esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Band Structure of Hierarchical Metamaterials
### Effect of Electromechanical Coupling Coefficients and Shunt Circuit Resistances
In contrast, the mechanically damped system does not affect the location of the band gap, with changes more closely related to the reduction in peak attenuation. In both electromechanically and mechanically damped hierarchical resonators, the attenuation curve widens, and the peak attenuation decreases as the electromechanical coupling coefficient and the mechanical damping ratio increase. Electromechanical resonators create an attenuation curve with extended peaks compared to mechanically damped resonators. As discussed previously, the electromechanical coupling coefficient and resistance of the shunt circuits do not affect the band structure of the weakly coupled hierarchical metamaterial. Additionally, the wavenumber analysis indicates that the attenuation constants and passbands of hierarchical metamaterials remain unaffected by the electromechanical coupling coefficient and the shunt circuit resistances in a weakly coupled system, as seen in Figure 6. The undamped tuning frequencies of the local resonators, \(\omega_{1}\) and \(\omega_{2}\), are perfectly aligned with the peak attenuation for weakly coupled hierarchical metamaterials. Based on wavenumber analysis, the local resonance band gap in hierarchical electromechanical metamaterials can be adjusted using hierarchy, electromechanical coupling coefficient, and shunt circuit resistance. This enables control over band gap properties, including location, width, peak attenuation, and resonance reduction, which are challenging to achieve with mass and stiffness changes alone.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 2 Research Progress in Smart Mobility and Gap Analysis  
This section provides a detailed description about the research progress made in the field of smart mobility. We also try to identify the research gaps in the existing literature. In order to conduct the survey, we have adopted the standard systematic literature review methodology [4]. Firstly, we performed a keyword based search using the online research paper databases Science Direct ([[https://www.sciencedirect.com/](https://www.sciencedirect.com/)]([https://www.sciencedirect.com/](https://www.sciencedirect.com/)) accessed on 15 January 2021) and IEEE Xplore ([[https://ieeexplore.ieee.org/Xplore/home.jsp](https://ieeexplore.ieee.org/Xplore/home.jsp)]([https://ieeexplore.ieee.org/Xplore/home.jsp](https://ieeexplore.ieee.org/Xplore/home.jsp)) (accessed on 15 January 2021)). We primarily used the keyword \"Urban Smart Mobility\" to search for articles. We received 851 results from IEEE Xplore and 10,439 results from Science Direct. We refined the search criteria by adding the terms \"Smart Cities\" along with smart mobility, which reduced the results to 440 (IEEE Xplore) and 8217 (Science Direct). We further refined the criteria by adding \"Enabling technology\" along with the previous two keywords, which further reduced the results to a total of 2486 papers. Next, refinement was performed on the basis of publication year. We analyzed the papers from 2011 to 2020. This refinement reduced the number of search results to 2049. We then filtered these articles based on the title contents to eliminate articles that were out of the scope of this review. Finally, 294 articles were left and a further selection has been made based on the abstract contents. Indeed, we read the abstracts of these 294 articles to further filter out articles which are not related to our prime focus. After this step, 186 articles were left. These 186 articles were read thoroughly to understand their key proposals, discussions and arguments. Finally, only 81 articles, which completely matched our criteria, were considered for the analysis. These articles were further analyzed and discussed to identify their key findings and research gaps. Figure 3 shows the word cloud of the results obtained by the search query terms.  
Figure 3: Word cloud of search query results.  
Figure 4 shows the steps involved in the review methodology that has been used:  
1. Classification of Articles on the basis of the type: we have chosen only peer reviewed research, review and survey articles satisfying our criteria;
2. Identification of Publication Year: articles from 2011 to 2020 only were analyzed in the literature review;
3. Classification of Articles on the basis of keywords: we have combined the following keywords  
1. Smart City
2. Urban Smart Mobility
3. Enabling Technology.  
Once the articles were filtered, a thorough study was conducted to identify the crux and key findings of each paper. The details about the state-of-the-art are discussed below.
