esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion  
### Data Description and Setup  
The air quality dataset from the Urban Air project of the Urban Computing team at Microsoft Research is used in this paper [39; 40; 41]. The dataset contains urban data, regional data, air monitoring station data, air quality data, meteorological data, and weather predict data for 43 cities, including Beijing, for the time from 1 May 2014 to 30 April 2015. All data are geographically aligned with latitude and longitude, with air quality data recorded every hour and meteorological data recorded every three hours.  
In this paper, air quality and meteorological data from this dataset are chosen as experimental datasets and preprocessed. The specific preprocessing is as follows:  
1. For air quality data, there are missing values in the air quality data for some time periods at each monitoring station, and at the same moment, certain gas concentration data may also be missing. To prevent information leakage and minimize its impact on the experimental results, missing values in the air quality data are filled with the gas concentration data from the preceding time point. For time periods with missing data, the data from the preceding time step of that period are used for supplementation, resulting in a complete set of air quality data comprising 8760 time steps.
2. Because meteorological data are recorded every three hours and cannot correspond with the gas concentration data at each moment, the meteorological data for each moment are applied to the data for the following two hours. This is restructured to include 8760 time steps of meteorological data.  
In this paper, the dataset of monitoring station 1001 is selected for ablation and comparison experiments of the model, and the generalization of the model is verified on the dataset of monitoring station 1002, monitoring station 1003, and monitoring station 1023. In this paper, sensor data samples were normalized using min-max normalization to a range of [0, 1]. The dataset was split into training, validation, and test sets with a ratio of 8:1:1.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 6 Open Research Issues and Challenges
### UAV Coverage  
The UAV coverage is another important issue. In UAWSNs, the coverage problem is categorized into three types: full/blanket coverage, target coverage, and path/barrier coverage [58]. The path coverage and target coverage are critical issues in UAWSNs. The existing routing protocols are based on full coverage in two-dimensional scenarios. In most of the existing routing protocols, a single UAV is deployed to collect data from the sensor nodes. Multi-UAV routing needs to be considered in future research for designing a routing protocol in UAWSNs.
## 2 Linearization System for Loudspeaker
### Compensation of Third-Order Harmonic Distortion  
Next, the relationship between the reduction ratio of the amount of computation and the compensation performance of the third-order harmonic distortion is examined. By applying singular value decomposition to the identified third-order Volterra filter in the linearization system shown in Figure 1 and applying the proposed method to remove paths with small singular values, the compensation performance of the third-order harmonic distortion is evaluated through simulations. For the evaluation of the third-order harmonic distortion, a sweep sine is used as the input signal, and the sampling frequency is 48,000 Hz and the sweep frequency is 240-5760 Hz. The third-order nonlinear distortion compensation was simulated using MATLAB.  
Figure 5 shows compensation results for third-order harmonic distortions. Here, the ratio of removed singular values \(r\) was set to 0, 60, 75, and 90%. Furthermore, the average compensation performance is shown in Table 3. Figure 5 shows that when the ratio of paths removed was 60%, the compensation performance for harmonic distortion up to about 6000 Hz was almost as good as when all paths were used. The harmonic distortion was also reduced for frequencies above 6000 Hz, although the compensation performance was slightly lower. Next, when the ratio of paths removed was 75% and 90%, the compensation performance for harmonic distortion up to about 4000 Hz was almost as good as when all paths were used. However, the 75% case shows little compensation for harmonic distortion above 4000 Hz, while the 90% case shows an increase in distortion above 4000 Hz. Table 3 also shows that the average amount of compensation decreased as the ratio of paths removed increased. Therefore, for the micro-speaker used in this experiment, it is reason  
\begin{table}
\begin{tabular}{r r r} \hline
**Removed Path Ratio \(r\) [\%]** & **\# of Multiplications** & **Average Compensation [dB]** \\ \hline original (no SVD) & 4,060,200 & 13.5 dB \\
0 \% & 2,050,500 & 13.5 dB \\
60 \% & 820,320 & 7.9 dB \\
75 \% & 512,775 & 3.5 dB \\
90 \% & 205,230 & –1.2 dB \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of number of multiplications for the ratio of removed singular values.  
able to limit the reduction of paths to about 60%. However, even with a 60% reduction, the amount of calculation was sufficiently reduced, thus demonstrating that the proposed method can reduce the amount of computation while maintaining the performance of harmonic distortion compensation.  
