esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Developed 3-D Profilometry Using Dual Sensing
### Optical System Setup  
Figure 3 illustrates the optical system configuration of the developed 3-D profilometry. As can be seen, it includes a low-coherent light source (1), a diffuser (2), which is collimated by a lens module (3), a sinusoidal grating (4), and a linear translator (5) for generating an accurate shifted phase. To achieve full-field surface profilometry using PSP, the light source is a low-coherent light source such as LED (light-emitting diode), instead of a coherent one like lasers. In the system, the structured light generated by the incident optical module passes through a telecentric lens (6) and is projected onto the measured surface (8) defined by the reference plane (7). The reflected light passes through two objectives (12 and 13) and is captured by two individual optical sensing cameras (10 and 11). The first objective (12) is arranged in vertical direction to capture the diffuse light while the second objective is set to a reflection angle with respect to the incident projection light angle, so that specular light can be well captured. With such an optical configuration, both diffuse and specular light beams reflected can be well detected by their corresponding objectives. When the projected sinusoidal fringe (9) hits the measured wafer surface, it becomes a deformed fringe according to the phase difference generated by the optical path difference (OPD) between the object profile and the reference plane. The deformed fringe image is transferred to a computer (14) and the phase map is then computed with multi-phase shifting and the wrapping principle using a set of deformed fringe images. Subsequently, phase unwrapping and phase-to-height transformation are performed to extract the profile of the measured surface.  
All the optical lenses employed in the developed system are telecentric to ensure the formation of a constant image size along the depth of field (DOF) of the measurement. When the measured surface located inside the overlapped with the DOF of the two cameras (indicated as the gray zone in Figure 3), the captured image size can be kept constant, so the FOV is kept as a rectangular or square shape according to the image sensor's design. Meanwhile, to keep the focus plane of the imaging  
Figure 3: Configuration of the developed dual-sensing measurement system.  
sensor to locate at the same focus plane with the projecting light side, the image sensor can titled with the principle optical axis by an adequate angle to compensate the tilting effect. Thus, both of the focus planes (light projection and imaging) are kept at the same focus plane and the image shape are rectangular or square in the same FOV. Meanwhile, an adequate camera calibration can be performed to identify all the intrinsic and extrinsic parameters of the coordinate transform, so 3D point cloud can be transformed with image coordinates accurately.
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### LoRaWAN Module Behavior in Testbed
#### 3.15.1 Behavior of the Network with the Packets Sent by SMs to the EPC  
As can be seen in the Figure 19, the graphs show a difference between the SF settings based on the size of the package to be sent. Considering the RSSI (a), the larger the SF the better the RSSI, but as the packet size increases this will directly affect the RSSI. With the SNR (b), the measure that the SF increases the SNR will be smaller, because of the sensitivity with possible barriers such as trees, buildings and homes, and the increase in the size of the packet also affects the SNR. What is important to highlight are the ToA (c) and delay (d) measures, as these are extremely important for network scalability. In this sense, as the SF is increased the ToA grows approximately twice as long, while the delay the difference follows around 1 to 2 s. In the cases of smaller SFs the ToA is very similar for all packet sizes however, the delay grows as the SF as well as the packet size increases. For these metrics the difference is approximately 260 ms for the worst and best case of ToA and 8 s for the delay.
## 4 Performance Evaluation of Post-Installed Anchors
### Static Loading Test
#### 4.1.2 Shear Performance Evaluation  
In the shear test results of the M10 and M12 set anchors, both anchors exhibited a steel failure instead of a concrete-anchor bond failure, as shown in Figure 9. It appears that the steel failure occurred before the load affected the concrete, owing to the sufficient bearing capacity of the concrete for the anchor installed at the center of the specimen and because of the distance of the concrete-anchor generated from the way in which the anchor was set. The differences in performance depending on the presence of a concrete crack were found to be insignificant, as shown in Figure 10. Table 4 summarizes the average maximum load for each test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Specimen Test Classification** & **Max. Average Load** \\ \hline \multirow{4}{*}{Shear tests} & \(\Phi\)10 & Non-cracked concrete & 16.08 kN \\  & \(\Phi\)10 & Cracked concrete & 15.85 kN \\ \cline{1-1}  & \(\Phi\)12 & Non-cracked concrete & 25.44 kN \\ \cline{1-1}  & \(\Phi\)12 & Cracked concrete & 24.52 kN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Respective results of the shear tests.  
