esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Results
### Simulation
#### 5.3.1 Scenario 1: Normal Traffic Conditions  
Scenario 1 shows normal and balanced traffic flow without extreme congestion or special priority requirements. This scenario reflects a moderate traffic flow where Regular vehicles (RVs) and occasional emergency vehicles (EVs) are available. Under these standard conditions, a smooth flow of traffic is the goal, with minimal waiting time and delays. In ideal steady-state traffic, this scenario provides a baseline for assessing the system's performance.  
Based on Table 1, in the hierarchical Stackelberg model, emergency vehicles (EVs) had a waiting time of 60 s, a 25% decrease compared to the basic model of 80 s. RVs also benefit from improved traffic flow and reduced wait times from 110 s to 80 s in the hierarchy model. Moreover, the hierarchical Stackelberg model significantly reduced intersection queues and congestion by 33% compared to the baseline model, demonstrating its efficiency in managing emergency and routine traffic. Standard Stackelberg models have moderate improvements over baseline models, but they are less effective than hierarchy models, especially for RVs.
## 3 Results and Discussion
### Carcass and Meat Quality Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{3}{c}{**Early Fattening Period**} & \multicolumn{3}{c}{**Late Fattening Period**} & \multicolumn{3}{c}{**Total Period**} \\ \cline{2-13}  & **CON1**  & **T 2**  & **SEM 3**  & **_p-Value_** & **CON** & **T** & **SEM**  & **CON** & **T** & **SEM**  & **_p-Value_** \\ \hline IBW (kg) & 376.4 & 415.0 & 11.50 & 0.26 & 575.8 & 624.4 & 1.47 & 0.03 & 376.4 & 415.0 & 11.5 & 0.26 \\ FBW (kg) & 538.8 & 595.1 & 14.80 & 0.04 & 696.0 & 751.6 & 17.4 & 0.03 & 696.0 & 751.6 & 17.40 & 0.03 \\ ADG (kg) & 0.83 & 0.93 & 0.04 & 0.05 & 0.76 & 0.83 & 0.05 & 0.20 & 0.79 & 0.88 & 0.02 & 0.22 \\ Feed intake (kg) & 11.50 & 16.29 & 0.73 & – & 14.36 & 17.79 & 0.54 & – & 12.93 & 17.04 & 0.49 & – \\ Concentrate (kg) & 8 & – & – & – & 12.57 & 6.43 & – & – & 10.29 & 3.21 & – & – \\ Rice straw (kg) & 3.5 & – & – & – & 1.79 & 0.79 & – & – & 2.64 & 0.39 & – & – \\ IRG silage (kg) & – & 3 & – & – & – & 1.29 & – & – & – & 2.14 & – & – \\ WCC silage (kg) & – & 5.86 & – & – & – & 0.43 & – & – & – & 3.14 & – & – \\ Fermented feed (kg) & – & 7.43 & – & – & – & 8.86 & – & – & – & 8.14 & – & – \\ FCR & 15.83 & 18.55 & 0.79 & 0.82 & 23.27 & 24.18 & 1.61 & 0.56 & 20.51 & 19.18 & 0.64 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effects of formulated feed on growth performance and feed intake of Hanwoo steers during early and late feeding periods.
## 3 Proposed Method
### The Scrambling Algorithm  
In many of the authentication-type watermarking methods, the image is divided into blocks and the blocks are paired as (B1, B2). An image digest, a binary image or image features are calculated from the B1 block and embedded in the B2 block. It is important to make the secret pairing of blocks because, if someone knows or predicts the block sizes and pairs, they could easily replace the block pair with a pair from an already authenticated image. In [25], it is mentioned that torus automorphisms can be used for that purpose. A torus automorphism is a dynamic stately system that has a beginning state and change state at t intervals.  
\[S_{t+1}=\int(S_{t}),t\epsilon\{0,1,2.....\} \tag{5}\]  
A two-dimensional torus automorphism can be defined by  
\[A=\begin{pmatrix}a_{1}&a_{2}\\ a_{3}&a_{4}\end{pmatrix},\begin{pmatrix}\frac{x_{t+1}}{y_{t+1}}\\ \end{pmatrix}=A\times\begin{pmatrix}\frac{x_{t}}{y_{t}}\\ \end{pmatrix}\mod N \tag{6}\]  
\(a_{i\epsilon}Z,determinant(A)=1\),  
\(A\) _has eigenvalues \(A_{1,2}\epsilon\ R-\{-1,0,1\}\),_  
The system is chaotic and repeats itself at every time step \(R\), that is \(S_{R}=S_{0}\). [PERSON] and [PERSON] [35] proposed a special type of matrix A for Equation (6), where A can be formed from one value \(k\).  
