esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
The prediction of Olympic medal outcomes has evolved through several key methodological breakthroughs. In 2004, [PERSON] pioneered the application of Cobb-Douglas production functions to medal forecasting, developing national-level prediction models based on macroeconomic indicators such as population size and GDP [3]. In 2007, [PERSON] advanced the field by introducing time series analysis techniques through ARIMA models to predict China's medal count at the 2008 Beijing Olympics [22], though these linear approaches showed limitations in capturing nonlinear relationships.  
In 2008, [PERSON]'s research team made a significant contribution by quantitatively establishing the host country effect, determining specific coefficients for home advantage (C = 0.298) and population size (P = 1.000) [23]. In 2010, [PERSON] developed specialized analytical methods through the gray relational analysis of technical indicators for China's men's basketball team, marking a shift toward sport-specific modeling [24]. In 2011, [PERSON]'s team achieved a methodological breakthrough by combining genetic algorithms with regression analysis, significantly enhancing prediction adaptability [25].  
The field saw further specialization in 2012 when [PERSON] applied gray prediction modeling to Olympic track and field performance analysis [26]. In 2013, [PERSON] expanded the spatial dimension of research through the dynamic evolution analysis of gold medal distributions in Olympic track and field events [27]. Most recently in 2022, [PERSON] et al. revolutionized the field by introducing machine learning approaches, using random forest models to not only improve prediction accuracy but also reveal the mechanistic roles of socioeconomic factors through feature importance analysis [28].  
While existing research has established fundamental theories for Olympic medal forecasting, significant limitations persist in terms of research scope, model sophistication, and data heterogeneity [18; 19; 20; 21]. These shortcomings have resulted in notable gaps in predict ing medal distributions at both national and individual event levels, with compromised accuracy in current projections. Building upon recent advancements in neural network algorithms, our study incorporates deep learning techniques to enhance predictive precision. The Medal Predict XGBoost Model (MPXG) and the First Medal Prediction Model (FMPM) not only forecast overall national medal standings but also specifically evaluate the probability of non-medal-winning nations securing their inaugural Olympic honors. Furthermore, our quantitative framework for host effect assessment and the coach effect evaluation metric provide data-driven insights for optimizing national training strategies.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 5 Experimental Verification  
To validate the design of the separation mechanism, vibration and separation tests were conducted. Following the structural analysis, vibration tests on the ground were used to determine the fundamental frequency and structural responses. The separation mechanism's functionality, including separation velocity and angular velocity, was evaluated through separation tests.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 2 Data and Methods
### GEO-LEO Satellite Image Fusion Algorithm
#### 2.3.1 Time Matching  
\[|t_{VISSR}-t_{AMSU-A}|<\delta_{\max,min} \tag{5}\]  
where t\({}_{VISSR}\) indicates the observation time of the VISSR pixel, \(t_{AMSU-A}\) represents the observation time of the AMSU-A pixel, and \(\delta_{max,min}\) is the time threshold. When the observation time difference between the VISSR and AMSU-A data is less than the empirical threshold \(\delta_{max,min}\), we consider the two observations to be \"simultaneous observations\". In this paper, \(\delta_{max,min}\) is 30 min.
## 3 Results and Discussion  
### Determination of the Number of Hidden-Layer Neurons  
The network was trained and tested to determine the optimal number of hidden-layer neurons, successively setting the number of hidden-layer neurons as integers in [4,13]. Owing to the characteristics of the gradient descent method, a BPNN tends to fall into local minima during training, which may result in the trained network not achieving optimal performance. Moreover, since the gradient descent path is not unique, the BPNN training is unstable [15], which means the network trained each time is inconsistent. Under each number of hidden-layer neurons, network training was repeated until 10 sets of network performance data with two output's test set R\({}^{2}\) values simultaneously greater than 0.8 were obtained. Then, the optimal set of data was selected from the 10 sets of data to represent the network performance under the current number of hidden-layer neurons. The network performance data were compared, as shown in Figure 9 and Table 16.  
Figure 8: BPNN-MOPSO process (BPNN-MOPSO denotes optimization method of the combination of the backpropagation neural network (BPNN) predictive model and multi-objective particle swarm optimization (MOPSO) algorithm).  
Figure 9: Comparison of the optimal network performance under each number of hidden-layer neurons (R\({}^{2}\), RMSE, and MAE denote the coefficient of determination, root mean square error, and mean absolute error between experimental and BPNN-predicted values, respectively; \({}^{-}\) denotes average value of network performance indices for missing index and reseeding index; TR and TS denote training set and test set, respectively).  
