esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Resistant Composite Membranes for Carbon Dioxide Separation from Methane
## 4 Conclusions  
Composite membranes of perfluorinated polymer Teflon AF1600 with the polyimide 6 FDA-TMPDA or PIM-1 were fabricated as water vapor resistant membranes for the separation of CO\({}_{2}\) from CH\({}_{4}\). The CO\({}_{2}\) permeability and ideal CO\({}_{2}\)/CH\({}_{4}\) selectivity of the composite membranes were clearly the function of the individual polymeric layers performance, with the permselectivity of the 6 FDA-TMPDA and PIM-1 layers comparable to pure membranes of these polymers. Upon exposure to water vapor, the water permeability through the composite membranes was higher, 1740-2900 barrer, than that of the pure Teflon AF1600 membrane, 425 barrer, but retarded compared to the pure 6 FDA-TMPDA and PIM-1 membranes (22,600-25,200 barrer). This reduction in water permeability was attributed to the perfluorinated polymer influencing water transport through the 6 FDA-TMPDA and PIM-1 layers by inducing water clusters, which reduced the apparent water diffusivity by 64%. Furthermore, below 0.2 water activity feeds the composite PIM-1 membrane had selectivity for CO\({}_{2}\) over water, with higher CO\({}_{2}\) permselectivity than that observed for the pure Teflon AF1600 membrane. The composite 6 FDA-TMPDA membrane did not demonstrate this reverse selectivity, which is partly attributed to the similar permselectivity of 6 FDA-TMPDA and Teflon AF1600 under mixed gas conditions.  
[PERSON] conceived and designed the experiments, performed the experiments and analyzed the data. [PERSON] wrote the paper.  
The author thanks [PERSON] at the University of Auckland for supplying the PIM-1 used in this research.  
The author declares no conflict of interest.
## 4 System Overview
### Digital FFT Spectrometers  
The system described involves the implementation of a spatial FFT that generates 32 parallel outputs, each corresponding to a distinct spatial RF beam. Subsequent to this spatial FFT, a temporal FFT spectrometer is applied to each of these 32 spatial RF beams, achieving channelization in the temporal frequency domain. Each temporal FFT is computed with 1024 points, necessitating the use of 32 parallel FFT cores. This parallel processing is facilitated by leveraging real-sampled biplex FFT blocks available in the Xilinx Virtex-6 SX475T FPGA.  
The real-sampled biplex FFT blocks exploit the Hermitian conjugation method, enabling the computation of the real-sampled FFT. This technique utilizes a complex FFT core to simultaneously transform two real-valued data streams. As a result, the FFT operation produces all frequencies, both positive and negative, with negative frequencies being the mirror images of their positive counterparts [100].  
The temporal FFT outputs for each beam yield 1024 complex values corresponding to 1024 discrete frequency bins. The power spectral density (PSD) is estimated by squaring the magnitude of each frequency bin. This estimate is derived by averaging across 1024 temporal FFT evaluations, resulting in a new PSD measurement every 1 million clock cycles. A block random access memory (BRAM) vector accumulator block collects the PSD values and transfers them to the software process supported by the CASPER design flow. Figure 6 shows the overall ADFT and temporal FFT flow, including energy integration and BRAM registers. The casperfpga Python library is used to interact with CASPER hardware, extracting values from the shared BRAM.  
The FPGA operates with a clock frequency set to 200 MHz. Given this configuration, the system frequency resolution for each beam is determined to be \(\Delta F=100/1024\) MHz, which is approximately 100 kHz. The Nyquist bandwidth of the system is thus 100 MHz. This configuration ensures precise channelization and high-resolution frequency analysis for each of the 32 spatial RF beams.  
Figure 6: ADFT and FFT calibration, energy integration, and overall system block including BRAM registers.
## 3 Results and Discussion  
### Precipitation Estimates  
#### 3.1.1 Amounts  
Table 7 compares the amounts of areal average precipitation total derived from seven rain gauges in the Yongdam Dam watershed with ground- and space-borne radar data. The areal-averaged values were calculated using Thiessen polygons for the gauged data, while the radar-based gridded data utilized the arithmetic mean over the grids. The table also presents the maximum and minimum values of the radar grid for each flood event.  
