at high temperatures up to 500 \({}^{\circ}\)C. For this reason this steel is used as tool steel, in moulding dies, missiles, rockets, engine casings and gears. Table 1 shows the composition of the C300 steel provided by the powder manufacturer (Oerlikon). In addition, the expected mechanical properties are also provided as a function of the printing orientation. For the vertical direction (Z) the expected yield strength (\(\sigma_{0,2}\)) is between 900 and 1100 MPa, the ultimate stress (\(\sigma_{uts}\)) between 1000 and 1200 MPa and the elongation (\(\epsilon_{r}\)) between 6% and 14% without any ageing heat treatment. Taking these values into account, the area under the stress-strain curve (\(A\)) is expected to be between 60 and 170 MPa.  
This steel has just recently started to be used for Additive Manufacturing processes, where the presence of temperature gradients and the rapid solidification leads to a more heterogeneous structure, resulting in better mechanical properties in terms of strength and hardness. However, this also leads to the appearance of cracks, and it is hence necessary  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Alloy** & **Ni** & **Mo** & **Co** & **Ti** & **C** & **Fe** \\ \hline C300 & 18 & 5.0 & 9.0 & 0.9 & \textless{}0.03 & Balance \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of C300 steel in weight percent (nominal).  
to relieve residual stresses by means of heat treatments [17]. The specimens used in this research have not received the ageing heat treatment before testing. The metal powders used have been atomized with argon gas, are spheroidal in morphology and particles have an average size of 45 \(\pm\) 15 \(\upmu\)m.
## 4 Results and Discussion
### Leakage Prediction
#### 4.1.2 Influence of the Hole Radius on Leakage  
Increasing the hole radius leads to an increase in the leakage (Figure 5). Additionally, it can be concluded based on the results that there is no linear relationship between the leakage and GMB radius. For example, the leakage for \(r_{G}\) = 0.05 m is 5.8 and 3.1 times larger than that for \(r_{G}\) = 0.005 and 0.025 m, respectively. However, according to the study of [PERSON] and [PERSON] [25], the increase in the leakage is slightly larger than linearly with the hole radius. The main reason for this difference is that [PERSON] and [PERSON] [25] considered the influence of the GMB hole when covered by tailings rather than embedded in the cut-off wall. The cut-off wall has low permeability and can prevent leakage. Thus, the effects of the geometric properties of the hole on the leakage will be weakened. Moreover, an interesting conclusion is found that the change of the leakage caused by the change of the cut-off wall hydraulic coefficient is related to the hole radius. For example, the leakages for \(r_{G}\) = 0.005, 0.025 and 0.05 m when \(k_{G}\) = 1 \(\times\) 10\({}^{-11}\) m/s are 2.4, 12.3 and 21.1 times lower than those  
Figure 3: Comparison with the existing analytical model by [PERSON] et al. (2021) [30].  
when \(k_{G}=1\times 10^{-9}\) m/s. The increases in leakages are approximately linearly with the hole radiuses.  
Figure 4: Leakages for different hydraulic conductivities and head losses when (**a**) \(T_{G}=0.001\) m and (**b**) \(T_{G}=0.002\) m.
## 3 Methods
### Functional Test  
In the satellite system, a functional test (FT) was performed to verify each subsystem's communications and interfaces, including the payload. Functional testing of pre-, during, and post-environmental testing was advisable to conduct in the same way as the mission plan of satellite operation in orbit. The FT was carried out on the ground during the space environment and long-duration operation tests (LDOT). The camera mission with DL execution is discussed in Section 4.  
