esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Considering the reliability of wireless information transmission in distributed network physical systems, this article proposed a reliability evaluation method for distributed cyber systems based on wireless access networks. It provided a new idea for quantifying information transmission performance reasonably and quickly. Based on the established switching model, this paper proposed a reliability calculation method combining the upward equivalence and the minimum path method, which greatly improved the calculation efficiency. Finally, a test system was established to verify the impact of information transmission performance on distribution network reliability, which verified the correctness and efficiency of the proposed method, and the main conclusions are as follows:  
(1) Among the information system components, the variation in the failure rate of IEDs has the greatest impact on CDDS reliability.  
Figure 7: System reliability under various weather conditions.  
(2) Information system failures cause a small decrease in the reliability of active distribution networks, and the allocation of distributed new energy generation devices with reasonable capacity to CPDSs is conducive to improving the reliability of system power supplies.  
(3) As the average load rate of the wireless access network increases, the number of withdraws of the channel detection increases, which will directly lead to the increase in information transmission delay, the probability of information link failure becomes larger, and the reliability of the CPDS decreases.  
(4) One notable limitation is the potential compatibility issue between 5G technology and existing wired communication infrastructures in traditional power distribution networks. Additionally, the high investment and construction challenges in certain physical environments may pose barriers. However, these limitations can be bypassed through strategic planning and incremental upgrades to the existing network, alongside cost-benefit analyses and collaborative efforts with stakeholders to facilitate a smoother transition to 5G-enabled power distribution systems.  
For subsequent research, we are preparing to introduce machine learning algorithms into the reliability assessment of power distribution physical systems to extend machine learning integration for predictive maintenance in a cyber-physical system.  
Methodology, C.L. and X.D. Software, S.Z. and X.W. Formal analysis, C.L. Writing--original draft, C.L., S.Z. and X.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20241269.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 6 Conclusions  
In summary, it seems that the TR controller developed by the author is an attractive alternative to the applied solutions for stabilising an inverted pendulum around its equilibrium point. The main advantage of this solution is simple tuning of the TR controller, and uncomplicated algorithm structure and good operating efficiency, even at a considerable distance from the operating point. However, it should be noted that the TR controller is a new proposal, and the research space regarding its properties is still open. In a further stage of work, it is possible, for example, to test the effectiveness of the TR algorithm within a wide range of set values of the pendulum inclination angle. The presented research was simplified to a pendulum model in which the righting force is set. In real models, an inverted pendulum is most often used and is placed on a moving cart. If the cart does not move in a circle, it has limited movement space, so in the next stage a test can be performed on such a model. In a system additionally equipped with a trolley, it is necessary to swing the pendulum from the pending position up to the inverted position. When preparing the TR controller to work with an inverted pendulum placed on a cart, it would be necessary to develop an algorithm that combines the possibilities of stabilising the inverted pendulum in the equilibrium position and swinging the pendulum from the pending position. The final test demonstrating the effectiveness of the TR controller should be conducted on a real inverted pendulum model.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PID & Proportional-integral-derivative controller \\ TR & Trigonometric controller of an inverted pendulum \\ CV & Control variable \\ PV & Process Variable \\ SP & Set point (value expected in the control process) \\ E & Control error (\(E=SP-PV\)) \\ IMU & Inertial Measurement unit \\ \end{tabular}
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 6 Conclusions
; project administration, R.J.; funding acquisition, R.J.; All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Academy of Finland under Project No. 311760 and Project No. 319003. Not applicable. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
3D Three-dimensional  
