esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Numerical Simulation
#### 2.3.2 Model establishment and boundary conditions  
A generalized numerical simulation of the reservoir with a trapezoidal section is established using computational fluid dynamics (CFD) software Fluent [25]. The trapezoidal cross-section has a top width of 78 m, a bottom width of 20 m and a water depth of 32.5 m. The calculation domain length is 165 m. To accurately simulate the flow, the grid is refined near the TCC area. The simulation model adopts an adaptive unstructured grid. The overall computing domain grid step size is 2 m, and the encrypted grid near TCC is 0.2 m. The total number of grids is approximately 2,210,000 (Figure 4).  
As a flexible structure, the TCC can experience large deformation and small strain in a hydrodynamic environment. The physical experiments and previous studies [26; 27] have shown that under tension boundary conditions and a stable flow environment, the TCC can maintain a relatively stable form. In the numerical simulation, the TCC is simplified to a solid wall boundary by ignoring the mechanical deformation due to the fluid-TCC interaction.  
The inflow boundary condition of the numerical simulation is set as a mass flow inlet, and a mass flow outlet is adopted for the outflow boundary condition. The free surface in the computational domain is assumed to be a rigid cover, and a symmetrical boundary condition is set to simulate the free sliding wall. Fixed-wall boundary conditions simulate that the side and bottom surfaces are nonslip walls. The vertical temperature distribution is imported by editing a user-defined function (UDF).  
The Reynolds stress turbulence model is adopted in this numerical simulation to ensure the accuracy of calculation. The SIMPLEC algorithm is applied for pressure-velocity coupling, and the second-order upwind scheme is selected for the discrete control equation. When the mean square deviation of the calculated results is less than \(10^{-4}\) and the surface pressure of the model tends to be stable, the calculated results are assumed to have converged.  
Figure 4: Schematic and meshes of the numerical simulation model: (**a**) computational domain of simulation model; (**b**) global mesh; (**c**) encrypted grid near TCC (model size: length 165 m, top width 78 m, bottom width 20 m, water depth 32.5 m).
## 3 Numerical Analysis of the Thermal Performance
### Thermal Resistance Analysis
#### 3.3.2 Interface between Insert and Tool-Holder  
Owing to the existence of surface roughness, there is a temperature drop across the interface between the insert and the tool-holder. Generally, the associated contact resistance is influenced by the  
Figure 8: Thermal resistance: (**a**) Coordinate definition; (**b**) Thermal resistance distribution.  
roughness level of the mating surfaces and the joint pressure [16; 22]. Treating insert and tool-holder as one-dimensional, the sketch is presented in Figure 9a.  
The equivalent thermal resistance between the insert and the tool-holder can be regarded as the parallel circuits of the resistances through different directions (Figure 9b). Therefore, the equivalent thermal resistance is written as:  
\[R_{th}=\frac{(R_{t1}+R_{tc1}+R_{1})(R_{t2}+R_{t2}+R_{2})}{R_{t1}+R_{t2}+R_{tc1 }+R_{tc2}+R_{1}+R_{2}} \tag{12}\]  
where \(R_{t1}\), \(R_{t2}\), \(R_{1}\), \(R_{2}\) are the thermal resistances associated with the process of heat conduction between the insert and the tool-holder. These quantities can be calculated using the formula \(R=1/(\lambda A_{1}/d+hA_{2})\), where \(\lambda\) is the thermal conductivity, \(d\) is the thickness of a part, \(A_{1}\) is the area of cross-section of a part, \(A_{2}\) is the area of the surface exposed to the air, and \(h\) is the heat transfer coefficient of the area contact with air. \(R_{tc1}\), \(R_{tc2}\) are the thermal contact resistance between the insert and the tool-holder and can be calculated using \(R_{tc}=d/(\lambda_{\text{w}}A_{\text{w}})\) where \(d\) is the thickness of the layer, \(A_{\text{w}}\) is the contact area, and \(\lambda_{\text{w}}\) is the thermal conductivity of water.  
