esa-sceva/satcom-chunk-collection huggingface dataset:
# Cognitive Routing in Software-Defined Underwater Acoustic Networks
## 5 Conclusions  
In this paper, we proposed a novel routing protocol for cognitive acoustic software-defined underwater networks. The idea of combining a cognitive capability with a routing scheme in software-defined underwater networks makes this protocol unique. The protocol has two phases: beaconing and route estimation. A main controller is responsible for network management, while AUVs serving as local controllers move on fixed trajectories to reduce the number of control messages and any network delay. The controllers are responsible for providing a stable route between source and destination for the querying node. A link is formed between two nodes if they have a consensus about a common idle channel, and a link with the minimum duration is selected to make a stable route. Both natural acoustic systems and artificial acoustic systems are considered to be PUs in this scheme. Therefore, spectrum sensing is performed with an OFDM-based energy detection scheme. Our results show better performance for average delay, packet delivery ratio, and routing overhead ratio.  
For sensing channels and then selecting relay, a large amount of energy is required in the proposed scheme. Therefore, it is an urgent problem to consider a good trade-off among energy consumption, overhead, and delay for cognitive routing schemes based on traffic and energy balancing. Also, energy harvesting techniques, such as harvesting energy from acoustic links to recharge sensors/AUV batteries, should be considered to prolong the network lifetime. We have left these research issues for future work.  
This work was supported by the National Research Foundation of Korea funded by the MEST under Grant NRF 2015R1A2A1A15053452 and NRF 2016K2A9A1A01950711.  
[PERSON] and [PERSON] proposed, analyzed, designed and contributed to writing the research idea all together.  
The authors declare no conflict of interest.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 6 Conclusions  
In this paper, a parameterized model for optimal DES planning in ADNs is presented. The typical scenarios for DES planning are generated by a clustering technique, containing the patterns of load demand, wind turbine output and photovoltaic output. To effectively solve the problem, the original MINLP model is transformed into a MISOCP model by convex conversion. The calculation and evaluation results show that through the presented method, the economic benefits of the distribution network have been significantly improved and the power fluctuation of DG has been smoothed through the optimal planning of DES.  
With the rapid development of energy storage technology, it is foreseeable that the energy storage cost will be greatly reduced, and the applications of DES will become more and more extensive. The proposed method considers the economic and technical characteristics of DES and can achieve optimal planning with multiple types of DES in ADNs. It can provide reasonable schemes based on the parameterized model and support the distribution network planners to make decisions on the optimal sizing, allocation, and type selection of DES. It also shows great potential in real applications with different temporal and spatial scales, providing more effective support for renewable resource accommodation, voltage and frequency regulation, and self-healing control by detailed type selection and reasonable planning of DES.  
[PERSON], [PERSON] and [PERSON] conceived and designed the study; [PERSON] and [PERSON] performed the study; [PERSON] and [PERSON] reviewed and edited the manuscript; [PERSON] wrote the paper; [PERSON] and [PERSON] made the critical and final reviewing.  
This research was funded by the National Natural Science Foundation of China, grant number 51807132 and U1866207.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Location** & **Power Capacity (kVA)** & **Energy Capacity (kWh)** \\ \hline \multirow{4}{*}{Only Li-ion battery} & 92 & 350 & 1350 \\  & 108 & 310 & 1280 \\  & 111 & 150 & 630 \\  & 116 & 190 & 740 \\ \hline \multirow{4}{*}{Only VRB} & 92 & 380 & 1390 \\  & 108 & 330 & 1320 \\ \cline{1-1}  & 111 & 120 & 610 \\ \cline{1-1}  & 116 & 170 & 680 \\ \hline Only lead-acid battery & — & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 14: Planning results of power fluctuation smoothing in IEEE 123-node system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Power Fluctuation Cost (S)** & **Investment Cost of DES (S)** & \(\mathbf{C_{2}}\) **(S)** \\ \hline Without DES & 117,620 & — & 117,620 \\ Only Li-ion battery allocated & 69,443 & 44,125 & 113,568 \\ Only VRB allocated & 59,125 & 31,200 & 90,325 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Power smoothing results of DES planning in IEEE 33-node system.
