esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We have successfully investigated the ability of an MRR-based sensor configuration to detect diabetes. MRR on the SOI platform has been fabricated using the EBL system and the HSQ negative tone as a resist layer. The morphology and quality factor of the fabricated MRR have also been experimentally characterized. The simulation value of quality factors is close to the experimental value. Therefore, the simulation method can be used and is sufficient to obtain the sensor's sensitivity. The resonance shifts were also observed with changes in diabetic glucose concentration. Sensor sensitivity values can still be optimized in order to detect changes at even lower glucose concentrations. Optimization of the MRR-based sensor sensitivity can be done by adjusting the size of the MRR geometric parameters so that it has a higher quality factor value, as there is linearity between the value of the quality factor and the sensitivity of the MRR-based sensor. Optimization by adjusting the size of the MRR geometric parameters can be done using a 3D FDTD simulation first by varying the size of the MRR geometric parameters to predict the value of the quality factor. The overall results will advance development in diabetes monitoring technology, especially in non-invasive sensing devices. Coupled with real-time detection and label-free features, MRR-based sensors will contribute to the increase in effectiveness while reducing operating costs in diabetes monitoring technology.  
Conceptualization, B.M., L.H., and R.M.; methodology, I.H. and K.K.; software, B.M.; H.S.N. and C.W.; validation, L.H., D.D.B., P.S.M., and A.R.M.Z.; formal analysis, H.S.N.; investigation, H.S.N., C.W., and M.H.H.; resources, A.R.M.Z.; data curation, L.H., D.D.B., and H.S.N.; writing--original draft preparation, H.S.N.; writing--review and editing, L.H., D.D.B., and P.S.M.; visualization, H.S.N.; supervision, L.H., B.M., D.D.B., and P.S.M.; project administration, B.M.; funding acquisition, B.M. All authors have read and agreed to the published version of the manuscript.  
We acknowledge the Directorate of Research and Community Service, Ministry of Research, Technology and Higher Education, Republic of Indonesia, for financial supporting through the grant of Penelitian Terapan Unogulan Perapuran Tinggi. The authors also acknowledge the Ministry of Education of Malaysia for financial support under the Fundamental Research Grant Scheme FRGS/1/2015/TK04/UKM/02/4.  
Figure 11: Resonance shift that occurs in MRR-based sensors due to the different glucose concentrations of a diabetic person.  
**Acknowledgments:** We acknowledge the administrative support given by Institute of Microengineering and Nanoelectronics (IMEN), UKM, and Universitats Pendidikan Indonesia (UPI) while doing this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### Engine Results
In this case the fuel jets are able to find relatively easily the oxygen inside the chamber, with small amounts of oxygen remaining in the crevice region and near to the injector at the end of combustion (CA90). For the baseline oxy-fuel combustion case, the CA10, CA25, and CA50 phases occur almost at the same crankangle position, during the premixed phase of the combustion. Higher quantities of oxygen remained in the squish region that did not reach the fuel, evidencing rich \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Reference** & **Baseline** & **Optimized** \\  & **Diesel** & **Oxy-Fuel C.** & **Oxy-Fuel C.** \\ \hline Efficiency [\%] & 40.1 & 26.6 & 35.7 \\ Soot [mg/s] & 0.35 & 11.4 & \textless{}0.0001 \\ CO [mg/s] & 177 & 1929 & 81 \\ HC [mg/s] & 28 & 206 & 4.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison between the efficiency, soot, and CO between the reference Diesel case, baseline and optimized oxy-fuel combustion cases. zones and incomplete combustion, responsible of the soot formation. On the contrary, the optimized oxy-fuel combustion case shows better oxygen consumption at the end of combustion (from CA50 to CA90) in comparison with the baseline configuration, with a similar distribution compared to the reference air-diesel engine, evincing the advantages of the optimization procedure. Figure 16: O\({}_{2}\) distribution inside the combustion chamber for several combustion periods (CA) and comparison between the reference diesel, baseline, and optimized oxy-fuel combustion case. Figure 15: Energy balance for the three simulated cases. Figure 17 illustrates the temperature contour inside the combustion chamber for the diesel reference and the optimized configuration. Although the new combustion chamber has smaller volume, the jet is able to distribute and progress within the domain until it reaches the bowl wall. The matching between the jet and the bowl corner can be seen, re-distributing the spray inside the bowl and some other part toward the squish, where fresh oxygen is available (in agreement with Figure 16). After CA50 phases, the temperature contours show higher values for the optimized case than the diesel one, which are also evenly distributed. This is also consistent with the temperature traces in Figure 14. In this combustion concept the high temperature levels can be afforded because if there is no N\({}_{2}\) in the trapped gas in the cylinder, then there is no NO\({}_{x}\) production. Also, relatively high temperature levels are desired in order to provide sufficient energy to the MIEC, as mentioned before.
