esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Framework
### Optimization Using Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA)
Hence, adaptable values present a unique enhancement to the production location enhancement updating Equation (7), and the production location formula is represented in Equation (11). \[x_{i,j}^{t+1}= \begin{cases}\omega\cdot_{i,j}^{t}exp\left(\frac{-i}{\alpha \cdot\textit{i}\textit{i}\textit{i}\textit{e}_{max}}\right)A_{2}<CD\\ \omega\cdot x_{i,j}^{t}+Q\cdot LA_{2}\geq CD\end{cases}  \tag{11}\]  
The precise computation of \(\omega\) is expressed in Equation (12). \[\omega= \begin{cases}\omega_{0}t\leq t_{0}\\ \left(\frac{1}{t}\right)^{0.9}t\leq t_{0}\end{cases}  \tag{12}\]where 0 represents the affirmative real number that is provided. The number of repetitions is indicated by \(t\). The specified range of repetitions is \(t_{0}\). In the sparrow searching phase, the system increases the scale factor in the initial iteration to increase the general searching area and the scale factor in the later repetition to increase the localized extortion capabilities. Figure 3 shows the flow chart of ECSSOA. A class of randomized non-Gaussian phenomena is called Levy flight. A high spotted probability of scatter plots best describes the probabilistic model of rise time. Levy flight can enable optimization techniques that are susceptible to running into the problem of the localized optimal to leap out of the area perfectly by a huge step with a greater chance of occurring in the stochastic process [31; 32]. Each sparrow location's Levy flying plan will be revised utilizing the greedy regulations to maintain the advantageous location. Reverse learning is a technique used to identify the inverse response corresponding to the present solution and keep the better alternative after assessment. Since backward training can locate the superior answer, programs often employ it to leave the optimum solution. Reverse learning was used to update the sparrow's location. When most iteration has been completed, it offers the precise location and accurate solution.
## 3 Method
### Discrete Fourier Transform  
The algorithm that transforms sampled data in the time domain into the frequency domain is called discrete Fourier transform (DFT). DFT connects the time domain and frequency domain of the sampled signal such that the phase and amplitude information can be easily analyzed. We use 1D-DFT here since our training data are a set of spectral vectors. Let \(\mathbf{x}=(x_{0},x_{1},\cdots,x_{119})^{T}\) be one of our training samples; its 1D-DFT is still the same number of samples but in the frequency domain representation, namely \(DFT[\mathbf{x}]=\mathbf{X}=\{X_{k}\}_{k=0}^{119}\). The relationship between them can be formulated as follows:  
\[X_{k}=\sum_{n=0}^{119}x_{n}e^{-j2\pi kn/120}\qquad(k=0,1,\cdots,119) \tag{1}\]  
By separating cosine and sine components as real and imaginary parts, respectively, each sample \(\mathbf{x}\) is transformed into complex numbers to enable the training of the complex neural network. For inference needs, this process is handed over to the fast Fourier transform (FFT) algorithm in particular applications.
## 4 KDN-Based Heavy-Hitter Identification Approach
### Architecture and Modules
#### 4.2.1 Heavy-Hitters Data Acquisition Module  
HH-DAM is responsible for performing two tasks. Firstly, it captures packets from some observation point in the network devices. That means a monitoring task needs to be carried out to collect and report network states in the data plane. Secondly, it generates a dataset for HH identification from the collected packets. In HH-DAM, since we do not implement the monitoring task, we build up the HH-identification dataset from a publicly accessible traffic trace collected in a university DCN, named UNIV1 [64]. UNIV1 was processed and organised into flow records by using the flowRecorder tool with an expiration time (\(f_{\text{it0}}\)) set to 15 s and 150 s. We wrote this tool in Python to turn IP packets, either in the form of PCAP (Packet CAPture) files or sniffed live from a network interface, into flow records that are stored in a CSV (Comma-Separated Values) file. Our flowRecorder supports the measurement of flow features in both unidirectional and bidirectional modes. Depending on the properties of the observed (incoming) packets, either new flow records were created or the flow features of existing ones were updated.  
Tables 3 and 4 show the UNIV1 flows dataset size distributions obtained using flowRecorder with \(f_{\text{it0}}=15\) s and \(f_{\text{it0}}=15\) s. To get details and use this tool, we invite the reader to review [25].  
