esa-sceva/satcom-chunk-collection huggingface dataset:
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 2 Related Work
### Concept Drift Detection Methods Based on Traditional Machine Learning Approaches  
Concept drift detection based on traditional machine learning models can be divided into three categories [19]: error rate-based concept drift detection methods, data distribution-based concept drift detection methods, and hypothesis testing-based concept drift detection methods.  
The drift detection method (DDM) is a representative approach for concept drift detection based on error rates. It determines a threshold by calculating the minimum error rate and standard deviation during the training process of the data stream. The current error rate and standard deviation are then compared with this threshold to assess whether a drift condition has occurred. While DDM demonstrates good generalization ability, it is less effective in detecting gradual concept drift. The early drift detection method (EDDM) [20] improves upon DDM's drift detection criteria, enhancing the detector's sensitivity to gradual drift, thereby making it more suitable for detecting gradual changes. The drift detection method based on [PERSON]'s inequality with the \(\mathcal{A}\)-test (HDDM_A) [21] is an extension of DDM that incorporates [PERSON]'s inequality, while the \(\mathcal{W}\)-test on HDDM (HDDM_W) [21] applies [PERSON]'s bounds. The ADWIN algorithm, a notable representative of concept drift detection using a sliding window, adaptively partitions two windows according to specific rules and computes the difference in certain statistical values within these windows. If the difference exceeds a predefined threshold, drift is detected. Compared to other methods, ADWIN demonstrates superior robustness and does not require prior knowledge of the data.  
The essence of concept drift lies in changes in the data distribution. Data distribution-based concept drift detection methods focus on measuring these changes, with variations in research methodologies primarily driven by the choice of different distance metrics. KSWIN [22] combines a sliding window approach with the Kolmogorov-Smirnov (K-S) test to detect drift by assessing the distribution differences between two windows. The concept drift detection method (CDDD) [23] introduces an innovative approach by collecting and storing data feature information either from previous drift detections or from the beginning of the data stream.  
Hypothesis testing-based concept drift detection methods leverage various hypothesis testing techniques to identify concept drift. [PERSON] et al. [24] introduced the Fisher Exact Test for concept drift detection, while [PERSON] et al. [25] employed the Kolmogorov-Smirnov (K-S) test for the same purpose.
# Preliminary Validation of Surface Reflections from Fengyun-3C Radio Occultation Data
## 3 Methods and Results
### Comparison of FY-3C Refractivity Profiles with COSMIC
Below 5 km, the relative biases of the refractivity profiles are within 1%, and the relative standard deviations are less than 2%. Due to reflection and atmospheric multipath below 2 km, the statistical differences increase significantly and show negative trends. Compared to the former comparisons (Figure 3a), the comparisons between FY-3C and COSMIC without reflections (Figure 3b) deviated more widely, from 0 km to 5 km. It is highly remarkable that the comparisons below 2 km show \(-\)3% mean bias and 5% standard deviation, which may be attributed to the different quality control method at each stage of the retrieval processes at a lower height between FY-3C and COSMIC. In practice, the GNSS-RO signals do not often reach the surface, there is no reflection on the surface and the cutting height in FY-3C and COSMIC is different, which might result in such wide deviations for the comparisons below 2 km. Figure 2: Monthly number of COSMIC occultations with reflection, no-reflection, FY-3C occultations and matched occultations from January to December 2015. From other previous studies, COSMIC occultation refractivity profiles with reflections in lower altitude have better performance with respect to ECMWF post-analyzed data than the refractivity profiles without reflections [36]. In their opinion, the reflected signals are derived from clear atmospheric conditions and can make better occultation signals to generate quality profiles at lower altitudes. Without considering the influence of surface reflections, [PERSON] et al. [33] identified a slight negative bias between FY-3C and ECMWF comparisons below 2 km; while, from 0 km to 35 km, FY-3C shows high consistency with respect to COSMIC, and the bias of raw bending angles between FY-3C and COSMIC was elaborated by a different retrieval data-processing algorithm and observational noise. Figure 3: (**a**) Refractivity deviation from matched profiles of FY-3C and COSMIC events with presence of reflected signals. (**b**) Refractivity deviation from matched profiles of FY-3C and COSMIC events without presence of reflected signals. Each panel from left to right and from top to bottom shows the mean bias (blue) and standard deviation (red) of one selected month. Based on our comparison results, the refractivity profiles of FY-3C and COSMIC have small systematic differences in a lower atmosphere when COSMIC events present reflected signals. The mean relative difference of the matched profiles with presence of reflected signals below 2 km is smaller than 0.5% (Figure 3a), which indicated both FY-3C and COSMIC events do indeed penetrate below 2 km, since the similar atmospheric and surface conditions are fine enough to attain clean raw data and generate quality profiles. The minor bias between the matched profiles may be due to observational noise rather than atmospheric influence. Therefore, we think that the FY-3C RO profiles which matched with the reflected COSMIC profiles also have reflection features near the surface.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece
### Security Analysis  
#### 3.4.1 Biometric Security Requirements  
Our proposal satisfies the security requirements (irreversibility, revocability and unlinkability) established in the ISO/IEC 24745 standard on biometric information protection [11].  
