esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
This was the first large scale study where a machine learning classification model was used to differentiate asthmatic coughs from normal-voluntary coughs. While further work is ongoing, we are already able to report a classification accuracy result exceeding 80%. This shows that computer assisted diagnosis of asthma using cough sounds is a promising clinical screening modality. As an objective assessment tool, it can aid physicians in clinical diagnosis, especially when wheeze is absent. It is also valuable in tracking asthma and its progression and response to medication. It is especially useful in younger children where conventional lung function tests such as spirometry are not practical. Another advantage is the potential of reducing the reliance on radiological investigations for diagnosis. Other benefits of automatic classification of cough sounds using machine learning, include its potential use in telemetric applications (e.g., allowing remote screening by parents--especially beneficial where access to a pediatric specialist is limited, enabling contact-free consultation and assessment, and follow-up in infectious respiratory diseases, etc.)  
In the future, this approach could be further extrapolated for preoperative screening and could potentially assess patients with asthmatic coughs, before surgery. Having early knowledge of such respiratory conditions are favorable for surgery scheduling optimization (e.g., preventing last minute case cancellations on the day of surgery).  
Conceptualization, H.H. and S.L.; Methodology, H.H. and S.L.; Patient recruitment, H.H., O.T., K.L., and S.T.; Software and investigation, B.B. and A.K.; Validation, Data curation and formal analysis, B.B., A.K., D.H., H.H., and J.C.; Writing--original and editing, B.B., D.H., H.H., and J.C.; Writing--review, H.H., O.T., K.L., S.T., B.B., A.K., D.H., and J.C.; Supervision, J.C. and H.H.; Project administration, H.H.; Funding acquisition, H.H.  
This work was supported by SMART Innovation Center, Ignition Grant to KK Women's and Children's Hospital. Grant Number ING000091-ICT (IGN) Acoustic Analytic Apps for Smart Telehealth Screening-Creating a Big Data Study. The contact person for the SMART innovation grant is [PERSON].  
We thank [PERSON] and [PERSON] for assistance in recruitment and administrative work related to the grant. We thank the staff at Children's Emergency, Children Day Surgery Unit, and the Respiratory Clinic and Wards for facilitating the recruitment of patients.  
The authors declare no conflict of interest. The work reported here was supported by SMART Innovation Center, ignition grant to KK women's and Children's Hospital. Grant Number ING000091-ICT (IGN) Acoustic Analytic Apps for Smart Telehealth Screening-Creating a Big Data Study. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results
## 2 Related Works and Contribution  
Recent field trials on VLC-based ITS have demonstrated a lot of advantages of using illumination purposed LEDs for road safety and traffic control [28; 29; 30]. Aside from the proven advantages (such as high levels of integration with existing infrastructure, transmission up to 50 m distance, etc.) in real-world environments, several connected challenges are experienced. These include saturation of the receiver due to strong sunlight, diffusion of traffic lights in bad weather conditions and degradation in performance due to the multiple reflections from the road surface. The use of MISO for performance improvement based on VLC has been presented previously using Vertical-Cavity Surface-Emitting Laser (VCSEL) [31] and LED [32] as the source. As a low-cost solution for the practical implementation of VLC on a large scale, LED is a more preferable choice as presented in [33], but the propagation channel has only been considered for indoor transmission and for very low data rates (in KBps). The multiple LED units-based transmission in [34; 35] provides a comprehensive study for such systems but does not include the effect of interference from neighboring optical sources. This work presents the characterization of VLC due to the outdoor environment such as sunlight interference and background/artificial light as noise effects. For the first time, a complete model with link budget analysis is presented for VLC-based ITS. The main contributions of this work are as follows:  
* A 4 \(\times\) 1 MISO-based VLC system for ITS is considered in this work, to enhance the reliability of the LED-based transmission system.
* The effect of link range and interference signal is analyzed in terms of bit error rate (BER) to quantify the performance improvement due to the proposed optimal receiver.
