esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We consider the problem of joint power allocation (JPA) in a coordinated multi-point (CoMP) joint diversity transmission (JDT) network with a total coordination point power (TCPP) constraint, aimed at maximizing the ergodic cooperative capacity (ECC) in Rayleigh fading channels. In this paper, we first extend the JPA problem in the coordinated two-point (Co2P) JDT networkto the case of a non-unity TCPP constraint. Furthermore, we introduce more accurate log-quadratic approximated (LQA) expressions to obtain the coordinated transmission point (CTP) powers. Next, we extend our study to a coordinated three-point (Co3P) JDT network. Given the mean branch gain-to-noise ratios, we first obtain a log-linear approximated (LLA) expression for obtaining the optimum power of the third CTP (i.e., the worst quality-providing CTP). After obtaining the third-CTP power, we obtain the CTP powers of two better quality-providing CTPs by invoking the LLA CTP power expressions for Co2P JDT power allocation, under the remaining power given by the TCPP minus the third-CTP power. The numerical results demonstrate that the LQA and LLA CTP power expressions for Co2P JDT and the LLA CTP power expressions for Co3P JDT are very efficient in terms of the simplicity for JPA and CoMP set selection, as well as the resulting ECC.  
CoMP network; joint diversity transmission; joint power allocation; total coordination point power constraint; ergodic cooperative capacity +
Footnote †: journal: Accepted: 14 January 2019; Published: 16 January 2019
## 1 Introduction
Large-scale tests are considered to yield more reliable results compared to small-scale tests, because they better replicate the physical structure of a reinforced soil mass, while allowing for a better distribution of stresses and strains on the geosynthetic, potentially due to a scale effect [6]. On the other hand, the reduced dimensions of small-scale test boxes may interfere with the soil-reinforcement interaction, thus potentially casting doubt on the reliability of the results. However, as reported by [PERSON] [39], adopting measures to minimize boundary effects of the small-scale equipment has led to obtaining pullout shear resistance and interface coefficients close to those from monotonic large-scale pullout tests when fine soils are used. Nevertheless, limited studies have previously compared the pullout behavior of geosynthetics derived from small- and large-scale equipment and further research is still required for a better understanding of the apparatus scale effect under different experimental conditions. In such earlier studies, geosynthetic specimens with distinct length/width ratios have generally been used, which may have jeopardized the comparison of results. Additionally, to the best of the authors' knowledge, no previous study has evaluated the influence of apparatus scale on the geogrid pullout behavior under both monotonic and cyclic/post-cyclic loading conditions. Considering that the conventional pullout tests using large-scale devices have several inconveniences, such as the high cost, long execution time and complexity of the assembly, it is important to assess the feasibility of using small-scale equipment for geosynthetic pullout testing. Given this, a series of pullout tests have been carried out to evaluate the influence of apparatus scale on the pullout response of a geogrid subjected to different confining stresses and varying loading conditions (i.e., monotonic, cyclic and post-cyclic). The use of small-scale equipment to estimate soil-geosynthetic interface strength parameters would represent significant benefits, including the easier and faster test assembly, as well as the possibility of obtaining a larger amount of data using less resources.
# Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm
## 3 DCFECF Model  
### Model Prediction Process and Framework  
Compared with the DCCF model, the DCFECF model adds the introduction of the time factor. Its prediction process is shown in Figure 1. Firstly, the initial graph structure is exported from the historical interaction record, including the bipartite interactive graph between users and items, and the initialization vector expression of nodes in the graph. Secondly, local and global convolutional channels are used to express the interaction relationship between higher-order nodes. Then, in the form of vector inner product, interactions that were not observed in historical interaction records are predicted. Finally, the time factor is added to the interactive prediction to further improve the prediction results.  
Based on the above analysis, the structure of the DCFECF model is shown in Figure 2, which is mainly divided into three parts: the local convolutional channel, the global convolutional channel, and the interactive prediction module.  
Where the local convolutional channel aggregates the feature information in the local neighborhood structure, it uses a state transition matrix to determine the high-order neighborhood range of nodes. By using the self-attention mechanism and the weighted average method, the threshold of the neighborhood transition probability of each order is obtained. Then, a single-layer graph convolutional network is used to aggregate local information between user nodes and item nodes, thereby suppres  
Figure 1: The prediction process of DCFECF.  
Figure 2: The model framework of DCFECF.  
problem. In the global convolution channel, the fuzzy C-means clustering algorithm optimized by K-means++ clustering is used for clustering, and a global interaction graph is constructed. According to the feature similarity between nodes, neighbor nodes with similar features are selected for user and item nodes from the global perspective to supplement global information and overcome the problem that interaction information is limited to the local neighborhood. In the interaction prediction stage, local and global information are integrated, and the inner product of the vector representations of user nodes and item nodes is used to predict unknown interactions. Finally, the time factor is added to the interactive prediction to further improve the prediction results.
