esa-sceva/satcom-chunk-collection huggingface dataset:
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 3 Experimental Setup
### Experimental Environments
During the experiments, changes in wind speed, temperature, and humidity could be neglected, and there were no other interferences such as walking people. There were two reasons for choosing the above three environments. First, the propagation characteristics of wireless signals were significantly different in these environments. The surface of the runway was relatively flat. Therefore, the received signal was mainly composed of the LOS component and the reflection component. In addition to the LOS propagation component, there were also scattering components in the artificial lawn, so the composition of the received signal was more complicated. There were many reflective surfaces on the signal propagation path in the corridor, such as the ceiling, ground, and single-sided wall. Therefore, the received signal was a combination of the LOS component and multiple reflection components. These diversities will lead to significant differences in the received signal strength and fluctuation range. Second, the background noise of these three environments were also different. Runway and artificial lawn are typical outdoor environments, in which background noise is typically lower than that in the corridor.
## 3 Results and Discussion
### Compressive Strength and Splitting Tensile Strength  
In order to understand the strength degradation under different FT cycles, tests on both compressive strength and splitting tensile strength were undertaken. The measured strengths at different FT cycles and exposure conditions are listed in Table 4. Figure 7A shows the dimensionless value of strength, i.e., relative residual compressive and splitting tensile strength, against FT cycles.  
In RAC subjected to sulfate-FT damage, the loss in compressive strength was smaller compared to the ones subjected to water-FT damage. For water- and sulfate-exposed samples subjected to 180 FT cycles, the residual compressive strengths were about 71% and 78% of the original strength, respectively. The residual strength of W180 was about 92% of that of S180. This phenomenon was also confirmed by [PERSON] [41] through studying the durability of high-performance concrete, which was subjected to the same exposed conditions.  
Moreover, for both RAC in this study and high-performance concrete (w/c = 0.56) in [41] subjected to FT cycles, their compressive strengths were in a pronounced linear relationship with FT cycles. Moreover, as a consequence of high porosity, the RCA concrete showed more loss in compressive strength compared with ordinary concrete in [41], despite adopting a lower w/b ratio.  
However, for the splitting tensile strength of RAC tested in this study, slight differences were noticed between different exposed conditions. For both water- and sulfate-exposed samples subjected to 180 FT cycles, the residual splitting tensile strength was about 65%  
Figure 6: Evolutions of damage parameter of water-exposed (W) and sulfate-exposed (S) RAC samples at different freeze–thaw cycles.  
Figure 7: Evolutions of residual strength (A) and interfacial transition zones (B,C) at different freeze–thaw cycles.  
of the original strength. For RAC subjected to sulfate-FT damage, except for 180 FT cycles, the loss in splitting tensile strength was smaller compared to the ones subjected to water-FT damage.  
After subjected to 180 FT cycles, there was about a 35% decrease in splitting tensile strength, which is higher than the loss in compressive strength. Therefore, compared with the compressive strength, the tensile strength is more sensitive to FT damage. It is generally recognized that the interfacial bonding between aggregate and mortar is vital for the tensile strength. Figure 7B,C shows the cutting face of the control specimen (0) and the water-exposed specimen with 120 FT cycles (W120). For the FT-damaged specimen, macro cracks, which mainly developed in regions close to the surface, were observed in the interface between the aggregate and mortar. Therefore, damage in the interfacial transition zones (ITZs) also occurs during cyclic FT damage, which contributes to more-serious degradation in splitting tensile strength. The quantitative analysis of the deterioration in ITZs will be discussed in the following section.