* Another variant of MISO is used with encoding performed at the transmitter side to compare the performance of the system in the presence of channel effects (multipath reflection and optical interference signal) using practical set of parameters for the VLC.
* An optimal design of the MISO receiver is proposed for the VLC transmission and analyzed for different transmission distances and received optical powers for practical channel conditions. The results shows the superiority of the proposed approach over conventional MISO.  
The rest of the paper is organized as follows: Section 2 presents the analytical model for the VLC-based MIMO processing and the use of spatial diversity to enhance the reliability of the system. The simulation model based on the mathematical analysis is presented in Section 3 and results are discussed in the following Section 4. The work is concluded in Section 5.
# Recent Advances in the Heterologous Biosynthesis of Natural Products from _Streptomyces_
## 4 Host Engineering Approaches
### tRNA Engineering  
Studies on tRNA have demonstrated the presence of 30-40 different tRNAs in bacteria. The tRNAs carry amino acids to build proteins. A few specific aminoacyl-tRNAs also participate in the biosynthesis of natural products, such as non-ribosomal peptides, tRNA-dependent cyclodipeptides, diketopiperazines, and cyclodipeptides, by using aminoacyl-tRNA to form peptide bonds. [PERSON] et al. reported that the active form of AlbC from Streptomyces noursei required aminoacyl-tRNA as a substrate to catalyze the cyclodopeptide formation [126]. Some BGCs required specific tRNAs to build the diketopierazine precursors for the biosynthesis of natural compounds. For example, the BGC of bicyclomycin from _Streptomyces aporonenensis_ ATCC 21532 was expressed in _E. coli_, suggesting that Leu-tRNALeu and Ile-tRNAIle are precursors to initiate the biosynthesis of bicyclomycin [127].  
The biosynthesis of some compounds occurs in different ways, such as the C4 ([PERSON] pathway) or the C5 pathway. For example, the biosynthesis of tetrapyrroles required 5-aminolevulinic acid as the precursor, which is generated via the condensation of glycine and succinyl-CoA in the C4 pathway in animal mitochondria and alpha-proteobacteria, whereas glutamyl-tRNA is needed in the C5 pathway in all other cells (chloroplasts) [128]. The heterologous expression of the gene encoding aminoacyl-tRNA led to increasing the titer of secondary metabolites in expression hosts. For example, the overexpression of _lemma1_ encoding glutamyl-tRNA in _S. roesoporus_ HCCB10043 and _Streptomyces ailosoporeus_ HCCB 13086 led to a 1.4-fold increase in daptomycin and a 1.64-fold increase in natamycin, respectively [129]. However, specific tRNAs also indirectly affect the morphology or the yield of secondary metabolites depending on the need for a specific tRNA as an adaptor molecule for the biosynthesis of polypeptide chains.  
The _bldA_ gene from _S. coelicolor_ encodes specific tRNA, which recognizes the rare UUA codon in _Streptomyces_. It can control the expression of phenotypic _carB_ gene in _S. coelicolor_[130] and the heterologous expression of _pur_ BGC from _Streptomyces alboniger_ in _S. lividans_[131]. The interaction between bldA-tRNA and DnrO led to increasing the daunorubicin production in _S. peucetius_[8]. Some natural products were produced by the heterologous expression of BGCs involving tRNA synthases genes; for instance, the valanimycin BGC consists of the gene _vlmL_ encoding Ser-tRNASer that produces seryltRNA, which may take part in the biosynthetic pathway of valanimycin [132].
## 7 Different Spatial Node Distributions
### Non-Uniform Random Distribution  
Table 13 exposes the results for a grid of 49 nodes where the coordinator is located in the center of the topology and all nodes generate and send traffic to it. The coordinator node is the destination. Moreover, we randomly localize 60% of nodes near the central part of the topology, including the jammer node, and the remaining nodes (40%) are distributed along the whole area in a random way.  
