esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Method
### Method  
SAR-estimated Doppler centroid \(f_{Dc}\) provided in the level-2 OCN product is associated with the relative motion between the radar sensor and the target. In the case of sea surface, \(f_{Dc}\) is influenced not only by the relative motion between the Earth and the satellite, but also by the surface currents and wind and wave fields. It can be decomposed into the following components:  
\[f_{Dc}=f_{Dp}+f_{em}+f_{sca}+f_{car}+f_{w} \tag{3}\]  
where \(f_{Dp}\) is the predicted (geometric) Doppler frequency, \(f_{em}\) is the antenna electronic miss-pointing term, \(f_{sca}\) is the scalloping signal term, \(f_{cur}\) is the current term, and \(f_{w}\) is wind-wave term. OCN RVL products contain the \(f_{Dc}\) term and the \(f_{Dp}\) term. The result obtained by subtracting \(f_{Dp}\) from \(f_{Dc}\) is the Doppler centroid anomaly \(f_{Dca}\). To obtain the geophysical components, the additional components of the non-geophysical Doppler term need to be removed first. The flowchart to correct DCA and the subsequent inversion is detailed in Figure 2. Each step is described in the next section.
## 3 Methodology
### Training (UMCL Dataset)
#### 3.6.2 Loss Function  
The loss function is essential to most machine learning pipelines. Usually, a model learns by minimizing its loss, which is derived from the difference between a ground truth sample and a set of predictions. In the case of class imbalance, many studies use Dice loss within the loss function [38]. A recent loss function known as the Tversky loss has also been created to mitigate this problem, but since DSC and IoU were the primary metrics, the loss function had incorporated Dice and IoU loss. However, few studies have shown that models trained on the Tversky Loss function have the potential to yield better segmentation performances if the weighted parameters of the loss function are experimentally determined [39]. Thus, using this function is a very viable future direction.  
The loss functions below include Dice loss and _IoU_ loss, which are simply the _DSC_ or _IoU_ subtracted from one.  
\[L_{Dice}=1-DSC \tag{3}\]  
\[L_{IoU}=1-IoU \tag{4}\]  
The binary cross entropy (_BCE_) function, also referred to as the log-loss function, is a standard loss function that is used in binary classification tasks [40]. Although the focus of this work had been on segmentation in lieu of classification, the segmentation process in this context is dependent on evaluating whether any given voxel is a lesion or not, which is a binary task. By using the logarithmic behavior in the _BCE_, these models had been much more penalized for wrong predictions of a given voxel.  
\[L_{BCE}=-\frac{1}{N}\sum_{i=1}^{N}(y_{i}\times log(p(y_{i}))+((1-y_{i})\times log (p(y_{i})) \tag{5}\]  
The _DSC_ and _IoU_ loss functions were weighted and combined with the _BCE_ function to obtain an aggregate loss function.  
\[L_{Aggregate}=1.0\times L_{Dice}+1.0\times L_{IoU}+0.8\times L_{BCE} \tag{6}\]  
The purpose of these weights were to allow the models' loss to correspond directly to the _DSC_ and _IoU_ while still being influenced by the _BCE_ loss but to a lesser extent. The theoretical maximum loss in this case would be infinite because _BCE_ does not have a maximum.
# Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking
## 1 Introduction
In [31], the hypergraph matching labeled multi-Bernoulli (HGM-LMB) filter is proposed to enhance the performance of data association. However, HGM-LMB cannot handle missed detections well. In order to overcome the limitation of HGM-LMB, we propose a joint probabilistic hypergraph matching labeled multi-Bernoulli (JPHGM-LMB) filter with all undetected cases being considered. In JPHGM-LMB, the likelihood is built based on group structure rather than distance between predicted states and measurements. Then, joint association probabilities can be obtained using the structure information. Next, these joint association probabilities are integrated into labeled multi-Bernoulli filter (LMB) by revising each single target likelihood. However, the computational complexity of the proposed approach exponentially grows with the number of targets since all undetected cases are considered, the proposed approach is usable in real time only for a limited number of targets. The rest of the paper is outlined as follows. Section 2 reviews the LMB filter and hypergraph matching. Our JPHGM-LMB is presented detailedly in Section 3. Extensive experiments are conducted to demonstrate the effectiveness of our proposed approach in Section 4. Conclusion is drawn in Section 5.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 2 Background
### FPGAs in Wearable Medical Devices  
Wearable medical devices require efficient power management to ensure long battery life and reliable performance. FPGAs offer unique opportunities for power optimization due to their reconfigurability, customizability, and granular control. However, selecting the right FPGA family and implementation technology is crucial. This section explores various FPGA families and implementation technologies optimized for low-power applications, particularly in wearable medical devices.
## 5 Experiments and Results
### Multi-Person Pose Estimation and Tracking
#### 5.1.1 Training  
During the training stage, we begin by pre-training our model on the COCO training dataset using annotations of the person category, which include bounding boxes and keypoints. We then fine-tune the model using the PoseTrack 2018 training dataset. To ensure consistent proportions, we use a fixed aspect ratio of 4:3 for the ground truth human bounding boxes. These boxes are subsequently cropped from the image and resized to a standardized resolution of 256 \(\times\) 192 pixels. Data augmentation techniques such as scale variation (\(\pm\)30%), rotation (\(\pm\)40%), and flipping are employed. Our feature extraction networks are pre-trained on the ImageNet Classification Task [35]. For optimization, we utilize the ADAM [40] optimizer with a mini-batch size of 32 images. The pose estimator model is trained for a total of 140 epochs on 4 GPUs. The learning rates for the first 90 epochs, the next 120 epochs, and the final 20 epochs are set to \(10^{-3}\), \(10^{-4}\), and \(10^{-5}\), respectively.
# NOMA-Based VLC Systems: A Comprehensive Review
## 6 Conclusions  
This article surveys the existing studies and research contributions of NOMA-based VLC systems. We provide a comprehensive review of NOMA schemes for VLC systems, which contains an extensive coverage of the existing literature. This study includes the integration of NOMA into VLC systems and indicates how it enhances the overall communication performance of VLC systems. We have shown, without a doubt, that the implementation of NOMA techniques in VLC is paramount to minimize the interference and enhance the performance of VLC systems. In this context, we provide detailed discussion about related works through existing literature. This article comprehensively discusses the extensive scope of NOMA-based VLC systems with the integration of several emerging technologies such as IRS, OFDM, UAV, ML, PLS, MIMO, etc. In addition, we briefly explain the benefits and shortcomings of incorporating NOMA-based hybrid RF/VLC systems. Furthermore, we highlight and summarize the potential challenges and open research issues. Finally, a number of future research directions associated with NOMA-based VLC systems have been presented for ameliorating the performance of the NOMA techniques into VLC systems. We firmly believe that this article will capture the interest of both academic and industrial framerities to investigate the potential of NOMA-based VLC systems in next generation wireless communication networks.  
Conceptualization, S.A.H.M. and Y.L.; methodology, S.A.H.M. and M.S. ([PERSON]): formal analysis, S.A.H.M., M.S. ([PERSON]) and S.V.S.; investigation, S.A.H.M., Y.L., S.V.S. and M.S. ([PERSON]); resources, S.A.H.M. and A.V.S.; data curation, Y.L. and A.V.S.; writing--original draft preparation, S.A.H.M. and Y.L.; writing--review and editing, S.A.H.M., Y.L., M.S. ([PERSON]), S.V.S. and M.S. ([PERSON]); visualization, A.V.S. and M.S. ([PERSON]); supervision, Y.L.; project administration, Y.L.; funding acquisition, A.V.S. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under grant 62261009, Ministry of Education Key Laboratory of Cognitive Radio and Information Processing (CRKL200106).  
Not applicable.  