## 5 Performance Analysis
### Cluster Lifetime  
The clustering algorithm's performance is assessed using some very important performance measures, such as network longevity as measured by the First Node Dead (FND) time, Half Node Dead (HND) time, Last Node Dead (LND) time, and residual energy. In the context of a cluster, a life cycle is a period that elapses between the cluster's creation and its eventual demise which is known as FND. The node with the best acceptable value is selected as CH during the algorithm run and assumes responsibility for managing a given cluster. There are two portions to the measurement parameter that we may use to track the life cycle of sensor nodes: the stable period and the unstable period. However, in our situation, we primarily utilize what is known as the unstable period between the first and last node deaths. This is generally used for environmental monitoring. If a large number of SNs dies, the information gathered will not make it to its intended destination because of the vast spread of SNs. There is uncertainty about the period based on the time from the FND to the Last Death Node (LND). In the case of many nodes dying, some of the gathered data cannot be used to adequately analyze the surrounding circumstances. While evaluating our suggested technique, this research analyzes network life using FND and reveals evident benefits over the other protocols. Compared with CUCA, CPCP, CACP, and LEACH, our proposed algorithm has better HND 11.72%, 4.37%, 13.83 as compared with the above algorithms. Furthermore, the experimentation shows that our proposed EEWBP algorithm increases LND by 81.48% compared with LEACH; CUCA increased by 56.84% and 64.56% over the CPCP algorithm. Figure 7 illustrates a comparison of residual network energy at FND, HND, and LND with EEWBP at the three different network timings [21; 50; 51; 52; 53; 54].  
Figure 7: Rounds vs. lifetime.
# Critical Review of Techniques for Food Emulsion Characterization
## 4 Emulsion Fractionation and Characterization Methods  
All the methods described above allow for detailed emulsion characterization. However, they are unable to separate emulsions into fractions of droplets differing in size or other physicochemical properties, such as charge, density, mass, surface properties, etc. Fractionating emulsions is extremely useful from both industrial and scientific standpoints as it allows for a more detailed downstream analysis of each droplet fraction. It can be applied in quality control to improve monodispersity and ensure consistent product quality, as well as during formulation development, or optimization of emulsion manufacturing, processing, and storage conditions [220]. The key methods that enable both separation and characterization of emulsions are field-flow fractionation (FFF) [221,222] and capillary hydrodynamic fractionation (CHDF) [223].
## 4 Conclusions and Discussion  
This paper evaluated the explanatory power of TIs by examining whether the inclusion of TI features could boost forecasting accuracies of DAM prices. Overall, we have demonstrated that TIs can capture the residual impacts from traders' behavioural biases; resulting in statistically significant reductions in forecast errors with ML models. Our case study has identified four TIs well suited to forecasting Belgian DAM prices: the EMA, the %B, the MOM, and the ROC. More specifically, our results advise using %B (\(n=58\)) and EMA (\(s=2\)) with linear models, MOM (\(n=58\)) with ensemble models, EMA (\(s=22\)) with NNs, and ROC (\(n=49\)) with CNNs. While we find evidence that TI performance is model dependent, the ROC and the EMA succeeded in reducing the RMSE and MAE of 90% and 80% of the ML models respectively.  
As the first to use TI features for DAM forecasting, we hope that this paper spurs others to examine the potentials of technical feature engineering. While our research examined the explanatory power of some of the simplest and widely used TIs in finance, further research could examine the predictive power of more complex TIs. Moreover, given the unique qualities of the DAM, future research may succeed in engineering new TIs, which incorporate DAM volumes, order book data, etc. to optimally capture short-term energy traders' behavioural biases. Faced with growing DAM volatility, we believe that the inclusion of tailored TIs alongside fundamental features shall become increasingly important, even if forecasting models grow in depth.  
All authors contributed their valuable insights to shape and improve this article. Specifically, [PERSON] and [PERSON] conceptualised and defined the methodology, developed the software, implemented the formal analysis, visualised the data and results, validated the methodology, and wrote the original draft. [PERSON] and [PERSON] reviewed and edited the original draft and supervised the research. [PERSON] managed the project administration. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scholt Energy Control, The Netherlands.  
