esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis
### Bit Error Rate for IRS-Assisted Bidirectional FD-MTC System  
Channel distribution \(A\) and the received SINR at two source nodes \(S_{1}\) and \(S_{2}\) determine the BER performance of the proposed system model. Since BPSK modulation is assumed, the average probability of error at both the source nodes \(S_{1}\) and \(S_{2}\) are given by,  
\[P_{error}^{S_{n}}=\int_{0}^{\infty}Q(\sqrt{2\gamma_{S_{n}}})f_{\Gamma S_{n}}( \gamma_{s_{n}})d\gamma_{s_{n}},n=1,2 \tag{20}\]  
where \(f_{\Gamma S_{n}}(\gamma_{s_{n}})\) is the PDF of \(\gamma_{s_{n}}\), it is obtained by taking the derivative of Cumulative Density Function (CDF) \(F_{\Gamma Sn}(\gamma_{s_{n}})\). By using (16), the CDF is defined as,  
\[F_{\Gamma Sn}(\gamma_{s_{n}})=P\bigg{[}\frac{P_{S}A^{2}}{N_{0}+\sigma_{w}^{2} }<\gamma_{s_{n}}\bigg{]},n=1,2 \tag{21}\]  
Here \(A^{2}\) follows a Chi-square distribution with one degree of freedom.  
The CDF \(F_{\Gamma Sn}(\gamma_{s_{n}})\) is expressed as,  
\[F_{\Gamma Sn}(\gamma_{s_{n}})=\frac{1}{\sqrt{2}\Gamma\Big{(}\frac{1}{2} \Big{)}}\int_{0}^{\Xi\gamma_{s_{n}}}a^{-\frac{1}{2}}e^{-(\frac{\pi}{2})}da,n=1,2 \tag{22}\]  
where \(\xi_{n}=\frac{1}{SNR}(a_{pt}+1),a_{pt}=\frac{\sigma_{w}^{2}}{\overline{N_{0}}}\), Equation (22) is simplified as,  
\[F_{\Gamma Sn}(\gamma_{s_{n}})=erf\Bigg{(}\sqrt{\frac{\xi\gamma_{s_{m}}}{2}} \Bigg{)},n=1,2 \tag{23}\]  
The corresponding PDF \(f_{\Gamma Sn}(\gamma_{s_{n}})\) is derived as,  
\[f_{\Gamma Sn}(\gamma_{s_{n}})=\frac{1}{\sqrt{2\pi\gamma_{s_{n}}}}\sqrt{\frac {(a_{pt}+1)}{SNR}}exp\bigg{(}-\frac{\xi\gamma_{s_{n}}}{2}\bigg{)},n=1,2 \tag{24}\]  
By using (24), (20) can be rewritten as,  
\[P_{error}^{S_{n}}=\frac{\sqrt{\xi_{n}}}{\sqrt{2\pi\gamma_{s_{n}}}}\int_{0}^{ \infty}Q(\sqrt{2\gamma_{S_{n}}})exp\bigg{(}-\frac{\xi\gamma_{s_{n}}}{2} \bigg{)}d\gamma_{s_{n}},n=1,2 \tag{25}\]Using the Q-function approximation \(Q(x)=\frac{1}{2}-\frac{1}{2}erf(\frac{x}{\sqrt{2}})\), the average probability of error expression is re-written as,  
\[P_{error}^{S_{n}}=\frac{\sqrt{\xi_{n}}}{2\sqrt{2\pi}}\int_{0}^{\infty}\left[ \frac{exp(-\frac{\xi_{n}\gamma_{S_{n}}}{2})}{\sqrt{\gamma_{S_{n}}}}-erf(\sqrt{ \gamma_{S_{n}}})\frac{exp(-\frac{\xi_{n}\gamma_{S_{n}}}{2})}{\sqrt{\gamma_{S_ {n}}}}\right]d\gamma_{S_{n}},n=1,2 \tag{26}\]  
The probability of error at the IRS-assisted bidirectional FD-MTC system is determined by solving (26), using [36],  
\[P_{error}^{S_{n}}=\frac{1}{2\sqrt{2\pi}}\sqrt{\frac{a_{pt}+1}{SNR}}\left[ \frac{2\pi}{\xi_{n}}-\frac{2}{\xi\sqrt{\frac{\xi_{n}}{2}}+1}\right],n=1,2 \tag{27}\]
###### Abstract  
The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of \(x\)- and \(y\)-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA's remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs.  
