esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Intervalley Scattering and Franck-Condon Transition
### Conventional Theories  
In the conventional theory of semiconductors, the microscopic quantum origin of the IVS has been understood in the context of the inelastic e-ph scattering [31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. In this mechanism, final L valley electrons and phonons must be independent of each other to achieve the process, while the scattering e-ph complex [41] is created after the excitation. For this purpose, the phonon should recoil and dissipate away from plane-wave electrons. To realize the energy and momentum conservation laws exactly in this inelastic and irreversible process, the phonon should oscillate at least a few periods or so, being inevitably slow [41], since the phonon oscillation period is \(\sim\)110 fs [40].  
This argument can also be seen from the viewpoint of the Markov approximation or the linear response theory [42; 43; 44]. As seen in Equation (8), for instance, the transition rate by the inelastic e-ph scattering does not depend on the phonon frequency directly, implying that the phonon field works as a heat reservoir for the electron. This situation is validated when the averaged amplitude of the randomly oscillating phonons in the whole system is almost zero. This is the white noise condition in the Markov approximation. In the present problem, however, the time scale to pay attention to is much shorter than the phonon oscillation period. Then, the inelastic e-ph scattering is not adequate for the explanation of the present ultrafast IVS, and the detailed and microscopic origin of the aforementioned ultrafast IVS is under significant debate and should be investigated further.  
On the other hand, the e-e scattering would be one of the promising mechanisms to realize the ultrafast transition. This mechanism has been examined by the ensemble Monte Carlo method, which has shown the time constant of the \(\Gamma\) to L (\(\Gamma\)-L) transition to be of the order of 100 fs [45]. Then, the e-e scattering is one of the promising mechanisms for the ultrafast IVS. However, the 2 PPES result has revealed that the time constant strongly depends on the temperature, implying that the understanding only by the e-e scattering is not enough.
## 3 Research Method
### Data Preprocessing  
To obtain high-quality audio, the sound data needs to be preprocessed before feature extraction. Data preprocessing mainly includes audio format conversion, denoising, and endpoint detection. The format conversion unifies the birdsong file format as \".wav\", and monophonic, and the sampling frequency is unified as 16 kHz. The wavelet threshold denoising is used to remove the noise, and the endpoint detection removes the silent part of the sound data. The data preprocessing process is shown in Figure 3:
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 3 Proposed Solution  
### Design Objectives  
1. Decentralization: Based on the flexible nature of MANETs, where nodes continuously enter and leave a network without administrative action, trust evaluation for resident nodes is an essential component. However, it is impractical to assess nodes within multi-hop distance using only a centralized node or set of nodes. Therefore, decentralized trust evaluation is crucial for MANETs, meaning the nodes in a MANET should perform mutual assessment that is decentralized and straightforward.
2. Availability & Consistency: For decentralized systems, such as MANETs, the availability of data is an important factor that must be considered. Each node should have trust values (TVs) for its relay nodes, even when they exist at multi-hop distance, so it can select the optimal nodes for forwarding data. Additionally, the data accessed by all nodes should be consistent for each node.
3. Tamper-Proofing: Based on some of the aforementioned design goals, such as decentralization and availability, all available information will be accessed by all nodes in the network. Even with reliable support from the proposed scheme, critical information should not be tampered with by malicious nodes in the network. In other words, the system should be resilient to a small set of collaborative adversaries.
4. Lightweight performance: Based on strong security goals, it is important that any trust solution provide reasonable performance, even with the complex computations required for authentication. However, for MANETs with limited resources, it is desirable to use a straightforward and effective solution, rather than a strongly secure, but complicated mechanism.
5. Efficiency of security model in MANETs: To the best of our knowledge, most trust solutions require repetitive processes to be performed based on the dynamic nature of MANETs. Moreover, a network is vulnerable to repeated attacks from the same attacker when there is no collaboration between individual nodes. Therefore, a trust solution with greater efficiency must be developed for MANETs.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 6 Results
### Resilience  
Figure 19 present the results for the resilience of the network, measured with a time-series produced by a variable associated to the packet retransmission average. The simulation time is 100 s. The jammer node is in the middle of the topology and the jammer attack starts at second 30. We can observe that MPH-M has around 15% lower exchange rate than AODV-M and DSR-M. In addition, the recovery time of MPH-M is 5 s faster than AODV-M and 2 s faster than DSR-M.  
