esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
###### Abstract  
Atom-thick two-dimensional materials usually possess unique properties compared to their bulk counterparts. Their properties are significantly affected by defects, which could be uncontrollably introduced by irradiation. The effects of electromagnetic irradiation and particle irradiation on 2H MoS2 two-dimensional nanolayers are reviewed in this paper, covering heavy ions, protons, electrons, gamma rays, X-rays, ultraviolet light, terahertz, and infrared irradiation. Various defects in MoS2 layers were created by the defect engineering. Here we focus on their influence on the structural, electronic, catalytic, and magnetic performance of the 2D materials. Additionally, irradiation-induced doping is discussed and involved.  
Keywords:MoS2; 2D material; irradiation; review +
Footnote †: journal: Applied Sciences
## 4 Proposed Solution Approach
### List Algorithm  
List algorithms are greedy heuristics that are widely used to solve scheduling problems. The standard list algorithm iteratively constructs a schedule by considering the jobs in a listed order and assigning each one of them to the first machine that becomes idle [26]. Examples of list algorithms applied to and activity planning and resource allocation in a hospital are presented in [27].  
In the injection molding problem described in this paper, we propose a list scheduling algorithm to schedule jobs and assign them to the machines and dates considering tool compatibility. One of the advantages of using a list algorithm is to provide a simple neighborhood structure for metaheuristics, which can browse the set of solutions by permutations between two items in a list, as we will see in the next subsection. The details of the list algorithm is described by Algorithm 1. The algorithm takes as input the number of machines, the number of molds, as well as a sorted list of \(N\) jobs \(\left(\sigma_{i}\right)_{i\in N}\) which states the order in which they will be selected to be assigned to the machines. Of course, the jobs' durations and their associated molds are part of the input. The algorithm gives as output the assignation of the jobs to the machines.  
```
Data: List of jobs \(\left(\sigma_{i}\right)_{i\in N}\) forall\(\mathit{job}\)\(\sigma_{i}\)do  Order the machines according to their release date: \(R[i]\) \(j\): = 0 while Job \(\sigma_{i}\) not assigned \(ANDj<\mathit{ND}_{machines}\)do if job \(\sigma_{i}\) and machine\(R[j]\) compatible with the moldthen ifNeeded mold availablethen  Release date machine \(R[j]\): = Release date machine \(R[j]\) + processing time \(\mathit{Job}\)\(\sigma_{i}\) + setup time  Release date needed mold: = Release date machine \(R[j]\)  Assign Job \(\sigma_{i}\) to machine \(R[j]\) else  Find on which machine the needed mold is used: _MachineUsing_ If the mold currently used in _MachineUsing_ is the one needed by _Job_\(\sigma_{i}\)then  Release date _MachineUsing_: = Release date _MachineUsing_ + processing  else  Release date _MachineUsing_: = Release date _MachineUsing_ + processing  time of _Job_\(\sigma_{i}\) + setup time  Release date needed mold: = Release date machine _MachineUsing_ Assign Job \(\sigma_{i}\) to machine _MachineUsing_  Next machine: \(j(j+1)\)%\(\mathit{ND}_{machines}\)
```  
**Algorithm 1** List algorithm for the injection problem  
Figure 2 shows an example of the algorithm's assignment rule for a 3-machines instance. At iteration \(u\), the mold required by the next job \(i\) on the list \(Y\) is not being used on any machine. \(i\) is thus assigned to the less loaded machine with a setup time. Next, job \(j\) needs a mold that is currently used on machine \(M3\) that has the lowest release time. \(j\) will therefore be assigned to \(M3\) without setup. For job \(k\) however, the required mold is currently used on the most-loaded machine \(M1\) and therefore the job has to wait for its release time before being processed. \(k\) is assigned to \(M1\) without setup time.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 7 Conclusions and Future Work  
### Conclusions  
In this paper, we began by examining over 20,000 articles related to data quality from five renowned databases. Subsequently, we categorized these studies into six distinct categories based on the specific DQ problems they address. These categories include redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse data. We further delved into the corresponding data-cleaning strategies associated with each category.  
Second, we utilized a real-world traffic big data platform and dataset to systematically investigate data quality issues and their corresponding solutions within the realm of public bus transportation systems. Finally, we provided two representative examples: one demonstrating GPS noise filtering and the other addressing missing-value cleaning, both illustrating the effectiveness of our data quality improvement efforts.  
The experimental results demonstrate that our GPS noise-filtering solution achieved an accuracy rate of 97%, surpassing the baseline method. Furthermore, our multi-source data fusion approach attained a 100% correct repair rate for bus arrival and departure information in the spatial dimension. The error margin between the repaired timestamps and the actual timestamps was less than 1%, and the correlation coefficient R was also close to 1. These findings provide valuable insights and lessons for enhancing data governance and improving data quality within the public transportation industry.
