esa-sceva/satcom-chunk-collection huggingface dataset:
# The Attachment of Carbon Nanotubes to Atomic Force Microscopy Tips Using the Pick-Up Method
## 3 Results  
### Techniques to Determine CNT Attachment to AFM Tips  
#### 3.1.1 AFM Imaging  
CNTs typically have diameters less than standard AFM tips and therefore it is expected that AFM images acquired before and after CNT attachment would have improved resolution. This is particularly evident when using the pick-up method and high-force tapping mode and is demonstrated in Figure 1. In Figure 1a the CNT covered surface is imaged with an FMV model AFM probe (Bruker Corporation, Billerica, MA, USA) and image indicates the tip is relatively blunt. Figure 1b shows the process of using high-force tapping mode to attach a CNT to the AFM tip. The region on the image where the tapping mode set-point is reduced is highlighted by a red rectangle and shows where the CNT has become attached and the resolution of the image has improved. In Figure 1c the same area is re-scanned and shows that the CNT attachment is stable and the image resolution is significantly Figure 1 shows the process of attaching a CNT to an AFM tip using the high-force tapping mode imaging method. Figure 1a is the image with the tip which is relatively blunt. Figure 1b shows the process of attachment by reducing the imaging set-point until a resolution change is observed. Figure 1c shows an improved resolution image with a stable CNT attached to the AFM tip. The height colour scale for images is 10 nm.  
Analysis of the CNT in Figure 1a,c (denoted by the number 1) shows that the FWHM of the CNT has decreased by a factor of approximately 4 before and after CNT attachment.  
This technique is particularly suited to the pick-up method as the same area can be re-scanned and the exact same CNTs FWHM compared before and after CNT attachment. A further advantage of high-force imaging is that it inevitably blunts the silicon AFM tips and, therefore, makes it obvious when CNT attachment has occurred. While this method is most applicable to the pick-up method it can also be used to verify CNT attachment using other techniques. Recent research has demonstrated that it is also possible to locate and relocate the same small areas and nanostructures on surfaces. This is typically done using techniques such as FIB milled or optical markers on the sample surface [26; 72]. [PERSON] et al. [48] also imaged and reimaged calibration grids before and after CNT attachment to demonstrate improved image resolution.  
Figure 1: Shows the process of attaching a CNT to an AFM tip using the high-force tapping mode imaging method. (**a**) is the image with the tip which is relatively blunt. (**b**) shows the process of attachment by reducing the imaging set-point until a resolution change is observed. (**c**) shows an improved resolution image with a stable CNT attached to the AFM tip. The height colour scale for images is 10 nm.
###### Abstract  
Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures.  
energy harvesting; hybrid; Internet of things; MIMO; perovskite photovoltaic; smart cities; wireless RF +
Footnote †: journal: _technologies_  
_Article_
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 3 Artificial Bee Colony Algorithm
### Onlookers' Phase  
During the onlookers' phase, they are standing by until employed bees finish the food-searching procedures. They receive information of the food sources from employed bees. However, different from the employed bees, onlookers select the food sources to develop according to the nectar amount. On the other hand, the food source with a better objective function value has a higher probability to select for searching near it. Then, the following procedures are similar to worker bees. Each onlooker provides a new food source near the previous one and applies the greedy selection between them. The detailed steps are described as follows:  
(i) First, define the \(fit_{i}\) function for \(i=1,2,\ldots,N_{p}\):  
\[fit_{i}=\frac{1}{(1+f(x_{i}))}. \tag{3}\]  
(ii) From (i), one can derive the probability value:  
\[\begin{split} probability_{i}=&\frac{fit_{i}}{N_{p}}. \\ &\sum\limits_{i=1}^{N}fit_{i}\\ x_{i,j}=& x_{\min,j}+\text{rand}(0,1)(x_{\max,j}-x_{ \min,j})\end{split} \tag{4}\]  
(iii) The onlooker chooses the food source to search depending on the _probability_. Let \(v_{kj}\) be the neighbor food source, for \(k=\) rand (\(1,Np\)). (iv) Express the relation between the selected and a new food source by the following equation:  
\[v_{ij}{}^{\prime}=x_{ij}+\varphi_{ij}(v_{ij}-v_{kj}), \tag{5}\]  
where \(\varphi_{ij}=\) rand (\(-1\), \(1\)), \(i=1,2,\ldots,N_{p}\) and \(j\) is the dimension. (v) Calculate the objective function value of \(v_{ij}\). (vi) Compare it to the objective function value of \(v_{ij}\) with \(v_{ij}{}^{\prime}\). (vii) Substitute a new memory of the food sources \(v_{ij}\), which has a better objective function value, into the old one of \(x_{ij}\). On the other hand, \(v_{ij}\) is reserved if it has a worse value.  
