esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Mathematical Preliminaries
#### 2.2.1 Moment Equations for GLFS Model with Markov Switchings  
Using the methodology proposed in [28; 29; 31; 32; 35; 36], and notation \(\rho_{1}=-\gamma_{11}\), \(\rho_{1}=\gamma_{12},\rho_{2}=-\gamma_{22},\rho_{2}=\gamma_{21}\) one can derive first and second order moment equations for a particular case of two subsystem models with Markov switchings  
\[\frac{dE[z_{1}(t,1)]}{dt} = \alpha_{x}^{1}-\beta_{x_{1}}^{1}\eta_{1}^{1}E[z_{2}(t,1)]-\rho_{1} E[z_{1}(t,1)]+\rho_{1}E[z_{1}(t,2)],\] (9) \[\frac{dE  
where  
\[u(t)=\left[\begin{array}{c}u_{1}(t)\\ u_{2}(t)\end{array}\right],\ \ \ A=\left[\begin{array}{cc}-\rho_{1}&\rho_{1}\\ \rho_{2}&-\rho_{2}\end{array}\right],\ \ \ v(t)=\left[\begin{array}{c}v_{1}(t)\\ v_{2}(t)\end{array}\right],\ \ \ u_{0}=\left[\begin{array}{c}u_{10}\\ u_{20}\end{array}\right]. \tag{24}\]  
The general solution of Equation (23) has the form  
\[u(t)=e^{At}u_{0}+\int_{0}^{t}e^{A(t-\tau)}v(\tau)d\tau, \tag{25}\]  
after algebraic manipulation, one can find the solutions for both coordinations of the vector \(u(t)\) in the form  
\[\begin{split} E[z_{1}(t,1)]&=u_{1}(t)=\left[e^{-\tilde{\rho}t}+ \frac{\rho_{2}}{\tilde{\rho}}\big{(}1-e^{-\tilde{\rho}t}\big{)}\right]u_{10}+ \left[\frac{\rho_{1}}{\tilde{\rho}}\big{(}1-e^{-\tilde{\rho}t}\big{)}\right]u _{20}\\ &\quad+\frac{\alpha_{x}^{1}\rho_{2}+\alpha_{x}^{2}\rho_{1}}{ \tilde{\rho}}t-\frac{\rho_{1}}{(\tilde{\rho})^{2}}(\alpha_{x}^{2}-\alpha_{x}^ {1})\big{(}1-e^{-\tilde{\rho}t}\big{)},\end{split} \tag{26}\]  
\[\begin{split} E[z_{1}(t,2)]&=u_{2}(t)=\left[\frac{ \rho_{2}}{\tilde{\rho}}\big{(}1-e^{-\tilde{\rho}t}\big{)}\right]u_{10}+\left[ e^{-\tilde{\rho}t}+\frac{\rho_{1}}{\tilde{\rho}}\big{(}1-e^{-\tilde{\rho}t} \big{)}\right]u_{20}\\ &\quad+\frac{\alpha_{x}^{1}\rho_{2}+\alpha_{x}^{2}\rho_{1}}{ \tilde{\rho}}t+\frac{\rho_{2}}{(\tilde{\rho})^{2}}(\alpha_{x}^{2}-\alpha_{x}^ {1})\big{(}1-e^{-\tilde{\rho}t}\big{)}.\end{split} \tag{27}\]  
If we assume \(u_{10}=\alpha_{x_{0}}^{1}\) and \(u_{20}=\alpha_{x_{0}}^{2}\) are constant parameters, and we use an approximation  
\[e^{-\tilde{\rho}t}\approx 1-\tilde{\rho}t, \tag{28}\]  
then we obtain approximate formulas for first-order moments in the form of linear time depending functions  
\[E[z_{1}(t,1)] =\alpha_{x_{0}}^{1}+\Big{(}\alpha_{x}^{1}+\rho_{1}(\alpha_{x_{0} }^{2}-\alpha_{x_{0}}^{1})\Big{)}t, \tag{29}\] \[E[z_{1}(t,2)] =\alpha_{x_{0}}^{2}+\Big{(}\alpha_{x}^{2}+\rho_{2}(\alpha_{x_{0} }^{1}-\alpha_{x_{0}}^{2})\Big{)}t. \tag{30}\]
