esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Band Medium-Frequency R-Mode Signal Quality Estimation +
## 1 Introduction  
The medium-frequency (MF) R-Mode system is an alternative navigation system that reuses terrestrial DGPS maritime beacons to transmit synchronized signals, allowing the users to obtain positioning, navigation, and timing (PNT) information [1, 2, 3, 4]. In recent years, a test-bed has been developed in the Baltic Sea region [5] as part of several regional projects led by DLR where research institutions, industry, and administrations from different countries were involved. The MF R-Mode concepts and working principles have been already demonstrated in the field [4, 6] where positioning accuracy better than 20 m could be achieved with the DLR receiver. Recently, a follow-up project has begun to further develop the system and bring it into an operational status with core service functionalities [7]. In general, an important feature of an R-Mode receiver is the ability to assess the received signal quality and predict its performance under certain assumptions. This is relevant for different reasons. On the one hand, it supports the service operator with monitoring activities on dedicated shore sites where the signals are received, processed, and compared with expected quality. In case of anomalies, the operator can issue warnings to the users. On the other hand, the indicator is useful onthe user side to predict the expected accuracy of the ranging signals and, therefore, implement weighting functions or exclusions in case of poor signal quality. Usually, for satellite navigation receivers the carrier to noise ratio (\(C/N\)) or carrier to noise density ratio (\(C/N_{0}\)) are used to define the quality of the received signals and assess the performance of the receiver [8]. The difference between the two is that for the \(C/N\), the noise power is considered in the computation, typically over a defined bandwidth, whereas for the \(C/N_{0}\) the noise power density is used i.e., the noise power over \(1\,\mathrm{Hz}\) bandwidth. In this paper, we define and describe the \(C/N_{0}\) as the quality factor of the MF R-Mode signals, and we explain how to exploit the discrete Fourier transform (DFT) to estimate it. The paper is divided as follows. Section 2 presents the MF R-Mode signal structure and the Cramer-Rao Lower Bound (CRLB) that describes the performance of the phase estimation and ranging accuracy. Section 3 describes the definition of the signal quality indicator (\(C/N_{0}\)) and its link to the ranging accuracy. In Section 4, the results obtained with Monte Carlo simulation for two scenarios are presented: the estimation of the \(C/N_{0}\) of a sinusoid in additive white Gaussian noise (AWGN) and the estimation of the \(C/N_{0}\) for the aiding carrier of the R-Mode signal in AWGN. The results are compared and discussed. Section 5 shows the results obtained with real data and demonstrates that the indicator can be used to predict the expected ranging accuracy. Finally, Section 6 concludes the paper.
## 4 Discussion  
In this study, we developed a process-based C-flux model constrained with data from forest monitoring plots that simulates the GPP and the NEP of Mediterranean _P. brutia_ stands. To do so, we used a set of field measurements to empirically parametrize parts of the model. After validating the model's predictive ability across a post-fire chronosequence and elevation gradient, we simulated stand-level C-fluxes for two future periods (i.e., 2051-2060 and 2091-2100) and two climate change scenarios (RCP4.5 and RCP8.5). Our simulations suggest that LAI, considered here as a proxy of a stand's structure and disturbance history, is the most important determinant of C-fluxes. Under climate change conditions and for the specific characteristics of our simulated plots, NEP remains positive, i.e., stands remain  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **RCP4.5 2060** & **RCP4.5 2100** & **RCP8.5 2060** & **RCP8.5 2100** \\ \hline GPP (\% change) & \(-13.8\) & \(-7.6\) & \(-8.5\) & \(-28.3\) \\ R\({}_{\rm het}\) (\% change) & \(11.4\) & \(14.2\) & \(17\) & \(84\) \\ NEP (\% change) & \(-86\) & \(-71.8\) & \(-52.3\) & \(-227.7\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean percentage difference in annual GPP (Gross Primary Productivity, g C m\({}^{-2}\) year\({}^{-1}\)), R\({}_{\rm het}\) (soil heterotrophic respiration, g C m\({}^{-2}\) year\({}^{-1}\)) and NEP (Net Ecosystem Productivity, g C m\({}^{-2}\) year\({}^{-1}\)) estimates per scenario and reference period compared to baseline conditions across all plots along the elevation gradient. Column names refer to the climate change scenario (RCP4.5 or RCP8.5) and the period (2051-2060 or 2091-2100, referred to as 2060 or 2100, respectively).  
