esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design Considerations
#### Determine the characteristic impedance  
In one of the boundary test conditions in this work the minimum load current is set as \(i_{Lmin}=0.2\)C = 500 mA and the maximum battery module voltage \(V_{Bmin}=25.2\) V\({}_{\text{DC}}\). Thus, when the resonant circuit is under a resonant status, we can determine the maximum characteristic impedance as below.  
\[|Z|=\frac{V_{bmax}}{i_{Lmin}}=\sqrt{\frac{L}{C}}=\frac{25.2\text{ V}}{500\text{ mA}}=50.4\text{ }\Omega \tag{34}\]
# Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
## 1 Introduction
As a result, the influence of the first three of the above-mentioned factors is usually not so significant as to make it completely impossible to determine the position of the receiver. The issue of deliberate interference should be treated separately, where an undesirable signal, even after its spectrum is dispersed, may significantly interfere with the useful signal [36].
## 3 Results
### Properties of Epoxyalkane Derivatives  
The conductivity of PEDOT:PSS varies by the molar ratio of PEDOT to PSS and particle size. According to the review papers, which summarized electrical properties of several grades of commercial PEDOT:PSS, the conductivities of Clevious(tm) P, which is used in this study, are in the range of 20-100 S\(\cdot\)m\({}^{-1}\)[28] or about 100 S\(\cdot\)m\({}^{-1}\)[29]. In this study, the conductivity value of PEDOT:PSS was found to be 132 S\(\cdot\)m\({}^{-1}\), as noted in Table 1. In the case of the derivatives, the greater the length of the reacted alkyl group, the lower the electrical conductivity, which is due to the fact that the amount of PEDOT responsible for conductivity in the entire molecule is relatively reduced as the chain length becomes longer. It is worth noting that the conductivities of PEDOT:PSS-C4 and -C6 are higher than those of pristine PEDOT:PSS. It is well known that the conductivity of PEDOT:PSS film significantly improves by the addition of organic solvents such as DMSO [30], ionic liquids [31], or surfactants [32], which is usually referred to as'secondary doping'. The conductivity enhancement of PEDOT:PSS film through a post-treatment with organic solvents [33] or acidic compounds [34] has been investigated in many studies as well. All of these methods are well summarized in a review paper [35]. However, the conductivity of PEDOT:PSS-C4 and -C6 itself, without an additive or any further post-treatment, is higher than that of pristine PEDOT:PSS. To our knowledge, it is the first example.  
All derivatives were not dispersed in water due to the absence of sulfonic acid groups of PSS, which mainly contribute to the hydrophilicity of PEDOT:PSS [7]. On the other hand, PEDOT:PSS-C6 was well dispersed in methanol at a concentration of 0.6 wt.%, which made it possible for us to prepare a transparent film. By studying the properties of this film, we attempted to elucidate the principle of the exceptional conductivity increase.  
Figure 5: XPS spectra of PEDOT:PSS and derivatives.
## 3 Overview of Molecularly Imprinted Polymers as Catalysts and Support for Catalysts  
### General Aspects  
MIPs are cross-linked synthetic polymers engineered with specific recognition sites, created using a target molecule as a template. This process yields high selectivity and affinity for the target, enabling a \"lock and key\" binding mechanism analogous to natural antibody-antigen systems. During MIP synthesis, the template forms a stable active complex with the functional monomer in a porogenic solvent containing initiators and a cross-link. This complex is then fixed within the rigid, porous polymer matrix via polymerization [64; 65]. Initially, the monomers are spatially arranged around a template molecule. Their positions are fixed through polymerization, resulting in a three-dimensional polymer matrix containing both macropores and microcavities. By washing this polymer matrix with specific solvents, the template molecule is removed, unveiling binding sites that are complementary in shape to the template. Consequently, the polymer can thus recognize and selectively bind template molecules dispersed, for instance, in an aqueous matrix. Figure 2 illustrates the process of obtaining MIPs. MIP's binding sites vary in characteristics depending on the interactions established during polymerization. The classification of molecular imprinting (typically covalent or non-covalent) depends on the nature of these monomer-template interactions.  
