esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Stage Deep Learning Framework for Video Surveillance
## 3 Proposed Method
### Proposed Overall Architecture
#### 3.2.1 First Stage  
The first stage involves extracting features from individual still-frame images. In this study, learning was not conducted separately to obtain spatial characteristics. Using networks that learned well with large amounts of data, features were quickly extracted for each frame image, and various pretrained networks were used with ViT [40] as the main focus in this study.  
ViT was developed to apply the Transformer architecture, as explained in Section 3.1, to image data analysis. The core concept of ViT, as depicted in Figure 4, involves dividing the input image into a series of fixed-size patches. Before being used as input to the Transformer, each patch is flattened into a single dimension. Subsequently, the flattened patches have a 'class token' added, similar to the 'CLS token' that is added to sentences in Transformers. Additionally, position embeddings are added to each patch image to recognize the relative positions of the patches. These preprocessed patches are then fed into the Transformer encoder, which learns the relationships between the image features and the patches.  
Figure 4: Architecture of Vision Transformer.  
Figure 3: Proposed overall framework architecture.  
Thus, ViT applies the characteristics of Transformers to image data, shows excellent performance when used with large datasets, and is used in various vision tasks.  
When the image feature extraction was completed, 768 features were outputted for each frame image, and when applied to each video, the final output was in the form of (number of videos, frame length, 768). The output data from the first stage are further processed through the combination layer of BiLSTM and multi-head attention in the second stage.
## 6 Discussion
### Comparison with Existing Methods  
The proposed approach offers distinct advantages over the methods mentioned in the introduction. Compared to mechanical reinforcement strategies, which aim to physically suppress vibration by stiffening components like the sensor bracket, our method is significantly less invasive, lower in cost, and more practical to implement. It obviates the need for potentially complex and space-prohibitive mechanical modifications by addressing the error in the signal-processing domain.  
Furthermore, while previous research such as that by [PERSON] et al. has focused on compensating for errors arising from sensor bracket vibration [23], our study targets the runout of the toothed disc itself. This is a more direct approach, as it addresses the error at its fundamental source--the dynamic change in the air gap between the sensor and the teeth--as modeled in our analysis. By using a symmetrically arranged dual-sensor structure to cancel this primary error component, the compensation mechanism is more direct and robust against the complex motion of the measurement target.
# Advanced Metal Matrix Nanocomposites
## 2 Fabrication Methods
### Accumulative Roll Bonding  
Originally introduced by [PERSON] et al. [67] in 1998, accumulative roll bonding (ARB) is based upon severe plastic deformations imposed by rotating rolls on stacked metallic sheets and comprise the following steps (Figure 12):  
* wire brushing of metal sheet surfaces to remove the oxide layer, contaminants, etc.;
* stacking of two sheets on top of each other;
* roll bonding the sheets together up to a minimum 50% thickness reduction; and
* dividing the roll-bonded sample into piece.s  
A one-piece entity can be attained following these steps and the process can be repeated many times. The process has been used for fabricating different metal matrix nanocomposites, i.e., Al/SiC [68; 69; 70], Al/Al\({}_{2}\)O\({}_{3}\)[71], Al/B\({}_{4}\)C [72], Al/CNT [73], Al/TiO\({}_{2}\)[74], Al/SiO\({}_{2}\)[75], Al/W [76; 77], Al/WC [78], Al-Al\({}_{2}\)O\({}_{3}\)-Mg multilayer composite [79], and Mg/CNT [80].  
Figure 11: Schematic of composite manufacturing [66].  
Al-based (AA1100) composite reinforced with nano- tungsten trioxide (WO\({}_{3}\)), with an average size of 80 nm and 1.0, 1.5, and 2.0 vol%, has been fabricated [81] for study of their microstructure and mechanical properties; good dispersion of nanoparticles within the matrix along with enhanced mechanical properties [81] was achieved upon 12 ARB cycles. Higher strength in the multi-pass ARBed samples is attributed to the grain refinement, strain hardening, and uniform distribution of the particles within the matrix (Figure 13).
