esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Patterning towards Devices: Conventional and Unconventional Methods
### Direct-write techniques
#### 4.3.2 Ink-jet printing
The low melting temperature of the Au NPs allows for their handling and treatment at a plastic-compatible low processing temperature for fabricating Au conductive patterns less than 50 \(\upmu\)m in width. The OFETs display a typical accumulation mode p-channel transistor behaviour with a carrier mobility of 0.002 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) and a Ion/Ioff ratio ranging from 103 to 104 [173]. Polymers composite films formed of carbon black (CB) NPs 12 nm in diameter and PS have been ink-jet printed as active layers in a chemoresistive sensor device to detect at room temperature acetone and toluene [174]. The active ink has been prepared by dissolving PS in hot 1-methyl-2-pyrrolidone (NMP) and finally adding CB NPs dispersed by ultrasonic bath. The final suspension has been filtered to remove agglomerates. The PS/CB/NMP ink presents a good time stability and a stable jet with reduced clogging risk thanks to the low polarity of the PS/NMP solution and the high boiling temperature of NMP. In addition, the sensitive PS/CB/NMP based printed structures show good and stable electrical resistance and high sensitivity towards the volatile molecules, especially at low concentration. [174] Self-standing luminescent single-and multi-color pixel arrays of chloroform nanocomposite inks based on PS functionalized with highly luminescent colloidal cadmium sulphide (CdS) and differently sized CdSe(r)ZnS NCs, the latter ranging from 2.7 to 4.6 nm, have been printed. [175] CHCl\({}_{3}\) has been used as common solvent to homogeneously disperse CdS NCs and differently sized CdSe(r)ZnS NCs in different PS concentrations, respectively with a stable and reproducible droplet generation. The authors have demonstrated that nanocomposite inks based on a single apolar, high vapor pressure and low boiling point (61.5 \({}^{\circ}\)C) carrier solvent, can be reliably dispensed once the ejection parameters are optimized, with no need of multi-solvent mixtures or post-preparative processing. The bright and non-bleachable size-dependent luminescence of the NCs is conveyed to the printed pixels which present a luminescence ranging from blue to red and a regular and reproducible shape that can be integrated in polymer displays and colored wall papers (Figure 6).
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 4 Results  
The IoT temperature anomaly detection system was deployed in three of the grocery distributor's last-mile delivery vehicles in Belfast, northern Ireland, in April 2022. The analysis presented in this section was completed after 6 months of recording data. By this stage, the three vans had cumulatively recorded 3154 journeys, on average 10.2 journeys per day.
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
## 3 Multipath/NLoS Bias Estimation Using a Clustering Algorithm
### DBSCAN, a Clustering Algorithm for Multipath/NLoS Estimation  
After having motivated the use of a clustering algorithm as a means to distinguish the presence of multipath/NLoS effects, this section will introduce the specific clustering algorithm, DBSCAN, employed in this work, discussing the parameter selection that makes it suitable to accomplish the task.  
DBSCAN is a minimum density level estimation that clusters data based on the density. This algorithm first specifies two parameters:  
1. **minPts**: the minimum number of points to form a cluster.
2. \(\varepsilon\): the maximum distance between two points to consider them neighbors. Then, every data point will be classified into three types:  
1. **Core points**: the data points can find at least minPts neighbors within the radius \(\varepsilon\).  
2. **Non-core points (border points)**: within radius \(\varepsilon\), the data points can find at least one core point but have no more than minPts neighbors.
3. **Outliers**: the data points do not satisfy either the definition of core points or the one of non-core points.  
DBSCAN can be described using the flowchart in Figure 1. In the first step of DBSCAN, the RangeQuery function is employed to identify all the neighbors of a specific point. This function finds all the data points in \(DB\) whose distance to the current point \(p\) is closer than \(\varepsilon\). Here, _dist_ is a function used to compute the distance between two data points.  
Upon identifying a core point, all its neighbors are assigned to the same cluster as that core point. If any of these neighbors is itself a core point, the neighbors of this new  
Figure 1: Flowchart of the DBSCAN algorithm.  
core point are classified into the same cluster. This process is iterated until all data points are clustered. Points that do not satisfy the aforementioned conditions are designated as outliers.  