The authors declare no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 2 Materials and Methods
### Experimental Set-Up  
Three-point bending static tests and fatigue lifetime tests on a rectangular beam (Figure 11) were performed using a servo hydraulic MTS-810 machine [41]. A 100-N/min loading rate was used for static testing, yielding an average ultimate stress of 289.38 MPa. A 0.75 Hz sinusoidal waveform at stress ratios (\(R\) = (\(\sigma\)/\(\sigma\)\({}_{\text{{\it{uhl}}}}\))) of 0.77, 0.70, 0.62, and 0.55 was used for fatigue testing. The contact stress was minimized by using damping rubbers around the loading pins. A total of twenty specimens were tested.  
Before any vibration test specimens were designed, the temperature behavior of several beams was examined under resonance, yielding a maximum increase of no more than 2 \({}^{\circ}\)C for all cases examined. This 2 \({}^{\circ}\)C increase in temperature was considerably less than that found in other works using three-point bending [12]. This effect was most probably due to the fact that the beam's motion under resonance provided a forced convective cooling effect not found in three-point bending tests.  
Vibration testing required the manufacturing of an optimal shape specimen. Specimen dimensions were exported to a computer numerical controlled (CNC) machine to manu  
Figure 11: Material MTS 810 test system.  
Figure 10: Comparison of the stress profiles obtained with Three-Point Bending and the final vibration test specimen (final [PERSON] design profile).  
facture the foam core shape. VARTM manufacturing was used to attach the face sheets. A 20-day drying period was used to ensure proper curing of the Epoxy resin. Figure 12 shows the final double cantilevered specimen used for cantilever beam testing.  
Vibration testing was performed with an electrodynamic VTS-VG150 shaker, with a loading capacity of 667 N and a maximum operating frequency of 4000 Hz. Shaker input signals were generated using a dynamic signal analyzer. The experimental setup is depicted in Figures 13 and 14. A verified FEA model, along with force transducer measurements, shaker amplifier input adjustments, and laser Doppler vibrometer measurements, ensured that the proper deflection was obtained, yielding the correct stress ratio at the desired area during testing. Beams were vibrated at their corresponding first resonance. As with three-point bending, five specimens were evaluated at each of the stress ratios used (0.77, 0.70, 0.62, and 0.55).  
Figure 12: Final sandwich specimens design used in fatigue vibration (total length 78 cm, end height 6 cm).  
Figure 13: Experimental setup for composite specimen vibration using the double cantilever technique.
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 2 Materials and Methods
### Sample Collection and Data Processing Scheme  
This study was conducted in Hefei, which is a typical prefecture-level city in eastern China. From 3 March to 6 March 2021, continuous observation of the wind field was carried out for 4 days. The experimental site was selected on the roof of the C4 Block in the Innovation Industrial Park (117.12\({}^{\circ}\)E, 31.85\({}^{\circ}\)N). The Lidar used was a 1550 nm eye-safe wind profile Lidar manufactured by Technovo Lidar High tech Co. Ltd. (model KC-WL-2006 Lidar). To prevent obstacles from blocking the laser beam, conical scanning using the probing beam at an elevation angle of \(\varphi=60^{\circ}\) was conducted in the experiment [29].  
Strict quality control of the data was carried out [33]. The sounding data were checked for error points using a threshold, and the error points were removed. The ground and other noises were removed from the instantaneous data. These quality-controlled datasets were used in our analyses. A combined vertical profile of the magnitude and direction of the horizontal wind speed was obtained every half hour. First, the three-dimensional wind field of the space atmosphere was measured using the wind profile coherent wind measurement Lidar via fixed detection at regular times. In the observation process, the horizontal and vertical wind profiles were analyzed online to verify the consistency of the wind field. In addition, a Lidar with the same parameters produced by the same company was used in the comparison experiment (KC-WL-2003 and KC-WL-2004). A total of 192 groups of wind profiles were collected. In the following sections, representative data were selected for analysis.  