Regarding the retransmissions, MPH has 16.8% less retransmissions per node than AODV and 12.9% less than DSR. With regard to the CSMA retries, MPH exhibits an average reduction of 13.6% against AODV and 11.4% against DSR. Concerning the tries by the coordinator, MPH is 30.2% less than AODV and 27.5% compared to DSR; this metric shows that the coordinator knows almost all routes to each node for the MPH protocol. Regarding achievable nodes by the coordinator, MPH has 16.7% more achievable  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Non-Uniform Random Distribution**} \\ \hline \multicolumn{1}{c}{**Retransmissions**} & \multicolumn{1}{c}{**CSMA Retries**} & \multicolumn{1}{c}{**Tries**} & \multicolumn{1}{c}{**Achievable Nodes by the Coordinator**} & \multicolumn{1}{c}{**Energy (J)**} \\ \hline
**AODV** & 2.91 & 4.42 & 3.14 & 35 & 4.86 \\
**DSR** & 2.78 & 4.31 & 3.02 & 36 & 4.56 \\
**MPH** & 2.42 & 3.82 & 2.19 & 42 & 4.05 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Scenario 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Topology of Figure 3 Where the Coordinator is Located in One Corner**} \\ \hline \hline  & **Retransmissions** & **CSMA Retries** & **Tries** & **Achievable Nodes by the Coordinator** & **Energy (J)** \\ \hline
**AODV** & 2.72 & 4.03 & 2.23 & 38 & 4.39 \\
**DSR** & 2.64 & 3.94 & 2.19 & 38 & 4.25 \\
**MPH** & 2.21 & 3.57 & 1.91 & 44 & 3.71 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Scenario 1.  
nodes than AODV and 14.3% more than DSR. Finally, with regard to energy, MPH has energy savings of 16.7% compared with AODV and 11.2% against DSR.  
The results of this scenario show that this configuration presents the worst case. This may be due to a high density of nodes being in the area near the coordinator node, and there is also the jammer node. This interfering node causes increased number of collisions and may actually cause more breakage of links because more nodes are affected. Because of the coordinator being the final destination, the packet loss is more affected by the presence of the jammer node.
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 2 Materials and Methods
### The Fuzzy-Logic-Based Offloading Algorithm  
The algorithm above provides the access policy for all users to prevent cells from overloading. However, with the irregular movements of users, some cells could still overload after a long enough period of time. At this moment, to ensure the service quality both at the time of offloading and in the future, FL-OL is proposed to select appropriate users to be offloaded. In the following, the proposed FL-OL is described.  
Consider a set of cells \(S=\{s_{1},s_{2},\cdots,s_{N}\}\). Whenever the resource utilization rate \(\eta_{i}\) of the TN cell \(s_{i}\in S,i\
eq N\) is higher than the threshold \(\Theta_{g}\), it is considered as an overloaded TN cell. Consider a set of users \(U^{i}=\{u^{i}_{1},u^{i}_{2},\cdots,u^{i}_{M_{i}}\}\) which are served by cell \(s_{i}\). \(M_{i}\) is the total number of users served by the cell \(s_{i}\). \(f^{i}_{j}\) is the FEM between cell \(s_{i}\) and user \(u^{i}_{j};j\in[1,M_{i}]\). \(u^{i}_{j}\) is the user with the minimum value of the evaluation metric \(f_{min}=f^{i}_{j}\). Calculate the FEM \(f^{i^{\prime}}_{j}\) between cell \(s_{i^{\prime}}\), \(i^{\prime}\in[1,N]\), \(i^{\prime}\
eq i\) and user \(u^{i}_{j}\). Due to the long distance new \(f_{min}\) and offload until the current cell \(s_{i}\) is no longer overloaded. The whole process is summarized by Algorithm 2 as follows.  