# Oxygen Isotopic Compositions of San Carlos Olivine Standard NMNH 111312-42
## 4 Discussion
### The Accuracy and Precision for Oxygen Isotope Analysis  
The oxygen isotopic compositions of olivine are critical for understanding geological and planetary processes, providing insights into research such as, but not limited to, mantle dynamics and crustal evolution [13]. An accurate determination of olivine's oxygen isotope values is essential for utilizing these data in tracing geological processes. Calibration during analysis relies on standard samples like the San Carlos olivine geostandard. Therefore, the uniformity of San Carlos olivine and potential differences between -42 and -44 olivine grains can significantly influence calibration accuracy.  
The precision required for oxygen isotope analysis varies depending on the geological application. For instance, oxygen isotope fractionation between olivine and basaltic melts at mantle temperatures is typically limited to \(\leq\)1%, while fractionations between olivine and coexisting minerals such as orthopyroxene and spinel are smaller than 1.5% at magmatic  
Figure 2: The comparison of \(\delta^{18}\)O values of NMNH 111312–44 vs. NMNH 111312–42. Note that the literature data are displayed by frequency instead of counts. The data are from [PERSON]. et al. [6], [PERSON]. et al. [5], and [PERSON]. et al. [7]. The value displayed in the blue range represents the average value of measurements taken in July and October.  
temperatures [14]. These small fractionations highlight the importance of high-precision measurements in resolving subtle isotopic variations. Oxygen isotopic compositions of olivine have revealed compositional heterogeneity in Earth's deep mantle, attributed to the subduction of oceanic slabs over 3.3 billion years ago [15]. In that study, two groups of olivine with distinct oxygen isotope compositions (4.9-5.4% and 3.6-4.7%) were identified. Accuracy requirements for such analyses typically range from \(\pm\)0.10% to \(\pm\)0.60%, with \(\pm\)0.20% sufficing in most cases. Even considering a slight \(\xi^{18}\)O discrepancy (0.06%) between -44 and -42 olivine grains, this is well within the acceptable precision for igneous rock analysis (\(\pm\)0.2% to \(\pm\)0.3% [16]).
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 4 Discussion  
### Multiple Prediction Times of LSTM Model  
In order to investigate the prediction capability of the LSTM model in a longer term, representative prediction times of 3 h, 7 h, and 15 h are selected to train the model and the ACC values of training set are all above 0.85 to ensure the model precision. Figure 5 shows the model results of testing set for the LSTM model with these four prediction times. It can be seen that the time series fit well with the observed values for the testing set morphologically. In terms of the difference between the modelled and observed values of the testing set, the maximum difference increases with prediction time which are 18 cm, 23 cm, 36 cm, and 45 cm at prediction times of 1 h, 3 h, 7 h, and 15 h respectively.  
Figure 6 shows the MAE, RMSE, and ACC values of testing set for the LSTM model with 1 h, 3 h, 7 h, and 15 h prediction times. Difference has a consistent augment trend for the maximum difference between the modelled and observed values, i.e., the MAE value increases sharply from 1 h prediction time to 3 h prediction time by 150%, which is followed by a slightly decrease from 3 h prediction time to 7 h prediction time by 6% and a moderately increase from 7 h prediction time to 15 h prediction time by 29%. It might be due to the nature of MAE which is the average of the absolute difference value between the modelled and observed values. The variation of RMSE with the prediction time displays a similar form to MAE while ACC shows an inverse pattern. For simplicity, it is feasible to select one of these three methods to evaluate the model. For example, when MAE is taken as the criterion, its value for 1 h and 15 h prediction times is 13.4 cm and 40.7 cm respectively. Based on the 20-year return period storm surge value of 186.5 cm [97], the percentage error of 1 h and 15 h prediction times are 7% and 22% respectively. Finally, it can be concluded that the LSTM model performs precisely for 1 h prediction of water  
Figure 4: (**a**) Model results of the LSTM model with 1 h prediction time, and (**b**) the zoom-in view of the testing set section where the light blue area is the cold start subset. Red dash line circle represents the maximum difference between the modelled and observed values at the water level peak.  
level during the storm surge period and it can also provide a 15 h prediction of water level within a limited error.