## 3 Results and Discussion
### Mass Yield, Moisture, Salt, and pH of Meat
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Analyses**} & \multirow{2}{*}{**Fresh Herring**} & \multicolumn{4}{c}{**Marinating Temperature**} \\ \cline{3-5}  & & **2 \({}^{\circ}\)C** & **7 \({}^{\circ}\)C** & **12 \({}^{\circ}\)C** \\ \hline Mass yield (\%) & 100 & 89.5 & 87.2 & 81.5 \\ pH & 6.52 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 4.15 \(\pm\) 0.01 \({}^{\mathrm{b}}\) & 4.16 \(\pm\) 0.01 \({}^{\mathrm{b}}\) & 4.17 \(\pm\) 0.01 \({}^{\mathrm{b}}\) \\ NaCl (\%) & 0.30 \(\pm\) 0.01 \({}^{\mathrm{d}}\) & 2.41 \(\pm\) 0.01 \({}^{\mathrm{b}}\) & 2.43 \(\pm\) 0.01 \({}^{\mathrm{b}}\) & 2.54 \(\pm\) 0.01 \({}^{\mathrm{a}}\) \\ Total acidity (\%) & 0.22 \(\pm\) 0.01 \({}^{\mathrm{d}}\) & 1.56 \(\pm\) 0.02 \({}^{\mathrm{c}}\) & 1.62 \(\pm\) 0.01 \({}^{\mathrm{b}}\) & 1.67 \(\pm\) 0.01 \({}^{\mathrm{a}}\) \\ Moisture (\%) & 66.6 \(\pm\) 0.29 \({}^{\mathrm{a}}\) & 59.9 \(\pm\) 0.6 \({}^{\mathrm{d}}\) & 62.0 \(\pm\) 0.3 \({}^{\mathrm{b}}\) & 61.3 \(\pm\) 0.3 \({}^{\mathrm{c}}\) \\ TN (g/100 g) & 2.30 \(\pm\) 0.00 \({}^{\mathrm{b}}\) & 2.45 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 2.43 \(\pm\) 0.03 \({}^{\mathrm{a}}\) & 2.44 \(\pm\) 0.02 \({}^{\mathrm{a}}\) \\ NPN (mg/100 g) & 233.6 \(\pm\) 3.16 \({}^{\mathrm{b}}\) & 218.8 \(\pm\) 1.6 \({}^{\mathrm{c}}\) & 237.9 \(\pm\) 1.6 \({}^{\mathrm{b}}\) & 280.5 \(\pm\) 0.0 \({}^{\mathrm{a}}\) \\ PHP(R) (mg/100 g) & 6.4 \(\pm\) 1.8 \({}^{\mathrm{d}}\) & 208.6 \(\pm\) 3.5 \({}^{\mathrm{c}}\) & 228.3 \(\pm\) 2.3 \({}^{\mathrm{b}}\) & 252.4 \(\pm\) 6.8 \({}^{\mathrm{a}}\) \\ PHP(A) (mg/100 g) & 12.9 \(\pm\) 0.2 \({}^{\mathrm{d}}\) & 35.2 \(\pm\) 0.9 \({}^{\mathrm{c}}\) & 42.3 \(\pm\) 0.2 \({}^{\mathrm{b}}\) & 55.2 \(\pm\) 0.4 \({}^{\mathrm{a}}\) \\ [PERSON] (U) & 3259 \(\pm\) 32 \({}^{\mathrm{d}}\) & 6077 \(\pm\) 22 \({}^{\mathrm{c}}\) & 6315 \(\pm\) 45 \({}^{\mathrm{a}}\) & 6118 \(\pm\) 22 \({}^{\mathrm{b}}\) \\ [PERSON] (U) & 214.7 \(\pm\) 1.2 \({}^{\mathrm{a}}\) & 136 \(\pm\) 1.5 \({}^{\mathrm{d}}\) & 190 \(\pm\) 4.8 \({}^{\mathrm{b}}\) & 168 \(\pm\) 2.3 \({}^{\mathrm{c}}\) \\ Cathepsin L (U) & 390 \(\pm\) 3.2 \({}^{\mathrm{a}}\) & 188 \(\pm\) 1.6 \({}^{\mathrm{b}}\) & 168 \(\pm\) 0.5 \({}^{\mathrm{c}}\) & 131.1 \(\pm\) 2.8 \({}^{\mathrm{d}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic composition, nitrogen fractions content, and cathepsin activity in the raw meat and marinated meat; TN—total nitrogen, NPN—non-protein nitrogen, PHP(R)—peptides fraction, PHP(A)—peptides fraction, PHP(A)—free amino acids fraction; \({}^{\mathrm{abc}}\) means within the same row with different lowercase letters differing significantly.