Figure 8: Test setup for performance evaluation of static loading: (**a**) shear tests; (**b**) pull-out tests.  
Figure 9: Failure mode for static shear tests: (**a**) M10 set anchor; (**b**) M12 set anchor.
## 4 Parameterization and Analysis of the Proposed TDL Model
### Average PDP and Tap Characteristics  
As described above, we calculated the APDP, where the PDF of the obtained APDP for the low approach scenario is visualized in Figure 5a. The power loss at delay 0 ns should be around 0 dB for the LoS path. Additionally, the APDP can be approximated as a unilateral exponential function, which decreases exponentially with the delay [24]. The normalized APDP is shown in Figure 5b, where the number of extracted effective taps \(M=10\) with the given threshold of 25 dB below the strongest tap power. The power threshold is a model empirical value, which is given to determine the number of taps, and excess delay of individual taps with respect to the LoS path for all measurement scenarios as summarized in Table 3.
## 4 Conclusions and Further Work  
This work showed a successful investigation, conducted through simulation and proven via empirical testing, of high frequency DC PLC. It demonstrated that a short-range wired environment can sustain a stable communication link through 868 MHz and 2.4 GHz at the highest IEEE 802.15.4 standard admissible data rates of 150 kbps and 250 kbps,  
Figure 7: Experimental measurement’s received signal strength indicator (RSSI) for the 868 MHz and 2.4 GHz communication over the 2.1 m separation between the two RF transceivers (**A**), connected to the capacitive coupling PCBs (**B**), linked to the DC bus composed of four Li-ion cells organized in parallel (**C**) and in series (**D**). The square marker shows the average measurement interval value in the context of the min–max range bars.  
respectively. Moreover, it showed that communication via interrupted wiring with an end-to-end gap of 0.1 m is also achievable close to the transceiver highest receiving sensitivity; therefore, both planned wired and unplanned wireless (e.g., accidental due to a wire cut or disconnection) communication are possible if RF transceivers are used for DC PLC. This demonstrates that the usage of RF transceivers on short-wired data links can mitigate network discontinuities, ensuring a higher reliability and possible support of new functions, such as communication failure point diagnosis and path estimations.  
This work demonstrates the advantages of using RF transceivers in short-range wired applications, which has a significant impact on the development of current and future BMS technology.  
Conceptualization, V.M. and J.F.; methodology, V.M.; formal analysis, V.M.; investigation, V.M.; writing--original draft preparation, V.M.; writing--review and editing, V.M., T.A., P.I., S.F., J.F.; visualization, V.M.; supervision, J.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Innovate U.K. project CALIBRE (Grant Reference Number 104176).  
Not applicable.  
The dataset generated and analyzed during this study are available from the corresponding author upon reasonable request, but restrictions apply to the commercially confident details.  
The authors declare no conflict of interest.
## 4 Results
### Spatio Temporal Fusion Features Compared to Original Time Series SAR Data  
We also used the t-SNE method for dimension reduction and visualization of the original time-series SAR data (the feature dimension was 18, i.e., 9 periods \(\times\) 2 backscattering coefficients) in the Shuangcheng area to analyze the separability of the original features for rice and other ground objects. Figure 22 shows the results. There was serious confusion between the rice pixels and pixels of other types of ground objects. This was mainly because the original features were purely temporal; the spatiotemporal relationship between each pixel and its surrounding pixels was not considered. Consequently, it was difficult to identify rice based solely on the original VV and VH time-series data. A comparison with Figure 21 showed that the deep learning model had powerful feature extraction abil  
Figure 21: Feature visualization based on the t-SNE method. Each graph was calculated and drawn from 10,000 rice and 10,000 non-rice pixels randomly selected from the corresponding feature map. The farther the separation between rice pixels and other pixels in the t-SNE map, the more effective the features are for rice classification, and vice versa. (**a**) t-SNE result in Mississippi Aluvial Plain in 2019, (**b**) t-SNE result in Sacramento Valley Plain in 2019, (**c**) t-SNE result before transfer in Shuangcheng in 2017, (**d**) t-SNE result before transfer in Shuangcheng in 2018, (**e**) t-SNE result before transfer in Shuangcheng in 2019, (**f**) t-SNE result before transfer in Shuangcheng in 2020, (**g**) t-SNE result after transfer in Shuangcheng in 2017, (**h**) t-SNE result after transfer in Shuangcheng in 2018, (**i**) t-SNE result after transfer in Shuangcheng in 2019, (**j**) t-SNE result after transfer in Shuangcheng in 2020.  
ity, which could mine deep-level spatiotemporal fusion features from time-series remote sensing images, thus providing the possibility for high-precision rice identification.
