esa-sceva/satcom-chunk-collection huggingface dataset:
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 2 Related Works
### Attention Mechanism  
Attention in human vision refers to the part of the human visual system (HVS) that adaptively measures visual information and focuses attention on salient areas [31]. Notably, not all the features assume an imperative part for the expectant reconstructed image in SR tasks lately. However, most recent SR methods treat all channels or spatial locations equally. The study in [15] shows that, in some cases, selectively focusing on a few important features of a specific layer can bring considerable improvements to the reconstruction performance of the model. Therefore, the SR methods based on the attention mechanism have received increasing attention [14; 15; 32; 33; 34; 35].  
[PERSON] et al. [32] developed a compact channel attention mechanism in the recursive unit to adaptively recalibrate the input channel features to flexibly adjust the weights of different channel features, thereby promoting the reconstruction quality to a certain extent. [PERSON] et al. [15] introduced the channel attention mechanism into ResNet and assigned different weights to each channel feature according to the different contribution of channel features to the super-resolution performance, which greatly enhanced the detailed information of the reconstructed image. This mechanism mainly utilizes a global pooling operator to extract channel feature statistics information, which can be named first-order channel attention. On the basis of the first-order channel attention, [PERSON] et al. [33] proposed a second-order attention network (SAN), which endeavors second-order feature statistics to exploit more remarkable representations. However, considering that it is difficult to weigh the features from multi-scale layers by using the channel attention mechanism alone, [PERSON] et al. [34] proposed a holistic attention network (HAN) for image super-resolution. HAN regards channel features from different layers as responses of specific classes and explores the channel features between different layers, which accelerates the feature extraction ability. In addition to the potential relevance of channel features, the potential dependence of spatial features has also attracted wider attention. For example, [PERSON] et al. [34] also explored the internal relevance of spatial features and proposed a channel-spatial attention module to boost the reconstruction performance. To adequately coordinate the extracted feature information, [PERSON] et al. [14] proposed an enhanced spatial attention module to make the extracted features focus on the significant spatial content. Furthermore, [PERSON] et al. [35] proposed a pixel attention based network (PAN) for image SR, which explores the dependence between the long-range space around each spatial location and the channel through adopting a self-calibration method, thereby improving the reconstruction accuracy.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 4 Results
### Analysis of the Impact of Different Modules on FRET
In Baseline+A+B+C, the GFRN module is applied first, enabling earlier optimization of feature information and reducing redundant computations. The GCEN module then further strengthens local feature extraction. In contrast, in Baseline+A+C+B, the GCEN module is applied first, enhancing local features, but the subsequent GFRN module's optimization effect is somewhat diminished. Therefore, Baseline+A+B+C achieves a better balance between global feature optimization and local feature enhancement, ultimately showcasing superior overall performance. Finally, the Baseline+A+B+C combination achieves the best MAE value of 2.8575, a reduction of 0.4610 compared to Baseline+A. SSIM is 0.9968, an increase of 0.0011, and PSNR is 63.0946, an increase of 1.2472. These results indicate that the combination of the GFRN and GCEN modules not only optimizes feature information and local feature extraction but also significantly improves image quality, achieving the best performance across all three metrics.
## 3 Results
### Question 4: How to Collect Data
#### 3.4.2 Questionnaire Survey  
Since occupants know the surrounding environment best, questionnaires were commonly used in previous studies to collect their perceptions of IEQ and determine the combined effect of the IEQ factors. The questionnaire design was similar among these studies. Most of them consist of questions on the occupants' personal information, along with their perceptions of and acceptance/satisfaction regarding the four IEQ aspects. The perception-related questions were, in general, answered using semantic differential evaluation scales (e.g., hot--warm--neutral--cool--cold), Likert scales (e.g., disagree--neither agree nor disagree--agree), or a visual analog assessment scale (e.g., 0-100 rating scales), while the acceptance/satisfaction-related questions were usually answered using a dichotomous assessment scale (e.g., acceptable/satisfied--not acceptable/unsatisfied) [79]. Apart from the occupants' questionnaires, some researchers also collected experts' questionnaires to better understand the importance of different IEQ aspects. A typical form of these questions served as a pairwise comparison. Through this process, experts' preferences could easily be collected. Usually, a questionnaire survey was conducted simultaneously, with the physical measurements being taken in the indoor environment. The combination of on-site measurements and questionnaire surveys allows researchers to develop an in-depth and comprehensive understanding of the IEQ performance in the investigated environment. However, as with on-site measurement, most questionnaires were designed by the researcher empirically. The number of questions included for one IEQ aspect varied from one [13] to five [56], which, according to [PERSON] et al. [16], might influence the weighting of IEQ aspects concerning overall satisfaction. Therefore, a uniform set of questionnaires is needed. Some well-designed questionnaires were either adopted or revised by later studies,such as the CBE occupant IEQ survey [62] and the OFFICAIR questionnaire [80]. However, these questionnaires mainly focused on office environments; standard questionnaires for children in school and older adults in care centers are yet to be created.
