esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Main Contributions  
The main contributions of this paper are as follows.  
* We propose some new ISAL system models, namely, the dual-slot synchronous ISAL network with spatiotemporal cooperation, the single-slot asynchronous ISAL network with spatial cooperation, and the dual-slot asynchronous ISAL network with spatiotemporal cooperation.
* We provide the derivation of corresponding FIM expressions for single-slot and dual-slot asynchronous ISAL networks, pointing out that the proposed RLM-OWR method can introduce temporal cooperation between the estimation of clock offsets in two time slots in the dual-slot asynchronous ISAL networks.
* We propose a step-by-step resource allocation scheme, which transforms the multi-parameter optimization problem into multiple sequential optimization problems with a single parameter, avoiding the difficulty caused by simultaneously dealing with too many parameters when solving optimization problems. We also propose an improvement method from the perspective of energy allocation to solve the problem of the high time complexity of the step-by-step scheme. By comparing the optimization results of the step-by-step scheme and the traditional integrated scheme, we summarize the suitable scenarios of each scheme.  
Notations: We use lowercase and uppercase bold symbols to denote vectors and matrices, respectively. The lowercase bold subscript symbols (e.g., \(\mathsf{ain}\)\(\mathbf{(A)_{a}}\)) represent the operation of taking the submatrix corresponding to the parameter vector (i.e., \(\mathbf{a}\) in the example) from the matrix (i.e., \(\mathbf{A}\) in the example). The notation \(\|\cdot\|\) is the Euclidean norm of its argument; \(\mathsf{tr}(\cdot)\) is the trace of a square matrix; \(\mathbb{E}(\cdot)\) is the expectation operator of its argument; \(|\cdot|\) represents the cardinality of the set; \(\mathbf{A}\succeq\mathbf{B}\) denotes that the matrix \(\mathbf{A}-\mathbf{B}\) is positive semi-definite.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 2 Algorithm Description
### Bi-LSTM for Track Outliers Detection
#### 2.3.2 Bi-LSTM Neural Network Model Construction  
1. In flight data, each track point is represented by a 5-dimensional vector: F\({}_{t}\)\(=\) {longitude, latitude, height, heading, speed}. Feature extraction is carried out on the data to form the training set of the model: \[F_{t}=\{f_{1},f_{2},\ldots,f_{n}\}\] (13)
2. Data standardization processing; \(X=\{X_{1},X_{2},\ldots,X_{n}\}\) represents the processed training set, which is used as the model input, with \(Y=\{Y_{1},Y_{2},\ldots,Y_{n}\}\) representing model output.
3. The Bi-LSTM network is initialized; the initial weight matrix is assigned as a random number evenly distributed between [0,1]. The maximum number of iteration training is set as 256, the window size is 32, the learning rate is 0.001 and the minimum error value is 0.
4. Forward calculation: the hidden layer is input by the training set \(X\) and the Bi-LSTM model contains bidirectional LSTM hidden layers. Two groups of features are extracted during training. Each LSTM hidden layer contains n cell units arranged  
Figure 3: Bi-LSTM structure.  
in chronological order and the output through the hidden layer can be expressed as: \(H_{\text{F}}=\{H_{\text{F0}},H_{\text{F1}},\ldots,H_{\text{Fn}}\}\) and \(H_{\text{B}}=\{H_{\text{B0}},H_{\text{B1}},\ldots,H_{\text{Bn}}\}\).
5. Reverse error calculation: the characteristics are transferred from the LSTM in the forward and the backward motions and the results are calculated to obtain the difference loss between the real value and the predicted value. According to the loss, the whole network is backtracked and the parameters are modified. In order to improve the generalization ability of the model, a dropout mechanism is added between the Bi-LSTM layer and the full connection layer of the first layer to prevent overfitting of the model [33].
6. When the training times and error values meet the set requirements, the model training is stopped and the test set is predicted.
# A Compact Sequentially Rotated Circularly Polarized Dielectric Resonator Antenna Array
## 2 Resonating Element
### Elliptical Slot  
Traditional rectangular and circular slot functions at a single-resonant frequency exhibit linear polarization. In contrast, the ES can excite the \(f_{11}^{e,o}\), the two-fold frequency corresponding to \(TM_{11}^{o}\) and \(TM_{11}^{e}\) modes, required for circular polarization radiation while retaining a single simple feed. The ESs have a characteristically thin bandwidth, low gain, and low efficiency. The ESs can resonate and emit radiation excellently only in the vicinity of the resonant frequency.  
