esa-sceva/satcom-chunk-collection huggingface dataset:
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### 5G and Beyond Technology and Architecture
#### 3.2.4 Beyond 5G Prospectives  
Research beyond 5G technology has already started [34]. 6G is the next generation of communication networks that will succeed the current 5G networks. 6G is expected to provide faster speeds, lower latency, enhance mobility, and more reliable connections than 5G [35]. It is also expected to enable new applications and use cases that are not currently viable with existing technology. Some of the potential use cases for 6G include advanced virtual and augmented reality (truly immersive XR), holographic communication (high-fidelity mobile hologram), and digital replicas (digital twin) [36]. These applications will shape new requirements and KPIs for 6G.  
Figure 5 shows the requirements for 6G. As can be observed, three of them are similar to 5G. However, there are three new categories [11]. Precision and accuracy are associated with sensing and localization capabilities, respectively. In this context, one way of measuring sensing precision is using missed detection (MD) and false alarm (FA) parameters. There is also adaptability response time, which is related to network automation and the quantity of manual intervention that it needs. Finally, end devices are expected to have intuitive interfaces, e.g., sensible to gestures, as well as to consume from low power to no power at all. Furthermore, some key enabling technologies that are expected to be part of 6G research include artificial intelligence/machine learning, terahertz technologies, spectrum sharing, and new network architecture paradigms such as converged RAN-Core and subnetworks [37].  
Figure 5: 6G key requirements [11].  
The exploration of potential applications and capabilities of 6G technology is an ongoing endeavor. It is anticipated to bring forth substantial advancements in communication networks and facilitate novel and inventive use cases spanning various industries.
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
## 1 Introduction  
Long-haul optical communication is seriously deteriorated by transmission impairments, e.g., chromatic dispersion (CD), polarization mode dispersion (PMD), laser phase noise (LPN), and Kerr fiber nonlinearities [1; 2]. The combination of coherent detection, digital signal processing (DSP), and advanced modulation formats offers a very promising solution for long-haul, high-capacity optical transmission, to offer great capabilities and flexibilities in the design, deployment, and operation of core telecommunication networks [3; 4; 5]. In the reported phase noise cancellation (PNC) methods, the radio frequency (RF) pilot tone scheme was verified to be an efficient approach to remove laser phasefluctuations [6; 7; 8; 9]. However, these works only studied the behaviors of RF pilot tones in short-reach systems, where the enhancement effect of fiber dispersion on the LPN was neglected [10; 11]. Actually, due to the interplay between the electronic dispersion compensation (EDC) module and the LPN from the local oscillator (LO), a phenomenon of equalization enhanced phase noise (EEPN) is induced and plays a significant role in the carrier phase recovery (CPR) in high-capacity optical communication systems [10; 11; 12; 13; 14; 15; 16]. However, so far, no DSP-based CPR has been developed to effectively compensate for EEPN [17; 18; 16]. Therefore, it will be of great significance to study the suppression of LPN and EEPN using an RF pilot tone scheme.  
In this work, the performance of an orthogonally polarized RF pilot tone scheme is investigated for eliminating both the LPN and the EEPN in long-haul optical transmission systems. A 28-Gsym/s quadrature phase shift keying (QPSK) system is numerically implemented, where the CPR is realized using a one-tap normalized least mean square (NLMS) estimation and a differential phase detection (DPD) scheme, respectively [19; 20; 21]. The results show that the LPN can be fully removed and the EEPN can also be effectively suppressed, when the RF pilot tone scheme is applied prior to the CPR.
## 4 Simulation and Experiment
### Simulation Results
#### 4.1.2 Example Two  
Although the WSAW signal is overall non-stationary, it is still a stationary signal during each single-frequency signal. Therefore, it is necessary to identify the interval points between the strong and the weak signals to achieve signal separation. The subsequent trial involves the 100 Hz and 200 Hz signal transmission simulation. In Figure 7, the number of sampling points of the overall signal is 60,000, and the number of sampling points for each single frequency point is 15,000. The intersection of the generalized inner product mean value and the fitted value determines the first interval point at 15,135, the same as the simulation setting frequency conversion point.
