esa-sceva/satcom-chunk-collection huggingface dataset:
# Biosurfactants' Potential Role in Combating COVID-19 and Similar Future Microbial Threats
## 2 Approaches That Can Use Biosurfactants in Outbreak Prevention and Management
### Therapeutics
#### 2.1.5 Probiotics  
Probiotics are living bacteria that have health benefits to the human gut microbiota, either by improving or restoring it. Besides improving the gut microbiota, they can contribute to improving the immune system. Probiotic lactic acid bacteria have been reported to produce various biosurfactants with important applications in biotherapeutics due to their important antimicrobial and anti-adhesive activity. They can significantly improve the defense of the gastrointestinal microbiota in outbreaks that cause gastrointestinal diseases. The main producers of probiotics include _Lactobacillus_ and _Bifidobacterium_. Other lactic acid bacteria include _Enterococcus_, _Leuconostoc_, _Pediococcus_, _Sporolactobacillus_, _Streptococcus_, _Lactococcus_, and non-lactic acid bacteria include _Propionibacterium_, _Bacillus cereus_, _Saccharomyces_, _Bifidobacterium lactis_, and _Escherichia coli_ strain _nissle_[46].  
Probiotic bacteria producing biosurfactants have shown anti-microbial activity against Gram-negative and Gram-positive bacteria, including yeast and other possible human pathogens [46]. These include _Lactobacillus paracasi_[47], glycolipid from _Lactococcus lactis_with activity against multidrug-resistant (MDR) pathogens [48], _Lactobacillus helevicus_[49], lipopeptide from _Bacillus cereus_ NK1 [50], _L. paracasei_ ssp _paracasei_ A20 [51], _Lactobacillus casei_ MRTL3 [52] and _Lactobacillus acidophilus_[53; 54].  
There is some evidence that probiotics may be able to decrease the risk and duration of viral respiratory infections. This may, therefore, be helpful for the management of COVID-19 infections. Although the actual mechanism of action is undefined, clear benefits were demonstrated in some initial trials. It was hypothesized that this may be through direct interaction with the virus or stimulation of the immune system. Research to investigate specific viral effects, especially on RNA viruses and other respiratory viruses, is recommended because this could provide a safe method to fight related infections in emergencies [55].
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 5 Results Analysis
However, the GRU model is effective only in capturing the short-term characteristics of traffic flow and fails to capture the long-term features. Therefore, we introduced the BILSTM model to store additional information by forward and reverse information propagation through the bidirectional LSTM model in order to achieve the effect of long-term temporal feature sequences and improve the stability of the model. Compared to the GRU model, the BILSTM model reduced the performance indices of MAE, MAPE, and RMSE by 15%, 29.5%, and 10.6%, respectively. Both the GRU and BILSTM models provided predictions based on the time series characteristics of traffic flow, but they could not reflect the spatial characteristics of traffic flow. Therefore, in this paper, we propose the combined CNN-BILSTM model. As evaluated from the following Table 1, the CNN-BILSTM model was further superior in performance indicators, which were 30.4%, 32.2%, and 39.6% greater than the BILSTM model, respectively. Figure 10: 30-minute forecast result graph. Figure 9: 15-minute forecast result graph.
# An Overview of GEO Satellite Communication Simulation Systems
## 4 Comparative Evaluation of Simulation Tools  
The development and evaluation of GEO satellite communication systems heavily depend on efficient and interoperable simulation tools. These tools must support modeling across several functional modules introduced in Section 1, including orbital and constellation modeling, physical-layer modeling, channel modeling, coverage analysis, and resource management and scheduling. In this section, we systematically compare widely adopted commercial and open-source simulation platforms across these identified modules using a structured, multi-dimensional approach. Table 2 presents a comprehensive comparison of these tools based on their respective capabilities in each functional module.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Tool**} & \multicolumn{2}{c}{**Orbit and**} & \multirow{2}{*}{**Physical-Layer Modeling**} & \multirow{2}{*}{**Channel Modeling**} & \multirow{2}{*}{**Coverage Analysis**} & \multirow{2}{*}{**Analys**} & \multirow{2}{*}{**Resource Management and Scheduling**} & \multirow{2}{*}{**Co-Simulation Interfaces**} \\ \cline{1-1} \cline{5-6}  & \multicolumn{1}{c}{**Constellation Modeling**} & & & & & & \\ \cline{1-1} \cline{5-6}  & \multicolumn{1}{c}{} & & & & & & \\ \hline STK & High-fidelity & Limited & Abstracted & Comprehensive & Manual via scripting & COM API, file exchange \\ \hline MATLAB & Indirect via STK & Extensive & \multirow{2}{*}{Customizable} & \multirow{2}{*}{Basic} & \multirow{2}{*}{Script-based} & STK Connect, FMU, Python API \\ \cline{1-1} \cline{5-6}  & & & & & & & \\ \hline GMAT & High-precision & Not supported & Not supported & Not supported & Not supported & Native scripting, file-based \\ \hline GNU Radio & Not supported & SDR Prototyping & Signal-level & Not supported & Not supported & TCP/UDP, ZeroMQ, Python API \\ \hline NS-3 & Not supported & Simplified & Network-level & Limited & Protocol-driven & Sockets, ns3-gym \\ \hline OMNeT++ & Not supported & Modular support & Detailed & Limited & Comprehensive & TCP/IP, file-based trace coupling \\ \hline OPNET & Not supported & Abstracted & Abstracted & Scenario-driven &
\begin{tabular}{c} Extensive \\ performance \\ analysis \\ \end{tabular} & File/API integration, MATLAB interface \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-dimensional comparative analysis of simulation tools.