\[A=\begin{pmatrix}1&1\\ k&k+1\end{pmatrix} \tag{7}\]  
In our study, the one-dimensional mapping method mentioned in [25] and given in Equation [8] is used.  
\[X^{\prime}=(k\times X\ mod N) \tag{8}\]  
where \(X,X^{\prime}\in\{0,1,\ldots.N-1\}\) are the block numbers at t and t+1, k is a prime number and the secret key, N is the number of blocks. The set of inner blocks is S1 and the number of elements in S1 is N1, whereas the set of outer blocks is S2 and the number of elements is N2. The scrambling algorithm maps the blocks in S1 to blocks in S2 and it is a one-to-one mapping.  
Although mapping between inner and outer blocks uses a scrambling algorithm and a secret key, it is not the only security prevention. The block sizes of the inner blocks and the outer blocks are also determined on the run which makes the attacks more difficult. The attacker will not know the blocking structure in the image. The number of excluded values in the DCT transform is also not known by the attacker. A little weakness in the pairing scrambling algorithm may be easily compensated for by the mentioned unknown parts of the algorithm.
# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 3 Results and Discussion
### Type 1 Maize
Figure 3 (Equation (10)) presents the estimation model as a dynamic explanatory response in time and the residuals of the estimation model given by Equation (10). The last considered model considering the dynamic structure of the time series and by means of transference functions is presented in Equation (11). The selected model was ARIMAX(2,0,0)X(1,0,1). \[Y_{t}=0.55+0.708Y_{t-1}+0.575Y_{t-2}+0.547Y_{t-12}+\varepsilon_{t}+0.533 \varepsilon_{t-12} \tag{11}\]  
where \(Y_{t}\) is type 1 maize at time \(t\). All estimated parameters were significant at the 0.05 level. The Sum of Squared Residuals (SSR) associated value for the model presented in Equation (11) is 0.563, and the Akaike info criterion (AIC) is \(-1.39\). We can observe a dynamic dependence. Ljung Box autocorrelation proof validates the hypothesis of white noise in the residuals of the model, confirming the adequacy and reliability of the estimation model. Figure 3 (Equation (11)) presents the estimation model as a dynamic explanatory response in time and the residuals of the estimation model given by Equation (11). The forecast model is constructed as a combination of models presented in Equations (7)-(11). Therefore, the dynamic structure of the type 1 maize time series is included as the effect of average temperature, precipitation, average humidity and radiation. The combination of the models was obtained through least squares and mean squares, with a resulting MSE of 0.01 and 0.011, respectively. Figure 4 presents the forecast values for a one-year time horizon given by the estimation models presented in Equations (7)-(11) and least squares and mean squares aggregation models. The associated MSEs for each estimation model presented in Equations (7)-(11) were 0.011, 0.017, 0.017, 0.019 and 0.019. The best forecast model attending to the best MSE value was obtained by least squares.
# Experimental Study of Entrainment and Mixing of Renewable Active Particles in Fluidized Beds
###### Abstract  
Fluidized bed combustors were initially designed and built basically for the utilization of fossil fuels, mostly coal. The actual worldwide trend of transitioning from fossil fuels to renewables requires sufficient knowledge on the fluid mechanics of these new particle types because of the significant differences in their shapes, sizes, densities, and homogeneities. This article presents experimental results on the particle entrainment and mixing of some industrially relevant fuels such as solid refused fuel/refuse derived fuel (SRF/RDF), bark, sunflower shell, and wheat shell. The measurements were performed on a lab-scale fluidized bed experimental facility. The results show that sunflower shell is entrained in the highest degree; however, at very low velocity, the entrainment of wheat shell is the most intensive. The entrainment behaviors of the investigated SRF and bark samples are similar. On the other hand, the mixing results showed that the SRF has relatively high mass fractions in the bottom and centeral regions of the fluidized bed at low superficial velocities, while at elevated velocities, the entire mass of this fuel is shifted upwards. Interestingly, just the opposite tendency can be observed in cases of all other investigated biomass fuels. Finally, the nonspherical renewable active particles have markedly higher concentrations in the bottom region of the bed compared to spherical ones.  
