esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Motion Data  
The motion data are processed by the PMTS. A system of cameras is used to record movements. The records are used for estimating the time required for manual tasks [8,9], mainly for assembly activities [10,11], but also for the operating tools [12] and vehicles [13].  
The MTM methods are widely used. In the MTM-1, basic movements are recorded for each hand, where eight basic hand and shoulder movements, two basic eye movements, and nine basic body and leg movements are defined. The smallest time unit is 1 TMU = 0.036 s. It is a very detailed method with a time consumption of 1:200, i.e., 200 min are used to analyze 1 min of movement [1]. MTM-2 does not differentiate between each hand, because a significant portion of the basic movements of the hands and shoulders are in combination. MTM-2 combines up to three basic movements. MTM-1 and MTM-2 are used in mass production. The methods used in serial production, the Universal Analyzer System (UAS), and for single and small series production (MEK) have been designed. UAS and MEK define nine standard actions consisting of a maximum of five basic movements. Contrary to MTM-1 and MTM-2, they are not based on knowledge of the sequence of movements but on the information of the general conditions, on the knowledge of the working system [2].  
[PERSON]'s Technique of Sequence of Operations (MOST) also uses TMU. In MOST, activities are described by a sequential model, a fixed string of letters. These are indexed. The index depends on the environmental conditions and expresses the time evaluation of the attribute. The sum of the indices is multiplied by the basic time unit to obtain the estimated time. The MOST methods are subdivided into MiniMOST for high-volume production, MaxiMOST for custom production, and BasicMOST for serial production with medium-job cycles. Approximately 10 h are required to analyze 1 h of motions [13].  
The Modular Arrangement of Predetermined Time Standards (MODAPTS) is based on the body actions that follow each other [8,14]. Namely, they are the actions of the movements of the different parts of the upper limb, the terminal action, the action of receiving, the action of placing, and the action of auxiliary movements [15]. MODAPS is used to integrate correction factors for implementation into DELMIA (Digital Enterprise Lean Manufacturing Interactive Application) software with the purpose of virtual simulation [16,17].
## 2 Selected Structure and Modelling Parameters
### Uncertain Parameters and Structural Modelling  
Taking into account the inadequate construction practices of school buildings built in the 1980s and damaged by the Pohang Earthquake, the effects of material properties and structural details on the seismic performance of the selected structure were evaluated. The compressive strength of concrete (\(f_{c}^{\prime}\)) and yield strength of the reinforcement (\(f_{y}\)) were considered as variables for the material uncertainty, while the longitudinal reinforcement ratio (\(\rho_{l}\)) and volumetric ratio of transverse reinforcement (\(\rho_{w}\)) were selected as variables for the structural details.  
As shown in Table 1, five cases for each variable were considered and thus, a total of 17 analytical models were obtained. The reference names listed in Table 1 were defined by considering the name and value of an uncertain variable. For example, CS18 is the analytical model with a concrete strength of 18 MPa. Note that the analytical models of CS24, YS400, LR2.00, and TR0.33 are identical and used as the reference structural model with a median value of each variable. It was assumed that the column section was \(400\times 400\) mm, and D10 with a yield strength of 400 MPa was used as shear reinforcement. In addition, the analytical variables for the longitudinal rebar ratios were considered by increasing and decreasing the cross-sectional area of eight reinforcements. Numerical models of RC structures with uncertain variables were implemented and analyzed using the OpenSees software package [18], which is a finite element analysis platform. To take into account the spread of plasticity along with the element, the RC frame members were modeled by the _Displacement-Based Beam-Column Element_ with the material constitutive laws of _Concrete02_ and _Steel01_ for concrete and steel, respectively, as shown in Figure 4. For the confined concrete, the confinement effect on the strength and strain was considered by utilizing the formulae developed by [PERSON] et al. [19].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Material**} & \multicolumn{2}{c}{**Structural Details**} \\ \cline{2-5}
**Reference Name** & **Concrete Strength, \(f_{c}^{\prime}\) (MPa)** & **Yield Strength of Longitudinal Reinforcement,\(f_{y}\) (MPa)** & **Longitudinal Reinforcement Ratio, \(\rho_{l}\) (\%)** & **Volumetric Ratio of Transverse Reinforcement, \(\rho_{w}\) (\%)** \\ \hline CS18 & 18 & & & \\ \hline CS21 & 21 & & & \\ \hline CS24 & 24 & 400 & 2.00 & 0.33 \\ \hline CS27 & 27 & & & \\ \hline CS30 & 30 & & & \\ \hline YS30
###### Abstract
They used a 3D-CNN to extract spatio-temporal features and an attention-based hierarchical LSTM module to capture the temporal correlations between video frames. Since most summarizing techniques analyze the visual components of the video and ignore audio elements, the authors of [30] provide a method that uses both visual and audio information. A structural similarity index was used to determine similarity among frames, and the mel-frequency cepstral coefficient was used for feature extraction from audio signals. The authors of [31] used GANs to extract representative parts of the videos as features through reconstruction loss followed by knowledge distillation, using a basic network for key frame selection. The authors of [32] used a bidirectional LSTM that took advantage of the underlying hierarchical structure of video sequences and learned temporal representations via intra-block and inter-block attention. They then partitioned shots and calculated shot-level importance scores to rank the frames that were included in the final video summary.
# Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology
## 3 Results and Discussion
### Visual Prediction of Okra Seeds  
Hyperspectral images contain the spectral and spatial information of the samples simultaneously, and there is a certain correspondence that can be used by image processing technology between spectral and spatial information. In order to verify the performance of the classification model, the okra seed prediction maps were plotted based on the average spectrum of each seed in the hyperspectral image. The combination of the model and the image processing technology can generate a pseudo-color map for predicting the type of the sample, and distinguish different sample types with different colors, as well as to visualize the classification results intuitively. Because of the large amount of data in the full spectrum, the computational complexity is high, which is not conducive to the rapid prediction of the sample. Therefore, this paper selected the SVM model based on the optimal wavelengths extracted by the CARS algorithm as the classification model. The average spectrum of each okra seed in the hyperspectral image was taken as input, and the three different types of okra seeds were selected to be 686 grains in total. The original seed map and prediction map are shown in Figure 4. In Figure 4, blue refers to the female parent (xianzhi), yellow refers to the father (xiaolusi), and red refers to the hybrid seed (penzai). Comparing Figure 4a,b, the three different kinds of okra seeds can hardly be distinguished by the naked eye in the original map. There were some misjudgments in the classification images of the three okra seeds, but the overall correct discrimination rate is 91.41%. Affected by factors such as hyperspectral image segmentation algorithms and image resolution, the okra seeds in the visualized pseudo-color map have undergone some deformation, but most of them are still intact and they do not affect the identification and analysis. This method can be used to make rough preliminary judgments on the species of hybrid okra seeds, which provides a new method for the rapid and accurate screening of seeds in the process of cross breeding.
# MCST Scheme for UAV Systems over LoRa Networks
## 6 Numerical Simulations
### Simulation Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**FRM Payload [Bytes]**} \\ \cline{2-6}  & **50** & **100** & **150** & **200** & **250** \\ \hline CSMA/LoRa & 8.41 & 8.58 & 8.75 & 8.92 & 9.09 \\ \hline MCST/LoRa & 8.19 & 8.25 & 8.31 & 8.36 & 8.42 \\ \hline mMCST/LoRa & 8.14 & 8.17 & 8.20 & 8.23 & 8.25 \\ \hline \hline \end{tabular} Notes: All the values are in the unit of m].  
