esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
## 5 Summary and Conclusions  
In this contribution, we comprehensively analyzed the characterization of inter-system biases (ISBs) with different precise products, receiver types and antenna types. We also investigated whether or not the ambient environment of the receiver affects the ISB characterization. Based on the analysis of ISB characterization, we implemented the GPS/BDS PPP with modeling the ISB as time constant, random walk process and white noise.  
A sudden jump exists in the ISB estimates between two adjacent days. It is caused by the change of satellite clock datum. The ISBs estimated with the precise products from different analysis centers have significant divergence. It is found that, in most cases, the estimated ISBs using WUM and CODE products are stable, while the variation of ISBs estimated by using GFZ products is remarkable. This is determined by the data processing strategies adopted in by different analysis centers. Moreover, even the stations equipped with the same type of receivers and antennas, the ISBs are inconsistent. This is because the stations are located in different places where the ambient environments (e.g., temperature) are different and the receiver-code biases are closely related to the temperature. The data from ultra-short baselines are used to confirm the same ambient environment, the result indicate that the receiver and antenna type both affects the characterization of ISBs. Therefore, besides the impact of different final precise products, the receiver type, antenna type and even the ambient environment affect the ISB characterizations.  
For GPS/BDS PPP solutions with WUM and CODE precise products, the positioning performances agree well among the three ISB processing methods. However, when the receiver-code biases vary significantly and GFZ precise products are used, the results of GPS/BDS PPP obtained by random walk or white noise are more accurate than those using the constant method.  
Conceptualization, C.K.; methodology, C.K.; software, C.K.; validation, Y.Z.; formal analysis, Y.Z.; investigation, C.K.; resources, S.W.; data curation, S.W.; writing--original draft preparation, C.K.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of Shandong Province, Grant Number 2019 JZZY010809. This work was supported by National Key Research and Development Program of Shandong Province, Grant Number No.2017 GHY15116.  
Figure 8: Positioning result of GPS/BDS PPP at station BALA on DOY 2, 2018, using constant, random walk and white noise methods.  
**Acknowledgments:** We would like to thank the IGS multi-GNSS experiment (MGEX) and Geoscience Australia for providing Multi-GNSS data and precise products.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 4 Multiple Biomarker Strategies  
Multiple biomarker predictive models are considered as an effective method to increase the specificity and sensitivity of a single biomarker tool [155]. Data confirm the superiority of multiple models compared with conventional models in risk stratification in HFpEF, whereas the adoption of serial biomarker measurements for risk stratification in HFpEF remains uncertain. However, different combinations of circulating cardiac biomarkers are likely a promising tool to improve prediction, risk stratification, and therapy in T2 DM with HF, although there are limited data on the optimal number of biomarkers that can be allocated to improve point-of-care therapy among both HFrEF and HFpEF patients [156]. There is no strong evidence that single biomarker use is superior to a multiple biomarker strategy for every clinical condition in HF patients. For instance, the MOLITOR (Impact of Therapy Optimisation on the Level of Biomarkers in Patients with Acute and Decompensated Chronic Heart Failure) study has shown that serial measurements of multiple biomarkers (C-terminal fragment of pre-pro-vasopressin, NT-proBNP, mid-regional pro-atrial natriuretic peptide, mid-regional pro-adrenomedullin, and C-terminal pro-endothelin-1) in advanced HF were no better than measurements of the C-terminal fragment of pre-pro-vasopressin [157]. [PERSON] et al. (2021) [158] evaluated the application of a biomarker-based risk score to identify patients with dysglycemia that were at high risk of incident HF. They enrolled individuals from three cohort studies (ARIC (Atherosclerosis Risk In Communities), DHS (Dallas Heart Study), and MESA (Multi-Ethnic Study of Atherosclerosis)). The original biomarker score included hs\(\leftarrow\)CTnT \(\geq\) 6 ng/L, NT-proBNP \(\geq\) 125 pg/mL, hs-CRP \(\geq\) 3 mg/L, and left ventricular hypertrophy identified by electrocardiography with one point for each abnormal parameter. The authors found that the 5-year risk for HF was associated with an increase in biomarker score; moreover,the highest risk was noted in patients with total scores of \(\geq\)3 (diabetes: 12.0%; pre-diabetes: 7.8%). Thus, it has been established that the biomarker score can stratify the HF risk among patients with T2 DM and pre-diabetes. [PERSON] et al. (2019) (2020) reported that the combination of NT-proBNP and ST2 had higher prognostic ability when compared with each biomarker alone in patients with acute HF, except for galectin-3 and hs-CRP, which did not increase in discriminative potency when compared to a multiple biomarker model in ischemia-induced HF. Consequently, these conflicting results deserve closer investigation in large clinical trials in the future.