3 GPP 3 rd generation partnership project  
AmBC Ambient backscatter communication  
BC Backscatter communication  
BD Backscatter device  
BLE Bluetooth low energy  
BPL Building penetration loss  
BPSK Binary phase shift keying  
CDF Cumulative distribution function  
CSI Channel state information  
CSS Chirp spread spectrum  
D2D Device-to-device  
D7 AP DASH7 alliance protocol  
FBR Front to back ratio  
GNSS Global navigation satellite system \begin{tabular}{l l} GO & Geometrical optics \\ HSR & High-speed railway \\ HPBW & Half power beamwidth \\ ICT & Information and communication technology \\ ID & Identification number \\ InH & Indoor hotspot \\ IT & Image theory \\ ITU-R & Radio communication sector of international telecommunication union \\ IoT & Internet-of-things \\ LPWAN & Low power wide area network \\ LoRa & Long range \\ LoS & Line of sight \\ LTE & Long term evolution \\ M2M & Machine-to-machine \\ MA & Multiple access \\ mMTC & Massive machine type communication \\ NB-IoT & Narrowband-IoT \\ NLoS & Non-LoS \\ RF & Radio frequency \\ RL & Ray launching \\ RT & Ray tracing \\ Rx & Receiver \\ SBR & Shoot and bouncing ray \\ SDR & Software-defined radio \\ SLL & Side lobe level \\ SNR & Signal-to-noise \\ Tx & Transmitter \\ UNB & Ultra-narrowband \\ WSN & Wide Sensor Network \\ \end{tabular}   
**Mathematical Symbols** \begin{tabular}{l l} \(PL_{LoS,dB}\) & Path loss value in LoS \\ \(PL_{LoS,dB}\) & Median path loss value in LoS \\ \(PL_{NLoS,dB}\) & Path loss value in NLoS \\ \(PL_{T,dB}\) & Median path loss value in NLoS \\ \(\Delta_{LoS}\) & Basic path loss in transition state distance \\ \(\Delta_{NLoS}\) & LoS location correction factor \\ \(f_{c,MHz}\) & NLoS location correction factor \\ \(d_{km}\) & Carrier frequency in MHz \\ \(d_{LoS}\) & separation between the terminals/devices in Km \\ \(\sigma_{LoS}\) & LoS distance \\ \(L_{urban}\) & Standard deviation of the shadowing in LoS \\ \(p\) & Urban environment correction factor \\ \(w\) & Location percentage \\ \(PL_{InH,dB}\) & Path loss value in an indoor environment \\ \(Pr_{LoS}\) & LoS probability \\ \(d_{3D,m}\) & 3D distance between the two points \\ \(f_{c,GHz}\) & Operation frequency in GHz \\ \(\xi_{LoS,dB}\) & Shadowing/slow fading factor in LoS \\ \(\xi_{NLoS,dB}\) & Shadowing/slow fading factor in NLoS \\ \(\sigma_{LoS}\) & Standard deviation of the shadowing in LoS \\ \(\sigma_{NLoS}\) & Standard deviation of the shadowing in NLoS \\ \end{tabular}
## 3 Time-Window-Based Security Orchestration  
Providing customized policies for services with different security levels is one of the key points to improve the performance of QKD-enabled optical networks. As mentioned before, different security requirements for services can be satisfied by setting different key update period. The shorter the key update period is, the higher security levels can the service achieve. Since the secure keys are changed more frequently, it will increase the difficulty of acquiring the secure keys for the eavesdropper. There are two different kinds of key requests in the process of secure data communications. One is the key provisioning request, which is generated when security service arrives; another is the key updating request, which is generated to improve security level during secure data transmission. We define the accepted delay range of key provisioning request and key updating request as initial time window (\(TW_{i}\)) and update time window (\(TW_{u}\)), respectively. The value of \(TW_{i}\) and \(TW_{u}\) is determined by the latency range of service. As shown in Figure 2a, the non-immediate service arrives at \(t_{a}\), and it only needs to transmit data before the deadline \(t_{s}\), thus a period from \(t_{a}\) to \(t_{s}\) can be obtained for the key configuration process. Obviously, the latency range of service must be larger than the key configuration time, and \(TW_{i}\) is equal to the latency range of service minus the key configuration time. Different from \(TW_{i}\), \(TW_{u}\) is related to the security levels of service requests. When there are multiple key updating requests, we can orchestrate them to acquire a reasonable deployment of key configuration channels (i.e., QKCh and MBCh) in time dimension. In this paper, a time-window-based security orchestration (TW-SO) strategy for key updating is proposed. When a key updating request is inserted into the queue, any request whose security level is lower will automatically slide backward within \(TW_{u}\) to satisfy the time delay. Suppose the key updating request is denoted as \(r(s,d,t_{a},TW_{u},t,l)\), where \(s\) and \(d\) are the source and destination nodes, \(t_{a}\) is the key-updating time, \(TW_{u}\) is the update time window, \(l\) is the security level, \(t\) represents the time required to complete the key configuration; and then the process of TW-SO is shown in details in Algorithm 1.  
