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
### SA for User Pairing
The objective function is evaluated after the exchange, and the new solution is accepted if it is better than the current best solution. The new solution of the user pairing is updated in the current best solution set, and the temperature is reduced in the current iteration. The user pairing scheme is described in Algorithm 1. In the hot configuration, the matching process pairs the highest channel gain user with the lowest channel gain user, regardless of the channel gain threshold. Meanwhile, the matching process in the cold configuration matches the highest channel gain user with the first listed user below the average channel gain. The condition in the cold configuration selected the first listed user below the threshold and multiplex it on the same subchannel with the highest channel gain user to suit the condition _Second user gain\({}_{n}\leq G_{Av}\)_. Hence, the matching process tests all possible users and subchannels involved in Throughput maximization. ```
1. Set the number of iterations \(j\in\{1,2,\ldots,J_{Max}\}\), the initial temperature T = 100:
2. Initialize the users set and subchannel set. 3. Estimate the channel gain \(G\in\left\|\hat{g}_{Max}\right\|_{U\times N}\). 4. Initialize the \(U_{Unsaligned}\) and \(N_{Unsaligned}\) in the system. 5. Pair two users on the same subchannel according to **Hot Config** as current Best Solution \(C_{B}\) Pair two users on the same subchannel according to **Cold Config** as current Best Solution \(C_{B}\) Pair two users on the same subchannel according to **Cold Config** as current Best Solution \(C_{B}\). 6. _Calculate_ Throughput function of the Initial design \(f_{C_{B}}\) :
7. System Mode \(f\
eq h=1\). 8. **Choose** two random subchannels \(1:N_{c}\) while fixing the initial values of \(a_{n}\) and \(\delta_{n}\). 9. **Exchange** the users with high channel gain for both selected subchannels. 10. **Check if** the Second selected subchannel is still giving high channel gain to the shifted user. 11. _If yes_; exchange the two users with high channel gain. 12. _If no_; check the user with low channel gain to exchange. 13. Check the low channel gain user \(Unsaligned_{n}<G_{Av}\); then exchange the users. 14. _If no_; check a different subchannel. 15. When Users and Subchannels are matched, denote the new configuration \(C_{A}\). 16. **Calculate** the Throughput function after \(f_{C_{A}}\) :
17. **If \(f_{C_{A}}<f_{C_{B}}\)**; accept the configuration. 18. **Else \(f_{C_{A}}>f_{C_{B}}\)**; Calculate: \(\Delta f=f_{b}-f_{a}\); Generate random number of \(\epsilon\in(0,1)\)
19. **If \(\epsilon<e^{-\frac{A}{2}}\)**; then accept the new solution Throughput function \(f_{C_{A}}\). 20. **Reduce** the temperature (\(T_{j}=T_{j-1}-\alpha\bar{T}_{j-1}\)). 21. Accept the solution. 22. End the Algorithm. ```  
**Algorithm 1**Proposed SA Subchannel and User Matching Scheme.
## 4 Identification of Faulty Events
### Phase-A and Phase-B to Ground Fault  
A fault on phases-A & B and involving ground (ABG fault) was simulated as an incident at the sixth cycle. The currents recorded on the node 650 were processed using the algorithm and the results for a period of 0.2 s (12 cycles) are depicted in Figure 6. It can be seen from Figure 6a that the magnitude of the currents corresponding to phases-A & B increase after the ABG fault, whereas the change in the current of phase-C is very small. Figure 6b depicts the WD-index corresponding to phases-A & B, which has a high magnitude during the incidence of a fault. The magnitude of this index corresponding to phase-C is low after the incidence of fault. Figure 6c illustrates the ALN-index with a high magnitude corresponding to all the phases at the time of a fault incidence. Figure 6d illustrates the fault index. It can be inferred that the magnitude of FI corresponding to the faulty phases (phases-A & B) is high compared to the threshold, whereas this FI corresponding to the healthy phase (phase-C) has a low magnitude compared to the threshold. Hence, the algorithm proposed in this paper effectively detects the ABG fault and also identifies the healthy and faulty phases.
