esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Drone Handover Methods Suitable for Multipath Interference Due to Obstacles
## 3 How to Evaluate Flying Drones  
A wireless network evaluation is typically performed layer-by-layer. Examples include antenna characteristics, propagation characteristics, network protocols, and the application quality of service. However, because drones can construct a three-dimensional network in the air, buildings and obstacles in urban areas can reflect and block the network, resulting in multipath interference. Unlike conventional methods for evaluating terrestrial networks, propagation conditions are highly complex and depend on the height of buildings, the drone's flight altitude, and the speed of movement of the drone. In addition, these propagation conditions have a significant impact on upper-layer communication protocols. In other words, the actual applications and quality of service also vary. Therefore, if a service operation is to be launched, it is necessary to conduct an evaluation and verification in a real environment; however, conducting experiments using actual flying drones is a significant hurdle. Moreover, apart from drone communications, ground station services also require significant operation time and cost for propagation experiments during station design. Propagation characteristics such as SINR and bit error rate (BER) of the physical layer have been used as evaluation indices for wireless communications. However, because the wireless communication services used by users include upper-layer protocol performance, such as streaming and net surfing, it is necessary to evaluate the communication quality of user-based applications. In addition to the difficulty in the analysis methods and evaluation indices that integrate the various layer technologies, the simultaneous analysis of signal processing at the physical layer and the analysis of radio propagation has resulted in sim  
Figure 6: Handover and multipath interference from drone flight.  
Figure 7: Conventional handover sequence.  
plified and inaccurate evaluation results [36]. In this study, we developed and evaluated a cross-layer computer simulator, from the physical layer to the internet protocol (IP) layer. This simulator is characterized by its ability to evaluate the impact of multipath interference caused by structures on throughput at the (IP) layer, such as the transmission control protocol (TCP) and user data gram protocol (UDP). Moreover, it can evaluate time-series data assuming a high-speed drone flight. The advantage of the simulator is that even engineers without expertise in the physical or higher layers can easily evaluate the performance of the entire system, regardless of where the service is provided. In other words, apart from drone networks, new communication methods devised in the future will be in great demand for virtual evaluation prior to the development of actual equipment.
###### Abstract  
Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.  
wireless power transfer; Petri net; trajectory optimization; Internet of Things; unmanned aerial vehicle
## 2 Materials and Methods
### Material Properties  
Two main types of units were used in the test as shown in Figure 3, which were bond-beam units and cleaning-hole units, respectively. The overall dimensions of the two kinds of units were the same: 390 mm (length) \(\times\) 190 mm (height) \(\times\) 190 mm (thickness). The ratio of the vertical cell to the entire volume of the unit was 47%. The face shell of the unit was 30 mm. Bond-beam units have the advantages of grouting continuity in both the vertical and horizontal directions. The cleaning-hole units were only constructed in the first course of the cast-in-place specimens where the redundant mortar was first eliminated and then the vertical reinforcement was overlapped. The material test was conducted on the same day as the wall test.  
The average compressive strength of the units was 17.4 MPa (c.o.v. = 8.3%) based on the gross area. The mortar cubes with dimensions of 70.7 mm \(\times\) 70.7 mm \(\times\) 70.7 mm had an average 28-day compressive strength of 13.1 MPa (c.o.v. = 4.8%). The dimension of grout samples was 100 mm \(\times\) 100 mm \(\times\) 100 mm. The premixed fine grout had a slump of 200 \(\pm\) 20 mm and was added with 5% volume of expanding agent, which satisfies [29]. The average compressive stress of grout was 45.0 MPa (c.o.v. = 4.0%). In addition to the above-mentioned material test, the prism test was also carried out because of the material complexity of masonry. The dimension of the prism was 590 mm (length) \(\times\) 990 mm (height) \(\times\) 190 mm (thickness). The average compressive strength of the prisms was 21.0 MPa (c.o.v. = 8.2%). All the samples were cured in the same conditions as the test walls. The average yield strength of the D8, D14, and D16 bars were 452 MPa, 463 MPa, and 455 MPa, respectively. The average ultimate strength of the D8, D14, and D16 bars were 637 MPa, 652 MPa, and 612 MPa, respectively. The number of samples for each size of reinforcement was three.
