esa-sceva/satcom-chunk-collection huggingface dataset:
# XAI-Based Clinical Decision Support Systems: A Systematic Review
## 2 Related Work
### Non-Knowledge-Based CDSS
Because ECG data also contain temporal information, ECG signals can be analyzed using RNN-based models to detect sleep apnea [52]. When dealing with clinical data, owing to its long-term properties, the problem of forgetting previous data and ignoring past information may arise. Therefore, studies using LSTMs [53] to predict future data by considering past data have been proposed. An LSTM was used to learn multiple diagnostic labels to classify diagnoses [54] and oral-nasal thermal airflow, nasal pressure, and abdominal breathing-guided plethysmography data from polysomnography were analyzed with a bidirectional LSTM model to diagnose sleep apnea [55]. Deep learning is frequently applied in medical image analysis. Chest radiographs can be analyzed using deep learning to diagnose chest diseases such as lung nodules [56], lung cancer [57], and pulmonary tuberculosis [58]. Unlike traditional supervised and unsupervised learning, reinforcement learning [59] generated its own training data by observing the current state and selecting future actions. Because existing CDSSs are trained based on evaluations made by different clinicians with different criteria, interrelated symptoms are not considered in some cases. This problem can be solved by applying reinforcement learning, which is used to learn complex environments. A CDSS based on a deep reinforcement learning algorithm has been introduced to determine the initial dose for ICU patients, where an accurate medication prescription is critical and prevents mis-dosing and complications. [60] Reinforcement learning of secure computations enables the implementation of patient-centered clinical decision-making systems while protecting sensitive clinical data. A privacy-preserving reinforcement learning framework with iterative secure computation was proposed to provide dynamic treatment decisions without leaking sensitive information to unauthorized users [3]. A reinforcement learning-based conversational software for radiotherapy was also studied, where the framework used graph neural networks and reinforcement learning to improve clinical decision-making performance in radiology with many variables, uncertain treatment responses, and inter-patient heterogeneity [61]. BERT [62], a large language model based on a transformer [63], was used to develop a CDSS with natural language understanding capabilities. To reduce diagnostic errors, a framework for multi-classification of diagnosis codes in EHRs using BERT [64] has been developed to help clinicians predict the most likely diagnosis. However, specialists have raised concerns about the reliability and responsibility of these deep learning and AI models because of their inability to explain their decisions. Therefore, they are often unwilling to use them in diagnosis. To this end, it is necessary to adopt AI, which provides evidence for prediction and an understandable explanation.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 3 CPWES Modeling and Assumption  
In this study, the Southwest offshore wind farm located in South Korea was considered as a case study. We aimed to design the communication infrastructure for phase 1 of the project, which consists of 20 wind turbines with a total capacity of 60 MW, as shown in Figure 2. The output voltage of each turbine was 690 V. The voltage is stepped-up to the collector bus with a typical voltage of 34.5 kV. All turbines were connected to an offshore substation. The spacing between turbines was about 800 m (along the rows and between the rows). The longest cable length was about 1.2 km between turbine No. 3 and the offshore platform, while the shortest cable length was about 524 m between turbine No. 1 and the offshore platform [18]. The electric topology configuration consisted of three feeders, one bus, and one HV transformer. We assumed that the communication network topology followed the WF electric topology, where the optical fiber cables are integrated with the submarine cables. The WF communication architecture is shown in Figure 3.  
Figure 2: Configuration of the Southwest wind farm project (Test WF).
## 4 Results and Discussion
### Specimen Force Chain Variation
Figure 7: Force chain distribution and skeleton particle position. In the figure, E is abbreviation for exponent, for example, the number 1.68E3 stands for 1.68. In the initial state, under the action of gravity, the bottom force chain was denser. As the vibration process proceeded under the dual action of gravity and vibration, the bottom material was continuously dense, the contact force between particles increased, and the strong force chain that formed was still concentrated at the bottom and transferred upward. At approximately 30 s, the vibration action's force on the upper material increased, and a strong force chain began to appear and transfer downward. After that, the material in the mold was subjected to axial pressure and vibration force, the force chain was transmitted from the upper and lower ends to the middle, and the force chain generated by the lower vibration was transmitted a greater distance. At the end of the molding process, the distribution of force chains in the specimen tended to be uniform. Figure 8: Skeleton particle force chain visualization of compaction molding. (**a**) Force chain before compaction; (**b**) force chain after 1 compaction; (**c**) force chain after 5 compactions; (**d**) force chain after 10 compactions; (**e**) force chain after 17 compactions; and (**f**) force chain after 42 compactions. In the figure, E is abbreviation for exponent, for example, the number 2E7 stands for \(2\times 10^{7}\).