After this selection, each onlooker bee searches for a new position based on the probability value.  
The onlookers guarantee to provide better food sources than the previous food sources provided by employed bees.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 2 Related Work
### Cooperative Caching in Satellite Networks  
Considering the characteristics of LEO satellite networks, several research works are focused on providing global content services for terminal users in satellite networks [32; 33]. For example, an effective load balancing scheme based on cache resource allocation is investigated [34], and a hybrid caching placement strategy is discussed to improve the quality of service [35]. Furthermore, a joint optimization problem is studied to minimize the total service delay of users [36], and a collaborative caching approach for content sharing among neighboring satellites is investigated to maximize the storage resource utilization [37].  
Note that the content service provisioning cannot meet with the service requirements for large number of users as the strictly limited on-board resources of satellite networks.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 5 Results and Comparison
### Performance Comparison
#### 5.3.4 Comparison on Virtex 7 Platform  
Upon comparing our proposed cryptographic accelerator design with the non-pipelined design in [16], we observed that the work in [16] utilizes 40.8% more resources and operates at a lower clock frequency. As a result, their design achieves a lower throughput/area ratio by a factor of 1.68 when compared to ours. These findings clearly demonstrate the superior resource efficiency and performance of our proposed design. Furthermore, when comparing our proposed two-stage pipeline architecture with the pipelined design in [16], we found that our design consumes 38.3% fewer hardware resources while operating at a significantly higher frequency. This leads to an impressive 1.84 times higher throughput/area ratio in our design. This highlights the effectiveness of our pipelining approach in achieving superior performance with optimized resource utilization.  
In the study conducted by [29] on hybrid architectures, two architectural designs are proposed. When the comparison is made for non-pipelined architectures, a significant reduction of 11.2% in resource usage is obtained. Additionally, the non-pipelined architecture in this article operates at a 36.9% lower frequency, suggesting reduced power consumption and improved energy efficiency. Furthermore, it achieves 1.22 times higher throughput/area, indicating superior performance and computational efficiency. When comparing the pipelined designs, it achieves 7.3% reduction in resource utilization, indicating improved hardware utilization. Moreover, the two-stage pipelined architecture outperforms the hybrid design with a 1.33-times higher throughput/area. This improvement highlights its capability to handle workloads more efficiently and deliver enhanced performance.  
In summary, the performance comparison clearly highlights the advantages of pipelining in achieving higher performance while using fewer hardware resources. Our proposed design stands out as a superior approach that effectively utilizes pipelining to achieve remarkable throughput/area ratios when compared to the designs discussed in [16; 23; 24; 25; 29; 38; 39]. These optimizations contribute to the advancement of cryptographic accelerators, enabling more efficient and secure cryptographic computations in practical applications.
## 4 Applicability of the Practical Criterion
### System Parameters Analysis  
In the following, the typical parameters of the large industrial motor were adopted [2]. The parameters of the single-load system are shown in Table 1. The load model was the constant torque load, and the load rate was 50%. The load rate is the ratio of the load torque to the maximum electromagnetic torque.  
Firstly, the influence of the stator reactance on the stability boundary was analyzed. Based on the parameters in Table 1, we changed the stator reactance value to 0.02 pu, 0.041 pu, and 0.082 pu, respectively. Three transient voltage stability boundaries under different stator reactance are plotted in Figure 5a. As can be seen from Figure 5a, the larger the stator reactance, the larger the unstable region. The increase of the stator reactance will increase the safe voltage magnitude and reduce the safe sag duration, which is not conducive to the stable operation of the motor. Besides, the influence of the rotor reactance on the stability boundary is the same as that of the stator reactance, as Figure 5b shows.  
Then, the influence of the rotor resistance on the stability boundary was analyzed. Similarly, three values of the rotor resistance were selected, i.e., 0.0031 pu, 0.0062 pu, and 0.0124 pu. Three transient voltage stability boundaries under different rotor resistance were plotted, as Figure 5c shows. It can be seen from Figure 5c that the larger the rotor resistance, the smaller the unstable region. The increase of the rotor resistance will increase the safe sag duration, which is beneficial to the stability of the motor. Besides, the rotor resistance has no effect on the safe voltage magnitude.  
