esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Sample and Data Extraction  
Data used in the case study include 6 cohorts of undergraduate students (between 2004 and 2009), who entered a medium-sized Chilean university, which encompasses both education and research in a variety of educational fields. In total, 12,840 students were enrolled in one of the 49 undergraduate programs in those years. The nominal duration of these undergraduate programs is between 4 and 7 years (median: 5 years). The observation period stretches from the beginning of 2004 to the end of 2018. This time window was chosen because by the end of 2018 more than 95% of the students of these 6 cohorts had already reached a final state (either dropout or graduation).  
The data extraction stage included the application of filters and a categorization of final state events regarding the data extracted from the academic records. Three group of students were filtered out: 603 students (4.70%) had not reached any final state on March 2019, 631 students (4.91%) stopped out immediately after initial enrollment, and 637 students (4.96%) chose to switch to a new curriculum while staying in the same program. Therefore, educational trajectories of 10,969 students were considered, corresponding to 85.43% of the global enrollment. Four final state events were defined; Table 1 shows their definition and the number of students who reached each final state. The number of undergraduate programs and students for each ISCED (International Standard Classification of Education) field is shown in Table 2.  
To achieve a greater representation of students, high-failure rate courses were selected from among those with a minimum of 30 students enrolled, considering the entire period of analysis. These represented 43.31% of all possible courses. Courses with a failure rate higher than the average plus one standard deviation were considered as high-failure rate courses. In the period under review (30 semesters), 3343 courses had at least 30 students enrolled, and 628 of them were considered as high-failure rate courses (18.79%).
## 2 Related Studies
The point of view used to analyze the rest of the literature covered in this research is through looking at how they solve the user interests mining problem. The factors considered are the type of study, demographic data, personality, heterogeneous information network, user content, and approach, as shown in Table 1. As a note, most of the studies did not use demographic and personal data with each other. Besides that, most of the content they studied are ratings of products instead of extractions of the topic of interest from the users' textual content. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author(s)** & **Type** & \begin{tabular}{c} **Heterogeneous** \\ **Network** \\ \end{tabular} & \begin{tabular}{c} **Personality** \\ **Age** \\ \end{tabular} & \
\begin{tabular}{c} **Demographic** \\ **Gender** \\ \end{tabular} &
\begin{tabular}{c} **User Content** \\ \end{tabular} & **Approaches** \\ \hline
[29] & \
\begin{tabular}{c} Recommender \\ System \\ \end{tabular} & Yes & No & No & No & Rating &
\begin{tabular}{c} Multiple \\ Similarity \\ Models \\ \end{tabular} \\ \hline
[35] & \
\begin{tabular}{c} Recommender \\ System \\ \end{tabular} & Yes & No & No & No & Rating &
\begin{tabular}{c} Multiple \\ Similarity \\ Models \\ \end{tabular} \\ \hline
[36] &
\begin{tabular}{c} User Interest \\ \end{tabular} & Yes & Yes & No & No & Text Content & Meta Path \\ \hline
[30] & \
\begin{tabular}{c} Recommender \\ System \\ \end{tabular} & No & No & No & No & Rating &
\begin{tabular}{c} Random \\ Forests and \\ Classification \\ Trees \\ \end{tabular} \\ \hline
[37] & \
\begin{tabular}{c} Recommender \\ System \\ \end{tabular} & No & No & Yes & Yes & Rating &
\begin{tabular}{c} Cascaded \\ Profiling \\ Approach \\ \end{tabular} \\ \hline
[38] & \
\begin{tabular}{c} Recommender \\ System \\ \end{tabular} & No & No & No & No & Rating &
\begin{tabular}{c} Hybrid \\ Similarity \\ Measure \\ \end{tabular} \\ \hline
[32] & \
\begin{tabular}{c} Recommender \\ System \\ \end{tabular} & No & Yes & No & No & Text Content &
\begin{tabular}{c} Recommender \\ Approach \\ \end{tabular} \\ \hline
[33] & \begin{tabular}{c} Recommender \\ System \\ \end{tabular} & No & Yes & Yes & No & \
\begin{tabular}{c} Music \\ Listening \\ Histories \\ \end{tabular} &
\begin{tabular}{c} Recommender \\ Approach \\ \end{tabular} \\ \hline
[34] & \
\begin{tabular}{c} Recommender \\ System \\ \end{tabular} & No & Yes & No & No & Ratings &
\begin{tabular}{c} Similarity \\ Based \\ Collaborative \\ Filtering \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of important factors in the literature.