As mentioned in Section 2, wildfire detection was an extra mission in the KITSUNE operation. The DL algorithm shall be executed only from an uplink command after camera mission execution. Any images captured by the payload have the option to downlinkimmediately or through the classification method. The image recognition result will be appended to the image data, not affecting the main mission. On the GS side, the satellite operator can classify the image using only one model or all three networks. This gives flexibility for the GS operator to determine which algorithm was well-suited to the image captured by the payload. Moreover, KITSUNE will execute the ShallowNet model in any captured images in an early demonstration of wildfire detection due to its very low computational memory yet high accuracy. Later, the CubeSat can be planned to take any wildfire events from a dedicated coordinate based on the news source. However, several places were identified as having the highest possibility for wildfire occurrence, namely, California (USA), British Columbia (Canada), New South Wales and Victoria (Australia), Sarawak (Malaysia), and Kalimantan (Indonesia). Utilizing STK software, the prediction of KITSUNE passing these locations was simulated based on the International Space Station (ISS) orbit (Figure 7). The blue dashed lines with red x's are the area covered by KITSUNE orbit between January and March 2022. However, British Columbia will be discarded from the prediction operation plan due to no passes towards the location in the designed orbit.  
Figure 6: ShallowNet, LeNet, and MiniVGGNet model architecture design.  
Figure 7: Satellite track (blue dotted lines) of KITSUNE between January and March 2022.
# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
###### Abstract  
Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm.  
Keywords:physical-layer security; deep learning; imperfect CSI; heterogeneous networks; channel estimation +
Footnote †: journal: Sensors
# A Survey of LoRaWAN for IoT: From Technology to Application
## 5 Physical Layer Performance Evaluation
### Interference Impact on LoRa  
LoRa self-interference plays a determining role in the network scalability. This interference can happen between transmissions using the same SF and different SFs. In addition, as LoRaWAN operates on unlicensed bands, interference from other network technologies should be considered too. To this end, empirical measurements, either in controllable setups or not, are one approach to study interference aspects of physical layer technology. This subsection covers studies related to interference impact on LoRa. Section 5.2.1 includes studies for interference impact from other technologies, while Section 5.2.2 includes studies related to LoRa self-interference impact.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 2 Passive Bistatic Radar and Mie Scattering  
Radars primarily fall under two configurations: monostatic and bistatic. In the monostatic configuration, the transmitter and receiver are either the same antenna or are co-located. Bistatic radars result from the separation of the transmitter (\(T_{x}\)) and receiver (\(R_{x}\)) modules by some significant distance, as seen in Figure 1.  
In Figure 1, \(R_{Tx}\), \(R_{Rx}\), and \(L\) represent the transmitter-target, receiver-target, and bistatic baseline distances, respectively. The angle subtended at the target (\(t_{g}\)) by these distances is known as the bistatic angle \(\beta\), while \(T^{\theta}\) and \(R^{\theta}\) are the transmitter and receiver look-angles with respect to a defined vertical direction or North.
## 4 Data Trend Term Removal Algorithm  
### Moving Average Method  
The moving average method is a widely utilized and simple method for long-term trend change analysis [13]. The principle of the method is as follows: average the data of two or more periods within the original time series, replace the trend value of the intermediate period with the average value, and form a new derivative series after moving the calculated average in a period-wise manner. This mean series eliminates the influence of random factors in the original time series, thus presenting a fundamental trend in the  
Figure 6: Zero-drift curve in wing working bridge load strain test.  
