esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### ROC Curves  
To identify the predictive value of fall event and frailty in all subjects evaluated in the study, ROC curves were obtained (Figures 1 and 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tests** & **Parameters** & **Statistic a** & \(p\)**-Value** \\ \hline
10 MWT & 10 MWT\_Cadence & 146 & 0.036 \\  & 10 MWT\_Speed & 91 & <0.001 \\  & 10 MWT\_Left\_Propulsion & 85.5 & <0.001 \\
6 MWT & 6 MWT\_Distance & 65 & <0.001 \\ TUG & TUG\_Duration & 151.5 & 0.050 \\  & TUG\_IR\_Duration & 115 & 0.005 \\  & TUG\_FR\_Duration & 110 & 0.004 \\  & TUG\_IR\_Speed & 82 & <0.001 \\  & TUG\_FR\_Speed & 103 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between Able and Frail groups.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tests** & **Parameters** & **Statistic a** & \(p\)**-Value** \\ \hline Stabilometry & Sway Area OE & 132 & 0.016 \\  & Max Speed\_Oscillation OE & 107 & 0.003 \\
10 MWT & 10 MWT\_Left Propulsion & 144 & 0.033 \\ TUG & TUG\_IR\_Duration & 147 & 0.038 \\  & TUG\_FR\_Duration & 131 & 0.015 \\  & TUG\_IR\_Speed & 134 & 0.019 \\  & TUG\_FR\_Speed & 151 & 0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between Fall and NFall groups.  
Related to falls, the maximum speed of oscillation at OE in the stabilometric test is considered as a predictive threshold value of fall event (Figure 1, panel A, \(p<0.001\); criterion \(>118\)). The path length at OE (Figure 1, panel B) could also identify the predictive threshold value of fall event (\(p=0.052\); criterion \(>522\)). The sway area at OE is a predictor of fall event with criterion \(>81\) (\(p=0.004\)) (Figure 1, panel C). The anterior-posterior (AP)  
Figure 1: Predictive ROC curves for Fall (1) and NFall (0) subjects. (**A**) Max_Speed_Oscillation OE; (**B**) Path length at OE; (**C**) Sway area at OE; Right (**D**) and left (**E**) anterior-posterior (AP) ground reaction force (GRF); (**F**) TUG duration. AUC = Area Under the Curve; Criterion = Youden’s Index; OE = open eyes; SE = sensitivity; SP = Specificity; TUG = timed up and go test.  
Figure 2: Predictive ROC curves for Able (1) and Frail (0) subjects. (**A**) TUG duration; (**B**) 6 MWT distance. AUC = Area Under the Curve; Criterion = Youden’s Index; SE = sensitivity; SP = Specificity; 6 MWT = six-minute walking test; TUG = Timed Up and Go test.  
ground reaction force (GRF) is a predictor of fall event for both limbs, with a criterion > 45 (\(p_{\text{right}}\) = 0.044, \(p_{\text{left}}\) < 0.001, Figure 1, panels D-E). The duration of the TUG (Figure 1, panel F) can predict the fall event with a criterion > 14 (\(p\) = 0.030).  
The duration of the TUG (criterion > 11, \(p\) = 0.049) and 6 MWT distance (criterion \(\leq\) 384; \(p\) < 0.001) are together parameters that could identify the predictive threshold value of the condition of frailty (Figure 2, panels A and B).  
The body mass index (BMI) had a positive correlation with the duration of the TUG, and a negative correlation with the acceleration in lifting from the chair and final rotation during the TUG (Table 5).
## 4 Case Study
### Result of Feature Extraction  
As in the preliminary study, the data was divided into segments of 1000 samples, resulting in a total of 7800 segments. Statistical features were extracted from each segment, and the results are shown in Figure 13.  
Figure 12: (**a**) Noise reduction normal data (High-pass). (**b**) Noise reduction failure data (High-pass).  
Figure 13: Results of feature extraction-Highpass (**(a)**: Mean, (**b**): RMS, (**c**): STD, (**d**): SK, (**e**): KU).  