## 3 Results and Discussion
### Gas Production and Composition  
Cumulative methane production was <1 mL g\({}^{-1}\) VS in all treatments for both corn silage and common reed substrates. Since only hydrolysis was conducted in this study, minimal methane volume was expected. Anaerobic fungi are known to produce H\({}_{2}\) and CO\({}_{2}\) during substrate hydrolysis [31]. [PERSON] and [PERSON] [18] demonstrated that bioaugmentation with anaerobic AF into a two-stage AD can increase H\({}_{2}\) production within the system in the days following inoculation. In our study, an initial increase in hydrogen (Figure 1) and CO\({}_{2}\) (Figure 2) production were observed with all fungal species that were added to digesters containing corn silage. Over the course of the 11-day hydrolysis experiment, all three fungal treatments produced similar cumulative volumes of H\({}_{2}\), in the range of 46-60 mL g\({}^{-1}\) VS (_p_ > 0.05) (Figure 1a). Similar trends were observed for CO\({}_{2}\) gas production, with cumulative CO\({}_{2}\) production for all three treatments of corn silage in the range of 78-93 mL g\({}^{-1}\) VS (_p_ > 0.05) (Figure 2a). The initial increase in gas production observed for corn silage may have been due to the fact that the material had already undergone ensiling. Additionally, background microbes such as H\({}_{2}\)-producing _Clostridia_ are known to be present in corn silage and were likely actively contributing to the gas production observed [32].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Corn Silage** & **Common Reed** \\ \hline TS 1 (\(\%\)) & 33.5 \(\pm\) 0.6 & 54.7 \(\pm\) 0.8 \\ VS 2 (\% TS) & 96.3 \(\pm\) 0.6 & 94.9 \(\pm\) 0.4 \\ VS added (g) & 30 & 30 \\ Moisture content (\(\%\)) & 66 \(\pm\) 0.6 & 44.3 \(\pm\) 0.8 \\ Total carbon: Total nitrogen ratio & 31.2 \(\pm\) 0.1 & 26.3 \(\pm\) 0.7 \\ Hemicellulose (\(\%\) of TS) & 12 \(\pm\) 4.3 & 28.7 \(\pm\) 0.4 \\ Cellulose (\(\%\) of TS) & 17.2 \(\pm\) 1.8 & 38.7 \(\pm\) 0.4 \\ ADL 3 (\(\%\) TS) & 2.7 \(\pm\) 0.0 & 7.9 \(\pm\) 0.3 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: The physicochemical characteristics of corn silage and common reed.  
During hydrolysis of common reed, smaller volumes of H\({}_{2}\) (<1 mL g\({}^{-1}\) VS) (Figure 1b) and CO\({}_{2}\) (<15.5 mL g\({}^{-1}\) VS) (Figure 2b) gas were evolved from all three treatments than was observed with corn silage (\(p>0.05\)). Further studies are required to either eliminate the contribution of background microflora present on the substrate, or to account for the magnitude of their activity within the overall microbial consortia present during substrate hydrolysis.
## 1 Introduction
Here, based upon the satellite-measured normalized difference vegetation index (NDVI), monthly maximum temperature (T\({}_{\rm max}\)), monthly minimum temperature (T\({}_{\rm min}\)), and the temperature difference (T\({}_{\rm D}\)) in the growing season (April to September), we investigated the spatiotemporal changes in plant growth using various temperature indices over the TP. A comprehensive attribution framework was then applied in order to systematically explore the functions of different temperature indices on plant growth. On top of this, the spatial heterogeneity of the effects of temperature indices on plant growth in various frozen grounds is further explored. The findings of this study will contribute to the conservation of the natural environment across the TP and promote its sustainable utilization. It is of great significance to understand the carbon cycle of plateau ecosystems and to thereby guide regional ecological environment construction.
