esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty-Aware Time Series Anomaly Detection
## 3 Proposed Approach
### Uncertainty Calculation  
In this work, we quantify aleatoric and epistemic uncertainty separately. Combining them results in predictive uncertainty. Definition 3 defines this formally.  
**Definition 3** (Predictive uncertainty).: _The uncertainty resulting from inherent data uncertainty and model uncertainty is defined as predictive uncertainty:_  
\[\mathcal{U}^{predictive}=\mathcal{U}^{aleatoric}+\mathcal{U}^{ epistemic}\]  
_with_  
* \(\mathcal{U}^{aleatoric}\) _as uncertainty in data and_
* \(\mathcal{U}^{epistemic}\) _as model uncertainty_  
Aleatoric uncertainty is learned by the network and serves as additional network output. To additionally quantify epistemic uncertainty we use MC Dropout. Sampling multiple times from the posterior distribution results in a set \(\{\hat{x}_{t},\hat{\sigma}_{t}^{2}\}_{t=1}^{T}\) of \(T\) sampled outputs.  
From that, we can derive epistemic uncertainty by calculating the variance over predictions:  
\[\hat{x}=\frac{1}{T}\sum_{t=1}^{T}\hat{\alpha}_{t}^{2}-\left(\frac{1}{T}\sum_{ t=1}^{T}\hat{x}_{t}\right)^{2}. \tag{3}\]  
Calculating the mean of uncertainties over all MC samples gives the final aleatoric uncertainty:  
\[\hat{\sigma}^{2}=\frac{1}{T}\sum_{t=1}^{T}\hat{\sigma}_{t}^{2}. \tag{4}\]  
Combined, we can summarize, with Equations (3) and (4) the predictive uncertainty for a given input vector \(x\) as follows:  
\[\underbrace{Var(x)}_{\text{predictive}}\approx\underbrace{\hat{x}}_{\text{ epistemic}}+\underbrace{\hat{\sigma}^{2}}_{\text{aleatoric}}. \tag{5}\]  
Algorithm 1 summarizes the steps from input to anomaly and uncertainty metric calculation in pseudocode.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 7 Conclusions  
The security of video conference systems is very important, and the content of the conference often involves military intelligence, commercial secrets, state secrets, and personal privacy. To solve the problem of communication data security, this paper designed a hybrid encryption scheme for quantum secure video conferencing combined with blockchain, making full use of the characteristics and advantages of the quantum key, integrating the quantum key distribution network with the classical network and using classical and quantum double-encryption schemes. At the same time, two quantum encryption schemes of \"one-time pad\" and AES were adopted to solve the problem of the low key rate of quantum keys. The system implements the QKD network operation monitoring scheme, enabling users to better grasp the QKD network operation. With the advantages of blockchain technology, such as tamper-proofness, decentralization, and traceability, the ciphertext of the meeting minutes is stored in the blockchain, so that the brief content of the meeting can be reviewed. Through the actual operation of the video conference system, the results show that the system has high stability and practicability. A cache-efficient query method based on B+ tree was designed, which was 3.15-times more efficient than the original query of the blockchain. In the future work, we plan to design a high-efficiency quantum key expansion algorithm. Since the video conference uses about 62.74 KB keys per second, the quantum key is expanded to 63 times, which can satisfy the requirements of encrypting and decrypting video conference data.  
Conceptualization, J.Z. and L.S.; methodology, J.Z. and D.Z.; software, J.W. and H.Z.; validation, J.Z. and L.S.; writing--original draft preparation, D.Z.; writing--review and editing, J.Z. and D.Z.; supervision, N.L. All authors have read and agreed to the published version of the manuscript.  
This work is funded by Jilin Development and Reform Commission Construction Fund (2020C020-2), the Education Department of Jilin Province with Grants No. JJKH20200577 KJ, the Project Plan of Science and Technology Development Center of the Ministry of Education (No. 2020 ITA07031) and the Jilin Science and Technology Development Plan Project (Project No. 20210201083 CX).  
Not applicable.  
