esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio-Visual Action Recognition Using Transformer Fusion Network
## 3 Method
### Audio-Visual Feature Extraction  
The feature extraction module is designed to extract learned features from the three processed inputs of spatial, temporal, and audio data, all uniformly transformed into a standardized format of \(W\times H\times C\). This standardized approach ensures consistency in the feature extraction process across all modalities. To facilitate reproducibility, we specify that the Swin transformer is employed as a unified feature extraction network. This network employs shared parameters in a single Swin Transformer to efficiently learn and handle all modalities rather than using separate transformers for each. This approach ensures a consistent and integrated method for feature extraction across the different types of data. The extracted features, denoted as \(f_{l}\) (spatial), \(f_{V}\) (temporal), and \(f_{A}\) (audio), are not combined all at once. Instead, they are paired and directed into three distinct modal fusion modules (MFMs). Each MFM is responsible for fusing two types of modalities: the first for spatial-temporal (\(f_{l}\) and \(f_{V}\)), the second for spatial-audio (\(f_{l}\) and \(f_{A}\)), and the third for temporal-audio (\(f_{V}\) and \(f_{A}\)) data. This pairwise fusion strategy, implemented in separate MFMs, allows for a more nuanced and effective integration of the multi-modal data. Through this approach, the model size is kept manageable, avoiding the complexity that would arise from employing distinct feature extraction networks for each modality.
## 3 Hybrid Service Placement Based on a Recommender System
### Proposed Architecture (System Model)
#### 3.1.3 Fog Orchestrator Control Node  
The role of the FOCN is to orchestrate Fog resources (Fog nodes) and services. In other words, to coordinate, organize, and manage them together to meet specific functional and non-functional requirements. Thus, in the proposed architecture, the FOCN's main responsibility is to resolve the SPP and ensure the efficient placement of services through an admission control unit (ACU) and a control loop called MADE-K.  
According to our proposal, within a Fog colony, the ACU plays a role in filtering requests at the local level and helps maintain an optimal performance, as illustrated in Figure 4. In the case of non-real-time applications, the ACU sends the request to the Cloud. Otherwise, it receives requests to place services in the current colony, filters these requests, and classifies them according to certain criteria, such as the deadline for completion, then sends these requests (indicating whether they have priority) to the MADE-k control loop, which will take charge of the placement decision. Otherwise, if the colony is not available, then the ACU sends the request to the recommendation system.  
The \"MADE-K\" or Monitoring, Analysis, Decision Making, and Execution with a knowledge base is a control loop model that can manage, plan placement requests, and solve the SPP based on four main phases. The monitoring phase consists of receiving  
Figure 3: The hierarchical structure of the proposed Fog layer architecture.  
placement requests sent by the ACU, following up on these requests, and monitoring node availability by updating the local knowledge base every period. The second phase consists of analyzing the placement requests received and prioritizing them according to the lead and waiting times. The decision phase is based mainly on executing a multi-objective optimization algorithm that will solve the SPP. Finally, the last phase of the MADE-k consists of placing the service based on the decision made in the previous phase. There are two possible outcomes: either the service is placed in an available node in the colony (selected in the decision phase), or, if unavailable, the placement request is transferred to the RS.