## 4 Results and Discussions  
All tests were conducted using the freely accessible AES HD Dataset, which contains more than 50,000 coherent-state transmissions modulated by Gaussian noise and recorded under 10 different channel-loss profiles. The original quadrature pairs (X and P) were taken straight from the HDF5 archives of the dataset. They were then adjusted to have unit shot-noise variance and divided into blocks of \(1\times 10^{4}\) samples for each loss level. Extra noise (0.01 SNU) was introduced in accordance with the meta data of the dataset, with the modulation variance swinging from 0.5 to 5 SNU for each block, while the channel transmissivity was stepped up from 0.1 to 0.8. A reconciliation efficiency above 90% was achieved by passing the empirical measurement vectors that were generated through an information-reconciliation module created by the researcher, which was constructed with CVXOPT's interior-point solver based on convex optimization and privacy amplification.
## 1 Introduction
The phased array can be employed in the channel sounder, where the electronically driven beamforming function will enable the channel sounder to reduce the channel measurement time considerably. To shorten the channel measurement time, the replacement of the conventional static antenna on the Tx/Rx of the channel sounder with a phased array is gradually being proposed and the associated measurements are being presented. In [33] a 28.5 GHz channel sounder with 8 \(\times\) 8 phased arrays at the Tx and Rx is proposed. The channel sounder sweep time for a 3-D double-omnidirectional dual-polarized channel is just 1.3 ms. In [34] an mmWave channel sounder that covers 360 degrees and 60 degrees in azimuth and elevation, respectively, with a sweep time of 6.25 ms is presented. The sounder also can be applied to dynamic channel measurements. In [35] the high resolution phased array radar system detection range can reach up to 250 m with 28 GHz 5G communications. Due to highly integrated AiP system design, it has only few external interfaces, which makes the channel sounder easier to implement and more efficient to set up than a channel sounder system using other type of phased arrays. The small size, low-weight and low energy consumption of the AiP also make it easy to carry and move around. This will make it more favorable in measurement scenarios where channel sounder Tx/Rx requires motion. In this paper, we apply the AiP system to channel sounding and propose a 26.5-29.5 GHz channel sounder with the 8 \(\times\) 8 AiP and a 4 \(\times\) 4 AiP employed as the Rx and the Tx, respectively. In addition, the effectiveness of different calibration methods for large arrays, and the effect of measurement noise on accuracy of different calibration methods have been investigated based on the AiP system. Practical channel measurements are carried out with the AiP system and the results are analysed. The rest of the paper is structured as follows. First, the 8 \(\times\) 8 AiP experimental platform is presented and the AiP operating performance is verified in Section 2. Section 3 describes the applications of AiP in verification of the accuracy for calibration method and in channel sounding, the corresponding measurements are carried out and the results are analyzed. Finally, Section 4 concludes the paper.
# Transition Metal Coordination Compounds as Novel Materials for Dye-Sensitized Solar Cells
## 3 Conclusions  
The conversion of solar radiation to electricity, achieved with the use of solar cells, such as dye-sensitised solar cells (DSSCs) is one of the most abundant and effectively inexhaustible sources of energy. Despite this abundance, practical considerations, such as availability of space and materials used to produce solar cells, necessitate the development and use of increasingly efficient and inexpensive solar cell designs for harvesting the available solar energy.  
The key advantage of DSSCs in comparison with other solar cell types (e.g., bulk heterojunction, multijunction, crystalline silicon) is that of cost-efficiency, as even though the PCE values achieved for DSSCs may not be considered particularly high among solar cells, DSSCs are fabricated from materials that can be considered relatively inexpensive when compared with the extremely pure raw materials required for producing other types of solar cells.  
The initial works on DSSCs were strongly focused on heavy-metal-based materials, such as the coordination compounds of ruthenium, which are becoming increasingly expensive and are hazardous to both the environment and human health. Even though such materials remain in use, the number of annually reported DSSCs utilizing such materials is rapidly dwindling as alternative materials are discovered. Coordination compounds of copper should be mentioned with particular emphasis as one of the most promising alternatives to heavy-metal-based DSSC materials. The reported coordination compounds of copper can be used in DSSCs as dyes, as redox mediators in liquid electrolytes and as hole-transporting materials due to their available electronic energy levels. Although copper-based compounds appear most prominently in recent works, zinc, iron and cobalt coordination compounds have also recently been reported as promising for DSSC applications.  
Data curation, writing--original draft, investigation, formal analysis, K.P.; conceptualization, writing--original draft, review and editing, supervision, T.J. All authors have read and agreed to the published version of the manuscript.  
