esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Ecological Footprint Spatial Pattern and Ecological Pressure  
Some researchers hold the view that the ecological footprint cannot express the true ecological pressure [45; 46]. However, in this study, the ecological footprint spatial pattern was showed, from which we could find where the ecological footprint was higher and the ecological capacity was lower in Xuzhou central area. The ecological pressure can be identified by the values of the ecological footprint and ecological capacity. According to the WWF's report about China's ecological footprint, China's ecological footprint depends on the urbanization level [47]. It can be seen from the results that in the southwest and center parts of Xuzhou central area, with its high urbanization level, there was high ecological pressure. From the spatial pattern of the ecological footprint, the unbalanced ecological pattern should be optimized. The ecological pressure in the southwest and center of Xuzhou central area needs to be released.  
However, there still remain some shortcomings in this study. Due to the availability of the MOD17A3 data, we could only measure the ecological footprint of Xuzhou central area until 2014. Additionally, the resolution of the MODIS data is relatively coarse.
# Tailored Magnetic Linear Birefringence in Wedge-Shaped Co Nanocluster Assemblies
## 1 Introduction
That transition from in-plane to out-of-plane magnetization is controlled by the film thickness, \(t\), and is reported to occur below a threshold value, \(t_{0}\), when the magnetic anisotropy energy, \(K_{eff}\), is dominated by the interface contribution, \(K_{S}\)[12; 15]. The research has been extended to wedged magnetic films [16; 17; 18; 19], where the commented spin-reorientation transition can be easily observed in a thickness range along the film size. For the same purpose, an original approach has been recently proposed in 2D magnetic systems whose magnetic anisotropy energy can be graded along the normal to the substrate plane, e.g., with a graded composition [20; 21]. In these last cases, two different magnetization directions could be coexisting in the same physical system, with respective mechanisms for magnetization reversal. That would open a new strategy to design eventually birefringent media from the magnetic point of view. We have focused our work on growing Co nanocluster assemblies with a strong thickness gradient and, therefore, a graded magnetic anisotropy along its direction. That method has been intended to induce two competing magnetization directions along the thickness gradient and, therefore, an MLB in such disordered 2D systems. We carried out an angular characterization of magneto-optical effects in these assemblies, in both reflection and transmission configurations, particularly concerned on their dependence parallel to the thickness gradient direction (TGD), i.e., thickness dependence. The resulting data show an induced uniaxial anisotropy arising from the linearly distorted surface, and a strong magnetic linear birefringence due to an emerging out-of-plane magnetization, M\({}_{out}\). Unlike the in-plane magnetization, M\({}_{in}\), the M\({}_{out}\) component is resolved by measuring the Faraday effect, showing a coherent rotation mechanism for M reversal. Its origin has been discussed in terms of the magnetic coupling amongst the Co nanoclusters of the terrace edges in our 2D assemblies.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 8 Conclusions  
In this paper, we studied the Markov decision policy-based base station selection policy for a C-V2X system. According to the vehicle's moving direction and moving speed, we proposed a method to predict the probability of next-step displacement. As vehicles compete for femtocell base station resources, the sorting of vehicles can obtain different sorting sequences. By comparing the maximum-value function for each strategy, we can obtain the best base station choice solution. The simulation compared effectiveness between the Markov decision policy and non-Markov decision policy. The Markov decision policy can help us ensure good signal quality of the system transmission. At the same time, it guarantees that the usage rate of the femtocell base station does not reach the upper limit. When a resource conflict occurs in the femtocell base station area, resources are reasonably allocated to improve the system's service quality.  
Figure 16: Average received signal strength of Markov and non-Markov policy.  
**Author Contributions:** Conceptualization, Q.H. and K.Y.; Data curation, Q.H.; Formal analysis, Q.H. and K.Y.; Funding acquisition, K.Y.; Methodology, Q.H. and K.Y.; Project administration, K.Y.; Software, Q.H.; Supervision, K.Y. and T.S.; Validation, Q.H.; Visualization, Q.H.; Writing--original draft, Q.H.; Writing--review and editing, Q.H., K.Y. and Z.W.  