```
0: Resource utilization rate \(\eta_{k},k\in[1,N]\) of cells in \(S=\{s_{1},s_{2},\cdots,s_{N}\}\)
1:while\(\eta_{i}\geq\Theta_{g},i\in[1,N-1]\)do
2: Get users \(U^{i}=\{u_{1}^{i},u_{2}^{i},\cdots,u_{M_{i}}^{i}\}\) severed by \(s_{i}\)
3:for\(u_{j}^{i}\) in \(U^{i},j\in[1,M_{i}]\)do
4: Get \(f_{j}^{i}\) by FEM and save in a set \(F\)
5:endfor
6: Sort \(F\) in ascending order and get the first user \(u_{j}^{i}\) with \(f_{min}=f_{j}^{i}\)
7:for\(i^{\prime}\in[1,N-1],i^{\prime}\
eq i\)do
8: Calculate \(\eta_{i^{\prime}}\) assuming \(u_{j}^{i}\) is offloaded to \(s_{i^{\prime}}\)
9:if\(f_{j}^{i^{\prime}}>f_{min}\) and \(\eta_{i^{\prime}}<\Theta_{g}\)then
10: Save \(f_{j}^{i^{\prime}}\) in a set \(F^{\prime}\)
11:endif
12:endfor
13:if\(F^{\prime}\) is not empty then
14: Sort \(F^{\prime}\) in descending order and get the first cell \(s_{i}\)
15: Offload \(u_{j}^{i}\) to \(s_{i}\)
16:else if\(\eta_{N}<\Theta_{g}\)then
17: Offload \(u_{j}^{i}\) to NTN cell
18:else
19: break;
20:endif
21: Update \(\eta_{k},k\in[1,N]\)
22:endwhile
```  
**Algorithm 2** The fuzzy logic offloading algorithm.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 2 Satellite Signal Acquisition
### Signal Propagation Time  
Let \(T_{r}^{i}\) be the system time that the start of the \(i\)th C/A code reaches the receiver. This is given by  
\[T_{r}^{i}=t^{i}+t_{u},\]  
where \(t^{i}\equiv t_{0}+\delta_{\phi}^{i}\tau\) is the measured time for the start of the sequence at the receiver, and \(t_{u}\) is the receiver clock offset. The system time at the receiver for the start of the C/A code from satellite \(i\) is given in terms of \(t_{0}\) and the code-phase as  
\[T_{r}^{i}=t_{0}+\delta_{\phi}^{i}\tau+t_{u}. \tag{3}\]  
At system time \(T_{r}^{i}\), the signal must have originated from the satellite at an earlier time \(T_{s}^{i}=T_{r}^{i}-\frac{\Delta_{r}^{i}}{c}\) due to the propagation velocity \(c\) of the signal over the distance, \(\Delta_{r}^{i}\), between receiver and satellite \(i\). We know that \(T_{s}^{i}\) is a time when an epoch starts, so it is an integer multiple of \(\tau\), i.e., \(M^{i}\tau\), so, in this case, we write  
\begin{table}
\begin{tabular}{c c c c c} \hline \(i\) & **SV** & \(X_{max}/\sigma_{X}\) & **Doppler-Shift \(\Delta f^{i}\)** & **Codephase \(\delta_{\phi}^{i}\)** \\ \hline
1 & 16 & 30.01 & \(+2185.15461\) & 0.503051794 \\
2 & 23 & 21.08 & \(+1632.13669\) & 0.576269412 \\
3 & 32 & 16.84 & \(-2624.85478\) & 0.209007485 \\
4 & 13 & 14.03 & \(+2335.25122\) & 0.197458456 \\
5 & 20 & 13.56 & \(-818.94480\) & 0.344973267 \\
6 & 4 & 10.70 & \(-155.43040\) & 0.439863498 \\ \hline \end{tabular}
\end{table}
Table 1: Typical acquisition results for a 4 ms sample of signal (shown in Figure 1). There are six satellite signals that exceed the threshold set for signal detection in this data.  
