esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Circular Rectenna for RF-Energy Harvesting at ISM Band
## 2 Rectenna Design  
### Monopole Receiving Antenna Design  
A simple compact microstrip circular patch (CMCP) antenna layout geometry and the fabricated antenna are depicted in Figure 1. The CMCP antenna is printed on a cheap FR4 material with an \(\varepsilon_{r}=4.3\), and loss tangent (tan) is 0.025. The substrate length and width are L\({}_{\text{S}}\) and W\({}_{\text{S}}\) with a thickness of 0.8 mm. A monopole circular-shaped patch of radius r is printed on the substrate and fed with a microstrip line of length and width of L\({}_{\text{F}}\) and W\({}_{\text{F}}\). To improve the impedance bandwidth, a reduced ground plane is placed on the back side of the substrate material with the length and width of L\({}_{\text{G}}\) and W\({}_{\text{G}}\). The geometrical variables of the CMCP antenna are depicted in Table 1.  
The circular patch radius r is obtained by using effective radius \(r_{e}\)  
\[r_{e}=\frac{8.79\times 10^{9}}{f_{r}\sqrt{\varepsilon_{r}}} \tag{1}\]  
\[r=\frac{r_{e}}{\left(1+\frac{2h}{\pi\varepsilon_{r}r_{e}}\left[\ln\left(\frac {1.57r_{e}}{h}\right)+1.78\right]\right)^{\frac{1}{2}}} \tag{2}\]A simple circular patch antenna can be analyzed as a parallel connection of capacitance \(C_{P}\), inductance \(L_{P}\), and resistance \(R_{P}\). The partial ground plane (PGP) is analyzed as a parallel combination of capacitance \(C_{g}\), inductance \(L_{g}\), and resistance \(R_{g}\). The two resonant circuits, one for the circular patch and another one for the partial ground plane, are coupled through the mutual capacitance \(C_{S}\). Thus, the overall analogous circuit of the designed CMCP antenna is depicted in Figure 2 and simulated using ADS. The S-parameter is compared with the CST model of the antenna for validation of the equivalent circuit model in the ADS and depicted in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values (mm)** \\ \hline r & 17.5 \\ \hline W\({}_{S}\) & 50 \\ \hline L\({}_{S}\) & 50 \\ \hline W\({}_{F}\) & 1.5 \\ \hline L\({}_{F}\) & 12 \\ \hline W\({}_{G}\) & 50 \\ \hline L\({}_{G}\) & 12.5 \\ \hline \end{tabular}
\end{table}
Table 1: The CMCP antenna’s optimal geometric dimensions.  
Figure 1: The geometry of the compact microstrip circular patch antenna and photo of the fabricated antenna: (**a**) top face; (**b**) rear face.
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles
### Using ERA5 Data for Validation  
We use a BA forward operator to convert ERA5 temperature/water vapor profiles to bending angle profiles. Note that the COSMIC-2 RO data in this study had not been assimilated into ERA5 for the study period (October 2019). The RO assimilation to ECMWF was started a few months later, on 25 March 2020. The ERA5 datasets are used to simulate the bending angle profiles, which are used to validate the bending angle profile derived from the STAR RO processing package. The ERA5 temperature, pressure, and moisture are collocated with the bending angle profiles based on RO tangent point longitude, latitude, and the RO occultation time. Then, the collocated ERA5 datasets are linearly interpolated in space and time to match the bending angle profile. The spatially and temporally interpolated ERA5 datasets are then converted to refractivity using:  
\[N\ =\ 77.6\frac{P}{T}+3.73\times 10^{5}\frac{e}{T^{2}} \tag{3}\]  
where \(N\) is the refractivity to be calculated, \(P\) is the pressure and \(T\) is temperature, and \(e\) is the moisture vapor pressure.  
Cubic spline vertical interpolation of the ERA5 profiles is carried out to get a finer resolution profile to reduce the vertical oscillation in bending angle. The super refraction points are firstly determined and removed from refractivity profiles. The forward operation uses composite Simpson's integration of the Abel integral equation to convert refractivity to bending angle. The composite Simpson's integration and the cubic vertical interpolation assure the smaller oscillation in the vertical integration of bending angle profiles than other tested schemes.