# Effective Behavioural Dynamic Coupling through Echo State Networks
## 2 Material and Methods
### Intrinsic Plasticity  
Selecting the spectral radius of the reservoirs weight matrix is one of the most important parameters while using dynamic reservoirs. Intrinsic Plasticity (IP) provides an unsupervised method for the adaptation of the Dynamical Reservoir [36; 37]. The idea is that the activation functions of the neurons are adapted to fire under a certain, usually exponential, distribution. This results to sparse activations of the reservoir neurons, with each one capturing only important features of the input signal. The IP rule is local in space and time and aims at maximizing input to output information transmission for each neuron.  
In our case, where the training data is noisy, IP is show to alleviate the overall performance helping in the decorrelation of the noisy input signals in the training procedure.  
Using a hyperbolic tangent as an activation function for the reservoir's neurons, the intrinsic parametrisation can be derived by adding a gain \(a\) and a bias \(b\), to the activation function \(f^{\prime}(x)=f(ax+b)\) and now working with f' as the activation function of the reservoir's neurons. Then, the online adaptation rule of IP according to [38] is derived to be,  
\[\Delta b=-\zeta(-\mu\sigma^{-2}+y\sigma^{-2}(2\sigma^{2}+1-y^{2}+\mu y)) \tag{4}\]  
\[\Delta a=\zeta a^{-1}+x\Delta b \tag{5}\]  
where \(\zeta\) is the learning rate for the IP, \(\mu\) the mean of desired activation distribution and \(\sigma^{2}\) it's variance. All signals, \(x,y\) and parameters \(a,b\) are of the same time step \(n\).
## 5 Conclusions  
1. The average values of soil salinity in each layer of soil in the Weigan River Basin oasis were generally high. The soil of the layer between 0 m and 0.25 m was severely salinized, while the soil of the layers between 1 m and 2 m and between 2 m and 3 m were moderately salinized. The three layers of soil salinity did not conform to a normal distribution, and showed strong variability, with the coefficients of variation ranging from 1.15 to 1.26;
2. According to [PERSON]'s formula for calculating the reasonable random sampling size, at the 95% confidence level and 20% relative error, the sampling numbers of the three layers of soil were 89, 73, and 61, respectively. When calculating the reasonable number of samples, not only the accuracy of the sampling should be taken into account, but also the economy and efficiency of sampling should be considered under the premise of ensuring accuracy;
3. The spatial variation direction of soil salinity in each layer was evident, and the long-axis range corresponding to the maximum anisotropy ratio first decreased and then increased as the depth decreased. The spatial variation structure of soil salinity in the layer between 0 m and 0.25 m was fitted with a nested model, and then interpolated with Kriging, and it was found that soil salinity showed a gradually increasing trend from the center to the edge of the oasis;
4. The analysis of variation function shows that soil salinity in the layer between 0 m and 0.25 m shows a moderate spatial correlation, while soil salinity in the layers between 1 m and 2 m and between 2 m and 3 m show strong spatial correlations, in which topography and groundwater level were the main structural factors affecting soil spatial variability, and farming and irrigation were the main random factors. Therefore, it is proposed that the comprehensive effect of various factors should be considered in the treatment of salinization, especially natural factors such as the local terrain, soil structure, and groundwater level, to build an efficient irrigation system and a reliable drainage system, and to effectively control soil salinization and realize gradual desalination, thereby achieving the effect of comprehensive treatment of salinized soil and improving land use efficiency.  
**Author Contributions:** Conceptualization, Y.G. ([PERSON]); Formal analysis, S.L., Y.G. ([PERSON]), Y.G. ([PERSON]) and Y.Z.; Investigation, L.L. and Y.G. ([PERSON]); Methodology, L.L. and Y.G. ([PERSON]); Software, Y.Z.; Validation, S.L. and Y.G. ([PERSON]); Writing--original draft, L.L.; Writing--review & editing, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number U1603243; Natural Science Foundation of Xinjiang Uygur Autonomous Region, grant number 2022D01C40.  
Not applicable.  
**Data Availability Statement:** Some data used during the study are available from the corresponding author by request (email: [EMAIL_ADDRESS] (L.L.)).  