## 5 Experiment and Evaluation
### Simulation Results
This is due to the fact that data with high TD-error decreases blind exploration for relay nodes. However, applying DAR to PM-DQfD has no effect on reducing exploration steps in one episode. This is because the DAR can only produce a higher reward in each learning episode, but is invalid for route selection. As shown in Figure 11, the Packet Delivery Ratio (PDR) increases for all algorithms with the increasing number of episodes. It can be observed that the PDR of DQfD-based schemes outperforms CBMQ, CRQ-routing and the two DQN-based methods. Among the DQfD-based methods, PM-DQfD with DAR achieves the highest PDR. Since PM-DQfD with DAR achieves the lowest end-to-end delay, as shown in Figure 9b, the portion of its packet delivery with a routing latency below the setting threshold exceeds that of other schemes. This illustrates that the PM-DQfD-based DAR is more robust and reliable compared to traditional algorithms. Figure 11: Packet delivery ratio versus number of episodes. Figure 10: Average number of hops along the path versus number of episodes. Figure 12a,b illustrate the trend of system PCR and routing latency as a function of PU arrival probability, respectively. In Figure 12a, the system PCR increases as PU arrival probability increases. This is because frequent arrival of PUs results in more data transmissions being interrupted. Increase in retransmission results in greater energy expenditure. For different PU arrival probabilities, PM-DQfD achieves the lowest system PCR, followed by DQfD. The PCR of PM-DQN is obviously higher than DQfD, and DQN has the greatest energy consumption. This demonstrates the advantage of learning from the expert demonstrations and memory structure optimization. Furthermore, it can be seen that the advantage of DQfD-based schemes decreases as PU arrival probability increases. This occurs mainly because the performance of the expert agent tends to deteriorate when the primary user returns frequently. Thus, even with the aid of the expert's demonstration data, the advantage to the apprentice node is still diminished. In addition, the change end-to-end delay with PU arrival probability given in Figure 12b is very similar to that in Figure 12a, and we will not repeat it here.
## 5 Formal Modeling and Analysis of SAPS
### Replay Attack  
Replay attacks can be launched while an attacker replays the original message parameters at some other time to impersonate any legal participant. In the proposed scheme, messages between the \(\mathrm{ID}_{\mathrm{A}}\), TS, and \(\mathrm{ID}_{\mathrm{B}}\) are transmitted on the public channel. An attacker might try to use these conversations to launch a replay attack. However, in our protocol, replay attacks can be easily thwarted because an adversary cannot produce an updated timestamp. Both end parties and the TS verify message freshness with a threshold for timestamp and nonce number. If the difference exceeds this threshold, it will abort the session. It is not easy for an adversary to impersonate participants' reply messages because the nonce numbers \(N_{1}\), \(N_{2}\), \(N_{3}\) during the authentication process, \(d_{A},\ d_{B}\) for key session key generation and \(\mathrm{R}_{\mathrm{Ts}}\) used by the TS are newly chosen for every session. For this scenario, an adversary cannot compute the \(\{X_{A},\ P_{A},\ T_{1}\}\), \(S_{K}=H(d_{A}X_{B}^{\prime},ID_{B},\ ID_{A})\), and \((\{X_{B},\ P_{B}\},T_{3}\ )\) communication patterns of the entire network.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Neural Network Model Performance
#### 5.3.6 ROC Curve Analysis  
To dig deeper into our model's capabilities, we plotted an ROC curve and calculated its AUC (please refer to Figure 3). These tools give us a more nuanced view of how well the model distinguishes between accurate and inaccurate shots.  
