esa-sceva/satcom-chunk-collection huggingface dataset:
# Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
## 1 Introduction  
The main technical advancements offered by Software Defined Networking (SDN) provides the unique separation of the control plane from forwarding plane in the hybrid SDN-based WSNs oriented Internet of Things (IoT) architectures [1]. IoT is advanced and heavily deployed Information and Communication Technology (ICT) application for modern-day communication systems. Some applications of IoT utilize the thousands of sensor nodes, that are organized by complicated wired and wireless environments to develop the information collection networks [2; 3]. Meanwhile, the advanced networking settings of SDN allow central control over deployed IoT sensornodes by the support of the SDN central controller. This central control, enable the SDN controller to act as a Network Operating System (NOS), which maintains a range of exceptional central control functions that include network topology to upper layer behaviour of the network. Network topology presents the nodes physical localization to wireless connectivity settings for deployed sensor nodes and their respective report transmission tasks within the data plane arrangements [4; 5]. Whereas these data plane tasks are centrally monitored and controlled by the central SDN controller at the control plane, this new primary controller interventions perform network management, which allows central network reconfigurations by global systems settings at SDN controller [6]. The SDN-based WSNs oriented IoT architecture basically introduce a new dimension of application-sensitive and heterogeneity-aware WSNs. These settings bring stability and offer alternative solutions for existing distributed WSNs network configurations [7]. These SDN-enabled IoT integration solutions alter the management systems of WSNs oriented IoT and offer practicality at the commercial level. In the general architecture of SDN-based WSN as shown in Figure 1, the very first requirement is to have the support of SDN in the WSN architecture [8].
## 3 Case Study
### Training the Network with Time-Domain Data
#### 3.1.2 Analysis of Training Results  
Another set of 12.8 s vibration signals collected from the same subway at the same location but at different times was used as a test network, and the LSTM network as trained above was used to predict the incomplete sequences that lacked data from Channels 7 and 8. Figure 9 indicates the prediction of the lost data in Channel 7, and Figure 10 represents the prediction of the lost data in Channel 8. As shown in the above figures, the vibration signal predicted by the multi-channel data recovery model proposed in this paper based on the lost data recovery model of adjacent channel time series is largely consistent with the real vibration signal curve.  
Figure 8: Effect of different hidden units on model accuracy.  
Figure 9: Comparison of vibration acceleration from Channel 7.  
The vibration acceleration signal is further wavelet-transformed to obtain a more intuitive time-frequency image of Channel 7's vibration acceleration frequency over time. Figure 11 shows the wavelet-transformed time-frequency image of the measured value, and Figure 12 shows the wavelet-transformed time-frequency image of the predicted value. It can be seen from the figures that the change trend in signal frequency of the missing data's predicted value and measured value over time is largely the same, and that the peak frequency appears at approximately the same position.  
Figure 11: Wavelet-transformed time-frequency image of the measured value from Channel 7.  
Figure 10: Comparison of vibration acceleration from Channel 8.  
A Fourier transform is performed on the vibration acceleration signal to obtain a more intuitive comparison diagram of the vibration acceleration amplitude loss data prediction and actual measurement of Channel 7, as shown in Figure 13, and the predicted value of the lost data is consistent with the actual value at all frequencies.  
Figure 14 shows the comparison between the predicted data of the LSTM neural network and the actual vibration acceleration data at different frequencies. It can be seen that the predicted data and the actual data have the same trend at all frequencies. Since Channel 7 is from the sensor on the tunnel wall, the accuracy of the predicted data between  
Figure 12: Wavelet-transformed time-frequency image of the predicted value from Channel 7.  
Figure 13: Comparison of acceleration frequency spectrum from Channel 7.  
10 and 100 Hz is mainly analyzed. The relative error between the predicted data and the actual data is calculated, as shown in Figure 15, and the error values at all frequencies are less than 10%, the overall accuracy rate is up to 95%, and for the 10-100 Hz frequency of interest, the accuracy rate reaches 97%.
