esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures and Analytics
#### 3.1.3 An Stripping Section  
In this section, the An(III)/Ln(III) separation is conducted. In the reference flowsheet [27], mixing of the loaded solvent stream with a fresh solvent stream with the same flow rate is foreseen. Therefore, the collected organic phase from the previous single centrifugal contactor test was mixed with the same volume of fresh ALSEP solvent and the mixture was analyzed (Table S3). The aqueous phase used in the An stripping section consisted of 0.015 mol L\({}^{-1}\) DTPA + 0.2 mol L\({}^{-1}\) ammonium citrate at pH 2.0. Figure 4 shows the Am, Cm, and Eu distribution ratios as a function of the experimental run time and equilibrium values in the An stripping section single centrifugal contactor test. Distribution ratios and mass balances calculated from the transient and equilibrium samples, and stage efficiencies for all metal ions, are shown in Table S3. The stage efficiencies were calculated by dividing the equilibrium distribution ratio by the mean value of the transient samples distribution ratios at 115, 125, and 140 min. The results show that Am and Cm were stripped into the aqueous phase with steady-state distribution ratios of ca. 0.3 and good separation from the Ln's (e.g., steady-state SF\({}_{\text{La/Am}}\) = 10, SF\({}_{\text{Nd/Am}}\) = 11, and the others were higher). The stage efficiency for An(III) was ca. 35-40%, whereas for Ln(III) it was ca. 55-75%. The concentration of several fission products was already quite low in the used organic feed. Therefore, it was not possible to properly quantify their concentrations in the effluents and calculate the distribution ratios, mass balances, and stage efficiencies, as indicated in Table S3. The Zr concentrations in the aqueous phases were below the detection limit, but they were measurable in the organic effluent. Hence, the Zr distribution ratios are quite high in that section, resulting in a good An(III)/Zr separation. The Mo stage efficiency was again very low (5%) but the distribution ratios did not show a clear trend, probably due to the low feed concentration. Nevertheless, it is anticipated that Mo would not be stripped to a large extend in that section. Ruthenium, Rb, Fe, and Y distribution ratios were \(>\)1; these elements would therefore not be stripped. Strontium distribution ratios were difficult to be measured due to the low Sr concentration in the feed. The distribution ratios decreased during the test to values below 1. Therefore, Sr could partially be stripped in this section, but it is believed that during a full test, Sr (as well as Ru and Rb) will already be scrubbed quantitatively in earlier sections.  
Figure 4: Am, Cm, and Eu distribution ratios as a function of the experimental run time (data points) and equilibrium values (horizontal lines) in the An stripping section single centrifugal contactor test. “g”, “a”, and “1” indicate values measured by gamma and alpha spectrometry as well as ICP-MS, respectively.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 1 Introduction
### Sensed Data Collection with OppNets
Their spatial analysis suggest that knowledge of daily travel patterns of users may contribute in designing more effective data collection protocols. [PERSON] et al. [25] identify significant patterns in the mobility traces of smartphone users that can be exploited to realize protocols for opportunistically conveying delay-tolerant data from sensors to their corresponding servers. The authors also identify important characteristics of user mobility--such as strong spatial and temporal localities--that need to be considered when designing related protocols and algorithms. [PERSON] et al. [20] implement an opportunistic network testbed for a large-scale Smart City platform (i.e., platforms used to analyze data and extract valuable informationfor city management and control), in order to evaluate solutions proposed for sensed delay-tolerant data collection with mobile phones. The authors propose a middleware that collects data from sensing infrastructure and sends them to the Smart City platform through multi-hop opportunistic routing. Regarding extending the lifetime of sensor nodes through duty-cycling, [PERSON] et al. (2016) propose a sensor node-initiated probing mechanism by exploiting rush hours, during which encounters with portable handheld user devices occur more frequently.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 4 Graph-Based RA in Cellular Homogeneous and Het-Nets
