esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spectrum Management Issues
### High-Spectrum Access
#### 3.4.2 NR mmWave (24-100 GHz)  
For high frequencies, such as 28 GHz, 850 MHz of bandwidth is available; for 39 GHz, two available bands offer 1.6 and 1.4 GHz bandwidths. For 73 GHz, 2 GHz of contiguous bandwidth, which is the widest one of the proposed frequency spectrum, is available for communication [161; 162; 163]. The FCC has defined several frequency ranges to be considered as potential candidates; for example, for the frequency range of 24 GHz, the 24.25-24.45 and 25.05-25.25 GHz ranges are the candidate bands. Similarly, for the 28-32 GHz range, the 27.5-28.35, 29.1-29.25, and 31-31.3 GHz ranges are considered. Similarly, for 39 GHz, 38.6-40, for 37/42 GHz: 37-38.6 and 42-42.5, for 60 GHz: 57-64 and 64-71, and for 70/80 GHz: 71-76 and 81-86 GHz frequency ranges have been selected [164]. Massive research on the propagation characteristics, implementation, and spectrum, as well as resource management techniques of mmWave band at 28, 38, 60, and 73 GHz, has been conducted in the last decade. Standard bodies, such as 3 GPP and 5G channel model, have suggested various channel models below the frequency of 100 GHz (e.g., CI, FI, CIF, and others). Furthermore, by using the data collected by standardization authorities, various firms have performed 5G field trials. For example, a data rate of 1.2 Gbps is achieved by a mobile user in a 400 MHz channel bandwidth at a 28 GHz frequency band, with a latency of 9-12 ms at 150 m far from the cell location of AT&T [165]. A comprehensive study is conducted, and the authors have explicitly mentioned that the mmWave spectrum is suitable for short-range communication and the well-suited propagation model for LOS to receive the desired results [166]. However, in some cases, where LOS propagation is not achievable, NLOS can use the conditions. The path loss and small- and large-scale fading must be addressed to comprehend the propagation  
Figure 5: High-spectrum access (mmWave).  
characteristics of indoor and outdoor locations for wireless communication channels. A study [167] has obtained remarkable results for NLOS mmWave environment by using passive reflectors. Another study [168] is performed for an NLOS indoor localization system by proposing an RF-ECHO method that attains high accuracy in long-distance indoor NLOS environments. The method presents a unique time-of-flight estimation technique to minimize the NLOS constraints. Moreover, it is worth mentioning that these frequency ranges are also suitable to consider as a wireless backhaul link [169]. The major motivation for using these mmWave frequency bands is the possibility of achieving the multi-gigabit wireless communication links. The frequency bands, such as 73 and 76 GHz, are also considered as the viable solution for wireless backhaul links [170; 171]. Several studies show the prominent results for 71-76 GHz and 81-86 GHz frequency bands by utilizing them as a backhaul link [172; 173]. Besides, the standardization organizations, e.g., ITU, have allocated 81-86 GHz frequency band for high capacity wireless backhaul links [174].
## 5 Conclusions  
Data collection is an essential task in photovoltaic systems as it can provide valuable information for fault detection, power prediction, load balancing, and other applications. In this paper, we investigate data collection in the UAV-assisted photovoltaic system to minimize data collection completion time without precise location information being known. We address the issue by proposing a novel data collection scheme based on DQN to acquire a satisfactory solution for the trajectory optimization. The optimization problem is formulated as an MDP, and the UAV agent interacts with the environment and receives rewards based on its actions. By learning in scenarios with different SN distributions, we demonstrate that the UAV agent can effectively adapt to changes in scenarios without expensive retraining or recollecting training data. We evaluate the proposed scheme in different scenarios with different sensor node distributions and compare it with benchmark algorithms. The performance of the proposal almost matches the performance of the benchmark algorithm with the precise position information known, which achieves 96.1% of the efficiency of the benchmark algorithm on average in all possible scenarios. The simulation results show the satisfactory convergence of the proposal and verify the effectiveness of the DQN-based scheme.  
Figure 8: Comparison with the benchmark algorithm.  
