esa-sceva/satcom-chunk-collection huggingface dataset:
# A Formula-Structure-Embedding-Based GNN for Formula Equivalence Determination
## 3 Method
### Encoder
#### 3.1.4 Graph Embedding Module  
In order to capture global features and combine node importance, a global context-aware attention mechanism is introduced to generate the global graph context vector by averaging and non-linearly transforming all node embedding vectors. Different attention weights \(W\) are assigned to the global context, and the global graph context vector calculated by Equation (2) reflects the overall structure information of the formula.  
\[h=\sum_{n=1}^{N}\sigma(u_{n}^{T}c)u_{n}=\sum_{n=1}^{N}f_{2}\Bigg{(}u_{n}^{T} \tanh\Bigg{(}\Bigg{(}\frac{1}{N}\sum_{m=1}^{N}u_{m}\Bigg{)}W\Bigg{)}\Bigg{)}u _{n} \tag{2}\]  
In Equation (2), \(N\) is the total number of nodes, \(u_{n}\in R^{D}\) is an embedding of node \(n\), \(c\in R^{D}\) is a simple average of node embedding, \(W\in R^{D\times D}\) is the learnable weight matrix, \(\sigma(\cdot)\) is the sigmoid activation function. This global context-based attention mechanism has an advantage of reflecting the overall structure features of formulas by flexible adjustment of node weights.
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 4 Experimental Methodology  
To evaluate the effectiveness of optical interconnects on the performance and energy efficiency of the multi-chip machine-learning system, we modeled DaDianNao multi-chip systems and used famous CNNs for image recognition as well as our arithmetic performance and energy model for measurements.
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 5 Results
### ICI Aware Progressive Iterative Receiver
#### 5.2.2 Experimental Results  
In order to verify the effectiveness of the proposed MIMO-OFDM ICI-aware progressive iterative receiver, this section tests the system experimentally by adding Doppler to the experimental setup discussed in Section 5.1.2. The Doppler is added manually to the system by moving the transmitters, while keeping all other conditions unchanged. The 10 received signals of the 2 nd, 3 rd, 5 th, 10 th and 8 th receiving hydrophones are sequentially combined to test the proposed MIMO-OFDM progressive iterative receiving algorithm. In the iterative process, the values of the Doppler factor \(N_{b}^{D}\) corresponding to \(D\text{max}=1,2\) are taken as 6 and 10 respectively, where \(D=1\) is iterated twice, therefore it is actually iterated three times in total. The BER values for different number of combined  
Figure 8: Comparison of the MIMO progressive and non-progressive iterative receivers.  
Figure 9: Comparison of complexity of MIMO progressive and non-progressive receivers.  
receiving hydrophones and different value of \(D\)max are given in Table 2. The table also compares the BER performance with the BER values of the ICI-unaware receiver BER for \(D\)max = 3.  
Table 2 shows that as the iterative process progresses, the value of \(D\)max increases gradually and the performance of the system is significantly improved, which proves the effectiveness of the progressive iterative receiver. At the same time, it can be seen that the BER values of the ICI-unaware receiver when iterated three times is very close to the BER values of the ICI-aware receiver after third progression. The reason behind this may be the smaller Doppler shift, which results in less ICI and therefore can be ignored in the actual processing.  
Furthermore, using the CP-based UWA Doppler scaling factor estimation method [25], the Doppler scaling factor is sequentially estimated for eight OFDM symbols in one frame of different receiving hydrophones, as shown in Figure 10. It can be seen that the Doppler scaling factor is in the order of \(10^{-5}\), which confirms that the Doppler shift is really small. This may be due to the slow speed of the movement of the array by hand, as the transmitting array is relatively heavy and might not be disturbed properly, therefore, causing a smaller Doppler scaling factor. In this case, it is recommended to use the ICI-unaware iterative receiving algorithm, because of its closer performance to the ICI-aware progressive receiving algorithm with fewer processing and calculations.
