esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Transition of Thermoacoustic Instability Modes
After jet control, the thermoacoustic oscillation transitions to a semi-stable intermittent state, with a phase difference below 90\({}^{\circ}\) (Figure 8c). This semi-stable state is highly prone to evolving into a complete limit cycle oscillation state. However, after the introduction of the Ar/He stratified jet, the thermoacoustic oscillation shifts to a chaotic state (Figure 8b). According to the Rayleigh criterion, the Ar/He stratified jet effectively suppresses the limit cycle thermoacoustic oscillation and disrupts the original flame-acoustic coupling process (Figure 8). The response characteristics of sound pressure during the opening and closing process of the jet were analyzed (Figure 8). The red line in Figure 8d represents the time series of sound pressure signals in the combustion chamber, while the black line indicates the time series in the premixed chamber. After the introduction of jet-in-crossflow, the sound pressure intensity in the combustion and premixed chambers significantly decreases. This reduction in sound pressure intensity within the methane and air-premixed chamber can minimize fluctuations in the flame equivalence ratio, thereby weakening the intensity of thermoacoustic oscillations. Nonlinear characteristics are crucial for predicting thermoacoustic oscillations. 56-60. The behavior of these thermoacoustic oscillations under Ar/He stratified jets provides valuable guidance for designing burners.
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
## 4 Proposed Adaptive DFSD Detection Scheme
### The Adaptive DFSD Detection Scheme According to the Number of Nodes at the \(N_{t}\)-th Layer  
Figure 3 shows the overall adaptive DFSD tree structure in \(4\times 4\) MIMO-OFDM system with QPSK modulation. There are too many metrics to include in Figure 3, so several metrics are omitted through the dotted lines like in Figure 2. At the \(N_{t}\)-th layer, the nodes are sorted according to ascending order of squared ED. The adaptive detection scheme uses an DFSD detection scheme with a different number of nodes in an ascending order of squared ED at the \(N_{t}\)-th layer depending on the channel condition. Figure 3 shows the DFSD detection scheme using one node at the \(N_{t}\)-th layer and Figure 3 shows the DFSD detection scheme using all nodes at the \(N_{t}\)-th layer. A description of the DFSD detection scheme using two and three nodes at the \(N_{t}\)-th layer is omitted. In Figure 3, from Equation (10), the first radius distance is calculated as follows,  
\[d_{1}^{1}(1)=\left\|z_{1}-\left\{r_{11}s(1)+r_{12}\hat{x}_{2}^{1}+r_{13}\hat{x }_{3}^{3}+r_{14}\hat{x}_{4}^{1}\right\}\right\|^{2}+d_{2}^{3}(1). \tag{11}\]  
According to Figure 3 and Equation (11), the accumulated squared ED of the first radius is 12. Also, the path of the first radius includes the reference symbols of corresponding nodes. Transmit symbols are estimated through the reference symbols. On the other hand, in Figure 3, the first radius distance is equal to that in Figure 3. However, by updating the radius distance and pruning the path, Figure 3 shows the shortest accumulated squared ED as follows,  
\[d_{1}^{4}(3)=\left\|z_{1}-\left\{r_{11}s(3)+r_{12}\hat{x}_{2}^{4}+r_{13}\hat{x }_{3}^{1}+r_{14}\hat{x}_{4}^{4}\right\}\right\|^{2}+d_{2}^{1}(4). \tag{12}\]  
According to Figure 3 and Equation (12), the accumulated squared ED of the updated radius is 11. Finally, the path of the updated radius includes the reference symbols of corresponding nodes.  
