esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Channel Model  
### Link Equation  
The link equation is widely used in communications to predict the power received at an optical detector, because it enables the analysis of the trade-offs between system parameters and channel performance. The intensity of a beacon beam is non-uniformly distributed in the spatial domain along the direction of beam propagation. Taking into account the non-uniform irradiance distribution and beam spreading due to atmospheric turbulence, the link equation is given by [18; 34]  
\[P_{R}=\frac{2P_{T}}{\pi W_{eff}^{2}(z)}\eta_{T}\eta_{R}A_{R}T_{A}T_{C}\exp \left[-2\frac{d^{2}}{W_{eff}^{2}(z)}\right], \tag{1}\]  
where \(P_{T}\) and \(P_{R}\) are the transmitting and received optical powers, respectively; \(W_{eff}\) is the effective spot size including the beam divergence and spreading; \(z\) is the axial distance from the beam waist; \(\eta_{T}\) is the transmitter optical efficiency; \(\eta_{R}\) is the receiver optical efficiency including the optical filter transmittance; \(A_{R}\) is the receiving area; \(d\) is the radial distance from the axis of beam propagation (i.e., \(d=z\theta_{e}\) for a small pointing error of \(\theta_{e}\)); and \(T_{A}\) and \(T_{C}\) are the atmospheric attenuations produced by atmospheric constituents and cirrus clouds, respectively. The last term indicates the pointing loss due to LOS misalignment between the transmitter and the receiver.  
The beacon beam suffers from power losses resulting from absorption and scattering by molecules and aerosols suspended in the atmosphere as well as cirrus clouds. The atmospheric attenuations resulting from the constituents and cirrus clouds are expressed as [35]  
\[T_{A}(\lambda,V,h_{\rm s})=\exp\biggl{[}-\sigma(\lambda,V,0)h_{SH}\sec(\theta_{Z} )\exp\biggl{(}-\frac{h_{\rm s}}{h_{SH}}\biggr{)}\biggr{]}, \tag{2}\]  
\[T_{C}=\exp\Bigl{[}-0.14(C_{T}\sec\theta_{Z})^{2}\Bigr{]}, \tag{3}\]  
where \(\sigma(\lambda,V,0)\) and \(V\) are the atmospheric attenuation coefficient and visibility at sea level, respectively, and \(\lambda\) is the beacon wavelength. \(h_{SH}=1.2\) km is the scale height; \(\theta_{Z}\) is the zenith angle of the satellite; \(h_{\rm s}\) is the height of the optical ground station; and \(C_{T}\) is the cirrus cloud thickness. It is worth noting that in the low elevation angle regimes, atmospheric attenuation by cirrus clouds is dominant compared to attenuation by atmospheric constituents.
## 6 Future Research Directions
### Channel Attistics  
When it comes to communication systems based on deep learning, the most significant challenge is creating a neural network. Using channel statistics, deep learning models with memory, like LSTM and RNN, may be used to forecast the channel's behavior. Because of the time savings, this data can be utilized in place of real-time CSI.
# Advances in Fermentation Technology: A Focus on Health and Safety
## 2 Microbial Control of the Pathogens' Growth--Biocontrol Strategies
### Potential Health Hazards in Traditional Fermentation
#### 2.1.4. Antimicrobial Resistance  
Fermented foods can serve as reservoirs for antimicrobial resistance genes (ARGs), particularly in spontaneously fermented plant-based foods, which cause added risks due to the lack of controlled starter cultures [44; 45]. ARGs, such as those conferring resistance to tetracyclines, penicillin, chloramphenicol, and macrolides, have been identified in LAB and coagulase-negative staphylococci [44]. Although many resistance traits in LAB are intrinsic, horizontal gene transfer remains a potential risk [46]. Furthermore, the food supply chain offers multiple pathways for ARG transfer, causing direct risks to humansfrom resistant microbes in fermented foods and raising safety concerns about a farm-to-gut antimicrobial resistance pipeline [44; 47].  
