esa-sceva/satcom-chunk-collection huggingface dataset:
Due to the clock synchronization and the clock drifts, the necessity of correction could additionally increase the power consumption. Pseudo-asynchronous communication exchanges data without the need for clock synchronization. The node is switching between the active and sleeping mode. The transmitter uses a preamble with an integrated address to communicate with the receiver according to an asynchronous schedule, where the reception readiness is confirmed with an acknowledgment. Usually, this leads to long latency. In order to optimize this approach, a wake-up receiver could be added. The wake-up receiver is always able to receive signals, so an on-demand communication could be realized, which reduces the power consumption of the WSN devices [18]. The so-called WuRx is connected to the sensor node, and the main microcontroller unit (MCU) (Figure 1) remains in the power-saving sleep mode. The WuRx listens for special telegrams called WuPts. If such a packet is received, an interrupt is generated and the MCU changes from the sleep mode to the active mode. The typical structure of a sensor node consists of an MCU, a radio transceiver, one or more sensor elements, and a power source [19]. Sensor elements acquire data on the environmental conditions, including the processing unit, to handle and forward information wirelessly to other sensor nodes or a base station via the radio transceiver. For WuRx reception, a second antenna is used. The WuRx is connected to the MCU for configuration and wake-up signaling. In this paper, the performance and reliability of sensor nodes with integrated wake-up receivers based on commercial of the shelf (COTS) components are investigated in an indoor environment. The goal is to achieve a very low PER up to the sensitivity limit in the sub-10 uW range. Therefore, two different LF amplifier are introduced to increase the WuRx sensitivity. The focus is on the reliable reception of the WuPts in view of obstacles in the interior, such as masonry and furniture. Sensitivity, range, data rate, and power requirements are taken into account. The structure of this paper is as follows: In Section 2, the approach is compared and discussed with techniques from State of the Art (SoA). Based on passive and low-power components, a WuRx architecture is built. Section 3 describes the theoretical investigation and simulation of the individual components of the structure with respect to the application of a wireless sensor node in the 868 MHz band. Subsequently, the results are verified with experiments in the laboratory and under real test conditions. The experimental setup and measurements are presented in Section 4. Finally, all results are discussed in Section 5. Figure 1: Block diagram of sensor node’s functional units.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Distributed Learning over the Joint Terrestrial and Non-Terrestrial Network  
The non-terrestrial networks have gained lots of attention in both 5G and 6G technologies in recent times [72; 73; 74; 75]. Various joint terrestrial and non-terrestrial networking solutions are considered for solving challenging problems in different wireless scenarios [76; 77]. Given that the ground-based fixed networking infrastructure is saturating in terms of resources, it is expected that the non-terrestrial networks, being easy to deploy, flexible, and highly secure entities, will be used more and more in the next generation of wireless technologies. Various air and space networking platforms, such as unmanned aerial vehicles (UAVs); high altitude platforms such as balloons; and very low earth orbit-based sidelights (VLEO), have already shown great promise and are expected to play important roles. With this in mind, the authors of [78] proposed the novel air-ground distributed-computing-enabled intelligent platform for the vehicular scenario. This work also highlighted the important problem to be solved for the traditional centralized FL process and proposed different FL-based distributed networking solutions for solving vehicular problems.  
A hierarchical FL process for the joint air-ground network with multiple edge computing layers was proposed for solving the FL communication problem (see Figure 7). By allowing the FL devices, i.e., vehicles, to transmit their local model updates towards the intermediate edge computing layers, the FL communication latency can be reduced. Additionally, link failures and resource requirements can be limited, which can improve the FL model's training performance compared with the traditional centralized FL training process. Another solution includes the computation offloading enabled by the FL process, where FL devices, i.e., vehicles, can employ the nearby edge servers for improving the performance of the FL process (see Figure 8). With vehicular mobility and resource limitations, vehicles alone can perform the local training operations with limited success only. By allowing vehicular nodes to use the nearby reliable edge servers and their computing capabilities through the split learning-based approaches, FL process performance can be successfully improved. Such solutions will be important to achieve the main goals of 6G technology in upcoming decade.  
Figure 6: Proposed scheme for the joint FL and task-offloading processes optimization. Reprinted/adapted with permission from Ref. [71]. ©2022, IEEE.
