esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Results and Discussion
#### 5.3.2 Results of the Model Compression  
We use a network pruning technique based on ResNet18 to reduce storage and computation costs while maintaining high accuracy. It has been verified by experiments that this  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & \multicolumn{2}{c}{**CEDAR**} & \multicolumn{2}{c}{**Uyghur**} & \multicolumn{2}{c}{**Parameter**} & \multicolumn{1}{c}{**Memory**} \\ \hline
**Compression** & **CBAM** & **FAR (\%)** & **FRR (\%)** & **ACC (\%)** & **FAR (\%)** & **FRR (\%)** & **ACC (\%)** & **(M)** & **(MB)** \\ \hline \(\times\) & \(\times\) & 7.58 & 2.27 & 95.08 & 5.83 & 2.88 & 95.65 & 11.18 & 31.03 \\ ✓ & \(\times\) & 6.06 & 4.54 & 94.70 & 6.15 & 2.92 & 95.47 & 0.08 & 1.83 \\ ✗ & ✓ & 6.44 & 1.52 & 96.02 & 3.75 & 3.23 & 96.51 & 11.87 & 31.14 \\ ✓ & ✓ & 5.30 & 2.27 & 96.21 & 4.17 & 3.02 & 96.41 & 0.08 & 2.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained from the ablation study.  
Figure 6: Performance indicators of different models on the self-built Uyghur dataset. (**a**,**b**) show the results of FAR, FRR, and ACC indicators, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **FAR (\%)** & **FRR (\%)** & **ACC (\%)** & \
\begin{tabular}{c} **Parameter** \\ **(M)** \\ \end{tabular} &
\begin{tabular}{c} **Memory** \\ **(MB)** \\ \end{tabular} \\ \hline ResNet101 & 6.06 & 2.65 & 95.64 & 44.5 & 202.3 \\ ResNet50 & 6.43 & 3.03 & 95.27 & 25.51 & 134.3 \\ ResNet34 & 7.19 & 3.41 & 94.69 & 21.28 & 45.89 \\ ResNet18 & 7.58 & 2.27 & 95.08 & 11.18 & 31.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance indicators of different models on the CEDAR public dataset.  
operation was successful. Specifically, the number of model parameters after compression is reduced from 11.18 M to 0.08 M, and the memory usage is also reduced from 31.03 MB to 2.06 MB, which shows that the compression technology is very effective in model optimization. The use of this technology can not only reduce the storage and computing overhead of the model but also improve the performance and efficiency of the model in practical applications, and it has broad application prospects.
## 2 One-Dimensional Optimization Design
### One-Dimensional Design Method  
The total-to-static efficiency \(\eta_{\rm s}\) can be calculated by considering several energy losses, which is given by:  
\[\eta_{\rm s}=[(\eta_{\rm u}-\zeta_{\rm f})\eta_{\rm\Delta}-\zeta_{\rm i}]\eta _{\rm e} \tag{1}\]  
where \(\eta_{\rm u}\) is the peripheral efficiency, \(\zeta_{\rm f}\) is the disk friction energy loss coefficient, \(\eta_{\rm\Delta}\) is the clearance efficiency, \(\zeta_{\rm i}\) is the incidence loss coefficient, \(\eta_{\rm e}\) is the partial admission efficiency.  
From the energy standpoint, the peripheral efficiency \(\eta_{\rm u}\) is calculated by:  
\[\eta_{\rm u}=1-\zeta_{\rm n}-\zeta_{\rm r}-\zeta_{\rm c2} \tag{2}\]  
where \(\zeta_{\rm n}\), \(\zeta_{\rm r}\), and \(\zeta_{\rm c2}\) are the energy loss coefficients in the nozzle, rotor blade, and the leaving velocity at rotor exit.  
The peripheral efficiency \(\eta_{\rm u}\) can also be expressed by the design variables as follow:  
\[\eta_{\rm u}=2x_{\rm a}\bigg{(}\varphi\cos\alpha_{1}\sqrt{1-\Omega}-\overline {D}_{2}^{2}x_{\rm a}+\overline{D}_{2}\psi\cos\beta_{2}\sqrt{\Omega+\varphi^{2 }(1-\Omega)+\overline{D}_{2}^{2}x_{\rm a}^{2}-2x_{\rm a}\varphi\cos\alpha_{1} \sqrt{1-\Omega}}\bigg{)} \tag{3}\]  
where \(x_{\rm a}\) is the velocity ratio, \(\varphi\) and \(\psi\) are the nozzle velocity coefficient and rotor blade velocity ratio, \(\alpha_{1}\) is the absolute flow angle at rotor inlet, \(\beta_{2}\) is the relative flow angle at rotor exit, \(\Omega\) is the reaction degree, \(\overline{D}_{2}\) is wheel diameter ratio. The definitions of \(x_{\rm a}\), \(\varphi\), \(\psi\), and \(\overline{D}_{2}\) can be referred to in [19].  