Figure 8: Estimated (a) GPP (Gross Primary Productivity), (b) R\({}_{\rm het}\) (soil heterotrophic respiration) and (c) NEP (Net Ecosystem Productivity) per elevation, scenario and reference period. Error bars represent \(\pm\) standard deviation during the baseline (1996–2005), mid-term (2051–2060, referred to as 2060) and long-term (2091–2100, referred to as 2100) periods per climate scenario.  
C-sinks for most future periods and climate scenarios, except in the case of the pessimistic RCP8.5 scenario in the longer-term 2091-2100 simulation period.
# Design, Implementation, and Field Testing
## 3 System Design
### System Requirement  
To achieve the objective of our project in helping nursing assistants to develop the good habit of using proper body mechanics while providing bedside care, the system should have the following properties:  
* Privacy protection. The system must track only the consented nursing assistant, not any other person that might appear in front of the Kinect sensor, including the patient in the room. This is essential to protect the privacy of the patients. The US federal regulation requires that an explicit consent is required before a patient can be tracked. Because it is unlikely for a patient to agree to be tracked all the time for any purpose, the system must provide maximum provision that he or she is not tracked.
* Reliable non-compliance activity recognition. The rooms in a nursing home typically are crowded with limited space. For example, a room may consist of several pieces of furniture such as wheelchairs, tables, walkers, etc. The activity recognition algorithm must be robust despite the occlusion caused by these pieces.
* Realtime alert. To have any positive impact to a nursing assistant, the alert should be delivered in realtime in a way that he or she could accept when he or she performed a non-compliant activity.
* Multi-room support. A nursing assistant is typically assigned to several rooms during a shift. To maximize the effectiveness of our system, we must be able to continuously track the nursing assistant regardless which room he or she goes in to do his or her job duties.
* Balance the privacy protection and usability of the system. While protecting the privacy of the patients, the system must strive to be usable by nursing assistants in a way that is too burdensome for them to use it.
###### Abstract  
The mix-zone method is effective in preserving real-time vehicle identity and location privacy in Vehicular Ad Hoc Networks (VANETs). However, it has limitations in low-vehicle-density scenarios, where adversaries can still identify the real trajectories of the victim vehicle. To address this issue, researchers often generate numerous fake beacons to deceive attackers, but this increases transmission overhead significantly. Therefore, we propose the Communication-Efficient Pseudonym-Changing Scheme within the Restricted Online Knowledge Scheme (CPCROK) to protect vehicle privacy without causing significant communication overhead in low-density VANETs by generating highly authentic fake beacons to form a single fabricated trajectory. Specifically, the CPCROK consists of three main modules: firstly, a special Kalman filter module that provides real-time, coarse-grained vehicle trajectory estimates to reduce the need for real-time vehicle state information; secondly, a Recurrent Neural Network (RNN) module that enhances predictions within the mix zone by incorporating offline data engineering and considering online vehicle steering angles; and finally, a trajectory generation module that collaborates with the first two to generate highly convincing fake trajectories outside the mix zone. The experimental results confirm that CPCROK effectively reduces the attack success rate by over 90%, outperforming the plain mix-zone scheme and beating other fake beacon schemes by more than 60%. Additionally, CPCROK effectively minimizes transmission overhead by 67%, all while ensuring a high level of protection.  
e acons; mix zone; privacy; pseudonym changing; RNN; transmission overhead; VANET +
Footnote †: journal: _Future Internet_  
_Article_
# Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters
## 4 Cluster-Centered Visualization Techniques
### Visual Comparison with Other Techniques  
The newly developed CCV techniques can be compared from a visual perspective with other techniques. Table 1 gives an overview of this comparison.  