Figure 5: Simulation results for compensation of third-order harmonic distortion. (**a**) Reduction rate \(r=60\%\). (**b**) Reduction rate \(r=75\%\). (**c**) Reduction rate \(r=90\%\).
## 3 Related Works
### Deep Learning Approaches for Combinatorial Optimization  
Beyond heuristic and approximate methods, DL approaches have been explored for solving NP-hard problems, yielding promising results [21; 22; 23]. Most research focuses on CO problems over static graphs [24], where the challenge is capturing the combinatorial structure of the network through spatial features. However, incorporating the temporal dimension significantly increases complexity, making existing DL-based methods ineffective in providing reasonable solutions. This limitation arises because representation learning techniques designed for static graphs struggle to handle temporal dynamics properly [25].  
For this reason, in recent years, temporal graph embedding has gained significant attention. Various approaches have been developed, and they can be categorized into several distinct research paradigms.  
* Event-Based Temporal Graph Embedding: These methods treat node interactions as timestamped events to capture precise temporal dependencies, like in _DyRep_[26] and _TGAT_[27]. They provide high temporal resolution but are computationally demanding, especially for high-frequency events.
* Snapshot-Based Temporal Learning: Temporal graphs are divided into snapshots, with conventional GNNs applied to each one. Models like _EvolveGCN_[28] and _DCRNN_[29] work well for gradual changes but require significant memory and may miss intermediate dynamics.
* Self-Supervised Learning and Pre-Training: These methods use automatically generated pseudo-labels for training, reducing the need for labeled data. Examples include _TRENND_[30] and _T-GCL_[31]. They are adaptable to low-data scenarios but can be sensitive to negative sampling strategies.  
* Hybrid Models: Combining components like temporal attention and convolutions, hybrid models capture complex interactions. _TMac_[32] and the _Temporal Graph Collaborative Transformer_[33] are flexible but can suffer from high complexity and training time.
* Inductive Learning and Scalability: Focusing on scalability, models like _GraphSAGE_[34] generate embeddings for new nodes and scale well to large graphs. However, they may lose some global context and require extensive hyperparameter tuning.
* Application-Specific Models: Tailored for tasks like link prediction and anomaly detection, these models, such as _JODIE_[35] and _DySAT_[36], perform well for specific applications but have limited generalizability and require domain-specific adjustments.  
The approaches demonstrate the diversity and complexity of temporal graph embedding methods, each addressing specific challenges in dynamic network representation.
## 4 Conclusions  
We have demonstrated that artificial void defects located inside XLPE plates for power cable insulation can be detected using a CW-THz imaging system. Because the THz technique based on two photomixers serving as THz transmitter and receiver enables coherent detection, the change in phase that originates from the variation in optical path length of the THz waves caused by the void defects inside the XLPE plates was measured by the change in the amplitude of the transmitted THz waves. Two-dimensional images of the amplitudes of THz waves transmitted through single- or double-void defects with various diameters and gap widths show that internal defects of up to 0.5 mm size can be detected by the coherent CW-THz imaging system, which is a technique that can be used as an internal noncontact, nondestructive examination method. Limitations of the CW THz imaging system based on transmission geometry are that the samples containing the layers that strongly reflect or absorb the THz waves cannot be measured and the samples with the multilayered structures with uneven surfaces are hard to measure. However, such problems can be overcome by the applications of measurement systems combined with various geometries, such as reflection or scattering, and signal processing methods to remove signals due to other factors. Technical developments based on CW-THz waves exhibit great potential in the context of various applications in the construction, automobile, manufacturing, electronics, energy, and electrical power industries, which require internal noncontact, nondestructive testing and inspection.  
