esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results & Discussion
### TGA--Thermograimetric Analysis
In all the developed bio-nano-composites a decrease in T\({}_{\text{onset}}\) and T\({}_{\text{max}}\) can be appreciated except for PHB226-T1. [PERSON] et al. reported that P3 HB-\(\infty\)-P4 HB samples containing a 10 wt % silica presented a higher T\({}_{\text{onset}}\) and T\({}_{\text{max}}\) than that of pure P3 HB-\(\infty\)-P4 HB, in particular being 2.08 and 6.28 \({}^{\circ}\)C higher, respectively [48]. Our samples containing a 3 wt % of nanoclay content seems not to induce a high thermal stability increase. Nevertheless, results show that sepiolites (T1 and T2) seem more suitable for these polyhydroxyalkanoates materials than montmorillonite for thermal stability purposes, as sepiolites maintain the thermal stability of the original matrix. For P3 HB-\(\infty\)-P4 HB composites, it can be observed that T1 presents the same T\({}_{\text{onset}}\), T\({}_{\text{max}}\) and T\({}_{50}\) while a slight decrease is found for T2. For T3, the overall decrease for T\({}_{\text{onset}}\), T\({}_{\text{max}}\) and T\({}_{50}\) is noticeable. A similar tendency is found for P3 HB composites. T1 is the nanoclay inducing a higher thermal stability for both degradation  
Figure 2: TGA curves: (**a**) neat matrices and nanoclays; (**b**) PHA1005 composites; (**c**) PHA3002 composites; (**d**) PHB226 composites. steps (including PBA), while T2 tends to maintain the original matrix behaviour and T3 induces a remarkable decrease. Results are detailed in Table 3.
# 2D Winograd CNN Chip for COVID-19 and Pneumonia Detection
## 1 Introduction
### Research Goal  
Generally speaking, chest X-rays are often used for diagnosis to observe the impact of the virus on the lungs, and the recognition of chest X-rays can be regarded as a grayscale picture in computer vision [11]. Algorithms play an important role in machine learning/deep learning systems and are to be used to train AI diagnostic and recognition capabilities and to ensure that training results can be achieved within a limited time for medical image analysis [8; 9; 12]. Because this medical image is a grayscale image, according to the analysis of the model, the Convolutional Neural Networks (CNNs) model has a greater advantage than other models in image analysis [6]. This research will enhance its computational power in the convolutional layers of CNNs models, showing the advantages of their algorithms on physical circuits. Although the CNNs model has relatively high advantages over other models in image analysis, this study will also analyze a variety of different models to show the model with the most advantages and reliability.  
According to much literature, although there are many kinds of accelerated convolutional, the most simple and effective implementation method is still Winograd [13; 14; 15; 16; 17]. Therefore, two-dimensional Winograd convolution algorithms using the FPGA platform are proposed [18; 19; 20]. This research will look forward to applying Winograd's algorithm to one layer of convolutional neural network chips and conducting chip-related experiments on it. According to the previous literature [21], this research will analyze different viewpoints on the overall content and redesign different processes, apply the Winograd algorithm to a layer of convolutional neural network chips, and conduct more chip-related experiments on it. In this study, it is expected that the advantages and disadvantages of Winograd will be clearly understood in practical circuits. The technical specification testing standard method of the low-power 0.18 \(\upmu\)m TSMC process used in this experiment will also be used to verify the reliability of the circuit [22; 23; 24; 25].
## 2 Reaction Mechanisms and Kinetics of Electrocatalytic Nitrate Reduction
### Advances in Electrocatalyst Preparation Techniques for Efficient Nitrate Reduction
#### 3.1.6 Impregnation Method  
The preparation of the electrocatalyst materials using the impregnation method requires immersing the noble metal in a solution, followed by drying and calcination to complete the process [95]. The impregnation method is commonly used to optimize the amount of noble metal to be added in order to minimize the loss of noble metals, as demonstrated in the study by [PERSON] et al. [96] who synthesized Pd-Cu/\(\gamma\)Al\({}_{2}\)O\({}_{3}\) catalysts with different metallic palladium loading rates. As reported in their study, a palladium loading rate of 5% demonstrated the best performance among the various tested rates and was, therefore, selected for subsequent experiments [96].
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 6 Conclusions  
This paper presented a deployment architecture of the NDN network over Edge Computing infrastructure to provide support for the disaster response phase. We showed a proof-of-concept system by implementing the architecture using the KubeEdge edge computing platform. Our system assists the disaster response phase by enabling emergency group communication and disaster information exchange through NDN device management. Moreover, the experimental and analytical results showed that our proposed architecture deals well with disaster challenges. It achieves faster network convergence time in case of node replacement over IP-based network, faster mobility handover time compared with MobileIP, and rendezvous NDN mobility method. Moreover, KubeEdge features lower information exchange transmission overhead and enables packet recovery capability in intermittent network conditions.  