## 2 A Hypothetical Example Application of the V-GSI Chart in a Tunnel
### Geotechnical Mapping Method  
Following excavation of each split heading and prior to a layer of steel fibre reinforced shotcrete (SFRS) being applied to the tunnel crown (and possibly sidewalls, depending on conditions encountered), the tunnel crown and sidewalls were photographed and mapped.  
Figure 4: Output map for heading excavation for the trailing left-hand-side of the M120 tunnel. **Upper left:** V-GSI chart with data calculated based on the collected discontinuity spacing (CF applied) and joint condition information. **Upper centre-left:** Stereonet showing collected discontinuity orientation measurements. **Upper right:** Geological sketch maps showing spatial locations of mapped discontinuities within the heading wall. “C\({}_{\text{B}}\)” refers to crossbedding and “LM” refers to laminate. **Lower left:** Discontinuity data collected during mapping.  
The tunnel sidewalls, crown and mined face were geologically and geotechnically mapped for each heading. Typical data collected while mapping included rock type(s), structure, weathering and intact rock strength; groundwater/seepage conditions; discontinuity conditions including dip, dip direction, trace length, aperture, infill type and thickness, shape, roughness and apparent joint spacings (as described in Section 2.1). Assessments were also made using the Q-system, for rock mass classification [23], the V-GSI system [1] and the Sydney Rock Mass Classification [24,25].  
For each map, apparent spacing data per discontinuity set was then corrected to true spacing data, which was used to calculate a typical _VFC_ and _VFC_ ratings for the vertical axis of the V-GSI chart. If more than one rock type was present within the tunnel face (for example, if the lower portion of the heading was within the Ashfield Shale, and the upper portion within the Hawkesbury Sandstone, two distinct V-GSI calculations would be undertaken and plotted automatically on a V-GSI chart embedded in the mapping data sheets (see example mapping sheet and associated V-GSI chart in Figure 4). The horizontal axis was quantified using the discontinuity conditions data that can be seen in the example mapping sheet in Figure 4. The mean V-GSI rating was plotted directly in the embedded V-GSI chart (red dot) for each map with a range (red box). The maps were used to inform the level of ground support required (based on pre-defined support classes for each ground type and tunnel geometric layout).
