esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Performance of Teaching Data with Only Biomechanics Materials
#### 3.1.3 Range Limitation of Torque  
In machine learning, the teaching data must be close to the range of the analyzed data for prediction as well as the number of such data. Therefore, because the peak torque of the  
Figure 4: Analysis results of (**a**) relationship between measured and predicted insertion torques and (**b**) Bland–Altman plot by augmentation of the teaching data by linear interpolation.  
Figure 3: Analysis results of (**a**) relationship between measured and predicted insertion torques and (**b**) Bland–Altman plot by teaching the dataset without additional processing.  
artificial bone was up to approximately 5 Nm, whereas the peak torque of the cadaveric bone data was up to approximately 1 Nm, the artificial bone data were analyzed using the teaching data by reducing the number of data to 18, limiting the peak torque range to the same as that of the cadaveric bone. The results of this analysis are shown in Figure 5. As shown in Figure 5a, different correlations were obtained from those in Figures 3a and 4a, although the accuracy of the analysis was worse because of the data range restrictions. The two variables used were frequency weighted (5000-10,000 Hz) and frequency weighted (1000-5000 Hz). The Bland-Altman plot shown in Figure 5b indicates that while systematic errors disappeared because of the limited data range, random errors appeared. This can be attributed to the small amount of available teaching data.  
The method of analyzing L-RFA data using an artificial bone as a teaching dataset to predict the peak torque by validating L-RFA data using cadaveric bone was found to have a clear emergence of systematic errors. However, there is a concern that an a priori evaluation and correction of the error will increase the data analysis process and reduce its generalizability, convenience, and accuracy. Therefore, it is necessary to improve accuracy by applying further innovations.
## 2 Materials and Methods
### Artificial Neural Network Model
#### 2.3.1 ANN (\(t\)-1) Model  
The ANN (\(t\)-1) model takes in as input seven residuals \(e_{t}^{*}\) produced by the DD-TSA application, prior to the one to be predicted. The single output of this feedforward neural network, \(\hat{e}_{t}\), represents the estimation of the stochastic term in Formula (1) that is used to implement the hybrid model. The structure of the ANN is summarized in Table 8.  
The dataset adopted to train the network is divided into three parts, one for the training (60% of the 1100 data), one for the validation and one for the test, which are both 20% of the entire dataset. Two cycles of training were executed, to obtain an optimum minimization of the objective function.  
The results are shown in Figure 4: the lower value of the objective function, in the validation data, is 0.30 for the first cycle and 0.25 for the second one. This result was achieved after 21 epochs on the first cycle and after 31 epochs on the second cycle. In Figure 5, regression lines are reported, with related functions, obtained comparing target data with the ones predicted by the network during the training in the two cycles: the correlation coefficient R, obtained considering the entire set of 1100 data of calibration, is 0.80 at the end of the first cycle and 0.82 at the end of the second cycle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inputs** & **One Hidden Layer** & **Output** \\ \hline \(e_{t-1}^{*}\) & & \\ \(e_{t-2}^{*}\) & & \\ \(e_{t-3}^{*}\) & & \\ \(e_{t-4}^{*}\) & 140 Neurons & \(\hat{e}_{t}\) \\ \(e_{t-5}^{*}\) & & \\ \(e_{t-6}^{*}\) & & \\ \(e_{t-7}^{*}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: The topological structure of the ANN (\(t\)-1) model.  
Figure 4: Performance achieved during the calibration process of the ANN (\(t\)-1) model. For the first tuning, the figure on the left (**a**), the mean square error after 21 epochs is 0.30. For the second tuning, the figure on the right (**b**), the mean square error after 10 epochs is 0.25. Circles indicate the values at the selected epoch.  
The network adopted in the following predictive application is the one with the weights calculated during the second cycle of training.
## 3 Materials and Methods: The SORA Risk Analysis Methodology
### Introduction to SORA  
JARUS pursues a consensus from various national aviation authorities and stakeholders on a common procedure to identify and qualitatively assess safety risks for drone operations. In particular, JARUS developed SORA [24] in 2019.  
Any risk assessment methodology uses a schema based on the ISO 31000 standard, providing a reference framework and guiding the general risk management process (see Figure 1).  
SORA is a method based on a holistic risk model that provides a generic framework to identify possible hazards and threats, as well as relevant harm and threat barriers applicable to drone operation. The aim is to establish a sufficient level of confidence that a specific operation can be carried out safely. To achieve this, SORA requires first the applicant to collect and provide the relevant technical, operational, and system information needed to assess the risk associated with the intended operation of the drone.  