Table 9 shows the numerical values in seconds of the resilience of the network under each of the protocols studied. We observed that the mechanism of mitigation allows the reconfiguration of the links in the nodes be done in a faster way in each protocol.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Without Mitigation Mechanism**} \\ \hline  & **Re-Transmissions** & **CSMA Retries** & **Overhead** & **Delay End-to-End** & **Energy** \\ \hline
**AODV** & 24.87\% & 25.09\% & 52.28\% & 69.74\% & 62.09\% \\
**DSR** & 20.21\% & 21.09\% & 41.97\% & 51.88\% & 53.18\% \\ \hline  & \multicolumn{4}{c}{**With Mitigation Mechanism**} \\ \hline
**AODV-M** & 72.97\% & 21.99\% & 78.39\% & 110.82\% & 138.13\% \\
**DSR-M** & 71.17\% & 6.28\% & 65.57\% & 102.85\% & 126.07\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: A demonstration to show, for each metric, how better MPH is more efficient than AODV and DSR.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Re-Transmissions** & **CSMA Retries** & **Overhead** & **Delay End-to-End** & **Energy** \\ \hline
**AODV-M** & 20.33\% & 32.27\% & 23.31\% & 21.41\% & 3.92\% \\
**DSR-M** & 18.10\% & 39.04\% & 23.65\% & 15.50\% & 3.49\% \\
**MPH-M** & 42.49\% & 30.55\% & 34.53\% & 36.73\% & 34.61\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: A demonstration to show, for each metric, how each protocol improves each metric when the mitigation scheme is used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Without Mitigation** & **With Mitigation** \\ \hline
**AODV-M** & 9 s & 7 s \\
**DSR-M** & 6 s & 5 s \\
**MPH-M** & 4 s & 2 s \\ \hline \hline \end{tabular}
\end{table}
Table 9: Resilience in seconds for AODV, DSR and MPH.
## 4 Mapping Algorithm  
We will describe a satellite network mapping method based on RL. The algorithm mainly learns optimal mapping strategies through interaction between agents and the environment. Specifically, during the learning process, the agent needs to interact with the environment, select corresponding actions based on current virtual network configuration, usage of physical network resources, network traffic, and other information, and receive feedback from the environment. Based on the feedback, the policy is updated. Through continuous learning and optimization, the agent can eventually learn an optimal virtual network mapping strategy, which effectively meets the ever-changing business requirements.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 2 Background
### Brief Review on O-RAN  
A number of recent research works have shown that O-RAN can support the next-generation smart IoT systems in several ways. For example, in two separate works, researchers in [PERSON] et al. [48] and [PERSON] et al. [49] considered the Internet of drones by integrating UAVs with O-RAN architecture to optimize user routes to the core network to improve resource allocation and enhance offloading tasks. Similarly, [PERSON] et al. [36] proposed a computation offloading strategy that minimizes energy consumption and reduces the communication latency of O-RAN-based IoT systems. [PERSON] et al. [50] leveraged the RAN intelligent controller (RIC) to enhance IoT handover management. [PERSON] et al. [51] emphasized that merging AI with 5G is essential in facilitating open networking (vendor independence) as well as realizing the next-generation smart IoT systems. [PERSON] et al. [35] adopted O-RAN to optimize federated learning (FL) operation in supporting distributed edge intelligence of 5G-based IoT systems. [PERSON] et al. [52] addressed network slicing security in cyber-physical systems (CPS), while [PERSON] et al. [53] considered extending O-RAN capabilities to virtual reality applications. [PERSON] et al. [54] presented the network digital twins (DT) concept to reflect the RAN's operation for taking the appropriate action. Likewise, [PERSON] et al. [55] demonstrated the integration of DT with O-RAN as inevitable for yielding the 6G RAN.  