## 3 An Implementation of the Proposed Architecture
### Combo Operation Layer and Smart Classroom Application Layer  
In the proposed model, the Raspberry Pi boards are located in the smart classrooms' private network, and a central web server is necessary for implementing combo operations and controlling all smart classrooms. The way for the central web server to access the layer 2 basic operations on Raspberry Pi boards is the port redirection settings, as shown in Table 13, on the NAT gateways of smart classrooms.  
Table 14 is a sample for showing how layer 2 operations are registered on the central web server. If the devices in a smart classroom do not need to be controlled outside the classroom, then the central web server is not needed because layer 2 operations can be sent from a control device located physically and logically in the smart classroom. However, the proposed smart classroom architecture is not only for building one smart classroom. Therefore, a central web server for registering layer 2 basic operations, defining layer 3 combo operations, and implementing other smart classroom control logics are necessary for the proposed architecture. The way of registering and recording layer 2 operations on the central web server can be implemented in different methods such as automatic register by the Raspberry Pi boards or manually predefined.  
Table 15 shows the way of implementing layer 3 combo operations on the central web server. Once a combo operation is defined, it can be called by any other application by sending HTTP requests. Multiple layer 2 basic operations will be triggered once a layer 3 combo operation is called. Therefore, complex smart classroom control logics, such as context-aware applications, can also be implemented, on or off the central web server, without any knowledge of direct device control.  
In Figure 10, the complete operation flow triggered by a Mobile App is demonstrated while a button for turning on all devices in H204 is pressed. In procedure A, the Mobile App sends a web application interface call to the central web server using classroom ID and layer 3 operation ID as parameters. When the central web server takes the call, corresponding layer 2 operations are founding in procedure B and C. Then the central web server sends multiple layer 2 operations, which are HTTP requests, to the NAT gateway of H204. In procedure D, when the NAT gateway of H204 gets the HTTP requests, addresses and ports are translated according to the port redirection settings on the NAT gateway. Then the HTTP requests are forward to Raspberry Pi boards, as shown in procedure E. Finally, in procedure F, the layer 2 operations are executed on the Raspberry Pi boards, and the corresponding device control commands are executed. Therefore, all devices are turned on by a simple click in a layer 4 smart classroom application. In Figure 11, the operations described above are shown as a sequence diagram.  
Figure 10: Operation flow in an implementation of the proposed model.
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 2 Materials and Methods
### Proposed Algorithm  
The noise reduction algorithm was operated using the stored learning model according to the environmental noise which had been trained using ten kinds of noise conditions for an effective noise reduction performance. For hearing aids operation, the DNNs were pre-trained and stored in each type of environmental noise.  
The storage elements of the trained model were the training configuration, such as the architecture of the model, the weight of each node in the layers, the cost function, and the optimization method. In particular, since the weight of each node was different based on the type of environmental noise, the weight value varied depending on the noise classification. As a result, the weight set increased in proportion to the type of environmental noise learnt using the DNNs, and it was possible to reduce this according to the noise.  
The process of the training stage and test stage of the DNNs for noise reduction are shown in Figure 1. First, the characteristic feature sets of noisy speech were extracted to generate input data for the DNNs [33]. The number of input data were 123, which consisted of mel-frequencycepstral coefficients (MFCCs), amplitude modulation spectrogram (AMS), relative spectral transformed perceptual linear prediction coefficients (RASTA-PLP), and 64 gamma-tone filterbanks (GFs) [34]. The DNNs had three hidden layers and an output layer, and there were 1024 nodes in the hidden layer.  
