esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results Analysis
### Database  
In this experiment, the CCS dataset [44] was used to compare and analyze the prediction performance of interval-based IGM, which comprises the data for the CCS of high-performance concrete (HPC). This dataset consists of eight input variables and one output variable. The input includes eight variables and one output variable of CCS.  
The ASN (Airfoil Self-Noise) data set is a measurement of noise generated by NACA 0012 in a wind tunnel environment of various speeds and angles. The ASN data set includes 5 inputs and 1 output variable. The input variables are frequency, angle, cord length in meters, free flow velocity, and suction side displacement thickness, and the output variable is the sound pressure level scaled in decibels.  
The MPG (miles per gallon) data set contains data that predict the fuel consumption of a vehicle. The MPG data set includes 7 inputs and 1 output variable. The input variables are cylinder, displacement, horsepower, weight, acceleration, model year, model name, and output variable is vehicle fuel consumption. The training and verification data were divided into two equal halves and normalized to a value between 0 and 1 to obtain a more accurate value.
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 5 The Proposed Prediction Models  
### The QoS Prediction Framework  
To recommend services with high quality, our paper focuses on achieving QoS prediction with high accuracy. Here is the proposed framework of QoS prediction (Figure 3). The proposed QoS prediction models include the user-based prediction model, the service-based prediction model and a hybrid model, which are stated below.  
1. **The user-based prediction model**. This model extends the user-based CF model, which improves the user similarity computation by integrating random walk model, to select similar neighbors. Both the random walk model and neighbor selection are capable of using the network location information. The unknown QoS values are predicted using the QoS records of similar neighbors collaboratively.
2. **The service-based prediction model**. This model extends the service-based CF model, which improves the service similarity computation also by integrating random walk model. Other technical details are similar to those of the user-based prediction model.
3. **The hybrid model**. To further improve the prediction accuracy, our idea is to fully utilize the similar user neighborhood and similar service neighborhood. In this paper, we propose a linear hybrid model, which combines the predicted results of the user-based model and service-based model.  
Figure 3: The proposed quality-of-service (QoS) prediction framework.
## 4 Intelligent Wearable Solutions for VRU Safety: Trends, Challenges, and Opportunities
### Wearable Assistive Devices for Visually Impaired Users
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Year** & **Interface Technology** & **Experimental Validation Method** \\ \hline [PERSON] et al. [61] & 2000 & Wearable computer with three interfaces: virtual sound beacon, digitized speech, and tactile tapping system & Field testing with 15 visually impaired subjects (aged 62–80) at three intersections; compared pre/post-baseline performance with each interface \\ \hline [PERSON] et al. [80] & 2001 & Wearable computer with voice I/O, differential GPS, electronic compass, and GIS database & Field testing with visually impaired users on campus; evaluated contextual information delivery, route optimization, and voice guidance through walkways \\ \hline [PERSON] et al. [79] & 2002 & Refined wearable orientation system with improved head/body referenced compass modes & Extended evaluation with visually impaired older adults focusing on interface preferences and improvement suggestions; measured performance ratios compared to baseline \\ \hline [PERSON] et al. [72] & 2007 & Wearable computer with non-speech audio, bone conduction headphones, and multisensor fusion & Lab and field testing with visually impaired and sighted users; evaluated audio beacon effectiveness and waypoint capture radius \\ \hline [PERSON] et al. [68] & 2010 & Miniaturized chest-worn ultrasonic device with dual transducers for bilateration, activity detection, and minimalist tactile/audio feedback & Laboratory testing with wooden targets at various distances and angles; evaluated range accuracy using correlation-based detection and bandpass sampling \\ \hline [PERSON] et al. [69] & 2013 & Necklace-mounted sonar system with ultrasonic transducer, adjustable detection range, and tactile feedback via vibration & Testing with 10 visually impaired subjects in both artificial indoor environment with simulated obstacles and live outdoor settings \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of orientation interfaces for visually impaired pedestrians (Part 1: 2000–2019).
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 2 Recent Advances on Catalysts for Syngas Production
### Transition Metals-Based Catalysts
#### 2.2.4 Bi- and Trimetallic Catalysts
The catalytic performance tests showed that the excess addition of Cu (Cu/Fe = 1.5) results in a decreased initial activity for CH\({}_{4}\) and CO\({}_{2}\) conversion and severe deactivation by carbon deposition (12.7 wt%), due to possible coverage of Ni active sites by Cu. Overall, the Cu addition in the ratio of Cu/Fe = 0.5 delivered the highest H\({}_{2}\)/CO = 0.60 at T = 750 \({}^{\circ}\)C with a CH\({}_{4}\) conversion of 50%. H\({}_{2}\) production of the trimetallic catalyst was comparable to the bimetallic NiFe catalysts, but the presence of Cu increased the amount of oxygen species on the catalyst surface and decreased carbon deposition by 50%.