Some key aspects related to the applicability of the methodology are the following.  
* It aims to assess the safety risks involved in the operation of drones of any class, size, and type of operation and particularly suited, but not limited to'specific' operations for which a risk and hazard assessment is required.
* The safety risks associated with collisions between drones and manned aircraft are within the scope of the methodology.
* Security aspects are excluded when not limited to those confined by the airworthiness of the systems (e.g., aspects relevant to the protection from unlawful electromagnetic interference).
* Privacy aspects are excluded from the applicability of this methodology.
# _Photonics_
###### Abstract  
The ultra-wideband polarization converters have been of interest to researcher due to their demand in satellite communication and navigation systems. This paper presents an ultra-wideband reflective cross-polarization converter comprising a stair-shaped metasurface. By observation, the alleged structure allows the conversion of linearly polarized waves to orthogonal components, having a polarization conversion ratio of greater than 90% spread across the large frequency range of 12.94 to 16.54 GHz and 17.54 to 26 GHz. A highly efficient, ultra-high frequency polarization conversion is achieved by the utilization of strong electromagnetic resonance coupling between the upper and lower layers of the metasurface. Further, it is depicted that the polarization converter has a wide obliquity of incidence wave. Moreover, the simulation and measured results show a good match. The linear polarization converter is simple in design but is of high performance, and therefore, might be useful in satellite communication, imaging systems, and navigation systems.  
metasurface; polarization converter; microwave frequency; angular stability +
Footnote †: journal: _Photonics_  
Mdpi  
Ultra-Wideband Cross-Polarization Converter Using Metasurface Operating in the X- and K-Band Photons202411863[[https://doi.org/10.3390/photonics1109863](https://doi.org/10.3390/photonics1109863)]([https://doi.org/10.3390/photonics1109863](https://doi.org/10.3390/photonics1109863))
## 3 Results
### Logistic Regression Analysis on Musculoskeletal Pain
#### 3.3.2 Logistic Regression Analysis on Upper Limb Pain
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Prevalence Rate (\%)**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{\(p\)-Value**} & \multirow{2}{*}{**OR**} & \multicolumn{2}{c}{**95\% C.L. for OR**} \\ \cline{5-8}  & & & & & & **Lower** & **Upper** \\ \hline Gender & & & & & & & \\ Male (ref) & 226 & 33.6\% & & & & & \\ Female & 326 & 51.8\% & 1.807 & \(<\)0.001 & 2.966 & 2.005 & 4.389 \\ Work experience & & & & & \(<\)0.001 & 2.967 & 2.005 & 4.389 \\ \(<\)3 years (ref) & 150 & 34.7\% & & & & & \\ \(3\)-9 years & 190 & 42.1\% & 0.444 & 0.065 & 1.558 & 0.972 & 2.497 \\ \(\geq\)10 years & 212 & 53.3\% & 0.964 & \(<\)0.001 & 2.621 & 1.634 & 4.206 \\ Vibration & & & & 0.041 & & & \\ \(\leq\)2h (ref) & 321 & 40.5\% & & & & & \\ \(2\)-4 h & 82 & 56.1\% & 0.597 & 0.031 & 1.816 & 1.057 & 3.119 \\ \(\geq\)4 h & 149 & 46.3\% & \(-\)0.136 & 0.573 & 0.873 & 0.545 & 1.399 \\ Low temperature & & & & 0.063 & & & \\ \(<\)2 h (ref) & 459 & 41.2\% & & & & & \\ \(2\)-4 h & 32 & 56.3\% & 0.195 & 0.627 & 1.215 & 0.554 & 2.664 \\ \(\geq\)4 h & 61 & 62.3\% & 0.741 & 0.019 & 2.098 & 1.128 & 3.900 \\ Handling heavy loads & & & & 0.002 & & & \\ \(<\)2h (ref) & 242 & 35.1\% & & & & \\ \(2\)-4 h & 181 & 48.6\% & 0.571 & 0.009 & 1.769 & 1.153 & 2.716 \\ \(\geq\)4 h & 129 & 55.2\% & 0.852 & 0.001 & 2.345 & 1.412 & 3.896 \\ Constant & & & \(-\)1.970 & \(<\)0.001 & 2.139 & & \\ \hline \hline \end{tabular} Note. * Significant at 0.05, ref = reference, OR = odds ratio, C.I. = confidence interval.  
\end{table} 
Table 7: Logistic regression analysis on upper limb pain.