## 4 Comparative Analysis Convolutional Codes with LDPC Codes, BCH Codes, and Turbo Codes
### Convolutional Codes vs. BCH Codes
Therefore, there are \(8\cdot 6\) = 48 comparisons for these time steps (note that the total of the comparisons is less than 48, because the comparisons number in time step 1 and 2 is less than 8). Another way to compare the complexity of BCH codes and Convolutional codes is to look at their codeword lengths. BCH codes tend to produce longer codewords than Convolutional codes for the same message length and error-correcting capability. This is because BCH codes have a fixed codeword length that depends on the size of the finite field, while Convolutional codes have a variable codeword length that depends on the constraint length and the code rate. For example, a (63,51) BCH code can correct up to two errors per codeword, while a (3,1) Convolutional code with constraint length seven can correct up to five errors per codeword. However, the BCH code has a codeword length of 63 bits, while the Convolutional code has a codeword length of 21 bits for a message length of 7 bits [28; 29]. When comparing the encoding and decoding operations, it is evident that Convolutional codes require fewer steps compared to BCH codes. Additionally, the mathematical operations involved in BCH codes are more complicated than in Convolutional codes. However, Convolutional codes still face a fundamental issue known as exponential complexity. Despite this drawback, Convolutional codes are considered the most appropriate choice for GPS communication systems. Overall, the high complexity associated with BCH codes makes them unsuitable for implementation in GPS systems and real-world applications. Consequently, Convolutional codes with Viterbi decoding, which are less computationally complex, are preferred over BCH codes.
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
## 3 Proposed Method  
The adaptive beamforming GSC [19] was originally proposed for narrowband applications. The ultrasonic echo data is the broadband signal [20]. Therefore, the broadband ultrasonic echo signals need be divided into a set of narrowband signals [21]. The discrete wavelet transform is introduced to divide the ultrasonic echo signals. Each separated subband fulfills the narrowband condition, and each separated subband is processed independently.  
For narrowband echo signals, the adaptive beamforming is an extension to the DAS. As illustrated in Figure 2, firstly, proposed beamforming uses the discrete wavelet transform (DWT) to divide the broadband echo signal into a set of narrowband signals. Then, the sets of narrowband signals after DAS are fed to the adaptive processor GSC, consequently, provides a set of adapted apodization weights for each wavelet subband. Later, beamformed narrowband ultrasonic echo signals are coalesced. Finally, the inverse discrete wavelet transform (IDWT) is adopted to deal with the coalesced broadband echo signals, and the broadband echo imaging will be achieved.  
For the given focus point \(\vec{\
u}_{\text{P}}^{\rightarrow}=(\text{x},\ \text{z})\), the beamforming output for each wavelet subband, \(\text{NB}_{\text{q}}(\vec{r_{\text{p}}})\), is given by:  
\[\text{NB}_{\text{q}}(\vec{r_{\text{p}}})=\sum_{\text{m}=1}^{\text{M}}\omega_{ \text{m}}^{\prime}(\omega_{\text{q}})\text{Y}_{\text{m}}(\omega_{\text{q}}) \tag{8}\]  
\(\text{Y}_{\text{m}}(\omega_{\text{q}})\) is the discrete wavelet transform response of the mth element's echo signal at qth wavelet subband. \(\omega_{\text{m}}(\omega_{\text{q}})\) is the adapted apodization weight for the qth wavelet subband, which is obtained with adaptive beamforming GSC. \(\{\cdot\}^{\prime}\) denotes the complex conjugate. For each wavelet subband, defining the apodization weight vectors and the wavelet subband response vectors:  
\[\boldsymbol{\omega}(\omega_{\text{q}})=[\omega_{1}(\omega_{\text{q}}),\ \omega_{2}(\omega_{\text{q}}),\ \cdots,\ \omega_{\text{M}}(\omega_{\text{q}})]^{T} \tag{9}\]  
\[\mathbf{Y}(\omega_{\text{q}})=[\text{Y}_{1}(\omega_{\text{q}}),\ \text{Y}_{2}(\omega_{\text{q}}),\ \cdots,\ \text{Y}_{\text{M}}(\omega_{\text{q}})]^{T} \tag{10}\]  
Superscripts \(\{\cdot\}^{T}\) denote the non-conjugate transpose. The beamforming output for each wavelet subband can be rewritten as:  
\[\text{NB}_{\text{q}}(\vec{r_{\text{p}}})=\boldsymbol{\omega}^{\text{H}}( \omega_{\text{q}})\mathbf{Y}(\omega_{\text{q}}) \tag{11}\]  
Using the inverse discrete wavelet transform to deal with the coalesced broadband echo signals \(\mathbf{NB}(\vec{r_{\text{p}}})=[\text{NB}_{1}(\vec{r_{\text{p}}}),\ \text{NB}_{2}(\vec{r_{\text{p}}}),\ \cdots,\ \text{NB}_{ 2}(\vec{r_{\text{p}}})]\). Q represents the total number of the wavelet subband. The final broadband echo imaging (Bi) will be achieved using inverse discrete wavelet transform (IDWT).  