\end{table} 
Table 3: Performance comparison between CSMA/LoRa, MCST/LoRa and mMCST/LoRa in energy consumption versus FRM payload for 30-node random topology. Figure 9: Performance of achievable BRF throughput and latency versus FRM payload for 30-node random topology. Table 4 depicts the performance comparison in terms of energy consumption when the number of nodes increases in the network with the fixed FRM payload size of 250 bytes. As we can observe, MCST/LoRa can save energy up to 29.97% compared to CSMA/LoRa when the number of nodes is 240 in the network. This is because MCST enables DATA transmission at a higher rate and results in a shorter time for successful transmission, leading to less energy consumption than the other MAC. Regardless of the number of nodes in the network and FRM payload size, the simulation results show that MCST/LoRa is superior in BRF throughput, latency, and energy consumption compared to CSMA/LoRa. Moreover, by reducing the control frame transmission, the achievable BRF throughput can be optimized, and the transmission latency and consumed energy can be minimized through the proposed mMCST/LoRa. For example, as illustrated in Figure 10 and Table 4, in the 240-node random topology, mMCST/LoRa can achieve 4.44 kbps BRF throughput, 0.45 s of transmission latency, and 9.07 mJ transmission energy consumption when MCST/LoRa gives 4.07 kbps, 0.50 s and 10.05 mJ, respectively, when FRM payload size is 250 bytes. In summary, applying the MCST scheme is beneficial as a contribution to the advantage in terms of achievable BRF throughput, transmission latency and energy consumption of the transmission scheme regardless of the FRM payload size and number of BRF transmissions in the LoRa network. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**No. of BRF Transmissions**} \\ \cline{2-9}  & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** \\ \hline CSMA/LoRa & 9.09 & 9.84 & 10.59 & 11.35 & 12.10 & 12.85 & 13.59 & 14.35 \\ \hline MCST/LoRa & 8.42 & 8.66 & 8.89 & 9.12 & 9.35 & 9.59 & 9.81 & 10.05 \\ \hline mMCST/LoRa & 8.25 & 8.37 & 8.49 & 8.61 & 8.72 & 8.84 & 8.95 & 9.07 \\ \hline \hline \end{tabular} Notes: All the values are in the unit of mJ.  
\end{table} 
Table 4: Performance comparison between CSMA/LoRa, MCST/LoRa and mMCST/LoRa in energy consumption versus the number of BRF transmissions. Figure 10: Performance of BRF throughput and latency versus the number of nodes in the network.
Moreover, the present Kalman filter technique applied to the inverse problem successfully reproduces, not only the real-time wave spectrum, but also the real-time wave elevation. In this paper, 'wave elevation' means the time series of irregular wave profile. The estimation of wave elevation is very important for the active control in many ocean-engineering applications. As far as the authors know, the real-time inverse estimation of wave elevation by the Kalman filter cannot be found in the open literature. The developed technology is relatively inexpensive and it has an advantage that the wave can be continuously estimated along a ship's passage. According to recent statistics, the number of merchant ships sailing around the world in 2017 is about 50,000 [11] i.e., there are already 50,000 potential sensors on those vessels over the world, which means that a continuous build-up of big real-time ocean wave data through the inverse algorithm is possible with a robust network among ships.
## 4 Environmental Parameters Measurement and Discussion of Results
### Impact of Both Turbulence and Thermal Factors on UWOC
#### 4.2.2 Cooling Experiment  
Because the average annual temperature of the earth is below 20 \({}^{\circ}\)C in high latitude areas, this experiment tests the communication quality of the UWOC system under the condition of low water temperature. The structure of this experiment is shown in Figure 7. The ice is wrapped in a sealed bag and put into the water to reduce the water temperature. Then, transmission loss and bit error rate are measured when the water temperature drops to 10 \({}^{\circ}\)C, 15 \({}^{\circ}\)C, and 20 \({}^{\circ}\)C.  
To ensure the accuracy of transmission loss measurement in this experiment, all the experiments above start with a 4.32 dBm input power. This experiment measures the optical transmission loss at low temperatures, as shown in Figure 8a.Based on the results, it is shown that the optical transmission loss increases as the temperature increase from 25 \({}^{\circ}\)C. Note at the original temperature is et at 25 \({}^{\circ}\)C. The BER values under different temperatures are shown in Figure 8b. At the same BER, the power penalty is 1 dB when the light power at a low temperature of 10 \({}^{\circ}\)C as compared to 25 \({}^{\circ}\)C at room temperature. Comparing the eye diagram at 25 \({}^{\circ}\)C and 10 \({}^{\circ}\)C shown in Figure 9a,b, the also shows the same result that 25 \({}^{\circ}\)C has better performance. Therefore, the low temperature on the UWOC systems has more significant changes than the high temperature.  
Figure 8: Transmission distance of 6 m (**a**) low-temperature optical transmission loss (**b**) measurement diagram of bit error rate when the temperature drops.  
Figure 7: The experimental water-cooling setup of UWOC.