## 4 Time Gating and Demodulation Algorithms
### OAM Recovery Algorithm
(The distance \(R\) from the beam axis may be deduced in non-fixed link via other signaling information such as timing-advance in TDD systems and from the TX antenna array geometry.) In practice \(\mathbf{U}\) could be obtained by measuring the spectral-envelope coefficients of each OAM mode using correlation techniques during periodic \"calibration\" intervals, when each OAM mode would be transmitted separately. The coefficients for each branch according to (40) are shown explicitly in a sketch of the recovery algorithm in Figure 14 below (based on Figure A6 on Appendix C). With suitable training signals for reference, an LMS type of algorithm can be formulated using well-known feedback loops to adapt the coefficients in the FIR-type of filter structure, as mentioned earlier. Moreover, including more than K spectral components and corresponding coefficient loops would provide more degrees of freedom, which could lead to further improved EVMs and potentially cancel external interference. The structure in Figure 14 is reminiscent of a spectral-analysis process, so it may be feasible to implement equivalent versions of it using Fourier transform techniques. An acousto-optic spectrum-analyzer configuration comes to mind and may be pursued in future work. It is also possible to make use of output \(Z_{2}\), noting that the output at \(Z_{2}\) can be derived by the same process as was that at \(Z_{1}\) but starting with  
Figure 13.0  
One obtains the corresponding result for \(Z_{2}\) prior to the gating operation, as  
\[ \begin{split}& Z_{2,k}(t)\cong\sqrt{2}S_{k}(t)e^{j(\Psi_{1}(t)- \frac{kt}{2K})}\times\ldots\\ &\cdots\times(\cos(P(t)-\pi/4))e^{j\tan^{-1}((\frac{kt}{2K})\tan (P(t)-\pi/4))}\end{split}  \tag{43}\]  
which looks like \(\pi/4\) was replaced by \(-\pi/4\) in \(Z_{1,k}(t)\). Although one can make (10) and (43) look like the input by substituting \(\cos(\Omega t-\pi/4)=\cos(\Omega t+\pi/4-\pi/2)=\sin(\Omega t+\pi/4)\) in (43), the change from \(\pi/4\) to \(-\pi/4\) also forces a change in the gating intervals. Using the same procedure as in Appendix D, it is found that (43) needs to be gated to intervals where  
\[0<\Omega t<\frac{\pi}{2} \tag{44}\]  
also separated by multiples of \(\pi\), which are exactly complementary to those shown in Figure 8. That means the useful outputs at \(Z_{1}\) and \(Z_{2}\) after their respective gating operations appear in disjoint time intervals, so it makes no sense to combine them even though they have the same fractional pseudo-Doppler shifts that are desired. What does make sense is to \"toggle\" them to the same final output in order to have a more time-continuous output signal for the OAM recovery algorithm leading to (41), which may become useful in future refinements. Figure 14: Functional OAM recovery algorithm from gated output of pseudo-Doppler modulation subsystem.
# Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
## 3 Network Throughput under Backoff Modification
### Simulation 1: Network Throughput and Fairness  
Now, we will make use of the expressions derived in the previous section to analyze the impact of having \(n_{2}\) stations that follow a uniform backoff, and hence, do not respect the binary backoff procedure. The values used for time durations are the same as in [22], extracted from 802.11 standard, and can be seen in Table 1. Observe that we consider two different payload lengths, a short one, \(T_{p,s}\), and a long one, \(T_{p,l}\). We consider that the stations of class 1 follow the IEEE 802.11 standard binary backoff mechanism (normal stations), with \(W_{1}=CW_{min,1}=32\), \(CW_{max,1}=1024\) and hence, \(m_{1}=5\). The stations of class 2 (malicious stations) will follow a uniformly distributed backoff in the interval \([0,W_{2}-1]\).  
With these values, we obtain the throughput for each station using (8) and (9) for these cases:  
* Using the large payload: \(T_{p}=T_{p,l}\). We test four cases: first, we consider that \(n_{2}=0\), that is, all stations follow the binary exponential backoff and we vary the number of stations for \(n\in[1,20]\). Then, we fix the number of stations to \(n=5\) and simulate for \(n_{2}\in\{1,2,4\}\), that is, for respectively 1, 2 and 4 malicious stations. We show the results for different values of \(W_{2}\), namely, for \(W_{2}\in[1,W_{1}]\). The obtained results are in Figure 1.
* Using the short payload, \(T_{p}=T_{p,s}\). We test the same four cases than we did for the large payload case. The obtained results are in Figure 1.
## 2 Materials and Methods
### Tool Force and Soil Disturbance Characteristics Obtaining in EDEM  
Much importance is attached to force and soil disturbance characteristics in assessing the tool performance. The tool force and the range of disturbance increase as the tool advances until the cutting is in a steady state. Then, they fluctuate within relatively constant ranges due to the periodic nature of soil-tool interaction in soil cutting [6; 39]. A failure boundary completely develops at the moment when the tool force is minimal [41]. The range of disturbance at that moment is typical. Therefore, the tool force in the steady state and the soil disturbance at the moment of minimal tool force were investigated in the Analyst module in EDEM.  