Figure 2: (**a**) Coordination of three kinds of channels; (**b**) Resource allocation conflicts in routing, wavelength, and timeslot allocation (RWTA).
###### Abstract  
Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN classifiers can learn and explore high-level abstract features, automatically reduce data dimensions, and perform classification well. According to the nearest neighbor criterion, the fuzzy membership weights of each test sample in each sub-DBNs classifier are calculated. The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that our proposed model has higher overall accuracy, recall, precision and F1-score than other well-known classification methods. Furthermore, the proposed model achieves better performance in terms of accuracy, detection rate and false positive rate compared to the state-of-the-art intrusion detection methods.  
intrusion detection; modified density peak clustering algorithm; restricted Boltzmann machine; deep belief networks; fuzzy aggregation +
Footnote †: journal: Applied Sciences
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
First, their relatively small structural dimensions and the complexities of the electrochemical mechanism often constrain the effective bandwidth, making it difficult to maintain reliable signals over the low-frequency range. Second, factors such as electrode degradation, electrochemical byproducts, and variations in environmental conditions can lead to gradual drift in the sensor's frequency response over time, undermining the reliability of long-term monitoring. These issues underscore the need for an enhanced system architecture that not only broadens the operational bandwidth but also suppresses long-term drift. Consequently, the introduction of a dynamic force balance feedback approach becomes pivotal, as it can both improve low-frequency responses and stabilize the hydrophone's characteristic performance in extended deployments. In order to solve the above limitations, a dynamic force balance negative feedback system based on a digital signal processor (DSP) is proposed to extend the working bandwidth of a MEMS-based electrochemical vector hydrophone. Dynamic force balance feedback strategies have also been explored in ground vibration sensor applications. For instance, [PERSON] et al. [28] introduced a scene-adaptive feedback approach for seismometers through real-time tuning of low-frequency drift compensation parameters. However, because the acoustic impedance and propagation medium underwater differ substantially from those on land, such methods cannot be directly applied to hydrophones. Consequently, this paper refines and extends the dynamic feedback method for hydrophone applications, with targeted improvements in sensor architecture and acoustic coupling mechanisms. The system has field adjustability and can resist the influence of long-term system frequency characteristic drifts. The structure of this paper is as follows. Section 2 introduces the structure and working principle of the MEMS electrochemical vector hydrophone with the feedback system. Section 3 elaborates on the parameter calculation and adaptive parameter adjustment of the dynamic feedback network. Section 4 provides experimental data and analyzes the adaptive effect. Section 5 discusses the main results.
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## 2 Methods  
In this paper, the theoretical expressions for the bit error probability (BEP) and the throughput of a wireless-powered dual-hop amplify-and-forward (WP DH AF) relaying system are considered. Moreover, we investigate a non-linear piece-wise EH model which is mathematically tractable in terms of the PDF, CDF of harvested power, and the system performance. Apart from the linear EH model described in the literature, this model provides practical performance analyses. Specifically, the non-linear EH model considered in [8; 11; 26; 27; 34; 45] is addressed since this model is mathematically tractable compared to other non-linear EH models [9; 10; 12; 13]. In contrast to the linear EH model, which overestimates system performance, the considered non-linear piece-wise EH model provides realistic system performance along with the practical non-linear EH models. Since most papers in the literature have considered the linear EH model, in this paper, the effect of the linear EH model is investigated along with the non-linear piece-wise EH model, to provide a comprehensive system analysis. Hence, comparisons of linear and non-linear EH models are also provided. To the best of the authors' knowledge, the BEP, outage probability, and throughput of the considered system have not yet been investigated in the literature. The contributions of this paper are summarized as follows:  
* Theoretical derivation of BEP expressions considering both linear and non-linear EH models is performed.
* Throughputs of the considered system for both linear and non-linear EH models are derived.
* Simulation and theoretical results of the considered system are provided for different system parameters, such as distance, power, and achievable rate.  
The paper is organized as follows: The system model is described in Section 3. Theoretical BEP derivations are provided in Section 4. Section 5 deals with the throughput analysis. Numerical results are presented in Section 6. Finally, Section 7 concludes the paper.
