esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & [25] & [26] & [27] & [28] & [29] & [30] & [31] \\ \hline \multirow{4}{*}{Methods} & \multirow{4}{*}{CLSA/ GC-MS} & SPME/ & \multirow{4}{*}{GC-MS} & \multirow{4}{*}{LGIS/ GC-MS} & \multirow{4}{*}{TPSS/ GC-MS} & \multirow{4}{*}{TD/} & \multirow{4}{*}{Portable} \\  & & HS- & & GC-MS & & \\  & & SPME/ & SPME/ & GC-MS & GC-MS & GC-MS & GC-MS \\ \cline{1-1}  & & GC-MS & & & & \\ \cline{1-1}  & & & FID/O & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1} Numbers & 47 & 164 & 56 & 2 & 83 & 35 & 33 \\ \hline \hline \end{tabular} Note: CLSA: Closed-loop stripping analysis, HS-SPME: Headspace-solid phase microextraction, LGIS: Liquid-gas impinger system, TSPS: Three-stage preconcentration system, TD: Thermal desorption, GC-MS: Gas chromatography-mass spectrometry, GC-FID/O: Gas chromatography-flame ionization detection and olfactory.  
\end{table} 
Table 1: The number of VOCs (volatile organic compounds) detected using different methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Odor Categories** & **Odor Descriptor** & **Chemical Compounds** & **References** \\ \hline Earthy/Musty & Earthy/Musty/Moldy & Geosmin & \\  & & & 2-Methylisobomeel & \\  & & & Trichtor anisole & \\ Oxidant/Chlorinous & Chlorinous & Monochloramine & \\  & & & Dichloramine & \\ Grassy/Woody & Green/Grassy/ Hay & cis-3-Hexen-1-ol & [27] \\  & & & Formaldehyde & \\ Sulfide/Cabbage/Garlic & Rotten Eggs/ & Hydrogen sulfide & [29] \\  & Decaying Vegetation & Dimethyl trisulfide & [25; 26] \\  & & & Dimethyl sulfide & [26; 29; 30; 31] \\  & & & Thiophene & \\  & & & Methyl mercaptan & [29] \\  & & & Dimethyl disulfide & [25; 26; 29; 30; 31] \\ Rancid/Putrid & Sour Milk/ & Methyl thiobutyrate & \\  & Putrid/ & Valeric acid & \\  & Rancid & Isovaleric acid & \\  & & & Butyric acid & \\  & & & Heptanal & [26] \\ Fragrant/Fruity & Soapy/ & 1-Dodecanal & \\  & & Fruity/ & Acetaldehyde & [27] \\  & & Citrus & Ethyl acetate & [26] \\  & & & d-Limonene & [25; 27; 31] \\ Ammonia/Fishy & Ammonia/ & Ammonia & [29] \\  & & Cat Urine/ & Pyridine & [26] \\  & & Fishy & Butylamine & \\ Solventy/Hydrocarbon & Solventy/ Gasoline & Benzene & [25; 26; 29; 31] \\  & & & Toluene & [25; 27; 29; 31] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main odor substances appeared in WWTPs (wastewater treatment plants) and their descriptors.
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 4 System Parameter Design
### Azimuth Sample Number and Referential Pulse Interval  
As has been discussion in Section 2, the whole non-uniform PI array should be generated based on an initially set referential PI, the design of which, as will be seen below, is based on the least required non-aliasing azimuth sample number. Similar to the range bandwidth, the azimuth bandwidth of the SSS-RSSSAR will also be changed by the 2D dechirp. After the range interpolation, the azimuth bandwidth of the total wavenumber spectrum, denoted as \(\Delta K_{\text{X}}\), yields  
\[\Delta K_{\text{X}}(i)=K_{\text{Y}}^{\prime}(i)\left(\tan\alpha\left(\frac{N_ {\text{a}}}{2}\right)-\tan\alpha\left(-\frac{N_{\text{a}}}{2}\right)\right) \tag{41}\]  
Note that \(\Delta K_{\text{X}}\) is actually wider than the azimuth wavenumber bandwidth of the signal because the total azimuth sample number \(N_{\text{a}}\) is larger than the azimuth sample number for an individual target, such as \(N_{\text{as}}\) in Equation (9). To avoid the azimuth aliasing, the azimuth swath of the reconstructed image should be wider than the BIS swath along the X-axis as  
\[\frac{2\pi}{\Delta K_{\text{X}}(i)}N_{\text{a}}\geq W_{\text{a}} \tag{42}\]  
By substituting Equations (25), (41) into Equation (42), the least required azimuth sample number \(N_{\text{amin}}\) can be estimated by assuming \(i=0\) for simplicity as  
\[N_{\text{amin}}\approx\sigma\frac{2f_{\text{c}}\sin\beta_{\text{c}}W_{\text{ a}}}{c}\left(\tan\alpha\left(\frac{N_{\text{a}}}{2}\right)-\tan\alpha\left(- \frac{N_{\text{a}}}{2}\right)\right) \tag{43}\]  
The final azimuth sample number \(N_{\text{a}}\) can be set slightly larger than \(N_{\text{amin}}\). Then by assuming the length of orbit over which the SSS-RSSAR flies during the DAP is \(D\), the referential pulse interval \(PI_{0}\) can be achieved as  
\[PI_{0}=\frac{D}{V_{\text{s}}N_{\text{a}}} \tag{44}\]  