Despite these inherent risks, LAB also produces bacteriocins, which are natural antimicrobial compounds effective against foodborne pathogens. These compounds present viable alternatives to conventional preservatives, especially amid the increasing consumer demand for clean labels and natural antimicrobials [48; 49]. Thus, monitoring ARGs throughout fermentation becomes essential as LAB can act as conduits and barriers for the spread of antimicrobial resistance (AR). Therefore, careful strain selection and continual monitoring during fermentation are crucial in mitigating the risks associated with AR [50]. Consequently, the standardization of ARG monitoring protocols and the development of comprehensive risk assessment frameworks represent urgent research priorities within the domain of fermented foods.  
In summary, effectively controlling microbial control through targeted biocontrol strategies is critical for maintaining the safety and quality of fermented foods. A more profound comprehension of the associated microbial risks and the implementation of suitable prevention measures is essential to safeguarding public health while allowing consumers to reap the benefits of fermented products.
# Building Survivable Software Systems by Automatically Adapting to Sensor Changes
## 5 Extensions
### Ability to Exploit Many Sensors  
As an increasing number of sensors are deployed in real-world systems, it is crucial for ASC to be able to exploit many sensors. This also enables our approach to be deployed in an open environment where new sensors continually emerge. Dealing with a large number of sensors is challenging in two aspects:  
* Noisy sensors are likely to be involved and can degrade adaptation performance. For example, if some reference sensors produce highly noisy values, the nearest neighbor distances can suffer from the noise. Additionally, noisy values in reference or new sensors can cause the optimization algorithm to get stuck in poor local minima.
* A large number of sensors leads to a large parameter space of \(\mathbf{\Theta}\), which significantly increases the computational cost of the adaptation algorithm.  
In addressing these issues, we developed a two-step procedure to select a subset of useful sensors:  
1. Selecting a subset of reference sensors: For each reference sensor, compute the average correlation between its sensor values and those from each replaced sensor, and then select \(N_{\text{ref}}\) reference sensors with the largest average correlation scores.
2. Selecting a subset of new sensors: For each new sensor, compute the average correlation between its sensor values and those from each replaced sensor as well as each selected reference sensor in Step 1, and then select \(N_{\text{new}}\) new sensors with the largest average correlation scores.  
Here, \(N_{\text{ref}}\) and \(N_{\text{new}}\) were set by the user in specific applications. We denote this improved approach as ASCSEL.
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 4 Experiments
### Discussion
#### 4.6.1 Impact of Different Relations  
The MRGCN combines three types of relations: composition relation, functional relation, and tagging relation. To demonstrate the necessity of the three relations in the model, we designed several variants of the model for comparison: MRGCN_D model only uses functional relation, the MRGCN_T model only uses tagging relation, the MRGCN_CD model uses both composition and functional relation, the MRGCN_CT model uses both composition and tagging relation, and the MRGCN_DT model uses both functional and tagging relations.  
The results of the comparison among the variants of our approach are shown in Figure 3 when evaluated with K = 5, 10, 15, 20. We can observe that, when either relation is dropped from the original model, the performance becomes worse, which suggests that both three relations can indeed help improve the model performance. The more relations are incorporated, the better the model performance we can obtain. Furthermore, MRGCN_CT outperforms MRGCN_CD which in turn outperforms MRGCN_DT, which shows that the composition relation is more important than the tagging relation and in turn more important than the functional relation for achieving a better recommendation  
Figure 2: Performance comparison of different approaches. (**a**) Recall@K; (**b**) NDCG@K.  
performance. However, when only a single relation is considered, MRGCN_D has a better performance over MRGCN_T. We conjecture the underlying reason is that some services do not have annotated tags, and they become \"cold-start\" services that have no connections with other nodes in the graph when only tagging information is used. MRGCN achieves the best performance as it jointly considers the three complementary relations, thereby enriching the information from different sources and mitigating the cold-start effect in the recommendation.
