esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 3 Methods and Datasets
### Model Tuning and Selection  
This section provides details on how the data used for training the models was split, the usage of hyperparameter tuning to get the most performant LSTM model, and the use of feature normalization.  
For the purpose of training and testing the models, the data was split into 80% training and 20% testing. For the LSTM models, out of the 80% kept aside for training, 20% was used for validation to tune weights via the loss functions. Another important parameter is the time window over past history, also known as the lag time, which is used for both training and testing the model. The optimal time window was found by doing trial and error experimentation. Four different values of lag were tested with window sizes of 1, 5, 7, and 14. The results of this analysis are shown and elaborated on in Section 4. The reason 5 was selected is because the number of working days in the week is 5 and, even with an overlap of the lag window, at least more than 1 day of the working week will be covered. Next, 7 and 14 were chosen to cover the full week including the weekend days and 2 consecutive weeks.  
To find the most optimal LSTM model, hyperparameter tuning was used to select the values of certain parameters such as learning rate, number of layers within the model, and loss functions. Table 3 summarizes the specifications that were found to result in the most optimal model.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Hyperparameter**} & \multicolumn{1}{c}{**Value**} \\ \hline Learning Rate & \(1\times 10^{-3}\) \\ Optimizer & Adam \\ Loss Function & MAE \\ No. of Epochs & \(1\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameter specifications.  
Normalization is generally a vital step, for almost all algorithms, as it can lead to faster convergence towards the minima, because the feature values are on a similar scale. In this work, Min-Max scaling is applied to the values such that the resulting range of values lies between 0 and 1 as follows:  
\[x^{\prime}=(x-x_{min})/(x_{max}-x_{min}), \tag{10}\]  
where \(x^{\prime}\) is the normalized value, \(x\) is the original value of the attribute, \(x_{max}\) is the biggest value in the column of the attribute, and \(x_{min}\) is the smallest.
## 3 Compact Model Validation
### De-Embedding Procedure
#### 3.1.1 InGaAs/InGaAsP UTC-PD De-Embedding
Extracting the inductive element, \(L_{Pad}\), from \(\Gamma_{Pad\_Short}\), yields an overestimated value (Figure 6a), as it actually includes a ground return inductance. A correction in the value of \(L_{Pad}\) can be made by optimizing the simulation on the imaginary part of \(Y_{Pad\_Short}/\omega\) extracted from measurement at high frequency. The capacitance of the pads, \(C_{Pad}\), can be extracted from \(Y_{Pad\_Open}/\omega\) at low frequency (Figure 6a). The same process was performed to reconstruct the other two sections using the mesa-free Open and Short test structures and the earlier test structures (Open and Short) that include the mesa accesses. The equivalent capacitances of the mesa-free access, \(C_{access}\), and mesa access, \(C_{mesa}\), can be determined at low frequency from the imaginary part of \(Y_{mesa\_free,Open}\) and \(Y_{Open}\), respectively (Figure 6b). The equivalent inductance of the mesa-free access, \(L_{access}\), and the mesa inductances, \(L_{mesa}\), were determined by optimization on the high frequency region of the measured \(Y\)-parameters. The access resistance, \(R_{access}\),  
Figure 5: (**a**) Extraction of capacitive, inductive, and resistive parasitic equivalents to the pads and access lines; (**b**) schematic of the complimentary test structures; (**c**) schematic of the UTC-PD depicting the three parasitic sections; (**d**) global electrical circuit equivalent to the pads and access lines. can be determined by averaging over the real part of \(Z_{meas\_free\_short}\). The complete set of parasitic lumped elements are summarized in Table 2.  
It is worth noting that this contribution was accepted at the EuMW 2022 conference [26].