Therefore, on the interface between the insert and the tool-holder, the heat flow can be expressed as:  
\[q_{th}=\frac{T_{insert}-T_{air}}{R_{th}} \tag{13}\]
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix A Literature Review
### Baseline Algorithms  
Anomaly detection aims to find an _\"observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism\"_[2]. The baseline anomaly detection algorithms described in [17] struggle to identify anomalies in time series, mainly because their assumptions are invalidated. If we consider models built for spatial data, a major assumption of these models is that observations are independent, whereas, for time series, high dependency exists between different time stamps. Clustering-based approaches, such as the density-based spatial clustering with noise (DBSCAN) method, are particularly impacted by this aspect; if an anomaly occurs at a given time stamp and is followed by incorrect values, clustering-based approaches consider that the observations of the time series belong to two different clusters and thus fail to identify the anomaly. Another limitation when considering these types of techniques is the choice of the similarity metric used for the data clustering. This task, although a crucial pillar of these approaches, is not trivial and becomes very challenging for high-dimensional problems.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Power Reduction Techniques  
WSNs contain numerous sensor nodes, which are utilized to measure ecological phenomena in real time and transmit information back to the master node through a wireless module. Given the absence of wire connections, sensor nodes are appropriate for different applications under strict environments. PA is one of the important applications relying on WSNs [67]. A sensor node is usually equipped with rechargeable batteries, which have limited capacity and pose a challenge to long-term application [68]. These batteries supply power to the sensor nodes by providing the necessary current to maintain each part of the sensor nodes working properly. The total power consumption of a sensor node is the sum of each element in the node (i.e., sensor, microcontroller unit, and radio module), and  
Figure 2: Energy-efficient schemes in agriculture based on wireless sensor networks (WSNs).  
each component may operate at different energy states. Therefore, the lifespan of a sensor node is the time consumed to exhaust its batteries under a sustainable operation threshold.  
Numerous scholars have developed power reduction techniques to provide sensor nodes an infinite lifespan. This section reviews several power reduction techniques of WSNs that can be used in agricultural applications. The purposes, wireless protocols, findings, advantages, and limitations of each technique are also discussed.
# An Analytic Transform Kernel Derivation Method for Video Codecs
## 4 Conclusions  
This study proposed an analytical kernel derivation method for the VVC video codec. Memory consumption to store different block size transform kernels is regarded as a major issue in video coding standardization. To address this scenario, the proposed method attempts to save static memory by storing only fewer elements of the transform kernels. Full-required transform kernels are derived using the stored transform kernels and generated unit-element matrices and a permutation matrix. In the proposed method, all the different block size transform kernels are generated dynamically during runtime. The static memory required is only for 1648 elements instead of 8180 elements, each with 8-bit precision, which means that approximately 80% of the static memory can be saved. The fast algorithm of DCT-2 and DST-7 can be implemented in the proposed method as the proposed signal closely overlaps with the original transform kernels, that is, DCT-2, DCT-8, and DST-7. Similarly, the proposed grouping concept of the DST-7 transform kernel shows the effect of tuning on one-row elements on the remaining rows that depend on the tuned row element using the permutation matrix.  
Conceptualization, A.K. and B.L.; methodology, B.L.; software, A.K.; validation, A.K. and B.L.; formal analysis, B.L.; investigation, B.L.; writing--original draft preparation, A.K.; writing--review and editing, B.L.; visualization, B.L.; supervision, B.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by Korea government (No. NRF-2019R11A3A01058959)[PERSON]  
Not applicable.  
The authors declare no conflict of interest.
# How to Open a Black Box Classifier for Tabular Data
## 2 Materials and Methods
### Data sets used
#### 2.2.2 Real-World data  
A description of the variables included in the starting pool for model selection and any standardisation that was applied to them is provided below.  
(a) Diabetes data set:  
The Diabetes dataset [29; 30] comprises measurements recorded from 768 women, who were at least 21 years old, of Pima Indian heritage, and tested for diabetes using World Health Organization criteria. One of the variables, \"Blood Serum\" Insulin, has significant amounts of missing data. These rows were removed along with all entries with missing values of \"Plasma Glucose Concentration\" in a tolerance test, \"Diastolic Blood Pressure\" (BP), \"Triceps Skin Fold Thickness\" (TSF) or \"Body Mass Index\" (BMI), resulting in a reduced data set with n = 532. In line with common practice, a subset was randomly selected for training (n = 314), and the remaining were used for testing (n = 268). The additional variables available are \"Age\", \"Number of Pregnancies\", and \"Diabetes Pedigree Function\" (DPF), a measure of family history of diabetes. A binary target variable indicated diabetes status, with a positive prevalence of 35.7%.  