The need to extract the target molecule post-use hinders MIP application in sensors, because residual target molecules can interfere with subsequent detection. However, despite this challenge, MIPs have successfully been commercialized. For instance, MIP Technologies, a Swedish company responsible for producing non-biological affinity reagents targeting small molecules, proteins, viruses, and other substances. Another prominent use of MIPs is in Sigma-Aldrich's SupelMIP line, which offers MIP-based extraction materials for solid-phase extraction (SPE) columns [64; 66].
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods
### Methods
#### 2.3.1 Feature Extraction
After this size, we chose larger increasing steps to calculate the GLCM window sizes in order to minimize spatial autocorrelation and computation time. We stopped the calculation after a window size of \(51\times 51\) pixels since no further increase in overall accuracy was visible for either PlanetScope or Sentinel-2 data.
# A Fast Point Clouds Registration Algorithm for Laser Scanners
## 3 Experimental Results
### Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**ICP**} & \multicolumn{3}{c}{**SAC-IA+ICP**} & \multicolumn{3}{c}{**3 DHoPD+ICP**} & \multicolumn{3}{c}{**Ours**} \\ \cline{2-13}  & **RMSE** & \(\beta\)**\%** & **ERMSE** & **RMSE** & \(\beta\)**\%** & **ERMSE** & **RMSE** & \(\beta\)**\%** & **ERMSE** & **RMSE** & \(\beta\)**\%** & **ERMSE** \\ \hline
**Bed** & 2.381 & 0.002 & 2.381 & 0.0763 & 75.2 & 0.0089 & 0.078 & **77.7** & 0.009 & 0.078 & 75.5 & **0.0079** \\
**Motorbike** & 0.6145 & 0.008 & 0.6145 & 0.0207 & **92.3** & 0.0117 & 0.021 & **92.3** & 0.0124 & 0.0221 & 91.3 & **0.0134** \\
**Piano** & 0.5435 & 0 & 0.5435 & **0.0007** & **100** & 0.0007 & 0.0009 & **100** & 0.0009 & 0.0058 & **100** & 0.0058 \\
**Dragon1** & 0.1573 & 0 & 0.1573 & 0.002 & 91.1 & **0.0005** & 0.0022 & **95.9** & 0.001 & 0.002 & 91.9 & 0.0007 \\
**Dragon2** & 0.0011 & 92.6 & **0.0005** & 0.0011 & 92.7 & **0.0005** & 0.002 & **95.8** & 0.0012 & 0.0011 & 92.4 & **0.0005** \\
**Bunny** & 0.0023 & 92.9 & **0.001** & 0.0026 & 91.5 & 0.0013 & 0.0024 & 91.1 & 0.0013 & 0.0026 & **94.6** & 0.0099 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMSE, \(\beta\), and ERMSE of ICP, SAC-IA+ICP, 3 DHoPD+ICP, and our method for the six models.
## 4 Proposed Inference Latency Prediction Approaches
### Inference Latency Depending on Devices  
This section deals with the relationship between computing power and inference latency when using the RFCN and SSD-MobileNet models for object detection. We measured the inference latency for the gRPC and REST communication protocols depending on three machines with different computing powers as described in Table 1. As shown  
Figure 4: Relationship between inference latency and image size with REST at the edge: (**a**) RFCN; (**b**) SSD-MobileNet.  
in Figures 5 and 6, we can observe that the SSD-MobileNet model has a much shorter inference latency than the RFCN model and that gRPC is slightly faster than REST. It can also be seen that the cloud shows the lowest inference latency, the edge shows a slightly longer inference latency than the cloud, and the client shows the longest inference latency. What we should pay attention to here is the tendency of inter-machine inference latency. For example, as shown in Figure 6b, although there are large differences between the absolute inference latencies of the client, edge, and cloud devices, it can be observed that the inference latencies of the client, edge, and cloud devices are relatively similar. In addition, the similarity is more evident in REST than in gPRC. Leveraging this similarity may help the client predict the inference latency of the edge and of the cloud.
# Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
###### Abstract  
Accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originating from global environmental change, and providing helpful information about carbon and water content for analyzing and diagnosing past and future climate change. The main focus of the current work was to investigate the feasibility of four comparatively new methods, including generalized regression neural network, group method of data handling (GMDH), extreme learning machine and adaptive neuro-fuzzy inference system (ANFIS), for elucidating the carbon and water fluxes in a forest ecosystem. A comparison was made between these models and two widely used data-driven models, artificial neural network (ANN) and support vector machine (SVM). All the models were evaluated based on the following statistical indices: coefficient of determination, Nash-Sutcliffe efficiency, root mean square error and mean absolute error. Results indicated that the data-driven models are capable of accounting for most variance in each flux with the limited meteorological variables. The ANN model provided the best estimates for gross primary productivity (GPP) and net ecosystem exchange (NEE), while the ANFIS model achieved the best for ecosystem respiration (\(R\)), indicating that no single model was consistently superior to others for the carbon flux prediction. In addition, the GMDH model consistently produced somewhat worse results for all the carbon flux and evapotranspiration (ET) estimations. On the whole, among the carbon and water fluxes, all the models produced similar highly satisfactory accuracy for GPP, \(R\) and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. Based on these findings, it was concluded that these advanced models are promising alternatives to ANN and SVM for estimating the terrestrial carbon and water fluxes.  
carbon fluxes; evapotranspiration; forest ecosystem; data-driven techniques; group method of data handling; extreme learning machine; adaptive neuro-fuzzy inference system +
Footnote †: journal: Journal of Geosciences
## 1 Introduction  
Drilling is the process of drilling a hole into the ground with a drill tool in order to discover underground mineral deposits [1]. Vibration occurs during drilling due to the collision of drilling tools with rock. Measuring vibration information is helpful for real-time monitoring and management of the drilling process, improving operation efficiency, reducing loss, and ensuring drilling safety [2]. As a result, it is necessary to monitor the vibrational condition of downhole drilling instruments in real-time. Due to complex and changeable downhole conditions, most scholars concentrate on the theoretical level of downhole vibration [3] such as mathematical models [4, 5], which cannot provide feedback to the actual downhole state. For the measurement method of downhole vibration, most scholars install sensors at the bottom of the hole to measure downhole vibration, such as acceleration sensors [6, 7], acoustic sensors [8], optical sensing technology [9], and so on. Furthermore, some researchers monitor vibration by affixing sensors to the hole's surface [10, 11].  
However, the aforementioned methods all have defects. If the sensor is installed on the surface of the hole, its measurement accuracy is not high, and it cannot correctly reflect the vibration signal at the bottom of the hole [12]. If the sensor is installed at the bottom of the hole, it is difficult to adapt to the complex and changeable underground environment, and more battery or cable power supply not only reduces construction efficiency but also increases drilling costs [13; 14]. Therefore, it is critical to create a self-powered sensor capable of adapting to the downhole environment and measuring the real-time condition of downhole drilling instruments.  
[PERSON] and colleagues proposed the Triboelectric Nanogenerator (TENG) based on triboelectrification and electrostatic induction in 2012 [15], and it has been utilized extensively in the fields of sensors [16; 17; 18] and energy collection [19; 20], such as marine energy harvesting [21; 22], wind energy harvesting [23; 24], body kinetic energy [25], water wave energy [26; 27], and so on. The energy harvested by triboelectric nanogenerators has been widely used in pressure sensors [28; 29], angle sensors [30; 31], health monitoring [32], motion detection [33; 34], vibration sensors [35; 36; 37], etc. In addition, some scholars have also extended TENG to the field of downhole sensors and realized the monitoring of drilling tools' rotational speed [38], vibration [39; 40], and some other parameters. However, most of the existing downhole self-powered sensors focus on the measurement of downhole parameters and lack the same type of products that can flexibly change their sizes according to the downhole working condition. As a result, this research proposes a honeycomb sensor with free combination based on a triboelectric nanogenerator (H-TENG), which can be freely combined with the changing downhole environment to collect the energy from the vibrations downhole and measure the frequency of drill string vibration in self-dpowered mode.