In the training stage, the cost function of the output layer was calculated as the mean square error between 64 IBM derived from the GFs and 64 output data applied to the sigmoid activation function. The ideal binary mask (IBM) is defined as [35]:  
\[IBM(t,f)=\sqrt{\frac{S^{2}(t,f)}{N^{2}(t,f)}} \tag{1}\]  
where \(S^{2}(t,f)\) and \(N^{2}(t,f)\) are the power of the speech signal and noisy speech signal, the range of values being from 0 to 1.  
In the test stage, 64 output data (\(t_{1}\), \(t_{2}\),..., \(t_{64}\)), which were the gain values of the power spectrum with the gamma-tone filterbank, were generated from 123 characteristic features of noisy speech from the input data [36]. The gain values were multiplied by the power spectrum that had been applied to the GF of the noisy speech, as follows:  
\[\left|\hat{S}(k)\right|^{2}=\sum_{k=1}^{64}t_{k}\left|\right.Y_{G}(k)\right|^ {2} \tag{2}\]  
The gain values were inversely transformed from the GF to the enhanced speech. Subsequently, the estimated enhanced speech was generated and evaluated using objective measures.  
Figure 1: Proposed speech enhancement algorithm using the deep neural networks (DNNs) based on noise classification.
# A Thermopile-Based Colorimetric Temperature Measurement Method for Arbitrary Bandwidth
## 2 Temperature Measurement Principles
### Principle of Colorimetric Temperature Measurement  
In reality, ideal blackbodies are nearly non-existent [16]. Therefore, it is necessary to introduce an emissivity coefficient \(\varepsilon(\lambda,T)\), which varies with material properties and surface conditions, known as spectral emissivity. The radiance emitted by a real object is given by:  
\[M(\lambda,T)=\varepsilon(\lambda,T)C_{1}\lambda^{-5}e^{-C_{2}/\lambda T}. \tag{4}\]  
Colorimetric temperature measurement determines temperature by comparing the ratio of radiative energies in two narrow-band wavelength ranges of the object being measured. Assuming the selected wavelengths are \(\lambda_{1}\) and \(\lambda_{2}\), and the widths of the two narrow-band ranges are \(\Delta\lambda_{1}\) and \(\Delta\lambda_{2}\) respectively, the radiative energies \(M_{1}(T)\) and \(M_{2}(T)\) in these two bands are given by:  
\[\begin{cases}M_{1}(T)=\int_{\lambda_{1}}^{\lambda_{1}+\Delta\lambda_{1}}M( \lambda,T)d\lambda\\ M_{2}(T)=\int_{\lambda_{2}}^{\lambda_{2}+\Delta\lambda_{2}}M(\lambda,T)d \lambda\end{cases}. \tag{5}\]  
Since the wavelength bands are narrow, the integral regions can be approximated by rectangles. Therefore, Equation (5) can be approximated as:  
\[\begin{cases}M_{1}(T)\approx M(\lambda_{1},T)\Delta\lambda_{1}\\ M_{2}(T)\approx M(\lambda_{2},T)\Delta\lambda_{2}.\end{cases} \tag{6}\]  
The ratio of radiative energies \(Q(T)\) is given by:  
\[Q(T)=\frac{M_{1}(T)}{M_{2}(T)}=\frac{M(\lambda_{1},T)\Delta\lambda_{1}}{M( \lambda_{2},T)\Delta\lambda_{2}}=\frac{\varepsilon_{1}(\lambda_{1},T)C_{1} \lambda_{1}{}^{-5}e^{-C_{2}/\lambda_{1}T}\Delta\lambda_{1}}{\varepsilon_{2}( \lambda_{2},T)C_{1}\lambda_{2}{}^{-5}e^{-C_{2}/\lambda_{2}T}\Delta\lambda_{2}}. \tag{7}\]Taking the logarithm of both sides of Equation (7), we obtain:  
\[T=\frac{C_{2}\Big{(}\frac{1}{\lambda_{2}}-\frac{1}{\lambda_{1}}\Big{)}}{\ln Q(T)- \ln\Big{[}\frac{\varepsilon_{1}(\lambda_{1},T)}{\varepsilon_{2}(\lambda_{2},T) }\Big{]}-5\ln\Big{(}\frac{\lambda_{2}}{\lambda_{1}}\Big{)}-\ln\Big{(}\frac{ \Delta\lambda_{1}}{\Delta\lambda_{2}}\Big{)}}. \tag{8}\]  
When using colorimetric temperature measurement, it is typically assumed that the object being measured is a grey body, which means \(\varepsilon_{1}(\lambda_{1},T)=\varepsilon_{2}(\lambda_{2},T)\). Therefore, the colorimetric temperature \(T\) can be calculated using Equation (9):  
\[T=\frac{C_{2}\Big{(}\frac{1}{\lambda_{2}}-\frac{1}{\lambda_{1}}\Big{)}}{\ln Q( T)-5\ln\Big{(}\frac{\lambda_{2}}{\lambda_{1}}\Big{)}-\ln\Big{(}\frac{ \Delta\lambda_{1}}{\Delta\lambda_{2}}\Big{)}}. \tag{9}\]