development of the phenomenon. Moving average processing of signals is actually a filtering process. Let the time series be \(X_{i}\), \((i=1,2,\cdots,n)\). The moving average trend term for the term \(K(K=2k+1)\) is as follows:  
\[\overline{Y}_{i}=\frac{X_{i-k}+\cdots+X_{i-1}+X_{i}+X_{i+1}+\cdots+X_{i+k}}{2k+1}. \tag{17}\]  
A more general sliding average method is to continuously slide \(N\) data points along the entire length, taking \(m\) adjacent data points as a weighted average to represent smooth data. The general formula is expressed as follows:  
\[f_{k}=y_{k}=\sum\limits_{i=q}^{p}\omega_{i}y_{k+i},\ k=q+1,q+2,\cdots,N-p, \tag{18}\]  
where \(\omega_{i}\) is the \(i\)th weight coefficient, \(\sum\limits_{i=q}^{p}\omega_{i}=1\); and \(p,\ q\) are positive integers such that \(p+q+1=m\). The different methods of taking these parameters result in different sliding average methods. If \(p=q=2\) and \(\omega_{i}=1/(2n+1)\) are the algorithms of Equation (17), it is called the equal weight center smoothing method. When \(p=0\) or \(q=0\), it is commonly used for endpoint smoothing. When \(\omega_{i}=1/m\) (for all \(i\)) is equal weighted endpoint smoothing, its formula is written as follows:  
\[f_{k}=y_{k}=\frac{1}{m_{i}}\sum\limits_{i=-m+1}^{0}y_{k+i}\ \ \mathrm{k}=1,2, \cdots,q \tag{19}\]  
\[f_{k}=y_{k}=\frac{1}{m_{i}}\sum\limits_{i=-m+1}^{0}y_{k+i}\ \ \mathrm{k}=N-p+1,N-p+2, \cdots,N \tag{20}\]  
The selection of these parameters results in different sliding average methods. The selection of parameters for the moving average method directly affects the smoothing effect on the data; for example, when a larger \(m\) is used in Formulas (19) and (20), more adjacent data are utilized for local averaging. Although the larger smoothing effect is conducive to suppressing random errors with frequent random fluctuations, it is also possible that the high-frequency varying deterministic components are averaged and weakened. Conversely, if a smaller \(m\) value is used, the low-frequency random fluctuations may be reduced without averaging (i.e., the method will not be conducive to suppressing random errors). Therefore, it is not suitable for the slowly varying zero-drift data in this test.
## 3 Materials and Methods
### Data Augmentation and Segmentation  
To increase the diversity and the volume of available data for training the model, various data augmentation techniques were applied. This approach is particularly useful when the original dataset is limited in size.  
After applying data augmentation, the total number of samples increased from 181 (85 non-fallers, 96 fallers) to 279 samples (136 non-fallers, 143 fallers). To address data scarcity and improve model robustness, data augmentation techniques were employed, which included:  
* Jittering: Adding random noise to simulate natural variability. This technique is commonly employed in time series data augmentation to enhance model robustness by introducing minor variations in the input data [67].
* Scaling: Applying small, constant variations to the time series. Scaling has been shown to improve model performance by generating variability in the amplitude of the input data [68].
* Time warping: Non-linear stretching or compressing of the time axis. Time warping is especially useful in time series classification tasks, as it allows the model to handle variations in the speed of events [68].
* Window slicing: Extracting different segments from the time series to create new, slightly shifted samples. This technique helps create additional data points by slicing the data into overlapping windows, increasing the effective size of the dataset [69].
* Interpolation: Generating new, smoothed samples by interpolating between existing data points. Interpolation techniques are widely used in augmenting time series data to create smoothed transitions between samples [68].