# Effect of Super Retarder on Recycled Water and Concrete Properties of Waste Slurry in Mixing Plant
## 2 Materials and Composition  
### Raw Materials  
Gelling material: In this study, P-I 42.5 ordinary silicate cement was used, which has a fineness of 0.8%, density of 3.12 g/cm\({}^{3}\), specific surface area of 356 m\({}^{2}\)/kg, satisfactory stability, initial setting time of 128 min, final setting time of 189 min, and a Dx(50) of 17.8 \(\upmu\)m. The chemical composition is listed in Table 1. Class F Class II fly ash, S95 grade mineral powder.  
Super Retarder: Tetrasodium hydroxyethylidene diphosphate (HEDP-4 Na); Butane 2-phospho-1,2,4-tricarboxylic acid (PBTCA); Tetrasodium iminodisuccinate (IDS); Acrylic acid-2-acrylamide-2-methylpropanesulfonic acid (AA/AMPS); Reclained water treatment agent (ACS); Polyamino polyether methylidene phosphonic acid (PAPEMP); Sediment Dispersant (SDP-90).  
Waste slurry recycling water: from a mixing station in Beijing, concentration: 7-20%.  
Sand: Zone II mechanism medium sand with a fineness modulus between 2.3 and 3.0 and Zone III fine sand with a fineness modulus between 1.3 and 1.6 were used.  
Stone: Use 5-25 continuously graded gravel with no more than 10% needles particles.  
Water reducing agent: Polycarboxylate super- plasticizer was employed as a super-plasticizer, and its water reduction rate and solid content were 40% and 50%, respectively.  
Water: Clean tap water.
# A New Period-Sequential Index Forecasting Algorithm for Time Series Data
## 4 Results and Discussion
### Periodic Recognition and Prediction on Electric Motors Exports (EME) Dataset  
For the EME datasets, the electric motors exports in China between January 1995 and June 2019 is used as an example [23] to show the correlation coefficient detection and prediction results. Using the reference historical two-cycle-years dataset from January 2016 to December 2017, the monthly electric motors exports from January to December of 2018 are predicted by using the SPNN-PSI, MA, ES, and ARIMA methods, as shown in Figure 4. It can be seen from Figure 4 that, compared with MA, ES, and ARIMA methods, the SPNN-PSI method demonstrates a better prediction trend with good volatility and following quality.  
Table 2 presents a more visual view of prediction errors of each model. According to the results obtained in Table 2, the corresponding error of _MAPE_ is 5.34%, 6.79%, 8.11%, and 6.97% for the PSI, MA, ES, and ARIMA methods, respectively. It also can be noticed that the four errors are within a reasonable range, but the developed SPNN-PSI algorithm is more suitable for the prediction of the EME dataset used in this paper due to lower _MAPE_, _RMSE_, and _MAE_, as compared to the other three prediction methods. In addition, the correlation coefficient of its historical reference data is shown in Table 1. The example shows that the proposed SPNN-PSI algorithm achieves satisfactory accuracy in time series prediction on the EME dataset whose historical reference data has a relatively higher correlation coefficient value of 0.9388.
# Study of Mesh Pattern for Optically Transparent Flexible Antenna with Feedline
## 3 Meshed Feedline and Antenna
### Meshed Feed Line  
In Figure 3, the line width \(w_{CPW}\) and spacing \(s\) of CPW are designed to have a value of 50 ohms for solid CPW. \(w_{CPW}\) is 2.263 mm, \(s\) is 0.12 mm, and the total length of the line is 40 mm. The thickness t of the commonly used copper is 0.018 mm, and the thickness of the dielectric h is 0.2286 mm.  
Figure 2: Manufactured CPWs, antennas; in order from the left of the photo: (**a**) solid CPW; (**b**) square-meshed CPW; (**c**) solid monopole antenna; (**d**) diamond-meshed monopole antenna with solid feedline; (**e**) square-meshed monopole antenna with meshed feedline.  