The ROC (Receiver Operating Characteristic) curve evaluates our binary classification model's performance in predicting shot accuracy. It illustrates our model's ability (orange line) to discriminate between successful and unsuccessful shots compared to random classification (dashed blue line, AUC = 0.5). With an AUC of 0.823, our model demonstrates strong discriminative ability, correctly ranking successful shots higher than unsuccessful ones 82.3% of the time when comparing random pairs. The curve's sharp upward trajectory towards the top left corner indicates a favorable balance between true positive and false positive rates across various classification thresholds. This top-left movement signifies high true positive rates with low false positive rates, which is desirable. While this performance surpasses random guessing, it also suggests potential for further optimization. For context,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Overall** & **Smashes** & **Drops** & **Clears** \\ \hline Accuracy & 85.70\% & 89.20\% & 84.50\% & 83.40\% \\ Precision & 88.30\% & 91.50\% & 86.20\% & 85.70\% \\ Recall & 83.10\% & 87.30\% & 82.90\% & 81.20\% \\ F1 Score & 0.856 & 0.893 & 0.845 & 0.834 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Neural network model performance.  
an AUC of 1 would indicate a perfect classifier, represented by a curve that reaches the top left corner.
# Face Gender and Age Classification Based on Multi-Task, Multi-Instance and Multi-Scale Learning
## 2 Facial Gender and Age Classification Based on MML and DRF
### Deep Feature Representation by MML
#### 2.1.3 Multi-Scale Integration Learning  
It is generally believed that in the backbone network, high-level features contain more semantic abstract information, while low-level features contain more detailed information. For facial gender and age recognition, we designed a lightweight decoder using the Compact Pyramid Refinement (CPR) [43] module as the basic unit. Because different levels of features correspond to different scales, multi-scale learning integrates features of different scales, so that the final extracted features not only have semantic abstract information, but also have details. Hence, we designed a multi-scale integration learning strategy to enhance facial feature extraction.  
Suppose that the input of a CPR module is \(\mathbb{C}\). First, the channels of \(\mathbb{C}\) are expanded M times by using a \(1\times 1\) convolution. Second, we apply three depth-wise separable convolutions with dilation rates of 1, 2 and 3 to obtain three different scale features. Finally, these multi-scale features are connected with a multi-scale fusion strategy, which can be denoted as:  
\[\begin{array}{l}\mathbb{C}_{1}{=\text{Conv}_{1\times 1}(\mathbb{C})},\\ \mathbb{C}_{2}^{d_{1}}{=\text{Conv}_{3\times 3}^{d_{1}}(\mathbb{C}_{1})},\\ \mathbb{C}_{2}^{d_{2}}{=\text{Conv}_{3\times 3}^{d_{2}}(\mathbb{C}_{1})},\\ \mathbb{C}_{2}^{d_{3}}{=\text{Conv}_{3\times 3}^{d_{3}}(\mathbb{C}_{1})},\\ \mathbb{C}_{2}=\text{ReLU}(\text{BN}(\mathbb{C}_{2}^{d_{1}}+\mathbb{C}_{2}^{d _{2}}+\mathbb{C}_{2}^{d_{3}})),\end{array} \tag{3}\]  
where \(d_{1}\), \(d_{2}\) and \(d_{3}\) are dilation rates. BN indicates batch normalization. Next, we used a \(1\times 1\) convolution to compress channels of \(\mathbb{C}_{2}\) to the same number as the input:  
\[\mathbb{C}_{3}=\text{Conv}_{1\times 1}(\mathbb{C}_{2})+\mathbb{C} \tag{4}\]  
Then, an attention vector \(v^{\prime}\) is computed by applying the attention mechanism in Equation (1), so that we have:  
\[X=v^{\prime}\otimes\text{Conv}_{1\times 1}(\mathbb{C}_{3}) \tag{5}\]  
Equation (5) uses global contextual information to recalibrate the multi-scale fusion features. As shown in Figure 4, at each decoding phase, the feature maps of the top decoder and the corresponding encoder are concatenated and then, the CPR module is used for fusion. In this way, the decoder can aggregate multi-level features from top to bottom.
