esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Classification of MAC Protocols Based on Channel
### Cooperative Single-Channel MAC Protocols
#### 4.6.3 UAV Relay  
The unmanned aerial vehicle-based MAC protocol was proposed for performance enhancement using a relay strategy. It improves the efficiency of communication hampered by interference or jammers at RSUs. UAVs use specific relay strategies to send messages from OBUs to RSUs. The Nash equilibria strategy was implemented, and UAV relay was optimized based on transmission cost and a UAV channel model. A hotbooting policy hill climbing (PHC) mechanism (a reinforcement learning technique) was used in the UAV relay strategy to resist jamming signals. The implementation of the network model was based on the calculation of bit error rate (BER) and SINR. Anti-jamming transmission stochastic game strategy (an extended work of [91]) was used depending on the quality of the channels and BER; this strategy minimizes the power consumption by making decision to choose the relay and whether to transmit. Dynamic anti-jamming game with  
Figure 13: Mechanism of packet exchange: (**a**) DT mode, (**b**) CR mode, and (**c**) MHR mode [90].  
the hotbooting PHC-based relay strategy was used to formulate the interaction between UAVs and jammers [92], determined the jamming power, and selected the relay based on the state of the system.  
The drawback of this protocol is communication and computation overhead. Highly mobile nodes and dense scenarios are not analyzed for this mechanism.
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
## 2 Problem of Interest  
Various studies have been published on cyberattacks on the ADS-B system commonly used in commercial aviation [5]. These studies reviewed and raised a variety of possible cyberattacks on the ADS-B system and highlighted the problems of this system's ability to defend against those attacks.  
Flight safety vulnerability is affected not only by cyberattacks, but also by environmental conditions, malicious attacks, or even military experiments. In 2017, the FAA commissioned a board to look into the effects of intentional GPS interference on civilianaircraft [9]. Its report found that the number of military GPS tests had almost tripled from 2012 to 2017, while, unsurprisingly, GPS jamming was also on the rise.  
Another flight safety incident report described how a passenger aircraft flew off course as a result of a GPS jamming attack and almost crashed into a mountain [10]. According to the report, two previous flights had advised that their GPS signals were interrupted, but then returned to working properly. The passenger aircraft reported a GPS problem as well and, shortly after, reported back that the problem was resolved. The aircraft began to descend before landing and was, therefore, transferred to a local airport control tower. Shortly thereafter, the ATC noticed that the plane had deviated from its path and approached a high-altitude mountain. Fortunately, the ATC contacted the local airport control tower and diverted the aircraft back to a safe trajectory.  
Moreover, the FAA has begun outlining a rule that requires every Unmanned Aircraft (UA) operator to own a digital license called a Remote ID [14]. In its most basic form, remote identification is a kind of \"digital license plate\" for UAs. Although this rule is for UA operations, it highlights the need to address aviation safety and security issues in the airspace system.
## 2 Materials and Methods  
### Literature Search Strategy and Eligibility Criteria  
This review adopted a comprehensive hybrid approach. While it employed systematic search strategies and structured inclusion criteria, it also integrated conceptual frameworks, narrative insights, and interdisciplinary perspectives, thus adopting a comprehensive review methodology. The aim was to investigate the relationship between soundscapes, user experience, and sustainable acoustic strategies in dental environments, taking into account literature found in healthcare settings. The review included multiple phases of data collection and analysis to ensure thematic relevance, methodological quality, and interdisciplinary depth [22]. The search was conducted across four major academic databases: PubMed, Scopus, Web of Science, and Google Scholar. Keywords used in various Boolean combinations included the following: \"dental acoustics,\" \"'dental clinic noise,\" \"'sound environment in healthcare,\" \"'soundscape in dentistry,\" \"'patient noise perception,\" \"sustainable architecture dental,\" and \"biophilic acoustic design.\" Additionally, a curated internal dataset in CSV format containing indexed noise-related research was analyzed. To enhance coverage, backward and forward citation tracking was performed, and additional peer-reviewed open-access articles were gathered via Google Scholar. Manual screening ensured that only articles aligned with the acoustic context of dental or healthcare environments were included.  
The inclusion criteria were peer-reviewed publications between 2014 and 2025, full-text availability (open access or PMCID), with a focus on acoustic environments, soundscapes, or noise in dental/healthcare settings, and studies assessing psychological, physiological, architectural, or experiential outcomes related to sound. The exclusion criteria were as follows: non-healthcare-related acoustic studies, conference abstracts or non-peer-reviewed documents, articles not in English or not available in full text, studies without methodological transparency or sound relevance, and those with inadequate methodological detail or duplicated content.