prognostic and health management; wafer-handling robot arm; feature extraction; general regression neural network; logistic regression
## 3 Results and Discussion
### Proofs of Concepts: Western Blotting Analysis
In this cell line,  
Figure 9: Expression of some representative proteins involved in the eicosanoid biosynthetic pathways of six human myeloma cell lines analyzed by western blotting. HEK-293 and HepG2 were used as positive controls for 17-\(\beta\)-HSD4, LTA4H, PGES2, ALOX-15B, GGT1 and ACAA1 expressions, respectively. HEK293-COX-1 cells treated with 10 ug/mL tetracycline for 24 h were used as a positive control for COX-1 expression. \(\beta\)-actin and vinculin were used as loading controls for 17-\(\beta\)-HSD4, COX-1, GGT1, LTA4H, ALOX-15B, and ACAA1, CBR1, CYP1A2, PGES2 respectively, except for the MM15 cell line where vinculin was used as the loading control for all the examined proteins. The image is the result of different western blotting experiments that were lined up in one single image. a good COX-1 expression was observed if considering that the reference cell line was stimulated to express COX-1. RPMI-8266 belonging to the cluster 4 was composed of the expression of genes belonging to metagene 2. Concerning HSD17B4, ACAA1, and ALOX15B, since their encoding genes were not present in the top most 20 representing MG2, we expected not to find them. Surprisingly, western blotting experiments (Figure 9) revealed the expression of HSD17B4, ACAA1, and ALOX15B with values of 0.44, 0.27, and 0.48, respectively. Their presence is justified by the fact that their codified genes are actually present but with a percentage of expression less than 1% and therefore not represented in Figure 8.  
The last but not least important cluster was represented from the cell line MM1S with a marked component of metagene 5 but a low contribution of metagenes 1, 2, and 5. This is the only HMCL used in which CYP1A2 was expressed and with a value comparable to the positive control. Similar expression values were found for the other proteins with the exception of GGT1 and ACAA1.
## 3 Results
### Urine Sample Donation
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **No SMFc 1** & **Any Type** & \begin{tabular}{c} **Mann–** \\ **Whitney 2** \\ \end{tabular} & \begin{tabular}{c} **White Fish** \\ **River** \\ \end{tabular} & \begin{tabular}{c} **White Fish** \\ **Sea** \\ \end{tabular} & \begin{tabular}{c} **Blue Fish** \\ **Sea** \\ \end{tabular} & \begin{tabular}{c} **Canned** \\ **Tuna** \\ \end{tabular} & \
\begin{tabular}{c} **Canned** \\ **Sardines** \\ \end{tabular} &