# A Hierarchical Parallel Graph Summarization Approach Based on Ranking Nodes
## 2 Materials and Methods
### Method Implementation  
The RHNS implementation is shown in Figure 4, the blue dots in the figure represent the merged super nodes, and the black dots represent the original nodes. The RNHS is constructed in two phases: 1. Hybrid importance metric generation; 2. Parallel graph summarization execution. **Step1**: This phase firstly performs node importance ranking, then applies weighted sums for normalized ranking results. The pseudocode is shown in Algorithm 1. ```
1. Input: Graph \(G\)
2. Output: Final sequence of weighted sums for node importance /* Let \(Vg\) denote the nodes of \(G\), \(I*\) denote the set of the node's importance with different metrics, and Sr denote the final weighted and sorted ranking results. The algorithm uses parallel calculation for three different weighted node importance policies, as examples. */
3. ThreadPool.start();
4. \(I_{s1}\) = startThread1(Vg, Pagerank); /* Importance metrics based on PageRank */
5. \(I_{s2}\) = startThread1(Vg, ClusteringCoefficient); /* Importance metrics based on ClusteringCoefficient */
6. \(I_{s3}\) = startThread1(Vg, EigenvectorCentrality); /* Importance metrics based on EigenvectorCentrality */
7. ThreadPool.stop();
8. avgResults = Normalized_weighted(\(I_{s1}\), \(I_{s2}\), \(I_{s3}\)); /* Normalize and weighted sums. */
9. \(Sr\) = Rank (avgResults); /* Sort the ranking results */
10. Output Sr
```  
**Algorithm 1** Hybrid Weighted Nodes Importance Ranking  
**Step 2**: Based on the prior results, the second phase performs hierarchical parallel summarization, as described in Algorithm 2, and comprises two sub-phases:  
* Initialization  
The original graph is divided into multiple partitions, and each partition adds the candidate pairs into the max heap in parallel. * Parallel summarization  
Figure 4: HRNS implementation architecture. Each partition owns a max heap and performs the following steps when the heap is not empty: (1) Each partition updates its own super-nodes set vs. and super-edges set \(Es\) in parallel; (2) Based on the importance strategy of Equation (2), each partition picks a candidate node and pushes it into its own max heap; (3) The partitions are merged in pairs based on a \"maximizing edges across the partitions\" strategy; (4) The summarization continues iteration until the compression ratio reaches the boundary threshold. Moreover, HRNS retains a configurable interface for the importance merging strategy, is applied to different scenarios, such as union or accumulation, and follows the union strategy by default. ```
1.Input: Origin graph \(G\)
2.Output: Summary graph \(Gs\) //* Let \(G_{i}^{k}\) denote the origin \(i\)th partition in \(k\) iteration, \(G_{i}^{k}\) denote the \(i\)th partition after summary in \(k\) iteration, \(G_{i}^{k}\) denote the final merged summary graph in \(k\) iteration, and \(H_{i}\) denote the makeup of the \(i\)th partition. */
3.\(Hi=\); /* Parallel initialization */
4.\((G1,\ldots Gi)=\) graphPartition(\(G\)); /* partition \(G\) */
5.
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 4 Experimental Results and Analysis
### Medical Image Denoising
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Noise Type** & **Images** & **RPCA** & **AMlogtv** & **NonRPCA** & **WNNM\(-\)RPCA** & **TDV** & **Ours** \\ \hline \multirow{8}{*}{Gaussian noise} & Butterfly & 0.9744 & 0.9835 & 0.9762 & 0.9807 & 0.8007 & 0.9900 \\  & House & 0.9690 & 0.9813 & 0.9664 & 0.9696 & 0.7821 & 0.9865 \\  & Peppers & 0.9499 & 0.9590 & 0.9521 & 0.9586 & 0.7899 & 0.9663 \\  & Lena & 0.9698 & 0.9847 & 0.9712 & 0.9766 & 0.7617 & 0.9890 \\  & Room & 0.9788 & 0.9762 & 0.9800 & 0.9765 & 0.6106 & 0.9919 \\  & Bird & 0.9527 & 0.9558 & 0.9523 & 0.9599 & 0.7583 & 0.9666 \\  & Camera & 0.9452 & 0.9488 & 0.9429 & 0.9419 & 0.7083 & 0.9596 \\  & Starfish & 0.9555 & 0.9592 & 0.9573 & 0.9633 & 0.7004 & 0.9687 \\  & **Average** & **0.9619** & **0.9685** & **0.9623** & **0.9658** & **0.7390** & **0.9773** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cont. Figure 10: Denoising images of LungCT image no. 2 by different methods (Poisson noise). (**a**) Singular value comparison; (**b**) original clean image; (**c**) noisy