## 2 Description of MCP Scenarios for Multiple Markets
### Building MCP Scenarios Based on the Copula Theory
the MCP of market \(i\) is subject to the Gaussian distribution with the mean value \(\hat{\lambda}_{i,t}\) and variance \(\sigma_{i,t}\). The probability density function (PDF) is:  
\[h(\lambda_{i,t})=\frac{1}{\sqrt{2\pi}\sigma_{i,t}}e^{-\frac{(\lambda_{i,t}- \lambda_{i,t})^{2}}{2\sigma_{i,t}^{2}}}\,\lambda_{i,t}^{\min}\leq\lambda_{i,t}\leq\lambda_{i,t}^{\max} \tag{4}\]  
Then, the marginal cumulative distribution function \(H\) is obtained by the integral of \(h(\lambda_{i,t})\). That is:  
\[H_{i}(\lambda_{i,t})=P\Big{\{}\xi\leq\lambda_{i,t}\Big{\}}=\int_{\lambda_{i,t} ^{\min}}^{\lambda_{i,t}}h(s)ds \tag{5}\]  
(2) Identify parameters for the Copula function. According to the characteristics of marginal distribution functions, appropriate Copula functions were firstly selected, and the unknown parameter \(\alpha\) was estimated by the maximum likelihood estimate method, and the estimated value is:  
\[\hat{\alpha}=\text{argmax}\!\!\sum_{k=1}^{K}\ln c(H_{1}(\lambda_{1,t}),H_{2}( \lambda_{2,t});\alpha) \tag{6}\]  
where, \(c(H_{1}(\lambda_{1,t}),H_{2}(\lambda_{2,t});\alpha)\) is the density function of the Copula function \(C(u_{1},u_{2};\alpha)\). (3) Check the fitting performance of the Copula function. The squared Euclidean distance \(d^{2}\) is used and its expression is:  
\[d^{2}=\sum_{i=1}^{n}\left|C(u_{1,i},u_{2,i};\alpha)-G(u_{1,i},u_{2,i};\alpha) \right|^{2} \tag{7}\]  
where, \(n\) is the number of samples, \(C(u_{1},u_{2};\alpha)\) is the Copula function, and \(G(u_{1},u_{2};\alpha)\) is the empirical distribution function. The smaller the value is, the better the fitting performance is. (4) Generate the combined MCP scenarios. \(S\times I\) dimensional data samples were generated by means of uniform discretization. \(S\) and \(I\) indicate, respectively, the number of samples and random variables. For each sample \(\left(u_{1}^{s},u_{2}^{s}\right)\), its combined MCP scenario vector \(\xi_{s}=\left(\lambda_{1,t}^{s},\lambda_{2,t}^{s}\right)\) could be obtained by the inverse operation of the marginal distribution function, i.e., \(\lambda_{i,t}=H_{i}^{-1}(u_{i})\), and the probability of the scenario is \(P_{r}(\xi_{s})=1/S\).
## 1 Introduction
### Related Work  
In recent work, we found some examples that used a DNN to solve optimization problems in wireless communications. In [10], [PERSON] et al., proposed a DNN scheme for real-time interference management over interference-limited channels. They provided a fairly accurate approximation of a popular algorithm, named weighted minimum mean squared error (WMMSE). In [14], [PERSON] et al., discussed a DL-based scheme to estimate channel parameters for wireless energy transfer. They proposed a deep autoencoder as a solution to learn the channel parameters autonomously at the energy transmitter based on the feedback from the energy receivers. They also proposed an adaptive solution to optimize the pilot signals. The proposed scheme optimized the channel parameter well, in comparison to the Gerchberg-Saxton method. In [15], [PERSON] et al., proposed learning-based wireless powered secure transmission, in which they exploited the potential of deep learning to maximize the effective secrecy throughput of wireless powered systems. They proved that their proposed schemes were much faster than a conventional exhaustive search scheme proposed in previous research. [PERSON] et al., [16] proposed a convolutional neural network (CNN)-based DNN to control the transmit power in device-to-device communications. They showed that their proposed scheme achieved high spectral efficiency while regulating the interference with low computation time in comparison to other conventional schemes from numerical optimization.
# Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
## 3 Results
### Social Media Posts  
For the social media entries dataset, we adhered to the same workflow as in the previous datasets. Consequently, we examined the intertopic distance map (Figure 17) and the HC dendrogram (Figure 18). Following this, we conducted a comprehensive review of the more representative Reddit posts. Upon the conclusion of our analysis, we proceeded to merge the topics, as outlined in the following list: {0, 6}, {1, 4, 24, 34}, {2, 8, 23, 36}, {7, 10}, {11, 16}, {13, 35}, {14, 20}, {19, 22, 32}, {21, 37, 39}, {26, 29, 38}]. Topics 25, 28, 31, 33, and 40 were considered as outliers.  
Figure 17: Intertopic distance map for social media posts.  
Figure 18: Dendrogram for social media posts.  
Subsequently, we analyzed the refined model and labeled the resulting topics. Figure 19 illustrates the labeled topics along with the c-TF-IDF scores of the most frequently occurring words within each topic. Figure 20 depicts the hierarchical structure of the refined topic model. Figure 21 portrays the hierarchical arrangement of the documents within their corresponding topics, enriched with embeddings. Figure 22 showcases a heatmap that presents a matrix illustrating the degrees of similarity among distinct topics.  
Figure 19: Topic word scores for social media posts.  
Figure 20: Dendrogram for the refined model of social media posts.  
Figure 21: Hierarchical document structure of corresponding topics for social media posts.  
Figure 22: Similarity matrix for social media posts.  
We observed the emergence of certain shared topics across all datasets, along with a variety of identical topics within each dataset, as summarized in Table 2.  
Certain topics are core concepts within the smart city paradigm, such as \"traffic management\", \"IoT\", \"air pollution\", \"healthcare\", \"security\", \"smart grid\", etc., as evidenced by their presence across all datasets. Within the dataset of scientific publication abstracts, we have identified more specialized topics, including \"edge computing\", \"machine learning\", \"semantics\", \"antennas\", \"blockchain\", and others, as detailed in the related list. It is interesting to focus on topics that are absent from the abstracts but prevalent in either the news blog posts or the social media posts datasets. For instance, topics like \"business\", \"investments\", and \"banking\" emerge in the news blog posts dataset, while topics related to the everyday lives of citizens, such as \"sidewalks\", \"green spaces\", \"shopping\", \"food\", \"street art\", \"suburban\", and \"career\" exclusively feature in the social media posts dataset. This phenomenon raises points for discussion, as elaborated upon in Section 4.
# Minimization of the Decoupling Circuit (DC-Link) in Induction Motors
## 2 Energy Flow Analysis of the Study Case
### Case Study Selection
#### 2.1.1 Torque Variation  
In this first operation profile, different slopes in the operation of the electric vehicle are emulated since these situations require different motor powers. For this purpose, a load variation technique is used to simulate the variation of the conditions of use of the electric vehicle.  
For the analysis, different levels of torque variation are used to evaluate the power behavior of the system. These levels range from the nominal torque of the motor up to 10% of the motor (Figure 3) and are essential to simulate high and low power demand situations in the vehicle applications.  
Figure 4 shows the inverter current (\(i_{inv}\)) at different intervals of the electric vehicle operation. It can be observed that there are intervals in which there is an energy return, which means that the current is negative. Emphasizing that the purpose of the variations presented is to observe the interval in which the greatest amount of energy is returned to the source.  
The previous figure is important because it allows observation of the direction and magnitude of the electric current flowing through the inverter, which provides information about the energy behavior of the system. With the data provided in Figures 3 and 4, it is emphasized that for this work scenario, the highest level of current return to the capacitor is presented at the moment of varying the torque from 50% to 10% of the nominal load.
## 3 Results and Discussions  
Numerical analyses were made to compare composite columns with standard and optimally designed steel profiles. As a result of the analysis, load-displacement curves of composite columns were obtained, and they can be seen in Figure 14. While the axial load was kept constant in the analyses, the lateral load was increased from zero to the maximum column-carrying capacity. In both composite column analyses, failure occurred due to the rupture in the longitudinal reinforcement of the columns. As can be seen from the graphics, increasing the height of the steel profile increases the maximum load-carrying capacity of the composite column by 19% and the energy absorption capacity by 24%. Figure 15 shows the von Mises stresses occurring in steel profiles in composite columns before failure.  
Figure 14: Load–displacement curves of composite columns designed with standard steel profiles, and optimally designed steel profiles.  
Figure 13: Section properties (**a**) composite column with normal steel profile (**b**) composite column with optimum designed steel profile.  