Similarly, there are several surveys and reviews on O-RAN, its application areas, and areas of challenges. For example, [PERSON] et al. [31] presented a comprehensive tutorial on O-RAN, discussing its architecture, interfaces, and workflows, giving a big picture of how O-RAN aims to transform the next-generation cellular networks. In addition to covering the areas of research successes and challenges, [PERSON] et al. [56] presented both threats and possible solutions that are associated with O-RAN's security and privacy concerns. [PERSON] et al. [57] presented a systematic adversarial machine learning (AML) threat analysis for O-RAN, discussing some of the possible AML countermeasures and a method for conducting risk assessments. [PERSON] et al. [58] reviewed O-RAN capabilities. Likewise, [PERSON] et al. [59] reviewed network slicing management in Industrial IoT. Thus, there is room for more effort within the public domain to provide a holistic investigation of the applications of O-RAN in smart IoT systems.
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 3 Materials and Methods
### Employed Datasets
#### 3.1.1 KU-HAR Dataset  
The KU-HAR dataset [54] was utilized for model selection and pre-training. It was released in 2021 and includes 20,750 non-overlapping time-domain samples from 18 classes, namely: stand, sit, talk-sit, talk-stand, stand-sit, lay, lay-stand, pick, jump, push-up, sit-up, walk, walk backwards, walk-circle, run, stair-up, stair-down, and table tennis. The sensor signals were collected from 90 volunteers aged 18 to 34 using a waist-attached smartphone and contain raw signals from a triaxial accelerometer and gyroscope. Each sample lasts 3 s and consists of six channels collected at a sampling rate of 100 Hz using a smartphone.  
During data acquisition, the gravitational acceleration was discarded, and neither denoising nor filtering was performed. KU-HAR is regarded as a realistic dataset, as it is an unbalanced dataset with no overlap between the samples and no denoising operations performed.
# Event Detection Using a Self-Constructed Dependency and Graph Convolution Network
## 4 Experiments
### Comparison Experiment  
To verify the effect of our model, several baseline models of event detection were compared. Three event detection models based on feature, sequence and continuous GCN were selected for comparison:  
1. Feature-based model: Event detection is performed by manually designing features. CrossEntity [23] uses cross-entity information to detect events, and MaxEnt [24] uses lexical and syntactic features to detect events.
2. Sequence-based model: Event detection is performed by processing and analyzing word sequences in sentences. DMCNN [8] builds a dynamic multi-pool CNN to learn sentence features, JRNN [9] uses bidirectional RNN to learn dependencies in sequences, and DBRNN [13] uses bidirectional RNN based on a syntax dependency tree to capture the dependencies between words.
3. GCN-based model: A graph is built according to the dependency of sentences, syntactic information is extracted through GCN, and events are detected. JMEE [5] combines an attention mechanism with GCN to improve the performance. RGCN [25] uses an adjacency matrix and a convolution filter of a specific relationship to model data to perform tasks. MOGANED [20] updates the node representation by aggregating the attention of each layer on GCN with an attention mechanism; EE-GCN [7] uses GCN to learn semantic information and add dependency information to dynamically update the representation of nodes and edges.  
Table 2 shows that our model can effectively improve the performance of event detection tasks. Of all considered models, the precision value and F1 score of our method are the highest, at 79.2% and 78.0%, respectively. We attribute the performance gain to twoaspects: (1) Compared with the other dependency-parsing-based event detection models, we do not use a full range of parsing labels, which reduces not only the training parameters of the model but also the introduction of redundant information and avoids the propagated error from the dependency parsing tool. (2) We use the multi-head attention mechanism to improve the GCN model to select important dependencies; finally, semantic representation and dependencies are dynamically fused through a gating mechanism. Although the EEGCN model also integrates this information, it does not distinguish the importance of dependencies, and does not use dynamic fusion mechanism, but simply splices.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 2 Materials and Methods
### Koch Fractal Antenna with Tunable FSS  
Initially, for tuning purposes, an FSS is introduced below the radiating patch at a height of 9.4 mm from the antenna radiator, as shown in Figure 6a. The distance between the two substrates (H) is kept equal to 7.8 mm, as displayed in Figure 6b. Both substrates of the patch and FSS are FR-4 with a thickness of 1.6 mm. After this, the FSS is varied from bottom to top at a 2.2 mm distance below the ground of the antenna in a step size of 0.8 mm. It is found that the antenna is tuned at a frequency of 3.5 GHz with a 3.8 mm air gap between the substrates, while an improved gain is obtained at an air gap of 7.0 mm (proposed design). The designed FSS is simple and contains only four square copper stubs with a side length of 5 mm and separated from each other by 10 mm. The top layer, bottom layer, and FSS surface views of the antenna are shown in Figure 6c. It is also noticed that the FSS below the antenna radiator improves the gain to a greater extent as compared to the FSS above the antenna radiator. The proposed antenna design model is represented in Figure 6d. All dimensions of the proposed antenna are illustrated in Table 1.  