Finally, the influence of the load rate on the stability boundary was analyzed. The load rate was 30%, 50%, and 80% to represent the three operation conditions of light load, normal load, and heavy load. The stability boundaries for the three load rates are plotted in Figure 5d. It can be seen from Figure 5d that the unstable region expands rapidly with the increase of the load rate. The values of the safe voltage magnitude and the safe sag duration vary greatly under the three load rates.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Item** & **R/pu** & **X/pu** & **X\_m/pu** & **R/pu** & **X/pu** & **H/s** & **R/pu** & **X/pu** \\ \hline Value & 0.007 & 0.041 & 3.62 & 0.0062 & 0.0267 & 1.6 & 0.015 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the single-load system.  
Figure 4: Diagram of the transient voltage stability boundary.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
## 1 Introduction
### Objectives
This study goes beyond the prior work on the Toshka lakes in a variety of ways. Unlike [23] and [53], it includes analysis of lake water level and volume, and provides a much higher temporal resolution analysis of surface area (812 dates of imagery, vs. 14 and 27, respectively in those prior studies). While [7] and [52] included water level and volume, this new study again provides a much higher temporal resolution and more than a decade of additional data. Additionally, in contrast to [7], it provides water levels and volumes for all lakes, and in contrast to [52], it provides a comparison between water level estimates derived from DEM analysis and from satellite laser altimetry. It also defines and evaluates a new lake index (ELI), compares two different remotely-sensed sources of DEMs, and compares two different algorithms for obtaining water levels from DEMs.
## 4 Evaluation
### Computational Complexity Results  
React-UCB's complexity analysis, as presented in Section 3.7, was verified with empirical results. These results were obtained on a MacBook Pro 2019, with 32 GB DDR4 and  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Null Hypothesis** & **Simulation Setup** & **Emulation Setup** \\ \hline B = BR & \(p<0.001\), \(d=4.97\) & \(p<0.001\), \(d=2.02\) \\ BR = BRC & \(p<0.001\), \(d=12.50\) & \(p<0.001\), \(d=2.93\) \\ BRC = BRCT & \(p<0.01\), \(d=0.71\) & \(p<0.001\), \(d=8.38\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Regret statistical tests: \(p\)-values and [PERSON]’s \(d\). The former test evaluates the significance of the statistical difference between the diverse configuration options of the MAB agent, and the latter test reveals the statistical practical significance of the agent components’ contributions to the final MAB agent’s learning performance.  
Figure 3: Average overhead comparison at both network (Net) and control (Ctrl) levels between controller enhanced by the MAB agent (Ag+SDN) and standalone controller (SDN). This considers several topology sizes.  
2.3 GHz 8-Core Intel Core i9 (Apple Inc., United States), using the simulation setup with 25, 100, 200, 300, and 400 arms.  
Figure 4 presents the obtained average CPU execution time per time-step \(t\) (i.e., per React-UCB's decision making) over a horizon of \(T=10^{4}\) time steps. Execution time was estimated with time.process_time() Python function in a conservative manner, that is, by including the whole algorithm, simulation setup, and corresponding interactions.  
A second-order polynomial curve was fit to the obtained execution timings to assess the theoretical algorithm's asymptotic execution time of \(T(n)=O(n^{2})\) (see Section 3.7). The quadratic order of complexity was confirmed by the almost perfect coefficient of determination achieved by the regression process, \(R^{2}=0.9995\). It is also notorious that the quadratic coefficient (\(9\times 10^{-5}\)) is several orders of magnitude smaller than the linear coefficient (\(1980\times 10^{-5}\)). This is due to the fact that the quadratic order of complexity was limited to the probabilistic inference step. The smaller quadratic coefficient results in a low execution time for a practical number of arms. For instance, considering 25 and 400 arms, the algorithm only takes, on average, 1.63 ms and 22.46 ms to output a decision per time step \(t\), respectively. These are adequate processing timings from a practical standpoint.
