esa-sceva/satcom-chunk-collection huggingface dataset:
# Subsampling Algorithms for Irregularly Spaced Autoregressive Models
## 5 Numerical Results  
We perform numerical simulation to evaluate the performance of the subsampling methods. We present the results on estimation efficiency in Section 5.1 and the results on computational efficiency in Section 5.2.  
We consider the performances of the uniform subsample estimator (**unif**), the A-optimal subsample estimator (**opt**), the information-based optimal subdata selection estimator (**iboss**), and the sequential thinning estimator (**thin**). We set the full-data sample size as \(m=10^{5}\) and consider different values of the true parameters; we allow \(\phi=0.1,0.5,0.9\) and \(\sigma=1,10\) and \(20\). The time gaps \(d_{j}\) are randomly sampled from the set \(\{1,2,3,4,5\}\).  
We consider different subsample sizes \(r=5000\), \(10\),\(000\), \(15\),\(000\), \(20\),\(000\), \(25\),\(000\), and \(30\),\(000\), corresponding, respectively, to \(5\%,10\%,15\%,20\%,25\%\), and \(30\%\) of the full dataset. We let \(r_{0}=1000\). For **opt**, we set the mixing rate \(\alpha_{1}=0.1\). To implement the **thin** subsampling procedure, we set the tuning parameters \(q=0.7\), \(\gamma=0.1\), and \(\epsilon_{1}=0.001\). We also implement the uniform subsampling method with a subsample size \(r+r_{0}\) as a benchmark for comparisons.
# Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area
## 5 Conclusions  
In this paper, the UV network model is creatively constructed in an arbitrary polygonal area, and the network coverage in a regular quadrilateral area is analyzed. The effects of different communication parameters on connectivity are simulated, and the results demonstrate that the smaller the transceiver elevation angles are, the better the network connectivity performance is. When \(\theta_{1}\) is the same and \(\theta_{2}\) is \(50^{\circ}\), the connectivity performance of the network is more prominent. Finally, we numerically compared the UV network models of polygonal and circular regions. The analysis results can provide theoretical guidance for actual networking.  
At present, we have begun to carry out the experiment of UV physical networking. In the next research, we will analyze and compare the simulation results and experimental results in order to realize the practical application of UV networking.  
Conceptualization, F.C. and Z.X.; Data curation, F.C.; Investigation, C.L.; Project administration, Z.X.; Writing--original draft, F.C. and C.L.; Writing--review & editing, Z.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
## 5 Proposed Method Description
### Proposed Method Architecture  
In the working environment, each user observes and submits requests for services. Additionally, users involve activities in the state; UAV takes action to design and learn suitable policies for the next state. The environmental capability of individual users has been observed. After the DRL agent builds its state based on previous actions and local observations, then determine the state of the environment. Local observations are used to provide and enhance the observation of the global state in the environment [26]. Figure 6 shows the structure of our proposed system. The figure considers the principle of DRL for the RM parameters of power (Pr), throughput (Tu), and SINR. Therefore, we need to correlate the state, environment, action, reward, policy, and training parameters. The format of the figure is as follows. Users and UAVs are regarded as agents. The data collection area of NTUT (500 \(\times\) 700 m) is the environment. The transmission of the A2G channel allowed for cooperation between users and UAVs, the resources (bandwidth, power, throughput, and SINR) are allocated. Then, each participating agent attempts to assign power with the throughput and management of SINR as an action. When resources are efficiently managed, lower than the threshold, it is a reward. Thus, when the threshold matches the situation, the reward value is effective; otherwise, the reward is zero. The action selection strategy is used to balance exploration and exploitation [13]. Hence, there may be actions conducted randomly for searching the effects of unknown actions, so it is easier to devise policies.  
By compiling all datasets, preprocessing was done. Initially, the DRL algorithm is initialized and trained. Then, the next action component is evaluated based on A2G access links with the multi-agent DRL. Mainly, information is transmitted from the A2G channel for IoT users. Then, the system took samples for policy formulation after saving to a mini-batch to a replay memory. Then, evaluate the capacity of SINR, power and throughput, and average values. Based on the generated results, a new policy is formulated for the next active state. Then, the policy is updated for the optimization of RM. These proposed rotations will have to be conducted until we find the optimal solution. Then, the reward offered to the agent included the average reward values of every agent. If the observable values changed and the value of each reward is more than the threshold, the event is activated. Then, the action and policy are updated by an agent unless the actions are ended.  
Generally, when an agent exceeds a given threshold, it learns from the environment for the new solution. Past experiences are used for learning through batch replay memory. Then, based on the previous experiences, the agent randomly picks from the uniformly distributed samples in the batch.  
