esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The exploration of anti-jamming and anti-interception technologies has emerged as a pivotal area of focus within the realm of satellite communications, advancements in which are crucial in ensuring the highest level of security for the transmission of information. This paper delves into the MP-WFRFT algorithm, which is known to induce rotation, diffusion, and aliasing in two-dimensional and three-dimensional signal constellation maps. In comparison to the amalgamation of DM with conventional modulation techniques, the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Situation** & **\(\alpha\) or \(\beta\)** & **\(\beta\) or \(\alpha\)** & **Azimuth (\({}^{\circ}\))** & **Curve** \\ \hline (a) & 0.50 & 0.50 & \(54^{\circ}\) & Green \\ (b) & 0.56 & 0.50 & \(54^{\circ}\) & Red \\ (c) & 0.58 & 0.50 & \(54^{\circ}\) & Yellow \\ (d) & 0.60 & 0.50 & \(54^{\circ}\) & Blue \\ (e) & 0.50 & 0.50 & \(60^{\circ}\) & Black \\ (f) & 0.56 & 0.50 & \(60^{\circ}\) & Purple \\ \hline \end{tabular}
\end{table}
Table 4: Parameter list of fusion algorithm.  
Figure 14: Performance of the proposed algorithm. (**a**) \(\cos\gamma\) direction (horizontal direction). (**b**) \(\sin\gamma e^{j\varphi}\) direction (vertical direction).  
integration of PM and DM offers distinct advantages in bolstering information security. Building upon the VPAPM framework, the scale vector parameters of MP-WFRFT are evaluated and ranked from highest to lowest in terms of their resistance to interception: \(m_{1}>m_{2}>m_{3}>m_{4}\), \(n_{1}\approx n_{2}\approx n_{3}\approx n_{4}\). Furthermore, based on an in-depth analysis of the simulation results, the \(\cos\gamma\) direction is more effective for improving the anti-interception performance of satellite communications systems. More importantly, this paper proposes a secure satellite transmission technique via directional variable polarization modulation with MP-WFRFT.  
The proposed scheme demonstrates superior performance over individual algorithms within a signal-to-noise ratio range of 0-10 dB. Beyond 10 dB, the anti-interception performance is improved by at least one order of magnitude. In summary, the scheme presented in this paper has the potential to significantly enhance spectral efficiency and electromagnetic countermeasures within the dynamic challenges of complex battlefield environments.  
Conceptualization, [PERSON] and Z.L. ([PERSON]); methodology, [PERSON] and Z.L. ([PERSON]); investigation, [PERSON], [PERSON] ([PERSON]), and [PERSON] ([PERSON]); resources, [PERSON] and Z.L. ([PERSON]); writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]), [PERSON], [PERSON] ([PERSON]), and [PERSON] ([PERSON]); visualization, [PERSON] and X.L. ([PERSON]); supervision, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grant U20A20193.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Computational Experiments
### Sensitivity Analysis  
The weight of the objectives in the presented model has an effect on the optimization results. Finally, we performed a sensitivity analysis to compare this impact. A series of experiments using the data in the above case are proposed, in which \(\theta_{1}\) is different and \(\theta_{2}\) is fixed to 1. Table 11 shows the experimental results. With increasing \(\theta_{1}\), the train travel time becomes longer, and remaining passenger demand becomes less. When \(\theta_{1}\) is larger, it is necessary to increase the stop frequency to transport more passengers, so the train travel time will become longer and the remaining passenger demand will be less. Hence, train travel time and remaining passenger demand can be balanced by adjusting the weights in the objective.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\theta_{1}\)} & \multicolumn{2}{c}{**Remaining Passenger**} & \multicolumn{2}{c}{**Train Travel**} & \multirow{2}{*}{**Number of Stops**} & \multirow{2}{*}{**Gap**} & \multicolumn{1}{c}{**Computation**} \\  & **Demand (Person)** & & **Time (min)** & & & **Time (s)** \\ \hline
0.01 & 5555 & 1312 & 48 & 1.56\% & 30,000 \\
0.1 & 241 & 1512 & 79 & 10.50\% & 30,000 \\
1 & 5 & 1564 & 87 & 12.30\% & 30,000 \\
10 & 1 & 1606 & 93 & 14.90\% & 30,000 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The results of the series of experiments.  
Figure 8: Seat occupancy of each train in the experiment.
