esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control Task** & **Control Gains** & \(1\,e_{ss}\,\mid\,\)**(Counts)** & **Overshoot (Counts)** & \(t_{ss}\) (s)** \\ \hline \multirow{4}{*}{Positioning Control} & \(k_{1}=15\), \(k_{2}=30\), \(w=0\) & 295 & - & 0.75 \\  & \(k_{1}=15\), \(k_{2}=30\), \(w=2500\) & 0 & 6 & 1.45 \\  & \(k_{1}=15\), \(k_{2}=30\), \(w=5000\) & 0 & 4 & 0.905 \\  & \(k_{1}=15\), \(k_{2}=30\), \(w=7000\) & 0 & 2 & 0.765 \\ \hline Control Task & Control Gains & Maximum Tracking Error \(\left|e\right|_{\text{max}}\)(counts) \\ \hline \multirow{4}{*}{Tracking Control} & \(k_{1}=15\), \(k_{2}=30\), \(w=0\) & 1584 \\  & \(k_{1}=15\), \(k_{2}=30\), \(w=5000\) & 544 \\ \cline{1-1}  & \(k_{1}=15\), \(k_{2}=30\), \(w=10\),000 & 280 \\ \cline{1-1}  & \(k_{1}=15\), \(k_{2}=30\), \(w=15\),000 & 173 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Control gains setting in experiments. Finally, for practical realization, the upper bound may not always be easy to identify. However, the robust gain \(w\) can be determined by increasing the value gradually. This feature has been verified through experiments.
## 4 Discussion  
### Comparison with Other Methods  
We include the comparison between the proposed cGAN-TV and other generative models to see how well it performed in comparison with others. In this case, the compared models are autoencoder and pix2 pix (cGAN) [8] because autoencoder is a part of our architecture and cGAN is the baseline of our method. The comparison of the results is shown in Figure 9.  
Visually, it is clear that the resulting image from our cGAN-TV (best scenario) is far superior and more similar to the original image. The autoencoder-generated image tends to be more blurry and has not been able to describe facial characteristics well, especially in the last dataset, where the autoencoder failed to generate an image. A more detailed comparison of SSIM and FID can be seen in Table 6. Quantitatively, the best GAN method scenario is also superior in both SSIM and FID metrics. The autoencoder failed to produce the face, as shown in the last dataset in Figure 9. This was due to the less varied and large dataset. Almost the entire training dataset was taken from the generated photos dataset [17], and because the testing dataset was not from the same source, the autoencoder failed to create images. On the other hand, GANs could still produce images, even though the resulting images are not as good as the validation dataset when viewed visually.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-5}  & **SSIM** & **FID** & **SSIM** & **FID** \\ \hline Autoencoder & 0.69 & 114.096 & 0.64 & 105.638 \\ \hline Pix2 pix & 0.815 & 97.078 & 0.71 & 101.376 \\ \hline cGAN-TV & **0.83** & **94.705** & **0.73** & **93.019** \\ \hline \hline \end{tabular}
\end{table}
Table 6: SSIM and FID comparison between cGAN-TV and other methods.  
Figure 9: Our method with best scenario compared with cGAN (pix2 pix) and autoencoder. The ground truth images are from generated photos [17] and volunteers (private collection).
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### PHY Layer Challenges
#### 7.1.1 Rendezvous  
The rendezvous problem is related to the realization of a common control channel (CCC) for control messaging in CR-LPWAN networks and may be experienced in ad-hoc-based CR-LPWAN network configurations where coordination will be required between end nodes. Rendezvous defines the time it takes for two or more CR end nodes to converge on a CCC. In practice, it is challenging for CR-LPWAN nodes to simultaneously agree on a free channel to be used as CCC for coordinating communication between CR-LPWAN end nodes. This challenge occurs because the probability of two CR-LPWAN nodes scanning and converging on a CCC without prior knowledge or agreement between both entities is often very low. This is because different CR-LPWAN nodes often start their search for white spaces at different frequency points, and may find these white spaces at different bands of the RF spectrum. Thus, it is often difficult for two CR-LPWAN nodes to start and discover the same white space at that same time and frequency location. This challenge is sometimes called the \"CCC setup challenge\" [28].  