With eight hidden-layer neurons, network performance is excellent, as the values of the training set \(\overline{\text{R}^{2}}\) (0.9641) and the test set \(\overline{\text{R}^{2}}\) (0.9290) are higher than those obtained under other numbers of the hidden-layer neurons. Except that the test set \(\overline{\text{MAE}}\) (1.7391) is slightly larger than that obtained with 4-7 neurons (1.6319-1.6981), the other performance indices are the optimal values. Thus, the number of hidden-layer neurons was set to 8.
# Game Theory-Based Smart Mobile-Data Offloading Scheme in 5G Cellular Networks
###### Abstract  
Mobile-data traffic exponentially increases day by day due to the rapid development of smart devices and mobile internet services. Thus, the cellular network suffers from various problems, like traffic congestion and load imbalance, which might decrease end-user quality of service. This work compensates for the problem of offloading in the cellular network by forming device-to-device (D2D) links. A game scenario is formulated where D2D-link pairs compete for network resources. In a D2D-link pair, the data of a user equipment (UE) is offloaded to another UE with an offload coefficient, i.e., the proportion of requested data that can be delivered via D2D links. Each link acts as a player in a cooperative game, with the optimal solution for the game found using the Nash bargaining solution (NBS). The proposed solution aims to present a strategy to control different parameters of the UE, including harvested energy which is stored in a rechargeable battery with a finite capacity and the offload coefficients of the D2D-link pairs, to optimize the performance of the network in terms of throughput and energy efficiency (EE) while considering fairness among links in the network. Simulation results show that the proposed game scheme can effectively offload mobile data, achieve better EE and improve the throughput while maintaining high fairness, compared to an offloading scheme based on a maximized fairness index (MFI) and to a no-offload scheme.  
cellular network; cellular offloading; device-to-device; cooperative game; fairness index; Nash bargaining solution; energy consumption. Article
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
## 3 Results and Discussion
### Honeycomb Shell Thickness  
The impact of the thickness of the honeycomb core shell on the energy dissipation efficiency is analysed by simulating varying values of shell thickness, \(t_{\rm h}\). Following the conclusions regarding the optimisation of the honeycomb core cell density in Section 3.1 above, model C-6 is used, with an AL2024-T3 honeycomb, and a shell thickness \(t_{\rm h}\) ranging from 0.01 to 0.5 mm, as listed in Table 2. The debris particle and impact zone have an SPH particle density of 7 particles/mm. The results in Figure 15 show a significant decrease in the kinetic energy of the debris particle at 17.5 us for increasing \(t_{\rm h}\). This trend shows that, for thicknesses above 0.3 mm, the increase in energy dissipation becomes less evident. The energy dissipation increases only 8.4% for thicknesses between 0.3 and 0.5 mm, compared to a 78.3% increase between 0.01 and 0.3 mm, which corresponds to model T-30 in Table 3.  
Figure 14: Simulation progress at \(t=16.2\) μs for an increasing number of cells across the width of honeycomb core for model C-0 (**top left**) to model C-10 (**bottom right**).  
Figure 13: Kinetic energy of the debris particle at \(t=20\) μs against the number of cells across the width of honeycomb core.  
To further support these conclusions, the simulation images in Figure 16 clearly show that, for shell thicknesses above 0.3 mm, there is no clustering of SPH particles (i.e., debris) reaching the back plate of the Whipple shield. A clear increase in debris fragmentation can also be observed from model T-01 to model T-50 at \(t=16.2\) us. Models with a smaller shell thickness exhibit a more tightly packed particle spread than models with a larger shell thickness, showing fewer particle clusters. The particle spread also appears much closer to the back plate for a smaller shell thickness. This further supports the results in Figure 15, as the increasing thickness of the honeycomb shell could be expected to positively influence the spread of debris fragments.  
Figure 16: Simulation progress at 16.2 us for an increasing thickness of honeycomb core from top left (model T-01) to bottom right (model T-50).  
Figure 15: Kinetic energy of the debris particle at \(t=17.5\) us against thickness of the honeycomb shell.