## 3 Machine Learning and Fault Diagnosis
### Simple Fault Diagnosis Models
#### 3.4.4 Fuzzy Logic (FL)  
Fuzzy logic is a branch of mathematics that allows a computer to model the real world in the same way that people do. It makes it possible to improve expert systems (appropriate where humans can linguistically describe the solution to the problem) because typically human knowledge is imprecise and vague. It was set up by [PERSON] in 1965. The principle of a fuzzy system is to be able to calculate output parameters by providing the system with a set of rules formulated in natural language and is composed of three parts as illustrated in Figure 14.  
The fuzzy logic model has the advantages of being simple, does not need a large amount of training data, and provides easy ways to deal with antagonisms in the well-defined knowledge base [30]. With fuzzy logic, rules can be generalized to cover a larger number of situations. However, in fault diagnosis, fuzzy systems are useful due to the fact that diagnosis often needs knowledge-based processing [30]. In practice, it is almost impossible to obtain adequate representations of the complex and highly nonlinear behavior of faulty systems using quantitative models. Several authors use  
Figure 14: Synoptic overview of a fuzzy logic system.  
also to resolve the uncertainties and inaccuracies associated with the description of the system [103]. In the work of [PERSON] et al. The fuzzy logic algorithm is used for the diagnosis of renewable energy systems. A comparison with the PSD (power spectral density) method showed better accuracy compared to PSD [104]. [PERSON] et al. examined the possibility of open circuit diagnosis in a three-phase inverter using the fuzzy logic controller. The simulation results show a better performance of the algorithm in the classification of desired defects [105]. Furthermore, [PERSON] et al. used fuzzy logic control to detect an open-circuit fault in a five-level cascaded inverter. The total harmonic distortion (THD) of the output voltage and the average output voltage are used as diagnostic variables. After simulation, the results obtained show the fault tolerance of the proposed method [106]. Mamdani-type fuzzy logic has been proposed to detect hot spot faults in PV systems. Three parameters are used as diagnostic variables (voltage drop, open circuit voltage, short circuit current). After simulation, the model shows an accuracy of 96.7% in detecting hot spots [30]. A fuzzy logic algorithm is used by [PERSON] et al. to detect and identify eight types of faults in a photovoltaic installation. It uses current ratios, maximum operating point voltage and open circuit voltage ratio as input parameters. All experimental defects were identified and classified with 99% accuracy [107]. Furthermore, it is also shown that fuzzy logic can be used to detect and classify faults in a solar photovoltaic system. The authors demonstrate that the proposed technique can accurately diagnose various types of faults with 98% [107].
# Application of Internet of Things in a Kitchen Fire Prevention System
## 3 Research Design
### Software and Hardware Used in this Study
#### MQ-4 natural gas methane sensor  
The gas-sensitive material used in the MQ-4 gas sensor (Figure 5) is SnO\({}_{2}\). When a combustible gas exists in an environment where the gas sensor is located, the conductivity of the sensor increases as the concentration of the combustible gas increases. The MQ-4 gas sensor is highly sensitive to methane as well as to propane and butane. It can detect numerous types of combustible gases, particularly natural gas, and thus is suitable for detecting methane gas and natural gas in households and factories. The detection range for natural gas and methane is from 300 to 10,000 ppm [71].  
Figure 4: DHT11 temperature sensor.  
Figure 3: Flame detector.  
(6) IP camera  
The camera employed in this study to monitor the gas stove is the 5 th generation advanced version Yosef 1080P surveillance IP camera (Figure 6). It was installed at a height in the kitchen that allows for monitoring the situation of the gas stove; therefore, the users can monitor the gas stove from a remote distance [72].