## 3 Results  
### SPME Optimization  
#### 3.1.1 SPME Coating Selection  
Seven commercially available SPME coatings were selected for optimization in the extraction of aroma compounds from Iowa red wines (Table 1). The response of the mass spectrometer to volatiles detected throughout the run was used to determine the extraction efficiency of the SPME coating. It was shown that the use of a 50/30 \(\upmu\)m DVB/CAR/PDMS SPME coating was most appropriate for the rest of the experiments. In Table 1, a coating with a number lower than 100 indicates a lower extraction efficiency for that analyte when compared to 50/30 \(\upmu\)m DVB/CAR/PDMS. These analytes spanned the entire chromatographic run, representing the range of analytes in wine aroma.
# Storing Set Families More Compactly with Top ZDDs
## 2 Preliminaries
### Succinct Data Structures
#### 2.2.1 Bit Vectors  
Bit vectors are the most basic succinct data structures. A length-\(n\) sequence \(B\in\{0,1\}^{n}\) of \(0\)'s and \(1\)'s is called a bit vector. On this bit vector we consider the following operations:  
* \(access(B,i)\) (\(1\leq i\leq n\)): returns \(B[i]\in\{0,1\}\), the \(i\)-th entry of \(B\).
* \(rank_{c}(B,i)\) (\(1\leq i\leq n,c=0,1\)): returns the number of \(c\) in the first \(i\) bits of \(B\).
* \(select_{c}(B,i)\) (\(1\leq j\leq n,c=0,1\)): returns the position of the \(j\)-th occurrence of \(c\) in \(B\).  
The following result is known.  
**Theorem 1**.: ([15]) _For a bit vector of length \(n\), using a \(n+\mathrm{O}(n\log\log n/\log n)\)-bit data structure constructed in \(\mathrm{O}(n)\) time, \(access(B,i),rank_{c}(B,i),select_{c}(B,j)\) are computed in constant time on the word-RAM with word length \(\Omega(\log n)\)._  
Consider a bit vector of length \(n\) with \(m\)\(1\)'s. For sparse bit vectors, namely, that consisting of a binary sequence with only \(m=\mathrm{o}(n/\log n)\)\(1\)'s, we can obtain a more space-efficient data structure.  
**Theorem 2**.: ([16]) _For a bit vector \(B\) of length \(n=2^{m}\) with \(m\) ones, \(select_{1}(B,i)\) is computed in constant time using a \(2m+\mathrm{O}(m\log\log m/\log m)\)-bit data structure._  
Note that on this data structure, \(rank_{0},rank_{1},select_{0}\) takes \(\mathrm{O}(\log m)\) time.