## Nomenclatures  
\begin{tabular}{l l} \(x\), \(y\), \(z\) & third local coordinate, mm \\ \(r\) & radial direction coordinate, mm \\ \(T\) & temperature, K \\ \(T_{c}\) & critical temperature, K \\ \(t\) & time, s \\ \(u\), \(v\) & velocity in the \(y\)-direction, velocity in the \(z\)-direction, m/s \\ \(\rho\), \(\rho_{c}\) & density and critical density, kg/m\({}^{2}\) \\ \(P\), \(P_{c}\) & pressure, critical pressure, MPa \\ \(P_{s}\), \(P_{d}\) & static and dynamic pressure, MPa \\ \(P_{t}\) & total pressure, MPa \\ \(P_{in}\) & nozzle inlet pressure, MPa \\ \(P_{am}\) & ambient pressure, MPa \\ \(P_{t}^{*}\) & perforation pressure, MPa \\ \(T_{am}\) & ambient temperature, K \\ \(V_{c}\) & critical specific volume, m\({}^{2}\)/ kg \\ \(R_{sp}\) & specific gas constant, dimensionless \\ \(K\) & kinetic energy of turbulence, dimensionless \\ \(\omega\) & specific dissipation rate of turbulence, dimensionless \\ \(v_{z}\) & axial velocity, m/s \\ \(v_{max}\) & max axial velocity, m/s \\ \(\mathrm{b}\), \(a_{0}\) & constants of PR equation, dimensionless \\ \(w\) & acentric factor, dimensionless \\ \(V\) & specific volume, m\({}^{2}\)/ kg \\ \(n\) & constants of PR equation, dimensionless \\ \(E_{T}\) & tangent modulus of shale rock, GPa \\ \(E_{T0}\) & average tangent modulus of shale rock, GPa \\ \(\alpha\) & non-uniform parameter of Weibull probability distribution \\ \(\beta\) & scale parameter of Weibull probability distribution \\ \(\sigma_{y}\) & \(y\)-directional stress, MPa \\ \(\sigma_{z}\) & \(z\)-directional stress, MPa \\ \(\sigma_{y}^{*}\) & failure tensile stress in the \(y\)-direction, MPa \\ \(\sigma_{z}^{*}\) & failure tensile stress in the \(z\)-direction, MPa \\ \(\sigma_{fy}\) & in situ stress in the \(y\)-direction, MPa \\ \(\sigma_{fz}\) & in situ stress in the \(z\)-direction, MPa \\ \end{tabular}
## 5 Performance Analysis
### Number of Clusters  
It has been seen that an increase in number of clusters will automatically reduce energy use because the number of nodes will be far from the CH causing a more rapid energy drainage [45]. A decrease in the number of clusters reduces the number of nodes close to CHs, increasing the rate at which network energy is used. The number of CHs required to transmit data to the BS over long distances will also increase if the clusters exceed the optimal number. In Figure 5, EEWBP has 24% fewer CHs than LEACH, CACP, CPCP, and CUCA at any given point in network operation.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Default Value** & **Parameter** & **Default Value** \\ \hline Monitoring area & 100 m \(\times\) 100 m & Amplifier constant [1; 2] & 10 pJ/bit/m\({}^{2}\) \\ Network node count & 200 & CH energy threshold [2] & 10–4 J \\ Shortest possible path between & 2 m & Size of packet [2] & 30 bytes \\ nodes & & & \\ Number of simulations & & & \\ performed & 100 & Rate of packets [2] & 1 packet/s \\ Simulation clock time & 120 s & Detection distance [2] & 10 m \\ Position of BS & (50, 50) & Size of a cluster [2] & 25 m \\ Initial energy & 0.5 J & Energy used to send each bit & 50 \(\times\) 0.00000001 \\ Transmission scope & 40 m & Energy used to receive & 50 \(\times\) 0.00000001 \\ Chance of a node becoming a CH & 0.1 & Tx/Rx electronics constant [2] & 50 nJ/bit \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feigning Parameter.  
Figure 4: The distribution of network nodes, in which \(\blacklozenge\) is conceived as SNs and \(\blacklozenge\) as BS.
###### Abstract  
Chromatic dispersion from optical fiber is the most important problem that produces temporal skews and destroys the rectangular structure of code patterns in the spectra-amplitude-coding-based optical code-division multiple-access (SAC-OCDMA) system. Thus, the balance detection scheme does not work perfectly to cancel multiple access interference (MAI) and the system performance will be degraded. Orthogonal frequency-division multiplexing (OFDM) is the fastest developing technology in the academic and industrial fields of wireless transmission. In this study, the radio-over-fiber system is realized by integrating OFDM and OCDMA via polarization multiplexing scheme. The electronic dispersion compensation (EDC) equalizer element of OFDM integrated with the dispersion compensation fiber (DCF) is used in the proposed radio-over-fiber (RoF) system, which can efficiently suppress the chromatic dispersion influence in long-haul transmitted distance. A set of length differences for 10 km-long single-mode fiber (SMF) and 4 km-long DCF is to verify the compensation scheme by relative equalizer algorithms and constellation diagrams. In the simulation result, the proposed dispersion mechanism successfully compensates the dispersion from SMF and the system performance with dispersion equalizer is highly improved.  
spectral-amplitude-coding optical code-division multiple-access (SAC-OCDMA); orthogonal frequency-division multiplexing (OFDM); balanced photo-detector (BPD); multiple access interference (MAI); dispersion equalizer