The authors declare no conflict of interest
# A Doppler Frequency-Offset Estimation Method Based on the Beam Pointing of LEO Satellites
## 4 Simulation Analysis
The simulation results are shown in Figure 8. The results indicate, based on the DFS-BP algorithm, that the satellite-to-ground frequency-offset estimation errors at different satellite orbital inclinations fall within the range of [\(-\)240 Hz, +240 Hz], and the RMSE of frequency-offset estimation errors fall within the range of [50 Hz, 150 Hz]. Figure 8: Simulation results of the RMSE of frequency-offset estimations for different orbital inclinations. (**a**) The Frequency shift error; (**b**) The Root Mean Squared Error. Figure 7: Simulation results of the RMSE of frequency-offset estimations for different orbital altitudes. (**a**) The Frequency shift error; (**b**) The Root Mean Squared Error. Based on the RMSE results of the Doppler frequency-offset estimation errors for different orbital altitudes and orbital inclinations, the 95% CI analysis of the satellite-to-ground Doppler frequency-offset estimation errors of the DFS-BP algorithm is conducted. The RMSE results of the Doppler frequency-offset estimation errors for different orbital altitudes are shown in Table 3. The RMSE results of the Doppler frequency-offset estimation errors for different orbital inclinations are shown in Table 4.  
Due to the small sample size for calculating the 95% CI, this paper selects the t-distribution model for confidence interval calculations. The calculated 95% CI of the Doppler frequency-offset estimation errors falls within the range of [80 Hz, 112 Hz].
###### Abstract  
A hydraulic engine mount (HEM) is an advanced tuned mass damper (TMD) system used to isolate the noise and vibration from the engine to the chassis. This paper aims to design a semiactive HEM based on the TMD model, which only tunes damping without affecting the other lumped parameters of the TMD system. Firstly, the dynamic equations of an HEM modeled as a TMD system are derived based on the linear lumped parameter model (LPM). Each lumped parameter of the HEM is analyzed to identify the relevant parameters that affect damping. Secondly, a newly designed semiactive HEM design is proposed that utilizes a helical moving plate to simultaneously control both the inertia track area and length, resulting in precise damping tuning. To illustrate the dynamic performance of the newly designed HEM, calculations are presented based on various parameters for its tunable damping range and dynamic stiffness spectrum. Additionally, to demonstrate the performance of vibration isolation, this paper determines the optimal length and dynamic stiffness of the inertia track to minimize the transmissibility. Thirdly, to reveal the superiority of the newly designed HEM over the MR fluid mount, an example is presented where the MR fluid medium is used in place of conventional hydraulic fluid in the HEM while keeping all other parameters constant. Specifically, the novel semiactive HEM employs conventional hydraulic fluid and is spiral-driven by a moving plate while the MR fluid-based HEM is controlled by an additional tunable magnetic intensity controller. The nonlinear LPM of the newly designed HEM is verified by comparing the dynamic stiffness spectrum with the experimental results in the published literature. Then, the nonlinear LPM of the MR fluid mount is established, and its dynamic stiffness spectrum is calculated and compared with that of the newly designed HEM. The results indicate that the newly designed HEM and MR fluid mount have similar ranges of dynamic stiffness control, but the newly designed HEM does not require expensive MR/ER fluid or additional continuous external energy input to regulate the dynamic stiffness. Moreover, when using inexpensive low-viscosity hydraulic fluid, the newly designed HEM can provide a wider range of dynamic stiffness control.  
hydraulic engine mount; semiactive control; magneto-rheological fluid; vibration isolation; dynamic stiffness +
Footnote †: journal: Applied Sciences
# Experimental Study on the Pre-Peak Mechanical and Seepage Characteristics of Granite
## 3 Analysis of Test Results
### Mechanical Characteristics
#### 3.1.2 Deformation Characteristics  
According to the stress threshold, the corresponding stress-strain curve can be divided into four parts: the crack closure stage I, linear elasticity stage II, stable crack expansion stage III, and unstable crack expansion stage IV, as shown in Figure 8. To better demonstrate the results, the stress-volumetric strain curve is divided into two parts, and the relevant points are interpreted in Table 3.  