Similar to the Gaussian noise-based results, clear separation between normal and fault data was observed in three out of five features, excluding skewness and kurtosis. The most notable difference appeared in the standard deviation feature. Unlike the Gaussian noise case, which showed overlapping regions between classes, the high-frequency noise-based model demonstrated more distinct separation between normal and fault data.  
In the case of kurtosis, the Gaussian noise-based features exhibited similar distributions for both classes across all segments, whereas the high-frequency noise-based results revealed some degree of distinction in specific regions.
# The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review
## 2 Materials, Processes and Methods
### Processes
#### 2.1.3 Gas Metal Arc Welding  
Gas metal arc welding is one of the most commonly used welding processes for WAAM and has been used for many decades in surfacing, cladding and additive manufacturing. In recent years, the development of new control systems for arc welding power sources has enabled the development of new welding process variants, such as controlled short arc and controlled spray arc processes. Modern controlled short arc welding processes in particular are used for additive manufacturing due to the reduced energy input they provide in comparison with conventional uncontrolled short arc processes [78; 79]. Furthermore, these controlled short arc welding processes can be exaggerated by innovative wire feed control to achieve different droplet detachment [78; 79].  
Due to the high achievable energy input with current welding machines, high throughput additive manufacturing processes can be achieved and are currently under investigation [80]. [PERSON] et al. showed that an additive manufacturing with a modified GMA welding process can have a material deposition rate up to 4.71 kg/h [80]. Another method to increase the mass of the deposited material is to use multi-wire processes [81]. Due to the changed energy input, the material behavior of the finished parts differs between the single-wire and multi-wire processes [44; 81]. One critical point for additive manufacturing with the GMA welding process is the movement of the wire tip and the resulting geometrical inaccuracy [82].  
On the basis of commonly buyable welding machines, different manufacturing systems have been developed [83] and are sold by various companies. Commonly used welding robots can be easily adopted for additive manufacturing.
## 2 Materials and Methods
### Data Collection  
For the data collection, the researchers used a dataset available from Kaggle entitled \"COVID-19 Symptoms and Presence\". This dataset has 20 attributes that are possible factors related to acquiring the virus, and 1 class attribute that determines the presence of COVID-19. Table 2 displays the features and descriptions of the dataset.  
This dataset is visible to the public; its sources are the World Health Organization (WHO) Coronavirus Symptoms and the All India Institute of Medical Sciences (AIIMS).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Attribute Name** & **Type** & **Percentage Level** & **Description** \\ \hline Breathing Problem & Nominal & 10\% & The person is experiencing shortness of breath. \\ Fever & Nominal & 10\% & Temperature is above normal. \\ Dry Cough & Nominal & 10\% & Continuous coughing without phlegm. \\ Sore Throat & Nominal & 10\% & The person is experiencing sore throat. \\ Running Nose & Nominal & 5\% & The person is experiencing a runny nose. \\ Asthma & Nominal & 4\% & The person has asthma. \\ Chronic Lung Disease & Nominal & 6\% & The person has lung disease. \\ Headache & Nominal & 4\% & The person is experiencing headache. \\ Heart Disease & Nominal & 2\% & The person has cardiovascular disease. \\ Diabetes & Nominal & 1\% & The person is suffering from or has a history of diabetes. \\ Hypertension & Nominal & 1\% & Having a high blood pressure. \\ Fatigue & Nominal & 2\% & The person is experiencing tiredness. \\ Gastrointestinal & Nominal & 1\% & Having some gastrointestinal problems. \\ Abroad Travel & Nominal & 8\% & Recently went out of the country. \\ Contact with COVID-19 Patient & Nominal & 8\% & Had some close contact with people infected \\  & & & with COVID-19. \\ Attended Large Gathering & Nominal & 6\% & The person or anyone from their family recently \\ Visited Public Exposed Places & Nominal & 4\% & Recently visited malls, temples, and other public places. \\ Family Working in Public & Nominal & 4\% & The person or anyone in their family is working in a \\ Exposed Places & Nominal & 4\% & market, hospital, or another crowded place. \\ Wearing Masks & Nominal & 2\% & The person is wearing face masks properly. \\ Sanitation from Market & Nominal & 2\% & Sanitizing products bought from market before use. \\ COVID-19 & Nominal & - & The presence of COVID-19. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features, percentage level and attribute descriptions of the COVID-19 symptoms and presence dataset.  