## 2 Description of the THz Wireless Communication Setup
### DSP Algorithm  
To recover the transmitted data, a Digital Signal Processing (DSP) routine was used to process the IF signal and retrieve the data. The receiver's DSP steps employed in the experiments are illustrated in Figure 4. First, the signal's clock is recovered by performing a cross-correlation with the transmitter waveform. This is followed by a down-conversion, inverse raised-cosine filtering, and resampling to achieve two samples per symbol. The equalization comprises a set of three blind equalizers: a (1) radius-directed equalizer (RDE), (2) carrier phase estimation (CPE), and (3) least-mean-square decision-directed equalizer (LMS-DDE). The carrier phase estimation is particularly critical, having to compensate for the phase noise contribution arising from the 80 kHz linewidth free-running lasers. While this contribution is confined to the transmitter in electrically pumped receiver cases, in the optically pumped scenarios, it is also present in the optically generated LO. To address this, the algorithm proposed in [38] is used, which can compensate for laser linewidths in the order of several MHz. In the last section of the article, the performances using a non-optimized algorithm for free-running lasers will be shown, highlighting the importance of an adequate DSP.  
Figure 4: DSP block diagram used in reception. RDE is the radius-directed equalizer, CPE is the carrier phase estimation, and LMS-DDE is the least-mean-square decision-directed equalizer.  
Figure 3: Measured noise figure over the LO power requirements of the two narrow-band subharmonic mixers. In (**a**), the measurements taken with the GaAs-diode-based mixer are shown. In (**b**), measurements obtained with the InGaAs mixer are shown.
## 4 Discussion
### Direct Oral Anticagulants  
Direct oral anticoagulants (DOACs), also known as novel oral anticoagulants (NOACs), have revolutionized anticoagulation therapy due to their predictable pharmacokinetics and lower risk of intracranial hemorrhage compared to vitamin K antagonists (VKAs) [14,15]. However, managing bleeding complications or urgent surgical procedures in patients on DOACs requires effective reversal strategies. In the last decade, several reversals have been proposed. For factor Xa inhibitors (apixaban, rivaroxaban, edoxaban), andexanet alfa (a recombinant modified human factor Xa decoy) has been approved as a specific reversal agent, demonstrating significant reductions in anti-factor Xa activity [16,17]. Alternatively, prothrombin complex concentrates (PCCs) may be used off-label when andexanet alfa is unavailable [18]. For dabigatran, a direct thrombin inhibitor, the specific reversal agent idarucizumab, a monoclonal antibody fragment, has shown the rapid and complete neutralization of anticoagulant effects [19]. Despite these advances, limitations exist, including high costs, restricted availability, and the need for further clinical data on the long-term outcomes of reversal strategies. Therefore, individualized patient management remains essential [20,21].
# Decoration of Polyfluorene-Wrapped Carbon Nanotubes with Photoleavable Side-Chains
## 2 Results and Discussion  
### Polymer Synthesis and Characterization  
To begin our study, we first synthesized an azide-containing polyfluorene (**PF-N\({}_{\bf 3}\)**) according to procedures in the literature [62]. Bromination of fluorene using \(N\)-bromosuccinimide (NBS) was performed to obtain precursor **1** (Scheme S1 (Supplementary Materials)), which was then alkylated with 1,6-dibromohexane to afford monomer **2**. Borylation of this monomer using Miyura conditions afforded the diboronic ester **3**. Monomers **2** and **3** were then copolymerized using a Suzuki polycondensation to obtain the homopolymer (**PF-Br**) (Scheme 1). Gel permeation chromatography (GPC) revealed an M\({}_{\rm n}\) of 33 kDa and a dispersity (D) of 2.2. **PF-N\({}_{\bf 3}\)** was then prepared via reaction between **PF-Br** and NaN\({}_{3}\) in the presence of \({}^{n}\)Bu\({}_{4}\)NBr. The homopolymers were characterized by \({}^{1}\)H NMR spectroscopy to confirm the presence of alkyl azides (3.15 ppm) in **PF-N\({}_{\bf 3}\)** and the disappearance of the signal of the alkyl bromides (3.31 ppm) in **PF-Br** (Figure S1).  
