esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Framework
### Experimental Configuration  
The experimental configuration for the 64 QAM-CAP-regulated UVLC technologies, which comprises the underwater path, the transmitters, and the receiver, is depicted  
Figure 3: Flow chart of ECSSOA.  
in Figure 4. A MATLAB application produces the initial digital data. To implement deep learning, predictive modeling, and statistical analysis methods, research groups use MATLAB 2022. Data for measured input and output are specified for an experiment. During the estimate, the model is simulated using the experiment input data, and the model output is contrasted with the actual experiment output data. The initial bit pattern is first translated into 64 QAM codes at the broadcaster using QAM mapping. The pattern is submitted for CAP modulated utilizing the cube-raised cosine function after four iterations of up-sampling. The random wave generation (Tektronix AWG 710B, Beaverton, OR, USA) is then fed with the standardized pattern to produce the analog electronic current. A hardware pre-equalization circuit prequalifies the electrical current, amplified by an electrical amplifier and linked with a bias tee to drive a Si-based LED with a silicon substrate. A lens then concentrates the photons so they may pass through the water tank. Due to technical restrictions, a 1.2 m water tank is employed as the underwater route. It should be emphasized that this research uses static water; further study will consider additional underwater pathway disruptions and greater transmission distances. An offerings PIN photodiode (Hamamatsu 10784, Bridgewater, NJ, USA) is utilized at the transmitter to employ differential power amplifier circuitry to transform the light brightness into an electronic current. A data memory analyzer then samples the incoming message impulses. The sinusoidal signal patterns first proceed through synchronization for the DSP on the receiving device. The linear distortion of the incoming signal is partially equalized using LMS. The QAM signs are acquired after the corresponding filters and down-sampling, and the suggested AFL-DLE equalizer processes them next. Here, evaluations are also made with other equalizers. The bit error rate (\(BER\)) is computed after 64 QAM demapping to assess system effectiveness.
## 3 Federated Learning for Space-Air-Ground Integrated Networks
### Device Selection Optimization Strategy Based on Hierarchical Reinforcement Learning  
In space-air-ground integrated networks, the dynamic availability of satellites, unmanned aerial vehicles, and ground devices leads to unstable device connection states, creating challenges for federated learning due to dynamic network topologies and unstable connections. To address these challenges, this paper proposes an optimization strategy based on hierarchical reinforcement learning, combining hierarchical attention mechanisms and meta-learning-based strategy optimization to intelligently select the optimal device set for federated learning, thereby enhancing model convergence speed and reducing communication overhead.
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 5 Results and Discussion
### Error Vector Magnitude  
The most common performance indicator utilised in 3 GPP for this research item's performance evaluation is Error Vector Magnitude. The difference between the symbol's demodulated 'anticipated' value and the demodulated received symbols'real' value is determined by EVM. EVM can be expressed as [5,55]:  
\[EVM\left(\%\right)=\sqrt{\frac{\frac{1}{M}\sum_{m=1}^{M}\mid S_{m}-S_{0,m}\mid^ {2}}{\frac{1}{M}\sum_{m=1}^{M}\mid S_{m}\mid^{2}}} \tag{7}\]  
where \(M\) is the number of constellation symbols, \(S_{m}\) denotes the constellation's real symbol associated with the symbol \"\(m\)\" and \(S_{0,m}\) is the real symbol associated with \(S_{m}\). The EVM limit for 3 GPP using 256 QAM is 3.5% [56].  
In Figure 5a, the Error Vector Magnitude EVM is represented for flexible waveforms compared for the case when there is no DPD improvement procedure employed when GMP DPD method is employed, and the CNN DPD method is utilized. It is observable that the CNN DPD method results in a better reduction as compared to GMP for all the flexible waveform architectures. Similarly in Figure 5b, it can be seen that when the RF input power is varied, the reduction in EVM due to the proposed CNN method is much better as compared to GMP method. For a high RF input power of 5 dBm, DPD CNN reduces EVM to approximately 2% while GMP has EVM around 5% so EVM reduction is about 9% with the CNN method bringing the performance within the limits set by 3 GPP.  
\begin{table}
\begin{tabular}{c c} \hline
**Framework** & **Parameters** \\ \hline Optimiser & ADAM \\ Type of activations function & ReLu \\ O/P layer type function & Softmax \\ Loss type & Mean Square Error (MSE) \\ Hidden Layers \(N\) & 15 \\ Neurons per layer \(K\) & 20 \\ \hline Regularization method & L2 \\ Regularization factor & 0.001 \\ Number of epochs & 100 \\ Validation split & 0.4 \\ Learning rate & 0.01 \\ Batch size & 16, 32, 64, 128, 256, 512, 1024 \\ Training specimens & 300,000 \\ Testing specimens & 300,000 \\ \hline Involution & \\ \((N-1)\)\(K^{2}\) + \((4+N)\)\(K\) + 6 & 5986 \\ \end{tabular}
\end{table}
Table 3: CNN parameters.