The average draught force (\(Fx\)) and average vertical force (\(Fz\)) in the steady state were obtained. The particles were displayed in gradient colors based on their velocity (0-1 m/s) so as to distinguish the disturbed particles from those undisturbed. The cross-sections perpendicular to the \(x\)-axis were selected at intervals of 50 mm within the range of disturbance (Figure 3b). The images of transverse disturbance were obtained by superimposing the cross-sections using the \"stack\" function in ImageJ (v.1.51J) software (National Institutes of Health, USA) (Figure 3c). The images of longitudinal disturbance in the plane of symmetry were also obtained.  
The disturbance was quantified using ImageJ software. The profiles of the disturbance were mapped by identifying the undisturbed particles. The values of disturbance width (\(W\)), upheaval height (\(H\)), and the area of transverse disturbance (\(Ayz\)) were obtained by measuring the images of the transverse disturbance. Forward rupture distance (\(L\)), rupture angle (\(\beta\)), and the area of longitudinal disturbance (\(Axz\)) were obtained by measuring the images of the longitudinal disturbance. These items are graphically illustrated in Figure 3. The measured values were transferred into actual values based on the scale of the image.  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Parameter** & **Value** \\ \hline Particle & Radius/mm & 3 \\ \hline Material & Density of particle/(kg\(\cdot\)m\({}^{-3}\)) & 2620 \\  & Poisson’s ratio of soil & 0.33 \\  & Shear modulus of soil/Pa & 1.02 \(\times\) 10\({}^{8}\) \\  & Density of steel/(kg\(\cdot\)m\({}^{-3}\)) & 7850 \\  & Poisson’s ratio of steel & 0.3 \\  & Shear modulus of steel/MPa & 7.9 \(\times\) 10\({}^{10}\) \\ \hline Interaction & Coefficient of restitution, soil-soil & 0.53 \\  & Coefficient of static friction, soil-soil & 0.53 \\  & Coefficient of rolling friction, soil-soil & 0.42 \\  & Coefficient of restitution, soil-steel & 0.27 \\  & Coefficient of static friction, soil-steel & 0.54 \\  & Coefficient of restitution, soil-steel & 0.28 \\ \hline \end{tabular}
\end{table}
Table 1: Particle, material, and interaction parameters used in EDEM.
### New Challenges and Related Issues When Using Double-Frequency Smartphones
They presented the first version of an Android application \"GNSSAlarm\" to perform RFI and spoofing detection via a combination of GNSS and inertial sensor data that takes advantage of native hardware inside the smartphone to increase the integrity of the positioning system. At that time, the question was raised about how to examine the characteristics of individual smartphones/devices so that an effective threshold could be defined. This, including all the available GNSS data from dual-frequency smartphones and the use of Leica GS15 and Leica GS18 geodetic receivers and several types of jammers with the added benefit of finding their position with a total station, was the starting point for the current research. In this study, however, we focused on two aspects, i.e., on the CNR characteristics and positional quality analysis, for both smartphones and professional receivers during jamming, with the goal to locate the jammer using the smartphones' raw measurements. Additionally, a relative carrier-phase positioning analysis was performed for the smartphones and geodetic receivers to assess their robustness in terms of positioning during the chirp jamming events.
# Deep Learning of GNSS Acquisition
## 1 Introduction
Section 2 recalls the basics of standard GNSS signal acquisition. Section 3 details the proposed DNN approach for signal acquisition, including a discussion on the models and its extension to noncoherent integration times. The model training setup is discussed in Section 4, and results are analyzed in Section 5. Finally, conclusions and future research directions are drawn in Section 6.
## 3 Experimental Results
### Analysis of the Forest Classification Situation  
In the HyRANK-Loukia dataset, the classification of Fruit Trees appears suboptimal, even with a 20-point enhancement by our algorithm, which only reaches about 28%. Similarly, our algorithm shows improvements of approximately 10% for Broad-leaved Forest, Coniferous Forest, and Mixed Forest, as evidenced by the prediction maps, which indicate a reduction in misclassification errors compared to other algorithms. In the Pavia Universit dataset, the accuracy for Trees is already quite high, yet our algorithm achieves an impressive 99%, approaching near-perfect classification. Additionally, our algorithm demonstrates varying degrees of enhancement for Trees in both the WHU-Hi-HongHu and WHU-Hi-HanChuan datasets. These results underscore the effectiveness of our algorithm in analyzing forest categories, which is highly beneficial for assessing forest degradation, deforestation, and forest change dynamics.  
Initially, Figures 2-6 display various components across panels (a) to (j): (a) the original image, (b) the label map, (c) AB-LSTM, (d) 3D-CNN, (e) SPEFORMER, (f) SSFT, (g) M3D-DCNN, (h) DFFN, (i) RSSAN, and (j) the method we propose. In Figure 2, methods (f), (i), and (j) exhibit promising outcomes. In Figure 3, (d) and (e) demonstrate the least effective performance, being more susceptible to errors. Figure 4 clearly indicates that (f) and (j) are the standout performers. In Figures 5 and 6, it is noticeable that in the boundary areas of the cultivated vegetation, our proposed method (j) achieves the superior results.  
The visual analyses provided in Figures 2-6 yield informative perspectives on the efficacy of various hyperspectral image classification techniques. Each figure's panels (a) to (j) illustrate unique elements, where (a) shows the original image, (b) the label map, and (c) to (i) signify a range of cutting-edge methods such as AB-LSTM, 3D-CNN, SPEFORMER, SSFT, M3D-DCNN, DFFN, and RSSAN, with (j) denoting our introduced approach.  
The examination of Figure 2 shows that methods (f) (SSFTT), (i) (RSSAN), and (j) (our proposed technique) exhibit commendable results, suggesting their proficiency in capturing the nuanced characteristics of the hyperspectral imagery. Conversely, as depicted in Figure 3, methods d (3D-CNN) and e (SPEFORMER) appear to have the least effective performance, being more prone to classification inaccuracies.  
Figure 4 distinctly demonstrates that methods (f) (SSFTT) and (j) (our proposed approach) are the leading contenders, indicating a superior level of classification accuracy. This pattern is corroborated by Figures 5 and 6, which clearly show that in the boundary areas of the cultivated vegetation, our proposed method (j) delivers enhanced outcomes  
Figure 8: The detailed epochs curve of training.  
when contrasted with the other techniques, underscoring its strength in delineating fine boundary details within HSI classification challenges.
# A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
###### Abstract  
A new non-stationary (NS) geometry-based stochastic model (GBSM) is presented for developing and testing the communication systems of vehicle-to-vehicle (V2V) applications, which considers the three-dimensional (3D) scattering environments and allows 3D velocity as well. In this paper, the proposed GBSM for NS V2V channels allowed 3D velocity variations and was more suitable for actual V2V communications because it provided smoother transitions between the consecutive channel segments. The time-variant channel coefficient and the channel parameters, i.e., Doppler frequencies, path delay and power, angle of arrival (AoA), and angle of departure (AoD), were analyzed and derived. Likewise, the theoretical statistical properties as the probability density function (PDF), the auto-correlation function (ACF), and Doppler power spectral density (DPSD) were also analyzed and derived under the von Mises-Fisher (VMF) distribution. Finally, the theoretical and measured results were well coordinated alongside the implemented results, which confirmed the feasibility of the introduced model along with the theoretical expressions.  
V2V channel; NS GBSM; 3D velocity variations; 3D scattering environments; statistical property +
Footnote †: journal: Sensors  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations.  
_Sensors_**2021**, _21_, 3271. [[https://doi.org/10.3390/s21093271](https://doi.org/10.3390/s21093271)]([https://doi.org/10.3390/s21093271](https://doi.org/10.3390/s21093271))
## 5 Trace Sampling Based on Type, Duration, and Errors  
Modern distributed applications trigger an enormous number of traces. The direct storage of hundreds of thousands of traces heavily impacts users' budgets. A sampling strategy is a procedure that decides which traces to store for further utilization. We describe a procedure that samples traces based on their types, duration, and/or errors. The final goal is to preserve sufficient information for application troubleshooting without possible distortion.  
A trace type can be approximately identified by the root span or the span that arrived the earliest. More strict identification is based on the analysis of its structure. However, the latest method is rather resourceful and will require additional grouping/clustering approaches. The durations of traces (in milliseconds) are available in the corresponding metadata. It is possible to extract the durations for different trace types and estimate the average/typical durations of the corresponding processes. The information on errors is also available in trace metadata. Traces are composed of spans. We have detailed information regarding the spans in the corresponding tags that describe the micro-processes. One of the tags indicates the health of a span. We can assume that the entire trace is erroneous if at least one of its spans has a \"True\" error label. We suggest a parametric approach for all sampling scenarios, allowing us to modify the required percentage of compression rates accordingly.