Figure 6: Proposed method architecture. A2G: air-to-ground; SINR: signal-to-interference-plus-noise ratio.
## 2 Materials and Methods
### Aqueous and Ethanolic Liquid Extractions  
The extraction procedures were performed considering three different parameters: solvent type, extraction temperature, and extraction time. In brief, 10 g of powered dried biomass were mixed with 100 mL of solvent (ethanol or distilled water). The mixtures were stirred at room temperature (RT) and protected from light for 5, 10, 30 min, 1 h, or 24 h (\(n=4\)). As a result of these preliminary tests, the extraction time was set to 30 min and different temperatures (room temperature (RT); 40 \({}^{\circ}\)C; 70 \({}^{\circ}\)C) were subsequently tested. The extractions with different solvents were done sequentially at the same temperature as indicated in Figure 1[36; 37]. Briefly, in the samples of sequence A, the biomass is first extracted with ethanol. Then, we proceeded with a second extraction from the same biomass again with ethanol, collecting the compounds that are soluble in this solvent and that may not have yet been removed in the 1 st pass of this solvent. Then, the same biomass was extracted twice with water. This process is repeated using the three different temperatures under test, separately, always for 30 min. The same applies to samples of sequence B but in this case the biomass is extracted first with water. Afterwards, the biomass is extracted again with water, followed by two more extractions with ethanol obtained after drying the four extracts. Again, the full sequence is repeated with the samples under testing using different extraction temperatures for 30 min. This allowed us to examine the properties of extracts that were sequentially obtained with the same solvent and realize the existence of compounds that despite being soluble were not removed with the first pass of the solvent. The extracts were then filtered through filter paper (Whatman No.1) and centrifuged at 8000\(\times\)\(g\) for 10 min at RT. The ethanol extracts were dried in a rotary evaporator at 40 \({}^{\circ}\)C (Heidolph, Laborota 4000) and the aqueous extracts were freeze-dried. The dried extracts were stored at 4 \({}^{\circ}\)C and dissolved in the respective solvents at a concentration of 50 mg mL\({}^{-1}\) (antimicrobial analysis) or 10 mg mL\({}^{-1}\) (antiv oxidant analysis). In total, four independent extractions sequences were performed both for sequence A and B and for the three different temperatures tested.  
Figure 1: Representative scheme of the two sequences of extraction (A and B) with different solvents (ethanol or water), temperatures (room temperature (RT); 40 \({}^{\circ}\)C; 70 \({}^{\circ}\)C), and the respective extracts obtained.
## 5 Prototype and Experimental Results
### Prototype Design
#### 5.1.1 The Size of the Mecanum Wheels  
For the chassis of the robot, on the one hand, the Mecanum wheels should have enough load capacity to carry goods; on the other hand, these wheels should not only be able to overcome speed bumps, but also adapt to the narrow space on the stair surface. Therefore, the diameter of the Mecanum wheels, \(d_{wheel}\), should meet the condition \(2h_{max}<d_{wheel}<w_{min}\), where \(h_{max}\) is the maximal height of the speed bumps, and \(w_{min}\) is the minimal width of the stairs. According to the Chinese standard [29; 30], the diameter of our robot's Mecanum wheels was finally chosen as 12.5 cm.