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 2 Problem Identification--Materials and Methods  
These limits are determined based on the physiological effects of these radiations and are detailed by the WHO (World Health Organization), ICNIRP (International Commission on Non-Ionizing Radiation Protection) and also at EU (European Union) level [12; 13]. In the case of the electric field, its physiological effects are based on surface charge accumulation. As a discharge occurs, an unpleasant sensation, glare, and nerve contraction can be felt around or above 10 kV/ m electric fields [10; 14; 15]. A low-frequency magnetic field can cause stimulation in vision-related nerves and affect mood, perception, and sleep. The long-term physiological effects of the magnetic field (such as cancer) are the subject of considerable research, but an exact correlation has not been found [11; 16; 17].  
According to the European Union's legally defined limit values listed in Table 1, for the electric field and the magnetic flux density. However, it is essential to mention that there could be more strict limit values for both parameters at a national level.  
Theoretically, there could be a combination of geometry and operating parameters for high-voltage power lines, which result in a higher electric field than the first limit [18; 19; 20; 21]. In the magnetic flux density, such problems do not occur in the general public [22; 23]. Exceeding the limit value can occur when linemen are in the vicinity of the line, such as climbing on the tower, working on the de-energized part of a double-circuit power line, or in the case of live working maintenance [24]. Due to the strict legal environment, the lines' design needs to consider these regulations, and for operating lines, it is assumed that there are no such problems. Yet, in the case of sensor installations and inclination measurements of FLEXTRANSTORE [25] and FARCROSS [26] pilot DLR projects in Europe, we found that there are operating power lines around in which the electric field value exceeds its limit value for the general public. This phenomenon is not rare, and in the case of some transmission lines, more than half of the tension spans are affected during a significant length of time. These kinds of issue occurred even if the necessary safety distances were observed.  
This paper's goal is twofold; first, it demonstrates an existing example for the phenomenon and notes that possible electric field problems need to be considered in DLR models. Second, it presents a new, DLR-based expert system to solve the electric field problem. By applying the expert system, the electric field strength is within the allowable limit all the time, while the power line's full utilization is also ensured.
## 5 Secure Key Agreement and Authentication Protocol for VCC  
This section provides the proposed protocol to resolve the security flaws in Limbasiya et al.'s protocol. We use only an OBU instead of a TPD. Limbasiya et al.'s protocol cannot provide secure key agreement, because the TPD sends the session key without encryption. Therefore, we register vehicles and RSUs at the \(TA\) to generate secure key agreement. Thereafter, the vehicle transmits the information encrypted with the session key to the RSU. RSUs validate the message and send it to the vehicular cloud. We also consider performance and storage of OBU because of its relatively low computational power and small storage. Thus, we design the protocol using only exclusive-or and one-way hash function, which have low computational cost.
## 3 Test Results and Discussion
### Prestressing Wires  
Figure 14 exhibits the hoop strain in the prestressing wires of Section 1 (Figure 14a), Section 2 (Figure 14b) and Section 3 (Figure 14c) with continuous load steps. The strains of Sections 1 and 2 changed obviously with the wires breaking, while the strains of the prestressing wires away from the wires breakage area (S3) did not show evident variation.  
The increase in the internal water pressure has little impact on the strains of Sections 1 and 2 during the first load stage (load stage I). The structural integrity was in a good condition and the concrete core took primary responsibility of sustaining the internal water pressure. Moreover, the strains at measuring point #1 of each section were nearly equal to #2, indicating that the stress in the prestressing wires was uniform before breaking wires.  
When the operation of wires breakage (load stage II) finished, the strains at measuring point #1 reflected a drastic drop owning to the abrupt loss of prestress. The strain of measuring point #2 at the crown only decreased to some extent. This phenomenon reveals that the mortar coating's bonding quality with wires was in a good state. Consequently, the prestress of wires resumed beyond a certain length from the broken point, which is in accordance with the conclusion summarized by [PERSON]. [5]. After the tensioning operation of strands, the tensile stress of prestressing wires declined to some extent considering that the responsibility of supplying the tensile strength was shared by the prestressing wires and the steel strands.  