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
###### Abstract
In terms of the estimation approaches used, supervised classifiers, including maximum likelihood [26], the Mahalanobis typicalities [3; 18], the Mahalanobis typicalities with a neural network [20], decision tree [7; 11; 13; 14; 15; 16; 18; 24; 25], K-nearest neighbor [11; 18; 27], support vector machine [18; 28], and random forest [17] classifiers, are commonly used to map rubber plantations based on remote sensing. Most previous studies used pixel information to estimate rubber plantation characteristics, and only three papers [11; 18; 27] have discussed the use of the object-oriented classification approach for monitoring rubber plantations. In these papers, spectral information was mostly considered; other features, such as textural features, have rarely been discussed. In addition, among these studies, the same supervised classifier used can provide different classification results because of different remote sensing images, features, methods, and study regions. For example, [PERSON] et al. [11], [PERSON] et al. [18], and [PERSON] et al. [24] used a decision tree classifier to map rubber plantations and obtained the classification accuracies of 77.5%, 80.80%, and 92.50%, respectively. The performances of these methods proposed in different studies cannot be comparatively evaluated directly. Thus, rubber mapping based on remote sensing still lacks systematic and comprehensive research with different remote sensing images, such as PlanetScope data, different approaches, such as object-based method and pixel-based method, and different features, such as texture features and spectral features, have not been fully considered. PlanetScope images were acquired by PlanetInc. in America, the world's largest satellite constellation, with more than 170 orbiting satellites. PlanetScope data have high spatial (3-4 m) and temporal (daily) resolutions, which has been successfully applied on many fields, for example, crop yield estimation [29], crop leaf area index estimation [30], pasture biomass [31], tropical forest canopy height [32] and so on. [PERSON] et al. [32] used the high temporal resolutions of Planet images, in view of limited capabilities of frequent updating of moderate resolution satellite data, to estimate top-of-canopy height of tropical forests. It has a significant advantage in acquiring high-quality images during the critical monitoring window period of rubber, which is very important to map rubber plantations by remote sensing. However, these data have not been used to monitor rubber plantations up to now [2]. Therefore, in this paper, we use PlanetScope images and comprehensively utilize four features, namely, spectral, vegetation index, first principal component, and textural features, to build pixel-based RF/SVM and object-oriented RF/SVM approaches to conduct rubber-monitoring research. The objectives are to assess the feasibility of monitoring rubber forests with PlanetScope data and to propose a practical and feasible monitoring method.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
###### Abstract  
A review of silicon photonics for space applications is presented. The benefits and advantages of size, weight, power, and cost (SWaP-C) metrics inherent to silicon photonics are summarized. Motivation for their use in optical communications systems and microwave photonics is addressed. The current state of our understanding of radiation effects in silicon photonics is included in this discussion. Total-ionizing dose, displacement damage, and single-event transient effects are discussed in detail for germanium-integrated photodiodes, silicon waveguides, and Mach-Zehnder modulators. Areas needing further study are suggested.  