Based on Equation (44) and the CVPI technique, the total non-uniform azimuth sampling array can be generated iteratively based on Equation (8). Due to the \(PI\) variation, the validity of the \(PI\) array should be checked to ensure that the echo from the ROI can be fully recorded in every \(PI\) as  
\[\min_{-\frac{N_{\text{a}}}{2}\ldots\frac{N_{\text{a}}}{2}}PI(m)>\tau_{\text{w}} \tag{45}\]  
Otherwise, \(PI_{0}\) should be further elongated for a new \(PI\) array generation until the condition in Equation (45) can be satisfied.
# A Combined Electromagnetic and Mechanical Approach for EU-DEMO Toroidal Field Coils
## 4 Electromagnetic Calculation Methodology-EMS Section
For the air domain (Figure 4a), needed to ensure the correct closure of the flux lines, infinite elements [12] are used to surround the magnets. The resulting magnetic field is reported in Figure 6, for the reference time points of the scenario. By means of this model, the Lorentz forces are computed as nodal forces for each magnet. Focusing on the TF system, it is useful to consider the distribution of the electromagnetic force and moment components along the curvilinear abscissa of the D-shaped magnet, shown Figure 7 with reference to a cylindrical coordinate system, where \(x\) is the azimuth; \(y\) is the radial distance, and \(z\) is the axial coordinate (height). Figure 7a shows that the radial component exhibits an intense centripetal action (i.e., toward the machine axis) along the inner leg and the adjacent curved region, while on the outer leg the radial action becomes centrifugal (positive values on the graph). The vertical component of the Lorentz force shows a nearly symmetrical top-down trend; the vertical force rises in the straight part joining the two lower curved sectors (Figure 7b). These two components are generated and influenced only by the magnetic field produced by the toroidal system. The toroidal (out-of-plane) component is due to the interaction of the TF current with the magnetic  
Figure 5: Current density [A/mm\({}^{2}\)] distribution in the CS modules and PF coils, modelled as smeared domains (**a**), and in the TF coil, modelled detailing its winding pack (**b**). field generated by the poloidal system, which varies with the time (Figure 7c). The same variation is highlighted also on the plots of the moments around the radial and the vertical axes, shown in Figure 7d,e, respectively. It is worth underlining that, within the TF system, the total moment around the machine vertical axis is zero. Finally, Figure 7f shows that the most severe action is exercised by the out of plane (OOP) moment, which does not change during the scenario, being composed of the constant components of the force. The effect of the rotation around the OOP axis is balanced by the reactions at the gravity supports and by the contact forces on the inner leg TF cases, where the D-shaped magnets are wedged. Figure 6: Magnetic field [T] distribution at (**a**) TF Energization, (**b**) premagnetisation, (**c**) start of flattop and (**d**) end of flattop of the plasma scenario considered.
## 4 Results and Discussion
### Spatial Characteristics of the Optimized DNN Models  
Sections 4.1 and 4.2 showed that the crop yield prediction accuracy was highest when using the optimized DNN model with the JA period data. To analyze the spatial characteristics of the result in more detail, we examined the annual predicted corn and soybean yields of the optimized DNN model for JA. Figures 7-10 are the maps of the annual county-level corn and soybean yields, respectively, for 2006-2015. The actual (Figures 7a, 8a, 9a and 10a) and predicted yields (Figures 7b, 8b, 9b and 10b) showed quite similar patterns. Figures 7c, 8c, 9c and 10c show the prediction errors for corn and soybean yields. Figures 7d, 8d, 9d and 10d show the Gi* for the prediction errors of our DNN model for corn and soybean yields. A somewhat spatially clustered pattern was found in a few areas for multiple years. This tendency of the spatial autocorrelation of the prediction error may be associated with the spatial distribution of the input data for land surface and weather variables. Indeed, the DNN model does not account for the spatial relationships between crop yield and agricultural variables. To examine the spatial clustering tendency of the prediction errors in more detail, and to solve the problem, the DNN model used in this study could be coupled with spatial statistical methods, such as geographically weighted regression (GWR), in a future work.  