# Multi-UAVs Communication-Aware Cooperative Target Tracking
###### Abstract  
A kind of communication-aware cooperative target tracking algorithm is proposed, which is based on information consensus under multi-Unmanned Aerial Vehicles (UAVs) communication noise. Each UAV uses the extended Kalman filter to predict target movement and get an estimation of target state. The communication between UAVs is modeled as a signal to noise ratio model. During the information fusion process, communication noise is treated as a kind of observation noise, which makes UAVs reach a compromise between observation and communication. The classical consensus algorithm is used to deal with observed information, and consistency prediction of each UAV's target state is obtained. Each UAV calculates its control inputs using receding horizon optimization method based on consistency results. The simulation results show that introducing communication noise can make UAVs more focused on maintaining good communication with other UAVs in the process of target tracking, and improve the accuracy of cooperative target tracking.  
Keywords:communication noise model; information fusion; receding horizon optimization +
Footnote †: journal: Applied Sciences
# 6G Visible Providing Advanced Weather Services for Autonomous Driving
## 5 Route Planning with Knowledge Modelling  
Knowledge graphs (KGs) are knowledge structures that consist of multiple entities and their relations. They are used in many Artificial Intelligence (AI) systems [18]. Part of the 6G Visible project is to test knowledge modeling with knowledge graphs for automatic route planning [7]. Figure 6 shows a part of an ontology created in the project. Ontologies are semantic data models that define the base for knowledge graphs. In the project, real-time weather and forecast data will be integrated with KGs. In addition, traffic congestion and accident data will be used if possible. The knowledge modeling will be tested in driving scenarios where there are three alternative routes, and the system should automatically decide the best route. The routes start at a neighborhood to the south of Oulu city and end at the Oulu University campus to the north. The distance is about 20 km along the shortest route. The first route goes along the highway across the city, the second route goes through the city center, and the third route bypasses both the city and the highway. There is one road weather station on the city center route and one on the highway route. Currently, there are forecast points only along the highway route, but additional forecast points can be added to the other routes. The forecast points on the highway occur every 50 m.  
Figure 5: Example of precipitation nowcast for northern Finland. 6G Visible piloting area in turquoise.  
Figure 6: Part of knowledge ontology created in 6G Visible.
## 2 System Layout  
The layout of pose-estimation system is shown in Figure 1, the pose-estimation module is mounted onto the robot manipulator, composed of one calibrated camera, a pair of high brightness LED light source, and two laser rangefinders. The LED light provides a bright and clear view of the battery for the Charge Coupled Device (CCD)camera to capture the image, and the data is then sent to the computer server for pose estimation. The keypoint \(P_{n}\) in the image is extracted from the battery with printing pattern, which is the key step for pose estimation. Laser rangefinders provide the real-time distance from the battery. The 3D position and orientation information of the battery, relative to the camera, is obtained and transformed to the pose relationship between the battery and the robot manipulator. The pose information is transmitted to the robot cabinet, which controls the robot manipulator to complete the process of battery replacement. The demand of the positioning precision would have to be within \(\pm\)2 mm in the x and y direction, and \(\pm\)3 mm in z direction. The positioning error of our system is lower than 1 mm, while the results are inside about 2 mm without spatial consistency, and the yaw errors is \(1^{\circ}\) without laser rangefinders.
## 4 Experimental Setup  
The same configurations described for the numerical models in Section 3 are experimentally measured. The test bench in which the measurements are carried out consists of a square duct with a length of 8 m and a cross section of 0.36 m\({}^{2}\) (0.6 m \(\times\) 0.6 m), which is open in one ending and has an axial fan attached to the other ending (see Figure 3).  
The major loss factor, \(h_{f}\), can be calculated by using the Darcy friction loss equation as follows:  
\[h_{f}=f_{D}\frac{L}{D}\frac{U_{0}^{2}}{2g} \tag{6}\]  
where \(f_{D}\) is the Darcy friction factor, \(L\) is the length of the duct, \(D\) is the diameter of the duct, \(U_{0}\) is the input face velocity (previously defined), and \(g\) is the acceleration due to gravity.  
