esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Effective user clustering and power allocation are significant for NOMA systems to cater to green communication and enhance the overall system performance. By formulating the energy efficiency maximization problem of downlink NOMA systems as an optimization problem with several constraints, we apply a quick user clustering strategy based on the analysis of downlink NOMA system characteristics. Given user clustering results, each user's optimal power allocation in each cluster is calculated by the Lagrangian multiplier method with Karush-Kuhn-Tucker optimality conditions. An inter-cluster dynamic programming algorithm is further developed to achieve the overall energy efficiency maximization. Numerical simulation results show the energy efficiency gain of the proposed solution. It is shown that, by using the proposed algorithms, the energy efficiency of NOMA systems is much higher than the systems that used OFDMA schemes and other user clustering strategies and power allocation schemes.  
Conceptualization, R.C. and J.W.; methodology, R.C.; software, R.C.; validation, R.C. and F.S.; formal analysis, F.S.; investigation, F.S.; resources, L.Z.; data curation, K.L.; writing--original draft preparation, R.C.; writing--review and editing, K.L. and J.W.; visualization, F.S.; supervision, L.Z.; project administration, K.L.; funding acquisition, K.L. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key-Area Research and Development Program of Guangdong Province (2020B0101090003), National Natural Science Foundation of China (NSFC 62072012), Shenzhen Research Project (JSGG20191129110603831), and Shenzhen Key Laboratory Project (ZDSYS201802051831427).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Sensor Calibration
### Camera Parameter Calibration
#### 2.1.3 Inclined Camera Imaging Model
Considering the definition of commonly used distortion models and removing the tangential distortion [28] that is coupled with camera tilt, it is necessary to add radial and  
Figure 3: Inclined camera imaging model. thin prism distortions to the normalized coordinates in the ideal camera coordinate system \(O_{\text{C}}-X_{\text{C}}Y_{\text{C}}Z_{\text{C}}\). \[ \begin{cases}X_{d}^{\prime}=\left(1+s_{1}r^{2}+s_{2}r^{4}\right)X_{\text{C}}^{ \prime}+k_{1}r^{2}+k_{2}r^{4}\\ Y_{d}^{\prime}=\left(1+s_{1}r^{2}+s_{2}r^{4}\right)Y_{\text{C}}^{\prime}+k_{3}r^ {2}+k_{4}r^{4}\end{cases}  \tag{9}\]  
where \(X_{\text{d}}^{\prime}\) and \(Y_{\text{d}}^{\prime}\) are the values of \(X_{\text{C}}^{\prime}\) and \(Y_{\text{C}}^{\prime}\) after distortion, respectively; \(r^{2}={X_{\text{C}}^{\prime}}^{2}+{Y_{\text{C}}^{\prime}}^{2};s_{1}\) and \(s_{2}\) are the first two coefficients of radial distortion; and \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\) are the thin prism distortion coefficients for \(X_{\text{C}}\) and \(Y_{\text{C}}\) in the coordinate system \(O_{\text{C}}-X_{\text{C}}Y_{\text{C}}Z_{\text{C}}\). Based on Equations (8) and (9), a tilted camera imaging model including lens distortion can be established, and this model can be used to calibrate the camera's intrinsic parameters.
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 5 Simulation Results
### Overall Complexity Evaluation  
In this set of experiments, the FRF interleaver is compared to the helical interleaver in terms of hardware complexity. The two interleavers were implemented in hardware using field-programmable gate arrays (FPGA) with a size of 128 \(\times\) 128. Additionally, both interleavers were developed using Altera's Cyclone II FPGA board. Figures 14 and 15 indicate the overall complexity in terms of the total logic elements and memory bits. The FRF and helical interleavers' required logic elements were estimated using Altera's Quartus II tools. As indicated in the figures, the FRF interleaver needs more logic elements than the helical interleaver, but the total memory bits are almost the same for both of them. The FRF interleaver is, therefore, more complicated than the helical interleaver; that is to say, the FRF interleaver requires more energy in computations. However, in wireless networks such as wireless sensor networks, the energy consumption in communication is identified as the major source of energy consumption and costs significantly more than computation. Indeed, increasing the BER will increase the number of lost packets, leading to an increase in energy consumption due to packet retransmission that inevitably affects the network  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Tap1** & **Tap2** & **Tap3** & **Tap4** & **Tap5** & **Tap6** \\ \hline
**Delay (ns)** & 0 & 310 & 710 & 1090 & 1730 & 2510 \\ \hline
**Power (dB)** & 0 & \(-1\) & \(-9\) & \(-10\) & \(-15\) & \(-20\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multipath profile of the vehicular A outdoor channel model.  