\[M^{i}\tau = T_{r}^{i}-\frac{\Delta_{r}^{i}}{c}, \tag{4}\] \[= t_{0}+\delta_{\phi}^{i}\tau+t_{u}-\frac{\Delta_{r}^{i}}{c},\] \[\delta_{\phi}^{i}\tau = M^{i}\tau+\frac{\Delta_{r}^{i}}{c}-(t_{0}+t_{u}).\]  
The codephase \(\delta_{\phi}^{i}\) changes rapidly with time due to satellite motion, i.e., \(\frac{d\delta_{\phi}^{i}}{dt}=\frac{\Delta_{r}^{i}}{c\tau}\sim 67\) cycles per second. In addition, uncertainty in the system time offset, of more than a fraction of a millisecond will make prediction of position from the codephase unfeasible. For example, an uncertainty in \(t_{u}\) of 100 \(\upmu\)s would lead to an uncertainty in \(\delta_{\phi}^{i}\) of 0.1, which would translate to a pseudorange error of \(\sim\)30 km.
## 2 Numerical Model
### Model Setup
#### 2.2.2 Formulation of the Heat Loss  
As outlined earlier, a temperature difference between the outer reactive-bed boundary and the vapor vessel is considered to cause the heat losses in the experiments of Reference [32]. It is assumed that the heat is transported between the two parallel plates (i.e., the filter plate and the vapor vessel casing, see Figure 1, left) with water vapor between them. The height of the reactive bed (2 cm) is much smaller than the length (1.6 m), so that we neglect heat losses on its vertical boundaries. According to Reference [36], several heat-transport mechanisms overlap and need to be considered: free convection, radiation, and heat conduction. The different heat-transport mechanisms share the same driving force, namely the difference in the temperature of the bounding walls of the reactive bed and the vapor vessel. For radiation, however, the driving force is proportional to the difference in temperatures raised to the power of four. Representing all mechanisms independently would produce several new parameters, all of which bear uncertainty. Additionally, the exact dimensions of the vapor vessel are not indicated in Reference [32]. We aim at determining the overall heat loss in a spatially resolved manner, regardless of which mechanism dominates. Thus, for simplicity, we choose the following form for the overall heat-loss flux:  
\[q_{e,loss}=(T(y=y_{max})-T_{BC})\alpha_{loss}, \tag{8}\]  
with the heat-loss coefficient \(\alpha_{loss}\), the temperature in the reactive bulk at the top boundary (at location \(y=y_{max}\)), and the temperature of the pressure-vessel casing \(T_{BC}\). As the temperatures measured at the locations T1, T3 and T7 all are different, we assign three degrees of freedom to the temperature distribution:  
\[T_{BC}=k_{1}x^{2}+k_{2}x+k_{3}. \tag{9}\]  
The constants \(k_{1}\) to \(k_{3}\) and the heat-loss coefficient \(\alpha_{loss}\) are calibration parameters in the inverse modeling. The loss term is assigned to the degrees of freedom at the upper boundary of the porous bulk simulation domain.
## 3 Results  
The emission signal \(S_{\rm E}\) was detected at the cryostat cold finger temperature \(T_{\rm c}\) ranging from 10 K to 150 K at applied pulse voltages of 60 V and 90 V. Such voltages are heating up the sample, therefore its temperature \(T_{\rm s}\) differs from \(T_{\rm c}\). To estimate \(T_{\rm s}\) at the bias, sample electrical resistance versus \(T_{\rm c}\) dependencies were measured (Figure 1B). The sample resistance dependence on temperature \(R(T_{\rm c})\) was measured at \(U_{\rm P}=5\) V. As the sample heating during this measurement was found to be small, the assumption that \(T_{\rm s}=T_{\rm c}\) was made. Later, this curve was used as a reference in order to estimate the sample temperatures at different cryostat temperatures when \(U_{\rm P}=60\) V and 90 V. It was found indeed that at the bias of 60 V and 90 V the sample and cryostat cold finger temperatures differ significantly.  
The emission intensity measured as a function of sample temperature \(T_{\rm s}\) is depicted in Figure 2A, whereas the current \(I_{\rm P}(T_{\rm s})\) flowing through the sample is shown in Figure 2B. The emission signal is higher at low temperatures and decreases gradually until full signal suppression at \(T_{\rm s}=155\) K (at applied electrical power of 0.72 W and 0.33 W for \(U_{\rm P}=60\) V and 90 V, respectively). However, such a local minima is not observed in \(I_{\rm P}(T_{\rm s})\) dependencies.  