In this study, the basic idea of coherent wind measurement Lidar data processing was to mix the received echo signal and to calculate the Doppler frequency shift through joint analysis of the time and frequency domains in order to synthesize the wind speed [42]. The time-domain analysis contains all of the information about the signal, which is more visual and can clearly illustrate the change in the signal. The frequency-domain analysis refers to the analysis of the time-domain signal through fast Fourier transform into the frequency domain, and the sampling of the time-domain signal follows the Nyquist sampling theorem. The specific data processing workflow is shown in Figure 3.  
Figure 2: Schematic diagram of eight-beam coherent wind measurement Lidar.  
As shown in Figure 3, the data processing was conducted as follows. The binary data collected by the wind profile Lidar were divided into several continuous distance gates according to chronological order through the measurement of the atmospheric wind field in Hefei during several consecutive days. In the digital processing circuit, the power spectrum of the sampled echo signal was estimated, and the Doppler frequency shift was extracted through cumulative averaging of the pulses [43,44]. Finally, the wind speed information was retrieved through the Doppler frequency shift in the echo signal.
## 4 Numerical Examples
### Nonuniformly Modulated Spectrum  
In this section, it was assumed that the autospectrum of the above nonstationary earthquake acceleration field was replaced by the nonuniformly modulated power spectrum model [26], given by  
\[S(f,t)={g_{1}}^{2}(t)\hat{K}_{1}(f)+{g_{2}}^{2}(t)\hat{K}_{2}(f) \tag{44}\]  
where \(g_{1}(t)\) and \(g_{2}(t)\) are the modulation functions and given by  
\[g_{1}(t)=\frac{\exp(-ct)-\exp(-dt)}{\max[\exp(-ct)-\exp(-dt)]} \tag{45}\]  
\[g_{2}(t)=\exp[\frac{(t-\varepsilon)^{2}}{2\mu^{2}}] \tag{46}\]  
in which \(c=0.25\), \(d=0.5\), \(\varepsilon=16\) s and \(\mu=4\) s, \(\hat{K}_{1}(f)\) and \(\hat{K}_{2}(f)\) are the stationary PSD functions determined by the one-sided Kanai-Tajimi spectrum, i.e.,  
\[\hat{K}(f)=4\pi S_{0}\frac{f_{g}^{4}+4\zeta_{g}^{2}f_{g}^{2}f^{2}}{(f^{2}-f_{g }^{2})^{2}+4\zeta_{g}^{2}f_{g}^{2}f^{2}} \tag{47}\]  
where \(S_{0}=0.1\) cm\({}^{2}\)/s\({}^{3}\); \(\zeta_{g}=0.25\); the parameters \(f_{g1}=5/(2\pi)\) Hz and \(f_{g2}=15/(2\pi)\) Hz were, respectively, used for \(\hat{K}_{1}(f)\) and \(\hat{K}_{2}(f)\). The coherence function was given by Equations (38)-(40).  
The basic parameters used for the simulation were given as: the total time \(T=102.4\) s; the cutoff frequency \(f_{u}=10\) Hz; the time step \(\Delta t=0.05\) s; and the frequency step \(\Delta f=1/102.4\) Hz. The POD interpolation parameters \(N_{\Phi}\), \(N_{\hat{\omega}}\) and \(N_{\hat{t}}\) and the interpolation parameter for Cholesky decomposition \(N_{\hat{\omega}}\) are given in Table 1.  
With a set of given phases \(\phi_{kl}(l=1,2,\dots,N)\), Figure 10 shows the simulated nonstationary earthquake acceleration time history of the first 30 s at the first simulation point. Further, 1000 earthquake acceleration samples were simulated to estimate the correlation function. The target correlation function could be calculated with Equation (7) based on the target spectrum and coherence function. As a typical example, Figure 11 compares the estimated correlation functions and the target at points one and five. Through the sample comparison and the correlation verification, the suggested method was also suitable for the nonstationary earthquake acceleration field described by the nonuniformly modulated EPSD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **\# = 50** & **\# = 100** & **\# = 150** & **\# = 200** \\ \hline
**Proposed** & 76.84 s & 243.64 s & 502.73 s & 832.91 s \\
**Traditional** & 805.35 s & 3227.53 s & 8501.64 s & 15241.56 s \\
**Time ratio** & 10.48 & 13.25 & 16.91 & 18.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation efficiency comparison of two methods (1000 samples).