Figure 13: BER performance for FFT\(-\)OFDM and DWT\(-\)OFDM systems over vehicular A channel.  
efficiency. Therefore, the FRF interleaver can improve the network throughput and energy efficiency compared with the others.
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 4 Simulations
### Sensor Coverage  
In this experiment, we measure sensor coverage, i.e., a proportion of unique locations where a sensor was able to boot and execute a task, i.e., represents a geographical coverage. The task run by a sensor node can be supported by a small 10-mF capacitor. For a fixed capacitor configuration, the coverage depends on the duty cycle, as shown in Figure 5, with maximum coverage achieved by the lowest duty cycle. Increasing the fixed capacitor size from 10 mF to 100 mF reduces the coverage by 42% at 0.1% duty cycle. Stepping up the duty cycle to 1% further reduces coverage by up to 80% compared to the 10-mF configuration. However, increasing the duty cycle beyond 1% does not have a significant effect on the coverage. The hybrid configuration provides a consistent near 100% coverage at all capacitor sizes. In hybrid configuration, the node boots on a small capacitor, which enables it to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline area & 3000 m \(\times\) 3000 m \\ avg speed & 1.38 m/s \(\pm\) 0.28 m/s \\ rest time, home station & 600 s \\ rest time, charging station & 5 s \\ leak current & 4 \(\mu\)A \\ sleep current & 4 \(\mu\)A \\ RF power output & 3 W \\ RF min distance & 1.5 m \\ \(V_{max}\) & 1.25 V \\ \(V_{min}\) & 1.02 V \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
boot at all locations. The performance of hybrid configuration does not depend on the larger capacitor size, as it performs the switch only when the available energy is sufficient to charge the larger capacitor.
# Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components
## 2 Materials and Methods
### Processing of 3D Point Cloud  
In these merged data sets the spatial component directly helps to simplify the problem as we can reduce the data set to regions of interest, such as the objects, and remove useless regions, such as the background. The second step of the proposed framework consists in removing the background. In our study case, the background can be easily removed using the depth information thanks to a plane estimation method. This has several advantages.  
Figure 4: Combined 3D point cloud and HSI. Each 3D point is assigned by a spectrum. In this figure, the average value over the entire spectrum is shown per pixel.  
Figure 3: Acquired datasets of electronic waste consisting of waste plastics and PCBs: **(a)** RGB; **(b)** 3D point cloud with a resolution of 0.5 mm and a depth accuracy of 0.05 mm; **(c)** HSI with a resolution of 636 \(\times\) 1118 pixels and 224 bands in the wavelength range from 900 nm to 1700 nm.  
Firstly, the amount of data are reduced, which has a positive effect on the further processing steps. Secondly, other disturbing effects such as dirt (e.g., on the conveyor belt) and shadow areas are removed. Areas of shadow in particular can have a negative impact on the quality of the results. An example for disturbing shadow effects and the influence on the classification result is shown in Figure 5. Shadow areas occur mainly on the border of 3D objects and also on the border of 3D components on the top of PCBs. The most disturbing shadows are on the border of the objects. Shadow areas are generally a problem when processing datasets as they reduce the reliability and success rate of algorithms for object recognition, classification and other types of processing. The removal of shadow areas is therefore an essential step towards improving image quality [27; 28] and thus also improve the base for further analysis steps.  