# A Circularly Polarized Antenna Array with Gain Enhancement for Long-Range UHF RFID Systems
## The Proposed Antenna Design
### Antenna Array Element Distance (d)  
Finally, a parametric study on the array element distance \(d\) was conducted. In fact, an increase of \(d\) reduced the mutual coupling among antenna elements, leading to a broadside gain enhancement; however, large \(d\) can cause the generation of grating lobes and the presence of border effects (because the metallic patch position is close to the ground plane edge) with a consequent reduction of the gain. For this reason, the parameter \(d\) was varied from \(0.5\lambda\) to \(0.75\lambda\), and simulation results are depicted in Figure 6b. The increase of \(d\) is beneficial for gain enhancement (in terms of peak value and bandwidth) up to a value of \(0.7\lambda\). Interestingly, peak gain and bandwidth mainly follow the \(S_{11}\) bandwidth behavior (revealing that, in this case, gain enhancement is mainly due to improved impedance-matching performance), while AR bandwidth exhibits an opposite trend. As for \(h\), a trade-off between LHCP broadside gain and AR bandwidth was made, and a final value \(d=0.6\lambda\) was selected.  
Figure 6: Normalized bandwidth (BW) and broadside LHCP peak gain simulation results: (**a**) as a function of \(h\) and (**b**) as function of \(d\).
## 1 Introduction
Besides generators of chaotic signals intentionally built by the design engineer, irregular noise-like behavior can be detected within non-chaotic analog building blocks where nonideal property of some circuit element is considered. For example, chaos can be detected in frequency filters commonly used in practice. Paper [20] brings detailed study of chaos that evolve within state variable filters (Kerwin-Huesmann-Newcomb configuration) within the basic concept of the phase locked loop [21]. Both isolated and driven systems of power electronics generally represent a good platform for evolution of dense strange attractors. Chaos was experimentally confirmed in wide class of power converters [22] as well as dc-dc converters [23]. Some papers study individual types of dc-dc converters separately. Bifurcations and chaos of the boost converter is the topic of work in [24] while the buck converter is addressed in paper [25]. Static nonlinear systems can be forced into chaotic operational states if high-frequency signals must be processed. This possibility was recognized in the case of multi-state memory composed by resonant tunneling diodes [26]. Obviously, the chaotic dynamical system can be claimed as novel if there is no change of the coordinates that transform \"already published system\" into the \"new discovered system\". Upcoming sections of this paper introduce and briefly analyze such a mathematical model, considered as both third order and fourth order depending on biasing circuitry, providing numerical results and analog realization of the corresponding realizable chaotic oscillator. To be more specific, the second part of this paper discusses mathematical description of two generalized bipolar transistors connected as fundamental two-terminal building block known as the lambda diode. The third section covers numerical analysis of the discovered mathematical model. All necessary numerical algorithms commonly dedicated for dynamical behavior quantification are provided: Lyapunov exponents, bifurcation diagrams, basins of attraction, etc. Normalized numerical values of internal parameters that leads to chaos and hyperchaos are provided in this section. The fourth section describes circuitry realization of chaotic oscillators based on a math model of the isolated lambda diode. The fifth part of this paper covers experimental confirmation of chaos, i.e., it contains rich gallery of oscilloscope screenshots. Visual agreement between theoretical expectation and examples of practical measurement is evaluated. Future work topics, new and still unsolved problems belong to next section--Discussion. Finally, concluding remarks are stated.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 4 Our Proposed Routing Schema
### CH Selection  
We expect to select the nodes with as much energy as possible. Therefore, the residual energy of nodes is a significant metric in CH selection. Another important metric for CH selection is the distance between the CH and the sink. CHs take the responsibility of transmitting their members' data to the sink and a great deal of energy is consumed. Therefore, it is necessary to select the nodes with as close a distance to the sink as possible. We use three different methods to calculate the weight of sensors and the result is discussed in the simulation. We used Equations (7)-(9) to calculate the weight of sensors:  
\[W_{i}=E_{residual\_i} \tag{7}\]  
\[W_{i}=\frac{E_{residual\_i}}{Dis_{i\_to\_sink}} \tag{8}\]  
\[W_{i}=\frac{E_{residual\_i}^{2}}{Dis_{i\_to\_sink}} \tag{9}\]  
The node closest to the sink will first broadcast its weight to the other members in the cluster and claim itself to be a tentative CH. Only nodes with the higher weight will broadcast their own weight and become a new CH candidate. Finally, the node with the maximal weight will be elected as the final CH in each cluster. The network after CH selection is shown as Figure 4.  