###### Abstract
As it will be described in this paper, there are several elements that must be secured in CPS deployed in open environments, such as radio communications, electronical components and any storage means used to store information, especially when data are saved outside the UAV. Another reason to include security in this kind of developments is providing support material for quality certifications. Being able to certify that the agricultural products come from livestock that was well taken care of can be an asset that will provide a competitive edge to the producer over other competitors that do not have this kind of certification. One example is the Animal Welfare certification provided by institutions belonging to the Welfare Quality network [8]. This certificate is obtained by means of _\"audits that are based on the direct observation of the animal itself, by assessing 4 principles: good feeding, good housing, good health and appropriate behaviour. Within these four principles, 12 measurable animal welfare criteria have been identified that complement each other.\"_[9]. Having a tool that will provide information about the welfare of the animals in the shape of pictures, video recordings or timestamped content with Distributed Ledger Technology (DLT) or distributed data storage like Interplanetary File System (IPFS) can be very useful, as it will aid in the assessment of how cattle is being treated. This manuscript provides a framework on the requirements to be considered when creating a system that makes use of UAVs with the purpose of monitoring livestock able to move throughout open spaces. This is the way cattle are held in a great number of larger exploitations all around the world, as it is usually more consistent with the idea of performing traditional cattle raising activities in a sustainable manner. In addition to that, it offers information about how to do it under major security parameters that must be taken into account for the system to be viable in a real environment, so that neither the UAV will malfunction as a result of a cyberattack, nor the data collected can be altered once it has been stored.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 4 Proposed Method
### Stacked-LSTM
#### 4.2.1 Linear Coding of Mixed Signals  
1-D convolution is used to extract linear coding feature representation from one-dimensional aliased communication source signals. Literature [12] proves that the effect of linear coding is better than non-linear coding, so we use this coding method. The purpose of linear encoding is to encode mixed signals for subsequent processing. A total of 512 convolution kernels is used to generate the multi-dimensional coding feature of the mixed signal, and the result is used as the input of the separation network:  
\[\mathbf{x}_{\text{encoder}}\ =\mathbf{h}_{\text{encoder}}\left(\mathbf{x}\right)=\mathbf{w}_{1} \ast\mathbf{x}+\mathbf{b}_{1} \tag{11}\]  
In the formula, \(h_{\text{encoder}}\left(\cdot\right)\) is the convolution operation. \(\mathbf{w}_{1}\) and \(\mathbf{b}_{1}\) are the weight and bias of the convolution kernel.  
Figure 4: Stacked-LSTM separation network structure.
## 4 Conclusions  
Direct-heated thermal desorption technology using fossil fuel has been considered as one of the typical thermal treatment methods for oil-contaminated soil. However, it has been limited in its applicability due to the inefficiency and high energy cost for the heat generation process necessary for the desorption of oil and organic matter. In this study, an indirect-heated microwave thermal desorption treatment technology has been developed in order to overcome disadvantage of conventional technology.  
In this study, thermal treatment was performed by heating the lubricant-contaminated soil up to 400-600 \({}^{\circ}\)C using an indirect-heated microwave thermal desorption technology to volatilize and desorb oil and organic pollutants from the soil. The following conclusions were made based on a review and analysis of the environmental and geotechnical properties of the soil before and after treatments.  
* The indirect-heated microwave thermal desorption equipment was preheated for 3 h with the electric power of 32, 24 and 16 kW to reach the target temperatures of 600, 500 and 400 \({}^{\circ}\)C, and the contaminated soil was put into the soil container for the desorption of the oil and organic pollutants from the soil. With treatment times of 5, 10 and 15 min as the main processes, environmental TPH concentrations were reduced to 2512, 652 and 38.2 mg/kg, respectively, indicating removal efficiencies of 90.56, 97.55 and 99.86% accordingly. Based on these environmental test results, it was found that the developed indirect-heated microwave thermal desorption method in this study has an excellent effect on removing semi-volatile organic pollutants.
* The analyses of the geotechnical properties are as follows: (1) The proportion of coarse-grained soils retained in sieve No. 200 (0.075 mm) was increased compared to the initial as well as contaminated soils due to the desorption of the oil and organic matters absorbed in the soil particles and even fine-grained soil particles in the form of dust and mist. It was found that the liquid limit and permeability coefficient were almost recovered to levels of the initial state due to the desorption of the oil occupying the pores between soil particles. (2) The internal friction angle, maximum dry density and permeability coefficient of the soil were reduced by oil contamination and were finally restored to the almost initial level of soil after treatment. (3) The cohesion of the soil was increased by oil contamination due to the lubrication effect of viscous oil and decreased after treatment.
* Due to the fossil-fuel-free heating mechanism designed for the remediation of lubricant-oil-contaminated soil at 600 \({}^{\circ}\)C in this study, newly developed indirect-heated microwave thermal desorption technology reduces energy costs and CO\({}_{2}\) emissions by 75% and 25%, respectively, compared with conventional thermal desorption methods using fossil fuels.