### RA Methods with Belief Propagation  
The RA problem with a focus on ICI coordination is considered in [28] for a Het-Net that includes a fixed number of UEs and channels and a variable number of BSs. A factor graph is formed, with its variable vertices illustrating the optimization variables (each optimized by a separate BS), while the factor vertices are utility sub-functions (which together form the RA optimization problem), and their number is equal to \(N_{BS}+N_{UE}\). Edges between the factor and variable vertices exist only if there is an opportunity for connection between the BS and UE, which are related to a particular factor and variable vertices. Then, the utility sub-functions are individually approximated (ICI is modeled as a Gaussian function) through belief propagation (BP) among the neighboring factor vertices. Due to the Gaussian approximation, each vertex needs to only propagate the mean and variance of the utility sub-function, which increases the computational efficiency. This method adapts the small cells' transmission power according to their density, to reduce the ICI. Q-learning is used in [29] to implement computationally efficient RA for a millimeter-wave (mmWave) Het-Net that encompasses a single macro BS and a fixed number of UEs, small cells, and channels. The time-averaged risk-averse rate \(AR\) (dependent on the throughput) of a particular BS-UE link is used to define the utility function. At each instance, a coordination graph between neighboring BSs and UEs is constructed to reflect the associations of UEs with BSs, as well as the interference between BSs. Then, through BP of the Q-function (dependent on the \(AR\)) among neighboring vertices, the RA is performed by the BSs in a distributed manner. If a node's \(AR\) is greater than a threshold, then its state changes and it obtains a new association and RA; otherwise, it is not included in the estimation process. Further computational speed is achieved by taking into account the past states of the Q-function. The simulations are performed with the use of real-world measurements, showing quick convergence of the learning algorithm and a linear dependency of the throughput with the BS transmission power.
# Wavelength Selection for Satellite Quantum Key Distribution
## 1 Introduction
The performance of each wavelength region is compared while operating through adverse weather conditions, such as haze, fog and rain. Section 6 concludes this paper by summarising the results and recommending future directions.
## 5 ANN-Based Prediction Models
### Transition Zone
#### 5.2.2 \(\varepsilon_{c1}\)  
The most accurate model for predicting \(\varepsilon_{c1}\) by variation of input data was obtained by using 30 neurons in one hidden layer and the Levenberg-Marquardt algorithm with the tansig transfer function. An in-depth investigation on the correlation between input variables was performed similar to the other prediction models, and the obtained result was similar to that of \(\varepsilon_{cu}\) and \(\varepsilon_{h,rup}\). The obtained results revealed that the Pearson's correlation for \(f^{\prime}_{\alpha}\) and \(\varepsilon_{\alpha}\) had the highest correlation coefficient, and only one of these input variables should be used to avoid multicollinearity, as illustrated in Table 8. However, the maximum accuracy was obtained when both \(f^{\prime}_{\alpha}\) and \(\varepsilon_{\alpha}\) were used. Equation (9) presents the input parameters, which offered the highest accuracy for \(\varepsilon_{c1}\) prediction.  
\[\varepsilon_{c1}=f\bigg{(}f_{\alpha},\ \frac{K_{l}}{f_{\alpha}},\ K_{l},\ E_{f},\ f _{f},\ f_{f},\ \varepsilon_{\alpha},\ D\bigg{)}, \tag{9}\]  
As displayed in Table 6, using two hidden layers with 25 and 20 neurons developed the most accurate predictions for \(\varepsilon_{c1}\). Furthermore, analysis of the transfer function showed that tansig offered the most accurate \(\varepsilon_{c1}\). The performance of the developed model for \(\varepsilon_{c1}\) is shown in Figure 8. Based on Figure 8a, similar to other key reference points, the predicted values of \(\varepsilon_{c1}\) were in good consistency with the recorded values in the experiments. Based on Figure 8b, the imposed criteria on MSE in the analysis was respected early in the calculation. Finally, as per Figure 8c, AAE varied significantly by the variation of test dataset of \(\varepsilon_{c1}\).  
Figure 8: Performance of a developed model for axial strain at a transition point (\(\varepsilon_{c1}\)): (**a**) Comparison of model prediction and experimental values, (**b**) performance accuracy, and (**c**) cross-validation analysis.