**Funding:** This research was funded by JSPS KAKENHI grant number JP18K18044.  
**Acknowledgments:** We would like to thank all the reviewers and editors for their valuable comments and suggestions which help improve this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 3 Experimental Setup and Results
### Dataset
It consists of applying and then subtracting a two-degree polynomial fit with a window of 151 points (approximate 3 days on Kepler), the _Sav-Gol_ filter [51]. Then, it subtracts a moving median filter with a window of 25 points. Finally, during the sigma-clipping, we remove positive outliers that are greater than \(5\sigma\) and remove negative outliers less than \(-40\sigma\).
Optimization of the Fermentation Conditions of Huaniu Apple Cider and Quantification of Volatile Compounds Using HS-SPME-GC/MS  
[PERSON]  
11 Agricultural Product Storage and Processing Research Institute, Gansu Academy of Agricultural Sciences,  
Lanzhou Yu30700, China; [EMAIL_ADDRESS] (Y.M.); [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (J.Y.); [EMAIL_ADDRESS] (J.Y.)  
1  
[PERSON]  
11 Agricultural Product Storage and Processing Research Institute, Gansu Academy of Agricultural Sciences,  
Lanzhou Yu30700, China; [EMAIL_ADDRESS] (Y.M.); [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] (J.Y.)  
1  
[PERSON]  
apple industry. Consequently, there is an urgent need for diversifying the processing of Huaniu apples.  
Currently, apple diversification processing includes various products such as cider, vinegar, brandy, and slices, in addition to fresh fruits [5; 6; 7; 8]. Cider, in particular, has experienced rapid growth, with the global cider industry's value exceeding USD 500 million in 2020 [9]. The characteristics of cider depend on the apple variety and the stage of maturity of the raw material. Different apple varieties exhibit distinct aroma and flavor characteristics due to variations in soluble sugars, organic acids, volatile compounds, and other components [10]. The quality of cider products is closely linked to the inherent characteristics of the apple varieties. The quality criteria for apple processing exhibit notable distinctions compared to the conventional cultivation practices intended for fresh consumption. Factors such as sugar/acid ratio, polyphenols, and antioxidant capacity play crucial roles. In particular, phenolic compounds in apples participate in fermentation, contribute to the aroma of the fermentation juice, prevent microbial spoilage, and control the fermentation rate [11; 12]. It has been reported that the concentration of polyphenols varies significantly among different apple varieties, with total phenolic content ranging from 107 mg/kg FW to 941 mg/kg FW [13; 14].  
Aroma is a vital indicator of cider quality and contributes to its distinctive flavor profile. The aroma of cider primarily consists of esters, alcohols, fatty acids, aldehydes, ketones, and terpen. Recent research on cider aroma mainly focuses on enhancing it through the optimization of fermentation strains and processes. However, there is a relative lack of research concerning the variations in cider aroma among different apple varieties. With its distinct flavor and nutritional value, the Huaniu apple holds great promise for making fruit wine. However, we currently have limited knowledge about how Huaniu apple cider is fermented and what gives it its unique taste. Therefore, the primary objective of this study is to investigate the distinct flavor components of Huaniu apple cider by optimizing the fermentation process. By doing so, the aim of this study is to improve both the production process and the quality of the final product. This research will emphasize the significance of Huaniu apple cider in the context of industrial development, showcasing its potential and value in the market.
# Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
###### Abstract  
Hyperspectral imaging technology was employed to detect slight Bruises on Korla pears. The spectral data of 60 bruised samples and 60 normal samples were collected by a hyperspectral imaging system. To select the characteristic wavelengths for detection, several chemometrics methods were used on the raw spectra. Firstly, principal component analysis (PCA) was conducted on the spectra ranging from 420 to 1000 nm of all samples. Considering that the reliability of the first two PCs was more than 90%, five characteristic wavelengths (472, 544, 655, 688 and 967 nm) were selected by the loading plot of PC1 and PC2. Then, each of the wavelength variables was considered as an independent classifier for bruised/normal classification, and all classifiers were evaluated by the receiver operating characteristic (ROC) analysis. Two wavelengths (472 and 967 nm) with the highest values under the curve (0.992 and 0.980) were finally selected for modeling. The classifying model was built by partial least squares discriminant analysis (PLS-DA) and the bruised/normal classification accuracy of the modeling set (45 damaged samples and 45 normal samples) and prediction set (15 damaged samples and 15 normal samples) was 98.9% and 100%, respectively, which is similar to that of the PLS-DA model based on the whole spectral range. The result shows that it is feasible to select characteristic wavelengths for the detection of slight bruises on pears by the methods combining the PCA and ROC analysis. This study can lay a foundation for the development of an online detection system for slight bruise detection on pears.  