## 3 Results and Discussion
### Chloroplast Genome Assembly of A. remotilora
\begin{table}
\begin{tabular}{l l l} \hline
**Category** & **Group** & **Name of Genes** \\ \hline \multirow{7}{*}{Photosynthesis} & Photosystem I & _psaA_, _psaB_, _psaC_, _psal_, _psal_ \\  & Photosystem II & _psbA_, _psbB_, _psbC_, _psbD_, _psbE_, _psbF_, _psbH_, _psbl_, _psbK_, _psbl_, _psbM_, _pspsbN_, _psbT_, _psbZ_, _ycf3_, _psbJ_ * \\  & Cytochrome 1 & _petA_, _petB_, _petD_, _petL_, _petN_, _petG_ * \\  & ATP synthase & _atpA_, _atpB_, _atpE_, _atpF_, _atpH_, _atpI_ \\  & NADH 2 & _ndhC_, _ndhD_, _ndhE_, _ndhF_, _ndhK_, _ndhA_ *, _ndhG_ *, _ndhG_ *, _ndhH_ *, _ndhI_ * \\  & Rubisco 3 & _rbcl_. \\ \hline \multirow{7}{*}{Self-replication} & SSU ribosome 4 & _rps2_, _rps3_, _rps4_, _rps7_, _rps8_, _rps11_, _rps14_, _rps16_, _rps18_, _rps19_, _rps12_ *, _rps15_ * \\  & LSU ribosome 5 & _rpl2_, _rpl14_, _rpl16_, _rpl20_, _rpl22_, _rpl32_, _rpl33_, _rpl36_ \\  & Polymerase 6 & _rpaA_, _rpsb_, _rpc1_, _rpc2_ \\  & rRNA genes & _rrnI_, _rms2_, _rms4_, _rms5_ *, _rm5_* \\  & & _trnC-GCA_, _trnD-GUC_, _trnI-UU_, _trnF-GAU_, _trnG-GCC_, _trnG-UICC_, _trnI-GUHG_, _trnK-UUUU_, _trnI-UIA_, _trnI-UIA_, _trnM-CAU_, _trnQ-UU_, _trnR-UICU_, _trnS-GCU_, _trnS-GGA_, _trnS-UGA_, _trnT-GGU_, _trnT-GGU_, _trnT-UGU_, _trnV-UAC_, _trnY-GUA_, _trnA-UGC_ *, _trnR-AGC_ *, _trnI-GAU_ *, _trnN-GLU_ *, _trnP-UGG_ *, _trnV-GAC_ *, _trnW-CCA_ *, _trnI-CAU_ ** \\ \hline Other genes & Cytochrome 7 & _ccsA_, \\  & Envelope 8 & _ccmA_, \\  & Maturase & _matK_ \\  & ORFs 9 & _ycf2_, _ycf4_, _ycf1_ * \\ \hline \end{tabular}  
* chloroplast cytochrome b\({}_{\text{f}}\) complex, 2 NADH dehydrogenase (quinone), 3 subunit of rubisco, 4 small subunits, 5 large subunits, 6 RNA polymerase, 7 C-type cytochromes genes, 8 envelope membrane proteins, 9 open reading frames, multicopy genes (* two, ** three copies).  
\end{table} 
Table 1: Chloroplast genome features and identified genes in _A. remotiflora_.
## Appendix A VOC Sampling Methods  
There are numerous techniques for sampling VOCs, the most common of which are whole-air sampling techniques and sorbent tubes. The choice of air sampling technique depends on the chemical and physical properties of the VOCs of interest and on preferences motivated by regulatory reasons [9].  
Whole-air sampling tools come in two common forms: evacuated stainless-steel canisters and sampling bags. In the US, evacuated canisters were introduced in the 1980s [15]. Today, canisters are applicable for sampling of up to 150 polar and nonpolar VOCs [16]. Canisters are equipped with flow controllers, particulate matter filters, and a vacuum gauge. For TWA sampling of VOCs in the field, the flow controller is pre-calibrated for the desired sampling time. Canister walls can modify the original content of sampled gas, as VOCs adsorb and undergo reactions, and samples can have poor recoveries [15; 17].  