Another advantage of adding 3D data is the possibility to partition the dataset by using cluster methods, which are addressing the shape content of objects. The aim of a cluster analysis is to find structures in a set of arbitrary data with different properties and to discover relationships in order to be able to group objects. This involves forming groups in such a way that the properties of the data within the group or cluster are approximately identical. A variety of cluster algorithms exists in the state of the art for this purpose. The task of cluster algorithms is to form clusters within datasets. Clustering methods differ in their approaches and strategies. This allows the separation of the individual objects in the dataset. Due to the homogeneous data available in our dataset, a cluster approach based on Euclidean distance was chosen for the separation of the individual objects. Cluster methods can be not only used for the individual object's separation (such as plastics and PCBs, as shown in Figure 6b), but also can be used for the separation of components on the boards (as shown in Figure 6c).
# Stimuli-Responsive Photonic Crystals
## 4 Active Tuning of the Photonic Band Gap
### Chemical Stimuli  
One of the most used methods to tune the PBG via chemical methods relies on the interaction between a soft structure (e.g., a hydrogel) and a given chemical species. For instance, the interaction with ions can generate a swelling and a shrinkage of the soft structure, which in turn changes the geometrical features of the PhC and leads to a shift of the photonic band gap [96; 97; 45; 98; 96]. Furthermore, this approach can be used for the detection H\({}^{+}\) ions, and thus for building up pH sensors [99; 45; 60; 29; 100]. Another class of chemically tuned PhCs consist in the integration of porous materials in the PhC, in which refractive index modulation is given by infiltration of vapors [55; 56; 101; 30; 57] or solvents [102; 20; 57; 58; 59; 103; 59; 104]. [PERSON] et al. fabricated 1D photonic crystals alternating films of poly methyl methacrylate-\(\infty\)-hydroxy methyl methacrylate-\(\infty\)-ethylene glycol dimethacrylate (PMMA-\(\infty\)-PHEMA-\(\infty\)-PEGDMA) and titania nanoparticles by spin-coating, drastically changing its structural color when immersed in different solvents (Figure 2a,b) [32].
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 4 Baseline Clustering Strategies under Jamming
### Power Domain
#### 4.1.1 Fixed Power (FP)  
In this strategy, we assume that clustered nodes use fixed transmit power to reach the CH. Therefore, the transmit power is chosen to ensure that the nodes communicate with the CH in a single hop. Setting the transmit power fixed to the required upper limit guarantees intra-cluster communication, but implies unnecessary power consumption for many of the established links.  
To illustrate the energy cost of this strategy, we establish a fixed transmit power for all nodes that guarantee communication. Therefore, the communication link between the node most affected by the jammer and the corresponding CH will be the constraint in the transmit power. Consequently, the fixed transmit power is acquired according to (3).
## 5 Conclusions  
In this paper, an innovative circuit for wireless power and data transmission is presented. This circuit incorporates MOSFETs and an inductor into the Class-E PA to adjust the voltage amplitude and enable the ASK modulation scheme for data transmission. The proposed circuit can effectively switch load networks for the ASK modulation scheme to maintain the high-efficiency transmission. Furthermore, the comprehensive parameters design process for the proposed circuit is introduced. Finally, simulations and experiments verify the performance of the proposed circuit. The measured results demonstrate that a power delivery of 84.1 mW can reach a power transmission efficiency of 70.8% at the data rate of 3 Mbps. Moreover, the measured BER is less than \(10^{-6}\) with a modulation index of 10%. Based on these results, both high data rate and power transmission efficiency targets are successfully achieved.  
[PERSON], [PERSON], [PERSON] and [PERSON] developed the idea of this study; [PERSON] and [PERSON] conducted the calculations and experiments; data analysis was performed by [PERSON] and [PERSON]; and [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **2018** & **2018** & **2018** & **2020** & **2022** & **This Work** \\  & [25] & [23] & [28] & [24] & [29] & \\ \hline Modulation & FSK & PSK & ASK & OOK & ASK & ASK \\ Carrier Freq. (MHz) & 2/4 & 13.56 & 10 & 2.5 & 10 & 16 \\ Data rate (Mbps) & 1 & 0.1 & 1 & 0.625 & 2 & 3 \\ Distance (mm) & 6 & 10 & 6 & 10 & 10 & 6 \\ BER & \(10^{-5}\) & / & \(10^{-5}\) & \(10^{-5}\) & \(10^{-6}\) & \(10^{-6}\) \\ PDL (mW) & 126 & 54 & 24 & 80 & 35 & 84.1 \\ PTE (\%) & 25 & 48 & 42 & 20 & 64.6 & 70.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with previous works.  