Irreversibility of the protected templates \(cb_{i}^{lIR}\) is satisfied since Classic McEliece is based on the NP problem of decoding random linear codes, Goppa codes able to correct up to \(t\) errors are employed and the number of errors in the \(eb_{i}^{lIR}\) is larger than \(t\). Hence, the protected templates cannot be decoded either by the authentication server, which knows the private key associated to the public key, employed in the encryption.  
Revocability is ensured because a user can remove his/her protected template associated with an authentication server and later create a new one that is different from the previous one. The user can generate different protected templates from the same biometric sample \(S^{lIR}\) by changing the secret padding \(p=(p_{1},\ldots,p_{N})\). Due to the security provided by the _ENCODE_ algorithm of Classic McEliece, the protected templates generated cannot be linked.  
Unlinkability of the protected templates associated with several authentication servers, i.e., several services, is also ensured because different public keys of the authentication servers are employed. Therefore, if there was information leakage from the database server(s), it is not possible to distinguish the protected templates that are associated with the same user.
# Experimental and Simulation Identification of Xanthohumol as an Inhibitor and Substrate of ABCB1
###### Abstract  
Xanthohumol (XN) is a well-known preplated flavonoid found in _Humulus lupulus_ L. It is involved in several pharmacological activities, including the sensitization of doxorubicin-resistant breast cancer (MCF-7/ADR) cells to doxorubicin (DOX) through a reduction in cell viability and stemness. In the present study, we revealed another mechanism to further explain the reverse of the drug resistance of XN. In the MCF-7/ADR cell line, we found that XN inhibited the efflux functions of ATP-binding cassette subfamily B member 1 (ABCB1). We also observed that XN was a substrate of ABCB1 and stimulated its ATPase activity. Moreover, our results revealed that XN showed a synergic effect with the ABCB1 substrate colchicine (COL) in the MCF-7/ADR cell line. Further, we showed that XN bound to the central transmembrane domain (TMD) site, overlapping with the DOX binding site. This mechanism was supported by molecular modeling and simulation data, which revealed that XN bound to the ABCB1 transmembrane domain, where doxorubicin also binds, and its binding affinity was stronger than that of doxorubicin, resulting in less protein and ligand position fluctuation. These results support the XN-induced reversal of drug resistance via the inhibition of ABCB1-mediated transport of doxorubicin, stimulating ABCB1 ATPase activity and acting as a substrate of ABCB1.  
