esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Classification Analysis
Figure 11 shows the training and validation accuracy and loss on both datasets for the proposed model using the SMOTE dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multicolumn{6}{c}{**Kaggle Dataset**} & \multicolumn{6}{c}{**FigShare Dataset**} \\ \cline{2-11}  & **A (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **RMSE (\%)** & **A (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **RMSE (\%)** \\ \hline Original dataset & 92.0 & 91.5 & 91.2 & 91.3 & 8.0 & 92.2 & 91.0 & 90.8 & 90.9 & 7.8 \\ \hline Basic augmentation & 95.0 & 94.6 & 94.4 & 94.5 & 5.5 & 95.3 & 94.7 & 94.2 & 94.4 & 5.3 \\ \hline SMOTE & 99.0 & 98.7 & 98.5 & 98.6 & 2.0 & 99.2 & 98.8 & 98.6 & 98.7 & 1.8 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance of the GATransformer model on different employed augmentation techniques. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multicolumn{6}{c}{**Kaggle Dataset**} & \multicolumn{6}{c}{**FigShare Dataset**} \\ \cline{2-11}  & **A (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **RMSE (\%)** & **A (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **RMSE (\%)** \\ \hline Original dataset & 85.0 & 84.2 & 83.8 & 84.0 & 6.6 & 85.3 & 83.4 & 82.0 & 82.7 & 9.5 \\ \hline Basic augmentation & 85.0 & 84.2 & 83.8 & 84.0 & 6.6 & 85.3 & 83.4 & 82.0 & 82.7 & 9.5 \\ \hline SMOTE & 87.1 & 86.7 & 86.3 & 86.5 & 5.5 & 87.5 & 86.4 & 86.0 & 86.2 & 8.3 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance of the best combination in LMA on different employed augmentation techniques. Figure 11: Comparison of the training and validation accuracy and loss of GATransformer model on the SMOTE augmented datasets; (**a**) training and validation accuracy comparison on Kaggle dataset; (**b**) training and validation loss comparison on Kaggle dataset; (**c**) training and validation accuracy comparison on FigShare dataset; and (**d**) training and validation loss comparison on FigShare dataset. Figure 10: Confusion matrix of GATransformer model on both datasets using SMOTE data; (**a**) confusion matrix for Kaggle dataset and (**b**) confusion matrix for FigShare dataset.
# VENuS-Derived NDVI and REIP at Different View Azimuth Angles
## 2 Materials and Methods
### Change Vector Analysis  
The change vector analysis (CVA) is a robust methodology for detecting multivariant changes from two or more spectral bands, spectral features, or biophysical indicators (e.g., spectral indices) associated with the pixel of an image [12]. In the current study, CVA was computed to measure the magnitude and direction of the change in NDVI and REIP (Equations (1) and (2)) versus the albedo \(\alpha\) (Equation (3)), between the two tiles (i.e., S01 with respect to W08), and across all pixels in the overlap zone. The VIs represent vegetated pixels since the high index value indicates more biomass, while the albedo represents bare light-colored soil since higher values indicate a more exposed non-vegetated surface. First, these variables were computed in the overlap zone, and then, the VIs were normalized to values between 0 and 1 to eliminate the bias of the CVA magnitude due to the dynamic range of each index. Finally, the CVA was performed pixel-by-pixel between the reference dataset W08 and the target dataset S01. The output of the CVA consists of the change vector (CV) magnitude (Equation (5)) and the change direction (\(\theta\), Equation (6)). The CV magnitude, \(|\)CV\(|\), is the Euclidean distance of the vector between the reference and the target datasets:  
\[|\text{CV}|=\sqrt{\left(\text{nVI}_{\text{S01}}-\text{nVI}_{\text{W08}}\right) ^{2}+\left(\alpha_{\text{S01}}-\alpha_{\text{W08}}\right)^{2}} \tag{5}\]  
where nVI is the normalized vegetation index, and \(\alpha\) is the albedo. A threshold was defined in order to distinguish between changed and unchanged pixels (i.e., noise). One standard deviation (1-STD) was selected as a threshold [13; 14; 15]. The direction of the change vector is represented by the angle \(\theta\), as depicted in Figure 2:  
\[\tan\theta=\frac{\text{nVI}_{\text{S01}}-\text{nVI}_{\text{W08}}}{\alpha_{ \text{S01}}-\alpha_{\text{W08}}} \tag{6}\]  
where nVI is the normalized vegetation index, \(\alpha\) is the albedo, and subscript S01 and W08 indicate the relevant tile.  
