esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## 3 Simulated Annealing Based Resource Allocation  
The simulated annealing algorithm is adopted to maximize the overall throughput for the downlink NOMA system. The SA algorithm allocates power for each subchannel and the proportional power for each multiplexed user on the same subchannel and assigns multiple users on the same subchannel. The SA algorithm imitates the physical annealing process by putting the metal into a high temperature with high energy, then reducing the heat to obtain the crystal structure with minimum energy [22]. The energy function in the physical annealing is defined as the throughput objective function in the proposed SA algorithm. The objective function is formulated to maximize the total throughput, as shown in (8). The algorithm evaluates the objective function based on the allocated power for each subchannel \(P_{n}\) and the power ratio \(\delta_{n}\) among the multiplexed users, as well as the user and subchannel matching. The SA design defines the parameters \(a_{n}=P_{n}/P_{Max}\), as a fraction of the total transmitted power \(P_{Max}\). The \(\delta_{n}\) is the power ratio for the multiplexed users on the same subchannel, and the channel gain difference is the main factor for the user and subchannel matching scheme. The SA system started by assigning random channel power based on the factional value of \(a_{n}\), random value of power ratio \(\delta_{n}\), and multiplexing two users randomly on the same subchannel. The initial design of the SA algorithm is considered as the current best solution for the objective function, then one parameter is changed according to the system mode. The SA algorithm accepted the solution after each change if the new solution is better than the current best solution, otherwise accepted with probability. The accepted new solution for each system mode is updated in the current best solution set, then the temperature is reduced and used for the next iteration temperature. The parameters are used to estimate throughput by changing one parameter every iteration while keeping the other two parameters fixed. This process can reduce the time complexity of re-calculating the objective function every iteration until the total throughput is achieved. The system initiates with two different configurations of user and subchannel matching. The change of one parameter every iteration can offer less computational time, and decouple the resource allocation problem since the problem is coupled. One parameter is changed in each iteration based on the system mode, and the mode is chosen according to the random value of \(\mu\). The user pairing is conducted when \(\mu=1\), the subchannel power is changed when \(\mu=2\), and the proportional power ratio is changed when \(\mu=3\). Moreover, SA is a flexible standalone algorithm that ensures an optimal solution for the coupled problem of resource allocation and user and subchannel matching. SA is a minimization algorithm, so we use a minus sign in the objective function. The SA algorithm is described in the three following subsection steps.
## 4 Configuration of BESS
### Model Constraints and Optimization Steps  
The configuration object is related to the power balance, the discharging energy, the SOC of the batteries, and the charging-discharging power of the BESS. The details of the constraints are shown as follows:  
(1) Power balance constraint  
During the station operation process, the BESS charging power is supplied by the grid, while the discharging power of the BESS only supplies the PEBs charging load.  
\[P_{\text{char},t}=P_{\text{grid},t}+P_{\text{BESS},t} \tag{18}\]  
where \(P_{\text{grid},t}\) is the power supplied by the grid in period \(t\); \(P_{\text{BESS},t}\) is the charging-discharging power of BESS; charging power is \(+\); discharging power is \(-\); and \(t\) is the period of \([t-1,t]\).  
(2) BESS energy constraint  
In order to ensure the sustainable operation of the BESS, the charging energy should match the discharging energy at the end of each operation cycle. The peak charging power constraint of the station is a constant condition, which is described in Equation (23).  
\[\sum_{1}^{T^{*}}p_{\text{dis},t}\Delta t=\sum_{1}^{T^{*}}p_{\text{ch},t}\cdot\Delta t \tag{19}\]  
(3) BESS SOC constraint  
During the BESS operational procedure, the SOC of the BESS should be constrained in a suitable range, called the depth of discharging (DOD), to keep the BESS working well. This also decides the total discharging and charging energy of the BESS. Usually, the SOC range of the BESS is 20% to 90%, which means the DOD is 80%.  
