esa-sceva/satcom-chunk-collection huggingface dataset:
# The Computability of the Channel Reliability Function and Related Bounds
## 5 Computability of the Zero-Error Capacity of Noisy Channels with Feedback
\), which means \(C_{0}(\hat{W}_{1})=C_{0}^{FB}(\hat{W}_{1})=R_{\infty}(\hat{W}_{1})=1\). For \(\hat{W}_{2}\), we take the three-ary typewriter channel \(\hat{W}_{2}(\epsilon)\) with \(\mathcal{X}_{2}=\mathcal{Y}_{2}=\{0,1,2\}\) (see [43]): \[\hat{W}_{2}(\epsilon)(y|x)=\left\{ \begin{array}{ll}1-\epsilon&y=x,\\ \epsilon&y=x+1\mod 3.\end{array} \right.\] Let \(\epsilon\in(0,\frac{1}{2})\) be arbitrary, then \(C(\hat{W}_{2}(\epsilon))=\log_{2}(3)-H_{2}(\epsilon)\). We have \(R_{\infty}(\hat{W}_{2}(\epsilon))=\log_{2}\frac{3}{2}\) and \(C_{0}(\hat{W}_{2}(\epsilon))=0\). This means that \(C_{0}^{FB}(\hat{W}_{2}(\epsilon))=0\). Thus, because \(C_{0}(\hat{W}_{1}\times\hat{W}_{2}(\epsilon))\geq C_{0}(\hat{W}_{1})=1\), \[C_{0}^{FB}(\hat{W}_{1}\otimes\hat{W}_{2}(\epsilon)) = R_{\infty}(\hat{W}_{1})=R_{\infty}(\hat{W}_{2}(\epsilon))\] \[= 1+\log_{2}(\frac{3}{2})>C_{0}^{FB}(\hat{W}_{1})+C_{0}^{FB}(\hat{ W}_{2}(\epsilon))\] and we have proven case 2.
# Intermitt Dynamic Compression Conters Anabolic Effects in Articular Cartilage
###### Abstract
IGF-1 is important for type II collagen synthesis and matrix incorporation [34; 31; 35; 36]. Previous studies have shown that IGF-1 and dynamic compression increase cartilage formation in an additive manner by matrix incorporation of radiolabeled proline and sulphate, suggesting that compression and IGF-1 act through different cellular pathways [35; 36]. IGF-\(\beta\) exists in three isoforms, namely TGF-\(\beta\)1, TGF-\(\beta\)2 and TGF-\(\beta\)3, which are all secreted by chondrocytes. The need for TGF-\(\beta\)-induced Smad2/3 signaling in preserving cartilage homeostasis through blocking of hypertrophic maturation is well documented [37; 38; 39; 40; 41]. Mechanical loading was previously suggested to induce TGF-\(\beta\) signaling, as upregulation of Smad2/3 response genes was reported in cartilage explants following physiological and excessive dynamic compression [42]. The transduction of homeostatic Smad2/3 signaling depends on ALK5, a TGF-\(\beta\) type I receptor [43]. TGF-\(\beta\) enhances the synthesis of proteoglycans [32; 44; 45; 46] and type II collagen [45; 46; 47] and promotes the production of pericellular matrix (PCM) components, including fibronectin [48]. Fibronectin is abundantly present in the cartilage PCM and binds the chondrocyte through membrane-spanning integrins [49]. Fibronectin has been suggested to be involved in sequestering the latent TGF-\(\beta\) complex in the cartilage matrix [50]. This study aimed to develop an ex vivo cartilage model that can be used to characterize the matrix turnover effects of long-term intermittent dynamic compression. Bovine cartilage explants were cultured for three weeks, and low-grade dynamic compression was performed five times weekly. We examined the effects on the cartilage matrix by biomarker measurements of type II collagen formation (PRO-C2) and fibronectin turnover (FBN-C) in the conditioned media from explants. We further investigated how long-term intermittent dynamic compression modulated the matrix in response to IGF-1 and TGF-\(\beta\)1.