**Author Contributions:** Conceptualization, Y.Y.; methodology, Y.Y.; software, H.Z. ([PERSON]) and Y.Y.; validation, Y.Y. and H.Z. ([PERSON]); formal analysis, Y.Y.; investigation, Y.L. and [PERSON]. ([PERSON]); resources, H.Z. ([PERSON]), Y.L. and [PERSON]. ([PERSON]); data curation, Y.Y. and [PERSON]. ([PERSON]); writing--original draft preparation, Y.Y.; writing--review and editing, Y.Y., [PERSON] ([PERSON]) and R.G.; visualization, Y.Y.; supervision, [PERSON] ([PERSON]) and [PERSON]; project administration, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported in part by the State Grid Corporation of China Project (5700-202316254A-1-2-ZN).  
**Data Availability Statement:** All datasets are publicly available.  
**Conflicts of Interest:** Author [PERSON] and [PERSON] are employed by the State Grid Shandong Electric Power Company. Author [PERSON] is employed by the Qingdao Power Supply Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Hardware Customization
### Site Health Monitoring Mote  
The site health monitoring mote consists of various sensors to monitor the environmental parameters, such as temperature, humidity, fire and smoke, and vibrations in the site, as shown in Figure 12a.  
Any disaster may occur in sites under construction, such as fire accident, collapsing of the building, gas leakage etc., which may lead to injuries to workers. A customized mote for detection of these important parameters, such as temperature, humidity, smoke/gas leakage, fire, and vibrations in the site is designed, as shown in Figure 12b. It consists of all  
Figure 11: (**a**) Gloves’ Detection System; (**b**) Customized Hardware of Gloves’ Detection System.  
Figure 12: (**a**) Site Health Monitoring Mote; (**b**) Customized Hardware of Site Health Monitoring Mote.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specifications** \\ \hline Microcontroller: & ATMega328P \\ Communication module: & nRF24 LO1+ Wireless module (2.4 GHz) \\ Voltage converter: & +5 V \\ Touch Sensor: & Force Sensor \\ Power jack: & +5 V External power supply \\ Programming pins: & FTDI and ICSP \\ \hline \hline \end{tabular}
\end{table}
Table 5: Technical Specifications of Gloves’ Detection Mote.  
of the sensors along with LCD to display the values and a rf module is also integrated to communicate wirelessly to the RF and LoRa coordinator. All of the technical specifications of each and every module used in the site health monitoring mote are listed in Table 6.
## 2 Theory and Models
### Relation to 1D Cross Correlation Function  
The one-dimensional (conventional) cross correlation function (CCF1) can be obtained as CCCF taken at zero cyclic frequency:  
\[R_{vi}(\tau)=\mathcal{R}^{0}_{vi}(\tau)=\sum_{k=-\infty}^{+\infty}\mathcal{S}^{ (0,k)}_{vi}\exp(j2\pi kF\tau). \tag{44}\]  
The frequency domain counterpart of CCF1 is known to be cross spectral density (CSD), which is, in turn, the CSCF at zero cyclic frequency:  
\[S_{vi}(f)=\mathcal{S}^{0}_{vi}(f)=\sum_{k=-\infty}^{+\infty}\mathcal{S}^{(0,k )}_{vi}\delta(f-kF) \tag{45}\]  
This consists of Dirac delta functions only since the voltage and current are periodic waveforms.  