The variation in the distance between the patch and FSS surface changes the volume of air between the surfaces. Therefore, the cumulative effective dielectric constant changes to a new value and is given by [37]  
\[\varepsilon_{\text{r\_cu\_effective}}=\frac{\varepsilon_{\text{reff}}\times \text{h}(\text{antenna})+\varepsilon_{0}(\text{air})\times\text{H}(\text{ Gap})}{\text{h}(\text{antenna})+\text{H}(\text{Gap})} \tag{8}\]  
Cumulative effective wavelength:  
\[\lambda_{\text{r\_cu\_effective}}=\frac{\lambda_{0}}{\sqrt{\varepsilon_{\text{ r\_cu\_effective}}}} \tag{9}\]  
\[\mathcal{O}_{\text{phase\_diff.}}=\mathcal{O}_{\text{FSS}}+2\text{gd} \tag{10}\]  
Figure 4: Frequency-selective surface: (**a**) FSS structure and (**b**) equivalent circuit of FSS.  
Figure 5: Step-by-step design development.  
where \(\beta\) is the propagation constant and d is the distance between the radiating patch and the surface of the FSS. Constructive interference will result when the \(\mathcal{O}_{\text{phase-diff.}}\) is equal to either \(0^{\circ}\) or \(2\pi\).  
Distance between antenna radiator and FSS layer:  
\[\text{d}=\frac{2\text{N}\pi-\mathcal{O}_{\text{FSS}}}{2\beta}=\frac{2\text{N} \pi-\mathcal{O}_{\text{FSS}}}{2\Big{(}\frac{2\pi}{\alpha_{\text{\tiny{$c$,$ cu$, effective}}}}\Big{)}} \tag{11}\]  
where N = 0, 1, 2, 3,...  
\[\text{d}=\text{h}(\text{substrate})+\text{H}(\text{gap}) \tag{12}\]  
Therefore, the gap between the substrates is  
\[\text{H}(\text{gap})=\text{d}-\text{h} \tag{13}\]  
Figure 6: [PERSON] fractal antenna (**a**) with FSS; (**b**) side view; (**c**) top patch, bottom ground, and FSS view; and (**d**) antenna 3D model.
## 2 Calculation Theory and Model
### Finite Element Analysis Model
#### 2.1.3. Suspension Bridge Model  
When the load is applied to the primary beam, it is transferred to the suspenders, and then to the main cables and the towers. The track-suspension bridge interaction finite element model is shown in Figure 4. The calculation model is spatial. The interactions of the main cables, suspenders, towers and the primary beam are well simulated. The multilayer structures including rail, fastener, sleeper, ballast bed, and beam are considered in this model to calculate the continuous welded rail force and deformation accurately.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Units** & **Value** \\ \hline Sleeper mass (half) & kg & 180 \\ Stiffness of rubber mat under rail & N/m & \(6.5\times 10^{7}\) \\ Damping of rubber mat & N-s/m & \(7.5\times 10^{4}\) \\ Fastener longitudinal resistance & kN/fastener & 15 \\ Sleeper pitch & m & 0.6 \\ Track bed density & kg/m\({}^{3}\) & 1800 \\ Vertical support stiffness of track bed & N/m & \(1.4\times 10^{8}\) \\ Shear rigidity of track bed & N/m & \(7.84\times 10^{7}\) \\ Shear damping of track bed & N-s/m & \(8.0\times 10^{4}\) \\ Vertical vibration quality of track bed & kg & 531.4 \\ Longitudinal resistance of track bed & kN/(m-rail) & 15 \\ Longitudinal damping of track bed & N-s/m & \(5.88\times 10^{4}\) \\ Longitudinal vibration quality of track bed & kg & 140.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vibration parameters of ballast track.  
Figure 3: Vibration quality model of ballast.  