For future works, our system can be improved by bringing enhanced NDN features that have been proven by simulation in previous studies into real implementation. Moreover, an edge computing platform can also be utilized to provide support for NDN IoT devices since these devices can greatly help to collect a huge amount of information in disaster areas.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compared Aspects** & **Our System Features** & **Compared Features** & **Results** \\ \hline Network convergence time & & & IP’s EIGRP routing & \\ after replacing & NDN’s NLSR routing protocol & protocol [34] & Faster \\ damaged nodes & & & \\ \hline \multirow{3}{*}{Mobility handover time} & Our NDN mobility & Mobile-IP [14] & \\  & support design & NDN rendezvous mobility & Faster than both \\  & & mechanism [15] & \\ \hline \multirow{3}{*}{Information transmission overhead} & NDN devices’ information & NDN Publish/Subscribe [38] & Equal to NDN-DM, lower \\  & management over Edge & NDN-DM’s push & than NDN Pub/Sub (4 times) \\  & platform (KubeEdge) & mechanism [39] & \\ \hline \multirow{3}{*}{Information recovery capability} & NDN devices’ information & NDN Publish/Subscribe [38] & Ensure recovery while NDN \\  & management over Edge & NDN-DM’s push & Pub/Sub and NDN-DM \\  & platform (KubeEdge) & mechanism [39] & can not \\ \hline \hline \end{tabular}
\end{table}
Table 3: Our system aggregated results.  
evaluation, as well as coordinating the work. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Government of Korea (MSIT) (No.2020-0-00946, Development of Fast and Automatic Service recovery and Transition software in Hybrid Cloud Environment) and under the ITRC (Information Technology Research Center) support program (IITP-2020-2017-0-01633) supervised by IITP.  
The authors declare no conflict of interest.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 4 Testing and Analysis  
### Receiver Signal Source: Long-Wave Signal from Analogue Source  
1.1. Indicators of the Signal Reception Strength and Signal-to-Noise Ratio (SNR) for Each Device  
Analogue sources were used to generate signal strengths ranging from 30 to 110 dB\(\mu\)V/m (in 10 dB\(\mu\)V/m intervals) for the 8390 station's chain positioning and 8390M station's timing signals. These signals are output through an adapter to various receiving equipment. Upon achieving stable signal reception, the measured signal strength and signal-to-noise ratio (SNR) parameters for each station were recorded, as shown in Figure 3.  
As shown in Figures 3-5, a repeat test of the simulated signal reception was conducted using Receivers 1 and 2. Figure 3 shows the results of the field strength SNR parameters of Receiver 1 with different analogue source signal strengths, Figure 4 shows the results of the field strength SNR parameters of Receiver 2 with different analogue source signal strengths, and Figure 5 shows a comparison of the field strength and the SNR of Receiver 1 and Receiver 2 with the same analogue source signal strengths. The test results indicate that both receivers exhibited good tracking and reproducibility for different signal strengths in the simulation. Notably, the output signal strength of Receiver 2 was approximately 15 dB higher than that of Receiver 1. This is primarily because the test uses an emulator that bypasses the antenna and antenna coupler. Consequently, the signal goes directly into the receiver, with a larger amplification factor for Receiver 2 than for Receiver 1. However, owing to the non-standardised output signal strength of the simulator, it is currently not possible to directly quantify the reception capability of the equipment.  
Figure 3: Receiver 1’s field strength and signal–to–noise ratio parameters.  
Figure 2: eLoran time service performance evaluation chart.
# Influence of Cracking on the Durability of Reinforced Concrete with Carbon Nanotubes
## 2 Materials and Methods
### Testing Procedures
#### 2.5.2 Carbonation Resistance  
The accelerated carbonation test was carried out according to LNEC E391 [36]. After curing, the artificial cracked specimens (\(\mathcal{O}\)100 \(\times\) 50 mm) were painted in all surfaces except the cracked one, to ensure the unidirectional carbonation flow through the specimen. Then the specimens were placed in a carbonation chamber with 23 \(\pm\) 3 \({}^{\circ}\)C, 60 \(\pm\) 5% relative humidity and 5 \(\pm\) 0.1% CO\({}_{2}\), for 28, 56 and 90 days. The natural cracked specimens were sealed with tape around the lateral surface and at the bottom. The cracked surface was left  
Figure 2: (a) Capillary absorption test. (b) Measure of carbonation depth in different sections, perpendicular to the crack.  