xanthohumol; doxorubicin resistance; synergism; ABCB1 2018 8681 doi:10.3390/app8050681
# Prediction Models for Diabetes in Children and Adolescents: A Review
## 2 Prediction Models of Type 1 Diabetes (T1D)
### Glycemic Control  
Despite the relatively short duration of T1D and fair to good glycemic control in children and adolescents, significant cardiovascular (CVD) changes can occur [40]. Compared to healthy children, patients with T1D show increased epicardial fat thickness, enlarged left atrial diameter and left ventricular mass, fractional shortening, as well as elevated triglyceride and irisin hormone levels. These findings underscore the importance of regular echocardiographic screening in T1D patients for the early detection of subclinical cardiac dysfunction. Early identification can help mitigate the risk of future complications, including morbidity and mortality. Further research is needed to better understand the role of the irisin hormone in T1D and its potential effects on cardiovascular health in this population. [PERSON] et al. [41] highlighted sex-related differences in CVD risk factors among children and adolescents with T1D. A review of the current literature reveals distinct disparities between boys and girls in their CVD risk profiles. Evidence shows that girls with T1D tend to have a more adverse CVD risk profile than boys, with greater exposure to multiple risk factors simultaneously. Girls often experience higher burdens of hyperglycemia, dyslipidemia, obesity, inflammation, physical inactivity, and poor diet compared to boys, placing them at a significantly higher risk of long-term CVD complications. By prioritizing early, tailored prevention strategies, healthcare providers can improve long-term cardiovascular outcomes in pediatric T1D populations and reduce the disproportionate burden of CVD risk among females. Hand tremor-based hypoglycemia detection and prediction in children and adolescents with T1D [42].  
In conclusion, further research is needed to enhance early detection, enable personalized treatments, and prevent severe complications in T1D among children and adolescents. Such efforts are critical for reducing morbidity and mortality and improving the quality of life for young patients.
## 3 Results and Discussion
### Stage 2: Effect of Additives in P-SiSO
It is possible that at equimolar concentration, the neutralizing effect was too high for the additive to be effective in this test. Interestingly, OPP, which was the only additive that did not improve performance at V(300 N/40-80 \({}^{\circ}\)C/60 min), performed well in two out of the three repeated tests. However, the only additive that consistently maintained low friction and wear, was PNA. As seen in Figure 9b, three repeated tests all generated low friction and \(W_{Hz}=1.15\). Images of all wear tracks can be found in the SI. Figure 9: Results from the experiments at C(300 N/80 \({}^{\circ}\)C/30 min). **(a)** Friction traces of Neat P-SiSO compared to P-SiSO with additives \(N\)-Phenyl-1-naphthylamine (PNA), _Ortho_-phenylene phosphate (OPP), and PNA + OPP. **(b)** Corresponding \(\mu\)-W chart with insets of ball wear scars. SEM images and EDS spectra of the wear track lubricated with P-SiSO + PNA added is shown in Figure 10. EDS analysis was performed on spots both in the center region and at the edge of the wear scar. In the center region of this effective boundary film, the composition was rich in the elements related to the silyl-sulfonate anion, i.e., Si, O and S. At the edge of the wear track, where the contact pressure was lower, only Si and O were found. P on the other hand, was not detected at all in this sample, and neither was N, which is an element found in PNA. Although PNA obviously affected the tribological performance in this experiment, we do not believe that it directly interacts to form the tribofilm. However, in ionic liquids, it is well-known that a change in the cation can have an effect on the ability of the anion to react and form tribofilms on the rubbed surface [13; 18]. For amines such as PNA, it remains possible that it can modulate the reactivity of the base fluid P-SiSO in a similar manner. PNA is commonly used as an antioxidant [1], and in the presence of H\({}_{2}\)O it can protolyze to become a cationic species. Tentatively, it can increase the adsorption of the P-SiSO anion through an ion exchange with the phosphonium cation. The fact that the anion seems to be the major contributor to the boundary film is also in agreement with the literature on ionic liquids [11; 12; 13]. Figure 10: Surface analysis of disc wear scar lubricated with P-SiSO + PNA at conditions V[300 N/40–80 \({}^{\circ}\)C/60 min]. (**a**) SEM image showing the tribofilm on the wear track; (**b**) wear scars are mild with peak-to-valley around 2 \(\mu\)m; and (**c**) SEM image of a representative area of tribofilm at 5000\(\times\) magnification; (**d,e**) EDS maps showing the distribution of the elements in the area shown in (**c**); (**f**) EDS-spectra comparing the composition of the tribofilm between the center and edge of the wear track.
## 3 Results  
### \(I_{E}-I_{c}^{-1}\) Curves  
The Figure 3 has been created from the data contained in Table 3 (Appendix C):  
In conclusion for Figure 3, when increasing \(n_{cont}\) there is an improvement in both \(I_{E}\) and \(I_{C}\), which is clearly smaller after every increment.  