As evident from the comparison of total amounts in the table, precipitation based on ground-borne radar tends to slightly underestimate compared to the gauged values. On the other hand, space-borne radar precipitation includes some instances of overestimation in relation to the gauge observations. This trend is also apparent in Figure 6, which shows all hourly data for the flood events used in this study. For RAR, the total sum is 689.6 mm, and for IMERG, it is 816.2 mm, resulting in underestimations of 28.2% and 15.1%, respectively, in comparison with the gauged total of 960.9 mm. The correlation coefficients (R\({}^{2}\)) further  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Storm Events**} & \multicolumn{6}{c}{**Precipitation Total (mm)**} \\ \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Period**} & \multicolumn{2}{c}{**Gauged Data**} & \multicolumn{2}{c}{**Ground-Borne Radar Data**} & \multicolumn{2}{c}{**Space-Borne Radar Data**} \\ \cline{3-10}  & & **Areaal Average** & **Min** & **Max** & **Areaal Average** & **Min** & **Max** & **Areaal Average** \\ \hline
1 & 17 August 2014 & 13:00–2014.08.20 12:00 & 108.3 & 64.8 & 146.9 & 88.7 & 96.5 & 174.2 & 134.4 \\
2 & 24 August 2014 & 01:00–2014.08.28 24:00 & 82.2 & 37.4 & 100.1 & 58.9 & 80.9 & 113.2 & 99.5 \\
3 & 8 August 2015 & 13:00–2015.07.11 12:00 & 57.3 & 16.8 & 65.3 & 38.1 & 9.2 & 81.2 & 39.0 \\
4 & 1 July 2016 & 13:00–2016.07.03 12:00 & 122.9 & 12.3 & 183.5 & 96.9 & 116.7 & 152.3 & 138.9 \\
5 & 16 September 2016 & 13:00–2016.09.19 24:00 & 146.1 & 15.3 & 145.2 & 101.1 & 91.6 & 121.3 & 102.0 \\
6 & 10 September 2017 & 01:00–2017.09.12 24:00 & 71.8 & 5.1 & 77.8 & 50.6 & 32.2 & 56.1 & 42.1 \\
7 & 25 August 2018 & 13:00–2018.08.30 12:00 & 286.3 & 22.7 & 305.9 & 198.6 & 128.4 & 218.9 & 189.4 \\
8 & 30 August 2018 & 13:00–2018.09.02 12:00 & 86.0 & 4.8 & 113.3 & 56.7 & 50.2 & 110.7 & 71.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Amounts of areal average precipitation total for gauged and radar-based data.  
provide insight into the relationship between the datasets. RAR illustrates a high correlation with the observations, with values of 0.86, while IMERG exhibits a significant difference in correlation, with a value of 0.46. Therefore, the relevant model parameter values were adjusted during the flood simulations to reduce the discrepancies between the IMERG outputs and the actual observations, and to incorporate them into the analysis.
## 3 System Modeling
### Evaluation Metrics
#### 3.3.1 Average Waiting Delay  
_AWD_ is used to measure the average number of waiting rounds from the entry to the current round for unresponsive demands in the system during each round. This metric reflects the timeliness of the system in handling requests and reveals the effectiveness of the management strategy in optimizing queue responses.  
Independently calculate the average waiting delay \(AWD_{i}\) for each queue. First, traverse all unresponsive demands in queue \(Que_{i}\) and calculate the difference between the entry round \(T^{in}\) and the current round \(T^{now}\) for each demand, thus obtaining the waiting delay for each demand. Then, accumulate the waiting time of all unresponsive demands to obtain the total waiting time for queue \(Que_{i}\) in that round. Finally, divide the total waiting time by the number of demands to obtain the average waiting delay \(AWD_{i}\) of queue \(Que_{i}\) in that round.  
\[AWD_{i}=\frac{1}{|Que_{i}|}\sum_{\text{Dem}\in Que_{i}}(T^{now}-T^{in}),\quad i \in\{low,medium,high\} \tag{9}\]  
The system's average waiting delay \(AWD\) is the average of each queue's \(AWD_{i}\).  
\[AWD=\frac{1}{3}\sum AWD_{i},\quad i\in\{low,medium,high\} \tag{10}\]  
_AWD_ dynamically tracks the enqueue time of demands to quantify the system's waiting time within a specific round. By analyzing the status of each queue \(Que_{i}\), the effectiveness of the in situ data management strategy can be evaluated, providing an optimization basis for subsequent queue responses. A smaller \(AWD\) value indicates that the management strategy effectively considers the waiting delay across all queues in the system. An effective strategy can reduce \(AWD\) by optimizing queue selection, minimizing the number of waiting rounds for demands, and enhancing the overall system responsiveness.
