esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 5 Conclusions  
An empirical, data-driven approach for the determination of a calibration model with an arbitrary number and selection of wavelengths for camera-based SpO\({}_{2}\) estimation was proposed and tested. An exhaustive search yields the SpO\({}_{2}\)-dependent signature vectors corresponding to the relative amplitudes in the different wavelengths. Based on this collection of signature vectors, a calibration model is estimated for the indirect APBV method. We performed 5-fold cross validation on recordings with relatively small variations in oxygen saturation and optimized for pulse signal quality. Results show that, with this approach, the performance improves when compared to a calibration model based on opto-physiological modeling, with an average RMSE reduction of 1.84%. Furthermore, we showed that the error only slightly increases, 0.29%, using solely near-infrared (NIR) wavelengths when compared to a combination of visible red and NIR wavelengths, typically used in pulse-oximeters.  
This observation has important implications for the number of possible applications of camera pulse-oximetry, e.g., for sleep or continuous 24/7 monitoring. This scalable approach is not only helpful to fine-tune the calibration model, but even allows computation of the calibration model parameters from scratch without prior knowledge of the data acquisition details, i.e., the properties of camera and illumination.  
Conceptualization, M.v.G. and G.d.H.; software, M.v.G.; validation, M.v.G.; formal analysis, M.v.G.; investigation, M.v.G. and G.d.H.; resources, M.v.G. and W.V.; data curation, M.v.G.; writing, original draft preparation, M.v.G.; writing, review and editing, M.v.G., W.V., and G.d.H.; visualization, M.v.G.  
This research received no external funding.  
The authors would like to thank their colleagues from Philips Research and the anonymous reviewers for their valuable feedback on the manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
APBV Adaptive PBV (blood volume pulse vector)  
BPf Bandpass filter  
CWL Center wavelength  
LPF Low pass filter  
NIR Near-infrared  
RMSE Root mean squared error  
ROI Region-of-interest  
SNR Signal-to-noise ratio
# Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies
## 5 A Solution to the Problem with Bounded Regret
Although setting a large \(\alpha\) is not necessary for the asymptotic scaling, it simplifies the analysis and ensures that the exploration term dominates initially in a way that would result in uniformly logarithmic regret over time. Such constants are influenced by the intricate structure of the underlying Markov chains. In special cases, these complexities can be simplified. In the next section, we present a specific example of the index policy. The above analysis and the resulting logarithmic regret guarantees rely critically on the assumption that the state transition probabilities for each arm are precisely known. Under this assumption, the decision-maker can form accurate estimates of each arm's mean reward and state distribution over time. If these transition probabilities are even slightly uncertain, the issue becomes significantly more complex. Suppose there exists a small but fixed deviation \(\delta>0\) such that for each arm \(i\), the true transition probability \(p_{uv}^{(i)}\) satisfies \(\left|p_{uv}^{(i)}-\hat{p}_{uv}^{(i)}\right|\leq\delta\) for the available (estimated) probabilities \(\{\hat{p}_{uv}^{(i)}\}\). Although \(\delta\) can be arbitrarily small, it introduces a persistent, non-vanishing discrepancy that compounds over time and directly impacts the estimation of the arms' stationary distributions and expected rewards. To illustrate the effect of this discrepancy, consider the long-term frequency of visits to a particular state \(x\in\mathcal{X}^{(i)}\). When the transition probabilities are exact, our analysis ensures that the empirical frequency closely matches the true stationary distribution \(\phi_{x}^{(i)}\). However, with even a small error \(\delta\), let the induced perturbed stationary measure be \(\hat{p}_{x}^{(i),\delta}\). As \(n\rightarrow\infty\), the difference \(|\phi_{x}^{(i)}-\phi_{x}^{(i),\delta}|\) does not vanish, and any reward estimation relying on the exact stationary distribution becomes systematically biased. This persistent bias undermines the correctness of confidence intervals derived under the assumption of known transition probabilities. Consequently, the index computations that yield logarithmic regret bounds no longer hold, and the regret is no longer guaranteed to remain bounded by a term of order \(\ln n\). Thus, incorporating uncertainty in transition probabilities would require a fundamentally different approach, and at present, the theoretical techniques employed here do not extend to handle unknown or partially known transition probabilities without sacrificing the uniform logarithmic regret properties.
# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 3 Methodology
### Normalization by Full-Rank LDA
For instance, when computing cosine distance, the between-class distance in a particular dimension should be more de-emphasized if this dimension exhibits a large within-class variation, as the large distance mainly reflects a large within-class variation. Importantly, if the within-class variation is not whitened, the cosine distance will be biased towards directions with large within-class variance. According to this analysis, if the main impact of domain mismatch in the embedding space is global shift and within-class distributional distortion, then the full-rank LDA can provide reasonable compensation. Another key insight is that LDA normalization is not affected by the between-class covariance. The positive side is that it does not require an accurate between-class covariance, which is often difficult to estimate in particular with limited data. The negative side, however, is that the LDA normalization cannot compensate for between-class distortion. It should be careful when interpreting the statement \"LDA does not require between-class covariance\": it does not mean estimating the LDA transform does not require a between-class covariance, it just states that any estimation of the between-class covariance does not change the results with cosine scoring.
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 3 Design of Target Importance Rank Algorithm
### TIR Algorithm Design and Prioritization Method
#### 3.2.4 TIR Scoring and Prioritization  
The score elements for each cyber target importance criterion from Sections 3.2.1-3.2.3 are summarized, as shown in Figure 8. Accordingly, Equation (4) for synthesizing the target importance score is derived. Equation (4) is an algorithm for deriving the target importance score using the PageRank algorithm proposed in this study. The parameters to be used comply with the contents of Table 9, but the added parameters and changed definitions are provided in Table 10.  
The network topology is transformed into a logical structure, as shown in Figure 9, to derive the target importance priority. Then, using the TIR algorithm, the results listed in  
Figure 8: Linking factors of research cases by Target Importance Criteria.  
Figure 7: Cyber Target Exposure score calculation method.  
Table 11 are obtained. Finally, if the TIR scores are sorted in descending order, the important targets are listed from the top.  
\[TIR(h_{i})=HE_{\text{C},I,A}(h_{i})*\left\{\frac{1-d}{|M(h_{i})|+1}+d\sum_{h_{j} \in M(h_{i})}\frac{TIR(h_{j})}{|L(h_{j})|}\right\} \tag{4}\]
## 3 Ultra-Long-Range Scanning and Acquisition Technology
### Scanning and Acquisition  
After achieving high-precision initial pointing, considering factors such as GPS measurement errors and PAT terminal execution errors, it is necessary to scan and cover the uncertain range. In this study, a rectangular spiral scanning strategy is employed. Taking into account platform attitude errors, positioning errors, PAT terminal execution errors, and some redundancy, scanning is conducted within an uncertain range of 0.2\({}^{\circ}\). The scanning trajectory is shown in Figure 5.  
The 400 \(\upmu\)rad coarse beacon is used to scan the uncertain area. The scanning terminal's beacon light has a transmission power of 1 W, with a scanning range of \(\pm\)0.1\({}^{\circ}\), a scanning step size of 0.01\({}^{\circ}\), and a dwell time of 0.1 s. The total scanning time is 46.3 s. The dual-terminal collection uses a staring scan approach. When the staring terminal receives the beacon light, it quickly tracks the signal and sends a signal light to the scanning terminal. The scanning terminal stops scanning once it receives the signal light from the staring terminal, completing the laser link acquisition between the two terminals. In this system, the scanning system is mainly used in the coarse alignment stage. It drives the fast reflector (FSM) through a preset program to search a wide range of angles and compensate for initial installation errors and environmental disturbances. In this stage, the system operates in an open loop, with a slow response speed and low link stability. The fine tracking system mainly controls the fast reflector based on the CCD camera's spot position error feedback to achieve high-precision closed-loop control. In this stage, the dynamic jitter caused by some turbulence can be suppressed to maintain link stability.