## 3 Fluctuation Correlations and Thermodynamics of Binary Symmetric Electrolyte Solutions
### Positive-Positive Ionic Correlations  
The Fourier transform of the positive-positive ions concentrations fluctuation correlation is derived from the first Equation (61). After averaging, the result is  
\[\begin{split}\langle\delta\mathcal{E}_{+}&(\mathbf{k}_{ 1},\omega_{1})\delta\mathcal{E}_{+}(\mathbf{k}_{2},\omega_{2})\rangle=\\ &-\frac{\mathbf{k}_{1}\cdot\langle\delta\mathbf{j}_{+}(\mathbf{k }_{1},\omega_{1})\delta\mathbf{j}_{+}(\mathbf{k}_{2},\omega_{2})\rangle\cdot \mathbf{k}_{2}}{(i\omega_{1}+Dk_{1}^{2})(i\omega_{2}+Dk_{2}^{2})}+\frac{D^{2} \kappa^{4}}{4(ze)^{2}}\frac{\langle\delta\rho_{e}(\mathbf{k}_{1},\omega_{1}) \delta\rho_{e}(\mathbf{k}_{2},\omega_{2})}{(i\omega_{1}+Dk_{1}^{2})(i\omega_{2} +Dk_{2}^{2})}\\ &-\frac{D\kappa^{2}}{2 ze}\Bigg{[}\frac{i\mathbf{k}_{1}\cdot \langle\delta\mathbf{j}_{+}^{\prime}(\mathbf{k}_{1},\omega_{1})\delta\hat{\rho} _{e}(\mathbf{k}_{2},\omega_{2})\rangle}{(i\omega_{1}+Dk_{1}^{2})(i\omega_{2} +Dk_{2}^{2})}+\frac{i\mathbf{k}_{2}\cdot\langle\delta\mathbf{j}_{+}^{\prime}( \mathbf{k}_{2},\omega_{2})\delta\hat{\rho}_{e}(\mathbf{k}_{1},\omega_{1}) \rangle}{(i\omega_{1}+Dk_{1}^{2})(i\omega_{2}+Dk_{2}^{2})}\Bigg{]},\end{split} \tag{62}\]  
Inverting Equation (62) (see Appendices B and C) and setting \(t_{1}=t_{2}=t\) leads to the following result for the concentration-fluctuation correlation in real space  
\[\langle\delta c_{+}\mathbf{r}_{1},t\rangle\delta c_{+}(\mathbf{r}_{2},t) \rangle=c\delta(\mathbf{r}_{1}-\mathbf{r}_{2})+c^{2}[g_{++}(r)-1]. \tag{63}\]  
where  
\[g_{++}(r)=1-\frac{(ze)^{2}}{4\pi\epsilon\epsilon_{0}k_{B}T}\frac{e^{-\kappa r} }{r} \tag{64}\]  
is the radial distribution function for the positive ions. Comparing (64) to the formal definition  
\[g_{++}(r)=e^{-\frac{w_{++}(r)}{k_{B}T}}\approx 1-\frac{w_{++}(r)}{k_{B}T} \tag{65}\]  
leads to the Debye and [PERSON] [11] result for the electrostatic energy of interaction between the ions in the solution  
\[w_{++}(r)=ze\psi(r)=\frac{(ze)^{2}}{4\pi\epsilon\epsilon_{0}}\frac{e^{-\kappa r }}{r}. \tag{66}\]  
The energy is positive, which indicates a repulsive interaction.
###### Abstract
This has culminated in the convergence and intensification of the impacts exerted by climate change and anthropogenic influences on terrestrial ecosystems. Furthermore, other driving factors, such as soil moisture (SM), and leaf area index (LAI), significantly influence the temporal and spatial variations in WUE in China. In the course of economic development, the alteration of land-use patterns can lead to soil moisture depletion, whereas soil acidity can impact the water equilibrium within plants, diminishing their capacity to generate organic matter and intensifying vegetation evapotranspiration. Consequently, this may result in a decline in WUE [30; 31]. In addition, the large-scale vegetation changes caused by ecological restoration also directly affect the changes in WUE [32]. Previous studies have also been conducted to assess the impact of China's ecological environment changes on water-use efficiency. However, these studies have mostly focused on the national scale and have not explored the impact of eco-environmental factors on WUE in different climate regions [33; 34]. Hence, conducting a comprehensive analysis of the intricate interplay between ecosystem WUE and environmental factors holds immense significance in evaluating ecosystem health, as well as in the restoration and preservation of ecosystems. In this study, we utilized remote sensing-based GPP and ET products to estimate the water-use efficiency (WUE) of China's terrestrial ecosystems. To explore the relationship between WUE and its driving factors, we employed partial correlation analysis, the geographic detector method, and ridge regression. Our objectives were to (1) investigate the spatiotemporal changes in WUE across China from 2001 to 2020, and (2) identify the dominant driving factors of WUE and their spatial characteristics.
