esa-sceva/satcom-chunk-collection huggingface dataset:
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
## 3 Results
### Assessment of Accuracy and Precision  
The mean, standard deviation, and \(p\)-value from one sample \(t\)-test for each parameter of the 3 study groups are shown in Table 1. The one sample \(t\)-tests were carried out with respect to the respective reference values. It also shows the mean and maximum deviation, which indicates the magnitude of error, as well as the CoV.  
All measurements were statistically different from the reference values, except for the ICW and AL in the SG group and the IMW in the BMC group. All mean deviation values were within the clinically acceptable range, which was set at <0.500 mm; the SG group demonstrated the smallest mean deviation for all 3 measurements with 0.088 mm for ICW, 0.126 mm for IMW, and 0.170 mm for AL, and the greatest mean deviation was observed in AL measurements for all 3 materials with 0.170 mm for SG, 0.307 mm for DLTV2, and 0.303 mm for BMC. However, the maximum deviation values for IMW in DLTV2 and BMC groups and AL for all 3 materials were greater than the clinically acceptable deviation, indicating that some of these splints were unacceptable for clinical use.  
The CoV values for the 3 resin materials varied depending on the parameter and they were in the ranges of 0.284-0.504% for ICW, 0.227-0.515% for IMW, and 0.709-0.802% for AL. Although the CoV values of the ICWs and IMWs were comparable, those of ALs were much higher than the other two for all 3 resin materials, with the highest value of 0.802% in the DLTV2 group. In terms of overall precision, the SG group had the lowest CoV values for all 3 parameters, indicating that it was more precise than the other resin materials.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Surgical Guide**} & \multicolumn{3}{c}{**Dental LT V2**} & \multicolumn{3}{c}{**BioMed Clear**} \\  & \multicolumn{3}{c}{**(SG)**} & \multicolumn{3}{c}{**(DLTV2)**} & \multicolumn{3}{c}{**(BMC)**} \\ \hline  & **ICW** & **IMW** & **AL** & **ICW** & **IMW** & **AL** & **ICW** & **IMW** & **AL** \\ \hline Mean (mm) & 38.157 & 50.876 & 29.791 & 38.283 & 50.874 & 29.559 & 38.254 & 50.843 & 29.574 \\ SD (mm) & 0.108 & 0.116 & 0.211 & 0.144 & 0.262 & 0.237 & 0.193 & 0.252 & 0.229
## Abbreviations  
Acc: Accuracy; AI: Artificial Intelligence; AUC: Area Under the Curve; BAcc: Balanced Accuracy; C-Index: Concordance Index; CAD: Computer-Aided Diagnoses; CE: Contrast Enhanced; CEA: Carcinoembryonic Antigen; CNN: Convolutional Neural Network; CR: Complete Response; CRC: Colorectal Cancer; CT: Computer Tomography; DBN: Deep Belief Network; DECT: Dual Energy Computer Tomography; DL: Deep Learning; DL-CAD: Deep Learning based Computer-Aided Diagnoses; EGFR: Epidermal Growth Factor Receptor; ExHOG: Extended Histogram of Oriented Gradients; FPSO: Fuzzy Particle Swarm Optimization; FS: Free Survival; GR: Good Response; HC: Hand Crafted; HER2: Human Epidermal Growth Factor Receptor 2; HGP: Histopathological Grow Pattern; HR: Hazard Ratio; IBSI: Imaging Biomarker Standardization Initiative; LARC: Locally Advanced Rectal Cancer; LASSO: Least Absolute Shrinkage and Selection Operator; LDCT: Low Dose Computer Tomography; LIDC-IDRI: Lung Image Database Consortium-Image Database Resource Initiative; LmCRC: Liver Metastases of Colorectal Cancer; LN: Lymph Nodes; IVI: Lymphovascular Invasion; M: Manual; MA: Mucinous rectal cancer; MD: Material Decomposition; MIP: Maximum Intensity Projections; ML: Machine Learning; MO-FS: Multi-Objective based Feature Selection; MRI: Magnetic Resonance Imaging; MS: Microsatellite Instability; N: Number of patients; NA: Not Available; NAR: Neoadjuvant Rectal score; NCE: Non-Constrated Enhocha; NMA: Non-Mucinous rectal cancer; NPV: Negative Predictive Value; OR: Odds Ratio; OS: Overall Survival; Prospective; PACS: Picture Archiving Communication Systems; PET: Positron Emission Tomography; PFS: Progression Free Survival; PM: Peritoneal Metastasis; PNI: Perineural Invasion; PPV: Positive Predictive Value; R: Retrospective; ResNet: Residual Network; RF: Radiomics Features; ROI: Regions Of Interest; RPN: Region Proposal network; rs: Spearman's rank correlation coefficient; S: Sensitivity; SA: Semi-Automatic; SE: Squeeze Excitation; Spec: Specificity; SVM: Support Vector Machine; TBS: Tumor Burden Score; TMB: Tumor Mutation Burden; WLTB: Whole Liver Tumor Burden.