DBSCAN is a suitable clustering solution to address the multipath/NLoS problem since the parameter minPts establishes the minimum number of leftover terms \(L^{s}(t)\) needed to confirm that they belong to the distribution \(\mathcal{N}\big{(}c\cdot dt_{\text{rcv}},\sigma^{2}\big{)}\). Meanwhile, the receiver noise \(e_{r}^{s}(t)\) acts as a reference for setting the parameter \(\varepsilon\), thus controlling the boundary of the cluster free from multipath/NLoS effects.
# Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks
###### Abstract  
There are many studies that seek to enhance a low resolution image to a high resolution image in the area of super-resolution. As deep learning technologies have recently shown impressive results on the image interpolation and restoration field, recent studies are focusing on convolutional neural network (CNN)-based super-resolution schemes to surpass the conventional pixel-wise interpolation methods. In this paper, we propose two lightweight neural networks with a hybrid residual and dense connection structure to improve the super-resolution performance. In order to design the proposed networks, we extracted training images from the DIVerse 2K (DIV2K) image dataset and investigated the trade-off between the quality enhancement performance and network complexity under the proposed methods. The experimental results show that the proposed methods can significantly reduce both the inference speed and the memory required to store parameters and intermediate feature maps, while maintaining similar image quality compared to the previous methods.  
deep learning; convolutional neural networks; lightweight neural network; single image super-resolution; image enhancement; image restoration; residual dense networks +
Footnote †: journal: Medical Image Computing  
**Keywords:** deep learning; convolutional neural networks; lightweight neural network; single image super-resolution; image enhancement; image restoration; residual dense networks +
Footnote †: journal: Medical Image Computing
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
###### Abstract  
The traditional Siamese object tracking algorithm uses a convolutional neural network as the backbone and has achieved good results in improving tracking precision. However, due to the lack of global information and the use of spatial and scale information, the accuracy and speed of such tracking algorithms still need to be improved in complex environments such as rapid motion and illumination variation. In response to the above problems, we propose SSTrack, an object tracking algorithm based on spatial scale attention. We use dilated convolution branch and covariance pooling to build a spatial scale attention module, which can extract the spatial and scale information of the target object. By embedding the spatial scale attention module into Swin Transformer as the backbone, the ability to extract local detailed information has been enhanced, and the success rate and precision of tracking have been improved. At the same time, to reduce the computational complexity of self-attention, Exemplar Transformer is applied to the encoder structure. SSTrack achieved 71.5% average overlap (AO), 86.7% normalized precision (NP), and 68.4% area under curve (AUC) scores on the GOT-10k, TrackingNet, and LaSOT. The tracking speed reached 28 fps, which can meet the need for real-time object tracking.  
object tracking; Siamese network; spatial scale attention; swin transformer; positional encoding +
Footnote †: journal: Applied sciences
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 2 Basic Theory
### Asymmetric Classical Communication  
In this paper, we also compare the proposed system with an ACC system that has no mode A--mode A can be considered to be the vacuum state \(|0\rangle_{\text{A}}\) in an AQC system without entanglement. Here, the coherent state \(|\alpha\rangle\) is prepared by Bob as the light source and is directed toward one of the two beam splitters operated by Alice. By switching the two beam splitters with their reflectivities of \(R_{0}\) and \(R_{1}(R_{0}<R_{1})\), Alice encodes binary information using the different reflectivities. The reflected light collected by Bob thus becomes the binary coherent state signal \(\{\{|\sqrt{R_{0}}\alpha\rangle,|\sqrt{R_{1}}\alpha\rangle\}\}\), and this can be considered to be an ASK modulation scheme. Assuming that the binary signal has equal _a priori_ probabilities and that the measurement in Bob's detector, which we call an optimum classical receiver, is a homodyne measurement, the error probability for Bob is then given as follows:  
\[P_{\text{e}}^{\text{(Hom)}}=\frac{1}{2}\Bigg{\{}1+\text{erf}\Bigg{(}\frac{ \sqrt{R_{0}|\alpha|^{2}}-\sqrt{R_{1}|\alpha|^{2}}}{\sqrt{2}}\Bigg{)}\Bigg{\}}. \tag{15}\]
# Technological Aspects and Potential Cutaneous Application of Wine Industry By-Products
## 5 The Use of Wine By-Products towards Sunscreen Development  
By-product ingredients from the wine industry can elevate the sun protection factor (SPF) of sunscreens using different mechanisms, contributing to reducing the concentration of UV filters, that could be potentially harmful to the environment, decreasing the use of natural resources and, in parallel, adding value to compounds from renewable sources. The efficacy profile of these innovative formulations has been studied both via in vitro and in vivo tests by our research group [28; 41; 76].  