Statlog German Credit Card data set:  
We used the numerical version of the Statlog German Credit Card database [31], which contains n = 1000 instances and 24 attributes. The first 700 observations were used for training, with a prevalence of bad credit risks being 29.6%. The remaining 300 observations were used for testing, with a prevalence of bad risks of 31%. The data set was used in the form created for the benchmarking study Statlog, where three categorical variables (\"Other Debtors\", \"Housing\", and \"Employment\") were coded in binary form with multiple columns.  
Statlog Shuttle data set:  
The Statlog Shuttle database [31; 32] from NASA comprises 9 numerical attributes and an outcome label. It is split into 43,500 cases for training and 14,500 for testing. There are 7 outcomes, of which 21% are in the category \"Rad Flow\". The binary classification task is to separate this category, Class 0, from the others, assigned to Class 1. Given the strong imbalance between classes, the default accuracy for a null model, i.e., predicting the predominant class for all rows, is 79%. The target accuracy is 99-99.9%.
## 3 Simulated Measurements  
To compare the reconstruction performances of the four methods (TRESM, CESM, CMESM, and ERDCESM) for different types of sources, simulated measurements were carried out. For CESM, CMESM, and ERDCESM, the CVX toolbox was used for the \(l_{1}\) norm optimization. For TRESM, the optimal regularization parameter was determined via the L-curve method [11]. In the measurements, a single source was selected to simulate the sparse source, and a simple supported plate was used as a type of spatially extended source. Simulations of ERDCESM-\(v\) were also implemented to verify the superiority of combining with FB.
## 4 Discussion
### Future Directions  
One possible future direction is to improve the accuracy of input datasets in calculating ET. The calculation of ET requires several variables, such as meteorological elements, land cover types, leaf area index, and land surface albedo, etc. Currently, these input datasets have different temporal and spatial resolutions at global and regional scale. Besides, current accuracies of some input datasets (e.g., precipitation, leaf area index) can hardly meet the demand of a highly accurate ET product. More research is needed to produce more accurate input datasets by improving their consistency in temporal and spatial resolutions and their algorithms. The second possible future direction is to fuse existing ET products. As shown in this study, most ET products perform best with a particular land cover type. The accuracy of the newly fused products would most likely be improved if a good fusion method were to be implemented or developed. A Generalized Three-Cornered Hat (GTCH) model may be a good candidate, as it can evaluate the uncertainty of more than three different datasets without an observation value [86]. Thirdly, more research is needed to improve the existing ET algorithms. Water balance in the soil-plant-atmosphere continuum is needed to be considered a priority. This is because water absorption from the soil layer and water storage change in the plant could greatly influence the escape of water from the leaf stomata [87]. The nitrogen cycle may be considered in the ET algorithm besides carbon and water, as coupling nitrogen deposition is found to improve the rate of photosynthesis [88]. When more carbon-dioxide is assimilated due to increased nitrogen supply, more water needs to transpire out from the stomata. Last, but not least, autocorrelation of series data should be conducted before conducting a linear trend, to evaluate its independence. If serial correlation is present in the time series data, it can considerably impact the outcome of trend analysis. Positive autocorrelation can artificially induce a trend in a time series, while negative autocorrelation can weaken the trend [89].
## 3 Results and Discussion  
Numerical results have been obtained for the parameters pertinent to the present problem in the following ranges: the dimensionless tube wall thickness, \(t_{w}\) = 0-0.5, and the wall-to-fluid thermal conductivity ratio, \(k_{wf}^{*}\) = 0.1-10. Other parameters were kept constant (\(c_{v}\) (0.1), \(Bi_{o}\) (1), \(Ste\) (0.1), and \(Pe\) (1000)).