Figure 6: (**a**) Two-dimensional amplitude images of transmitted CW–THz waves for samples with various depths, D, starting from 1.0 mm and increasing up to 1.7 mm with a step size of 100 \(\upmu\)m. The diameter of the artificial void defects is 1.0 mm. The actual values of the depth may be slightly different from the designed parameters shown here. (**b**) Cross-sectional profiles along the \(x\)-axis at the center of the amplitude dips that originate from the defects shown in (**a**).  
**Author Contributions:** Conceptualization and methodology, J.W.L.; writing--original draft preparation, I.-S.L.; writing--review and editing, J.W.L.; data acquisition and analysis, I.-S.L. and J.W.L.; supervision, project administration, and funding acquisition, J.W.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Korea Electric Power Corporation (Grant Number: R18 XA06-79).  
**Acknowledgments:** We acknowledge and thank [PERSON] for the illustration in Figure 1a.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 2 Theoretical Foundations and Key Components  
This section provides the relevant theoretical foundations of our proposed defogging algorithm. Specifically, we detail two core components of our approach: an image enhancement method based on Retinex theory for solving the non-uniform illumination problem and enriching the detail information (Section 2.1) and an atmospheric scattering model combined with the dark channel prior (DCP) for estimating the key parameters related to haze, i.e., the transmittance map and atmospheric light intensity (Section 2.2).
###### Abstract  
Due to system failures and the limited overcurrent capability of semiconductor devices, overcurrent in modular multilevel converters (MMC) is a key factor affecting the safe and stable operation of offshore wind power MMC-HVDC (modular multilevel converter high-voltage direct current) transmission systems. This paper proposes a field-circuit coupling analysis method for overcurrent research in MMC valve. The method integrates the electric field characteristics of valves with the analysis of MMC-HVDC systems. Firstly, the development process and influencing factors of overcurrent in valves in offshore wind power MMC-HVDC systems are analyzed. A field-circuit coupling model and an electric field calculation model for MMC valves are established. The electric field characteristics and stray parameters of MMC valves are analyzed synchronously and the result are incorporated into the field-circuit coupling model. The nonlinear transient parameters of surge arresters are calculated, and the results are incorporated into the field-circuit coupling model. Finally, a reasonable overcurrent suppression strategy for offshore MMC-HVDC valves is proposed based on the proposed method. The effectiveness and practicality of the field-circuit coupling overcurrent analysis method are verified through comprehensive case studies conducted on the \(\pm\)500 kV offshore MMC-HVDC valve overcurrent calculation and suppression.  
modular multilevel converter (MMC); overcurrent; field-circuit coupling analysis; valve stray parameters Article
## 4 Case Study
### Results and Discussion
#### 4.2.2 Cognitive Emotion Recognition
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Average F1-Score** & **Data Requirement and Dataset** & **Protocol** \\ \hline LBP + SVM [31] & 90.82\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ CNN-SIFT + SVM [66] & 65.58\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ IL-VCG [67] & 86.05\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ IF-GAN [68] & 88.33\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ TransFER [69] & 88.25\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ DANet [70] & 87.83\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ cross-fusion DANet [71] & 91.68\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ IFSL + SVM [72] & 91.56\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ IFSL + KNN [72] & 86.82\% & Static image (mentioned above) & 5-fold CV (mentioned above) \\ PLBP + RF [73] & 90.30\% & Dynamic video (mentioned above) & 5-fold CV (mentioned above) \\ Geodesic distance + Sample & 86.25\% & Dynamic video (mentioned above) & 5-fold CV (mentioned above) \\ entropy + RF & \multirow{2}{*}{\ \begin{tabular}{} \end{tabular}  } & Dynamic video & \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } \\ Proposed method & 91.92\% & & \\ \cline{1-1} \cline{3-3} \cline{5-3}  
Through the entire modeling and testing process, some limitations of this method were identified:  
1. The generalization ability of this method still needs to be improved. We found that databases lacking sample diversity can lead to bias. For example, there are almost no Asian faces in the MMI Facial Expression Database. Therefore, we had to supplement the training set with images of East Asians before developing the cognitive emotion recognition application. Furthermore, we believe that the main reason for this bias may lie in the training of the AAM, where the emotional description points trained from European faces are not applicable to East Asians. By increasing the sample diversity or pre-training AAMs for different human races, the generalization ability of the proposed method could be improved, making it capable of handling various tasks. 2. The proposed feature descriptor based on \"geodesic distance + sample entropy\", at present, does not have the ability to update the extracted spatiotemporal feature vectors in real time based on each frame of the input video stream. Therefore, in real-time emotion recognition, the video must be truncated before feature extraction, resulting in imperfect real-time emotion recognition. 3. High numbers of feature dimensions and classifier parameters can affect the real-time performance of cognitive emotion recognition. Although a delay of 2 s is acceptable, we still want to further improve the model's real-time performance by appropriately reducing the number of feature description points or using more efficient classifiers.