\[SOE(t+1)=SOE(t)-\frac{p_{\text{dis},t}\cdot\Delta t}{\eta_{\text{dis}}\cdot E} \tag{20}\]  
\[SOE(t+1)=SOE(t)+\frac{\eta_{\text{ch}}\cdot p_{\text{ch},t}\cdot\Delta t}{E} \tag{21}\]  
\[SOE_{\text{min}}\leq SOE(t)\leq SOE_{\text{max}} \tag{22}\]  
where \(SOE(t)\) is the state of energy of the battery at \(t\), \(\eta_{\text{dis}}\) is the discharge efficiency, and \(\eta_{\text{ch}}\) is the charge efficiency.  
(4) BESS power constraint  
The maximum charging and discharging power should be set to keep the batteries safe during the operation, which is also related to the energy and power of the BESS.  
\[-P_{\max}\leq P_{\text{BESS},t}\leq P_{\max} \tag{23}\]
# Intelligent Interference Management in UAV-Based HetNets +
## 3 System Model
### SE with 3 GPP Release-10/11 ICIC Techniques
Using this framework of eICIC and FeICIC and following an approach similar to that of [17,22] for a HetNet, the SIR (\(\Gamma\), \(\Gamma_{\text{csf}}\), \(\Gamma^{\prime}\), \(\Gamma^{\prime}_{\text{csf}}\)) and the SE (\(C^{\text{mbs}}_{\text{usf}}\), \(C^{\text{mbs}}_{\text{csf}}\), \(C^{\text{uabs}}_{\text{sf}}\), \(C^{\text{uabs}}_{\text{sf}}\)) experienced by an arbitrary UE \(n\) can be defined for four different scenarios as follows:  
(1) UE associated with MOI and scheduled in USF:  
\[\Gamma=\frac{S_{\text{mbs}}(d_{mn})}{S_{\text{uabs}}(d_{un})+Z} C^{\text{mbs}}_{\text{usf}}=\frac{\beta\text{log}_{2}(1+\Gamma)}{N^{ \text{mbs}}_{\text{usf}}}\,. \tag{8}\]  
(2) UE associated with MOI and scheduled in CSF:  
\[\Gamma_{\text{csf}}=\frac{\alpha S_{\text{mbs}}(d_{mn})}{S_{\text{uabs}}(d_{ un})+Z} C^{\text{mbs}}_{\text{csf}}=\frac{(1-\beta)\text{log}_{2}(1+\Gamma_{ \text{csf}})}{N^{\text{mbs}}_{\text{csf}}}. \tag{9}\]  
(3) UE associated with UOI and scheduled in USF:  
\[\Gamma^{\prime}=\frac{S_{\text{uabs}}(d_{un})}{S_{\text{mbs}}(d_{ mn})+Z^{\prime}} C^{\text{uabs}}_{\text{usf}}=\frac{\beta\text{log}_{2}(1+\Gamma^{ \prime})}{N^{\text{uabs}}_{\text{usf}}}. \tag{10}\]  
(4) UE camped on UOI and scheduled in CSF:  
\[\Gamma^{\prime}_{\text{csf}}=\frac{S_{\text{uabs}}(d_{un})}{\alpha S_{\text{ mbs}}(d_{mn})+Z^{\prime}} C^{\text{uabs}}_{\text{csf}}=\frac{(1-\beta)\text{log}_{2}(1+ \Gamma^{\prime}_{\text{csf}})}{N^{\text{uabs}}_{\text{csf}}}. \tag{11}\]  
Figure 3: 3 GPP Release-11 FeICIC with reduced power, almost blank subframes, and different power reduction factors for each MBS for the scenario in Figure 2.  
In (8)-(11), \(Z\) and \(Z^{\prime}\) are, respectively, the interference at a UE from all the MBSs and the UABSs except the UOI and except the MOI. They have the same meaning but different values.