# Fabrication of Random Microlens Array for Laser Beam Homogenization with High Efficiency
## 3 Fabrication of Random Microlens Array (rMLA)
### Fabrication of rMLA
#### 3.2.1 Fabrication of Honeycomb rMLA  
For the fabrication of the honeycomb rMLA, a chromium film with a thickness of about 140 nm was first coated on a glass substrate, as shown in Figure 5a. Secondly, the glass substrate was put into the chromium removal solution and soaked for 10 s to etch a series of random micropores on the chromium film, as shown in Figure 5b. Finally, the preprocessed substrate was put into the chemical etching solution to fabricate the honeycomb rMLA. During the etching process, the etching solution was continuously oscillated and the substrate was cleaned every 10 min in order to prevent the detached chromium film from affecting the profile of the rMLA. Meanwhile, the total etching time was about 25 min. The experimental result observed by an OLYMPUS BX51 microscope is shown in Figure 6. As can be seen from the microscopic image of the fabricated honeycomb rMLA, the surface of the rMLA is composed of irregular microstructures, making the overall profile look like a honeycomb. Meanwhile, the surface of the honeycomb rMLA has quite a high filling ratio, which guarantees that the whole beam passing through the rMLA is able to be modulated, so that the uniformity of the generated light filed is further improved. Moreover, the surface of the honeycomb rMLA is extremely smooth, with a roughness of 13 nm, as shown in Figure 6, which verifies that high-quality rMLA can be fabricated by our method. However, due to the irregularity of the structure parameters of the honeycomb rMLA, it is difficult to measure the sagittal heights and the apertures.  
Figure 4: The rMLA fabricated by the HNO\({}_{3}\) chemical etching solution.  
Figure 5: Fabrication schematic of honeycomb rMLA: (**a**) chromium plating; (**b**) corroded chromium film; and (**c**) honeycomb rMLA obtained by chemical etching.
# Mesoporous Niobium Oxyhydroxide Catalysts for Cyclohexene Epoxidation Reactions
###### Abstract  
Mesoporous catalysts were synthesized from the precursor NbCl\({}_{5}\) and surfactant CTAB (cetyltrimethylammonium bromide), using different synthesis routes of, in order to obtain materials with different properties which are capable of promoting the epoxidation of cyclohexene. The materials were characterized by X ray diffractometry (XRD), thermogravimetry (TG), acidity via pyridine adsorption, Hammet titration and N\({}_{2}\) adsorption/desorption. The characterization data indicate that the calcination process of the catalysts was efficient for elimination of the surfactant, but it caused a collapse of the structure, causing a Brunauer Emmett Teller (BET) specific area decrease (CINbS-600, 44 and CINbS-AC-600, 64 m\({}^{2}\) g\({}^{-1}\)). The catalysts that have not been calcined showed high BET specific areas (CINbS 198 and CINbS-AC 153 m\({}^{2}\) g\({}^{-1}\)). Catalytic studies have shown that mild reaction conditions promote high conversion. The catalysts CINbS and CINbS-AC showed high conversions of cyclohexene, 50 and 84%, respectively, while the calcined materials showed low conversion (<30%). The epoxide formation was confirmed by nuclear magnetic resonance (NMR).  
epoxidation; cyclohexene; niobium oxyhydroxide catalysts +
Footnote †: journal: Applied Physics Letters
# On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio
## 2 System Model
### Energy Detection and Sensing Performance Metrics
Although multiple PUs can arrive or depart randomly, this assumption is still reasonable based on the long time average scenario of observed PU behavior. One may refer to [26; 27] to optimize both the sensing time and sensing period (duty cycle) so that instantaneous sensing performance improves under random arrivals of multiple PUs in cooperative sensing.