Table 1 shows a comparison between the classical visualization techniques and our new CCV techniques for the consideration of single and all possible clusters. The main important difference between the presented visualization techniques is the consideration  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Consideration of Clusters**} \\ \hline
**Visualization Technique** & **Single** & **All** \\ \hline CPC & Yes & No \\ A2C & Yes & No \\ AM & Yes & No \\ Scatterplot & No & Yes \\ Heat map & No & Yes \\ Dendrogram & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of visualization techniques based on the consideration of single or all clusters.  
of clusters. Our new CCV techniques consider only single clusters because sometimes a specific single cluster is more interesting than focusing on all possible clusters within the data set. In contrast, the classical techniques of scatterplot, heat maps, and dendrogram visualize all possible clusters and their dependencies. Focusing on interesting single clusters is not possible. The following Figure 1 presents a visual comparison between a classical visualization technique (scatterplot) and one of the newly developed visualization techniques (CPC).  
Figure 1 shows an example of a visual comparison between a scatterplot and the CPC technique. The visualization is based on an ambiguous data set and fuzzy clustering with three clusters. The generation of an ambiguous data set is described in Section 5.1. The scatterplot on the left shows which data point belongs to which cluster. However, it is not recognizable how close or far away the clusters are from each other. The new CCV techniques are particularly advantageous at exactly this point. Here, the CPC visualization technique is applied to the same data set. By focusing on one single cluster (the black one), it is clearly visible that the green cluster is very close to the black cluster and the red cluster is clearly separated. All three new CCV techniques focus on one specific cluster and visualize the dependencies among the other possible clusters. This is validated by different examples in the following section.
## 3 Results and Discussion
### Study Limitations and Future Directions  
The methods and results from this study were constrained by several factors including the use of spatially coarse SIF and meteorological data, and global relationships defined by a monthly climatology. Our study also assumes that SIF is a linear approximation of GPP, which has been demonstrated from previous field, airborne, and satellite-based studies [28; 58; 59; 60]. While our results confirm the linear correspondence between SIF and GPP for the global scale and monthly climatology examined, the mechanisms of the linear SIF-GPP relationship are still an ongoing study topic and may vary at finer spatial and temporal scales.  
Improving the understanding of processes affecting relationships between SIF and GPP would be valuable for further improving vegetation productivity modeling in the context of our study. Further research is also needed to clarify the effects of temporal trends and inter-annual climate variability on ecosystem productivity. Additionally, climate parameters from global reanalysis are associated with uncertainties, while potential environmental lag effects on plant growth, and other influences beyond temperature and moisture were not addressed in this study. Nevertheless, the satellite SIF record provides the means for global verification and potential refinement of model assumptions and environmental response characteristics developed from sparse tower observations.
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 5 Experimental Evaluation
### Clustering Techniques
#### 5.4.3 Grey Wolf-Optimized Self-Organizing Maps Clustering Technique  
The mechanism we used to optimize the SOM is described in Section 4. The following are the steps we used for evaluating the optimized SOM. Note that these steps are executed on all datasets (MSA and NADA) with both representation methods (CV and TF-IDF), and 10-fold cross-validation is performed to validate the obtained results:  
1. Divide the dataset into three portions: 60% for training, 20% for validation, and 20% for testing.
2. Set the parameters of the GWO as follows:  
Figure 5: Results of fine-tuning the number of epochs parameter for Self-Organizing Maps.  
Fitness function: We used the QE function provided by the MiniSom python library as a fitness function for the Grey Wolf Optimization algorithm.
* Dimension: We set the dimension to be the product of the total number of corpus features by the selected SOM dimensions, \(dim_{x}\) and \(dim_{y}\).
* The number of search agents: For the purpose of this paper, we chose the number of search agents to be five. Note that the initial experiments with different numbers of search agents were performed and the difference between their results was negligible.
* Initiate the positions of the most powerful wolves, alpha, beta, and delta. The initial positions are set as a matrix of 0 s.
* Initiate random values for the search agents with the same GWO dimension.
* Calculate the fitness value using the QE, on the validation data portion, provided by the MiniSom Python library.
* With every fitness calculation, the result is compared against the alpha, beta, and delta wolves. Accordingly, if the new fitness is less than the values of these wolves, update the wolves' values with the new fitness.