pear; damage detection; hyperspectral imaging; characteristic wavelength
## 4 Discussion
### Limitations and Further Development  
The work presented in this paper focused on the corrosion performance of arc-sprayed aluminium with predefined circular damaged areas. The damaged areas had coatings completely removed to expose the underlying steel substrate. The geometry of the damaged area was circular in nature. Although the size and the number of such 'damaged areas' varied, the geometry still remained circular. It was also assumed that the production of such damaged areas did not result in any changes to the kinetics or nature of the oxide formed on the TSA surface. However, it must be noted that the concentration of electronic defects have a bearing on the nature of Al\({}_{2}\)O\({}_{3}\) produced [41; 42]. It might also influence the way in which aluminium deteriorates [43]. Nonetheless, as the 'damages' were substantial compared to the defects that might have been generated during the sample production phase, it is fair to assume that the impact of electronic defects would be significantly less and the nature of the response would be dominated by mechanical 'damages' [43].  
The electrochemical monitoring presented in the paper is not novel. Electrochemical techniques such as OCP, LPR and Tafel analysis are regularly carried out to ascertain the corrosion behaviour on a regular basis. The intension of the work was not to develop new methods of assessment or coating production, but to provide detailed experimental data on arc-sprayed TSA. Such detailed work on the performance of damaged coatings in seawater has not been previously reported in open literature. However, further development of electrochemical monitoring methods would be beneficial for better understanding of the anodic and cathodic reactions. Some progress has been made to understand the cathodic reaction by using zero resistance ammeter (ZRA) between the steel and the TSA [25]. EIS also allows better understanding the corrosion mechanism once a suitable electrical equivalent model has been identified [44]. Modelling work is also considered beneficial in theoretically looking at the possible mechanisms [33]. It also allows one to carry out'simulated' experiments without the expense and time constraints. These developments are likely to improve our ability to better understand the mechanisms that govern the performance of TSA in service.
## 5 Application of the Approach and Experimental Results
Observing the measurements reported in Figure 4 it is clear that CPU001 is in charge of the master Docker and of one of the Slaves. Also, from Figure 5 it is possible to determine that not many process switches take place during the execution. Overall, the Raspberry are not overwhelmed by the computations, so they can be still be exploited for other concurrent tasks. The execution times are, of course, much different if we compare the Raspberries with the centralized server. As shown in Figure 6, the centralized server is far more efficient than the single Raspberries, the performances of which slightly differ from one another. However, this last fact is simply due to the difference in the reading speed of the SD cards used in the two Raspberries, despite it being rather small: 22.9 MB/s for Raspberry 1 and 23.1 MB/s for Raspberry 2. Figure 4: CPU utilization in one of the Raspberries. Figure 5: Process Switching during execution. If we take in consideration the medium size of a cluster, which in our case has be considered to be of 1000 points, the server will take 986 s on average to complete the computation. If we consider a configuration with N Raspberries, with each of them being given a portion of the points to be clustered, we would roughly obtain an execution time of 800 s if we considered 10 Raspberries working in parallel, and not completely focused on the specific clusterization task. We are not taking in consideration data transmission times at them moment, as all data will be transmitted within the same local network, with small to negligible delays. Furthermore, the Raspberries would be available to host the computation of data coming from different households in the neighborhood, provided they can access a common communication network, as in the current CoSSMic scenario.