## 3 Experiments
### GNSS RO Dataset  
Scintillation in the polar and equator regions strongly affects the phase and amplitude of GNSS carrier signals [31]. In this study, some measurements recorded using the MetOp-A  
Figure 3: Structure of the SSA-XGBoost model.  
occultation [32] between 2015 and 2018 are used. The navigation receiver on MetOp-A can receive GPS L1 C/A code signals [33] and is capable of outputting raw satellite signal strengths sampled at 50 Hz, and Figure 4 shows some examples of satellite signal strength variations from MetOp-A occultation measurements.  
The signal strength of a satellite may be utilized to compute the power spectral density of the satellite signal. This PSD [34] is the Fourier transform of the autocorrelation function of the signal strength and can be evaluated through using the Welch method [35,36] (modified periodogram averaging method). The Welch method computation steps are as follows:  
1. The signal with data length N is divided equally into L segments, each with data length M;
2. The selected window function is added to each small segment of data, and the periodogram for each segment is derived to form a modified periodogram, which is then averaged over each modified periodogram;
3. Each corrected periodogram is approximated as uncorrelated with each other, and the result (processed using a smoothing algorithm) is used as the final power spectral density map.  
In particular, this study uses occultation events for which scintillation has been recorded [37], and these occultation events are labelled by setting label 1 for scintillation events and label 0 for non-scintillation events. The average duration of an occultation event is 22 s, which means that the power spectral density of the satellite signal is computed within an average time window of 22 s and is input into the classification model as a data sample along with the labelled values of the corresponding occultation events. Table 2 provides an overview of the detailed structure of this dataset, with the first column being the class labels and the second to last column being the PSD computed using the Welch method, with each row constituting a row vector entered into the model as a data sample.
# A Secure Auditable Remote Registry Pattern for IoT Systems
## 2 Motivation and Background
### Motivating Scenario
As a relevant summary, we will discuss the cases of the EU and the USA. In the European Union, the Cybersecurity Act [13] proposes a voluntary cybersecurity certification framework, which allows companies to certify their ICT products, services and processes according to EU standards. On the other hand, opposed to the voluntary character of the Cybersecurity Act, the recent EU Cyber Resilience Act [14] imposes legal obligations for stringent cybersecurity requirements on digital products, including IoT devices, to be fulfilled from design to marketing. The European NIS2 Directive [15] extends the scope of the original NIS Directive, to include more sectors and types of entities that must comply with stricter cybersecurity standards. This directive had to be translated into national-level laws by member states by 17 October 2024. The NIS2 Directive proposes more concrete measures to reduce discrepancies between countries and ensure a common high level of cybersecurity across all member states. In particular, it establishes the obligation to facilitate \"regular and targeted security audits carried out by an independent body or a competent authority\" and further elaborates by establishing that \"the results of any targeted security audit shall be made available to the competent authority\". Finally, it establishes that \"costs of such targeted security audit carried out by an independent body shall be paid by the audited entity\", which means that it is in the interest of the entities managing those IT or IoT systems to implement measures to facilitate the audits and reduce the costs associated with them. In the USA, on 12 May 2021, a Presidential Executive Order on Improving the Nation's Cybersecurity commissioned the definition of two labeling programs on the cybersecurity capabilities of Internet of Things (IoT) consumer devices and software development practices from the National Institute of Standards and Technology (NIST), which, in February 2022, released a White Paper on \"Recommended Criteria for Cybersecurity Labeling in the IoT\" [16] that contains guidance for IoT manufacturers and integrators. The content of this guide is voluntary, but it is important to mention that, apart from the basics, such as unique device identifiers (a serial number, for example), data protection or firmware updates, it refers to the need to maintain a cybersecurity event log. In particular, it mandates that \"the IoT product captures and records information about the state of IoT components that can be used to detect cybersecurity incidents affecting or affected by IoT product components and the data they store and transmit\".