## Materials and Methods
### Gas Chromatography-Tandem Mass Spectrometer  
Gas chromatography-tandem mass spectrometry (GC-MS) method was appropriately modified based on the reference of [PERSON] et al. [19]. The volatile compounds of the coconut jam were analyzed with a GC - MS instrument (GC-MS-QP2010, Shimadzu, Kyoto, Japan). The analysis was performed on a ZB-5 MS silica capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m) equipped with a mass detector. Helium (99.999% purity) was used as the carrier gas with a flow rate of 1.0 mL/min. The temperature of both the injector and detector was set at 270 \({}^{\circ}\)C. The programmed sequence for the column was set as follows: an initial temperature of 45 \({}^{\circ}\)C was held for 4 min and increased at 5 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C/min prior to being increased to 220 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held at 220 \({}^{\circ}\)C for 5 min. The mass detector was equipped and set in electron impact mode at an ionization voltage of 70 eV in the 50-500 amu (atomic mass unit) scan range for mass spectrum collection, and the ion source temperature was 250 \({}^{\circ}\)C.  
Volatile compounds in coconut jam were identified according to the method of [PERSON] et al. [20]. The volatile compounds were identified by searching the NIST spectrometry library and the retention index (RI) was calculated using a linear heating formula (Equation (1)). Finally, the chemical structure with the closest similarity to the mass spectrum and RI value was selected as the best identification result. Quantitative (relative content) analysis of aroma substances was achieved by peak area normalization [3]. The final volatile components and relative contents determined are shown in Table 1.  
\[\mathrm{RI}=100_{\mathrm{n}}+\frac{100(t_{x}-t_{n})}{t_{n+1}-t_{n}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Descriptors** & **Definitions** \\ \hline \multirow{4}{*}{Odor/Flavor} & Fruity & May resemble the odor of coconut, pineapple, apple, or other fruits \\ \cline{2-3}  & Caramel & Cooked sugar, all which reminds sugar cooking, caramel \\ \cline{2-3}  & Acid & \
\begin{tabular}{c} Sour off-flavor due to acid-producing organisms such as _Lactococcus lactis ssp. cremoris_ \\ \end{tabular} \\ \cline{2-3}  & Fatty &
\begin{tabular}{c} Aromatics associated with stale fats \\ \end{tabular} \\ \cline{2-3}  & Honey & Aromatics associated with the sweet fragrance of honey \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description and definition of the aroma of coconut jam.  
where \(t_{x}\), \(t_{n}\), and \(t_{n+1}\) are the retention times of the outflow peaks of the component analyzed and the n-alkanes (\(t_{n}<t_{x}<t_{n+1}\)) with carbon numbers of n and n + 1, respectively.
# K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony
## 4 Numerical Experiments
### CAABC Performance Analysis
& \(1.65\times 10^{-128}\) & \(6.90\times 10^{-63}\) & **0.00** & **0.00** & **0.00** & **0.00** & **0.00** \\ \cline{2-8}  & CPUtime & 31.72 & 23.42 & 19.34 & **18.21** & 20.35 & 20.42 & 23.22 \\ \hline \multirow{3}{*}{\(f_{12}\)} & Mean & \(3.98\times 10^{-5}\) & \(1.83\times 10^{-5}\) & \(2.04\times 10^{-5}\) & \(\textbf{2.07\times 10^{-8}}\) & \(2.31\times 10^{-5}\) & \(2.25\times 10^{-5}\) & \(2.82\times 10^{-5}\) \\ \cline{2-8}  & Std. & \(8.19\times 10^{-6}\) & \(3.31\times 10^{-6}\) & \(3.87\times 10^{-7}\) & \(\textbf{3.84\times 10^{-5}}\) & \(5.99\times 10^{-7}\) & \(5.06\times 10^{-7}\) & \(9.00\times 10^{-8}\) \\ \cline{2-8}  & CPUtime & 34.13 & 18.34 & 17.15 & **12.66** & 17.68 & 18.97 & 20.88 \\ \hline \multirow{3}{*}{\(f_{13}\)} & Mean & \(6.10\times 10^{-225}\) & \(1.32\times 10^{-8}\) & \(6.29\times 10^{-226}\) & \(\textbf{1.08\times 10^{-231}}\) & \(5.96\times 10^{-238}\) & \(3.07\times 10^{-238}\) & \(4.11\times 10^{-236}\) \\ \cline{2-8}  & Std. & \(0.00\) & \(1.12\times 10^{-9}\) & 0.0 According to the results in Tables 3 and 4, it shows that CAABC is superior to or at least equal to other algorithms in the rest of benchmark functions except for the several  
