esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Parameter Settings  
For performing review text data preprocessing, we applied the NLTK (Natural Language Toolkit) package to remove stopwords, special characters, symbols, numbers, etc., included in the review [65; 66]. For training CNN-BiLSTM hybrid model, we set an embedding dimension of 300, filter windows of 3, 4, and 5, a filter size of 100, and the number of hidden units in Bi-LSTM of 64. In addition, to solve the overfitting problem, the dropout rate was set to 0.5, batch size was set to 50, and Epoch was set to 100. As the optimization algorithm, Adam, which is widely used in previous studies, was applied, and the learning rate was set to 0.05 [67]. We set the review length of average and maximum length and the vocabulary size of several sizes [68]. Then, we set the optimal review length and vocabulary size based on classification performance. We applied the same parameters to baseline algorithms and compared the classification performance. For the CF algorithm, Pearson Correlation Coefficients measures similarity between users, and the neighbor size is set from 1 to 100. Furthermore, we set the latent factor sizes of SVD and NCF techniques to 8, 16, 32, 64, and 128 [60]. Experiments in this study were conducted using TensorFlow, Keras, and Surprise packages. All experiments were conducted in a computer environment with CPU Intel Core i9-9900 KF, 64G of memory, and a GeForce RTX 2080 Ti.
# Status of the SXFEL Facility
## 2 The SXFEL Test Facility
### Injector  
The SXFEL photo-injector, as shown in Figure 2, consists of an S-band photo-cathode radio frequency (RF) gun, emittance compensating solenoids, a drive laser, two S-band accelerating structures, and a laser heater. To generate a flat-top driving laser pulse, the pulse stacking technique is adopted in the temporal shaping system. Three diagnostic stations are employed in the injector. The 6D phase space of the electron bunch can be reconstructed by the combination of a transverse deflecting cavity and a 2-cell FODO lattice, each cell contains a focusing quadrupole (F), a space (O), a defocusing quadrupole (D) and a space (O).  
The beam parameters at the exit of the injector are shown in Table 2. The injector aims at achieving sub-\(\upmu\)m level normalized emittance with the bunch charge of 0.5 nC, and the operation parameter errors of photo-cathode gun, drive laser, solenoids, and accelerating structures are well controlled to make sure the beam parameters lie within the specifications.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 3 Simulation Results
### Processor Utilisation Analysis  
A processor's computation time is estimated by the total number of simulation iterations in which the processor is busy with computation. The overall computation time is the sum of the computation times of all processors. The PU is the overall computation time divided by the number of nodes then divided by the simulation time (in iterations) expressed as a percentage.  
The experiments with ABC (Figure 9) show that in many cases PU increases with an increase in comm/comp ratio. It is only at a very high data rate of 1 Tb/s that PU levels (or drops a little bit) compared to 100 Gb/s. The simulated grid yields better PU with larger sizes of data. The best results (up to 84%) are achieved with 10 MB of data and the comm/comp ratio above 10,000. Regarding the available technologies (data rates between 10 to 100 Gb/s), PUs of around 60% to 80% are predicted (see blue plots in Figure 9c,e,f). Note that a multi-channel communication scheme is envisaged for the ABC. The available bandwidth will be divided between channels which lowers the available bandwidth for each channel, and the PU will be lower as a result.  
The results with a 2000-node ABC (in Figure 9b,d,f) are the same as with 1000 nodes. However, there is a decrease in PU with 2000 nodes compared to 1000 nodes. The nature of the task model and the packet-switching method can be responsible for this. Expansion of a network brings potential for an increase in PU and consequently faster task completion; however, the task model used in these simulations seems unable to take advantage of that opportunity.  
The same experiments are made with a 5D grid (Figure 10). The PUs in ABC and 5D grids are comparable. In some cases, the ABC performs slightly better than the corresponding experiment with 5D. Like the ABC grid, with a 5D grid the same trend of increase in PU with an increase in comm/comp ratio and data size is observed. Again (like with ABC), there is a performance plateau with some data points with a data rate = 1 Tb/s.  