unprotected. Afterwards, the specimens were moved to the same carbonation chamber and tested at 7 and 28 days. The specimens were first sectioned perpendicularly to the crack, then air dried for 24 h and placed back in the carbonation chamber. This procedure was carried out at each testing age, as exemplified in Figure 2b. The carbonation depth was measured after spraying a 0.2% phenolphthalein solution on the freshly broken surfaces. In cracked specimens, two different measures were taken, one in the cracked region and the other in the uncracked zone. One specimen per age was tested for each combination of composition and crack width. The carbonation coefficient, \(K_{c}\), was obtained from the linear regression between the carbonation depth, \(x_{c}\), and the square root of time, \(t^{1/2}\), by applying the [PERSON]'s first law of diffusion through Equation (1).  
\[x_{c}=K_{c}t^{1/2}\ \left[mm/year^{1/2}\right] \tag{1}\]
# Behaviour of Square and Rectangular Tunnels Using an Improved Finite Element Method
## 2 Methods Calculation
### FEM Calculation  
In this study, ground parameters of the 3 rd line Nhon--Kim Ma belonging to the Hanoi metro system are used as a reference case [23; 24; 25; 26]. The size of the square tunnel cross-section is 5.5 m. The rectangular tunnel is 6.0 m in width and 5.0 m in height. The tunnel is located 20 m below the ground surface. The tunnel is assumed to be supported by a continuous lining. The other parameters of the tunnel lining and the surrounding ground are shown in Table 1.  
Square and rectangular tunnels are simulated using the plain strain FEM model. While [PERSON]'s constitutive model is adopted for the ground, the behaviour of the tunnel lining is assumed to be linear elastic. The ground surrounding the tunnel is simulated using a 15 nodes element. Plate elements are used to model the tunnel lining. An interface is also modelled to simulate the interaction between the tunnel lining and the surrounding ground [27; 28; 29; 30].  
The dimensions of the model built in FEM are 100 m in wide and 40 m in height. The model includes 33,185 nodes and 4092 zones (see Figure 4). While the top of the model is free, the bottom of the model was fixed in the horizontal direction and the vertical sides of the model were fixed in the vertical direction. The influence of groundwater is not taken into consideration.  
The construction process is divided into the following phases:  
Phase 1--Setup the model: build up the model's mesh and set up the boundary conditions and initial stress state;  
Phase 2--Construction phase: the soil in the tunnel area is removed;  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline _Parameters of tunnel lining_ & & \\ Young’s modulus, \(E_{I}\) & MPa & 35,000 \\ Poisson’s ratio, \(\
u_{I}\) & - & 0.15 \\ Tunnel lining thickness, \(t\) & m & 0.35 \\ Overburden, \(H\) & m & 20 \\ _Parameters of soil_ & & \\ Young’s modulus, \(E\) & MPa & 10 \\ Poisson’s ratio, \(\
u\) & - & 0.34 \\ The unit weight, \(\gamma\) & kN/m\({}^{3}\) & 18.1 \\ Lateral earth pressure factor, \(K_{0}\) & - & 0.5 \\ Cohesion, \(c\) & kPa & 22.5 \\ Internal friction angle, \(\varphi\) & degrees & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the soil and tunnel lining.  
Figure 4: Geometry of the FEM model.  
Phase 3--Setting of the tunnel lining: the tunnel lining was installed on the tunnel periphery. It should be mentioned that the relaxation process induced in the ground surrounding the tunnel after the excavation and before the tunnel lining installation is not considered in this study. This case means the worst situation of active loads applied on the tunnel lining.
## Appendix A Special Port for IoT Devices  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device Brand** & **Protocol Type** & **Ports** \\ \hline \multirow{4}{*}{HIKVISION} & HTTP/HTTPS & 81, 80, 82, 443, 8443 \\  & RTSP & 554 \\  & DateService & 8000 \\  & Onwif & 80 \\ \hline \multirow{4}{*}{Dahua} & HTTP/HTTPS & 80, 8080, 443, 8443 \\  & RTSP & 554 \\  & DateService & 37777 \\  & Onwif & 80 \\ \hline \multirow{4}{*}{TP-Link} & HTTP/HTTPS & 80, 443, 8080, 443 \\  & RTSP & 554 \\  & Onvif & 2020, 80 \\ \hline \multirow{4}{*}{D-Link} & HTTP/HTTPS & 80, 443, 8080 \\  & RTSP & 554 \\  & Onvif & 80 \\ \hline \multirow{4}{*}{VIVOTEK} & HTTP/HTTPS & 80, 443, 8080 \\  & RTSP & 554 \\  & Onwif & 80 \\  & FTP & 21 \\ \hline \multirow{4}{*}{AXIS} & HTTP/HTTPS & 80, 81, 8081, 8080 \\  & RTSP & 554 \\  & Onwif & 80 \\  & FTP & 21 \\ \hline \multirow{4}{*}{Panasonic} & HTTP/HTTPS & 80, 443, 81 \\  & RTSP & 554 \\  & Onwif & 80 \\  & FTP & 21 \\ \hline \multirow{4}{*}{Cisco} & HTTP/HTTPS & 80, 443 \\  & RTSP & 554 \\ \cline{1-1}  & FTP & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Special Port For Webcam.  