The disk friction loss coefficient \(\zeta_{\rm f}\) is given by:  
\[\zeta_{\rm f}=\frac{1000}{G\Delta I_{\rm s}}\cdot\frac{f\rho_{1}D_{1}^{2}}{1.3 6}\cdot\Big{(}\frac{u_{1}}{100}\Big{)}^{3} \tag{4}\]  
where \(G\) is the mass flow rate, \(\Delta I_{\rm s}\) is the turbine isentropic enthalpy drop,\(f\) is the coefficient with a constant value of 4, \(\rho_{1}\), \(D_{1}\), and \(u_{1}\) are the density, diameter, and peripheral velocity at the rotor inlet.  
The expression of clearance efficiency \(\eta_{\rm\Delta}\) is as follows:  
\[\eta_{\rm\Delta}=1-1.3(\Delta r/l_{\rm m}) \tag{5}\]  
where \(\Delta r\) is the radial clearance between the shroud and the casing, \(l_{\rm m}\) is the arithmetic mean blade height.  
The current radial inflow design assumes the full admission pattern and the partial admission efficiency \(\eta_{\rm e}\) equals 1.  
The incidence loss \(\zeta_{\rm i}\) is additionally considered in the current study, which is given by:  
\[\zeta_{\rm i}=\frac{{w_{1}}^{2}\sin^{2}i_{1}}{2\Delta I_{\rm s}} \tag{6}\]  
where \(w_{1}\) is the relative velocity and relative flow angle at the rotor inlet, \(i_{1}\) is the incidence angle at the rotor inlet. The definition of \(i_{1}\) is as follows:  
\[i_{1}=\beta_{\rm 1b}-\beta_{1} \tag{7}\]  
where \(\beta_{\rm 1b}\) is the rotor inlet blade angle of 90\({}^{\circ}\), \(\beta_{1}\) is the relative flow angle at the rotor inlet.
## 2 Methodology
### Mathematical Model
\(Y_{M}\) represents the contribution of pulsatile expansion. For the constants \(C_{1\varepsilon}\), \(C_{2\varepsilon}\), and \(C_{3\varepsilon}\), we adopt values of 1.44, 1.92, and 0.09, respectively. Additionally, \(S_{k}\) and \(S_{\varepsilon}\) represent the source terms related to turbulent kinetic energy and dissipation. In this study, ammonia combustion was simulated using a premixed turbulent combustion model implemented in the FLACS-CFD. Despite the inherently poor combustion characteristics of ammonia, such as its low laminar burning velocity and narrow flammability limits, the model incorporates experimentally derived correlations for both laminar and turbulent burning velocities. An external energy source is not used to initiate ignition. Instead, flame propagation begins when the local thermodynamic conditions meet the flammability criteria, specifically within the lower and upper flammability limits (LFL and UFL). Under these conditions, the flame propagates at a laminar burning velocity. To provide further clarity, the sub-models implemented in the FLACS-CFD to estimate the laminar and turbulent flame speeds are detailed as follows. The laminar burning velocity \(S_{L}^{0}\) is modeled as a function of the fuel type and equivalence ratio \(\Phi\), which are both set to zero outside the flammability limits to represent non-reactive mixtures:  
\[S_{L}^{0}=S_{L}^{0}(fuel,\Phi) \tag{4}\]The turbulent burning velocity \(S_{T}\) is computed using empirical correlations developed by [PERSON] and [PERSON], which incorporate the turbulence intensity \(u^{\prime}\), integral length scale \(l_{I}\), and kinematic viscosity \(\
u\)[40]:  
\[S_{T1}=1.8S_{L}^{0.784}u^{0.412}l_{I}^{0.196}\
u^{-0.196},\ S_{T2}=0.96S_{L}^{0. 284}u^{0.912}l_{I}^{0.196}\
u^{-0.196}+S_{L} \tag{5}\]  
\[S_{T}=min(S_{T1},S_{T2}) \tag{6}\]  
Turbulent combustion is modelled based on the turbulent burning velocity, which is calculated from the local diffusion and reaction rates in the numerical flame zone. The model distinguishes between laminar, quasilaminar, and turbulent flow regimes, and accounts for flame surface area enhancement through a flame folding mechanism that includes sub-grid obstructions, turbulence, and Rayleigh-Taylor instabilities. The interaction between the chemical reactions and turbulence is handled using the flamelet approach, where an array of finite-thickness laminar flamelets represents the turbulent flame. Furthermore, the model employs [PERSON]'s correlation for turbulent flame speed and [PERSON]'s \(\beta\)-flame model, which introduces artificial flame thickening by increasing the diffusion and reducing the reaction rate to ensure numerical stability [33]. Although autoignition is not explicitly modelled, the combustion process is initiated when the mixture composition and thermodynamic conditions satisfy the flammability limits.