* When a stopping criterion is met, whether completing the maximum number of iterations or having no improvement in fitness for five consecutive iterations, the GWO function terminates by passing the value of the fitness into the MiniSom to train the model.
* Test the model using the testing data portion and report the results as shown in Table 4.
## 5 Discussion
### Performance Comparison of Traditional and Proposed Method
#### 5.1.1 Dataset 1: Nearby Parking Lot  
From 15 September 2022 to 14 November 2022, a total of 6 scenes of ground truth data were collected for this parking lot. Table 4 shows the absolute value of detection for missing cars in the proposed method and the traditional pairwise comparison method.  
The graph of the deviation for detected cars by the traditional method and the proposed method is shown in Figure 25.  
It can be seen from the graph that the deviation for detected cars by the proposed method is lower than that of the traditional method. The overall detection performance is evaluated with the RMSE metric.  
Based on calculations, the experimental results obtained from the Logarithm Background Subtraction method yield an RMSE value of 8.534. From the perspective of quantitative analysis indicators, there are certain errors in the detection results, but these are within a reasonable range. Considering the low resolution of Sentinel-1, this method can obtain more accurate results for higher-resolution images. Taking data from 21 October 2022 as a case study, the following section presents an error analysis of vehicle distribution in the parking lot.  
Figure 23: Logarithm Background Subtraction result. (**a**) Input image. (**b**) Background. (**c**) Change part.  
Figure 24: The CFAR detection result.  
Figure 26: Comparison of actual distribution and detection result of vehicles in the parking lot. (**a**) Actual distribution. (**b**) Detection result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date of Image** & **2022.9.15** & **2022.9.27** & **2022.10.09** & **2022.10.21** & **2022.11.02** & **2022.11.14** \\ \hline Truth data (number of vehicles) & 114 & 134 & 109 & 140 & 46 & 44 \\ \hline Traditional method (number of vehicles) & 145 & 110 & 121 & 167 & 39 & 42 \\ \hline Deviation for detected cars (traditional method) & 31 & 24 & 12 & 27 & 7 & 2 \\ \hline Proposed method (number of vehicles) & 108 & 126 & 96 & 150 & 48 & 36 \\ \hline Deviation for detected cars (proposed method) & 6 & 8 & 13 & 10 & 2 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The detection result and performance comparison of the proposed method and traditional pairwise based method.  
Figure 25: Deviation for detected cars.  
hand, the Background Subtraction method is not limited by the number of targets and only requires sufficient similarity between the background and detection images. Additionally, the pairwise difference method requires image registration and subtraction for each pair of images, which is computationally intensive and vulnerable to registration errors, leading to lower efficiency and accuracy. Compared to the pairwise comparison method, the Background Subtraction method reduces false detections caused by noise and illumination changes by modeling the static background.
## 4 Experiment Study
### Ablation Study
The results for the 3 h ahead forecasting are shown in Figure 8. As can be seen in Table 2, the values of the RMSE, MAE, SMAPE, MAPE, and R of the proposed ELFNet model are 0.0417, 0.0333, 0.2813, 0.4118, and 0.9892, respectively, under the 3 h ahead forecasting value of 0.0440, and the MAE value is reduced from 0.0440 to 0.0377 after using the CA module, showing an improvement of 13.32%; similarly, after using the SA module, the MAE value is decreased from 0.0440 to 0.0379m with an improvement of 13.86%; after using both the CA and SA modules, the improvement reaches 24.32%. Figure 8: ELFNet forecasting results for 3 h ahead. (**a**) is the prediction result, and (**b**) is the local enlargement. Figure 6: ELFNet forecasting results for 1 h ahead. (**a**) is the prediction result, and (**b**) is the local enlargement. Figure 7: ELFNet forecasting results for 2 h ahead. (**a**) is the prediction result, and (**b**) is the local enlargement. It can be shown from the aforementioned analysis and experimental data that both the CA and SA modules are crucial to increasing the model's forecast accuracy at various prediction horizons.