## 3 Results and Discussion
When the synthesizing temperature was 1100 \({}^{\circ}\)C and 1200 \({}^{\circ}\)C, the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors had less variation in their lifetimes. In Figure 6 we use a fitting curve to find the relationship between lifetime and synthesizing temperature; the equation is:  
\[t=-0.0016\times T+3.543 \tag{2}\]  
where \(t\) represents the emitting lifetime of the luminescent material and \(T\) represents the synthesizing temperature (\({}^{\circ}\)C). In other words, the luminescence lifetime of Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors can be obtained by using Equation (2), as the synthesizing temperature is changed from 900 \({}^{\circ}\)C to 1200 \({}^{\circ}\)C. These results suggest that the luminescence lifetime of Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors can be a function of synthesizing temperature, and that the concentration of Eu\({}^{3+}\) ions not only can affect their emitting intensity but also can affect their luminescence lifetime. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Synthesizing Temperature** & \(\mathbf{X=0.04}\) & \(\mathbf{X=0.06}\) & \(\mathbf{X=0.08}\) & \(\mathbf{X=0.10}\) & \(\mathbf{X=0.12}\) \\ \hline
900 \({}^{\circ}\)C & 2.05 & 2.1 & 2.04 & 2.04 & 2.05 \\
1000 \({}^{\circ}\)C & 2.03 & 2.03 & 1.93 & 1.97 & 1.99 \\
1100 \({}^{\circ}\)C & 1.81 & 1.83 & 1.78 & 1.82 & 1.82 \\
1200 \({}^{\circ}\)C & 1.65 & 1.65 & 1.66 & 1.66 & 1.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Luminescence lifetimes of Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors as a function of the synthesizing temperature and concentration of Eu\({}^{3+}\) ions. Figure 5: Luminescence lifetime of Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors synthesized at (**a**) 900 \({}^{\circ}\)C; (**b**) 1000 \({}^{\circ}\)C; (**c**) 1100 \({}^{\circ}\)C; and (**d**) 1200 \({}^{\circ}\)C, respectively. Figure 6: Fitting curve of lifetime for Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors as a function of the synthesizing temperature and concentration of Eu\({}^{3+}\) ions.
## 4 Calculation Example
### Establishing Finite Element Model
#### 4.1.1. Loads and Constraints  
1. For the whole structure, purlins and radial beams are connected by pin shafts, and the rotation constraints need to be released at the nodes. The purlins and supporting purlins adopt chute beams, and the rotational and axial constraints need to be released at the connecting nodes. The short axis bending moment of all reinforced purlins and the torsional bending moment at the other end need to be released. The braces are connected to the radial beam and tension ring clamp by pin shafts, and the rotation constraints are released.
2. The load condition of the whole structure consists of dead load and pre-tension load. The dead load is the weight of structural components, and the pre-tension load includes equivalent pre-tension in the cable net and outer compression ring, which can be achieved by applying an equivalent temperature difference calculated through cable forces using Equation (16): \[\Delta\mathrm{T}=\frac{\mathrm{P}}{\alpha\mathrm{EA}}\] (16) where P pre-tension force, \(\alpha\) is temperature linear expansion coefficient, E is elastic modulus, A is section area, and \(\Delta\mathrm{T}\) is equivalent temperature difference. Considering the symmetry of the structure, Table 4 shows the equivalent temperature difference of \(1/4\) CR, and Table 5 shows the equivalent temperature difference of \(1/4\) radial cables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CR Element** & **PCR (k
###### Abstract
It achieves approximate load balancing on different computing nodes by using the idea of approximate processing, dividing the data into cells and performing pseudo-random partitioning based on the cells. However, the RP-DBSCAN algorithm is still essentially based on clustering spatial data, and therefore it does not make good use of the temporal attribute information contained in the spatial-temporal trajectory data. In this paper, we present STRP-DBSCAN, a parallel DBSCAN algorithm based on spatial-temporal random partitioning for clustering trajectory data. STRP-DBSCAN is an improvement on the RP-DBSCAN algorithm. It assigns data that are close in both the spatial and temporal attributes to the same partition as far as possible for computation by introducing temporal partitioning based on the idea of the spatial random partitioning of RP-DBSCAN. In this way, STRP-DBSCAN can further improve the efficiency of clustering spatial-temporal data and speed up the clustering computation. Aiming at the problem that DBSCAN is more sensitive to the global parameters, we propose a PER-SAC algorithm based on deep reinforcement learning (DRL), which automatically tunes the optimal parameters to improve the clustering accuracy. The contributions of this paper are summarized as follows:  
* The STRP-DBSCAN algorithm is proposed, by introducing the idea of spatial-temporal random partitioning, data that are close in spatial and temporal attributes are divided into the same partition as far as possible to achieve a better load balancing among different computing nodes, which reduces the communication of parallel computation and improves the clustering efficiency. * The DRL-based PER-SAC algorithm is proposed to solve the parameter sensitivity problem of DBSCAN, which uses the prioritized experience replay (PER) mechanism combined with the soft actor-critic (SAC) algorithm to construct an optimal parameters autotuning framework for DBSCAN in a simple, fast, and efficient way. * The proposed algorithm is extensively evaluated on the acquired real dataset of public automatic identification system (AIS), and the experimental results show that the proposed algorithm effectively improves the clustering speed and clustering accuracy of the spatial-temporal trajectory data and achieves a better stability of the clustering results. The rest of this paper is organized as follows. Section 2 introduces related work. Section 3 presents the STRP-DBSCAN algorithm. Section 4 describes the implementation of the PER-SAC algorithm. Section 5 shows the evaluation results. Section 6 concludes this paper.