An increase in the transparency of the mesh pattern increases the ohmic loss, and an index to compare the effect of this on the transmission line performance is needed. The loss factor \(\alpha\) was calculated using the scattering parameters from the following Equation (3).  
\[|S_{11}|^{2}+|S_{21}|^{2}=1-\alpha. \tag{3}\]  
However, since there is an open space in the conductor layer, conductor loss due to the change in sheet resistance is dominant. The loss factor of 40 mm, which is the total length of the simulation model, is affected by the change of the parameters of the mesh pattern. Since the characteristic impedance of CPW changes according to the size of the open space of the mesh, all lines were adjusted to 50 ohms by adjusting \(w_{CPW}\). Although meshed CPW has a higher loss factor than solid CPW, it can be compensated for by gaining transparency. However, it is important to design in a line that does not impair the purpose of the existence of transmission line. The loss factor consists of various loss terms.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 4 Results
### Grouped Scenario Analysis  
#### 4.2.1 Overlapped vs. Separated Obstacles  
Selecting the object configuration as grouping factor, overlapping scenarios 1, 2, 3, and 4, on one side, and scenarios 5, 6, 7, and 8, on the other, have been compared. The mult-compare graphs are shown in Figure 20. Configuration 16 is the top ranked, producing better statistically significant results for all other alternatives, except 13, 14, 15, 17, 18, and 22 in overlapped group, and 13, 17, 18, 22, 25, and 26 for separated group.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Headings** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline
**Indiv-1** & 101.843 & 3.475 & \(-2.828\) & 3.576 & 1.797 & \(-7.925\) & 4.228 & 5.304 & \(-7.232\) & 2.118 \\
**Indiv-2** & 115.325 & 2.387 & 3.369 & 2.691 & \(-14.945\) & \(-8.553\) & \(-8.460\) & 12.263 & \(-1.276\) & 1.708 \\
**Indiv-3** & \(-105.884\) & 129.065 & 19.088 & \(-9.868\) & \(-6.821\) & 55.787 & \(-11.275\) & 17.250 & 8.822 & \(-7.443\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Optimized glider headings for scenario 8.  
Figure 19: Selected individuals for scenario 8.  
Figure 20: Mult-compare (Tukey–Kramer) results for overlapping scenarios 1–4 (**left**) and separated scenarios 5–8 (**right**).
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 3 Proposed Method for Range Extension
### Computational Complexity of the Proposed Method  
The computational complexity is measured in terms of number of complex multiplications and additions. It is considered that removal of the CPI is negligible in complexity, divisions are equivalent to multiplications, and performing an IFFT/FFT of size \(N\) requires \((N/2)\text{log}_{2}(N)\) number of complex multiplications and \(N\text{log}_{2}(N)\) number of complex additions [27]. Table 1 provides the complexity of the proposed method and the conventional OFDM-based sensing. The complexity of the proposed method is higher by a factor of\(\approx\)\(Q\)\(N_{\text{s}}\) because whole chain of operation, for range detection, is performed after each shift in \(k\) with maximum shifts as \(Q\)\(N_{\text{s}}\). For speed, \(N\) number of \(M\)-point FFTs is increased to \(Q\)\(N_{\text{s}}\).
## 2 Materials and Methods
### Unsupervised Machine Learning
#### 2.7.1 K-Means Clustering  
[PERSON] [50] introduced k-means clustering in 1967, which is a simple yet powerful algorithm used to partition a set of observations into \(k\) (\(\leq\)\(n\)) sets, where each observation is d-dimensional. The primary goal of the algorithm is to minimize the variance within the clusters. Given an observation set \(\{x_{1},x_{2},x_{3},...,x_{n}\}\), the k-means algorithm iteratively assigns each observation to one of the \(k\) clusters \(S=\{S_{1},S_{2},S_{3},...,S_{k}\}\) by minimizing some sort of distance (usually the Euclidean distance) between the observation and the centroid. Mathematically, the objective is to find the following:  
\[arg_{S}min\sum_{i=1}^{k}\sum_{x\in S_{i}}\|x-\mu_{i}\|^{2}=arg_{S}min\sum_{i=1 }^{k}|S_{i}|VarS_{i} \tag{9}\]  
where \(\mu_{i}\) is the mean value of all the points in \(S_{i}\). This procedure is equivalent to minimizing the pairwise squared deviations in each cluster:  
\[arg_{S}min\sum_{i=1}^{k}\frac{1}{|S_{i}|}\sum_{x,y\in S_{i}}\|x-y\|^{2} \tag{10}\]
## 3 Model Structure  
In this paper, we propose an orbital satellite region tracking prediction model based on graph convolutional networks (GCNs). Figure 3 illustrates the overall framework of the proposed model. The model consists of four parts: preprocessing of satellite target video frames, construction of intra-frame and inter-frame graph structures (Sections 3.1 and 3.2), spatiotemporal prediction model for spatial targets (Section 3.3), and the model training module.  