## 3 Methodology
### Data Preparation
#### 3.3.2 Handling Outliers  
Since outliers can significantly deviate from the majority of the dataset, they often exert an undue influence on the interpretation of relationships among variables. Therefore, identifying and properly addressing outliers is crucial in data analysis and modelling.  
Box plots were used to analyse the numerical variables in the dataset. According to [PERSON] et al. [32], a box plot is a straightforward graphical technique for identifying outliers. It provides a summary of a variable's distribution, showing the minimum value (excluding outliers), the lower quartile (Q1), the median (Q2), the upper quartile (Q3), and the maximum value (excluding outliers). In a box plot, outliers are identified as data points that lie beyond the whiskers, which extend from the minimum to the maximum value within the range of typical non-outlier data.  
Box plot analysis of the numerical variables revealed outliers in the \"Foil_thickness\" variable, as depicted in Figure 2. This visualization clearly illustrates points that fall outside the typical range, offering a comprehensive view of the distribution and the presence of outliers for this variable.  
The presence of outliers in the dataset can be definitively attributed to data entry errors. This conclusion is based on the recorded values for foil thickness associated with the outlier observations (0.023 micron), which do not align with the company's specified foil material specifications. Therefore, these outliers are considered error outliers, reflecting significant deviations from expected values due to inaccuracies in the data collection process [32].  
The appropriate approach for addressing error outliers is to either correct the data points to their accurate values or remove the corresponding observations from the dataset [33]. Since the \"Foil_thickness\" variable had only 32 outlier observations, accounting for 0.53% of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Missing Values** & **Number of Observations** & **Incidence** \\ \hline
45 & 6081 & 0.74\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Missing value incidence in “Foil_thickness” attribute.  
the dataset, it was decided to exclude these observations. As a result, the total number of observations decreased from 6036 to 6004.
# Cyber-Security Challenges in Aviation Industry: A Review of Current and Future Trends
## 4 Cyber-Attack Surfaces and Vulnerabilities in the Civil Aviation Industry  
[PERSON] [48] attests that only attackers with a broad understanding of how an aircraft or aviation system functions can successfully disrupt normal operations, citing that an attack on an entire aircraft or aviation system is non-trivial. [PERSON], [PERSON] and [PERSON] [8] highlighted that technologies such as Wireless Fidelity (WiFi), Internet, IoT, Global Positioning System (GPS), open-source systems, virtualisation and cloud computing services have been central in the optimisation of aviation operations, reducing costs and response times through enhanced inter-operability. Integrated systems, however, can be targeted remotely due to their inherent vulnerabilities, an assertion shared by [2,49] and [PERSON] et al. [9]. [PERSON] et al. [9] also added that the practise of Bring Your Own Device (BYOD) by airport customers, travellers and employees creates a rich attack surface. As an example, the work in [50] reports an attacker's access of the in-flight entertainment by simply attaching a catc cable with a modified connector through his laptop; other aeroplane network systems could also be commandeered, as confirmed by [PERSON] in [51].  
[PERSON], [PERSON] and [PERSON] [52] postulate that the increase in the number of operational aircraft coupled with the innovation driving the development of smaller and more sustainable air vehicles render air communication protocols a high-profile target for cyber-attacks. [PERSON], [PERSON] and [PERSON] [1], [PERSON] et al. [20] and [PERSON] [21] are of the view that the reliance on computer-based IT systems in the day-to-day management of the industry--which has enabled improvements in the sophistication of air navigation, on-board aircraft control and communication systems--increase the cyber-attack surface. Furthermore, airport ground systems, which include flight information, security screening and day-to-day data management systems, are also identified as targets. An aggregation of the above targets comprises the spectrum of feasible attack surfaces in the Civil Aviation Industry (CAI), with associated vulnerabilities.  
[PERSON] [53] discovered security flaws in Immarsat and Iridium Satellite Communication (SATCOM) terminals in 2014, infrastructure in routine use within the aviation industry. Researchers concluded that malicious attackers have the potential to exploit the vulnerabilities inherent in the design of the system through back-doors; exploitable were hard-coded credentials, an insecure protocol and weak encryption algorithms. [PERSON] [54] reported in 2017 that a team of government, industry and academic researchers successfully hacked into a legacy Boeing 757 commercial aircraft, remotely, in a non-laboratory setting, by accessing its systems through radio frequency communications.  
Figure 3: Cyber-Attacks by Location.