esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Test of Developed BIOS1 + BIOS2 Sensor Duplex on Real Samples and Validation with A-D Strips  
To demonstrate that the developed BIOS1 + BIOS2 sensor duplex can be successfully applied for automated real-time determination of acetic acid concentrations in film boxes, it was used to quantify the acetic acid vapor released by the film samples described in Section 2.5. For each measurement, the following protocol was employed:  
* The BIOS1 + BIOS2 sensor duplex was placed on the top cover of a circular film box of fixed diameter, with the sensor reaching inside the container (see Section 2.8 Sensors positioning in film boxes) the material of which simulates those used in real storage conditions. The box was put in a refrigerator at 4 \({}^{\circ}\)C, with controlled relative humidity at 50%, to simulate the conditions used in conservation institutes for cinematographic films to simulate storage conditions used in conservation institutes
* The film to analyze was placed in the box, having care to not touch the sensors surfaces. * The box was dosed, and the data acquisition was started along with the data processing software. * After 10 min (time required empirically determined as required for homogeneous diffusion of volatiles in the box and thus obtain stable values from the BIOS1 + BIOS2 duplex) acetic acid concentration was calculated from the acquired data. The data obtained for each film, and the comparison with the acetic acid concentrations determined employing the official methodology (A-D strip placed in box with film for 24 h) are reported in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Acctic Acid** & **Butanol Concentration** & **Acctic Acid Concentration** \\
**Concentration (ppb)** & **(ppb)** & **Determined by Algorithm (ppb)** \\ \hline
1000 & 0 & \(995\pm 15\) \\ \hline
800 & 200 & \(807\pm 11\) \\ \hline
600 & 400 & \(595\pm 10\) \\ \hline
400 & 600 & \(402\pm 5\) \\ \hline
200 & 800 & \(200\pm 4\) \\ \hline
100 & 900 & \(99\pm 1\) \\ \hline
50 & 950 & \(50\pm 2\) \\ \hline
10 & 990 & \(<\)30 \\ \hline
0 & 1000 & \(<\)30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm test on different acetic acid-butanol proportion in closed box to verify linearity of system. Each reported value is the average of algorithm calculations performed on three independent measurements.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 5 Performance Evaluation
### Network Configuration: Sensors, Gateway and Server  
In order to provide more effectiveness and significant experimental comparison and results, we developed two kinds of WSNs, which differ only for the number of deployed sensors. Before describing how networks are composed, we have to mention that in the \"omnetpp.ini\" we set the \"send_interval\" parameter to 1 s, so each node could send one packet per second. Considering the two developed networks, we have:  
* A network composed of 4 sensors, a gateway and a server: in this setting, each sensor will send 100 packets during a simulation time of 100 s. So, considering four sensors, 400 packets will be sent.
* A network composed of 8 sensors, a gateway and a server: also in this case, in 100 s of simulation time, each sensor sent 100 packets so 800 packets will be sent. For simplicity, we show a picture which describes the network with four sensors: To better understand how the overall communication works, each sensor node transmits, in a periodic manner, data towards a gateway node using a wireless channel, which in turns, transmits this data to a server using a cabled connection. In the Figure 10, an ideal warehouse blueprint in which all the described elements are placed in is depicted. As we can see from the picture, the network is characterized by a star topology. The wireless interface of sensor nodes and gateway are specified, also, in the \"omnetpp.ini\" and, all the specifications, used to set all the wireless interface parameters, are listed in the Figure 9. Each sensor node sends a UDP packet with a fixed payload size of 10 bytes, each second, to the server. Packets will be characterized by a fixed UDP header of 8 bytes and an IPv4 header of 20 bytes so, for the MAC layer a frame header of 38 bytes has been considered. Each packet sent by a sensor to the server will be routed by the gateway.  
Figure 9: Wireless Interface parameters.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 3 Background and Preliminaries
### Markov Decision Policy  
In probability theory and statistics, the Markov decision policy provides a mathematical architecture model on how to make decisions in a state where part of the randomness is partially controlled by the decision-maker [23]. For a system, there is transition probability from one state to another, and this transition probability can be derived from the immediately preceding state [24,25]. Therefore, we determine the best base station selection strategy by combining the transfer function of the base station condition in the macro-femtocell system [26].