Examination of soil properties was also effective for analyzing the spatial pattern of prediction errors. In the case of Illinois, the spatial clustering of prediction errors was found in almost every year, for both corn and soybeans. This may be related to the spatial characteristics of the distribution of soil property values, such as bulk density and sodium content. High bulk density can prevent plants from rooting, and high sodium content can result in water stress for crops. Hence, we referred to the soil property maps from the Harmonized World Soil Database (HWSD) for the study area. Presumably, the spatial clustering of prediction errors in Illinois can be associated with the values of bulk density and sodium content, which were higher than in the other states. Additional treatment aimed at the spatial distribution of soil properties can contribute to improving the accuracy of crop yield prediction.  
Figure 7: Maps of the actual and predicted corn yields, prediction errors, and Local Geary index (G\({}^{\prime}\)) of the prediction errors, 2006–2010.  
Figure 8: Maps of the actual and predicted corn yields, prediction errors, and Local Geary index (G\({}^{\prime}\)) of the prediction errors, 2011–2015.  
Figure 9: Maps of the actual and predicted soybean yields, prediction errors, and Local Geary index (Gi\({}^{\prime}\)) of the prediction errors, 2006–2010.  
Figure 10: Maps of the actual and predicted soybean yields, prediction errors, and Local Geary index (G1) of the prediction errors, 2011-2015.
## 3 Results and Discussion
### Chemical Composition Assays  
Results of chemical composition analyses are shown in Table 2. An increase in fat content can be seen by a comparison against 0 h. It has been reported that _L. paracasei_ is capable of producing short-chained fatty acids (SCFA). However, levels seen are in the mmol/kg order and in human feces [38]. To the best of our knowledge the highest production of SCFA seen in a sourdough model has peaked at 360 mg/kg [39]. Seeing that there is no statistical difference between treatments, these results suggest that the commercial formulation of the lactic acid bacteria contains an additive. Further studies out of the scope of this work should be carried on determining if there was a production of SCFA.  
Ash content could be explained in a similar manner. Following L * results from Table 1, maillard reactions decreased biscuit's lightness which could explain the difference between ash content in control biscuit (0 h) and those made from fermented flours.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fermentation Time**} & \multicolumn{3}{c}{**Color**} \\ \cline{2-4}  & **L*** & **a*** & **b*** \\ \hline
**0 h** & 73.92 \(\pm\)3.88 \({}^{\text{a}}\) & 13.78 \(\pm\)1.41 \({}^{\text{a}}\) & 33.14 \(\pm\)1.43 \({}^{\text{a}}\) \\ \hline
**2 h** & 54.99 \(\pm\)3.17 \({}^{\text{b}}\) & 18.39 \(\pm\)1.71 \({}^{\text{a}}\) & 30.92 \(\pm\)1.73 \({}^{\text{a}}\) \\ \hline
**4 h** & 54.88 \(\pm\)2.16 \({}^{\text{b}}\) & 20.12 \(\pm\)1.51 \({}^{\text{a}}\) & 32.65 \(\pm\)1.12 \({}^{\text{a}}\) \\ \hline
**6 h** & 51.71 \(\pm\)3.28 \({}^{\text{b}}\) & 19.20 \(\pm\)2.65 \({}^{\text{a}}\) & 30.96 \(\pm\)2.73 \({}^{\text{a}}\) \\ \hline
**8 h** & 52.28 \(\pm\)8.64 \({}^{\text{b}}\) & 19.69 \(\pm\)4.63 \({}^{\text{a}}\) & 30.66 \(\pm\)0.54 \({}^{\text{a}}\) \\ \hline \hline \end{tabular}  
* In both a* and b* there was no significant difference between 0 h and treatment biscuits. These results suggest that in order to see significant changes in color, longer fermentation times should be employed as in other works [6; 14; 37].  
\end{table}
Table 2: Biscuit color analysis results from different fermentation times a, b different letters indicate significant differences within the same column (\(p<0.05\)).  
Figure 6: Picture of transversal cut of two of the baked biscuits. 0 h control biscuit, 4 h bscuit made from flour fermented for 4 h.  
Noticeably, there is no statistical difference between treatments in protein content. This differs from the scientific literature, where it has been seen that flour fermentation with lactic acid bacteria has an effect on both amino acid and protein content [40]. It has been seen that fermentation can significantly increase crude protein content and be detected by the assays done in this paper [41]. However, fermentations last considerably longer in research (72 h).