# An IoT-Based Fog Computing Model
## 6 Experiment and Discussion of Results
### Simulating the Terminal Layer  
Experiments were also carried out to simulate the terminal layer using the Cooja emulator on Contiki [32]. Cooja is a simulator/emulator embedded in Ubuntu 16.04 operating system. Using Cooja [33] we were able to implement and test the robustness and energy efficiency of the multi-sink LIBP used at the terminal layer. The experiments were run on a network with 50 nodes. When the radio chip hardware was turned on, but in passive mode (that is, not transmitting or receiving), the energy consumption was almost the same as in the receive mode. Though in passive mode, the energy consumed was not zero, because keeping the receive-machinery active and continuously sampling the medium for a potential packet transmission also consumed energy. For this experiment, the energy consumption test was done with various numbers (between 1 and 5) of sink nodes.
## 4 Methodology
### Doa-Bpnn Model
```
1:Input: Initial BPNN parameters \(W_{init}\), Training dataset \(D_{train}\), DOA parameters: \(\{N_{pop}\), \(T_{max}\), \(T_{d}\), \(u\), \(lb\),\(ub\}\)
2:Output: Optimized BPNN parameters \(W_{best}\)
3: Compute dimension \(D\) = number of parameters in BPNN
4:\(k_{max}\leftarrow\lfloor k\cdot D\rfloor\)
5: for \(t\gets 1\) to \(T_{max}\) do
6: if \(t<T_{d}\) (Exploration Phase) then
7: Divide population into 5 groups:\(G_{0}\), \(G_{1}\),..., \(G_{4}\)
8: for \(q\gets 0\) to 4 do
9:\(X_{q,best}^{t}\leftarrow\arg\max_{X_{q}^{t}\in G_{q}}F(X_{q}^{t})\)
10:\(k_{q}(t)\leftarrow\left[1+(D-1)\cdot(1-\frac{t}{t_{d}})\cdot(\frac{5-q}{5})\right]\)
11: for each \(X_{i}\) in \(G_{q}\) do
12:\(X_{i}^{t+1}\gets X_{q,best}\)
13:\(\Omega_{forget}\leftarrow\)Randomly select \(k_{q}(t)\) dimensions
14: if \(rand()<u\) then
15: Random forgetting
16: else
17: Dream sharing
18: end if
19:\(X_{i}^{t+1}\gets clip(X_{i}^{t+1},lb,ub)\)
20: update \(F(X_{i}^{t+1})\)
21: end for
22: end for
23: else (Exploitation Phase)
24:\(X_{best}^{t}\leftarrow\arg\max_{X_{i}\in\{X_{1},X_{2},\ldots,X_{pop}\}}F(X_{i})\)
25:\(k_{r}(t)\leftarrow\left[1+(k_{max}-1)\cdot(1-\frac{t-T_{d}}{T_{max}-T_{d}})\right]\)
26: for \(i\gets 1\) to \(N_{pop}\) do
27:\(X_{i}^{t+1}=X_{best}^{t}\)
28:\(\Omega_{forget}\leftarrow\)Randomly select \(k_{r}(t)\) dimensions
29: Apply forgetting strategy
30:\(X_{i}^{t+1}=clip(X_{i}^{t+1},lb,ub)\)
31: update \(F_{i}^{t+1}\)
32: end for
33: end if
34: if \(F_{i}^{t+1}>F_{best}\) then
35:\(F_{best}\gets F(X_{i}^{t+1})\)
36:\(W_{best}\gets X_{i}^{t+1}\)
37: end if
38: end for
39: Return \(W_{best}\)
```  
**Algorithm 1** DOA Optimization for BPNN Parameters
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 5 Simulation Results
### Performance Comparisons
Compared with other protocols, CLIC with \(\xi=0.8\) achieves improvements in energy efficiency because of its focus on network interference.
# Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
## 3 Machine Learning Method
### Machine Learning Models
#### 3.1.5 K-Nearest Neighbor (KN)  
K-nearest neighbor is a non-parametric statistical method mainly used for classification and regression [32]. It is efficient and enjoys a wide range of applications. The principle of this algorithm is that the category of the input instance is identical to the dominant category of its K closest neighbors. The average value of the instances in this category is the predicted value obtained by the K-nearest neighbor regression algorithm. The essence of this algorithm is the selection of the K value. If the K value is small, only the training instance closer to the input instance will affect the prediction result, which makes it prone to overfit. Otherwise, if the K value is large, the training instance that is far away from the input instance will also affect the prediction result, causing prediction errors. A reasonable selection of the K value can obtain a model with high prediction accuracy and avoid overfitting issues.