## 2 Materials and Methods
### Random Patch Extraction
#### 2.2.3 Inference Pipeline  
During inference, we employed the same tiling procedure as the baseline (FPE + NCB), ensuring that any performance difference stemmed solely from the training scheme. Specifically, each parcel image was divided into overlapping patches, covering the entire farmland region. Any patch whose valid area was below 10% was discarded to avoid low-quality inputs. As illustrated in Figure 8, the trained network then computed softmax probabilities (\(p(c\mid\text{patch}_{i})\)) for each retained patch.  
We aggregated these patch-level predictions by multiplying the class-wise probabilities and selecting the class that maximized the product:  
\[\hat{y}_{\text{parcel}}=\arg\max_{c}\Bigl{\{}\prod_{i=1}^{N}p\bigl{(}c\mid\text {patch}_{i}\bigr{)}\Bigr{\}}.\]  
Hence, the inference process remains identical to that of FPE + NCB, ensuring a fair comparison that isolates the effects of CBS and RPE on final performance. In this manner, the final parcel label (\(\hat{y}_{\text{parcel}}\)) reflects the combined evidence across all retained patches. Details of the inference configuration and parameter choices are provided in Appendix A.
## 5 Key 6G Technologies
### New Spectrum
#### 5.1.3 Optical Wireless Technology  
Optical wireless communications (OWCs), extending beyond the terahertz spectrum, offer high-density broadband communication services, boasting advantages like inherent physical layer security, ultra-low latency, absence of electromagnetic interference, cost-effectiveness, access to abundant unlicensed spectrum, and simple deployment [57; 58]. The optical band encompasses visible light, ultraviolet (UV), and infrared (IR) each with distinct applications and considerations.  
IR communication, operating between 760 nm and 1 mm wavelength, facilitates remote data transmission with simple structures but is susceptible to atmospheric effects like -fog [57]. Visible light, within the 360-760 nm range, serves dual purposes of communication and illumination, additionally enabling energy harvesting using solar cells [59]. The concept of light-based IoT has been proposed, and innovative lighting devices based on blue lasers have entered the market, achieving an aggregate data rate of 26 Gbps [60; 61].  
UV, ranging from 10 to 400 nm, stands out for potential non-line-of-sight (NLOS) communications due to lower background noise and higher atmospheric scattering, though health and safety implications warrant thorough evaluation before practical application [62; 63; 64].  
Optical wireless technologies, including light fidelity (LiFi), visible light communication (VLC), optical camera communication (OCC), light detection and ranging (LiDAR), and free-space optics (FSO), have gained prominence, catering to applications in indoor, underwater, vehicular, and long-distance communications [65; 66]. Although optical bands have three orders of magnitude more spectrum resources than RF bands, optoelectronic devices' electrical bandwidth restricts their use. Advances in high-performance devices, such as fast organic light-emitting diodes (OLEDs) and silicon photomultipliers, have been instrumental in achieving data rates exceeding 1 Gbps [67; 68].  
Understanding OWC channels, characterized by non-isotropic properties, requires considerations for device orientation, leading to proposals like multi-directional transmitters with adaptive spatial modulation [69; 70]. Innovations such as time-domain spatial modulation and non-orthogonal multiple access (NOMA) schemes contribute to optimizing the performance and security of OWC systems [71; 72; 73]. Extensive research in these areas reflects the increasing attention given to optical wireless as a promising technology for various communication scenarios [74; 75].