# Aerodynamic Enhancement of Vertical-Axis Wind Turbines Using Plain and Serrated Gurney Flaps
## 2 Simulation Setup
### Isolated Airfoil
#### 2.1.2 Mesh Topology  
The computational domain is composed of three parts. A circular zone with a diameter of 3c is created around the blade in order to facilitate local grid refinement near the blade [32] and this part is meshed by prismatic elements. A boundary layer of 33 layers is laid perpendicularly over the airfoil surface. The second part is a C-type region outside the circular zone and connects with the third part of a rectangular region. Outside the boundary layer, unstructured polyhedron grids are adopted to provide adaptability and efficient solutions [20]. The mesh topology is shown in Figure 3 with enlarged views of the leading and trailing edges. Computation starts from a coarse grid with the first grid height of \(7\times 10^{-6}\) m. The grid is successively refined with a normal extending rate of 1.2 to generate a medium and a fine grid (see Table 1) which are used to study the grid-dependence of the results.
## 8 Conclusions  
In this paper, a distributed cooperative dispatch method for DN with DHN and BESS considering flexible regulation capability is proposed. According to the simulation results, the following conclusions are drawn:  
(1) The proposed distributed cooperative dispatch method does not fall into the local optimum during the solution process and differs from the centralized dispatch method by only 0.52% in the total system cost, which indicates that it can achieve the same solution accuracy as the centralized dispatch method.  
(2) Compared with the independent dispatch method, the proposed distributed cooperative dispatch method realizes the efficient synergy between DN and the two types of flexible resources, DHN and BESS, which can effectively improve the absorption capacity of DN for PV and WT and then reduce the operation cost of the system by 35.6%.  
(3) The proposed method, considering the heat storage capacity of DHN, can provide additional regulation capacity for DN by flexibly adjusting the water supply temperature of DHN, which in turn improves the absorption capacity of DN for PV and WT and then reduces the system operation cost.  
Figure 9: Trend in total system cost and curtailed energy for PV and WT of the four cases.  
In the future, the authors will further consider flexible resources such as electric vehicles in distributed cooperative dispatch to provide more regulation capability for the distribution network and further investigate how to take into account the impact of uncertainty of renewable energy in the distribution network in cooperative dispatch. In addition, the authors will apply the proposed method to a specific case in Wuxi City in the future to further test the performance of the proposed methods in practical applications.  
Conceptualization, X.F., S.Y. and Z.L.; data curation, C.C. and C.N.; formal analysis, Q.H. and L.W.; funding acquisition, X.F., Q.H. and L.W.; investigation, X.F. and S.Y.; methodology, X.F., S.Y. and Q.H.; project administration, X.F. and Z.L.; resources, Q.H. and Z.L.; software, S.Y., L.W. and C.N.; supervision, Z.L.; validation, S.Y., C.C. and Z.L.; visualization, C.C. and C.N.; writing--original draft, X.F, S.Y. and Q.H.; writing--review and editing, S.Y., C.C. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Science and Technology Project of State Grid Jiangsu Electric Power Co., Ltd. (No. J2023083).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author(s).  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company State Grid Wuxi Power Supply Company of Jiangsu Electric Power Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Typical Weight Models
### Exponential Model  
In the model suggested by [6], the weights are given as exponential functions of the SNR. Equation (2) shows the typical implementation of an exponential model.  
\[\sigma_{i}^{2}=a+b\cdot e^{k(SNR_{min}-SNR_{i})} \tag{2}\]  
In Equation (2), \(SNR_{min}\) and \(SNR_{i}\) represent the preset minimum possible SNR and SNR measurements at the \(i\)-th epoch, respectively. Parameters \(a\), \(b\), and \(k\) should be empirically determined according to the receiver hardware and signal-reception environments. The positioning sensitivities of these three parameters were examined, and it was found that changing them did not have a significant effect on the positioning accuracy. Thus, we used the same values as those suggested by [6], for example, \(a=0\), \(b=1\), \(k=0.3\).