* It is finally found that soil treated by an indirect-heated microwave thermal desorption technology is expected to be reusable for geotechnical construction purposes such as road and railway fill materials.
## 4 Case Study
### Sumo Simulation  
The simulation scenario is constructed based on the actual intersection, and the lane layout is shown in the intersection channelization diagram in Figure 3a, adopting a single ring intersection network topology structure, including two adjacent intersections. The setting of the basic signal phase is shown in Figure 3b. The implementation of the signal control program is achieved by predefining all possible signal combinations (such as green light stage, yellow light stage, etc.) in the road network file, setting the current stage code through the TraCI interface, and switching the signal light control to green light according to the relevant road-environment status information in Table 4 to meet the requirements of autonomous bus passing.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Related Indicators** & **Code** \\ \hline Vehicle speed & traci.vehicle.getSpeed(vehicle\_id) \\ Vehicle location & traci.vehicle.getPosition(vehicle\_id) \\ Vehicle arrival time & traci.vehicle.getNextTLS(vehicle\_id) \\ Number of vehicles in the lane & traci.lane.getLastStepVehicleNumber(lane\_id) \\ Traffic light status & traci.trafficlight.getRedYellowGreenState(traffic\_light\_id) \\ Traffic light duration & traci.trafficlight.getNextSwitch(traffic\_light\_id) \(-\) \\  & traci.simulation.getCurrentTime() \\ \hline \hline \end{tabular}
\end{table}
Table 4: Road environmental status information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Value** \\ \hline batch\_size & Number of samples per training batch & 100 \\ gamma & Discount factor, controlling the importance of future rewards & 0.75 \\ learning\_rate & Learning rate, controlling the speed of weight updates & 0.001 \\ target\_network\_update\_frequency & Target network update frequency, controlling how to update & 10,000 \\  & the target network & 1.0 \\ epsilon\_start & The probability of initial random actions in greedy strategies & 1.0 \\ epsilon\_end & The probability of final random actions in greedy strategies & 0.1 \\ epsilon\_decay & The decay rate of random action probability in a greedy strategy & 100,000 \\ memory\_capacity & Maximum capacity of experience-playback memory & 50,000 \\ num\_hidden\_layers & Number of hidden layers & 4 \\ num\_neurons\_per\_hidden\_layer & Number of neurons per hidden layer & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings for DQN agent.
# Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation
## 3 Results
### Parameter Design
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Run** & \(\mathbf{l_{1}}\) **[mm]** & \(\mathbf{l_{2}}\) **[W]** & \(\mathbf{l_{3}}\) **[mm]** & \(\mathbf{l_{4}}\) **[mm]** & \(\mathbf{\sigma_{f}^{2}}\)**[\({}^{\circ}\)C]** \\ \hline
1 & 142.81 & 246.17 & 2.28 & 2.35 & 11.97 \\
2 & 150.00 & 255.99 & 2.30 & 2.41 & 11.75 \\
3 & 182.72 & 216.52 & 2.23 & 1.98 & 10.87 \\
4 & 177.48 & 223.02 & 2.26 & 1.96 & 10.97 \\
5 & 153.98 & 238.46 & 2.60 & 2.37 & 12.84 \\ \hline \hline \end{tabular} The values for the hyperparameters of the covariance function of the trained Gaussian processes for each of the five runs.  
\end{table} 
Table 1: Hyperparameters of the trained Gaussian processes. from our dataset. When the heat load is below 500 W, we can no longer detect defects that are lower than 9.8 mm below the surface._  
**Example 3**. : _The model can also be used to visualize regions in the input space that result in undesirable temperatures for the sample. For instance, we can highlight regions where the temperature of the sample would become too high. This serves as a warning, as temperatures that are too high might cause damage to the sample under inspection. In Figure 4, we provide an overview of the predicted temperature differences per defect diameter. Regions that are colored are to be avoided when designing the dynamic line scan thermography setup. These plots also reveal that some regions of the input space are workable for some defects, but not for others. The full benefit of these plots comes into its own when using software that allows the end user to rotate the generated cubes, which is trivial to set up in Matlab or any Python environment equipped with a graphing library, such as Matplotlib._  
**Example 4**. : _In this last example, we visualize the plots from Example 3 in a different way to highlight regions of the input space that correspond to appropriate temperature differences. In Figure 5, we color regions that result in hard-to-detect (or even undetectable) temperature differences red. For this example, we set the threshold to an arbitrary value of 5 \({}^{\circ}\)C. Regions that result in temperature differences above 25 \({}^{\circ}\)C are colored yellow. Ideal regions lie in between those values and are given the color green (see  
Figure 4: Visualization of the temperature difference for six different defect diameters. (**a**) 12 mm, (**b**) 14 mm, (**c**) 16 mm, (**d**) 18 mm, (**e**) 20 mm and (**f**) 22 mm. Red indicates temperature differences that might result in damaging the sample under inspection. These plots serve as a warning when designing a setup. Figure 5: Visualization of the temperature difference for six different defect diameters. (**a**) 12 mm, (**b**) 14 mm, (**c**) 16 mm, (**d**) 18 mm, (**e**) 20 mm and (**f**) 22 mm. Red are temperature differences below 5 °C, yellow above 25 °C and green in between. Only the green regions are of practical value in real-world applications.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