# Manufacture of a Potential Antifungal Ingredient Using Lactic Acid Bacteria from Dry-Cured Sausages
## 3 Results and Discussion
### In Vitro Antifungal Study of the MB Formulated
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fungi**} & \multicolumn{8}{c}{**BFS Concentration (g/L) of the Fermented MRSb**} \\  & \multicolumn{2}{c}{_Lactiplantibacillus plantarum C60_} & \multicolumn{2}{c}{_Pediococcus pentosaccus_ C12} & \multicolumn{2}{c}{_Pediococcus pentosaccus_ C15} \\ \cline{2-10}  & **MIC 1** & **MIC 2** & **MFC** & **MIC 1** & **MIC 2** & **MFC** & **MIC 1** & **MIC 2** & **MFC** \\ \hline _A. flavus_ & 11 & 85 & 170 & 21 & 85 & 170 & 11 & 21 & 85 \\ _A. parassiticus_ & 11 & 43 & 170 & 11 & 43 & 170 & 11 & 21 & 85 \\ _P. commune_ & 21 & 43 & 85 & 21 & 43 & 85 & 11 & 21 & 85 \\ _P. griseofulum_ & 21 & 43 & 170 & 21 & 43 & 170 & 5 & 21 & 170 \\ _P. nordicum_ & 11 & 43 & 85 & 11 & 43 & 85 & 5 & 21 & 21 \\ _P. verrucosum_ & 21 & 43 & 85 & 21 & 43 & 85 & 11 & 43 & 43 \\ \hline Mean & 16 & 50 & 128 & 18 & 50 & 128 & 9 & 25 & 82 \\ \hline \multicolumn{10}{c}{BFS concentration (g/L) of the MB10} \\ \cline{2-10}  & MIC 1 & MIC 2 & MFC & MIC 1 & MIC 2 & MFC & MIC 1 & MIC 2 & MFC \\ \hline _A. flavus_ & 21 & 85 & 170 & 43 & 85 & 170 & 21 & 43 & 85 \\ _A. parasiticus_ & 21 & 43 & 170 & 21 & 43 & 170 & 21 & 43 & 85 \\ _P. commune_ & 43 & 85 & 85 & 43 & 85 & 85 & 21 & 21 & 85 \\ _P. graisefulum_ & 43 & 85 & 170 & 43 & 85 & 170 & 11 & 21 & 170 \\ _P. nonficum_ & 21 & 43 & 85 & 43 & 85 & 85 & 11 & 21 & 43 \\ _P. verrucosum_ & 43 & 85 & 85 & 43 & 85 & 85 & 11 & 43 & 43 \\ \hline Mean & 32 * & 71 & 128 & 39 * & 78 * & 128 & 16 * & 35 & 85 \\ \hline \hline \end{tabular}  
* 1: 50% of visual inhibition growth; 2: 100% of visual inhibition growth. *: results were statistically analyzed by the _t_-test, comparing the Bacterial free supernatant (BFS) of MRSb with the BFS of MB10. Means are significantly different _it p_ \(\leq\) 0.05.  
\end{table} 
Table 4: Minimal inhibitory concentration (MIC) and minimal fungicidal concentration (MFC) of bacterial-free supernatant (BFS). MRS broth (MRSb) and meat broth (MB10) were fermented by LAB for 48 h at 37 \({}^{\circ}\)C and centrifuged to obtain the BFS. _verticilliloides_ growth and reduced the production of aflatoxin and fumonisin in cereals such as maize [26]. Therefore, MRSb has proven to be an interesting medium for fermenting bacteria as antimicrobial metabolites are produced. However, the use of MRSb has some disadvantages, such as its high cost. In addition, the complexity of some of their ingredients that convert MRSb might not be allowed as an ingredient in meat products. Therefore, one of the objectives of the work was to develop a meat broth with similar antifungal characteristics but, at the same time, could be incorporated as an antifungal ingredient. Overall, the _P. pentosacous_ C15 showed higher antifungal potential in the agar diffusion test and microdilution of BFS; hence, these results led us to select this strain for the fermentation of MB10 and elaborate a postbiotic antifungal ingredient.
