esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 3 Literature Review  
There is a lot of work in the literature comparing routing protocols in VANET, and the need of this comparative study is very interesting to enhance the communication between vehicles and to avoid the risky situations for drivers and passengers.  
[PERSON] et al. [9] discussed and compared OLSR, AODV, and DSDV. They concluded that OLSR is the best protocol, which gives the best performance in the VANET scenario in the case of the packet delivery ratio and end-to-end delay, especially when the density of vehicles and speed increase. The simulation is performed under the NS3 simulator, and the mobility and movement nodes are created by BonnMotion.  
The paper [16] compared between OLSR, AODV, GPSR, and GPCR using SUMO to extract mobility on a real map and NS3 as a network simulator, varying the number of vehicles. Their results prove that the OLSR is better in the case of packet delivery ratio, whereas, in the case of the end-to-end delay, GPSR presents the best performance due to the use of the greedy forwarding method.  
The authors in [17] compared the AODV, DSDV, DSR, and GPSR routing protocols in the VANET environment, using NS2.35. The best performance is obtained for the GPSR protocol in the case of end-to-end delay, whereas the AODV protocol is better in the case of the packet delivery ratio.  
[PERSON] et al. (2020) [18] presented the improvement of the GPSR protocol called AGPSR. This improvement is based on the greedy forwarding mechanism that makes the GPSR protocol more efficient in the event of a delay in E2E and the packet delivery ratio, hence the importance of the greedy forwarding mechanism.  
[PERSON] et al. (2020) [19] compared OLSR and GPSR, and they concluded that OLSR outperforms GPSR in terms of PDR and throughput.  
All works previously presented prove that the OLSR presents the high and best performance in the case of end-to-end delay and packet delivery ratio. In addition, the integration of the greedy forwarding mechanism makes the routing protocol more efficient than the classic version.
# Wind Load Distribution in Float Photovoltaic System
## 2 PV Generation System  
### Overview  
When installing a solar module, the structure is fixed to the ground to resist the load on the ground, but when installed on the water surface, the boundary conditions are unstable, so proper design considerations are needed as a floating structure. Floating PV generation is a method of constructing a solar power plant on the surface of the water, and although it is similar in terms of system to existing solar power generation, it shows a big difference in installation method [1; 2; 3].  
In the case of existing solar power plants, piles are installed on the ground, or existing structures to install solar modules and structures for fixing solar modules are built, and then solar systems are installed. Therefore, all the load on the solar module is transmitted to the foundation or ground through the piles.  
Floating PV, however, does not require piles or foundations and is a system that floats on water using buoyancy and moorings, as shown in Figure 1. The transmission system from the power plant to the electric room and transmission track uses the same method as the existing ground solar power generation, except for the cable installation method. The cable from the solar power plant to the electric room is a method of building an underground line on the ground or transmitting power through an electric pole. However, water solar power generation mainly uses a method of diving cables underwater from power plants to the electric room through the riverbed topography or floating on the water to directly connect to the electric room.  
The floating photovoltaic power system consists of floating structures to fix solar modules, buoyancy resisting gravitational loads transmitted from the structure, and a mooring system for fixing horizontal loads. The floating structure must provide strong support for the solar modules, be sufficiently resistant to external forces such as wind and wave loads, and ensure long-term durability against corrosion and fatigue given the special conditions of the water. In particular, in terms of structural safety and constructability, the structure is designed as a unit structure, and in order to be sensitive to the movement of the water surface, the connection of each structure is hinged to minimize the transmission of bending moment.  
Figure 1: Configuration of floating photovoltaic system.
## 5 Experiment Results  
The outdoor experiments were conducted in an open field near National Formosa University in Yunlin County, Taiwan. Figure 6 shows the entire system mounted on the drone, and Table 1 lists the payload weight and power budget. The total payload of all items at an external fixation and equipped on the drone was 1053 g, with a power budget smaller than 17.5 W. The total payload included the SWIR camera and its kits, fixation, onboard computer, GPS, battery, cable, and frame grabber. The payload weight and power budget management are crucial in an aerial mission and affect the drone's flying time. In our experiment, our proposed framework ensured the mission time being more than 30 min.  