When adding one container for the first time, payload (associated with capacity) grows by roughly 30 t. This is a 100 % growth, from 30 to 60 t. When adding one container again,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Value** & **Refs.** & **Variable** & **Value** & **Refs.** \\ \hline \(a_{1}\) (m/s\({}^{2}\)) & 14.72 & [N/A] & \(m_{tara}\) (kg) & 2180 & [24] \\ \(a_{2}\) (m/s\({}^{2}\)) & 14.72 & [N/A] & \(p_{t}\) (Pa) & 250 & [24] \\ \(C_{DEDS}\) (\(\phi\)) & \(3\times 10^{-3}\) & [24] & \(R\) (J/(kg\cdot\mathrm{K})\)) & 287 & [28] \\ \(C_{Det}\) (\(\phi\)) & 0.60 & [24] & \(R_{1}\) (\(\Gamma\)) & 8 & [24] \\ \(c_{i}\) (m/s) & 0 (const.) & [N/A] & \(T_{i}\) (\(\mathrm{\SIUnitSymbolC}\)) & 20 & [24] \\ \(D_{cont}\) (m) & 3.558 & [24] & \(\gamma\) (\(\phi\)) & 1.40 & [28] \\ \(e_{bat}\) (Wh/kg) & 225 & [29] & \(\gamma_{o}\) (\({}^{\circ}\)) & 15 & [24] \\ \(g\) (m/s\({}^{2}\)) & 9.81 (const.) & [N/A] & \(\delta_{lc}\) (m) & 0.04 & [24] \\ \(L_{cont}\) (m) & 6.058 & [24] & \(\delta_{rc}\) (m) & 0.05 & [24] \\ \(m_{carga}\) (kg) & 28,300 & [24] & \(\eta_{bat}\) (p.u.) & 0.90 & [29] \\ \(m_{EB}\) (kg) & * & [24] & \(\eta_{EDS}\) (p.u.) & 0.73 & [24] \\ \(m_{PDS}^{\prime}\) (kg/m) & 32 & [24] & \(\tau\) (\(\%\)) & 30 & [24] \\ \(m_{cs}^{\prime}\) (kg/m) & 500 & [24] & \(\varphi\) (\({}^{\circ}\)) & 30 & [24] \\ \(m_{Li^{+}}\) (kg) & * & [24] & & & \\ \hline \hline \end{tabular} For *: \(m_{Li^{+}}=350\) kg for \(n_{cont}=1\) and 50 kg is added per each additional container. \(m_{EB}=750\) kg for \(n_{cont}=1\) and 250 kg is added per each additional container, and 350 and 750 kg have been used to start the series.  
\end{table}
Table 2: Input variables with their respective units, their values, and their associated references to their right.  
payload grows by roughly 30 t with respect to the initial 60. This is a 50 % increase. The next time there is a 33 % increase (30/90) and, finally, 25% (30/120). This results in a slowing-pace of increase in \(I_{C}\) (the contrary for \(I_{C}^{-1}\)).  
Besides this, the dead weight also grows increment by increment: \(m_{Li^{+}}\) and \(m_{EB}\) grow as established in Table 13, \(m_{est}^{\prime}\) and \(m_{EDS}^{\prime}\) multiply a longer length (\(n_{cont}L_{c}\) according to Equation (A22)) and \(m_{tara}n_{cont}\) according to the same formula. This and the slowing-pace improvement in capacity explains the slowing-pace decrement in \(I_{E}\), which is mainly governed by the ratio \(m_{tot}/\left(m_{carga}\sum_{i=1}^{i=n_{cont}}f_{i}\right)\) (the difference between the numerator and denominator is the deadweight) and by losses independent from \(m_{tot}\) (chiefly \(P_{D}t_{v}\) and \(E_{bat}\)) divided by payload.  
In the end, \(n_{cont}\) is set to 5 because the improvement from 5 to 6 will be predictably tinier and over-dimensioning of the system is undesirable.