Figure 1 depicts an ES carved on the bottom side and coupled to a microstrip on the top side. The semi-major \(a\) and semi-minor \(b\) axes of the ES are chosen to maintain a low \(b/a\) ratio, thereby increasing the eccentricity. Owing to the axis similarity, the ES generates two resonating frequencies corresponding to the dominant even and odd modes. The eccentricity as a design factor offers supplementary elasticity and improves the practicality of elliptical structures. A higher eccentricity leads to a larger bandwidth [13]. Consequently, accurate resonant frequency calculations are critical and essential. The theoretical phenomena for calculating the resonance frequency of an ES were reported in [14; 15]. The eccentricity relation can be described by Equation (1).  
\[e=\sqrt{1-\left(\frac{b}{a}\right)^{2}} \tag{1}\]  
Figure 1: Schematic of open-ended microstrip line with ES: (**a**) Front plane; (**b**) Ground plane.  
The ES generates two resonant frequencies corresponding to the dominant even and odd modes. These two resonant modes can be determined using Equation (2), as reported in [16]:  
\[f_{11}^{e,o}=\frac{15}{\pi ea}\sqrt{\frac{q_{11}^{e,o}}{\varepsilon_{r}}}\text{ GHz} \tag{2}\]  
where \(f_{11}^{e,o}\) is the two-fold frequency corresponding to \(TM_{11}^{e}\) and \(TM_{11}^{o}\) modes, respectively, while \(\varepsilon_{r}\) is the permittivity of the used substrate.  
The approximated Mathieu function \(q_{11}^{e,o}\) for even and odd mode is given by Equations (3) and (4):  
\[q_{11}^{e}=-0.0049e+3.7888e^{2}-0.7228e^{3}+2.2314e^{4} \tag{3}\]  
\[q_{11}^{o}=-0.0063e+3.8316e^{2}-1.1351e^{3}+5.2229e^{4} \tag{4}\]  
The resonant frequencies come close to each other as the eccentricity decreases and vice versa.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band OFF and K/Ka-Band ON
#### 7.3.4 Stability  
Figure 28 illustrates the unconditional stability of the design for the entire 23-28 GHz band. This ensures reliable operation in satellite and 5G applications where large signal variations and different load conditions can occur.  
Figure 27: Input and output reflection coefficient performance of the switchable wideband receiver front-end with the X-band arm OFF and K/Ka-band ON.  
Figure 28: Stability performance of the switchable wideband receiver front-end with the X-band arm OFF and K/Ka-band ON.
## 4 Bearing Capacity Analysis
### Constructing the Equivalent Curve  
Based on two Q-\(s\) curves (for pile base and shaft) it is possible to draw the so-called equivalent load-settlement diagram, which approximates the traditional static test loads. Several papers have described this problem [6; 12] over the years. At the beginning, both curves from the bi-directional test are divided into a series of points. Then, any two points from both curves are selected for the same displacement values and a new point is created. The new point has the same value of displacement, but the ordinate of loads results from summing the loads for the two points selected. This procedure is repeated for successive points until the whole equivalent curve \(Q\)-\(s\) can be drawn (Figure 10).  
Figure 10: Method for constructing exemplary \(Q\)-\(s\) curve.
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
## 2 Materials and Methods
### Data Analysis  
The results of the plate counts are shown as the mean \(\pm\) standard error. Data are expressed in CFU after conversion to log10 for improved analysis. Multiple samples were analyzed to test for the presence of viable bacteria in each experimental replicate. The entire experimental procedure, along with immunosensor identification, was replicated at various time intervals, with incubation times of approximately 3 h and 24 h. The validation process was based on an independently prepared _Salmonella_ spp. inocula. The validation process included several replicates of food samples that were naturally contaminated from the same batch of fresh salad and fresh chicken. These replicates allowed for multiple independent tests with identical samples to ensure the reproducibility and accuracy of the detection method.  
Analysis of variance (ANOVA) was used to assess the impact of each variable. Variations in the frequency of positive samples were evaluated by a chi-square test with a  
Figure 5: Immunousensor-based detection of _Salmonella_ spp. (**a**) Binding event ensues with the immobilized antibodies on the PIC surface. Throughout the sample flow process, a meticulous monitoring of the correlation between optical resonance, in picometers (pm), and the elapsed time in seconds (s) is performed. (**b**) The optical signal transduction performed by the system is elucidated, in the form of a calibration curve. This calibration curve articulates the resonance in pm with the concentration of _Salmonella_ spp. bacteria present in the sample. The curve’s development entails measurement of the net difference in resonance between the reference rings (rr3, rr4) and the rings with functional antibodies (rr1 and rr2). The reference resonators function as controls, and any discernible change in resonance concerning the functionalized rings relative to the reference rings signifies the occurrence of _Salmonella_ spp. binding.  
confidence level of 95%. Data analysis was performed using Systat version 9 software (SPSS Inc., Chicago, IL, USA). Statistically significant differences in the ANOVA were considered to confirm when the \(p\)-value was less than or equal to 0.05.  