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 5 ETC-Based Urban Traffic Management: Applications and Practices
### ETC Technology Protocols and System Design
Particularly, the standardized implementation of DSRC protocols and the optimized design of lane system software provide important guarantees for stable ETC system operation in complex urban traffic environments.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 4 Conclusions  
In this paper, a hybrid PCA and LSTM prediction method is presented. PCA is used to process original meteorological data. LSTM is optimized by the DE algorithm to obtain the best prediction model. Combining PCA and LSTM shows great advantages. The proposed method is applied to predict wind speed and the results prove that the method has strong predictive ability for time series data. Based on the analyses of Cases I-III, the proposed model not only requires less data than other models, but also largely improves the accuracy of forecasting results.  
In our future work, hybrid methods using different deep learning models will be considered for time series prediction. In addition, we will improve the PCA method to make it more applicable and efficient.  
[PERSON] and [PERSON] proposed the algorithm. [PERSON] implemented the experiments and wrote the manuscript. [PERSON] supervised the study and revised the manuscript. [PERSON] co-supervised the study. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under Grant No. 61703268, and Open Project Program of Shanghai Key Laboratory of Intelligent Manufacturing and Robotics under Grant No. zk1703.  
The authors would like to thank the anonymous reviewers and the editor for their valuable comments which are extremely helpful for improving the quality of this paper.  
The authors declare no conflict of interest.
## 4 Conclusions  
This study evaluated the feasibility of thermal-RGB imagery and computer vision techniques to identify water stress in oka in a two-year field study. Observations indicated a significant effect of irrigation method as well as the level of irrigation on okra yield and water use efficiency. During high temperatures in summer, flood irrigation may have led to favourable soil moisture and canopy temperature, resulting in higher yield over sprinkler irrigation. For the combination of epochs (30) and batch size (16), both (ResNet-50 and MobileNetV2) deep learning models exhibited optimal results for water stress identification. The accuracy of water stress identification was higher using thermal imagery inputs (87 and 84%) over RGB imagery inputs (78 and 74%) for the DL models (ResNet-50 and MobileNetV2). A maximum yield of 10,666 kg ha\({}^{-1}\) and crop water use efficiency of 1.16 kg m\({}^{-3}\) was recorded for flood irrigation, while 9876 kg ha\({}^{-1}\) and 1.24 kg m\({}^{-3}\), respectively, for sprinkler irrigation at a 100% irrigation level was recorded.  
While ResNet-50 was computationally accurate, MobileNetV2 was computationally efficient. Thus, each is fit for different computational resources. MobileNetV2 could be more handily implemented in microcomputers or embedded systems to be deployed for precision irrigation management in agriculture.  
Conceptualization, Y.A.R. and N.S.C.; data curation, K.D.; funding acquisition, Y.A.R. and N.S.C.; methodology, N.S.C., K.D. and A.S.; project administration, V.P.C. and K.V.R.R.; resources, Y.A.R. and A.K.C.; software, S.K.S., K.D., A.S. and M.M.; supervision, V.P.C.; validation, K.D., A.S. and M.M.; writing--original draft, Y.A.R., N.S.C. and A.K.C.; writing--review and editing. A.K.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Indian Council of Agricultural Research ICAR-CIAE-824--project, Govt. of India, New Delhi.  
Not applicable.  
Data Availability StatementData will be made available on request.  
Authors would like to acknowledge the technical and farm management team for their assistance in maintaining experimental plots and harvesting.  
The authors declare no conflicts of interest.
## 2 Experimental Setup
### Reference Laser System  
To compare the performance of the monolithic laser diode with other laser sources, the measurements have been repeated with a state-of-the-art 1550 nm fiber laser system, from Menlo Systems, with 90 fs pulse duration. This system replaces the red part (optical setup) of Figure 1, while the blue part (THz Setup) remains unchanged.
