esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Ultrasound Homogenization in Milk Ice Cream Mixes
## 2 Materials and Methods
### The Analysis of Rheological Properties of Milk Ice Cream Mixes  
The analysis of rheological properties of the cream mixes was performed by using the Haake Mars 40 rheometer (Thermo Scientific Inc., Karlsruhe, Germany) in a rotational mode within a shear rate of 0-100 s\({}^{-1}\) at a constant temperature of 25 \({}^{\circ}\)C. The apparent viscosity (\(\eta_{\text{app}}\)) curve as a function of the shear rate (\(\dot{\gamma}\)) in the semi-logarithmic scale (the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Median D\({}_{50}\)**} \\  & **TSI** &
\begin{tabular}{c} **Before Maturation** \\ \end{tabular} & **After Maturation** \\ \hline CH & 2.5 \(\pm\) 0.3 \({}^{\text{b}}\) & 22.97 \(\pm\) 1.13 \({}^{\text{a}}\) & 20.11 \(\pm\) 0.29 \({}^{\text{ab}}\) \\ CU & 2.2 \(\pm\) 0.1 \({}^{\text{ab}}\) & 31.12 \(\pm\) 2.70 \({}^{\text{bc}}\) & 23.55 \(\pm\) 0.95 \({}^{\text{ac}}\) \\ IH & 7.3 \(\pm\) 0.3 \({}^{\text{e}}\) & 25.17 \(\pm\) 0.99 \({}^{\text{ab}}\) & 18.63 \(\pm\) 0.32 \({}^{\text{b}}\) \\ IU & 2.0 \(\pm\) 0.2 \({}^{\text{ab}}\) & 28.92 \(\pm\) 3.67 \({}^{\text{ab}}\) & 26.96 \(\pm\) 2.82 \({}^{\text{c}}\) \\ AH & 4.1 \(\pm\) 0.8 \({}^{\text{c}}\) & 25.25 \(\pm\) 1.62 \({}^{\text{ab}}\) & 20.79 \(\pm\) 0.51 \({}^{\text{ab}}\) \\ AU & 1.5 \(\pm\) 0.1 \({}^{\text{ab}}\) & 39.72 \(\pm\) 1.70 \({}^{\text{d}}\) & 37.14 \(\pm\) 1.33 \({}^{\text{de}}\) \\ BH & 4.2 \(\pm\) 0.5 \({}^{\text{c}}\) & 25.41 \(\pm\) 0.75 \({}^{\text{ab}}\) & 23.95 \(\pm\) 0.16 \({}^{\text{ac}}\) \\ BU & 1.3 \(\pm\) 0.1 \({}^{\text{a}}\) & 33.72 \(\pm\) 0.48 \({}^{\text{cd}}\) & 33.93 \(\pm\) 0.98 \({}^{\text{d}}\) \\ LH & 5.8 \(\pm\) 0.2 \({}^{\text{d}}\) & 24.45 \(\pm\) 1.00 \({}^{\text{a}}\) & 22.87 \(\pm\) 1.12 \({}^{\text{ab}}\) \\ LU & 1.5 \(\pm\) 0.1 \({}^{\text{a}}\) & 39.78 \(\pm\) 4.63 \({}^{\text{d}}\) & 39.75 \(\pm\) 2.14 \({}^{\text{e}}\) \\ \hline \hline \end{tabular} The values are represented as the mean \(\pm\) standard deviations. The different superscript letters in the column represent significant differences in the means (\(p<0.05\)). TSI—Turbiscan Stability Index, D\({}_{50}\)—median diameter.  
\end{table}
Table 2: The physical properties of milk ice cream mixes.  
flow curves) was plotted by theoWin v.4.86. Job Manager (Thermo Scientific). To describe the flow curves and to determine the flow behavior index, the Ostwald de Waele model (1) was applied:  
\[\eta_{app}=K\dot{\gamma}^{n-1} \tag{1}\]  
where \(K\) is the consistency index (Pas\({}^{n}\)), \(\dot{\gamma}\) is the shear rate (s\({}^{-1}\)), and \(n\) is the flow behavior index (dimensionless). Three repetitions for each variant of ice cream mixes as well as before and after maturation were examined.
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH  
LoRa is an LP-WAN technology of low cost modulation, low energy consumption and that has as main proposal the long range [43]. Like any technology, the LoRa also has disadvantages, after all, to achieve long range without consuming much energy and that is low cost, some factor should be affected and this is the bandwidth. Thus, LoRa is not a technology like WiFi that enables streaming, lives, donwloads and extensive package uploads. However, LoRa will be widely used for applications that don't need large bandwidth, but that need scalability, low cost and especially long reach. Among the IoT technologies presented, this work proposes the implementation of a two-way communication infrastructure using LoRa technology through LoRaWAN, LoRaMESH and hybrid between the two protocols.