silicon photonics; deep-space optical communications; radiation effects; integrated photonics +
Footnote †: journal: _photonics_
# Evaluation of Eccentric Compressive Behavior Between Active and Passive Preloading in CFST
## 4 Finite Element Analysis and Discussions
### Load-Displacement History for Different Preloading Methods
As a result, the peak load for CFST-CC increased and reached its peak early but weakened its service load due to the excessive compressive preload. When the longitudinal pre-compression was applied to the steel tube only, the peak load of the CFST-SC column was only 0.96 times that of the ordinary CFST column (Figure 12d and Table 4). In Step 1, only the steel tube was subjected to the longitudinal load, and its small lateral deflection caused the interface between the tube and the concrete to separate. In Step 2, although the peak load on the CFST-SC column was slightly lower than those of the other specimens, the concrete in the compression bending zone gradually expanded as the service load increased. When the deformation of the concrete increased faster than that of the steel tube, the confinement effect was enhanced. Therefore, the later curve of CFST-SC almost coincided with that of the ordinary CFST column. To discuss the ductile properties of different preloaded CFST specimens, the ductility index (\(\mu=\Delta_{2}/\Delta_{1}\)) was introduced, as shown in Figure 13. As shown in Table 4, in comparison to the ordinary CFST column, CFST-SC had the largest ductility index because the interaction between the concrete and the steel worked later than those of the other specimens. Thus, the curve softened slowly. In contrast, the softening of the second half of the CFST-CP curve was evident, and therefore, the ductility index was the lowest. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & _elr_ & \(\mathbf{P_{u}}\) (MPa) &
\begin{tabular}{c} **Peak Load** \\ **Ratio** \\ \end{tabular} & \(\mathbf{\Delta_{u}}\) (mm) & \(\mathbf{\Delta_{1}}\) (mm) & \(\mathbf{\Delta_{2}}\) (mm) & \(\mu\) \\ \hline CFST-SP & & 3020 & 1.04 & 3.2 & 1.0 & 7.1 & 7.1 \\ CFST-CP & & 3244 & 1.15 & 2.0 & 1.1 & 5.9 & 5.4 \\ CFST-CC & & 2977 & 1.02 & 2.1 & 0.8 & 7.5 & 9.4 \\ CFST-SC & & 2751 & 0.96 & 3.4 & 0.9 & 8.8 & 9.8 \\ CFST & & 2811 & 1.00 & 3.2 & 1.1 & 8.5 & 7.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Load-bearing capacity ratio and ductility index (\(\mu\)).
## 7 Conclusions  
This paper proposes a secure communication scheme based on a multicast mechanism in software-defined MIoT. Firstly, we designed a multicast tree protocol based on DBWST. This protocol incorporates multicast mechanisms to quickly establish a multicast tree among affected online learning devices after the occurrence of emergencies. Secondly, an adaptive signature authentication scheme is devised to ensure the security of multi-party communication, enabling the system to meet security requirements such as anonymity, privacy preservation and traceability. Additionally, our scheme supports batch authentication, effectively saving resources. Furthermore, a security proof conducted under the  
Figure 5: The time consumed for batch verifications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Signature** & **Batch Authentication** & **Communication Cost** \\ \hline \multirow{2}{*}{[47]} & \(4T_{bm}+T_{bm}\) & \((n+2)T_{m}+(3n-1)T_{a}+2 nT_{h}\) & 296 bytes \\  & \(+T_{mtp}+2T_{h}\) & \((2n+1)T_{ba}+(2n+1)T_{bm}\) & \\ \hline \multirow{2}{*}{[48]} & \multirow{2}{*}{\(3T_{bm}+2T_{h}\)} & \((2n+1)T_{ba}+(n)T_{h}\) & \multirow{2}{*}{388 bytes} \\  & & \(+2T_{bp}+(n)T_{h}\) & \\ \hline \multirow{2}{*}{[6]} & \(1T_{em}+2T_{h}\) & \((2n+2)T_{em}+(n)T_{ea}+(2n)T_{h}\) & 144 bytes \\ \hline Our & \(1T_{em}+T_{h}\) & \(2 nT_{em}+(n)T_{ea}+(n)T_{h}\) & 184 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of computational and communication costs.  
random oracle model demonstrates that the proposed scheme satisfies the security requirements for secure communication in software-defined MIoT. The performance comparison of the scheme indicates its superior performance in both computing and communication. However, our system does not consider the actual distribution of online learning devices and does not achieve optimal efficiency. In the future, we will focus on how to group online learning devices based on the proposed scheme, which will help in managing online learning devices more efficiently in device-intensive areas.  
In this research paper, [PERSON] conceived, designed, and wrote the cryptographic protocol; [PERSON] performed the experiments and analyzed the collected experimental data. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Foundation of Anhui Educational Committee grant number 2020 jyxm0326, 2022 sx020, 2022 jy55; National Natural Science Foundation of China grant number 62272002.  
Not applicable  
Due to restrictions on user privacy, we cannot make the data publicly available. If readers require access to the data, they can contact the authors via email to obtain it.  
The authors declare no conflict of interest.