## 3 Acoustic Models
### Delay Computation Model  
In this section, we suggest an analytical model to calculate end-to-end delay in data transmissions. We study the effects of acoustic channel characteristics on the speed and propagation delay of the signal. The propagation delay of acoustic signal is five times greater than the RF signal. Figure 2 shows the description of propagation delay.  
Figure 2: Propagation delay in multi-hop communication.  
The end-to-end delay between the sender and receiver is given by:  
\[T_{E-E}=(n+1)(T_{tx})+n(T_{rx})+T_{p}^{d} \tag{13}\]  
where \(T_{tx}\) and \(T_{rx}\) are the consumed transmission and reception time of a packet. \(n\) is the number of hops for a specific packet, whereas, \(T_{p}^{d}\) is the overall propagation delay of packet from source to sink and is expressed as:  
\[T_{p}  
acoustic signal. It also suggests a model for the calculation of combined acoustic absorption loss. Thus, at a given frequency \(f\), thorp model calculates the total absorption loss as follows:  
\[10 log\alpha(f)=\begin{cases}0.11f^{2}/(1+f^{2})+44f^{2}/(4100+f)\\ +2.75*104f^{2}+0.003,&\text{f}\geq 0.4\\ \\ 0.002+0.11(f/(1+f))+0.011f,&\text{f}<0.4\end{cases} \tag{20}\]  
where \(\alpha(f)\) is measured in dB/km and \(f\) in kHz. By using above calculated loss, we calculate the value of \(\alpha\) as follows:  
\[\alpha=10^{\alpha(f)}/10 \tag{21}\]  
All the tunable parameters are given in \(dBre\mu Pa\). The total attenuation can be calculated by adding absorption and spreading loss:  
\[10 logA(l,f)=k*10 log(l)+l*10 log(\alpha(f)), \tag{22}\]  
The first term in Equation (22) shows the spreading loss and the second term is the absorption loss. \(l\) is the transmission distance in meters. The spreading coefficient \(k\) shows the geometry of the signal propagation. (_i.e._, \(k\) = 1 is cylindrical, \(k\) = 2 is spherical, and \(k\) = 1.5 is practical spreading [29]).
# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 4 Simulation
### Evaluation Results
#### 4.2.1 Comparison of Performance with Conventional Data Augmentation Techniques  
We compared the proposed technique with conventional data augmentation techniques. The data augmentation methods used for comparison were mix&shuffle [13], mixup [27] and specaugment [14]. When using mix&shuffle and specaugment together with our proposed method, half of the data to be augmented were applied to the proposed method, and the remaining half was applied to mix&shuffle or specaugment. Moreover, when applying mixup together with the proposed method, mixup was applied during the training of the augmented data generated by the proposed method.  
As demonstrated in Table 4, the proposed algorithm improved performance across all metrics compared to the model without augmentation. Furthermore, the results from the development and evaluation datasets demonstrated that the proposed method achieved the best performance in both the micro and weighted F1-Scores.  