## 3 Experimental Study
### Compared Methods  
We have compared the proposed method with other predictive methods using the Scikit-learn Python library, which contains implementations of machine learning and statistical modeling for classification, regression, and clustering [23]. The performance comparison was conducted with the commonly used techniques in previous studies.  
Multiple Linear Regression (MLR) is commonly used for regression tasks [24]. It estimates coefficients that explain the correlation between the response and explanatory variables.  
Least Absolute Shrinkage and Selection Operator (LASSO) is one kind of regularized linear regression. It minimizes the prediction error by shrinking the coefficients of some input variables that are irrelevant to the prediction task to zero [25].  
Support Vector Regression (SVR) is a type of Support Vector Machine [26] used as a regression method. SVR works by using the \(\varepsilon\)-tube that best approximates the continuous-value function for balancing model complexity and prediction error [27]. We have built an SVR model with a C regularization parameter is equal to 0.1 and a linear kernel function.  
Extreme Gradient Boosting (XGBoost) is an ensemble algorithm based on a decision tree and is formed on a gradient-boosting framework. It consists of multiple decision trees based on different subsets of features and combines their predictions to generate a final prediction. Each decision tree is built on the errors of the previous trees [28]. We have configured the learning rate to 0.3; the booster is gradient-boosting linear (gblinear), and the number of boosting iterations is 100.  
Random Forest (RF) is widely used in classification and regression tasks. It builds several decision trees on dissimilar sub-samples from the training dataset. In the case of regression, the final result is generated by their average [29]. We set the number of trees to 500.  
Long Short-Term Memory (LSTM) is one kind of Recurrent Neural Network (RNN) used in sequence processing [30]. It solves the problem of RNN that cannot predict accurately from the long-term information by extending the memory cells using input, forget and output gates. Those gates are employed to add useful information to the cell (input gate), remove unnecessary information from the cell (forget gate), and extract applicable information from the current cell (output gate). The LSTM model used in the experimental study had a single hidden layer with 16 neurons. The model was trained with Adam optimizer [31], batch size to 16, epochs to 100, and learning rate to 0.001.  
Multilayer Perceptron (MLP) constructs multiple hidden layers in between input and output layers [19]. Each layer consists of artificial neurons connected with its following layers' neurons by weight parameters. The weight parameters are optimized by the backpropagation algorithm. The training configuration of the MLP was Adam optimizer, batch size to 16, epochs to 100, and learning rate to 0.001.
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 1 Introduction
### Contribution  
The main contributions of this paper are as follows:  
* We develop two new DL strategies for signal detection in MIMO communication systems. Both strategies perform close to the optimal ML detection scheme.
* We show that the proposed strategies have a tradeoff between BER performance and complexity and could be used under different hardware constraints.
* We show that the activation function at the output layer has an impact on the NN-based detector performance.  
The remainder of the paper is organized as follows. Section 2 gives a description of the methods used for system design. Section 3 presents the proposed DL detection scheme for MIMO communication systems. Section 4 provides several simulation results and compares the system performance considering related approaches. Finally, Section 5 summarizes the main conclusions and future directions of this research.  
_Notation:_ Bold-face lowercase letters and bold-face uppercase letters indicate vectors and matrices, respectively. The notations \((\cdot)^{T}\), \((\cdot)^{H}\), and \(\|\cdot\|\) indicate the transpose, the conjugate transpose, and the norm of a vector or matrix. The Gaussian distribution of a complex variable with mean \(\mu\) and \(\sigma^{2}\) variance is denoted as \(\mathcal{CN}(\mu,\sigma^{2})\).