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
## 3 Physical Layer Security Technology Based on Coded FTN Signaling
### Block-Wise Coded FTN Signaling  
For the convenience of implementing the proposed coded FTN signaling, this subsection considers a block-wise coded FTN signaling method, where a block consists of some transmission symbols. In this block-wise coded FTN signaling, the same \(\tau\) value is assigned to symbols in the same symbol block. The \(\tau_{code}\) of the block-wise coded FTN signaling is described as follows:  
\[\tau_{code}=[\tau_{bkl1}\tau_{bll2}\tau_{bll3}\cdots\tau_{bllL-1}\tau_{bllL}] \tag{5}\]  
where \(L\) is the number of symbol blocks, \(L=K/B;B\) is the number of symbols in a symbol block; and \(\tau_{bllk}\) is the \(l\)-th \(\tau\) block, which can be written as  
\[\tau_{bllk}=[\underbrace{\tau_{l}\tau_{l}\tau_{l}\cdots\tau_{l}}_{\text{ length: $B$}}]. \tag{6}\]  
In Equation (6), the \(l\)-th \(\tau\) block with a length of \(B\) has an identical \(\tau_{l}\). Since the identical symbol time can be used for consecutive \(B\) symbols, the implementation efficiency of the coded FTN signaling can be improved. However, in symbol blocks with low \(\tau\) values, all the B symbols have a short symbol time, and relatively high interference occurs continuously in the symbol block. Therefore, performance degradation occurs in symbol blocks with low \(\tau\) values.
## 4 Methodology
### Proposed Hybrid Framework
#### 4.3.1 MC-CNN-Encoder  
For the spatial encoder component, an effective multi-channel convolutional encoder is proposed. Each channel of the encoder takes multivariate time series data from different spatial neighbors as input. Using the multichannel approach, spatio-temporal features from spatial neighborhood of data points can be extracted. As in image data, multivariate spatio-temporal data are processed by 2D kernels and 2D pooling operations. In contrast to other deep learning approaches which apply CNN on time series data with 1D kernels to extract temporal features [50; 51; 52], 2D kernels are applied to extract spatial features.  
MC-CNN-Encoder component is composed of two convolutional blocks, and each block is made up of four cascading layers: 2D convolution kernel layer, followed by an activation layer, followed by 2D max-pooling layer and a channel-wise batch normalization layer. The first convolutional layer has 16 filters of size \((2\times 2)\) with zero-padding and no striding (or with strides \((1\times 1)\)). The second convolution block has convolutional layer with 32 filters of size \((3\times 3)\) without striding and with zero padding. The max pooling operation with pool size \((2\times 2)\) and strides \((2\times 2)\) is used to cut temporal dimensions by half at each max pooling layer.  
The MC-CNN-Encoder takes three-dimensional multivariate spatio-temporal input data and generates a higher order representation of it. The dimension of the output of the MC-CNN-Encoder is (_timestamps \(\times\) features \(\times\) feature maps_). Thus, we need to add a layer to reshape the output before passing to the LSTM layer. The output of the MC-CNN-Encoder is flattened over spatial dimensions into a feature vector and sent to the LSTM Decoder. As we go deeper in the network, the width and height dimensions (the temporal and feature dimensions representing multivariate time series) tend to shrink as a result of max pooling operation, while the number of feature maps (spatial dimension) for each Conv2D layer controlled by number of filters increases. This architecture helps to extract complex spatio-temporal dependencies for each data point in the dataset.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 5 Attack Description and Explanation
### Explanation of AMQP Brute Force Attack
#### 5.4.3 Traits and Signature  
Key features, such as tcp.dstport=5672, tcp.len=0, seq.num=509, nxtseq.num=509, window.size=508, and tcp.flag=0x014, are essential for identifying potential security threats, including brute force attacks. These features serve as a foundation for detecting anomalies associated with repeated login attempts. Below is a summary of the traits and their significance:  
* **TCP Destination Port** (tcp.dstport=5672): Critical for AMQP protocol connections.
* **TCP Length** (tcp.len=0): Indicates no data in the packet payload.
* **Sequence and Next Sequence Numbers** (seq.num=509, nxtseq.num=509): Tracks packet order.