According to the Figure 3, Equations (11) and (12), the DFSD detection scheme using all nodes at the \(N_{t}\)-th layer detects the signal more accurately than the DFSD detection scheme using one node at the \(N_{t}\)-th layer. However, the DFSD detection scheme using all nodes at the \(N_{t}\)-th layer passes through  
Figure 3: Adaptive DFSD tree structure in \(4\times 4\) MIMO-OFDM system with QPSK modulation. (**a**) Proposed detection scheme in the best channel condition; (**b**) Proposed detection scheme in the worst channel condition.  
more nodes than the DFSD detection scheme using one node at the \(N_{t}\)-th layer. So, when the channel has the best condition, the proposed detection scheme uses one node at the \(N_{t}\)-th layer. On the other hand, when the channel has the worst condition, the proposed detection scheme uses all nodes at the \(N_{t}\)-th layer.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 2 Driver Behavior Overview
### Factors Affecting Drivers' Behavior  
While driving, a driver's behaviors change when the driver is engaged in other, non-driving activities. This causes risks in terms of road safety as the drivers should be fully concentrated on their driving task. A driver in a distracting situation will most probably fail to respond to hazards and/or to anticipate them. Driver distraction has been classified by several researchers into two categories: internal and external distractions. Internal distraction can happen when the driver is eating, chatting and using entertainment systems. External distraction occurs when a driver looks at the outside of their vehicle as well as roadside advertising panels. Another classification presented in the literature classifies driver distraction into different types: most commonly auditory, biomedical, cognitive and visual. Auditory distraction happens when a driver is disturbed by a sound that breaks their concentration and focus on the road. This can be due to music, listening to the radio or chatting with other passengers [4]. Biomedical or physical distraction occurs when drivers are not controlling the steering wheel because their hands are used to answer a call, eating or adjusting vehicle settings [5]. Cognitive distraction is related to the driver's mind. This is represented by a reduction of the driver's awareness and concentration on the road. It can be due to mental pressure or personal/financial issues [6; 7; 8]. Visual distraction occurs when the driver's eyes are off the road. This can happen when the driver is adjusting a GPS address or looking at a billboard on the roadside [9]. A combination of these distraction factors will affect the driver's behavior and will lead to very dangerous driving situations [10].
## 2 Materials and Methods  
### Antenna Geometry  
The geometrical configuration of the proposed antenna is illustrated in Figure 1. The antenna is designed using ROGERS RT/droid 6010 having dielectric loss tangent (tan \(\delta\)) of 0.0023, relative permittivity (\(\varepsilon_{r}\)) of 10.2 and 1.9-mm thickness (H) [18]. The overall dimension of the reconfigurable antenna is \(A_{X}\times A_{Y}\). It can be seen from Figure 1a that the proposed monopole antenna consists of a triangle-shaped radiator having a dimension of \(P_{X}\times P_{Y}\) and the feed line dimension is \(F_{X}\times F_{Y}\). Two V-shaped slots were etched to notch the desire bands from wide bandwidth, the total length of the long slot is \(2\times L_{1}\), whereas the length of the shorter slot is \(2\times L_{2}\). The width of both slots had a dimension of d while the gap between two slots is \(g\). A truncated ground plane of length \(G_{X}\) was used to achieve wideband operational bandwidth. Table 1 shows the optimized parameters of the antenna.
## 4 Innovation through SDN-Based Network Applications
### SDN-Based Network Virtualization  
Multiprotocol Label Switching (MPLS) is a network technology that provides various network features with explicit tunneling. The MPLS tunnels may be used for traffic engineering (known as MPLS-TE), as well as for MPLS-based VPNs (MPLS-VPN). The former is often enabled by the Resource Reservation Protocol (RSVP-TE), and the latter often appears in the context of BGP and MPLS/IP virtualization. In [66], the authors discuss how MPLS-TE and MPLS-VPN solutions can be implemented as plugins to the NOX controller. The authors highlight the advantages of the OpenFlow-based approach that can effectively reuse network functionality from the OpenFlow protocol to implement the complex MPLS-TE and MPLS-VPN functionality with little effort. Generalized MPLS (GMPLS) is an extended variant of the MPLS control plane. It can create tunnels on the optical network layer. The authors of [67] provide a unified OpenFlow/GMPLS control plane that can be used to provide GMPLS-specific features to OpenFlow networks.  