A double-blind assay was sufficient to determine the sensitivity and specificity of the method assay in which negative fresh chicken and Batavia samples were deliberately contaminated with _Salmonella enterica_ serotype _Typhimuriumum_ CECT4266 and _Salmonella enterica_ serotype _Enteritidis_ CECT 7236. The results were statistically analyzed to determine their significance [19]. The reproducibility of the method was assessed by multiple identifications using a PIC or immunosensor surface for each entire range of concentrations of the study strains under identical conditions, using similar chemicals and instrumentation elements.  
One-way ANOVA was carried out for the antibody array (immobilization control test) to evaluate the presence of statistically significant differences in fluorescence rates between activated and non-activated surfaces.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 5 Performance Evaluation  
This section evaluates the performance of the DWSIGF and the proposed FuGeF protocol using MATLAB simulation. The protocols were evaluated in terms of packet delivery ratio, end-to-end delay, energy consumption and probability of choosing an attacker metrics. The simulation parameters used were radio bandwidth, payload size and Constant Bit Rate (CBR) streams as 200 kbps, 32 bytes, and 100 packets respectively, to emulate the type of traffic expected in low-bandwidth networks. The simulation setting presented in Table 1 was used for all experiments and assumptions made in [3; 38] were maintained. In the simulation, a terrain of 150 square meters containing 196 sensor nodes, each having a communication range of 40 m, was prepared and the Gaussian distribution with standard deviation of four meters was used over the terrain to achieve uniform node distribution for the network.
## 3 Results and Discussion
### Image Processing Outputs  
The sample images were processed as mentioned in Section 2.3. An example of the processed images is shown in Figure 7. These images were used for feature extraction which was utilized for a fuzzy model generation. The images are consisting of both whole and broken grain rice. After segmentation and morphological operations, each object of an individual image is labelled as shown in Figure 7h-l. The features associated with each object (e.g., Object 1 in Figure 7h) are stored with the associated objectID in a tuple. These data set are further being used in training and testing of the classifier.
# Prevalence and Predictors of Osteoporosis Among the Chinese Population in Klang Valley, Malaysia
## 4 Discussion  
Osteoporosis is a skeletal metabolic disease with increasing prevalence among the ageing population. In the current study, the prevalence of osteoporosis was higher among older subjects. The prevalence of osteoporosis was 32.6% in subjects aged \(\geq\)71 years compared to subjects in younger age groups (4.2%, 8.8%, and 21.9% in age groups 40-50 years, 51-60 years, and 61-70 years, respectively). This was a common finding in many human epidemiological studies. A study conducted in 7163 Koreans aged above 50 years showed that 4.0% of men in their 50s were osteopororotic. The prevalence increased to 7.2% for those in their 60s, 15.1% in their 70s, and 26.7% in their 80s. The prevalence in women was 15.2%, 36.5%, 62.7%, and 85.8%, respectively [19]. A similar trend was found in 7042 Han Chinese adults (age range: 20-85 years), whereby men (21.7%) and women (59.3%) in the age group 75-79 years were reported to have the highest prevalence of osteoporosis as compared to other age groups [20]. The pooled prevalence of osteoporosis in 2016 (34.65%) in China was reported to be twice the pooled prevalence in the year 2006 (15.7%), and the study also observed that osteoporosis increased with age [21]. It was evident that age is one of the most important determinants of osteoporosis. Women are known to be more susceptible to osteoporosis compared to men. This is due to a phase of accelerated bone loss after menopause [22; 23] and a lower peak bone mass in women [24]. In this study, the prevalence of osteoporosis was 18.9% among women compared to 11.5% in men. This was in agreement with the study by [PERSON] et al. (2005), which reported that the prevalence of spine and hip osteoporosis among 514 women (mean age: 51.07 \(\pm\) 5.28 years) was 8.6% and 21.4%, respectively [9]. In the Korean population (n = 126,269; age range: 50-64 years), a 10-fold difference in the prevalence of osteoporosis between women (25.2%) and men (2.3%) was observed [25]. Menopausal women in our study had the highest prevalence of osteoporosis compared with premenopausal or perimenopausal women, and this was analogous to previous studies [26; 27]. [PERSON] et al. showed that the prevalence of osteoporosis in postmenopausal women (42.1%) was four-fold higher than that of premenopausal women (11.1%) [26]. Another Korean study (1547 men and 1991 women; age range 40-79 years) also found that postmenopausal women were at greater risk for osteoporosis at lumbar spine compared to premenopausal women [27]. After menopause, deprivation of estrogen will alter the composition of the mineral and organic matrix of bone tissue [28; 29]. The progressive increase in bone resorption and the decrease in bone formation due to estrogen deficiency will deteriorate the microarchitecture and strength of bones [29]. This study also showed that the prevalence of osteoporosis was higher among underweight subjects compared to those with normal BMI or who were overweight. This was in line with a