## 2 Theoretical Noise Model
Equation (11) shows that \(\delta P_{out}\) is determined by the seed power fluctuations, the gain of the amplifier, the fluctuations induced by the fiber splitter and photodetector, and the negative feedback gain. We focus on the power fluctuations of the out-of-loop and expect it to be as small as possible. For a given seed, the noise suppression scheme involves choosing a low feedback gain, keeping the coupling ratio \(\epsilon\) stable, and controlling the temperatures of the photodetector and amplifier to reduce the thermal noise. This is the guideline for our experimental investigations.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 5 Mror: Mobile Reliable Opportunistic Routing for CRSN
### VCG-Based Data Forwarding Initiative Determination  
The data forwarding in MROR is based on VCG receiver-based contention. After all initialization processes, all nodes switch to the data channel (as determined from the VCG formation phase) in preparation for the forwarding phase. A node that has a data packet encapsulates its location information and its VCG LMR node's tag on the data packet and then broadcasts the packet afterwards. The data initiative forwarding determination (DIFD) operation is then performed by all eligible VCG nodes that received this data packet successfully. The DIFD binary operation is performed as follows:  
\[I_{DIFD}=\begin{cases}1,\begin{cases}d_{sk}\leq d_{sk}^{fd}\\ B>B_{th}\\ \xi_{pkt}\geq\xi_{th}\\ R(t)\geq R_{t}\\ \text{else = 0}\end{cases}\end{cases} \tag{35}\]  
A node's initiative is regarded as successful only if the \(I_{DIFD}\) operation returns a one. Such a situation can only arise if the receiving node's POS, \(d_{sk}\), is less than \(d_{sk}^{fd}\), which is the forwarding node's POS. This condition ensures two important issues: (i) that the packet maintains a progressive movement to the sink; and (ii) to eliminate the possibility of the occurrence of an infinite routing loop. The condition \(B>B_{th}\) is the local congestion control parameter. It prevents packet drops (_i.e._, buffer overflow) by ensuring that relay node's buffer occupancy level, \(B\), does not exceed the acceptable threshold, \(B_{th}\). This metric also helps ensure that network load is fairly distributed among potential eligible relay nodes. An unbalanced network load distribution can occur when hot spots are created. This is when a node becomes a favorite relay node, due to its position in the network. Thus, the generated traffic will favorably be channeled to this node, which usually results in a buffer overflow in relay nodes. This, consequently, results in adverse end-to-end network performance. Thus, with this condition, \(B>B_{th}\) ensures the network load distribution by giving an opportunity to equally eligible nodes to relay packet. The third condition is the link quality indicator, \(\xi_{pkt}\geq\xi_{th}\); the third condition is used to always ensure the availability of a reliable link. It ensures that any relay node must have received the packet at a signal-to-noise ratio of \(\xi_{pkt}\), which is above the set threshold, \(\xi_{th}\). Otherwise, \(I_{DIFD}\) returns a null. If a positive result is returned after completing the \(I_{DIFD}\) operation, the successful node will delay sending its generated acknowledgment (ACK) packet according to a scheme referred to as the receiver contention prioritization scheme.
# Data Gathering Techniques in WSN: A Cross-Layer View
## 5 Routing for Data Gathering in WSN  
We keep climbing the layers, and in this section we address issues related to the Network layer. We start with a short review of WSN routing protocols. We note that routing-related aspects were also referred to in other sections of this survey. We mainly focus on the prominent and more recent protocols. We do not provide a comprehensive review of routing protocols in multihop WSNs and mainly explore routing protocols suitable for data gathering. Figure 4 provides a schematic distribution of the discussed papers into main topics. As with the schematic partition in the other sections, in order not to have too many, the topics are chosen such that the theme on each one encompasses several papers. The papers' partition is rough: some papers can appear in more than one topic while others are only related to the topic.  
We start with general WSN settings and then continue with routing protocols under energy harvesting constraints. Then we explore the utilization of network coding for data gathering, which leverages the multi-hop routing, allowing relays (intermediate nodes) to code the incoming packets before forwarding them toward the sink. We conclude this section by examining a different paradigm. Rather than utilizing the traditional approach of forwarding the sensed data via multiple relay nodes before reaching the sink, this paradigm relies on a Mobile Sink (MS) that traverses the network and collects the sensed data from the sensors it passes through.
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 3 Analysis of the Positioning Solution and Observation Data Quality
### Signal Strength  
Signal strength in the context of GNSS is usually expressed as carrier-to-noise-density ratio, C/N\({}_{0}\). This parameter is one of the observations reported by the receiver. It is a key parameter for evaluating the quality of the signal and thence affecting the positioning. Figure 5 shows the GPS and GLONASS C/N\({}_{0}\) values of Round 5, where the color bar at the bottom shows the solution statuses as introduced earlier in this section (cf. Figure 2). Before way-point C, as can be seen from the color bar, the solution proceeds to float type. At this moment, we can see a sudden drop in very high values that results in changing the solution type. Additionally, after way-point D, the solution proceeds intermittently to code, float and navigated types. It can be seen that at this time, there is a sudden drop in the signal strength for some of the satellites. The minimum C/N\({}_{0}\) observation (GSIC) is 17.4 dB-Hz. Looking at the carrier phase observations, we can detect a threshold of 22 dB-Hz for tracking GL1C.  
Figure 4: Histogram of available dual-frequency (L1 and L2) phase GPS (G) and GLONASS (R) signals for four types of position solutions: (**a**) fixed, (**b**) float, (**c**) code and (**d**) navigated in all twelve rounds.
# A Survey of Deep Learning for Data Caching in Edge Network
## 3 Deep Learning Outline
### Auto Encoder  
Auto Encoder is a stack of two neural networks (NNs), named encoder and decoder, respectively, where the former tries to learn the representative characteristics of input and generate a related code, and the later reads the code and reconstructs the original input. In order to avoid the auto encoder simply copying the input, some restrictions are considered, like the dimension of code is smaller than input vector [43]. The quality of auto encoder can be measured via reconstruction error, which estimates the similarity between input and output. In most cases, the auto encoder is used for the proper representation of the input vector so the decoder part is removed after unsupervised training. The code can be employed as input for further deep learning models. Additionally, the code after dimensional reduction is easier for data visualizing analysis compared with the original high dimensional input data. Because it is unsupervised learning, the auto encoder is expensive to pretrain with big data.
## 3 Capacitive Sensing with Electronic Detection
### Capacitive Sensor Design and Fabrication
#### 3.2.3 Surface de-Functionalization by Laser Erosion  
Since both comb capacitors of one sensor are covered with a monolayer of sensing molecules during the functionalization process with silanes, one of the sensors has to be de-functionalized in  
Figure 11: The result of XPS analysis of the APS modified SiO\({}_{2}\) surface: (**a**) XPS of -NH\({}_{2}\) group of a freshly prepared sample; (**b**) XPS of a -NH\({}_{2}\) group of 1 month old sample; and (**c**) XPS of a -NH\({}_{2}\) group of three months old sample.  
Figure 10: Chemical modification of SiO\({}_{2}\) surface with: (**a**) 3-trimethoxysilyl-propan-1-amine (APS); (**b**) 3-triethoxysilylpropylurea (UPS); and (**c**) trimethoxyphenylsilane (APhS).  
order to obtain a differential response. The removal of surface-adsorbed silane molecules has to be localized and well controlled, as the surface of the active functionalized area is only 250 by 350 \(\upmu\)m, and the separation between two capacitors that form a differential pair is only 30 \(\upmu\)m. We developed a laser-assisted process of removing the silane monolayer based on controlled surface illumination with focused Ar\({}^{+}\) laser light with a wavelength of 514.5 nm. The surface of the sensor was positioned in the focal plane of a 60\(\times\) objective, focusing the 750 mW laser light to a diffraction-limited spot of \(\sim\)1 \(\upmu\)m diameter. During a 10 s illumination, the local temperature of the sensor surface increased to approximately 80-100 \(\,\)C. The sensor was moved in a regular manner, so that the illumination spot gradually scanned the whole silane functionalized surface of the selected comb capacitor. The effects of laser-assisted erosion were clearly observed on the test surfaces by the XPS. The results indicate that a five second illumination with a fully focused power of 750 mW is sufficient to decompose and remove different silane modification monolayers on the SiO\({}_{2}\) surface of silicon.