## 4 Results  
The initial UAV image processing of 417 images (0.016 m/pixel ground resolution at a flying height of 50 m) through the feature matching process, implemented in the SfM algorithm, produced a point cloud comprising 915,832 features over an area of 2020 m\({}^{2}\). The 3D UAV-SfM's model accuracy was evaluated by 12 GCPs, uniformly distributed over the study area using the following equations (Equations (1) to (3)):  
\[\text{RMSE} =\sqrt{\frac{\sum_{i=1}^{n}\left(\text{x}_{\text{RTK,i}}-\text{ x}_{\text{computed,i}}\right)^{2}}{n}} \tag{1}\] \[\text{RMSE} =\sqrt{\frac{\sum_{i=1}^{n}\left(\text{y}_{\text{RTK,i}}-\text{ y}_{\text{computed,i}}\right)^{2}}{n}}\] (2) \[\text{RMSE} =\sqrt{\frac{\sum_{i=1}^{n}\left(\text{z}_{\text{RTK,i}}-\text{ z}_{\text{computed,i}}\right)^{2}}{n}} \tag{3}\]  
where  
Figure 7: Camera position of the area of Wat Maha That, Ayutthaya flight.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**GCP**} & \multicolumn{4}{c}{**Field Survey Data**} & \multicolumn{4}{c}{**Diff**} \\ \cline{2-9}  & \multicolumn{1}{c}{**X (m)**} & \multicolumn{1}{c}{**Y (m)**} & \multicolumn{1}{c}{**Z (m)**} & \multicolumn{1}{c}{**dX (m)**} & \multicolumn{1}{c}{**dY (m)**} & \multicolumn{1}{c}{**Z (m)**} \\ \hline
1 & 669082.4191 & 1587840.2374 & 17.9260 & 0.021 & \(-\)0.009 & 0.043 \\
2 & 669064.9475 & 1587827.1337 & 19.0470 & \(-\)0.012 & 0.012 & \(-\)0.036 \\
3 & 669010.3487 & 1587839.8734 & 18.5760 & 0.028 & \(-\)0.029 & \(-\)0.049 \\
4 & 668978.6815 & 1587827.1337 & 18.1510 & 0.008 & 0.009 & \(-\)0.086 \\
5 & 668983.0494 & 1587790.0066 & 18.5300 & 0 & \(-\)0.001 & 0.021 \\
6 & 668998.6810 & 1587807.1142 & 18.2430 & 0 & 0.001 & \(-\)0.081 \\
7 & 669065.5757 & 158778.3588 & 18.2350 & 0.047 & 0.003 & 0.026 \\
8 & 669086.0590 & 158798.7424 & 19.3790 & \(-\)0.027 & 0 & 0.068 \\
9 & 668984.1413 & 1587740.1397 & 20.1880 & 0.051 & \(-\)0.040 & \(-\)0.012 \\
10 & 669018.7205 & 1587733.2239 & 19.8580 & 0.001 & 0 & \(-\)0.057 \\
11 & 669052.2078 & 1587737.2278 & 19.2190 & 0 & 0.001 & 0.021 \\
12 & 669093.3349 & 1587739.7757 & 17.0110 & 0 & 0.001 & \(-\)0.062 \\  & \multicolumn{1}{l}{RMSE H = sqrt ((\(\mathrm{sum}(\mathrm{dX})^{2}+(\mathrm{sum}(\mathrm{dY})^{2}/\mathrm{n})\))} & \multicolumn{1}{c}{0.028 m} & & \\  & \multicolumn{1}{l}{RMSE V = sqrt ((\(\mathrm{dZ})^{2}/\mathrm{n}\))} & \multicolumn{1}{c}{0.052 m} & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unmanned aerial vehicle structure-from-motion’s (UAV-SfM) model accuracy assessment.  
Figure 8: Comparison of the terrestrial and UAVs point cloud computed on 20 checkpoints for the Prang structure. (**a**) Point clouds from terrestrial laser scanning (TLS). (**b**) Point clouds from UAV.
## 6 Results and Discussion
### Evaluating the Flexibility of HPWH: Peak Savings  
The ability of the loads to modify their consumption to reduce their maximum peak (from 10% to 50%) was also analysed. Specifically, this is accomplished by applying peak shaving policies to respond to a DR event. Again, the temperature of each HPWH load must be in the range between 50 \({}^{\circ}\)C and 62 \({}^{\circ}\)C, and the demand must not be reduced but delayed to maintain load service.  