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 2 System Modeling
### FSO Link  
The FSO link follows the Gamma-Gamma fading statistics with the impact of the angle-of-arrival of the FSO beam. The Gamma-Gamma fading model is a general and mathematically tractable composite FSO model [4]. The probability density function (PDF) of instantaneous electrical SNR, \(f_{\gamma FSO}(\gamma)\), is given, as in [29; 30], as  
\[\begin{split} f_{\gamma FSO}(\gamma)&=\exp\Big{(}- \frac{\theta_{FOV}^{2}}{2\sigma_{\theta}^{2}}\Big{)}+\Big{[}1-\exp\Big{(}- \frac{\theta_{FOV}^{2}}{2\sigma_{\theta}^{2}}\Big{)}\Big{]}\\ &\times\Big{[}\frac{\zeta^{2}\gamma_{1}^{-1}}{2\Gamma(\alpha_{f}) \Gamma(\beta_{f})}\times G_{1,3}^{3,0}\Big{(}\frac{\alpha_{f}\beta_{f}}{A_{o} h_{l}}(\sqrt{\frac{\gamma}{\tau_{FSO}}})\Big{|}\frac{1+\zeta^{2}}{\zeta^{2}, \alpha,\beta}\Big{)}\Big{]},\end{split} \tag{2}\]  
where \(\tilde{\gamma}_{FSO}=\frac{(\theta^{2}P_{1}^{2})}{\sigma_{\theta_{1}}^{2}}\), \(\rho\) is optical-to-electrical efficiency, \(P_{1}\) is source-transmitted optical power, \(\sigma_{n_{1}}\) is the standard deviation of Gaussian noise, \(\sigma_{\theta}^{2}\) is the variance of \(T_{x}-R_{x}\) misalignment orientations, and pointing error \(\zeta=\frac{w_{e}}{2\sigma_{\theta}}\); here \(\sigma_{s}\) stands for the standard deviation of the pointing error displacement, and \(w_{e}\) is the equivalent beam width at the receiver. \(\alpha_{f}\) and \(\beta_{f}\) represent the atmospheric turbulence parameters associated with the atmospheric conditions and \(\Gamma(\cdot)\) stands for the standard Gamma function. \(A_{0}=\)_erf_\({}^{2}(v)\), where \(erf(.)\) denotes the error function and \(h_{l}\) stands for atmospheric path loss, given as in [31]. Now integrating (2) using ([Equation (07.34.21.0084.01) [32]), we derive the closed-form expression for the CDF of instantaneous SNR for the FSO link given as  
\[\begin{split} F_{\gamma FSO}(\gamma)&=\exp\Big{(}- \frac{\theta_{FOV}^{2}}{2\sigma_{\theta}^{2}}\Big{)}+\Big{[}1-\exp\Big{(}- \frac{\theta_{FOV}^{2}}{2\sigma_{\theta}^{2}}\Big{)}\Big{]}\\ &\times\Big{[}\frac{\zeta^{2}}{\Gamma(\alpha_{f})\Gamma(\beta_{f })}\times G_{2,4}^{3,1}\Big{(}\frac{\alpha_{f}\beta_{f}}{A_{o}h_{l}}(\sqrt{ \frac{\gamma}{\tau_{FSO}}})\Big{|}\frac{1,1+\zeta^{2}}{\zeta^{2},\alpha,\beta,0}\Big{)}\Big{]}.\end{split} \tag{3}\]
## 4 Experiments
### Semantic Segmentation
#### 4.2.3 Significance of Skip Connections for Feature Forecasting  
Table 5 investigates the influence of skip connections for both the single-frame (oracle) and the forecasting model. The top section refers to the ResNet-based models and the bottom section to the DenseNet-based models. For the segmentation model, we observe 2.3 pp drop in accuracy for RN18 and 2.8 pp for DN-121 when we do not use skip connections. Although the forecasting model (denoted as ML-F2 MF) does not achieve comparable improvement, we still observe a consistent gain in accuracy when we use skip connections. For RN-18, we achieve 68.3% mIoU for short-term and 55.9% mIoU for mid-term, which is 0.6% and 0.5% better, respectively, compared to the setting in which our model does not use skip connections. For DN-121, this improvement is 0.4% and 0.2% for short-term and mid-term, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Accuracy (mIoU)**} & \multicolumn{2}{c}{**Short-Term**} & \multicolumn{2}{c}{**Mid-Term**} \\ \cline{2-5}  & **All** & **MO** & **All** & **MO** \\ \hline Oracle (DN-121-MixSkip) & 78.3 & 78.8 & 78.3 & 78.8 \\ \hline Copy last (DN-121-MixSkip) & 52.6 & 48.5 & 38.6 & 30.0 \\
3 DConv-F2F [34] & 57.0 & / & 40.8 & / \\ Di110-S2S [5] & 59.4 & 55.3 & 47.8 & 40.8 \\ LSTM S2S [28] & 60.1 & / & / & / \\ Mask-F2F [26] & / & 61.2 & / & 41.2 \\ FeatReproj3D [35] & 61.5 & / & 45.4 & / \\ Bayesian S2S [27] & 65.1 & / & 51.2 & / \\ DeformF2F [13] & 65.5 & 63.8 & 53.6 & 49.9 \\ LSTM AM S2S [36] & 65.8 & / & 51.3 & / \\ LSTM M2M [6] & 67.1 & 65.1 & 51.5 & 46.3 \\ ApaNet [12] & / & 64.9 & / & 51.4 \\ F2 MF [8] & 69.6 & 67.7 & 57.9 & 54.6 \\ Panoptic Forecasting [7] & 67.6 & 60.8 & 58.1 & 52.1 \\ LSTM-VAE-ML-F2F [9] & 71.1 & 69.2 & 60.3 & 56.7 \\ \hline ML-F2 MF-DN-121 (ours) & 70.2 & 69.0 & 58.5 & 55.9 \\ \hline \end{tabular}
\end{table}
Table 4: Evaluation of short-term and mid-term forecasting accuracy (mIoU) on Cityscapes val. _All_ denotes all classes; _MO_ denotes moving objects.