## 1 Introduction
However, the influence of angle-of-arrival (AoA) fluctuations was rarely investigated. The AoA fluctuations because of orientation deviations of the receiver can cause fluctuations in the signal-to-noise ratio (SNR) and substantially reduce the communication performance of long-range FSO systems, such as drone-assisted FSO communication systems [8; 23]. Therefore, the AoA fluctuations should be considered when assessing the performance of drone-assisted FSO communication systems. [PERSON] et al. modeled a multi-rotor UAV-assisted FSO link considering the influence of AoA fluctuations [24]. However, the proposed model might need more flexibility due to its complexity. A simpler and easy-to-handle FSO system under AoA fluctuations was further proposed in [25]. However, only the outage probability was studied, which may be insufficient for constructing the drone-assisted FSO system. It is also important to note that the average BER and ergodic capacity of the FSO communication system under AoA fluctuations are also important metrics of the system's performance and have hardly been investigated. Therefore, research on the effect of the AoA fluctuations on the system performance of the drone-assisted FSO system is still in the initial stage. In this paper, a drone-assisted downlink FSO communication system is established in response to the abovementioned discussions. Comprehensive consideration is given to the impact of atmospheric attenuation, atmospheric turbulence, pointing errors, and AoA fluctuations on the communication system. Note that this is widely available in most practical communication scenarios, such as post-disaster rescue, emergency communication and mobile communication supplementation. Furthermore, the channel fading of the FSO link was also modeled using the Malaga distribution. To evaluate system performance,exact closed-form formulae are presented for the average BER and the ergodic capacity using the IM/DD and the heterodyne detection techniques, respectively. Hence, the main contributions are listed below:  
* A drone-assisted FSO communication system is established, considering the effects of atmospheric attenuation, atmospheric turbulence, pointing errors, and AoA fluctuations for the first time, where the FSO link undergoes the Malaga fading. * The statistical expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the drone-assisted FSO link are derived using the IM/DD and the heterodyne detection techniques, respectively. In addition, closed-form expressions for the average BER and the ergodic capacity are derived. * The asymptotic expression for the average BER under the heterodyne detection technique at high optical power is also proposed to offer enhanced insights into the system and gain useful engineering applications. The rest of the paper is structured as follows. Section 2 proposes the system and channel models. The closed-form formulae for the average BER and the ergodic capacity are deduced in Section 3. Section 4 offers simulation results and discussions. Finally, the whole paper is summarized in Section 5.
## 3 Dark Current Measurement Based on Focal-Plane Temperature and Dark Current Correction Experiment
### Dark Current Measurement Experiment  
As already described in Section 2.3, it was first necessary to carry out calibration experiments on the temperature of the chiller with the temperature measurement pin voltage to calculate the parameters in Equation (9). The refrigeration equipment is shown in Figure 5a using the equipment to adjust multiple chiller temperatures \(T_{dio}\) and measure the corresponding temperature measurement pin voltage \(U_{tem}\). The experimental results are shown in Figure 5b, and \(U_{tem}\) and \(T_{dio}\) have good linearity. The linear fit yields an \(a_{ref}\) of \(6.3\times 10^{-3}\) and \(b_{ref}\) of 1.44 for this chiller. Most of the target cooling temperatures \(T_{set}\) for QWIP in engineering applications are in the range of 40 to 50 K. Therefore, this temperature interval was chosen for the experiments.  
With reference to the design of the information acquisition circuit, its specific parameters in Equations (10) and (11) were obtained as shown in Table 2.  
Figure 5: (a) Calibration equipment for temperature measurement pins of refrigeration machines; (b) Calibration data for temperature measurement pins of refrigeration machines.  
The dark current values of the device at 40 K and 50 K were measured at the device preparation stage as \(3.30\times 10^{-12}\) A and \(6.72\times 10^{-11}\) A, respectively. By solving Equation (12), it was found that \(k_{I}\) was \(-0.24\) and \(b_{I}\) was \(-7.02\) for this device. The dark current value between 40 and 50 K could be accurately measured. Table 3 shows the dark current values at some temperatures in the range of 40 to 50 K. Figure 6 shows the dark current versus temperature curve, with the horizontal axis being the temperature \(T_{set}\) and the vertical axis being the dark current value \(l_{dark\_T}\).  
With this method, the focal-plane temperature data can be obtained to monitor the dark current value of a QWIP in satellites in space in real-time, providing more references for the research and development of LWIR QWIP detection systems.