## 3 Application to Cultural Heritage Science
### Isotopic Analysis  
The determination of isotope composition can be a very useful tool for the study of a sample, especially for provenance studies. Most of the research [44; 45], however, has a destructive approach; a non-invasive approach could be performed by muon spectroscopy. It has been observed that, especially for heavier elements, there is an isotopic shift in muonic K-line energies. The nuclear size difference of the isotopes (owing to the Coulomb attraction being diluted when the isotope size is bigger) affects the muonic X-ray, resulting in a small shift in these energies. In [PERSON] et al. [46], measurements were performed on lead. Lead is the most common material used for provenance studies since ores can be distinguished by the comparison of lead isotope ratios. In the work, small but measurable shifts were observed in the K\(\alpha\) peaks. Another way of assessing the isotope composition is to use the gamma rays that can be emitted after negative muon capture by the nucleus. As reported in Section 2, if the muon is captured by the nuclei, it could lead to gamma-ray emissions. Hence, the emitted gamma rays can be detected and used to identify the parent nuclide responsible for the emission. There are a number of studies investigating this aspect [47; 48; 49; 50] and values are tabulated on the IEAE website [51], while the first experiment with lead is reported in [52].
## 4 Discussion
### Uncertainties and Future Works  
This paper has revealed the slight increasing of GPP\({}_{\text{GS}}\) in the NEC from 1982 to 2015, and simultaneously identified the effects of climate change on GPP\({}_{\text{GS}}\). Nevertheless, some uncertainties still exist for this analysis.  
Figure 8: Relationship of GPP\({}_{\text{GS}}\) with (**a**) Cropland Area, (**b**) Crop Yield per Area, and (**c**) Irrigated Area.  
First, we used the GLASS-GPP products which can provide vegetation productivity from 1982; however, the spatial resolution of GLASS-GPP (around 5 km) is rather rough, and thus some variations of GPP may be overlooked, especially in heterogeneous regions, leading to overestimation or underestimation of the trend of vegetation productivity.  
Another uncertainty comes from correlation analysis between GPPGS and three meteorological factors. In this study, we chose the fixed growing season (April to October) of each year as our study period, and then considers the impacts of the three meteorological factors (daily average temperature, precipitation, and daily radiation) within the growing season, nevertheless, these three factors may influence vegetation growth during the whole year. Take temperature as an example, winter temperature also influences spring phenological events of the next year [66]. Therefore, using fixed growing season period as well as considering annual average temperature during growing season to analyze the effects of warming on vegetation productivity may bring some uncertainties. In the next step, we should consider a variable growing season length, such as using temperature thresholds (e.g., 0 and 5 \({}^{\circ}\)C) as a sign for growing season, so that a more comprehensive impact of meteorological factors on GPP can be taken into account.  
Finally, uncertainty may exist when quantified the effects of each meteorological factors on GPP using SMLR models. The SMLR model assumed that the combined effect of meteorological factors is linear, actually, there is a more complex relationship between GPPGS and meteorology, in addition, there is a coupling effect between vegetation and atmospheric dynamics, that is to say, apart from the effect brought by climate change on vegetation, the variation of vegetation would impact climate simultaneously as a lot of studies [67; 68; 69] have revealed. The relationship between weather and vegetation is too complex to quantify using a statistic-based model. So, a land process models based on biochemical mechanisms, such as CABLE [70], ORCHIDEE [71], and LPJ [72], is needed for more accurate analysis. Apart from reducing uncertainties mentioned above, one important thing that needs further exploration is the relationship between GPPGS and LIAGS. This paper found that the relationship between GPPGS and LIAGS across the NEC has large spatial heterogeneity, indicating that the widely reported greening is not bound to have great contribution to improve carbon sink capability. Whether this relationship between GPPGS and LIAGS found in this paper is specific to the NEC or a common phenomenon in the terrestrial ecosystem is still to be seen.
## 3 Results and Discussions
### Analysis of Experimental and Numerical Data Comparisons  
In this study, the heat transfer performance of two TPMS blocks in gyroid form, made of Al and Ag materials, was investigated using two different fluids. The first fluid, selected as the reference fluid, was pure distilled water, while the other was a specially prepared nanofluid. Al nanoparticles with an average diameter of approximately 32 nanometers (nm) were added to distilled water at a volume fraction of 0.6% to create a nanofluid. The experimental results of the control group (water) and the nanofluid were compared using the finite element method with the numerical analysis software ANSYS Fluent 2023 R2.  
Figure 8: Ag TPMS structure using nanofluid: (**a**) temperature, (**b**) temperature difference, (**c**) dimensionless temperature distributions.