## 2 Analysis of Cable Vibration under Double-End Excitation
### Model Verification  
The longest cable of a rail-cum-road cable-stayed bridge, whose length, inclination angle, elastic modulus, linear density, initial tension, and cross-sectional area are 295.995 m, 25 degrees, 2 \(\times\) 10\({}^{11}\) pascals, 153.55 kg per meter, 9438 kilometres, 0.01828 square meters, respectively, was taken as the prototype. The cable was simulated using 60 link180 elements in ANSYS software. The link180 element is a three-dimensional uniaxial bar element with a tension-only capacity, which is useful for modeling the cable's properties. A form-finding analysis [26] was first executed to obtain the initial shape of the cable under the action of initial tension and gravity, as shown in Figure 3. The form-finding analysis should be performed first because the gravity of the cable will generate an initial sag, which is considered for accurate simulation purposes. Then, the first four modal frequencies of the cable, which are compared with the numerical model results in Table 1, can be obtained by conducting a linear perturbation modal analysis with prestress [27]. The linear perturbation method is commonly used to solve a linear problem with a preloaded status. After conducting a linear perturbation analysis, the tangent stiffness matrix of the preloaded structure can be gained, which will be used for the subsequent modal analysis.  
The comparison results of the first four modal frequencies of the cable demonstrate a high level of agreement between the finite element model and the numerical model, thus confirming the accuracy and validity of the numerical model. Only the first four modal frequencies are compared because the cable's n-th frequency is approximately n times the fundamental frequency, so comparing the higher frequencies is redundant.
# Multidisciplinary Aerodynamic Design of a Rotor
## 4 Results and Discussion
### Blade Parameter Determination  
The GOE design scheme following Equations (23)-(26) is implemented to find out the optimum rotor performance for an ORS-helicopter. The design variations in this study include blade twist, taper ratio, tip sweep angle, sweep start position and rotor speed. The DPL improvement, with its corresponding blade parameter variations of GOE method, are shown in Figure 14. As mentioned, the DPL is the component that measures rotor performance and design merit directly. Its improvement means a better aerodynamic performance of the rotor itself. The square-point lines express the global optimized solution in overall freestream velocities. In Figure 14a, it can be seen that variable blade technology has a satisfactory improvement for rotor performance in different freestream velocities. The DPL in hover reduces to 83.2% of the baseline, and it has a better improvement in forward flight, benefitting from the swept tip. The DPL reduces to 74.7% of the baseline at 124.8 km/h. The optimum rotor speed is depicted in Figure 14b. In hover and at low freestream velocity, it is allowed a wider range of rotor speed to operate. The consumption of profile power will decrease with a reduced rotor speed. Through a proposed GOE design scheme, the performance improvement, optimum rotor speed, and blade parameters are shown by circle-point lines in Figure 14. It can be seen from Figure 14a that DPL improvement becomes worse than with variable blade technology. Drops of 1.6%, 1.9% and 3.0% are suffered in hover, at moderate freestream velocities like 124.8 km/h, and at maximum freestream velocities, respectively. Comparing blade parameters by GOE and global optimized solutions, the drops are a consequence of compromises in taper ratio and tip sweep in hover, and taper ratio and blade twist in forward flight.  
Figure 13: Induced power and profile power with rotor speed variation.  