## 2 Modeling of InGaP/GaAs Tandem Solar Cells
### Recombination Model  
Semiconductor imperfections, such as impurities and defects, introduce trapping levels that impact charge carrier lifetimes and recombination processes. Indeed, the Shockley-Read-Hall (SRH) model in semiconductors describes how electrons are trapped by defect states and subsequently recombine with holes in the valence band, leading to the decrease in the number of free charge carriers. This versatile model accounts for recombination in different regions of the device, taking into account factors such as trap density and energy levels, making it essential for optimizing semiconductor and photonic devices.  
This model takes into consideration the dynamics of electrons during their interactions with trapping sites acting as donors and acceptors within the device. To integrate this model, simply activate the SRH parameter in the MODELS instruction of the ATLAS code. The formulation of the SRH model is expressed as follows [35]:  
\[R_{SRH}=\frac{n_{p}-n_{ie}^{2}}{\tau_{p}\left[n+n_{ie}exp^{(\frac{EtRAP}{kT_{L} })}\right]+\tau_{n}\left[p+n_{ie}exp^{(\frac{EtRAP}{kT_{L}})}\right]} \tag{1}\]  
In the context of this model, the symbols \"\(n\)\" and \"\(p\)\" represent the concentrations of electrons and holes, respectively. The term \"\(n_{ie}\)\" denotes the \"intrinsic carrier concentration\", while \"\(Et_{RAP}\)\" signifies the gap between the trap energy level and the intrinsic Fermi level. Additionally, \"\(T_{L}\)\" corresponds to the lattice temperature measured in degrees Kelvin.  
Furthermore, \"\(\tau_{n}\)\" and \"\(\tau_{p}\)\" represent the lifetimes of electrons and holes, respectively. It is crucial to note that the values of \"\(\tau_{n}\)\" and \"\(\tau_{p}\)\" are contingent on the type of trap and the volume density of trapping \(T_{L}\) effects. It should be emphasized that users have the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Layer Properties** & **AlGaInP** & **GaInP** & **GaAs** & **AlGaAs** \\ \hline \(E_{g}\) (eV) & 2.3 & 1.9 & 1.42 & 1.8 \\ \(\alpha\) (Å) & 5.56 & 5.56 & 5.56 & 5.64 \\ \(\varepsilon_{r}\) & 11.7 & 11.6 & 11.0 & 11.0 \\ \(\chi\) (eV) & 4.2 & 4.16 & 4.07 & 4.1 \\ \(Nc\) (cm\({}^{-3}\)) & 1.2 \(\times\) 10\({}^{20}\) & 1.3 \(\times\) 10\({}^{20}\) & 4.7 \(\times\) 10\({}^{17}\) & 4.35 \(\times\) 10\({}^{17}\) \\ \(Nb\) (cm\({}^{-3}\)) & 1.28 \(\times\) 10\({}^{19}\) & 1.28 \(\times\) 10\({}^{19}\) & 7.0 \(\times\) 10\({}^{18}\) & 8.16 \(\times\) 10\({}^{18}\) \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of materials used in studies already carried out in [22; 23].  
flexibility to customize these electron and whole lifetime parameters, \"\(\tau_{n}\)\" and \"\(\tau_{p}\)\", using the MATERIAL instruction [12]. These parameters play a pivotal role in characterizing the dynamic behavior of the semiconductor, offering a nuanced understanding of carrier concentrations, intrinsic properties, and thermal effects within the material.