## 4 Material and Methods
### Code Availability
#### 4.8.3 ncRNAs Differential Expression  
The first read of each pair was aligned to _S. ambofaciens_ genome as previously described for gene expression and FatureCounts (Subread v2.0) was adopted to obtain read counts based on the annotation reported in Supplementary Tables S4-S7. The following command line was used:  
featureCounts -a <ncRNA.saf> -o <ncRNA_counts> -F SAF -t gene -s 1 -T 15 -p -B -C -J -G <GCF_001267885.1_ASM126788v1_genomic.fna> -- fracOverlap 0.5 <BAM file list>  
DESeq2 version 1.26.0 in R 3.6.3 with default parameters was run to obtain differentially expressed ncRNAs, manually defining the size factors according to the ones estimated on the entire transcriptome and already used for standard gene expression analysis.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/antibiotics10080947/s1](https://www.mdpi.com/article/10.3390/antibiotics10080947/s1)]([https://www.mdpi.com/article/10.3390/antibiotics10080947/s1](https://www.mdpi.com/article/10.3390/antibiotics10080947/s1)), Figure S1 ncRNA identified by analysis of 3' or 5' UTRs. Figure S2. Codon frequencies in _S. ambofaciens_ ATCC 23877 CDSs. Table S1. List of monocistronic and polycistronic transcriptional units identified in this study. Table S2. Re-defined transcriptome mapping statistics. Table S3. List of predicted sRNA. Table S4. List of predicted Class I asRNA. Table S5. List of predicted Class II asRNA. Table S7. Differential expression (log2 Fold Change) of sRNA and Class I asRNA in w.t. strain at different growth time points (48, 72, 96 and 120 h). Table S8. Rfam predictions. Table S9. GlassGO conservation analysis of _S. ambfacing_sRNAs. Table S10. IntaRNA target predictions of _S. ambfacing_sRNAs. Table S11. GlassGO conservation analysis of _S. ambfacing_s Class I asRNA. Table S12. MaSigPro analysis of gene expression. Table S13. Differential expression (log2 Fold Change) of Class I asRNA obtained comparing w.t., \(\Omega\)pirA and \(\Omega\)pirA rpoB(R) strains, according the 4 time points (48, 72, 96 and 120 h) and \(\Omega\)pirA, and \(\Omega\)pirA rpoB(R) mapping statistics.  
Conceptualization, P.A.; data curation, E.P., M.C. and C.P.; formal analysis, E.P., M.C. and C.P.; funding acquisition, P.A.; investigation, E.P., M.C., A.T., F.D., C.P., G.E.D.B. and A.P.; project administration, P.A.; supervision, A.T., F.D., G.D.B. and P.A.; writing--original draft, E.P., M.C. and P.A.; writing--review and editing, A.T., F.D., L.S., G.D.B. and P.A. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Italian Ministry for Education, University and Research (grant no. PON01_02093 and PRIN 2017 SFBFER to [PERSON]).  
Not applicable.  
Raw data are publicly available at Sequence Reads Archive under accession numbers: PRJNA342588 for the w.t. strain, PRJNA430852 for _pirA_::PYM-18 (\(\Omega\)pirA) and _pirA_::PYM-rpoB(R) strains.  
We would like to thank [PERSON] for technical support.  
The authors declare no conflict of interest.
## 2 Robust Design Optimization Methods and Challenges
### Methods for Computational Cost Reduction  
Electrical machines' accurate modeling requires solving partial differential equations in many physical domains (electrical, mechanical, thermal, etc.) simultaneously. Moreover, the number of possible designs is very large. If we are thinking back to the preliminary design problem of a large power transformer, where 20 design parameters were optimized simultaneously, there exists millions of possible designs for the same specification [9]. The design task can be more expensive numerically in a system-level design of a rotating machine, where the electrical machine and the control are designed together. To reduce the computational demand of these tasks, a wide variety of meta-modeling techniques can be employed to evaluate the fitness function and avoid complex numerical simulations [48; 196; 197]: response surface methodologies [198; 199], multilayerperceptron [194; 200; 201; 202; 203], radial basis function neural networks [204; 205], kriging models [206; 207], Gaussian processes [208], support vector machines [209; 210; 211], fuzzy logic [212].  