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 5 Experiments
### Baseline Models
#### 5.1.3 PSPNet  
PSPNet [43] leverages a pyramid parsing module to extract global contextual information through multi-region context aggregation. This approach optimizes both local and global feature extraction, improving segmentation accuracy. We re-implemented PSPNet with the Semantic Models library using ResNet-34 as the backbone and ImageNet pretrained weights.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 6 Results and Discussion
### Comparison NOMA vs. OFDMA  
Figure 10 illustrates the boundary rate comparison of OFMA and NOMA. To better illustrate this, a pair of users communicating with a BS over an AWGN channel is considered as an example. As it can be observed from Figure 10, utilizing NOMA leads to a larger capacity region compared to OFDMA. The rate of user 2 with NOMA is about 3 times higher than the one with OFDMA. That is regarded as a significant superiority of NOMA over OFDMA. Intuitively, the users associated with small normalized channel gains should be allocated higher power levels to improve their received SINR.More particularly, the users with higher channel gain require less power and they can successfully detect the data with a high probability as a benefit of SIC. In the absence of preventative measures, the SIC induced error propagation may have a severe effect on the error probability as the number of users increase. Based on this optimal SIC-detection order, any user can detect its information without substantial interference-contamination imposed on the other users whose normalized channel gain is smaller than that of this user.  
Figure 9: The flowchart of orthogonal pairing algorithm.  
In Figure 11, we investigate the performance of EE and SE. We assume the power consumed due to hardware and signal processing operations is \(Ps=100\) W. For Equation (19) the EE peak can be assumed in NOMA is 3696.4 bit/joule, and OFDMA's peak is 4.5 bit/joule. The result of NOMA is much better 370 times than the result of OFDMA. Furthermore, the NOMA scheme has the potential of striking a more attractive trade-off between SE and user-fairness. However, some advanced user pairing and power allocation methods and powerful channel coding schemes reduce the error probability. Furthermore, NOMA's down-link could achieve the capacity region, while OFDMA is suboptimal in general. However, rate-fairness is not maintained at this optimal point since the low-power user rate is much lower than that of the higher-power user when the distinction between the two users' received powers is high. Note that the simple two-user case results can be extended to the general case of an arbitrary number of users. In this way, the user with the most substantial normalized channel gain can cancel interference from all the other users and thus is least contaminated. Then, simulations in Figure 12 compare the sum data rate in a rural area at a given EE and SE when using NOMA versus OFDMA. The simulation were performed in MATLAB, and the results showed that NOMA achieves a better performance than OFDMA in the categories compared.  
Figure 11: The energy efficiency and spectral efficiency comparison for NOMA and OFDMA.  
Figure 10: The boundary rate between NOMA and OFDMA for downlink.
## 3 KA-DDPG-Based Direct Torque Distribution Method
### KA-DDPG-Based Driving Torque Distribution
#### 3.2.3 Reward Function  
The reward is the only feedback signal available for the agent's learning, which is used to evaluate the action taken by the agent. The reward function designed in this paper combines a dense reward with a sparse reward, for resolving the problem of driving torque distribution. Three types of excitations are considered in the dense reward: (1) a reward \(r_{1}\), regarding the desired value tracking errors; (2) a reward \(r_{2}\), regarding the convergence  
Figure 5: Knowledge-assisted learning method—criteria action method. of tracking errors; (3) and a reward \(r_{3}\), which encourages reducing the driving torques. The specific rewards are defined as follows:  
\[r_{1}=v_{delta}^{2}+5\cdot\omega_{delta}^{2}, \tag{15}\]  
\[r_{2}=\left(v_{xa}^{\prime}\right)^{2}+\left(\omega_{\phi a\prime}^{\prime} \right)^{2}, \tag{16}\]  
\[r_{3}=T_{fl}^{2}+T_{rl}^{2}+T_{fr}^{2}+T_{rr}^{2}. \tag{17}\]  
The dense reward part of the learning process is defined as follows:  
\[r_{dense}=w_{1}\cdot r_{1}+w_{2}\cdot r_{2}+w_{3}\cdot r_{3}. \tag{18}\]  
The dense reward is a punishment that helps the agent minimize the tracking error of the desired value. In addition, the sparse reward is used as a positive reward, rewarding the agent for controlling the vehicle in an ideal state. The sparse reward is defined as follows:  
\[r_{sparse}= \begin{cases}3000,&\text{if}\left|v_{delta}\right|<0.01\times \left|v_{val}\right|\text{and}\left|\omega_{delta}\right|<0.01\times\left| \omega_{\phi d}\right|\\ 0,&\text{otherwise}.\end{cases}  \tag{19}\]  
The final observed reward is the sum of the dense reward \(r_{dense}\) and the sparse reward \(r_{sparse}\), expressed as follows:  
\[R_{o}=r_{dense}+r_{sparse}. \tag{20}\]  
Unlike the original DDPG algorithm, which directly uses the observed reward as the updating reward to update the policy directly, in the KA-DDPG algorithm, we adjust the observed reward, with the help of the evaluation method, to sharpen the updating reward, which can accelerate the agent's learning process. The guiding reward method is proposed to adjust the updating reward, and its framework is shown in Figure 6.  
