esa-sceva/satcom-chunk-collection huggingface dataset:
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 5 Setting up the PrECast Infrastructure
### The PrECast Algorithm
#### 5.1.5 Robustness of the PrECast Protocol  
In this subsection, we analyse the robustness of the PrECast protocol. As we mentioned before, a switch that does not implement the PrECast protocol can still coexist with the PrECast infrastructure. However, the broadcast-based IPv4 attacks still exist and are confined to hosts that are connected to the unsupported switches. The PrECast switch-port to which a non-supportive switch is connected will record the <MAC, IP> mapping of all the hosts connected with the non-supportive switch in its MCAM table.  
We now discuss the case when a PrECast supportive switch, say \(S_{1}\), goes down. Assume that the switch \(S_{1}\) is connected with port \(P\) of a switch \(S_{2}\). Whenever, \(S_{1}\) is down, \(S_{2}\) will not detect any Layer-1 activity on port \(P\). If there are no activities for the time set out by the administrator, \(S_{2}\) will initiate the forced-time-out procedure set in Section 5.1.4. \(S_{2}\) will multicast an unsolicited ARP-reply for all the hosts connected with switch \(S_{1}\). All active switches that receive this unsolicited ARP-reply will remove the host entries from their MCAM table. A similar procedure is followed if a host connected to a switch port is down.  
If the binding between a host and the switch is broken and still there is a Layer-1 activity between the switch and the host, a forced-time-out procedure will not be invoked; rather, the respective MCAM entry will be marked as dormant. Whenever the broken link is restored, the MCAM entry will be changed from dormant to active. Thus, this process will not incur an extra overhead.
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
###### Abstract  
As one of the key techniques used in the perception layer of the Industrial Internet of Things (IIoT), radio frequency identification (RFID) has been widely applied for object tracing, smart warehouse management, product line monitoring, etc. In most applications, conveyor belts are prevalently implemented to accelerate the sorting efficiency for goods management. However, in such a system, tags quickly go through the reader's reading range resulting in constant changing of the tag set and limited participating time of moving tags. As a result, it poses more challenges to the tag identification problem in mobile systems than in traditional static applications. In this work, a novel admission-control-based dynamic query tree (ACDQT) protocol is proposed for fast-moving tag identification. In ACDQT, two main strategies are developed, i.e., multi-round admission control (MRAC) and dynamic query tree recognition (DQTR). In MRAC, the reading process of multiple rounds is analyzed, and the number of admitted tags in each round is optimized. Thus, the tag lost ratio is guaranteed, and the identification process can be effectively accelerated. In DQTR, colliding tags are grouped into multiple subsets with the help of consecutive colliding bits in tag responses. By constructing a dynamic query tree, the number of collision slots is greatly reduced, and the identification efficiency in a single round is improved significantly. With MRAC and DQTR, ACDQT can support higher tag flow rate in mobile systems than existing works. Both theoretical analyses and simulation results are presented to demonstrate the effectiveness of ACDQT.  
IIoT; Industry 4.0; mobile RFID system; conveyor belt; tag identification; anti-collision
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 6 Open Issues and Future Research Directions
### Ethical and Privacy Concerns  
A critical issue surrounding deepfake detection models is the ethical and privacy concerns they raise, particularly when analysing large volumes of video data [44]. These systems often require access to personal or sensitive content, raising the risk of privacy violations, especially if the data are collected or processed without consent [7]. Additionally, there is the potential for these detection models to be misused in non-consensual settings, such as mass surveillance or monitoring, further complicating their ethical deployment. The balance between effective deepfake detection and the protection of individual privacy is a challenging ethical dilemma that needs careful consideration [89]. Future research should focus on developing privacy-preserving video deepfake detection techniques that can analyse video content while adhering to privacy laws and regulations. This could involve techniques like federated learning, where models are trained on decentralised data without the need for raw data collection, or differential privacy, which ensures that individual data cannot be easily extracted from the model's output.
# Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
###### Abstract  
To achieve persistent systems in the future, autonomous underwater vehicles (AUVs) will need to autonomously dock onto a charging station. Here, reinforcement learning strategies were applied for the first time to control the docking of an AUV onto a fixed platform in a simulation environment. Two reinforcement learning schemes were investigated: one with continuous state and action spaces, deep deterministic policy gradient (DDPG), and one with continuous state but discrete action spaces, deep Q network (DQN). For DQN, the discrete actions were selected as step changes in the control input signals. The performance of the reinforcement learning strategies was compared with classical and optimal control techniques. The control actions selected by DDPG suffer from chattering effects due to a hyperbolic tangent layer in the actor. Conversely, DQN presents the best compromise between short docking time and low control effort, whilst meeting the docking requirements. Whereas the reinforcement learning algorithms present a very high computational cost at training time, they are five orders of magnitude faster than optimal control at deployment time, thus enabling an on-line implementation. Therefore, reinforcement learning achieves a performance similar to optimal control at a much lower computational cost at deployment, whilst also presenting a more general framework.  
autonomous underwater vehicle; reinforcement learning; optimal control +
Footnote †: journal: Applied  
_Article_
# Spectrum Allocation Using Integer Linear Programming and Kerr Optical Frequency Combs
###### Abstract  
The rapid increase in Internet usage has led to a growing demand for bandwidth. Optical microring resonators (MRRs) are emerging as a promising solution to meet this need. MRRs generate optical frequency combs (OFCs) that provide multiple wavelengths with high phase coherence, enabling communication via wavelength division multiplexing (WDM). Spectrum allocation methods, such as the Routing, Modulation Level, and Spectrum Assignment (RMLSA) approach, play a crucial role in executing this strategy efficiently. While current algorithms have improved allocation efficiency, further development is necessary to optimize network performance. This paper presents an integer linear programming (ILP)-based method for network resource allocation, aiming to maximize the number request and the bandwidth assigned to each. The proposed approach offers a flexible cost function that prioritizes system constraints such as transmission distance and bandwidth requirements, resulting in significant improvements to the bandwidth blocking rate (BBR). By integrating multilevel modulation and using wavelengths generated by MRRs, this method efficiently handles up to 1075 requests, achieving a BBR of zero. This dynamic and adaptable allocation strategy ensures optimal resource utilization, enhancing overall network performance.  
WDM-PON; RMLSA; optical microresonators; bandwidth blocking ratio; optimization Article
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 3 Time Series Forecasting Experiments  
### Data Description  
#### 3.1.1 General Overview  
The data comes from the actual operation monitoring records of a single wind turbine in a wind farm from 1 January to 31 December 2018. The data collection interval is 10 min, including the wind direction and wind speed of the location of the unit, theoretical power, and actual output power at the current wind speed. From the monthly wind speed distribution shown in Figure 5, this area has good wind power generation conditions, and the continuously working units can provide sufficient data for our research.
## 3 On-Chip Circuit Implementation
### \(V_{\text{DC}}\) Detector  
The rectifier output voltage V\({}_{\text{DC}}\) is quantized into a digital word for uplink transmission. Quantization noise could be reduced by using more quantization bits, at the expense of a longer uplink data package. We decided to use 3 bits, and V\({}_{\text{DC}}\) is quantized into V\({}_{2.0}\) by a 3-bit flash analog-to-digital converter (ADC) as shown in Figure 5a. As V\({}_{\text{DC}}\) is regulated to around 3.5 V in the power-regulation phase, to make full use of the 3 bits the quantization range is designed to be from 3.2 V to 4.0 V with a resolution of 0.1 V. The quantization code is shown in Figure 5b. The bandgap reference generates the reference voltage V\({}_{\text{ref}}\) of 1.2 V. The flash ADC consists of a resister ladder with 7 outputs V\({}_{\text{01-07}}\) that are compared to V\({}_{\text{ref}}\); and the outputs of the comparators are encoded into the 3-bit word V\({}_{\text{20}}\). The resistor ladder has a total resistance of 1 M\(\Omega\).