However, it is difficult to make a meta-model from an unknown expensive optimization problem, which approximates the function of the whole region (Figure 8). Generally, the kriging or Gaussian process approximations can evaluate the function with sufficient accuracy in a given region of the function. However, they cannot approximate the minimum functions or make false global minimums, which is not part of the original function. To overcome these problems, these approximate functions are usually used together with the original function, and if the error indicator is small enough, the calculation uses the approximate value [171; 202]. Kriging based techniques are widely used for electrical machine design problems [109; 213; 214; 215; 216]. For example, [PERSON] et al. [217] used it together with PSO to optimize a permanent magnet synchronous machine for hybrid and electric cars, and [PERSON] [218] used it to find the optimal shape of the rotor structure. In [219], the authors explored the importance of achieving an appropriate balance between exploration and exploitation. They proposed two kriging based strategies to approximate the fitness functions of expensive electromagnetic design problems with high accuracy and low computational effort. Many electrical machine design paper use the response surface methodologies [8; 21]; however, as previously mentioned, they can converge to a sensitive solution, because the sensitive part of the objective function can be ignored [62].
## 3 Results and Discussion
### Co-Treatment of Caco-2 Cells with 2 mg/mL_HB-AuNPs and Dox  
The unique optical properties of AuNPs resulted in multifunctional biomedical activities such as drug delivery, photothermal, and drug-sensitizing agents, etc. [18]. The drug-sensitizing effects of AuNPs are currently being explored in the fight against cancer to enhance chemotherapeutic drug efficacy while reducing their side effects. Previous studies have demonstrated that co-administration of plant-mediated AuNPs and Dox has the potential to enhance the anti-cancer effects of Dox [6; 53]. Of interest, unlike other studies, HB-AuNPs_ci were able to augment the cytotoxicity of Dox when used at a very low and non-toxic dose [4]. This finding was especially interesting as Dox is one of the most potent chemotherapeutic drugs but has limited application due to cardiotoxicity and nephrotoxicity. The idea of using chemotherapeutic drugs (not limited to Dox) at non-toxic doses therefore holds promise in the development of highly potent cancer treatment strategies. Nano-based strategies have shown the ability to enhance the drug's biocompatibility, bio-availability, and efficacy [6]. The current study also investigated the drug-sensitizing effects of HB-AuNPs on Dox-treated Caco-2 cells to investigate whether the two HB species behaved in a similar manner. Co-treatment of Caco-2 cells with Dox (1.56 \(\upmu\)g/mL) and 100 \(\upmu\)g/mL of 2 mg/mL_HB-AuNPs resulted in a significant reduction in the cell viability (Figure 9) when compared with their individual treatments. Unlike HB-AuNPs_ci, the concentration of 2 mg/mL_HB-AuNPs was ten-times lower [4], suggesting that these NPs can also be effective at a very low dosage. Co-treatment was also investigated in clinical trials, where orally administered biogenic AuNPs in combination with Dox and cyclophosphamide were well tolerated in metastatic stage III breast cancer patients [28]. These studies suggest that biogenic AuNPs can be safely used alone or in combination with conventional therapy for disease treatment to enhance drug efficacy.  
Figure 9: Co-treatment with 2 mg/mL_HB-AuNPs and Dox enhanced cell death in Caco-2 cells. Individual treatments demonstrated insignificant effect on the viability of Caco-2 cells after 24 h exposure, whereas combination of Dox (1.56 \(\upmu\)g/mL) and 2 mg/mL_HB-AuNPs (100 \(\upmu\)g/mL) significantly reduced cell viability. \(p\leq\) 0.05 was considered as statistical significant for untreated vs. treated samples, ***\(p\) value = 0.001.