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 4 Experiment
### Further Discussion  
Previous methodologies employed to detect space objects require algorithms which are complicated to design and dependent on handmade feature vectors. This study evaluates an alternative robust solution adopting a supervised learning algorithm to avoid selecting  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Inference Time(s)** \\ \hline GEO-PP-YOLOv2 & 0.932 \\ GEO-PP-YOLOv2 (with TensorRT) & 0.706 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average Inference Time on the NVIDIA Jetson Nano platform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Participant Name** & **F1(\%)** & **MSE** \\ \hline
1 & AgeniumSPACE & 94.83\% & 33,838.9931 \\
2 & POTLAB@BUAA & 94.43\% & 30,541.73189 \\
3 & duwizila & 92.89\% & 41,194.85863 \\
4 & Magpies & 90.43\% & 48,919.9227 \\
5 & Mr\_nuangLTZaaa & 88.42\% & 62,021.80923 \\
6 & francescodg & 87.89\% & 65,772.46337 \\
7 & mhalford & 87.70\% & 69,566.90857 \\
8 & PedroyAgus & 86.61\% & 70,104.96654 \\
9 & elmhailol & 86.11\% & 83,172.81408 \\
10 & Barebones & 83.66\% & 10,5518.4199 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Final ranking of top 10 teams in the SpotGEO challenge.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**AI Performance** & **GFLOPs** \\ \hline CPU & 4-core Cortex A57 \\ GPU & 128-core NVIDIA Maxwell \\ Memory & 4 GB 64 bit LPDDR4 \\ Size(mm) & \(100\times 80\times 29\) \\ Power(W) & 5 W (or 10 W) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specifications of NVIDIA Jetson Nano.  
Figure 10: _Cont._  
Figure 9: Performance of the proposed multi-frame space object detection method on the test set: (a-c) show the space targets that can be detected correctly in different spatial distribution within one sequence. The prediction results of PP-YOLOv2 detector are marked by the green solid circle; the final results after the post-process procedure of CFS are marked by the red cross symbols. Additionally, the blue cross symbols indicate the ground truth.  
Furthermore, the ability to detect truncated and occluded objects is highly favorable for space object detection tasks as such instances regularly occur for images. Here, we also present several challenging examples in the dataset in Figure 10. The blue cross symbols in the left column and the green circle symbols in the right column both indicate the ground truth. Those misdetected samples in blue stand out in the image where there are none of the same red symbols nearby. In future work, we will continue to refine our approach to achieve greater detection accuracy.
## 3 Low-Complexity Decoding Algorithm  
### Mas  
The process of LDPC decoding has been enhanced through the utilization of various algorithms, such as the Belief Propagation (BP) algorithm, Likelihood Ratios BP Algorithm (LLR-BP), and the MSA [27; 28; 29]. The MSA, in particular, has garnered widespread adoption due to its ability to simplify hardware implementation without compromising decoding performance. Notably, [PERSON] contributed to further streamlining the MSA's initialization steps by deriving formulas that enable the algorithm to decode without requiring prior knowledge of channel information [30]. In certain operations, direct assignment of initial probability values as inverses of received signal amplitudes enhances operational efficiency.  
The complete MSA is shown as Algorithm 2, where \(rx\), \(it_{max}\) stand for receiving message and the default iteration time, \(L(q_{n})\) stands for the soft message, \(N(m)\) stands for nodes participating in the m-th check equation, \(M(n)\) stands for check equations connected with the n-th variable node, \(N(m)\backslash n\) stands for \(N(m)\) without the n-th node, \(M(n)\backslash m\) stands for \(M(n)\) without the m-th equation, \(r_{mn}\) is the message from the m-th check equation to the n-th node, \(q_{mn}\) is the message form the i-th node to the j-th check equation, and \(L(P[x_{n}\mid y,S])\) is the posteriori probability.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 3 System Model and Problem Formulation  
In this section, we first introduce the system model of a two-tier HetNet. Then, we discuss the handover issue formulation to support utility maximization while allowing load-balancing.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 2 VMD Parameter Optimization and Wavelet Noise Reduction
### IMF Component Screening  
Following the optimized VMD of the signal, \(K\) IMF components are obtained, comprising both effective and noise components. Each IMF component is adaptively selected based on the Pearson correlation coefficient, which reflects the degree of noise within the time series.  