###### Abstract  
Missing data are common in industrial sensor readings owing to system updates and unequal radio-frequency periods. Existing methods addressing missing data through imputation may not always be appropriate. This study presented a sorted missing percentages technique for filtering attributes when building machine learning classification models using sensor readings with missing data. Signal detection theory was employed to evaluate the distinguishing ability of resulting models. To evaluate its performance, the proposed technique was applied to a publicly available air pressure system dataset, which then was used to build several classifiers. The experimental results indicated that the proposed technique allowed a logistic regression model to achieve the best accuracy score (99.56%) and a better distinguishing ability (response bias of 0.0013, adjusted response bias of 0.0044, and decision criterion of \(-\)1.8994) compared with the methods applied to the same dataset and reported in papers published between 2016 and 2019 March on binary classification, wherein attributes with more than 20% of missing data were filtered out. The proposed technique is suitable for industrial sensor data analysis and can be applied to the scenarios dealing with missing data owing to unequal radio-frequency periods or a system being updated with new fields.  
sorted missing percentage; signal detection theory; classification model 2020104920201049202020
# Modeling the Response of Magnetorheological Fluid Dampers under Seismic Conditions
## 3 Numerical Model
### Computational Domain  
In order to simplify the underlying problem and reduce the computational power required, only MR fluid around the annular orifice region (see Figure 2) was modeled in this study. A two-dimensional axisymmetric configuration was implemented to reduce the computational effort. The computational domain is shown in Figure 4. Numerical results were extracted at six equally spaced locations throughout the domain marked as 1-6 The shaded region between locations 3 and 4 represents the annular orifice where the MR fluid is affected by the external magnetic field.
## 4 Experiment
### Parameter Analysis
#### 4.1.3 Are Multiple Layers Necessary?  
In this subsection, we examine the necessity of multiple layers; 19 kinds of layer configurations are studied, as shown in Table 2, which includes various types of combinations: low-low-low, low-middle, middle-high, low-middle-high, etc. The layer number ranged from 3 to 16. It should be noted again that there are 175 layers of tensors for the network ResNet50 when it is unrolled, including the prediction probability layer of size \(1\times 1\times 1000\). The recognition accuracy and feature length are shown in Figure 6. The greater the feature length, the higher the recognition rate generally, because more features are utilized. Obviously, one can conclude that more layers will lead to better recognition performance. Although the 9 th, 12 th, 14 th and 15 th configuration indexes obtained good enough accuracy, the 17 th and 19 th configuration indexes obtained higher recognition rates. Considering the trade-off between feature length and recognition rate, we select the 17 th configuration index; in other words, the layers of {90, 100, 110, 120, 130, 140, 152} for ResNet are used in this work.  
Figure 5: Comparison of recognition results between different convolution networks on the Flavia dataset.  
Figure 6: Accuracy and feature length for different layer configurations in Table 2.
## 4 Experimental Setup
### Parallel Application Instances  
The applications used in the experimentation are:  
* Double precision floating point FORTRAN benchmark (Fpppp) [35].
* The Laser Interferometer Gravitational-Wave Observatory (LIGO) application [36].
* Robot control application (Robot)
* Sparse matrix solver (Sparse).
* A benchmark of fourteen small synthetic instances from [11].  
The applications Fpppp, Robot, and Sparse are included in the Standard Task Graph Set (STG) in [37]. We performed the same treatment to the original applications instances as in [8], considering different (Communication to Computation Ratios, CCRs) CCRs \(=\{0.1,0.5,1,5,10\}\), (Heterogeneity Factors) \(\text{HFs}=\{0.1,0.25,0.5,0.75,1\}\), and number of machines \(|M|=\{8,16,32,64\}\). The combination of the mentioned configurations for the four parallel applications gives a total of \(4\cdot 5\cdot 5\cdot 4=400\) instances. The nomenclature for the large instance set of Fpppp, Robot, and Sparse instances used in this work is Application-Machines-Tasks-HF-CCR. For the small benchmark in [11], the nomenclature is Name-Machines-Tasks. We made available the complete instance set in [38].
## 4 Results
### Deployment  
The metrology system has been deployed in the shop floor of an automotive warm forge of motorcar stub axles, next to one of the press lines. The press line forges around 1200 workpieces per hour. As shown in Figure 13, in this initial setup an operator places manually the warm workpiece into the scanner directly from the press ramp. In the future this operation will be assumed by a robot arm to obtain higher measuring cadences.  