There are a number of efforts geared towards addressing this challenge such as in [104; 105; 106; 107; 108; 109; 110; 111; 112; 113]. For example, in [104], a rendezvous system was proposed wherein end nodes search for free bands without any coordination. The developed system contributed mainly in extending rendezvous convergence to multiple users by inheriting hopping sequences from other users. Their distributed algorithms called coordinated channel hopping (CCH) achieved 80% lower time to rendezvous than existing algorithms. Similarly, while noting that implementing rendezvous is challenging since CR end nodes are unaware of each other before rendezvous, authors in [112] proposed a fast rendezvous algorithm based on channel hopping. It was noted that the proposed fast rendezvous algorithm can guarantee rendezvous in \(2N^{2}+N\) timeslots under asynchronous environments, where \(N\) is the number of licensed channels in the network. [PERSON] and [PERSON] in [113] proposed a channel hopping algorithm for rendezvous in CRN using swarm intelligence. They deployed an ant colony optimization algorithm to guide nodes towards converging on the best channel.  
Essentially, we note that the literature on the rendezvous problem in CR is quite rich, and it is a contemporary challenge that would affect CR-LPWAN systems. As such, it is pertinent to address the rendezvous problem towards developing effective CR-LPWAN systems. Specifically, further research is required to ensure that rendezvous algorithms are fast, simple, and energy efficient for CR-LPWAN purposes.
## 5 Conclusions  
In this paper, the particle swarm optimization algorithm combined with Kriging surrogate model is applied to optimize the flap position parameters of three-dimensional wing and flap configuration. The aerodynamic and aeroacoustic predictions are calculated by numerically solving Large Eddy Simulation (LES) equations with Ffowcs-William and Hawkings (FW-H) equation. In the optimization, coordinates of the flap leading point (x_flap, y_flap) and angle of the flap (angle_flap) were taken as the design variables, and the maximum overall sound pressure level (OASPL) of far-field noise were set as the objective function. The following conclusions are drawn:  
1. The numerical results of three-dimensional wing and flap configuration show reasonable agreement with the experimental data. Thus, the aerodynamic noise of a simplified wing model can be accurately assessed by means of high-fidelity CAA/CFD numerical simulation.
2. The relationships between the design variables and the objectives were analyzed by correlation coefficients and scatter plots. The two design variables of x_flap and y_flap have positive correlation with the maximum OASPL and the lift-drag ratio (L/D). Meanwhile, a strong correlation exists between angle_flap and L/D.
3. Under aerodynamic constraints, the maximum overall sound pressure level is reduced by 3.99 dB after optimization. At the same time, the optimized values of x_flap and y_flap are located near the lower limit value, while the optimized values of angle_flap are located in the middle part over the range.
4. With the deflection angle of the flap almost unchanged, the aerodynamic noise can be reduced, while ensuring a large L/D by changing the positional relationship between the flap and the main wing. The research results can provide suggestions for the design of low-noise & high-lift configuration, as well as their application in high-lift devices.  
Methodology, S.J. and Z.S.; validation, Y.W. and C.Y.; formal analysis, S.J. and Z.S.; investigation, D.G. and G.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Youth Innovation Promotion Association CAS (2019020) and the EU-China IMAGE project (Grant No. 688971). Innovative Methodologies and technologies for reducing Aircraft noise Generation and Emission (IMAGE) is an EU-China collaborative project between a European team (Chalmers, AGI, CFDB, CIMINE, KTH, NLR, NUMECA, ONERA, RWTH-Aachen, TU-K, UPM, and VKI) and a Chinese team (ASRI, ACAE, ARI, BASTRI, BUAA, FAI, IMech, NPU, and THU).  
The authors declare no conflict of interest.  
Figure 25: Iso-surfaces of Q-criterion(Q = 5000): (a) initial shape: (b) optimal shape.