\begin{tabular}{c} **Median** \\ **Test 3** \\ \end{tabular} \\ \hline  & n = 113 & n = 33 & \(p\) & n = 4 & n = 6 & n = 8 & n = 11 & n = 2 & \(p\) \\ \hline As & 47.7 & 107.3 & **0.009** & 45.7 & 189.9 & 106.0 & 103.5 & 124.3 & **0.025** \\ Ba & 1.774 & 2.239 & 0.400 & 4.132 & 1.846 & 1.399 & 2.543 & 0.809 & 0.218 \\ Be & 0.015 & 0.025 & 0.547 & 0.035 & 0.020 & 0.069 & 0.001 & 0.018 & 0.257 \\ Cd & 0.190 & 0.122 & 0.158 & 0.177 & 0.114 & 0.078 & 0.021 & 0.074 & 0.453 \\ Co & 0.292 & 0.463 & **0.037** & 0.139 & 0.543 & 0.534 & 0.375 & 1.151 & 0.206 \\ Cu & 7.293 & 8.506 & 0.392 & 8.037 & 7.640 & 6.963 & 9.247 & 8.749 & 0.453 \\ Hg & 6.030 & 7.475 & 0.232 & 4.911 & 2.413 & 11.865 & 6.332 & 19.125 & **0.028** \\ Li & 14.0 & 16.0 & 0.719 & 16.8 & 18.6 & 10.8 & 18.3 & 18.2 & 0.078 \\ Mo & 33.8 & 28.4 & 0.320 & 29.1 & 36.5 & 32.7 & 27.2 & 17.3 & 0.148 \\ Pb & 2.570 & 3.630 & 0.100 & 11.260 & 2.286 & 4.416 & 3.283 & 18.730 & 0.103 \\ Se & 42.2 & 47.4 & 0.311 & 63.1 & 60.8 & 53.7 & 38.0 & 46.9 & 0.391 \\ Sr & 152.1 & 154.2 & 0.724 & 112.7 & 232.5 & 145.8 & 137.0 & 143.0 & 0.584 \\ Tl & 0.046 & 0.071 & 0.619 & 0.064 & 0.062 & 0.069 & 0.122 & 0.117 & 0.359 \\ V & 0.281 & 0.175 & 0.118 & 0.155 & 0.179 & 0.170 & 0.175 & 0.125 & 0.453 \\ W & 0.204 & 0.187 & 0.898 & 0.218 & 0.129 & 0.191 & 0.187 & 0.176 & 0.703 \\ Zn & 416.7 & 409.2 & 0.465 & 537.3 & 368.2 & 523.8 & 283.8 & 409.5 & 0.970 \\ \hline \hline \end{tabular}  
* No fish consumption in the 8 h prior to donating the urine sample. 2 No fish consumption vs. any type of fish consumption. 3 No fish consumption vs. type of fish consumption.  
\end{table} 
Table 2: Comparison of the median metal levels (ppb) in urine samples according to fish consumption in the 8 h prior to sample donation. Statistically significant results are highlighted in bold. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **No SMFc 1** & **Any Type** & \begin{tabular}{c} **Mann–** \\ **Whitney 2** \\ \end{tabular}  & \begin{tabular}{c} **Shell** \\ **Mollusks** \\ \end{tabular}  & \begin{tabular}{c} **No-shell** \\ **Mollusks** \\ \end{tabular}  & \ \begin{tabular}{c} **Shrimps,** \\ **Prawn, etc.** \\ \end{tabular}  & \begin{tabular}{c} **Median Test 3** \\ \end{tabular}  \\ \hline  & n = 131 & n = 16 & \(p\) & n = 1 & n = 5 & n = 8 & \(p\) \\ \hline As & 55.1 & 112.2 & **0.009** & 274.6 & 72.3 & 102.8 & 0.144 \\ Ba & 1.837 & 2.762 & 0.675 & 5.034 & 2.458 & 1.425 & 0.637 \\ Be & 0.020 & 0.003 & 0.205 & 0.035 & 0.006 & 0.009 & 0.637 \\ Cd & 0.183 & 0.106 & 0.4
# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 3 Results
### Sentinel-1 and AIS Datasets, Benchmarking Methods
Due to the dense maritime traffic and a large number of AIS terrestrial stations located in this area, the number of AIS positions per ship in a time range of 30 min was sufficiently high to find out if there was an AIS position for each SAR detected target. The second test case aimed at analyzing the maritime traffic in open sea areas located in the vicinity of the Pacific Mexican coastline. The location of the Sentinel-1 images used for this case study isgiven in Figure 2b. Due to a relatively sparse maritime traffic and a lack of AIS terrestrial stations, the corresponding AIS data were extracted within a time interval of two hours (X = 60 min). For both datasets, more than one Sentinel-1 image acquired at a different time can can correspond to a single SAR frame illustrated in Figure 2.  