Figure 2: A graphical scheme illustrates how the change vector analysis (CVA) equations were applied to calculate the change magnitude and change direction (angle \(\theta\)) for the VEN\(\upmu\)S-derived NDVI, REIP, and albedo and between the reference tile W08 and the target tile S01. The change magnitude is represented by the distance of S01 from W08, and the change direction is represented by the degree angle in the range between 0\({}^{\circ}\) and 360\({}^{\circ}\). The direction value range is divided into four main classes of 90\({}^{\circ}\) (i.e., quadrant).
# Can Blockchain Strengthen the Energy Internet?
## 1 Introduction
The significance of inherent features of the blockchain, utilized towards realization of the next generation of smart grids in the identified directions, have been illustrated in Figure 2. Challenges pertaining to each identified direction and the role of blockchain utilization have been discussed, summarizing the future research directive in achieving fully autonomous and distributed electricity distribution networks [15]. Hence, this paper aims to present a clear insight of blockchain utilization for strengthening EI grids to deliver maximum benefits to all participating stakeholders. Figure 1: Overview of the bi-directional energy and information flows in the applications of the envisaged EI grids. The rest of the paper is organized as follows: Section 2 provides an insight regarding how blockchains can facilitate futuristic electricity distribution to be sustainable through heterogeneity. Section 3 highlights the improvement achieved in security, privacy and trust establishment without the involvement of a third party, related to operations of the future EI grids. The benefits identified through the integration of blockchains include mitigating cyber-physical attacks leading to disruptions in the electricity supply, preventing breaches of privacy and instating trust in distributed operation. Section 4 signifies the contribution of blockchain-based EI grids towards achieving grid stability and reliability, while Section 5 discusses the benefits offered through improved scalability to cater to the increasing number of stakeholders connecting. Sections 6 and 7 elaborate on the role of blockchain to facilitate predictive data analysis integrated with EI data management. Each of these sections highlight the role of blockchains in this progression and the way forward in each individual aspect.
# A Comparative Study of On-Body Radio-Frequency Links in the 420 MHz-2.4 GHz Range
## 2 Materials and Methods
### RF Technologies
#### 2.2.1 915 MHz Printed Folded Dipoles for UHF RFID
During these measurements, we used the same antennas, VNA, on-body setup, statistical processing and path loss model as described above for the reference scenario.
# Artificial-Intelligence-Based Charger Deployment in Wireless Reshargeable Sensor Networks
## 5 Simulation Results
### Comparison of Computational Time  
In this section, we compare the computational time while the number of sensor nodes increased from 50 to 400. The \(x\)-axis represents the number of sensors, and the \(y\)-axis represents computational time. To ensure objectivity and accuracy, all of the algorithms performed 1000 iterations.  
In Figure 4, the computational time of LSABC is less than that of SABC because the layoff algorithm can remove unnecessary candidate chargers to reduce computational time. Most of the computational time is used for calculating the covered sensor nodes.  
Figure 4: Comparison of computational time.  
The computational time of LTSBC is less than that of TSBC. The reason is the same as for TSABC; the layoff algorithm can remove unnecessary candidate chargers to reduce computational time. In LTSBC and TSBC, most of the computational time is used for calculating covered sensor nodes and checking the tabu list, so it will take more computational time than TSABC.  
In LGABC and GABC, the computational time is used for checking which sensor nodes are covered by candidate chargers. The computational time will increase if more candidate chargers are deployed. The layoff algorithm can improve the convergence of LGABC, so the computational time can be reduced at the same time.  
The computational time of LACOBC is less than that of ACOBC; thus, LACOBC will record the CCL in the beginning. However, ACOBC will calculate the number of covered sensor nodes when ants arrive at a new node in every iteration, and it will require more computational time. The difference in computational time between LACOBC and ACOBC will be large if the sensor nodes increase. The layoff algorithm uses the CCL table to record the covered sensor nodes and change the method used to find the solution; this application reduces the computational time.  