Figure 14: Power transmission efficiency and output power under different distances \(d\). (**a**) Power transmission efficiency with different distances d. (**b**) Output power with different distances \(d\).  
**Funding:** This work was supported by the National Natural Science Foundation of China (under Grant 52177199) and the Zhejiang Provincial Natural Science Foundation of China (under Grant LGG22E070013).  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Manganese Oxide Films  
The amorphous manganese oxide films were produced on glass substrates [168]. Experimental results showed the highest specific capacitance was 243 F/g when the scan rate was 10 mV/s. Moreover, two peaks could be observed in the cyclic voltammogram attributed to Mn\({}^{2+}\) and Mn\({}^{3+}\), respectively. Highly adherent and compact morphology of films were produced by [PERSON] and co-workers [169]. Researchers concluded that hausmannite (Mn\({}_{3}\)O\({}_{4}\)) films were changed to manganese dioxide (MnO\({}_{2}\)) after chemical treatments (with hydrochloric acid (HCl) solution). The obtained films showed different specific capacitance values through electrochemical transformation (72 to 393 F/g) and chemical transformation (72 to 258 F/g). Enhanced physical properties (from amorphous to crystalline, irregularly fused capsule-like grains) improved the band gap and transmittance of SILAR-deposited Mn\({}_{3}\)O\({}_{4}\) films decorated on graphene [170]. The films showed the highest specific capacitance (194 F/g), indicating these materials could be used in electrochemical storage devices. [PERSON] and co-workers [171] reported that the SILAR-deposited MnO\({}_{2}\) films (tetragonal and birnessite phase) could be used in supercapacitor devices. [PERSON] and co-workers [172] proposed synthesizing thin films under different cationic precursors. The films prepared using manganese (II) acetate showed spike morphology; the contact angle was 35\({}^{\circ}\) and the specific capacitance was 222 F/g (5 mV/s c scan rate). The films produced using manganese (II) chloride (MnCl\({}_{2}\)) indicated void morphology, the contact angle was 26\({}^{\circ}\), the specific capacitance was 375 F/g, (5 mV/s c scan rate), the maximum energy density was 17 Wh/kg, and the power density was 999 W/kg. The films synthesized from manganese sulfate (MnSO\({}_{4}\)) exhibited porous morphology, thecontact angle was 29\({}^{\circ}\), and the specific capacitance was 248 F/g, (5 mV/s scan rate). The experimental results concluded the best electrochemical properties by using manganese (II) chloride and retained stability of 94% after 4500 cyclic voltammetry cycles. NaOH and MnSO\({}_{4}\) were used to produce thin films on a stainless steel substrate at 273 K, the rinse time was 10 s, and the immersion time was 20 s [173]. In the FTIR studies, 615, 499, 3746, and 1544 cm\({}^{-1}\) contributed to MnO in tetrahedral, the MnO band in octahedral, and the stretching and bending vibrations of hydroxide (OH) molecules, respectively. In the TEM studies, the nanorod-like structure, d-spacing of 0.26 nm, and tetragonal phase could be observed in the films deposited at 75 deposition cycles.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.5 AODV with Predicting Node Trend (AODV-PNT)  
AODV-PNT is a novel routing protocol proposed in [36], suitable for vehicular networks, which considers VANET topology features. It is an enhanced version of AODV. This proposal includes two main changes: the routing metric improvements and the estimation of the total weight of the route (TWR). This TWR includes analysis of movement direction, acceleration, vehicle speed, and link quality. Due to frequent topology changes, future TWR was included where it was attempted to calculate a relatively stable relay vehicle over a lapse of time in the future.  