The experimental results were carried out using a computer with a processor i7-6800K CPU of 3.40 GHz and a memory of 64 GB. In order to process a Sentinel-1 IW image, acquired over the English Channel and containing about 200 targets, the detection algorithm took about 7 minutes for the CFAR detector that handled a GRD image of 24,720 \(\times\) 12,599 (range \(\times\) azimuth) pixels and about 10 minutes for the proposed detector computing the un-normalized coherence considering a SLC image of 69,009 \(\times\) 13,122 (range \(\times\) azimuth) pixels.
## 2 Engineering Overview
### Analysis of Disturbance Mechanism of Transferring Pressure Across Goaf Effect to Gob-Side Roadway  
The lateral roof fracture in the goaf upon the completion of the mining of the strip working face 1513 is shown in Figure 3a; meanwhile, the position of gob-side roadway driving with the small coal pillar is usually located in a low-value stress area below block A and block B [17; 18; 19]. During the driving of the BHR 1513, the disturbance to its overlying strata will not affect the stability of block A and B. At this time, no changes are observed in the deformation and mechanical characteristic of block A and B, which bear the weight of the collapsed strata of the overlying strata to shield the roadway. During driving, the deformation of the surrounding rock is mainly caused by the stress concentration of the broken coal rock mass within the lateral low-value stress zone in the goaf due to the driving disturbance.  
Before mining the working face 1511, only KS1 in the roof overlying strata of the strip goaf 1513 breaks and collapses, while high KS do not break. Therefore, block A and B only need to bear the weight of the strata below the unbroken KS2. The rotation deflection of block A and B at this stage are relatively small as it is related to the weight of the strata that it bears (Figure 3a). After the completion of the mining of the working face 1511, the two goafs connect, causing the high KS in the roof of the overlying strata of the strip goaf 1513 to break. At this time, block A and B further rotate and subside towards the strip goaf 1513 due to increased load-bearing. Due to the rotation and subsidence of block A and B, the roof of the roadway further tilts towards the goaf, and the coal pillar sidewall squeezes into the roadway space (Figure 3b).  
Figure 3: Fracture and rotation orientation of main roof of gob-side roadway: (**a**) before mining of working face 1507 and (**b**) after mining of working face 1507.
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 3 Experiments and Results
### The Quality of the PPP-B2b Corrections Degradation over Time  
#### 3.2.1 Accuracy of the PPP-B2b Corrections  
In this study, the GBM final products provided by the GFZ during DOY 168-198 in 2022 were used to evaluate the performance of the PPP-B2b satellite orbit and clock offsets [22]. Figure 7 shows the RMSE of the BDS-3 and GPS satellite orbits recovered with the PPP-B2b signals with respect to GBM products. For the BDS-3 MEO satellites, the mean RMSE values were 0.14 m, 0.22 m, and 0.20 m in the radial, along-, and cross-track directions, respectively; for the BDS-3 IGSO satellites, the mean RMSE values were 0.15 m, 0.28 m, and 0.23 m in the radial, along-, and cross-track directions, respectively; for the GPS satellites, the mean RMSE values were 0.06 m, 0.25 m, and 0.23 m in the radial, along-, and cross-track directions, respectively. Generally, the PPP-B2b orbit had a much better accuracy in the radial direction than in the along- and cross-track directions. In addition, the orbit accuracy of the BDS-3 IGSO satellites was slightly worse than that of the BDS-3 MEO and GPS satellites.  
Figure 6: Re-edit of the time series of the clock corrections of the BDS-3 and the GPS satellites on DOY 192 in 2022.  
Figures 8 and 9 show the RMSE and STD of clock offsets of the BDS-3 and GPS satellites. For the BDS-3 MEO satellites, the mean RMSE and STD values were 1.67 ns and 0.09 ns; for the BDS-3 IGSO satellites, the mean RMSE and STD values were 1.85 ns and 0.11 ns, which are slightly worse than those of the BDS-3 MEO satellites; for the GPS satellites, the mean RMSE and STD values were 3.67 ns and 0.08 ns. The larger RMSE values associated with the GPS satellite clock corrections may come from the incompleteness of the network distribution used to generate the corrections [18].  
Figure 8: RMSE of the BDS-3 and GPS satellite clock offset recovered with the PPP-B2b signals using the GBM orbit products as a reference during DOY 168–198 in 2022.  