In contrast to the one-dimensional autocorrelation function (ACF1), which could have been constructed for the voltage waveform \(R_{v}(\tau)\) itself, the CCF1 is not obliged to exhibit the even symmetry as a function of lag shift \(\tau\). Similarly, CSD should not be thought of as a real valued function, or possessing real valued coefficients at Dirac delta functions, in the case of a generalized function, whereas power spectral densities (PSD) that could have been derived for the voltage waveform \(S_{v}(f)\) would be real valued. However, even-odd decomposition is applicable to the real valued CCF1 and, according to the properties of the Fourier transform, it relates to the real-imaginary decomposition made for CSD:  
\[\begin{split}\mathcal{R}^{0}_{vi}(\tau)=\mathrm{Ev}\big{[} \mathcal{R}^{0}_{vi}(\tau)\big{]}+\mathrm{Odd}\big{[}\mathcal{R}^{0}_{vi}( \tau)\big{]},\\ \mathcal{S}^{0}_{vi}(f)=\Re\mathcal{S}^{0}_{vi}(f)+\,j\Im\mathcal{S }^{0}_{vi}(f),\end{split} \tag{46}\]  
where the vertical arrows designate the pairs \"signal-spectrum,\" interconnected by the Fourier transform. The decomposition of the CSCF can be moved down to the weights of Dirac delta functions in its representation:  
\[\mathcal{S}^{(0,k)}_{vi}=\Re\mathcal{S}^{(0,k)}_{vi}+j\Im\mathcal{S}^{(0,k)} _{vi}. \tag{47}\]
## 5 Performance Evaluation
### Experimental Setup  
We describe the experimental setup in this subsection, which we used to implement and test the 3-Tier AKA protocol against the well-established alternatives mentioned above. The experiments were conducted using Google Colab [56], and Jupyter Notebook [57] provides a powerful and flexible environment for implementing and testing the proposed cryptographic protocol. By leveraging the capabilities of Python and its libraries, we were able to perform complex cryptographic operations and secure communications efficiently. The hardware configuration, featuring an Intel (R) Xeon (R) CPU at 2.20 GHz, ensures adequate computational power to handle the cryptographic operations required in our experiments. We utilized Python as the programming language for the experiments, utilizing its extensive libraries for cryptographic operations and network communications. Table 4 shows the Python libraries utilized in our setup.  
The chosen setup allows for reproducibility and ease of collaboration, making it an ideal choice for cryptographic research and development.
# A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects' 6D Pose Estimation
## 1 Introduction
Recently, finding dense correspondences using the deep neural networks has shown advantages in 6D pose estimation [25; 26]. The per-pixel matching scheme was utilized to design and train the network. In [26], a pseudo-siamese matching network was proposed to match dense correspondences in high-dimension; then, the dense correspondences were used to calculate the target pose through Perspective-n-Points (PnP) method [27]. This method achieved state-of-the-art performance in LineMod [28] and Occlusion-LineMod [29] datasets. However, both datasets contain only one instance for each object. The network is designed to directly segment the object in the image. Thus, it is not applicable for multitarget pose estimation tasks. In this paper, we improve this method in two main aspects for industrial usage. (1) We adopt EfficientDet [30] to first detect every object in the image. Each object in the image is then cropped through the bounding box provided by the EfficientDet and resized into fixed value. All the resized images are fed into the correspondences matching network to predict dense correspondences. After obtaining the correspondences, PnP-Ransac method is used to calculate the 6D pose of the target. By adopting the two-stage network structure, we solve the problem for multi-instance 6D pose estimation. (2) We introduce the circle loss, a well-known loss function in metric learning, to measure the similarities between pixelwise deep features from a 2D image and nodewise deep features from a 3D mesh model. We analyze the reason why the softmax cross-entropy loss [31] used in [26] is not suitable for dense correspondences matching and compare the proposed masked circle loss with the softmax cross-entropy loss though ablation studies to show the superiority of the proposed loss. In summary, our main contribution lies in the framework of the 6D pose estimation that can deal with multiple instances in single frame and a novel metric learning loss that efficiently constrains the matching of the 2D-3D correspondences. The remainder of this paper is organized as follows: In Section 2, we introduce the whole two-stage 6D pose estimation framework for multi-instance textureless objects. The masked circle loss for 2D-3D correspondences matching is introduced in detail. In Section 3, we test our proposed method on the pose estimation problem for the antenna support and compare it with some other state-of-the-art methods to show the effectiveness and advantages of our method. Conclusions are drawn in Section 4.
## 2 Predictive Model Based on ESN  
### Echo State Networks  
For status monitoring and fault early warning of components in an electronic motor, it is necessary to build regression models for the relevant parameters of components, so as to predict and monitor the parameters that can more intuitively describe the status of components. The Echo state network (ESN) [15; 16] is a kind of recurrent neural network (RNN). It is a time series model, and its training process is relatively simple. It overcomes the training difficulties and shortcomings of traditional recurrent neural networks in large-scale applications. Therefore, it has great potential to depict the variation of operating state parameters with time. In the following discussions, we will first briefly introduce ESN and then describe the details of its learning.