[PERSON] and coworkers developed a sunscreen system containing UVA/B organic filters associated with 10.0% \(w/w\) of grape pomace (_V. vinifera_ cv. C. Sauvignon). A significant increase was observed in the in vitro SPF value (172%) and antioxidant ability (710% in Trolox equivalents), in comparison to the extract-free formulation. Moreover, the study probed in vivo the photoprotective capacity of grape pomace. The sunscreen formulation containing pomace extract delayed by -20% the appearance of the initial signs of erythema, compared to the control formulation [28].  
Recently, the same research group reported the biocompatibility of two fractions of grape pomace (C. Sauvignon), chloroform (GPE-CHF), and ethyl acetate (GPE-EAF), using a fibroblast culture (NIH 3T3). When the cells were stressed with H\({}_{2}\)O\({}_{2}\), an oxidative agent, the authors observed a cytotoxic effect from the GPE-CHF, whereas the ethyl acetate fraction (GPE-EAF) was non-cytotoxic in the used conditions. According to these findings, GPE-EAF (10.0% \(w/w\)) was associated with a mixture of UV filters, and the in vitro SPF was determined using a diffuse reflectance spectrophotometer with the integrated sphere as well the functional photostability. GPE-EAF increased the photoprotective efficacy by 66% and improved the photostability of the sunscreen system after 1 h of artificial UV exposure [76].  
These studies are indicative of the potential of using extracts of winemaking residues in the development of sunscreens aligned with the challenges of sustainable development to face poverty, inequalities, and climate change.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 4 Discussion
### Future Directions  
From the reviewed literature and analysis emerge a number of promising research directions for designing the future wireless positioning systems. In this section, we discuss the fusion of different techniques and technologies, real-world testing and deployment, cloud and edge computing, and new techniques and technologies, from smart sensors to 6G.
## 4 Case Study
### Results and Analysis  
Figure 6 shows that the system's anti-jamming capability gap has the highest priority, followed by both the security-and-confidentiality capability and the system survivability, with the navigation-and-positioning capability gap having the lowest priority. In order to improve the degree of requirement satisfaction in the entire space-navigation-and-positioning system of systems, measures must focus on improving the system's anti-jamming capability.  
In Figure 7, \"Gap Priority\" indicates the capability-gap priority, \"IBF\(-\)\" indicates the inverse belief function value, and \"RSD+\" indicates the belief with a high degree of requirement satisfaction, calculated using the method in Section 3.1.  
While, as shown in Figure 6, the degree of requirement satisfaction of the anti-jamming capability has the highest value, it also remains the top final gap priority as it has the largest inverse-belief-function value.  
We used a conditional evidential network in order to assess the prioritization of capability gaps in space-navigation-and-positioning system of systems. This network is able to efficiently handle the experience of relevant experts while making full use of the input of uncertain knowledge. This all helps to improve the credibility of capability assessment in space-navigation-and-positioning system of systems.  
Figure 6: Prioritization of capability gaps.
## 5 Algorithmic Complexity  
Table 1 compares the computational complexity in each step of NDA-EVM-AM and DA-SNR-AM algorithm (proposed by [14]). Note that \(M_{m}=\max(M)\) is the highest modulation order of MQAM signals, \(n\) is the number of modulation modes, and \(n<M_{m}\).  
Figure 2: The state machine for NDA-EVM-AM.  
Analyzing each step of NDA-EVM-AM algorithm, the processes with the highest complexity are calculation of NDA-EVM and SER with all constellation sizes, (i.e., both complexities are \((M_{m})\)). Thus, NDA-EVM-AM algorithm has a complexity of \((M_{m})\), which is the same as the DA-SNR-AM algorithm.  