Figure 7: RMSE of the BDS-3 and GPS satellite orbits recovered with the PPP-B2b signals in the radial, along-, and cross-track directions using the GBM orbit products as a reference during DOY 168–198 in 2022.
## 3 Robot Calibration Principle  
### TCF Calibration  
The calibration of the robot tool coordinate system is directly related to the positioning of the tool pose, which can affect the accuracy of the robot machining or assembly parts. The function of TCF calibration is teaching the robot to the top of the calibration rod and obtaining the pose transformation matrix from the tool coordinate system to the end of the robot body by the known tool coordinate system Euler angle, position and robot joint angles, and other information.  
As shown in Figure 2, defining the tool coordinate system of the robot as \(\{T\}\), the body flange end coordinate system as \(\{E\}\), and the base coordinate system as \(\{B\}\). According to the coordinate system conversion relationship:  
\[H_{E}^{T}=\left(H_{B}^{E}\right)^{-1}H_{B}^{T} \tag{1}\]  
Figure 1: Schematic diagram of dual-robot collaboration.  
Figure 2: Layout of TCF calibration coordinate system.  
In Formula (1), \(H_{B}^{T}\) is the transformation matrix of the tool coordinate system \(\{T\}\) relative to the robot base coordinate system \(\{B\}\), \(H_{B}^{E}\) is the transformation matrix of the robot flange end coordinate system \(\{E\}\) relative to the base coordinate system \(\{B\}\), and \(H_{E}^{T}\) is the transformation matrix of the robot tool coordinate system \(\{T\}\) to be obtained relative to the flange end coordinate system \(\{E\}\).  
When the teaching robot is at the top of the calibration rod, \(H_{E}^{T}\) can be calculated from the positive kinematics by changing the angle of each joint. By reading and recording Euler angles \(\alpha\), \(\beta\), \(\gamma\) and their positions \(p_{x}\), \(p_{y}\), \(p_{z}\) from the teach pendant, the transformation matrix of the tool coordinate system \(\{T\}\) relative to the robot base coordinate system \(\{B\}\) can be expressed as:  
\[H_{B}^{T}=Rot_{z}(\alpha)Rot_{y}(\beta)Rot_{x}(\gamma)Trans\big{(}p_{x},p_{y},p_{z}\big{)}=\begin{bmatrix}n_{11}&n_{12}&n_{13}&p_{x}\\ n_{21}&n_{22}&n_{23}&p_{y}\\ n_{31}&n_{32}&n_{33}&p_{z}\\ 0&0&0&1\end{bmatrix} \tag{2}\]  
In Formula (2), \(n_{11}=\cos\alpha\cos\beta\), \(n_{12}=\cos\alpha\sin\beta\sin\gamma-\sin\alpha\cos\beta\), \(n_{13}=\cos\alpha\sin\beta\cos\gamma+\sin\alpha\sin\gamma\), \(n_{21}=\sin\alpha\cos\beta\), \(n_{22}=\cos\alpha\sin\beta\sin\gamma+\cos\alpha\cos\gamma\), \(n_{23}=\sin\alpha\sin\beta\cos\gamma-\cos\alpha\sin\gamma\), \(n_{31}=-\sin\beta\), \(n_{32}=\cos\beta\sin\gamma\), \(n_{33}=\cos\beta\cos\gamma\).
## 4 _P. gingivalis_ and Systemic Diseases
### Other Diseases
#### 4.5.1 Oncology  
The oral cavity hosts a diverse microbial community that lives in a symbiotic relationship with each other and with the host immune system. Dysbiotic oral microflora subverts host defense mechanisms, resulting in chronic periodontal disease and chronic bacterial infection that may be a promoter or cause of oral cancer [77]. Growing evidence from epidemiological and genetic studies suggests a significant link between oral pathogens and certain cancers, including oral, digestive tract, esophageal, pancreatic, and colorectal cancers. The chronic inflammation caused by oral pathogens plays a critical role in the development and progression of these malignancies [78]. [PERSON] et al. conducted a comprehensive review evaluating the correlation between chronic oral inflammation and the development of oral cancers (OCs). The microbial species _P. gingivalis_ has been implicated in periodontal disease (PD), which may act as an etiological factor in the pathogenesis of some cancers. A multicenter study involving 405 pancreatic cancer patients and 416 controls found that _anti-P. gingivalis_ antibody levels were significantly higher in pancreatic cancer patients compared to healthy controls [79].  