## 4 Application of Cyclodextrin-Based Adsorbents
### Removal of Organic Compounds
#### 4.3.1 Endocrine Disruptors
Moreover, after using the CD polymer on contaminated water, the authors treated it with pulsating light, by which the presence of contaminants was further reduced by up to 91%. The CD polymer was shown to be reusable at least ten times while eliminating one of the most popular NSAIDs, ibuprofen. For this reason, a CD polymer system, combined with pulsating light, can be an innovative and efficient method of removing NSAIDs from water [146]. Drugs often used to treat chronic diseases, such as Salbutamol (SAL) and Atenol (ATL), were tested in 2021 for their removal from the environment. Unfortunately, traditional wastewater treatment plants cannot cope with their complete removal. To efficiently remove the above-mentioned compounds, silanized adsorbents of \(\beta\)-cyclodextrintrin compounds (GD-EDTS) were designed and tested. Due to the introduction of carboxyl groups into this composite, it was possible to sustain satisfactory adsorption efficiency over a \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**adsorbent** & **Pollutant** & **qmax** &
\begin{tabular}{c} **Removal** \\ **Efficiency [\%]** \\ \end{tabular} & **References** \\ \hline CNPs & Tetracycline (TC) & 543.5 & 93.8 & [72] \\ \(\beta-\)CD\(-\)M & Ibuprofen & 86.2 & 90.0 & [147] \\  & Procaine & 48.0 & 86.0 & \\ CS-ED-CD & Imipramine & 47.1 & 85.0 & [148] \\  & Ciprofloxacin & 47.1 & 83.0 & \\ \(\beta\)-CD/rGO & Naproxen & 361.9 & 79.3 & [144] \\ \(\beta\)-CD COF & Naproxen & - & 98.0 & [149] \\  & Ibuprofen & - & 78.0 & \\ Ca(II)-doped chitosan/\(\beta\)-CD & Acetaminophen & 200.9 & 99.9 & [150] \\  & CD@Clay-PVP & Ibuprofen & 3.46 & 83.9 & [151] \\ \(\beta\)-CD polymer with & Carbamazepine & 136.4 & 65.0 & [83] \\  & & Salbutamol & 140.2 & 71.3 & \\  & & Atenol & 236.9 & 70.4 & [146] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum adsorption capacities and removal efficiencies of various CD-based adsorbents for selected pharmaceutical compounds. broad pH range. The maximum SAL and ATL adsorption capacities were 140.24 mg/g and 236.92 mg/g. Moreover, the poor rivalries behavior between SAL and ATL resulted in the fact that both impurities could be removed simultaneously with the test adsorbent.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 2 Performance Evaluation
### Parameters to Evaluate Radiation Performance
#### 2.2.2 Single Event Effects  
The characterization of an ADC for a particular non-destructive SEE--SEFI, SET or SEU (single event functional interrupt, transient or upset, respectively)--can be summarized with the limits of the resultant Weibull fit--the LET\({}_{th}\) (measured in MeV\(\cdot\)cm\({}^{2}\)/mg) and the saturation cross section \(\sigma_{sat}\) (given in cm\({}^{2}\)/device) --. On the other hand, destructive SEE--SEB, SEDR, SEL or SESB (single event burnout, dielectric rupture, latch-up or snap-back, respectively)--are characterized only with the LET\({}_{th}\).  
Sensitivity to non-destructive SEE has varied with technology scaling, making the characterization of SEFI, SET and SEU already necessary in SEE tests fifteen years ago. In addition, upsets could be difficult to be discerned from registered transients; tests reports shall clearly define these events to avoid confusions. Furthermore, in ADCs with high conversion rates, a SET could spread out through several samples; if this occurs, only one event is counted but the length of the burst of samples is also reported.  
