esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrogenation of Adiponitrile to Hexamethylenediamine over Raney Ni and Co Catalysts
## 2 Experimental Section
### Hydrogenation Reaction  
Batch reactions of ADN hydrogenation were carried out in a 100 mL high-pressure batch reactor (M-Series reactor system, Rexo Engineering Inc., Seoul, Korea) equipped with a magnetic drive agitator. A thermocouple was also equipped on the reactor to measure the actual temperature of the reaction. For an experiment, water, ethanol, ADN, HMDA (total volume of 56 mL), and a catalyst (Raney Ni or Raney Co) with a desired ratio of ADN/HMDA and ADN/catalyst were loaded into the reactor. Considering that the Raney Ni and Co catalysts are highly pyrophoric, great care was taken to prevent exposure to air when loading them into the reactor. Thus, we loaded the catalyst into the reactor according to the following procedure: (1) Thermogravimetric analysis of the Raney Ni and Raney Co catalysts was conducted in order to determine their water content (note that the catalysts were delivered as a slurry in water); (2) water (mass of water required for an experiment - the mass of water contained in a catalyst slurry (i.e., catalyst + water) required for the experiment) was placed on a weighing dish and then weighed; (3) the catalyst slurry was taken on the water-loaded weighing dish and then weighed; and (4) the water with the catalyst slurry was poured together into the reactor.  
After all reactants and solvents were loaded into the reactor, the reactor was sealed, pressurized and purged three times with the UHP hydrogen, and subsequently pressurized to the desired pressure. The pressurized reactor was heated at 6 \({}^{\circ}\)C min\({}^{-1}\) and then kept at the desired reaction temperature. The reactant solution was stirred at a constant rate of 500 rpm. After the reaction finished, the reactor was cooled to room temperature in an ice bath and then depressurized to atmospheric pressure. The ADN conversion, product selectivity, and product yield were calculated according to the following equations:  
\[\text{Conversion}\;(\%) = \frac{\text{moles of carbon in converted ADN}}{\text{moles of carbon in ADN}}\times 100 \tag{1}\] \[\text{Selectivity}\;(\%) = \frac{\text{moles of carbon in a product}}{\text{moles of carbon in converted ADN}}\times 100\] (2) \[\text{Yield}\;(\%) = \frac{\text{moles of carbon in a product}}{\text{moles of carbon in ADN}}\times 100\] (3) \[\text{Carbon balance}\;(\%) = \frac{\sum\text{moles of carbon in a product}}{\text{moles of carbon in ADN}}\times 100 \tag{4}\]
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 2 Materials and Methods
### Network Architecture  
In 2016, [PERSON] et al. introduced the Efficient Neural Network (ENet) for segmentation tasks [PERSON] et al. (2016), which is depicted in Figure 2. Despite sharing an encoder-decoder-based architecture with UNet, ENet exhibits significant differences. Notably, it features a small decoder, along with early and limited downsampling to preserve spatial fine-level features, serving as both an alternative and a complement to the skip-connections method. The ENet architecture is made up of six stages comprising an initial stage, followed by three stages forming the encoder and the last two stages forming the decoder.  
The initial stage contains a single block shown at the top left of Figure 2. Stage 1 is composed of five bottleneck blocks while stages 2 and 3 share a similar structure, with the distinction that stage 3 lacks the downsample bottleneck. The different color-coded bottleneck modules correspond to a downsampling bottleneck, implemented by using a maxpooling parallel branch; the dilated, asymmetric or regular bottlenecks implemented by using corresponding convolutions; and the asymmetric bottleneck implemented by using a sequence of \(5\times 1\) and \(1\times 5\) convolutions. A detailed description of the architecture can be referred to in the original work [PERSON] et al. (2016).  
The novelty of the ENet architecture lies in the various design choices made with a specific focus on improving the computational complexity. One such choice was the use of dilated convolutions and using saved indices from max pooling layers to produce sparse upsampled maps in the decoders. Other choices include the application of early downsampling to heavily reduce the input size, a smaller decoder size, using Parametric Rectified Linear Units (PReLU) instead of RELUs, implementing information-preserving dimensionality changes by performing pooling operations in parallel with a convolution of stride 2 and concatenating the resulting feature maps, among others.  