Figure 5: Aerial SWIR remote-sensing map construction using two SWIR images. (**a**) The key feature selection process. (**b**) The result of aerial SWIR image map construction.  
Figure 6: Prototype of the intelligent aerial remote-sensing system. (**a**) Drone flying using the proposed system. (**b**) The whole payload of the proposed system.
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 4 The Implementation and Optimization of Efficient GPU Tensor Sensing
### Design and Implementation of the GPU Tensor Sensing
#### 4.1.4 Least Squares Minimization  
As shown in Algorithm 2, least squares minimization (LSM) is the major step of the tensor sensing approach, which is the most time-consuming part of the entire approach. There are many approaches to perform least squares minimization. QR factorization is one of the most efficient approaches, which is well supported by CUDA.
## 3 Result and Discussion
### Wavelengths Selection by the Hybrid ANN-SA  
Diagrams of the reflectance spectra and the spectra converted to absorption of Fuji apples were created from samples at different maturity times. Different peaks in the absorption spectral graphs represented useful information to predict Fuji apples' starch and TSS properties. Estimating the starch content for non-destructive Fuji apple, the most effective wavelengths selected by hybrid ANN-SA algorithm were 852.58, 855.54, 849.03, 855.83, 853.47, 844.90 nm, while estimating TSS, the most effective wavelengths selected by hybrid ANN-SA algorithm were 967.86, 966.67, 964.90, 958.40, 957.22, 963.97 nm. Table 2 gives the optimal structure of the ANN hidden layers adjusted by the bio-based optimization algorithm to predict starch and TSS. As can be seen, the best ANN structure for predicting starch content and TSS is three layers and two layers, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & **Number of Layer** & **Number of Neurons** & **Transfer Function** & **Backpropagation Network Training Function** & **Backpropagation Weight/Bias Learning Function** \\ \hline \multirow{3}{*}{Starch} & \multirow{3}{*}{3} & 1 st Layer
## 3 Methodology Steps
### Multi-Agent System (MAS) Architecture Design  
A structured design of the multi-agent system architecture multi-agent system (MAS) model is developed, including:  
1. Definition of intelligent entities and their respective functions (orchestration, space allocation, route optimization, flow control, boarding management, and supervision).
2. Specification of communication between agents, defining information exchange protocols, and collaborative decision-making models.
3. Development of an architecture diagram, visualizing the key interactions between agents to ensure an efficient flow of information and operations.  
The integration logic between heterogeneous optimization algorithms is managed by the orchestrator agent (EOO), which coordinates task allocation and data exchange among specialized agents through standardized communication protocols such as contract net protocol (CNP). Each agent operates its assigned optimization function autonomously, minimizing the need for centralized processing. Regarding computational resources, the system design is distributed to enable scalability, with each agent requiring moderate processing capabilities suitable for typical industrial IoT or edge computing environments.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### ML Models
#### 2.4.1 Logistic Regression  
The LR is a technique applied to problems with binary response variable \(y\), i.e., the number of available categories is two. The model conducts a linear mapping \(\theta x\), on the \(d+1\) dimensional input feature space of \(x\) considering a bias term, where \(\theta\in\mathbb{R}^{d+1}\). Subsequently, through a sigmoid transformation (Equation (6)), the probability of a given data vector \(x_{i}\) classified as 1 is calculated by Equation (7).  
\[h(x)=\frac{1}{1+e^{-x}}, \tag{6}\]  
\[P(y_{i}=1|x_{i})=h(\theta x_{i}), \tag{7}\]  
where \(\theta x_{i}\) is a linear combination of the features and 1 is the damage class (0 is for the undamaged class). For \(m\) samples, \(\theta\) is determined by minimizing the loss function, as follows,  
\[L(\theta)=\sum_{i=1}^{m}\Big{\{}y_{i}\ln[h(\theta x_{i})]+(1-y_{i})\ln[1-h( \theta x_{i})]\Big{\}}. \tag{8}\]  
When the number of categories is more than two, the LR can still be applied for classification using an approach called \"one vs all\" approach where one damage class is labeled as 1 and the others are labeled as 0 for training and the process is repeated for each class. Using Equation (7) and the training set, the coefficient vectors \(\theta\) are evaluated. In this study, the four damage categories were assigned 0 (undamaged), 1 (minor damage),  
Figure 5: Plots of the relationships of the proposed features and the displacement ductility.  