The authors declare no conflict of interest.
## 4 Experimental Results  
The primary evaluation metric for our system was the mean accuracy of the system. To compute the mean accuracy, we adopted the 10-fold cross-validation technique so that the results are more general and dependable. All the experimentation was performed on a Windows-10 operating system having 16 GB RAM, and a processor of core-i7-7500U CPU @2.70 GHz. After a brief description of the datasets, we present the results of four experiments that we performed on each dataset. In the first experiment, we generated receiver operating characteristics (ROC) curves for each class separately. In the second experiment, we compared the accuracies for the classification of individual classes with the help of confusion matrices. In the next experiment, we analyzed the proposed system's performance while using some other well-known classifiers. Finally, we made a comparison of our system with the available state-of-the-art techniques.
## 5 A Hysteresis Model with Damage Accumulation of Welding Materials
### Parameter Fitting of the Model
#### 5.5.1 The Parameter \(\lambda\) of EvolutionEquation of Damage Accumulation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{4}{c}{**I + II**} & \multicolumn{4}{c}{**III**} \\ \cline{3-10}  & & \(\mathbf{h_{2}}\) & \(\mathbf{g_{2}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{h_{3}}\) & \(\mathbf{g_{3}}\) \\ \hline \multirow{4}{*}{Butt weld of Q235 steel} & WA1(1) & 0.045 & 0.991 & 0.889 & 2.083 & 2.466 \\  & WA1(2) & 0.024 & 0.982 & 0.881 & 1.694 & 2.433 \\  & WA1(3) & 0.024 & 0.989 & 0.879 & 1.668 & 2.453 \\  & Model Parameters & 0.031 & 0.987 & 0.883 & 1.815 & 2.446 \\ \hline \multirow{4}{*}{Left fillet weld of Q235 steel} & WA2(1) & 0.020 & 0.997 & 0.875 & 1.751 & 2.553 \\  & WA2(2) & 0.021 & 0.998 & 0.882 & 1.751 & 2.503 \\  & WA2(3) & 0.020 & 0.997 & 0.879 & 1.709 & 2.513 \\  & Model Parameters & 0.020 & 0.997 & 0.879 & 1.737 & 2.523 \\ \hline \multirow{4}{*}{Right fillet weld of Q235 steel} & WA3(1) & 0.056 & 1.000 & 0.887 & 1.345 & 2.134 \\  & WA3(2) & 0.020 & 0.997 & 0.886 & 1.384 & 2.196 \\  & WA3(3) & 0.113 & 1.000 & 0.886 & 0.981 & 1.796 \\  & Model Parameters & 0.063 & 0.999 & 0.886 & 1.225 & 2.044 \\ \hline \multirow{4}{*}{Right fillet weld of Q345 steel} & WB1(1) & 0.109 & 0.999 & 0.890 & 1.519 & 2.269 \\  & WB1(2) & 0.111 & 1.000 & 0.882 & 1.807 & 2.499 \\  & Model Parameters & 0.103 & 1.002 & 0.886 & 1.445 & 2.196 \\  & Model Parameters & 0.107 & 1.000 & 0.886 & 1.509 & 2.321 \\ \hline \multirow{4}{*}{Left fillet weld of Q345 steel} & WB2(1) & 0.113 & 0.997 & 0.879 & 1.005 & 1.825 \\  & WA2(2) & 0.094 & 0.999 & 0.881 & 1.083 & 1.878 \\ \cline{1-1}  & WA2(3) & 0.104 & 1.001 & 0.877 & 1.054 & 1.883 \\ \cline{1-1}  & Model Parameters & 0.104 & 0.999 & 0.879 & 1.047 & 1.862 \\ \hline \multirow{4}{*}{Right fillet weld of Q345 steel} & WB3(1) & 0.083 & 0.999 & 0.857 & 1.198 & 1.956 \\ \cline{1-1}  & WA3(2) & 0.057 & 1.000 & 0.887 & 2.365 & 3.043 \\ \cline{1-1}  & WA3(3) & 0.089 & 0.999 & 0.890 & 1.789 & 2.513 \\ \cline{1-1}  & Model Parameters & 0.076 & 0.999 & 0.878 & 1.784 & 2.504 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Values of parameters \(g\), \(h\).