## 2 Design Method
The fourth optimization variable \(\phi_{cent,k}\) fixes the phase of the central element, shifting the phases of all the element in order not to change the phase distribution. Optimizing this variable allows having the different elements in the most efficient distribution according to their real losses. In [14], the main beam-width and SLL were optimized only in two planes (azimuth and elevation), by employing two masks. In the present work, the cost function to be minimized has been redefined as expressed Equation (4), being the SLL value minimized in all the space \((u,v)\). A mask in the azimuth plane (\(mask_{inf}\)) is employed to force the design to fulfil the required \(-3\) dB beamwidth, but the SLL is estimated in all the space by means of a two-dimensional mask (\(mask_{2D}\)) that delimits the area outside the main beam, being defined by the beamwidths at \(-10\) dB (Figure 3). Only the points of that area in which the radiation is above a determined level are computed in the cost function, trying to minimize the radiation outside the main beam. A pseudo-directivity (\(\widehat{dir}\)) is considered as the ratio between the maximum radiation of the main beam and the mean radiation in the area outside the main beam (delimited by \(mask_{2D}\)). Three constants are employed to control the importance of each of the parameters in the optimization cost function: K\({}_{1}\) for pseudo-directivity, K\({}_{2}\) for azimuth beamwidth and K\({}_{3}\) for SLL. \[C=-K_{1}\widehat{dir}+K_{2}\sum RP_{azi}<mask_{inf}+K_{3}\sum RP_{2D}>mask_{2D} \tag{4}\]  
where:  
\[\widehat{dir}=\dfrac{max(E_{r}(u,v))}{\overline{E_{r}}(u_{m},v_{m})};\quad(u _{m},v_{m})\in mask_{2D}\]  
\[RP_{azi}=\dfrac{E_{r}(u_{b},v)}{max(E_{r}(u_{b},v))};\quad v\in[-1,1]\]  
\[RP_{2D}=\dfrac{E_{r}(u,v)}{max(E_{r}(u,v))};\quad(u,v)\in[-1,1]\]
# Design of Inverted Nano-Cone Arrayed SERS Substrate for Rapid Detection of Pathogens
###### Abstract  
Rapid detection of bacteria is a very critical and important part of infectious disease treatment. Sepsis kills more than 25 percent of its victims, resulting in as many as half of all deaths in hospitals before identifying the pathogen for patients to get the right treatment. Raman spectroscopy is a promising candidate in pathogen diagnosis given its fast and label-free nature, only if the concentration of the pathogen is high enough to provide reasonable sensitivity. This work reports a new design of surface-enhanced Raman spectroscopy (SERS) substrate which will provide high enough sensitivity and fast and close contact of the target structure to the optical hot spots for immunomagneticly densenetrate-concentrating technique. The substrate uses inverted nanoconte structure arrays made of transparent PDMS (Polydimethylsiloxane) to funnel the light from the bottom to the top of the cones where plasmonic gold nanorods are located. A high reflective and low loss layer is deposited on the outer surface of the cone. Given the geometry of cones, photons are multi-reflected by the outer layer and thus the number density of photons at hotspots increases by an order of magnitude, which could be high enough to detect immunomagnetically densified bacteria.  
SERS; pathogen detection; gold nanorods; nanocone array +
Footnote †: journal: Applied Sciences
# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 3 Simulation
### Optimized CEEMD Method
Finally, the relationship between the MSE value of each signal component and the dynamic threshold is shown in Figure 11. In Figure 11, the MSE values of IMF1-IMF10 are all smaller than the dynamic threshold. Figure 10: CEEMD decomposition reconstructed signals. Figure 4:0 Comparison of the original signal and reconstructed signal.
## 5 Conclusions  
This paper primarily investigates the reflection of elastic waves by considering the influence of the microstructural effect and applied magnetic field. Through rigorous theoretical analysis, formula derivation, and numerical simulations, the following key conclusions were drawn:  
1. In classical elastic solids, the reflected waves consist solely of P and SV waves. In dipole strain gradient elastic solids, the reflection phenomenon becomes more com  
Figure 8: The influence of microstructural parameters on a critical angle when SV wave is incident (\(\overline{\lambda}\)\(=\) 8).  
Figure 7: The influence of the applied magnetic field on the reflected wave when SV wave is incident (\(\epsilon\)\(=\) 0.05 and \(\overline{\lambda}\)\(=\) 8).  
plex, and there are two types of inflected waves involving both body waves and surface waves: reflected P-waves, the reflected SV waves, the reflected SP waves, and the reflected SS waves.
2. Both the microstructural effect and Lorentz force affect the propagation of elastic waves. Notably, the impact of the magnetic field intensity and microstructural effects on surface waves surpasses that of body waves.
