esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
###### Abstract  
The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatalities in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). Therefore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication technologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium-range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency. This research presents a comprehensive study of AV technologies and identifies the main advantages, disadvantages, and challenges.  
autonomous vehicles; Bluetooth; dedicated short-range communications; intelligent transport system; V2I; V2V; V2X; VANET; vehicular communications; ultra-wide band; ZigBee 2021
# A Survey of Graphene-to-Phoneme Conversion Methods
## 5 Dataset and Evaluation Metrics  
### Dataset  
#### 5.1.1 Monolingual Dataset  
The most widely used English G2P conversion datasets are CMUDict and NetTalk [54]. The CMUdict is an open-source pronouncing dictionary for North American English developed by Carnegie Mellon University, it contains 134,000 words and their pronunciations that are represented in ARPAbet format. NetTalk contains a list of 20,008 English words, with a phonetic transcription for each word, the NetTalk dataset is created for training a proper converter task. Besides the English G2P dataset, there are also various monolingual datasets for different languages. For example, ref. [55] created a Mandarin dataset that consists of 99,000+ sentences for Chinese polyphone disambiguation; ref. [56] proposed a Kurdish text corpus for the Central Kurdish (Sorani) branch; ref. [57] introduced an end-to-end Persian G2P converter with Persian dataset; ref. [58] provided an Arabic phonetics database that contains 46,000 files; ref. [59] described a 150,000-entry Japanese phonetic database; ref. [60] developed a text corpus for the Malayalam language with 62,000 words.
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 2 An Overview of Literature on Denial-of-Service in Control Systems
### Networked Control Problem
#### 2.1.2 Continuous-Time Setting
The performance of periodic and event-triggering networked control approaches under different types of jamming attacks was investigated in [58]. Additionally, frequency regulation problems in power networks under DoS attacks are considered in [59; 60]. In both studies, the nodes in the power network are assumed to communicate over insecure communication channels that are subject to DoS attacks. In the problem formulation in [59], nonlinear dynamics are explored. Moreover, the researchers propose an event-based control approach in [60]. In [61], researchers considered networked control problem under DoS attacks and data-rate constraints, where state measurements are quantized through a dynamic quantization scheme. We note that, among the different control approaches used for networked control systems under DoS attacks, event-triggered control (see, e.g., [25; 38; 48; 54; 58], and the references therein) appears to be the most commonly considered approach. We discuss the resiliency of event-triggering approaches against DoS attacks further in Section 5.1.1.
## 5 Results and Discussion
### Average Energy Consumption  
The amount of energy consumed during data transmission is determined by the energy consumption. Figure 6 presents the average energy consumption for several network rounds. The average energy consumptions of the EECHS-ARO, EECHS-ISSADE, PDU-SLno, and EACH-COA were 0.55 J, 0.50 J, 0.47 J, and 0.39 J, respectively. The proposed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{4}{c}{**Network Longevity**} \\ \cline{2-5}  & **EECHS-ARO** & **EECHS-ISSADE** & **PDU-SLno** & **EACH-COA** \\ \hline
100 & 130,000 & 140,000 & 170,000 & 180,000 \\ \hline
200 & 220,000 & 200,000 & 240,000 & 260,000 \\ \hline
300 & 310,000 & 300,000 & 360,000 & 400,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Throughput vs. number of nodes.  
Figure 5: Throughput vs. number of nodes.  
EACH-COA approach minimizes energy consumption by 16%, 6%, and 8% compared to EECHS-ARO, EECHS-ISSADE, and PDU-SLnO, respectively, for the 3000 th network round. The proposed approach has a shorter convergence time for CH selection. This is because the COA considers the RER and distance parameters to compute the fitness function. It has been noted that there is an escalation in energy consumption as the number of network rounds continues to escalate.  
The average energy utilization of several techniques in association with several network rounds is listed in Table 5. The proposed EACH-COA consumed less energy than the other algorithms. It was found that energy consumption increased as the number of network rounds increased.