image (Poisson noise); (**d**) RPCA (PSNR = 25.99 dB, SSIM = 0.9620, FSIM = 0.9656); (**e**) AMIgqv (PSNR = 29.25 dB, SSIM = 0.8792, FSIM = 0.9870); (**f**) NonRPCA (PSNR = 37.75 dB, SSIM = 0.9732, FSIM = 0.9918); (**g**) WNNM\(-\)RPCA (PSNR = 31.22 dB, SSIM = 0.9776, FSIM = 0.9786); (**h**) TDV (PSNR = 23.51 dB, SSIM = 0.6897, FSIM = 0.8187); (**i**) our method (PSNR = 41.38 dB, SSIM = 0.9895, FSIM = 0.9956). Figure 11: Denoising images of LungCT image no. 3 by different methods (Gaussian noise). (**a**) Singular value comparison; (**b**) original clean image; (**c**) noisy image (Gaussian noise); (**d**) RPCA (PSNR = 33.85 dB, SSIM = 0.6098, FSIM = 0.9916); (**e**) AMIogluv (PSNR = 25.68 dB, SSIM = 0.3639, FSIM = 0.9968); (**f**) NonRPCA (PSNR = 34.89 dB, SSIM = 0.5844, FSIM = 0.9867); (**g**) WNNM\(-\)RPCA (PSNR = 35.28 dB, SSIM = 0.5988, FSIM = 0.9890); (**h**) TDV (PSNR = 23.30 dB, SSIM = 0.2879, FSIM = 0.8248); (**i**) our method (PSNR = 37.94 dB, SSIM = 0.6233, FSIM = 0.9980). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Noise Type** & **Images** & **RPCA** & **AMlogtv** & **NonRPCA** & **WNNM\(-\)RPCA** & **TDV** & **Ours** \\ \hline \multirow{8}{*}{Impulse noise} & 1 & 34.1086 & 20.8109 & 39.3910 & 30.0661 & 22.6606 & 43.9398 \\  & 2 & 29.7339 & 20.7488 & 39.8981  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Noise Type** & **Images** & **RPCA** & **AMIogtv** & **NonRPCA** & **WNNM\(-\)RPCA** & **TDV** & **Ours** \\ \hline \multirow{8}{*}{Impulse noise} & 1 & 0.9904 & 0.9630 & 0.9924 & 0.9761 & 0.8230 & 0.9968 \\  & 2 & 0.9761 & 0.9656 & 0.9928 & 0.967
## 3 Results and Discussion
### The Validation of the Methods
#### 3.3.2 Detection and Quantitation Limits  
Under the optimal conditions of headspace solid-phase microextraction (extraction fiber 50/30 \(\upmu\)m DVB/CAR/PDMS, ion concentration 20%, extraction time 87 min, and extraction temperature 50 \({}^{\circ}\)C) and the experimental method and instrumental parameters determined in Section 2.4, the mixed standard stock solution of three DMSs was diluted step by step with ultrapure water, and then prepared as mixed standard work solutions of 100 ng/L and 200 ng/L. The limit of detection limit (LOD) was determined using a 3-fold signal-noise ratio (S/N = 3), and the lower limit of quantification (LOQ) was determined using a 10-fold signal-noise ratio (S/N = 10). Standard working solutions were prepared for five parallel samples. The results are summarized in Table 4. The LOD of DMS was 29 ng/L and the LOQ was 95 ng/L. The LOD of DMDS was 1.2 ng/L and the LOQ was 4.1 ng/L. The LOD of DMTS was 5.0 ng/L and the LOQ was 17 ng/L. A ng/L level of detection was reached for all DMSs. [PERSON] et al. used GC-FPD to determine the contents of DMS and DMDS in various treatment units of a wastewater treatment plant, and the limits of detection could reach 4.2 ng/L and 2.6 ng/L, respectively [17]. [PERSON] et al. determined five organosulfides containing DMS, DMDS, and DMTS using GC-MS with detection limits ranging from 2.2 to 4.0 ng/L [18]. [PERSON] et al. used SPME-GC-MS to determine the amount of DMSs produced by Flagellates, and the limit of detection could reach 6.8 ng/L [30]. [PERSON] et al. used a novel adsorption tube to enrich DMSs in situ and determined DMS and DMDS in water samples using thermal desorption gas chromatography coupled with a sulfur chemiluminescence detector (TD-GC-SCD) with detection limits of 6 and 3 ng/L, respectively [15]. Similar limits of detection were achieved using the optimized method of this study; however, the versatility of the instrument was enhanced, which increased its usability.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dimethyl Sulfides** & **Linear Range/(ng/L)** & **Regression Equation** &
\begin{tabular}{l} **Coefficient of** \\ **Determination R\({}^{2}\)** \\ \end{tabular} & \(p\)**-Value** \\ \hline DMS & 100\(\sim\)10,000 & y = 1.6503x \(-\) 3.0502 & 0.9967 & \(<\)0.001 \\ DMDS & 10–1000 & y = 2.0681x \(-\) 0.5219 & 0.9907 & \(<\)0.001 \\ DMTS & 5–1000 & y = 2.3945x \(-\) 3.1269 & 0.9994 & \(<\)0.001 \\ \hline \hline \end{tabular} Note: x is the logarithmic value of the sulfide concentration (ng/L) and y is the logarithmic value of the instrumental determination of the peak area.  