# Design and Research of a Virtual Laboratory for Coding Theory
## 3 Results
### Analysis of the Results of the Student Success Rate
#### 3.2.2 Statistical Testing of Hypotheses About the Final State of the Two Groups
\begin{table}
\begin{tabular}{l l r} \hline \multicolumn{3}{c}{**One-Sample Kolmogorov–Smirnov Test 1**  } \\ \hline \multicolumn{3}{c}{**Average Test Scores**} \\ \hline N & & 59 \\ \hline \multirow{2}{*}{Normal Parameters 2  } \\ \cline{2-3}  & Mean & 5.1371 \\ \cline{2-3}  & Std. Deviation & 0.70284 \\ \hline \multirow{3}{*}{Most Extreme Differences} & Absolute & 0.132 \\ \cline{2-3}  & Positive & 0.110 \\ \cline{1-1} \cline{2-3}  & Negative & \(-\)0.132 \\ \hline \multicolumn{3}{l}{Test Statistic} \\ \hline \end{tabular}
\end{table} 
Table 6: Results of the Kolmogorov–Smirnov test for the distribution of mean scores (average grades) for the control group at the final state. \begin{table}
\begin{tabular}{l l r} \hline \multicolumn{3}{c}{**One-Sample Kolmogorov–Smirnov Test 3**  } \\ \hline N & & 59 \\ \hline \multirow{2}{*}{Normal Parameters 2  } \\ \cline{2-3}  & Mean & 5.6400 \\ \cline{2-3}  & Std. Deviation & 0.36713 \\ \hline \multirow{3}{*}{Most Extreme Differences} & Absolute & 0.177 \\ \cline{2-3}  & Positive & 0.163 \\ \cline{1-1} \cline{2-3}  & Negative & \(-\)0.177 \\ \hline \multicolumn{3}{l}{Test Statistic} \\ \hline \multicolumn{3}{l}{Asymp. Sig. (2-tailed)} \\ \hline \multicolumn{3}{l}{a. Group = experimental. b. Test distribution is normal. c. Calculated from data. d. Lilliefors Significance Correction.[ENDFOOTNOTE] } \\ \hline \end{tabular}
\end{table} 
Table 7: Results of the Kolmogorov–Smirnov test for the distribution of mean scores (average grades) for the experimental group at the final state. * Analysis of the results Comparing the value \(\alpha_{\rm{s}}\) (_Asymp. Sig. (2-tailed)_) from Table 9 with the pre-set significance level \(\alpha\) yields \[\alpha_{\rm{s}}=0.000<\alpha=0.05,\] (7) i.e., the hypothesis \(H_{1}\) is accepted, which states that there is a significant difference in mean scores (average grades), i.e., there was a statistically significant difference between the control and experimental groups at the end of the study. Based on dependencies (3) and (4) for the final state, the following effect size results are obtained:  
\[SD_{pool}=0.56070 \tag{8}\]  
\[d=-0.89691 \tag{9}\]  
In the final state, a value of \(d=-0.89691\) was obtained, which means a significant difference between the two groups. A negative sign indicates that the difference is in the gain of the experimental group.
## 4 Energy Management Strategy  
The adopted EMS is based on a PI controller. Only proportional and integral action were considered sufficient for the case study of this paper. In the emulated powertrain, the FC is the primary energy source and must follow the low-dynamic part of the load. Thus, the EMS was developed such that the FC follows the load demand with a slow time of response. From the measurement of the batteries' current and the load current, the control unit evaluates indirectly the current provided by the emulated fuel cell downstream from the power converter (\(I_{DC\text{-}DC}\)). The PI controller evaluates the current reference signal (\(I_{DC\text{-}DC_{ref}}\)) to the DC-DC so that the batteries current is equal to zero or the power delivered by the emulated FC corresponds to the power demand. As a consequence, the adopted EMS behaves as a charge-sustaining strategy with low reactivity. If the external load power demand is negative, as during regenerative braking, the PI setpoint FC current is set equal to zero. If the external load power demand exceeds the power capabilities of the emulated FC, the reference current for the PI controlled is saturated to a value that corresponds to the nominal power of the emulated FC. The PI gains can be set such that the response time is slow, allowing for a significant reduction in the FC output power oscillations. However, a tuning procedure for the determination of the PI gains must be performed. Figure 6 shows a schematic representation of the control strategy for the current reference signal. As for the voltage reference signal, the value is set equal to the maximum allowable recharging voltage for the batteries. According to this, if the load demand drops abruptly so that the current provided by the emulated FC would exceed the maximum charging current for the batteries, the voltage reference becomes the limiting signal and the current does not exceed the limit.  