Figure 2: The ENet architecture (**bottom**) depicting the initial block (**top left**), the bottleneck module (**top right**) and the complete architecture (**bottom**). The output shapes are shown for an input image of size \(256\times 256\) with and an output of C classes. \(\mathtt{conv}\) is either a regular, dilated or full convolution (also known as \(3\times 3\) filters or a \(5\times 5\) convolution decomposed into two asymmetric ones).  
The used ENet architecture in itself makes use of skip connections following the work of [PERSON] et al. (2018). However, these connections are applied only over the maximum argument parameters extracted from early pooling layers and are passed to the unpooling layers of the decoder.  
These strategies, among other minor tweaks, enable the architecture to learn with a wide reception field while keeping computational costs low. Despite the network's structure comprising 358 different layers including 89 associated with convolutions, the amount of parameters remains low, totaling 0.373M.
# Metrology of Nanostructures by Tomographic Mueller-Matrix Scatterometry
###### Abstract  
The development of necessary instrumentation and metrology at the nanoscale, especially fast, low-cost, and nondestructive metrology techniques, is of great significance for the realization of reliable and repeatable nanomanufacturing. In this work, we present the application of a homemade novel optical scatterometer called the tomographic Mueller-matrix scatterometer (TMS), for the measurement of photoresist gratings. The TMS adopts a dual rotating-compensator configuration and illuminates the nanostructure sequentially under test conditions by a plane wave, with varying illumination directions and records. For each illumination direction, the polarized scattered field along various directions of observation can be seen in the form of scattering Mueller matrices. That more scattering information is collected by TMS than conventional optical scatterometry ensures that it achieves better measurement sensitivity and accuracy. We also show the capability of TMS for determining both grating pitch and other structural parameters, which is incapable by current zeroth-order methods such as reflectometry- or ellipsometry-based scatterometry.  
Keywords:ellipsometry; scatterometry; Mueller matrix; diffraction grating; inverse scattering; pitch measurement +
Footnote †: journal: Applied Sciences
# Detecting Axial Ratio of Microwave Field with High Resolution Using NV Centers in Diamond
###### Abstract  
Polarization property characterization of the microwave (MW) field with high speed and resolution is vitally beneficial as the circularly-polarized MW field plays an important role in the development of quantum technologies and satellite communication technologies. In this work, we propose a scheme to detect the axial ratio of the MW field with optical diffraction limit resolution with a nitrogen vacancy (NV) center in diamond. Firstly, the idea of polarization selective detection of the MW magnetic field is carried out using a single NV center implanted in a type-IIa CVD diamond with a confocal microscope system achieving a sensitivity of 1.7 \(\upmu\)T/\(\sqrt{\mathrm{Hz}}\). Then, high speed wide-field characterization of the MW magnetic field at the submillimeter scale is realized by combining wide-field microscopy and ensemble NV centers inherent in a general CVD diamond. The precision axial ratio can be detected by measuring the magnitudes of two counter-rotating circularly-polarized MW magnetic fields. The wide-field detection of the axial ratio and strength parameters of microwave fields enables high speed testing of small-scale microwave devices.  
Keywords:MW magnetic field; axial ratio; polarization; NV center +
Footnote †: journal: Sensors
# Analysis of Injected Electron Beam Propagation in a Planar Crossed-Field Gap
## 1 Introduction
This paper primarily focuses on validating the 1-D space charge limited theory for a crossed-field device [5; 11] by performing a 3-D simulation of a simple, planar crossed-field injected beam geometry, which will ultimately be validated experimentally. While this work starts with the simple planar geometry, it is a necessary step to compare the 3-D simulation with the 1-D theory to ensure no discrepancies in the approach. The next program steps will include study of a simple cylindrical crossed-field geometry which again will be analyzed theoretically, by 3-D simulation, and by experiment. Validation of these models will allow study of the more complicated magnetrons and CFAs to determine the critical current density and magnetic field tilt limitations on the electron hub stability. This study considers a crossed-field device consisting of a sole electrode, an injected beam electron source, an anode, end-hats, and an end-collector. Section 2 summarizes prior theoretical predictions for our device configuration. Section 3 presents the model used for the simulations. We present simulation results for different current densities and magnetic field tilts with a fixed anode-sole gap distance of 20 mm and then compare these to theory in Section 4. Section 5 provides the discussion and Section 6 provides the concluding remarks.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 4 Dataset Validation
### Error Statistics and Analysis  
An absolute error analysis of the GNSS initial-aided positioning trajectory and the GNSS continuous-assisted trajectory with respect to the true trajectories was carried out. As Figure 10 shows, the red meter sign indicates the GNSS initial-aided positioning error and the blue circle indicates the GNSS continuous-assisted error. Overall, the x, y, and z axis errors appeared to increase with the number of frames, but the GNSS continuous-assisted error had a decreasing trend relative to the GNSS initial-aided positioning error.  