We thank Scholt for their support, and in particular to [PERSON] for his invaluable insights.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AB & AdaBoost \\ BBAND(s) & Bollinger Band(s) \\ CNN & Neural Network with CONV \\ CONV & Convolutional Layer \\ COPP & Coppock Curve \\ DAM & Day-ahead Market \\ DM & Diebold-Mariano (test statistic) \\ EMA & Exponential Moving Average \\ FCL & Fully Connected Layer \\ GB & Gradient Boosting \\ HR & Huber Regression \\ LR & Linear Regression \\ MACD & Moving Average Convergence Divergence \\ MAE & Mean Absolute Error \\ ML & Machine Learning \\ MOM & Momentum \\ \end{tabular}
# Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification
## 4 Results and Discussion  
### Signal Purity Identification  
Before the classification measurements with micro particles and cells, in order to clarify if there were some micro scale objects that can induce unwanted SMI bursts thus inducing confusion with the meaningful ones from the cells, the PBS sheath liquid and the DMEM culture medium were characterized using our system over 2 h. As expected, none of the liquids generated extra bursts, and these results validate the particle/cell-induced signal purity in the measurements.
# Decorrelation-Based Deep Learning for Bias Mitigation
## 3 Materials and Methods
### Distance Correlation  
Distance correlation measures not only linear, but also non-linear dependencies between two random variables \(B_{1,\dots,p}\) and \(F_{1,\dots,p}\), unlike the Pearson correlation coefficient [21] which measures only linear dependencies. In our proposed approach, \(B\) is the dataset bias variable whereas \(F\) is features extracted from ANN and CNN and \(p\) is the total number of samples. The distance correlation is the square root of:  
\[\mathcal{DC}^{2}(B,F)=\begin{cases}\frac{V^{2}(B,F)}{\sqrt{V^{2}(B,B)V^{2}(F,F )}}&\text{if }\mathcal{V}^{2}(B,B)\mathcal{V}^{2}(F,F)>0\\ 0&\text{else }0\end{cases} \tag{1}\]  
where \(DC^{2}(B,F)\) varies between 0 and 1 and indicates that variables \(B\) and \(F\) have dependencies, and \(DC(B,F)=0\) only when the variables \(B\) and \(F\) are independent. \(v^{2}(B,F)\) is the distance covariance between a pair of variables and \(v^{2}(B,B)\), \(v^{2}(F,F)\) is the distance variance as defined in [22]. The distance covariance is normalized by the distance variances.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 3 Results
### DCCA Coefficients on Each Scale and Their Relationship
#### 3.2.2 AQI Correlation with Humidity  
As shown by the green lines in Figure 3, almost all of the inter-relationship curves between the AQI and the humidity were negative, indicating that there was a negative correlation between the AQI and the humidity. Furthermore, the inter-relationship curves generally reflected a decreasing and then increasing trend with the change in window size, and the curves reflected a decreasing and then increasing trend in the interval \(n<1000\). The intercorrelation number between the AQI and the humidity remained above \(-0.8\) after the window size \(n=5100\), indicating a strong negative correlation.
# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 4 Results and Analysis
### Changes of Coastline in the Regions Around Six Ice Shelves
When the ice shelf advanced into the ocean, oceanic factors (increased bottom melting and associated ice shelf thinning and recession) contributed to the structural weakness of the shelf, and consequently lead to the ice shelf breakup [39]. Another such example is MGT. From the result of the coastline in 2005, the shelf protruded into the ocean for more than 100 km, and in 2010 the unstable ice tongue broke into the ocean with the collision of the B-09 iceberg. Among ice shelves with area expansion from 2005 to 2017, the area change, as well as the rate of the change, of the RIS and the RSIS were much more pronounced. Apart from these ice shelves with larger area change, there are also some other smaller ice shelves that underwent area and shape changes from 2005 to 2017. The Shackleton Ice Shelf, for example, enlarged by 1238 km\({}^{2}\) from 2005 to 2010. The area of the Cook Ice Shelf decreased by 498 km\({}^{2}\) from 2010 to 2017, while the area of the Lazarev Ice Shelf increased by 437 km\({}^{2}\) over the same time.
## 4 Simulation Study and Results
### Results of the Simulation Study
This substudy shows the importance of the scaling factor \(k_{flex}\) for real planning in industrial applications since the corresponding number of workstations must also be provided for the respective organisational principles, depending on the assembly times. The plausibility of the scaling with an allowance of 75% was confirmed by the real times of the practice partners. **Substudy 3:** After selecting the hybrid value \(PUD_{hy}\) and the scaling factor \(k_{flex}\), the hybrid organisation structure was compared with the dedicated line and the dedicated job shop organisation structure. The performance of these organisation structures was examined under the assumption of changing production programmes. Figure 6 shows the results of the various simulation runs with different production programmes. With an increasing \(C_{pp}\) value, the complexity of the production programme increases. The target variables of flow rate and throughput time were evaluated; the throughput time is shown in Figure 6. It can be seen that the line achieves the lowest throughput time, whereas the hybrid organisation structure converges the line's throughput time when complexity is high. In this context, it should be noted that the hybrid organisation structure achieves the same performance with lower capacities because it uses only eight workstations. When considering the line, it can be seen that at a certain level of complexity, the throughput time increases due to an increasing cycle time; correspondingly, the flow rate decreases with increasing complexity. By contrast, the flow rate increases for a hybrid organisation structure. Accordingly, the more complex the production programme in a line, the lower the degree of utilisation of the individual workstations. The hybrid organisation structure and the division of assembly processes into line and flex zone according to their frequency increase the degree of utilisation of the workstations and can even save capacity. In summary, it should be said that the selection of a suitable organisation structure depends largely on the production programme to be produced. The complexity of a production program must be evaluated, and this must serve as a basis for decision-making when choosing a pure or hybrid organisation structure.
# Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System
## 4 Simulation Results  
In order to demonstrate the performance of the proposed algorithm, we compare it with various conventional methods, including those of Bertalmio [16], Telea [17], Criminisi [18], Liu [19], GiliSoft Stamp Remover [33], and Theinpaint [34] from the subjective and objective viewpoints. These methods were explained in Section 1.  
We captured picture sets using the built-in camera of a Samsung Galaxy 9 smartphone. There were more than 100 pictures in each set. The pictures within a single set were stitched using APIS to create the stereoscopic VR images. Figure 8 shows the left or right VR images of the stereoscopic VR images having holes of various sizes and locations. The VR images including the holes were used as test sets to evaluate the performances of the proposed algorithm and various conventional methods. In Figure 8a-f, the holes have been generated through the stitching algorithm owing to the reason described in Section 2. Those pictures are used only for subjective evaluations because there is no reference VR image without any hole. When we created Figure 8g,h, the stitched VR images did not have any holes. Therefore, we have made an artificial hole in each VR image. Consequently, Figure 8g,h can be used for objective evaluation, because the difference between the reference image (without any hole) and the concealed images (the results of various algorithms) can be calculated numerically.  
Figure 8: Equirectangular projection images generated from test sets to evaluate different methods. (**a**) Laboratory, (**b**) Brick Road, (**c**) Alleyway, (**d**) Tree, (**e**) Bench, (**f**) Street, (**g**) Hallway, and (**h**) Mozart Hall.
## 4 Simulation Experiment and Results  
In Section 4, we will evaluate the performance of the PH-RPM proposed in this paper through simulation experiments, which will be modeled using a real production environment on the eighth deck of a large cruise ship. We use some well-known path planning algorithms to compare with the PH-RPM of this paper in two scenarios, respectively.
###### Abstract  
This study investigates the influence of self-assembled monolayer treatment of gate insulators on the electrical characteristics of bottom-gate/bottom-contact organic field-effect transistors (OFETs) with short channel lengths of 5 \(\upmu\)m to 30 nm. The treatment of 3-chloropropyltrichlorosilane (CPTS) with large dipoles produces a high built-in electric field perpendicular to the SiO\({}_{2}\) gate insulator surface, which results in a threshold voltage shift and enhanced hole injection compared to the treatment of phenethyltrichlorosilane (PETS) with small dipoles. Pronounced parabolic drain current-voltage (\(I_{D}\)-\(V_{D}\)) characteristics due to a space-charge limited current are observed in short-channel OFETs based on poly(3-hexylthiophene) with CPTS-treated gate insulators. CPTS treatment on short-channel OFETs based on poly(9,9-dioctylfluorene-co-bithiophene) (F8T2) suppresses the nonlinear \(I_{D}\) increase in the low \(V_{D}\) region caused by the voltage drop at the Au/F8T2 contact. The influence of the increase in the net source-drain electric field associated with the reduced voltage drops on the channel-length dependence of the field-effect mobility of short-channel F8T2 FETs is also discussed.  
organic field-effect transistor; short-channel effect; self-assembled monolayer; dipole electric field +
Footnote †: journal: Applied Physics Letters
## 2 Manufacturing and Measurement Techniques
### Quantitative Analysis  
Profile surface texture measurements (contact profilometer--2 \(\upmu\)m diamond tip radius and 60\({}^{\circ}\) tip angle) (SJ-400, Mitutoyo, Japan), and areal surface texture measurements (laser scanning--2 \(\upmu\)m spot diameter) (Talyscan 150, Taylor-Hobson, UK) were used to quantify the surfaces under study. All the major industrial and academic questions (refer to Section 1) were addressed by examining the roughness profile at various \(\lambda_{\text{c}}\) and \(\text{l}_{\text{n}}\)-s. The outcomes from each case are elaborately discussed. The waviness of AM components is often ignored during surface quantification. Nevertheless, in this study, the resulting waviness and roughness topography at each filtering condition are presented, and the method proposed to understand the resulting topography is explained.