# Measurement of Unit Weight of Dry Sand Using Piezoelectric Sensor
## 4 Discussion  
In this study, the unit weight of sand with three different particle sizes was evaluated by an EMI sensing technique. The EMI peak magnitude was decreased with the increase of surcharge on sand. At the same time, the EMI peak frequency shifted toward a higher frequency domain with the increase of surcharge on sand.  
As the sand became stiffer due to unit weight increase caused by surcharge, the vibration of the PZT sensor became less and the peak frequency increased. This relationship was most dominant for the small grain sized Nakdong river sand. As a key finding of this study, the relationship between the dry unit weight (r\({}_{\text{d}}\)) and the peak frequency (f\({}_{\text{0}}\)) for the small Nakdong river sand was derived from Equation (1) from Figure 8a. As shown in the figure, as unit weight increased, the peak frequency increased and the peak conductance decreased. By using this kind of result, we can predict the variation of unit weight in the field due to compaction or surcharge loading according to  
\[\text{f}_{\text{0}}\left(\text{kHz}\right)=1.593\times\text{r}_{\text{d}} \left(\text{kN}/\text{m}^{3}\right)+148.71. \tag{1}\]  
In the previous studies using EMI sensing techniques, the EMI signatures were analyzed with converting data, i.e., RMSD (root mean square deviation) and Mahalanobis distance, because the variation of EMI signatures was very small [13; 14]. However, in this study the clear changes in EMI signatures were found and the unit weight of soil was evaluated using only the EMI signatures, without the conversion of data. Therefore, it can be concluded that the unit weight of soil can be effectively monitored through the changes of EMI signatures.
## 1 Introduction
Decision support systems can also complement the analysis of people's health status, such as infrared body temperature screening at airports and other places of public circulation, being able to detect people who may be suspected of suffering from some pathology that poses a danger of contagion, as repeatedly observed in the media in the current context of the pandemic [15]. Several reports published before the COVID-19 pandemic show that the proportion of those classed as part of the ageing population is increasing in Portugal, without support units being able to provide an efficient and timely response to all requests [16]; thus, one of the solutions will be to keep people in their homes as long as they can be properly followed and monitored, keeping them in their comfort zone. In this way, elderly people will feel more comfortable in their residence, maintaining their habits and routines, a situation that contributes to active and healthy aging. Nevertheless, recent studies show that those suffering or who will suffer from some type of mental disorder is growing considerably throughout the world, so it is imperative to assess the existing technologies for the benefit of the people, minimizing the negative impact that these pathologies have on their quality of life [17; 18]. In the context of social isolation and confinement caused by COVID-19, this became even more evident, with several studies proving that these measures are risk factors for the health of the population and for the elderly population in particular [2; 19]. Based on this reality, using the concepts already defined in other monitoring and follow-up environments based on miniaturized sensors and telecommunications equipment, namely the work referred to in [20], we present, in this article, an answer to our question, with a system model for following, monitoring and protecting old people who are in a stable state of health, allowing them to maintain their autonomy within their homes and eventually abroad. The main goal of the proposed system is, therefore, the monitoring of the health status of elderly people who are in their homes, sometimes a few kilometers away from support centers and local community centers (Parish/County), in a discreet and non-intrusive way, through information and communication technologies. Thus, both support institutions, as well as family members, friends, or other entities, can monitor the status of these people in real time. Considering that systems aimed at monitoring and managing people's data are currently undergoing great evolution, and that pervasive and ubiquitous computing is already part of everyday life, with this work, the authors hope to contribute positively to improving people's quality of life, especially in the senior population with low economic resources who are still in a state of health that allows them to maintain their autonomy.
# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 3 Experiments
### Experiments and Analysis  
To demonstrate the effectiveness of the algorithm we proposed for infrared dim and small target detection, we conducted ablation experiments based on YOLOv7, as well as comparative experiments with existing advanced algorithms on datasets derived from a variety of scenarios.
