esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DQN-Based Priority/Demanding Resource Management
### DQN-Based Solutions
(2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Spectrum- and Energy- Efficiency Analysis under Sensing Delay Constraint for Cognitive Unmanned Aerial Vehicle Networks. _KSII Trans. Internet Inf. Syst._**2022**, _16_, 1392-1413. * [PERSON] and [PERSON] (2023) [PERSON], [PERSON] Computation Offloading in MEC-Enabled IoV Networks: Average Energy Efficiency Analysis and Learning-Based Maximization. _IEEE Trans. Mob. Comput._**2023**, _23_, 6074-6087. [CrossRef]
* [PERSON] and [PERSON] (2022) [PERSON]; [PERSON] Energy-Efficient Beamforming and Resource Allocation for Multi-Antenna MEC Systems. _IEEE Access_**2022**, _10_, 18008-18022. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Resource Management for Computation Offloading in D2D-Aided Wireless Powered Mobile-Edge Computing Networks. _IEEE Internet Things J._**2021**, \(8\), 8005-8020. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Dynamic Energy-Saving Offloading Strategy Guided by Lyapunov Optimization for IoT Devices. _IEEE Internet Things J._**2022**, \(9\), 19903-19915. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Joint Optimization of Caching and Association in Energy-Harvesting-Powered Small-Cell Networks. _IEEE Trans. Veh. Technol._**2018**, _67_, 6469-6480. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine Learning-Based XApp for Dynamic Resource Allocation in O-RAN Networks. _arXiv_**2024**, arXiv:2401.07643. [CrossRef]
* [PERSON] (2021) [PERSON] An RL Approach for Radio Resource Management in the O-RAN Architecture. In Proceedings of the 2021 18 th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Rome, Italy, 6-9 July 2021. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Intelligent-Driven Green Resource Allocation for Industrial Internet of Things in 5G Heterogeneous Networks. _IEEE Trans. Ind. Inform._**2022**, _18_, 520-530. [CrossRef]
* [PERSON] (2021) _3 GPP TS 23.203 V17.2.0_; Technical Specification Group Services and System Aspects. Policy and Charging Control Architecture; ETSI: Valbonne, France, 2021. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Optimized Multi-Service Tasks Offloading for Federated Learning in Edge Virtualization. _IEEE Trans. Netw. Sci. Eng._**2022**, \(9\), 4363-4378. [CrossRef]

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# Optimizing Assembly in Wiring Boxes Using API Technology for Digital Twin
## 1 Introduction  
Currently, the production of automotive wiring harnesses is still quite dependent on human resources, with the assembly of the harnesses largely done through manual operations. Increasing productivity is primarily achieved through efficient workload distribution among workstations, ensuring ergonomic workstations, and streamlining the component feeding process for the assembly line.  
The continuous evolution of the security and multimedia systems integrated into vehicles poses a challenge for wiring harness manufacturers. They must provide the automotive industry with complex wiring harnesses with a lean architecture that can be easily integrated within the vehicle's architecture. Components such as terminals, connectors, and connector seals are becoming smaller, making manual handling increasingly difficult. Other components, like fuse boxes and relays, are evolving technically, with manufacturers providing new technological solutions to enhance the flexibility of the wiring harness, such as introducing integrated circuit boards into fuse boxes and relays.  
However, even as these components evolve, a part of the process remains constant in current practice, specifically the manual insertion of fuses and relays into the box. Therefore, this research aims to increase the level of automation in the assembly process by offering an automation solution that can be easily implemented by manufacturers.  
The studied solution consists of using a robotic arm for the insertion of fuses and re-lays, considering the technical constraints related to insertion, force, and angle, which are difficult to control in manual operations. This study aims to provide solutions to eliminate the quality issues caused by factors that are challenging for an operator to control, suchas angled insertion (that leads to an imperfect contact between the fuse and terminal), incomplete insertion (difficult to detect, caused by insufficient force applied during the fuse insertion operation), fuse/terminal damage due to excessive force application, and fuse insertion into the wrong cavity (leading to an operational time increase due to the need for rework).  