## 1 Introduction  
In orthodontic treatment, lateral expansion of the dental arch can be accomplished using either a rapid or a slow expander. Rapid expanders separate the midpalatal suture and require several kilograms of force. In contrast, slow expanders apply a light force of a few hundred grams to the teeth, expanding the arch through the remodeling of the alveolar bone, that is, through orthodontic tooth movement.  
The quad-helix is one of the most commonly used slow expanders [1, 2, 3, 4, 5, 6]. By modifying its shape, the expansion force can be easily controlled, allowing for either parallel or fan-shaped expansion. Due to its ease of use, the quad-helix has been widely utilized in clinical treatments.  
The quad-helix acts as an elastic spring, and its mechanics have been studied. [PERSON] et al. measured the orthodontic forces produced by the quad-helix during parallel and fan-shaped expansions [7]. [PERSON] et al. calculated the relationship between the shape and the mechanical properties of the quad-helix [8, 9]. [PERSON] et al. measured the forcesgenerated by two types of stainless-steel quad-helices [10]. These studies focused on the force systems immediately after the quad-helix was placed on the arch.  
The quad-helix is constructed by incorporating four helices into a W-shaped spring (W-arch) [1]. These helices reduce the stiffness of the W-arch, allowing for larger expansion with a light force. In addition, they minimize force decay during expansion, leading to more efficient expansion.  
Instead of using helices, the stiffness of the W-arch can be reduced by decreasing the wire size (diameter of the cross-section). By selecting the appropriate wire size, the stiffness of the W-arch can be made equivalent to that of the quad-helix. If the W-arch can achieve the same expansion as the quad-helix, it could serve as a viable alternative. The W-arch without helices is easier to fabricate and more comfortable for patients. Currently, differences in performance between these two expanders remain unclear, and no guidelines are available for selecting one over the other.  
The purpose of this study was to examine whether there was a difference in the expansion ability of the quad-helix and the W-arch. If no difference is found, the W-arch could be used instead of the quad-helix. Using the finite element method (FEM), we simulated expansion with both the quad-helix and the W-arch. The present study is valuable not only for clarifying the mechanisms of expanders, but also for selecting an appropriate expander in clinical settings.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 5 The Structure of the Digital Signal Control System
### Control Strategy  
In this research, the constant current-constant voltage control is determined as the charging strategy of battery storage. In the battery discharging mode, the constant voltage control is applied to keep the stability and reliability of the DC power grid.  
Figure 12: The voltage gain curves operating in the forward and reverse charging modes.  
Taking the battery storage as an example, Figure 14 shows the control system block diagram of the battery storage [22; 23], with the reference voltage signal \(V_{b\_ref}\) being 95% of the upper limit battery voltage and \(I_{b\_ref}\) being the reference current signal according to the battery charging current. When the battery is charged in the constant-current mode, the analog-to-digital converter (ADC) of the DSP will fetch the output signal \(I_{b}\) and compare it with the reference current signal \(I_{b\_ref}\) to obtain the difference. With the compensation by a Proportional integrator (PI) controller and modulation by a frequency modulation generator (FM generator), a frequency modulated pulse-width modulation (PWM) is achieved to accomplish the constant current control.
# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
## 5 Parameter Optimization for NOMA System with Laser Chaos Decision-Maker and Error Correcting Codes
### Parameter Optimization
Hence, our proposed scheme can pair users with smaller BER compared to C-NOMA and UCGD-NOMA. Moreover, compared to \(\epsilon\)-greedy based scheme, our proposed LCDM-NOMA scheme can search for pairing options more efficiently, by which pairing options with smaller BER can be decided by the BS. Specifically, when the cell radius is 400 m, our proposed optimized LCDM-NOMA scheme can get 99.97% improvement compared to C-NOMA and UCGD-NOMA and  
Figure 11: Adaptive parameter optimization in LCDM-NOMA. (The thick solid line is the adaptive parameter optimized LCDM-NOMA. The dashed lines with \(Z\) values are based on \(ECC_{\tau 7}\) while using the specified \(Z\) values for the threshold levels. The solid lines with \(ECC_{i,n}\) employ only ACK and NACK in the ECC). 99.41% improvement compared to the epsilon greedy-based user-pairing scheme. In the meantime, the BER performances do not show evident differences among the strategies when the cell radius is large. We speculate that the pairing may not be too difficult in situations where the users are spatially sparsely distributed when the cell radius is large, leading to negligible performance differences among the strategies.
