esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Variation in pH along the Depth of Cover  
Variation in pH along the cover depth for uncracked samples with minor corrosion is shown in Figure 6a. The pH along the cover depth ranged from 8.5 to 12.1. The pH values at depths of 3.5 mm, 10.5 mm, and 17.5 mm from the concrete surface were in ranges of 8.5-11.3, 9.5-11.9, and 11.3-12.1, respectively.  
It was found that pH increases with the increase in depth, as shown in Figure 6. Figure 7a shows that the full carbonation depth is much less than one-third of the cover depth. This indicates that the proportion of semi-carbonated and uncarbonated zones is higher. Based on the carbonation mechanism, carbon dioxide diffused from outside to inside of the concrete and carbonated with the alkaline substance. The carbonation reaction resulted in an increase in the content of calcium carbonate CaCO\({}_{3}\) and a reduction of calcium hydroxide Ca(OH)\({}_{2}\) from the concrete surface to the inside. Thus, carbonated products alter the pH of the concrete pore solution; CaCO\({}_{3}\) reduces the pH value, and Ca(OH)\({}_{2}\) increases the pH values.  
Figure 6: Variation in pH along the cover depth: (**a**) uncracked samples with minor corrosion; (**b**) uncracked samples with moderate corrosion; (**c**) cracked samples with severe corrosion.  
Variation in pH along the cover depth for uncracked samples with moderate corrosion is shown in Figure 6b. The pH along the cover depth was between 8.7 and 10.8. The pH values at depths of 3.5 mm, 10.5 mm and 17.5 mm from the concrete surface were in ranges of 8.7-10.5, 9.5-10.6, and 10.2-10.8, respectively. Compared with the minor corrosion samples, the pH of moderate corrosion samples reduced significantly at the same depth and varied in a narrower range.  
Figure 7b shows that the full-carbonation depth was much less than one-half of the cover depth. This indicates that the proportion of fully carbonated zone increases with the corrosion degree. This result agrees with findings obtained with the pH test.  
Variation in pH along the cover depth for cracked samples with severe corrosion is shown in Figure 6c. The pH of the concrete solution along the cover was between 8.0 and 10.0. The pH at depths of 3.5 mm, 10.5 mm and 17.5 mm from the concrete surface were in ranges of 8.0-9.5, 8.5-10.0, and 9.0-10.0, respectively. Compared with the uncracked samples, the pH of cracked samples reduced significantly at the same depth.  
Figure 6c also shows that the pH of cracked samples with severe corrosion slightly changed along the cover depth, and its value fluctuated at pH = 9.0. This is because corrosion-induced cracks along the rebar provide a convenient channel for the diffusion of carbon dioxide; thus, a steady stream of carbon dioxide accelerates the carbonation process of the cover concrete for cracked samples. Figure 6c confirms this, showing where the carbonation depth of the cracked sample exceeds or is close to the cover depth. Thus, the cover is completely carbonized, allowing little variation in pH to occur along the cover depth.
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
## 3 Materials and Methods  
In the first instance, we installed the LoRa gateway in an elevated position [32], in the headquarters of an agricultural cooperative from which coverage of nearby crops could be given. Thanks to the LoRa technology, we could easily cover an area of about 5 km around this cooperative; this is less than other studies, but we did not modify the LoRaWAN PHY layer and did not point the antenna toward the test area [32,33]. Then, end-devices (BoXmotes, Bo True Activities SL, Huelva, Spain) were installed in the crop, which were equipped with the most suitable sensors, depending on the parameters that were to be measured. In this way, the BoXmote took measurements at regular intervals and sent them to the gateway through the LoRa technology. Once the gateway received the data, they were sent to a server via the ethernet connection. When the data reached the server, the farmer could consult them through a mobile application.