## 2 Methods
### Interferometric Synthetic Aperture Microscopy (ISAM)
ISAM is a technology for correcting \((\overrightarrow{Q_{//}},-2k)\) to \((\overrightarrow{Q_{//}},-2k_{x}\ (\overrightarrow{Q_{//}}/2))\), which results in the correction of lateral resolution in the whole depth range. To illustrate more clearly the advantages of ISAM in terms of lateral resolution, numerical simulations were performed for both SD-OCT and ISAM. The simulation method has been introduced in our previous published work, as well as in many other studies [16; 18; 22; 23]. The simulation steps are briefly summarized as follows: First, the object matrix \(\eta(\overrightarrow{r})\) was defined; then, the detection signal \(S\) was generated on the basis of Equation (1) and Equation (2); finally, for SD-OCT, the inverse fast Fourier transform (_IFFT_) was applied directly to \(S\) to obtain the spatial structure signal, while for ISAM, the axial spatial frequency was corrected before the _IFFT_ was applied. In the simulation, the center wavelength of light source was 1.3 \(\upmu\)m, the spectral resolution was 0.1 nm, and the bandwidth was 80 nm. The Gaussian beam used in the simulation had a DOF of approximately 200 \(\upmu\)m and a focal spot with a diameter of approximately 10 \(\upmu\)m. Figure 1b,c show the results of SD-OCT and ISAM, respectively, when imaging randomly distributed isotropic point scatterers with a diameter of 2 \(\upmu\)m. We can see that the ISAM image had spatially invariant lateral resolution over the entire depth compared to the SD-OCT image, whose lateral resolution was degraded away from the focus. It was measured that the full width at half maxima (FWHM) of each scattering particle was the same throughout the depth, as shown in Figure 1c.
## 3 Results and Discussion  
### Soil Electrical Conductivity  
The soil electrical conductivity linearly increased in all of the saline treatments, but with different trends; at the last harvest the increase over the first harvest was more than double in EC2, and about three times in EC4 as well as EC6 (Figure 1). Moreover, the EC values were significantly higher in EC6 compared to the rest of the salinity levels, while the EC0 treatment presented the lowest overall values in all of the harvestings, being significantly lower than the rest of the treatments at the last four harvestings (II-IV). Finally, all of the salinity treatments differed among each other in the last three harvestings. Similarly to our findings, [PERSON] et al. [46] also suggested an increase in soil EC values with the increasing salinity of the nutrient solution, which is attributed to the gradual buildup of Na and Cl in soil solutions due to inhibited water uptake from plants. Moreover, [PERSON] et al. [47] also suggested a gradual increase in soil EC with increasing salinity and multiple harvestings in wild rocket plants, although the increase was less profound between different harvesting times of the same salinity level compared to the increase between the different salinity levels. According to [PERSON] et al. [48], the application of saline irrigation water resulted in an increase in EC levels in soil solutions which evolved with the progression of the growing cycle, while similar findings were recorded for the use of brackish water in cotton plant irrigation [49].
# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 2 Related Works
### Methods Based on Loss Function and Training Strategy Optimization  
[PERSON] et al. [10] addressed the class imbalance in dense object detection by proposing Focal Loss, which down-weights easy samples and emphasizes hard examples during training, thereby improving the model's focus on challenging instances. Additionally, they integrated this loss function into RetinaNet, effectively enhancing the detection of small and heavily occluded objects while maintaining computational efficiency. [PERSON] et al. [20] improved UAV image target detection by enhancing YOLOv8 with Wise-IoU loss, which optimizes the training process for better localization accuracy. Additionally, they designed a context enhancement module and a spatial-channel filtering module to refine feature representation, effectively boosting the detection performance of small targets in UAV imagery. Experimental results on the VisDrone2019 dataset demonstrated a 7.3 percentage point increase in [EMAIL_ADDRESS] compared to the original YOLOv8 model.  