The characteristics of each deformation stage are analyzed as follows.  
Crack closure stage I: As shown in Figure 8, it is found that the stress-strain curve and the stress-volumetric strain curve are concave and highly coincident during the crack closure stage, indicating that the rock volume gradually becomes dense when the confining pressures and axial pressures start to act on the rock samples, and only longitudinal cracks occur in response. The primary cracks in the rock samples are closed until the crack closure strength (\(\sigma_{cc}\)).  
Linear elastic stage II: The stress-strain curve of the rock samples shows a linear trend until the crack initiation strength (\(\sigma_{ci}\)), which reflects the internal deformation of the rock samples as elastic deformation during the gradual increase in axial stress. Observing the volumetric strain curve, the volume of the rock samples is found to be in compression, and the primary crack continues to be closed.  
Stable crack expansion III: As shown in Figure 8, when the axial stress starts to exceed the crack initiation strength (\(\sigma_{ci}\)), it is found that there is no significant change in the volumetric strain, and the crack stability development stage is short. [PERSON] [36] suggests that only secondary cracks occur parallel to the load direction change in this stage.  
Unstable crack expansion IV: This is the final stage of the progressive failure process, the unstable expansion stage of cracks. According to Figure 8, it can be found that when the stress in the rock specimen exceeds the crack damage strength, the direction of the volumetric strain is deflected from positive to negative. This indicates that the volume of the rock sample is transformed from compaction to expansion, and a large number of secondary transverse and vertical cracks are developed in the inner part of the rock sample, and the process lasts until the peak stress (\(\sigma_{p}\)).
## 2 External Load Monitoring
### Repetition Method and Volume Load  
The repetition method is a simple tool for measuring the total number of repetitions and volume (i.e., when combined with the number of sets) performed in an exercise, session, week, or training cycle. Due to its simplicity, absolute volume load is often more suitable for measuring external load and monitoring the balance between exercise intensity and volume in resistance training [36]. Volume load is a method for quantifying external load by calculating the product of load and volume during resistance training (load in kg \(\times\) number of sets \(\times\) number of repetitions). This method allows for the determination of the exercise volume load, and the sum of the volume loads of the exercises in a session quantifies the total volume load [36; 37].  
While absolute volume load is widely used, it has some limitations, particularly regarding differences in movement force-velocity profile and endurance between individuals with varying levels of strength. A more effective way to compare volume load between two individuals is a calculation using the number of sets \(\times\) number of repetitions \(\times\) % 1 RM (in arbitrary units), which is better for comparing volume load between two individuals [37].  
Lastly, a recommended strategy for determining the intensity of a session or exercise in resistance training is to divide the volume load (session or exercise) by the number of repetitions performed (session or exercise), providing the average load (kg) lifted per repetition [37]. In addition, movement velocity is another variable of exercise intensity, which, in combination with the load lifted, can be useful for monitoring the level of exertion when planning training to develop muscle power (i.e., the ability to increase work per time) [34]. In practical terms, velocity is indeed a costly variable to be measured accurately [35], and power measurements are restricted to laboratory conditions. While this information can provide valuable insights for external load analysis, there is still not easy applicability to real-world training management [7].
## 7 Performance Evaluation
### Simulation Results
#### 7.2.1 Impact of Sink Number  
As many users may simultaneously access the sensor network, it is important to consider the impact of the sink number. For the sensor network area of 2,000 m\(\times\)2,000 m, we set the number of sink varying from 1 to 8. Each sink moves with the maximum speed 10 m/s and a 5-second pause time. The number of total nodes and the number of sources are not changed.  