## 3 Sources of Discrepancies in LCA Stages
### Stage 3: Impact Assessment
In fact, according to the two most established midpoint environmental methodologies: CML and the Tool for the Reduction and Assessment of Chemical and other environmental Impacts (TRACI), GWP is based on the aggregation of carbon dioxide emissions, methane and nitrogen dioxide. CML was developed in 1992 by the Institute of Environmental Sciences of the University of Leiden, and TRACI was prepared by the US Environmental Protection Agency's (US EPA's) National Risk Management Research Laboratory in 2003. The second reliability issue with this choice is that GWP is not the only significant environmental impact indicator. As seen in Figure 11, more than 10% of the authors opted to use, along with GWP, the cumulative energy demand (CED). More than half of the authors opted to present several midpoints, according to the following methodologies: CML (32%), Integrated Material Profile And Costing Tool (IMPACT) (10%) and TRACI (7%). These midpoint indicators include, besides GWP, ozone depletion potential (ODP), acidification potential (AP), eutrophication potential (EP), and abiotic depletion potential (ADPE), among others. Considering these indicators would include emissions such as carbon monoxide, sulphates and ammonia [67]. This concludes that in order to provide a reliable assessment of the environmental impact of a concrete alternative, it is advisable to calculate it using several midpoint indicators. However, it remains up to the user to remove any that are deemed irrelevant. For example, [PERSON] et al. [103] argue that since the ODP potential of 1 tonne of geopolymer concrete is almost equal to \(1.34\times 10^{-5}\), equivalent to operating a household lamp for 2.5 years, this is not a significant indicator to consider.
###### Abstract  
In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. However, convolutional neural network models encounter challenges in hyperspectral image classification due to limitations in their receptive fields. Conversely, the global modeling capability of Transformers has garnered attention in hyperspectral image classification. Nevertheless, the high computational cost and inadequate local feature extraction hinder its widespread application. In this study, we propose a novel fusion model of convolutional neural networks and Transformers to enhance performance in hyperspectral image classification, namely the dual-branch multi-granularity convolutional cross-substitution Transformer (DMCCT). The proposed model adopts a dual-branch structure to separately extract spatial and spectral features, thereby mitigating mutual interference and information loss between spectral and spatial data during feature extraction. Moreover, a multi-granularity embedding module is introduced to facilitate multi-scale and multi-level local feature extraction for spatial and spectral information. In particular, the improved convolutional cross-substitution Transformer module effectively integrates convolution and Transformer, reducing the complexity of attention operations and enhancing the accuracy of hyperspectral image classification tasks. Subsequently, the proposed method is evaluated against existing approaches using three classical datasets, namely Pavia University, Kennedy Space Center, and Indian Pines. Experimental results demonstrate the efficacy of the proposed method, achieving significant classification results on these datasets with overall classification accuracies of 98.57%, 97.96%, and 96.59%, respectively. These results establish the superiority of the proposed method in the context of hyperspectral image classification under similar experimental conditions.  
hyperspectral image classification; convolutional neural network; transformer +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Set-Up for Experimental Characterization of SWCNT Film and NSH PD  
The current as a function of voltage, (I-V) measurements were acquired using a Keithley 2602A source/meter directly connected to the PD electrodes with coaxial cables through a special reading card, realized and purposely printed for the NSH PDs, which allows the insertion of the sample inside it. All the external controls are adapted to 50 \(\Omega\) without the use of any filter or amplifier.  