To appeal to wiring harness manufacturers, the automation solution needs to be flexible, be easily adaptable to changes in the fuse and relay box configuration, consider the cost factors for investment and implementation, and have compact dimensions to avoid the major reconfiguration of the assembly line. Additionally, it should aim to facilitate the professional reconversion of the operator (e.g., training in robotic arm operation). The main goal of increasing automation is to minimize quality risks by increasing the efficiency of the process, not to reduce the workforce.
## 2 Methods
### ANN and DT Models  
#### 2.4.1 Ann  
Various ML techniques are available for solving regression problems. Two of the most commonly used ML techniques are ANN and DT. An ANN is a modeling procedure stimulated by the human nervous system that enables learning through representative data instances, which explains a physical aspect or a choice process. One different characteristic of ANN is that they can ascertain practical associations between inputs and target variables and also obtain exact knowledge and complicated information from representational datasets. Associative relationships between inputs and target variables are ascertained without presumptions on the analytical description of the phenomes. This technique presents destined benefits over regression-based techniques, including its capability to handle noisy data. NNs comprise a layer of input nodes and a layer of output nodes joined by one or more layers of hidden nodes.  
An ANN ensemble is a learning model where several NNs are trained for a similar assignment [60]. It was shown that the generalization capability of the NN model was remarkably enhanced by ensembling various NNs [61]. Typically, an NN ensemble is built in a couple of steps: (1) training several elements of NNs and (2) consolidating the element forecasts.  
There are two common approaches for training component NNs, including boosting and bagging. The process of boosting and bagging is shown in Figure 3. Despite the fact that both bagging and boosting involve N students, they are fundamentally distinct. Unlike the bagging technique, which combines predictions of the same type, the boosting method incorporates predictions of various kinds. In bagging, each model is constructed independently of the others, whereas, in boosting, the outcomes of earlier constructed models are impacted. In the bagging technique, every model is given equal weight, while, in the boosting technique, the new ones are weighted according to their performance. In boosting, new components of training data contain observations that the old model incorrectly classified. Bagging utilizes randomly generated subsets of training data.
## 2 Materials and Methods
### Free Fatty Acids  
Free fatty acid levels were determined according to the method of [PERSON] et al., with slight modifications [19]. Each 100 mg sample was mixed with 4 mL of chloroform, vortexed for 30 s, and centrifuged at 3500\(\times\)\(g\) for 15 min at room temperature. The lower organic phase was collected, and the extraction was repeated twice using 2 mL of dichloromethane each time. The combined organic extracts were dried under a gentle stream of nitrogen with mild heating (\(\sim\)35 \({}^{\circ}\)C). Derivatization was then carried out by adding 2 mL of methylation reagent, vortexing for 30 s, and incubating in a water bath at 80 \({}^{\circ}\)C for 2 h. The container was loosely covered with gauze to minimize gas loss. After cooling to room temperature, 2 mL of n-hexane and 1 mL of water were added and thoroughly vortexed. The mixture was centrifuged at 2000\(\times\)\(g\) for 5 min, and the upper organic phase was collected and evaporated under a gentle stream of nitrogen with mild heating. An appropriate volume of iso-octane was added based on the sample concentration, vortexed, and left to stand. The final solution was filtered through a 0.22 \(\upmu\)m PTFE syringe filter before being transferred to a sample vial for analysis. Quantification was performed using the external standard method with a mixed standard solution containing 36 fatty acids (Sigma-Aldrich, St. Louis, MO, USA).  