## 1 Introduction  
The importance of Artificial Intelligence (AI) grows significantly, predominantly for the analysis of large amounts of data, as industries recognize and prepare to take advantage of the data they collect every day [1]. Industries have the desire to maximize the efficiency of their production lines, controlling variables that manufacturing has, for example, waste management or quality control [2]. However, it is still recent the perception that the functional performance depends inherently on the processing and visualization of information in real-time in a clear way. Only in this way can companies reduce the risks to which they are exposed [3]. Business Intelligence (BI) processes, thus, become a key technology for any industry that wants to manage and serve its customers in the best possible way [4]. BI is a learning process that must provide intuitive information analysis, the ability to perform assertive analysis of what is happening, and where the company should go towards [5]. Real-time processing and visualization involve principles that allow companies to take advantage of the information that is stored on their computer systems as it arrives [6].  
The Complex Event Processing (CEP) paradigm allows the information contained in events that flows through all layers of the (Information Technology) IT infrastructure to be discovered, understood, and made available in real-time [7]. Complex Event Processing is defined as the use of technologies capable of consulting data even before it is stored [6]. This process is essential for the processing of information in real-time, as it performs the analysis of incoming events in a continuous way, providing an updated view of what is happening in the business domain [8]. Therefore, CEP is a helpful ally, since it tracks data flows from several sources simultaneously, analyzing trends, patterns, and even events in real-time [9]. CEP allows users to access events that happened in the past and use them in any order. These events may come from various sources and may occur for a long period of time. CEP approaches require sophisticated event interpreters, event pattern definitions, and matching of event patterns, together with correlation techniques [9; 10; 11]. With CEP, the received data are being continuously monitored and acted upon based on the use of declarative conditions, with almost zero latency [9].  
Complex events are commonly related to important business events (such as opportunities or threats), with the implication that they will be responded to in real-time or at least in something approaching real-time or zero-latency [8; 9; 12]. From databases and dashboard visualizations, to the presentation platforms, everything must support the processing of complex events in real-time. In the following chapters, a discussion about the pros and cons found when adopting these tools is presented.
## 4 Results and Discussion
### Hardware in the Loop Analysis  
To enhance the validity of the proposed controller, hardware in the loop (C-HIL) analysis evaluates the real-time performance of the BFASMC controller before its practical  
Figure 16: Grid voltage during grid-connected mode.  
Figure 17: Frequency comparison.  
Figure 18: Grid current comparison.  
implementation and HIL setup is shown in Figure 19. To generate the control signals, this hardware in loop analysis uses C2000 Delfino MCU F28379D LaunchPad with dual-core central processing units (CPUs) with an operating frequency of 200 MHZ, and the interface is provided with MATLAB/Simulink using Delfino T1C2000 support through embedded coder. The average small-signal model of hybrid renewable energy sources (PV and WES), including energy storage system (battery), power converters, and grid, is modeled on MATLAB and machine coding is done on MCU F28379D Launchpad-1. Moreover, control laws of BFASMC are implemented on Launchpad-2 with a 10 kHz switching frequency. The general-purpose input/output (GPIO) ports at Launchpad-1 based on the machined model have connected with the pulse width modulation (PWM) output ports at Launchpad-2. Both the launchpads establish a close loop of 12-bit built-in digital to analog converter (DAC) and an analog to digital converter (ADC). C-HIL experiment is facilitated by using the power of the wind energy system to supply the constant load power. For C-HIL experiments, islanding and grid-connected mode are considered and compared with simulation results.