fluidized bed; SRF/RDF; bark; sunflower shell; wheat shell; entrainment; mixing; renewable; fuel; active particle Article
## 3 Data Sources and Research Methodology
### Research Methodology
#### 3.2.4 Correlation Analysis  
Pearson's coefficients were used to analyze the correlation between WUE and climate factors as follows.  
\[\mathbf{R}=\frac{\sum(x-\overline{x})(y-\overline{y})}{\sqrt{\sum(x-\overline {x})^{2}}\sqrt{\sum(y-\overline{y})^{2}}} \tag{7}\]  
In the above formula, R is the Pearson coefficient, x represents the climate factors, and y is the WUE.  
Partial correlation analysis was used to calculate the partial correlation coefficient between two variables while eliminating the influence of other variables. This analysis can make one of the three variables fixed so as to explore the correlation between the other two variables [28; 29]. In this paper, image element-based partial correlation analysis can further investigate the classification of influence of temperature and precipitation on ecosystem WUE, and the calculation formula is as follows:  
\[\mathbf{R_{xyz}}=\frac{R_{xy}-R_{xz}R_{yz}}{\sqrt{\left(1-R_{xz}^{2}\right) \left(1-R_{yz}^{2}\right)}} \tag{8}\]  
In Formula (8), x represents the dependent variable (WUE), y and z are the independent variables (climate factors), and Rxyz is the partial correlation coefficient between x and y. In order to calculate the significance level of the correlation, a \(t\)-test was used as follows.  
\[\mathbf{t}=\frac{\mathbf{R}\sqrt{n-1}}{\sqrt{1-\mathbf{R}^{2}}} \tag{9}\]  
where n is the number of samples. At given significance levels \(\alpha 1\) (\(p<0.05\)) and \(\alpha 2\) (\(p<0.01\)), the correlation is significant if t\(>\) t\({}_{\alpha 1}\), and the correlation is extremely significant if t\(>\) t\({}_{\alpha 2}\). Otherwise, the correlation is not significant.
# Numerical-Experimental Analysis to Optimize the Geometry of a Prosthetic Abtment
## 7 Conclusions  
This paper presents a comprehensive study on optimizing the geometry of prosthetic abutments, validated through both numerical simulations and experimental testing. The proposed crown-abutment coupling model is based on a combination of theoretical analysis, numerical investigation, and experimental validation, closely reflecting the shape typically assumed by machined abutments.  
A key contribution of this study is the development of a double algorithm that accurately calculates the axial and cusp resistance areas of prosthetic abutments under varying geometric configurations. This algorithm exhibits significant novelty compared to the existing literature, providing valuable insights into abutment stability and dislodgement resistance.  
The study also includes a 10:1 scale prototype, tested in a compressive testing machine, where the pressure distribution on the coupling surface was measured through pressure-sensitive films. The close alignment between numerical and experimental results further validates the proposed model.  
The simulation part was carried out with FEA numerical code (simulation), while the experimental part was carried out on the 10/1 scale sample and instrumented to verify that the loads predicted in the simulation were also results in the real experiment.  
Moreover, the research explored various types of luting cements through FEA, with self-adhesive resin showing superior performance in terms of tensile strength and dislodgement resistance, making it the optimal choice for enhancing the structural integrity of the abutment-crown system.  
In addition to the novel algorithm and material findings, the methodology developed in this study is applicable to a variety of teeth, making it versatile and highly adaptable for broader clinical applications.  
The main limitation of this research lies in the fact that the results obtained could not be cross-verified with a clinical trial, which could have certainly provided further insights into the effectiveness of the approach. Therefore, future work will focus on refining the algorithms, exploring additional types of cement, and optimizing prosthetic performance under various clinical conditions. Additionally, we will consider the opportunity to initiate a clinical trial campaign to address the current gaps in research.  