# Self-Tuning Algorithm for Tuneable Clamping Table for Chatter Suppression in Blade Recontouring
## 7 Conclusions  
The wide variety of different blade geometries present in a single engine makes blade manufacturing and repairing processes a complex task, since specific stiffening fixtures are needed for each geometry in order to avoid machining chatter when performing the recontouring operations. Similarly to TMDs, the tuneable clamping table can damp the critical modes by means of mode tuning, but thanks to its in-series configuration, the tuning can be achieved without interfering with the cutting area, enabling a more universal clamping. Aiming at maximising its chatter suppression capabilities, a self-tuning algorithm has been developed for TCT architecture. The algorithm iteratively measures the frequency of the arising chatter and optimally tunes the device for that frequency, outperforming other wide-bandwidth tuning strategies. The effectiveness of the algorithm has been validated through semidiscretization calculations and initial value time domain simulations, where the stability enhancement provided in the ideal linear case has been validated. The industrial application of the algorithm would require studying the robustness of the algorithm with respect the uncertainty of the part dynamics due to initial deviations or material removal. Conceptualization: M.S.-C., Z.D. and J.M. ; software: M.S.-C. and Z.D. ; investigation: M.S.-C., writing--original draft preparation: M.S.-C.; writing--review and editing: Z.D. and A.I. ; supervision: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This project has been funded by the MIRAGED: Poscionamiento Estrategico en Modelos Virtuales y Gemelos Digitales para una Industria 4.0 (CER-20191001), supported by CDTI-Acceditacion y Concesion de Ayudas Destinadas a Centros Tecnologicos de Excelencia Cervera; the Hungarian NKFI FK 124361 and the TiMachina project (IDI-201904196) from the International Technological Corporation, and by R+d projects program of the Spanish Centre for the Development of Industrial Technology (CDTI). Not applicable. This research work has been done under the framework of the project MIRAGED: Poscionamiento Estrategico en Modelos Virtuales y Gemelos Digitales para una Industria 4.0 (CER-20191001), supported by CDTI-Acceditacion y Concesion de Ayudas Destinadas a Centros Tecnologicos de Excelencia Cervera; the Hungarian NKFI FK 124361 and the TiMachina project (IDI-201904196) from the International Technological Corporation, and by R+d projects program of the Spanish Centre for the Development of Industrial Technology (CDTI). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} DDE & delay differential equation \\ FFT & fast Fourier transform \\ MRO & maintenance, repair and overhaul \\ SLD & stability lobe diagram \\ TMD & tuned mass damper \\ TCT & tuneable damping table \\ ZOA & zeroth order algorithm \\ \end{tabular}   
**Nomenclature**
## 2 Materials and Methods
### Water and Sediment Analysis  
Water samples were analyzed [22] after being filtered through 0.45 \(\upmu\)m pore filters. The phosphate, iron, and manganese contents were measured using Merck or Nanocolor spectrophotometers. TN was analyzed using an IL 550 TOC-TN analyzer. Measurements included pH, EC, iron, manganese, and phosphates by spectrophotometer; calcium was measured by the titration method using EDTA and eriochrome black T [22].  
Water content in fresh sediment was measured gravimetrically. Results were used for calculating the dry weight (DW) of sediment samples.  
The bottom sediment was dried and milled using an A10 Basic mill (IKA Works GmbH & Co. KG, Staufen, Germany). The dry weight, contents of organic matter with CO\({}_{2}\) regeneration, and inorganic carbon were measured gravimetrically. Dried sediments were mineralized according to the methodology described by [9], and SiO\({}_{2}\), Fe, Mn, Al, Ca, Mg, and total Kjeldahl nitrogen (TKN) contents were analyzed after mineralization using a Merck spectrophotometer. Phosphorus fractions (labile P, redox-sensitive P, aluminum-bound P and P connected to organic matter, calcium-bound P, and residual P) were analyzed according to the [23] method using an Innova 40 shaker with incubation. Soluble phosphates (SRP) and total P(TP) in extracts were measured using molybdenum blue method spectrophotometrically. The results of the water-sediment interface chemistry (significance  
Figure 3: Scheme of Lake Kortowskie with hypolimnetic withdrawal installation.  
Figure 2: Bathymetric map of Lake Kortowskie with the location of sampling stations [19] (modified).  
of differences between St. 1 and St. 2) were statistically analyzed using a \(t\)-test for independent samples (Statistica 13.5), and \(p\) was set as 0.05. Obtained research results were log, log + 1, or arc sin transformed (data approximation to normal distribution).
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 4 Experiments
### Relationship between Molecular System and Partitions  
Figure 9 does not seem to exhibit a correlation between the molecular connectivity (measured with the average graph degree) and the normalized number of operations (NNO), defined as the sum of cubes criterion (3) normalized by the complexity \(n^{3}\) of the dense matrix-matrix multiplication. The observation also applies to the polyethylene dense crystal, whose normalized number of operations remains low although its average degree is high. Similar observations can be made for other molecules with a smaller average degree.  
Our G-SP2 algorithm with METIS being used in the graph partitioning step is capable of finding the lowest NNO for 1-dimensional systems such as the polyethylene linear chain and the polyethylene sparse crystal (compare the MMPN values in the brackets in Figure 9 to the measured normalized number of operations on the \(y\)-axis). According to [22], regular agglomerates of polyethylene chains align along a particular direction with a large chain-to-chain distance.  