## 2 Materials and Methods
### Biofilm Evaluation with Fluorescence Microscopy  
Biofilm formation was visualised using FilmTracer(tm) LIVE/DEAD(r) Biofilm Viability Kit (Invitrogen, USA) containing SYTO 9 dye (3.34 mM) and propidium iodide (20 mM). Briefly, the biofilm formed on 96-well, flat-bottomed, sterile polystyrene microplates as previously described, was stained for 30 min in the dark at room temperature, as manufacturer's instructions, then visualised with fluorescence microscopy. Fluorescent images were collected using a Nikon Ti2-U research inverted fluorescence microscope, (Nikon Instruments, Amsterdam, Netherlands) with beam path settings for FITC and TRITC-like labels. Stacks of about 20 images were collected using a 20 \(\times\) objective lens. The stain differentiates live bacteria from dead by staining living bacteria fluorescent green and the dead ones fluorescent red, while the background remains virtually non-fluorescent.
## 5 Conclusions  
This study showed that the modified DDPG and NSGA-II algorithms could work in conjunction in the RL framework, combining the universality of RL agents and the effectiveness of the GA process and demonstrating the corresponding advantages. The trained agent modified one of the GA result cases, improved the pressure ratio from lower than the reference to higher than the reference, and maintained high efficiency.  
The new rotor obtained a sweep and lean feature, and the associated 2D airfoils were modified. The flow field analysis evidence suggested that the modifications made by the agent and GA process were explicable. It was revealed that the efficiency of the redesigned rotor improved because the separation near the tip was released, the shock wave weakened, the influence of the TLV was reduced, and the radial secondary flow was alleviated. These beneficial features remained after agent modification, and the pressure ratio improved mainly because of the increase in the flow turn angle.  
The agents with the modified DDPG algorithm learned a valid policy to improve the pressure ratio across the whole design space. The results of the NSGA-II algorithm indicated that novel fitness functions reduced the variation in geometry modification and working points. The integration of the trained agent and GA process yielded favorable  
Figure 24: Convergence history of the agent.  
results with a higher pressure ratio and efficiency. Moreover, the computational costs were compared, showing that agents modified the design much faster than the GA process, with more training of agents cost.  
The results of this study indicated that the RL method could incorporate other methods and that better designs could be generated by combining agents with different policies, GA methods, prior knowledge, and other optimization methods. More efforts are needed to improve the RL methods and surrogate models, involving, for example, hierarchical RL to extend the generalization ability. The RL methods can be further applied to assist future compressor design and have the potential to complete the design automatically and utilize more existing knowledge after improvement.  
Conceptualization, X.X.; methodology, X.X.; software, X.X.; validation, X.X.; formal analysis, X.X. and D.B.; investigation, X.X.; resources, X.H. and M.Z.; data curation, X.X.; writing--original draft preparation, X.X. and D.B.; writing--review and editing, X.X., D.B. and X.H; visualization, X.X. and D.B.; supervision, M.Z.; project administration, X.H.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Theory and Experimental Instrument  
### Measurement Principle  
Dual-wavelength IPDA lidar systems, presented in this study, consist of two continuous wave seed lasers. One wavelength is tuned onto or near the center of an absorption line, denoted as the on wavelength, and a reference off wavelength is used with significantly less absorption. For simplicity, the lidar equation for the received signal power at wavelength \(\lambda_{0}\), from a scattering target at range \(R\), is given by [22]:  