## 2 Natural Bioactive Compounds for Food and Therapeutic Applications
This is basically done to protect the core bioactive material from degradation and maintain their stability, bioactivity, and bioavailability to a great extent [4; 25]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Class** & **Bioactive Compounds** & **Source** & **Technique** & **Ref.** \\ \hline \hline \multirow{4}{*}{Rutin} & Resveratrol & & Emulsion/freeze drying & [73] \\ \cline{3-4}  & \multirow{2}{*}{Commercial} & Supercritical fluid & [74] \\ \cline{3-4}  & & Liposomal encapsulation & [75] \\ \hline Hydroxycinnamic acid & Commercial & Ionic gelation & [76] \\ \hline Caffeic acid, carvacrol, thymol & & Liposomal encapsulation & [77] \\ \hline Resvertol, methoxy & & & \\ pectin and epigallocatechin gallate & Orange juice & Liposomal encapsulation & [78] \\ \hline Procyanidins & Litchi pericarp & Liposomal encapsulation & [79] \\ \hline \hline \multirow{4}{*}{Phenolics} & Jabuticaba (_Myricaria jabuticaba_ ([PERSON].) [PERSON]) peel & Freeze drying & [80] \\ \cline{3-4}  & Laurel (Litsea glaucescens) & Spray drying & [81] \\ \cline{3-4}  & \multirow{2}{*}{Pomergranate flavedo} & Freeze drying & [82] \\ \cline{3-4}  & \multirow{2}{*}{Mate leaves (_Ilex paraguariensis_)} & Spray drying & [83] \\ \cline{3-4}  & \multirow{2}{*}{Red chicory and red cabbage} & Spray drying & [84] \\ \cline{3-4}  & \multirow{2}{*}{Oat bran} & Complex coaervation & [85] \\ \cline{3-4}  & \multirow{2}{*}{_Morinda citrifolia_ leaf extract} & Spray drying & [86] \\ \cline{3-4}  & \multirow{2}{*}{Avocado peel extract} & Complex coaervation & [87] \\ \cline{3-4}  & \multirow{2}{*}{Cocoa extract} & Ionic gelation & [88] \\ \cline{3-4}  & \multirow{2}{*}{Yarrow extract} & Supercritical fluid technology & [89] \\ \cline{3-4}  & \multirow{2}{*}{Raspberry leaf, hawthorn, ground ivy, yarrow, nettle, and olive leaf extracts} & Extrusion technology & [90] \\ \hline Aqueous extract of _Piper_ _sarnentosum_ & Extrusion technology & [91] \\ \hline Aqueous extracts of _Ilex_ _paraguariensis_ (yerba mate) & Extrusion technology & [92] \\ \hline Ethanolic grape extract & Supercritical fluid technology & [93] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Furthermore, a regulated and controlled release of the core material can be ensured to sustain the bioactivity for a longer duration [3]. This technique is immensely useful in reducing the evaporation as well as transfer rate of core material, thus masking the intense flavor and aroma, which may otherwise affect the acceptability of food products. This technique is also used for keeping food ingredients that are cross-reactive separate. However, the efficiency and success of microencapsulation techniques are affected by many critical factors associated with the process as well as with the properties of bioactive compounds along with the encapsulating materials.