## 2 Problem Formulation
### Control Problem Formulation
\(\phi_{ik,1}\) and \(\phi_{ik,2}\) are depicted respectively by  
\[\phi_{ik,1}=\gamma_{ik,1}\dot{e}_{D01}+\lambda_{ik,1}e_{D11}^{q_{21}/p_{21}}, \ \ \ \phi_{ik,2}=\gamma_{ik,2}\dot{e}_{D02}+\lambda_{ik,2}e_{D12}^{q_{22}/p_{22}}. \tag{24}\]  
The derivative and integral terminal sliding mode control law is set as  
\[ \begin{array}{l}u=\dot{H}_{ik}^{-1}\Big{[}\ddot{y}_{ik}^{d}-k_{eik}\parallel \phi_{ik}\parallel diag\Big{[}\frac{q_{11}}{p_{11}},\frac{q_{12}}{p_{12}} \Big{]}A_{\pi ik}^{-1}\frac{s_{ik}}{\|s_{ik}\|_{1}}\Big{]}\\ -H_{ik}^{-1}\Big{(}\overline{d}_{ik}(t)-k_{ik}sign(s_{ik})-\eta_{ik}s_{ik}\Big{)} \end{array} , \tag{25}\]  
where \(sign(s_{ik})=sign\Big{[} \begin{array}{cc}s_{ik,1}&s_{ik,2}\end{array} \Big{]} ^{T},k_{eik}\), \(\eta_{ik}\) are all the positive constant set by designer. \(\overline{d}_{ik}(t)\) is the upper bound of the unmatched uncertainties. \[A_{\pi ik}=\left[ \begin{array}{cc}A_{\pi ik,1}&A_{\pi ik,2}\end{array} \right] ^{T},A_{\pi ik,K}\ (K=1,2)\]  
\[A_{\pi ik,K}=\left\{ \begin{array}{c}\dot{e}_{ik,0K}^{(p_{1K}/q_{1K}-1)},for \Big{|}\dot{e}_{ik,0K}^{(p_{1K}/q_{1K}-1)}\Big{|}\geq\varepsilon_{ik}\\ \varepsilon_{ik},otherwise\end{array} \right.. \tag{26}\]  
Here, \(\varepsilon_{ik}>0\), \(\chi_{ik}\) and \(k_{eik}\) meet the following conditions  
\[\parallel\Big{(}A_{\pi ik}-diag\Big{[}\dot{e}_{ik,D01}^{(p_{11}/q_{11}-1)}, \dot{e}_{ik,D02}^{(p_{2}/q_{12}-1)}\Big{]}\Big{)}A_{\pi ik}^{-1}\parallel \leq\chi_{ik}<1, \tag{27}\]  
\[k_{eik}>1/(1-\chi_{ik}). \tag{28}\]Substitute the control law (25) into (23), considering the conditions (26). Then, \(\dot{V}_{ik}<0\) can be guaranteed when \(k_{ik}>\overline{d}_{ik}\) holds true. However, apart from Hypothesis 2, the unmatched uncertainties \(d_{ik}\) in (13) are unknown, which means that the upper bound is also unknown. Therefore, it cannot select an appropriate parameter \(k_{ik}\) to guarantee \(\dot{V}_{ik}<0\). Therefore, the stability of control system cannot be guaranteed.
# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion
### Practical Implications for Landscape Sustainability
#### 3.4.1 Increasing Green Areas  
Expanding the presence of green areas, especially in urban zones with dense built-up surfaces, is essential for mitigating the UHI effect. Broad-leaved shade trees should be strategically planted in areas with a high concentration of impervious surfaces, such as campus parking lots and roads, to reduce surface temperatures and improve user comfort [84; 88].
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 1 Introduction
### Mobility Management
Thus, machine learning (ML) algorithms can also be used to optimize the handover process by adapting to the specific characteristics of the network and mobile devices. For example, ML algorithms can learn to adjust the handover threshold and the timing of the handover based on the type of application and the mobility of the mobile device. Figure 2: Procedure for Conditional Handover.
# BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
## 4 Experimental Results
### Comparison with the State-of-the-Art  
First, we compare the performance of BTDNet to that of the state-of-the-art methods: the unimodal 3D-Resnet10-Trick [8] (winner of the RSNA-ASNR-MICCAI BraTS 2021 Challenge), the multimodal EfficientNet-LSTM-mpMRI [10] (runner up in the challenge), the multimodal EfficientNet-Aggr [11] (third place in the challenge), the unimodal YOLO-EfficientNet [9] (fourth place in the challenge), and the multimodal stats-EfficientNet [12] (fifth place in the challenge). For a fair comparison with our method, which utilized segmented 3D volumes, we re-implemented these methods and also utilized the same segmented data. Table 1 shows the performance of the state-of-the-art methods and our method. The results are presented in the form of an average of five folds \(\pm\) spread (i.e., difference between max. and min. performance within the five folds). It can be seen in Table 1 that our method outperformed all the other state-of-the-art methods by large margins. In more detail, it outperformed the winner of the challenge, 3D-Resnet10-Trick, by 3.3%, and it outperformed all other methods by at least 18.8%. Table 1 also shows that our method achieved the minimum spread among all the state-of-the-art methods.  