Figure 1: The five phases of this study are data collection, preprocessing, modelling, comparative analysis and finding the best model to determine COVID-19 presence.  
The spatial coverage of this dataset is worldwide, and the date coverage is from 17 April 2020 to 29 August 2020 [23].
## 4 Experimental Results
### Probabilistic and Temporal Characteristics of Attacks against SDN  
The simulation results are used for calculating the probabilistic temporal characteristics of attacks using TTSN.  
Using the obtained values as initial data, the dependences \(F(t)\) and \(\bar{t}_{\text{CA}}\) were obtained; they are presented in Figures 14-19. The graphs indicated in these figures as \"\(a\)\" show the dependence of the average time \(\bar{t}_{\text{CA}}^{-}\) on the probability of the CA implementation \(P_{n}\). Graphs marked as \"\(b\)\" show the dependence of the integral probability distribution function \(F(t)\) on the time \(t\) spent on CA implementation. The following values of time and probability are used as initial data, corresponding to the profile model of the CAs; the average time periods for each stage of the CA are the values from Table 9: \(P_{\text{res}}\) = 0.2; 0.6; 0.8.  
Figure 14: Probabilistic temporal characteristics of the “man-in-the-middle” attack (**a**) dependence of the average time on the probability of the implementation of the CA; (**b**) dependence of the integral probability distribution function on the time of the implementation of the CA.  
Figure 16: Probabilistic temporal characteristics of the “Service Chain Interference” attack. (**a**) dependence of the average time on the probability of the implementation of the CA; (**b**) dependence of the integral probability distribution function on the time of the implementation of the CA.  
Figure 15: Probabilistic temporal characteristics of the “Hacking/Cashing controller” attack. (**a**) dependence of the average time on the probability of the implementation of the CA; (**b**) dependence of the integral probability distribution function on the time of the implementation of the CA.  
Figure 17: Probabilistic temporal characteristics of the “Internal Storage Abuse” attack. (**a**) dependence of the average time on the probability of the implementation of the CA; (**b**) dependence of the integral probability distribution function on the time of the implementation of the CA.  
Figure 18: Probabilistic temporal characteristics of the DoS attack. (**a**) dependence of the average time on the probability of the implementation of the CA; (**b**) dependence of the integral probability distribution function on the time of the implementation of the CA.  
The presented dependences make it possible to determine the probability \(P_{n}(t\leq T_{3})\) for the implementation of each CA for a time not exceeding the specified \(T_{3}\), the average time of their implementation, as well as the time corresponding to the given level of threat of their implementation. So, for example, if the probability of network disruption is \(P_{n}=0.8\), which essentially determines its accessibility to the means of the attacker's CA, after 17 min of operation, the network will be inoperable with a probability of \(F(t=17)=0.8\), while the average time of CA implementation is about 10 min (Figure 14).
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 4 Efficiency Analysis  
Quantum communication systems rely on rare and expensive quantum resources. Improving efficiency means transmitting more information while maintaining the same level of resource consumption, thereby optimizing resource utilization and minimizing costs. The information-theoretic efficiency defined in reference [34] is given by:  
\[\eta=\frac{c}{q+b} \tag{21}\]  
where \(c\) represents the number of bits used to transmit secret messages, \(q\) denotes the total number of qubits employed in the protocol, and \(b\) indicates the number of classical bits consumed. Notably, decoy qubits employed for eavesdropping detection are excluded from this calculation. As [PERSON] et al. [35] demonstrated that when the number of decoy particles reaches 20, the probability of detecting an eavesdropper approaches 100%. In the proposed protocol's large-scale data transmission, decoy particle quantities exhibit negligible impact on total efficiency and are therefore omitted from the efficiency analysis. The total number of qubits consumed in the protocol is \(q=4n\), which includes: \(n\) pairs of hyperentangled photon prepared by [PERSON]\({}_{1}\), and \(2n\) new separable single-photon qubits prepared by [PERSON] in Step 3. During the transmission process, [PERSON] needs to announce the correct order of the permutation operators, which consumes \(2n\) classical bits. Therefore, \(b=2n\). The protocol successfully transmits \(c=4n\) secret message bits. Substituting these values into Equation (21), the efficiency of the protocol is calculated as: \(\eta=\frac{4n}{4n+2n}=66.7\%\).  