K.P. acknowledges the support of the Silesian University of Technology project no. 04/040/BKM21/0183.  
Not applicable.  
The authors declare no conflict of interest.
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 2 Hybrid FSO/RF Link System
### Free Space Optical Channel  
The received signal \(r\) with the optical wireless system can be modeled as [15]  
\[r=g_{1}P\eta h_{FSO}x+n_{1}, \tag{1}\]  
where \(P\), \(x\), and \(\eta\) represent the average transmitted optical power, the transmitted signal, and the receiver's optical-to-electrical conversion efficiency, respectively. \(h_{FSO}\) is the instantaneous optical channel intensity gain, \(n_{1}\) is the additive white Gaussian noise, \(g_{1}\) represents the specific attenuation resulting from the weather attenuation coefficient (\(\alpha_{1}\)), and their relationship is expressed as \(g_{1}=exp(-\alpha_{1}L)\), and \(L\) is the link distance. Assuming that \(h_{FSO}\) obeys the gamma-gamma distribution, the instantaneous SNR is expressed as:  
\[\gamma_{FSO}=\overline{\gamma}_{FSO}h_{FSO}^{2}, \tag{2}\]  
where \(\overline{\gamma}_{FSO}\) denotes the average SNR. The probability density function (PDF) of the FSO link due to the Gamma-Gamma fading model is [3]  
\[f_{\gamma_{FSO}}(\gamma_{FSO})=\frac{(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{ \Gamma(\alpha)\Gamma(\beta)\overline{\gamma}_{FSO}^{\frac{\alpha+\beta}{4}}} \gamma_{FSO}^{\frac{\alpha+\beta}{4}-1}K_{\alpha-\beta}\bigg{(}2\sqrt{\alpha \beta\sqrt{\frac{\gamma_{FSO}}{\overline{\gamma}_{FSO}}}}\bigg{)}, \tag{3}\]  
where \(K_{\alpha-\beta}(.)\) represents the modified Bessel function of the second kind of order \(\alpha-\beta\) and \(\Gamma(.)\) denotes the Gamma function. \(\beta\) and \(\alpha\) given by [6] indicate the effective number of large-scale and small-scale eddies of the scattering process, respectively.
## 4 Results
### Relationship with the United Nations' SDGs
The challenge of aligning long-term corporate strategies and planning contributes to SDG 9 (Industry, Innovation, and Infrastructure), as integrating ESG strategies into long-term corporate planning drives innovation and promotes sustainable development of infrastructure and the construction industry. The lack of standardized definitions and metrics for ESG is linked to SDG 12 (Responsible Consumption and Production), as resolving this challenge involves standardizing definitions and metrics to facilitate the assessment and monitoring of organizations' environmental and social performance, thereby encouraging more responsible practices. The lack of transparency, reliability, consistency, audit, control, and internal assurance of non-financial indicators is associated with SDG 16 (Peace, Justice, and Strong Institutions), emphasizing the importance of transparency and effective governance in assessing and disclosing non-financial indicators related to ESG. The reliability and consistency of these indicators promote a more ethical and responsible organizational culture. The lack of encouragement for innovation, incentives, investment, rewards, support, and government investment in ESG practices is a challenge that, when addressed, contributes to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 17 (Partnerships for the Goals), emphasizing the need for policies and incentives that promote investments and innovations in sustainable practices in construction. The lack of organized waste management is associated with SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), highlightingthe importance of proper waste management in promoting more sustainable cities and reducing the environmental impact of the construction industry. Corruption, political uncertainty, and political instability are related to SDG 16 (Peace, Justice, and Strong Institutions), emphasizing how mitigating corruption and ensuring political stability are essential for ensuring a fair and transparent regulatory environment for the implementation of ESG practices. These associations highlight that addressing these challenges can improve the implementation of ESG in construction and positively impact multiple Sustainable Development Goals, promoting effective transformation towards more sustainable, ethical, and inclusive practices in the sector.