Figure 9: Trajectory comparison.  
Here, a further statistical analysis of the GNSS initial-aided positioning error accuracy and the GNSS initial-aided positioning error accuracy was carried out. The mean absolute error (MAE) was defined as follows:  
\[MAE=\frac{\sum\limits_{i=1}^{n}\left|x_{\mathcal{C}_{i}}^{GNSS}-x_{\mathcal{C}_{ i}}^{GNSS}truth\right|}{n} \tag{16}\]  
where \(x_{\mathcal{C}_{i}}^{GNSS}truth\) represents the true value of the GNSS coordinates of the camera center in frame \(i\).  
To analyze the applicability of the GNSS-aided visual dynamic localization method proposed in this study, Table 4 shows the following statistics for the different frame numbers' (20, 70, 100, and 143) MAEs.  
As Table 4 shows, from Frame 4 to Frame 20 and then to Frame 70, the mean absolute errors and root mean square errors increased, but the overall error increased slowly. However, for GNSS initial-aided positioning, from 70 frames, and especially after 100 frames, the error increased exponentially. For the GNSS continuous-aided positioning, the error growth was slow, and compared with the GNSS initial-aided positioning error, the accuracy was improved by two to three times. The proposed algorithm could provide stable and continuous global location services.
# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
## 4 Results and Discussion  
### Series 1  
The LSTM, Bi-LSTM, and GRU networks yielded RMSEs of 29,537, 30,264, and 30,531 for the testing dataset, respectively. The results are compared with those of the previous fuzzy time series studies [37; 38; 39], as shown in Table 1. RMSE of [PERSON] et al. [39] is smaller than those of [PERSON] [37] and [PERSON] et al. [38]. The RMSEs for the period from May 2000 to September 2005 obtained by [PERSON] et al. [39], LSTM, Bi-LSTM, and GRU are 30,789,  
Figure 3: Monthly tourist number of Taiwan from January 1984 to May 2021. Figure 2: Monthly tourist number of Taiwan from January 1984 to September 2005. 31,182, 34,872, and 34,770 respectively. The actual and various predicted tourist numbers of Taiwan from May 2000 to September 2005 are depicted in Figure 4. Considering the SARS period only (November 2002 to June 2003), the RMSEs for [PERSON] et al. (2019), LSTM, Bi-LSTM, and GRU are 61,863, 59,276, 59,480 and 59,369 respectively. These RMSEs are almost twice the corresponding RMSEs of the entire period, indicating that TD forecasting during this period is difficult. However, the RMSEs of this study achieved 4% better error rates than that of the aforementioned research, indicating that the prediction model can be used to predict time series with catastrophic events. The comparison results show that the LSTM model is slightly better than that of the Bi-LSTM, and GRU in terms of RMSE. The RMSEs for the entire period, including the SARS period, are compared in Table 1.  
The reported fuzzy time series models have achieved successful prediction results under the modelling frameworks. However, the models have a potential vulnerability (without long-term dependency). Due to the lack of \"memory\" functions in the structure, the model is more sensitive to short-term relationships than long-term dependencies, and cannot capture some important recurring features. Furthermore, deep learning approaches are non-parametric and are more generalizable without fuzzification and de-fuzzification.
# On Beamforming with the Single-Sideband Transform
## 3 Signal Model and Beamforming
### Theoretical Disadvantages  
There are two apparent weaknesses with the SSBT: the first is a byproduct of working with a real-valued transform, where each frequency is influenced by its conjugate when dealing with convolution. This is inherent to any time-frequency transform that operates on a real-valued frequency domain, assuming a correct model that preserves the phase. The need to work with two frequencies simultaneously implies that, for each constraint in the problem, two constraints are needed in the mathematical model, even in an ideal scenario. This adds further load to the minimization problem, limiting how much noise it can minimize.  