Figure 1: (**a**) Optical transmittance (\(T\)) of single wall carbon nanotube (SWCNT) films as a function of the incident photon wavelength, \(\lambda\). (**b**) Optical transmittance (\(T\)) of SWCNT films at fixed incident wavelength \(\lambda\) = 550 nm vs their sheet resistance (\(R_{S}\)). The red curve is a fit obtained following the Equation (1). The free parameter in the fit is the ratio \(\sigma_{ac}/\sigma_{dc}\) which is found to be equal to 1.2 \(\pm\) 0.1. (**c**) Top panel: Sketch of the device. In order to better display the multifinger top electrode, the SWCNT film is artificially reported lifted with respect to the active area. Red arrows indicate where the SWCNT film was deposited through the dry-transfer printing process. Bottom panel: a picture of the device. Note the high transparency of the SWCNT film that allows to still see the interdigitated contacts.  
The external quantum efficiency (EQE) signal was acquired by an Ametek (Berwyn, PA, USA) 7265 lock-in amplifier upon the device active area illumination through a monochromatic light modulated by a Thorlabs (Newton, NJ, USA) MC 1000A optical chopper system (operating modulation frequency 22 Hz). The incident radiation is obtained by using a monochromator (LOT (Darmstadt, Germany) MSH-300, wavelength range 300-1000 nm) after a LOT LSE 140 Xenon lamp.  
The optical spectroscopy measurements were carried out using a Perkin-Elmer Lambda 19 spectrophotometer with unpolarized light acquiring the transmitted optical signal from 200 to 3000 nm. The sheet resistance was measured through the van der Pauw method recording the typical I-V curves in the four-contact geometry.  
For fast switching signal acquisition, a 30 GHz Tektronix oscilloscope directly connected with the device reading card was used without any support electronics. The light sources used to illuminate the sample in the measurements shown are manifold: a LOT-Oriel solar simulator AM 1.5 G spectral illumination of 100 mWcm\({}^{-2}\) (1 sun), a light emitter diode (LED Kingbright L-7113 QBC-D, emission spectrum peaked at \(\lambda\) = 460 nm) and a ultra-fast laser system consisting of a regenerative amplifier, seeded by a Ti:Sa oscillator, coupled with an Optical Parameter Amplifier (OPA) which produces a 35 femtoseconds laser pulse at 1 kHz of repetition rate and in a wavelength range that goes from 240 nm to 20 \(\upmu\)m [29; 30].
## 3 Results and Discussion
### Synthetic Example
Additionally, we also observed the mean square error (MSE) loss function curves between the network outputs within 1000 iterations and clean seismic records without ground roll for these four scenarios, as shown in Figure 10.  
From Figure 10, it is evident in Figure 10a,e that when using the L2 loss function, the network tends to fit the reflected waves within a certain number of iterations. However, as the number of iterations increases, the network quickly starts fitting seismic records containing ground roll, leading to a rapid increase in the mean square error between the network output and clean seismic records without ground roll. Furthermore, it is also  
Figure 8: The training process of synthetic seismic data when \(\lambda_{1}=40\) and \(\lambda_{2}=1\) (where **(a–i)** represent the output of the network at iterations 0, 80, 100, 140, 160, 180, 340, 800, and 1000 and **(j)** is the loss function after every 1000 iterations of network training). Figure 9: The training process of synthetic seismic data when the loss function is the L2 loss function (where **(a–i)** represent the output of the network at iterations 0, 80, 100, 140, 160, 180, 340, 800, and 1000 and **(j)** is the loss function after every 1000 iterations of network training). clearly observable from Figure 10 that when \(\lambda_{2}\) is fixed, a larger \(\lambda_{1}\) results in a faster fitting speed of the network; the more layers the network has, the faster the fitting speed. In conclusion, from Figure 10, we can observe that in the initial iteration phase, the network tends to prioritize fitting the reflected waves, with the best fitting effect, as shown in Figure 10a-d. Particularly, the surface wave suppression effect in Figure 10c is the most effective, with a more coherent and clear waveform compared to Figure 10a-c while maintaining the mean square error within a smaller range. However, as the number of iterations increases, the mean square error of the network gradually increases and eventually stabilizes. Therefore, when denoising actual seismic records, we selected the six-layer skip connection network with a faster processing speed as the overall structure of the WPI-SD ground roll suppression network. Additionally, when suppressing ground roll, it is important to choose appropriate loss function coefficients and optimal iteration numbers to achieve better ground roll suppression effects on seismic records.