## 2 Methodology
### Life Cycle Impact Assessment (LCIA)  
The life cycle assessment (LCIA) phase was carried out based on a limited set of impact categories for all the output flows quantified in the LCI phase. They were grouped into damage categories, namely environmental compartments suffering the damage caused by the product in its life cycle. The assessment was drawn-out to the end-point approach to understand the degree of damage and understood that there would be high uncertainties in the damage characterization [34]. Associated environmental impacts in system investigation could quantitatively be represented with all the included materials and processes. Thus, most impacting ones could be highlighted. The damaging impact was assessed based on three damage categories at the end-point level by utilizing the ReCiPe method, the most common method in SimaPro software [35]. The end-point factors of characterization used in the ReCiPe method are described as follows: 1. Human health: stated as the number of years life lost and the number of years lived disabled and termed as disability adjusted life years (DALYs), an index used by the World Bank and World Health Organization (WHO). DALYs measure the difference between an ideal situation in which everyone lives to the standard life expectancy in perfect health and the actual situation. The metric is a combination of the years of life lost (YLL) due to premature mortality and the years of life lost due to disability (YLD) when living with the disease or its consequences: DALY = YLD + YLL [36]. The DALY unit is defined by WHO measured from '0' for no effect or safe, '0.67 and above' for danger or critical, and the value of '1' for fatal damage or death [37] or generally \"0\" indicating indifference between the health state and full health and \"1\" indicating indifference between the health state and death [38; 39]; 2. Ecosystems: expressed as the loss of species over a particular area during a specific time. It also includes altered pH, nutrient availability, and concentration in soil. The formula to calculate the damage to ecosystems is (plant species (%) \(\times\) affected area (m\({}^{2}\)) \(\times\) number of production years), known as ecosystem damage potential (EDP), and the unit is species.year; 3. Resources scarcity: represented in surplus costs of future resource production over an infinitive time frame (assuming constant annual production), considering a 3% discount rate. The unit is USD ($) [37].
## 3 MEC Implementation in Vehicular Networks
### Core Network Layer  
Cloud servers have a huge capacity to cover all needs of the networks and make optimal decisions. Those are deployed in remote data centers and store the transferred data from MEC servers. The role of processing uploaded data and information is performed by cloud servers, and the cloud creates a global and centralized control and management over the whole VN.
## 6 Experimental Evaluation
### Experiment I: Service Classifier
#### 6.1.2 Evaluation Metrics  
The primary goal of evaluating the classification model is to assess its ability to categorize network traffic based on latency and bandwidth accurately. Such categorization is essential for optimal resource allocation and QoS, ultimately impacting QoE and network efficiency. We calculated several key metrics to achieve this goal: accuracy, precision, recall, and F1-score. These metrics are widely used because they offer a comprehensive view of the performance of machine learning classifiers, covering different aspects of the model's predictive capacity. They are considered classic for their simplicity, effectiveness, and ability to assess overall accuracy, the quality of optimistic predictions, the ability to identify relevant examples, and the balance between accuracy and sensitivity. This combination is essential in contexts with unbalanced classes or different costs for errors, guaranteeing a reliable and comparable analysis between different models. Our metrics were derived by comparing the labels predicted by the Gaussian naive Bayes model with ground truth labels found in the test set. Specifically, the ground truth labels were established by categorizing network traffic into low latency and high bandwidth based on predefined thresholds. Each data point was labeled according to these criteria, such as traffic length thresholds, to represent the traffic type accurately.  
To validate the generalization capacity of the classifier, we used cross-validation with five divisions on the training set. This technique calculates the average performance in several subdivisions, ensuring that the high accuracy of the model is consistent across different subsets of data.  
Finally, we performed a comparative analysis. We evaluated the model's performance by comparing its predictions with known labels (ground truth) to confirm that it correctly classified the services in the low latency and high bandwidth categories. This final check ensures that the model's decisions are aligned with the characteristics defined for each class, validating the accuracy and reliability of the classification. Next, we conducted confusion matrix analyses to compare the model's predicted and ground truth labels.