Many researches regarding LEACH, TTDD, and CCMAR conduct the selection of CHs during each round. However, frequent CH selection will greatly increase the control messages such that much energy is wasted. In our presented schema, the CHs selection only happens when any of the CHs do not satisfy the condition of maximal weight. During each round, each member node will upload a data package that contains its weight for selection to its corresponding CH. When CHs find that their member nodes have higher weight than themselves, a re-clustering message will be broadcast in those clusters by the CHs.  
Figure 3: Clustering formation.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Control Models
This architecture has higher scalability and higher resilience to failures due to the hybrid mode of operation. For instance, when communication with the centralized controller is unavailable, this architecture can run in full distributed mode, and vice versa, depending on the network conditions. As an example of the hybrid mode, since the SDN controller has an overview of the network, instead of sending whole flow tables to the nodes, it can instruct the nodes to use a specific routing algorithm, but nodes should exchange control messages in a distributed manner to build the flow tables. Dey et al. have proposed hybrid routing to address scalability issues found in the logically centralized architecture, where a local router in each data plane element handles some part of routing and most of routing is controlled by the controller [332]. Considering the coexistence of legacy switches and SDN switches, consistent forwarding graphs are constructed for coordination of forwarding of SDN switches and distributed routing in hybrid control architecture, which also secures a high throughput while maintaining forwarding consistency [333]. DevoFlow (Devolved Flow) is a hybrid control approach that reduces frequent interactions with the control plane and the data plane in order to reduce overhead and delay by distributing some of the control plane to the data plane. In the DevoFlow approach, the controller is only involved in controlling significant and long-lived flows, whereas switches make local control decisions [310]. Distributed Flow Architecture for Network Enterprise (DIFANE) is a scalable hybrid control architecture where authority switches are assigned rules by the controller, which has an algorithm to partition rules and minimize rule fragmentation [334]. DIFANE keeps all traffic in the data plane by selectively directing packets through intermediate switches that store rules. OpenRouteFlow, which enables legacy routing as a software-defined routing service that provides path-oriented and traffic-oriented subscription and publication services for different scenarios of network control, has been used to achieve hybrid network control [335]. Fibbing is a hybrid control framework where the controller tricks the routers into seeing a fake topology that is constructed to achieve the desired forwarding information base. It applies central control over traditional distributed link state protocols such as Open Shortest Path First (OSPF), where computation and installation of forwarding information base on the data plane are carried out by traditional distributed protocols [336]. Hybrid control is secured in HybridFlow, which is a control architecture that controls legacy devices using an SDN control plane that abstracts the hybrid SDN network as a logical SDN network. HybridFlow, which was originally implemented in the POX [337] controller, maps logical ports of the SDN network to physical ports of the actual network [338]. Table 6 summarizes the classification and details of controller frameworks based on the degree of distribution of control.