## 4 Results and Discussion  
This section consolidates the experimental findings for both FDM (PLA and ABS) and SLA (general-purpose photopolymer resin), covering surface finish, dimensional accuracy, mechanical behavior, production cost, energy usage, environmental factors, and reliability. By evaluating the same geometries under standardized settings (Section 3), we aim to present a balanced, data-driven comparison of these two additive manufacturing (AM) technologies. The results are organized to highlight the strengths and limitations of each process, providing actionable insights for practitioners and researchers.
## 2 Materials and Methods
### Statistical Analysis  
In order to compare the influence of compost and fly ash on the tested parameters, Cu and Zn levels in plants, total and bioavailable amounts of metals, \(\text{BCF}_{\text{T}}\), \(\text{BCF}_{\text{B}}\), \(\text{EDI}_{\text{W}}\), \(\text{EDI}_{\text{M}}\), \(\text{HRI}_{\text{W}}\), and \(\text{HRI}_{\text{M}}\), were compared for the following pairs: T0 and TI, T0 and TII, as well as TI and TII with respect to the amounts of Cu and Zn. In the first step, it was checked whether the samples came from the normal distribution. Normality was verified with the Shapiro-Wilk Test (Shapiro-Wilk Normality Test). If the sample showed a normal distribution, the appropriate Student's \(t\)-test was selected for analysis in order to compare the mean Cu and Zn amounts in plants and soil, as well as mean values of the parameters \(\text{BCF}_{\text{T}}\), \(\text{BCF}_{\text{B}}\), \(\text{EDI}_{\text{W}}\), \(\text{EDI}_{\text{M}}\), \(\text{HRI}_{\text{W}}\), and \(\text{HRI}_{\text{M}}\). On the other hand, when testing normality if it was concluded that the sample did not come from a normally distributed population, the Wilcoxon test was used to compare the two samples. Regardless of the soil or the additive used, or the cultivated plant, all analyzed samples had the same number of replications, \(n=8\). Therefore, the design of the considered experiment was treated as a completely random design, and appropriate statistical tools were applied. All the analyses were performed in the R environment (R version 4.1.2). The violin charts were used for the visualization of Cu and Zn contents in plants and soil. These graphs show all the basic statistics from the samples. The bottom line of each violin chart represents the minimum metal amount in plant and soil (total and bioavailable amounts), whereas the top value of the violin is the maximum metal amount. The lower range of the black box represents the lower quartile of Q1, below which 25% of the sample observations (in terms of metal content) are found. The upper range of the black box represents the upper quartile of Q3, below which 75% of the sample observations are found. The white dot in the black box represents the median Q2 (middle value), below which 50% of the observations in terms of metal amount are found.
# A Secured Intrusion Detection System for Mobile Edge Computing
## 2 Literature Review
### IDS (Intrusion Detection System)  
Intrusion detection systems play a pivotal role in identifying and mitigating cyber-security threats, offering organizations a proactive defense mechanism. However, understanding their limitations is paramount in designing a comprehensive security strategy that addresses the evolving nature of cyber threats [7]. As cybersecurity landscapes continue to advance, the need for innovative approaches and the integration of complementary technologies becomes imperative to enhance the overall resilience of network defenses. Edge-based IDS security architecture represents a crucial advancement in the domain of cybersecurity, tailoring intrusion detection mechanisms to the specific requirements and constraints of edge computing environments. Unlike conventional IDS that operate within centralized data centers, edge-based IDS is strategically positioned at the network periphery, closer to the data sources and endpoints. This proximity not only reduces latency but also enables timely detection and response to security incidents, a critical consideration in the context of emerging applications such as autonomous vehicles, smart cities, and industrial IoT. The architecture of edge-based IDS encompasses a range of components and functionalities designed to fortify the security posture of edge computing environments. This includes the deployment of intrusion detection sensors, distributed detection engines, and secure communication protocols [8]. The dynamic and resource-constrained nature of edge devices necessitates the optimization of detection algorithms and the efficient utilization of computing resources. Consequently, the security architecture must strike a delicate balance between detection accuracy and minimal impact on system performance.