Conceptualization, G.M.G.; methodology, G.M.G.; software, M.B.; validation, D.A.; formal analysis, G.M.G. and F.L.S.; data curation, M.B. and D.A.; investigation, D.A.; resources, writing--original draft reparation, G.M.G. and F.L.S.; project administration, G.M.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 3 Results  
### Whole Exome Sequence Sequencing and Bioinformatic Analysis  
Whole exome sequence analysis of each case did not reveal any candidate genes with autosomal recessive inheritance. However, a heterozygous frameshift variant in TGFBR2 (chr3: g.30691880 del; c.458 del; p.Lys153 SerfsTer35) was identified and confirmed by [PERSON] direct sequencing in all four unrelated patients (Figure 4). This variant is not seen in over 250,000 alleles reported in gnomAD v2.1.1 ([[https://gnomad.broadinstitute.org/](https://gnomad.broadinstitute.org/)]([https://gnomad.broadinstitute.org/](https://gnomad.broadinstitute.org/)) (accessed on 14 November 2022)), although a frameshift impacting the same codon, c.459 del; p.Lys153 AsnfsTer35, has been seen in one person of South Asian ancestry. In total, only 11 frameshift and five possible splice site variants have been reported in TGFBR2 in gnomAD.  
The c.458 del; p.Lys153 SerfsTer35 variant was found in 17 individuals in our in-house exome database of 725 individuals of Thai ancestry with normal controls who had no history of pushular skin lesions. Thirty-six additional Thai patients with either GPP or AOID did not carry the truncating TGFBR2 variant as determined by whole exome sequencing. [PERSON]'s exact test revealed a statistically significant difference in the variant frequency between the diseased cases and control groups: 10.0% vs. 2.3%, respectively (\(p\) = 0.020). In addition to the truncating TGFBR2 variant, an additional variant in TGFBR2 (NM_001024847.2: c.1019C\(>\)T; NP_001020018.1: p.Thr340 Met; rs34833812) was also identified in patient 2. The allele frequency for this variant is 0.001062, according to gnomAD, and was detected in four of 725 normal Thai controls who had no history of pushular skin lesions. This variant has been classified by ClinVar as benign based on supporting modeling and functional data.  
In addition to the TGFBR2 variants, rare variants in other immunodeficiency genes were also found in the four cases reported here (Table 1). Of note, the CARD14 variant c.2473G\(>\)A; p.Ala825 Thr found in patient 1, who was from the Karen tribe, was also seen in 90 of 100 Karen tribe individuals we investigated by Sanger sequencing, suggestive of a polymorphism.  
Figure 4: Electrophrograms of the _TGFBR2_ variant in patients 1–4. The heterozygous base deletion c.458 del is predicted to cause premature truncation of TGFBR2 protein (p.Lys153 SerfsTer35).
## 2 Governing Equations and Numerical Methods
### Governing Equations
\[\frac{\partial\overline{\rho}}{\partial t}+\frac{\partial}{\partial x_{i}}( \overline{\rho}\overline{u_{i}})\ =\ 0 \tag{5}\]  
\[\frac{\partial}{\partial t}(\overline{\rho}\overline{u_{i}})+\frac{\partial}{ \partial x_{j}}\Big{(}\overline{\rho}\overline{u_{i}}\overline{u_{j}}+p^{+} \delta_{ij}\Big{)}\ =\ \frac{\partial\tau_{ij}^{+}}{\partial x_{j}}+\frac{\partial}{ \partial x_{j}}\Big{(}\mu\overline{S}_{ij}\Big{)} \tag{6}\]  
\[\frac{\partial}{\partial t}(\overline{\rho}\overline{e_{t}})+\frac{\partial} {\partial x_{i}}\Big{[}\Big{(}\overline{\rho}\overline{e_{t}}+p^{+}\Big{)} \overline{u_{i}}\Big{]}\ =\ \frac{\partial Q_{i}}{\partial x_{i}}+\frac{\partial}{ \partial x_{i}}\Big{(}\overline{k}\overline{\frac{\partial T}{\partial x_{i}} }\Big{)}+\frac{\partial}{\partial x_{i}}\Big{(}\mu\overline{u_{j}}\overline {S}_{ij}\Big{)} \tag{7}\]  
\[T^{+}\ =\ \overline{T}-\frac{\tau_{kk}}{2c_{v}\overline{\rho}},\ \ p^{+}\ =\ \overline{\rho}RT^{+}+\Big{(}\frac{R}{2c_{v}}-\frac{1}{3}\Big{)}\tau_{kk},\ \ \tau_{ij}^{+}\ =\ \tau_{ij}-\frac{1}{3}\tau_{kk}\delta_{ij}\ =\ \overline{\rho}\
u_{t} \overline{S}_{ij} \tag{8}\]  
LES is represented by the physical quantity that can be resolved by a grid and the physical quantity that needs to be modeled, as expressed in Equation (4). The superscript \(\bar{\ }\) denotes the Favre- (or mass-) averaged physical quantity and is used to consider the compressibility of the turbulent structure. \(e_{t}\) is the total energy, and the physical quantity by the superscript \({}^{+}\) is given by Equation (8). \(c_{v}\) is the static specific heat, \(R\) is the gas constant, and \(\
u\) is the kinematic viscosity [12]. Unlike the RANS equation using time averaging, LES does not use time averaging, so it is suitable for solving unsteady flow with time variation. However, the computation cost is very high, and the solution can be unstable according to initial conditions. In this study, to effectively and accurately consider the influence of the unsteady flow, the steady-state flow field was calculated by solving the RANS, and the LES was performed by applying the RANS results as the initial condition for the unsteady flow field simulation.