###### Abstract  
Titanium dioxide (TiO\({}_{2}\)) and TiO\({}_{2}\)/copper (denoted as TC) composite were prepared via hydrothermal process. In the meantime, divinylbenzene (DVB) and bismaleimide (BMI) monomers were allowed to participate in in-situ radical polymerization in the presence of azobisisobutyronitrile (AIBN) initiator to afford porous polymers (abridged as PP). The as-obtained PP were mixed together with tetrabutyl titanate (TBT) and CuSO\({}_{4}\)-5H\({}_{2}\)O in vacuum to obtain PP/TC composite (denoted as PPTC) containing incorporated TC composite in the pores of PP. The as-prepared TiO\({}_{2}\), TC, and PPTC were characterized by transmission electron microscopy, scanning electron microscopy, X-ray diffraction, fluorescence spectrometry, and electron spin resonance spectrometry, and so on. Furthermore, their photocatalytic activity for the degradation of \(N\),\(N\)-dimethylformamide, methyl orange, phenol, and methylene blue under the irradiation of simulated sunlight (Xe lamp light) and natural sunlight were investigated. Findings indicated that, whether under simulated sunlight or nature sunlight irradiation, PPTC exhibited much better photocatalytic performance than TiO\({}_{2}\) and TC for the degradation of the tested organic pollutants. Particularly, it allowed \(N\),\(N\)-dimethylformamide (DMF) to be degraded by a rate of 73.7% under simulated sunlight irradiation and it retained photocatalytic activity even after six cycles of reuse, exhibiting promising potential for the removal of organic pollutants in wastewater (including industrial water, aquaculture wastewater, and domestic sewage). The desired photocatalytic performance of the as-prepared PPTC is attributed to two aspects. Namely, the incorporation of Cu\({}^{2+}\) into the fine structure of TiO\({}_{2}\) contributes to increasing photocatalyst activity and producing more free radical while the embedding of TC composite into the PP pores improves to the contact area between the photocatalyst and organic pollutants, and both are beneficial for improving the adsorption capacity and activity of the photocatalyst, thereby enhancing the degradation of the organic pollutants.  
porous polymer; TiO\({}_{2}\)/Cu composite; photocatalyst; characterization; wastewater; organic pollutant; degradation; photocatalytic activity +
Footnote †: journal: Journal of Materials Research  
_Acticle_  
_Acticle_  
**2020**, _10_, 310; doi:10.3390/catal10030310
## 4 Experiments  
### Dataset and Backbone  
We conduct experiments on action recognition datasets Kinetics-400 [26]. Kinetics-400 encompasses an extensive collection of 400 distinct human action classes, accompanied by a substantial collection of training videos totaling approximately 240 k as well as 20 k validation videos. Our method is trained on the training set and evaluated on the validation set. Consistent with prior studies, we adhere to the convention of presenting the top-1 and top-5 recognition accuracy as performance metrics.  
The primary model used in this study is the video transformer UniFormer. The choice of this model is attributed to its superior performance coupled with a reasonable computational cost. Notably, UniFormer's structure is characteristic of a hierarchical vision transformer, which includes a convolution in its self-attention block. This effectively validates our method's efficacy, as it operates without compromising the original structure of the transformer. UniFormer [17] is intricately crafted, combining both local and global multihead relation attention (MHRA) blocks. Convolutions replacing self-attention at the earlier stages of this design do not allow for the implementation of naive token pruning. Consequently, token sparsification and asymmetric processing are co-applied in the deeper layers while in the shallower layers, only asymmetric processing is utilized in the feed-forward network (FFN). To further affirm the versatility and adaptability of our approach across varying transformer architectures, we carry out additional experiments with Video Swin Transformer [12] as well.
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 5 Results
### Relative Selfish Revenue
#### 5.1.2. Hashing Power Distribution  
We are interested in learning the effect of varying hashing power distributions on relative selfish revenue, in order to understand the robustness of the selfish mining strategy. Figure 3 shows relative selfish revenue as a function of selfish mining power \(\alpha\) under different hashing power distributions, namely a uniform random, power law, and exponential distribution. We show results for two different latency regimes.  