\[P(\lambda_{0},R)=P_{0}\eta A_{R}R^{-2}\rho^{*}\exp\biggl{[}-2\int_{0}^{R}q_{ \text{CO}_{2}}(r)\bigl{(}1-q_{H_{2}O}(r)\bigr{)}N_{air}(r)\sigma(r)dr\biggr{]}. \tag{1}\]  
where \(R\) is the integral path, \(P_{0}\) is the laser emission power, \(\eta\) is the optical efficiency of the system, \(\rho^{*}\) is the target reflectivity, \(A_{R}\) is the effective receiving area, \(q_{\text{CO}_{2}}(r)\) is the dry air volume mixing ratio of CO\({}_{2}\), \(q_{H_{2}O}(r)\) is the volume mixing ratio of water vapor, \(N_{air}(r)\) is the air number density, and \(\sigma(r)\) is the absorption cross-section. Knowledge of the water vapor mixing ratio is necessary in order to derive the dry air mixing ratio of CO\({}_{2}\). For a DIAL that uses on and off wavelengths which are very close together (<0.2 nm), differences in the reflectivity, backscatter coefficients, and the absorption of other molecules are negligible. By taking the ratio of the two simultaneous measurements, and assuming that the instrument is designed properly such that the receiver dependent differences at the two wavelengths are either negligible or constant and well calibrated, one arrives at the standard DIAL equation:  
\[\int_{0}^{R}q_{\text{CO}_{2}}(r)\bigl{(}1-q_{H_{2}O}(r)\bigr{)}N_{air}(r) \Delta\sigma(r)dr=\frac{1}{2}\ln\frac{P_{0}(\lambda_{\text{ON}})P_{R}(\lambda_ {\text{OFF}})}{P_{R}(\lambda_{\text{ON}})P_{0}(\lambda_{\text{OFF}})}. \tag{2}\]  
The column-averaged dry air CO\({}_{2}\) mixing ratio (\(XCO_{2}\)) is given by [23]:  
\[XCO_{2}=\frac{N_{\text{CO}_{2}}(R)}{N_{dry}(R)}=\frac{1}{2N_{dry}(R)R\Delta \sigma}\ln\frac{P_{0}(\lambda_{\text{ON}})P_{R}(\lambda_{\text{OFF}})}{P_{R}( \lambda_{\text{ON}})P_{0}(\lambda_{\text{OFF}})}. \tag{3}\]  
where \(N_{dry}(r)\) is the dry air number density, and the differential absorption cross-section is given by \(\Delta\sigma=\sigma(\lambda_{\alpha n})-\sigma\bigl{(}\lambda_{off}\bigr{)}\).
## 2 Mathematical Models of the 4 WS System
### The 8 DOF Vehicle Model and Model Validation  
#### 2.2.1 The 8 DOF Vehicle Model  
Although the 2 DOF vehicle model mentioned above can be used for the controller design, it cannot be used to represent a vehicle-controlled plant because of its simplicity and linearity. In this study, a nonlinear 8 DOF vehicle model is regarded as a vehicle-controlled plant for the 4 WS control system's evaluations through computer simulations and hardware-in-the-loop experiments. Figure 2 shows the nonlinear 8 DOF vehicle model, which mainly includes the rotational dynamics of the four wheels and the longitudinal, lateral, roll, and yaw motions of the vehicle.  
(1) Longitudinal motion:  
\[\begin{array}{c}m(\dot{v_{x}}-v_{y}r)-m_{s}h\dot{r}\phi=\sum\ F_{xi}\\ \sum\ F_{xi}=\left(F_{xfl}+F_{xfr}\right)cos\delta_{f}+\left(F_{xrl}+F_{xrr} \right)cos\delta_{r}-\left(F_{yfl}+F_{yfr}\right)sin\delta_{f}-\left(F_{yrl}+ F_{yrr}\right)sin\delta_{r}.\end{array} \tag{7}\]  
(2) Lateral motion:  
\[\begin{array}{c}m(\dot{v_{y}}-v_{x}r)+m_{s}h\dot{\phi}=\sum\ F_{yi}\\ \sum\ F_{yi}=\left(F_{yfl}+F_{yfr}\right)cos\delta_{f}+\left(F_{yrl}+F_{yrr} \right)cos\delta_{r}+\left(F_{xfl}+F_{xfr}\right)sin\delta_{f}+\left(F_{xrl}+ F_{xrr}\right)sin\delta_{r}.\end{array} \tag{8}\]  
Simulink. The simulation condition (\(\delta_{f}=3*pi/180*~{}(3.33t),\delta_{r}=0,V=20\) m/s) is set under the full-vehicle model and the 8 DOF vehicle model. The simulation results for the sideslip angle, yaw rate, and lateral acceleration are shown in Figure 3. As can be seen in Figure 3, the magnitudes and trends of the full-vehicle model and the 8 DOF vehicle model results are roughly consistent, which proves the correctness of the 8 DOF vehicle model. Thus, the 8 DOF vehicle model is used as the vehicle model in this paper to evaluate the 4 WS control system.
## 4 Analysis of Simulation Findings
### Analysis of Simulation Findings  
In this section, the motor speed waveforms of three controllers, SMO, ADRC, and Fuzzy-ADRC, are compared and analyzed when the motor speed is set to 1000 revolutions per minute, as shown in Figure 6.  