## 3 Analysis of Flow Force Characteristics of Slide Valves
### Effects of Viscous Forces and Vortex Dissipation
An arc-shaped flow channel was implemented to suppress the formation of vortices within the valve chamber, as illustrated in Figure 13. The arc-shaped flow channel constrains the cross-sectional area of the annular cavity, enhancing the jet velocity, and the arc transition concentrates the outlet jet. By integrating the entropy production rate over the volume, the pulsation and total entropy production for both the original and arc-shaped flow channel were calculated, as shown in Figure 14. The results indicate that the entropy production corresponding to the arc-shaped flow channel is significantly lower, particularly at larger openings, effectively suppressing the formation of oil vortices within the valve chamber. Figure 12: Cloud plot of entropy production rate for two throttling modes. **Left**: outlet throttling, **right**: inlet throttling. Figure 15 presents the absolute values of the steady flow forces for the two flow channels under inlet and outlet throttling modes. As the valve opening increases, the introduction of the arc-shaped flow channel reduces the flow force in the outlet throttling mode, while increasing the flow force in the inlet throttling mode. This results in the diminished asymmetry of the steady flow forces between the inlet and outlet throttling modes. Therefore, it is concluded that suppressing the vortices within the chamber contributes to weakening the differences in steady flow forces of the slide valve under bidirectional throttling modes.
## 2 Design of Integrated Passive Components
### Filter, Diplexer and Coupler Design  
The selection of bandpass filters for this demonstration is based on the types of resonators. Hairpin filters are designed using coupled half-wavelength (\(\lambda/2\)) open-circuited stub resonators. The interdigital filters are composed of quarter-wavelength (\(\lambda/4\)) short-circuited stub resonators, and edge-coupled filters are formed using parallel-coupled \(\lambda/2\) resonators. Both the hairpin and interdigital filters are designed using a 0.043 dB Chebyshev equal-ripple response with fifth-order g-values, and the edge-coupled filters are designed using fourth-order g-values. All filters are doubly terminated: they have 50 \(\Omega\) impedance at both ports. Their order is selected based on acceptable passband insertion loss and stopband attenuation of more than 30 dB. Hairpin and interdigital filters are designed for bands n257, n258 and n260, whereas edge-coupled filters are designed for combined bands n257 and n258 (24.25-29.5 GHz) and band n260. The design procedure for the filters is described in [21; 22; 28].  
Since the 28 and 39 GHz 5G NR bands are separated by a frequency range of 29.5-37 GHz (not including edge frequencies), the diplexer structures for these bands are non-contiguous and can be designed using the aforementioned doubly terminated filters. A T-junction with special properties can be designed to connect two doubly terminated filters in such a way that they do not load each other and effectively appear \"open\" in the stopband of the other filter [29]. This configuration results in a diplexer. Using the doubly terminated filters designed for this demonstration, hairpin, interdigital and edge-coupled diplexers are designed. The simulated response of a diplexer based on interdigital filters for bands n258 and n260 and its layout are shown in Figure 4.  
For the directional coupler, a passive, three-section coupled-line coupler with a 20 dB coupling factor is designed which can be connected to a power detector to measure the output power of the antenna. Since the coupling factor is 20 dB, only 1% of the power is coupled from the main line of the coupler, ensuring better signal integrity in the critical signal path to the antenna. The coupler covers the desired bandwidth of 24.25-40 GHz and has a maximum passband insertion loss of 0.35 dB with small variation in coupling in the desired frequency range. One critical condition for the constant coupling factor is the termination at the isolation port of the coupler. Variations in impedances at the coupler termination ports can lead to an undesired response, but this can also be compensated for using complex termination techniques at the isolated port, as discussed in [30]. The simulated response of the coupler along with its layout is shown in Figure 5.  
Figure 4: Simulation results of an interdigital diplexer for bands n258 and n260 along with its layout.