We observe that hashing power following an exponential distribution has a negligible effect on the dynamics of the system, for either latency regime. The dynamics are indistinguishable from the previous results with a uniform random distribution. However, the left column of Figure 3 shows remarkable differences for hashing power following a power-law distribution with very high block latency (see Figure 3c where \(\lambda_{nd}=0.03\)). First, relative selfish revenue crosses the fair share threshold for higher values of \(\alpha\). Second, the slope of the regression of relative selfish revenue on selfish mining power is notably less steep. The reason for this might be that hashing power following a power-law distribution implies that it is relatively more likely that an honest miner has a very large share of the network's hashing power. This in turn implies that when block diffusion is very slow, it is more likely that an honest miner manages to outperform the selfish miner, thus creating the longest chain. For this high latency, the main chain is effectively built by one single miner, because block diffusion time exceeds the expected time between new blocks. However, [PERSON] [19] finds that Bitcoin mining power trends can be fit as exponential distributions.  
Figure 3: _Cont._  
Figure 2: Relative selfish revenue as a function of: (**a**) network latency (\(\lambda_{nd}\)) and (**b**) selfish hashing power (\(\alpha\)).
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 3 Solution-Phase Ligand Exchange
### Flat Energy Landscape and High Packing Density in Quantum Dots  
In CQD solids, the inhomogeneous energy distribution is an important problem limiting the further improvement of device performances [38; 39; 40]. Poor monodispersity, disordered packing, and aggregation of CQDs can produce additional energy distribution at the band edge including both conduction and valance band [41; 42; 43; 44; 45]. Additionally, energetic inhomogeneity arises from the solid-state ligand exchange procedure [42]. The solid-state ligand exchange has disadvantages, such as insufficient ligand exchange, CQD fusion, and remained organic debris. The solution-phase ligand exchange approach can be considered as a solution to overcome these problems. However, previous CQD ink-based PVs have not yet outperformed the conventional solid-stated ligand-exchanged CQD PVs [38]. The lesser performance of CQD ink-based PVs originates from the counter-ions, which are essential for colloidal stability under polar solvents, and the residual counter-ions in CQD film can hinder charge transport [34; 35]. Counter ions can be removed by a post-annealing process; however, halide ligands on the CQD surface also can be damaged during this process, resulting in the poor surface passivation and aggregation of CQDs [34]. Therefore, the CQD PV performance can be improved by the removal of unwanted counter ions before film deposition. [PERSON] et al. suggested a new solution-phase ligand exchange process using lead halide and ammonium acetate salts [46]. The hybrid PbS CQD ink exhibited increases in both the incorporation of halide ligands on the PbS CQD surface, as well as the elimination of native oleic acid ligand residuals remaining in the resultant PbS CQD solutions [46]. This new PbS CQD ink demonstrated multiple benefits, including reduced band tails that improved the open-circuit voltage (\(V_{\text{OC}}\)), better carrier transport, and improved light absorption [46]. The densified halide ligands also exhibit improved surface passivation, allowing  
Figure 5: Schematic of the solution-phase ligand exchange and film fabrication. Ligands of CQDs are exchanged in the solution-phase first. After that, CQD films can be fabricated just by spin-coating the CQD inks on the substrate.  
thicker PbS CQD film, which is beneficial for light harvesting. These benefits enable the fabrication of PbS CQD PVs with a certified _PCE_ of 11.28%.
# Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus
## 4 Speech Synthesis Experiments
### Naturalness Evaluation  
To fully compare the naturalness, we compared the baseline systems FastSpeech2 + HiFi-GAN, VITS with ground truth speech. In addition, to verify the performance of the neural vocoder, we added the FastSpeech2 + Griffin-Lim baseline model for further comparison. The Griffin-Lim algorithm can directly obtain the phase information of audio to reconstruct the waveform without additional training. For each speaker, we randomly selected 20 sentences as the evaluation set, which were not used for training. The model-generated and real audio were randomly disrupted and assigned to listeners. During the evaluation process, 10 native Mongolian speakers were asked to evaluate the naturalness of the generated 600 audible speeches in a quiet environment. We report the naturalness results with naturalness mean opinion score (N-MOS) [34].  
The main results for N-MOS are given in Table 2. Undoubtedly, the best performance was obtained for real speech. VITS achieved scores extremely close to real speech and had the best results among the two baseline models. FastSpeech2+HiFi-GAN outperformed FastSpeech2+Griffin-Lim and also achieved good results. Specifically, VITS achieved an N-MOS score of 4.62 on F3, which is a small difference compared to the true value. Meanwhile, FastSpeech2+HiFi-GAN also achieved the best score of 4.29 on F3, but there is still a significant difference compared to VITS.  