The aforementioned models have provided new methods and insights for improving the accuracy and speed of small target detection in UAV applications. However, existing models still exhibit certain limitations. First, in traditional convolutional neural networks (CNNs), each convolutional layer typically employs fixed and static kernels to extract image features. While this approach performs well for ordinary scene images, it struggles with drone aerial images, where targets are usually tiny in scale and numerous. The limited receptive field of traditional kernels results in weak feature extraction capabilities, adversely affecting detection performance. Second, the YOLOv8 neck structure suffers from a lack of diversity in feature fusion methods, making it ineffective in integrating critical features for multi-scale and complex aerial images. Additionally, in the YOLOv8s model, the input image size is 640 \(\times\) 640, with a minimum detection scale of 80 \(\times\) 80. However, in UAV aerial scenarios, small targets are numerous and even smaller in scale, making it difficult for traditional networks to recognize target features within grids, thereby impairing small target detection capabilities.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### What Values Should be Used for Batch Size?  
This section aims at studying the influence of the batch size on the classification performance and on the training time. For this purpose, the model of Section 4.5 is trained for the following batch sizes: \(\{8,16,32,64,128\}\). Table 7 displays the OA values and also the training time for each studied batch size.  
Table 7 shows that for our experiments the batch size influences the training time, but not the accuracy of the trained models, as all OA values are comparable. This result implies that large batch sizes can be selected to speed up training.
## 3 Results and Discussions
### Ultrasonic Velocity Studies  
Studies on ultrasonic velocity are useful for extensive applications towards the evaluation of the thermodynamic and physicochemical properties of simple, binary and ternary mixtures [49; 50]. Considering the extensive reports in the literature on binary mixtures, it is understood that relatively less attention has been focused on the mixtures based on ethanol [51; 52; 53]. In the present work, the experimental ultrasonic velocities are compared with values derived through various theoretical methods: the [PERSON] relation (U\({}_{\text{N}}\)) [35], [PERSON] and Vangeel relation (U\({}_{\text{IMR}}\)) [36], Impedance relation (U\({}_{\text{IR}}\)) [37], [PERSON]'s specific velocity relation (U\({}_{\text{R}}\)) [38] and the [PERSON] relation (U\({}_{\text{J}}\)) [39]. The experimental values, along with the theoretical values calculated using various models, are presented in Tables 6 and 7 along with standard relations [35; 36; 37; 38; 39]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{\
\begin{tabular}{c} **Mode Fraction** \\ **of Ethanol** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Ultrasonic** \\ **Velocity** \\ **(Experimental Values) m/s** \\ \end{tabular} } & \multicolumn{4}{c}{**Theoretical Ultrasonic Velocity (m/s)**} \\ \cline{3-7}  & & \multicolumn{1}{c}{**[PERSON] and**} & \multicolumn{1}{c}{**Indence**} & \multicolumn{1}{c}{**[PERSON]’s Specific**} & \multicolumn{1}{c}{**[PERSON] Relation (U\({}_{\text{IR}}\))**} & \multicolumn{1}{c}{**Relation**} \\ \cline{3-7}  & & **Relation (U\({}_{\text{IR}}\))** & \multicolumn{1}{c}{**Relation (U\({}_{\text{IR}}\))**} & \multicolumn{1}{c}{**Relation (U\({}_{\text{IR}}\))**} & \multicolumn{1}{c}{**Relation (U\({}_{\text{IR}}\))**} & \multicolumn{1}{c}{**Relation (U\({}_{\text{IR}}\))**} & \multicolumn{1}{c}{**Relation (U\({}_{\text{IR}}\))**} & \multicolumn{1}{c}{**Relation (U\({}_{\text{IR}}\))**} \\ \hline