A comparison of the proposed MCSQD protocol with related SQD protocols is shown in Table 2. It is evident that the proposed protocol achieves a relatively high communication efficiency. Moreover, it is suitable for a wide range of complex scenarios and offers significantly richer functionality compared to the SQD protocols listed in the table.
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 3 Methodology
### Experimental Setup  
The experiments performed were comparative analyses to evaluate the performance of the six ML classifiers described above using five performance metrics, four missing data methods to handle missingness in the dataset, and incorporating three-dimensionality reduction methods to reduce the feature scope of the RTA dataset. The experiments were conducted using the Python platform. During the investigation, the results were generated using the default setting and parameter tuning. The experiments were performed using a real-life RTA dataset that contained primary and secondary accident parameters. As outlined above, the applied ML classifiers were NB, LR, k-NN, AdaBoost, RF, and SVM, and the missing data methods applied to the dataset were the mean and median, k-NN, and MICE. The LDA and PCA were the dimensional reduction techniques used. Additionally, as outlined, the abovementioned methods were employed in the study due to their frequent use in related studies. The introduction of LDA and PCA methods to the study was to observe whether they could contribute positive outcomes to constructing the RTA model.  
This section depicts the stages of the experimental process that were followed during the construction of the RTA model. The process consisted of five layers: type of dataset; data pre-processing, which involved data cleaning, dimensionality reduction, and preparation; data pre-processing was followed by sub-processes, namely, data training and testing; comparison analysis of the ML methods; and finally, the predicted RTA model evaluated. The process is illustrated in Figure 1 below.  
Figure 1: RTAs experimental procedure.
# Dual Quaternion Embeddings for Link Prediction
## 5 Experiments
### Experiment Settings
#### 5.1.2 Evaluation Metric  
Similar to [6], we used three metrics to measure our approach, i.e., Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hit@n. To calculate those metrices, we first replace \(h\) by all entities \(h^{\prime}\in\mathcal{E}\) for each testing triplet \((h,r,t)\in\mathbb{T}\) (where \(\mathbb{T}\) is a set of testing triplets) and compute score \(f_{r}(h^{\prime},t)\) for each triple \((h^{\prime},r,t)\). After that we sort \(h^{\prime}\) according to score \(f_{r}(h^{\prime},t)\) ascendingly and get the rank of the original entity \(h\), denoted by \(\mathcal{K}(h)\). Note that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\#E** & **\#R** & **\#TR** & **\#V** & **\#TE** \\ \hline FB15k & 14,951 & 1345 & 483,142 & 50,000 & 59,071 \\ FB15k-237 & 14,541 & 237 & 272,115 & 17,535 & 20,466 \\ WN8 & 40,943 & 18 & 141,442 & 5000 & 5000 \\ WN8 RR & 40,943 & 11 & 86,835 & 3034 & 3134 \\ YOGA3-10 & 123,182 & 37 & 1,079,040 & 5000 & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific information of the experimental datasets. #E and #R denote entities and relations number in datasets, #TR, #V and #TE denote the size of training set, valid set and test set.  
\(\mathcal{K}(h)\) is the \"rank\" of \(h\) instead of the score of \(h\), e.g., if the score of \(h\) the smallest, \(\mathcal{K}(h)\) is 1. We can calculate MR as shown below:  
\[\text{MR}=\frac{\sum_{(h,r,t)\in\mathbb{T}}\mathcal{K}(h)}{|\mathbb{T}|}\]  
which means MR is an average of ranks of all the original entities in the testing triplets. Likewise, MRR can be calculated as follows:  
\[\text{MRR}=\frac{\sum_{(h,r,t)\in\mathbb{T}}\frac{1}{\mathcal{K}(h)}}{| \mathbb{T}|}\]  
which indicates MRR is an average of inverse ranks of all the original entities in the testing triplets. Hit@n suggests the proportion of original entities in the top \(n\) entities, which can be calculated by:  
\[\text{Hit@n}=\frac{\sum_{(h,r,t)\in\mathbb{T}}one(\mathcal{K}(h)\leq n)}{| \mathbb{T}|}\]  
where \(one(\mathcal{K}(h)\leq n)\) is 1 if \(\mathcal{K}(h)\leq n\), and 0 if \(\mathcal{K}(h)>n\). We tested different values \(n=1\), 3, 10 in the evaluation, similar to the setting used in reference [6].