Air sampling bags are used for the sampling of odorous gases [14; 15]. Sampling bags are simple (consisting of a polymer film and a connector) and inexpensive to use. Despite their simplicity and cost-effectiveness, there are several limitations with regards to poor sample recoveries [14]. For example, Tedlar bags can desorb acetic acid and phenol, and absorb indole, \(p\)-cresol, nonanoic and octanoic acids, and some other VOCs resulting in an increased or decreased total mass of those VOCs in every sample [14]. Metalized bags can improve sample recovery for selected odorous VOCs [18]. Nalophane is the least expensive material; however, the material is not recommended for benzene and other petrochemicals, and it is not recommended to store samples for more than 6 h [19]. Teflon FEP bags are considered the most chemically inert among other bags, but they have a higher cost [20].  
Sorbent tubes can be a good alternative to canisters and bags. Sorbents can be selected for application to a wider range of analytes including odorous VOCs [10]. Unlike canisters, sorbent tubes are compact and are easier to transport and store. Moreover, sorbent tubes have greater stability when exposed to polar compounds (most odorous VOCs are polar). In this approach, contaminated air passes through a tube containing sorbent material, which adsorbs VOCs. Usually, to facilitate this process, the contaminated air passes through the tube at a constant rate with the help of an air sampling pump. Sampling with sorbent tubes is one of the conventional sampling procedures for VOC quantification in ambient air [10; 13; 14; 21]. All methods are summarized in Table 14.
## 3 Results  
### Analysis Result  
Analysis Result of Safety Criteria  
In this study, the measurement locations for life safety criteria were chosen as the farthest points from the ignition source, based on the height of 1.8 m from the floor, representing the breathing area. Table 5 details the location of each measurement point based on aspect ratios.  
The arrival time of the life safety criteria can be defined as ASET (available safety egress time), and the measurement results showed that the ASET varied depending on the building finishing material. Figure 3 illustrates the variation in the ASET for each building finishing material according to the aspect ratio, while Table 6 presents the ASET values for each building finishing material.  
Figure 3: Trends in safety criteria arrival time variation for each building finishing materials.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{2}{c}{**W, L, H (m)**} \\ \cline{2-3}  & **Fire Source** & **Measurement Point** \\ \hline Aspect ratio 1 & & 1.9, 1.9, 1.8 \\ \hline Aspect ratio 2 & & 1.9, 3.9, 1.8 \\ \hline Aspect ratio 3 & 0.1, 0.1, 0.2 & 1.9, 5.9, 1.8 \\ \hline Aspect ratio 4 & & 1.9, 7.9, 1.8 \\ \hline Aspect ratio 5 & & 1.9, 9.9, 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fire room measurement point by aspect ratio.  
The simulation results confirmed that the ASET was delayed as the aspect ratio increased. With the aspect ratio increasing from 1 to 5, the arrival times of temperature, visibility, and FED safety criteria for polyurethane foam were delayed by 6.0 s, 6.0 s, and 4.4 s, respectively. For MDF, similar delays were observed: 7.6 s for temperature, 8.0 s for visibility, and 8.4 s for FED criteria. CFRPBM exhibited the most significant delays with increasing aspect ratio. The reference excess times for temperature and visibility increased by 8.0 and 7.6 s, respectively. However, the FED excess time saw a much larger delay of 117.6 s.  
The average increase in the ASET for temperature, visibility, and FED with the increase in aspect ratio was 1.5 s, 1.5 s, and 1.1 s, respectively, for polyurethane foam and 1.9 s, 2.0 s, and 2.1 s for MDF. CFRPBM exhibited an average increase of 2.0 s, 1.9 s, and 29.41 s, respectively, indicating its superior performance in the FED category compared to the building finishing material of the control group.