Figure 16 depicts some results for the peak shaving control. HPWH loads were committed to reducing their peak from 10% to 50%, but in every case, the real peak reduction reached is less than committed. The average temperature of the mixed water inside the tanks is always in the range from 51 \({}^{\circ}\)C to 59 \({}^{\circ}\)C, as shown in Figure 16b. Table 7 presents the results of the peak reduction dispatch. As said before, the maximum peak is always less reduced than expected, reaching a maximum percentage (\(-34.4\%\) in \(-40\%\) and \(-34.6\%\) in \(-50\%\) peak shaving dispatches). At this point, the loads do not have more capacity to reduce their peak or phase out their consumption without losing the service conditions (a minimum temperature of warm water). In this way, the limits of flexible demand can be defined and controlled more accurately through PBLM models.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Indicator** & **Individual Day** & **November** & **July** \\ \hline PV generation & 103 kWh & 130 kWh & 251 kWh \\ HPWH agg demand without DR & 160 kWh & 163 kWh & 147 kWh \\ HPWH agg demand with DR & 163 kWh & 165 kWh & 169 kWh \\ Net demand without DR & 112 kWh & 108 kWh & 86 kWh \\ Net demand with DR & 82 kWh & 76 kWh & 55 kWh \\ Use of own PV without DR & 46.6\% & 39.9\% & 29.4\% \\ Use of own PV with DR & 79.1\% & 69.0\% & 45.3\% \\ Increase in self-consumption & 32.5\% & 29.1\% & 15.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of HPWH following a PV dispatch.
# RIS-Assisted Fixed NOMA: Outage Probability Analysis and Transmit Power Optimization
## 7 Conclusions  
The analytical outage probability expressions for RIS-SR-FNOMA and RIS-AP-FNOMA systems are derived in this paper. For larger numbers of reflecting elements, there is a close match between the MC simulations and analytical curves. The FU outage performance is superior to that of NU in both RIS-SR-FNOMA and RIS-AP-FNOMA systems. The outage performance for NU and FU improves as the elements in RIS increase. The optimal powers for NU and FU are also determined to maximize the sum capacity and satisfy the QoS constraints of both users. The sum capacities of RIS-SR-FNOMA and RIS-AP-FNOMA are clearly superior to FNOMA. Even with negative SNR values, the RIS-SR-FNOMA and RIS-AP-FNOMA systems can provide higher sum capacities. Over sub-optimal power assignments, the optimal power assignment maximizes the sum capacity. In terms of outage and sum capacity, RIS-AP-FNOMA is marginally better than RIS-SR-FNOMA. FNOMA paired with RIS, it may be argued, performs better in terms of outage and sum capacity. As a result, RIS-assisted NOMA will be a viable alternative for constructing next-generation networks that can handle massive connections. As a future work, the proposed model can be modified for uplink communication. The proposed system can be extended for ordered NOMA and cooperative NOMA.  
**Author Contributions:** The manuscript was written through the contributions of all authors. V.B.K., P.G.S.V. and S.J.T. were responsible for the conceptualization of the topic; article gathering and sorting were carried out by V.B.K., A.L.I, F.R.C.S., P.G.S.V., S.J.T., D.-T.D. and A.M.; resources, V.B.K., P.G.S.V., S.J.T. and A.M.; supervision, V.B.K., A.L.I, F.R.C.S., P.G.S.V., S.J.T. and D.-T.D.; validation, V.B.K., A.L.I., F.R.C.S., P.G.S.V., S.J.T. and D.-T.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work of [PERSON] was supported in part by the Nigerian Petroleum Technology Development Fund (PTDF) and in part by the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data are available from the corresponding author with a reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 5 Conclusions  
This paper presents a non-uniform-illumination image enhancement algorithm based on the Retinex theory, addressing the problems of low brightness and unclear details in non-uniform-illumination images and shortcomings observed in the application of traditional methods in enhancing non-uniform-illumination images, such as local over-enhancementand insufficient parameter adaptability. The proposed algorithm follows a systematic approach to achieve comprehensive image enhancement. It begins by employing the color balance method to address the color differences in the non-uniform-illumination image. After obtaining a uniformly colored non-uniform-illumination image, the image is converted from the RGB space to the HSV space. In combination with the Retinex theory, the multi-scale Gaussian function is utilized to obtain the precise illumination component and reflection component in the HSV space. The illumination component map is then divided into distinct blocks, categorizing them into high-light and low-light areas for independent enhancement. The low-light area is enhanced using an adaptive gamma correction algorithm, while the high-light area is enhanced by a function based on the Weber-Fechner law. Furthermore, image fusion and detail improvement are applied to obtain high-quality enhanced images. Through a comparison experiment with typical algorithms, the efficacy of the algorithm proposed in this paper is proven. Specifically, it achieves a better balance of the overall brightness in the image while retaining image details, avoiding the distortion caused by excessive enhancement. As a result, the enhanced image retains more information with clearer details.  