The second disadvantage is a direct consequence of the first. As explained in Section 24, the SSBT transform is less robust than the STFT in terms of RFR estimation errors, this being caused by correlation between conjugate frequencies on different frames (see Section 2.4), having more error terms when compared to the STFT. While it is impossible to bypass the first one since it is a modeling obstacle, the second can be worked around as it is an outcome of non-ideal considerations. For example, these effects are lessened by minimizing the impact different windows of the CTF model have on each other (or by using the MTF model).
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 2 Related Work
### Correlation Filter Tracking  
Correlation filter has been used for tracking with excellent tracking accuracy and speed. In recent years, it has become the mainstream object tracking method. The method trains the correlation filter according to the information of the current frame and the previous frame. The introduction of the Fourier transform speeds up the computation, so the tracking speed of single-target tracking can reach hundreds of frames.  
The Minimum Output Sum of Squared Error (MOSSE) [26] filter, which uses adaptive training strategy, was the first to employ the correlation filter method for tracking. Because of MOSSE's robust and fast results, many MOSSE-based algorithms have been proposed. For instance, in the Circulant Structure with Kernal (CSK) algorithm [27], target samples generated by a circulant structure are used to train classifiers. Meanwhile, calculating in the Fourier transform domain reduces the complexity and greatly improves the speed. The KCF [10] algorithm adopts multichannel features and uses kernel correlation filtering. However, this algorithm is insensitive to scale changes. When the target is removed from view or blocked for a long time, the correlation filter often performs poorly. On the other hand, combining deep learning and correlation filtering, [PERSON] proposed the C-COT [12] and ECO [11] algorithms, which achieve high accuracy. Among them, C-COT won the VOT2016 competition. However, the C-COT and ECO cannot be real-time, which is an important aspect in target tracking. Deep learning also imposes high requirements on the platform, which often requires a highly configured personal computer (PC) for operation.
## 5 Results and Analysis
### Misalignment Velocity Analysis  
Our factorial analysis determined that the relative standard error margin was less than 4.78%, ensuring an overall reliability of 99.64%. As shown in Figure 10, the pick-and-place machine achieved a maximum test speed of 0.288 m/s. We found the average deviation to be 0.0711 mm, which is about 25.14%, significantly lower than the error tolerance limit of 0.2829 mm. Our probability assessments revealed that the mean velocity probability for misalignment is 0.832 m/s, and the median probability for maintaining the IC within 0.2829 mm alignment at maximum velocity is 1.5466 m/s. We identified that dielectric fluids with lower viscosity, such as FR3, DTE-150, and PDMS, allow better alignment of ICs at maximum speeds of 1.778 m/s, 1.727 m/s, and 1.499 m/s, respectively.  
Figure 9: Correlation of dispersion matrix plot: X and Y axes deviation.  
Figure 11 shows the cumulative deviation statistics of a SOIC package model as it moves through a specific dielectric fluid. We calculated the maximum deviation using the test pick-and-place machine's maximum velocity. We then compared these data with the permitted maximum alignment on both the X and Y axes.  
We determined each set's maximum alignment speed by multiplying the deviation frequencies with the test's velocity, assuming linear dispersion correlation.  
The results obtained present margins of relative standard errors lower than 4.78%, as shown in Figure 12. It was observed that the SOP24 package presented more deviations in the test with DPMS dielectric fluid. The SOP20 package maintained the most stable behavior, with the least deviation in the different dielectric fluids.  
Figure 11: Accumulated deviation in the results obtained.  
Figure 10: Maximum velocity for testing SOIC packages aligned.