## 4 Results and Discussion
### Evaluation of the ANN-Based LP Model
#### 4.3.2 Comparison with Shake Table Test  
For further validation, a time history response comparison was performed between the proposed ANN-based method and a shaking table test of a single-story RC frame [43]. As shown in Figure 14, the frame consists of two identical columns and a rigid link beam. Each column is modeled by one elastic element and two flexural springs at the two ends. Based on their properties, the ANN model was used to predict their strength and deformation capacities, as presented in Table 6. Therefore, the parameters for bilinear model (\(M_{y},K_{e},b\)) and tri-linear model parameters (\(K_{e},M_{y},\beta_{l},\beta_{c},M_{c}/M_{y}\)) of their LP models were calculated, based on which the FE model was developed on the OpenSEES platform.  
During the test, the structure was subjected to a scaled ground motion with PGA = 1.28 g (recorded by TCU076 NS in the 1999 Chi-Chi earthquake), as shown in Figure 15a. After performing the dynamic time history analysis, the drift response of the specimen was obtained and compared with the test result, as is shown in Figure 15b. It is observed that the ANN-based LP models can provide an acceptable estimation of the drift response before the collapse of the frame structure, especially for the tri-linear one. The corresponding maximum error of the drift response before the time of 22.6 s between the ANN-based tri-linear model and the test is very small. However, due to the limitation of the LP model itself, the collapse phenomenon of the specimen could not have been directly represented during the simulation. In spite of that, the proposed ANN-based model-free and data-driven method is still an effective and reliable candidate for the researchers and engineers to determine the key parameters of RC columns during finite element analysis.  
Figure 14: Prototype and model of the shaking table specimen.  
Figure 13: Comparison of the ANN-based LP model and test result [42].  
The developed ANN-based model for the LP parameters of RC columns has been already implemented in an efficient Matlab GUI, anyone has access to it in the supplementary materials.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 2 Preliminaries and State of the Art  
Extensive research has been conducted regarding indoor localization systems, and products are available. This section provides a SOTA overview in which, in addition to the performance of different systems, the energy consumption of a mobile node is also assessed as there is increasing demand for the indoor positioning of energy-neutral devices. The emphasis in this paper is on acoustic and hybrid RF-acoustic systems given their promising capabilities in terms of accuracy and low-power.
## 4 Experimental Results and Security Analysis
### Statistical Random Number Tests  
We tested the randomness properties of the output keystreams by FIPS PUB 140-1 [21] and SP800-22 [22], respectively. In this section, we present the test results of the statistical random number tests. We discard the first 64 outputs of the CML. The reason is to avoid the attack on either the initial values or the system parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Length of the Sequence** & \multirow{2}{*}{**Number of 0**} & \multirow{2}{*}{**Number of 1**} & **Disequilibrium Degree (\%)** \\ \hline \multirow{3}{*}{(e)} & 10,000 & 4847 & 5153 & 3.06 \\  & 50,000 & 24,245 & 25,755 & 3.02 \\  & 100,000 & 48,507 & 51,493 & 2.99 \\ \hline \multirow{3}{*}{(f)} & 10,000 & 4851 & 5149 & 2.98 \\  & 50,000 & 24,263 & 25,737 & 2.95 \\  & 100,000 & 28,559 & 51,441 & 2.88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Proposed Chaotic Stream Cipher** & \(kdr\) \\ \hline The original CML based on logistic map & 16.49\% \\ The original CML based on tent map & 4.27\% \\ The improved CML based on logistic map & 46.97\% \\ The improved CML based on tent map & 46.23\% \\ The compound CML combining both logistic map and tent map in a single stage & 48.68\% \\ The compound CML combining logistic map and tent map in an alternate stage & 48.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The keystream difference rate (\(kdr\)) of the proposed chaotic stream cipher.