# Collagen Nanoparticles in Drug Delivery Systems and Tissue Engineering
## 3 Collagen-Based NPs in Biomedicine
### Collagen Based NPs in TE
For implants in vascular medicine, collagen NPs are used as a scaffold in tissue engineered vascular grafts with the addition of different compounds. Collagen NPs behave as a template for growth into the vascular graft and cell recognition [91]. Injections based on collagen NPs can be provided to locally deliver therapeutic components or drugs to hinder neurodegeneration and they contribute structural foundation [70]. [PERSON] et al. showed the usage of collagen nanoparticle or nano-sized \(\beta\)-tricalcium phosphate conduits along with filaments of collagen and nerve growth agents for regeneration of facial nerves [90]. [PERSON] et al. conducted an experiment to study the effects of cell attachment of collagen NPs on crosslinked electrospun nanofibers. Collagen NPs enhanced various properties such as viability of the cell, adhesion and more spreading on the scaffold. Regarding crosslinking with glutaraldehyde, collagen NPs mimicked the extracellular matrix better than collagen nanofibers [91]. Renal cartilage sponge collagen NPs were utilized as an osmotic accelerator for hormone replacement therapy for transdermal delivery of 17\(\beta\)-estradiol-hemihydrate. Conclusions displayed that the hydrogels in which estradiol collagen NPs were present prolonged the release time of estradiol and improved the absorption of estradiol to a great extent. Hence, sponge collagen NPs can act as prospective carriers for transdermal drug delivery [92; 93]. These investigations on collagen in TE are summarized in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Agent (Crosslinking/Stabilising/Optimising)** & **Effect of Collagen NPs** & **Application** & **Reference** \\ \hline Gold NPs & Reduction of inflammation and formation of granulation tissue with no problems of rejection & Wound healing & [31] \\ \hline Gold NPs and cryopreserved human fibroblasts & Increased rate of healing and enhanced collagen deposition & Burn and wound healing & [80] \\ \hline Silver and pectin & Greater antibacterial activity and improved viability of the cell toward keratinocytes. & Dermal TE & [81] \\ \hline Hydroxyapatite and silver & & Burn and wound healing, skin repair, bone graft materials & [82] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Investigations on Collagen NPs in TE.
## 2 Materials and Methods
### Identification of Polyphenols by HPLC  
Individual flavonoids and phenolic acids were examined by HPLC [33]. Briefly, 1 mL of the samples was extracted with 5 mL of 80% methanol by sonication for 10 min at 6 kHz and 30 \({}^{\circ}\)C. After that, the samples were centrifuged (Hermle Z 300 K, Wroclaw, Poland; 10 min, 6000 rpm, temp. 0 \({}^{\circ}\)C). Fifty microliters of sample extract was analyzed by the HPLC system with a Fusion RP-80 A column (250 \(\times\) 4.6 mm\({}^{2}\); Phenomenex, Warsaw, Poland). The analysis was carried out with the use of Shimadzu equipment (USA Manufacturing Inc., New Providence, IA, USA; two pumps LC-20 AD, controller CBM-20A, column oven SIL-20 AC, spectrometer UV/Vis SPD-20 AV). The mobile phase was prepared from acetonitrile and water with phosphoric acid (pH 3.0). The gradient flow of 1 mL min\({}^{-1}\) was applied as follows: 1.00-22.99 min, 95% phase A and 5% phase B; 23.00-27.99 min, 50% phase A and 50% phase B; 28.00-28.99 min, 80% phase A and 20% phase B; and 29.00-38.00 min, 95% phase A and 5% phase B. The single run lasted 42 min. The detection wavelength for flavonoids was at 360 nm, and the detection of phenolic acids was at 250 nm. Polyphenols were identified based on the retention time of external standards (gallic, chlorogenic, caffeic, \(p\)-coumaric acid, catechin, epigallocatechin, epigallocatechin gallate, quercetin-3-\(O\)-rutinoside, kaempfer-3-\(O\)-glucose, quercetin, and kaempfer) and were calculated using calibration curves (R\({}^{2}\) = 0.999). All standard curves are presented in Supplementary Materials as Figures S2 and S3.