The Darcy friction factor is calculated by using the Colebrook-White equation as follows:  
\[\frac{1}{\sqrt{f_{D}}}=-2\log\left(\frac{\varepsilon}{3.7D_{h}}+\frac{2.51}{ Re\sqrt{f_{D}}}\right) \tag{7}\]  
where \(\varepsilon\) is the duct's effective roughness (in this case, \(\varepsilon=0.045\)), and \(D_{h}\) is the hydraulic diameter of the duct.  
When started, the fan sucks the air entering the duct through the open-ended termination, causing a pressure difference between the downstream and upstream sections in the presence of the splitters. The pressure is measured at the center of each wall of the duct by placing eight probes, four downstream and four upstream, at the distances specified in the ISO standard (0.5\(d\) downstream and 6\(d\) upstream), as it is shown in Figure 4a. The pressures measured in the four probes placed downstream are averaged, as well as those measured upstream, in order to obtain the average total pressure at both sides of the silencer. The position of the splitter silencers in the duct is shown in Figure 4b.  
In order to properly set the input face velocities to be tested, a Pitot tube is placed in the center of the cross section of the duct at a distance of 30 cm from the open-ended termination. The rotation speed of the fan is set in order to obtain the desired flow speed at the measurement point.  
Figure 3: A 3D model of the test bench setup used for the experimental measurements of pressure loss.
## 3 Shape-Memory Polymer Nanocomposites (SMPNCs)
### Other Non-Metal Nanocomposites
The introduction of SNPs created multifunctional cross-links and reinforced fillers, significantly enhancing the mechanical and shape-memory properties [268]. However, the high surface area to volume ratio of SNPs posed challenges in forming organic-inorganic nanocomposites due to poor dispersion within organic polymer matrices. To address this, chemical surface modification of SNPs was employed to improve interfacial interac  
Figure 26: (**a**) (i) TEM image of PDANPS (scale bar: 1 \(\upmu\)m), (ii) schematic of the chemical structure of PDANPs incorporated PCL-PDA polyurethane nanocomposite, (iii) NIR-responsive shape-recovery behavior of the nanocomposite, and (iv) recovery ratio variations for PCL-PDA with increasing irradiation time under 808 nm laser at intensities of 0.33, 0.66, and 1.00 W/cm\({}^{2}\). Reproduced with permission from ref. [260]. Copyright 2022 American Chemical Society. (**b**) (i) Digital photographs and optical microscope images illustrating the appearance of the damaged and healed samples, (ii) illustrations showing the photo-induced shape-memory and self-healing mechanisms in EVA/PCL/PPy blend composites, and (iii) mechanical performance of the healed nanocomposite. Reproduced with permission from ref. [266]. Copyright 2021 Elsevier. tions between the organic matrix and inorganic SNPs [269]. Consequently, the modified SNP-SMPU nanocomposites exhibited enhanced shape-memory stability, maintaining a shape-recovery ratio (R\({}_{\text{r}}\)) of over 80% even after three test cycles. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Nanofilers** & **Polymer Matrix** &
\begin{tabular}{c} **Temperature** \\ **(T\({}_{\text{max}}\))** **\texttexttexttext  
[PERSON] et al. developed a multifunctional, thermo-irritated SMP, poly(ethylene-co-vinyl acetate) (EVA), doped with non-close-packing SNPs. This composite exhibited reversible transparency changes during shape deformation due to light scattering in the internal cavities formed between the NPs and the polymer matrix [270]. Furthermore, the composite demonstrated excellent stability, enduring at least 20 stretch/reverse cycles without degradation. Other types of non-metallic NPs, such as lignin nanotubes [271], hexagonal boron nitride (HBN) NPs [272], poly (9,9-dioctylfluorene-alt-benzothiazole) (F8 BT) NPs [273], and hydroxyapatite (HA) NPs [274], have also been occasionally used to improve the mechanical and shape-memory properties of nanocomposites.
## 6 Conclusions  
The predominately positive feedback received from SCAN-ECHO participants suggests that this is a viable alternative/supplement to traditional in-person specialty care appointments, offering efficient and effective localized care to patients and facilitating continuing education and professional community building among providers.  