# A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study
## 3 Material and Methods  
This section describes the evolutionary multi-objective approach for voice disorder detection. It starts by explaining the datasets and extracted features, how the problem is formulated and the methods used. Finally, the evaluation measures and the experimental setups are described.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Method** & **Results** & **Objective** \\ \hline [PERSON] et al. [15] & Hand-crafted features + CNN + SMOTE & Accuracy: 94.3\% & Multi-label voice disorder detection using SVD dataset \\ \hline [PERSON] et al. [16] & ML algorithms (DT, RF, SVM) + filter-based feature selection & Accuracy: 99.97\% (SVM) & Dysphonia detection using SVD and MEI datasets \\ \hline [PERSON] et al. [17] & Inductive CP + RF + Test-time augmentation & Reliable uncertainty estimates & Improve confidence in silent speech recognition \\ \hline [PERSON] et al. [18] & SL-RANN + DFT + MFCC & Accuracy: 94.35\% & Vocal cord disorder detection \\ \hline [PERSON] et al. [19] & Fast Learning Networks (FNN) + MFCC (SVD) & G-mean: 86.81\% & Voice disorder detection using FLN \\ \hline [PERSON] et al. [20] & Literature Review & – & Identified research gaps in deep learning for voice disorder detection \\ \hline [PERSON] et al. [21] & AOA + CNN features + KNN/SVM & Accuracy: 96.62\% & [PERSON]’s disease detection using spectrograms \\ \hline [PERSON] et al. [22] & Whale-Ant (WO-ANT) optimization & Accuracy: 90.29\% & PD detection using speech samples \\ \hline [PERSON] et al. [23] & NN + Multi-agent Salp Swarm (MASS) & F1-score: 0.995 & PD detection using optimized neural network \\ \hline [PERSON] et al. [25] & NSGA-II + wavelet features & Accuracy: 98.33\% & [PERSON]’s detection from spontaneous speech \\ \hline [PERSON] et al. [26] & NICGAR (multi-objective association-rule mining) & – & Diagnose PD using acoustic characteristics \\ \hline [PERSON] et al. [27] & Multi-objective Java + KNN & Accuracy: 92.26\% & PD detection with feature reduction \\ \hline [PERSON] et al. [28] & MOEA/D & – & Feature selection for medical diagnosis \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related works on voice disorder detection.
## 6 Results
### Load Forecast  
The main results presented for load forecasting show the efficiency in solving problems based on the temporal and spatial flow of users. The models were able to predict congestion in the Manhattan area with over 85% accuracy, taking into account seasonal and regional trends found in the database.  
In Table 3, it took the GRNN hours to complete the training, while the random forest took only minutes to perform the same work. Thanks to the efficiency of the random forest, it is possible to train the model in real time with new data and thus improve the prediction accuracy. We present the results of the prediction of the models for the two times defined for the case study to confirm that the random forest model selected to predict the UAV-BSs in the simulation is more efficient than the GRNN  
In Table 4 we evaluated the discrepancy between the two models using the metrics MAE and RMSE. The random forest model showed better values than the GRNN model, showing the efficiency of the model at certain time points in the case study. Table 5, shows that the differences between the models are smaller, as can be seen from the behavior of the prediction in Figure 6. When more regions are affected by the congestion, the prediction of the GRNN model is more efficient because the data have similar behavior. Some works, such as [60; 61; 62], point out the difficulty of modeling solutions that can solve problems that include both the spatial and temporal segments due to the large randomness, but models that can represent trends or seasonality in the database can accurately predict these types of problems. Therefore, we can say with the results that the random forest can be used to solve the problems in this work.  
Figure 6a,b show the prediction results for the case study times compared to the actual values. We found that the two models accurately predict loads of each macrocell, following the behavior of the real values. Figure 6a shows that the prediction behavior of the random forest model is closer to the real behavior, having better accuracy and relatively lower errors compared to the GRNN model. At 6 p.m., as shown in Figure 6b, the random forest model continued to show efficiency in predicting network utilization, but the GRNN model showed better results with an accuracy of 97% compared to its prediction at 7 a.m., with an accuracy of 84%, which could be related to the model achieving better results when the data start to show patterns in behavior. Despite the significant increase in the GRNN model's result, random forest was superior in predicting the two time points with better accuracy, proving that the model is efficient for nonlinear data.
###### Abstract  
To address the issue of blurred images generated during ice wind tunnel tests, we propose a high-resolution dense-connection GAN model, named Dense-HR-GAN. This issue is caused by attenuation due to scattering and absorption when light passes through cloud and fog droplets. Dense-HR-GAN is specifically designed for this environment. The model utilizes an atmospheric scattering model to dehaze images with a dense network structure for training. First, sub-pixel convolution is added to the network structure to remove image artifacts and generate high-resolution images. Secondly, we introduce instance normalization to eliminate the influence of batch size on the model and improve its generalization performance. Finally, PatchGAN is used in the discriminator to capture image details and local information, and then drive the generator to generate a clear and high-resolution dehazed image. Moreover, the model is jointly constrained by multiple loss functions during training to restore the texture information of the hazy image and reduce color distortion. Experimental results show that the proposed method can achieve the state-of-the-art performance on image dehazing the in icing wind tunnel environment.  
generative adversarial network; image dehazing; sub-pixel convolution; high resolution; meteorological environment