## 3 Image Processing Technique Used
### Morphological Operations  
Mathematical morphology is based on two basic operations: dilation, which fills holes and smoothens the contour lines, and erosion, which removes small objects and disconnects objects connected by a small bridge. Morphology is based on set theory. The shapes of objects in a binary image are represented by object sets. This theory can be extended to gray-scale images. Morphology operations can simplify image data, preserving the object's essential shape characteristics. They can also eliminate irrelevant objects. The main advantage of a morphological filter is the ability to preserve the shape of objects that are large enough. A morphological filter has also been proven for segmenting brain and other image analyses. Figure 7 shows initial treatments with erosion and dilatation on an image of 94 \(\times\) 94 pixels (0.2 mm \(\times\) 0.2 mm). The erosion and dilation was coded with a rolling ball structuring element, which automatically takes advantage of the decomposition of the structuring element. For the rest of the touching object after applying erosion and dilation, it can be breakable while applying the watershed splitting method.
## 3 Turning Gait Planning in the Non-Inertial Reference (IRF)
### Foot Position and Allowable ZMP Region Planning  
The foot position determines the area supporting the robot, so it must be determined first. For the turning walk, we use a circular curve interpolation to generate the foot trajectory, which is shown in Figure 7. In the single support period, the foot position of the swinging leg will follow the curve of the circle.  
At this stage, we know the foot step, the walking cycle period, and the turning angle. In turning walk, the foot steps of the two legs are not the same. Therefore, we must first calculate the right foot radius (\(r_{\text{str}}\)). The foot step is approximately equal to the arc length.  
The radius of the left foot should be calculated as shown in Formula (19) in order to calculate the right foot radius:  
\[r_{\text{st,l}}=l_{\text{st,l}}/\theta. \tag{19}\]  
Suppose now that the robot turns right while walking. Here, \(l_{\text{st,l}}\) is the right foot step, and \(\theta\) is the turning angle. The right foot radius is \(r_{\text{st,r}}=r_{\text{st,l}}+w_{\text{hip}}\). We can also obtain the right foot step as \(l_{\text{st,r}}=\theta(r_{\text{st,l}}+w_{\text{hip}})\), and \(w_{\text{hip}}\) is the distance between two feet. The foot trajectory is then generated by spline interpolation. The foot position can be calculated when the turning angle (\(\theta(t)\)) is known. The turning angle (\(\theta(t)\)) is obtained by cubic spline interpolation. This can guarantee that the foot speed at the beginning and end of the single support period is zero.  
In straight-line walking, the foot coordinate only has translation related to the hip coordinate, so the hip yaw joint does not turn, but in turning walk, besides the translation, the foot coordinate also has rotation related to the hip coordinate, so the hip yaw joint will play a role in coordinate rotation shown in Figure 8.  
After the foot positions have been determined, the next step is planning the allowable ZMP region (AZR). This is a prerequisite for planning the ZMP trajectory. The AZR is the polygon that is surrounded by the supporting feet. However, for turning walk, the front foot direction differs from that of the rear foot. Therefore, in the double support period, the AZR is as shown in Figure 9.  
The directions of the \(x\) and \(y\) coordinates are the same as those in the IRF. We suppose that the robot is at the position shown in Figure 9, and the position of AZR at the world coordinate reference is also known. So, we should express the AZR in the IRF. After we have determined the \(\alpha\) and the position of \(\
u_{\text{com}}^{\text{IRF}}\), we can easily obtain the expression of the AZR in the IRF through the positive kinematics.  
Figure 7: Foot position planning.
## 6 Discussion
Finally, despite studies commonly reporting that most energy consumption by mobile sensors is caused by the movement of the sensors and is related to the distance traveled, this explanation is not practical in some situations. For example, when a small drone is used as a mobile platform to carry sensors, the energy consumption will also vary according to weather conditions, changes in the communication environment, or flight altitudes. Therefore, additional factors that may affect energy consumption must be considered in practical applications.