A chromatographic analysis was performed using an Agilent 6890 gas chromatograph (Agilent Technologies, Santa Clara, CA, USA) equipped with a CP-Sil 88 capillary column (100 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). A 1 \(\upmu\)L sample was injected with a split ratio of 10:1, and helium was used as the carrier gas at a constant flow rate of 1.0 mL/min. The initial oven temperature was set at 100 \({}^{\circ}\)C and held for 5 min, then increased at a rate of 4 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C, and held at 240 \({}^{\circ}\)C for 15 min. Mass spectrometric analysis was conducted using an Agilent 5977 mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) equipped with an electron ionization (EI) source and operated using the MassHunter Workstation software package. The analysis was conducted in selected ion monitoring (SIM) mode, with the ion source temperature set at 260 \({}^{\circ}\)C, quadrupole temperature at 150 \({}^{\circ}\)C, ionization voltage at 70 eV, and scan rate at 57.7 cycles per second. In SIM mode, only specific characteristic ions (\(m/z\)) for each target fatty acid were monitored, all of which fall within the scan window of \(m/z\) 30 to 550. Information on the purity, CAS numbers, monitored \(m/z\) values, and retention times of the fatty acid standards is summarized in Supplementary Table S2. Compound identification was performed by matching retention times and mass spectral data with those of authentic standards. Data pre-processing--including baseline correction, peak detection, alignment, and retention time calibration--was performed using the Quant-My-Way Agilent B.09.00 software program.
# A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems
## 6 Conclusions  
This paper proposed an HCSA approach to solve the PFSP. The proposed HCSA adopts an SPV rule to convert the continuous position values to job permutations and incorporates an NEH-based heuristic algorithm for population initialization. It also adopts the SA-based VNS local search method  
Figure 4: Comparison of the results for various metaheuristic algorithms.  
\begin{table}
\begin{tabular}{l l l l l} \hline  & & **HCSA** & **MGELS** & **PSOVNS** & **PSOMA** \\ \hline Problem & & & & & \\ ta01 & BRPD & **0.00** & 1.48 & 0.23 & **0.00** \\  & ARPD & **0.00** & 2.45 & 1.63 & **0.00** \\ ta11 & BRPD & **0.00** & 2.84 & 0.26 & **0.00** \\  & ARPD & **0.00** & 4.95 & 2.28 & 0.10 \\ ta21 & BRPD & **0.00** & 2.84 & 0.74 & **0.00** \\  & ARPD & **0.18** & 4.14 & 1.77 & 0.20 \\ ta31 & BRPD & **0.00** & 0.77 & 0.00 & **0.00** \\  & ARPD & **0.00** & 1.65 & 0.51 & 0.04 \\ ta41 & BRPD & **1.24** & 8.42 & 1.61 & 1.61 \\  & ARPD & **1.70** & 8.82 & 1.75 & 2.43 \\ ta51 & BRPD & **1.98** & 7.74 & 2.91 & 2.15 \\  & ARPD & **2.25** & 10.26 & 4.31 & 2.54 \\ ta61 & BRPD & **0.00** & 0.52 & 0.00 & **0.00** \\  & ARPD & **0.02** & 1.10 & 0.02 & 0.11 \\ ta71 & BRPD & **0.23** & 4.92 & 1.19 & 0.26 \\  & ARPD & **0.50** & 5.70 & 1.98 & **0.50** \\ ta81 & BRPD & 1.59 & 9.38 & 2.33 & **1.29** \\  & ARPD & 2.10 & 10.51 & 3.82 & **1.62** \\ ta91 & BRPD & **0.16** & 2.17 & 1.12 & 0.16 \\  & ARPD & 0.56 & 4.17 & 1.17 & **0.21** \\ ta101 & BRPD & **1.26** & 8.50 & 1.96 & 1.45 \\  & ARPD & **1.39** & 9.47 & 3.03 & 1.53 \\ ta111 & BRPD & **0.80** & 7.40 & 1.64 & 1.35 \\  & ARPD & **1.06** & 8.82 & 2.37 & 1.48 \\ \hline Average & BRPD & **0.61** & 4.75 & 1.15 & 0.69 \\  & ARPD & **0.81** & 6.00 & 2.05 & 0.90 \\ \hline \end{tabular}
\end{table}
Table 8: Comparison tests of various meta-heuristic algorithms.  
to balance exploitation and exploration, and ultimately enhance the quality of the solution. Simulation results demonstrate that the proposed HCSA, which incorporates NEHLJP1 and an SA-based VNS local search, produced a better makespan for all benchmark datasets than other algorithms. In our future research, we hope to analyze the following four aspects of the HCSA: (1) applying the HCSA to large real-world problems, such as nurse scheduling, basketball tournament scheduling, automotive manufacturing, and the vehicle routing scheduling problem; (2) adapting the Levy searching strategy to improve the local search; (3) using graph structures and neighborhood structures to speed up the local search; and (4) implementing a new NEH-based heuristic algorithm and solving the larger benchmarks.  