The Pearson correlation coefficient is a widely used metric for assessing relationships [24]. It finds extensive application in fields such as data analysis and fault diagnosis [25]. The definition of this coefficient is provided in Equation (10).  
\[\rho_{x,y}=\frac{\sum\limits_{i=1}^{n}\big{(}X_{i}-\overline{X}\big{)}\big{(}Y _{i}-\overline{Y}\big{)}}{\sqrt{\sum\limits_{i=1}^{n}\big{(}X_{i}-\overline{X} \big{)}^{2}}\sqrt{\sum\limits_{i=1}^{n}\big{(}Y_{i}-\overline{Y}\big{)}^{2}}} \tag{10}\]  
where \((X_{i},Y_{i})\) represents the \(i\)th values of two sets, \(X\) and \(Y\), respectively; \((X,Y)\) denotes the means of these sets; \(n\) is the number of elements in each set. The correlation coefficient \(\rho_{x,y}\) ranges from \(-1\) to \(1\), reflecting the strength of the correlation between each component and the original signal.  
In general, the degree of correlation between variables can be assessed according to the standards outlined in Table 1.  
After VMD decomposition, each modal function component, or IMF, encompasses distinct frequency bands. The presence of abundant abnormal signals in components dominated by noise tends to decrease their correlation with the original signal, resulting in lower correlation coefficients. Conversely, components primarily composed of useful signal elements exhibit a better correlation with the original signal, reflected in higher correlation coefficients. Consequently, the magnitude of the correlation coefficients between the original signal and each component can serve as a criterion for selecting effective components.  
Initially, the signal undergoes VMD to extract \(K\) modal components. Subsequently, the correlation coefficients \(\rho\) between each modal component and the original signal are computed. If the absolute value of \(\rho\) is less than \(0.2\), the variables are considered to have negligible correlation. Thus, if the minimum \(\mid\!\rho\!\mid\) exceeds \(0.2\), it suggests that all modal components are significant.
# A Novel Benthic Phage Infecting _Shewanella_ with Strong Replication Ability
## 3 Results and Discussion
### Phylogenetic Analysis  
We used the gene-encoding DNA polymerase subunit B, which has been widely used as a marker gene to study phage diversity to determine the phylogeny of phage S0112. Here, the phylogenetic analysis based on phage DNA polymerase subunit B gene and the viral proteomic tree constructed by the ViPTree both indicated that the closest relative of phage S0112 was _Vibrio_ phage VpKK5 (Figures 7 and 8), while, in the whole genome level, phage S0112 and VpKK5 share very low similarity with each other (Figure S1). Phylogenetic analysis combined with morphological assessment indicated that phage S0112 could be a representative of a novel genus belonging to the family _Siphoviridae_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detected** & **Predicted Function** & **Molecular Mass (kDa)** & **Number of Peptides** & **Sequence Coverage (\%)** & **Protein Score** \\ \hline S0112\_001 & Uncharacterized protein & 11.2 & 3 & 33.7 & 175 \\ S0112\_065 & Uncharacterized protein & 52.6 & 17 & 46.7 & 3837 \\ S0112\_066 & Head-tail connector & 19.6 & 7 & 49.7 & 1873 \\ S0112\_068 & protein & 16.9 & 5 & 30.1 & 575 \\ S0112\_069 & Uncharacterized protein & 43.1 & 4 & 9.4 & 220 \\ S0112\_071 & Major capsid protein & 37.3 & 13 & 42.1 & 9723 \\ S0112\_074 & Tail completion protein & 13.7 & 4 & 43.7 & 418 \\ S0112\_075 & Tail terminator protein & 16.1 & 6 & 44.3 & 694 \\ S0112\_076 & Major capsid protein & 54.1 & 17 & 58.7 & 15,158 \\ S0112\_079 & Tail length tape measure & 99 & 24 & 31.4 & 5007 \\  & protein & & & & \\ S0112\_080 & Uncharacterized protein & 38.4 & 5 & 27.1 & 1859 \\ S0112\_081 & Virion structural protein & 30.8 & 7 & 30.8 & 709 \\ S0112\_082 & Tail assembly structural & 62.2 & 8 & 27.1 & 1781 \\ S0112\_083 & Minor tail protein & 30.8 & 8 & 37.7 & 1024 \\ S0112\_086 & Tail assembly structural protein & 83.3 & 12 & 23.9 & 1972 \\ S0112\_087 & Minor tail protein & 14.9 & 4 & 45.5 & 696 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mass spectrometry analysis of phage S0112 virion.  
Figure 7: The maximum likelihood phylogenetic tree of phages based on their DNA polymerase subunit B amino acid sequences. The red star represents _Shewanella_ phage S0112 isolated in this study. The reference sequences were collected from the NCBI database. The tree was constructed based on the MUSCLE alignment of MEGA 7.0. The bootstrap values were based on 1000 replicates.