To introduce our photocleavable side-chains, we first prepared an _o_**NB-TEG-alkyne** via activation of triethylene glycol monomethyl ether with toosyl chloride (Scheme S4), followed by alkylation with 5-Hydroxy-2-nitrobenzaldehyde to obtain compound **5**. Compound **5** was then reduced using sodium borohydride to afford the corresponding alcohol **6**. Nucleophilic substitution of this alcohol with propargyl bromide was finally performed to obtain _o_**NB-TEG-alkyne**. The non-cleavable analog was prepared via nucleophilic substitution of triethylene glycol monomethyl ether (TEG-OH) with propargyl bromide to give **TEG-alkyne** (Scheme S5). **PF-N3** was then functionalized with either **TEG-alkyne** (**P1**) or _o_**NB-TEG-alkyne** (**P2**) (Figure 1) using copper-catalyzed azide-alkyne cycloaddition (CuAAC) (see Supplementary Materials for details). The reaction was monitored by infrared (IR) spectroscopy via the disappearance of the polymer azide stretch at \(\sim\)2090 cm\({}^{-1}\) (Figure S2). The resulting polymers were also characterized by \({}^{1}\)H-NMR spectroscopy to confirm the disappearance of the alkyl azides (3.15 ppm), the appearance of the aromatic proton in the triazole ring (7.51 ppm), as well as the appearance of alkyl protons from the side-chain between 3 and 5 ppm (Figure S3).
###### Abstract  
This study describes a contactless vital sign monitoring (CVSM) system capable of measuring heart rate (HR) and respiration rate (RR) using a low-power, indirect time-of-flight (ToF) camera. The system takes advantage of both the active infrared illumination as well as the additional depth information from the ToF camera to compensate for the motion-induced artifacts during the HR measurements. The depth information captures how the user is moving with respect to the camera and, therefore, can be used to differentiate where the intensity change in the raw signal is from the underlying heartbeat or motion. Moreover, from the depth information, the system can acquire respiration rate by directly measuring the motion of the chest wall during breathing. We also conducted a pilot human study using this system with 29 participants of different demographics such as age, gender, and skin color. Our study shows that with depth-based motion compensation, the success rate (system measurement within 10% of reference) of HR measurements increases to 75%, as compared to 35% when motion compensation is not used. The mean HR deviation from the reference also drops from 21 BPM to \(-\)6.25 BPM when we apply the depth-based motion compensation. In terms of the RR measurement, our system shows a mean deviation of 1.7 BPM from the reference measurement. The pilot human study shows the system performance is independent of skin color but weakly dependent on gender and age.  
title=1,outerline=0  
depth information can be used to differentiate the grayscale intensity variations caused by underlying heartbeat versus the user's motion.  
We conducted a pilot human study with 29 participants of varying demographics to show that when using distance-based motion compensation, the success rate of HR measurement increases to 75%, as compared to only 35% when distance-based motion compensation is not used. The mean deviation from the reference HR measurement is \(-6.25\) BPM if we apply the depth signal for motion compensation, while the deviation increases to 21 BPM when motion compensation is not used. At the same time, our study shows that the mean deviation of our RR measurement is 1.7 BPM, which is comparable to the pulse oximeter we used in this study. Our results further show that the system performs well among participants of various demographics such as skin color, gender, and age. Such systems could be useful in applications such as vehicle driver/occupant monitoring systems, remote and in-patient patient monitoring systems, telemedicine systems, or unobtrusive health screening systems.
# Research on Large Divergence Angle Laser Ranging System +
## 3 Design of Large-Divergence Transmitter and Wide-Field-of-View (WFOV) Receiver Systems
### Design of Wide Field-of-View (WFOV) Optical Reception System for Laser Echo Detection  
The laser receiving the optical system collects the laser energy reflected from the target and focuses it onto the light-sensitive surface of an avalanche photodiode (APD) [9]. When designing the receiving system, it is necessary to both enhance the ranging capability for small targets by increasing the receiving field of view and to balance the need for suppressing background stray light (especially from close-range backgrounds) [10]. According to the requirements of the receiving FOV, the APD with a photosensitive surface size of 500 \(\mu\)m is selected as the receiving detector. The main parameters of the receiving detector are shown in Table 2 below:  