Regarding the monitoring task, it is important to highlight that to avoid the SDN controller overload regarding traffic and processing caused by gathering statistics from a central point and aiming at getting better performance in collecting and reporting the network flows state, we plan in a future work to use Inband Network Telemetry (INT) [65] by using the Programmable, Protocol-independent Packet Processor (P4). P4 was created as a common language to describe how packets should be processed by all manner of programmable packet-processing targets, from general-purpose CPUs, NPUs, FPGAs, and high-performance programmable ASICs [59]. The main advantages of P4 are: _(i)_ protocol independence meaning that collecting network devices information should be protocol agnostic, _(ii)_ target independence indicating programmers should be able to describe packet-processing functionality independently of the underlying hardware; and _(iii)_ reconfigurability in the field highlighting programmers should be able to change the way switches process packets after their deployment in the network.
### Major Contribution  
Thus, we proposed k-medoids with an improved-artificial-bee-colony (IABC)-based clustering approach and hybrid cross-layer-based Harris-hawks-optimization algorithm (CL-HHO) for effective routing in WSN. Because k-medoids with the improved artificial bee colony operate and calculate quickly, we used k-medoids with IABC for cluster formation and CH selection. K-medoids provide high efficiency, while defining the behavior of complex nodes and speeding up the procedure with IABC. Therefore, we chose CH using k-medoids and IABC. We used CL-HHO for the routing process because it has the advantage of HHO's quick search speed, which provides a quick response and convergence. The primary concerns of the introduced research work are:  
* Cluster formation and CH selection are mediated by k-medoids and IABC.
* A hybrid CL-HHO algorithm is presented for choosing the shortest path.
* A novel, well-known technique is used to compare the methodology's performance.
* The effectiveness of the proposed CL-HHO improves the overall QoS performance.  
The remaining article is explained below, and the following section reviews the related research. Section 3 describes the cluster formation, CH selection and routing, in detail. Section 4 describes the proposed work performance using MATLAB. The final section summarizes the research paper's findings and makes recommendations for future research.
## 3 Mathematical Modeling
### Adsorption Isotherm Model of Type I
#### 3.1.2 Langmuir and Toth Model  
The classical Langmuir and Toth models are still widely used in the current adsorption systems to describe the adsorption process and characterize the adsorption conditions. Furthermore, the Toth model is often used together with the D-A model, and together they are frequently used in various forms and with the addition of some corrections for the adsorbed phase. [PERSON] et al. [41] summarized common adsorption models for adsorption cooling and heating applications and provided an overview of some of the water- and ethanol-based systems' capabilities.  
Equations (10)-(13) express the Langmuir and Toth model and expand the necessary parameters in the form that will be used in this work:  
\[w=w_{0}\frac{bP}{1+bP} \tag{10}\]  
\[w=w_{0}\frac{bP}{\left(1+\left(bP\right)^{t}\right)^{1/t}} \tag{11}\]  
\[b=b_{0}\exp\left(\frac{Q_{a}}{RT}\right) \tag{12}\]  
\[t=t_{a}+\alpha\left(1-\frac{T_{0}}{T}\right) \tag{13}\]The Toth model fundamentally builds on the Langmuir model by extending the original Langmuir Equation (10) with an additional parameter, the Toth isotherm's exponent \(t\) as demonstrated in Equation (11). The interaction constant \(b\) (kPa\({}^{-1}\)), generally denoted as \(K\) in the Langmuir isotherm, has a similar meaning in both models, and it is related to the binding affinity of the respective adsorbent/adsorbate system. The preceding additional Toth exponent in Equation (11) then corrects the model in relation to the heterogeneity of the surface. From the form of the equation, it is clear that once the exponent \(t\) reaches unity, signifying high homogeneity of the surface, the Toth equation takes the shape of the Langmuir equation. Since adsorption is a fully reversible process, the amount of energy exchanged with the environment can be described as a change in the system's energy potential formally explained by Gibbs free energy \(\Delta G^{\circ}=-RT\ln(K/K_{0})\). Substituting the equilibrium constant \(K\) and the reference state \(K_{0}\) with the interaction constant \(b\) and reference state \(b_{0}\), we get Equation (12). In this modification, the energy term \(Q_{a}\) corresponds to the adsorption energy as a combination of phase change enthalpy and binding energy. Lastly, the \(R\) and \(T\) terms are the common gas constant and thermodynamic temperature, respectively. The additional Toth isotherm constant can also be expressed as a function of temperature, as in Equation (13). The constant parameters \(t_{a}\) and \(\alpha\) do not carry theoretical meaning and serve only as fitting parameters [42]. In this work, we assume the reference temperature \(T_{0}\) = 298.15 K.