Conceptualization and methodology, K.-W.H., A.S.G. and Z.-X.W.; writing--original draft preparation, K.-W.H., A.S.G., Z.-X.W. and Y.-W.C.; writing--review and editing, K.-W.H. and Y.-W.C.  
This work was supported in part by the Ministry of Science and Technology, Taiwan, R.O.C., under grants MOST 107-2218-E-992-303.  
The authors declare no conflict of interest.
# Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels
## 4 Methodology
### Point Cloud Registration
Given the system calibration, zero shifts and zero rotations could be also used when registering two point clouds captured by two scanners in a given drive run. The transformed point (Pt) is then utilized to identify the three closest points in the reference surface. If (Pt) belongs to a bi-pyramid created by these points and two vertices that belong to the orthogonal to the triangle, which is defined by these three points, through its centroid given a predefined normal distance threshold (n), then the closest three points are accepted as a possible match (Figure 9). It is worth noting that the predefined normal distance threshold is defined according to the noise level within the data in question as well as the quality of the current estimate of the transformation parameters. Therefore, the normal distance threshold would reduce as we proceed with the iterations as the quality of transformation parameters improves. Instead of minimizing the squared sum of the distances between the transformed point and its projection onto the corresponding patch (which is implemented in ICPP), this study utilized a modified weight function proposed by [PERSON] et al. [28]. Through this weight modification, one can use non-conjugate points from a point-patch pair while minimizing the squared sum of normal distances between transformed points from the source surface and the corresponding patches in the reference surface. The key advantages of the proposed hybrid approach, when compared to either the original ICPatch or ICPP strategies, include less sensitivity to the existence of erroneous points (i.e., outliers), higher computational efficiency, and ability to incorporate vertical patches [29]. The first two advantages are attributed to the use of a simpler similarity constraint using pseudo-conjugate points for a point-patch pair. A similar registration procedure could be also used for the registration of multiple TLS scans as well as TLS and MLS point clouds. Just as an example, Figure 10 illustrates the registration outcome of three TLS point clouds together with MLS scans from neighboring drive runs.
## 5 Discussion  
It is widely known that the inevitable side channels are detrimental to the security of the practical QKD systems. For this reason, how can [PERSON] exploit these side channels and how much the systems may be affected are worth researching. The side-channel attack proposed above on the imperfect implementation of the SNS protocol, which applies an imperfect IM, truly proves that even small side channels at the light source can compromise the secret key rate severely.  
We note that there are two key points of the side-channel attack. The first and the most important is the imperfect IM is applied, which produces the decoy and signal states differently in the frequency domain, i.e., the frequency side channels. The second is the long-distance key distribution where the channel attenuation is large enough, which could be utilized by [PERSON] to amplify the effects of the side channels. Since the long-distance key distribution is a primary goal of the SNS protocol, the above attack emphasizes the harmfulness of the side channels specifically and may provide a reference for the practical implementation.  
To guarantee the security in the practical systems with the side channels, the first potential way is to improve the experimental techniques or modulation methods to restrain the side channels [57; 58]. For example, three IMs can be used to modulate the signal  
Figure 5: The estimated lower bound of the secret key rate \(R_{e}\) and the upper bound of the real secret key rates \(R_{ul}\) (\(l\in\{1,2,3\}\)) in logarithmic scale versus transmission distance (between Alice and Bob) under the passive frequency-shift attack. The experimental parameters \(r_{||\alpha}\) about the side channels are listed in the Table 3.  