\end{table}
Table 3: Standard curves and correlations of DMSs.
## 3 Near Field Models of Reflectarray and Transmitarray Antennas
### Discussion  
The proposed wideband near field beam-shaping technique was able to achieve a maximum ripple of 1.5 dB in the whole 5G NR n257 band, corresponding to a 10.7% relative bandwidth. It is worth mentioning that specifications have been met employing a unit cell consisting of two stacked rectangular microstrip patches backed by a ground plane, offering up to four degrees of freedom to perform a direct layout optimization. However, bandwidth could be further improved by employing a more suitable unit cell. For instance, the three stacked patches configuration is known to offer higher bandwidth of operation than only two layers [38]. Alternatively, a unit cell comprised of two layers of parallel and coplanar dipoles is known to be able to offer, after a direct layout optimization, relative bandwidths of up to 20% [36]. When applied to near field beam-shaping, this would allow offering coverage in both the n257 and n258 bands, covering from 24.25 GHz up to 29.5 GHz, which represents 19.5% of relative bandwidth. Additionally, even though the use of a MoM-LP tool directly in the optimization loop penalizes computational performance to achieve wideband results, some techniques could be employed to accelerate the process. In this work, the differential contributions technique [37] was used to accelerate some building blocks of the GIA. However, the unit cell analysis could also be sped up by substituting the MoM-LP with a database of reflection coefficients [39] or the use of machine learning techniques [40].
## 6 Applying Anomaly Detection in Water Systems
### Feature Selection  
One of the most important considerations in machine learning is that of feature selection which will directly impact how well the algorithm will work in the application scenario. When considering feature selection for anomaly detection in critical water system infrastructure, it may be useful to first consider how fault detection mechanisms in these systems work. As mentioned in the previous section, data integrity anomalies are more difficult to classify and have more devastation consequences than network anomalies so that will be the focus of this discussion. These FD algorithms use the fact that ICS applications change the physical state of their application environments and these changes should thus obey the laws of physics [69]. For example, if a flow valve from a specific tank is opened, then the flow of water into/out of that tank should follow the laws of fluid dynamics. If the sensor readings and corresponding control commands do not result in physical changes that obey these laws then there is a fault in the system.  
Knowledge of these laws above can be used to predict what the system state should be, given a set of time-series inputs, and if the actual value falls outside of a predetermined threshold it is flagged as a system fault. Using machine learning, designers of the algorithms do not need to have an in depth understanding of these laws because the detection algorithm would be able to learn the input/output relationship from historical data. A basic knowledge of these laws would however enable designers to choose the features that would produce the best results for their specific applications. Designers would also then be able to make adequate decision regarding the data pre-processing required before training commences.
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 4 Results and Discussion  
### Analysis of Prediction Results of Air Temperature in Cable Trench  
A comparison between the air temperature distribution in the cable trench based on the RF-GPR cable temperature prediction model and the air temperature distribution in the  
Figure 4: Accuracy analysis of RF-CNN model prediction results.  
Figure 3: Accuracy analysis of RF-GPR model prediction results.  
cable trench obtained by the finite element calculation method is shown in Figure 5. It can be seen from Figure 5 that the distribution trend of the model prediction results trained by the machine learning method is basically the same as that of the finite element calculation results. Through the error analysis of the temperature calculated by the finite element and predicted by machine learning, the relative error percentage of the machine learning model to predict the air temperature in the cable trench is obtained, as shown in Figure 6. It can be seen from the relative error distribution diagram that most of the regional error is below 4%, and the relative error of the lower part of the cable trench is smaller than that of the middle and upper parts. It can also be seen from the relative error distribution diagram that the relative error near the outer skin of the cable is larger than that far away from the cable. The interior of the cable belongs to solid heat transfer, and the air in the cable trench belongs to convective heat dissipation, which lead to a large temperature gradient near the cable skin, resulting in the poor prediction accuracy of the machine learning model; thus, the relative error near the cable skin is large.  
The relative errors of the machine learning predicted temperature values are further numerically analyzed below, and the relative error distribution of all data is shown in Table 4. According to the data analysis, the relative error of the temperature value predicted by machine learning is mostly between 1% and 2%, accounting for 41.22%, while the relative error of data above 5% accounts for only 0.08%. The relative error of most data is below 3%, which accounts for 91.93% of the whole data. The maximum relative error of the overall data is 6.18%, and the average relative error is 0.72%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Error Distribution & 0–1\% & 1–2\% & 2–3\% & 3–4\% & 4–5\% & \textgreater{}5\% \\ \hline Proportion & 22.38\% & 41.22\% & 28.33\% & 7.02\% & 0.97\% & 0.08\% \\ \hline \end{tabular}
\end{table}
Table 4: Proportion of relative error distribution.  