## 2 Literature Review
These architectures are considered robust because they can learn complex patterns and relationships within the data, which is crucial for accurately distinguishing between falls and various ADLs that may exhibit similar motion characteristics [9; 10]. The ability of mmWave sensors to provide high-resolution data that is less affected by environmental factors likelighting conditions or privacy concerns further enhances the robustness of deep learning models trained on such data [9,10]. Among them, recurrent neural networks (RNNs), especially LSTM models, have been widely used to capture temporal dependencies in human motion [24, 25, 26]. However, recent studies have revealed limitations in LSTM-only approaches, particularly in distinguishing falls from ADLs that may exhibit similar motion patterns [24, 25, 26]. Despite this over 90% accuracy success, LSTM-based models often treat all time steps equally, which can degrade the performance of identifying critical motion cues. As a solution to this drawback, an attention mechanism has been applied to improve the effectiveness of the model [28, 29]. The attention mechanism allows the model to pinpoint the most important features, such as noticeable movements or posture changes, within the input sequence, which improves the accuracy of detecting falls compared to ADLs. Incorporating the attention mechanism into the LSTM architecture allows the model to weigh more critical temporal features, which improves detection accuracy and reduces false positives. This is especially important in real-world scenarios, where radar data may be noisy or where falls may resemble other rapid movements. To enhance the accuracy of fall detection, fall detection algorithms combining LSTM with attention mechanisms have been studied in wearable devices or vision-based sensors [13, 14, 29, 30]. However, a comparative study on the performance of applying x-y scatter and Doppler range images measured by FMCW mmWave sensors using only LSTM versus combining LSTM with attention has not yet been reported.
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 1 Introduction
First, through information flooding, all nodes acquire the positions of anchor nodes and their minimum hop counts to each one. Second, each anchor node calculates its average hop distance and broadcasts this information. Finally, each unknown node estimates its distance to each anchor based on the hop count and average hop distance, and then determines its position. The DV-Hop algorithm assumes a uniform distribution of nodes within the network. Under this assumption, hop counts can reliably approximate physical distances. However, in real-world IoT networks, node distributions are often non-uniform, leading to irregular and complex topologies. Such non-uniformity can cause significant errors in hop count estimation, which further result in inaccurate average hop distance calculations. The accumulation of these errors ultimately leads to a significant decline in the localization accuracy of DV-Hop under non-uniform topologies. To enhance the localization performance of the original DV-Hop algorithm under non-uniform topologies, we integrate the Grey Wolf Optimization (GWO) algorithm [13] into the DV-Hop framework. Anchor nodes with large distance estimation errors are eliminated, and a subset of high-quality anchor nodes is selected to improve the localization accuracy of unknown nodes. The primary contributions of this study are summarized as follows:1. The impact of non-uniform node distribution on hop count and average hop distance was analyzed. A binary Grey Wolf Optimization (BGWO) algorithm was employed to encode anchor nodes and establish an optimal anchor node selection mechanism. 2. During the multilateration stage, a continuous GWO algorithm was applied to replace the least squares method for position optimization, enhancing the accuracy of solving the distance equations. 3. The overall performance of the proposed algorithm was thoroughly evaluated through simulation experiments. The organization of this paper is structured as follows: Section 2 reviews the related research background; Section 3 introduces the fundamental principles of the DV-Hop algorithm and discusses the impact of non-uniform node distribution on localization; Section 4 presents the GWO algorithm and its binary form; Section 5 provides a detailed explanation of the proposed improved algorithm; and Section 6 validates the localization performance of the proposed algorithm through simulations. Finally, Section 7 concludes this paper.
## 3 Batch Authentication in the Vehicle-to-Grid (V2G) Network
### Existing Authentication Protocols in the Vehicle-to-Grid (V2G)
#### 3.2.1 Digital Signature Algorithm (DSA)-Based Protocols  
[PERSON] _et al._[78] constructed a digital signature algorithm (DSA)-based batch verification protocol where a signer generates n-signatures through the interactions with the verifier, and then the verifier validates these n-signatures simultaneously. After that, [PERSON] [79] introduced an interactive batch protocol, in which the same private key (same sender) is used to sign a batch of packets. However, this scheme is inefficient due to pre-computation of all generated signatures for the batch. [PERSON] _et al._[80] proposed a protocol for a V2G network based on a variant of DSA. However, it increases the authentication time due to large modular exponential and multiplication computations. Further, [PERSON] [81] proposed another multiple DSA-type signature scheme. However, it does not always produce the true results when it groups the same base terms together and adds the exponent terms together.