Figure 5: The structure of the VITS model. We implemented the multi-speaker VITS by adding the speaker encoder module.  
For F1 and F2, VITS scored 4.60 and 4.55, respectively, while FastSpeech2 + HiFi-GAN achieved 4.02 and 4.15, respectively, which is encouraging. Overall, the VITS model still works better than FastSpeech2 + HiFi-GAN. In short, all our results demonstrate that the MnITS2 dataset can be used to build a robust TTS system to generate high-quality speech.
## 3 Analysis of Simulation Results  
### Analysis of Temperature Changes  
After running the FDS simulation, the temperature distribution of the temperature slicing output of the third floor is obtained, which is shown in Figure 5. According to the literature [29], it is known that the human body can tolerate a temperature below 60 \({}^{\circ}\)C for more than 0.5 h, and more than 60 \({}^{\circ}\)C will cause discomfort, which makes the tolerance time greatly reduced and is not conducive to safe evacuation. As can be seen from Figure 5, the temperature at B is the most variable among the four measurement points, with the highest temperature around 47 \({}^{\circ}\)C, while the temperatures of the other three measurement points are all below 40 \({}^{\circ}\)C. This indicates that, except for the fire room, the temperature change in other spaces in the simulation field is not the dominant factor affecting evacuation.
## 4 Case Study  
### Data Description  
In Korea, the Korea Transport Institute (KOTI), a government-funded institute, periodically distributes KTDB data, including O/D trip and network data, to increase public confidence in long-term demand predictions. The KTDB data consist of 250 zones defined by local governments, and 247 zones were used in this study, as shown in Table 1; Jeju, Seogwipo, and Ulleung were excluded since they are islands that do not have HSR lines, and thus have zero trips. The O/D trip frequency in Korea's HSR was 225,495 trips/day in 2018, and the number of inter-region (16 metropolitan local governments) O/D trips is shown in Table 2. There were no O/D trips between Gangwon and North/South Chungcheong, North/South Jeolla, and North/South Gyeongsang, as well as between North/South Jeolla and North/South Gyeongsang, in the KTDB. This is because travel between these regions is not directly connected by HSR, and thus travel time and distance between these regions is so long. Therefore, it was assumed that there was no transfer demand in the HSR network and only trips between stations connected by the same line. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Region** & **No. of Zones** & **Region** & **No. of Zones** & **Region** & **No. of Zones** & **Region** & **No. of Zones** \\ \hline Seoul & 25 & Gwangju & 5 & Gangwon & 18 & Jeonnam & 22 \\ Busan & 16 & Daejeon & 5 & Chungbuk & 14 & Gyeongbuk & 24 \\ Daegu & 7 & Ulsan & 5 & Chungnam & 16 & Gyeongnam & 22 \\ Incheon & 10 & Gyeonggi & 42 & Jeonbuk & 15 & Sejong & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of zones in the KTDB data used in this study. Figure 8: Calculation of the boarding/alighting volume by stations. The HSR network consists of railway stations and lines. There are 51 HSR stations and three major lines in Korea: Gyeongbu (Seoul-Busan), Honam (Seoul-Gwangju), and Gangneung (Seoul-Gangneung). Figure 9 shows HSR network data schematized using EMME, where the pink boxes represent the zones (centroids), and the blue lines represent the HSR lines. Two influential variables, namely the access distance from the zones to stations and the train frequency at the stations, were considered in this study. The access distance from zones to stations was calculated using the Euclidean distance from the centroid (a point representing each zone) to the station node. The KTDB network data used a local coordinate system (Bessel ellipsoid, Korean 1985 datum) to express the geographical location, and the coordinate system unit was in meters. Therefore, the Euclidean distance from zones to stations was calculated using Equation (2) [47]. The Euclidean distances were calculated by extracting only the information of the 51 HSR station nodes and 247 centroids in the KTDB network data. \[d_{is}=\sqrt{\left(x_{i}-x_{s}\right)^{2}+\left(y_{i}-y_{s}\right)^{2}} \tag{2}\]
## 2 Materials and Methods
### Physicochemical Parameter Analysis
#### 2.3.9 Determination of Total Phenolic Content  
The total phenolic content of _Lavandula stoechas_ was determined in its aqueous and methanolic extracts using the Folin-Ciocalteu assay according to [PERSON] et al. [15]. In particular, in a 5 mL volumetric flask, 0.2 mL of herb extract, followed by 2.5 mL of distilled water and 0.25 mL of Folin-Ciocalteu reagent, were added. After 3 min, 0.5 mL of saturated Na\({}_{2}\)CO\({}_{3}\) (30%, \(w\)\({}^{-1}\)) was also added and the final volume of 5 mL was reached using distilled water. The flask was then stored in a dark place and the absorbance was measured after 2 h at 760 nm in a UV/VIS Spectrophotometer (SHIMADZU, UV-1280, Kyoto, Japan). Before any absorbance measurements, the solutions were filtered using Whatman filters (Whatman plc, Buckinghamshire, UK) with a pore size of 0.45 \(\upmu\)m. For the quantification analysis, a calibration curve of standard gallic acid was constructed in wide range of concentrations: 0-6240 mg L\({}^{-1}\). The equation obtained was of the following form:  
\[y=0.0004x+0.1479,\,R^{2}=0.9895 \tag{3}\]  
Total phenolic content was expressed as mg of gallic acid equivalents per mL (or g L\({}^{-1}\)) of _Lavandula stoechas_ extract. Each sample was analysed in triplicate (\(n=3\)).  