# _Applied_
## 3 Methodology
### Metrics
#### 3.4.1 Accuracy  
The evaluation of the accuracy uses the \(F_{1}\) score, which is a metric well-suited for multi-classifiers. The \(F_{1}\) score is the harmonic mean of two other metrics: precision and recall, both metrics are based on the true positives, the false positives and the false negatives. Precision (Equation (1)) measures the ability of a classifier to identify only the correct instances for each class and recall (Equation (2)) is the ability of a classifier to find all correct instances per class. They are expressed as follows:  
\[\text{Precision}\;=\;\frac{t_{p}}{t_{p}+f_{p}} \tag{1}\]  
\[\text{Recall}\;=\;\frac{t_{p}}{t_{p}+t_{n}}, \tag{2}\]  
where \(t_{p}\) and \(t_{n}\) are the number of true positives and true negatives respectively, while \(f_{p}\) is the number of false positives. Both parameters are used to obtain the \(F_{1}\) score as follows:  
\[F_{1}\text{ score}\;=\;\frac{2\times\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}}. \tag{3}\]  
The value of the \(F_{1}\) score is normalized between 0 and 1, a score of 1 indicating a perfect balance as precision and recall are inversely related [38]. Moreover, the \(F_{1}\) score takes into account imbalanced class distribution, such as that which occurs with the one-vs-all approach and the MosAic approach evaluated in Section 6.  
The \(F_{1}\) score can be averaged in three different ways: the micro average (\(F_{1}\)-micro), the macro average (\(F_{1}\)-macro) and the weighted average (\(F_{1}\)-weighted). The \(F_{1}\)-micro uses the individual true positives, false positives and false negatives to calculate the performance. Since the \(F_{1}\)-micro is not sensitive to the accuracy of individual classes, it can be a misleading metric in cases where the class distribution is imbalanced such as the UrbanSound dataset. A classifier obtaining a high micro average means that it performs well overall. The \(F_{1}\)-macro is calculated using larger groups, namely the performance of individual classes. Therefore it is more suitable when the data have an imbalanced class distribution. A high \(F_{1}\)-macro indicates that the classifier performs well for each individual class. Like the \(F_{1}\)-macro, the \(F_{1}\)-weighted is also calculated with the performance of individual classes, but it alters the result to account for label imbalance [39]. All three different averages of the \(F_{1}\) score are used in this evaluation of the classifiers.
# COVID-19 Diagnosis from Crowdsourced Cough Sound Data
## 2 Related work
### Cough Testing  
[PERSON] et al. [12] collected cough and breath sound data, as described in Section 2.1, to distinguish between the cough sounds of COVID-19-positive individuals and those of healthy people. [PERSON] et al. trained a classification model with 477 handcrafted features and features extracted using VGGish [22]. The handcrafted features consisted of features extracted at the segment level and features extracted at the frame level. Duration, onset, tempo, and period were extracted at the segment level, and RMS energy, spectral centroid, roll-off frequency, zero-crossing, MFCC, \(\Delta\)-MFCC, and \(\Delta^{2}\)-MFCC were extracted at frame level. Therefore, a total of 477 handcrafted features were extracted to train the classification model. The researchers also used VGGish to extract features. With a sampling frequency of 16 kHz, a pre-trained VGGish model returns 128-dimensional features every 0.96 s. The researchers trained a classification model by extracting 256 features using VGGish. The classification model used a support vector machine. In an experiment by [PERSON] et al. extracting and training the VGGish feature and segment level features using only cough sound data produced an area under the receiver operating curve (AUC) of 0.82 and a sensitivity of 0.72. These were the best results of their experiments.  
[PERSON] et al. [13] used the COUGHVID dataset to identify the cough sound of a person who was COVID-19 positive. They extracted MFCC and spectrogram images from the data to train a multi-branch network. The spectrogram images were input into a ResNet50 model, and the 13 MFCCs extracted were input to the fully connected layer. Clinical features such as fever symptoms and respiratory diseases were input to the fully connected layer, and combined with the MFCC model. Then, the model was combined with the spectrogram model and trained. The multi-branch network had a sensitivity of 85% and a specificity of 99.2%. The model excluding clinical features had a sensitivity of 93% and a specificity of 86%.