\[\text{Bi}=\text{IDWT}(\mathbf{NB}(\vec{r_{\text{p}}})) \tag{12}\]  
Figure 2: The block diagram of broadband generalized sidelobe canceler.
## 3 Results  
### Evaluation and Comparison of the Forecasting Abilities of Two Base Models  
#### 3.1.1 Comparison of Test Dataset Accuracy Between the VGG11 and AlexNet Models  
According to Figure 4A, the accuracy of the VGG11 model was higher only for 0 ELG, e.g., if the dropout was set to 0, and the accuracy for 0 ELG became stable after about 400 iterations, but the accuracies for other scenarios were low or unstable. When the accuracy curves stabilized, the mean accuracy values for the five models were 0.7508, 0.7110, 0.5238, 0.5099, and 0.4863, respectively.  
According to Figure 4B, the accuracies stabilized in all ELGs after about 300 iterations, and the mean accuracy values for the five ELGs were 0.7378, 0.7434, 0.7377, 0.7326, and 0.7165, respectively. When comparing each ELG, it was observed that, specifically for 0 ELG, the VGG11 model exhibited an average accuracy that surpassed that of the AlexNet model by a margin of 0.013; for scenarios 0.2, 0.4, 0.6, and 0.8 ELGs, the average accuracy of AlexNet exceeded that of VGG11 by 0.0334, 0.2139, 0.2227, and 0.2302, respectively. As shown in Figure 4, the accuracy curve of the AlexNet model was more stable than that  
Figure 4: Test set accuracy of the VGG11 **(A)** and AlexNet **(B)** models.  
of the VGG11 model. Considering the model's accuracy curve and accuracy mean, it was concluded that the AlexNet model was superior to the VGG11 model.
###### Abstract  
In order to deeply investigate the effects of various factors on the shear behavior of RC beams strengthened with fiber-reinforced polymer (FRP) grid-polymer cement mortar (PCM) composite, and to construct a more accurate formula for the shear behavior of reinforced concrete beams, the following work is carried out in this investigation: Firstly, the finite element numerical simulation of FRP grid-PCM composite RC beams model is carried out using ABAQUS and compared with the test results to verify the correctness of the model; then, the effects of the amount of FRP grid reinforcement, the elastic modulus of the FRP grid, the shear span ratio of the beam, the concrete strength, and the shear reinforcement ratio on the shear performance of the strengthened beams are analyzed; finally, based on the effective strain of the FRP grid to quantify its actual shear contribution, a calculation formula of the shear behavior Capacity of RC Beams Strengthened with an FRP grid-PCM composite is proposed. The results show that the model established in this paper can effectively simulate the shear behavior of the beams in the test; additionally, the effects of the amount of FRP grid reinforcement, the shear span ratio, and the concrete strength are more significant. Finally, the theoretical results of the calculation formula fit well with the collected experimental ones.  