## 3 Results
### Optimization Possibilities  
In a third phase of analysis, it was proposed to use the Monte Carlo method to the building model in order to observe the impact of the available efficiency measures and obtain an optimization method that maximizes efficiency, minimizing investment. For this, different possibilities of action are proposed like:  
* Carpentry (permeability ACH): category 1, 2 or 3 (50, 27 and 9 m\({}^{3}\)/h m\({}^{2}\), respectively);
* Transmittance of current facade (UF) 1.69, being able to improve to 1.1 or even 0.59 W/m\({}^{2}\) K;
* Transmittance of current cover (UC) 2.27 and can improve to 1 or even 0.45 W/m\({}^{2}\) K;
* Combustible (Comb): current diesel (0), possible change to natural gas (1);
* Incorporation of photovoltaic energy source (PV): From 0 (current), 5.00, 10.00, 15.00, 20.00, to 25.00 kWp (Maximum). In order to determine the impact of each measure and the impact of each of the possible combinations, we have proceeded to establish the matrix of combinations and in each of them Monte Carlo method was employed in order to determine the average value of the probability of consumption and its related economical cost. The result was sum up in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Test**} & **ACH** & **UF** & **UC** & **Comb\_Heat** & **Comb\_DHW** & **PV** & **Cost** \\  & **(m\({}^{3}\)/h m\({}^{2}\))** & **(W/m\({}^{2}\)**-K)** & **(W/m\({}^{2}\) K)** & **(kWh/(m\({}^{2}\) year))** & **(kWh/(m\({}^{2}\) year))** & **(kWh/(m\({}^{2}\) year))** & **(kWp)** & **(Euros)** \\ \hline
1 & 50 & 1.69 & 2.27 & 0 & 0 & 0.000 & 30,950 \\  & 27 & 1.69 & 2.27 & 0 & 0 & 0.000 & 33,950 \\  & 9 & 1.69 & 2.27 & 0 & 0 & 0.000 & 36,950 \\ \hline
2 & 50 & 1.69 & 0.45 & 0 & 0 & 0.000 & 59,105 \\  & 27 & 1.69 & 0.45 & 0 & 0 & 0.000 & 62,105 \\  & 9 & 1.69 & 0.45 & 0 & 0 & 0.000 & 65,105 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of Monte Carlo method over each different combination of retrofit options. Figure 10: Probability distribution of the energy demand before any retrofit action. With these experiments, a response surface was carried out by Minitab software, trying to obtain a polynomial capable of determining the energy and economical effects of the possible retrofit combinations. The obtained polynomials are defined by the Equations (5) and (6) with a determination factor of 85.26 and 97.42, respectively. \[ \begin{array}{ll}\mathrm{Cost}=254641&-198\cdot ACH-107018\cdot UF-15,470\cdot UC +29,037\cdot Comb_{heat}+1200\\ &\cdot PV+0.88\cdot ACH^{2}+7735\cdot UC\cdot Comb_{heat}-1338\cdot Comb_{heat} \cdot PV\\ &9&1.69&0.45&0\\ \hline 13&50&1.69&0.45&1\\ &27&1.69&0.45\\ &9&1.69&0.45\\ \hline\end{array}  \tag{5}\]  
Energy consumption  
\[ \begin{array}{ll}=209-0.99\cdot ACH-14\cdot UF-1.9\cdot UC-65.2\cdot Comb_{heat }-2.46\cdot PV\\ +0.0232\cdot ACH^{2}+0.1044\cdot PV^{2}+0.075\cdot ACH\cdot UF-0.09\cdot ACH \cdot UC\\ -0.596\cdot ACH\cdot Comb_{heat}+0.0053\cdot ACH\cdot PV+14.09\cdot UF-UC+34 \cdot UF\\ \cdot Comb_{heat}-25.16\cdot UC\cdot Comb_{heat}-1.239\cdot Comb_{heat} \cdot PV\\ \end{array}  \tag{6}\]
## 3 Knowledge-Guided Parallel Hybrid Local Search Algorithm
### Knowledge-Guided Parallel Hybrid Local Search  
Meta-heuristic algorithms, such as tabu search and late acceptance, have more robust global search capabilities. They are widely used in real satellite scheduling problems due to their greater flexibility and adaptability. As shown in Algorithm 1, a hybrid local search algorithm is designed.  