While the standard steel profile reached a maximum Von Mises stress of 272 MPa before failure, the maximum stress in the optimally designed steel profile increased to 288 MPa. This shows that the optimally designed steel profile works more effectively. The failure of the two composite columns was caused by the breakage of the longitudinal reinforcement. A comparison of composite columns is shown in Table 2.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
###### Abstract  
A lane change is one of the most important driving scenarios for autonomous driving vehicles. This paper proposes a safe and comfort-oriented algorithm for an autonomous vehicle to perform lane changes on a straight and level road. A simplified Gray Prediction Model is designed to estimate the driving status of surrounding vehicles, and time-variant safety margins are employed during the trajectory planning to ensure a safe maneuver. The algorithm is able to adapt its lane changing strategy based on traffic situation and passenger demands, and features condition-triggered rerouting to handle unexpected traffic situations. The concept of dynamic safety margins with different settings of parameters gives a customizable feature for the autonomous lane changing control. The effect of the algorithm is verified within a self-developed traffic simulation system.  
autonomous driving; lane change; trajectory planning; safety margin Article
# Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
## 3 Materials and Methods
### Extrinsic Evaluation on NLP Downstream Task  
To understand if the synthetic data and annotations have sufficient utility to be used for training of NLP models, we measure effectiveness in a de-identification downstream task. The objective of de-identification is to detect instances of PHI in text, such as names, dates, addresses and professions [9]. Ideally, a de-identification model trained on synthetic  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **LSTM** & **GPT2** \\ \hline Tokenizer & spaCy, replace low-frequency tokens (\(<=10\)) with \textless{}unk\textgreater{} &
\begin{tabular}{c} Trained English “ByteLevelBPE \\ Tokenizer” on Dutch corpus, while \\ keeping embeddings for common tokens. \\ \end{tabular} \\ \multirow{3}{*}{Model} & 2-layer LSTM (650 input & GPT-2 English small (12-layer, \\  & embedding size, 650 hidden & 768-hidden, 12-heads, 117M parameters \\  & units, softmax output) [7] & before fine-tuning) [6] \\ Vocabulary & 49,978 tokens & 50,257 tokens \\ Parameters & 39,307,380 & 163,037,184 (after fine-tuning) \\ Perplexity & 32.1 & 38.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of language models used to generate synthetic text. Note that the test perplexity cannot be directly compared due to the difference in vocabulary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Corpus** & **Model** & **Generation Method** & **Tokens/Doc.** \\ \hline LSTM-p & LSTM & p-sampling (\(p=0.95\)) & 50–400 \\ LSTM-temp & LSTM & Temperature sampling (\(t=1\)) & 50–500 \\ GPT-p & GPT-2 & p-sampling (\(p=0.95\)) & 50–400 \\ GPT-beam & GPT-2 & Beam search (beams \(n=5\)) & 50–500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of language model decoding parameters to generate four synthetic corpora.  
data performs as good or better than a model trained on real data. To evaluate this, we train a BiLSTM-CRF de-identification model in three settings: (1) using real data, (2) extending real with synthetic data, and (3) using only synthetic data (Figure 3). As implementation for the BiLSTM-CRF, we use \"deidentify\" ([[https://github.com/nedap/deidentify](https://github.com/nedap/deidentify)]([https://github.com/nedap/deidentify](https://github.com/nedap/deidentify)), accessed on 19 May 2021) with the same architecture and hyperparameters as reported in the original paper [10]. As real data, we use the NUT corpus of that study with the same test split such that results are comparable. NUT consists of 1260 records with gold-standard PHI annotations.  
The effectiveness of the de-identification models is measured by entity-level precision, recall and F1. The BiLSTM-CRF trained on real data is considered as the upper baseline for this problem. We also report scores of a rule-based system (DEDUCE [30]) which gives a performance estimate in the absence of any real or synthetic training data.
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 6 Conclusions  
In this paper, we have presented a multitier hierarchical network architecture with the support of edge computing that includes the underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork. Based on the network architecture, we have formulated an MO problem to minimize the total network deployment cost and maximize the network lifetime. To solve the MO problem, we have proposed an efficient algorithm, namely, AC-ETO, and analyzed its time complexity. The proposed algorithm approaches the optimal solutions under different network scales with polynomial time. We will jointly study the network deployment of static ocean sensors and the trajectory design of mobile ocean vehicles in our future work.  