Remarkably, in the aspect of constellation size adjustment, NDA-EVM-AM algorithm guarantee the transmitter can \"jump\" to the optimal constellation size \"in one step\", thus has \((1)\) operations. However, to optimize the set of switching levels, SNR-AM algorithm requires continuously refreshing the relationship of SNR-SER for every modulation mode, which requires \((n)\) operations. Thus, the NDA-EVM-AM has less computational complexity in adaptation strategy, which means that NDA-EVM-AM has greater real-time in adjustment.  
On the other hand, in the aspect of initializing the adaptive modulation system, the DA-SNR-AM algorithm needs to train known sequences (i.e., data aided symbols) to each modulation mode when the channel varies, which requires \((n)\) operations, much more complex than the initialization of NDA-EVM-AM with \((1)\), and the non-real time in train sequences will lose the accuracy of adaptive modulation.  
Above all, to improve the real-time and accuracy of adaptive modulation, NDA-EVM-AM algorithm is significantly complex in calculation of NDA-EVM values and SER with all constellation sizes. However, the computational complexity of NDA-EVM-AM is linear, and has high efficiency, even over rapidly time-varying channels.
# An Agile Approach to Improve the Usability of a Physical Telechabilitation Platform
## 4 Results and Discussion
### Second Iteration  
The second iteration began in February 2018 and the same methods as in the first iteration were used. However, a new team of three experts was invited to participate, and the evaluation was performed using a mock-up. The improvement list obtained in the previous iteration was incorporated in the new mock-up set. Figure 6 shows a comparative chart of the heuristic violations found in the two iterations for the exploratory prototype and mock-up of the questionnaire interface.  
In general, the number of usability problems decreased. However, two atypical cases were presented: (1) An increase in the number of usability problems for HP3 and SH2; (2) The number of usability problems did not vary for the heuristics HP2 and HP8. It is necessary to understand the experts' rationale to explain these atypical cases. For the first case, the experts recommended increasing the font size of the letters and not saturating the space. For the second case, the experts recommended using appropriate terms (medical terms) and validation controls for information input (medical criteria). Thus, the recommendations of the experts were due to a refinement of the interface rather than an increase in the number of usability problems attributable to the construction of the mock-up.  
For the cognitive walkthrough, a customized survey was used to guide participants in completing a list of the tasks proposed. Experts acted as observers and registered user actions performed to complete the tasks. For the experiment, 19 volunteers participated and four questions were defined:  
1. Are the controls for the action visible? This question evaluated if the controls developed to complete a task were easily accessible or identified by the user.
2. Will the interface allow the user to produce the effect the action has? This question evaluated if there was concordance between the objective of the task and the objective reached by the user.  
Figure 5: Proposed fuzzy model for exercise detection in telerehabilitation [29].  
3. Will users succeed in performing this action? This question sought to quantify the number of users who fulfilled the objective of the task.
4. Will users notice that the correct action has been executed successfully? This question sought to quantify the number of users who identified that their actions performed to complete the task were correct.  
Table 5 shows the comparative results for first and second iterations.  
The results for the second iteration showed that the controls were visible and that 100% of users fulfilled the objective of the task. However, the interface only facilitated concordance between the objective of the task and the objective reached by the user in 67% of them.
# Photodynamic Inactivation of Human Coronaviruses
## 2 Materials and Methods  
### Cells, Viruses, and Reagents  
Human embryonic kidney (HEK) 293T and 293A cells, and Huh7.5 (human hepatoma) cells were cultured in Dulbecco's modified Eagle's medium (DMEM; 11965118, Thermo Fisher Scientific, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS; A31607-02, Thermo Fisher Scientific), 100 U/mL penicillin/100 ug/mL streptomycin (Pen/Strep; 15140-122, Thermo Fisher Scientific), and 20 mM L-glutamine (Gln; 25030-081, Thermo Fisher Scientific). HCT-8 (human adenocarcinoma) cells were cultured in 10% FBS/DMEM + Pen/Strep/Gln and further supplemented with 1X MEM Non-Essential Amino Acids (NEMA; 11140050, Thermo Fisher Scientific). BHK-21 (baby hamster kidney) and Vero (African green monkey kidney) cells were cultured in 5% FBS/DMEM + Pen/Strep/Gln. HEK 293A cells stably expressing angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) were generated by sequential transduction with pJM1-based ACE2 and TMPRSS2 lentiviral vectors with selection using puromycin (Puro) and blasticidin (BSD), respectively. All cells were maintained at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) atmosphere.  