## 4 Experiments
### Low- and High-Variability Cells
It is interesting to notice that the gap between ARIMA and deep learning approaches is much reduced (ARIMA showing even some outliers with very large errors), thus suggesting that deep learning approaches and in particular CNN-LSTM can successfully learn anomalous behaviors even in the case of high-variability time series cells. Finally, based on the experiments we presented so far and trying to summarize the results in the clearest possible way, we present a list of conclusions as follows:  
* Both statistical and deep learning methods show interesting results. In particular, the ARIMA statistical method and the CNN-LSTM deep learning model have proven to be the top-performers for each category. Figure 19: RMSE error distribution across the cells for both low (**left**) and high (**right**) variability scenario. Graphics shows that both the ARIMA method and CNN-LSTM deep learning method can give good results in high and low variability scenario. Figure 20: The boxplot graphics illustrates the distribution of MAPE across the cells and for each method. for both low and high variability scenario. Graphics shows how the ARIMA method can outperform deep learning methods for both high and low variability scenario. * Performance measures such as RMSE and MAPE show that both ARIMA and CNN-LSTM are capable of delivering good forecasting results. These two measures show that none of such methods is significantly better than the other. * The MAXAE measure shows that deep learning methods such as CNN-LSTM are significantly better than the statistical methods such as ARIMA in handling the anomalies in particular in high variability context and keeping the MAXAE low even for longer forecasting horizons. * The ease of use of deep learning methods when compared to statistical methods makes them a better choice when the forecasting service has to be delivered for thousands of different time series as we do in this work. In this sense they provide a one-size-fits-all solution which as our work shows can yield interesting results.
## 4 Case Study and Results
### Energy Consumption Results  
This section presents the results of the simulations conducted under the four mission scenarios described in Section 3.2. For each scenario, the total energy consumed by the UAV to complete deliveries along each bus line was computed for payload masses ranging from 1 kg to 10 kg.
## 6 Evaluation Metrics
### Efficiency and Dynamic Response  
* **Conversion Efficiency (\(\eta\))**: Quantifies the energy harvesting capability: \[\eta=\left(\frac{\overline{P_{out}}}{\overline{P_{in}}}\right)\times 100\%\] (21) where \(\overline{P}\) denotes average power over the simulation period.
* **Convergence Time (\(t_{conv}\))**: Determined when the system reaches and maintains steady-state operation, calculated using MATLAB's lsiminfo function. The threshold (2%) was set to ensure stable power delivery, with validation through moving average analysis of the error signal.
## 5 Experimental Validation
### Experimental Results  
The active steering control was tested on a 1:5 scale test railway track, the layout of which is illustrated in Figure 10. The operation followed a \"straight-curve-straight\" arrangement, including 10 m straight sections and a 10 m curve with a 15 m radius. During the experiment, the control effect of the PER-MADDPG controller was evaluated. The scaled vehicle ran at 1 m/s, and the test lasted for 20 s.  
The experimental data are shown in Figure 11, which shows the lateral displacement, attack angle, and active guidance torque of each wheelset. In the absence of active control, the maximum lateral displacement reached 8.02 mm on straight tracks and increased to 10.09 mm on curved tracks, indicating poor steering behavior. Conversely, with the proposed controller, the vehicle exhibited effective centering on straight tracks, limiting  
Figure 10: Schematic of the scale model experiment. (**a**) Scale model vehicle, (**b**) Digital model for MARL training, (**c**) Layout of the scale track.  
the maximum lateral displacement to 2.05 mm. Flange guidance was avoided on the curved tracks, leading to strong curve-negotiation performance, with a maximum lateral displacement of 2.51 mm. The yaw angles were markedly reduced for both the front and rear IRWs and were maintained within 7.8 mrad. The control torques applied to the left and right wheels, which are equal in magnitude but in opposite directions, peaked at less than 2.5 N-m, satisfying the motor's rated maximum output. With each time step, the front and rear IRW agents independently make decisions on the control command, demonstrating the optimized dynamic performance of the scale vehicle for various operation tracks and underscoring the controller's real-time operational efficiency.