# From 5G to 6G--Challenges, Technologies, and Applications
## 4 Current Research  
We devote this section of the paper to presenting up-to-date information about ongoing trials to investigate future networks for B5G and/or 6G by, mainly, research groups and organizations worldwide. We give our findings in two parts: the first looks at the published works that include an entire or partial discussion of these future networks, and the latter part sheds light on the efforts committed to bringing 6G networks to life by worldwide standards organizations, universities, research centers, countries, and more.
## 5 Field Testing
### Integrated Path Tracking Controller Testing  
In order to investigate the performance of the NMPC path tracking controller, the path tracking experiment of the articulated dump truck on a paved road is performed. As shown in Figure 15, the path tracking control system can be divided into three major subsystems: the vehicle (including sensors and actuators), the upper-level controller containing an extended Kalman filter (EKF) fusion positioning module and an NMPC path tracking controller, and the lower-level controller including load calculation module, the driving controller, and steering controller.  
During the experiment, the vehicle's GPS and front IMU provide the vehicle's latitude, longitude, and heading in the geodetic coordinate system to the reference path module in the upper-level controller. The module outputs the local reference path for the NMPC path tracking controller. Meanwhile, the EKF module fuses the GPS and IMU data to calculate and output the vehicle status (the coordinates in the vehicle body coordinate system, the heading angle, and the current speed of the vehicle). The NMPC path tracking controller collects the data and computes the vehicle's following control outputs (desired speed and desired steering rate). The upper-level controller broadcasts the outputs to the lower-level  
Figure 14: Experimental verification results of driving controller: (**a**) no load; (**b**) full load.  
Figure 15: Diagram of articulated dump truck path tracking system.  
controller via the CAN bus. The load calculation module in the lower-level controller calculates the vehicle load based on the data from the IMU on the rear body and transmits the data to the driving controller. The driving and steering controllers in the lower-level control calculate the corresponding control commands and forward them to the vehicle's actuators, respectively. After implying the commands, the sensors collect new information and perform the next tracking cycle.
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 5 Results and Discussions  
Here, the effectiveness of the proposed algorithm, in terms of the outage ratio of all the users, the total power consumption of transceivers at all the BSs, and the average transmission rate of all the users, in the context of the macrocell--small cell network system, as conceptually shown in Figure 2, is evaluated and compared with that of two conventional methods. The two conventional methods are set as follows: (1) a system that is the same as the proposed system, except that all SBSs are continuously in the cell-enlarged (i.e., high power output) state, and (2) a system that is the same as the proposed system, except that the cell of each SBS is randomly determined to be closed or enlarged. For convenience, the two conventional methods are called SBS-Cell-Enlarged and SBS-Random, respectively.  
Figure 14: Conceptual illustrations of (a) NND and (b) NNO.  
Figure 12: Conceptual illustrations of (a) NYD and (b) NXD.  
Figure 13: Conceptual illustrations of (a) NYO and (b) NXO.  
In addition, it is important to note that, typically, a given SBS is classified as a microcell, picocell, or femtocell BS, and that in recent studies, the use of increasingly small BSs has become a notable trend [2]. As a result, femtocell BSs were chosen as the SBSs used in this study.
## 2 Materials and Methods
### Cretan Electric Grid Interconnection  
The Greek government's priority is focused on connecting the unconnected islands in order to reduce energy production costs. Notwithstanding the project's technical problems, it was intended, in earlier years, to link the island's electric grid to the mainland's electric grid via two underwater cables. One cable can carry 150-180 MW, while the other can transport 350 MW. The installation of the smaller cable was completed by the end of 2020, whilst the second was anticipated to be completed by 2023. The net position of 2023 imports-exports is shown in Figure 5:  
The interconnection is utilized most extensively during the summer season, as demand surges due to sudden increases in population and commercial traffic. In the past, when Crete's electric grid operated on its own, there were restrictions placed on the maximum installed power of intermittent renewable energy systems such as solar-PV and wind-generated electricity. These restrictions were implemented for reasons related to maintaining the stability of the grid. Despite the fact that previous research has shown both their technical and economic viability, hydro pump storage systems for the purpose of power storage have not yet been developed on the island of Crete. With the aforementioned limitations, the contribution of renewable energies to the total mix of local energy sources is now slightly greater than 23%. Due to the operation of undersized and inefficient thermal power facilities, the price of energy in Crete is at present costly, accompanied by high greenhouse emissions. Crete has an abundance of sun and wind, and the technology to turn those resources into power is established and cost-effective. Despite investor enthusiasm for implementing these projects, the island has attracted energy investment in solar-PV systems and wind farms that are much lower than its capacity. Wind farms and solar energy production systems are anticipated to replace the polluting and inefficient thermal power facilities in Crete.