## 3 Results
### Optimization of Final Volume  
The modified final volume has a significant impact on the enhancement factor. High enhancement factors can be attained by reducing the organic phase's dilution and the final volume; nevertheless, there is a threshold value for the minimum final volume due to some reasons. The reasons can be explained by the fact that there is an ideal final volume for the AAS system to satisfy at least three replicate analyses in order to the find standard deviation. Also, the viscosity of the final volume should be proper to avoid blocking in the tip during sucking and injecting processes in the auto-sampling step. Another reason is that when the tip of the autosampler is immersed in the vial, there should be enough volume height to sample an accurate amount of standard/real sample. An experiment was carried out to investigate the effect of the organic phase's ultimate volume on the extraction efficiency. Solutions with different final volumes, ranging from 100 to 800 \(\mu\)L, were used in the suggested procedure (Figure 9). Naturally, the enhancement factors automatically decrease as the preconcentrated solution's final volume rises. The solution with the highest absorbance value was 200 \(\mu\)L, the  
Figure 8: Effect of added amount of organic drop.  
one with the smallest final volume. The volume value of 100 \(\mu\)L has a high standard deviation value, probably due to its high viscosity. Its high viscosity causes a decrease in the absorbance value with high possibility.  
Conditions: C\({}_{0}\): 2.5 \(\mu\)g/L Hg(II); pH: 4.0, 2 mL; ionic liquid amount: 15 \(\mu\)L HMIMPF6; extraction time: 30 min; extraction temperature: 26 \({}^{\circ}\)C; extraction speed: 750 rpm; added dodecanol amount: 80 \(\mu\)L.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 1 Introduction
### Contributions  
Based on the FSL framework, this paper designed a method for inland ship object detection and object tracking using the YOLOv5s lightweight detection network and DeepSORT tracking network. The proposed method is characterized by fast training speed and high accuracy with small datasets. The main contribution of this manuscript is as follows:* Multi-object detection and tracking for inland ship situation awareness based on FSL are proposed, which achieve 84.9% ([EMAIL_ADDRESS]) with only 585 training images.
* The transfer learning strategy is designed by innovatively using an open dataset of vehicles on highways to improve object detection accuracy for inland ships.
* Introducing the Shuffle Attention mechanism and smaller anchor boxes in the object detection network can somewhat improve the detection accuracy of different targets in different scenes.
## 4 Validation Results
### Along-Track Results
The DEM window size was increased to reduce the DEM noise, as the JER area is relatively flat compared to the WG area. All of the JER tracks used the same IGOT parameters. A summary of these parameters and the track information is provided in Table 2.  
The JER track 1 peak reflectivity of each of CYGNSS and the IGOT model is shown in Figure 8b, which shows that the reflectivity of the IGOT model follows the CYGNSS reflectivity. Furthermore, the median difference of reflectivity between CYGNSS and IGOT is about 4 dB. For JER track 2, the peak reflectivity of each of CYGNSS and the IGOT model is shown in Figure 9b. The median difference of reflectivity between CYGNSS and IGOT is about 4 dB. For most of the DDMs, the IGOT model reflectivity follows the CYGNSS reflectivity. However, there is a jump in CYGNSS reflectivity of the DDM with a longitude of \(-107.01^{\circ}\) that the IGOT model did not capture. This increase in CYGNSS reflectivity may be due to a vertically variable soil moisture profile that is wetter beneath the surface since the SP of this DDM is located in a valley, as shown in Figure 9. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **WG Track 1** & **WG Track 2** & **JER Track 1** & **JER Track 2** \\ \hline Date & 5 February 2019 & 15 September 2019 & 30 May 2022 & 30 May 2022 \\ Time [UTC] & 19:41 & 14:31 & 14:27 & 17:57 \\ Spacecraft ID & 8 & 6 & 4 & 5 \\ Channel number & 4 & 4 & 3 & 4 \\ Starting sample ID & 70,680 & 104,540 & 10,419 & 128,830 \\ Number of DDMs & 25 & 20 & 14 & 18 \\ SNR range [dB] & 4–13 & 5–10 & 2–10 & 2–10 \\ Soil moisture [m\({}^{3}\) m\({}^{-3}\)] & 0.18 & 0.51 * & 0.01 & 0.01 \\ Soil clay percentage & 20\% & 20\% & 20\% & 20\% \\ \(\sigma_{L}\) & 0.4\({}^{\circ}\) & 0.4\({}^{\circ}\) & 0.1\({}^{\circ}\) & 0.1\({}^{\circ}\) \\ \(\sigma_{S}\) [cm] & 1.25 & 1.25 & 1.25 \\ DEM window size & 9 & 9 & 15 & 15 \\ \(xd\) & 0 & 0.2 & 0 & 0 \\ \hline \hline \end{tabular} \({}^{*}\)Saturated soil moisture.  