## 3 Results and Discussion
### PAH and Trace Metals Concentrations
#### 3.1.2 Trace Metals  
The concentrations of the heavy metals identified in the dust samples are summarized in Table 2. Among the elements that were examined, Fe was found to have the highest concentration (4.9 ug/g), followed by Al (4.2 ug/g), Zn (0.4 ug/g), Cu (0.34 ug/g) and Ba (0.25 ug/g). The elevated concentrations of both soil-related (Fe, Al) and anthropogenic (Zn, Cu, Ba) elements in the dust samples implies that the dust accumulation in the residential indoor Environment is mainly affected by the resuspension of natural dust (soil) and road dust. The elements Hg and Cd had the lowest concentrations.  
The highest concentration of Fe (26.1 mg/g) was observed in a residence located in an urban area on a high circulation road. Indeed, Fe has been linked with traffic as it originates either from road dust resuspension or emissions from brake wear [33]. The highest concentration of Al (45 mg/g) was observed in a residence which was located in a rural area, while the highest levels of Zn, Cu, and Sr were detected in a ground floor house located in the Athens city centre, and thus significantly affected by vehicular emissions [34]. Due to the absence of established legislation, limits for trace metals in indoor dust were compared with concentrations reported in the literature. Compared to the metal concentrations reported in a recent study examining residential dust samples in Toronto, Canada, the levels of the present study were all higher (Cr (65.2 vs. 42 ug/g), Cu (339 vs. 136 ug/g), Ni (29.9 vs. 23 ug/g), Pb (46.1 vs. 36 ug/g), and Zn (401 vs. 386 ug/g), except for Cd (0.5 vs. 1.7 ug/g) [12]. Higher concentrations compared to those of the present work were also presented in an earlier study conducted in Japan [31], where [PERSON] 2013, reported lower concentrations for Cr, Ni, Pb, and Zn, but higher for Cd (0.5 vs. 8.2 ug/g) in Kathmandu, Nepal [32].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Elements** & **Mean** & **SD** & **Min** & **Max** \\ \hline Zn & 401 & 211 & 136 & 1031 \\ Fe & 4913 & 5650 & 851 & 26100 \\ Al & 4217 & 9391 & 269 & 44900 \\ Hg & 0.4 & 0.5 & 0.0 & 2.1 \\ V & 9.0 & 5.4 & 2.4 & 21.3 \\ Cr & 65.2 & 36.4 & 14.2 & 147 \\ Mn & 128 & 67.9 & 28.9 & 272 \\ Ni & 29.9 & 23.9 & 5.2 & 103 \\ Cu & 339 & 711 & 11.8 & 3051 \\ As & 4.0 & 4.4 & 1.3 & 19.8 \\ Se & 1.0 & 0.6 & 0.2 & 2.8 \\ Sr & 118 & 97.6 & 37.7 & 396 \\ Cd & 0.5 & 0.6 & 0.1 & 2.3 \\ Ba & 251 & 237.0 & 35.0 & 1130 \\ Pb & 46.1 & 71.5 & 6.5 & 343 \\ \hline \end{tabular}
\end{table}
Table 2: Metal concentrations in indoor dust samples (ug/g).
# Ozone Activation on TiO\({}_{2}\) Studied by IR Spectroscopy and Quantum Chemistry
###### Abstract  
The adsorption of different isotopic ozone mixtures on TiO\({}_{2}\) at 77K was studied using FTIR spectroscopy and DFT calculations of cluster models. In addition to weakly bound ozone with band positions close to those of free or dissolved molecules, the spectrum of chemisorbed species was observed. The splitting of the \(\
u_{1+3}\) combination band to eight maxima due to different isotopomers testified to the loss of molecule symmetry. The frequencies of all the isotopic modifications of the ozone molecules which form monodentate or bidentate complexes with four- or five-coordinated titanium atoms were calculated and compared with those of experimentally observed spectra. The four considered complexes adequately reproduced the splitting of the \(\
u_{1+3}\) vibration band and the lowered anharmonism of chemisorbed O\({}_{3}\). The energetically most favorable monodentate complex with four-coordinated titanium atoms showed good agreement with the observed spectra, although a large difference between the frequencies of \(\
u_{1}\) and \(\
u_{3}\) modes was found. For better coherence with the experiment, the interaction of the molecule with adjacent cations must be considered.  