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
## 6 Results and Discussion  
### Encoding Schedule Optimization Results  
Optimization of the processing order provided a strong decrease in the encoding time. Examples of optimized encoding schedules are shown in Figure 12. Examples are shown for a processing order when four CSs are available. It is obvious that rows are grouped in a way that the same information bits are used for multiple CPMs at the same time. This results in a significant increase in the encoding speed. Moreover, the core parity bits processing is merged with the processing of some information bits when free CSs are available. This repositioning is of great importance for the additional encoding speed increase.  
Table 2 summarizes the results of GA optimization. The improvement metric describes how much faster the optimized schedule is, compared to the unoptimized. A large reduction in the number of clock cycles necessary for one codeword encoding can be observed. The BG2 optimization provides better improvements because a larger percentage of total CPMs are located in the core parity columns and since all these CPMs are processed together with other CPMs from information bit columns.  
Moreover, the improvement is calculated with respect to the original serial processing. The results show that the proposed architecture granted almost three times faster encoding for codes with lifting sizes of 96 and smaller, and almost twice as faster for codes with lifting sizes between 96 and 192. The hardware overhead is expected to be small and comes  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Encoding** \\ **Schedule** \\ \end{tabular} } & \multirow{2}{*}{\(N_{\textit{CPC}}\)} & \multirow{2}{*}{\(N_{\textit{CPC, Optimized}}\)} & \multicolumn{2}{c}{\
\begin{tabular}{c} _Improvement wrt_ \\ _Unoptimized (\%)_ \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} _Improvement wrt_ \\ _Serial (\%)_ \\ \end{tabular} } \\ \cline{2-9}  & **BG1** & **BG2** & **BG1** & **BG2** & **BG1** & **BG2** & **BG1** & **BG2** \\ \hline Serial & 265 & 150 & - & - & - & - & 0 & 0 \\ Serial (2 CSs) & 223 & 136 & 165 & 86 & 35.15 & 58.14 & 60.61 & 74.42 \\ Serial (4 CSs) & 180 & 94 & 107 & 53 & 68.22 & 77.35 & 147.66 & 183.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the GA optimization.  
Figure 12: A scatter diagram of the optimized processing order for BG1 and BG2 codes when four CSs are available. Circles represent CPMs that are used for information bits processing, whereas other shapes represent CPMs that are used for core parity bits processing. All core parity bits processing is merged with some information bits processing when free CSs are available.  
mainly because of the shifting network's flexibility, which will be shown in Section 6.2. Therefore, it is expected that the HUE will be significantly increased.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Pulse Features  
Spatiotemporal feature enhancement based on pulse features captures lip motion and spatial information by processing binary pulse signals, primarily through event cameras and spiking neural network enhancement methods.  
The intention of enhancement based on pulse features is to process binary pulse signals and preserve latent pulse feature information in lip motion and spatial information. By harnessing these innovative approaches inherent in event camera technology and advanced spiking neural network methods, the system can effectively discern intricate dynamics of lip movement, thus enriching the representation of spatial information pertinent to visual communication.
## 2 Material and Methods  
### Study Site Descriptions  
Two artificial forests (_Larix principis-rupprechtii_ and _Pinus tabualeformis_) were selected as the research objects, which were monospecific plantations, in the dryland of Northwest China. There were some sparse shrubs (_Rosa omeiensis_, _Cotonester multiflorus_, _Lonicera hispida_, _Cerasus tomentosa_, etc.) and herbs (_Carex rigescens_, _Aconitum sinomontanum_, _Fragaria orientalis_, _Potentilla bifurca_, _Medicago lupulina_, etc.) under the forest, which had little impact on the carbon and water flux of the ecosystem. Further details of the study site specifics are summarized in Table S1. The study region is a temperate continental monsoon climate, with remarkable temperature differences among four seasons. According to the climate characteristics, the growing season and non-growth season are classified as the periods from May to October and from November to April, respectively [46].  