## 2 Background on SSI
### Blockchain & DLT  
Blockchain technology is closely related to SSI. Section 5 surveys the security issues of blockchains and DLT. In general, blockchains are a subset of DLT. They are characterized by combining transactions into blocks, which are committed to the ledger. DLT in general can use any number of methods to implement a distributed, append only data storage system.  
Common to all DLT is the need to form the consensus among all participants about the current state of the data store. This consensus protocol is one of the main differences between blockchain implementations. Bitcoin famously uses a proof-of-work (PoW) approach, where miners, i. e., the participants aiming to combine pending transactions together to write an updated state, need to solve a cryptographic puzzle in the form of calculating a hash value with specific properties [15]. The hash values can only be generated by brute-forcing inputs until the output fulfills the needed properties. Additionally, properties can be adjusted to keep the puzzle hard or easy enough, depending on the available processing power.  
As PoW is criticized for its \"useless\" waste of processing power, many DLT projects are looking for suitable replacements. Ethereum uses a proof-of-stake (PoS) approach, which does not need the raw processing power and is seen as a replacement for PoW [16]. Non-public DLT can use a simpler form of consensus, where the duty to update the database's state is deterministically decided.  
Besides storing transactions, some DLTs also feature so-called smart contracts, which are programs that are \"run on the DLT\" [17]. Like a transaction, every participant can view the smart contract's code and each invocation's inputs to verify the output, which is computed by the miner as part of updating the state.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
## 4 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Name** & **2-Opt++** & **NN** & **No.** & **Name** & **2-Opt++** & **NN** \\ \hline
1 & eil51 & 2.65 & 18.56 & 34 & fl3795 & 6.21 & 18.95 \\ \hline
2 & berlin52 & 0.03 & 8.50 & 35 & a280 & 6.03 & 19.98 \\ \hline
3 & st70 & 3.08 & 12.82 & 36 & pr299 & 2.05 & 24.3 \\ \hline
4 & eil76 & 5.3 & 13.80 & 37 & lin318 & 3.16 & 28.56 \\ \hline
5 & pr76 & 0.56 & 21.05 & 38 & rd400 & 6.32 & 19.23 \\ \hline
6 & rat99 & 2.99 & 13.03 & 39 & fl417 & 1.69 & 27.43 \\ \hline
7 & kroa100 & 1.13 & 16.05 & 40 & pr439 & 6.79 & 22.45 \\ \hline
8 & krob100 & 0.71 & 31.68 & 41 & pcb442 & 5.24 & 16.10 \\ \hline
9 & kroc100 & 0.32 & 26.89 & 42 & d493 & 4.64 & 24.7 \\ \hline
10 & krod100 & 2.44 & 26.56 & 43 & u574 & 4.69 & 27.03 \\ \hline
11 & kroe100 & 1.15 & 25.01 & 44 & rat575 & 8.02 & 24.75 \\ \hline
12 & rd100 & 1.13 & 25.68 & 45 & p654 & 3.45 & 25.31 \\ \hline
13 & eil101 & 6.19 & 17.01 & 46 & d657 & 5.01 & 27.12 \\ \hline
14 & lin105 & 1.38 & 41.61 & 47 & u724 & 8.02 & 31.77 \\ \hline
15 & pr107 & 0.89 & 5.36 & 48 & rat783 & 7.96 & 27.81 \\ \hline
16 & pr124 & 1.07 & 17.4 & 49 & pr1002 & 5.34 & 21.83 \\ \hline
17 & bier127 & 0.87 & 14.77 & 50 & u1060 & 5.02 & 25.68 \\ \hline
18 & ch130 & 2.55 & 17.82 & 51 & vm1084 & 5.49 & 25.98 \\ \hline
19 & pr136 & 2.32 & 24.81 & 52 & pcb1173 & 7.37 & 23.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Error comparison of 2-Opt++ algorithm with nearest neighbour drawn by graph compression technique in all categories. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **TSP** & \multicolumn{5}{c}{**Error \%**} \\ \cline{2-9}  & **Instance** & **TPO** & **NN** & **GA** & **SA** & **TS** & **ACO** & **2-Opt++** \\ \hline
1 & eil51 & 2.64 & 18.56 & 6.60 & 3.08 & 3.08 & 9.73 & **2.37** \\ \hline
2 & berlin52 & 2.17 & 8.50 & 5.36 & 5.55 & 2.63 & 5.04 & **0.03** \\ \hline