3. A critical angle arises when the SV wave is incident, and intriguingly, the critical angle increases proportionally with the augmentation of microstructural parameters.
4. In the classical scenario, elastic waves exhibit non-dispersive propagation characteristics. However, in dipolar gradient elastic solids, the introduction of the microstructural effect results in the emergence of novel wave modes and the manifestation of dispersion properties in elastic waves. Conversely, the applied magnetic field solely affects the amplitude of the elastic wave propagation through the influence of the Lorentz force without producing new wave modes or altering the dispersion characteristics of the elastic waves.  
Conceptualization, W.P. and T.W.; methodology, Z.W.; validation, C.Z., R.Z. and Y.G.; formal analysis, Z.W.; investigation, Y.J.; data curation, Y.J.; writing--original draft preparation, T.W.; writing--review and editing, W.P.; supervision, C.Z.; project administration, R.Z.; funding acquisition, Y.G. and R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Sichuan Science and Technology Program (No. 2022 YFG0274) and the National Natural Science Foundation of China (Grant No. 51875146).  
Not applicable.  
The authors confirm that data supporting the findings of this study are available within the article.  
The authors declare no conflict of interest.
## 5 Multi-Dimensional Operational Performance Evaluation of Apron U-Shaped Area
### Safety Operating Performance Indices  
We describe the proposed safety operation performance indices at both macro as well as micro levels. The probability of conflict between flights is used as a macro-level index of apron safety performance. The inefficiency of the surface operation due to the excessive pursuit of U-shaped area operation safety can also be reflected in the high probability of inter- aircraft conflict. The conflict variable \(N_{f}^{m}\) is introduced for a queue containing \(m\) aircraft. If a flight is delayed due to a conflict within the U-shaped area, \(N_{f}^{m}\) is 1; otherwise, it is 0. The conflict probability is calculated in Equation (14). Conflict resolution time as well as instantaneous traffic volume indices are used as micro-level indices. The delay time can reflect the severity of the conflict from the side, and the delay time can also be referred to as the conflict resolution time. \(w_{f}\) is introduced as the waiting time of the aircraft. For a queue containing \(m\) aircraft, the total conflict resolution time is calculated in Equation (16). Relying on the spatial-temporal network of aircraft operations, the instantaneous traffic volume index we proposed can reflect the magnitude of potential unsafe events with aircraft. Based on the macroscopic distribution network (MDN) modeling method ([PERSON], 2018) [21] of scene traffic proposed by [PERSON], as shown in Figure 8, we can consider that there are two aircraft with which there is a potential risk of collision during the operation of aircraft \(f_{m}\) in the U-shaped area, because there are two aircraft in operation other than \(f_{m}\) during the operation period of \(f_{m}\) in the U-shaped area. Introducing the instantaneous traffic for a queue containing m aircraft, the total instantaneous traffic is calculated in Equation (17).  
The conflict variable \(N_{f}^{m}\) is introduced. For a queue containing \(m\) aircraft, the following conflict judgment conditions are defined:  
\[N_{f}^{m}=\begin{cases}1&w_{f}>0\\ 0&w_{f}=0\end{cases}\quad f\in\mathcal{F}_{A}\cup\mathcal{F}_{D} \tag{14}\]  
Figure 8: Macroscopic Distribution Network of U-shaped Area.  
where \(w_{f}\) is the waiting time of aircraft \(f\), then the probability of conflict is  
\[P=\frac{\sum\limits_{m=1}^{|m|}N_{f}^{m}}{m}\quad f\in\mathcal{F} \tag{15}\]  
and the conflict resolution time is  
\[C_{t}=\sum\limits_{m=1}^{|m|}\sum\limits_{f\in\mathcal{F}}\left(N_{f}^{m}\times w _{f}\right) \tag{16}\]  
A discrete time window \(\mathcal{T}=\{1,2,\cdots,|\mathcal{T}|\}\)([PERSON], et al., 2017) [36] is assumed, and in a queue containing \(m\) aircraft, the U-shaped area traffic volume at time \(i\) can be expressed as  
\[Q_{m}(i)=Card\Big{\{}f\Big{|}T_{in}^{f,m}\leq i\leq T_{out}^{f,m}\Big{\}} \tag{17}\]  
where \(T_{in}^{f,m}\) and \(T_{out}^{f,m}\) are the time when the \(m\) aircraft enters or leaves the U-shaped area.