During the preprocessing stage, we use the Simple Linear Iterative Clustering (SLIC) method to perform superpixel segmentation on two consecutive frames of the images. In the graph structure construction stage, we use the superpixel seeds from the segmentation as graph nodes. The intra-frame graph structure (intra-frame adjacency matrix) is established based on the principle that adjacent nodes are connected. Then, the inter-frame graph structure (inter-frame adjacency matrix) is constructed by connecting all superpixel nodes that exceed the segmentation threshold between two frames.  
Next, we built a feature extraction model composed of two layers of graph convolutions. The first layer consists of two sets of graph convolution structures, taking the tracking target features and the intra-frame adjacency matrix as inputs. The second layer accepts the concatenated results of the two sets of GCN outputs from the first layer and the inter-frame adjacency matrix as input, ultimately outputting the predicted superpixel node features, which are then reconstructed into an image.  
Figure 2: Satellite target image.  
During model training, we treat the video frames from the third frame onward as the control group. The threshold segmentation results of the control group images are used for superpixel segmentation, with the pixel values of the superpixel seeds serving as the expected output of the model, used to train the proposed GCN model.
## 3 Optomechanical Design  
This section presents the optomechanical configuration of the constructed prototype. The different components are chosen on the basis of the parameters established in Section 2. It was decided to opt for a biaxial configuration as, unlike a coaxial configuration, it does not require compensation systems for the internal optical cross talk. Figure 5 shows the optomechanical configuration of the constructed prototype and Table 6 presents the system specifications.  
Figure 5: Picture of the lidar prototype showing the emitting and receiving subsystems.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Wavelength** & \multicolumn{3}{c}{**905 nm**} & \multicolumn{3}{c}{**1064 nm**} & \multicolumn{3}{c}{**1.5 \(\upmu\)m**} \\ \hline Pulse Energy & 15 \(\upmu\)J & 75 \(\upmu\)J & 300 \(\upmu\)J & 25 \(\upmu\)J & 100 \(\upmu\)J & 500 \(\upmu\)J & 50 \(\upmu\)J & 200 \(\upmu\)J & 1 mJ \\
1 Hz & 51 & 115 & 230 & 34 & 67 & 151 & 0.8 & 1.6 & 3.6 \\
10 Hz & 69 & 153 & 307 & 45 & 90 & 201 & 2.5 & 5.0 & 11 \\
100 Hz & 91 & 204 & 409 & 60 & 120 & 268 & 8.0 & 16 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Required beam diameters (in mm) at several repetition rates for the studied wavelengths and pulse energies.
## 4 Discussion
Tools that favor ease of use over diverse functionality and application, such as ML-MEDIC, limit the barrier of entry and create a more gradual learning curve and can potentially act as a bridge to tools with fully customizable user-specified settings and complex functionality. We limited ML-MEDIC's machine learning methods to supervised learning due to the ability of supervised learning to generally provide more clinically relevant results and the input from the medical experts interviewed. The domain experts interviewed affirmed that comparing predictive supervised machine learning models to statistical models such as survival or logistic regression was most likely to facilitate the adoption of machine learning. Currently, ML-MEDIC does not support interactive data preparation for machine learning analyses. A potential next step would be to provide interactive methods for data preparation prior to performing machine learning. In addition to expanding the methods available, features further facilitating the interpretation and evaluation of models, such as adding a precision-recall curve to the current tabular display, or incorporating more model specific visualizations, such as graphical tree displays for decision-tree-based methods, can be explored [30]. The current evaluation of ML-MEDIC is limited to more of a qualitative assessment of the benefit of GUI-based, user-interactive tools by the clinical community to facilitate and improve the use of ML in clinical applications. Further testing and quantitative analysis is needed to assess resources such as ML-MEDIC and the potential for use in clinical decision support and various clinical research applications [31].