It is worth mentioning that the target tensile strength of strands (Mentioned in 2.1.2) is determined on the condition that the prestress of the wires after breakage is fully removed. However, as depicted  
Figure 14: The strain curves of prestressing wires: (**a**) Section 1; (**b**) Section 2; (**c**) Section 3.  
in Figure 14, the actual prestress loss of the broken wires existed within a certain length and the strains of wires beyond a certain length decreased to some extent, which is not zero. This phenomenon indicates that it is conservative and safe to determine the target tensile strength of strands based on the former assumption.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 2 Background
### Classification of UAVs  
Each UAV has its own characteristics, which will depend greatly on the application in which it will be used and achieve the desired results. UAVs are classified based on several factors, either by their altitude, size, or shape [1]. For example, multi-rotor drones are the most used, such as quadrotors which are the cheapest, fastest, most efficient, and with better maneuverability. However, hexacopters and octocopters have better flight stability, fault tolerance, and more payload capacity [2]. Figure 1 presents a general classification of UAVs by altitude, size, and shape.
# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
## 4 Results
### Parameter Analysis  
As shown in Figure 4, we delve into a comprehensive analysis of our model's behavior under varying IOU threshold \(P_{t}\) in the collaborative open-world classifier. The performance of our framework in terms of unknown class mAP and Recall for different \(P_{t}\) values is shown in Figure 4a, while UDP and UDR metrics are illustrated in Figure 4b. From the figures, it can be observed that when \(P_{t}\) is set to 0 or 0.2, the framework exhibits lower UDR, UDP, and U-Recall values. As the value of \(P_{t}\) increases, U-Recall and UDR increase accordingly. This phenomenon can be attributed to the larger number of retained unknown-class candidate boxes with high Intersection over Union (IOU) scores. However, it also implies a higher occurrence of known-class detections being misclassified as unknown-class, resulting in fluctuations in UmAP and UDP with the increasing value of \(P_{t}\). In general, when \(P_{t}\) takes other values, UDR, UDP, and U-Recall exhibit relatively stable performance, indicating that our method can detect many unknown objects with small overlaps with known objects, and these detections are less affected by changes in \(P_{t}\). When \(P_{t}\) is set to 0.4, the framework achieves its highest U-mAP value of 4.83. Therefore, our method effectively achieves the detection of unknown categories with good stability and fewer uncertainties.
## 4 Experimental Study
### Experimental Design  
Due to the varying capabilities of different satellites and significant differences in constraints and management modes of satellite management systems among countries, providing a uniform standard for scheduling AEOS is not feasible. The experiments compared the more effective agile satellite scheduling algorithms found in the current research literature, i.e., ALNS-I [16] and ALNS-TPF [17].  
In our study, we considered the performance of the same model (AS-01) agile satellite as described in ALNS-I [16]. Therefore, we adopted a similar construction method to validate the efficiency of the proposed algorithm. A total of 15 scenarios are used in the dataset, with the number of tasks ranging from 50 to 400 with an increment of 25, i.e., the numbers of the tasks are \([50,75,100,\cdots,375,400]\). The scheduling period is from 2013/04/20/00:00:00 to 2013/04/20/23:59:59, and the targets are distributed in the range of \(3^{\circ}\) N-\(53^{\circ}\) N and \(74^{\circ}\) E-\(133^{\circ}\) E. Each target's profit and observation duration are uniformly distributed within \([1,10]\) and \([15,30]\) seconds, respectively.  
We evaluated the algorithms using an equal number of iterations to ensure a fair comparison of experimental results. All solution results are obtained from 10 independent experimental runs for each case. The values of other parameters of the algorithm are as follows:  
* M = 2400, E = 2400;
* length for \(k_{n}=0.1S\), length of \(U_{t}=0.1S\), length of \(U_{L}=0.05S\), where \(S\) represents the number of tasks;
* maximum number of elite solutions = 50, maximum number of consecutive generations without improvement = 50;
* number of solutions for data mining = 10, maximum number of generations = 100, number of threads = 5.  