## 1 Introduction
### Background and Main Goals of Investigations  
As an improvement of the traditional partition method of NEE [111], the leaf relative uptake rate (LRU = \(\frac{\text{F}_{\text{COS}}\times\text{C}_{a,\text{CO}}}{\text{GPP}\times \text{C}_{a,\text{COS}}}\)) has been proposed for use in estimating GPP from the COS flux [51; 56; 59; 96; 112]. LRU has been estimated to be in the range of 1.5-4.0 for many species [51; 54; 56; 113]. On the other hand, the LRU is also an ecophysiologicalindicator used in estimating \(\text{C}_{\text{i,CO}_{2}}\)/\(\text{C}_{\text{a,CO}_{2}}\) and \(\text{g}_{\text{s}}\)[56; 90]. COS assimilation of different plant components [51], litters and soil responses vary distinctly during at diel and seasonal scales, leading to a fluctuation in the LRU value [90]. Further, the differential behavior of COS and \(\text{CO}_{2}\) in physiological and meteorological processes causes small fluctuations in the plant assimilation ratio of the two, which further affects the LRU value [51; 114; 115; 110]. Generally, LRU is controlled by \(\text{g}_{\text{s}}\), as well as concentrations of either COS or \(\text{CO}_{2}\) in the leaf boundary layer [54]. The environmental conditions, atmosphere and vegetation at certain stations may influence the LRU [51; 65; 88; 116]. In the species-dependent LRU environment response process [65; 101; 113; 114], light intensity, temperature and water limitation [115], as well as atmospheric conditions, such as the frictional wind speed, \(\text{C}_{\text{a,COS}}\) and \(\text{C}_{\text{a,CO}_{2}}\), are influential [51; 68]. In this study, we hypothesized that regarding diurnal, daily and seasonal variation, the above environmental conditions (i.e., light, temperature, water conditions, \(\text{C}_{\text{a,COS}}\) and \(\text{C}_{\text{a,CO}_{2}}\)) co-regulate \(\text{g}_{\text{s}}\), photosynthesis and COS assimilation, and this species-dependent relationship will affect the LRU (Figure 1). By providing perspectives on the development of COS-constrained GPP simulations and the key physiological processes within the LRU regulation mechanism, this study aimed to determine the relationship between environmental conditions and LRU variations, to describe how stomata regulate the relationship between \(\text{F}_{\text{C,COS}}\) and canopy \(\text{CO}_{2}\) flux (\(\text{F}_{\text{C,CO}_{2}}\)) and to constrain the accuracy of LRU estimates.
## 2 Methods and Design
### Overall Design of the Fundus Optical System  
In this section, we first clarify the function and desired purpose of each part. The lighting system is mainly to illuminate the fundus area [12], introduce suitable light into the fundus, and cause the fundus to produce reflected light to obtain an image with sufficient contrast and clarity when shooting. The imaging system is mainly used to gather the reflected light from the fundus and clearly image the actual appearance of the fundus on the sensing component of the camera. The imaging system and illumination system need to work together to ensure the clarity and accuracy of the fundus image.  
Second, we identify the inadequacy of traditional imaging. The optical architecture of the traditional fundus imaging system is complex, including a condenser, illumination diaphragm, relay lens group, hollow mirror, quantum objective [13], etc. The objective lens is divided into an eyepiece objective near the human eye and an imaging objective near the negative; the two sets of objectives make the total length of the system longer and the illumination system mostly adopts the coaxial illumination method, but, due to the common light path of the illumination system and the imaging system [14], it is difficult to suppress stray light caused by the cornea and quantum objective lens group of the human eye.  
On this basis, by establishing a model of fundus imaging field of view and non-coaxial annular array illumination, the design for the illumination and imaging of a miniaturized and large-field fundus system is proposed. The structure we designed is shown in Figure 2. The human eye is a very sophisticated and complex optical imaging system; to make the design more in line with the application of practical medicine, the analysis is carried out in combination with an eye model.