Figure 9: Processor utility for FFTTM on ABC with two sizes: 1000 nodes (**a**,**c**,**e**) and 2000 nodes (**b**,**d**,**f**). Data sizes are 100 KB (**a**,**b**), 1 MB (**c**,**d**) and 10 MB (**e**,**f**). Blue, red, green, orange and black graphs represent the computational ability of 0.01, 0.1, 1, 10 and 100 GIPS respectively.
###### Abstract  
With recent advances in remote sensing image acquisition and the increasing availability of fine spectral and spatial information, hyperspectral remote sensing images (HSI) have received considerable attention in several application areas such as agriculture, environment, forestry, and mineral mapping, etc. HSIs have become an essential method for distinguishing crop classes and accomplishing growth information monitoring for precision agriculture, depending upon the fine spectral response to the crop attributes. The recent advances in computer vision (CV) and deep learning (DL) models allow for the effective identification and classification of different crop types on HSIs. This article introduces a novel squirried search optimization with a deep transfer learning-enabled crop classification (SSODTL-CC) model on HSIs. The proposed SSODTL-CC model intends to identify the crop type in HSIs properly. To accomplish this, the proposed SSODTL-CC model initially derives a MobileNet with an Adam optimizer for the feature extraction process. In addition, an SSO algorithm with a bidirectional long-short term memory (BiLSTM) model is employed for crop type classification. To demonstrate the better performance of the SSODTL-CC model, a wide-ranging experimental analysis is performed on two benchmark datasets, namely dataset-1 (WHU-Hi-LongKou) and dataset-2 (WHU-Hi-HanChuan). The comparative analysis pointed out the better outcomes of the SSODTL-CC model over other models with a maximum of 99.23% and 97.15% on test datasets 1 and 2, respectively.  
hyperspectral remote sensing; crop mapping; image classification; deep transfer learning; hyperparameter optimization
## 4 Discussions
### GW and SW Interaction  
The coupled model resulted in intense interactions among SW and GW. Long term, SW flows follow the pattern of GW levels in the defined catchment with the higher GW flows followed by higher SW discharges. Model calibration of SW discharges was difficult due to the limited monitoring data availability of SW discharges, and that highlights the significance of high resolution field monitoring data in hydrological modelling. GW is a major contributor to the balance of SW flows during the low flow periods, and GW contribution rises up to 45% of total SW flows during the observed partial drought in the catchment; and, with exclusion of river flows from lake Tollense, GW contributes mainly to the Tollense river nearly up to 95% of total SW flows. The simulated hydrograph shows relatively overestimated river flows during peak flow periods. A successful calibration of SZ boundary conditions and geological layers' vertical discretization, saturated hydraulic conductivities, drainage time constant, and leakage coefficient play a vital role to successfully quantify the SW and GW interactions. The above discussed differences between simulated and monitored GW levels and SW discharges are due to a combination of different sources of uncertainty: Structural (grid size, simplification of geology), input data (climate data), and parameter uncertainty (e.g., saturated hydraulic conductivity); see also Sections 3.2 and 3.3.
## 3 Full Device Fabrication
### Ge PD Fabrication  
Ge p-i-n PDs are fabricated with a process compatible with standard CMOS technology. After the 1 \(\upmu\)m thick Ge film is grown on an 8-inch p-type Si substrate by RPCVD, the device active area is defined by double mesa patterning with i-line lithography, followed by dry etching processes. The p-i-n junction is formed by first implanting phosphorus into the Si substrate and then implanting boron into the Ge film surface. A film of 600 nm thick silicon dioxide (SiO\({}_{2}\)) is subsequently deposited by PECVD as an inter layer dielectric (ILD), followed via opening with a reactive ion etching (RIE), and Ti/TiN/Al/TiN deposition/patterning with physical vapor deposition (PVD)/transformer-coupled plasma (TCP) etching. A silicon nitride layer (SiN) is then deposited as an anti-reflection coating, followed by removal of the silicon nitride on top of the metal pad. The schematic plot of the device structure is shown in Figure 8a. In Figure 8b,c, the cross-sectional SEM image and top-view OM image of the fabricated devices are shown.  
Figure 7: (**a**) Cross-sectional TEM image of the Ge film deposited on a Si substrate using RPCVD. (**b**) Top-view AFM image obtained by the Ge etch pit method. (**c**) Top-view SEM image obtained by the Ge etch pit method.