Figure 5: Experimental setup of the scaled test bench.  
Figure 6: PI-based control strategy for the current reference signal.
## 4 Role of sRNA in Antibiotic Resistance
### Modification of Cell Envelope  
Lippolysaccharides (LPSs) are crucial components of the outer membrane of Gram-negative bacteria, playing key roles in maintaining membrane integrity, stress response, and virulence [189]. LPSs consist of three main parts: lipid A, a core oligosaccharides, and an O-antigen. The lipid A portion anchors LPS to the membrane and is a target for the polymyxins class of antibiotics such as colistin [190]. However, modifications to LPS, such as the addition of phosphoethanolamine, which changes the charge and structure of the lipid A, can reduce susceptibility to polymyxins. Polymyxin resistance in _E. coli_ ismediated by the enzyme EptB, which modifies LPS with phosphoethanolamine [184]. The translation of _eptB_ mRNA is inhibited by the sRNA MgrR, which is itself suppressed by the sponge sRNAa SroC. As a result, the loss of MgrR enhances, while the loss of SroC reduces resistance to polymysxin B. Additionally, _eptB_ expression is negatively regulated by the sRNA ArcZ, whose levels are governed by the ArcA-ArcB two-component system, which senses aerobic and anaerobic conditions [191]. Interestingly, the deletion of Hfq, a protein essential for the activity of these sRNAs, results in increased susceptibility to polymysxin B. This phenomenon may be attributed to a compromised cell envelope stress response mediated by the sigma factor RpoE. RpoE not only promotes _eptB_ transcription but also regulates other Hfq-dependent sRNAs involved in LPS biogenesis and modification. The following section discusses the role of two small RNAs that provide antibiotic resistance by modification of the bacterial cell membrane.
## 2 Materials and Methods
### Soil Spectrum Preprocessing  
[PERSON] et al. (2009) [27] reported that preprocessing can be utilized to remove physical phenomena from spectra to improve multivariate regression, classification models, etc. Different preprocessing methods have a significant impact on the outcome of data analysis and collecting spectra without preprocessing results in less accurate data due to the ambient environment in the field [26; 28]. [PERSON] et al. (2015) [29] reported that preprocessing is effective at removing noise from spectra and enhancing their characteristics and accuracy. [PERSON] et al. (2017) [18] applied SG and SNV preprocessing to predict soil properties such as soil organic carbon (SOC), TN, pH, and soil texture (sand, silt, and clay). As a result, the following R\({}^{2}\) values were obtained for each property: 0.92, 0.85, 0.73, 0.84, 0.74, and 0.83, respectively.  
In this study, Savitzky-Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing were applied to develop the optimal soil property prediction regression model, and the preprocessing of the spectra was performed with Unscrambler (ver. 10.4, CAMO, Inc., Oslo, Norway). Spectral data in the 350-400 nm range were removed due to the low signal-to-noise ratio. The reflectance of the spectra for the predicted and post-dried soil samples collected in the fields was converted into absorbance (A = log [1/Reflectance]), and the spectra were analyzed by applying SNV and SG smoothing, as shown in Figure 3.  