\begin{table}
\begin{tabular}{c c c} \hline
**Device Brand** & **Protocol Type** & **Ports** \\ \hline \multirow{4}{*}{Cisco ASA} & HTTP/HTIPS & 443, 80, 8443 \\  & SSH & 22 \\  & Telnet & 23 \\ \hline \multirow{4}{*}{Fortinet Gate} & HTTP/HTIPS & 10443, 443, 80, 8443 \\  & SSH & 22 \\  & Telnet & 23 \\ \hline \multirow{4}{*}{Huawei} & HTTP/HTIPS & 443, 8443, 80, 8888 \\  & SSH & 22 \\  & Telnet & 23 \\  & SNMP & 161 \\ \hline \multirow{2}{*}{D-Link DFL} & HTTP/HTIPS & 80, 443, 8080, 8443 \\  & Telnet & 23 \\ \hline \multirow{4}{*}{Ruijie} & HTTP/HTIPS & 443, 80 \\  & Telnet & 23 \\ \cline{1-1}  & SNMP & 161 \\ \cline{1-1}  & SSH & 22 \\ \hline \end{tabular}
\end{table}
Table 10: Special Port For Firewall.
# Temperature Impact on Engineered Cementitious Composite Containing Basalt Fibers
## 3 Result and Discussions
### Results of the Flexural Strength Test  
Flexural strength is the bending property in a concrete beam under flexural loading. The flexural strength test at 28 days of ECC prism specimens was performed with a three-point loading configuration conforming to ASTM C348-08. The reinforcing effect of the BF on the flexural behavior of the ECC mix was specifically manifested by an increase in flexural strength. The test results showed that the compressive strength of the samples in BF-reinforced and controlled specimens was 10.4 MPa and 8.6 MP, respectively. The results  
Figure 5: The result of the change in tensile strength of control and fiber-reinforced specimens exposed to elevated temperatures up to 400 \({}^{\circ}\)C.  
of the flexural strength tests are illustrated in Figure 6 for the control and BF-reinforced ECC mixes. The results in each point were calculated from the average of triplicate specimens.  
It was observed that the increase in temperature in the BF-reinforced ECC up to 100 \({}^{\circ}\)C increased the tensile strength, while exposure to a higher temperature range (i.e., 100-400 \({}^{\circ}\)C) resulted in its decrease. The flexural strengths of the R-ECC and BF-ECC at 400 \({}^{\circ}\)C were 16% and 7% lower than those at room temperature. The maximum growth in the compressive strength of BF-ECC was achieved at 100 \({}^{\circ}\)C. The pattern of change in compressive strength of the BF-ECC was similar to the one for the reference state. However, the range of variation in R-ECC was much higher compared to BF-ECC due to the exposure to an elevated temperature. The rate of flexural strength development under elevated temperature in BF-ECC up to 100 \({}^{\circ}\)C temperature was 3% higher, while the strength loss from 100 to 400 \({}^{\circ}\)C was 9% lower compared to control specimens. Reduction in the tensile strength of the ECC under elevated temperature up to 400 \({}^{\circ}\)C may be due to the fact that this concrete completely lost its water up to 400 \({}^{\circ}\)C. Up to 100 \({}^{\circ}\)C, the hydration of cement was improved due to the internal autoclave conditions.
## 2 Materials and Methods
### Firi  
In addition to comparing the GNSS-RO profiles with ionograms, they were also compared against corresponding FIRI-2018 modeled profiles. FIRI was created as an improved empirical model of the lower ionosphere using rocket-borne wave propagation experiments [34]. Further, all altitudes are treated independently in the model and analytic expressions are derived for neutral density levels instead of altitude. The Faraday rotation measurements from rocket-borne experiments used to create FIRI result in a substantial improvement in electron density estimates at D-region altitudes compared to the standard International Reference Ionosphere (IRI) [34; 35]. Further incoherent scatter radar (ISR) comparisons with IRI and FIRI found closer agreement with FIRI predictions of the lower ionosphere [36; 37], which motivated our use of FIRI instead of the commonly used IRI.  