0.00 & 1281.7 & 1193.9 & 1281.7 & 1281.7 & 1192.3 & 1281.1 \\ \hline
0.14 & 1270.6 & 1183.6 & 1188.1 & 1261.0 & 1172.1 & 1268.1 \\ \hline
0.28 & 1257.9 & 1172.0 & 1131.8 & 1240.8 & 1152.8 & 1253.8 \\ \hline
0.49 & 1245.9 & 1150.6 & 1088.0 & 1210.5 & 1124.5 & 1228.7 \\ \hline
0.64 & 1217.3 & 1129.5 & 1079.0 & 1187.0 & 1103.1 & 1205.4 \\ \hline
0.77 & 1194.0 & 1108.9 & 1085.7 & 1168.4 & 1086.4 & 1183.9 \\ \hline
0.87 & 1177.3 & 1088.8 & 1100.2 & 1153.2 & 1073.1 & 1163.9 \\ \hline
0.95 & 1145.3 & 1069.0 & 1119.2 & 1140.6 & 1062.1 & 1145.1 \\ \hline
1.00 & 1133.3 & 1056.1 & 1133.3 & 1133.3 & 1055.9 & 1133.3 \\ \hline APD from experimental values & 7.06\% & 6.49\% & 1.34\% & 8.24\% & 0.55\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental and theoretical ultrasonic velocity of binary liquid system of Ethanol + 1-Hexanol at 303.15 K.
## 3 Results
### ROC Analysis  
ROC curve analyses demonstrated that serum 20S proteasome and UCH-L1 levels were capable of distinguishing patients with muscle-invasive tumour from patients with non-muscle-invasive tumour, with areas under the curve of 0.64 (\(p\) = 0.029) and 0.74 (\(p\) = 0.003), respectively (Figure 3A,B). Furthermore, serum 20S proteasome and UCH-L1 levels were capable of distinguishing patients with high-grade tumour from patients with low-grade tumour, with areas under the curve of 0.65 (\(p\) = 0.019) and 0.64 (\(p\) = 0.020), respectively (Figure 3C,D).  
Additionally, serum levels of proteasome and UCH-L1 in patients with primary and recurrent tumour were compared. The ROC curve for discriminating between those groups yielded an AUC of 0.62 (\(p\) = 0.49) for serum 20S proteasome and 0.53 (\(p\) = 0.62) for serum UCH-L1 (Figure 3E,F). For each variable cut-off level, positive predictive value (PPV) and negative predictive value (NPV) were calculated. The results are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Proteasome 20S**} & \multicolumn{3}{c}{**UCH-L1**} \\  & \multicolumn{3}{c}{**Concentration**} & \multicolumn{3}{c}{**Concentration**} \\  & \multicolumn{3}{c}{**[\(\mu\)g/mL]**} & \multicolumn{3}{c}{**[ng/mL]**} \\ \hline  & **Range** & **Median** & \(p\)**-Value** & **Range** & **Median** & \(p\)**-Value** \\ \hline \multicolumn{6}{c}{Primary/recurrent} \\ \hline Primary (35) & 0.32–3.62 & 2.25 & 0.0446 & 0.28–1.07 & 0.71 & 0.0892 \\ Recurrent (47) & 0.38–3.81 & 1.81 & 0.0446 & 0.26–1.05 & 0.67 & (NS) \\ \hline \multicolumn{6}{c}{Multiplicity} \\ \hline Single (46) & 0.38–3.81 & 2.08 & 0.5656 & 0.28–1.07 & 0.7 & 0.1722 \\ Multiply (36) & 1.13–3.51 & 1.85 & (NS) & 0.26–1.05 & 0.67 & (NS) \\ \hline \multicolumn{6}{c}{Stage} \\ \hline Non-muscle & & & & & 0.0574 \\ invasive (51) & 0.38–3.81 & 1.82 & 0.27–1.05 & 0.66 & (NS) \\ (Ta + T1) & & & 0.0448 & & \\ Muscle invasive (31) & 1.23–3.62 & 2.25 & 0.26–1.07 & 0.77 & \\ (T2 + T3 = T4) & & & & & \\ \hline \multicolumn{6}{c}{Grade} \\ \hline Low-grade (34) & 0.38–3.38 & 1.7 & 0.0033 & 0.28–1.07 & 0.67 & 0.4409 \\ High-grade (48) & 1.13–3.81 & 2.21 & 0.221 & 0.26–1.05 & 0.73 & (NS) \\ \hline \multicolumn{6}{c}{Size (mm)} \\ \(<\)30 (49) & 0.38–3.81 & 1.81 & 0.2258 & 0.26–1.05 & 0.66 & \\ \(>\)30 (33) & 0.42–3.62 & 2.21 & (NS) & 0.28–1.07 & 0.85 & 0.0184 \\ \hline \hline \end{tabular} NS–no statistically significant values.  