FTIR; adsorption; ozone; TiO\({}_{2}\); DFT calculation +
Footnote †: journal: Applied Sciences
# A Hybrid Model for Freight Train Air Brake Condition Monitoring
## 2 Materials and Methods
### Main Components Implemented in the Model
#### 2.3.6 Relay Valve  
The relay valve (labelled with (6) in Figure 6) controls the amount of air flux that passes from the auxiliary reservoir to the brake cylinder. The relay valve opening area is controlled by the pressure difference between the distributor pressure and the brake cylinder pressure during the first braking phase and by the pressure difference between the command pressure and the brake cylinder pressure during the brake build-up. In Figure 15, a simplified scheme for a relay valve is shown.  
As shown in Figure 16, the pressure variation in the brake cylinder (\(\dot{\mathrm{p}}_{\mathrm{bc}}\)) is modelled as the saturation of the G I P mode device according to the variation of the alimentation valve section. This variation is derived as the pressure difference between the command pressure (\(\dot{\mathrm{p}}_{\mathrm{c}}\)) and the brake cylinder pressure (\(\dot{\mathrm{p}}_{\mathrm{bc}}\)). If the pressure difference exceeds a certain threshold (identified as EQP in Table 1), the brake cylinder pressure variation follows the G I P mode; if the pressure difference is lower than the threshold, the brake cylinder pressure variation follows the saturation of the G I P mode. The threshold has been empirically tuned to simulate the equivalent stiffening spring contribution of the relay valve components. The G I P mode has been modelled with a linear behaviour of the brake cylinder pressure with respect to the filling time, as already highlighted in Section 2.3.4. From a physical point of view, the G I P device changes the cross-sectional area connecting  
Figure 14: Kink valve transfer function of (**a**) the braking phase and (**b**) the releasing phase.  
the auxiliary reservoir to the brake cylinder, resulting in different filling times. The freight-passenger device was developed because freight trains are usually longer and heavier than passenger ones. Considering the delay in the propagation of braking command from the train head to its tail, freight trains are, in general, subjected to higher coupling forces, which can undermine both their safety and integrity [27]. The G brake mode allows pressure build-up to be completed within a time of around 24 s, while the P brake mode allows pressure build-up to be completed within a time of around 4 s. For long freight trains, to reduce the coupling forces difference between wagons, usually, the first five wagons after the locomotive are in G mode while the following wagons are in P mode.
# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
## 3 Methodology
### Agents
#### 3.5.1 OSM Agent  
The OSM agent was developed to augment instantiated buildings [56]. The agent incorporates building usage and address data from OpenStreetMap (OSM) [28] to enrich building information in the KG via the following process:  
1. Equivalent buildings in the KG and the OSM dataset are matched through comparison of the building footprints from the two datasets [56].
2. After a match is found, the agent extracts OSM tags that describe the usage (e.g., office and gym) and address information from the OSM data.
3. The agent creates ontological instances of the extracted usage and address information in the KG.
4. The newly created instances are then linked to the corresponding instantiated building in the KG, enriching the KG with usage and address information from OSM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Agent** & **Data Required** & **Data Added to KG** \\ \hline \multirow{2}{*}{OSM agent} & Building footprints from OSM & Building usage, usage share, \\  & and KG & and address \\ \hline Building floor agent & Building address & Number of floors \\ \hline \multirow{2}{*}{GFA agent} & Building footprint area and & \\  & number of floors & \\ \hline \multirow{2}{*}{Cost agent} & GFA, building usage and & \\  & usage share & \\ \hline \multirow{2}{*}{Sea level impact agent} & Elevation, locations of objects & Linkage between sea level rise \\  & of interest, sea level rise & scenarios with vulnerable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of data required by agents and data added by the agents.  