* **Window Size** (window.size=508): Reflects the data transmission capacity for the RST/ACK packet.  
Figure 8: Analysis of AMQP brute force attack packets.  
* **TCP Flags** (tcp.flag=0x014): Used to reset connections during repeated failed attempts.  
Consistent values in RST/ACK packets throughout repeated experiments indicate that the targeted machine terminates the connection using identical values. The attacker manipulates these packets to disrupt the system and prolong the attack, aiming to obtain valid login credentials.
# An Asymmetric Bargaining Model for Natural-Gas Distribution
## 3 Pakistan's Natural-Gas Production, Consumption, and Supply-Demand Gap  
According to the BP Statistical Review of World Energy Report published in June 2017, the total natural-gas consumption in the world reached 3.551 Tcm in 2016 with an annual growth of 0.3%. According to a report [4], the share of natural gas in energy resources is going to increase globally. Some of the world's largest gas producers are Iran, the United States, and Russia. Pakistan's gas production in 2016, in comparison with some of the major gas-producing nations, is shown in Table 1. In 2016, Pakistan shared 1.2% of the global gas production. In 2016, 1464 BCF natural gas was produced by Pakistan, which increased to 1658 in 2020. Currently, Pakistan faces a huge shortfall of gas due to non-efficient distribution. The country's economy has also been affected due to this inefficient allocation of gas. Figure 1 shows that gas reserves in Pakistan have been in sharp decline since 2005. It is evident from the figure that gas production and consumption were almost equal in 2015. The consumption and production trends, however, started to separate in 2016. To cope with this supply-demand gap, the government and policymakers must ensure that the gas allocation is done among the provinces of Pakistan in the most efficient way.  
As per the report of the \"Development Plan of Pakistan Oil and Gas Industry\" in 2020, the supply-demand balance for years 2020-2028 [46], are shown in Table 2. It can be seen from the table that domestic natural-gas production in Pakistan continues to decrease whereas the overall gas supply is increasing due to LNG supply, Iran-Pakistan gas pipeline, and The Turkmenistan-Afghanistan-Pakistan-India Pipeline (TAPI), also known as the Trans-Afghanistan Pipeline. TAPI is a natural-gas pipeline developed by the Galkynysh--TAPI Pipeline Company Limited, with the participation of the Asian Development Bank.
# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 2 State of the Art
### Digital Maps  
In contrast to navigation maps, digital maps hold additional contextual information, such as information regarding speed limits, road topology information, traffic signs, traffic congestion, signal phase and more. However, they are a layer built on top of the basic maps and navigation maps and can come as a standalone or as part of navigation. The accompanying number of layers and details of the context depend on the level of automation they support, see Figure 5.  
Figure 4: Typical architecture of the early fusion algorithm.  
Figure 3: Typical architecture of late fusion algorithm.  
Maps that support up to SAE Level 2 of automation normally require road-level accuracy, which means that an accuracy of several meters is sufficient. Such maps used in ADAS include intelligent speed assistance (ISA), adaptive cruise control (ACC), lane keeping assist (LKA) and predictive powertrain control (PPC). Yet, for SAE Level 2+, those maps are not enough, and lane-level accuracy is needed for such advanced systems; therefore, so-called high definition (HD) maps were introduced. These maps offer centimetre precision, so they can be used for localisation tasks. However, due to the lack of vehicles equipped with the ADAS of a higher automation level, it is economically unjustified for map makers requiring commercial solutions, so HD maps currently tend to be restricted to research purposes. Despite that, the map makers such as TomTom, HERE, Bosch, and others in collaboration with car manufacturers, are actively working on scanning environments to collect all information, and it can be presumed that they already have some solutions on the shelf [41, 42]. There are several ways in which a vehicle could be provided with those maps. One way is that they can be stored directly within a vehicle, while the other way is cloud-based solutions. That means the maps are offered through vehicle manufacturers' cloud or are directly streamed from map providers. The benefit of streaming maps is the real-time service and up-to-date information. Those maps would be updated via crowdsourcing, which uses data collected through public and private vehicles and automatically sends them to the cloud, where they are merged with already existing maps [43, 44]. Besides having real-time information about the road and traffic ahead, HD maps offer redundancy for onboard sensors and support localisation, perceptions beyond FOV and more precise path planning. Those are the main fields of their application according to [45], and a summary is provided in Table 4.  