Figure 5: Frequent pattern mining to AEOS scheduling problem.
# Horizontal Loading Performance of Offshore Wind Turbine Pile Foundation Based on DPP-BOTDA
## 2 Field Experiment  
### DPP Measurement System  
A typical DPP-BOTDA scheme was implemented to realize high spatial resolution offshore wind turbine pile monitoring, as shown in Figure 1. A narrow-linewidth (40 kHz) optical fiber laser was used as the light source, which operates at a wavelength of 1550 nm and outputs a power of 80 mW. Then, a 3 dB coupler was used to split the output beam into two arms. In the upper arm, the laser beam was double-sideband modulated by means of an intensity electro-optic modulator (EOM1), which was driven by the microwave generator (MG). Then, the lower sideband was selected by a fiber Bragg grating (FBG), working as the probe wave. An orthogonal polarization scrambler (PS) was used to reduce polarization fading. Right before being launched into the fiber under test (FUT), the probe wave went through an optical isolator (OI), which was used to prevent the high-power pump pulse.  
In the lower arm, the laser with a 50% power component was firstly modulated into an optical pulse pair (38 ns and 40 ns) by the second electric-optic modulator (EOM2), which was driven by the arbitrary function generator (AFG). Then, an erbium-doped fiber amplifier (EDFA) was utilized to amplify the optical pulse pair, providing a 500 mW peak power. The pump pulse pair was obtained and injected into the FUT from the other end through an optical circulator. The frequency difference between the pump and probe waves was scanned from 10.6 GHz to 11 GHz, covering the sensing fiber Brillouin frequency shift (BFS), with a frequency step of 4 MHz. As a result, the pump and probe waves interacted in the FUT via stimulated Brillouin scattering, and then the amplified probe wave was detected by a 350 MHz bandwidth photodetector (PD). Finally, the detected signal was collected by the data acquisition (DAQ) card with a sample rate of 2 GHz.  
The DPP technique is a good candidate for overcoming the limited metric spatial resolution, limited by a non-instantaneous (10 ns) response time of the acoustic wave generation. The sub-meter spatial resolution offered by the DPP scheme is achieved based on the subtraction of two temporal traces using long pump pulse widths with slightly different durations. Here, 20 cm spatial resolution is obtained by 2 ns difference between the pump widths, and a high-frequency fitting accuracy is maintained due to a narrow spectrum with long pump pulses. Besides, the DPP scheme is simple and reliable, which is suitable for the field experiment in an extreme environment, especially marine.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 4 Mission Process Control System Modeling  
### System Hypothesis  
**Hypothesis 1**.: _The execution of each mission in a plan is an independent process. Let mission \((s_{1},f_{1})\) be completed as event \(A\) and mission \((s_{2},f_{2})\) be completed as event \(B\). Then the probability \(p(AB)\) that both the mission \((s_{1},f_{1})\) and the mission \((s_{2},f_{2})\) are completed can be denoted by_  
\[p(AB)=p(A)\cdot p(B) \tag{16}\]  
**Hypothesis 2**.: _Both the executors set \(\mathcal{E}\) and the missions set \(\mathcal{M}\) remain constant during mission execution. That is, changes in the number of executors due to entity failures or breakdowns are not taken into account. Furthermore, all missions in the plan must be executed._  
**Hypothesis 3**.: _Mission progress bias is categorized into advance and delay. Let the actual start time of the mission \((s,f)\) be \(T^{(s,f)}_{AS}\) and the actual end time be \(T^{(s,f)}_{AF}\). The mission advance and mission delay can be expressed as (17) and (18), respectively._  
\[T^{(s,f)}_{AF} <T^{(s,f)}_{PF} \tag{17}\] \[T^{(s,f)}_{AF} >T^{(s,f)}_{PF} \tag{18}\]  
**Hypothesis 4**.: _Mission progress control is only for the one case of delay. For mission advance, the system defaults to extending the mission by waiting until the planned mission end time, and the system does not need to specifically control this._  
Figure 9: Diagram of critical path switching.