The inputs for FIRI are the day-of-year (1-365), solar zenith angle (0\({}^{\circ}\)-130\({}^{\circ}\)), geographic latitude (0\({}^{\circ}\)-60\({}^{\circ}\)), and the solar F10.7 index in solar flux units (75-200) [35]. The solar zenith angle was calculated using latitude and local solar time for each ionogram, and the F10.7 data were retrieved from NASA OMNIWeb [38]. If the observed F10.7 value was less than 75 or greater than 200, then 75 or 200 respectively was used as they are the limits of the input. The latitude input for FIRI requires positive values. To account for the southern hemisphere locations, the absolute value of latitude is used, and six months must be added to the day-of-year input. Finally, FIRI is focused on non-auroral zones, so it has a maximum latitude of 60\({}^{\circ}\). Since Gakona is above this latitude, 60\({}^{\circ}\) was used, and the FIRI profiles may not be representative at this location.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 3 Data Services
### Panoply  
Only Level-3 datasets are included in Giovanni. Another powerful and free tool, Panoply (2016), developed by NASA GISS, is available for viewing both Level-2 and 3 datasets. Data files can be imported in Panoply or via remote URL access such as OPeNDAP or CDS. There are many features built in Panoply for data analysis and visualization. For analysis, simple map combinations (e.g., subtraction) are included. Vector plots (e.g., wind) are supported. For visualization, features include scale, maps,  
Figure 2: The landing page of Giovanni, developed by GES DISC to facilitate data analysis and visualization without downloading data and software. A keyword search box is provided for searching over 1500 variables for the time being and more variables to be added. As the cloud version of Giovanni is being developed, better performance and more data from other Distributed Active Archive Centers (DAAC) will be expected.  
overlays, shading, contours, vectors and labels. Output can be saved in different popular formats (e.g., PNG, KMZ (Keyhole Markup language Zipped)).
## 4 Materials and Methods
### Solid-Phase Peptide Synthesis (SPPS)  
The chemicals with analytical grade were obtained from commercial sources, and the solutions were prepared using ultrapure water from a Millipore purification system (Milford, MA, USA). Fmoc-Lys(Boc)-OH, Fmoc-Leu-OH, Fmoc-Val-OH and Fmoc-Phe-OH and piperidine were purchased from Fluka (UK). The Fmoc-Rink-Amide-(aminomethyl)-Resin (0.48 mmol/g, 200-400 mesh, 1% DVB) was obtained from Intavis Bioanalytical Instruments (Koln, Germany) and benzotriazole-1-yl-oxy-tris-pyrrolidone-phosphonium hexafluorophosphate (PyBOP) from Novabiochem (Rosh Ha'ayin, Israel). \(N\),\(N\)-dimethylformamide (DMF), diethyl ether, dichloromethane, acetonitrile (AcCN) and ethanol were purchased from Roth (Karlsruhe, Germany), whereas acetic acid, trifluoroacetic acid (TFA) and \(N\)-methyl-morpholine (NMM) were from Merck (Darmstadt, Germany). Triisopropylsilane (TIS) and \(\alpha\)-cyano-4-hydroxycinnamic acid (\(\alpha\)-CHCA) were obtained from Sigma Aldrich (Gillingham, UK), while the nicotinic acid was purchased from Chinoin (Budapest, Hungary). In addition, the bromophenol blue indicator was bought from Scharlau (Hamburg, Germany).  
For each synthesis, we determined the quantity of protected amino acids and reagents required to obtain 250 uM of peptide-based drugs. Thus, the resin was placed in a 2 mL syringe reactor and swelled for 30 min with DMF, followed by deprotection with a piperidine/DMF solution (1:4). The washing steps were performed using DMF. The amino acid coupling was achieved with NMM and PyBOP for 50 min at room temperature (25 \({}^{\circ}\)C). The coupling step was repeated twice for each amino acid. Following peptide synthesis, nicotinic acid (NA) was added similarly with Fmoc-amino acid coupling, except for the incubation time, which was 2 h instead of 50 min. Furthermore, bromophenol blue test was used to monitor the coupling reaction [71]. The synthesis was mixed by stirring, and the resin was filtered with polyethylene.  
The peptide cleavage from the resin was performed with a solution of TFA, TIS and ultrapure water (95:2.5:2.5, \(v/v/v\)). Consequently, the peptides were precipitated with 5% acetic acid (in ultrapure water) and subjected to lyophilization (Liophilizer Alpha 1-2 LD Plus 101521, [PERSON], Osterode am Harz, Germany).