2 (moderate damage), and 3 (major damage). The LR model finds the linear boundary that separates each damage category from the others.
# Dynamic Simulation of a 5 Degrees of Freedom Rotor Dropping on a Protective Bearing
## 3 Characteristic Analysis of Dynamic Simulation Model of 5-Dof Rotor-Protective Bearing System
### Simulation Analysis of Contact Force Characteristics of Rotor Dropping  
ADAMS can also be used to analyze the contact force. Through the post-processing function of ADAMS, the contact force between the rotor and the protective bearing during the rotor drop is studied. Compared with the numerical calculation, ADAMS has significant advantages. For the contact force characteristics of the rotor drop, the friction coefficient between the protective bearings and the initial speed are studied by changing the rotor. ADAMS simulation is carried out with 6000 r/min as the initial speed, and the simulation time is 0.1 s. The results are shown in Figure 12 below.  
It can be seen from Figure 12 that the rotor is in intermittent contact with the inner ring in the process of falling into the protective bearing. At the beginning, the impact force is strong and the impact interval is large, and then the impact force begins to attenuate and  
Figure 11: Simulation of bearing inner ring speed fluctuation when the initial rotor speed is 8000 RPM.  
Figure 12: Simulation of contact force between rotor and bearing inner ring at initial speed of 6000 rpm.  
the impact interval is also reduced. At the same time, it can be seen from the results that at the beginning, the contact forces on the inner rings of the left and right protective bearings are synchronous and similar in size. The contact forces on the left and right inner rings begin to be asynchronous with the drop process, so it can be analyzed that the rotor begins to fall in a cylindrical motion during the drop process, and the motion state of the rotor evolves from cylindrical motion to conical motion with the drop process.  
In order to study the relationship between the initial speed of the rotor and the contact force between the rotor and the inner ring of the bearing in the process of rotor dropping, the initial speed of the rotor is set from 6000 r/min to 8000 R/min and ADAMS simulation is carried out. The results are shown in Figure 13 below.  
It can be seen from Figure 13 that with the increase in the initial speed of the rotor, there is no significant change in the contact force of the rotor falling on the inner ring of the protective bearing. At this time, the contact forces on the left and right protective bearings are synchronous at the beginning, and with the falling, the contact forces on both sides begin to be asynchronous. It can be analyzed that the rotor motion is cylindrical motion at the initial stage of the drop process, and then the contact force begins to be asynchronous, which can be inferred that the rotor evolves from cylindrical motion to conical motion. With the increase in rotating speed, the impact interval of contact force decreases and the occurrence of contact increases.
# Antimicrobial Effect of Titanium Hydroxapatite in Denture Base Resin
###### Abstract  
In the current study, we investigated the antimicrobial effect of titanium hydroxyapatite (TiHA), a photo-oxidizing organic material, in denture base resin on single-species biofilms formed by laboratory bacteria and on multispecies biofilms formed by bacteria from the human saliva. Although TiHA reportedly restricts the growth of planktonic bacteria upon ultraviolet A (UVA) irradiation, the antimicrobial effect of TiHA on bacterial biofilms remains to be elucidated. Resin specimens were prepared by adding TiHA to polymethyl methacrylate-based, denture base resin. The specimens were incubated with biofilm-forming _Streptococcus sanguinis_, _Actinomyces naeslundii_, _Staphylococcus aureus_, _Escherichia coli_, or bacteria from the human saliva obtained from volunteers. After UVA irradiation, the colony-forming units (CFUs) from the biofilms formed on the specimens were determined. CFU numbers for _S. sanguinis_, _A. naeslundii_, _S. aureus_ that formed biofilms on TiHA-containing specimens were significantly lower than those formed on specimens without TiHA. TiHA did not reduce the CFUs of biofilm-forming _E. coli_. In all cases, CFU numbers in the biofilms formed on TiHA-containing specimens by the salivary bacteria were significantly reduced. In addition, neither a 56 h UVA irradiation nor a 28 d soaking in water diminished the antibacterial effect of TiHA. TiHA in denture base resin exerts an antimicrobial effect on single-species bacterial biofilms and biofilms formed by a wide variety of bacteria from human saliva.  