## 3 Results and Discussion  
The experimental operating platform is a Windows system, using an Intel (R) Core (TM) i7-10700F CPU, an NVIDIA Quadro P4000 GPU, and 16 GB of memory. The network model is implemented using the Pytorch 2.1.0 deep learning framework, and the raw dataset is constructed using the Matlab R2016a simulation program. In order to more accurately characterize the impact of phase disturbances caused by atmospheric turbulence on OAM beams, this study uses power spectrum inversion to simulate atmospheric turbulence and compensates for its low-frequency deficiency using the subharmonic method [26]. Under three turbulence intensities of weak, medium, and strong (\(C_{n}^{2}=1\times 10^{-16}\) m\({}^{-2/3}\), \(C_{n}^{2}=1\times 10^{-15}\) m\({}^{-2/3}\), \(C_{n}^{2}=1\times 10^{-14}\) m\({}^{-2/3}\)), 1000 images of 15 OAM modes with topological charges l = 1-15 were simulated as datasets, totaling 15,000 images. Figure 4 shows partial dataset images of LG beam transmitted through a 1000 m turbulent channel. The dataset was divided into a training set and a validation set in an 8:2 ratio and then input into the Transfer OAM modal classifier designed in this paper for training. After extensive training and adjustment, appropriate hyperparameters were selected: batch size of 16, iteration times of 200, and learning rate of 0.0002.
# UAV-Based Evaluation of Rockfall Hazard in the Cultural Heritage Area of Kipinas Monastery, Greece
## 3 Stability Analyses  
### Kinematic Analysis  
In general, from the field survey, it is preliminarily comprehended that the main failure mode in the whole area comprises rock block detachment followed mainly by toppling or wedge failure and secondarily by planar sliding failure. The fallen blocks reveal fresh surfaces and as a result, the lateral blocks exhibit a lack of confining stress. Consequently, new cracks emerge and the same process accelerates.  
Figure 8: (**a**) Stereographic projections of the DSE-derived discontinuity sets for U3 and (**b**) the visual interpretation of the four (4) main sets in the 3D point cloud.  
Figure 9: (**a**) Stereographic projections of the DSE-derived discontinuity sets for U4 and (**b**) the visual interpretation of the four (4) main sets in the 3D point cloud.  
In order to determine the optimal failure model for each sub-region, a kinematic analysis was performed based on the calculated major discontinuity sets from DSE software, which were previously validated according to the field-obtained structural data [60,62]. This analysis was executed, for each area, via DIPS v.7 software (RocScience Inc., Toronto, ON, Canada). The geometrical characteristics of each slope and of each discontinuity set were imported, in addition to the friction angle of the bed rock, which was estimated as \(\varphi=30^{\circ}\), and a stereographic projection occurred for every failure type. These produced stereonet plots, identified any potential failures and highlighted the discontinuity set(s) which contributed to those. The results of this process are demonstrated in Table 3 and the stereographic projections for each sub-region are shown in Figures 10-13.  
\begin{table}
\begin{tabular}{c} J\({}_{1}\) \& J\({}_{2}\) \\ \(J_{1}\) \& J\({}_{3}\) \\ \(J_{2}\) \& J\({}_{3}\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: A concise review of the kinematic analysis per sub-region.  
Figure 10: Kinematic analysis for: (**a**) wedge sliding and (**b**) flexural toppling failure in sub-region U1.
# Healing Effects of Photobiomodulation on Diabetic Wounds
## 3 Photobiomodulation (PBM)
### Effects of PBM Using Blue Light on Bacterial Growth and Fibroblasts
Four different assays were carried out to determine cellular viability 24 h post-irradiation. The MTT assay showed that irradiation with 55, 110 and 220 J/cm\({}^{2}\) significantly impairs mitochondrial activities and decreases fibroblast viability. When cellular viability was determined by the neutral red assay as well as the Trypan blue assay, a significant decrease was observed following irradiation with 110 or 220 J/cm\({}^{2}\), indicating a disruption to the cell membrane integrity. Data attained from the live/dead fluorescence assay showed only slight, insignificant decreases in cell viability at all fluences tested. Overall, these results showed that there was a dose response in fibroblast cells in response to irradiation at 470 nm, with fluences above 110 J/cm\({}^{2}\) becoming intolerable to cells. [PERSON] and colleagues [48] found that irradiation of fibroblasts at wavelengths of 410, 420, 453, and 480 nm (irradiance of 50 mW/cm\({}^{2}\)) at different fluences produced different results. Fibroblast numbers were significantly decreased when irradiated with 410 nm and 420 nm at 60 and 90 J/cm\({}^{2}\), whereas irradiation with 480 nm resulted in a significant increase at 30 and 60 J/cm\({}^{2}\). Irradiation with 453 nm produced no difference. Irradiations with 410 nm and 420 nm at a fluence of 10 J/cm\({}^{2}\) resulted in increased intracellular oxidative stress, while wavelengths of 435 nm and 480 nm produced no effect at the same fluence. This increase in oxidative stress was partly due to an increase in the production of singlet oxygen. Irradiations at 410 nm, 420 nm, and 435 nm significantly reduced cellular proliferation when cells were irradiated daily with 10 J/cm\({}^{2}\) over 4 days, while irradiations with 480 nm had no effect on cellular proliferation. Since PBM in the blue spectrum has been found to be lethal to bacterial growth, there is promise for this therapy to be used in the treatment of infected wounds, however, more detailed in vivo studies on infected wounds need to be conducted. An advantage of such a treatment is that there are no external drugs involved, and it is highly unlikely that bacteria will develop resistance to this kind of treatment. Caution should still prevail, however, as some wavelengths and fluences have been shown to increase bacterial cell proliferation, and the effects on fibroblasts are not well established and vary according to wavelengths used. Despite this, PBM using blue light may be a viable alternative to drug treatment.
# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 1 Introduction
### Methodology  
We had to deal with a specific problem and we proposed a concrete solution. Taking into account the requirements, it was necessary to find a solution based on a monocular system and with limited processing capacity (a solution that can be run in low power, smaller computers). Whereas it was intended to estimate the relative position and attitude of the AUV in relation to a dock using a single camera, and taking into account that, in the vicinity of the dock, the AUV's camera may not necessarily be facing the dock, we chose to construct a target to attach to the dock and whose markers were visible on different points of view. On the other hand, we chose to use different colors for each of the markers in order to avoid ambiguities in the relative pose estimation. In order to estimate a 3D relative pose of the AUV in relation to the built target, we propose an algorithm of low computational cost that detects, identifies and locates the spherical color markers in the image and then estimates the position and orientation of the target with regard to the camera frame mounted onboard AUV. The module developed for the detection of markers and relative pose estimation presents fairly satisfactory estimates and with sufficient accuracy to feed the control, especially with accurate orientation information needed in the final docking phase. We propose here a particular solution to deal with situations of partial occlusion of the target (absence of one or more markers) or appearance of outliers that affect the estimate of the 3D relative pose. Our particular solution, based on particle filters, rather than following the more common approach of throwing particles over the target as a whole, considers a particle filter for each of the markers and considers the geometric constraints of the target and the constraints of the markers in the color space. The proposed solution also considers real-time color adjustments which allow, in a scenario with features of color similar to our markers, a greater number of outliers is avoided and the weight of the particle of the filter has a more adjusted value and that the results are more accurate.  
\begin{table}
\begin{tabular}{c c} \hline Sensor Size (mm) & 3.2 \(\times\) 2.4 \\ \hline Resolution (pixels) & 704 \(\times\) 576 \\ \hline Pixel Size (\(\upmu\)m) & 6.5 \(\times\) 6.25 \\ \hline \end{tabular}
\end{table}
Table 1: Camera sensor specifications.  
\begin{table}
\begin{tabular}{c c} \hline \hline Focal length (mm) & 3.15 \\ \hline Diagonal Field of View (_degrees_) & 65 \\ \hline Maximum Aperture & \(f1.4\) \\ \hline \end{tabular}
\end{table}
Table 2: Camera lens specifications.
## 3 Results and Discussion
### Effect of Inlet Temperature  
The influence of the inlet temperature on signal detection intensities during the detection process on the basis of the mentioned GC-QqQ-MS/MS conditions was investigated. A 1 \(\upmu\)L mixed standard solution of the selected analytes at individual concentration of 20 \(\upmu\)g/L was injected into GC-QqQ-MS/MS detection system with matched autosampler under six individual injection-port temperatures (150, 170, 190, 210, 230 and 250 \({}^{\circ}\)C) in splitless mode. Each detection of the sample was repeated three times. The relative signal intensities of these identified peaks of the selected analytes with reference to those at 250 \({}^{\circ}\)C were calculated using Equation (1). The results are shown in Figure 1a,b.  
\[\text{Ratio (\%)}=\text{(Peak area at T/Peak area at 250 ${}^{\circ}$C)}\times 100\% \tag{1}\]  
As can be seen from Figure 1, the effect of the injection port temperature on signal detection intensities of the selected analytes was complex. For MTBE, the effect of temperature on signal detection intensity was negligible. For ETBE and TBA, the intensity of signal increased obviously as the injection port temperature increased from 150 to 230 \({}^{\circ}\)C. However, as the temperature further increased to 250 \({}^{\circ}\)C, the intensity exhibited decreased tendency. The intensity of response signal for the other analytes exhibited increased tendency as the temperature increased from 150 to 210 \({}^{\circ}\)C, then decreased as the temperature further increased to 250 \({}^{\circ}\)C. Hence, the temperature of injection port was fixed at 210 \({}^{\circ}\)
## 4 Results and Analysis  
### Single Passage Model  
#### 4.1.1 Overall Performance  
The overall performance of impellers is shown in Figure 7. In this study, the largest mass flow rate (Q/Qd) of all the impellers is selected as 1.2. In fact, it is due to low rotational speed that this kind of impeller has an excellent performance under the working conditions with the large mass rate. So, the choke performance will not be analyzed in the followed words.  
