esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 2 Motivation and Gap Analysis  
This research aims to propose an efficient and cooperative optimization technique for V2V and V2I communication in VENs. In existing work, several meta-heuristics-based (nature-inspired) optimization techniques have been used to find the optimal path to store the data in the nearest station in the vehicular network, such as genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), and ant colony optimization (ACO). GA is an evolutionary algorithm based on selecting a parent's chromosomes for reproduction. Crossover and mutation are carried out to obtain offspring or new solutions. It cannot process in the case of a single chromosome; it starts from many points, having a higher fitness value. GA cannot search from a particular area, so it takes much time to explore the whole search space [14]. The time complexity of GA is \(O\) (\(n^{3/2}\)\(log\)\(n\)) when a gene has the same length. In PSO, the swarm of n particles flies over a search space, and during iteration, each particle updates its current position according to its past and neighbor's experience. If one particle flies in the wrong direction, it can mislead the whole swarm; moreover, it cannot work in the problem of scattering. PSO has random walks and is not suitable for continuous problems.  
The expected runtime of PSO is \(\Omega\) (\(n\)/\(log\)\(n\)) and \(O\) (\(n\)\(log\)\(n\)), which is between the lower and upper bound. Its average case of asymptotic notation is \(\Theta\) (\(n\)\(log\)\(n\)). In ACO, the initial parameter for ants is randomly assigned, and then the path length is calculated to find the optimal solution by updating the pheromone. Improper selection of initial value may lead to local optima [15]. The time complexity of ACO is \(O\) (\(n^{*}\)(\(n-1\))\({}^{*}\)\(m^{*}\)\(T/2\)). As Big-O represents the worst-case scenario; so, it takes maximum time to process the large-scale problems and easily falls into the second-best solution, and many ants cause congestion.  
In contrast, cuckoo search (CS) solves continuous problems. Cuckoo species inspire it, laying their eggs in the nest of other species, which increases their survival and productivity [16]. There is a 10% probability that the host bird discovers an egg and reproduces stably. Moreover, CS has great robustness in terms of convergence, assigning initial parameters and dependencies on other swarms [17]. Therefore, we use CS in our research to develop an optimized algorithm that finds the best path to pre-cache the requested content, minimize the response time and cache cost, and enhance the cache hit ratio.
## 3 Numerical Experiments  
In order to study the effectiveness of the smooth local subspace projection method applying to mechanical fault signal analysis, Lorenz model [48] and frequency modulation (FM) signals [49] are adopted to assess the performance of the algorithm. The Lorenz model is one kind of typical nonlinear dynamic signals, and frequency modulation signals also usually exist in mechanical fault signals [50; 51]. Hence, it is meaningful to analyze these two kinds of simulated signals to verify the effectiveness of this denoising algorithm.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
PCA shows that the first two components, the eigenvalue of which is >1, accounted for 75.2% of the total variance (Table 6). PC1 shows strong loading for pH, EC, TDS, cations, anions, and nitrogen-based nutrients. This shows the common origin and combined contribution of all ions in the water. PC1 displays the lithological control including carbonate or silicate weathering [74]. PC2 showed strong loadings for TP and SRP. This shows their common origin and combined contribution to the water. TP and SRP may originate from agricultural activities indicating substantial anthropogenic activities in the area studied [11; 69; 74]. Biologically, previous studies of [PERSON] et al. [83], [PERSON] et al. [21], and [PERSON] et al. [84] coincide with this observation where they also reported several pollution-tolerant cyanobacterial and microalgal species inhabiting the springs and drilled wells of the El-Farafra Oasis.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.2.6 Energy-Efficient Grid-Based Clustering Routing Protocol (E2 GCRP)  
A clustering and energy efficient routing protocol based on the grid (E2 GCRP) for UWSNs was proposed by the author in [141]. Each grid is created by using this technique as one cluster in the network. Based on the energy left over, the CH is placed there. The network's communicating nodes with the most remaining energy will transform into a CH. A coordinator communicating node, which is installed to assist with inter-cluster communication and data packet delivery to the BS, is a particular type of communicating node in the network. The technique cluster based underwater wireless sensor network (CUWSN) that has been proposed has the ability to boost network's throughput with the coordinating node's assistance. However, it is anticipated that the coordinator node and CH will terminate soon.
## 2 Materials and Methods  
The Institutional Review Board of Pusan National University Dental Hospital approved this study (PNUDH-2022-03-011). The sample size was calculated as described [15]. Here, we compared the results of treating mild skeletal Class III malocclusion in pediatric patients using a facemask and a tooth (FM)- or a miniscrew (FM + MS)-anchored appliance. The angle formed by the intersection of sella-nasion and nasion-A lines (SNA) was significantly increased in the FM + MS, compared with the FM group (\(2.2^{\circ}\pm 1.3^{\circ}\) vs. \(1.1^{\circ}\pm 1.0^{\circ}\), \(p<0.01\)). A study power of 90% was assumed; significance level was 5%. The calculation of sample size was based on the previous study using the G*power software 3.1.9.7 (Universitat Dusseldorf, Dusseldorf, Germany) [8]. They investigated the treatment outcomes of mild skeletal Class III malocclusion between FM and FM in combination with a miniscrew for anchorage (FM + MS) in growing patients. The SNA values were significantly higher in the FM + MS group than that in the FM group (FM; \(1.1\ \pm\ 1.0^{\circ}\), FM + MS; \(2.2\ \pm\ 1.3^{\circ}\)). The sample size was calculated based on an alpha significance level of 0.05 and 90% power to detect an effect size of 0.948 between measurements. The power analysis showed that at least 25 patients were needed. Therefore, this retrospective study included 50 patients (28 males and 22 females) who underwent orthodontic treatment at the Department of Orthodontics at the Pusan National University Dental Hospital from 2013 to 2017. During the retrospective study period (2013-2017), patients presenting in the early years were treated primarily with tooth-anchored devices, while patients presentingin the later years were treated primarily with miniscrew-anchored as MSE devices became more widely available. All patients satisfied the following inclusion criteria:  
1. Skeletal Class III malocclusion (relative anteroposterior position between the maxilla and mandible (ANB) <2\({}^{\circ}\)) measured on initial lateral cephalograms and evaluator.
2. Anterior cross (including edge-to-edge) bite.
3. Cervical vertebral maturation stages (CVMS) I-III.
4. No craniofacial malformations or systemic disease.
5. No temporomandibular joint disorder (TMD).
6. No history of orthodontic treatment.  
The patients (n = 25 per group) in the group with tooth-anchored (T-A) devices comprised 9 males and 16 females (mean age, 7.9 years) and those in the miniscrew-anchored (M-A) group comprised 11 males and 14 females (mean age, 9.9 years). Lateral cephalometric radiographs were obtained from all patients before (T1), immediately after (T2), and during follow-up periods, at an average of 37.11 months (standard deviation (SD), 16.74) after maxillary protraction (T3).
# An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data
## 2 Background
### Regression  
After building the HI, if the index score continues to rise, it can be assumed that the equipment is out of order. That is, if the HI of the new data is larger than the score of the trained data, it can be said that abnormal data is generated from the equipment, and if there is a trend of the index score, the failure of the equipment can be predicted. Therefore, it is necessary to regard the HI score as time series data and predict the future score of the HI. Regression is a simple, fundamental method among the various algorithms used for time series prediction. In particular, linear regression is a linear approach to modeling the relationship between a dependent variable and one or more independent variables. Among them, the simple linear regression is the simplest prediction method that can be used when there is only one variable in the data.  
Given data set \(\mathbf{X}=\left\{\mathbf{x}_{1},\;\mathbf{x}_{2},\;\ldots,\;\mathbf{x}_{p} \right\}\in\mathbb{R}^{n\times p}\), the general form of the linear regression can be expressed as follows:  
\[\begin{array}{ll}y_{i}&=\beta_{0}+\beta_{1}x_{i1}+\beta_{2}x_{i2}+\ldots,+ \beta_{p}x_{ip}+\epsilon_{i}\\ &=\mathbf{x}_{i}^{T}\boldsymbol{\beta}+\epsilon_{i},i\in\{1,\;\ldots,\;p\} \end{array} \tag{6}\]  
where \(T\) denotes the transpose, so that \(\mathbf{x}_{i}^{T}\boldsymbol{\beta}\) is the inner product between vectors \(\mathbf{x}_{i}\) and \(\beta\). And \(y_{i}\) is a dependent variable, \(x_{ip}\) is the \(p\)-th independent variable of timestamp \(i\), respectively. Furthermore, \(\beta_{0}\) is an intercept, \(\beta_{p}\) is the \(p\)-th coefficient of each independent variable and \(\epsilon_{i}\) is an error term. In this paper, as the HI is adopted for the regression's input vector, a simple linear regression is used. A least-squares estimator is utilized to fit the model and tries to minimize the sum of squares of an error term. The least-squares estimator finds the optimal solution, seeking the slope \(\beta_{1}\) and the intercept \(\beta_{0}\) for \(\underset{\beta_{0},\beta_{1}}{\arg\min}S(\beta_{0},\beta_{1})\) as in (7)  
\[\begin{array}{ll}S(\beta_{0},\beta_{1})&=\sum\limits_{i=1}^{m}\left(\epsilon _{i}\right)^{2}\\ &=\sum\limits_{i=1}^{m}\left(y_{i}-\beta_{0}-\beta_{1}x_{i}\right)^{2}\end{array} \tag{7}\]  
where \(m\) is the number of training data.  
In this paper, the slope represents the trend of the HI. For example, if the health of the equipment is good, then there is no trend in the HI, the slope of the regression model can be either very small or negative. On the contrary, when an abnormality or aging occurs in the equipment, the trend of the HI may rise, and the slope value of the regression model becomes large.
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### Problem Definition
#### 3.1.3 Network Portioning Due to Constant Use of Minimum Cost Forwarding Paths  
The potential issue associated with the multi-hop based minimum cost and multipath data forwarding schemes is that the minimum cost path is constantly used until it fails. This scheme does not provide the best network lifetime. Constant use of a minimum cost path results in depletion of energy at nodes on the same path and causes a network partition.
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 4 Results  
### Test Data  
Six datasets from VISO [27] were selected to verify the validity and practical performance of the method. The dataset was captured using the Jilin-1 satellite constellation. The spatial resolution of the Jilin-1 video satellite is 1.03 m, with an altitude of 535 km, an  
Figure 5: Diagram of the angular difference in the direction of motion. The green and red dots represent the positions of the two moving vehicles in the video in multiple frames, with the solid line indicating their actual trajectory, and the dashed line indicating the predicted trajectory. \(a_{1}\) indicates the angle between the green vehicle detection and predicted targets, \(a_{2}\) indicates the angle between the red vehicle prediction and detection targets, and \(a_{3}\) indicates the angle between the green vehicle prediction and red vehicle detection targets.  
orbital inclination of 97.54\({}^{\circ}\), a standard view area of 11 km \(\times\) 4.5 km, and an uncontrolled positioning accuracy of approximately 200 m. The data include an RGB color video, and the dataset is created by intercepting standard images. The videos cover several square kilometers in real scenes. Figure 6 shows the first video frame for each area. The video was taken in St. Paul, USA, and Muharraq, Bahrain. For each scene, 100 frames were used for testing. The video includes not only roads but also residential land, wood, arable land, water bodies, and other feature types. Specifically, Area 1 comprises a main road with a large area of rural residential land and vegetation in the background. Area 2 comprises a main road with a large area of a lake in the background. Area 3 comprises mainly an overpass with a large area of residential land in the background. Area 4 comprises a road and a path with a town settlement in the background. Area 5 consists of a crossroad with a town settlement in the background. Area 6 comprises the coastal highway with a large body of water in the background. The above 6 are sufficient data for a more comprehensive assessment of the actual performance of DTSTC.
## 3 Results
### Simulation of Damage Detection  
After correcting the temperature influence, a hypothesis test can detect a change in the frequencies' mean. In this section, we illustrate the necessary properties of the test to detect a change in the previously estimated frequencies. A simulation of a test measurement can indicate the required minimum difference between the test distribution and the reference distribution. We assume that the test distribution consists of frequency estimations from 30 min of measurements with a standard deviation equal to the reference distribution. Furthermore, both distributions are normally distributed, and the significance level is defined as 5%. The least squares approach with GBR measurements would result in approximately 50 frequency estimations and a minimum detectable difference of 8 mHz. The FDD with a 5 min window generates six frequency estimations resulting in a difference of 27 mHz. In the case of the strain sensors' result, the required difference is 13 mHz for the least squares approach.
## 2 Motion Planning of XXY Stage Using the ANN
### Motion Planning of XXY Stage  
In this study, the coplanar XXY stage is controlled by a PC+PLC-based architecture. After capturing the positioning mark in the image space, the image processing and pattern matching methods are studied and discussed to extract the positions of the fiducial mark, such as (1) pattern matching; (2) Sobel edge finding; (3) morphology processing and (4) Hough transform. After obtaining the positon of the two sets of the fiducial mark images, the relationship between the image coordinate and the actual coordinate in Cartesian space should be established. However, the relationship between the XXY stage's movement and the commands of the three motors is difficult to compute, because the movements of the three motors on the same plane are coupling. Therefore, an artificial neural network is studied to establish a nonlinear mapping from the desired position and orientation of the stage to three motors' commands.  
Without considering the rotational movement of \(\theta z\), the relationship between the XXY stage's displacement (\(\delta_{x}\), \(\delta_{y}\)) and the stepper motors' command is described as follows.  
\[\left[\begin{array}{c}dx1\\ dx2\\ dy\end{array}\right]=\frac{R_{m}}{l_{p}}\left[\begin{array}{cc}1&0\\ 1&0\\ 0&1\end{array}\right]\cdot\left[\begin{array}{c}\delta_{x}\\ \delta_{y}\end{array}\right] \tag{1}\]  
where \(R_{m}\) is the motor resolution (unit: pulse/rev); \(l_{p}\) is the lead of the ball-screw (unit: mm/rev); \(dx1\), \(dx2\) and \(dy\) are the commands for the \(X1\)-axis, \(X2\)-axis and \(Y\)-axis motors (unit: pulse). To make \(\delta\)= 0, the commands of the X1-motor and the X2-motor should be the same. However, considering the rotational movement of \(\theta z\), because the rotational center of the XXY stage is not fixed as the stacked XY-\(\Theta\)z stage, the kinematic relationship is very complicated. The kinematic of the coplanar XXY stage can be formulated as follows [11].  
\[\left[\begin{array}{c}dx1\\ dx2\\ dy\end{array}\right]=\left[\begin{array}{ccc}R_{m}/l_{p}&0&k_{a}R_{m}/l_{p} \\ R_{m}/l_{p}&0&k_{b}R_{m}/l_{p}\\ 0&R_{m}/l_{p}&k_{c}R_{m}/l_{p}\end{array}\right]\cdot\left[\begin{array}{c} \delta_{x}\\ \delta_{y}\\ \delta_{\theta}\end{array}\right] \tag{2}\]  
where \(\delta_{x}\) and \(\delta_{y}\) is the desired translation displacement of the stage and \(\delta_{\theta}\) is the desired angular displacement of the stage; \([dx1\ dx2\ dy]^{T}\) is the motors' commands (unit: pulse); \(k_{a}\), \(k_{b}\) and \(k_{c}\) are related to the distance between the XXY stage center and the working stage center. The relationship between \([dx1\ dx2\ dy]^{T}\) and \([\begin{array}{cc}\delta_{x}&\delta_{y}&\delta_{\theta}\end{array}]^{T}\) is complicated and nonlinear.  
Figure 2: (**a**) Traditional XY-\(\Theta\)z stage [13]; (**b**) Coplanar XXY stage [14].
## 5 Calculation Results and Discussion
The PV generates electricity from sunrise to sunset, operating both to cover the load as such and to charge the BS. The DPP is switched on at certain hours, tracking the load profile. Figure 6: Capacities of energy sources under conditions favorable for RES ((a)—winter day, (b)—summer day). In winter days (Figure 6a), the DPP is switched on when the load increases and is switched off during periods of simultaneous operation of the WT, PV, and GPP. During the period of the evening load increase when the PV and WT stop their generation, the GPP and the DPP operate until the next wind speed increase, when the next cycle of battery charging begins. During the summer day, the DPP can be switched off, the power of the PV increases, and the contribution of the WT decreases (Figure 6b). At certain hours (at night, in the morning, and in the afternoon), the load profile is completely covered by the output of the GPP. The BS is charged at night by the WT, during the day it is charged by the PV. As the load increases in the morning and evening hours, the electric batteries are discharged. In this case, the optimal solution is the joint use of all of these types of RES, if necessary; in periods of increased load, the DPP is switched on (see Figure 6). At that, the main part of generation is provided by the GPP (more than 60%), the share of WT is about 18%, PV--12%, and the contribution of the DPP does not exceed 10%. In addition, we conducted calculations with constraints on some technologies. Rejection of one of the components (PV, WT, or GPP) increases the cost of the system by no more than 5-20% (Figure 7). Therefore, all the considered renewable energy sources (except for PV in areas with solar radiation of 1100 kWh/m\({}^{2}\) and below) in the hybrid power system are desirable, but the exclusion of any one of the components is not critical for the economic performance of the power supply system. The most expensive option is without RES, i.e., when consumers are supplied with electricity from the DPP only (Figure 7). Improved economic performance, when using RES, is associated with saving expensive diesel fuel and reducing the installed capacity of the DPP: up to 90% of the maximum load when included in the system of WTs and PVCs, and up to 60%--GPP. The most economical option is the unrestricted option, i.e., when the system includes RES of all the considered types. In this case, the total discounted costs are reduced by almost 2.5 times compared to the DPP option. Calculations with a decrease in the load power (up to 1-10 kW) demonstrate that the share of solar cells increases in the optimal structure due to the change in specific capital investments in favor of solar cells.
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### Goals
#### 3.2.4 Identification of Unhealthy Nodes Near the BS  
Our proposed scheme determines the qualification of the potential filtering path and identifies those unhealthy nodes on the path whose energy is below a threshold value given the distance between the e-CH and the BS.
## 2 Measurements and Quality Control Processes
### Parameterizing Daytime Longwave Downward Radiation  
#### 3.7.1 Clear-Sky Conditions  
Since the downward longwave radiation is greatly affected by air temperature and/or vapor pressure at the screen level parameterizing was based on these variables. [PERSON] et al. [9] have estimated daytime longwave downward irradiance under clear sky conditions using five different models. For this purpose, hourly temperature and relative humidity data were used to estimate \(L_{dt}\). The classification of the clear-sky conditions was based on the value of the clearness index \(k_{t}\), which is defined as the hourly ratio of the measured global solar irradiance to the value of extraterrestrial horizontal solar irradiance (\(k_{t}=G/G_{0}\)). Then, the sky can be considered as clear when (\(k_{t}\geq 0.65\)), as partly cloudy when (\(0.35\leq k_{t}<0.65\)), and cloudy when (\(k_{t}\leq 0.35\)) [39].  
The best performance of the test models was obtained with [PERSON] [40] and [PERSON]'s [41] models using local calibrated coefficients based on data measurements covering the period 2013-2014 (training dataset). The equations involved in the calculations of the longwave  
Figure 15: Quadratic relationship between the daily longwave upward irradiation (I\({}_{upd}\)) and the daily shortwave reflected irradiation (R\({}_{d}\)).  
downward irradiance under clear sky conditions (\(L_{dnc}\)) with local calibrated coefficients have the following form:  
\[L_{dnc}=[0.71+4.09*10^{-5}e*\exp(1500/T_{a})]*\sigma*T_{a}^{4}\ (\text{Idso model}) \tag{13}\]  
\[L_{dnc}=(0.62+0.046*e^{0.5})*\sigma*T_{a}^{4}(\text{[PERSON] model}) \tag{14}\]  
where, \(e\) is the actual vapor pressure in hPa, \(T_{a}\) is the air temperature at screen level in Kelvin and \(\sigma\) is the Stefan-Boltzman constant. The above models were tested with the current data set hourly values. The performance of the above models was based on the root-mean-square error (RMSE), and the correlation between the estimated (\(E_{i}\)) and the measured irradiance values (\(M_{i}\)). The RMSE has the following form:  
\[\text{RMSE}=[\frac{1}{n}\sum_{i=1}^{n}{(E_{i}-M_{i})^{2}}]^{0.5} \tag{15}\]  
where \(n\) is the number of data pairs. The results of the evaluation of both models for the clear sky conditions are shown in Table 6. The Table shows that the slope of the lines and the coefficients of determinations are close to one for both clear sky models. However, [PERSON]'s model shows slightly better performance with respect to [PERSON]'s model, since it has slightly lower RMSE compared to [PERSON]'s model.