titanium hydroxyapatite; photocatalyst; bacterial biofilms; antimicrobial effect; denture
## 4 Results and Discussions
### Effect of Frequency Contents on the Seismic Responses  
To clarify the effects of the frequency contents on the seismic responses of the electric cabinet, a comparison between the responses obtained under the two ground motion groups is illustrated in Figure 11. The scatter points in each subfigure are the maximum acceleration values of the structural response in both cases with and without TMD. The horizontal lines are the mean values of each case. As can be seen from these figures, the mean value of the maximum acceleration in both cases of high- and low-frequency ground motions indicates that TMD is an effective device to significantly mitigate the acceleration response of the cabinet by absorbing undesirable dynamic excitation.  
Another finding can be explained through the acceleration amplification factor (\(AAF\)) that is defined as the ratio of mean acceleration response at the top of the cabinet (\(A_{cabinet}\)), and the PGA of input excitation (\(A_{excitation,0.5}\)) that is equal to 0.5 g. The \(AAF\) is calculated as follows:  
\[AAF=A_{cabinet}/A_{excitation,0.5} \tag{7}\]  
The comparative results of \(AAF\) are tabulated in Table 2. In the case of without TMD, the \(AFF\) value of HF earthquakes is 3.84 that greater than three times compared to the LF group. When TMD is considered, a reduction of around 50% compared to without TMD system is found for both HF and LF earthquakes, corresponding to the \(AFF\) values of 2.07 and 0.74, respectively. It is found that the structural response of the cabinet depends strongly on the variation of the earthquake frequency contents.  
In addition, the spectral acceleration of the electric cabinet according to the numeric model with and without TMD in Figures 12 and 13. From these figures, it can be seen that the models with TMD have lower spectral acceleration responses compared to those models without the TMD system. Moreover, the trends of spectral acceleration of LF earthquakes in both cases with and without TMD are the same with the acceleration responses in Figure 11, with a smaller response compared to the HF group. This leads to the conclusion that the HF earthquakes amplify the structural response of the cabinet structure significantly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **wo TMD** & \multicolumn{2}{c}{**w TMD**} \\ \hline  & **HF** & **LF** & **HF** & **LF** \\ \hline \(AFF\) & 3.84 & 1.36 & 2.07 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(AFF\) of the cabinet for different cases.  
Figure 11: Maximum acceleration responses: (**a**) High-frequency; (**b**) low-frequency.