## 3 ANN Architectures and Training Algorithms
### Kohonen SOM Network and Unsupervised Training  
Unlike the trainings of MLP, RBF and ERNN networks, Kohonen SOM networks are trained using an unsupervised algorithm, by which the input data are self-organized into clusters or classes, i.e. similar input data will activate the same network output node. The networks construct one-dimensional, two-dimensional, and even three-dimensional arrays of output nodes to form self-organized feature maps. Figure 4 is the structure of Kohonen SOM network with a two-dimensional array of output nodes used to form feature maps.  
This is a two-layer network. The output nodes are orderly arranged in a two-dimensional grid. Each input is connected to all output nodes through a weight. The network training process has two stages. The first is to generate a coarse mapping, i.e. to create some form of topological ordering on the map of randomly oriented nodes. There may be large changes to the orientation of the nodes on the map, so the gain term or adaption rate, \(\eta\), needs to be kept high to allow large weight modifications and settle into an approximate mapping quickly. Once a stable coarse map is found, the training process goes into the second stage. At this stage, the nodes within the localized regions of the map are fine-tuned to the network inputs. For this fine-tuning much smaller changes of weights must be made at each output  
Figure 3: ERNN network structure.  
node, so the adaptation rate, \(\eta\), is reduced as training progresses. The weight updating in training is iteratively operated along with winning output node neighborhood:  
\[\begin{split}&\Delta w_{ijk}=-\eta(w_{ijk}\text{ - }x_{k})\\ &\text{ij}\in\text{NE}_{ijk}\\ &(\text{i}=1,2,...,\text{p};\text{j}=1,2,...,\text{q};\text{k}= 1,2,...,\text{n})\end{split} \tag{9}\]  
where p and q the first and second dimension of the feature map array, respectively, n is the number of network inputs, and ij* is the location of optimal output node. The optimal node is determined by:  
\[\begin{split}&\text{MIN}(\text{D}_{ij})\\ &\text{D}_{ij}=\sum_{k=1}^{n}(x_{k}-w_{ijk})^{2}\end{split} \tag{10}\]  
The neighborhood function \(\text{NE}_{ij}\) is set large at very beginning of training, and slowly decreases in size with the progress of the training (Figure 5). The output nodes of the network are activated using a winner-take-all method:  
\[y_{ii}=\begin{cases}1\text{ if }\text{ ij}=\text{ij}*\\ 0\text{ otherwise}\end{cases} \tag{11}\]  
That is that if (i,j) is the location of the optimal output node, the output node takes the value of 1; otherwise it takes 0. Accordingly, the weights connected to the neighborhood of the optimal node are updated.  
Figure 4: Kohonen SOM network with two-dimensional array of output nodes used to form feature maps.
# PPP and PPP-AR Kinematic Post-Processed Performance of GPS-Only, Galileo-Only and Multi-GNSS
## 3 Results  
The following section shows some examples of PPP and PPP-AR positioning in detail as well as global network summary graphs in East (E), North (N) and Up (U) components.
# Effects of Atmospheric Turbulence on Lensless Ghost Imaging with Partially Coherent Light
## 2 Theory of Ghost Imaging in Monostatic Turbulence and Bistatic Turbulence
### Bistatic Turbulence Case
\(\overline{M}_{1}^{-1}\), \(\overline{S}_{1}\), \(\overline{S}_{2}\), \(\overline{N}_{1}^{-1}\), \(\overline{N}_{2}^{-1}\) and \(\overline{B}_{1}\) are all \(2\times 2\) matrices, given by:  
\[\overline{M}_{1}^{-1}=\left( \begin{array}{cc}-\frac{i}{k}\Big{(}\frac{1}{2 \sigma_{1}^{2}}+\frac{1}{\sigma_{2}^{2}}\Big{)}&\frac{i}{k\sigma_{2}^{2}}\\ \frac{i}{k\sigma_{2}^{2}}&-\frac{i}{k}\Big{(}\frac{1}{2\sigma_{1}^{2}}+\frac{ 1}{\sigma_{2}^{2}}\Big{)}\end{array} \right),\overline{N}_{1}^{-1}=\left( \begin{array}{cc}-i\frac{\left[1/\left(2\sigma_{1}^{2}\right)+1/\sigma_{2}^{ 2}\right]}{k}&0\\ 0&-i\frac{\left[1/\left(2\sigma_{1}^{2}\right)+1/\sigma_{2}^{2}\right]}{k} \end{array} \right), \tag{17}\]  
\[\overline{N}_{2}^{-1}=\left( \begin{array}{cc}0&i/\Big{(}k\sigma_{g}^{2} \Big{)}\\ i/\Big{(}k\sigma_{g}^{2}\Big{)}&0\end{array} \right),\overline{S}_{1}=\frac{-2 i}{k}\left( \begin{array}{cc}1/\rho_{01}^{2}&-1/\rho_{01}^{2}\\ -1/\rho_{01}^{2}&1/\rho_{01}^{2}\end{array} \right), \tag{18}\]  