# On the Capacity of Optical Backbone Networks
## 2 Basics on Optical Networks and Physical Impairments  
### The Concept of WDM  
Figure 1 helps us to understand the concept of WDM. This figure shows \(N_{ch}\) optical channels, each characterized by its carrier frequency \(\
u_{k}\) (where\(k\in\{1,\ldots,N_{ch}\}\)) and symbol rate \(R_{s}\) (in symbols/s or baud), which is related the channel bandwidth through  
\[B_{ch}=(1+\beta)R_{s}, \tag{1}\]  
with \(\beta\) (\(0\leq\beta\leq 1\)) being the roll-off parameter. It is important to note that the frequency \(\
u\) in Hz is related to the wavelength \(\lambda\) in meters via \(\lambda\
u=c\), where \(c\) is the speed of the light in the vacuum (approximately \(3\times 10^{8}\)m/s). The signal transmitted through the optical channel is assumed to be based on a sequence of symbols at the rate of \(R_{s}\). Each symbol encodes multiple bits of information using the amplitude and the phase of the signal. This process is known as modulation. In a modulation scheme with a symbol constellation of size \(M\), each symbol corresponds to \(\log_{2}M\) bits of information. Examples of modulation schemes used in digital communications include M-ary phase-shift keying (MPSK), which utilizes the phase of the optical signals, and M-ary quadrature amplitude modulation (MQAM), which utilizes both the phase and amplitude.  
Furthermore, the spacing between the different channels is denoted as \(\Delta\
u_{ch}\), subject to the condition \(\Delta\
u_{ch}\geq B_{ch}\). As a consequence, the bandwidth occupied by the WDM signal is equal to \(B_{WDM}=N_{ch}\Delta\
u_{ch}\).  
When the spacing \(\Delta\
u_{ch}\) is very narrow (on the order or tens of GHz) this technique is designated as dense WDM (DWDM). DWDM can be implemented using two approaches: fixed-grid and flexible-grid. In the fixed-grid approach, the channel spacing \(\Delta\
u_{ch}\) is kept constant typically at 50 GHz. In contrast, the flexible-grid approach sets \(\Delta\
u_{ch}\) as a multiple of an elementary spectral slot of 12.5 GHz and is adjustable according to the requirements of the optical signals being transmitted. DWDM is typically employed in long-haul and high-capacity applications, where it can support over 100 optical channels per fiber. As seen, the number of channels provided depends on \(\Delta\
u_{ch}\) and \(B_{WDM}\), with the latter parameter being limited by the bandwidth of the EDFAs. Most of the currently deployed DWDM networks operate in the C-band, which is a wavelength range centered around the wavelength of 1548 nm (193.7 THz). Nowadays, standard EDFA technology offers a bandwidth of approximately 4800 GHz (Extended C-band), although more advanced solutions can achieve values up to 6000 GHz (Super C-band). It is worth mentioning that for a channel spacing of 50 GHz, the first solution can support up to 96 channels, while the second one can accommodate up to 120 channels [19].
## 3 Initial Results
### Discussions: Towards Real-World Application and Stakeholder Alignment
#### 4.4.1 Empowerment Through Data Justice and Participation  
Decentralization affects more than just control of individual information--it alters the very structure of power in data ownership. Historically, data were at risk of misuse, as access to them was gate-kept by a small number of large corporations and government agencies, as described in [151]. This status quo is rebalanced through blockchain technology, which allows individuals to own and control their digital assets, leading to consumer empowerment and fairness in making algorithmic decisions. For the use of legal and social services, this builds on a more inclusive dataset, providing a scenario in which algorithmic decision-making can be less biased than currently.  