\end{table}
Table 4: Diagnostic characteristics of urine 20S proteasome and UCH-L1 concentration ratio compared with various clinicopathological parameters.  
Figure 3: ROC curves for 20S proteasome in serum as diagnostic tests for (**A**) muscle-invasive tumour, (**B**) high-grade tumour, (**C**) recurrent tumour, and for UCH-L1 in serum as diagnostic tests for (**D**) muscle-invasive tumour, (**E**) high-grade tumour, (**F**) recurrent tumour.
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 6 Conclusions  
Four distinct ML-based TAS techniques are suggested and implemented for a small-scale MIMO-based FGSM system in this work. For different values of \(N_{T}\), \(N_{tx}\), and \(M\), the recommended algorithm's ABER performance is compared to those of FD-TAS and FGSM-NTAS. The SVM-based TAS scheme surpasses all proposed ML-based TAS approaches in the simulations. The ABER performance of SVM is poorer than FD-TAS but the SVM-based TAS scheme is more computationally efficient than FD-TAS. SVM-based TAS attains the highest classification accuracy (\(\sim\)71.1%) and a minimal SNR gain of \(\sim\)2.2 dB compared to FGSM-NTAS. As a result, it may be a viable option for future-generation networks. These schemes, as well as DL-based architectures, can be implemented in the future for advanced SM variants. The proposed TAS strategies could be extended to massive MIMO systems.  
All authors contributed to the manuscript. Conceptualization: H.K.J. and V.B.K.; article gathering and sorting: H.K.J., V.B.K., A.M., A.L.I, P.S. and A.C.; resources: H.K.J., V.B.K., A.M., A.L.I, P.S. and A.C.; supervision: V.B.K., A.M., A.L.I, P.S. and A.C.; validation: V.B.K., A.M., A.L.I, P.S. and A.C. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] is supported in part by the Nigerian Petroleum Technology Development Fund (PTDF) and in part by the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under Grant 57473408.  
Not applicable.  
All data are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**ML-Based TAS Scheme**} & \multicolumn{4}{c}{**Improvement in SNR gain (dB)**} \\ \cline{2-5}  & **Configuration 1** & **Configuration 2** & **Configuration 3** & **Configuration 4** \\  & **(\(N_{T}\) = 4, \(N_{tx}\) = 3, & **(\(N_{T}\) = 4, \(N_{tx}\) = 3, & **(\(N_{T}\) = 5, \(N_{tx}\) = 4, & (\(N_{T}\) = 6, \(N_{tx}\) = 4, \\  & \(N_{rx}\) = 4, and & \(N_{rx}\) = 4, and & \(N_{rx}\) = 4, and & \(N_{rx}\) = 4, and \\  & **\(M\) = 4)** & **\(M\) = 16)** & **\(M\) = 4)** & **\(M\) = 4)** \\ \hline SVM-based & & & & \\ TAS [22] & 1.76 & 1.89 & 1.44 & 2.2 \\ NB-based & & & & \\ TAS [21; 27] & 1.49 & 1.65 & 1.21 & 1.86 \\ DT-based & & & & \\ TAS [21; 28] & 0.59 & 1.08 & 0.46 & 1.03 \\ KNN-based & & & & \\ TAS [22] & 1.27 & 1.39 & 0.96 & 1.61 \\ \hline \hline \end{tabular}
\end{table}
Table 9: SNR gain achieved by distinct TAS practices over FGSM-NTAS.