\end{table} 
Table 2: Analyzed track information. Figure 7: WG track 2 : (**a**) Track elevation map; (**b**) Peak reflectivity. The SoilSCAPE sites are between the orange vertical lines.
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 5 Technical Challenges
### Witness Swarm Integrity  
It is very important that the PoL created by the prover (in case of QuietPlace it is the LPA message) contains all the LPSs received from all witnesses, regardless of whether they confirmed or rejected the prover's assertion. Even if the witness identity and the answer it gives is hidden, the prover can still learn if an LPS comes from a colluding witness via a private communication link. The prover could then include only the LPSs from the colluding sources, creating a fake proof.  
In [25], the authors propose that the nodes (in their case provers) include in their messages a list of all the other nodes that they see in their vicinity. In this way, a server can create a graph relating the nodes that saw each other, detecting nodes that are hiding themselves. Inspired by the above, we use a similar technique to satisfy the inability to add or remove LPS specification.  
In QuietPlace, we require that each witness declares their presence after they receive the proof request by the prover. Each witness \(W_{i}\) then creates a list with every witness \(W_{j}\) that declared their presence, including themselves. This is called the presence list. We also require that each witness keeps track of the witnesses that sent an LPS, creating a second list, called the LPS list. Every witness sends its two lists to the prover in an encrypted message for the CA to handle. Given n witnesses that sent a presence message, the CA expects to receive n presence lists and n LPS lists. All the lists should have the same information (the n witnesses) and the LPSs that the prover sent in his LPA message should belong to exactly the n witnesses in the LPS lists. Witnesses that are missing from other witnesses' lists but with an LPS message included in the LPA are suspected for collusion with the prover. Also, witnesses that exist in the LPS lists but missing from the LPA message means that the prover decided not to include their LPS. With this mechanism we can make sure that the prover did not add an LPS from a possible colluding witness or remove an LPS from his LPA message.  
The combination of the two lists helps the CA in detecting not only whether a prover added or removed an LPS, but also whether a witness refused to help, even though it declared its presence in the first place, accusing in this case the witness and not the prover. Generally, the CA assumes that a P-W collusion took place if a witness was not seen by the majority of other witnesses but an LPS was present. It also assumes the existence of a malicious prover if it does not include an LPS from a witness that was seen by the majority of the other witnesses. The exact rules that the CA follows to accuse a witness or a prover as malicious is configuration-specific and is out of the scope of our work.
## 3 Results and Discussion
### Sensory Evaluation  
To evaluate the impact of two native non-_Saccharomyces_ yeast strains (_C. famata_ WB-1, \(H\). _uarum_ S-2) in pure or sequential fermentation on Tamjanika wine sensory profile, a sensory evaluation was carried out (Figure 2). Slight differences in the gustatory (taste) attribute scores of the wines produced by different yeast strains and fermentation protocols were found, while more pronounced differences were observed for olfactory attribute scores. Still, significant variation in taste and odor scores was detected among all wine samples (\(p<0.05\)), except for the taste typicality and astringency (\(p>0.05\)).  