## 3 Proposed Design Scheme
### Firmware Design
In this design, the DDM-related specifications for the SFP optical transceiver in Standard Document SFF-8472 were implemented to satisfy the DDM-related specifications and were designed to calculate DDM values using higher-order polynomials to increase DDM accuracy [20]. The DDM formula representing the receiving optical power is shown in Equation (1):  
\[RXPWR=\sum\limits_{n=1}^{m}C_{n}A^{n}+Offset(i) \tag{1}\]  
where \(RXPWR\) is the received optical power DDM value for the receiving channel and \(C_{n}\) is the coefficient value of the \(n\)th order and performs the calculation up to the \(n\)th difference. The maximum value of m is four. \(A\) is the value monitored from the ADC, and \(Offset\) represents the channel offset. All coefficient values and offsets were assigned to the register so that the coefficient values and offsets for each order could be set and calculated in floating point format (IEEE 754). ```
1 WriteCount \(\leftarrow\) Number of I2C Write Count
2 StartAddress \(\leftarrow\) Start Address of I2C Write Count
3 CurrentPage \(\leftarrow\) Current Upper page index
4 WriteValue\([128]\)\(\leftarrow\) Array of I2C Write Value
5 Lowerbuffer\([128]\)\(\leftarrow\) Array of I2C Lower page access buffer
6 UpperBuffer\([128]\)\(\leftarrow\) Array of I2C Upper page access buffer
7 FunctionI2C Slave Stop (void)
8 SaveFlag <- false
9 if\(StartAddress==PageSelect\) and \(WriteCount==1\)then
10\(LowerBuffer[PageSelect]\) <- \(WriteValue[0]\)
11\(CopyFromFLASH(UpperBuffer,WriteValue[0],128)\)
12\(CurrentPage\) <- \(WriteValue[0]\)
13 else
14 while\(i<WriteCount\)do
15 if\(LowPageAccess\)then
16\(LowerBuffer[StartAddress+i]\) <- \(WriteValue[i]\)
17 elseif\(UpperPageAddress\)then
18\(UpperBuffer[StartAddress+i-128]\) <- \(WriteValue[i]\)
19\(Processing(CurrentPage,StartAddress+i)\)
20 if\(IsWrite EnableAddress\)then
21\(SaveFlag<\)- True
22\(i++\)
23 if\(SaveFlag==True\)then
24\(CopyToFLASH(UpperBuffer,CurrentPage,128)\)
```  
**Algorithm 1**QSFP28 register control.
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 1 Introduction
### Gold Standard for Diagnosis  
There is no universally accepted gold standard for diagnosing cardiac fatigue. However, it is typically assessed using a multimodal approach involving ECG (e.g., decreased heart rate variability--HRV; increased QT variability), echocardiography [5] and cardiac imaging (e.g., echocardiographic measures of ejection fraction and diastolic filling). Cardiopulmonary exercise testing, valuable even when performed by a portable measurement system (Cosmed(tm) K5, Rome, Italy) [6;7], is used to test the efficiency of the cardiopulmonary system and the metabolic thresholds. Because cardiac fatigue is reversible and dynamic, real-time or continuous monitoring (e.g., Holter ECG, wearable sensors) is often more informative than static assessments.
## 5 Materials and Methods
### Quantitative Performance Results  
To provide clearer insights into the system's behavior under varying loads, we conducted a series of stress tests and measured latency as well as data throughput. Table 1 summarizes the mean values (\(\pm\) standard deviation) of these key metrics under three different scenarios:  
* Scenario A: Only the S7-400 (Profibus) transmitting data at 50 Hz;
* Scenario B: Combined data acquisition from S7-400 (50 Hz) and S7-200 (20 Hz) via IBH Link S7++;
* Scenario C: Full integration (S7-400, S7-200, coating sensor at 10 Hz via OPC UA).  
As illustrated in Table 1, each data source (S7-400, S7-200, and the coating sensor) exhibits stable latencies in their respective communication paths, even when the system is fully loaded in Scenario C. Meanwhile, the overall throughput can reach up to 15 MB/s in total, which aligns with our initial QoS provisions. Figure 7 provides a visual breakdown of how the data rate evolves over time when transitioning from Scenario A (single data source) to C (all sources active).  
\begin{table}
\begin{tabular}{l c c c} \hline
**Data Source** & **Protocol/Path** & **Sampling Rate** & **Latency (ms)** \\ \hline S7-400 & Profibus (1.5 Mbit/s) & 50 Hz & 5.2 \(\pm\) 0.8 \\ S7-200 & IBH Link S7++ (Ethernet) & 20 Hz & 10.3 \(\pm\) 1.5 \\ Coating Sensor & OPC UA & 10 Hz & 15.6 \(\pm\) 2.1 \\ \hline \end{tabular}
\end{table}
Table 1: Measured latencies per data source and communication path.  
Figure 7: Graphical representation of latency (figure by [PERSON]).  
A slight increase in latency can be observed once the coating sensor (OPC UA) is integrated, reflecting the overhead of an additional communication protocol. Nonetheless, the measured values stay within acceptable bounds for real-time monitoring in our targeted industrial context.