## 2 Model Design
### Modified Noncircular Rolling Bearing Quasi-Dynamic Model
Variable \(\delta_{1 jkh}\) and \(\delta_{2 jkh}\) can be obtained based on the geometric shape of the deformed raceways, thus, with the rollers' loads on the inner ring and the applied load of the ring from the shaft, the ring balance equation can be obtained, as expressed in Equation (3). The cage balance equation can be obtained using the method of Cui [16]. Thus, the modified noncircular ring bearing quasi-dynamic model is established. \[ \begin{split}& F_{y}-F_{lr}\text{sin}\psi-F_{lt}\text{cos}\psi -\sum\limits_{j=1}^{N}\varpi\sum\limits_{k=1}^{n}\frac{\mathcal{Q}_{2 jk}^{2}}{l _{e}}\text{sin}\ \phi_{j}=0\\ & F_{z}-F_{lr}\text{cos}\psi-F_{lt}\text{sin}\psi-\sum\limits_{j=1 }^{N}\varpi\sum\limits_{k=1}^{n}\frac{\mathcal{Q}_{2 jk}^{2}}{l_{e}}\text{cos} \ \phi_{j}=0\\ & M_{y}-\sum\limits_{j=1}^{N}\left(\varpi\sum\limits_{k=1}^{n} \frac{\mathcal{Q}_{2 jk}^{2}}{l_{e}}\right)\cdot\frac{\sum\limits_{k=1}^{n} \mathcal{Q}_{2 jk}^{2}\cdot\left[\left(\xi-0.5\right)\varpi-0.5l_{e}\right]}{ \sum\limits_{k=1}^{n}\mathcal{Q}_{2 jk}^{2}}\cdot\text{cos}\ \phi_{j}=0\\ & M_{z}-\sum\limits_{j=1}^{N}\left(\varpi\sum\limits_{k=1}^{n} \frac{\mathcal{Q}_{2 jk}^{2}}{l_{e}}\right)\cdot\frac{\sum\limits_{k=1}^{n} \mathcal{Q}_{2 jk}^{2}\cdot\left[\left(\xi-0.5\right)\varpi-0.5l_{e}\right]}{ \sum\limits_{k=1}^{n}\mathcal{Q}_{2 jk}^{2}}\cdot\text{sin}\ \phi_{j}=0\\ & M_{x}-\frac{D_{y}}{2}\sum\limits_{j=1}^{N}\left(\varpi\sum \limits_{k=1}^{n}\left(T_{2 jk}+P_{2 jk}\right)\right)=0\end{split}  \tag{3}\]  
The proposed model can be used to accurately analyze the load distribution and dynamic characteristics of the deformed bearing by taking into consideration the influence of the noncircular raceway following structural deformation, and also the microscopic contact and the rheological lubricant properties in the bearing. By setting the values of \(\delta_{1 jkh}\) and \(\delta_{2 jkh}\) as zero, this model can also analyze load distribution without structural deformations, which can be the initial values of finite element analysis.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Pros and Cons of the Proposed Framework  
The proposed smart clothing framework is suggested for material selection to find a textile and sensor that could be useful to develop durable smart clothes. Most importantly, the framework has suggested an IoT cloud service systems where the signal data are collected from various parts of the human body through internet, processed, and analysed through AI techniques which can provide the best decision-making strategy for taking necessary action before causing any serious health damage. Clothes manufacturing industries can take this idea forward to develop advanced smart clothing technology for identifying any abnormal health condition and taking necessary actions. However, the further development of different technologies required for this framework is important to ensure its effectiveness in real-life applications.
## 1 Introduction
At present, there are roughly two methods for determining the FOV of the space-based observation system. Method 1: Assume the space target to be a Poisson distribution, and use the spherical formula and statistical star table to determine the FOV [19]. Method 2: Assume the spatial target as a Gaussian distribution, and use the probability distribution model to determine the FOV [20]. This article examines the plan wherein the DDSSO satellite observes the GEO targets. First, we identified the categories of GEO targets, analyzed the orbital evolution law of the failed satellites and other space debris, established the evolution model, and obtained the trajectory distribution characteristics of all targets in the GEO belt. In addition, an improved observation model based on the natural rendezvous model was developed. After that, a scheme for determining the cross-orbit field of view (COFOV) and the along-orbit field of view (AOFOV) was formulated, and the correctness of the conclusion was verified by simulation experiments. Finally, the threshold FOV that meets the requirements of both coverage rate and revisit period was obtained, and the corresponding orbital altitude and inclination of the satellite were determined. The traditional FOV determination methods were all studied with the detection probability as the index. Compared with the traditional method, the FOV determination method in this paper not only considers the detection probability, but also considers the observation plan, the revisit period, and the satellite orbit and inclination. The research in this paper is more comprehensive and more universal.