In the Figure 7a, at the design point, the LA impeller achieves to the highest efficiency and pressure ratio among four impellers, all the same the AL impeller has the lowest efficiency and pressure ratio. Meanwhile it is found that for the LA and prototype impellers, their peak efficiency points are very close to the design point. But, when the flowrate is decreased, performance of LL impeller appears to be better.  
In this research, the compressor characteristics close to stall point are evaluated by the stall margin, as shown in Table 2. The stall margin (_SM_) is defined as  
Figure 6: 2D cross-section.  
Figure 7: Performance curve.  
\[SM=1-Q_{stall,outlet}/Q_{d}, \tag{5}\]  
where, \(Q_{stall,outlet}\) is mass flow rate close to the stall point and \(Q_{d}\) the design flow rate. In Table 2, LL impeller has the largest stall margin, which is increase by 50% compared with that of prototype. It means that after the simplifying of the meridional profile, performance at design point of LL impeller is not obviously deteriorated (efficiency and pressure ratio is decreased by about 0.5% compared with that of the prototype as is shown in Figure 7), while in some respects, its off-design performance is improved somewhat.  
Though LA impeller presents more excellent capability with large mass rate, performance of the LL impeller seems to be better than that of another three impellers when the machine is slipping into the working conditions with low mass rate.
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 4 Simulations and Results  
In Section 3, our constructive MAC layer was presented for streaming images in time-restricted applications. In this section, experimental results achieved in our implementations using MATLAB are demonstrated.
## 3 Reliability Sensitivity Analysis of Emergence Extension
### Sensitivity Analysis
#### 3.2.2 Effect of Main Torque on Failure Probability of Emergency Extension  
To determine the significant torques on the failure probability of emergency extension, ranking method is adopted to vividly order by the sensitivities of torques to the failure probability of emergency extension with the changes of strut rotation angle. The ranking is shown in Figure 5.  
As revealed in Figure 5a, both the damping torque M\({}_{5}\) and the aerodynamic torques (M\({}_{7}\) and M\({}_{8}\)) of after and forward door are the three largest influencing the failure probability of emergency extension. When the strut rotation angle changes in [0\({}^{\circ}\), 55.1759\({}^{\circ}\)], M\({}_{5}\) has the largest factor affecting the failure probability of emergency extension. M\({}_{8}\) will take the place of M\({}_{5}\) and becomes the most influential torque when the strut rotation angle varies in [55.2889\({}^{\circ}\), 79.2622\({}^{\circ}\)]. With the further increase of the strut rotation angle, M\({}_{5}\) and M\({}_{7}\) will become the most influential torques as the strut rotation angle in [79.5208\({}^{\circ}\), 92.0710\({}^{\circ}\)] and [92.5061\({}^{\circ}\), 101.1959\({}^{\circ}\)], respectively. Similarly, we can see the variations of the second and third torques with the strut rotation angle, as shown in Figure 5b,c. The ranking of torques is useful to guide the design of a feasible plan in emergence extension reliability improvement.
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 4 Performance Analysis
### Adaptive Transmission  
In order to adapt to the actual scenario, both \(E\in\Phi_{1}\), \(E\in\Phi_{2}\) and \(E\in\Phi_{3}\) are considered together, and the adaptive transmission scheme is adopted. That is, when the eavesdroppers are beyond the sector secrecy guard zone, the system adopts direct transmission, and when the eavesdroppers are in the sector secrecy guard zone, AN assisted transmission is used. Considering the probabilities aforementioned that eavesdroppers may be beyond the sector secrecy guard zone and within the sector secrecy guard zone, we study the secrecy performance of an adaptive transmission scheme.  