The algorithm mixes a late acceptance hill climbing algorithm and a tabu search algorithm and introduces a new solution construction method based on knowledge guidance. The algorithm uses the prior knowledge of the satellites to construct a new solution and decides whether to accept the current solution according to the late acceptance algorithm. If the new solution is accepted, the tabu table and the deferred table are updated, where the tabu table is used to record the tasks deleted and inserted each time a new solution is accepted. The functions in the tabu table are prohibited from being deleted or inserted in the next \(\theta\) iteration, and the size of \(\theta\) is set to be a random number between \([0,\sqrt{(S/2)}]\), where \(S\) is the number of tasks. The deferred table is used to record historical data.  
However, within the domain of satellite resource scheduling problems, algorithm runtime stands as a crucial metric. Including multiple strategies in local search algorithms inevitably elongates the algorithm's runtime. Parallel computing is introduced to reduce search times, allowing more searches within the same runtime. During parallel computation, each thread utilizes a distinct initial solution. High-quality solutions are continuously recorded in an elite solution set throughout the iteration process. Ultimately, the elite solutions and the best solution acquired by each thread are presented, initiating a new round of parallel searches based on these newfound solutions.  
```
Input: Initial solution\(R_{I}\), elite set \(U_{x}\), tabu set \(U_{I}\), and deferred set \(U_{L}\); Output: The current solution \(R\)
1\(R=R_{I}\);
2\(R_{L}=\) The last element in the overdue table;
3 while termination condition is not satisfieddo
4 Constructing new solutions based on knowledge guidance \(R_{x}\);
5 ifthe size of elite set \(U_{x}<K\)then
6 Record the current solution \(R_{x}\) in \(U_{x}\);
7
8 else
9 Find \(R_{m}\leftarrow\{\arg\min\{F(x):x\in U_{x}\}\}\);
10 if\(f(R_{x})>f(R_{m})\) or \(f(R_{x})>f(R_{L})\)then
11 Replace \(R_{m}\) by \(R_{x}\);
12 Record the current deleted and inserted tasks to the tabu table \(U_{I}\);
13 Record the current solution to the overdue table \(U_{L}\);
14
15 return \(R\);
```  
**Algorithm 1**Knowledge-guided parallel hybrid local search algorithm.
## 5 Results and Discussion
### Implications of Regulation and Certification
#### 5.2.2 Alignment with Further Regulations in the Energy Industry  
A major factor for the acceptance of SMGWs by solution providers, such as DSMgrs, is the certainty that SMGWs as a platform will be classified as appropriate for all possible business cases within the energy industry. Therefore, the SMGW ecosystem should be well aligned with further regulations and market processes in the energy industry. For example, it should be possible to utilize the SMGW platform to satisfy all needed requirements for the provisioning of ancillary services with DERs. Consider, for instance, the pre-qualification criteria by the German transmission system  
\begin{table}
\begin{tabular}{c c c c} \hline
**Manufacturer** & **HKS3** & **HKS4** & **HKS5** \\ \hline Devolo/Kiwigrid \({}^{\star}\) & & X & X \\ EFR \({}^{\star\star}\) & X & (?) & (?) \\ Power Plus Communications \({}^{\star\star}\) & X & X & X \\ Theben \({}^{\star}\) & X & X \\ Sagemcom Dr. Neuhaus \({}^{\star\star}\) & & X & X \\ \hline \end{tabular} \({}^{\star}\) Declaration on request; \({}^{\star\star}\) derived from published product information.  