Obviously, LGABC and LACOBC will require more computational time than LTSABC and LSABC because they are complicated methods. In LTSBC or LSABC, there is only one solution in every iteration, whereas LGABC and LACOBC have multiple solutions in every iteration; therefore, they will require more computational time to find the best solution.
## 4 Discussion
### Limitations  
Several limitations must be considered when interpreting these findings. These findings are only generalizable to PWD in residential care facilities with the ability to stand and walk with minimal assistance. We only recruited from two residential care facilities, and there was limited population heterogeneity among participants. The participants varied in functional impairments and dementia severity. Dementia severity also impacts mobility [4]. Polypharmacy was observed in most participants. Furthermore, our participant sample included only 35.7% females, even though females are more likely to acquire dementia, and it is unclear whether this affected the results [60]. While all participants had a confirmed diagnosis of dementia, the dementia subtype was often not available. This missing data may limit and/or confound our findings, as different causes of dementia and dementia subtypes manifest differently and have varying disease progression. Specifically, some subtypes of dementia, such as progressive supranuclear palsy, dementia with Lewy body dementia, hydrocephalus, and vascular dementia, alter gait and fall risk more than other subtypes, such as Alzheimer's dementia or frontotemporal dementia [61]. Our small sample size limited our statistical comparisons. Larger and more longitudinal trials are needed to ascertain the effect of exercise on functional mobility among PWD. There may have been attrition bias, as only \(n=9\) participants adhered to at least 2x/week exercise for our Per Protocol analysis; however, similar results were observed in the ITT analysis and Per Protocol analysis. While the inertial sensor placement may have varied across assessors, all assessors were trained to ensure standardization. The scalability, implementation, and cost-effectiveness of tailored exercise interventions for PWD require further research.
# Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
## 2 Problem Statement and Model Setup
### Optimization Model M0
#### 2.1.1 Pre-Processing Procedures for Categorical Features  
Given the feature sets, we assume that the index set of categorical features is denoted by \(C\), and the index set of numerical features is denoted by \(U\); thus, we have \(|C|+|U|=p\). Our challenge revolves around appropriately integrating categorical features \(c\) (\(c\in C\)) into the optimization model, while numerical features \(u\) (\(u\in U\)) do not pose such a concern.  
To accommodate the categorical features, one-hot encoding is employed. Essentially, this process takes a categorical feature and produces binary columns for each category of the feature. Assume that feature \(c\) (\(c\in C\)) is a categorical feature with \(l_{c}\) categories; for \(x_{i}\) (\(i\in\{0,1,\ldots,N\}\)) (note that we simultaneously consider known data samples and the new observation), we can obtain an \(l_{c}\)-dimensional column vector \(x_{i}^{
###### Abstract  
In 2023, we proposed the modified delay and Doppler profiler (mDDP) as an inter-carrier interference (ICI) countermeasure for underwater acoustic orthogonal frequency division multiplexing (OFDM) mobile communications in a multipath environment. However, the performance improvement in the computer simulation and pool experiments was not significant. In a subsequent study, the accuracy of the channel transfer function (CTF), which is the input for the mDDP channel parameter estimation, was considered insufficient. Then a sequential ICI canceling mDDP was devised. This paper presents simulations of underwater OFDM communications using an iterative one- to three-step mDDP. The non-reflective pool experiment conditions are a two-wave multipath environment where the receiving transducer moves at a speed of 0.25 m/s and is subjected to a Doppler shift in the opposite direction. As NumCOL, the number of taps in the multitap equalizer which removes ICI, was increased, the bit error rate (BER) of 0.0526661 at NumCOL = 1 was significantly reduced by a factor of approximately 45 to a BER of 0.0011655 at NumCOL = 51 for the sequential ICI canceling mDDP.  