The core idea behind the guiding reward method is to sharpen the updating reward that the agent uses to update the policy. The guiding reward, like the criteria action, needs to eliminate the effect after a period of training. Thus, by combining the guiding reward \(R_{g}\) and the observed reward \(R_{o}\), the updating reward \(R_{u}\) is defined as follows:  
\[R_{u}=\left(1-\gamma^{i}\right)\cdot R_{o}+\gamma^{i}\cdot R_{g}. \tag{21}\]  
The distance between the agent action and criteria action is used as the guiding reward, which is a simple but effective evaluation method. The agent action and criteria action are both four-dimensional vectors. The guiding reward is defined as:  
\[R_{g}=-\sqrt{\left(a_{a}-a_{c}\right)^{T}\left(a_{a}-a_{c}\right)}. \tag{22}\]  
Figure 6: Knowledge-assisted learning method—guiding reward method.
## 2 Materials and Methods
### Laboratory Setting  
In order to ensure high-quality performance, the laboratory must meet certain requirements, such as performing validation studies on authorized available tests, the use of internal quality controls, and involvement in external quality assessment plans (EQA). Since the WHO and the Innovative New Diagnostic Foundation started organizing to deal with the pandemic, only reference materials and EQA recommendations for SARS-CoV-2-based molecular detection have become available to date [22]. To obtain fast, sensitive, highly specific and reliable results about the presence of specific viruses, the quantitative real time polymerase chain reaction (RT-qPCR) is the method of choice.  
The AccuPlexTM SARS-CoV-2 Reference Materials Kit (SeraCare, Milford, MA, USA) was used as the test kit for viral aerosols [23]. This positive reference material contains recombinant virus particles composed of the SARS-CoV-2 genome sequence, including the E gene, RdRp (RNA polymerase dependent on RNA), ORF1a gene and N gene. In addition, the positive references have high similarity with the SARS-CoV-2 virus, which results in the advantages of more realistic virus detection and lower measurement uncertainty.  
Figure 1: The schematic design of the protection tent.  
The Reference Materials Kit has been approved by the US FDA (U.S. Food and Drug Administration) and is compliant with federal regulations and laboratory standard quality control procedures.  
The external control used the AccuPlexTM SARS-CoV-2 Reference Material Kit and the Reliance One-Step Multiplex Supermix (Bio-Rad Laboratories, Hercules, CA, USA). External positive controls were handled like patient samples to check the RNA extraction, reverse transcription, and RT-qPCR amplification and detection. The supposed outcome was obtained with the external positive control, along with performing positive (template) and negative (non-template) RT-qPCR controls, to clarify the results obtained from patient samples. Primers and probes were synthesized by Tri-I Biotech, New Taipei City, Taiwan. Viral extraction was performed using a Quick-RNA Viral Kit (Zymo Research, Irvine, CA, USA). Two hundred microliters of the sample of the AccuPlexTM SARS-CoV-2 Reference Material Kit was added to the column and eluted in 15 \(\upmu\)L of DNase/RNase-Free water, provided in the kit.  
The SuperScriptTM III PlatinumTM Taq Mix (Thermo Fisher Scientific, Waltham, MA, USA) was applied for the RT-qPCR with specific primers and probes tagged with FAM. The final concentration of the primers in the reaction was 200 nM in a 50 \(\upmu\)L reaction. The final concentration of the probe was 100 nM. The primers and probes described by the WHO for the diagnostic detection of the SARS-CoV-2 virus by RT-qPCR are shown in Table 1. Thermal cycling was performed at 50 \({}^{\circ}\)C for 15 min for reverse transcription, followed by 95 \({}^{\circ}\)C for 2 min and then 40 cycles of 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for 30 s. Table 2 shows the setup for the RT-qPCR for the 2019 novel coronavirus.