To gain a deeper understanding of the emission signal behavior described above, the spectra were measured at cryostat temperatures \(T_{\rm c}\) of 22 K, 52 K, 121 K, and 131 K, taking a few measuring points below and two above \(T_{\rm s}=155\) K. Figure 3 shows the emission spectra with the influence of the TPX window transmission and spectrometer's beamsplitter efficiency removed. The spectra obtained below 155 K demonstrate the broad asymmetric peak with the weak spectral features in wavenumber range between 150 and 384 cm\({}^{-1}\). The upper range limit corresponds to the transverse optical sapphire phonon energy [13]. At higher wavenumbers, the sharp emission peak at the energy of longitudinal sapphire optical phonon energy \(\omega_{\rm LO}=387\) cm\({}^{-1}\) appears. Also, an intensive peak at 394 cm\({}^{-1}\) is present, which could account for emission caused by radiative electron transitions in nitrogen vacancies in GaN [14].
## 4 Experimental Results and Analysis
### Syndrome Generation Analysis  
Syndrome generation is the critical area where the proposed hybrid method provides an advantage over the GPU methods [8; 9]. Figure 7 shows the plot of the syndrome computation time vs. different bit errors \((t=2,...,40)\) across different finite fields \((m=12,13,14,15)\) for different architectures: GPU [9], hardware [21], and hybrid (proposed). The computation of the SRU engine depended on the number of clock cycles required for a page read. Since all the \(res_{i}(x)\) that were necessary for the key-equation solver were calculated in tandem, the latency only depended on the read cycles for flash memory. The hardware architecture for syndrome generation consumed the same clock cycle as the hybrid approach since the approach to syndrome generation was similar. It should be noted that the GPU unit was used as a display unit, so the results of kernel profiling depended on the load of the GPU during the execution of the kernel routine. The execution time for the syndrome on the GPU architecture depended on the number of threads getting executed, and typically, it was from 30-100 us.
## 7 Risk Analysis
### Emergency Response Service
#### 7.3.2 Denial of Service  
1. **Component**  
_Security risk_: A DoS performed on the ERS could lead to a situation where the service becomes unavailable, potentially preventing critical crash notifications. This could be due to overwhelming traffic, malicious attacks, or technical failures.  
_Security measure_: It is possible to prevent DoS attacks through multiple mechanisms that can be applied directly to the server. By enforcing rate limits on incoming requests, it is possible to prevent excessive traffic from a single source. By applying anomaly detection mechanisms, the system can identify unusual traffic patterns that could indicate an attack. web application firewall (WAF): Employ a WAF to filter and block malicious traffic before it reaches the web service. content delivery network (CDN): Use a CDN to distribute traffic and absorb distributed denial of service (DDoS) attacks, improving availability. Redundancy and failover: Set up redundant servers in different geographical regions and implement failover mechanisms to maintain service in case of a failure.
2. **Ports**  
_Security risk_: DoS attacks on ports are high risk because they disrupt critical network services, causing downtime, data loss, and financial harm. Limited resources, collateral damage, reputation loss, and potential legal consequences make these attacks highly damaging.  
_Security measures_: Network-level protection: Firewalls--deploy firewalls to filter incoming traffic and block suspicious or excessive requests to the ports. Intrusion detection/ prevention systems (IDS/IPS)--use IDS/IPS systems to detect and prevent unauthorized access or abnormal traffic patterns. access control list (ACL)--configure ACLs on network devices to allow only legitimate traffic to reach the ports. Encryption andauthentication: Encryption--use encryption (e.g., hypertext transfer protocol secure (HTTPS)) to secure communication between clients and the web service, preventing unauthorized access and tampering. Authentication--install strong authentication mechanisms to ensure that only authorized users can access the service.