## 4 Results and Discussion
### Ablation Study
#### 4.4.2 Effect of Confidence Threshold  
The confidence threshold \(\sigma_{btm}\) is an important hyperparameter of the label-matching refinement method, which determines whether clustering needs to be fine-tuned and the range involved in the fine-tuning process. Thus, we investigate how the confidence threshold \(\sigma_{btm}\) influences the quality of images generated by GDUI, and analyze the results accordingly. As depicted in Figure 4, for the majority of cases where the confidence threshold \(\sigma_{btm}\) ranges from 0 to 1, GDUI exhibits superior performance over the unconditional diffusion model across various evaluation metrics. It is worth noting that in the majority of cases, GDUI also exhibits superior performance over the diffusion model based on ground-truth datasets in all evaluation metrics except Recall. As can also be observed from Figure 4, when the confidence threshold \(\sigma_{btm}\) approaches a small value close to 0, the overall performance of GDUI degrades to be similar to only using the pseudo-label generation (PLG) method. This phenomenon can be understood as the label-matching refinement method determining that no fine-tuning is needed for the labels within a cluster when the confidence threshold \(\sigma_{btm}\) is set to a low value. When \(\sigma_{btm}\) tends to 1 with a large value, the overall performance of GDUI becomes similar to the diffusion model based on ground-truth datasets. This can be interpreted as the label-matching refinement method considering that almost all image labels need to be fine-tuned.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Real Class** & **FID \(\downarrow\)** & **sFID \(\downarrow\)** & **IS \(\uparrow\)** & **Precision\(\uparrow\)** & **Recall\(\uparrow\)** \\ \hline Baseline & NO & 26.81 & 48.74 & 9.76 & 0.59 & **0.45** \\ +PLG & NO & 18.74 & 48.67 & 10.86 & 0.69 & 0.38 \\ +PLM & **YES** & 18.73 & 48.66 & 10.85 & 0.69 & 0.38 \\ +LMR & **YES** & **16.66** & **48.44** & **11.64** & **0.74** & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation studies of GDUI on the whole STL10 dataset. Here, baseline refers to the unconditional diffusion model trained for 400K iterations on the unlabeled dataset. Qualitative comparisons are also conducted to evaluate whether the models can generate images of specific real categories. The best results are highlighted in **bold**.  
Figure 4: Ablation results of various \(\sigma_{btm}\) values on the STL10 dataset. As \(\sigma_{btm}\) increases, the general trend for all metrics is to first improve and then worsen.
## 1 Introduction
Therefore, to apply SnS to energy devices, it is necessary to obtain a defect-free film and control the grain growth of the SnS film according to the various applications. As the described problems coexist, the research on SnS remains stagnant. Therefore, we present an efficient thick-film process using ALD and a method of grain growth control to optimize the film characteristics. In the ALD process using tetrakis (dimethylamino) tin (Sn[N(CH3)2]4, TDMASn) precursor and H2S reactant, three crystal phases of amorphous SnS\({}_{2}\) (a-SnS\({}_{2}\)), crystalline hexagonal SnS\({}_{2}\), and crystalline orthorhombic SnS can be obtained depending on the deposition temperature. The ALD process using tetrakis (dimethylamino) tin and H2S was first reported by [PERSON] et al. [31]. In the report, amorphous SnS\({}_{\mathrm{x}}\) films were deposited at temperature below 120 \({}^{\circ}\)C. At the deposition temperature of 140 and 150 \({}^{\circ}\)C, hexagonal SnS\({}_{2}\) phase was observed. Further, orthorhombic SnS films were deposited at the temperature of 160 and 180 \({}^{\circ}\)C. As the deposition temperature increased, the crystallinity of thin film increased, and the growth rate decreased. Although the composition of amorphous SnS\({}_{\mathrm{x}}\) is not described, X-ray photoelectron spectroscopy (XPS) results showed that the SnS\({}_{\mathrm{x}}\) film had SnS\({}_{2}\) bond. This SnS\({}_{2}\) bond was identified again in this study. Here, the a-SnS\({}_{2}\) with a high growth rate was deposited and annealed in a vacuum atmosphere to form a thick SnS film using phase transition. Through this method, we obtained a thick SnS film with the advantages of ALD, such as uniformity and conformality. In addition, changes in grain growth behaviors in the SnS film were performed using the ALD-deposited crystalline SnS and SnS\({}_{2}\) seed layers. All the processes were performed in the same chamber as an in-situ process. The entire SnS film had phase continuity because the vacuum annealing results of crystalline SnS and SnS\({}_{2}\) were also SnS phase.