Figure 2: UML sequence diagram summarising agent interactions.  
By having building information linked to the central building concept, the instantiated building in the KG enables consistent building information within TWA for other agents' operation. For example, the added ontological concepts of building usage by the OSM agent can be used by the cost agent in Section 3.5.4 to estimate the construction costs of buildings based on property usage. Furthermore, the building address information instantiated by the agent can be used by the building floor agent in Section 3.5.2 to derive the number of floors of buildings.  
The example of property usage concepts are shown at the bottom of Figure 1. For example, if an instantiated building in the KG is matched with an OSM building that is tagged as a hotel, the instantiated building is tagged with the ontological concept \(\mathtt{obe}\):\(\mathtt{Hotel}\). To account for multi-purpose buildings, the OSM agent also instantiates the usage share based on the occupied area. For example, a building may be occupied by offices (60% by area) and retail stores (40% by area); this information is also instantiated in the knowledge graph via the data property \(\mathtt{obe}\):hasUsageShare and made available to query.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 5 Immersive Audio-Visual Experiments
### Statistical Analysis Methods  
The scores given by human participants to any test stimuli are called perceptual scores. The perceptual scores from all participants are averaged for each PVS, resulting in a mean opinion score (MOS). For the three experiments, quality and content scores associated with each PVS were gathered. The scores were averaged according to the type of HRC.  
The quality scores were processed to generate the Mean Quality Score (MQS) per-HRC, given by:  
\[\text{MQS}_{\text{HRC(j)}}=\frac{1}{n}\cdot\sum_{i=0}^{n}S_{j}(i), \tag{1}\]  
where \(S_{j}(i)\) is the score given by the \(i\)th subject for the \(j\)th element of the set of \(m\) HRCs and \(n\) is the total number of subjects. In other words, \(\text{MQS}_{\text{HRC(j)}}\) gives the average quality score for the \(j\)-th HRC, measured across all subjects and content originals.  
Similarly, the Mean Content Score (MCS) per-HRC is obtained by taking the average of the content scores given by all subjects:  
\[\text{MCS}_{\text{HRC(j)}}=\frac{1}{n}\cdot\sum_{i=0}^{n}CS_{j}(i), \tag{2}\]  
where \(CS_{j}(i)\) is the content score given by the \(i\)-th subject to the \(j\)-th HRC test sequence, with \(j=\left\{1,2,\ldots,m\right\}\).
# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
###### Abstract  
This study develops a progressive navigation and guidance model for the route selection of cyclists executed in a designated area. The route selection of cyclists is modeled as a Pareto multi-objective optimization problem which is solved with the NSGA-II algorithm. The study aims to contribute to the ongoing efforts to create efficient and cyclist-friendly navigation tools to promote sustainable urban mobility. Data collection methods include GPS tracking, field measurements, and qualitative approaches to understand cyclists' behavior and preferences. Nine objective functions are constructed based on criteria related to safety and comfort, incorporating decision variables related to cyclists riding on sidewalks, capturing the complexity of urban cycling infrastructure. Tests are performed in a defined area in the center of Athens, Greece. The NSGA-II algorithm is executed with modifications and the Pareto front is constructed, which consists of 28 alternative routes between two origin-destination points. The four routes that optimize the nine criteria of the objective functions are presented, with most routes passing through the Zappeion Gardens. The NSGA-II algorithm is proven to be a suitable approach for applications in networks with complex characteristics and for capturing cyclists' choices when they face conflicting options. The study presents how a novel approach for the multi-objective optimization of cyclists' route choice, which considers a wide range of cyclists' needs and preferences, can be implemented in an urban environment with a lack of cycle infrastructure.  
bicycle; route choice; MOOP; Pareto optimal; NSGA-II +
Footnote †: journal: Applied sciences