The receiving optical system is composed of spherical lenses, which are mainly made of Radiation-resistant glass, and the numerical aperture F# is 0.54. The optical path diagram is shown in Figure 8. Lens 1 in the figure is a narrow-band filter, which can filter out the background light clutter beyond 1535 nm in the ambient light. Lenses 2, 3, and 4 are laser convergence optical systems, and lens 5 is the APD optical window.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **Symbol** & **Test Conditions** & **Min** & **Typical** & **Max** & **Units** \\ \hline Wavelength Response & \(\lambda\) & - & 1000\(\sim\)1700 & nm \\ \hline Active diameter & D & - & 500 & \(\mu\)m \\ \hline Vop for specified responsivity & \(\mathrm{V_{br}}\) & dark current \(\mathrm{I_{D}}\) = 100 \(\mu\)A, optical power\(\varphi_{\mathrm{e}}\) = 0 & 30 & 70 & V \\ \hline Responsivity & \(\mathrm{R_{V}}\) & M = 10, \(\lambda\) = 1.55 \(\mu\)m, pulse width\(\mathrm{Tw}\) = 100 ns; & 300 & & KV/W \\ \hline Outpoput impedance & Ro & - & 50 & \(\Omega\) \\ \hline Positive supply current & \(\mathrm{I_{CC}}\) & \(\mathrm{V_{CC}/V_{EE}}\) = \(\pm\)5 V & - & 30 & mA \\ \hline Negative supply current & \(\mathrm{I_{EE}}\) & & - & 10 & mA \\ \hline Output voltage swing & \(\mathrm{V_{O}}\) & - & 0.7 & & V \\ \hline DC output offset voltage & \(\mathrm{V_{offset}}\) & - & 0 & V \\ \hline \end{tabular}
\begin{tabular}{c c c c} \hline  & The parameters of the receiving optical system designed are as follows: APD photosensitive surface diameter: 500 \(\mu\)m; entrance pupil diameter: 33 mm; FOV: \(\geq\)30 mrad; Optical Efficiency: \(\geq\)0.8; Focal Spot Diameter: \(\leq\)20 um (0 FOV); \(\leq\)30 um (1 FOV).  
\end{table}
Table 2: APD core parameters.  
Figure 7: Illuminance simulation diagram at 1000 mm.  
In the whole temperature range of \(-40\)\({}^{\circ}\)C\(\sim\)60 \({}^{\circ}\)C, the receiving optical system has good energy distribution and spot size, which can meet the requirements of a large field of view reception.
###### Abstract  
A poly(ionic-liquid) (PIL) matrix can be altered by incorporating additives that will disrupt the polymer chain packing, such as an ionic liquid (IL) and inorganic salts to boost their exploitation as materials for membrane production to be used in CO2 capture. Herein, potential of PIL/IL/salt blends is investigated on the example of poly(diallyldimethyl ammonium) bis(trifluoromethylsulfonyl)imide (P[DADMA][Tf2N]) with N-butyl-N-methyl pyrrolidinium bis(trifluoromethylsulfonyl)imide ([Tyrr14][Tf2N]) and zinc di-bis(trifluoromethylsulfonyl)imide (Zn[Tf2N]). Composite material with IL and a higher amount of Zn2+ showed an increase in the equilibrium CO2 sorption capacity to 2.77 cm3 (STP) cm-3 bar-1. Prepared blends were successfully processed into thick, dense membranes and thin-film composite membranes. Their CO2 separation efficiency was determined using ideal and mixed-gas feed (vol% CO2 = 50, dry and with 90% relative humidity). The dominant role of solubility in the transport mechanism is confirmed by combining direct gravimetric sorption measurements and indirect estimations from time-lag experiments. The maximum incorporated amount of Zn2+ salts increased equilibrium solubility selectivity by at least 50 % in comparison to the parent PIL. All materials showed increased CO2 permeance values by at least 30% in dry conditions, and 60 % in humidified conditions when compared to the parent PIL; the performance of pure PIL remained unchanged upon addition of water vapor to the feed stream. Mixed-gas selectivities for all materials rose by 10 % in humidified conditions when compared to dry feed experiments. Our results confirm that the addition of IL improves the performance of PIL-based composites due to lower stiffness of the membrane matrix. The addition of Zn2+-based salt had a marginal effect on CO2 separation efficiency, suggesting that the cation participates in the facilitated transport of CO2  
**Keywords:** flue gas; poly(ionic liquid); CO\({}_{2}\) transport; thin-film composites; relative humidity; zinc; polymeric membranes +
Footnote †: journal: Journal of Low Temperature Physics
## 4 Analysis of Experimental Results
### Experimental Environment Parameter Settings  
In several experiments, we set the BILSTM model to three layers: the input layer, the hidden layer, and the output layer, where the fully connected layer was used to adjust the dimension of the output layer. Parameter setting: The Adam function was optimized, the number of iterations of model training was 400, the batch size was 24, the initial learning rate was 0.005, the period in which the learning rate decreased was set to 100, and the learning rate was adjusted by setting the weight factor to 0.8. After adjustment, the learning rate was equal to the current learning rate \(\times\) weight factor, and the dropout value was set to 0.2.