## 3 Current Space Segment of BDS-3/BDS-2 Constellations  
As of September 2020, the in-orbit BDS satellites include 24 MEO, 3 IGSO and 3 GEO satellites for BDS-3, and 3 MEO, 7 IGSO and 6 GEO satellites for BDS-2. Table 1 details the current space segment of BDS-3 and BDS-2. In addition to the BDS-3/BDS-2 satellites, there are also five in-orbit validation satellites of BDS-3, namely BDS-3S satellites. The PRN is not clear for one BDS-3S MEO satellite which suffers from a failure of the transmitting antenna. So far, there is no receiver tracking of this satellite. The BDS satellites with a PRN in the parenthesis in Table 1 cannot be supported by the real-time stream CLK93, including one BDS-2 GEO satellite (i.e., C18), all BDS-3S satellites, and all BDS-3 satellites except for the MEO satellites with a PRN smaller than C38. Currently, the real-time stream CLK93 can offer the precise corrections of 18 BDS-3 MEO, 3 BDS-2 MEO, 7 BDS-2 IGSO and 5 BDS-2 GEO satellites. The orbital altitude is 35,786 km for the GEO and IGSO satellites, whereas the MEO satellites with an altitude of 21,528 km operate in nearly circular orbits. For the GEO satellites, the non-zero inclination falls between 0.7\({}^{\circ}\) and 1.7\({}^{\circ}\), while both MEO and IGSO satellites are inclined at 55\({}^{\circ}\). As for the orbit period and orbital repeat period, they are 12 h 53 min and approximately a week for MEO satellites, respectively, but both of them are 23 h 56 min for non-MEO satellites. The ground track for BDS-3/BDS-2 satellites supported by real-time precise products on the day of the year (DOY) 140 of 2020 is illustrated in Figure 1. Except for the Arctic and Antarctica regions, most regions of the world are covered by the ground track of MEO satellites of BDS-3 and BDS-2. Two figure-of-eight loops with a respective ascending node longitude of 118\({}^{\circ}\) E and 95\({}^{\circ}\) E can be identified with respect to the ground track of BDS-2 IGSO satellites. For the five BDS-2 GEO satellites (i.e., C01-C05), the ground track is fixed over the equator at 140\({}^{\circ}\) E, 84\({}^{\circ}\) E, 110.5\({}^{\circ}\) E, 160\({}^{\circ}\) E and 58.75\({}^{\circ}\) E longitude, respectively. Thus, in the performance enhancement of BDS PPP, both the IGSO and GEO satellites have potential for users in the Asia-Pacific regions. As for the GPS system, the corresponding information is detailed in [PERSON] et al. (2016).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Generation** & **Constellation** & **PRN** \\ \hline \multirow{3}{*}{BDS-2} & GEO & C01–C05/(C18) \\  & IGSO & C06–C10/C13/C16 \\  & MEO & C11/C12/C14 \\ \hline \multirow{3}{*}{BDS-3S} & IGSO & (C31/C56) \\  & MEO & (C57/C58/C7) \\ \hline \multirow{3}{*}{BDS-3} & GEO & (C59–C61) \\  & IGSO & (C38–C40) \\ \cline{1-1}  & MEO & C19–C30/C32–C37/(C41–C46) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Space segment of BeiDou navigation satellite system (BDS)-3 and BDS-2 as of September 2020.  
Figure 1: Ground track of BDS-2 and BDS-3 satellites on day of the year (DOY) 140, 2020. Different colors in the figure refer to different satellites.
# Research on Environmental Vibration Induced by High-Speed Maglev Transportation
## 4 Environmental Ground Surface Vibration
### Vibration Propagation Perpendicular to the Maglev Line  
The direction perpendicular to the maglev line is most widely studied in previous research for both wheel-track and maglev transportation. Researchers usually ignore the traveling wave effect and assume that the points under the column have the largest vibration, which was found not to be so accurate in the former section. In spite of this, the direction perpendicular to the maglev line and column is still important to evaluate the ground vibration magnitude and is widely utilized in standards and criteria. In this research, test points with distances of 0 m, 7.5 m, and 10 m (points G, K, and L in Figure 4) were set to measure the ground acceleration when maglev trains passed through. The measured results and calculated frequency spectra are shown in Figure 9 (listed in order of distance).  
Figure 9 above shows that, with the increase in the distance to the maglev line, the ground vibration attenuation speed is considerably quicker than that in the guideway structures. At the test point 10 m away from the maglev line, the peak acceleration reduces by over 90% compared to the foundation center, and it does not show any tendency towards an amplifying phenomenon. The peak acceleration 10 m away from the maglev line is no more than 0.015 m/s\({}^{2}\), which is the perception threshold suggested by international standards based on the evaluation of human exposure to whole-body vibration [18]; thus, the ground vibration can barely be perceived at this distance if resonance does not occur.  