## 4 Discussion  
In this study we developed an Artificial Neural Network Sequential Downscaling Method (ASDM) with Transfer Learning Enhancement (ASDMTE) that enabled coarse-scale AOD data (\(\sim\)50 km) to be downscaled to a finer-scale (\(\sim\)7 km) where training occurred only on a limited sample of temporally overlapping images. The ASDM/ASDMTE approach took point-wise inputs of lagged fine-scale AOD data, coarse-scale AOD data, latitude,  
Figure 6: Downscaled (by method) and G5 NR data over the study region on 29 July 2006: (a) ASDMTE, (b) ASDM, (c) SRDRN, (d) dissever GAM, (e) dissever LM, (f) G5 NR. longitude, time and elevation to predict the fine-scale AOD generated from GSNR. We found that this neural network approach was able to learn complex relationships and produce reliable predictions. Based on the comparison of image-wise \(R^{2}\) and \(RMSE\) shown in Appendix Figures A1-A4 and Table 1, ASDM/ASDMTE showed superior downscaling performance that outperformed the CNN-based neural network--SRDRN--and statistical downscaling approaches in dissever (GAM, LM). Statistical downscaling has a long history, rooting from the demand to generate local-scale climate information from GCMs with less computational cost. Traditional statistical approaches focus on establishing empirical associations between coarse-scale and fine-scale variables [22; 49]. For instance, [PERSON] et al. (2008) modeled the associations between soil moisture at 40 km resolution and its corresponding fine-scale (1 km) observations using linear regression [50]. Leveraging temporal replicates, they fit separate linear regression models independently to each fine scale grid, ignoring spatial and temporal associations in either the fine- or coarse-scale data. Recently, deep learning approaches have been used that address spatial features, such as [PERSON] et al. (2021) [26], who developed a CNN-based method, Super Resolution Deep Residual Network (SRDRN), to downscale precipitation and temperature from coarse resolutions (25, 50 and 100 km) to fine resolution (4 km) by learning the between-scale image-to-image mapping function. However, they ignore the temporal associations between images. Current downscaling methods focus only on modeling between-scale relationships and ignore any inherent temporal associations in the data. As observed in Figure 5, there are inherent within-scale temporal associations in the fine- and coarse-scale data, where at the same location temporally near observations tend to be correlated to each other. These associations provided essential support for downscaling and resulted in better fine-scale
# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
###### Abstract  
Water distribution infrastructure (WDI) is well-established and significantly improves living quality. Nonetheless, aging WDI has posed an awkward worldwide problem, wasting natural resources and leading to direct and indirect economic losses. The total losses due to leaks are valued at USD 7 billion per year. In this paper, a multi-classification multi-leak identification (MC-MLI) scheme is developed to combat the captioned problem. In the MC-MLI, a novel adaptive kernel (AK) scheme is developed to adapt to different WDI scenarios. The AK improves the overall identification capability by customizing a weighting vector into the extracted feature vector. Afterwards, a multi-classification (MC) scheme is designed to facilitate efficient adaptation to potentially hostile inhomogeneous WDI scenarios. The MC comprises multiple classifiers for customizing to different pipelines. Each classifier is characterized by the feature vector and corresponding weighting vector and weighting vector pertinent to system requirements, thus rendering the developed scheme strongly adaptive to ever-changing operating environments. Hence, the MC scheme facilitates low-cost, efficient, and accurate water leak detection and provides high practical value to the commercial market. Additionally, graph theory is utilized to model the realistic WDIs, and the experimental results verify that the developed MC-MLI achieves 96% accuracy, 96% sensitivity, and 95% specificity. The average detection time is about 5 s.  
Keywords:adaptive kernel; multiple classifiers; graph theory; hydraulic model; multi-criteria decision-making +
Footnote †: journal: Applied sciences
# Modeling Population Dynamics and Assessing Ecological Impacts of Lampreys via Sex Ratio Regulation
###### Abstract  
Regulating lamprey populations is crucial for maintaining ecological equilibrium. However, the unique sex determination process of lampreys is constrained by multiple factors, complicating intuitive analysis of population dynamics and their impact on the natural environment. This study employed a two-species competition mechanism to elucidate the factors influencing sex ratios and their mechanistic effects on lamprey population size. Using the Lotka-Volterra equations, we investigated how sex ratios affect trophic levels both upstream and downstream of lampreys in the food web. A logistic population growth model was applied to assess the impact of sex ratio variations on symbiotic parasitic species, while the Analytic Hierarchy Process (AHP) was utilized to explore the dynamic relationship between sex ratio changes and ecosystem stability. To validate model efficacy, we manipulated temperature and food availability under controlled disturbance conditions, analyzing temporal variations in lamprey population size across different disturbance intensities to evaluate model sensitivity. The findings indicate that the variable sex ratio's benefit is in facilitating the lampreys' population's enhanced adaptation to environmental shifts. The coexisting species exhibit a similar pattern of population alteration as the lampreys, albeit with a minor delay. A definitive link between the quantity of lampreys and the parasitic species is absent. A male ratio of 0.6 optimally contributes to the ecosystem's equilibrium. Over time, the configuration of our model's parameters proves to be sensible. This research provides robust theoretical support for developing scientific strategies to regulate lamprey populations.  