Conceptualization, J.-L.D. and Y.-X.L.; methodology, J.-L.D., L.X.C., and B.L.; software, J.-L.D.; validation, J.-L.D. and L.X.C.; writing--original draft preparation, J.-L.D., L.X.C., and B.L.; visualization, J.-L.D.; supervision, J.-L.D., B.L., and Y.W.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China under 61971083 and 51939001 and Dalian Science and Technology Innovation Fund under 2019J11 CY015.  
Figure 8: Comparison of AC-ETO and a greedy algorithm in terms of the average deployment cost and energy consumption of twenty execution times under various scenarios.
## 3 Simulation Examples
### Mode Shape Extraction Using Displacement Approximation of Contact Points
#### 3.3.4 The Influence of Noise Interference at the Specified Frequency Band  
By controlling the level of white noise (1%, 5%, 10%) and combining with a low-pass filter to generate three kinds of band-limited noises of 10 Hz, 20 Hz, and 50 Hz, the influence of noise frequency components on the modal recognition of vehicle vibration signals was systematically evaluated. The experimental results show that, as shown in Figures 26-28, at the 10 HZ frequency component, the MAC is close to 1, verifying the robustness of this method at the 10 Hz frequency component, which is highly consistent with the anti-interference requirements of the main frequency band in the actual bridge monitoring scenario.  
Figure 25: The first three beam mode shapes from the approximate displacement residual STFT-based extraction after being denoised by CEEMDAN-IWT under noise levels of 1%, 5%, and 10%, compared to the analytical first mode shape (**a**), the second mode shape (**b**), and the third mode shape (**c**).  
Figure 27: The first three beam mode shapes using the approximate displacement residual STFT-based extraction when adding the band-limited noise of 0–20 Hz at the levels of 1%, 5%, and 10%, respectively, compared to the analytical first mode shape (**a**), the second mode shape (**b**), and the third mode shape (**c**).  
Figure 26: The first three beam mode shapes using the approximate displacement residual STFT-based extraction when adding the band-limited noise of 0–10 Hz at the levels of 1%, 5%, and 10%, respectively, compared to the analytical first mode shape (**a**), the second mode shape (**b**), and the third mode shape (**c**).
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Ratio Features from PPG Signals
#### 2.5.1 AC/DCs as Features  
The AC-to-DC ratio (AC/DC) represents the ratio of the pulsatile (i.e., AC) to the baseline or static (i.e., DC) component of the PPG signal. The relation between the AC/DC value and blood glucose level may be causal. However, there is not much research on  
Figure 2: IMFs for the blue channel of the first segment of subject 1 obtained through EMD. (**a**) original signal, (**b**) IMF 1, (**c**) IMF 2, (**d**) IMF 3, (**e**) IMF 4, (**f**) IMF 5, (**g**) residual.  
this subject. A proportional association between AC/DC values and glucose levels was demonstrated in one study [15]. The relationship between blood glucose levels and AC/DC values of PPG signals was demonstrated in another study [16]. A recent study [17] showed a correlation between perfusion index (PI) and blood glucose concentration. The perfusion index is a measure of pulse strength in a specific area of the body and is a relative assessment of the blood flow to that area and similar to the AC/DC. They suggested that AC/DC as PI could be a reliable indicator for noninvasive blood glucose estimation. Blood glucose affects light scattering, and as glucose level in the blood rises, the amount of light scattered decreases. As a result, the total absorbed light decreases. PI, which depends linearly on the magnitude of the pulsatile component of blood flow, also decreases. They showed a negative linear correlation between PI and blood glucose concentration. The AC (or DC) detection from a PPG signal was performed using the AC (or DC) determination algorithm [18]. Figure 3 shows AC/DC values versus glucose levels for 34 subjects used in this study.  
The observed characteristics of the PPG signal are influenced by the wavelength of the light used in PPG sensors. In Figure 3, we can see the higher AC/DC for green wavelength compared with red and blue wavelengths. The AC/DC value of the reflected PPG signal, which is a critical factor in determining the quality of the PPG signal, varies with the light wavelength. Specifically, green and yellow light yield higher AC/DC values, typically between 3 and 5%, while red and infrared (IR) light produce a significantly lower value, about 0.5 to 1% [19]. This variation is important because a higher AC/DC value generally indicates a stronger PPG signal. Also, the green light has an inherently higher perfusion index (PI), which correlates with a stronger PPG signal [20].