Figure 6: Comparison with other improved Artificial Bee Colony algorithms in 30 dimensions. Figure 7: Comparison with other improved Artificial Bee Colony algorithms in 60 dimensions. benchmark functions. The case shown in Table 3 happens only on \(f_{9}\), \(f_{11}\) and \(f_{14}\). In addition, in these functions, the difference between the improved algorithm and the others is less than 5%. However, in 60 dimensional comparison experiment, in Table 4, the improved algorithm achieves good results in \(f_{15}\). At the same time, it can be clearly seen in Figure 5 that CAABC also has a better convergence rate. Based on the above experimental sresults, the superiority of this algorithm is proved. With the increase of dimension, the results of CAABC are even closer to ideal results. It indicates that the optimization effect of the CAABC is better than canonical ABC and other mentioned algorithms. The best value, the worst value, the average value, or the standard deviation, might be more ideal when the running time is basically the same. By comparing with CAABC-1 and CAABC-2, the effectiveness of the improved algorithm components has also been verified. Overall, compared with classic ABC and other improved ABC algorithms, the accuracy and efficiency of convergence have been enhanced. The exploration and exploitation performance are productively balanced at the same time.
# Rock Breaking Performance of TBM Disc Cutter Assisted by High-Pressure Water Jet
###### Abstract  
A high-pressure water jet can break rock efficiently, which is of great potential to overcome the problems of a tunnel boring machine (TBM) in full-face hard rock tunnel digging, such as low digging efficiency and high disc cutter wear rate. Therefore, this paper presented a new tunneling method that is a TBM coupled with a high-pressure water jet. The rock failure mechanism under the coupled forces of a disc cutter and water jet was analyzed at first. Then, the finite element method (FEM) and smoothed particle hydrodynamics (SPH) method were used to establish a numerical model of rock broken by the disc cutter and water jet. Effects of parameters on rock breaking performance were studied based on the numerical model. Moreover, an experiment of the water jet cutting marble was carried out to verify the reliability of the numerical simulation. Results showed that the high-pressure water jet can increase the TBM digging efficiency and decrease the forces and wear rate of the disc cutter. The optimum nozzle diameter is 1.5 mm, while the optimum jet velocity is 224.5 m/s in this simulation. The results can provide theoretical guidance and data support for designing the most efficient system of a TBM with a water jet for digging a full-face hard rock tunnel.  
TBM; high-pressure water jet; disc cutter; rock breaking mechanism; numerical simulation +
Footnote †: journal: Applied Physics Letters  
Article
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
###### Abstract  
Many natural signals exhibit quasi-periodic behaviors and are conveniently modeled as combinations of several harmonic sinusoids whose relative frequencies, magnitudes, and phases vary with time. The waveform shapes of those signals reflect important physical phenomena underlying their generation, requiring those parameters to be accurately estimated and modeled. In the literature, accurate phase estimation and modeling have received significantly less attention than frequency or magnitude estimation. This paper first addresses accurate DFT-based phase estimation of individual sinusoids across six scenarios involving two DFT-based filter banks and three different windows. It has been shown that bias in phase estimation is less than 0.001 radians when the SNR is equal to or larger than 2.5 dB. Using the Cramer-Rao lower bound as a reference, it has been demonstrated that one particular window offers performance of practical interest by better approximating the CRLB under favorable signal conditions and minimizing performance deviation under adverse conditions. This paper describes the development of a shift-invariant phase-related feature that characterizes the harmonic phase structure. This feature motivates a new signal processing paradigm that greatly simplifies the parametric modeling, transformation, and synthesis of harmonic signals. It also aids in understanding and reverse engineering the phasegram. The theory and results are discussed from a reproducible perspective, with dedicated experiments supported by code, allowing for the replication of figures and results presented in this paper and facilitating further research.  
Keywords:phase estimation; harmonic phase structure; harmonic magnitude and phase parametric modeling; harmonic signal processing +
Footnote †: journal:  
_Acticle_