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 7 Tests and Results
\begin{table}
\begin{tabular}{c c c} \hline
**Data Integrity Algorithm** & **Secure Boot Time (ms)** & **Secure Boot Execution Rate (ms/MB)** \\ \hline SHA 256 & 7.35 & 52.27 \\ HMAC SHA 256 & 7.5 & 53.36 \\
**AES 128 CMAC** & **3.67** & **26.07** \\
**ECDSA P256 SHA 256** & **23.58** & **167.67** \\ RSASSA–PSS 2048 SHA 256 & 167.44 & 1190.8 \\
**ECDSA P256 AES 128** & **19.81** & **140.83** \\
**CMAC** & **19.81** & **140.83** \\ \hline \end{tabular}
\end{table} 
Table 4: The results of secure booting a total of 147,455 bytes of code using different data integrity algorithms that are implemented using the HSM module of the AURIX TC399 microcontroller, with a 100 MHz clock speed.
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 3 Receiver for Short-Reach Transmission Systems
### Monolithic Integrated Receiver  
Various detection methods with discrete components have been applied and demonstrated in short-reach transmission systems. In order to further reduce footprint size, costm and power consumption, many efforts are being made to push discrete components to monolithic integration in both InP-based and SiP-based monolithic integration [104]. Recently, based on a silicon-on-insulator platform, [PERSON] et al. demonstrated a waveguide-coupled integrated graphene plasmonic photodetector with beyond 110-GHz bandwidth [22]. [PERSON] et al. demonstrated a 400-Gbit/s (16-channels 25-Gbit/s OOK) receiver where PIN + TIA arrays were integrated on a silicon PLC platform [105]. For another alternative cost-effective receiver, [PERSON] et al. proposed and demonstrated an InGaAsP/InP waveguide-based polarization-analyzing circuit in integrated Stokes vector receiver [106]. Figure 7a depicted a schematic top-view of the proposed InP-based Stokes vector receiver, where a 1 \(\times\) 4 multimode interference (MMI) splitter was used to split the input light equally into four ports. As illustrated in Figure 7b,c, through the Stokes vector converter (SVC) sections consisting of a symmetric waveguide (SW) and a half-ridge asymmetric waveguide (ASW), the light can be converted into four different states depending on the design of SVC. Finally, these four output lights were detected by four polarization-selective PDs (PS-PDs), which had higher sensitivity for the transverse-electric (TE) mode than the transverse-magnetic (TM) mode. Four basis vectors were located at the vertices of a regular tetrahedron inscribed on the Poincare sphere as depicted in Figure 7d. The application of the integrated Stokes vector receiver had been demonstrated in 10-Gbaud transmission systems. In addition, [PERSON] et al. reported the first PDM Kramers-Kronig coherent receiver in photonic integrated circuit with a capability to detect 256-Gbit/s 16-QAM signal, which showed the most compact size among silicon coherent  
Figure 6: Different structures of Stokes vector direct detection: (**a**) structure I [19]; (**b**) structure II; (**c**) structure III [21]; PBS: polarization beam splitter; OC: optical coupler; BPD: balanced photodetector; pol: polarizer.  
receivers [107]. We find that monolithic integrated receiver not only makes monolithic integrated detection chips practically feasible, but also makes it excel discrete components in many aspects, including cost, power consumption, and footprint.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology  
In this study, we adopt a structured methodology to investigate the application of TinyML in handling Big Data challenges within extensive IoT systems. Initially, our approach involves integrating IoT devices with Raspberry Pi units, which are crucial for managing the complexities of Big Data characterized by high volume, rapid velocity, and diverse variety while ensuring accuracy and value extraction.  
Subsequently, we concentrate on the technical deployment of TinyML on Raspberry Pis, focusing on essential tasks such as data cleaning, anomaly detection, and feature extraction. The effectiveness of these processes is comprehensively evaluated through a series of tests, ensuring that our approach aligns with the desired outcomes. Moreover, we introduce a feedback mechanism linked to the central Big Data system, enabling continuous updates and enhancements to the TinyML models on Raspberry Pis. This methodology is designed to create an efficient and adaptable system capable of addressing the dynamic needs of Big Data management in large-scale IoT applications and systems.  