The authors calculated the TWR from a source node to the next hop, as shown in Equation (5).  
\[TWR=f_{s}\times|S_{n}-S_{d}|+f_{a}\times|A_{n}-A_{d}|+f_{d}\times|\theta_{n}- \theta_{d}|+f_{q}\times Q, \tag{5}\]  
where,  
* \(\theta_{n}\), \(S_{n}\), \(A_{n}\): Direction, next-hop node's speed and acceleration.
* \(\theta_{d}\), \(S_{d}\), \(A_{d}\): Direction, destination node's speed and acceleration.
* \(f_{s}\): Speed weight factor.
* \(f_{a}\): Acceleration weight factor.
* \(f_{d}\): Direction weight factor.
* \(f_{d}\): Link-quality weight factor.
* \(Q\): Link quality between source and next-hop vehicle.  
Note that the TWR is defined by differences of link quality, speed, direction, and acceleration. The next-hop node is best with the least TWR and similar acceleration, speed, and direction compared  
Figure 8: Path considered by source in analytical hierarchical process (AHP)-based multimetric geographical-routing protocol (AMGRP) and in GPSR to reach destination [35].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Mobility metric & 0.413846 \\ Node density & 0.256923 \\ Node status & 0.216923 \\ Link lifetime & 0.112308 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relative ranking of criteria.  
to the destination vehicle and sound link quality between source node and next-hop node, as shown in [36].
# Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification
## 3 Our Approach  
In this part, we accurately describe our network structure and the innovations we propose in the network. In Section 3.1, we explain the role of our proposed NLAM in the frame-level feature extraction process. In Section 3.2, we take one sequence of a video as an example to analyze our proposed NLTP method. In Section 3.3, we explain the loss function we adopted. The structure of the entire network is shown in Figure 2:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Temporal**} & \multicolumn{4}{c}{**MARS**} & \multicolumn{4}{c}{**DukeMTMC-VideoReID**} \\ \cline{3-10}  & & **Aggregation** & **mAP** & **R1** & **R5** & **R20** & **mAP** & **R1** & **R5** & **R20** \\ \hline ResNet50 & TPavg & 75.8 & 83.1 & 92.8 & 96.8 & 92.9 & 93.6 & **99.0** & **99.7** \\ ResNet50 & TA & 76.7 & 83.3 & 93.8 & **97.4** & 93.2 & 93.9 & 98.9 & 99.5 \\ ResNet50 & RNN & 73.8 & 81.6 & 92.8 & 96.7 & 88.1 & 88.7 & 97.6 & 99.3 \\ SE-ResNet50 & TPavg & **78.1** & 84.0 & **95.2** & 97.1 & **93.5** & 93.7 & **99.0** & **99.7** \\ SE-ResNet50 & TA & 77.7 & **84.2** & 94.7 & **97.4** & 93.1 & **94.2** & **99.0** & **99.7** \\ SE-ResNet50 & RNN & 75.7 & 83.1 & 93.6 & 96.0 & 92.4 & 94.0 & 98.4 & 99.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different basic frameworks on MARS (Motion Analysis and Re-identification Set) [30] and DukeMTMC-VideoReID (Duke Multi-Target Multi-Camera Video Reidentification) [31]. TPavg, TA, RNN stand for average temporal pooling, temporal attention, and recurrent convolution network, respectively. The best results are shown in bold.  
Our overall network architecture is shown in Figure 2. Similar to a standard video-based Re-ID framework, it mainly includes two parts: Feature extractor and temporal feature aggregation. The difference is that we insert the non-local attention module (NLAM) we proposed in the feature extractor, and we adopt the non-local temporal pooling (NLTP) method we proposed in the temporal feature aggregation part. NLAM is used to insert between CNN blocks for spatial attention and channel attention extraction. It helps CNN to determine the location of the target person in each frame and reduce the interference caused by occlusion in the image. NLTP improves on the previous temporal pooling method by acquiring temporal attention in a non-local way in the first step and embedding temporal features into video-level features through pooling in the second step. The primary purpose of NLTP is to give a higher weight to frames that are more representative of the entire sequence, thereby obtaining a more robust video-level representation.