## 3 Experiments
### Experiments on Public Datasets
#### 3.2.2 Parametric Analysis Experiment  
It can be seen from Equation (9) that \(\alpha_{1}\) and \(\alpha_{2}\) are important hyperparameters in the process of distilling the student network. Initially, we conducted experiments in which we temporarily ignored the influence of global knowledge by setting \(\alpha_{1}\) to 0. Through this analysis, we observed that \(\alpha_{2}\) near 1 yielded the best model's performance. Building upon this observation, we proceeded to fix \(\alpha_{2}\) at 1, and the value of parameter \(\alpha_{1}\) was varied with a step size of 0.1 within the range of [0, 1]. The test accuracy of the student network under different values of \(\alpha_{1}\) is shown in Figure 3a,b. The results indicate that the model performance is optimal when the value of \(\alpha_{1}\) is 0.5. Therefore, the value of \(\alpha_{1}\) was set to 0.5, and then \(\alpha_{2}\) was varied with a step size of 0.01 within the range of [0, 0.1]. The test accuracy of the student network under different values of \(\alpha_{2}\) is shown in Figure 3c,d. The results reveal that the optimal value for \(\alpha_{2}\) is 0.1.  
Figure 3: Test experiments under different values of \(\alpha_{1}\) and \(\alpha_{2}\) on three public datasets. (**a**) 1-shot test accuracy under different values of \(\alpha_{1}\); (**b**) 5-shot test accuracy under different values of \(\alpha_{1}\); (**c**) 1-shot test accuracy under different values of \(\alpha_{2}\); (**d**) 5-shot test accuracy under different values of \(\alpha_{2}\).  
Additionally, after completing the search for \(\alpha_{1}\) and \(\alpha_{2}\) within their respective ranges, we extended our exploration beyond the boundaries of [0, 1] and [0, 0.1]. However, we found that no values outside of these ranges yielded superior results.
# Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism
## 6 Discussion
### Performance Analysis of the Proposed Model under Different Murmur Characteristics  
Figure 6 in Section 5.4.2 shows the different performances of the proposed model under different murmur characteristics. This section conducts a performance analysis based on it.  
The significantly low accuracy of \"Early-systolic\" and \"Decrescendo\" was attributed to the fact that the early-systolic murmur disappears shortly before the mid-systole period [2]; thus, it has a short duration, which makes it difficult for the model to extract features.  
In this dataset, grade labeling may have deviated from the original definition [2]. The murmurs were classified as I/VI by default when not all of a patient's auscultation locations were recorded. So, the PCGs of I/VI may be louder while still being classified as I/VI. Therefore, among the three grading types of PCGs, II/VI may have the highest proportion of low-intensity PCGs in fact. Based on this fact, due to the model's poor ability to detect the quality of low-intensity murmurs, the PCGs of II/VI had a lower accuracy than the other two types.  
In addition, the accuracy of the \"High\" murmur was much higher than that of the \"Low\" and \"Medium\" murmur. This discrepancy may indicate that the \"Low\" and \"Medium\" murmurs are more likely to overlap with the normal heart sounds (the first (S1) and second (S2) heart sounds) in the frequency domain (because the frequency range of normal heart sounds is relatively low (mainly 20-150 Hz [47])), making feature extraction and detection more challenging.  
To visualize the above discussion, Figure 7 shows the waveforms and log-Mel spectrograms of different types of PCGs for 0-1.5 s (about 3 cardiac cycles) to succinctly present the PCGs. Figure 7a is a correctly categorized log-Mel spectrogram of PCG, which has the timing of \"Holosystolic\", the grading of \"III\", and the pitch of \"High\". Figure 7b-d shows three misclassified log-Mel spectrograms of \"Early-systolic\" timing, \"II\" grading, and \"Low\" pitch, respectively. As Figure 7, the duration of the systolic murmur is shorter in \"Early-systolic\" PCG compared to \"Holosystolic\" PCG; the color of the systolic log-Mel spectrogram is lighter in PCG with II grading compared to PCG with III grading, which means that the systolic murmur is less loud for the PCG with II grading; and \"Low\" PCG has a lower frequency distribution than \"High\" PCG. This is consistent with the discussion above.  
In conclusion, the model shows inadequacy in detecting the murmur quality for murmurs of short duration, low intensity, and low pitch. These findings can point the way to future research.