Forest ages, management practices, and historical land use were all likely to affect the annual carbon and water fluxes of forest ecosystems [47; 48]. Therefore, two forests selected for this study had similar ages (30-40 years), and neither the forests nor the soil were disturbed and managed by humans. More basic information of two forest stands is summarized in Table S1. The differences in carbon and water fluxes between two forest stands were attributed to tree species.
## 4 Results and Discussion
### Generation Events
#### 4.1.2 How Are Interactions Distributed throughout the Day?  
We defined the day time slots in blocks of three hours, from midnight (00:00 to 02:59) to night (21:00 to 23:59). The **timestamp** field (see Table 4) captures time of day at which the interactions take place in local time; we can study how they are distributed over said time slots. Table 11 displays the overall distribution of interactions, and Table 12 shows how each category is distributed throughout the day.  
Overall results show that users make the greatest use of the application in the mid-morning (09:00 to 11:59) and evening (18:00 to 20:59) slots.  
This time distribution is coherent with time for activities (mid-morning) such as school, work, or therapy and social time at home (evening). It is relevant that the evening slot is the one with the most communicative activity.  
Regarding the categories, we observe that emotions shift their interactions from the bands of greatest activity, with a very representative block in the afternoon (15:00 to 17:59). In the case of nutrition, care, and health, these are more distributed throughout the day. It is also important to point out that a significant percentage of social interaction, which more than doubles mid-morning activity and leisure, takes place in the evening slot, when most educational, occupational, and health activities take place, presumably with partners.  
A plausible hypothesis that deserves further research is that for the users of these accessible tools, family is still the main source of social interaction despite inclusion efforts. It is also interesting that conversations on emotions are equally prevalent in mid-morning and evening. Usage during night hours results as expected, with few interactions mainly associated with well-being categories.
# Analytical Methods to Assess Polyphenols, Tannin Concentration, and Astringency in Hard Apple Cider
## 2 Materials and Methods
### Polyphenol, Astringency, and Tannin Analyses  
Color characteristics of the ciders were evaluated by UV-Vis spectrophotometry (Lambda 25 UV-Vis spectrophotometer, PerkinElmer Inc., Waltham, MA, USA) at 420 nm representing yellow and 520 nm representing red. The sum of these two absorbances is used as total color and is interpreted as the slight yellow to orange color range observed in apple ciders. Total polyphenols were photometrically analyzed using the Folin-Ciocalteu method as described by [PERSON] and [PERSON] [25], with modifications from [PERSON] and [PERSON] [26]. Total tannins were characterized using the methylcellulose (MC) precipitation assay according to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [23] with minor modifications. The calibration curve was made with (+)-Catechin hydrate (Sigma-Aldrich, St. Louis, MO, USA) in a range between 10 and 500 mg/L. In order to test a different precipitation agent in the cider matrix, MC was substituted by bovine serum albumin (VWR, Radnor, PA, USA) at a concentration of 1 g/L in ethanolic tartrate buffer (2.5 g/L potassium bitartrate in 12% ethanol, pH 3.6). Total iron-reactive phenolics were analyzed as described by [PERSON], [PERSON] and [PERSON] [18]. Cider astringency was analyzed according to [PERSON], [PERSON], [PERSON] and [PERSON] [24] with modifications suggested by [PERSON] [27] and further modifications as follows. Cider samples were not diluted prior to analysis due to the low tannin concentration compared to wine. Bovine Serum Albumin (VWR, Radnor, PA, USA) was used as a protein source and turbidity was analyzed by spectrophotometry at 860 nm. An astringency index was calculated by setting the ratio between treatment with protein and control. In order to be able to compare the astringency results to samples with no known interferences, a total of ten red wines made from Barbera, Syrah, Cabernet Sauvignon, and Touriga Nacional were randomly selected from Fresno State Winery. The wines were analyzed using the MCP total tannin assay and the astringency assay described above. Monomeric phenolics were analyzed using an Infinity II 1260 LC-DAD instrument (Agilent Technologies Inc., Folsom, CA, USA) with a method modified from a protocol previously described by [PERSON] and [PERSON] [28] for anthocyanins. The analytical column was changed to a Poroshell 120 EC-C18 with the dimensions 4.6 \(\times\) 100 mm and a particle size of 2.7 um (Agilent Technologies Inc., Folsom, CA, USA). The gradient was adjusted to the new column length and was set as follows. Conditions started at a 100% mobile
# Light Harvesting and Optical-Electronic Properties of Two Quercitin and Rutin Natural Dyes
## Results and Discussion
### Absorption Properties  
Table 3 shows the calculated absorption data and Figure 5 shows the absorption spectrums. From the experiment, it can be clearly seen that the trend of the absorption peaks of S-Q and S-R are in the following order: S-Q (352 nm) \(\approx\) S-R (354 nm). The theoretical simulated maximum absorption peaks of S-Q and S-R are S-Q (355.50 nm) \(>\) S-R (336.92 nm). When comparing the experimental data with theoretical simulated data, it can be clearly seen that S-R (336.92 nm) is about 17 nm shorter than the value of its experimental data (354 nm), and S-Q (355.50 nm) is about 3.5 nm longer than the value of its experimental data (352 nm), and there is very little change in the value. It can be inferred that the theoretical simulated data can reflect the experimental data well.  