Figure 5: The four-layer local dynamic map model [40].
# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 5 Conclusions  
This paper introduced a dynamic ensemble selection framework for EEG signal classification in distributed data environments. The proposed method uses coalition formation based on conflict metrics to dynamically integrate predictions from locally trained models. Our findings suggest that the framework has potential for effective classification in scenarios where data centralization is infeasible.  
In particular, unified coalitions at the measurement level consistently achieved strong results, especially when paired with Gradient Boosting classifiers. These results underscore the promise of context-aware ensemble strategies in handling data dispersion and heterogeneity--common challenges in clinical and telemedicine applications. The findings emphasize the following:  
* Coalition-based ensemble models outperform traditional voting approaches.
* Measurement-level prediction aggregation preserves more discriminative information than abstract label-level fusion.
* Boosting methods (Gradient Boosting, AdaBoost) are suitable for decentralized EEG classification tasks.  
While the results are encouraging, we acknowledge that further validation on larger and more diverse datasets is necessary to fully establish the generalizability of the approach. Future work will focus on expanding the framework to more complex clinical scenarios and integrating it with real-time EEG acquisition systems. Future work should also investigate the robustness of the proposed framework in the presence of noise, missing data, and high variability across different patients. Transfer learning or domain adaptation techniques could be incorporated to improve generalization in dispersed system. Overall, this study contributes to ongoing efforts in developing adaptive, privacy-aware EEG analysis methods, with potential applications in both telemedicine and decentralized healthcare infrastructures.  
**Author Contributions:** Conceptualization, M.P.-K. and J.S.; methodology, M.P.-K. and J.S.; software, J.S.; validation, M.P.-K. and J.S.; formal analysis, M.P.-K. and J.S.; investigation, M.P.-K. and J.S.; writing--original draft preparation, M.P.-K.; writing--review and editing, M.P.-K. and J.S.; visualization, M.P.-K. and J.S.; supervision, M.P.-K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in the paper are freely available [23].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Novel Metasurface Lens Design for Synthesizing Plane Waves in Millimeter-Wave Bands
## 5 Conclusions  
This paper describes the design, simulation, and measurement of a novel metasurface lens for millimeter-wave RF tests. The metasurface lens, fed by a planar 2 \(\times\) 2 feeding-antenna array, was designed for synthesizing plane waves in a near field at millimeter-wave frequency bands. The designed lens antenna was verified in terms of amplitude and phase-field distribution at a reduced distance of 1.2 m through using a simulated FDTD method. Compared with the single feeding-antenna array, the phase-shift capability of the lens significantly reduces the distance needed to synthesize the plane wave. Moreover, the QZ is broadened and the amplitude and phase uniformity are greatly optimized. The designed metasurface lens is manufactured by employing a chemical-etching facility based in LDI technology, and periodic copper Jerusalem-cross patterns are etched onto the PCB substrate. The asymmetry of the lens makes it easy and convenient to fabricate. The above designed metasurface lens was quantified and verified through measurements in  
Figure 10: Measured amplitude and phase distribution of the feeding-antenna array with the metasurface lens in the QZ: (**a**) f = 24.25 GHz, (**b**) f = 25 GHz, (**c**) f = 26 GHz, and (**d**) f = 27.5 GHz.  
Figure 9: Photograph of the proposed metasurface lens fed by planar 2 \(\times\) 2 patch-antenna array.  
an anechoic chamber. A very satisfactory agreement was obtained between experimental and simulated results carried out with a planar scanner. In addition, with the metasurface lens, cost and weight are significantly reduced compared with the CATR. Consequently, the proposed metasurface lens can be a practical alternative for millimeter user-equipment RF and RRM tests in anechoic chambers, which require single and multiple angles of arrival.  
Conceptualization, Y.Z. and Z.W.; methodology, J.Z.; investigation, Z.W. and C.P.; validation, L.J. and X.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, J.Z.; visualization, Y.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program (no. 2020 YFB1807401).  
The authors declare no conflict of interest.
## 4 Objective Function Under Different Transmission Modes  
The transmission mode is one of the primary factors influencing outage capacity and interference. The objective function is designed to represent the network capacity during connection. During transmission, signals attenuate from one terminal to another, limiting direct communication between terminals to nearby ones. If terminals are distant from each other, intermediate terminals are needed to relay information until the message reaches the destination terminal. This paper considers two transmission modes: direct transmission and two-hop transmission.