\[\overline{S}_{2}=\frac{-2i}{k}\left( \begin{array}{cc}1/\rho_{02}^{2}&-1/\rho _{02}^{2}\\ -1/\rho_{02}^{2}&1/\rho_{02}^{2}\end{array} \right),\overline{B}_{1}=\left( \begin{array}{cc}1/z&0\\ 0&1/z\end{array} \right), \tag{19}\]  
After vector integrating over \(\widetilde{\mathbf{r}}\), Equations (20) and (21) become:  
\[I_{4}^{(b)}(u_{1},u_{2})=\frac{\left|H(u_{1})\right|^{2}}{\left[\det\!\left( \widetilde{B}^{-1}\widetilde{M}^{-1}+\widetilde{B}^{-1}\widetilde{S}+\widetilde {I}\right)\right]^{1/2}}\exp\!\left(\frac{ik}{2}\widetilde{u}^{T}\widetilde{M} _{01}^{-1}\widetilde{u}\right), \tag{20}\]  
\[\Gamma_{4}^{(b)}(u_{1},u_{2})=\frac{\left|H(u_{1})\right|^{2}}{\left[\det\! \left(\widetilde{B}^{-1}\widetilde{M}^{-1}+\widetilde{B}^{-1}\widetilde{S}+ \widetilde{I}\right)\right]^{1/2}}\exp\!\left(\frac{ik}{2}\widetilde{u}^{T} \widetilde{N}_{01}^{-1}\widetilde{u}\right), \tag{21}\]  
with \(M_{01}^{-1}=\widetilde{B}\!\left(\widetilde{N}^{-1}+\widetilde{S}+\widetilde {B}\right)^{-1}\!\widetilde{B}\). The superscript \"\((b)\)\" denotes the case of bistatic turbulence. Equations (20) and (21) represent the background noise and the ghost image information terms, respectively.
## 3 Results
### Single Actions
#### 3.2.1 Comparison of CEP and NPV of Single Actions  
Figure 2 compares single energy-efficiency actions represented in terms of the CEP from [37] to the same actions, evaluated using the LCC method and represented by NPV for a 50-year period. The comparison is made on all actions proposed in [37], but for clarity purposes, only envelope actions are shown in Figure 2. A complete list of all results is provided in Appendix A, while the detailed cost structure of each action is accessible through the Data Availability Statement. As it can be observed in Figure 2 and in the appended information, there are multiple solutions of the same element and type. These are cases with several solutions for a given element type. For example, an external wall type one with nine variations of insulation thickness or windows of the same material and class, but different opening mechanisms.  
By comparing the graphs in Figure 2, a few similarities and differences can be noted. The most significant differences are the change in slope of the usable attic solutions, wall type one and two. While their CEP decreases with increase of energy savings (increased insulation thickness), the NPV for those elements increases. Furthermore, the NPV of wall types two and three are comparable to the usable attic solution. This is not the case when the CEP is applied. Nevertheless, the remaining actions maintain similar disposition (relative to each other), when applying either the CEP or NPV. For example, it is evident that wall type one, windows, attic solutions, terrace doors, and ground floor insulation all preserve the same distribution.  
While the CEP represents the cost of saved kWh for a given solution over its lifetime, NPV determines the total cost over the calculation period. Furthermore, CEP considers only implementation cost, lifetime, and saved energy by the solution. In addition to that, NPV takes into account inflation, price increases, interest rate, maintenance, replacement cost, and residual value at the end of the calculation period. Even though those parameters are an inseparable part from global solution (package) evaluation, the comparison in Figure 2 shows that omitting them seems to have a limited effect on the ranking of single actions. At this relatively early stage of the methodology, it is valuable to identify actions with low investment relative to the obtained savings rather than find the solutions with the least required investment over a defined period of time. Moreover, the majority of building owners are still not completely clear on the practical use and application of LCC calculations, and its uptake is still rather weak [45]. Regarding those considerations, in combination with the great difference in calculation methods for the CEP and NPV, it can be argued that the CEP provides satisfactory results for this stage of a renovation project, despite its simplified approach.