## 3 Results and Discussion
### Photoelectrochemical Testing  
#### 3.4.1 The Effect of the Type of Electrolyte on the Photocurrent Generation  
The photoelectrochemical activity of the samples was measured based on different types of electrolytes that represented different pH values, namely, 0.5 M Na\({}_{2}\)SO\({}_{4}\) (pH 6.7), 0.5 M KOH (pH 13), and 0.5 M H\({}_{2}\)SO\({}_{4}\) (pH 1) (Figure 6). Based on the linear sweep voltammetry data, both photocathode samples exhibited similar patterns of photocurrent generation with similar photocurrent density values in all types of electrolytes. Based on Figure 6a-c among the electrolytes under investigation, the photocurrent generation measured in H\({}_{2}\)SO\({}_{4}\) solution was the highest. Photocurrent produced by the pre-annealed sample is \(\sim\)\(-\)0.023 mA cm\({}^{-2}\). This photocurrent value was approximately four-fold greater than that achieved in neutral and basic electrolytes. Meanwhile, the highest photocurrent produced by sample 450 \({}^{\circ}\)C (\(\sim\)\(-\)2.3 mA cm\({}^{-2}\) at \(-\)0.5 V vs. RHE) was much higher than the Co\({}_{3}\)O\({}_{4}\) pre-annealed sample. According to [PERSON] et al. (2012) increasing annealing temperature may improve catalytic activity because high temperature may increase the structural defects and the number of active sites, which would have positive impacts on the catalytic activity. However, at sample 550 \({}^{\circ}\)C, the photocurrent decreased to \(\sim\)\(-\)1.2 mA cm\({}^{-2}\) at \(-\)0.5 V vs. RHE because the phase of the sample has changes. These results suggest that H\({}_{2}\)SO\({}_{4}\) electrolyte solution enriched with protons (H\({}^{+}\)) could sufficiently attract opposite-charged electrons. The present study suggested that the recombination rates of electrons and holes were remarkably reduced, leading to enhanced photocurrent generation. Furthermore, a chronoamperometry stability test was conducted for samples of 450 \({}^{\circ}\)C and 550 \({}^{\circ}\)C in 0.5 M H\({}_{2}\)SO\({}_{4}\) electrolyte (Figure 6c). Sample 450 \({}^{\circ}\)C shows better stability compared to sample 550 \({}^{\circ}\)C. Under constant simulated solar illumination and fixed current density of 0.4 mA cm\({}^{-2}\), the 450 \({}^{\circ}\)C sample was stable until 4000 s of irradiation time before slowly decreasing. Meanwhile, the 550 \({}^{\circ}\)C sample was only stable for less than 500 s and decreased drastically.
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
## 2 Materials and Methods
### Network Training  
Training was performed on a desktop PC with an Intel(R) Core(TM) i7-4790 CPU at 3.60 GHz, 16 GB of RAM, and an NVIDIA RTX 2080 graphics card with 8 GB of memory. The software tools included Linux OS with Python 3.6 and the TensorFlow Model Garden framework. The fine-tuning strategy was performed using models that were pre-trained on the COCO dataset. The learning rate was set to 0.02 for the MobileNetV1 and MobileNetV2 models and to 0.0455 for the MobileNet Edge TPU and MobileDet models. The number of training steps was 30,000 for the MobileNetV1 and MobileNetV2 models and 35,000 for the MobileNet Edge TPU and MobileDet models.
## 2 Related Work
### Context-Free TV Recommender Systems  
A number of works tried to enhance the performance by adapting CBF approaches [3; 4; 5; 6; 7]. [PERSON] et al. proposed a personalized EPG based on the individual view preferences for attributes of TV programs such as category, title, participants, and producers [3]. [PERSON] et al. proposed a personal video recorder-based TV recommender system calculating users' preference with a Bayesian classifier based on both explicit feedback and users' recording history [4]. [PERSON] and [PERSON] proposed a group recommender system by using Bayesian networks and an analytic hierarchy process [5]. [PERSON] et al. proposed a heterogeneous information network-based TV program recommendation method that utilized the relationship information between the target user and the TV program based on meta-paths of its genre, channel, and timestamp [6]. [PERSON] et al. considered viewers' properties, such as activities, interests, moods, and these data were fed into a neural network model to predict viewers' program preferences [7]. These methods need a lot of detailed descriptions of programs such as titles, genres, actors, synopsis, and so on. Such information is difficult and costs a lot to be gathered in standalone TVs.  