The SCAN-ECHO system could be enhanced via increased administrative support (e.g., allocating reserved hours for participation and encouraging/incentivizing engagement in SCAN-ECHO). Additionally, utilizing a secured Internet-based videoconferencing platform that supports multiple (mobile) devices could expand accessibility to these clinical videoconferences.
## 5 Conclusions  
In this work, the thermal conductivities of 4D C/C composites are predicted by UC1 and UC2, where UC2 is constructed by two 180\({}^{\circ}\) rotational transformations of UC1, and its volume is a quarter of UC1. According to the equations of temperature relations derived from 180\({}^{\circ}\) rotational transformation in Section 2.1 and the temperature boundary conditions imposed on UC1, the temperature boundary conditions of UC2 are derived. The accuracy of the imposed boundary conditions in UC2 is verified when compared the temperature and heat flux at the specific positions of UC1 and UC2. The thermal conductivities of 4D C/C composites calculated by UC1 and UC2 are compared with the experimental values in [27]. The results in transverse direction predicted by UC1 and UC2 are both higher than experimental values, which are 5.8 to 6.2% and 7.3 to 8.2%, respectively. In longitudinal direction, the measured results of UC1 and UC2 are 6.8% and 6.2% higher than the experimental results, respectively.  
According to the results calculated in the above, two conclusions can be drawn:  
In general, the unit cells are constructed by translational transformation used to predict the thermal conductivities of 4D C/C composites. However, a smaller-size unit cell (UC2) is proposed by two 180\({}^{\circ}\) rotational transformations based on UC1 in this work. They can both predict the thermal conductivities of 4D C/C composites accurately. The smaller-size unit cell can save computing time and computing resources greatly.  
The carbon fiber rods with different diameters have an impact on the effective thermal conductivities of 4D C/C composites. The longitudinal thermal conductivity of the 4D C/C composites increases as the diameter of the fiber rods increase, while the transverse thermal conductivities decrease.  
Conceptualization, C.X. and Z.S.; methodology, C.X.; software, C.X.; validation, C.X. and Z.S.; formal analysis, C.X. and G.S.; investigation, C.X.; writing--original draft preparation, C.X.; writing--review and editing, C.X. and Z.S.; visualization, C.X. and G.S.; supervision, Z.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data also forms part of an ongoing study. The authors declare no conflict of interest.
## Geography Geo-Wiki in the Classroom: Using Crowdsourcing to Enhance Geographical Teaching  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1 and [PERSON] 1**  
1 Ecosystems Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg 2361, Austria; E-Mails: see(@iiasa.ac.at (L.S.); [EMAIL_ADDRESS] (S.F.)  
2 Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +43-2236-807-357; Fax: +43-2236-71-313.  
External Editor: [PERSON]  
_Received: 21 March 2014; in revised form: 11 September 2014 / Accepted: 18 September 2014 / Published: 29 September 2014_  
**Abstract:** Geo-Wiki is a crowdsourcing tool used to derive information, based on satellite imagery, to validate and enhance global land cover. Around 5000 users are registered, who contribute to different campaigns to collect data across various domains (e.g., agriculture, biomass, human impact, _etc._). However, seeing the Earth's surface from above does not provide all of the necessary information for understanding what is happening on the ground. Instead, we need to enhance this experience with local knowledge or with additional information, such as geo-located photographs of surface features with annotation. The latest development in enhancing Geo-Wiki in this context has been achieved through collaboration with the University of Waterloo to set up a separate branch called Geography Geo-Wiki for use in undergraduate teaching. We provide the pedagogical objectives for this branch and describe two modules that we have introduced in first and third year Physical Geography classes. The majority of the feedback was positive and in, many cases, was part of what the student liked best about the course. Future plans include the development of additional assignments for the study of environmental processes using Geo-Wiki that would engage students in a manner that is very different from that of conventional teaching.  