Our approach involves deploying these algorithms on Raspberry Pi units, utilizing their strengths and capabilities in federated learning, anomaly detection, data compression, caching strategies, and data quality assessment. We systematically evaluate each algorithm's performance in real-time IoT scenarios, focusing on their efficiency in processing and managing data. This includes assessing the scalability, responsiveness, and accuracy of each algorithm in handling the unique data streams generated by IoT devices. By incorporating these algorithms into our methodology, we aim to provide a comprehensive solution for Big Data challenges in IoT systems, ensuring robust and efficient data management.
## 1 Introduction
### Baikal Region  
The Baikal region (Figure 1) includes the territories of the Irkutsk region, the Republic of Buryatia, and Zabaykalsky Krai (formerly Chita region). In the satellite image (Google Maps image was used), the main part of the territory is colored green. This is the woodlands.  
The area of the Baikal region exceeds 1.55 million square kilometers (9.1% of the territory of the Russian Federation), and it is home to about 4.5 million people [14]. Almost half of the region's territory is in the Irkutsk region, where 2.4 million people live.  
It is known that in the Baikal region multi-year average wind speeds (at a height of 10 m) are 2-6 m/s (they are the highest in the middle part of Lake Baikal), and the annual solar radiation incident on a horizontal surface ranges from 900-1000 kW\(\cdot\)h/m\({}^{2}\) in the north to 1300-1400 kW\(\cdot\)h/m\({}^{2}\) in the south [8].  
One of the features of the region is its high forest cover. Forest area occupies nearly 64% of the territory of Buryatia, 70% of the territory of Zabaykalsky Krai, and 83% of the territory of the Irkutsk region [15]. For comparison, the forest cover ratio for the Russian Federation as a whole is 46.4% [16]. Forests are not only a source of timber and various products, they also perform essential ecosystem functions by helping to stabilize the composition of the atmosphere.  
About 17% of Russia's total timber reserves are concentrated in the Baikal region, including 12% in the Irkutsk region [11]. The available significant timber reserves served as the basis for the timber industry. Enterprises of the sector produce industrial wood, lumber, pulp, cardboard, and paper.
# General Position Subset Selection in Line Arrangements +
## 4 Another Application  
In this section, we restrict ourselves to General Position Subset Selection in grid-like sets, that is, sets with at most \(\ell=O(\sqrt{n})\) points collinear and with a line cover of size \(\kappa=O(\sqrt{n})\). The approximation is obtained with an approach similar to that in the proof of Theorem 3.  
Proof of Theorem 4.: By the first assumption and Lemma 1, the number of collinear triples in \(P\) is  
\[T=O(n^{2}\log\ell)=O(n^{2}\log n). \tag{4}\]  
Since an optimal solution can contain at most two points of \(P\) from each line in an optimal line cover of \(P\), we have \(\mathsf{OPT}\leq 2\kappa=O(\sqrt{n})\). The subset selection algorithm consists of two steps: (i) random sampling from \(P\) and (ii) the application of the deletion method. In the first step, a random subset \(X\subset P\) is chosen by selecting points independently with probability \(p=k/n\), for a suitable \(k\) to be determined. Note that \(\operatorname{E}[|X|]=n\cdot k/n=k\).  
As in the proof of Theorem 3, it suffices to choose \(k\) so that  
\[\operatorname{E}[Tp^{3}]\leq k/2.\]  
Let \(c_{1}\) denote the constant hidden in (4). Using the upper bound on \(T\) in (4), it suffices to choose \(k\) so that  
\[2c_{1}k^{2}\log n\leq n.\]Setting \(k=c^{\prime}\sqrt{\frac{n}{\log n}}\) for a sufficiently small \(c^{\prime}>0\) satisfies the inequality, proving the lower bound on the size of the output. In addition, this setting is valid for ensuring that \(p=k/n<1\).  
The expected number of points in general position found using the algorithm is \(\Omega\bigg{(}\sqrt{\frac{n}{\log n}}\bigg{)}\). By recalling that \(\mathsf{OPT}\leq 2\kappa=O(\sqrt{n})\), it follows that the approximation ratio is  
\[\frac{\mathsf{ALG}}{\mathsf{OPT}}\geq\frac{1}{2\kappa}\cdot\Omega\bigg{(} \sqrt{\frac{n}{\log n}}\bigg{)}=\Omega\bigg{(}\frac{1}{\sqrt{\log n}}\bigg{)}\,,\]  
as claimed.