Generally, Tamjanika wines produced in all fermentations resulted in similar taste profile for the wines produced with the same non-_Saccharomyces_ yeast, but with statistically different scores for the majority of gustatory attributes. The increase in fresh fruit, citrus, and floral odor attributes was observed in wines produced in fermentation with pure _C. famata_ WB-1 and \(H\). _warum_ S-2 compared to the control or wine produced in sequential fermentation. The wine produced in sequential fermentation with \(H\). _warum_ S-2 was highly rated for all taste attributes, except for the acidity, achieving the highest score for the honey, tropical, or dry fruit olfactory attribute. Additionally, this wine sample had the best scores for complexity (8.3), duration (8), fullness (8), intensity (7.7), and harmony (8). Samples fermented by _C. famata_ WB-1 in sequential fermentation were characterized by the highest score for acidity and spice aroma notes. Compared with the control sample, used non-_Saccharomyces_ strains produced wines with stronger fruity and floral flavor and a weaker herbal flavor.  
Figure 2: **Gustatory (a)** and olfactory (**b**) attribute scores for Tamjanika wine samples produced in pure and sequential fermentations inoculated with a two non-_Saccharomyces_ strains (_C. famata_ WB-1 and \(H\). _warum_ S-2) and \(S\). _cerevisiae_ QA23 (SC). Data were the mean of two biological replicates of fermentation. Asterisks indicate that there is no significant differences between the means in attribute intensities (\(p<0.05\), [PERSON]’s HSD test).
## 4 Morphology of the Blastocyst and Quality Assessment
### Blastocyst Grading Approaches in IVF
#### 4.2.2 Semi-Automated  
Alternatively, a semi-automated system has been practically integrated to the manual grading system to generalize the final decision. Likely, semiautomated grading systems usually focused on extracting specific morphological features of the blastocyst, i.e., segmentation, feature extraction (FE), or classification of blastocyst expansion size, ICM, and TE [27; 43] to standardize the grading methods. In addition, the evaluation of blastocyst morphology with digital image systems has been found useful particularly for the embryo selection task since the quantitative parameters' evaluation is more reliable. As an example, the findings of a semi-automated system suggested that the blastocyst with area \(\geq\) 18,500 \(\upmu\)m\({}^{2}\), with an elevated number of TE cells (25.6 \(\pm\) 11.3 \(\upmu\)m\({}^{2}\)), and a large well-defined ICM area (3122.7 \(\pm\) 739.0 \(\upmu\)m\({}^{2}\)) has been highly considered for the transferring task [27].  
In fact, blastocyst grading of TE and ICM are two important features that are highly evaluated because the metabolism of the blastocyst occurs in these two different places, i.e., in TE cells where glucose consumption occurs and half is converted to lactate, whereas glycolysis occurs in ICM [46]. Hence, these ICM and TE will turn into a fetus and a placenta, respectively. However, several limitations have been highlighted in semi-automated approaches for blastocyst grading, mainly on the need of user intervention [43] and sample size issue [27; 47] i.e., a larger training set may require providing a more robust classifier.
## 3 Results and Discussion
### Breakthrough Volume and Reusability

Table 1: Accuracy, precision, limit of detection and limit of quantification of colistin A, colistin B and polymyxin B from different kinds of water samples spiked with the mixture of target analytes (10 \(\upmu\)g L\({}^{-1}\) for each) after MISPE treatment. conditioned with 5 mL of MeOH and 5 mL of water. Impurities were removed by 3 mL of 50% MeOH-water solution and, finally, the target compounds were eluted with 2 mL of 2% FA in MeOH. It is obvious that MIP cartridge (Figure 6c) was able to significantly reduce the amount of co-extracted matrix compounds impurities allowing the detection of target analytes. However, the corresponding chromatogram obtained after SPE on the C\({}_{18}\) cartridge (Figure 6a) contained large amounts of unknown peaks at retention times of target analytes and presents a noisy baseline, thus hampering their accurate determination. Figure S4 shows the obtained chromatograms after MISPE of target compounds from lake and spring water samples. As can be observed, there were no interference peaks at the retention times of target analytes allowing their unequivocal detection. The obtained chromatograms confirmed the excellent performance of the synthesized MIP as SPE sorbent for the enrichment and purification of trace amounts of polymyrins in water samples from different sources. Finally, there was no template bleeding in the development of the present work, and thus guaranteeing the accurate quantification of analytes.