## 3 Passive Thermal Control Methods
### Thermal Straps  
Thermal straps are excellent passive heat transfer devices that are commonly used on space missions to conduct the heat from inaccessible regions of the spacecraft and radiate into space. Thermal straps come in various shapes and lengths according to the requirement. They are comprised of thin wires of high thermal conductivity metals or foils which make them flexible and efficient thermal links. Thermal straps are made of different metals and some of them are listed in Table 2 with their thermal properties.  
Thermal contact conductance (TCC) is a property of heat conduction between solid bodies in thermal contact. This property is highly dependent on contact pressure and surface flatness. TCC influences the overall performance of the thermal strap and the relationship is given in Equation (7) [26].  
\[Q_{strap}=\frac{\lambda A}{l}\Delta T \tag{6}\]  
\[C=\frac{Q_{strap}}{\Delta T}=\frac{Q_{htr}-Q_{leak}}{\Delta T}=\frac{Q_{htr}-Q _{rad}-Q_{htrleads}-Q_{TCIeads}}{\Delta T} \tag{7}\]  
where, \(\lambda\)--Thermal conductivity (W/mK), \(A\) and \(l\) are, respectively, the cross-section (m\({}^{2}\)) and the length (m) of the heat path inside the medium, \(C\)--Thermal contact conductance (\(W/K\)), \(Q_{strap}\)--applied heat load into the thermal stray (W), \(Q_{htr}\)--heat load from the heater pad, \(Q_{leak}\)--total heat lost from the heater pad due to radiation (\(Q_{rad}\)), from the heater wire leads (\(Q_{htrleads}\)), and thermocouple leads (\(Q_{TCIeads}\)). \(\Delta T\) is the difference in temperature measured at the ends (\({}^{\circ}\)C). Variation in thermal strap conductance is shown in Figure 2. The lower the thermal strap conductance, the higher the difference in temperature between two ends of the thermal strap will be.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Name** & **Thermal Conductivity, k, W/m\_K** \\ \hline Graphene & \(\sim\)3500 \\ Pyrolytic graphene & \(\sim\)1500 \\ Graphite fiber & \(\sim\)800 \\ Copper & \(\sim\)450 \\ Aluminium & \(\sim\)225 \\ \hline \hline \end{tabular} Note: \(\sim\): Actual values may change but they remain close to the tabulated values.  
\end{table}
Table 2: Thermal strap materials.
## 3 Approach
### Speech Emotion Databases  
**ASED**[10] is for Amharic and was created by the authors in previous work. It uses five emotions and consists of 2474 recordings made by 65 speakers (25 male, 40 female). Recording was performed at 16 kHz and 16 bits. The ASED dataset is accessible to the public for research purposes (see URL earlier).  
**RAVDESS**[27] is for English, uses eight emotions, and contains just two sentences. The 24 speakers (12 male, 12 female) are professional actors. Interestingly, emotions in this dataset are'self-induced' [28], rather than acted. Moreover, there are two levels of each emotion. There are 4320 utterances. Project investigators selected the best two clips for each speaker and each emotion. Recording was at 48 kHz and 16 bits, and it was carried out in a professional recording studio at Ryerson University.  
**EMO-DB**[29] is for German, uses five emotions, and contains ten everyday sentences: five made of one phrase, and five made of two phrases. There are ten speakers (five male, five female), nine of whom are qualified in acting, and about 535 raw utterances in total. Recording was performed at 16 kHz and 16 bits and was carried out in the anechoic chamber of the Technical Acoustics Department at the Technical University Berlin.  
**URDU**[17] is for Urdu, uses four emotions, and comprises 400 audio recordings from Urdu TV talk shows. There are 38 speakers (27 male, 11 female). Emotions are not acted but occur naturally during the conversations between guests on the talk shows.  
Figure 1: Network architecture of proposed VGGE based on well-known VGG model.