Savitzky-Golay smoothing uses polynomial regression to correct the wavelength between two points, and this process is repeated to correct the entire spectrum Formula (1).  
\[\text{SG}=\frac{\sum_{i=-m}^{m}C_{i}Y_{j}}{N} \tag{1}\]  
where \(Y_{j}\) represents the original spectrum data, \(C_{i}\) denotes the filtering coefficient, and \(N\) reflects the number of points in the moving window (\(N=2m+1\)).  
This technique is used to smooth the spectrum by removing high-frequency noise, while allowing low-frequency signals to pass through, and because it benefits from a broader range of data compared to other preprocessing techniques, it is often used in spectroscopic analysis [30; 31; 32]. In this study, the second derivative with nine smoothing points was applied in the preprocessing of SG smoothing.  
Standard Normal Variate preprocessing was reported by [PERSON] et al. (1989) [33]. This is a widely used preprocessing technique to reduce the multiplicative effects of scattering and grain size and to reduce the overall intensity differences in the signals; each spectrum is centered and then rescaled by dividing by its standard deviation Formula (2).  
\[x_{i,j}^{SNV}=\frac{x_{i,j}-\overline{x}_{i}}{\sqrt{\frac{\sum_{j=1}^{p}\left( x_{i,j}-\overline{x}_{i}\right)^{2}}{p-1}}} \tag{2}\]  
where \(x_{i,j}\) represents the \(j\) spectrum of the \(i\) sample, \(\overline{x_{i}}\) denotes the average of spectrum \(i\), and \(p\) reflects the number of samples.
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 5 Experiments and Evaluation
### Evaluation on Surface Reconstruction Quality Estimation  
To evaluate the performance of 3D reconstruction quality prediction, the CatBoost-based method is compared to multiple prevailing regression methods. As shown in Table 3, CatBoost-FE-LASSO (prediction with feature enhancement and selection), CatBoost-FE (prediction with feature enhancement without selection), and CatBoost-LASSO (prediction with feature selection without enhancement) are compared, as well as the SVR-based [34] and XGBoost-based [35] methods.  
Table 3 demonstrates that the CatBoost with feature enhancement and selection (CatBoost-FE-LASSO) proposed in this paper performs the best on all evaluation metrics. According to the results, the proposed feature enhancement is superior to feature selection in improving the CatBoost method, because CatBoost-FE outperforms CatBoost-LASSO on all metrics. However, both of them are surpassed by CatBoost-FE-LASSO, which will be employed in quality-driven planning. The advantage of the proposed feature enhancement is also demonstrated on improving the SVR and XGBoost models. Both regressions can be improved by using feature enhancement to learn the SVR-FE and XGBoost-FE models.  
The scatter plots illustrating the predicted and true values are shown in Figure 6 for both training and testing sets. An ideal reference line, which represents perfect prediction, is included at a 45-degree angle. From the visualization, we can see that the data points in both training and testing sets are well-distributed, clustering closely around this ideal line. For example, most points in the test set are clustered around the ideal line, indicating that the model has a strong generalization ability. This can also interpret the advantage of CatBoost-FE-LASSO over other models based on SVR and XGBoost, 0.3427 vs. 0.6994 and  
Figure 5: Visualization of satellite orbits and observation area. The yellow rectangle on the right side enlarges the rectangular region to be observed.  
0.4981 on \(RMSE\) in Table 3 for instance. Specifically, the \(R^{2}\) value of CatBoost-FE-LASSO on the testing set is close to 0.9, reflecting that the model maintains a high prediction accuracy on unseen data. Despite the usefulness of the modeling approach in predicting the quality of reconstruction, there is still much room for further improvement by utilizing external factors such as atmospheric conditions and satellite attitude stability, etc.