###### Abstract  
Near-infrared (NIR) spectroscopy is widely used for non-destructive detection of fruit quality, but the transferability of NIR models between different fruits is still a challenge. This study investigates the transferability of NIR models from strawberry to grape and apple using two case studies. A total of 94 strawberry, 80 grape, and 125 apple samples were measured for their soluble solids content (SSC) and NIR spectra. Partial least squares (PLS) regression was used to establish a model for predicting strawberry SSC, with an acceptable root mean square error of prediction (RMSEP) and correlation coefficient (R) of 0.53 \({}^{\circ}\)Brix and 0.91, respectively. Directly applying the strawberry model to grape and apple spectra significantly degrades the performance, increasing the RMSEP up to 3.47 and 16.40, respectively. Spectral preprocessing can improve the predictions for all three fruits, but the bias cannot be eliminated. Global modeling produces a generalized model, but the prediction for strawberry degrades. Calibration transfer with SS-PFCE and PLS correction, which are calibration methods without standard samples, was found to be an effective way to improve the prediction of grape and apple spectra using the strawberry model. Therefore, calibration transfer may be a feasible way for improving the transferability of NIR models for multiple fruits.  
near-infrared spectroscopy; fruit quality; multivariate calibration; calibration transfer; soluble solids content +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 3 Results  
### Effect of MFSCE on Viability of SH-SYSY Cells Treated with A\(\beta_{25-35}\)  
To evaluate the neuroprotective effect of MFSCE, we measured the cell viability (Figure 1). We observed a significant decrease in cell viability to 63.76% in A\(\beta_{25-35}\)-treated cells. However, MFSCE-treated cells at concentrations of 0.5, 1, 2.5, and 5 ug/mL significantly increased cell viability to 80.46%, 85.48%, 86.80%, and 87.02%, respectively. This result indicates that MFSCE elevated cell viability of SH-SYSY cells induced by A\(\beta_{25-35}\).  
Effect of MFSCE on Lactate Dehydrogenase (LDH) Activity in SH-SYSY Cells Treated with A\(\beta_{25-35}\)  
We investigated the effect of MFSCE on LDH activity (Figure 2). LDH activity was 137.30% in A\(\beta_{25-35}\)-treated cells, compared with 100.00% in the control group. On the other hand, MFSCE-treated cells dose-dependently inhibited LDH activity, compared with the A\(\beta_{25-35}\)-treated group. LDH activity at MFSCE doses of 0.5, 1, 2.5, and 5 ug/mL was 126.82%, 119.48%, 109.76%, and 100.90%, respectively. This indicates that MFSCE inhibited LDH activity in neuronal cells induced by A\(\beta_{25-35}\).  
Figure 1: Effect of membrane-free stem cell extract (MFSCE) on viability of SH-SYSY cells treated with A\(\beta_{25-35}\). Values are presented as mean \(\pm\) SD (\(n\) = 6). The different letters (a–d) among groups represent significant differences determined by ANOVA and [PERSON]’s multiple range test (\(p\) < 0.05). *\(p\) < 0.05 vs. A\(\beta_{25-35}\)-treated cells.  
Figure 2: Effect of membrane-free stem cell extract (MFSCE) on lactate dehydrogenase release activity in SH-SYSY cells treated with A\(\beta_{25-35}\). Values are presented as mean \(\pm\) SD (\(n\) = 6). The different letters (a–d) among groups represent significant differences determined by ANOVA and [PERSON]’s multiple range test (\(p\) < 0.05). Asterisk (*) indicates significant differences using Mann–Whitney U-test (\(p\) < 0.05).