Figure 15 depicts the time history plot of DPL by GOE design scheme, with respect to the baseline DPL. It can be seen that the most improvement is attained at the outer portions of the blade, both in hover and in forward flight. That means the parts of power consumed the most is moved towards inner blade portions by the GOE design scheme, including blade twist, taper ratio, tip sweep and rotor speed optimization in this study, making power consumption more homogeneous in the blade.  
Figure 14: DPL improvement and its corresponding design parameters in GOE method. (**a**) DPL improvement; (**b**) Optimum rotor speed; (**c**) Taper ratio; (**d**) Blade twist; (**e**) Tip sweep angle; (**f**) Start position of tip sweep.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 2 Related Work
### Image Classification Methods
#### 2.3.3 GP-Based Image Classification Methods
\begin{table}
\begin{tabular}{p{142.3 pt}} \hline \hline [PERSON] et al. [30] developed a new GP-based image classification method that utilizes Multi-Objective Genetic Programming (MOGP) to automatically generate feature descriptors adaptable to various image domains. Tests on multiple datasets have shown that its classification accuracy significantly surpasses that of comparative methods. \\ \hline [PERSON] et al. [31] proposed a new GP-based program structure that automatically and simultaneously extracts both global and local features from original images. The performance of the proposed method is significantly superior to or on par with all comparative methods across four image classification datasets of varying difficulty. \\ \hline [PERSON] et al. [32] proposed a new GP-based method specifically for the classification of low-quality fish images. This method automatically selects image operators to process images and extract features. Its performance on a well-known fish image dataset is significantly better than various benchmark methods. \\ \hline [PERSON] et al. [16] proposed a new GP-based method for low-quality image classification, capable of extracting discriminative features from low-quality images. This method has been tested for performance in various scenarios, including original (clean), blur, low contrast, and noise scenarios. The test results demonstrate that this method excels in classifying low-quality images. \\ \hline [PERSON] et al. [33] designed a new program structure that allows for the flexible reuse of features generated by different nodes. Through this design, a combination of low-level and manual features has been achieved. The new method integrates classification algorithms into the program structure, enabling the automatic selection of suitable classification algorithms during the evolutionary process. \\ \hline [PERSON] et al. [34] integrated the selection and combination of base classifiers into a single tree, ultimately ensemble-classifying with multiple base classifiers, achieving higher generalization performance than a single classifier. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: GP-based multiclass image classification methods. when the fitness of the best individual stagnates. Simultaneously, this paper introduces a multi-generation individual ensemble strategy. This strategy selects efficient individuals based on the frequency of different functions appearing in each individual and integrates them into a solution for classification tasks.
## 3 Coupled Fluid-Solid Simulation Model
### Model Validation  
The numerical simulation results of the NSC soil are shown in Figure 12. As can be seen from the figure, the numerical simulation reproduces the key phenomena observed in the test. After the tunnel leakage, the soil particles were lost continuously with the seepage erosion, and no obvious soil cave phenomenon appeared in the process of erosion development.  
The numerical simulation results of the USC soil are shown in Figure 13. As can be seen from the figure, the numerical simulation reproduces the phenomena of seepage erosion induced by tunnel leakage and the development of unstable soil caves as observed in the tests. The strata surface remained stable during the development of seepage erosion.  
Figure 12: Simulation results of NSC soil: (**a**) tunnel leakage; (**b**) slip surfaces; (**c**) subsidence within the slip surfaces.  
The soil caves expanded upward with the development of seepage erosion and eventually induced the strata surface subsidence.  
The numerical simulated phenomena in the SSC strata are shown in Figure 14. It can be seen that the numerical simulation reproduced the phenomenon of stabilized soil cave in the SSC strata as observed in the model test, which appeared in the soil near the top of the tunnel after seepage occurred, but stabilized spontaneously with the continuation of seepage flow.  
Figure 14: Simulation results of SSC soil: (**a**) initial soil cave at the tunnel top; (**b**) soil remains stable.  
Figure 13: Simulation results of USC soil: (**a**) leakage at the tunnel top; (**b**) initial soil cave; (**c**) secondary soil cave; (**d**) surface subsidence.  
From the comparison between simulation results and the test results shown in Section 2.2, it can be seen that the numerical model can effectively reproduce the seepage erosion development characteristics of the four different soils. Therefore, the model can be further used to analyze the key factors in the seepage erosion process induced by tunnel leakage in different strata.