# Optical Injection Locking for Generation of Tunable Low-Noise Millimeter Wave and THz Signals
## 2 Optical Injection Locking for Sub-THz and THz Signal Generation
Similarly, it is important to take into account the cavity response for external light; for example, injection into a ring cavity or through a DBR can provide additional filtering, which can either be detrimental or, on the contrary, useful for the system as a whole. Combined use of OFC and IL incorporate some advantages that should be given special attention. The combination of OIL with an array of slave lasers allows increasing power in each of the selected optical tones above that originally available from the comb and equalize power among all the selected tones, which is important since generation of broadband flat OFC requires additional efforts [36; 37]. Moreover, from a system point of view, it may be interesting to transmit signals simultaneously at different frequencies, which can provide multiple user access. The same can be used in spectroscopy to provide synchronous scanning at different frequencies, which may significantly reduce scanning time of the sample. This could be integrated in the system by adjusting each of the slave lasers for different tones in OFC and using a combination of them with a different spacing. Another useful case for generation of two different THz tones by means of injection locking at two different sets of the comb lines is that one of the THz tones can be modulated and used as a main carrier of the data and another tone may play a role of the local oscillator for downconversion at the receiver or may be reused for uplink data transmission. The next step in the generation of the THz signal can be optical signal processing. Usually, it can be a fast electro-optical modulator for data modulation and/or slow phase shifters to perform beamforming in the optical domain [38]. Due to the high propagation losses in sub-THz and THz communication links, there is a strong need to use beamforming to compensate wireless propagation losses and improve the power budget. Finally, the selected and amplified optical tones from the lasers are fed to a photoconductive antenna or assembly of photodiode and antenna to emit THz radiation in free space. In the case where a separate photodiode is used, it is possible to add amplification or signal processing in the electrical domain before the signal is radiated.
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 6 Implications of Persister Cells in the Food Industry
### Physical Treatments
However, its effectiveness varies based on factors such as food type, microbial species, and treatment duration, with some resistant biofilms requiring prolonged exposure for significant reductions. However, its application requires further optimization to ensure uniform effectiveness without affecting sensory properties. While this method offers a promising non-thermal approach to food preservation, additional research is necessary to refine its application, address safety concerns, and expand its usability across diverse food products. Additional physical strategies discussed in the literature include the use of magnetic fields and ionizing irradiation [74; 75; 76]. Thus far, the inactivation of microorganisms using a magnetic pulsed field has shown limited efficacy in preventing bacterial biofilm formation [77]. However, its specific impact on persister cells has not been thoroughly investigated. In contrast, ionizing irradiation is a well-established method that can damage microbial DNA, contributing to food safety and quality when applied at appropriate doses [76]. The Food and Drug Administration (FDA) has approved irradiation as a technique to control the growth of microorganisms on food surfaces [77]. Although irradiation has been effectively employed to manage bacterial biofilms, its effects on persister cells remain unexplored. Further research into these physical strategies could provide valuable insights into their potential to target persister cells, thereby enhancing food safety protocols in the industry. In addition to the previously mentioned physical techniques for treating persisters in the food industry, several other methodologies may contribute to preventing the development of foodborne microorganisms. These non-thermal techniques include cold plasma, ultraviolet (UV) light irradiation, high-intensity light pulses (HILP), pulsed electric fields (PEFs), and ultrasound (US) [78]. However, further research is needed to optimize their efficacy related to persisters.