The spectral analysis results clearly show that the major components of the maglev-induced vibration are in the high-frequency range, and they also have weak penetrating power in soft soil and sand. The high-frequency components of the ground vibration waves  
Figure 8: Acceleration time history and frequency spectra of each test point along the maglev line.  
Figure 7: Test point layout at center of maglev line.  
attenuate rapidly with the increase in the distance, with low-frequency components under 50 Hz mainly being reserved 10 m away from the maglev line.
# Hyperbolic Asynchronous Method of a Radio Navigation Technique
## 2 Basic of the Hyperbolic Technique
### Selected Localization Algorithms for Hyperbolic Solution
 \tag{10}\]  
With assumptions (10), it can be shown that a simple linear relationship of a known form is correct [21]:  
\[y_{UT}=a\cdot x_{UT}+b,\] (11a) in which \[a=\frac{\left(\frac{\Delta d_{P2}}{\Delta d_{P1}}\cdot x_{P1}-x_{P2}\right)}{y _{P2}},\] (11b) and \[b=\frac{x_{P2}^{2}+y_{P2}^{2}-\Delta d_{P1}\cdot\Delta d_{P2}\cdot\left(1- \left(\frac{x_{P1}}{\Delta d_{P1}}\right)^{2}\right)}{2y_{P2}}. \tag{11c}\]  
However, before this, a square equation of the following form should be solved, in which one of the coordinates in Equation (11a) is unknown--e.g., \(x_{UT}\):  
\[a_{x}\cdot x_{UT}^{2}+b_{x}\cdot x_{UT}+c_{x}=0, \tag{11d}\]  
in which  
\[a_{x}=-\left(1-\left(\frac{x_{P1}}{\Delta d_{P1}}\right)^{2}+g^{2}\right), \tag{11e}\]  
\[b_{x}=x_{P1}\left(1-\left(\frac{x_{P1}}{\Delta d_{P1}}\right)^{2}-2\cdot g \cdot h\right), \tag{11f}\]  
\[c_{x}=\left(\frac{\Delta d_{P1}^{2}}{4}\right)\cdot\left(1-\left(\frac{x_{P1 }}{\Delta d_{P1}}\right)^{2}-h^{2}\right), \tag{11g}\]  
whereby  
\[g=\frac{\Delta d_{P2}\cdot\left(\frac{x_{P1}}{\Delta d_{P1}}-x_{P2}\right)}{ y_{P2}}, \tag{11h}\]  
while  
\[h=\frac{x_{P2}^{2}+y_{P2}^{2}-\Delta d_{P2}^{2}+\Delta d_{P2}\cdot\Delta d_{ P1}\cdot\left(1-\left(\frac{x_{P1}}{\Delta d_{P1}}\right)^{2}\right)}{2y_{P2}}. \tag{11i}\]  
Solving Equation (11d), two solutions are obtained, in this case the coordinates \(x_{UT}\) to which the comment expressed in the description of the solution of Equation (7a) refers. Then, by substituting the calculated value of one of the search coordinates appearing in relation (11a), the other is simply calculated. The result is the location of the localized terminal. The algorithms described above are characterized by different usability, as described below:  
* The Chan algorithm can be applied to any number of reference stations. Therefore, from the point of view of the asynchronous system that is being developed, it is the most useful method in practice. * The Foy algorithm can also be applied to any number of reference stations. However, it is an iterative method, which entails specific requirements, especially for the time necessary to obtain a solution with the required accuracy. If an unfavorable starting point is adopted, there may be difficulties in achieving convergence of the solution. In addition, it must also be taken into account that the navigation system under development will operate in real time rigor. * The Fang algorithm, on the other hand, is a simplified version of the Chan algorithm which can be used in a clearly limited range of only three reference stations.
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 4 Discussion
### Protocols Designed for Plain Networks
#### 4.1.1 Protocols that Use A Global Time Reference  
All protocols from this category [9; 19; 20; 21] are based on the same idea of using a combination of logical and physical clocks to ensure causal message order. To achieve this, protocols are required to be able to synchronize time with a time server and have knowledge about the minimum and maximum delays of messages in communication channels and maximum clock drift. To determine the order of two messages \(m_{1}\) and \(m_{2}\), these protocols use the physical time. If the difference in physical time when the messages were sent is greater than the maximum delay, then the messages can be ordered based on physical time. In addition, they can determine that a message \(m_{1}\) cannot be received when a message \(m_{2}\) was sent (difference in physical time is smaller than the minimum delay). If these protocols cannot determine a message order based on these rules, a logical clock is used to order messages.