## 4 Seismic Performance of a Masonry Structure with RC Frames on the First Story with CFST Dampers
### Result Analysis of Three Structures JG-1, JG-2, and JG-3
#### 4.2.2 Interlayer Displacement of Three Structures JG-1, JG-2, and JG-3  
It can be seen from Figure 19 that due to the large lateral stiffness of the upper masonry structure, the interlayer displacement of JG-1 and JG-2 is greater on the first story than that of the upper layers. Under the action of the EL-Centro wave and TJYY wave, the maximum interlayer displacement of JG-3 appears on the second story, which is due to the large stiffness of the frame structure with a shear wall on the first story, resulting in the large interlayer displacement of the upper part of the structure.  
Figure 17: The Fourier spectrum of three waves. (**a**) EL Centro wave; (**b**) Taft wave; (**c**) TJY wave.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**JG-1**} & \multicolumn{2}{c}{**JG-2**} & \multicolumn{2}{c}{**JG-3**} \\ \cline{2-7}  & **Frequency/Hz** & **Period/s** & **Frequency/Hz** & **Period/s** & **Frequency/Hz** & **Period/s** \\ \hline
1 & 1.748 & 0.572 & 1.905 & 0.525 & 2.357 & 0.424 \\
2 & 1.816 & 0.551 & 2.167 & 0.451 & 2.648 & 0.377 \\
3 & 2.257 & 0.443 & 2.671 & 0.374 & 3.548 & 0.282 \\ \hline \end{tabular}
\end{table}
Table 3: Modal analysis of the three structures JG-1, JG-2, and JG-3.  
Figure 19: The interlayer displacement. (**a**) Interlayer displacement under the action of EL-Centro wave; (**b**) interlayer displacement under the action of Taft wave; (**c**) interlayer displacement under the action of TJY wave.  
Figure 18: Maximum absolute acceleration response of each floor. (**a**) Under the action of EL-Centro wave; (**b**) under the action of Taft wave; (**c**) under the action of TJY wave.  
Compared with JG-2 and JG-3, JG-1 has a large interlayer displacement on the first story, but the interlayer displacement on the second story is significantly reduced, and the interlayer displacement of the upper masonry of JG-1 is smaller than that of JG-2 and JG-3.  
The displacement of JG-1 on the first story is slightly larger under the earthquake action, but the response of the upper masonry structure is relatively light. The displacement of JG-2 on the first story is slightly smaller than that of JG-1 (and can be ignored), but the interlayer displacement of the upper masonry structure is larger than that of JG-1. Due to the large stiffness of JG-3 on the first story, the displacement response on the first story is reduced, but the interlayer displacement of the upper masonry structure is increased. Considering the interlayer displacement of each story, JG-1 has the least damage and the best anti-seismic efficiency.
# The Ethanol Oxidation Reaction Performance of Carbon-Supported PtRuRh Nanorods
###### Abstract  
In this study, carbon-supported Pt-based catalysts, including PtRu, PtRh, and PtRuRh nanorods (NRs), were prepared by the formic acid reduction method for ethanol oxidation reaction (EOR) application. The aspect ratio of all experimental NRs is 4.6. The X-ray photoelectron spectroscopy and H\({}_{2}\)-temperature-programmed reduction results confirm that the ternary PtRuRh has oxygen-containing species (OCS), including PtO\({}_{\rm x}\), RuO\({}_{\rm x}\) and RhO\({}_{\rm x}\), on its surface and shows high EOR current density at 0.6 V. The corresponding physical structure results indicate that the surface OCS can enhance the adsorption of ethanol through bi-functional mechanism and thereby promote the EOR activity. On the other hand, the chronoamperometry (CA) results imply that the ternary PtRuRh has the highest mass activity, specific activity, and stability among all catalysts. The aforementioned pieces of evidence reveal that the presence of OCS facilitates the oxidation of adsorbed intermediates, such as CO or CH\({}_{\rm x}\), which prevents the Pt active sites from poisoning and thus simultaneously improves the current density and durability of PtRuRh NRs in EOR.  
PtRuRh; ethanol oxidation reaction; nanorods; bi-functional mechanism; oxygen containing species +
Footnote †: journal: Applied Sciences