Figure 9 shows total energy consumption of SCODE as the number of sinks varies from 1 to 8. It demonstrates that SCODE_plus_ is more energy efficient than SCODE, TTDD, and DD. This is because of three reasons. First, SCODE_plus_ and SCODE are based on a coordination network, so that nodes in each cell negotiate among themselves to turn off its radio to significantly reduce energy consumption. Meanwhile, TTDD and DD must turn on all nodes to participate in routing.  
\begin{table}
\begin{tabular}{l l} \hline
**Simulation Parameters** & **Value** \\ \hline \(N\) (total nodes) & 400 nodes \\ \(A\) (network size) & 2,000 m \(\times\) 2,000 m \\ Transmission & 0.66W \\ Reception & 0.359W \\ Idle & 0.035W \\ RC5 encryption of 64 bits & 0.26 ms \\ Pseudorandom number generation & 0.26 ms \\
4-byte MAC generation & 0.13 ms \\ \(R\) & 250 m \\ Data packet size & 64 bytes \\ Other packet size & 32 bytes \\ Number of mobile sinks & 8 \\ Default speed of mobile sinks & 10 m/s \\ Data generation interval & 1 second \\ \hline \end{tabular}
\end{table}
Table 3: Simulation parameters and defined values.  
Second, SCODE_plus_ and SCODE optimize a number of transmission hops between sources and sinks that is based on the cell size to maximize the communication distance between two adjacent cells. Third, SCODEplus is based on the hexagonal network topology. Compared with square-grid used in SCODE, the hexagon decreases the number of cells, reduces transmission hops, and increases the number of sleeping nodes to save energy. Also, using _Gaussian_ network deployment can reduce the number of void cells, thus reduce the number of round paths compared with SCODE.  
Figure 10 plots the average end-to-end delay of SCODE. The figure shows that the delay of SCODE_plus_ is much smaller than SCODE. Figure 11 shows that the success rate of SCODE_plus_ is always above 97 percent. It means that SCODE_plus_ delivers most of packets successfully.  
Figure 9: Energy consumption vs. sink number.  
Figure 10: Average delay vs. sink number.
# A Weighted Histogram-Based Tone Mapping Algorithm for CT Images
## 3 Theory
### Contrast Limit  
Contrast-limited adaptive histogram equalization (CLAHE) [7] introduced the idea of clipping histogram peaks and redistributing the clipped area to all histogram bins, as is depicted in Figure 3.  
In our approach, a radially decreasing weight function yields good locality and avoids halos due to the large weighting kernel, but it still may over-stretch contrast between similar shades. Limiting contrast stretching effectively mitigates this issue, and avoids over-amplification of noise.
# Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
## 4 Simulation and Evaluation
### Safety Analysis
#### 4.4.2 Resilience
Due to the randomness of the large integer \(R\), even if the private key is decrypted, the session key established before will not be revealed. 3. The analysis of dynamic security of the whole network: when the MS completes a traversal round, the base station will start a new round of clustering and the topology structure of the network will be also changed as well. At the same time, the session keys between cluster heads and the MS, and the session keys between cluster heads and the member nodes will all be re-established, which can realize the keys' instantaneous updating in each round of clustering to prevent the pseudo nodes from capturing the information in the data link by long-term detection. 4. The analysis of performance of resistance to possible attacks: the simulation results show that compared to the E-G scheme, AP scheme and q-composite scheme, the proposed scheme has higher connectivity and less storage overhead with the same number of keys stored in each node. When there are external attacks and the malicious nodes try to capture the keys, due to the higher connectivity, any data transmission anomalies are more likely to be detected by the network, and the attacks could thus be identified. In addition, the capture probability of the keys will be lower due to the higher storage efficiency of the keys. On the other hand, the proposed scheme realizes the random dynamic key management by dynamic clustering and optimal route planning of mobile sinks, which can greatly reduce the capture possibility of the keys between the MS and cluster heads and the keys between the cluster heads and the member nodes within the cluster through regular listening by the malicious nodes. Apparently, the proposed scheme could have better resistance to possible attacks, and ensure the network security.