**Keywords:** crowdsourcing; Volunteered Geographic Information; land cover; Geo-tagged pictures; Climatology; Physical Geography; web based teaching
Thus, the theoretical analyses may run the risk of overestimating the signal performance and ignore some critical factors for practical applications. In this paper, the UWOC transmission system with a 450 nm blue laser source is designed and established in a 1.5-m water tank. Then, the UWOC 1.25 Gbps data rate digital transmission was carried out under 6-m transmission with a BER value better than 10 \(\times\) 10\({}^{-8}\) at room temperature. Several parameters such as temperature, turbulence, artificial seawater by adding salt to simulate practical application in a river or sea were studied and discussed. The paper is organized as the following. Section 2 explains the system architecture. Calculation of transmission distance in the ideal state will be addressed in Section 3. The optical loss due to coupling, mirror reflection and water absorption will be discussed. In Section 4, the environmental factors were carried out under different parameters such as temperature, turbulence, water refractive index/quality variation will be discussed. Finally, Section 5 provides the conclusion. The paper's results may be useful in facing a real UWOC system in the river or sea. It also provides useful references and suggestions for further research in UWOC.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 4 Common Preparation Methods
### Gas Phase Method
#### 4.3.2 Physical Vapor Deposition (PVD)  
Physical vapor deposition (PVD) is a method in which a paint is vaporized by a physical method to form a film on the substrate surface. In addition to conventional vacuum deposition and sputter deposition techniques, PVD includes various ion beam deposition, ion plating, and ion beam assisted deposition techniques that have prospered in recent years. Types of deposition include vacuum deposition, sputtering, and ion plating [185; 186; 187; 188]. PVD is a common technique for producing hard coatings (hard films). Compared with CVD, the PVD method has a low deposition temperature and does not easily cause deformation and cracking of the substrate. Films produced by PVD are uniform and possess easy to control structures and properties. However, the PVD preparation of the film must be carried out under a vacuum, and the equipment necessary for film formation is expensive.  
To date, PVD methods can deposit not only alloy films and metal films but also ceramics, compounds, polymer films, semiconductors, and so on.
## 5 Recent Experiments
### Relation of the Boson Heat Capacity Peak to the Defect Structure
Using differential scanning calorimetry, the excess enthalpy can be determined as \(\Delta H=\dot{T}^{-1}\int_{T_{i}}^{T_{f}}W(T)dT\), where \(W\) is the heat flow measured by the calorimeter, \(\dot{T}\) is the heating rate, \(T_{i}\) can be accepted equal to room temperature and \(T_{f}\) is the temperature leading to the full crystallization. Figure 10 shows the dependence of the boson peak height \(h_{B}\) calculated using Equation (15) together with the experimental \(h_{B}\)-data as function of the variable \(ln\left[\mu/\left(\mu-\rho\beta\Delta H\right)\right]\)[74]. It is seen, first, that the calculated and experimental \(h_{B}\)-points are quite close. The experimental dependence \(h_{B}(\Delta H)\) nicely falls onto a straight line and the slope of this dependence equals to \((5.14\pm 0.29)\times 10^{-4}\) J/(mol \(\times\) K\({}^{4}\)). This agrees with this slope given by Equation (15) as \(\frac{\Gamma}{\alpha\beta}=4.9\times 10^{-4}\) J/(mol \(\times\) K\({}^{4}\)). Thus, the IT-based approach reproduces the boson peak height and provides a good description of its height on the excess enthalpy of the glass. Figure 9: The height of the boson peak as a function of the defect concentration \(c\) calculated using Equation (2). The line gives the least square fit [73]. With permission from [PERSON], 2019. An interesting study of the boson peak was recently reported by [PERSON] et al. (2019). They performed a molecular dynamic simulation of an equiatomic CuNiCoFe alloy in the crystalline and amorphous states alternatively with chemical disorder (high-entropy state), structural disorder and reduced density. They found that the density reduction and fluctuations of the elastic constants cannot be responsible for the boson peak. However, they revealed that the boson peak in the crystal increases with the concentration of dumbbell interstitials while other defects (e.g., dislocations) do not contribute to it. Interstitial atoms even at a small concentration lead to a boson peak, which is close to that in the glass of the same composition. At that, the vibrational modes of interstitial defects in the crystal resemble those of glass. Finally, the authors of ([PERSON], 2019) concluded that the softened regions provided by interstitials resemble the \"soft spots\" discussed in the literature on MGs ([PERSON], 2019; [PERSON], 2019) and the boson peak is due to quasi-localized defect-related modes.