## 2 Background and Motivation
In this study, we improve upon the methods proposed in [28] and focus on the more complex application scenario of in-vehicle HR and RR monitoring. Specifically, we not only use the depth information to distinguish intensity changes caused by body motion versus heartbeat but also to assess the quality during different sections of the HR measurements, which further increase the robustness of the HR measurements against motion-induced artifacts [28]. Since our depth-based method does not make the sparse frequency assumption of the PPG signal, this system would work even if a compact/localized VCSEL illuminator is used. Furthermore, even though RR has been shown to be correlated with the drowsiness or stress level of the driver [8], the existing camera-based system usually does not have an adequate signal quality to extract RR from the HR signal [29; 30]. To circumvent the limitation of low signal quality, in this study, we use the ToF camera to measure RR directly by measuring the chest wall displacement [28; 31], avoiding the potential complexity of fusing the data from the radar sensor and the camera, respectively, and reducing the cost for the entire system. To the best of our knowledge, this is the first study where a ToF camera is used to simultaneously perform HR and RR measurements in a vehicle. Indirect ToF cameras are becoming increasingly popular on various platforms and are already deployed in vehicles for functions such as Smart Restraint Control Systems (Smart-RCS). Given the benefits brought by the active illumination and depth information, our CVSM system would enable compact, reliable, and multi-modal physiological signal monitoring in a vehicle, improving the safety and the driving experience for both the drivers and passengers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Sensor**} & \multirow{2}{*}{**HR?**} & \multirow{2}{*}{**RR? \({}^{*}\)**} & \multicolumn{2}{c}{**HR Robust against**} & \multirow{2}{*}{**Ambient Light**} & \multirow{2}{*}{**Independent of Skin Color?**} \\  & & & & & **Motion?** & & \\ \hline
**Electocardiogram**[9; 32] & \(++\) & Indirect & N & \(++\) & \(++\) & \(++\) \\ \hline
**Radar**[15; 16] & \(--\) & Direct & Y & \(--\) & \(++\) & \(++\) \\ \hline
**Ballistocardiograph**[33] & \(--\) & Direct & N & \(--\) & \(++\) & \(++\) \\ \hline
**Thermography**[8; 20] & N/A & Direct & Y & N/A & \(++\) & \(++\) \\ \hline
**PPG with** & & & & & \(+\) & \(--\) & \(--\) \\
**RGB Camera**[12; 14] & \(++\) & Indirect & Y & \(+\) & \(--\) & \(--\) \\ \hline
**PPG with** & & & & & & \(+\) \\
**IR Camera**[11; 13] & \(+\) & N/A & Y & \(--\) & \(+\) & \(+\) \\ \hline
**PPG with** & & & & & & \(+\) \\
**indirect ToF Camera** & \(+\) & Direct & Y & \(+\) & \(++\) & \(+\) \\
**[This Study]** & & & & & & \\ \hline \hline \end{tabular}  
* Direct measurement measures chest movement meanwhile an indirect RR measurement uses the modulation of the HR signal to extract RR; \(+/-\) indicates the system has advantage/ disadvantage in a certain category.  
\end{table} 
Table 1: Summary of HR/RR measurement techniques used in vehicle.
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 3 Methods
### Defining Anomalous Deformation Behaviour  
A deformation behaviour may be described as anomalous if a measurement falls outside the \"normal\" variation of the data. In the case of GPR, the bounds between normal and anomalous behaviour are defined by the 95% confidence interval. Prediction uncertainty is not obtained using the EE-DL and RF approaches. Therefore, if a measurement is beyond the background noise level of the data, it will be outlined as an anomaly for the purposes of comparing the performance of the various models at detecting anomalous behaviour. The background noise level will be estimated using the RMSE between the measurements and predictions of the EE-DL approach.  
Moreover, the anomalies are then clustered spatially so that anomalies next to other anomalies are given higher importance than those occurring on their own. Therefore, an arbitrary number (four) was chosen here as the minimum number of anomalies requiredfor an anomalous cluster. For simplicity, any anomalous clusters inside the failure area were classed as true positives, and any outside the immediate failure area were treated as false positives. A simple definition of anomalous behaviour was adopted, intentionally, to showcase the potential of the algorithms in a direct and concise manner.