## Nomenclature  
* [rightmargin=*]
FD-MAC Full-Duplex Medium access Control  
FS-MACA Future Scheduling Multiple Access with Collision Avoidance  
FDCA-MAC Full-Duplex Collision avoidance Medium Access Control  
FDCP-MAC Full-Duplex Collision avoidance Protocol Medium Access Control  
HS Hand-Shaking  
HD Half-Duplex  
HSIC Hybrid Self-Interference Cancellation  
IBFD In-Band Full-Duplex  
IBFD-MAC In-Band Full-duplex Medium access control  
IBFD-UWAC In-Band Full-Duplex Underwater Acoustic communication  
IFFT Inverse Fast Fourier Transform  
ICI Inter Carrier Interference  
ISI Inter Symbol Interference  
IUI Inter User Interference  
IoUWT Internet of Underwater Things  
LDP Low Detect Probability  
LNA Low Noise Amplifier  
LTI Local Transmission Interference  
LEI Local Emission Interference  
MAC Medium Access Control  
ML Maximum Likelihood  
ML-SC-IBFD Maximum Likelihood Sparse Constraint In-Band Full-Duplex  
MSE Mean Square Error  
MUAC Multi-User Acoustic Communication  
NPML Non-Parametric-Maximum-likelihood  
NLMS Normalized Least Mean Square  
NACK Non-Acknowledgement  
OFDM Orthogonal Frequency Division Multiplexing  
OFDM-IBFD Orthogonal Frequency Division Multiplexing In-Band Full-duplex  
OPRLS Over-Parameterization based Recursive Least Squares  
PRP Pairwise Error Probability  
PPM Pulse position modulation  
PAPR Peak-to-Average-Power Ratio  
PVDF Poly Vinylidene Fluoride  
QoS Quality of Services  
RA Random Access  
RSI Residual Self-Interference  
RLS Recursive Least-Square  
RST Request-to-Send  
SIR Signal-to-Interference-Ratio  
SI Self-Interference  
SIC Self-Interference Cancellation  
SS Spread Spectrum  
SS Spectrum Sensing  
SLI Self-Loop Interference  
SDFE Soft-Decision Feedback Turbo Equalization  
SNR Signal-to-Noise Ratio  
TH Time Hopping  
TDMA Time Division Multiplexing  
TRDC Time Reversal Digital Cancellation  
TH-CDMA Time Hopping Code Division Multiple access  
TFH-MAC Two and fold hand-shaking medium access control  
UWAC Underwater Acoustic Communication
# Cavitator Design for Straight-Running Supercavitating Torpedoes
## 3 Verification of Mathematical Models
### Forces on Disk- and Cone-Type Cavitators  
The hydrodynamic forces on the disk-type cavitator inclined to the inflow at an angle of \(\alpha_{c}\) were calculated using the following approximate formulas [16; 17]:  
\[F_{xc}=X_{c0}\cos^{2}\alpha_{c},\ \ F_{zc}=X_{c0}\sin\alpha_{c}\cos\alpha_{c},\ X _{c0}=0.8275(1+\sigma)\rho V^{2}S_{c}/2 \tag{8}\]  
where \(X_{c0}\) represents the cavitator drag force and \(\alpha_{c}=\delta_{c}+\alpha_{B}\) is the angle of attack of the cavitator.  
In the case of cone-type cavitators, the hydrodynamic drag force \(F_{xc}\) comprises cavitation and viscous drag and can be calculated as follows [18]:  
\[F_{xc}=0.5\rho V^{2}S_{c}\Big{(}c_{d1}+c_{f}/\sin(\beta_{\rm cone}/2)\Big{)} \tag{9}\]  
In Equation (9), the cavitation drag coefficient \(c_{d1}\) is calculated using [PERSON]'s formula, which is valid when the cone angles \(\beta_{\rm cone}\) are not too small [19]:  
\[c_{d1}(\beta_{\rm cone},\sigma)=c_{d0}(\beta_{\rm cone})(1+\sigma) \tag{10}\]  
The cavitation drag coefficients for the cone when \(\sigma=0\) were calculated by approximating the numerical calculation results [17]:\[c_{d0}(\beta_{\rm cone})=0.5+1.81(\beta_{\rm cone}/360-0.25)-2(\beta_{\rm cone}/36 0-0.25)^{2},30^{\circ}\leq\beta_{\rm cone}\leq 180^{\circ} \tag{11}\]  
The viscous drag coefficient for the cone was calculated using formulas for the turbulent boundary layer [18; 20]:  
\[c_{f}=1.16c_{fp},\ c_{fp}=0.075/(\log({\rm Re}_{cone})-2)^{2},\ {\rm Re}_{ cone}=d_{c}V/(2\
u\sin(\beta_{\rm cone}/2)) \tag{12}\]  
where \(c_{fp}\) represents the viscous drag coefficient for a plate, \({\rm Re}_{cone}\) is the Reynolds number based on the cone-type cavitator base diameter, and \(\
u\) is the kinematic viscosity.  