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 4 Optical Intensity Modulation
fabricated a high-precision sub-wavelength nano-grating based on photolithography and designed a wide-band high-sensitivity displacement sensor based on this grating and an arrayed vertical cavity surface laser [40; 41]. [PERSON] et al. applied the grating to a high-sensitivity optical accelerometer, as shown in Figure 10. This optical accelerometer achieves a resonant frequency of 36 Hz and a sensitivity of 590 V/g [42]. Similar to the principle of GS, an optical shadow sensor (OSS) realizes sensing by detecting changes in shadows projected onto the photosensitive surface of a photodetector [43]. [PERSON] et al. used an OSS to measure the motion of seismic mass, as shown in Figure 11a [44; 45]. The system consists of a suspended seismic mass and a complementary metal-oxide-semiconductor (CMOS) chip with a detection photodiode and a readout circuit. When acceleration acts on the system, the seismic mass moves, causing a change in the shaded portion of the CMOS chip's photosensitive surface, which, in turn, causes a change in the chip's output signal, from which the acceleration value can be calculated. The performance of the accelerometer is affected by the resolving power of the CMOS chip at the micron level. [PERSON] et al. used high-sensitivity photodetectors instead of CMOS chips to improve the accuracy of OSS [46]. This photodetector is produced by Siemens, model BPX-65, and has a sensitivity of 0.55 A/W, a quantum efficiency of 80%, and a NEP of 3.3 \(\times\) 10\({}^{-14}\) W/Hz\({}^{1/2}\). The accelerometer designed by [PERSON] et al. also uses an inverse spring with a very low resonance frequency (2.3 Hz) with an accelerometer sensitivity as low as 41 ng/Hz\({}^{1/2}\). [PERSON] et al. and [PERSON] et al. also designed accelerometers with very low resonant frequencies [47; 48]. [PERSON] et al. used an asymmetric spring structure that consisted of two curved and two folded beams, as shown in Figure 11b, to significantly improve the sensitivity of the optical accelerometer to 8.1 ng/Hz\({}^{1/2}\). Figure 10: GS-based accelerometer. Reprinted with permission from Ref. [42]. Copyright 2008 Elsevier. Figure 9: OWG accelerometer: (**a**) cantilever type and (**b**) shutter type. In summary, optical accelerometers based on optical intensity modulation are similar to those based on optical path modulation, with a simple structure and lower cost. This type of optical accelerometers can achieve extremely high sensitivity by using a highly sensitive mechanical sensing unit [46, 47, 48]. However, the measurement accuracy of light intensity-modulated accelerometers is directly dependent on the stability of light intensity and has high requirements for the light source and its driver. Therefore, the cost of highly sensitive optical intensity-modulated optical accelerometers is high. The parameters of the structures and the optical accelerometer based on optical intensity modulation (i.e., resonant frequency, working frequency band, and sensitivity) are provided in Table 2.
## 3 Validation of MANTiS-32 Sensing Performance
### Integrated Measurement Performance Evaluation of the MANTiS-6050 Sensor and MANTiS-32 System
#### 3.4.3 Modal Sensing Performance Evaluation of MANTiS-32  
To assess the modal sensing performance of the MANTiS-32 wireless measurement module, eight MANTiS-605 accelerometers were uniformly mounted along the top surface of the cantilever beam at intervals of 80 mm, as shown in Figure 12. Each sensor was connected to a single MANTiS-32 hub via RS485 communication, and the acquired data were transmitted in real time to the PBM system through a Wi-Fi access point. The sampling frequency for all sensors was set to 200 Hz, and data were recorded continuously for a duration of 16 s. An impulse load was applied to induce free vibration in the beam, and the resulting acceleration responses were used for output-only ambient modal analysis. The analysis was performed using the Time Domain Decomposition (TDD) technique, following the method proposed by [PERSON] et al. (2005) [PERSON] et al. (2005), enabling real-time identification of modal parameters. As representative results, Figure 13(a) presents the acceleration response measured at sensor location S8, while Figure 13(b) illustrates the corresponding cross-correlation function. The power spectral density of the signal, shown in Figure 13(c), clearly reveals the beam's dominant natural frequencies, with the first and second modes identified at 8.406 Hz and 53.864 Hz, respectively. These results closely align with the finite element predictions, confirming the reliability of the MANTiS-32 system for accurate wireless modal sensing.