It should be noted that all the state-of-the-art methods utilized an ad hoc strategy of selecting a fixed input length by removing or duplicating slices within each 3D volume. Furthermore, some methods utilized CNN-RNN architectures, in which the 3D volume annotation was propagated to each slice within the volume. Finally, we performed an extra experiment, which is not shown in Table 1 so as not to clutter the results. In more detail, we implemented 3D-Resnet10-Trick (winner of the challenge) and utilized non-segmented data. Surprisingly, we noticed that the method's performance increased from \(62.9\pm 4.8\) (which was the case when segmented data were used) to \(63.3\pm 4.4\). This indicates that the method focused more on regions outside of the brain, which played a role in the increase in performance (however, this is not correct, as the MGMT promoter methylation status exists in the brain region and should be predicted only from that region).
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
###### Abstract  
Network RTK (NRTK), one of the primary means of high-precision real-time kinematic positioning (RTK), has been widely used. The key to providing highly accurate positioning is the ambiguity of the reference station being correctly fixed, but the atmospheric errors must be handled carefully, which seriously affects the efficiency of ambiguity fixing. This paper aims to improve the efficiency of ambiguity fixing by studying the time-varying characteristics of atmospheric errors. Once reasonable constraints are imposed on atmospheric parameters in the uncombined observation model, it can better fix ambiguity. Atmospheric parameters are estimated by random walk at the reference station, and the power spectral density (PSD) of atmosphere is determined by real-time observations, instead of using empirical values or empirical models that do not consider atmospheric variations. The experimental results showed that the real-time estimated PSD can improve the ambiguity fixing time by 18.4% and the ambiguity fixing success rate for the reference station by 11.7%, compared with using empirical PSD for atmospheric parameters. Unlike general NRTK positioning based on differential error correction values, undifferenced NRTK estimates the integer ambiguity and undifferenced error correction value at a single reference station, ensuring the independence of the error correction value of each reference station, and it can be easily broadcast and received through the network, which is more convenient for realizing high-precision RTK positioning for users.  
network RTK; atmospheric error; random walk; ambiguity fixed; error correction value +
Footnote †: journal: remote sensing
## 2 Experimental
### Electrochemical Experiments
#### 3.2.3 Ethanol Electrooxidation: Potentiostatic Measurements  
In order to consider the stability of mono and bimetallic Pt and Pd nanoparticles towards ethanol oxidation in the alkaline media, the chronoamperometric experiments were performed at a constant potential of 0.56 V vs. RHE. This potential was selected since the electrocatalytic oxidation of ethanol has been begun at this potential, but the current is not controlled by the ethanol diffusion, so that any diminution of the current cannot be attributed to this phenomenon [19; 20]. Figure 9 shows the transients measured at 0.56 V for each nanoparticle sample for 1000 s. The general behavior of the transients shows a fast current decay during the first minute followed by a quasi-steady state in the middle region. Additionally, the reactivity trend follows the same behavior as that recorded in Figures 6 and 7. The highest and lowest current densities during CA experiments are obtained for Pt\({}_{3}\)Pd\({}_{2}\) and Pd nanoparticles, respectively. The highest ratio of the current at 1000 s to the initial current corresponds to the Pt\({}_{3}\)Pd\({}_{2}\) nanoparticles. This result indicates that the general behavior obtained for the voltammetry is also applicable to the potentiostatic currents, that is, PtPd alloy acts as a bifunctional catalyst in which Pt sites are used to dehydrogenase ethanol to produce acetaldehyde, and Pd sites proved the oxygen group required to oxidize acetaldehyde to acetate.  
Figure 8: Evolution with the nanoparticle composition of the specific activity (red histogram) and residual specific activity after 20 consecutive cycles (blue line) for the EOR. Data obtained from Figures 6 and 7.  
Figure 9: Chronoamperometric curves for the synthesized nanoparticles in 0.1 M NaOH solution containing 0.1 M EtOH at 0.56 V.