# TransPGC: Point Cloud Geometry Compression Based on Transformers
## 3 Methods
### Pre-Processing  
The original point cloud's representation format preserves discrete geometric information in a three-dimensional space. However, this format cannot be directly utilized for 3D convolutions. Therefore, the TransPCGC method initially processes the original point cloud representation, converting it from the discrete point set format to a volumetric model represented by voxels. Voxelized point clouds describe correlations between voxels in a three-dimensional space. This aids deep learning techniques in extracting meaningful features from point clouds. The voxel representation format has a unit length of 1, and when scaling or downsampling is necessary, the voxel size is typically set to a value greater than 1. End-to-end neural network algorithms depend on the backpropagation function. To ensure differentiability, a method involving the addition of stochastic quantization noise is employed. This allows gradients propagation and facilitation of updates to model parameters during the training process, as illustrated in Equation (1). Here, \(t\) represents the  
Figure 1: TransPCGC. “Conv” denotes the number of output channels, and the kernel size, “Transformer “1/“2”, indicates the cascading of one/two Transformer modules. “\(\uparrow\)” and “\(\downarrow\)” signify the operations of zoom-in and zoom-out, “ReLU” refers to the rectified linear unit, Entropy is expressed as Entropy Rate Modeling.  
quantized data, \(\mu\) represents random uniform noise with a distribution range from \(-\frac{1}{2}\) to \(\frac{1}{2}\), and \(\hat{t}\) represents the simulated quantized data after noise addition.  
\[\hat{t}=t+\mu. \tag{1}\]  
The cost of processing a whole voxel rises dramatically when voxels are used, leading to a large increase in data volume. The processing cost can be decreased by dividing the voxels into non-overlapping chunks. These blocks are typically of a smaller size, denoted by \(M\times M\times M\), as expressed in Equation (2). Here, \((b_{c},n_{c},g_{c})\) represents the specified position of a block, \(\hat{T}_{n}\) denotes the global coordinates of the voxel, and \(\hat{T}_{n}^{loc}\) signifies the local coordinates of all blocks \((b_{c},n_{c},g_{c})\) encoded using an octree encoder.  
\[\hat{T}_{n}^{loc}=\hat{T}_{n}-(b_{c}\times M,n_{c}\times M,g_{c}\times M). \tag{2}\]
## 5 Nutritional Tips for an Adequate and Balanced Diet
### Increase the Consumption of Naturally Gluten-Free Cereals, Including Pseudocreals
#### 5.5.1 Rice  
There are different types of rice grains that vary considerably from each other, such as milled rice, brown and wild rice. Milled rice (or white rice) is one of the principal cereals of the gluten-free diet, as a component of cakes, biscuits, and bread. Milled rice has its husk, bran, and germ removed, while brown rice consists of endosperm, the embryo, and the bran layers. Compared with milled rice, brown rice contains relatively higher amounts of dietary fiber, unsaturated lipids, micronutrients (calcium, phosphorus, magnesium, potassium). This is because the embryo and the bran layers present in brown rice are rich in lipids, proteins, dietary fiber, and other components [86; 87]. Several cohort studies have shown that brown rice consumption reduces the risk of developing metabolic diseases, such as obesity and T2D [88; 89]. On the other hand, milled rice has higher glycemic index values than whole grains, such as brown rice [90]. A recent meta-analysis [91] found that high white rice consumption could be associated with an increased risk of metabolic syndrome development, particularly in Eastern Asia.  
Wild rice also contains more proteins, dietary fiber, starch, vitamins (thiamine, vitamin B3), and minerals (phosphorous, magnesium, potassium, zinc, iron, calcium) than milled rice. Moreover, wild rice is a rich source of phenolic compounds, flavonoids, and phytosteros [92]. Milled rice is composed of very low amounts of dietary fiber, while wild rice is a rich source of resistant starch and dietary fiber [92]. Resistant starch is considered to provide health benefits as it cannot be digested and absorbed in the small intestine reaching the colon, where it is slowly fermented by microorganisms to produce short-chain fatty acids [93]. Compared with milled rice, the nutritional profile of wild rice is associated with improved lipid effects [94; 95] and higher antioxidant effects [96].