## 3 Results and Discussion  
### Physicochemical Parameters  
The evolution of the physicochemical parameters during ripening is presented in Figure 1. The moisture content (%) decreased significantly (\(p\leq 0.05\)) during ripening from initial levels around 88% to 35-38% at the end of ripening in all batches studied (Figure 1a). These values are similar to those found in soft-ripened cheeses by [PERSON] et al. [22]. There were no differences between batches C and Lc throughout the ripening period; although, the moisture content values of the batch inoculated with _Lc. garoviaee_ (Lg)were significantly lower at days 30 and 45 (Figure 1a). However, there were no significant differences (\(p>0.05\)) between batches at the end of the ripening time.  
The \(\mathrm{a_{w}}\) ranged from initial values of 0.975 to 0.938 at the end of ripening (Figure 1b). Similar evolution in the \(\mathrm{a_{w}}\) was reported by [PERSON] et al. [22] in \"Torta del Casar\" cheese. No significant differences (\(p>0.05\)) in \(\mathrm{a_{w}}\) values were found between batches during the ripening.  
The pH values decreased until day 30 of ripening in the three analyzed batches to reach levels of around 5.20 (Figure 1c). However, at days 45, 60 and 90 the pH increased to reach levels around 5.8 at the end of ripening. The increase in pH values of ripened cheese might be due to the decomposition of lactic acid and the formation of basic compounds derived from the hydrolysis of proteins [23; 24]. Small differences in the pH values between batches were found, only at day 90, the batch in which _L. casei_ was inoculated (batch Lc) showed a pH significantly (\(p\leq 0.05\)) higher than that found in the control batch (batch C).  
Thus, only little and punctual differences over the physicochemical parameters of \"Torta del Casar\" cheese (humidity and pH) as a result of the inoculation of the selected  
Figure 1: (**a**)Evolution of moisture content; (**b**) water activity and (**c**) pH during ripening of “Torta del Casar” cheese. Mean values with different lowercase letters indicate significant differences between batches at the same incubation day. Control (uninoculated batch), Lc (inoculated with _Lacticasibacillus casei_) and Lg (inoculated with _Lacticoccus garview_).  
LAB strains _L. casei_ and _Lc. garvieae_ were observed, but in general, these microorganisms did not affect these parameters on soft-ripened cheeses. These results contrast with those found by [PERSON] et al. [23] in semi-hard goat cheese, who reported that the addition of selected starter cultures could decrease the pH of matured cheese.
## 4 Results and Interpretations
### Facies Associations and Lobe Complexity  
In this section, the outcrop sections are discussed with respect to various thickening or thinning cycles based on the range of thickness of the individual sand units. These cycles or trends are quite useful to relate the outcrop sections with lobe elements and lobe degradation, aggradation or cessation. The dynamics of the lobe in a deep marine system were quite evident from the thickening or thinning trends. Additionally, the lithological units were categorized into lobe elements, which were grouped into lobes and further into lobe complexes. Several individual sedimentary facies were identified for any rock formations, which were later grouped and categorized into facies associations [42]. Several facies associations were identified based on the facies analysis of lithological beds, including sandstone, siltstones and shales or mudstones. These facies associations are essentially connected with various components of submarine lobe deposits [17] equivalent to the proximal lobe or axial lobe (FA1), lobe off-axis (FA2), lobe fringe (FA3) and distal lobe fringe or interlobe (FA4).
## 2 Materials and Methods
### Data
#### 2.2.3 Input Data Processing
The EVI can mitigate the saturation problem of the normalized difference vegetation index (NDVI), thus the EVI is commonly used in forest studies because it is more suitable for the representation of vegetation greenness in densely vegetated areas [37; 38; 39]. The MOD13A2 (Terra) and the MYD13A2 (Aqua) products provide 16 day composites for NDVI and EVI. Because the 16 day periods of the Terra and the Aqua products were crossing each other, we could obtain 8 day composites by combining the Terra and the Aqua products. The 8 day EVI was then converted to daily value using a cubic spline interpolation method because the forest greenness usually shows gradual changes such as the cosine curve during a year.