FRP grid-polymer cement mortar; concrete beam; effective strain; shear behavior capacity formula; finite element analysis Article
## 3 Results and Discussion
### (Pyrolytic WC)/MWCNT Nanocomposite Research
The pyrolytic tungsten carbide coating on the surface of MWCNT was identified as an ultra-disperse WC\({}_{1\text{-x}}\) Fm3m phase with lattice parameter a = 4.14 A (fcc structure; JCPDS card no. 20-1316) with no indication of WC or W\({}_{2}\)C. Earlier [46] formation of this tungsten carbide phase was identified where mixtures of MWCNTs and W(CO)\({}_{6}\) were subjected to ultrasonic treatment in argon flow at T = 95 \({}^{\circ}\)C for 3 h in hexadecane. The nonstoichiometric WC\({}_{1\text{-x}}\) phase is likely stable only in the case of nanosized crystallites. The MWCNTs coated with WC\({}_{1\text{-x}}\) nanoparticles TEM results are given in Figure 4. The TEM image of WC\({}_{1\text{-x}}\)/MWCNTs in Figure 4a,b shows that WC\({}_{1\text{-x}}\) in initial stages formed aggregates of small nanoparticles. Furthermore, with the continuation of the deposition process, these aggregates  
Figure 3: The SEM images of the composite material sample coated with pyrolytic tungsten carbide MWCNTs with initial mass ratios of precursors (MWCNTs:W(CO)\({}_{6}\)) of 1:3 (sample 1) (**a**), 1:4 (sample 2) (**b**) and 1:5 (sample 3) (**c**). (**d**)—the decrypted EDS profile obtained from the MWCNT coating shown in (**c**). subsequently form islands of coatings on the MWCNT surface and then continuous coatings. Figure 4b insert shows the HRTEM image of a cluster nanoparticle. The blue color indicates the system of clusters interplanar distances in a reflective position. Crystal clusters are disoriented within a single nanoparticle as evidenced by the FFT shown on Figure 4c. The MWCNTs coated with WC1-x nanoparticles TEM results show that their interior is entirely composed of tungsten carbide. This allows us to conclude that tungsten oxides are formed not during pyrolysis, but as a result of contact with atmospheric oxygen and form a thin oxide layer on the tungsten carbide nanoparticles surface.
## 4 Discussion
This is possible because the typical communication between the flow computer and the Supervisory Station is executed through a TCP-IP or similar digital Ethernet network, which is the same system that supports communication between the Supervisory Station and the generation of the Monthly Report. In this case, both the flow computers and the Supervisory Station would have unique addresses through the MAC (Media Access Control address). The MAC address is the most basic identification of a network card or device. Each card has a unique address, which is the base address of all network devices. There is only one MAC address for each board produced. When packages are sent or received, the devices compare the destinations and the adaptor's MAC addresses. If they are the same, the package is processed; otherwise, the information is discarded [91]. MAC is a 48-bit serial number whose function is to identify an Ethernet or Wi-Fi network device globally. The connections work with two protocols: the IP address and the MAC. The IP address is located one layer above the Ethernet, being the first option used in sending packets. If the destination is not in the same IP network as the sender, it is necessary  
Figure 4: Construction of Historical Databases by Flow Computers. to send it to the configured router. Ethernet uses MAC addresses for these submissions. The ARP (Address Resolution Protocol) is used to recover the destination's MAC address. With the recovered address, the sender records the MAC address in the package and sends it to the recipient, where it is received and processed at its destination [92]. With the use of the MAC address, it is then possible to create the block of the blockchain and ensure data protection of the flow computer and the Supervisory Station itself, which in turn propagates this safety to the production report generator (Monthly Report). With the unique address of the information, we can then run the blockchain algorithm, which will add the transaction code to the block through its own algorithm. Put simply, the regulator would then have a Monthly Report protected by blockchain up to the level of the flow computer, as well as from the latter to the sensors. Figure 5 summarizes how the proposed system would work for the incorporation of the blockchain algorithm in the information of the measurement of the production of the field. Evidently, using the MAC address is not a perfect solution. Many controllers and operating systems can change this address. Nonetheless, in oil and gas measurement systems, operators have a record, in their databases, of the data from the flow computers used in the calculation of product volumes (including the MAC address of the communication). Consequently, any change in the MAC address would make the received data incorrect. In other words, the use of the blockchain would have the main function of connecting the data received from the operator to its origin, the flow computer. This aspect alone justifies the use of the proposed solution.