Figure 2: Resistance-displacement diagram: (**a**) Type-V fastener; (**b**) Ballast bed.  
The primary beam is a space truss structure. The chord, web, and cross members are simulated by the Bernoulli beam element, which can consider the shear deformation, and in which the members are rigidly connected. Steel bridge decks are simulated by shell elements. The suspenders, main cables, and wind-resistant cables are simulated by tension-only rod elements. The main cables are connected to the upper part of the tower by a rigid connection without longitudinal constraint. The anchor side of the cable is treated with full constraint. The main tower and the cross beam of the main tower are simulated by Euler beam elements connected from end to end in sequence. The suspension bridge is a semi-floating system. The primary beam and tower are coupled with vertical and lateral constraints, and damping devices are used longitudinally. The damping devices are modeled by linear springs, which are only activated under earthquake and braking load. The lateral and vertical stiffness of auxiliary piers is simulated by the linear spring element.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 1 Introduction
The third type of algorithm is mainly designed and implemented based on the idea of a matrix gradient, including the conjugate-gradient (CG) method [17] and the steepest descent (SD) method [18]. This type of algorithm uses the matrix gradient search method and avoids the high-dimensional matrix inversion problem. However, compared to the method of series expansion, the CG and SD algorithms bring about great improvement in detection performance, but calculation of the matrix gradient after each iteration also causes higher complexity. In this paper, a low-complexity joint detection algorithm was proposed. The SD algorithm had a good convergence direction at the beginning of the iteration, and the Gauss-Seidel (GS) algorithm with low complexity mentioned in [19] was combined with the SD method (called SDGS), which provided an effective search direction for GS iterations, speeding up convergence and improving the detection performance. Furthermore, applying it to soft output detection gave an approximate calculation method for the bit log-likelihood ratio (LLR) of the channel decoder input. A good compromise between detection performance and computational complexity was achieved. The rest of this paper is organized as follows: Section 2 discusses the system model and analytical derivations, Section 3 explains signal detection, while Section 4 explains the mixed iterative algorithm and the proposed algorithm. Section 5 provides the simulation results, while Section 6 concludes the paper.
## 3 Experimental Setup and Accuracy Evaluation of the 3D Shape Measurement  
### Experimental Setup  
Figure 5 shows the prototype of the 3 DM-PM device. A light source, a grating plate, and a camera were fixed as a unit to realize the 3D shape measurement. A projector was mounted as a unit for projection mapping. Figure 5a shows a photograph of the 3 DM-PM device. Figure 5b presents the configuration diagram of the 3 DM-PM device. The measurement projector and the mapping projector were located 350 and 80 mm apart from the camera. Table 1 shows various parameters of the 3 DM-PM device at the time of measurement. Table 2 presents the hardware information of the 3 DM-PM device.  
Figure 6 displays the light-source-stepping method [13; 14; 15; 16], where the grating patterns were projected by switching the lighting position of the light source. The prototype device used this method to realize a high-speed phase shift by controlling the lighting position of the LED device. A signal was sent from the PC to the microcomputer. The timing of the LED lighting and that of the camera capture were synchronized by a microcomputer. A high-speed phase shift was realized by electrically switching the lighted line.  
Figure 5: Configuration of the 3D shape measurement projection mapping (3 DM-PM) device: (**a**) Photograph of the prototype 3 DM-PM device and (**b**) configuration diagram of the 3 DM-PM device.  
Figure 6: Diagram of the light-source-stepping method: (**a**) Fine pitch grating and (**b**) large pitch grating.  
The measurement depth in the \(z\) direction can be expanded projecting two types of grating with different pitches [16]. Figure 6 shows how grating with different pitches were projected using two light sources installed at different distances from the grating plate.  
The pitch of a grating plate can be denoted as \(p_{0}\). The distance between a plane \(z=z_{0}\) and the grating plate is \(b\). The distance between light source A and the grating plate is \(a_{A}\). The distance between light source B and the grating plate is \(a_{B}\). The pitches \(P_{A}\) and \(P_{B}\) of the projected grating at the plane \(z\) = \(z_{0}\) are calculated based on Equation (3).  
\[P_{A}=\frac{a_{A}\ +\ b}{a_{A}}P_{0} \tag{3}\]