###### Abstract  
Signal detection in massive MIMO systems faces many challenges. The minimum mean square error (MMSE) approach for massive multiple-input multiple-output (MIMO) communications offer near to optimal recognition but require inverting the high-dimensional matrix. To tackle this issue, a Gauss-Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing (CJGS) is presented. In order to accelerate algorithm convergence, the signal is first initialized using the optimal initialization regime among the three options. Second, the signal is processed via the CJ Joint Processor. The pre-processed result is then sent to the GS detector. According to simulation results, in channels with varying correlation values, the suggested iterative scheme's BER is less than that of the GS and the improved iterative scheme based on GS. Furthermore, it can approach the BER performance of the MMSE detection algorithm with fewer iterations. The suggested technique has a computational complexity of O(U\({}^{2}\)), whereas the MMSE detection algorithm has a computational complexity of O(U\({}^{3}\)), where U is the number of users. For the same detection performance, the computational complexity of the proposed algorithm is an order of magnitude lower than that of MMSE. With fewer iterations, the proposed algorithm achieves a better balance between detection performance and computational complexity.  
massive MIMO; conjugate gradient; Jacobi; Gauss-Seidel; Kronecker channel +
Footnote †: journal: Applied  
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Footnote †: journal: Applied  
1
Footnote 1: email: _Applied_
# On the Performance of Interleavers for Quantum Turbo Codes
## 2 Classical Interleavers for Quantum Turbo Codes
### Classical Interleavers for QTCs  
The concatenation between the outer and inner QCCs is done by the quantum interleaver \(\Pi\) of size \(N\), which is an \(N\) to \(N\)-qubit symplectic transformation (The notation we utilize to describe the operation of a quantum interleaver is the so-called symplectic notation as used in Reference [3].) composed by a permutation \(\pi\) of the \(N\) qubit registers and a tensor product of single-qubit symplectic transformations. It acts by multiplication on the right on \(\mathrm{G}_{N}\) as  
\[(P_{1},\cdots,P_{N})\rightarrow(P_{\pi(1)}K_{1},\cdots,P_{\pi(N)}K_{N}), \tag{2}\]  
where \(K_{1},\cdots,K_{N}\) are some fixed symplectic matrices acting on \(\mathrm{G}_{1}\), where \(\mathrm{G}_{N}\) refers to the symplectic representation of the \(N\)-fold Pauli group [3].  
In order to use classical interleavers in QTCs, they must properly match the definition of quantum interleavers in (2). Classical interleavers consist only of permutations of the information arrays with no such thing as individual symplectic transformations. Consequently, those operations will be taken as identity operators acting on the registers \(K_{i}=I,\forall i\); and so the only non-trivial operation of such quantum interleavers will be the scrambling of the qubits in the quantum information stream (The random interleavers used in References [3; 4; 5; 6; 7; 8; 9] also follow this approach.). Following this approach, the quantum interleavers presented in this paper will be \(N\) to \(N\) qubit symplectic transformations acting as  
\[(P_{1},\cdots,P_{N})\rightarrow(P_{\pi(1)},\cdots,P_{\pi(N)}) \tag{3}\]  
to the qubit data stream, and where the permutation pattern \(\pi\) corresponds to the classical interleaver under consideration.
## 5 Evaluation: Influence of the Main RPL Parameters on Network Convergence Performance
### Influence of the Minimum Interval on the Network Convergence Process
On the contrary, for low \(I_{min}\) values (e.g., \(I_{min}\) = 4 ms), the number of collisions is already high for low redundancy constant values, since such \(I_{min}\) values approach the DIO transmission time in the 2.4 GHz IEEE 802.15.4 physical layer (_i.e._, 2.82 ms), and thus the number of collisions increase with the redundancy constant is smaller than that observed for greater \(I_{min}\) values. From the study carried out in this subsection, it can be derived that there exists a tradeoff between network convergence time and DIO message overhead (and collisions) that depends on \(I_{min}\). Varying \(I_{min}\) in the range of values considered in this study has a greater quantitative effect on the number of DIO messages sent than on the network convergence time. Fine-tuning \(I_{min}\) has a greater impact on the number of DIO messages transmitted during network convergence as both the redundancy constant and network density grow, whereas the relative impact of \(I_{min}\) on network convergence time does not vary significantly with the redundancy constant, network size or network density.