###### Abstract  
The observational requirements for space-based quantification of anthropogenic CO\({}_{2}\) emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based monitoring of CO\({}_{2}\) emissions: pixel size and revisit rate, and we introduce a new method utilizing multiple images simultaneously to significantly improve emission estimates. The impact of pixel size ranging from 2-10 km for space-based imaging spectrometers is investigated using plume model simulations, accounting for biases in the observations. Performance of rectangular pixels is compared to square pixels of equal area. The findings confirm the advantage of the smallest pixels in this range and the advantage of square pixels over rectangular pixels. A method of averaging multiple images is introduced and demonstrated to be able to estimate emissions from small sources when the individual images are unable to distinguish the plume. Due to variability in power plant emissions, results from a single overpass cannot be directly extrapolated to annual emissions, the most desired timescale for regulatory purposes. We investigate the number of overpasses required to quantify annual emissions with a given accuracy, based on the mean variability from the 50 highest emitting US power plants. Although the results of this work alone are not sufficient to define the full architecture of a future CO\({}_{2}\) monitoring constellation, when considered along with other studies, they may assist in informing the design of a space-based system to support anthropogenic CO\({}_{2}\) emission monitoring.  
CO\({}_{2}\) emissions; satellite; coal; climate change; anthropogenic; power plant; greenhouse gases +
Footnote †: journal:  
1
## 3 Results
### Anthropometric Indicators  
The height, body mass, and BMI of children in both groups at the beginning and end of the study are presented in Table 2. In the course of the study, the children's body height increased on average by nearly 14 cm (EPA 13.50 cm; SPA 14.17 cm; \(p\) = 0.000). The body height of girls increased by 13.98 cm (SPA 14.17 cm; EPA 13.62 cm; \(p\) = 0.021), and that of boys increased by 13.89 cm (SPA 14.16 cm; EPA 13.34 cm; \(p\) = 0.014). The weight gain of the entire group was on average 13.83 kg (SPA 14.62 kg; EPA 13.05 kg; \(p\) = 0.001), including 13.62 kg in girls (SPA 14.04 kg; EPA 13.21 kg; \(p\) = 0.002) and 14.04 kg in boys (SPA 15.20 kg; EPA 12.88 kg; \(p\) = 0.041). Girls had a lower body weight compared to boys (54.91 kg vs. 60.73 kg, respectively; \(p\) = 0.000).  
Overall, SPA girls had a higher BMI than EPA girls (Table 2). The average BMI increase in SPA girls over the entire study period was 1.80 kg/m\({}^{2}\) and in EPA, this was 1.25 kg/m\({}^{2}\) (\(p\) = 0.001). SPA boys started the study with a higher BMI than EPA boys (SPA 25.92 kg/m\({}^{2}\); EPA 22.65 kg/m\({}^{2}\); \(p\) = 0.000). The average BMI increase in SPA boys was 1.31 kg/m\({}^{2}\) and in EPA boys, this was 1.56 kg/m\({}^{2}\) (\(p\) = 0.004). Girls had a lower BMI compared to boys (23.17 vs. 25.01 kg/m\({}^{2}\), respectively; \(p\) = 0.003).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measurement** & \multicolumn{3}{c}{**SPA**} & \multicolumn{3}{c}{**EPA**} & \multicolumn{3}{c}{**\({}^{*}\)\(p\)**} \\ \cline{2-7}
**Session** & **Girls** & **Boys** & **Mean** & **Girls** & **Boys** & **Mean** & \\ \hline \multicolumn{7}{c}{Overweight} \\ \hline Initial & 59\% & 55.5\% & 57.25\% & 58\% & 50\% & 54\% & 0.030 \\ Final & 47\% & 61\% & 54\% & 62.5\% & 67\% & 59.75\% & 0.021 \\ \multicolumn{7}{c}{Obesity} \\ Initial & 41\% & 44.5\% & 42.75\% & 42\% & 50\% & 46\% & 0.045 \\ Final & 53\% & 39\% & 46\% & 37.5\% & 33\% & 35.25\% & 0.050 \\ \multicolumn{7}{c}{A Change in the BMI Category from Overweight to Normal} \\ \multicolumn{7}{c}{Entire study period} & 3.33\% & 0 & 3.33\% & 7.71\% & 11.43\% & 17.14\% & 0.001 \\ \hline \hline \end{tabular} EPA— elevated PA group; SPA—standard PA group; \({}^{*}\)\(p\) = \(t\)-test; \(p\) \(\leq\) 0.05.  