Headspace Solid Phase Microextraction Coupled to Gas Chromatography/Mass Spectrometry (HS-SPME/GC-MS)
## 1 Introduction
Furthermore, even if the actual channel state is obtained, AMC needs to adjust the coding scheme dynamically. Some works [15; 16] adopted a look-up table method to obtain the optimal coding scheme. However, the conventional look-up table method cannot cope with multiple antennas scenario. Reinforcement learning can adapt to the complicated satellite-to-ground channel and multiple antennas scenario. [PERSON] et al. [31] proposed a Q-learning algorithm with constrained exploration spaces. Some methods [32; 33] proposed a multi-objective Q-learning algorithm to select a coding scheme for satellite-to-ground communication. The satellites move in traditional ways, and therefore, there exist relationships between the current channel state and the historical ones. Unfortunately, these works have not considered the past channel state, which may limit their performance and feasibility. This paper proposes an AMC method based on deep learning (DL) and deep reinforcement learning (DRL) for ubiquitous satellite-to-ground communications. The estimation model solves the drawback of inaccurate channel estimation in the previous works by establishing a global real-time weather model and considering the precious channel information. The decision model can only cope with the multi-antenna scenario that the table look-up method in the previous works can cope with. Nevertheless, the throughput of the satellite-to-ground communication can be improved by using an actor-critic network. The proposed method consists of a DL-based estimation model and a DRL-based decision model. It is proved that the proposed AMC scheme can improve the throughput performance. This paper's remaining sections are organized as follows: Finally, Section 2 describes in detail the work in recent years that is similar to this paper. Section 3 introduces a satellite-to-ground channel and formulates the AMC problem into a Markov decision process (MDP). Section 4 presents the proposed intelligent weather-conscious AMC method. Section 5 describes the performance validation procedure and the simulation results. Section 6 concludes this paper.
## 3 Machine Learning Techniques Based on Linear and Regularized Linear Models  
### Linear and Regularized Linear Models  
#### 3.1.1 Multiple Linear Regression (MLR)  
Linear regression is one of the most widely used regression methods, aiming to identify the best-fit line that represents the relationship between the dependent and independent variables. Multiple linear regression, a type of linear regression, models the relationship between multiple independent predictor variables and a single dependent outcome variable. This stands in contrast to simple linear regression, which focuses on the relationship between a single independent variable and a single dependent variable [49]. If the predictor variables are denoted as \(x_{1}\) to \(x_{n}\) and the outcome variable as \(y\), the relationship between \(x\) and \(y\) can be expressed as shown in Equation (1):  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{n}x_{n}+\varepsilon \tag{1}\]  
where \(\beta_{0}\) denotes the intercept, \(\beta_{1}\) to \(\beta_{n}\) denote the weights, and \(\varepsilon\) denotes the error term. The intercept and weights together are referred to as regression coefficients. The weights of the regression equation were calculated using the ordinary least squares (OLS) method, which minimizes the residual sum of squares (RSS), defined as the sum of the squared differences between the observed data points and the values predicted by the regression equation [29,50]. For a training dataset consisting of m samples, the cost function for linear regression was defined as shown in Equation (2). The primary goal of linear regression is to identify the regression coefficients that minimize this cost function.  
\[MLR_{cost}=\sum_{i=1}^{m}(y_{i}-\widehat{y_{i}})^{2} \tag{2}\]  
Here, \(y_{i}\) represents the actual value of the \(i\)-th training data, while \(\widehat{y_{i}}\) denotes the predicted value of the i-th training data.