###### Abstract  
Smart grid technology based on renewable energy and energy storage systems are attracting considerable attention towards energy crises. Accurate and reliable model for electricity prediction is considered a key factor for a suitable energy management policy. Currently, electricity consumption is rapidly increasing due to the rise in human population and technology development. Therefore, in this study, we established a two-step methodology for residential building load prediction, which comprises two stages: in the first stage, the raw data of electricity consumption are refined for effective training; and the second step includes a hybrid model with the integration of convolutional neural network (CNN) and multilayer bidirectional gated recurrent unit (MB-GRU). The CNN layers are incorporated into the model as a feature extractor, while MB-GRU learns the sequences between electricity consumption data. The proposed model is evaluated using the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. Finally, our model is assessed over benchmark datasets that exhibited an extensive drop in the error rate in comparison to other techniques. The results indicated that the proposed model reduced errors over the individual household electricity consumption prediction (IHEPC) dataset (i.e., RMSE (5%), MSE (4%), and MAE (4%)), and for the appliances load prediction (AEP) dataset (i.e., RMSE (2%), and MAE (1%)).  
bidirectional gated recurrent unit; convolutional neural networks; electricity consumption prediction; hybrid deep learning model; residential load prediction +
Footnote †: journal: Applied Sciences
# Teenagers and Automated Vehicles: Are They Ready to Use Them?
## 4 Results and Discussion
### Focus Group Results
#### 4.1.1 Which Are Your Mobility Habits and Needs?  
In the beginning of the FGs, to better understand their current mobility, the teenagers were asked to describe the transport modes usually chosen in their daily mobility to school and other activities. The teenagers mainly indicated passive modes of transport, such as the school bus, public transport, private car driven by parents, or carpooling with friends. Among the least mentioned modes of transport, the active modes were listed, and more specifically bicycling and walking. The bicycle was mentioned only once as a main travel mode and more often as an alternative to passive modes of travel when the weather conditions allowed it. Walking was generally preferred due to the residence's proximity to the school, as an alternative to the school bus or when chained with other transport modes to reach the final destinations.  
_\"I live really near by the school; I mostly walk there\"_. [PERSON], 17 y/o  
These findings of the reduced use of active modes of transport in teenagers' daily commuting to school are in line with the observations presented in the literature [30; 32; 33; 36].  
An important aspect that was emphasised during their descriptions was the combined use of transport modes to reach school or other destinations. Some explained that they were driven by parents or walked to bus/train stops, others that they combined public transport (PT) with public shared/private bicycle, or that they used multiple PT modes (bus, train, and tram).  
_\"I take the school bus and to go to a bus stop my parents bring me there by car or I walk there\"_. [PERSON], 17 y/o  
_\"I have to take the train early morning and then bicycle from the train station\"_. [PERSON], 15 y/o  
We consider important the finding related to the combination of different transport modes resulting in complex mobility patterns to reach school or other destinations since, to the best of our knowledge, similar patterns were not identified in the literature.  
Exploring the reasons behind these travel mode choices, the FG participants flagged the proximity to school as an enabler to walking and biking. The findings are in line with the aspects pointed out in the literature regarding the influence of distance to school on the use of active modes of transport [18; 31; 34; 35; 36; 37]. On the other hand, the lack of reliable PT options in terms of frequency, arrival times, and proximity to their residence pushes teenagers to depend on their parents who drive them either to the closest station or to their final destination.  
The participants were also asked to provide details on their mobility for leisure activities (e.g., sports, meetings with friends, and shopping). All of them mentioned the use of PT as the main or secondary transport mode or combined with rides offered by the parents, the use of a bike, and walking.
## 3 Problem Definition  
This paper is motivated by recent works on dynamic packet size control in WSNs which tries to minimize the energy consumption by dynamically adapting packet size depending on the link quality [15; 16; 17; 22; 23]. Large packets can improve the energy efficiency by keeping the overhead of the header low, but at the same time large packets can reduce the energy efficiency by raising PER. In contrast, the use of small packets leads to low PER, but is not good in terms of the header overhead. Therefore, these works adjust the packet size appropriately depending on the link quality.  