Network virtualization can also be achieved with pure SDN-based solutions. They are commonly based on the idea of explicit flow table entry installation based on virtual network definitions. The work in [68] describes a virtual network definition and highlights how the virtual network definitions are converted to OpenFlow entries. The authors describe the SDN control plane decisions and how they verify the isolation properties of virtual networks. Hierarchical policies for network virtualization are discussed in [69]. The authors propose PANE, a system that creates flow-specific entries out of virtual network definitions. PANE exposes an API through the northbound interface that can be used to request virtual networks. The system is able to detect conflicts when new virtual networks are requested. PANE can resolve conflicting virtual networks automatically. Finally, the authors of [70] implement an SDN network virtualization component for Openstack [71]. Openstack is an open-source platform for building and maintaining private and public clouds. Their prototype is called Meridian; it is integrated in Openstack, and provides network-as-a-service features to Openstack. This allows Openstack to provide a virtual network for a cloud that has specific network requirements. For example, a complex cloud application can consist of a firewall, a database server and several web-servers. Meridian allows one to define a virtual network that connects the elements and provide consistent routing for external and internal traffic.
# Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling
## 3 DS-FedIDS
### Feature Distributions by Network Type
#### 3.6.2 Attack Distribution Analysis  
Table 3 shows diverse attack patterns across networks. CDN faces primarily Exploit attacks (61.87%), while IoT networks experience mostly Generic attacks (78.90%). Database networks show high Fuzzer activity (49.11%), with significant Exploit (22.74%) and Reconnaissance (17.59%) attempts.
## 4 Discussion
Third, this was a cross-sectional study that compared three walking speed conditions at a single measurement point, and thus repetitive training or changes over time were not considered. Therefore, it is not possible to confirm the effectiveness of fast or slow speed walking training. Finally, since this study's gait analysis was conductedbased on spatiotemporal indicators, kinematic or kinetic information that includes specific compensatory movements (including increased hip hiking or circumduction) could not be analyzed. Future research should involve subgroup analyses (comparing walking with and without a cane) with sufficient sample sizes to examine the influence of assistive device usage. Additionally, longitudinal or randomized controlled trials are also needed to investigate how repeated gait training at different walking speeds (including progressive speed-increase protocols) influences muscle activity and gait patterns over time. Furthermore, using 3D motion analysis would permit a more comprehensive assessment of joint kinematics and muscle activation responses across different walking speeds. In summary, this study revealed that increasing walking speed improved certain aspects of lower limb muscle activity and gait performance; however, it did not enhance overall gait quality or symmetry. Faster walking may improve biomechanical elements, including propulsion; nevertheless, it does not necessarily result in better gait symmetry. Gait symmetry depends more on complex neuromuscular factors such as balance, proprioception, and coordination than just speed. Therefore, clinical gait interventions for patients post-stroke should include multidimensional training approaches that target neuromuscular control, instead of focusing solely on speed enhancement.
## 4 Effect of Essential Factors on CE
### Frequency  
A wide range of ultrasonic frequencies was implemented during the extraction of natural products depending on the matrix type. The extent of structural damage of cell surface was observed to be the highest for frequencies under 100 kHz. The capillary effect is frequency dependent, as the rigid structures require high frequency (\(\leq\) 500 kHz), whereas low frequency (20-40 kHz) is suitable for flexible materials (vegetable matter, algae, etc.). The mechanism behind the capillary effect can be attributed to the peristaltic action of ultrasound waves resulting in expansion and contraction of capillary channels. However, in the case of high-intensity low-frequency sonication, the capillary effect is attributed to reciprocating pumping action at the open ends of capillary rather than peristaltic action.  
Low-frequency sonication is known to generate extreme cavitational conditions as compared to high frequency, which could be favorable for better extraction yield under optimum conditions. As low-frequency ultrasound exhibits a stronger sonophysical effect compared to high frequency, the reported data indicated an enhancement in substrate porosity and better mass transfer rate for the frequency range of 20-100 kHz. [PERSON] et al. [8] observed a better yield of salvianolic acid B from _Salvia milliorrhiza_ root under 28 and 45 kHz frequency as compared to 100 kHz. [PERSON] et al. [80] suggested 40 kHz be the most effective during the extraction of phenolics from grape pomace as compared to 80 and 120 kHz. Comparatively better extraction yield was reported for the frequency range of 20-40 kHz. Overall, even though low frequency has demonstrated better extract yield, but the specific frequency is substrate dependent. Hence, the effect of cavitation on the extraction efficiency depends upon numerous intrinsic characteristics, such as ultrasound frequency, intensity, temperature, HC pressure and the properties of mediums, such as viscosity.
## 2 Method
Thus, a neural network can be represented in terms of nested transformations of linear combinations of input units, where the initial input units are the predictor variables themselves, so that the overall network function with \(H\) hidden units and activation functions \(\phi\) and \(\psi\) becomes \[P\left(D_{i}|AD_{1}\cdots D_{i-1}\right)=\psi\left\{\sum_{h=0}^{H}\omega_{h}^{ \left(2\right)}\phi\left[\sum_{h=0}^{H}\omega_{h}^{\left(1\right)}P\left(D_{j}|A \right)\right]\right\},\,j\in\left\{1,\,2,\cdots i\right\},\,i=2,\cdots,N \tag{3}\]  
where \(\omega_{h}^{\left(1\right)}\) and \(\omega_{h}^{\left(2\right)}\) indicate the weights in the inner and outer layer, respectively, and the bias parameters are absorbed into the set of weight parameters by defining an additional input variable \(P\left(D_{0}|A\right)\) whose value is clamped at \(P\left(D_{0}|A\right)=1\). Using [PERSON] theorem (or the definition of conditional probability), we can decompose the conditional PMF as follows  
\[ \begin{array}{l}P\left\{C\left(x_{0}\right)=k|C\left(x_{1}\right)=l_{1}, \cdots,C\left(x_{N}\right)=l_{N}\right\}\\ =P\left(A|D_{1}D_{2}\cdots D_{N}\right)\\ =\frac{P(D_{1})P(A|D_{1})P(D_{2}|A)\cdots P(D_{N}|AD_{1}\cdots D_{N-1})}{P(D _{1}D_{2}\cdots D_{N})}\end{array}  \tag{4}\]  
where \(x_{1}\) is the nearest neighbor of \(x_{0}\) among the \(N\) locations, \(l_{1},\cdots l_{N}\in\left\{1,2,\cdots,K\right\}\). Therefore, we can compute the posterior occurrence probabilities of different classes according to Equation (4), and then estimate the category at the target location based on the \"maximum a posterior\" criterion. According to the above description, the proposed approach can be coded easily on the computer and the flowchart can be summarized as in Figure 1.  
One of the commonly used methods to compute the multi-point posterior PMF is the MCRF model. The highlight of this theory is that there is only a single spatial Markov chain that moves (or jumps) in  
Figure 1: Flowchart of the proposed method. a space, which avoids the small-class underestimation problem in modeling the categorical random field [2]. Nevertheless, the multi-point probabilities \(P\left(D_{i}|AD_{1}\cdots D_{i-1}\right)\), \(i=2,\,3,\cdots,N\) are difficult to calculate. Hence, the general solution of the MCRF model is deduced based on the conditional independence assumption, which is  
\[P\left(D_{i}|AD_{1}\cdots D_{i-1}\right)=P\left(D_{i}|A\right),\,i=2,\cdots,N \tag{5}\]  
By plugging Equations (3) and (5) into Equation (4), the posterior PMF can be determined. Note that \(P\left(A|D_{1}\right)\) is the two-point transition probability and can be estimated from paired samples. Additionally, \(P\left(D_{1}\right)\) and \(P\left(D_{1}D_{2}\cdots D_{N}\right)\) are the normalization constants and should be computed using the sum rule, since they do not contain the unknown event \(A\).