The lift force on the inclined cone-type cavitator can be predicted on the basis of the results of experiments performed by the Institute of Hydromechanics (IHM) at the National Academy of Sciences of Ukraine (NASU), with the practice accuracy in the range of \(-20^{\circ}\leq\alpha_{c}\leq 20^{\circ}\)[21]:  
\[F_{zc}=0.5c_{z}\rho V^{2}S_{c},\ c_{z}=c_{z0}(1+\sigma)\alpha_{c} \tag{13}\]  
where \(c_{z0}\) represents the derivative of the lift force coefficient of the cone-type cavitator with respect to the angle of attack when \(\sigma=0\), and it can be approximated using the following polynomial:  
\[c_{z0}=-2.05\times 10^{-7}\beta_{\rm cone}{}^{3}+1.355\times 10^{-4}\beta_{\rm cone }{}^{2}-3.325\times 10^{-2}\beta_{\rm cone}+2 \tag{14}\]
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
###### Abstract  
The colonoscopy procedure heavily relies on the operator's expertise, underscoring the importance of automated polyp segmentation techniques in enhancing the efficiency and accuracy of colorectal cancer diagnosis. Nevertheless, achieving precise segmentation remains a significant challenge due to the high visual similarity between polyps and their backgrounds, blurred boundaries, and complex localization. To address these challenges, a Multi-scale Selective Edge-Aware Network has been proposed to facilitate polyp segmentation. The model consists of three key components: (1) an Edge Feature Extractor (EFE) that captures polyp edge features with precision during the initial encoding phase, (2) the Cross-layer Context Fusion (CCF) block designed to extract and integrate multi-scale contextual information from diverse receptive fields, and (3) the Selective Edge Aware (SEA) module that enhances sensitivity to high-frequency edge details during the decoding phase, thereby improving edge preservation and segmentation accuracy. The effectiveness of our model has been rigorously validated on the Kvasir-SEG, Kvasir-Sessile, and BKAI datasets, achieving mean Dice scores of 91.92%, 82.10%, and 92.24%, respectively, on the test sets.  
polyp segmentation; context fusion; edge aware; high-frequency information; deep learning 2025
## 7 Adaptive Single-Point and Multi-Point Equalization
### SISO/SIMO Approaches
#### 7.1.1 Time Domain Approaches  
A first adaptive equalizer was proposed in [77], considering the variability of the environment from different points of view. The approach was based on a single-point technique, adaptively minimizing in the time domain the mean-squared error between the equalized response and a delayed single-channel version of the original signal using a filtered-x algorithm. The equalization was effective  
Figure 8: Equalization procedure: (**a**) impulse responses (IRs) with prototype function \(H_{\textit{prot}}\), multi-point equalizer \(EQ_{\textit{multi}}\), single-point equalizer \(EQ_{\textit{single}}\). Equalization results applying (**b**) single-point equalizer \(EQ_{\textit{single}}\); and (**c**) multi-point equalizer \(EQ_{\textit{multi}}\).  
for the considered position, but a degradation in other points of the enclosure was introduced, as also described in Section 6. Therefore, a multi-point approach was also presented by the same authors in [77], where the equalizer was designed by adaptively minimizing the sum of squared errors between the equalized responses in several positions and a delayed version of the input signal. Unfortunately, the approach is very sensitive to peaks and notches in the room response and to room response variations at different positions. As a consequence, pre-echo problems can easily be experienced.
## 4 Discussion
### Future Work  
Suggestions for future work that are raised by specific results were discussed above. More broadly, future work could look at subgroups of the already enrolled participants to identify whether there are non-voice factors that affect how voice responds to HF status. For example, any effects of age, sex, HF subtype (systolic or diastolic failure), or previous medical history could all be relevant. Additionally, data from the intermediate recordings (between admission and discharge) could be used to train a linear model that predicts, for example, how many days from admission a sample was recorded on. In contrast to the logistic models presented here, predicting a continuous dependent variable could provide more information about the trajectory of voice changes during treatment for HF.  
This study examined voice and speech changes in patients recovering from decompensated HF. In future data collection, it would be useful to collect data from HF patients who are not decompensated but are at risk of developing ADHF. For example, the 30-day readmission rate after an episode of ADHF has been reported to be approximately 24% [8]. Analyzing voice and speech patterns from patients who have just recovered from ADHF could be used to predict readmission. This prospective monitoring could also test hypotheses in the preventative direction (starting with stable patients and attempting to predict decompensation) rather than following patients as their HF status improved in this study. Monitoring discharged patients may also provide a clearer idea of how closely voice changes leading up to decompensation actually mirror voice changes during treatment.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 4 Results and Discussion
### Case II  
In this case, the LSTM hyperparameters were optimized with a GA method. The purpose of using this algorithm was to find the best LSTM hyperparameters that minimize the MSE of the network (Table 4). Table 5 shows the GA parameters used in this paper.  
Table 6 shows the LSTM hyperparameter optimization results for each variable. These results correspond to the parameters that minimize the MSE metric for each variable, according to the time period of the forecasts.  