Project management guide, X.J.; Writing--original draft, S.G.; Writing--review and editing, H.Z.; Survey--collecting data, W.X. All authors have read and agreed to the published version of the manuscript.  
This project is supported by the Science and Technology Project of State Grid Jilin Electric Power Co., LTD., Jilin, China (Contract No.: 512342220003).  
Not applicable.  
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.  
Here we would like to express our sincere thanks to [PERSON] and [PERSON] for their help in providing data and relevant materials in the research work, as well as funding.  
The authors declare no conflict of interest.
## 1 \(C\)
###### Abstract  
To predict the corrosion failure of carbon steel oil and gas pipelines more accurately, a new corrosion failure prediction model for submarine oil and gas transport pipelines was constructed. A corrosion failure prediction management system was also developed based on the constructed model. To construct the model, corrosion experiments were carried out to analyze the influences of temperature, partial pressure of CO\({}_{2}\), pH value, and flow rate acting on the corrosion rate. Based on the analysis results and the corrosion experiment data, a new corrosion failure prediction model containing the time and flow rate for oil and gas pipelines was constructed. The model is based on the existing corrosion prediction model and has a determination coefficient, \(R^{2}\), of 0.9573, which indicates good prediction accuracy. A machine learning prediction model was also used to predict, and the prediction results are compared with that of the proposed model, which further verifies the accuracy and feasibility of the proposed model. A corrosion failure prediction management system for carbon steel oil and gas pipelines was developed based on the constructed model, which makes corrosion failure prediction more convenient and faster and provides a reference for the accurate prediction and efficient control of oil and gas pipeline corrosion failure.  
submarine oil and gas transport pipeline; corrosion experiment; corrosion prediction model; corrosion prediction management system
## 3 Results and Discussions
### Fabrication of Porous Scaffolds Using FDM  
Given that the FDM PLA/15 wt.% WSP biocomposite possessed a good mechanical property in this study, it was chosen to further fabricate porous scaffolds with various porosities (40-75%) and interconnected pores in sizes of 50-200 \(\upmu\)m. It can be seen from Figure 11, with an increasing porosity, the yield strength and modulus experienced a drop, getting to a highest value of 19.96 MPa and 104.1 MPa at 43.43% of porosity; and, getting to a minimum value of 1.67 MPa and 10.93 MPa at 74.41% of porosity, respectively. The results showed that the scaffolds with higher porosity would have reduced strength and modulus. The strain went a contrary route, as compared to the strength, climbing sharply to its peak (25.96%) at 64.17% of porosity, followed by a sharp drop to its minimum (15.28%) at 74.41% of porosity. It deserves to be observed that the scaffold with porosity of 64.17% possessed the highest strain, a decent strength and modulus, showing an excellent ability to sustain the compression load.  
The compression process included two stages, the yield plateau and densification. The width of yield plateau played a key role in deciding the ability of compression energy absorption. The scaffold with porosity of 64.17% possessed the biggest yield plateau width, so that it could not be crushed, even at the deformation of 55%. However, the plateau width decreased when increasing the porosity to 74.41%.  
From the picture of the scaffold before compression, it can be observed that the pores were distributed between 50-200 \(\upmu\)m. The result showed that not only the porosity, but also the pore size, could be controlled through FDM. The results in the literature revealed that the compression modulus of a trabecular bone was 10 to 2000 MPa [51; 52]. If the size of the pores is in the range of 10-75 \(\upmu\)m, the fibrous tissue was able to penetrate into the pores of the scaffold. If the range is 75-100 \(\upmu\)m, it led to the ingrowth of un-mineralized osteoid tissue. If the size was larger than 200 \(\upmu\)m, it resulted in substantial bone ingrowth [51; 52]. The higher porosity and larger interconnected pores benefit the bone ingrowth and osseointegration of implants after surgery [35]. Therefore, in this work, the scaffold with porosity of 40-75%, the pore size in the range of 50-200 \(\upmu\)m, and the compression modulus of 10-104 MPa can satisfy the requirements of the trabecular bone implants, and it has the potential to be used in tissue engineering. The incorporation of 15 wt.% WSP can cut down the cost of PLA by about 15% and enhance the degradation speed of PLA.  
Figure 11: The relationship between porosity and compression properties.