underwater acoustic communication; orthogonal frequency division multiplexing; channel estimation; inter-carrier interference; modified delay and Doppler profiler
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 4 Simulation Results and Analysis
### Fairness Analysis  
In this section, we compare the fairness of the algorithms, as shown in Table 3. Assuming that the dimension of the transmission signal corresponding to each user is equal to the number of receiving antennas, we use the fairness factor to measure the fairness of the algorithm, and define fairness factor as [19]:  
\[F(i)=\frac{\left(\sum\limits_{i=1}^{k}x_{i}\right)^{2}}{\sum\limits_{i=1}^{k}x _{i}^{2}} \tag{19}\]  
where \(x_{i}\) represents the average transmission rate of the user \(i\); \(F(i)\) ranges from 0 to 1, and the greater the value, the higher the fairness; \(F(i)=1\) means that users have received the same resources, and the fairness is highest at this time.
## 6 Discussion  
The results obtained from the mathematical modeling of the induction motor's starting behavior provide valuable insights into the transient and steady-state performance of the motor. The developed model, which takes into account current displacement in the rotor slots, shows a significant improvement in the prediction of starting current and torque characteristics. Specifically, the inclusion of current displacement results in a faster transition to steady-state operation and a reduction in the amplitude of the starting current, which are critical factors in enhancing energy efficiency and reducing mechanical stress during startup.  
In comparison to the existing literature, traditional models often neglect the current-displacement effect, leading to discrepancies in predicting motor dynamics, particularly during the transient phase. Studies such as those by [PERSON] [49] and [PERSON] [54] highlight the challenges of accurately capturing transient phenomena without considering these nonlinear effects. By incorporating current displacement, our model reduces these discrepancies, thus providing a more accurate representation of the induction motor's starting characteristics. Additionally, [PERSON] et al. [60] discuss various control strategies, including AI-based controllers for improving dynamic response, which also acknowledge  
Figure 14: IM-starting characteristics: rotation speed obtained through mathematical modeling and experimental measurements.  
Figure 15: IM-starting characteristics: electromagnetic torque obtained through mathematical modeling and experimental measurements.  
the importance of accurate modeling in transient conditions. Similarly, [PERSON] et al. [5] emphasize the impact of starting methods on induction motor performance. This research aligns with these studies in recognizing that accounting for complex physical behaviors, such as current displacement, significantly enhances the accuracy of motor simulations, particularly during start-up.  
While the model presented in this paper performs well under steady-state conditions and closely aligns with experimental data, certain discrepancies are observed during the initial transient phase. These differences are primarily attributed to model simplifications, such as assuming linear magnetic properties and excluding mechanical factors like friction and vibration. All these limitations have also been noted in previous studies, indicating that the inclusion of these factors could further enhance the model's accuracy.  
The comparison between the experimental and simulated data suggests that the proposed model effectively captures the general trends in the motor's behavior, but improvements are needed for better transient accuracy. Future work will focus on incorporating more detailed representations of mechanical losses and magnetic saturation effects to enhance the model's capability in predicting dynamic behavior during motor startup.
###### Abstract  
The large-scale production of high-purity hydrogen via pressure swing adsorption (PSA) remains a prominent research focus. This study develops a multi-component heat and mass transfer model for a lean hydrogen mixture (\(\mathrm{N_{2}/CO_{2}/H_{2}/CO}\) = 44.6 /35.4/19.9/0.1 mol%) on a coal-derived activated carbon (AC)/zeolite 13X layered bed to investigate its breakthrough curve and PSA purification performance. The model is implemented on the Aspen Adsorption platform and validated with published data. Parametric analysis of the breakthrough curve reveals that a high pressure and a low feed flow rate can delay the breakthrough of impurity gases. The simulated variations in pressure, purity, and recovery during the PSA cycle align with the published results. Studies on PSA cycle parameters show that, in general, a high pressure, a low feed flow rate, a short adsorption time, and a high P/F ratio improve purity but reduce recovery. The purity and recovery of the layered bed outperform those of the single-layer bed. Specifically, gradually modifying the AC/zeolite 13X length ratio from 10:0 to 5:5 enhances hydrogen purity, while adjusting it from 10:0 to 3:7 enhances hydrogen recovery. At AC/zeolite 13X = 5:5, the highest purity was 97.38%, while at AC/zeolite 13X = 3:7, the highest recovery was 49.13%.  
pressure swing adsorption; hydrogen purification; breakthrough curve; lean hydrogen mixture; layered bed
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 4 Baseline Clustering Strategies under Jamming
### Power Domain  
The use of optimum transmit power improves energy efficiency and extends the network lifetime. Therefore, the comparison between strategies that use fixed power (FP) in all nodes with strategies that use power allocation (PA) conveniently is the first starting point to establish benchmarks in our research.  