When the proposed method was combined with conventional techniques (mixup [27] and specaugment [14]), there was a tendency for performance improvement in the evaluation dataset. Notably, although the specaugment technique exhibited the best macro-averaged performance in the development dataset, its performance was significantly reduced in the evaluation dataset. However, when used together with the proposed method, this performance degradation was mitigated. Since the proposed method is designed to achieve better performance with the evaluation dataset than the development dataset by in  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Development Dataset**} & \multicolumn{3}{c}{**Evaluation Dataset**} \\ \hline
**F1-Score** & **Micro** & **Macro** & **Weighted** & **Micro** & **Macro** & **Weighted** \\ \hline Without augmentation & 0.9001 & 0.8612 & 0.8974 & 0.8861 & 0.8397 & 0.8848 \\ \hline mix\&shuffle [13] & 0.9006 & 0.8642 & 0.8979 & 0.8874 & 0.8417 & 0.8873 \\ \hline mixup [27] & **0.9050** & **0.8700** & 0.8975 & 0.8905 & **0.8460** & 0.8890 \\ \hline Specaugment [14] & **0.9055** & **0.8756** & **0.9036** & 0.8897 & 0.8445 & 0.8887 \\ \hline RTFs (proposed) & **0.9060** & 0.8692 & **0.9037** & **0.8939** & 0.8433 & **0.8922** \\ \hline mix\&shuffle \& RTFs & 0.9029 & **0.8704** & 0.9009 & 0.8854 & 0.8397 & 0.8860 \\ \hline mixup \& RTFs & 0.9040 & 0.8694 & **0.9015** & **0.8935** & **0.8445** & **0.8917** \\ \hline Specaugment \& RTFs & 0.9023 & 0.8370 & 0.9003 & **0.8920** & **0.8476** & **0.8910** \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1-Score comparison of existing data augmentation techniques. The three highest scores are bold, and the highest scores are underlined.  
cluding recordings from microphones in unknown positions, we observe that even though specaugment outperforms in terms of the macro average on the development dataset, there is also great improvement in the macro average on the evaluation dataset.
# Spatial Image-Based Walkability Evaluation Using Regression Model
## 2 Proposed Method
### Feature Analysis  
#### 2.2.1 Regression Model Training  
To predict the walkability scores for the evaluation, linear (multiple linear, ridge, LASSO [33]), nonlinear (SVR [34]), and tree-based (XGBoost [35]) regression models are built. The following environment is used for model training: Windows 10, AMD RYZEN 9 5950X CPU, NVIDIA RTX 3080 TI GPU, and 64 GB RAM. Scikit-learn is used to build the multiple linear regression, ridge regression, LASSO regression, and SVR, whereas the XGBoost library 2.0.3 is used to build eXtreme Gradient Boosting (XGBoost). These five regression models are trained on the results of the walkability evaluation and the model exhibiting the best performance is identified by comparing the accuracies of all models. The following performance metrics are used for the evaluations: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R\({}^{2}\)) [32].
# Experimental Study of Coaxial Jets Mixing Enhancement Using Synthetic Jets
## 3 Results and Discussion
### The Parameter Distribution of Coaxial Jets
#### 3.1.4 Analysis of Parameter Distribution Results  
The above results show the characteristics of flow field for coaxial jet nozzle A. The streamwise velocity gradient was large in the two shear layers, but the velocity of annular jet between the layers was relatively stable in the radial direction. The distribution of radial velocity in shear layers was in accordance with the streamwise vortices, showing a \"peak-valley\" shape. Both the [PERSON] shear stress and the turbulence intensity showed two peaks on the radial distribution curve. The peak in the inner shear layer became flat with the development of mixing in the axial direction.  
After the actuators were turned on, the vortex pairs formed at the outlet of actuators entered the shear layer with the synthetic jet, enhancing the development of streamwise vortices in shear layers. The primary and secondary vortices both expanded the influence region, and the inner and outer shear layers became thicker. The streamwise velocity rose in the inner shear layer, then decreased on the inside of the outer shear layer and increased on the outside. The radial velocity significantly increased, indicating that the radial penetration and mixing between jets is enhanced. Because the velocity gradient became smaller and the mainstream momentum exchange range expanded, the Reynolds shear stress curve around the two peaks flattened and the span became larger. The synthetic jets caused the velocity pulsation in the outer shear layer to become intense, so the turbulence intensity increased significantly. The distribution and change of the above parameters indicated that the synthetic jets effectively enhanced the coaxial jets mixing and shortened the time and space required for mixing. This is of great significance for practical applications.