## 4 Conclusions
Farmers can access and analyze real-time data from DTs, enabling them to make informed decisions and optimize their farming practices. In addition, DTs can serve as educational tools, providing interactive and immersive experiences for students and professionals to learn about sustainable farming techniques and resource management. However, the farmer's participation in these actions for crop improvement through technology is not always uniform. Here, the critical point is always the cost and financial commitment of the agricultural community [2]. They always appreciate systems without any cost, but this is not always a possibility. They are usually reluctant to invest in research and automated new technologies, as their knowledge on maintaining the systems is usually low. To sum up, Agriculture 6.0 can facilitate knowledge sharing and capacity building through digital platforms, enabling farmers to access information, best practices, and expert advice. This can enhance agricultural productivity, improve farming techniques, and empower farmers with the necessary skills and knowledge to adapt to changing agricultural landscapes, but the involvement and commitment of all the social network, and even the agricultural community is of major importance for the successful implementation of this type of technology. Conceptualization, M.G.-P., E.A.N. and J.S.-G.; methodology, M.G.-P. and P.C.-R.; software, M.G.-P., J.S.-G. and P.C.-R.; validation, J.S.-G., P.C.-R. and M.G.-P.; formal analysis, [PERSON] and E.A.N. ; investigation, [PERSON]; resources, E.A.N. ; data curation, [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Spanish Ministry of Science and Innovation/Spanish Research Agency (MCIN/AEI) within the project Agriculture 6.0 with reference TED2021-131040B-C33, funded by MCIN/AEI/10.13039/501100011033 and by the European Union \"NextGenerationEU\"/PRTR. It was also funded by the Spanish Ministry of Universities with grant PRX22/000503. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Acknowledgments:** The authors would like to thank the technical staff from the IDS company for their support on the drone flights. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Evaluation
### Evaluation Criteria
#### 4.1.3 Fiber Area Fraction  
To compute the fiber area fraction, the area \(A_{\text{s}}\) of the triangulated surface is first extracted by summing the area of its triangles. Then, the geodesic distance field \(\phi_{path}\) on the mesh is calculated with the path points serving as the source \(\gamma\). The area for which \(\phi_{path}(x_{p})>d\) is the uncovered area \(A_{u}\). This is calculated by summing the triangle areas of a subdivided mesh, where all vertices fulfill \(\phi_{path}(x_{p})>d\). Lastly \(\frac{1-A_{u}}{A_{\text{s}}}\) gives us the index for the area fraction. This value indicates the amount of used area, with 0 representing no fiber deposited and 1 meaning complete coverage.
## 3 Results and Discussion
### Flexural Strength  
Figure 5 shows the flexural strengths of 28-day old base concrete. When the vol% of hybrid fiber was 0.54%, the flexural strength generally decreased as the volume proportion of polyolefin fiber increased. With 0% polyolefin fiber (100% steel fiber), the flexural strength of the base concrete was 7.1 MPa. As the volume proportion of polyolefin fiber increased to 25%, the flexural strength of the base concrete decreased to 6.38 MPa. The flexural strengths of base concrete with 50% and 75% polyolefin fiber slightly increased to 6.72 and 6.74 MPa, respectively, but it was still less than that with 0% polyolefin fiber. The flexural strength of the base concrete with 100% polyolefin fiber (0% steel fiber) was 5.85 MPa, showing the lowest flexural strength.  
In the case of base concrete with a fiber content of 1.08 vol%, the flexural strength of base concrete was 7.92 MPa for 0% of polyolefin fiber and increased to 8.55 and 8.56 MPa as the incorporation ratio of polyolefin fiber increased to 25% and 50%, respectively. A synergetic effect was observed up to 50% polyolefin fiber replacement although there was some level of standard deviation in the flexural strength data. An interpretation of such a result is that, at a total fiber content of 1.08%, a steel fiber ratio up to 50% did not decrease the flexural strength as much, and some contribution from the polyolefin fiber was present to increase the flexural strength. When the amount of polyolefin fiber exceeded 75%, a considerable drop in flexural strength was observed. The flexural strengths of base concrete with 75% and 100% polyolefin fiber were 6.78 and 6.32 MPa, respectively.  
Figure 5: The 28-day flexural strength of hybrid fiber reinforced base concrete.  
The flexural strength generally increased as the total volume of fiber increased up to 50% of polyolefin fiber reinforcement. When the amount of polyolefin fiber was 75%, the differences in the flexural strengths with an increase in the total amount of fiber were considerably reduced. This phenomenon was identical to the case of compressive strength, indicating that polyolefin fiber also contributes much less to flexural strength than steel fiber. Consequently, considering the data obtained from the compressive strength and flexural strength, it was confirmed that the amount of polyolefin fiber should not exceed 50% of replacement when designing optimal proportions of hybrid fiber composed of steel and polyolefin fiber.