# Research on Multipath Correlated Imaging with the Grayscale Target in Endoscopic Applications
## 4 Conclusions  
In conclusion, we have theoretically and experimentally investigated the influence of multipath effect on CI with a grayscale object in endoscopic applications. When the multipath effect exists, the path length of the reflected light is larger than that of the direct light. What is more, the power of reflected light is much weaker than that of the direct light due to the reflection. Based on this theory, the model of CI with multipath effect is established. The mismatch ratio and the reflection ratio are defined. It is noted that the time delay is smaller than the coherent time of the source in this paper. In this case, the disturbance of multipath effect on the reconstructed image is the most serious. For the case that the time delay is larger than the coherent time of the source, the background noise may be enhanced by the reflected light, which may affect the imaging of object with lower grayscale values. This case will be discussed in the following work.  
The results show that a multipath effect has an influence on the grayscale distribution and imaging quality, and the effect from the mismatch ratio is stronger than that from the reflection ratio. As the mismatch ratio increases, a defocused image with the decreasing size and poor resolution is generated, meaning the decreased variation area of the grayscale values for the reconstructed image. For the larger reflection ratio, the variation amplitude of grayscale values for the reconstructed image increases due to the enhanced disturbance of the defocused image. It is found that the disturbance from multipath effect can be ignored when the mismatch ratio is smaller than 0.3 or the reflection ratio is smaller than 0.2. Thus, in the closed endoscopic environment, the disturbance of multipath effect on the reconstructed image can be ignored within a certain range. Our study paves the way for practical endoscopic applications.  
Data curation, J.L.; writing--review and editing, Y.B., S.N.; methodology, X.H.; formal analysis, W.T.; investigation, X.Z.; conceptualization, X.F. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Experimental results of reconstructed images under different \(\eta\) and \(\rho\) values: (**a1**) no multipath effect, \(\rho=0.5\); (**a2**) \(\eta=0.4\), \(\rho=0.5\); (**a3**) \(\eta=0.6\), \(\rho=0.5\); (  
**Funding:** This research was funded by National Natural Science Foundation of China (NFSC) (61871431, 61971184, 62001162).  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### RQ1: What Are the Existing Recognition-Based Methods?  
A total of 28 RBGP schemes have been developed and reported in the literature. Figures 3-5 depict the analysis of publishing channels and the years associated with the largest and lowest quantities of studies pertaining to RBGPs.
## 4 Discussion
### Image Compositing  
The number of cloud-free images available at a given location can influence the map quality [113]. Creating a composite image from optical data is considered a suitable solution when the study area is large and susceptible to persistent cloud coverage [124]. Our study managed to generate a quality and cloud-free Sentinel-2 median composite to capture the cocoa temporal dynamics in 2021. However, it might be expected that the dry and wet seasonality did not equally contribute to the composite image, and as a result, the annual cocoa dynamics might not have been represented appropriately. Future cocoa studies in West Africa could further explore the effectiveness of using multiple composites from the dry and wet seasons and/or deriving features from time-series images to account for the seasonal vegetation and the classes prone to changes. Another shortcoming of our compositing method is that the median reflectance values of neighbouring pixels might have been retrieved from different image scenes. This could have resulted in deviating values for pixels that were supposed to be classified in the same class but were assigned to different ones.  
In total, three Sentinel-2 orbits were required to mosaic our composite image covering the study area extent. However, satellite orbits have different revisit times, and consequently, the swath areas from adjacent orbits are captured under different viewing conditions. This issue, which was also encountered in other studies [62; 113], caused streak artefacts in the Sentinel-2 composite image (Figure 10) and the final cocoa map (Figure 3); however, these streaks are not evident at a local zoom-in level.