In some cases, SEL and SESB can be mitigated to non-destructive SEE if over-current watchdogs are implemented in the power supply at system level. In other words, SEL and SESB could be turned from hard to soft errors with an opportune power cycling.
# Subsampling Algorithms for Irregularly Spaced Autoregressive Models
## 4 Subsampling Methods for the IS-AR(1) Model
### Optimal Subsampling Under A-Optimality
The final subsample estimator \(\tilde{\mathbf{\theta}}\) is obtained by combining the pilot estimator \(\mathbf{\theta}^{\mathrm{(plt)}}\) and the optimal subsample estimator \(\tilde{\mathbf{\theta}}\):  
\[\tilde{\mathbf{\theta}}=\Big{(}r_{0}\bar{\mathbf{M}}_{r_{0}}^{\mathrm{(plt)}}(\bm {\theta}^{\mathrm{(plt)}})+r^{*}\bar{\mathbf{M}}_{r}^{\mathrm{(opt)}}(\tilde{ \mathbf{\theta}})\Big{)}^{-1}\Big{(}r_{0}\bar{\mathbf{M}}_{r_{0}}^{\mathrm{(plt)} }(\mathbf{\theta}^{\mathrm{(plt)}})\mathbf{\theta}^{\mathrm{(plt)}}+r^{*}\bar{ \mathbf{M}}_{r}^{\mathrm{(opt)}}(\tilde{\mathbf{\theta}})\tilde{\mathbf{\theta}}\Big{)}, \tag{9}\]where  
\[\tilde{\mathbf{M}}_{r}^{\rm(opt)}(\tilde{\mathbf{\theta}})=\frac{1}{m}\sum_{j=1}^{m} \delta_{j}\frac{\tilde{\mathbf{\theta}}(\tilde{\mathbf{\theta}};y_{j},y_{j-1},d_{j})}{ \hat{\eta}_{\alpha_{1},j}^{\rm(opt)}}.\]  
We summarize the A-optimal subsampling strategy in Algorithm 2. * **Step 1--Pilot Estimates and Preparations:** 1. Take a simple random sample of size \(r_{0}\) by sampling without replacement and set indicators \(\{\delta_{j}^{0}\}_{j=1}^{m}\). 2. \(\theta^{\rm(plt)}\leftarrow\arg\max_{\theta}\sum_{j=1}^{m}\delta_{j}^{0}\ell( \mathbf{\theta};y_{j},y_{j-1},d_{j})\) 3. \(\mathbf{M}^{\rm(plt)}\gets r_{0}^{-1}\sum_{j=1}^{m}\delta_{j}^{0}(\mathbf{ \theta}^{\rm(plt)};y_{j},y_{j-1},d_{j})\) 4. Calculate \(\kappa_{a}\). * **Step 2--Subsampling:** for i=1 to m 1. \(u\leftarrow\text{Unif}(0,1)\) 2. \(\eta_{j}\leftarrow(1-\alpha)\kappa_{a}\|\mathbf{M}^{\rm(plt)}{}^{-1}\hat{\ell} (\theta^{\rm(plt)};y_{j},y_{j-1},d_{j})\|+\alpha(r/m)\) 3. if \(u<\eta_{j}\)do \(\delta_{j}\gets 1\) else \(\delta_{j}\gets 0\)  end if
* **Step 3--Estimation:** 1. \(\tilde{\mathbf{\theta}}\leftarrow\arg\max_{\theta}\sum_{j=1}^{m}\delta_{j}\ell( \mathbf{\theta};y_{j},y_{j-1},d_{j})/(\eta_{j}\wedge 1)\) 2. Combine \(\mathbf{\theta}^{\rm(plt)}\) and \(\tilde{\mathbf{\theta}}\) using (9) to obtain \(\tilde{\mathbf{\theta}}\). ```  
**Algorithm 2** Poisson sampling under A-optimality.