\end{table}
Table 3: HAN communication scenarios (“HAN-Kommunikationsszenario”, HKS) implemented by SMGW manufactures. No statement was acquired from EMH metering, Discovery, and Landys + Gyr.  
operators regarding the communicative connection. One restriction in this context is the maximum duration of the connection between CLS and aEMP, which is limited to 48 h. After this time frame, the transparent data tunnel needs to be re-established. This may be an important factor for applications that require (near) real-time responses in the area of seconds as the reestablishment of the communication temporarily leads to a short unavailability. The alignment of SMGW-related regulation and other regulations in the energy sector is also addressed in the standardization strategy [41] of the BSI and the German Federal Ministry for Economic Affairs and Energy (BMWi).
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 3 Proposed Approach
### Arabian Camel Bred Dataset  
To the best of our knowledge, there is no existing image-based dataset for Arabian camel breed classification that can be used to train our model. Therefore, we constructed a novel dataset for this task from scratch by collecting and annotating images of different breeds of Arabian camels from various sources. The images were labeled with six distinct Arabian camel breeds: Waddeh, Majaheem, Homor, Sofor, Shaele, and Shageh. We obtained 1073 images in total with approximately 180 images per breed. We also consulted three experts in the field to review the dataset and remove any ambiguous or mislabeled images. The images were organized into six separate folders corresponding to the six breeds.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 5 Joint Optimization Scheme for Communication Rate
### Relay Drone Modulation Method Optimization  
To study the optimization problem of the relay drone modulation method \(U(k)\), it is necessary to fix variables such as the power allocation factor, \(\lambda^{(c-1)}(k)\), of the cluster drones, the transmission power, \(P^{R,(c-1)}(k)\), of the relay drone, and trajectory, \(Q^{(c-1)}(k)\)during the \((c-1)\)th iteration in time slot \(k\). Thus, the original optimization problem \(C_{1}\) can be simplified as follows:  
\[C_{2}:\ \max\limits_{\{U\}}:\sum\limits_{k=1}^{K}\sum\limits_{i=1}^{M}c_{1}^{ UL}log_{2}(U^{(c)}(k)) \tag{49}\]  
Wherein \(c_{1}^{U}=B_{n}^{Ruav}log_{2}(1+\lambda_{i}^{(c-1)}(k)P^{F}(k)P^{R,(c-1)}(k)(G _{t}G_{r}(\lambda^{G})^{2})^{2}/(I_{1}^{t}+I_{2}^{t}I_{3}^{t^{\prime}})^{(c-1)})\), and \(I_{3}^{t^{\prime}}\) is given by \(\sum\limits_{j\in\Gamma}\lambda_{j}^{(c-1)}(k)P^{F}(k)H_{j}^{w(c-1)}(k)+n_{0} B_{n}^{Ruav}\). Because of the inconvenient computation of discrete variable values involved in the above optimization problem, the auxiliary variables \(b_{1},b_{2},b_{3},b_{4}\) are introduced, thus transforming the optimization problem \(C_{2}\) into the following:  
\[\begin{array}{c}C_{3}:\max\limits_{\{U\},b_{1},b_{2},b_{3},b_{4}\}:\sum \limits_{k=1}^{K}\sum\limits_{i=1}^{M}c_{1}^{UL}log_{2}(U^{(c)}(k))\\ U^{(c)}(k)=2b_{1}+4b_{2}+16b_{3}+64b_{4}\\ \dfrac{P^{R,(c-1)}(k)H_{i}^{u,(c-1)}(k)}{n_{0}B_{n}^{Ruav}}\geq b_{1}f_{1}+3b_ {2}f_{2}+15b_{3}f_{2}+63b_{4}f_{2}\\ f_{1}=\left(erfc^{-1}(BER_{q})\right)^{2}/2\ f_{2}=\left(erfc^{-1}(BER_{q}/4 )\right)^{2}/3\\ b_{1}+b_{2}+b_{3}+b_{4}=1b_{1},b_{2},b_{3},b_{4}\in\{0,1\}\end{array} \tag{50}\]  
The above issue is a 0-1 integer programming problem, which can be directly solved using the Mosek optimization toolkit to obtain the \(U^{(c)}(k)\) value.