We do not observe any advantage of our approach (measured via NNO) for _regular_ systems (polyethylene linear chain, polyethylene sparse crystal, polyethylene dense crystal, and urea crystal).  
A difficult case for our graph partitioning task is the phenyl dendrimer (expressed through its high NNO values). This is due to the fractal-like structure of the graph associated with the molecule.  
Another class with a large NNO is proteins (solvated polyalamines). We conjecture that the large average node degree (in comparison to peptides, i.e., small proteins) is responsible for the large NNO measurements. Based on Figure 9, we further conjecture that the difference of maximum and minimum partition norms \((\max-\min)/n\) (MMPN) is correlated with the NNO. Here, the number of vertices is denoted as \(n\), and the maximal and minimal sizes of the obtained blocks are denoted as \(\max\) and \(\min\), respectively. The conjectured correlation is clearly visible in Figure 9: the dendrimer tends to both large MMPN and NNO values, proteins exhibit intermediate values of both MMPN and NNO and finally, we observe low values of both MMPN and NNO for sparse ordered systems such as polyethylene chains.  
Figure 9: Sum of cubes criterion (3) normalized by the complexity \(n^{3}\) of the dense matrix-matrix multiplication for G-SP2 with METIS. Number of vertices \(n\), number of edges \(m\), and average degree \(m/n\). Test systems of Table 1. Brackets show fractions of \((\max-\min)/n\) (MMPN). Figure taken from [12]. Copyright ©2016 Society for Industrial and Applied Mathematics. Reprinted with permission. All rights reserved.
## 2 Stack Long Short-Term Memory Network  
### LSTM Network  
Recently, due to the advantages of deep learning in automatic feature extraction and high recognition rate or prediction accuracy, it has been successfully applied to speech recognition [17], action recognition [18], remaining useful life prognosis [19], traffic flow prediction [20], and other fields. As a commonly used deep learning model, the recurrent neural network (RNN) is an effective method for modeling dynamic sequences [21; 22; 23; 24; 25; 26; 27; 28]. However, a RNN struggles to model long sequences because of gradient disappearance. To solve this problem, [PERSON] et al. proposed the long short-term memory (LSTM) network [29]. It uses multiple functionally distinct gates to control neurons and store information, thus having the ability to store important information for longer periods of time. The information is achieved by dot product \(\otimes\) of activation function. Each gate state relies on a series of parameters trained by the gradient descent method.  
Figure 1 shows the structure of the LSTM network in detail. Each gate in the LSTM network has a specific and unique function. Forget gate \(f\) decides which information should be discarded from the previous state \(h_{t-1}\). Input \(x_{t}\) and previous state \(h_{t-1}\) after update gate \(u\) operation,  
\(y_{t-1}\). Each gate depends on the current external input \(x_{t}\) and the previous output \(y_{t-1}\). The updated process of the LSTM network is given by Equation (1).  
\[\left\{\begin{array}{l}\begin{aligned} & forget\;gate:f_{t}=\sigma(W_{f}x_{t}+R_{f}y_{t-1}+b_{f})\\ & candidate\,state:\overline{h_{t}}=g_{1}(W_{f}x_{t}+R_{f}y_{t-1}+b_{f})\\ & update\,state:u_{t}=\sigma(W_{u}x_{t}+R_{u}y_{t-1}+b_{u})\\ & cell\,state:h_{t}=u_{t}\otimes\overline{h_{t}}+f_{t}\otimes h_{t-1}\\ & output\,gate:_{t}=\sigma(Wx_{t}+Ry_{t-1}+b)\\ & output:y_{t}=_{t}\otimes g_{2}(h_{t})\end{aligned}\right. \tag{1}\]  
where \(x_{t}\) is the input vector at \(t\) time; \(W_{f}\), \(W_{h}\), \(W_{u}\), and \(W\) are the weight matrices associated with the input unit; \(R_{f}\), \(R_{h}\), \(R_{u}\), and \(R\) are the weight matrices for hidden layer connections; \(b_{f}\), \(b_{h}\), \(b_{u}\), and \(b\) are bias vectors; \(\sigma\) is the sigmoid or tanh function; \(g_{1}\) and \(g_{2}\) are the non-linear activation functions; and \(\otimes\) denotes the dot product operation.