## 2 Methods and Materials  
### Methods  
All investigations were carried out on a servo-controlled Rock and Concrete Mechanics Testing System (MTS) (Figure 1), with the triaxial tests additionally using the MTS (MTS Systems, Eden Prairie, MN, USA) triaxial cell, model 656.11 (Figure 1c). The measurement of the axial force was carried out with a force transducer MTS (MTS Systems, Eden Prairie, MN, USA) type 661.23 with a force capacity of 500 kN (Figure 1a,b). In the case of triaxial tests installed inside the pressure cell, model MTS 661.97B-02 (MTS Systems, Eden Prairie, MN, USA) with axial rated force capacities of 1532 kN (Figure 1c) was used, while the displacements were measured with the use of extensometers MTS type 632.90F-04 Kit 01 (MTS Systems, Eden Prairie, MN, USA) (Figure 1a-c). Radial displacements were determined through the measurement of changes in the sample circumference with a chain put around it. All tests were conducted according to the International Society for Rock Mechanics (ISRM) suggested methods [22] and ASTM standards [23]. The testing was conducted at room temperature and humidity, with the axial strain rate of \(5\times 10^{-5}\) s\({}^{-1}\) in all the compression experiments.  
The triaxial testing procedure was as follows: After placing the sample inside the triaxial cell and filling it with mineral oil, confining pressure was applied until the desired value was reached (Figure 1d). Then, the axial force impacting the piston of the cell was increased to induce sample failure. Finally, axial force and confining pressure were reduced to zero at the end of the test.  
In the second part of the article, presenting the applicability of the extension strain criterion to evaluate the failure process of sandstone samples, DIC analyses, simple shear, and Brazilian tensile strength tests were used. The camera used in the experiment was Nikon D60 (Nikon Corporation, Tokyo, Japan) (10 Mpx, 3872 x 2592 px). Regarding the DIC method, the Ncorr system was applied [24].  
To ensure the compatibility of the DIC analysis with the extensometer sensor measurement results, a comparison of the tensile stress-tensile strain curve obtained with the MTS extensometer and the DIC analysis (a virtual extensometer) is shown in Figure 2c. It can be seen that there is a good concordance between the two methods used to analyse strain in the specimen in the Brazilian test. Finally, three measurement methods were used simultaneously in these two tests: (1) an extensometer to measure crack width displacement in the Brazilian test, (2) a linear variable differential transformer (LVDT) sensor to measure piston displacement under a vertical load in the shear test, and (3) DIC to analyse the strain field on the lateral surface of the specimens (Figure 2a,b) in the Brazilian and shear tests.  
Figure 1: Experimental setup: (**a**) the Brazilian test, (**b**) the unconfined compression test, (**c**) the MTS confined compression test, and (**d**) the loading scheme in the triaxial compression test.
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 1 Introduction
Nevertheless, against the background of the COVID pandemic outbreak, existing studies analysing shipping emissions worldwide have not reflected the influence of this pandemic and port policy's effectiveness. Furthermore, research is yet to be conducted to analyse the significance of launching the shore power policy. In response to filling these gaps, this study makes the following contributions:  
1. We establish a high-precision estimation method for missing ship data to improve the existing \"'bottom-up\" approach for calculating ship emissions. 2. We detail the emissions based on different ship types and ship engines in the POLB region before and after the COVID pandemic. 3. As the Port of Long Beach has adopted new shore power policies, the calculations should be revised in line with the policies. Through the results, we discuss the significance of the shore power policy. The remainder of this paper is organised as follows: Section 2 summarises the methods for calculating ship emissions and the existing research. Section 3 presents the data-processing and calculation methods of this paper. Section 4 analyses the experimental results and implements data visualisation. Section 5 provides the conclusion.