3 & st70 & 3.28 & 12.82 & 3.81 & 3.16 & 2.26 & 12.08 & **1.96** \\ \hline
4 & eil76 & 3.49 & 13.80 & 5.95 & 5.42 & 4.41 & 9.78 & **2.87** \\ \hline
5 & pr76 & 5.26 & 21.05 & 13.70 & 4.48 & 1.64 & 9.78 & **0.94** \\ \hline
6 & ch150 & 6.35 & 8.42 & 7.30 & 8.18 & 5.12 & 12.60 & **2.54** \\ \hline
7 & a280 & 7.89 & 19.98 & 8.82 & 9.74 & 8.60 & 11.20 & **6.29** \\ \hline
8 & rd400 & 19.04 & 19.23 & 8.42 & 10.05 & 35.62 & 26.03 & **5.89** \\ \hline
9 & pcb442 & 19.64 & 16.10 & 9.73 & 13.08 & 63.70 & 24.93 & **3.98** \\ \hline
10 & rat99 & 4.53 & 13.03 & 6.16 & 5.48 & **2.68** & 9.36 & 2.79 \\ \hline
11 & eil101 & 7.31 & 17.01 & 9.04 & 6.86 & **6.14** & 19.70 & 6.27 \\ \hline
12 & d198 & 5.49 & 14.46 & 5.09 & 3.81 & **1.92** & 14.27 & 2.74 \\ \hline
13 & kroA100 & 5.55 & 16.05 & 6.79 & 4.68 & 5.82 & 7.80 & **1.13** \\ \hline
14 & ch130 & 6.63 & 17.82 & 8.20 & 7.34 & 9.94 & 13.16 & **2.34** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of 2-Opt++ algorithm with existing algorithms.
# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 5 Discussion and Conclusions
### Conclusions  
This study explores the convergence of blockchain, AI, NFC, and mobile technologies in e-payment systems through a robust combination of descriptive, network, and evolutionary scientometric analyses. It identifies the conceptual transition from isolated technological innovations to an integrated, sociotechnical ecosystem--with trust, security, and usability as core pillars.  
The findings reveal that blockchain, artificial intelligence (AI), mobile platforms, and contactless interfaces (e.g., NFC) have emerged as dominant, interlinked themes within the e-payment literature. Thematic evolution maps and keyword co-occurrence networks show that these technologies increasingly co-appear and form core \"motor themes,\" underscoring their centrality in driving the digital transformation of payment systems.  
This study highlights several opportunities: improved transaction security through decentralized systems, predictive fraud detection via AI, and enhanced user convenience through seamless mobile interfaces. However, these benefits are accompanied by critical challenges--such as the lack of interoperability across systems, data privacy risks, regulatory misalignment, and the potential widening of the digital divide. These insights are supported by the convergence of theoretical models emphasizing trust, security, and risk perception in e-payment adoption.  
By integrating both macro-level patterns and micro-level conceptual linkages, this study contributes to theoretical development, practical design strategies, and policy formulation in the digital finance domain. It also highlights challenges such as interoperability, regulatory harmonization, and digital exclusion, calling for multi-disciplinary collaboration among technologists, economists, and institutional actors to co-create an inclusive, secure, and user-centered digital finance future.  
Conceptualization, K.-K.L. and P.C.B.; methodology, P.C.B.; software, P.C.B.; validation, K.-K.L., Y.-C.H., and V.A.D.; formal analysis, P.C.B.; data curation, P.C.B. and V.A.D.; writing--original draft preparation, P.C.B.; writ-ing--review and editing, Y.-C.H.; visualization, P.C.B. and V.A.D.; supervision, K.-K.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The bibliographic data used in this study were retrieved from the Web of Science (WoS) database, which is a sub-scription-based and not publicly available source. Data analysis was conducted using the Bibliometrix R package (version 4.2.1). This study did not generate any new data. Due to license restrictions, the raw dataset cannot be shared publicly.  
The authors declare no conflict of interest.