## 3 Methodology
### Performance Measures  
The following four commonly used and easily understood performance measures were used to qualitatively evaluate the performance of the ML models: root mean squared error (\(RMSE\)), coefficient of determination (\(R^{2}\)), \(PBIAS\), and \(MAE\). Their detailed expressions are as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{sim}-y_{obs})^{2}}{n}} \tag{1}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{obs}-y_{sim})^{2}}{\sum_{i=1}^{n}(y_{obs}- \overline{y}_{obs})^{2}} \tag{2}\]  
\[PBIAS=\frac{\sum_{i=1}^{n}(y_{sim}-y_{obs})}{\sum_{i=1}^{n}y_{obs}}\times 100 \tag{3}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{sim}-y_{obs}\rvert \tag{4}\]  
where \(n\) is the number of observations; \(y_{sim}\) represents the simulated GPP; \(y_{obs}\) is the target GPP; and \(\overline{y}_{obs}\) denotes the average of the target GPP. The \(R^{2}\) ranges from 0 to 1. A value of 0 indicates that the model does not explain any variability in the dependent variable, whereas a value of 1 indicates that the model explains all of the variability. The \(RMSE\) measures the standard deviation of the differences between the predicted and target variables. Lower \(RMSE\) values are preferable, signifying closer agreement between the predictions and targets. A \(PBIAS\) value of 0% indicates a perfect model fit with no bias. Positive values indicate that the model predictions are, on average, higher than the observed values (overestimation) and vice versa. The \(MAE\) provides a straightforward measure of the prediction accuracy, with lower values indicating better model performance.
## 5 Results and Discussion
### Univariate Models  
Following the reasoners' development, the LSTM model results are shown in Figure 7 and Table 7. It can be seen that the model has wrongly identified two cases of OC (label 1) as jamming faults (label 3) and one instance of jamming as OC. It is also worth noting that all instances of IOC (label 2) were correctly identified, and no false positives were generated for this type of fault. The results obtained for LSTM univariate model are shown in Table 7.  
The TSF model showed high accuracy consistently, with the average being 99.34% and not dropping below 97%. The model showcases 100% accuracy for 8 out of 10 iterations. The only misclassification during this iteration is the classification of an instance of IOC as an OC fault. Figure 8 and Table 8 show the TSF confusion matrix and univariate performance values, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Average Accuracy**} & \multicolumn{2}{c}{**85.3\%**} \\ \hline \multicolumn{2}{c}{**Average Precision**} & \multicolumn{2}{c}{**Average Recall**} & \multicolumn{2}{c}{**Average F1-Score**} \\ \hline OC & 89.5\% & 71.7\% & 79.4\% \\ IOC & 92.8\% & 100\% & 96.1\% \\ Jamming & 77.1\% & 90.0\% & 83.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: LSTM Univariate Performance.  
Figure 7: Confusion Matrix for LSTM Univariate Model.  
Lastly, the k-NN univariate model was tested ten times, and the average accuracy for this model was 72.3%, being the lowest and highest accuracy achieved 80% and 66.7%, respectively. Figure 9 and Table 9 displays the k-NN confusion matrix and performance, respectively. From the confusion matrix, one instance of OC was misclassified as jamming, two IOC as OC, and three cases of jamming as OC. It is also worth noticing that no false negatives were predicted for jamming. It can also be observed that the recall rate for jamming is very low, even though the precision rate is very acceptable.
## 2 Methods and Materials
### Variable Selection and Parameter Estimation
#### 2.4.2 Parameter Estimation  
The parameters to be estimated are obtained using either the maximum likelihood estimation (MLE) approach or the Bayesian approach. We have adopted the Bayesian approach to estimation because it captures uncertainty as compared to the MLE and also the framework upon which we are forecasting is based on Bayesian assumptions. Bayesian estimation makes use of either informative or non-informative priors. In this study, we used the informative priors which are from the kernel simulations we developed.  
Bayesian approach is more attractive than the frequentist MLE technique since it combines prior information to data, allows analysis with small samples and the forecasting is robust. The Bayesian analysis does not depend on asymptotic estimation and follows the likelihood principle that involves applying a subset of the selected data on the two selected probability models basing on whether they have the same likelihood outcome producing the same assumptions.