To avoid damages to the optical equipment, all the devices are protected with a metal cover. The chassis has been mounted on a foam cushion layer to absorb the vibrations caused by the press.  
The measurement software is connected to the Manufacturing Execution System (MES) of the factory. This integration allows automatic loading of the measurement program for the reference that is being manufactured, as well as other data, such as the manufacturing order, the operator name, etc. This data is stored in the report for each workpiece along with the calculated measurements for traceability purposes.  
Figure 12: Boxplot of the runout measurements presented in Table 3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Source** & **Variance** & **\% of Total Variance** \\ \hline Operators (Reproducibility) & 0.0000 & 0.04\% \\ Vision System (Repeatability) & 0.0017 & 22.07\% \\ Gauge R\&R (GRR) & 0.0017 & 22.11\% \\ Interaction & 0.0000 & 0.00\% \\ Workpieces & 0.0059 & 77.89\% \\ Total & 0.0076 & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Gauge Repeatability and Reproducibility (GRR) table.  
The implemented system automatically assesses the runout of the revolution-like workpieces in less than 60 s. Current measurements are performed with the warm workpiece at approximately 600 \({}^{\circ}\)C. The system works as an early detection mechanism for manufacturing and process failures, mainly due to deviations between the forging punch axis and the forming matrix axis. The previous dimensional assessment mechanism (cold-state CMM) used to take only one measurement for every 400 fabricated parts (i.e., every 20 min). On the contrary, our system performs a measurement every 20 fabricated parts (i.e., every minute). Therefore, the system improves the previous measurement method by reducing the number of defective workpieces by around an estimated 95%.
## 3 System Model  
The symbols are transmitted based on allocations on time and frequency domains. Time-frequency allocation depends on transmission schemes. Thus, the allocation of single- and multicarrier transmission, time and frequency division transmission differs. GFDM is a waveform considered frequency division and multicarrier. The time-frequency allocation of GFDM is designed as a hexagonal scheme. The time-frequency allocations of the GFDM and hexagonal GFDM are given in Figure 1. Accordingly, each subsymbol of the hexagonal GFDM is shifted in both frequency domain and time domain.  
In this section, hexagonal GFDM and its efficient polyphase decomposition are presented in the following subsection.
# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 4 Results
### Case Study: Performance Analysis for TCN-Based Model (Scada Wind Farm, Turkey)  
Step 1. Data preprocessing phase  
In the experiment, an open historical dataset from Scada wind farm in Turkey, including real weather observations and wind turbine power outputs, was used in model pretraining. Each record in the Scada dataset contained five parameters sampled every 10 min, with a total of 52,560 samples listed [31]. Notably, some null fields contained linear proportions of the neighbouring observation data in advance. Our training dataset comprised samples from 1 January 2018, to 26 December 2018, and the test dataset used samples from three days, namely December 27 to 29, 2018.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**IP** & **Programming Language** & **Operating System** & **Numerical Library** \\ \hline \hline \multirow{4}{*}{192.168.1.10 (AMD Ryzen Threadripper, 1920X, 3.4G)} & & \multirow{4}{*}{Pardas 0.23.4 Ubuntu 14.04 LTS 64} \\  & & & \\ \cline{1-1} \cline{3-4}  & & & \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental environment for TCN-based prediction model.  
Scada Systems measured and saved data including wind speed, wind direction, generated power, etc. This file was taken from a Scada Systems wind turbine working and generating power in Turkey. The data in the file are listed as follows: [30]  
1. Date/Time: 10 min intervals
2. LV ActivePower (kW): The power generated by the turbine for that moment.
3. Wind Speed (m/s): The wind speed at the hub height of the turbine.
4. Theoretical Power Curve (KWh): The theoretical power values that the turbine generates with that wind speed, which is given by the turbine manufacturer.
5. Wind Direction (\({}^{\circ}\)): The wind direction at the hub height of the turbine  
Step 2. Model training phase  
To examine model efficiency, four deep neural models were incorporated for series data processing, namely, RNN, LSTM, GRU, and TCN, in order to conduct wind power forecasting 72 h ahead of time. We set the initial values of TCN model parameters as in Algorithm 1, and the experiment parameters for the RNN, LSTM and GRU models as in Table 9.  
Step 3. Model validation phase  
Two experiments with training datasets of different sizes (i.e., one month and one year) were conducted to verify the effectiveness of the four DNN-based models for wind power forecasting.  