Figure 4: Electrostatic potential of S-Q and S-R.  
Figure 5: Absorption spectrum of S-Q and S-R in experiment and theoretical simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dyes** & **States** & **E (eV)** & **\(\lambda\) (nm)** & **CI** & **F** \\ \hline \multirow{2}{*}{S-R} & S1 & 3.6800 & 336.92 & H\(\rightarrow\)L/0.682 & 0.3600 \\  & S2 & 3.7822 & 327.81 & H-1\(\rightarrow\)L/0.655 & 0.0104 \\ \multirow{2}{*}{S-Q} & S1 & 3.4876 & 355.50 & H\(\rightarrow\)L/0.675 & 0.4010 \\  & S2 & 3.7928 & 326.89 & H-1\(\rightarrow\)L/0.678 & 0.1096 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculated transition energies and oscillator strengths of S-Q and S-R.  
From the theoretical analysis, S-Q has red-shifted absorption spectrums when compared with S-R. which illustrates that S-Q can absorb more sunlight than S-R [54]. The largest oscillator strength of S-Q (0.4010) and S-R (0.3600) in their first excited state (S1) with the maximum absorption peaks both corresponding to HOMO-LUMO transition, and the relative electron transfer of S-Q and S-R both take place among the black region (see Figure 1). It can be reflected by the (FMOs) [55] of S-Q and S-R, as shown in Figure S4. Figure 5 shows that S-Q has larger molar extinction coefficients than S-R in both the experiment and theoretical simulation, which demonstrates that S-Q has a better property to harvest sunlight.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 3 State of the Art
### Destination-Less Forwarding Protocols
#### 3.1.1 Epidemic Routing  
When two nodes using Epidemic routing [4] meet, they fully synchronise their cache contents. The node with the smaller node ID starts the synchronisation process by sending its summary vector (SV), a list of message identifiers currently residing in its cache, to the node with larger node ID. The receiver of the SV compares it to its own cache and requests the sender to send the missing items. When the process is completed in one direction, it repeats in the opposite direction. Thus, after this two-way synchronisation, both nodes have identical caches. In the case the nodes remain in contact for a longer time, they will re-synchronise only after some predefined amount of time to avoid unnecessary network overhead. Epidemic has two main parameters: the re-synchronisation period and a maximum number of hops the data can travel before being dropped from the cache. These parameters balance the performance between low delay and high delivery rate on one hand and low network overhead on the other hand.  
Epidemic does not differentiate between messages in its cache, apart from the number of hops they are allowed to travel or an optional TTL field (time-to-live). There exist some extensions of Epidemic, which target priority-based traffic [8], which we discuss in more detail in Section 3.2.
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 2 Related Work  
### Multi-View Learning  
Multi-view learning algorithms can be divided into co-training, multi-kernel learning, and subspace learning [21, 22]. The co-training algorithm iteratively maximizes mutual conventions on two distinct views to ensure consistency on the same validation data, such as multi-view collaborative clustering algorithm and research [23] using multiple collaborative training for document classification [24]. The multi-kernel learning (MKL) algorithm uses the kernels corresponding to different views and combines them linearly or nonlinearly to improve performance. For example, the support kernel machine (SKM) model introduced in [25], and the sequential minimum optimization (SMO) algorithm is developed to solve it. Multi-kernel framework with nonparallel support vector machine (MKNPSVM) works by integrating non-parallel support vector machines into the MKL framework to learn the optimal kernel combination [26]. The subspace learning algorithm assumes that the input view comes from a potential subspace and aims to realize the potential subspace shared by multiple views, such as SVM-2K combined with two support vector machines and kernel canonical correlation analysis (KCCA) [27], SVM classification method with coupling privileged kernel method [28], etc.