## 4 Performance Evaluation in Edge Scenarios
### Performance Improvement Strategies  
Based on the performance tests, it is evident that SGX experiences significant performance degradation in edge intelligence scenarios. Although different SGX implementations excel in specific areas, such as Gramine in CPU-intensive tasks and Occlum in process management, the overall performance of SGX in real-world tasks like I/O and network programming remains poor. To improve SGX's performance in edge scenarios, three main approaches can be considered: optimizing enclave entry and exit, bypassing the kernel for I/O operations and adopting confidential virtual machine (CVM)-based TEE.  
Figure 10: Throughput versus latency of Memcached.
# The CYGNSS Mission: On-Going Science Team Investigations
## 3 Conclusions and Final Remarks  
NASA's CYGNSS 8 microsatellite-constellation is the first-ever operational GNSS-R mission, revolutionizing several scientific fields within our community, and attracting new members from different communities such as microwave radiometry and SAR. The number of GNSS-R scientific publications within our community continues to increase significantly thanks to the open-access CYGNSS products available at the PODAAC. This was a nice start, but the CYGNSS Science Team is a dynamic community always looking to improve products and apply them to new scientific applications. In this scenario, around 50% of the Science Team members are now involved with applications over land surfaces, including SMC, AGB, and water surface. The key enabling events were improving calibration procedures, product spatial resolution (e.g., reducing date integration to 0.5 s from 1 s), geolocation, metadata quality, and a more agile web for downlinking data. The recent mission extension by NASA will enable the continuation of investigations by the CYGNSS Science Team. We will continue to contribute to the wider remote sensing community with our new and novel results, with the objective of advancing our understanding of key dynamic processes over the Earth's surface. With this article, we welcome new members to participate in the CYGNSS mission. Conceptualization, C.R. and H.C.-L.; methodology, C.R. and H.C.-L.; writing--original draft preparation, C.R., H.C.-L., R.A., A.B., J.A.C., M.M. and A.W. ; writing--review and editing, C.R., H.C.-L., R.A., A.B., J.A.C., M.M. and A.W. ; visualization, C.R. and H.C.-L.; supervision, C.R. and H.C.-L.; project administration, C.R. ; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript. This research was supported in part by the NASA Science Mission Directorate contract NNL13 AQ00C with the University of Michigan. Figure 10: CYGNSS-derived wind speed retrievals over the Hurricane Florence by [PERSON] et al. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
# Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service
## 1 Introduction
Applying a stochastic model in kinematic PPP has shown improvement in the kinematic positioning [22], and frequency stability also improves in real-time PPP time transfer with a receiver clock between-epoch constrained model as described in [23]. In this paper, a simple and well-defined receiver clock model is introduced and tested in the post-processing mode. Furthermore, the constrained PPP is also shown to be able to keep the continuity of the clock solution in case of satellite tracking loss or to retrieve the clock solution from very noisy measurements. Finally, Atomium integer PPP will be presented. This approach fixes the carrier phase ambiguities as integer numbers in the least square computation. The old version of Atomium PPP in DEMETRA treats the carrier phase ambiguities as float values, ignoring the fact that these ambiguities should be integers. It was reported in [24] that using the carrier phase measurements with integer ambiguity resolution will improve the stability of GNSS time transfer and also eliminate the clock jumps or random walk introduced by float ambiguity solutions. In [25], a new method for fixing integer ambiguity on un-differenced phase measurements was described, and it leads to a PPP with centimeter-level precision. Since the GPS satellite integer products started to be available at the IGS [26], it has become possible for the single receiver user to perform the PPP time transfer with integer ambiguity resolution. Due to the features of the techniques, the independent solution of integer PPP can be only used for frequency transfer, while it cannot be used for time transfer. However, it shows that integer PPP can also perform excellently in long term time transfer, mitigating the problem of time discontinuity at the daily batch boundary if its solution can be calibrated by another time reference [27]. The mathematic details of the three upgrades of Atomium PPP are presented in Section 2, and the associated clock solutions, compared to the current PPP performances, are given in Section 3.