\end{table}
Table 1: The BMI categories during the study period.  
SPA children were characterized by an average higher BMI percentile then EPA (\(p=0.014\)). During the study SPA boys' BMIs indicated 97 c (\(p=0.024\)); among girls, it increased from 90 c to 97 c (\(p=0.004\)). In EPA boys, a decrease in BMI was observed from 90 c to 85 c (\(p=0.001\)); in EPA girls, it was at the same level of 90 c (\(p=0.030\)).
# Varietal Authentication of Extra Virgin Olive Oils by Triacylglycerlycerols and Volatiles Analysis
## Materials and Methods
### Preparation of Methyl Esters of Constituent Fatty Acids and Analysis  
Fatty acid methyl esters (FAME) were prepared by transesterification, as previously reported [24] and analyzed by high resolution gas chromatography (HRGC). A DANI 1000 DPC gas-chromatograph (Norwalk, CT, USA) provided with a split-splitless injector and a flame ionization detector (FID) was used. A fused silica capillary column, named CP-Select CB for FAME (50 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m f.t.; Varian, Superchrom, Milan, Italy), was used for the chromatographic separation. The injector and detector temperature was 250 \({}^{\circ}\)C. The initial oven temperature, 180 \({}^{\circ}\)C, was held for 6 min, raised at 3 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, and finally maintained for 10 min. Carrier gas was helium with flow rate of 1 mL/min; the injection volume was 1 \(\upmu\)L with a split ratio of 1:70. To identify the FA, the standard mixture containing 37 FAME was used. The percentage of each FA was calculated using the peak area of the samples. The chromatograms were acquired and processed using Clarity integration software (DataApex Ltd., Prague, Czech Republic). The data were normalized considering only the main reported FA (% mol mean values \(\geq\)0.1).
## 2 Materials and Methods
### Overall Process of Sea Ice Image Segmentation
As regards the coarse granularity of the CIS validation data, after obtaining the sea ice classification results, the conditional random field (CRF) [18] algorithm was used to effectively remove the noise from the classification results and to optimize the boundaries. The results were then transformed into CIS-like ice maps via morphological post-processing, and the final sea ice classification results were obtained by combining the results of the two types of segmentation tasks.
# A Novel Neural Network-Based Method for Medical Text Classification
## 2 Related Work  
### Gated Recurrent Unit  
Recurrent neural networks (RNNs) are a kind of feedforward neural networks which have a recurrent hidden state and the hidden state is activated by the previous states at a certain time. Therefore, RNNs can model the contextual information dynamically and can handle the variable length sequences. Gated recurrent unit (GRU) is a kind of RNN architecture and has become the mainstream structure of RNNs at present [6]. GRU addresses the problem of vanishing gradient by using a gating mechanism which tracks the state of sequences without using separate memory cells. There are two types of gates in GRU--the reset gate \(r_{t}\) and the update gate \(z_{t}\). They control how information is updated to the state together. A GRU unit consists of the four components and it is as illustrated in Figure 1[7].  
The mathematical form of GRU shown in Figure 1 is given below. The hidden state \(h_{t}\) given input \(x_{t}\) is computed as follows:  
\[r_{t}=\sigma(W_{r}x_{r}+U_{r}h_{t-1}+b_{r}) \tag{1}\]  
\[z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}) \tag{2}\]  
\[\tilde{h_{t}}=\tanh(W_{h}x_{t}+r_{t}\otimes(U_{h}h_{t-1})+b_{h}) \tag{3}\]  
\[h_{t}=(1-z_{t})\otimes h_{t}(t-1)+z_{t}\otimes\tilde{h_{t}} \tag{4}\]  
where, \(z_{t}\) is the update gate. \(r_{t}\) is the reset gate. \(h_{t-1}\) is a previous state. \(\tilde{h_{t}}\) is the candidate state at time \(t\). \(x_{t}\) is the sequence vector at time \(t\). \(\sigma(.)\) and \(\tanh(.)\) is sigmoid and hyperbolic tangent function respectively. \(b_{z}\), \(b_{h}\), \(b_{r}\) are bias terms, respectively. The operator \(\bigotimes\) denotes element-wise multiplication.