# Two Designs of Automatic Embedded System Energy Consumption Measuring Platforms Using GPIO
## 5 Evaluations and Results
### STM32F4 in Different Power Modes  
The STM32F4 was designed with three different power-saving modes, two of which also use different voltage regulators, namely, the sleep and stop modes. These different power modes obviously have different runtime power consumption levels. As we mentioned at the beginning of the previous set of tests, in addition to time, the other major factor affecting energy consumption is power. In this set of tests, we designed six groups of test cases to measure the operating current of the STM32F4 in different power states. These included the normal working mode, the sleep and stop modes using the main regulator, the sleep and stop modes using the low-power regulator, and the standby mode. The results are shown in Figure 6, with (a) showing the working current of different power modes and (b) showing the difference between the standalone design and the simplified auxiliary design.  
Although the power measurement is done using off-the-shelf instruments, we added some new content unique to our design to this set of tests. We used the intermediate device to send the changed level signal to the GPIO of the target board as an external interruption signal to wake the target board from the sleep, stop, or standby modes.
## 4 Experiments And Results
### Feature Visualization  
In Sections 3.1 and 3.4, we introduced the 3D nested convolution structure and dense skip connection in Render 3D U-Net. In this section, we visually displayed the features of the output layer, which more intuitively confirms the superiority of the network structure for semantic feature extraction.  
As we can see in Figure 7, we used four different networks for the liver CT images segmentation task and visualized the features of the output layer \(X_{0\_N}\)\((N\in[0,1,2,3,4])\). These four networks included 3D U-Net [7], 3D UNet3+ [40], Render 3D U-Net without deep supervision, and Render 3D U-Net with deep subdivision.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Preprocessed Volume Size** & **Volume Number** & **Data Provider** \\ \hline Liver & 256 \(\times\) 256 \(\times\) 32 & 130 & 2017 MICCAI LiTS \\ Kidney & 256 \(\times\) 256 \(\times\) 32 & 210 & 2019 MICCAI KiTS \\ Heart & 256 \(\times\) 56 \(\times\) 32 & 40 & 2019 ISBI SegTHOR \\ \hline \hline \end{tabular}
\end{table}
Table 1: An overview of some important information of the medical image datasets used in this experiment.  
We observe that the feature representation of U-Net is not clear enough and that the complete semantic information is not extracted. This is because the original skip connection in U-Net is  
Figure 7: Visualization and comparison of feature maps along skip connections for heart CT images: note that the red area denotes the ground truth and that the blue area denotes the segmentation output.  
relatively simple and the lower layer features are directly connected to the encoder features after upsampling. In comparison, the features in UNet++ and Render 3D U-Net are connected by more complex skip connections, which merge more semantic information and gradually form a clearer feature representation. In addition, we can also observe that, due to the introduction of hybrid loss (Section 3.4), the loss of each output node can be calculated, which helps Render 3D U-Net to extract semantic information better.
# Driver Clustering Based on Individual Curve Path Selection Preference
## 1 Introduction
These algorithms use very little memory while having relatively fast convergence. Moreover, if the driver behavior and hence their parameters are changed (e.g., due to influencing factors of exhaustiveness, traffic, mood...etc.) they can automatically adapt the parameters. In this paper, we introduce a comprehensive solution for how lane centering systems can be made more human-like. We exploit existing components of the literature, from parametric driver model clustering to parameter identification techniques. To the best of our knowledge, there are no existing solutions in the literature that cover the entire driver modeling and clustering chain, including real-time parameter identification options. Also, using simple and robust driver modeling, applications with lower computational capacity--especially car manufacturers in the lower vehicle categories--are possible, enhancing the ADAS functionality in a wide range of vehicles. The main contributions are the following:  
* We cluster drivers based on their curve path preferences, using the Linear Driver Model and its parameters, and in this way, behavioral differences are quantified. It is shown that the clusters are indeed different in their curve driving behavior. Two groups of dynamic and cautious drivers are created that are used as reference groups for the classification. * An Extended Kalman Filter-based approach is proposed to learn the driver model parameters. It is proven that this solution is suitable for embedded applications as its computational needs are low, while its convergence to the real parameter values is fast. This way, a compact solution is introduced that can be directly used for the next generation of lane centering systems.