Using the concept of decentralization, as delivered by BIAI, alongside transparency and enhanced security, we can identify several directions in which economic and social equity in the context of mitigating sextortion may be discussed. The structure of the discussion moves from micro to macro and from victim to perpetrator, highlighting how BIAI can positively affect this chain. First, BIAI ensures democratized access to financial resources and digital services, promoting economic equity according to the token economic model shown in the previous section. This feature may lead to providing a secure platform for sextortion victims to seek help without the danger of additional exploitation.  
Second, BIAI supports social equity by increasing security through privacy-aware data processing enabled by ZKP systems. For victims of sextortion or potential victims, this level of security and accountability is essential because it gives them confidence that their data is being stored and handled properly and that there is accountability in such instances when a data breach occurs. This empowers victims, encouraging them to seek help and resources for protection; in addition, the blockchain side of BIAI facilitates decentralized reporting systems, which in turn permit sextortion victims to report incidents securely and anonymously. As a consequence, the risk of social ostracism is mitigated and social equity is advanced by safeguarding the well-being of victims.
## 3 Results and Discussion
### Numerical Results of DFT Full Spectrum  
Spectral analysis is an alternative way of identifying and analyzing signals, which becomes complementary to time domain analysis. For this, the signal is decomposed in the frequency domain, allowing the identification of the components of that signal. A known tool for the treatment of discrete and aperiodic signals is the DFT (\"Discrete Fourier Transform\"). A conventional DFT is called a \"half spectrum\", as it relates only the positive part of the frequency domain, commonly in harmonics, e.g., \(1\times,2\times,3\times\) etc. For cases in which information about the relative phase is important, such as in the diagnosis of unbalance and misalignment faults, among others, the application of the \"DFT full spectrum\" is indicated [27]. In these situations, the full spectrum enables the determination of the rotor's behavior under \"forward\" and \"backward\" precession conditions. Each orbit has, in general, an elliptical shape, which can be represented as the sum of two circular orbits, one in forward rotation and the other in backward rotation, both rotating at the same frequency, which is the frequency of the filtered orbit. Thus, the orbit will be more or less circular, depending on the amplitude of the signal components in the frequency domain. Therefore, Figure 9 shows the first results for the elliptical bearing 1, with preload 0.25, using the DFT full spectrum decomposition. To facilitate the interpretation of the graphs, Table 1 brings a comparison of the magnitudes of the most relevant amplitude peaks for all analyzed cases. The intention is to compare the influence of linear and nonlinear models on the frequency spectrum for the different bearing types. Figure 9: DFT full spectrum of simulated results in elliptical bearing 1 (preload 0.25): (**a**) linear, (**b**) nonlinear. It is possible to observe the presence of the \(-1\times\) component in all rotations, for both models, on the left-hand side of the spectrum, i.e., frequency related to backward mode, due to the fact that hydrodynamic bearings inherently introduce some anisotropy into the system. In linear cases, there are only the \(1\times\) and \(-1\times\) components, indicating the purely elliptical shape of the orbit, as can be attested in Figure 5. For nonlinear cases, there are significant values related to high order harmonics, such as \(2\times\) and \(-2\times\). For 35 Hz, the components \(-3\times\) and \(3\times\) appear, indicating the proximity of the system's critical speed region, which is an indication that the system is more nonlinear under these conditions. These additional harmonics influences the orbit size and shape, which can again be clearly observed in Figure 5.  
By increasing the preload to 0.8, for the elliptical bearing 1, it is possible to observe similar behavior to the case of smaller preload, as shown in Figure 10 and Table 2. The amplitudes are at least 1 order of magnitude lower, and there is no presence of \(-3\times\) and \(3\times\) components for the rotation of 35 Hz, due to the smaller orbit originated by the low journal eccentricity.