sex ratio; Lotka-Volterra equation; population size; logistic model Article
# Logit Averaging: Capturing Global Relation for Session-Based Recommendation
## 3 Results
### Ablation Study
#### 3.3.1 Effectiveness of Augmentation  
We utilized augmentation methods with four baselines in order to overcome the data sparsity problem in session datasets. We conduct four different data augmentation methods, which are GraphMix [36], FLAG [37], Random deletion, and Random insertion. GraphMix [36] is proposed as a regularization method for GNN based semi-supervised object classification. It interpolates the hidden states and the corresponding labels to make a better feature representation. Even though it targets object classification tasks, we find this technique also applicable to session-based recommendation tasks as well. We interpolate the feature representations of two random sessions within a batch and let there be two target values, making it similar to a multi-label classification task. The equation is shown below. \[F_{mix}=\lambda\cdot F_{a}+(1-\lambda)\cdot F_{b} \tag{7}\]  
\[L_{m}=\lambda\cdot CrossEntropyLoss(pred,y_{a})+(1-\lambda)\cdot CrossEntropyLoss( pred,y_{b}) \tag{8}\]  
\[L=L_{o}+L_{m}, \tag{9}\]  
First, we interpolate two random feature representations of sessions a and b, \(F_{A}\) and \(F_{b}\) with respect to \(\lambda\) value. We set \(\lambda\) value as 0.6 in all experiments. Then the _pred_ is calculated using the session model, and the loss value is calculated using cross entropy \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Model** & **HR@10** & **MRR@10** & **Coverage@10** & **HR@20** & **MRR@20** & **Coverage@20** \\ \hline \multirow{3}{*}{Tmail} & GCE-GNN & 23.76 & 15.20 & 16.18 & 26.29 & 15.35 & 25.71 \\  & TAGNN++ & 30.77 & 14.55 & 16.36 & 36.42 & 14.96 & 25.42 \\  & TAGNN++ & GraphMix & **34.27** & **17.11** & **17.50** & **43.33** & **17.76** & **28.06** \\ \hline \multirow{3}{*}{Yocohose 1/64} & GCE-GNN & 60.32 & 29.66 & **29.79** & 71.02 & 30.40 & **34.98** \\  & TAGNN++ & **60.84** & **31.11** & 28.79 & **71.16** & **31.83** & 33.46 \\ \cline{1-1}  & TAGNN++ & GraphMix & 60.75 & 30.86 & 27.64 & 71.15 & 31.59 & 32.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance comparison on our proposed model with TAGNN++ and GCE-GNN using Tmall and Yoochoose 1/64 datasets.
###### Abstract  
This article proposes the design of a dual-band coupled-fed dipole antenna for passive coherent location (PCL) systems in the very high frequency (VHF) band. The proposed indirect coupled feed mechanism, which is often employed in microstrip patch antennas, is first applied to VHF band dipole elements for dual-band matching. To confirm the effectiveness of the proposed design, we fabricate the coupled-fed dipole element and measure antenna characteristics, such as the voltage standing wave ratio (VSWR) and the antenna gain. The proposed antenna element is then applied to an eight-element circular array to form the reference and surveillance beams for PCL systems. Finally, the target location is estimated by constructing amplitude-range doppler (ARD) maps for one frequency modulation (FM) and two terrain digital multimedia broadcasting (T-DMB) illuminators in the Seoul-Gyunggi urban area. The results confirm that the proposed element is suitable for dual-band PCL systems in the VHF band compared to a conventional dipole antenna.  
VHF; FM; dipole antenna; PCL; passive radar; coupled feed; dual-band +
Footnote †: journal: Applied Sciences
## 4 Experiment and Results  
In this section, we evaluate the performance of the unsupervised SSIN through experiments. Synthetic and real-world road datasets are used for the experiment. The synthetic dataset MICC-SRI [9] includes paired ground-truth static labels, but the real-world dataset Cityscapes [10] does not have ground truth for the static labels. A comparative experiment was conducted with the proposed SSIN and non-preprocessing methods, the DCGAN [20], an adversarial entropy network (AdvEnt) [27], and the preprocessed method, the segmentation adversarial network (SegAdv) [29], using MICC-SRI. The results for the Cityscapes images are also compared qualitatively and quantitatively.