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 4 Results
### FPGA Results  
Table 1 presents the performance comparison between the regular CCRV32 ST processor core and the dual-core lockstep implementation using the VDCLS technique. The results were obtained by implementing both processor versions in the Xilinx VCU108 board. As previously noted, the bare fact that stall cycles have to be injected means that the VDCLS performance will be impacted. The extent of this impact depends on the executed software profile, bus utilization, etc. For example, the negative impact on CoreMark execution is much lower than that on Dhrystone. The presented results show that the negative impact is low and depends on the target application.  
Figure 9: Trace of the delay between the master and slave cores in VDCLS execution.  
Table 2 presents the FPGA resource utilization comparison between the regular CCRV32 ST dual-core processor implementation and the VDCLS one. Every dual-core lockstep implementation requires additional resources, such as error detection logic or switches that reorganize cores and memories on the system busses. In the VDCLS implementation the interrupt and multicore logic is much bigger due to the implementation of VCDLS Controller Module and Interrupt Injection Module. The price of additional 4% system resource utilization is compensated by the flexible split/core processing mode, where lockstep mode ensures fast error detection and makes security attacks more difficult. The processor system components used for comparison are presented in Figure 3. AMBA APB interconnect and peripherals as well as DMA block were excluded from the comparison.
### IoT Factors to Consider  
Several variables must be considered when choosing an LPWAN technology for an IoT application.  
Scalability and length of payload are essential factors. While all three technologies are very scalable, NB-IoT performs more effectively, with more than 100,000 devices per base station. NB-IoT also supports payload lengths up to 1600 bytes, which is longer than Sigfox (12 bytes) and LoRaWAN (243 bytes). The decision is based on the requirements of the application.  
Considering factors such as coverage and range, Sigfox performs better with a range that exceeds more than 40 km, and it is suitable for entire city coverage with minimal base stations [8]. NB-IoT has the lowest range, typically less than 10 km, targeting locations not covered by cellular mobile networks. In comparison, LoRaWAN offers a similar, yet slightly extended, range of up to 20 km.  
While Sigfox and LoRaWAN ecosystems are developed and commercially accessible, NB-IoT is still in the process of expanding internationally [5]. NB-IoT could use more energy with synchronous communication, which might reduce the lifetime of the device. LoRaWAN-Class-C and NB-IoT are recommended for low-latency applications [4]. Sigfox and LoRaWAN prioritize energy conservation and enhance battery life with end-device sleep modes. On the other hand, synchronous communication in NB-IoT may result in increased energy consumption and a lower device lifetime. While Sigfox and LoRaWAN-Class-A serve latency-insensitive applications, LoRaWAN-Class-C requires energy efficiency (EE) tradeoffs to achieve low bidirectional latency [8].  
An overview of the technical aspects and performance characteristics of Sigfox, LoRaWAN, and NB-IoT is given in Table 1.  
Intensive research has been done in the past about LoRa and LoRaWAN technology and its resources. This survey offers an in-depth study of various LoRaWAN resource features such as spreading factor, Bandwidth, Transmission power, and energy consumption. The study offers insights into the recent developments of LoRaWAN technology, considering factors such as network performance and stability. The study explores advanced strategies for optimizing EE by discussing state-of-the-art methods. The study investigates the integration of ML and AI algorithms to optimize LoRaWAN performance. Scaling LoRaWAN networks for extensive IoT deployments is also explored, considering key trends and challenges. The survey also identifies gaps and opportunities for the future, specifically focusing on performance enhancement and EE optimization.  
The rest of the survey is organized as follows. Section 2 offers the methodology used to gather the state-of-the-art information along with the research questions formulated. The properties and most relevant algorithms of LoRa and LoRaWAN technologies are discussed in Section 3. Section 4 presents the state-of-the-art with LoRaWAN and its integration with ML. Section 5 discusses the implications and analyzes the literature review based on research questions. Section 6 presents the conclusion and future work.