# Precise Orbit Determination of FY-3C with Calibration of Orbit Biases in BeiDou GEO Satellites
## 3 FY-3C POD Results and Analysis
### Validation of FY-3C Orbit Precision  
The GPS-derived precise orbit for FY-3C is regarded as the true reference orbit to evaluate the BDS-derived orbit since the SLR data are not available. Daily RMS series of orbit differences in along-track, cross-track, radial, and 3D for the three solutions are shown in top, middle, and bottom part of Figure 7, respectively.  
The average daily RMS of orbit differences compared to GPS-derived orbit are listed in Table 3. The 3D precision of FY-3C orbit obtained from solution A is only 56.9 cm, while the precision are 7.8 and 6.2 cm for solution B and C, respectively. The orbit precision would be greatly decreased when ignoring the GEO orbit errors. The results at the centimeter level from solution B and C emphasize the significant positive contribution determined by excluding GEO and including GEO with the daily constant orbit biases calibration. A promising orbit solution for FY-3C can be achieved following the strategies of handling daily orbit biases in GEO proposed in this study. The 3D precision of the final orbit can reach 6.2 cm and have impressive improvements of 22.6%, 11.3%, 22.2%, and 20.2% in along-track, cross-track, radial, and 3D, respectively.
## 3 Performance Analysis of Direct Detection CPolSK System
### Bit Error Rate
For a direct-detection CPolSK modulation scheme, the BER can be expressed by  
\[f(I)=\frac{2(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{\Gamma(\alpha)\Gamma(\beta)I }\left(\frac{I}{\langle I\rangle}\right)^{\frac{\alpha+\beta}{2}}K_{\alpha- \beta}\left(2\sqrt{\alpha\beta\frac{I}{\langle I\rangle}}\right),\text{I>0}. \tag{22}\]1  
where \(\overline{\gamma}=\frac{\langle i_{s}\rangle}{\sigma_{n}}\) represents the equivalent signal-to-noise ratio (SNR), \(p\) denotes the optical power, _erfc_ denotes the complementary error function, and \(I=\frac{i_{s}}{\langle i_{s}\rangle}\) denotes the normalized optical intensity. Consequently, the average BER of the transmitted optical signal can be formulated as follows:  
\[f(I)=\frac{2(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{\Gamma(\alpha)\Gamma(\beta)I }\left(\frac{I}{\langle I\rangle}\right)^{\frac{\alpha+\beta}{2}}K_{\alpha- \beta}\left(2\sqrt{\alpha\beta\frac{I}{\langle I\rangle}}\right),\text{I>0}. \tag{22}\]2
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 1 Introduction
Current methodological frameworks--including empirical models (Table 1), analytical solutions (Table 1), and CFD simulations (discussed in the preceding section)--demonstrate constrained applicability across diverse engineering applications. This limitation primarily stems from their dependence on narrowly scoped experimental data and project-specific prototype parameters. For example, the empirical model has strict application conditions; the existing analysis model focuses on spillway tunnels that have a single air vent immediately downstream of the gate; CFD models can simulate only the air characteristics of spillway tunnels with an applicable theoretical model and a large amount of computational resources (time and computational power). This paper focuses on the governing parameters of air characteristics of a spillway tunnel with multiple air vents, advancing the mechanistic understanding of air-water interactions in such systems. To achieve this objective, a hydraulic model experiment that allowed for the variation of hydraulic factors (such as flow velocity and water depth) within a certain range, as well as adjustments to structural parameters including the cross-sectional area and position of the vent, was used. Through analysis of the experimental data, we developed a predictive air demand equation that quantitatively integrates the coupled effects of these critical hydraulic and geometric parameters.
# A Continuous Music Recommendation Method Considering Emotional Change
## 3 Generation of Music Emotion Vectors
### Emotion Analysis of Music and Vector Generation  
In this section, we explore the relationship between the valence and arousal of music and each emotion, detailing the process of generating emotion vectors for music. We employed a multiple regression model to analyze the relationship between valence and arousal within the four emotional domains. Initially, we refined the data by comparing the representative emotions of the music, as classified in Section 3.2, with emotions categorized based on their valence and arousal values. Subsequently, for the multiple regression analysis, we further refined the data to include only those instances where emotions matched the valence, arousal, and representative categories within each emotional area.  
Based on the refined data, we analyzed the relationship between valence, arousal, and each emotion using the Pearson correlation equation shown in Equation (7). The correlation coefficient r measures the strength and direction of the liner relationship between two variables:  
\[\mathbf{r}=\frac{\sum{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{\sum(x_ {i}-\overline{x})^{2}\sum(y_{i}-\overline{y})^{2}}} \tag{7}\]  
Here, \(r\) represents the correlation coefficient, and \(x\) and \(y\) denote the variables under analysis. As a result of the correlation analysis between valence, arousal, and each emotion, significant correlations were observed. Subsequently, we computed the emotional value of the music based on its valence and arousal values across the four emotional areas.  