# Agricultural Residue Management for Sustainable Power Generation: The Poland Case Study
## 4 Results
### Crop Residue Availability for Power Generation  
Agricultural crop field residues are straw, stover, stalks, stubble, seed pods, etc. They can be used for energy production. In this paper widespread crops have been selected: wheat, triticale, rye, barley, oats, mixed grain, and rape. Productions of their residues are examined. To calculate residue production, we use the Equation (1). There is stability in sown area. Since 2010 there has been a rise in sown area by 3.7% (from 10,366 to 10,757 thousand ha) [68]. Since 1999 the share of the above crops has increased from 75.02% to 81.25%.  
Crop production is growing (Figure 2). Gross grain crop production ranged from 21.34 to 31.79 Mt [82]. Average, minimum and maximum grain crop harvests over the years 1999-2018 are listed in Table 1. The main crops are wheat and triticale. Their average harvest was 13.49 Mt or 49.87% of the national harvest.  
Applying [PERSON]'s method, clusters of voivodships were identified for the year 2018. The cluster analysis was based on official statistical data on crop harvest [72; 82], the Residue-to-Crop Ratios [65; 69; 70; 71], quantity of straw for animal feeding and bedding [72; 73; 74; 75; 76]. The crop residue energy potential is calculated by the Equation (2). Since 1999 the total energy potential has increased by 168.55 PJ. It is the result of an increase in the total harvest (Figure 2) and a decrease in the livestock population [81].  
The average national density of crop residue energy potential is 13.62 GJ/ha. This value is somewhat higher compared to Ukraine (13.45 GJ/ha) [66]. The potential power  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crop** & **Average** & **Maximum** & **Minimum** \\ \hline Wheat & 9.46 & 11.67 & 7.06 \\ Triticale & 4.03 & 5.34 & 1.90 \\ Rye & 3.16 & 4.86 & 2.01 \\ Cereals mixed for grain & 3.40 & 4.32 & 2.25 \\ Oats & 1.33 & 1.52 & 1.03 \\ Barley & 3.40 & 4.16 & 2.78 \\ Corn & 2.87 & 4.47 & 1.26 \\ Total & 27.05 & 31.79 & 21.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average, minimum and maximum grain crop harvests over the past 20 years, Mt (from 1999 to 2018).  
Figure 2: Total cereal production.  
generation of straw-based power plants was estimated at 14.99 TWh of 8.82% of the total national generation. Potential power generation is determined by the Equation (3).  
Biogas production from livestock waste can increase the energy potential and, therefore, power generation.
# Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors
## 5 Simulation Results
### Network Lifetime  
The number of live nodes is shown in Figure 6. These numbers are for the proposed method, the LEACH method [23], the RED-LEACH method [27], and the gravitational search algorithm (GSA)-based method [37]. In this diagram, the initial energy of the sensor nodes is assumed to be half a joule. Figure 6 shows the network lifetime based on the analyzed methods, and Figure 7 shows the number of off nodes.  
It can be perceived from Figure 7 that, by increasing the number of steps, the energy of the live nodes will eventually run out and turn off. In the LEACH method, after 350 steps, the RED-LEACH method, after 750 steps, and the GSA-based method, after 915 steps, the nodes start shutting down, which occurs after 1227 steps for the proposed method. This means that the nodes are turned off later. Since the first node's shutdown time is considered in defining the lifetime of the network, this figure indicates that the suggested method offers a longer lifetime. The reason for this improvement is that the proposed method distributes the consumed energy in a balanced way among the network nodes. On the other hand, by increasing the number of steps, the percentage of live nodes in the proposed method works better than that of the other methods. This method means that after almost 2155 steps all of its nodes are turned off, and, until then, the probability of establishing a route for sending packets is higher than that of the other methods.  
As can be seen in Figure 6, all of the network nodes in the LEACH method lost their energy after approximately 1418 steps, after 1859 steps for the RED-LEACH method, and after 1971 steps for the GSA-based method, while in the proposed method this event occurs after 2155 steps. This makes the nodes active in the network for a longer period of time and raises the network's lifespan.  
Figure 6: Network lifetime based on the analyzed methods.