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 3 Experiments and Results
### Performance Comparison
#### 3.4.3 Ablation Comparison  
To determine whether the internal graph convolutional network or the external heterogeneous data fusion network played a key role in the heterogeneous graph convolution network in improving the accuracy, we conducted ablation experiments by removing a certain portion of the proposed network. The experimental results are shown in Table 4, where Ours* is the prediction result of the static graph convolution network without the internal graph convolution network and Ours** is the prediction result without the external heterogeneous data fusion network. Since the accuracies of Ours** and Ours* are similar and much lower than for the full structure, this indicates that both structures play an important role in the whole framework, and they are both indispensable in improving the accuracy.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PeMSD4**} & \multicolumn{3}{c}{**PeMSD8**} \\ \cline{2-7}  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline Ours** & 26.221 & 37.542 & 14.468\% & 26.462 & 38.403 & 19.135\% \\ Ours* & 25.437 & 35.416 & 16.105\% & 25.154 & 35.168 & 20.896\% \\ T-GCN & 25.842 & 31.457 & 14.657\% & 24.643 & 30.665 & 15.691\% \\ ASTGCN & 19.531 & 26.924 & 14.522\% & 18.685 & 27.651 & 16.650\% \\
**Ours** & **16.165** & **24.656** & **12.413\%** & **15.486** & **22.513** & **13.641\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison for ablation experiment.  
Figure 10: Prediction results for the non-working day in PeMSD8.  
Figure 9: Prediction results for the working day in PeMSD4.
# Tilt-Induced Error Compensation with Vision-Based Method for Polarization Navigation
[25] developed a novel method for modeling and compensating heading errors in polarized light compasses under attitude variations, using Gated Recurrent Unit (GRU) neural networks. The approach achieves a 64.09% reduction in heading error, contributing to improved navigation accuracy. Nevertheless, the complexity of the algorithm limits its deployment to upper computer platforms. [PERSON] et al. [26] proposed a tilt-compensated polarization orientation scheme for sloped road conditions, which overcomes the field-of-view limitations of imaging polarization compasses through a visual modulation model. This method achieves a remarkable improvement, reducing the root-mean-square error (RMSE) by 82% compared to traditional polarization-based orientation approaches. However, this method still relies on inertial navigation systems (INS) for horizontal attitude angles, consequently inheriting their characteristic cumulative errors. Despite the application of techniques like inertial navigation and neural networks to improve the orientation accuracy under tilted conditions to some extent, these methods are hindered by cumulative errors and significant processing complexity. In this context, the incorporation of visual methods provides a new way for the rectification of polarized light tilt errors. This paper proposes a dark channel defogging algorithm based on Retinex theory to boost the measurement accuracy of horizontal attitude angles, hence improving adaptability in low illumination and hazy conditions. Furthermore, leveraging the distinct color contrast between the sky and ground, we develop a dynamic HSV-based threshold segmentation method for horizon detection to increase extraction accuracy. The approach then employs an improved adaptive bilateral filtering Canny operator in conjunction with a progressive probabilistic Hough transform for precise horizon extraction, subsequently estimating the UAV's horizontal attitude angles from these horizon parameters. Finally, the obtained horizontal attitude angles are utilized to compensate for heading calculation errors in polarized light navigation under tilted conditions. The organizational structure of this paper is as follows: Section 1 presents the background and current research status of visual attitude estimate methods and the polarized light tilt error compensation. Section 2 introduces the fundamental principles and algorithmic steps of the proposed horizon detection method. Section 3 elaborates on the theoretical framework for polarized light heading compensation. Section 4 provides a comprehensive description of experiments conducted on UAV platforms to validate the effectiveness of the proposed approach. Finally, Section 5 concludes the paper with key findings and implications.
## 4 Proposed Scheme
### System Setup Phase  
In the system setup phase, the TA executes the following processes.  