Multiple regression analysis was used to calculate the emotional value of music. During the regression analysis, valence and arousal were used as independent variables. Equation (8) creates a multiple regression model using valence and arousal.  
\[E_{m,i}=\alpha_{i}+\beta_{1,i}\times V_{m}+\beta_{2,i}\times A_{m} \tag{8}\]  
In this equation, \(E_{m,i}\) represents the emotion value \(i\) of music \(m\), \(V_{m}\) denotes the valence value of music \(m\), \(A_{m}\) denotes the arousal value of music \(m\), \(\alpha_{i}\) signifies the regression coefficient of emotion \(i\), \(\beta_{1,i}\) represents the regression coefficient of valence for emotion \(i\), and \(\beta_{2,i}\) represents the regression coefficient of arousal for emotion \(i\).  
Each emotional value of the music, calculated through multiple regression analysis, was represented as an emotion vector. Equation (9) defines the emotion vector for content.  
\[\overset{\rightarrow}{V_{m}}=\left(E_{m,happy},E_{m,angry},E_{m,sad},E_{m, peaceful}\right) \tag{9}\]  
In this equation, \(\overset{\rightarrow}{V_{m}}\) denotes the emotion vector for music \(m\), where \(E_{m,happy}\), \(E_{m,angry}\), \(E_{m,sad}\) and \(E_{m,peaceful}\) represent the calculated emotional values for happiness, anger, sadness, and peacefulness, respectively.
## 2 Mathematical Dynamic Model of the IRW
### Gyroscopic Moment
According to mechanical principles, a gyroscopic moment exists to resist the direction change of \(\overset{\rightarrow}{H}\) and to maintain its original rotational direction, which means that the gyroscopic moment is in the yaw angular direction of the IRW, and its magnitude is  
\[M_{\text{G}}=\left|-\frac{\overset{\rightarrow}{dH}}{dt}\right|=J_{\text{Wy} }\dot{\alpha}(\omega_{\text{L}}+\omega_{\text{R}}) \tag{14}\]  
If both two wheels with positive linear conicity tread are installed on the carrier, the roll velocity \(\dot{\alpha}\) will present a negative linear relationship with lateral displacement speed \(\dot{y}\), which can be determined by the IRW's geometric parameters and given by  
\[\dot{\alpha}=-\frac{\lambda}{b}\dot{y} \tag{15}\]  
Then, \(M_{\text{G}}\) with adaptation of positive linear conicity tread wheels can be written as  
\[H=H_{\text{L}}+H_{\text{R}}=J_{\text{Wy}}(\omega_{\text{L}}+\omega_{\text{R}}) \tag{9}\]0  
Moment \(M_{\text{G}}\) is the gyroscopic moment in the yaw direction of the IRW, and its magnitude is relative to the carrier's lateral translational speed and the wheels' rotational angular speeds. The effects brought out by gyroscopic moment can be analyzed through Equation (16) and can also be described visually in \[H=H_{\text{L}}+H_{\text{R}}=J_{\text{Wy}}(\omega_{\text{L}}+\omega_{\text{R}}) \tag{9}\]1 When the positive linear tread conicity wheels are adopted, lateral velocity \(\dot{y}\) and gyroscopic moment \(M_{\text{G}}\) present an opposite reaction, which forms a type of self-guidance ability on the IRW, and the derailment tendency can be resisted. According to Equation (16), a large equivalent wheel tread conicity and high wheel rotational speeds are necessary for acquiring a dominant self-guidance ability. Furthermore, compared to the inertia moment of the whole IRW in the yaw direction, the inertia moment of the wheel in the \(y\) direction is much smaller. Thus, the self-guidance ability caused by gyroscopic effects is relatively slight to some extent, especially at low longitudinal speeds. However, due to the loss of longitudinal creep moment on the IRW, the gyroscopic moment can significantly affect the dynamic characteristics of the IRW, especially at high longitudinal speeds. The existence verification of the self-guidance ability caused by gyroscopic effects through simulations, as well as experiments, and how the gyroscopic moment affects the lateral dynamics of the IRW will be demonstrated in Sections 3-5.