Another important aspect of code dissemination is to guarantee the authenticity of the distributing node (e.g., BS) and the integrity of the code image which is normally ensured by the authentication tokens, such as MACs and digital signatures, attached to each packet by the source node. It is important to note that source authentication, not intermediate nodes authentication, is required during code dissemination since the sensor nodes are vulnerable to node capture attacks. When applying existing source authentication schemes into code dissemination supporting dynamic packet size, a new problem arises as shown in Figure 3 since the packet size can be different in each hop.  
The BS first determines the packet size on the basis of the link quality between the BS and the sensor node 1. Once the packet size is determined, the BS generates an authentication token which provides the authenticity and the integrity of the packet, and sends the token with the packet to the node 1. Upon receiving the packet, the node 1 verifies the authentication token using a shared key with the BS or a public key of the BS according to the used cryptography. To forward the packet to the node 2, the node 1 determines the packet size depending on the link quality between the node 1 and the node 2. In this case where the optimal packet size is different from that of the first hop as shown in Figure 3, the node 1 cannot generate a valid authentication token of the BS for the packet with the optimal size since the key for generating an authentication token is only known to the BS.  
Therefore, we define the problem to be resolved in this paper as \"providing source authentication under the environment where the packet size changes in each hop during code dissemination in order to improve the energy efficiency.\"  
Figure 3: The problem of existing source authentication schemes in code dissemination supporting dynamic packet size.
## 3 Experimental Results and Analysis  
### Results of Automatic Feature Region Searching for Image Registration  
The experimental results show that the proposed shape searching algorithm based on the combination of contour point distribution information and edge gradient direction can select the good shape in the image stably and quickly. Figure 10 provides an example of the result of a printed image. Figure 10a shows all shapes extracted after edge detection and the connected component analysis. Figure 10b shows the good shapes searched. Figure 10c presents a partially enlarged view.  
Figure 10: First set of searching results for good shapes. (**a**) All shapes detected; (**b**) good shapes searched; (**c**) partially enlarged image of Figure (**b**).  
In addition to the results in Figure 10, the results of the test on the automatic registration feature region searching algorithm for other printed packaging images are given. The red contours in Figures 11a and 12a present all the shapes detected by the preliminary shape extraction method, and the red contours in Figures 11b and 12b represent good shapes searched. Figures 11c and 12c represent, respectively, the partially enlarged image of Figures 11b and 12b.  
Figure 11: Second set of searching results for good shapes. (**a**) All shapes detected; (**b**) good shapes searched; (**c**) partially enlarged image of Figure (**b**).  
Figure 12: Third set of searching results for good shapes. (**a**) All shapes detected; (**b**) good shapes searched; (**c**) partially enlarged image of Figure (**b**).
###### Abstract
Section 1 presents a review on the application of the FRA method on transformers and IMs. In addition, the problem statement and study objective are stated in this section. Section 2 presents the FRA application for power transformers. In Section 3, the FRA application for IMs is presented. Section 4 presents the obtained results and analysis using several statistical indicators. A comparison of the FRA application on transformers and IMs is presented in Section 5. Finally, the main conclusions are drawn in Section 6. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Scope and Main Findings** & **Limitations** \\ \hline
[14] & Detecting broken bars in three-phase squirrel-cage IM using FRA. Results show a variation in the FRA signature within the frequency range 1 kHz to 300 kHz. & Only one fault was investigated, and no FRA interpretation methodology was presented. \\ \hline
[15] & Detecting short circuit faults within IMs. Findings show that turn\(-\)to\(-\)turn has a small effect on the motor FRA signature whereas a huge variation was observed for phase-to-phase short circuit fault. & Did not cover a variety of faults, and statistical indicators that are employed to interpret the results. \\ \hline
[16] & Detecting emerging winding faults. Reported results show that even a small percentage like 1\% of winding defects can be detected by FRA. & Only turns fault at one phase is investigated and a few statistical indicators were used. \\ \hline
[17] & Detection of short circuit faults within IM. & Only Phase-to-phase fault is investigated with one assessment factor used to analyze the results. \\ \hline
[18] & Detecting winding faults within IM using FRA. & Interpretation of the result was based on personnel expertise. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research published on the application of the FRA on induction motors.
# Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net
###### Abstract  
Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.  
t +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences