esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Calculation of Outage Probability Using (5)
Utilizing the CDF expressions from Table 2 in (5), the outage probability for scenario 4 can be calculated as  
\[p_{out}(\gamma_{th})=1-\frac{\Gamma\left(m,\frac{m}{\tilde{ \gamma}}\gamma_{th}\right)}{\Gamma(m)}\Bigg{[}Q\bigg{(}\alpha(q)\sqrt{\frac{ \gamma_{th}}{4\tilde{\gamma}}},\beta(q)\sqrt{\frac{\gamma_{th}}{4\tilde{ \gamma}}}\bigg{)}\] \[-Q\bigg{(}\beta(q)\sqrt{\frac{\gamma_{th}}{4\tilde{\gamma}}}, \alpha(q)\sqrt{\frac{\gamma_{th}}{4\tilde{\gamma}}}\bigg{)}\Bigg{]} \tag{9}\]  
Scenario 5: In scenario 5, the links between S-R and R-D take the Rayleigh PDF and the \(\eta-\lambda-\mu\) PDF. The \(\eta-\lambda-\mu\) PDF is a generalized small-scale fading distribution which contains \(\lambda-\mu,\eta-\mu\), Gamma, exponential, Hoyt, Nakagami-m, Rayleigh, and one-sided Gaussian fadingas special cases [10]. This scenario is used to model multipath fading in both links in which one link, represented by Rayleigh fading, is subjected to intense scattering with no line of sight of transmission and the other link, represented by \(\eta-\lambda-\mu\), experiences a variety of multipath fading that varies from worse to better than Rayleigh fading. Utilizing the CDF expressions from Table 2 in (5), the outage probability for scenario 5 can be calculated as:  
\[p_{out}(\gamma_{th})=1-\exp\left(\frac{-\gamma_{th}}{\tilde{ \gamma}}\right)\Bigg{[}1-\Bigg{(}\frac{2\sqrt{\eta(1+\lambda^{2})}\tilde{b}}{ \tilde{r}^{2}}\Bigg{)}^{2\mu}\frac{\sqrt{\pi}}{\gamma(u)\tilde{c}^{2\mu}\tilde {d}^{\mu-0.5}}\] \[\sum_{j=0}^{\infty}\frac{1}{j!\Gamma(0.5+\mu+j)}\Bigg{(}\frac{ \hat{d}}{2\hat{c}}\Bigg{)}\bigg{(}\Gamma(2(\mu+j))-\Gamma\bigg{(}2(\mu+j), \frac{\hat{c}\gamma_{th}}{\tilde{\gamma}}\bigg{)}\bigg{)}\Bigg{]} \tag{10}\]  
An important point to note here is that the derived expression can be used to calculate the outage performance of the system for any fading scenario. The only condition is that the CDF of SNR should be known.
# The Design of an Energy Harvesting Wireless Sensor Node for Tracking Pink Iguanas
## 6 Related Work
### Comparison with Commercial Devices  
Several commercial devices to track movements of animals in the wild have been made available in recent years. A systematic review of the design trade off (including the cost) is reported in [1]. Table 2 lists the main properties of some devices commercially available, for a comparison with the system presented in this paper.  
The main characteristic that distinguishes our device from other products is the absence of the battery. Compliance with such a requirement has been obtained through the solution of several hardware and software challenges as discussed in the previous sections. The communication technology is rather variable, the available solutions include: cellular networks for the data collection in urban and suburban regions, satellite networks [35] such as Iridiurm [36] or Argos [37] for world wide coverage. VHF or UHF radios are chosen for local coverage associated with portable o fixed gateways deployed in the monitoring area.  
Finally it is worth to note that all the commercial devices are based on a communication technology that requires a large antenna (between 17 and 20 cm) in addition to the GPS antenna. In our device, a small antenna can be used because communications occur at higher frequency. The choice of using a small antenna was in our case determined by the fact long antenna could imply a higher risk of breakage. This would be detrimental for the purposes of long-term studies of iguanas.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 2 Related Work
### Opportunistic Duty Cycle Based Routing in WSNs
The proposal addresses this problem by considering the residual energy as a criteria to select the forwarder and by controlling the number of forwarders based on forwarding cost estimation. When applying the opportunistic routing with the duty cycle, the multihop broadcasting becomes a very challenging problem when every node has its own working schedule. This issue is addressed for instance by [PERSON] et al. in [33; 34]. The strategy of these references is to minimize the number of broadcast transmissions while keeping a good latency. The authors start by proving that this problem is NP-hard (Non-deterministic Polynomial-time). Then, the basic idea of the proposed solution is that for each node there are two types of neighbors: those which receive the data packets immediately, and those which postpone their wakeup schedules to receive the broadcast data from the first category of neighbors. The authors model the problem as the Directed Latency-optimal Group Steiner Tree Problem (DLGST). Another issue of opportunistic routing is addressed in [35]. Indeed, authors argue that opportunistic routing should take into account the correlation between links (the packet receptions across multiple receivers have certain correlation and are not independent). Thus, authors propose a metric by selecting the nodes with diverse low correlated links as forwarder candidates. They evaluate the proposal with testbed implementation and simulations.
# A Dynamic Programming Approach to Ecosystem Management
## 2 Materials and Methods
### Minimum Cost Flow Problems  
Minimum cost problems are a class of network optimization models. They consider flow through a network with limited arc capacities and cost, or distance, assigned to each arc. Several important problems in practical applications are their subcases, e.g., finding the shortest path problem, the maximum flow, or the transportation and assignment problems in operations research.  
A _minimum cost flow problem_ is characterized by the following features:  
1. The network is directed and connected;
2. At least one node is a supply node, and at least another one is a demand node; all other nodes are transshipment nodes;
3. The network has enough arcs with sufficient capacity to enable all the flow generated at the supply nodes to reach all the demand nodes;
4. The cost of the flow through each arc is proportional to the amount of that flow. Each concrete operation implies an effort that results in positive total costs for each arc of the whole network;
5. Directional flow through an arc is allowed according to the arc direction, and the maximum amount of flow cannot exceed the arc capacity;
6. The objective is to minimize the total cost of shipping the available supply through the network to satisfy the given demand (or, alternatively, to maximize the total profit for the same operation); Specific features for our situation, referring to the above points, are the following ones:
2. In our application, we take the source node as the current state of the system, and consider a single destination;
4. In our case, the total costs for each arc depend on the type of human operations necessary for driving the system from the current state to one of its adjacent nodes.
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 4 Discussion
### The Effect of Imagery Resolution
Testing the ability of Sentinel to identify these small-scale refugia would be a valuable next step towards assessing whether mapping burn severity at a higher resolution would provide a tangible benefit to the ecological interpretation of fire effects.
## 4 Design Scheme and VISSIM Simulation
### Safety Evaluation  
#### 4.5.1 Selection of Evaluation Indexes  
In addition to the analysis of the above indexes, the design should be evaluated from the perspective of safety, and the safety of the design schemes can be reflected in the collision frequency. Therefore, the SSAM(X86) software proposed by the Federal Highway Administration (FHWA) was used to evaluate the safety performance of the freeway. By inputting the trajectory data file obtained from the VISSIM simulation into the SSAM(X86) software, the probability of conflict can be predicted, and three indexes can be generated. These indexes include time to collision (TTC), post-encroachment time (PET), and number of conflicts, all of which help characterize safety aspects.  
In the SSAM(X86) software, the minimum TTC is set to 1.5 s, and less than 1.5 s is considered to have the possibility of a conflict. PET refers to the duration between the leading vehicle leaving a designated area and the following vehicle arriving at the same location. A traffic conflict is considered to occur when the PET is less than 5 s [53]. TTC and PET can better predict the propensity of collision.  
However, due to the high speed of vehicles, the freeway diverging and emerging areas often produce serious consequences once a collision occurs. Therefore, using the CSI index to evaluate the collision's severity is also necessary. TTC and Max-delta V are collision-related indexes, and based on their relationship, the CSI index can be calculated as shown in (7) [54].  
\[CSI=e^{-TTC}\left(\frac{1}{TTC+\frac{a}{MaxDeltaV}}\right) \tag{7}\]  
where _Max-Delta V_ is the vehicle speed change of pre-crash, which can be output via the SSAM(X86) software, and the a is a constant to balance the contribution of the _TTC_ and _Max-Delta V_ to severity, which is taken as a = 20.  
In addition, based on the angle between two conflicting vehicles, conflicts can be categorized into rear-end conflicts, lane change conflicts, and crossing conflicts. The total number of the various conflicts is the number of conflicts [48].  
Among the selected indexes mentioned above, TTC and PET are positive indexes, while CSI and the number of conflicts are negative indexes.
# Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
## 6 Prototype Description and Evaluation  
To assess the feasibility and efficiency of the proposed solution, we developed and experimentally tested a proof-of-concept prototype. In the following, we first describe the testbed setup in Section 6.1. Then, we detail experiments and analyze performance results in Section 6.2.
## 3 Methodology
### Spatial Analysis  
Global Moran's I and local Moran's I were utilized to analyze the spatial autocorrelation of the estimated crash frequency. Global Moran's I and local Moran's I [37; 38] spatial dependence tests were applied to measure spatial autocorrelation for estimated crash frequency [39]. The global Moran's I test [38] is widely used to measure how related the values of a variable are based on the locations and the values of their neighbors. The Local Indicators of Spatial Association (LISA) test assumes that global Moran's I is a summation of individual cross-products.  
The global Moran's I statistic [37] assesses whether the value of one variable at one location is associated with its value at neighboring locations. The z-score of Moran's I (\(Z_{I}\)) and pseudo \(p\)-value [40] obtained from the permutation test are used to assess the significance of Moran's I. \(Z_{I}\) can be computed as:  
\[Z_{I}=(I-E[I])/SD[I] \tag{3}\]  
where \(E[I]\) is the expectation of I and \(SD[I]\) is the standard deviation of \(I\). A positive \(Z_{I}\) indicates that the observation distribution is spatially clustered [41] and a pseudo \(p\)-value less than 0.05 confirms that \(I\) is statistically significant at the confidence level of 95% [42]. More details about global Moran's I practice can be found in previous studies [34; 41; 43].  
LISA is often used to capture local spatial patterns, and evaluates whether the patterns are clustered, dispersed, or randomly distributed. Local Moran's I for the observation z\({}_{i}\), z\({}_{j}\) in cell \(i,j\) with weight matrix wij and N observations [44] can be computed as:  
\[I_{i}=\frac{z_{i}}{\left(\frac{\sum z_{i}^{2}}{N_{cb}}\right)}{\sum_{j}w_{ij} z_{j}} \tag{4}\]  
Figure 1: Grid generation in the study area.
# Numerical Analysis for Critical Structures Protection against Blast Loading Using Metallic Panels
###### Abstract
[PERSON] and [PERSON] [30] investigated the abilities of metallic foam cladding to protect reinforced concrete slabs against air explosions. Furthermore, [PERSON] et al. [31] investigated the dynamic responses of monolithic steel plates, multilayer steel plates, and hybrid plates constructed from steel and Kevlar layers against a blast load. [PERSON] et al. [32] numerically studied the fluid-structure interaction effect on a coated concrete slab subjected to blast loading. Furthermore, the amount of explosives was determined by knowing the incident pressure peak value, while the negative pressure was determined from the pressure after decay [33]. Additionally, [PERSON] et al. [34] investigated the dynamic performance of multilayered hybrid aluminum foam and Ultra High Molecular Weight PolyEthylene (UHMWPE) laminates core sandwich panels subjected to air blast. The effects of foam core gradation and the utilization of UHMWPE laminates on blast mitigation performance were studied. The results revealed that deploying the UHMWPE laminate as a first core layer helped in decreasing the front face velocity. [PERSON] et al. [35] studied the dynamic response of two-layer graded aluminum foam to blast loading. [PERSON] et al. [36] numerically studied polyvinyl chloride (PVC) foam-filled corrugated core sandwich panels subjected to an air explosion using ANSYS AUTODYN. [PERSON] et al. [37] evaluated the dynamic responses of sandwich-panel structures subjected to blast loading. The results revealed that the deflections were reduced by approximately 50%, and at the same time the weight of the panel was decreased by 2.3%. [PERSON] and [PERSON] [38] demonstrated that the sandwich beam could withstand a higher blast load than the solid beam with the same mass and material. [PERSON] et al. [39] investigated the dynamic responses of corrugated sandwich plates with different core arrangements to maximize the mitigation of blast effects on the structures. [PERSON] et al. [40] presented a mitigation system to protect the substructures of military vehicles against blast loading by using V- shape elements. [PERSON] et al. [41] studied the effects of blast loads using a thin oriented strand board (OSB) cantilever wall. It was found that the OSB reduced the blast pressure by 20%. Previous studies have presented the mitigation performance of steel barriers against blast load. It is clear that the barriers attained various values of plastic deformation without reaching complete failure. Thus, more research is needed to enhance the design of barriers to withstand a blast load while reducing the plastic deformation values. As a result, this paper introduces an improved steel protective panel design that could sustain a blast load with minimum plastic deformation and avert any failure. This panel design includes a modified structural design. The modifications include reinforcement techniques using shear connectors and horizontal and vertical walls.
## 2 Materials and Methods  
### Acquisition Energy Metric  
The acquisition energy cost refers to the amount of energy consumed by the GPS receiver when a particular satellite signal is captured, and is closely related to GPS acquisition computation overhead. Therefore, this paper proposes a metric \(\overline{C}_{acq}\) to measure acquisition mean computation overhead (AMCO) as shown in Equation (1), where \(\overline{N}_{iter}\) represents the average number of iterations required by the first successful acquisition, and it is only determined by the detection probability \(P_{D}\). The computation overhead of a single acquisition \(\overline{c}\) depends on the particular integration algorithm and acquisition strategy. Thus, the acquisition energy optimization is a balance of detection probability and computation overhead.  
\[\overline{C}_{acq}=\overline{c}\ \times\ \overline{N}_{iter}=\overline{c}\ \times\ \sum_{k=1}^{\infty}\ kP_{D}(1-P_{D})^{k-1}=\frac{\overline{c}}{P_{D}} \tag{1}\]  
GPS civil signal L1 C/A is a Code Division Multiple Access (CDMA) system where signals are modulated by Binary Phase-Shift Keying (BPSK). GPS signal acquisition is the detection, and the estimation of code phase and frequency shift is essential. According to the acquisition strategy, correlation results are first generated with parameters in searching space, after the estimated code phase \(\tau\) and Doppler frequency \(f_{d}\) are given by the decision algorithm. This DD/MAX method divides the whole searching space into \(\eta\) subsets. It generates test variables twice in each subset so that two peaks are obtained. If these two peaks occur in the same code phase, it deems the phase as the final acquisition result. Otherwise, the next subset will continue to be searched until all subsets are completed. Therefore, its acquisition single computation overhead includes two possibilities. One is that acquisition is declared in the \(k\)th subset with the possibility (\(P_{D}+P_{F}\))/\(\eta\) after \(k\)N/\(\eta\) searching cells. Another is that acquisition is never declared with the possibility \(1-P_{D}-P_{F}\) after searching all \(N\) cells. The AMCO of DD/MAX can be derived as  
\[\overline{C}_{DD/MAX}=\frac{1}{P_{D}}[\frac{P_{D}+P_{F}}{\eta}\Sigma_{k=1}^{ \eta}\ (\frac{2 kN}{\eta}T_{\text{int}}C_{cell})+(1-P_{D}-P_{F})\times 2 NT_{ \text{int}}C_{cell}], \tag{2}\]  
where \(P_{D}\) is the detection probability of DD/MAX, \(P_{F}\) is the false alarm probability of DD/MAX, \(N\) is the correlation result number of the searching space, \(T_{\text{int}}\) is integration time for acquisition, and \(C_{cell}\) is the computation overhead of a correlation per unit integration time for one cell.
## 3 Numerical Simulation
### Analysis of Numerical Calculation Results  
For the scenario with a water level of 215 m, numerical simulation calculations were performed on Tunnel No.1 of the main channel. The settlement data of the overburden soil after tunneling were obtained. Taking the calculation result with the maximum deformation as an example, the vertical and horizontal displacements of the model are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & **Density** & **Elastic** & **Poisson’s** & **Cohesion** & **Friction Angle** & **Saturated** & **Saturated** \\
**(kg/m\({}^{3}\))** & **Modulus (Pa)** & **Ratio** & **(kPa)** & **(\({}^{\circ}\))** & **Cohesion (kPa)** & **Friction Angle** & **(\({}^{\circ}\))** \\ \hline Loess 1 & 1720 & \(1.60\times 10^{7}\) & 0.35 & 21 & 20 & 17 & 14 \\ Loess 3-1 & 1850 & \(2.10\times 10^{7}\) & 0.34 & 21 & 14 & 21 & 12 \\ Loess 5 & 1890 & \(3.50\times 10^{7}\) & 0.33 & 23 & 21 & 21 & 14 \\ Silty Sandstone & 2500 & \(2.00\times 10^{7}\) & 0.24 & 150 & 33 & - & - \\ Grouting Area & 2600 & \(4.00\times 10^{7}\) & 0.22 & 500 & 40 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of soil and support.  
Figure 2: Stratigraphic information and tunnel 3D model diagram. (**a**) Tunnel and stratigraphic distribution map (unit: m), (**b**) 3D computational model.  
From the simulation results, it can be observed that after excavation of the tunnel to station ZG20+400, there is approximately 0.46 m of settlement deformation at the top of the tunnel, and about 0.32 m of horizontal displacement at the face.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 6 Discussion and Future Recommendation
The computer vision and image processing techniques can complement chemical approaches for microbiological corrosion assessment, detection, and maintenance [110]. Furthermore, crucial elements to develop the computer vision and image processing prediction models include the level of knowledge or expertise, availability of real-time data and historical data, computational power, and prediction or detection accuracy. In the case of insufficient data from images and videos, corrosion prediction and detection can be implemented using the probabilistic model. This prediction model is suitable for predicting the failure states of materials with higher accuracy. Nevertheless, the probabilistic model requires a high level of modelling expertise and computational expense. In cases of data abundance, the knowledge-based and statistical models can perform better in terms of computational power, accuracy, and expertise level. It is also recommended to use fuzzy logic for prediction modelling if the data are not available, ambiguous, and/or vague. Fuzzy logic can utilize engineering judgments and expert opinions when developing the corrosion prediction model. The poor quality of corrosion images, because of the presence of noise, interference, distortion, illumination, and image stabilization, is the main concern for computer vision and image processing techniques. Automated image acquisition is highly recommended when pre-processing techniques are all embedded in the imaging equipment, thus, it can produce a high-quality image. On the other hand, most of the developed computer vision and image processing techniques were implemented on simple curves and flat surfaces. It is recommended to include other complex structures and geometry, as observed on heavy machines, ship hulls, bridges, etc. Furthermore, the trend analysis can be implemented to predict corrosion progression or growths on materials. The prediction model can be used to predict maintenance in order to invent an automated corrosion maintenance system.
## 3 Theoretical Part
### Gamification in Education
#### 3.1.2 Psychological and Pedagogical Aspects of Gamification  
Gamification is based on several key psychological principles that foster motivation and engagement. One of the main principles is intrinsic motivation, which refers to an individual's internal drive to engage in activities for personal satisfaction or interest rather than external rewards [9]. Gamification uses elements such as challenges, competitions, and reward systems to enhance students' intrinsic motivation. Through goal achievement, feedback, and recognition of accomplishments, students develop a sense of competence and achievement, further boosting their motivation [10].  
From a pedagogical perspective, gamification can be integrated into various learning methods, including problem-based learning (PBL) and project-based learning. These approaches allow students to explore complex problems and develop solutions through play, increasing their engagement and active learning. Furthermore, gamification can help develop social skills and collaboration among students, as many gamified activities involve teamwork and collective problem solving [11].  
Self-determination theory (SDT), which relates to the three basic psychological needs--autonomy, competence, and relatedness--is also crucial in understanding the success of gamification in education. Gamified systems can meet these needs by providing a controlled environment where students can experiment and learn at their own pace (autonomy), feel capable and competent (competence), and connect and collaborate with others (relatedness) [12].  
Gamification also allows for personalized learning, adapting content and task difficulty to the individual abilities and preferences of students. This approach not only increases learning effectiveness but also reduces stress and anxiety associated with learning, as students have control over their progress and pace [13].  
Based on these psychological and pedagogical aspects, it is clear that gamification has the potential to significantly improve the quality of the educational experience, especially in contexts where traditional learning methods fall short of achieving high engagement and student motivation.