## 4 Influence of the System Parameters
### MED (or RLRT) and ED  
We now turn our attention to MED (or RLRT) and ED. In both cases the noise variance is assumed to be known. Figure 5 shows the ROC curves for both detection strategies, again adopting the parameters in Table 1, but only for \(n=50\). Although severely degraded in performance, MED still works, unveiling a behavior similar to GLRT and MMED (see Figures 3 and 4) concerning the way it is influenced by the variations of the system parameters. Again one can notice the small susceptibility of an eigenvalue-based detection to the variations on the temporal signal correlation, which are produced in the _R-model_ by varying \(L\), the impulse response length of the MA filters. The situation for ED in Figure 5, however, is quite dramatic: it produces useless values of \(P_{\rm fa}=P_{\rm d}\) for the whole range of variations of the decision  
Figure 4: ROC curves for MMED (or ERD) under parameter variations.  
threshold and system parameters. This poor performance of ED remains unchanged for \(n\) as large as 500. An explanation for this can be found on the presence of the AGC at the receiver, which makes the received sampled noise and signal power vary dynamically. This should be taken into account when the detection thresholds are set or, equivalently, when the decision statistic is computed. This was not considered in this paper.
# Clustering of Heart Failure Phenotypes in Johannesburg Using Unsupervised Machine Learning
## 3 Results
### Cluster Analysis on a Dataset without the Left Ventricular Ejection Fraction  
Using a dataset without the LVEF, the algorithm that achieved the highest silhouette score of 0.37 was the Gaussian mixture model with seven clusters (Table 6). The most distinct clinical findings were venous hypertension in cluster 1, with 100% of patients in the cluster hospitalised with raised jugular venous pressure.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Clinical Parameters**} & **All** & \multirow{2}{*}{**Cluster 1 (\(n\) = 38)**} & **Cluster 2 (\(n\) = 56)** & **Cluster 3 (\(n\) = 118)** & **Cluster 4 (\(n\) = 134)** & **Cluster 5 (\(n\) = 49)** & **Cluster 6 (\(n\) = 123)** & **Cluster 7 (\(n\) = 118)** & \multirow{2}{*}{**\(p\)-Value**} \\  & & & & & & & & & \\ \hline Age (years), mean & 55.6 \(\pm\) 16.9 & 53.3 \(\pm\) 19.4 & 50.9 \(\pm\) 16.3 & 53.7 \(\pm\) 17.6 & 57.3 \(\pm\) 16.9 & 58.0 \(\pm\) 13.9 & 56.0 \(\pm\) 15.7 & 56.9 \(\pm\) 17.4 & 0.0283 \\ Male sex, n (\%) & 352 (55.3) & 15 (39.5) & 30 (53.6) & 69 (58.5) & 74 (55.2) & 32 (65.3) & 70 (56.9) & 62 (52.5) & 0.3323 \\ Ethnicity, n (\%) & & & & & & & & \\ African & 380 (59.7) & 27 (71.1) & 33 (58.9) & 69 (58.5) & 82 (61.2) & 30 (61.2) & 75 (61.0) & 64 (54.2) & 0.6979 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Baseline demographic and clinical characteristics of the study patients stratified according to clusters identified by the Gaussian mixture model using the dataset without the left ventricular ejection parameter.  
Figure 4: Kaplan–Meier survival curves for the three clusters of heart failure patients. The \(x\)-axis shows the duration of hospitalisation in days, and the survival rate is on the \(y\)-axis. Patients in cluster 3 (congested cluster) did not survive beyond 42 days, while the survival rate in cluster 1 and 2 is comparable.  
Cluster 6 (hyponatraemic cluster) had the lowest mean serum sodium of 138 \(\pm\) 5.8 (confidence interval: 137-138) mmol/L, while cluster 7 (hyperuaremic cluster) had 118 patients and the highest mean urea of 16.8 \(\pm\) 39.8 mmol/L. Clinical parameters that had a significant impact on model output included systolic blood pressure (mean SHAP value = 0.27), diastolic blood pressure (mean SHAP value = 0.16), and age (mean SHAP value = 0.14). Serum sodium levels were the only clinical parameter that showed statistical significance on the post hoc analysis, confirming low sodium levels in cluster 6.