Figure 5: Comparison of finite element calculation results (**left**) and machine learning prediction results (**right**) of air temperature distribution in cable trench.  
Figure 6: Relative error percentage of air temperature predicted by machine learning.
## 3 System Framework and Algorithm Procedure
### Frame Format  
DMAC separates the channel time into a series of super-frames, which consists of one CP, one beacon, and one TP slots. Figure 1 gives the format of superframe, DTR frame, and ACK frame. Figure 1 depicts the superframe format. During the CP period, all of the nodes may transmit a DTR frame to the central node AP. The Figure 1 depicts the format of DTR, which includes the Transmission node's MAC Address (TA) and Receiving node's Address (RA). After CP, the AP will activate the data transmission period by broadcasting a beacon. The first node in the sequence queue of the AP will be paged to send a frame, and then the subsequent node also will be successively activated to send a frame by an ACK frame from AP. Finally, all of the potential transmission nodes in the queue can be scheduled to send a frame without any collision occurring. The central node AP transmits periodically the beacon frame, which activates the data transmission procedure and transmits some control information.  
Figure 1: The format of super-frame, Data Transmission Request (DTR) and acknowledgement (ACK) frame. (**a**) superframe; (**b**) DTR frame; (**c**) ACK frame.
# When Maritime Meets Aviation: The Safety of Seaplanes on the Water
## 2 Materials and Methods
### Description of the Database of Saplane Occurrences in Canada  
Transport Canada (TC), the safety regulator, has collated a database that consists of 1771 sealame occurrences (accidents and incidents) in Canada, from 1990 to 2022. In this database, there are 466 occurrences on water involving private recreational operations, during the following phases of flight: standing, taxi, take-off, landing, landing run (roll out), and docking (including parked).  
While TC provided a comprehensive dataset, certain data fields had to be derived from the narratives, such as the cause of the occurrence (categorization), number of injuries and fatalities, and route. The data fields include general information (e.g., date, time, phase of flight), details of the surrounding environment (e.g., route, location), aircraft specifications (e.g., damage level, tonnage, landing gear), and environmental conditions (e.g., weather). To enhance the reliability of the data extraction process, an inter-rater verification method was applied, whereby a second reviewer independently coded a randomly selected subset of narrative-derived fields. Discrepancies were discussed and resolved through consensus to ensure consistent interpretation and minimize potential bias.  
Details of the individuals affected (e.g., gender, age, injuries) were unavailable, as were any details about the pilot's performance. Complementary data collection methods such as interviews, focus groups, and decision-making studies, e.g., those by [PERSON] et al. [11], are considered in this paper. The underreporting of incidents, either due to minimal reporting requirements or fear of repercussions, further limits the dataset's accuracy, potentially skewing risk assessments. Comprehensive reporting and interdisciplinary approaches are therefore essential to address these limitations, ensuring a holistic understanding of seaplane safety and enabling meaningful recommendations for mitigating risks in shared waterways.
## 3 Results
### Feature Overall Importance Analysis  
The global feature explanation obtained from the SHAP method for the three tree-based models is shown in Figures 3 and 4. We represent the average impact of each variable on soil organic matter based on the global feature importance, which is the mean absolute representative SHAP value for that feature over all the given samples (Figure 3). Figure 4 illustrates the impact of the features on the model's predictions. Higher SHAP values for precipitation, indicated in red, signify a large and positive average impact on predictions, suggesting a higher likelihood of increasing the prediction accuracy due to the high mean absolute SHAP values.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model Parameter** & **Parameter Grid** & **Decision Tree** & **Random Forest** & **Gradient Boosting** \\ \hline Maximum depth of a tree & [6; 8; 10; 12] & 6 & 12 & 8 \\ Minimum samples per leaf & [8; 12; 18] & 12 & 8 & 18 \\ Minimum number of samples & [8; 16; 20] & 8 & 8 & 8 \\ Number of trees & [10; 100] & - & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal hyperparameters of three tree-based models.  
Figure 2: Scatter plots of predicted-versus-observed organic matter stocks in soil by using tree-based model. Points are field-wide averages for the last three years (2020–early 2023). Dashed line is 1:1.  
The features are ordered from top to bottom by their predictive importance. In this case, we found that all models consistently identified the mean annual precipitation as the primary predictor. This was closely trailed by exchangeable K content in the DT and RF models (Figure 4). Similarly, the GB model acknowledged exchangeable K as one of the top five predictors; however, it attributed relatively less importance to it (Figure 4).