Combined with the probability \(p_{e1}\) that the eavesdroppers may not be in sector secrecy guard zone, we deduce the SOP, which is derived as  
\[\begin{split} p_{soc1}&=p_{e1}\times\Pr\left(\max \left(\gamma_{E_{A}},\gamma_{E_{A1}}\right)>2^{R_{B}-R_{S}}-1\right)\\ &=p_{e1}\times\left(1-\exp\left(-\theta_{S}\lambda_{E}\left(B+A _{N}-A_{L}\right)\ -\left(2\pi-\theta_{S}\right)\lambda_{E}\left(D+C_{N}-C_{L}\right)\right) \right).\end{split} \tag{36}\]  
Correspondingly, considering the probability \(p_{e2}\) of eavesdroppers in sector secrecy guard zone, we deduce SOP, which is expressed as  
\[\begin{split} p_{soc2}&=p_{e2}\times\Pr\left(\max \left(\gamma_{E_{A1}},\gamma_{E_{B}}\right)>2^{R_{B}-R_{S}}-1\right)\\ &=p_{e2}\times\left(1-\exp\left(-\theta_{S}\lambda_{E}\left(F+E_ {N}-E_{L}\right)\ -\left(2\pi-\theta_{S}\right)\lambda_{E}\left(D+C_{N}-C_{L}\right) \right)\right).\end{split} \tag{37}\]  
Finally, we derive the SOP of the whole system under the random distribution of eavesdroppers, which is written as  
\[\begin{split} p_{soc}&=p_{soc1}+p_{soc2}\\ =& p_{e1}\times\left(1-\exp\left(-\theta_{S}\lambda_{E} \left(B+A_{N}-A_{L}\right)\ -\left(2\pi-\theta_{S}\right)\lambda_{E}\left(D+C_{N}-C_{L}\right) \right)\right)\\ +& p_{e2}\times\left(1-\exp\left(-\theta_{S}\lambda_{E }\left(F+E_{N}-E_{L}\right)\ -\left(2\pi-\theta_{S}\right)\lambda_{E}\left(D+C_{N}-C_{L}\right) \right)\right).\end{split} \tag{38}\]As a result, the total secrecy throughput of the whole system is derived as  
\[\begin{array}{ll}\eta&=\left(1-p_{cob}\right)\left(1-p_{soc}\right)R_{S}\\ &=\left(1-\sum\limits_{i\in\{L,N\}}\left(\frac{\mathrm{v}\left(\frac{\mathrm{z}^ {\left(R_{S}-1\right)r_{D}^{2}}N_{i}}{P_{M_{S}L}\left(r_{D}\right)}N_{i}}{ \Gamma\left(N_{i}\right)}\right)P_{i}\left(r_{D}\right)\right)\\ &\times\left(1-p_{e1}\times\left(1-\exp\left(-\theta_{S}\lambda_{E}\left(B+A_{N }-A_{L}\right)\right.\right.\right.\left.-\left(2\pi-\theta_{S}\right)\lambda_ {E}\left(D+C_{N}-C_{L}\right))\right)\\ &\left.-p_{e2}\times\left(1-\exp\left(-\theta_{S}\lambda_{E}\left(F+E_{N}-E_{L }\right)\right.\right.\right.\left.-\left(2\pi-\theta_{S}\right)\lambda_{E} \left(D+C_{N}-C_{L}\right))\right)R_{S}.\end{array} \tag{39}\]
# Production Plant and Warehouse Automation with IoT and Industry 5.0
## 1 Introduction
### Industry 4.0
#### 1.2.3 IoT-Based Architecture in Industrial Automation  
The architecture followed by industrial automation systems, based on IoT, generally follows architecture that has 3 layers [22], which are:  
* Detection/sensors layer;
* Communications layer;
* Application layer.  
The smart sensors, remote I/O, smart camera, radio frequency identification (RFID) tag, and switches are held in the sensing layer to collect information. The network or communication layer contains the communication technology, fiber optic, 2G/3G mobile communication network, Wi-Fi technology. The data will be transferred from this, i.e., the network layer, to the application layer. There are several protocols of communication in the network layer, according to the different requirements, such as security [6]. Every protocol has its pros and cons, but MQTT is mostly used as it can handle high latency networks. The IIoT application layer is the industrial control center. This layer contains the display units, final controlling elements, auto-contractors, and resource planning, safety, and security management; furthermore, users can integrate data analysis to obtain some intelligent decisions of the control and services. All the technologies in the architecture are effectively employed together to provide the automation of the process, communication, and calibration [23].