From the speed curves, it can be seen that all three controllers have different degrees of overshooting oscillations for a period of time after the motor starts. The SMO speed-exceeded percentage was measured to be 70% with a regulation time of 0.012 s, the ADRC speed-exceeded percentage was 4.1% with a regulation time of 0.026 s, and the Fuzzy-ADRC speed-exceeded percentage was 2.9% with a regulation time of 0.017 s. Among them, the SMO has a large speed-exceeded percentage but a rapid dynamic response. The ADRC has a small speed-exceeded percentage but a long dynamic response time. The Fuzzy-ADRC has the smallest speed-exceeded percentage and has a shorter dynamic response time than the ADRC. Therefore, by combining the two indicators of speed-exceeded percentage and regulation time, the Fuzzy-ADRC has the lowest speed-exceeded percentage and better dynamic performance than the remaining two controllers.
## 3 Results  
### Phytochemical Composition of Resin in P. densiflora  
P. densiflora and P. koraiensis plants were observed at 10-day intervals after infection of PWNs. Lower leaves of P. densiflora and P. koraiensis exhibited disease symptoms (wilting with discoloration) after 20 days of PWN infection and showed obvious wilting symptom after 40 days. The two pine trees were completely widtered after 70 days of PWN infection (Figure 1). The chemical compositions of resin after 20 days of PWN infection were analyzed by GC-MS in PWN-infected and non-infected P. densiflora plants. A total of eight monotereponoids (\(\alpha\)-pinene, camphene, \(\beta\)-pinene, \(\beta\)-myrcene, 3-carene, \(\beta\)-phellandrene, terpinolene, and L-pinocarveol), five sesquiterpenoids (\(\alpha\)-cubebene, copaene, \(\beta\)-caryophyllene, \(\beta\)-cubebene, and \(\delta\)-cadinene), and diterpenoids (isocrhrol and resin acids) were identified in resin of P. densiflora. These compounds were identified by comparison with the mass spectral library of GC-MS (Figure 2). The most abundant compounds were diterpene resin acids followed by \(\beta\)-pinene, \(\alpha\)-pinene, and \(\beta\)-phellandrene from P. densiflora. Among the substances investigated, 3-carene was the most markedly changed substance. It was faintly detected in the resin of P. densiflora without PWN infection, whereas the peak area of 3-carene was strongly enhanced (14.75 times) in PWN-infected pine tree (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \multirow{2}{*}{**Peak**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Retention Time (min)**} & \multicolumn{2}{c}{**Peak Area \(\times\) 10\({}^{-7}\)**} \\ \cline{4-6}  & & & & **Control Tree (\%)** & **Infected Tree (\%)** \\ \hline
1 & \(\alpha\)-pinene & Monoterpene & 4.41 & 6.32 \(\pm\) 0.07 (100) & 5.94 \(\pm\) 0.16 (94.00) \\
2 & camphene & Monoterpene & 4.70 & 0.33 \(\pm\) 0.05 (100) & 0.53 \(\pm\) 0.03 (160.61) \\
3 & \(\beta\)-pinene & Monoterpene & 5.47 & 12.89 \(\pm\) 0.51 (100) & 9.83 \(\pm\) 0.01 (76.26) \\
4 & \(\beta\)-myrcene & Monoterpene & 5.77 & 2.65 \(\pm\) 0.57 (100) & 0.55 \(\pm\) 0.06 (20.75) \\
5 & 3-carene & Monoterpene & 6.04 & 0.08 \(\pm\) 0.02 (100) & 1.18 \(\pm\) 0.15 (1475.00) \\
6 & \(\beta\)-phellandrene & Monoterpene & 6.81 & 3.14 \(\pm\) 0.88 (100) & 6.87 \(\pm\) 0.03 (218.79) \\
7 & terpinolene & Monoterpene & 8.39 & 0.93 \(\pm\) 0.20 (100) & 1.52 \(\pm\) 0.04 (163.44) \\
8 & L-pinocarveol & Monoterpene & 9.88 & 1.30 \(\pm\) 0.00 (100) & 1.57 \(\pm\) 0.02 (120.77) \\
9 & methylthymylether & - & 12.65 & 0.35 \(\pm\) 0.06 (100) & 0.51 \(\pm\) 0.01 (145.71) \\
10 & acetic acid & - & 14.06 & 0.71 \(\pm\) 0.20 (100) & 1.69 \(\pm\) 0.19 (238.02) \\ \end{tabular}
\end{table}
Table 1: The comparative peak area of resin compounds of control and pine wood nematode-infected _Pinus densiflora_.
# Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images
## 3 Results
Figure 7: The quality of ResNet-34 prediction for all classes: (**a**) ROC-AUC metric and (**b**) PR-AUC metric. Figure 8: Examples of CNN predictions. The patches were painted when the predicted class probability was greater than 0.5. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**Level**} & \multicolumn{6}{c}{**Classes**} \\ \cline{3-8}  & & **NG** & **AKG1** & **AKG2** & **AKG3** & **TA** & **VA** \\ \hline \multirow{2}{*}{Accuracy} & Patch & 0.905 & 0.828 & 0.730 & 0.833 & 0.793 & 0.855 \\ \cline{2-8}  & WSI & 0.838 & 0.871 & 0.876 & 0.974 & 0.664 & 0.886 \\ \hline \multirow{2}{*}{Precision} & Patch & 0.944 & 0.200 & 0.339 & 1.000 & 0.308 & 0.612 \\ \cline{2-8}  & WSI & 0.939 & 1.000 & 0.625 & 1.000 & 0.368 & 0.500 \\ \hline \multirow{2}{*}{Sensitivity} & Patch & 0.463 & 0.009 & 0.669 & 0.000 & 0.190 & 0.372 \\ \cline{2-8}  & WSI & 0.553 & 0.000 & 0.577 & 0.000 & 0.318 & 0.277 \\ \hline \multirow{2}{*}{Specificity} & Patch & 0.994 & 0.992 & 0.741 & 1.000 & 0.914 & 0.952 \\ \cline{2-8}  & WSI & 0.981 & 1.000 & 0.933 & 1.000 & 0.794 & 0.964 \\ \hline \multirow{2}{*}{NPV} & Patch & 0.902 & 0.833 & 0.918 & 0.833 & 0.849 & 0.883 \\ \cline{2-8}  & WSI & 0.813 & 0.871 & 0.920 & 0.974 & 0.756 & 0.912 \\ \hline \multirow{2}{*}{F1-score} & Patch & 0.622 & 0.017 & 0.450 & 0.000 & 0.236 & 0.463 \\ \cline{2-8}  & WSI & 0.696 & 0.000 & 0.600 & 0.000 & 0.341 & 0.357 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Metric values for EfficientNet-b4 predictions. Patch-level evaluation corresponds to the WSI level in a way similar to the micro-averaging corresponding to the macro-averaging. As an output of our program, we received the probability (the value from 0 to 1) for each of the six tissue types that the patch belonged to. If all the probabilities were below the threshold, we did not assign any class to the patch. In this case, the patch may have belonged to the unknown class. When several classes had probabilities greater than the threshold, the prediction was <<mixed>>; that is, the patch consisted of several areas belonging to different tissue types. It would be inconvenient to analyze the result of the painted WSI if we used special markers for all mixed types. Such patches could be highlighted for the specialist; otherwise, we painted the mixed patches with the color of the most probable type, while the threshold could be chosen by the user.
# On the Optimal Calculation of the Rice Coding Parameter
## 7 Conclusions  
In this article, we designed several methods for the calculation of the optimal Rice coding parameter and then evaluated them using real data.  
Our analysis and numerical verification showed that the optimal Rice coding parameter for a data sequence can be quickly bounded as expressed by (21). In addition, we also observed that the output bit-length of Rice codes can be estimated by (27) knowing the mean and variance of the dataset.  
In around 10% of the experiments, it was observed that partitioning the sequence into sub-sequences, such that each sub-sequence is coded with a different Rice parameter, can be profitable. An algorithm for finding the optimal partitioning solution for Rice codes was proposed, as well as fast heuristics, based on the understanding of the problem trade-offs.  
This work was supported in part by the FP7 Goldfish project, which received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No. 269985, as well as by the H2020 Micromole [24] and H2020 SYSTEM [25] projects, which have received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 653626 and No. 787128, respectively.  
**Acknowledgments:** The author would like to thank [PERSON] for the implementation of early versions (not presented in this manuscript) of the algorithms described and evaluated in this article.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 13: Ratio of optimal Rice coding considering partitioning versus the best heuristic partitioning solution found. The mean is calculated considering only those heuristic solutions for which the optimal could not be found, which account for 35% of the batches.
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
## 3 Proposed UAV Topology Control Algorithm  
The mobility of the drones is essential to keep the system functional and to adapt the formation to the movements of GNs. UAV-enabled LoRa network implements three mechanisms that together control the mobility behavior of the UAVs: the VSF mobility algorithm, the Connection Recovery, and Maintenance (CRM) algorithm and the Movement Prediction (MP) algorithm. These mechanisms are described in detail in the following sections.