However, a recent study demonstrated that _P. gingivalis_ is closely related to oral squamous cell carcinoma (OSCC), and outer membrane vesicles (OMVs) are the main pathogen-associated factor [80]. For instance, _P. gingivalis_ has been detected in 61% of esophageal cancer tissues, compared to 12% in adjacent non-cancerous tissues and 0% in normal esophageal mucosa [81]. Similarly, periodontal disease pathogens have also been implicated in colorectal cancer by inducing excessive immune responses and activating cancer growth genes. The bacteria preferentially accumulate in adenomas--benign bowel growths that can progress to cancer--and thrive in the nutrient-rich microenvironment of colonic lesions due to their asaccharide metabolism [82]. This suggests that the oral microbiota, particularly in individuals with periodontal disease, could serve as a potential biomarker for various human diseases. In addition, a significant association between head and neck squamous cell carcinoma and periodontal disease has been reported, supporting the hypothesis that periodontal disease may be an independent risk factor. These findings have important implications for the prevention, early diagnosis, and treatment of these tumors, with the potential to improve patient outcomes. Advances in understanding host-microbe interactions and underlying cause-effect mechanisms have expanded our knowledge of the role of microbiome in both health and disease, opening new therapeutic avenues for clinical practice [83].
## 5 Results
### Testing and Validation
In fact, whenever we consider PEG, we can cite [37; 58] for a PH of one hour. On one hand, the study by [PERSON] [37] considered nine real individuals and obtained a PEG of 87.22% falling in zones A + B, while the study by [PERSON] et al. [58] considered 21 real individuals and obtained a Clarke error grid (CEG) of 94% falling in zones A and B. It is worth noting that both PEG and CEG are comparable error grids [59]. Tesseratus outperformed both for the same PH, achieving a PEG of 97.9% falling in zones A and B for daytime, and 98.9% at night-time. Whenever we consider MAE as the error, we can cite [60], who obtained an MAE equal to 51.3 mg/dL for a PH of 60 min, whereas Tesseratus reached 26.41 mg/dL, both at daytime. It is worth noting that the smaller the MAE, the better the values. Finally, if the considered error is MAPE, [PERSON] et al., 2019 [58] presented interesting results for a PH ranging from 30 to 360 min. A comparison with Tesseratus is presented in Table 11 considering night-time PH. Observe that Tesseratus outperforms them for a PH of 120, 180 and 360 min, the last one being the highest PH that they achieve. Nevertheless, they outperform Tesseratus for short PHs such as 30 and 60 min. We advocate that this is not an issue, since the need for long-term prediction is paramount for night-time. In addition to the results achieved, it is worth mentioning the main contributions of the Tesseratus model in a summarized way: (1) a combination of different techniques in the same model working in an orchestrated way, from mathematics to ML (supervised and reinforcement learning), represented by agents; (2) continuous learning for applicability in real individuals with T1D; (3) delegating the task of a continuous self-adjustment process about prediction errors to the agents; (4) Tesseratus works independently of sex and age; (5) it was tested with real individuals; (6) applicability in technology-based healthcare; (7) prediction of glucose oscillation of up to eight hours, depending on the individual's lifestyle, with an acceptable absolute error; (8) support for personalized recommendations on macronutrients, insulin and physical exercise, based on ML models, and not just fixed rules. All of the aforementioned contributions indicate the feasibility of Tesseratus to be embedded as the underlying model to: an open source artificial pancreas system [61]; a commercial artificial pancreas (closed-loop) [62]; insulin pumps [63; 64]; and a recommendation system, to cite a few. It is our claim that it must facilitate the daily life of T1D individuals, automating most of the individual T1D's 180 extra daily health-related decisions [65].