## 1 Introduction
Based on the predicted frequency, a triangular shape inference function which was based on predicted frequencies were generated prior to the panel and cavity average response. The inference function was used as an input of the neural network to predict the panel's averaged vibration response. In the same way, cavity natural frequency was predicted using the GCN and the average pressure transfer function from the area where coupling with the panel to measurement point was predicted using the inference function based on the cavity natural frequency. The inference function is a domain-knowledge-based proposal, for which the main response peak has a strong relationship with the system's natural frequency, and it works efficiently to predict the system's response with a limited number of datasets. Panel average vibration response and cavity average acoustic response are aligned to 2D images as input data, and the structure-borne noise of the whole system was predicted using 2D-CNN. The proposed model trained for the finite element panel-cavity model to minimize the mean squared error loss for the prediction of sound pressure level from an approximately 10 to 500 Hz range in the 1/12 octave band in a cavity. Various kinds of shapes/sizes of the panel and cavity were used for the training, validation, and test. Using this proposed method, predicted structure-borne noise shows an 0.858 R-Square value.
## 3 Results
### MCA Identified on Real Data  
We now explore the performance of the model of Equation (2) in analyzing real data sets. We considered MCA protocols set by \(\lambda_{m}(u)\) and \(\pi_{m}(u)\) given by Equations (12) and (17) respectively, and the PLA forms \(\Omega_{\text{BL}}(u)\), \(\Omega_{\text{ML}}(u)\) and \(\Omega_{\text{TSK}}(u)\) given by Equations (25), (28) and (30) one to one. For comparison aims, we also acquired the \(\text{H}_{TSK}(x)\) proxy fitted in direct arithmetical scales As already pointed out, the case \(\lambda_{0}(u)\) identifies a TAMA protocol.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AIC** & \(\rho\) & \(R^{2}\) & **SEE** & **MPE** \\ \hline \(E_{\lambda 0}(y|x)\) & \(-\)11,994.77 & 0.7369 & 0.6496 & 0.0220 & 2.4649 \\ \(E_{\lambda 2}(y|x)\) & \(-\)19,114.93 & 0.9897 & 0.9798 & 0.0053 & 0.5929 \\ Huxley-DNLR & \(-\)18,731.51 & 0.9832 & 0.9763 & 0.0057 & 0.6407 \\ \(\text{E}_{\text{HTSK}}\left(y|x\right):(q=4)\) & \(-\)19,089.31 & 0.9897 & 0.9796 & 0.0053 & 0.5958 \\ \(\text{E}_{\text{\tiny{\text{$\Omega$TSK}}}}\left(y|x\right):(q=4)\) & \(-\)19,096.41 & 0.9897 & 0.9796 & 0.0053 & 0.5949 \\ \(\text{E}_{\text{\tiny{\text{$\pi$m}}}}\left(y|x\right):(m=5)\) & \(-\)18,666.28 & 0.9873 & 0.9758 & 0.0058 & 0.6486 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance statistics for addressed proxies fitted on data simulated by the procedure of Equation (44) (non–normal errors). AIC stands for [PERSON]’s information criterion, \(\rho\) symbolizes [PERSON]’s concordance correlation coefficient, \(R^{2}\) denotes determination coefficient, SEE standard error of estimation and MPE mean prediction error.  
Figure 4: Comparison of \(\lambda_{2}(u)\) and \(\Omega_{TSK}(u)\) MCA fits the simulated non-lognormally distributed data. Panel (**a**) comparison of mean response functions \(E_{\lambda 2}(u|p)\) and \(E_{\OmegaTSK}(u|v)\). Panel (**b**) compares related retransformed forms \(E_{\lambda 2}(y|x)\) and \(E_{\OmegaTSK}(y|x)\). Panel (**c**) compares \(E_{\lambda 2}(y|x)\) to \(E_{\text{HTSK}}(y|x)\) acquired from regression model of Equation (34) fitted in direct arithmetical scales. Panel (**d**) is a comparison of \(E_{\OmegaTSK}(y|x)\) to \(E_{PQ2}(y|x)\), the directly acquired projection function. This is obtained by replacing fitted parameter vector \(PQ\) into addressed MCA form in arithmetical space. We can be aware of dependable projections even without a correction factor (CF).