* TA uses a randomly chosen value \(s\in Z_{q}^{*}\) as its secret key and then calculates its relevant public key \(P_{pub}=sP\);
* TA selects two large prime values \(q\) and \(p\), where \(q\leq[p/4]\) and \(p>q\), \(p\) is utilized for identifying a group of multiplicative \(Z_{q}^{*}\), and \(q\) is utilized for selecting the domain key;
* TA utilizes the randomly selected value \(sk_{i}\) from the group of multiplicative \(Z_{q}^{*}\) for 'n' number of vehicles, which is given to the users during the offline registration time;
* TA computes \(x_{i}=\frac{\tau_{i}}{sk_{i}}\), where \(\zeta_{g}\)=\(\prod_{i=1}^{n}(sk_{i})\) and \(i=1,2,3,\ldots,n\);
* TA computes \(y_{i}\) such that \(x_{i}\times y_{i}\equiv 1\) mod \(sk_{i}\);
* TA multiplies all users \(x_{i}\) and \(y_{i}\) numbers, saves them in the variables \(vbs_{i}=x_{i}\times y_{i}\), and computes the number \(\mu=\sum_{n}^{i}(vbs_{i})\);
* TA utilizes three selected secure one-way hash functions \(H_{i}:\{0,1\}^{*}\to Z_{q}^{*}\) (\(i=1,2,3\));
## 4 Experimental Results and Discussion
### First-Order Deposition Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**\(R^{2}\)**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAPE**} \\ \cline{2-9}  & **Height** & **Width** & **Height** & **Width** & **Height** & **Width** & **Height** & **Width** \\ \hline
1 & 0.0361 & 0.0337 & 0.9847 & 0.9894 & 0.0282 & 0.0082 & 2.8151\% & 0.8235\% \\
2 & 0.0580 & 0.2053 & 0.9888 & 0.9242 & 0.0291 & 0.0301 & 2.9098\% & 3.0145\% \\
3 & 0.0392 & 0.0154 & 0.9818 & 0.9652 & 0.0345 & 0.0037 & 3.4547\% & 0.3704\% \\
4 & 0.0388 & 0.0094 & 0.9717 & 0.9300 & 0.0408 & 0.0030 & 4.0777\% & 0.2951\% \\
5 & 0.0176 & 0.0258 & 0.9954 & 0.9947 & 0.0232 & 0.0047 & 2.3192\% & 0.4720\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model performance for different samples. The results from Table 2 shows that the best test trial reported is sample 5, as it shows good values of prediction accuracy measurements both for bead width and height. This good result was obtained due to the smooth thickness transition across layers and the good distribution of the layer thicknesses by using a high wire-feed rate with low power and travel speed compared with the other measured samples. On the other hand, the worst performance reported is Sample 2, which shows the worst values of prediction accuracy measurements. It is noted that Sample 2 has the highest RMSE, MAE and MAPE; however, \(R^{2}\) was good. This is due to the fact that \(R^{2}\) gives an estimate of the strength of the relationship between \(y^{\prime}\) and the prediction curve \(F(x)\), the value reported for this test does not provide a formal explanation of the fitting. Another explanation of the value obtained obtains from the distribution of the \(y^{\prime}\) in sample 2 that shows a similar trend with the prediction curve, which does not fit the \(y^{\prime}\) values. These prediction results indicated that having a high laser power and travel speed with a low wire-feed rate will lead to bad results on the final bead geometry. Finally, these results are summarized in Table 3, which illustrates the influence of deposition parameters on both the prediction model and the bead geometry's accuracy. It is worth mentioning that the designation \"Acceptable bead prediction\" in Table 3 takes the trend of the set of parameters with the best correlation coefficients (RMSE, \(R^{2}\), MAE, MAPE). While the designation \"Good layer deposition\" refers to those experimental tests that showed process stability (no stuck wire, stable metal vapour plume, etc.) for a low laser power/travel speed coupling and a high wire-feed speed. Figure 13: _Cont._