However, in practice the utilized hardware and/or legislation may impose upper and lower limits of potential transmit powers. Therefore, in some cases, the necessary power to transmit and ensure the total reception of the messages cannot be ensured. Consequently, some messages will be lost by the effect of the disruption signal generated by the jammer. Based on [47; 51], exists an \(SINR\) threshold that permits decoding the majority of packets correctly. Therefore, meeting this requirement according to the established maximum transmit power constraint determines the nodes that can communicate directly with their CH in each cluster.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 3 Preliminary
### The Need for an Asynchronous Stream-Sensing Technique
Under the LOS environment, the trigger message can be delivered to mobile devices over more than 400 m for all pathloss models. In almost all cases, the maximum distance is 600 m. That is, for all the path loss models, the maximum distance is larger than 30 m. For UMa and RMa even in NLOS, the maximum distance becomes greater than 120 m as the altitude of the UAV increases. Therefore, these results confirm that asynchronous transmission can frequently occur. Figure 1(b) shows the results only under the NLOS environment for various transmission powers and an UAV altitude of 25 m. The transmission power ranges from 22 to 36 dBm for the outdoor scenario [36]. The maximum communication distance is larger than 30 m for all the pathloss models. Moreover, for UMa and RMa, the maximum communication distance is greater than 120 m in almost all cases. Figure 2: The maximum communication distance for various pathloss models. In summary, we can conclude that it is necessary to develop a generally available asynchronous stream-sensing technique. Through our analysis, even in the NLOS environment, asynchronous transmission can occur frequently depending on the characteristics of the disaster area, the altitude of the UAV, and the transmission power.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 5 Conclusions  
This study has demonstrated the use of the tools Gmsh and GetDP, known together as ONELAB, as a single simulation platform for modeling both optical, as well as RF-induced, thermoacoustic computed tomography, i.e. PACT and RACT, respectively. To achieve PACT and RACT results, the propagation of optical, radio frequency, and acoustic waves were effectively modeled using solutions of the optical diffusion equation, Maxwell's equations, and time-domain wave equations. We validated our PACT and RACT algorithms using two types of tissue mimicking phantoms: a homogeneous phantom consisting of two absorbing targets and a breast phantom consisting of a tumor, with pre-defined optical and RF properties. Our results demonstrated that the optical and RF absorption properties of the respective tissue phantoms were accurately reconstructed using the proposed dual-modality computed tomography simulations in ONELAB. The use of the ONELAB software package allows for accurate multimodal thermoacoustic modeling, in order to develop and validate more robust algorithms for functional imaging of the human body.  
[PERSON] designed the simulations, analyzed the results, and wrote the paper. [PERSON] assisted in both the analysis of the results and writing of the paper.  
The authors would like to thank the NIH for funding this work under NIH-NIBIB 4R00 EB017729 ([PERSON]) and PennState Hershey Center Institute Cancer Startup Funds ([PERSON]).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ONELAB & Open Numerical Engineering Laboratory \\ GetDP & General Environment for the Treatment of Discrete Problems \\ RF & radio frequency \\ CT & computed tomography \\ PET & positron emission tomography \\ MRI & magnetic resonance imaging \\ UCT & ultrasound computed tomography \\ PACT & photoacoustic computed tomography \\ RACT & radio frequency acoustic computed tomography \\ TACT & thermoacoustic computed tomography \\ FEM & finite element method \\ FDM & finite difference method \\ PDE & partial differential equation \\ CAD & computer-aided design \\ \end{tabular}  
IGES initial graphics exchange specification  
STL stereolithography  
DICOM Digital Imaging and Communications in Medicine  
NIR near infrared  
TM transverse magnetic  
PML perfectly matched layer