## 2 Materials and Methods
### Analysis  
The resolution of different data sets used in this study varied at the spatial and temporal scale. In order to minimize potential differences due to the mismatch in spatial and temporal resolution, we aggregated all the variables to \(0.5^{\circ}\times 0.5^{\circ}\) and monthly mean to maintain the consistency with GOME-2 SIF data.  
To obtain signals related to the drought stress, the spatial distribution of normalized anomalies of SIF, \(\mathrm{SIF_{yield}}\), EVI, temperature, precipitation, and radiation were computed at the pixel level. The anomaly in a pixel (i, j) for the drought year 2015 and 2016 was calculated as a departure from that averaged of the period 2007-2014 and normalized by the standard deviation (std):  
\[x_{2015/2016\ anomaly}((i,j))=\frac{x_{2015/2016}(i,j)-mean(x_{2007-2014}(i,j ))}{std(x_{2007-2014}(i,j))} \tag{4}\]  
The anomalies indicated the departure from their relevant multiyear mean for each month. The GOSAT XCO\({}_{2}\) data were only available from 2009 to 2016; thus anomalies were calculated as deviations from the average of the period 2009-2014.
## 2 Materials and Methods
### Analysis of Ion Concentration  
ZnO NPs were dispersed in DI water, OECD medium, ISO medium, and dechlorinated tap water at 50 mg/L concentrations for measuring dissolved Zn\({}^{2+}\) concentrations. The NP-dispersed solutions were then analyzed with and without NOM. OECD medium, ISO medium, and dechlorinated tap water were used as the test mediums following OECD TGs 201, 202, and 203 for each toxicity test at different exposure times of 72, 48, and 96 h, respectively, for each test medium. At 24 h intervals, the ionizations in the OECD medium, ISO medium, and dechlorinated tap water were measured three,four, and five times, respectively. The ionization in DI water was measured five times for an exposure time of 96 h. Ten mL of each solution was injected into ultra-4 centrifugal tubes (Amicon Ultra\(-\)15, Centrifugal Filters Ultracel-3K/30K, Merck Millipore Ltd., Burlington, MA, USA) containing three filters with pore sizes of 3 and 30 kDa and centrifuged at 5000 rpm for 30 min. Most of the solutions that passed through the 3 kDa filter possessed free Zn\({}^{2+}\), whereas those that passed through the 30 kDa filter contained free Zn\({}^{2+}\) and conjugated ions. For analyzing the ion concentrations of ZnO NPs, an appropriate acid treatment was required. We collected 1 mL of the centrifuged solutions that passed through the 3 or 30 kDa filter, then added 1 mL nitric acid (HNO\({}_{3}\), 69%, GR, WAKO, Tokyo, Japan), and mixed them gently. The mixed solution was allowed to settle for 15 min [37; 38] and was then diluted with 8 mL of DI water.  
For analyzing the ion concentrations of MNs, a proper acid treatment is required to ionize the particles. One mL of centrifuged supernatant solution was collected and acidified in a 15 mL tube. ZnO NPs were pretreated by stirring with 1 mL of nitric acid, settling for 15 min [37; 38], and dispersing with 8 mL of DI water.  
ZnO NPs were analyzed using an ICP/MS (Agilent 9700 \(\times\) quadrupole, Agilent Technologies, Santa Clara, CA, USA) and the data were processed using the Mass hunter 7.0 software in spectrum mode. An element measurement was conducted using the spectrometer parameters. The analysis conditions were 1550 W of output power from a RF generator (radio frequency generator) and a 1 L/min nebulizer gas flow rate; these are important parameters for sensitivity interrupting oxidation and double-charge ion formation. With a sample-uptake flow rate of 1 mL/min and sweeps-counting signal pulses of 100, the samples were measured with three replicates.