Figures 9-11 show the prediction results for case II. In general, the behavior of each variable is different for each time period chosen. In this case, the predicted values behave similarly to the observed data, with a more fluctuating shape than in case I.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Variable** & **LSTM Layers** & **LSTM Units** & **Batch Size** & **Epochs** \\ \hline  & T\_MED & 3 & 87 & 49 & 34 \\  & DD\_MED & 2 & 70 & 38 & 20 \\
3 days & FF\_MED & 3 & 98 & 50 & 27 \\  & FF\_MAX & 3 & 50 & 17 & 21 \\  & RG\_TOT & 1 & 76 & 41 & 31 \\ \hline  & T\_MED & 1 & 86 & 34 & 39 \\  & DD\_MED & 2 & 91 & 21 & 21 \\
7 days & FF\_MED & 3 & 57 & 31 & 21 \\  & FF\_MAX & 2 & 72 & 16 & 30 \\  & RG\_TOT & 1 & 116 & 16 & 30 \\ \hline  & T\_MED & 1 & 118 & 31 & 30 \\  & DD\_MED & 2 & 120 & 20 & 39 \\
15 days & FF\_MED & 2 & 124 & 54 & 29 \\  & FF\_MAX & 3 & 134 & 25 & 20 \\  & RG\_TOT & 2 & 109 & 42 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimization results, case II.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of LSTM layers & [1; 3] \\ Number of LSTM units & [50; 150] \\ Batch size & [16; 64] \\ Number of epochs & [20; 40] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Range of LSTM hyperparameters.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum number of iterations & 5 \\ Population size & 10 \\ Mutation rate & 0.1 \\ Elit rate & 0.1 \\ Crossover rate & 0.5 \\ Parents portion & 0.3 \\ Crossover type & Two point \\ \hline \hline \end{tabular}
\end{table}
Table 5: Genetic algorithm parameters.  
Figure 9: Last 15 days of the testing set (blue) and the predicted data (red) for 3 days ahead in the future for **(a)** daily average temperature, **(b)** daily average wind direction, **(c)** daily average wind speed, **(d)** daily instant wind speed, and **(e)** daily solar radiation predicted values (red), using an optimized LSTM.  
Figure 10: Last 15 days of the testing set (blue) and the predicted data (red) for 7 days ahead in the future for **(a)** daily average temperature, **(b)** daily average wind direction, **(c)** daily average wind speed, **(d)** daily instant wind speed, and **(e)** daily solar radiation predicted values (red), using an optimized LSTM.
## Appendix A Description of Relaxation in Time and Frequency
### B. Convolution with a Resolution Function  
In an inelastic scattering experiment, the observed spectrum is usually well approximated as a convolution  
\[S_{R}(\omega)=\int_{-\infty}^{+\infty}\!\!{\rm d}\omega^{\prime}\,R(\omega^{ \prime})S(\omega-\omega^{\prime}) \tag{81}\]  
of the physical spectrum \(S\) with the instrumental resolution \(R\). Since the observed spectrum and the observed resolution are both given as histograms on a discrete \(\omega\) mesh, the convolution integral is normally computed as a sum  
\[S_{R}(\omega_{i})\simeq\sum_{j}\Delta\omega_{j}R(\omega_{j})S(\omega_{i}- \omega_{j}) \tag{82}\]  
This is a reasonable approximation as long as both \(S\) and \(R\) vary slowly from one \(\omega\) channel to the next. As for \(R\), this condition should be of no concern: in a well designed spectrometer, the channel width is chosen considerably smaller than the width of \(R\).  
However, the condition is not always fulfilled for \(S\). Typical resolution functions have shapes between Gaussian and Lorentzian. If \(S\) is the Fourier transform of a stretched exponential with \(\beta<1\), then the top of \(S\) can be much sharper than \(R\), while the wings of \(S\) exceed those of \(R\) and cause noticeable quasielastic scattering (see, e.g., Figure 1 in [41]). In such a case, discrete values, especially the value \(S(0)\), are not representative for entire \(\omega\) channels. Instead of Equation (82), one needs to compute  
\[S_{R}(\omega_{i})\simeq\sum_{j}R(\omega_{j})\int_{-\Delta\omega_{j}/2}^{+ \Delta\omega_{j}/2}\!{\rm d}\omega^{\prime}\,S(\omega_{i}-\omega_{j}+\omega^{ \prime}) \tag{83}\]  
The integration must not be carried out explicitly, since libkww provides a function kwwp that directly yields the primitive Equation (7) of \(Q_{\beta}(\omega)=\pi S(\omega)\), without passing through an evaluation of \(Q_{\beta}\). In consequence, fit programs should compute resolution-broadened Kohlrausch-Williams-Watts spectra as  
\[\pi S_{R}(\omega_{i})\simeq\sum_{j}R(\omega_{j})\left[P(\omega_{i}-\omega_{j} +\Delta\omega_{j}/2)-P(\omega_{i}-\omega_{j}-\Delta\omega_{j}/2)\right] \tag{84}\]