Figure 6: The upper bounds of the percentage of the secret key bits \(R_{rl}=R_{ul}/R_{e}\), \(l\in\{1,2,3\}\).  
pulses and reference pulses, where the last IM is used to eliminate the side-channels [35,36]. The second alternative is to develop the mathematical models in theory to include the side channels, such as the loss-tolerant method [59, 60, 61, 62, 63] with the characterization of the real apparatuses [64]. Finally, it would be a good choice to improve the protocol theoretically to resist the side-channel attack [65]. An ongoing search for the side channels may be needed to guarantee the practical security of QKD systems.
## 2 Conventional 3-D Sound Image Reproduction Based on VBAP  
VBAP is a 3-D amplitude panning method based on vector synthesis that localizes any 3-D sound image by using three loudspeakers. By using the position of a sound image and three loudspeakers, the sound image can be reproduced at the desired position. Figure 3 shows an overview of VBAP. In 22.2 multichannel audio, the three loudspeakers, with the exception of the LFEs, are used to reproduce the 3-D sound image by VBAP. The 3-D sound image is reproduced by the following procedure.  
1. Obtaining the 3-D sound source position vector \(\mathbf{p}\). Panning requires the position vector of the 3-D sound image \(\mathbf{p}=\left[p_{x}\ p_{y}\ p_{z}\right]^{\mathsf{T}}\) to generate signals for reproduction of the sound image. The vector \(\mathbf{p}\) is acquired automatically or manually from media such as video.
2. Calculation of the gain vector \(\mathbf{g}\). VBAP calculates the gain vector \(\mathbf{g}=\left[g_{1}\ g_{2}\ g_{3}\right]^{\mathsf{T}}\) to control the sound image by using \(\mathbf{p}\) and each unit vector \(\mathbf{l_{1}}=\left[l_{1x}\ l_{1y}\ l_{1z}\right]^{\mathsf{T}}\), \(\mathbf{l_{2}}=\left[l_{2x}\ l_{2y}\ l_{2z}\right]^{\mathsf{T}}\), \(\mathbf{l_{3}}=\left[l_{3x}\ l_{3y}\ l_{3z}\right]^{\mathsf{T}}\) from the listening point to the three loudspeakers. In VBAP, it is assumed that the 3-D sound image is on the median point among the three loudspeakers. Hence, the position of the 3-D sound image is represented as:  
Figure 3: Overview of vector base amplitude panning (VBAP).  
\[\mathbf{p}=\mathbf{L}\mathbf{g},\] (1) where \(\mathbf{L}=[\mathbf{l_{1}}\ \mathbf{l_{2}}\ \mathbf{l_{3}}]\) is the matrix of unit vectors. From Equation (1), the gain vector \(\mathbf{g}\) is obtained by: \[\mathbf{g}=\mathbf{L}^{-1}\mathbf{p}. \tag{2}\]
3. Normalize the gain vector \(\bar{\mathbf{g}}\). To prevent excessive sound pressure, the gain vector \(\mathbf{g}\) should be normalized by the L\({}_{2}\) norm \(\|\mathbf{g}\|\) as: \[\bar{\mathbf{g}}=\frac{\mathbf{g}}{\|\mathbf{g}\|},\] (3) where \(\bar{\mathbf{g}}=[\bar{g_{1}}\ \bar{g_{2}}\ \bar{g_{3}}]\) is the normalized gain vector.
4. Generation of the input signals \(y_{i}(t)\) of the three loudspeakers. The input signals \(y_{i}(t)\) are generated by the object signal \(x(t)\) and the calculated gains \(\bar{g}_{1}\), \(\bar{g}_{2}\), and \(\bar{g}_{3}\) as: \[y_{i}(t)=\bar{g}_{i}x(t),\] (4) where \(t\) is the time index and \(i\in\{1,2,3\}\) is the loudspeaker index.  
From these procedures, VBAP can reproduce the 3-D sound image by using the three loudspeakers.  
However, VBAP has an issue regarding accurate 3-D sound image reproduction in that it cannot represent the distance of a 3-D sound image. This is because the gain vector \(\mathbf{g}\) is calculated by using the radial unit vector. In other words, the gain vector \(\mathbf{g}\) only considers the direction of the sound image. To solve this problem, we propose a novel 3-D sound image reproduction method that controls both the directivity and distance of the target sound image on the basis of SH expansion and mode matching.