Also, some researchers adapted CF approaches for TV recommender systems [8; 9; 10; 11; 12]. [PERSON] and [PERSON] proposed an Internet-based personalized television listing system that provided a daily TV guide by adapting CF approaches [8]. [PERSON] et al. and [PERSON] et al. proposed TV recommender systems based on matrix factorization, which is a model-based CF method [9; 10]. [PERSON] et al. proposed a tag-based collaborative TV recommender system based on user-added tags for TV programs and collaborative tag generation [11]. [PERSON] et al. proposed an automatic TV program recommendation scheme based on CF [12]. They first clustered users based on their genre preferences and performed CF for the group that the active user belongs to. However, the CF approach has a problem that it requires other users' information, which is difficult to be gathered in standalone TV environments.  
Fewer works integrated social information into TV recommender systems for better performance [22; 23; 24]. [PERSON] et al. and [PERSON] et al. assumed that a user's preference was learnable from viewing history and information in social networks [23; 24]. However, this approach also has a problem that personal social data are hard to be gathered in off-line TV environments.  
The problem of context-free methods mentioned above is that they calculate only a user's overall preference without consideration of the time context. However, time contextual preferences are different from overall preference, so that these methods can be biased to each user's average favorites.
## 1 Introduction
[44] have determined that demand-controlled ventilation based on occupancy could lead to energy savings of up to 93% in pharmaceutical cleanrooms. Also, [PERSON] [45] has assessed through simulations that a substantial amount of energy may be saved by allowing wider limits for temperature and relative humidity (RH) when there is no surgical activity in the ORs. This paper aims to explore the extension of the current set point ranges for hygothermal conditions during unoccupied periods as a strategy to minimize the energy demand in surgical areas. Such an approach is not contemplated by the actual Spanish standard, which recommends keeping the same ventilation and hygothermal conditions as for periods with surgical activity; that is, a room temperature between 22 and 26 \({}^{\circ}\)C and a RH range of 45-55% [41]. However, results from the literature discussed above suggest that there is a substantial margin for reducing the ventilation loads in ORs during off-use hours without compromising the safety of the indoor environment. In fact, more up-to-date guidelines are beginning to promote the energy efficiency through this kind of strategy, with the French standard allowing the widest range for temperature during unoccupied hours [46]. In particular, this code permits the air temperature to vary between 15 and 30 \({}^{\circ}\)C during off-use periods, instead of the normal operation range of 19-26 \({}^{\circ}\)C, and with no defined limits for the RH in conventional ORs. Therefore, the principal objective of this investigation is to assess the potential energy savings derived from applying the aforementioned recommendation of the French code to the case of an existing surgical suite of a Spanish hospital. First, the authors develop a dynamic energy model; second, it is calibrated using experimental data collected during occupied and unoccupied periods in the surgical suite. Such a simulation model, created in TRNSYS, is used to evaluate the annual energy performance of the whole suite under different ventilation scenarios. The scenarios were defined following the Spanish [41] and American standards. Temperature and RH setpoint ranges were, during operations, the same as currently used in the surgical suite; during unoccupied periods, temperature limits of 15-30 \({}^{\circ}\)C with non-controlled HR were employed. In principle, the safety of the surgical environment is guaranteed because both the ventilation and the hygothermal conditions will be within the limits established by widely accepted standards. However, the hygothermal conditions during transitions from unoccupied to occupied periods will be carefully studied, as well as the possibility of moisture condensation.
## 2 Materials and Methods
### Parametric Images from Radiofrequency Signals
Images representing ultrasound parameters quantitatively, such as an attenuation image, can be called the Q-mode image. The idea of multiple parametric images from raw RF signals for deep learning approaches can be extended to data augmentation in a broad sense and compensates for both limitations of datasets and low image quality simultaneously in medical ultrasound applications. Figure 2 shows the overall signal processing procedures for computing multiple parametric images from the RF signals. While B-mode images are conventionally generated from IQ data, they are also computed from RF signals by taking the Hilbert transform and absolute value followed by log compression. The envelope image before log compression to adjust the wide dynamic range, is an intermediate image that represents the echogenicity of the scanned region by taking only the magnitude of the received RF signals (i.e., ignoring phase information). Using this envelope image, two meaningful parametric images, entropy  
and phase images, can be obtained by signal processing and transformation techniques. In addition, attenuation images are estimated from raw RF signals through FFT and power spectrum analysis and applied to a deep learning network as an input representing a spectral feature in the frequency domain. The idea of \"multiple parametric images\" can be extended to other types of feature maps or parametric images, such as Nakagami images in either time (or spatial) or frequency domains, according to the purpose of clinical application. The detailed methods for each parametric image are described in the subsections that follow.