## 3 The RNN-Based Algorithm for Resource Allocation
### Algorithm Complexity  
The complexity of the solution of Equations (1) to (3) is \(O(I\cdot N^{2})\)[48], where \(I\) is the number of iterations required for convergence. However, we can reduce the complexity of each iteration by taking advantage of the special structure of the RNN-based algorithm [27].  
In Equation (17), there are \(N=|V||R|\) neurons; thus, its computational complexity is \(O(I\cdot|V|^{2}|R|^{2})\). According to the definition of inhibitory weights, Equation (17) can be rewritten as [27]:  
\[q(r,v)=\Lambda(r,v)/[\lambda(r,v)+f(r,v)+\theta_{1}(v)+\theta_{2}(r)-2q(r,v) max\{0,b(r,v)\}] \tag{18}\]  
where:  
\[\theta_{1}(v)=\sum_{r^{\prime}\in R}q(r^{\prime},v)max\{0,b(r^{\prime},v)\}, \forall v;\;\;\;\theta_{2}(r)=\sum_{v^{\prime}\in V}q(r,v^{\prime})max\{0,b( r,v^{\prime})\},\forall r\]  
Figure 1: Graph representation of the interactions of a four neuron network.  
Based on Equation (18), the computation for each neuron requires \(O(1)\) operations; hence, the computational complexity of \(N\) neurons where \(N=|V||R|\) is \(O(|V|\cdot|R|)\). Moreover, the computation of the terms \(\theta_{1}(v)\) and \(\theta_{2}(r)\) is also of complexity \(O(|V|\cdot|R|)\). Therefore, the complexity of solving Equation (18) is \(O(I\cdot|V|\cdot|R|)\). Because there are \(|R|\) rescuers, Equation (18) will be performed at most \(|R|\) times. The complexity of the RNN-based task assignment algorithm is \(O(I\cdot|R|^{2}\cdot|V|)\).
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 4 Experiments  
In order to explore the effectiveness of different features and various distance metrics for SCD, we designed a set of experiments and report them in the following order:  
(i) the threshold is a hyperparameter in metric-based SCD that determines the sensitivity of the detection process. This experiment seeks to effectively identify the optimal \(\tau\) threshold for different feature and distance metric combinations on a subset of the validation set. (ii) considering a situation of imbalanced problem, the dataset may contains many negative samples, while only a small portion are positive samples. Under these conditions, a naive system that only outputs negative sample labels (in this case, 0) would achieve high accuracy but would fail to correctly identify positive samples. (iii) evaluate each combination of audio feature with different methods on the test data, and report the evaluation results. (iv) averaging the results for each method across all features to identify the most suitable features to SCD. (v) compare the empirically obtained results with the statistically obtained insights, and identifying the robust features by visualizing the comparison between the maximum probability of \(P(SC|FC)\) and a combined score.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature** & **Crossing Points** & **Max \(P(SC|FC)\)** \\ \hline Pitch & 0.2869 & 0.4008 \\ \hline MFCC & 0.3153 & 0.5340 \\ \hline Zero crossing rate & 0.2194 & 0.2931 \\ \hline RMS energy & 0.2879 & 0.3196 \\ \hline Mel spectrogram & 0.3614 & 0.3944 \\ \hline Chroma & 0.4171 & 0.5149 \\ \hline Spectral centroid & 0.3593 & 0.3941 \\ \hline Spectral bandwidth & 0.3844 & 0.4521 \\ \hline Spectral contrast & 0.4090 & 0.4746 \\ \hline Spectral rolloff & 0.3332 & 0.4611 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Crossing points and the maximum values of \(P(SC|FC)\) for various audio features.  
Robust features were clearly identified by visualizing the comparison between the maximum probability of \(P(SC|FC)\) and the methods' results. To effectively compare the metric-based approach, we designed an experiment using two naive systems as a baselines for comparisons.