pJM1-ACE2-BSD was cloned from Caco-2 cell cDNA using primers: ACE-F 5\({}^{\prime}\)-AATTACCGGATATGCAAGCTCTTCCTGGC and ACE2-R 5\({}^{\prime}\)-AATTGTCAGACTTAAAAAGGAGCTCTGAACATCATCAG. pLJM1-TMPRSS2-V5-Puro was generated by subcloning from pLVX_317 (Broad Institute GPP TRCN0000467270) into pJM1 [21] using Nhel and Mlul restriction endonucleases. Lentiviruses were generated by transfecting HEK 293T cells with lentivirus backbone pJM1, a Gag-Pol encoding packaging vector (psPAX2, a kind gift form [PERSON]; Addgene #12260), and plasmid encoding VSV glycoprotein (pMD2.G, a kind gift from [PERSON]; Addgene #12259) using polyethyleneimine MAX(r) (Polysciences, Warrington, PA, USA). Lentiviruses were harvested 48 h after transfection, filtered with a 0.45 \(\upmu\)m filter, and stored at \(-\)80 \({}^{\circ}\)C until use.  
Stocks of human coronavirus OC43 (HCOV-OC43; VR-1558, ATCC) were propagated in Vero cells. Cells were infected at a MOI of 0.05 for 1 h at 33 \({}^{\circ}\)C in serum-free DMEM. After 1 h, the infected cells were maintained in DMEM supplemented with 1% FBS and Pen/Strep/Gln for five days at 33 \({}^{\circ}\)C. Upon harvest, the culture supernatant was centrifuged at 1000\(\times\)\(g\) for 5 min at 4 \({}^{\circ}\)C, the supernatant aliquoted, and then stored at \(-\)80 \({}^{\circ}\)C. Stocks of human coronavirus 229E (HCOV-229E; VR-740, ATCC) were prepared using a similar protocol, using Huh7.5 cells and DMEM supplemented with 2.5% FBS and Pen/Strep/Gln.  
The preparation of PhytoQuin(tm) from an ethanolic extract of _Polygonum cuspidatum_ has been previously described [20]. PhytoQuin (provided by PhotoDynamic Inc., Lot# SMKW115, Halifax, NS, Canada) and emodin (13109, Cayman Chemical, Ann Arbor, MI, USA) were solubilized in DMSO. All oligonucleotides were synthesized by Thermo Fisher Scientific (Pleasanton, CA, USA).
# A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation
###### Abstract  
This study proposes a novel factorization method for the DCT IV algorithm that allows for breaking it into four or eight sections that can be run in parallel. Moreover, the arithmetic complexity has been significantly reduced. Based on the proposed new algorithm for DCT IV, the speed performance has been improved substantially. The performance of this algorithm was verified using two different GPU systems produced by the NVIDIA company. The experimental results show that the novel proposed DCT algorithm achieves an impressive reduction in the total processing time. The proposed method is very efficient, improving the algorithm speed by more than 4-times--that was expected by segmenting the DCT algorithm into four sections running in parallel. The speed improvements are about five-times higher--at least 5.41 on Jetson AGX Xavier, and 10.11 on Jetson Orion Nano--if we compare with the classical implementation (based on a sequential approach) of DCT IV. Using a parallel formulation with eight sections running in parallel, the improvement in speed performance is even higher, at least 8.08-times on Jetson AGX Xavier and 11.81-times on Jetson Orion Nano.  
parallel algorithms; discrete trigonometric transforms; DCT-IV; GPU +
Footnote †: journal: Applied  
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Citation: [PERSON]; [PERSON] A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation. _Appl. Sci._**2024**, _14_, 7491. [[https://doi.org/10.3390/app1477491](https://doi.org/10.3390/app1477491)]([https://doi.org/10.3390/app1477491](https://doi.org/10.3390/app1477491))  
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