## 2 Reliability Evaluation Technique for NDT  
A representative technique for quantitative reliability evaluation of NDT is calculating the POD. The POD is the probability of detecting flaws according to the flaw length \(a\). It is expressed as POD (\(a\)), and its graphical representation is referred to as the POD curve. In general, through the RRT, according to the measured information on the presence or absence of flaw and sizing, a POD curve is derived and used for reliability evaluation for various conditions and factors. The following models are used to obtain POD values: a binary POD that reflects the flaw detection status with binary data (hit/miss) and a signal-response POD based on the correlation between the measured flaw size and the real flaw size (\(\hat{a}\) vs. \(a\)). For hit/miss data, the value assigned depends on whether a previously identified flaw was detected with the technique under evaluation. The real flaw size was measured by an inspector with ASNT Level III based on RT and PAUT data. A value of 1 is assigned in the case of detection; otherwise, 0 is assigned. This method is suitable for the reliability analysis of techniques such as magnetic testing, penetrant testing, and RT, with small differences between the real flaw sizes and experimental values. However, because this approach uses simple binary data that do not take into account various parameters of the inspection system, a large amount of data is needed to achieve high reliability [18].  
The \(\hat{a}\) vs. \(a\) approach is a reliability evaluation method using signal response information based on the flaw length. It is suitable for techniques where the signal response depends on the flaw size, such as eddy current testing and UT. In particular, it is possible to obtain a POD with high accuracy in an inspection technique using ultrasound, such as UT or PAUT, where the sizing error can be large, depending on the inspection conditions and inspector performance. Thus, in this study, a reliability analysis was performed by applying the \(\hat{a}\) vs. a method.
## 4 Inversion Effect Analysis
### Effect of the Constraint Method  
Four inversion regularization constraints are presented: smallest model constraints (SMC), flattest constraints (FC), smoothest constraints (SC), and dynamic prior model composite constraint (DPMCC). The dynamic prior model composite constraint use the t0 difference method to obtain the a priori model. The parameters of the four methods are the same. Offset distance is 4 m, shot spacing is 2 m. Trace spacing is 1 meter. A total of 49 seismic sources and 97 receivers are used.  
In this section, we will compare the constraints of these four constraints in the travel time tomography inversion of the fault model. In order to control the variables, we set the default forward travel time method as MSFM, with its higher accuracy and efficiency. The LSQRs algorithm was chosen for solving a large sparse matrix.  
As is shown in Figure 14a, there are many false anomalies and the boundaries of the layers are not clear enough. Figure 14b,c are both in better smoothness than that of Figure 14a. The inversion result of the algorithm we proposed is shown in Figure 14d. It can be seen that the interface of the layer is most clearly depicted, and there are few false anomalies. The inversion speed is closer to the real value, so its stability and convergence are the best.  
Figure 14: Different constraints: (**a**) Smallest Model Constraints (SMC); (**b**) flattest Constraints (FC); (**c**) smoothest Constraints (SC); (**d**) dynamic Prior Model Composite Constraints (DPMCC).  
Figure 13: Error fitting curves using different forward travel time algorithms.  
As shown in Figure 15, when the number of iterations is the same, the constraint method proposed in this paper has the best effect. Therefore, DPMCC is a more effective method for inversion calculation.
## 6 Performance Analysis
### Simulation Setup and Environmental Parameters
#### 6.1.2 Dataset  
To ensure a fair and consistent comparison across the algorithms, the parameters utilized to generate the dataset for the other algorithms (best fit decreasing and modified best fit decreasing) are identical to those used in training the neural network for this study [55]. Other scholars [56; 57] have used well-known parameters, such as application inter-arrival time and IoT network traffic pattern. These are listed in Table 2. This approach ensures that any observed differences in model performance can be attributed directly to the models' inherent characteristics rather than discrepancies in the experimental setup.