In the first experiment, the one-month dataset was used to train four DNNs. The LSTM model had the lowest prediction error (MAPE = 3.8%), followed by the GRU (9.09%) and the TCN (10.93%) models, with the RNN model exhibiting the poorest performance (11.21%), as detailed in Figure 11.
# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 3 Analyzing the AI Hub Corpus Using LIWC
### Linguistic Inquiry and Word Count (LIWC)
Furthermore, LIWC has been recently utilized in numerous studies on natural language processing (NLP), and its effectiveness and relevance in the field of NLP has been demonstrated. For instance, the performance of misinformation detection [52], sentiment analysis, and plagiarism detection [53] can be improved by applying LIWC, and the effectiveness of LIWC can be evaluated through its comparison to BERT [54]. Following these trends, we aim to investigate all the parallel corpora for Korean released by AI Hub through morphological, semantic, sentence-syntactic, sentimental, and gender-bias aspects. For Tables 2-4, the largest values of each category are shown in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**KR-En Domain-Specialized Parallel Corp**} & \multicolumn{2}{c}{**KR-En Parallel Corpus**} \\ \hline
**Categories** & **Features** & **Train** & **Valid** & **Total** & **Total** \\ \hline \multirow{4}{*}{summary language variables} & **Analytic** & **97.14** & **97.26** & **97.16** & **94.16** \\  & Clout & 61.1 & 61.17 & 61.11 & 65.39 \\  & Authentic & 25.7 & 25.37 & 25.66 & 27.76 \\  & Tone & 49.14 & 49.08 & 49.13 & 54.48 \\ \hline \multirow{4}{*}{Linguistic dimensions} & WC & 33,750,816 & 4,222,191 & 37,973,007 & 38,481,936 \\  & WPS & 27.33 & 27.29 & 27.33 & 25.39 \\  & Sixltr & 26.5 & 26.49 & 26.5 & 25.69 \\  & Dic & 76.33 & 76.3 & 76.32 & 79.25 \\  & function & 43.78 & 43.78 & 43.78 & 45.24 \\  & pronoun & 4.9 & 4.87 & 4.89 & 6.73 \\  & ppron & 1.54 & 1.54 & 1.54 & 3.08 \\  & **i** & **0.21** & **0.21** & **0.21** & **1.02** \\  & we & 0.23 & 0.23 & 0.23 & 0.38 \\  & you & 0.3 & 0.3 & 0.3 & 0.56 \\  & shehe & 0.42 & 0.42 & 0.42 & 0.67 \\  & they & 0.38 & 0.38 & 0.38 & 0.45 \\  & ippron & 3.36 & 3.33 & 3.35 & 3.64 \\  & article & 10.4 & 10.42 & 10.4 & 10.2 \\  & prep & 15.51 & 15.52 & 15.51 & 15.1 \\ \hline \multirow{4}{*}{grammar} & auxverb & 6.07 & 6.05 & 6.06 & 6.75 \\  & adverb & 2.46 & 2.47 & 2.46 & 2.55 \\  & conj & 5.92 & 5.92 & 5.92 & 5.43 \\  & negate & 0.63 & 0.62 & 0.63 & 0.7 \\  & **verb** & **9.94** & **9.91** & **9.94** & **11.36** \\  & adj & 4.49 & 4.46 & 4.49 & 4.47 \\  & compare & 2.35 & 2.34 & 2.35 & 2.25 \\  & interrog & 1.21 & 1.2 & 1.21 & 1.3 \\  & number & 3.27 & 3.28 & 3.27 & 2.53 \\  & quant & 1.36 & 1.36 & 1.36 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: LIWC results of Korean–English Domain-Specialized Parallel and Korean–English Parallel Corpus.
# Model Test Analysis of Subsurface Cavity and Ground Collapse Due to Broken Pipe Leakage
###### Abstract  
Urban ground collapse is a common geological disaster characterized by its invisible nature, particularly in China, and results in significant socioeconomic losses and even loss of life. Underground pipeline breakage is the most common factor leading to urban ground collapses. Hence, it is essential to study how different types of pipeline breakages initiate the collapse mechanism. In this study, an indoor model test was conducted to directly observe the process of collapse due to broken pipe leakage. A broken pipe was put into a model box and tested by an experimental device. The results showed that among the different pipeline breakage types, vertical damage had the greatest influence on the degree of cavity development and ground collapse. Similarities were observed in the patterns of cavity evolution development and the extent of ground collapse as well, further revealing the significance of the cavity evolution process in predicting ground collapses.