## 2 Modeling Method
### Improved Particle Swarm Optimization (PSO)  
PSO algorithm was first developed by [PERSON] and [PERSON] in 1995 based on a distributed behavior model [26]. This model is initialized with a swarm of particles in a D-dimension space, where D represents the number of parameters to be optimized. Each particle is composed of a speed vector (\(V_{i}\)) and a individual position vector in D dimensions. During the iteration, individual optimal position (\(pbest\)) of each particle is selected from individual position in the history and the global best optimal position (\(gbest\)) is determined among \(pbest\) for the whole swarm. To identify the optimal solution, each particle adjusts its flying according to the two optimal positions:  
\[V_{i}^{t+1} =\omega^{t}V_{i}^{t}+c_{1}r_{1}(pbest_{i}^{t}-X_{i}^{t})+c_{2}r_{2 }(gbest^{t}-X_{i}^{t}), \tag{13}\] \[X_{i}^{t+1} =X_{i}^{t}+V_{i}^{t+1}, \tag{14}\]  
where \(V_{i}^{t}\) denotes the speed of particle \(i\) at iteration \(t\), \(X_{i}^{t}\) denotes the position of particle \(i\) at iteration \(t\), \(pbest_{i}^{t}\) is the individual optimal position of particle \(i\) at iteration \(t\), \(gbest^{t}\) is the global optimal position at iteration \(t\), \(c_{1}\) and \(c_{2}\) are acceleration constant with the default value, \(2\), \(r_{1}\) and \(r_{2}\) are the random numbers ranging from \(0\) to \(1\), and \(\omega^{t}\) is the inertia factor. Equation (13) contains three parts. The first part is called the momentum part, reflecting the flying inertia of the particle. The second part represents the particles approximating individual optimal position in the history, and the last part reflects the tendency to the global optimal position.  
Previous studies have proved the performance of PSO in optimizing SVR parameters [34; 35; 36], while the local optimal solution has prevented the PSO to further optimize parameters after a certain number of iterations. Self-adaptive evolution strategy (ES) is a method used to enhance the operation of PSO when the global best position cannot be improved after some successive generations. This method applies small Gaussian mutations; thus, it is suitable for local optimization [37]. The Gaussian mutation is used by mutating the particle speed \(V_{i}\) and inertia factor \(\omega_{i}\):  
\[V_{i}^{\prime} =V_{i}exp[\tau^{t}N(0,1)+\tau N_{i}(0,1)], \tag{15}\] \[w_{i}^{\prime} =w_{i}+V_{i}^{\prime}N_{i}(0,\alpha T(g)),\] (16) \[T(g) =1-exp(-fitness(g)), \tag{17}\]  
where \(N_{i}(0,1)\) is the normal distribution for each particle, the value of \(\tau\) and \(\tau^{\prime}\) are \((\sqrt{2n})^{-1}\) and \((\sqrt{2\sqrt{n}})^{-1}\), respectively, \(N_{i}(0,\alpha T(g))\) represents the random value created by normal distribution, \(\alpha\) is a positive proportional constant, \(fitness(g)\) is the fitness value of individual, and \(T(g)\) is the degree of mutation. After the resetting of velocity and inertia factor, \(pbest\) and \(gbest\) are evolved by self-adaptive evolution strategy. This has proved the diversity of the particles and enabled the particles to find a better optimal solution in the next iteration.
## 4 Elementary State Components
### Rotations  
The elementary pure state vectors are in the irreducible rotational multiplets, \(\mathcal{H}_{\ell}\), of dimension \(2\ell+1\). The elementary mixed components belong to the linear subspaces \(\mathcal{A}_{n_{-},\ell_{-},m_{+},\ell_{+}}\), spanned by the dyadic products \(|n_{-},\ell_{-},m_{-}\rangle\langle n_{+},\ell_{+},m_{+}|\), where \(n\) stands for the rotational independent quantum numbers. The elementary state components of the rotationally invariant open dynamics are given by the subspaces, \(\mathcal{A}_{n,\ell}\), spanned by the tensor operators \(\{T_{m}^{(n,\ell)}\}\). The structure of an elementary open component in terms of the elementary components of the closed dynamics,  
\[\langle\langle T_{m}^{(n,\ell)}||n_{-},\ell_{-},m_{-}\rangle \langle n_{+},\ell_{+},m_{+}|\rangle\rangle\] \[=\langle n_{+},\ell_{+},m_{+}|T_{m}^{(\ell)}|n_{-},\ell_{-},m_{-}\rangle\] \[=\langle n_{-},\ell_{-},m_{-}|T_{m}^{(n,\ell)}|n_{+},\ell_{+},m_{ +}\rangle^{*}\] \[=(\ell_{+},\ell,m_{+},m|\ell_{-},m_{-})\langle\langle n_{-}, \ell_{-}|T^{(n,\ell)}|n_{+},\ell_{+}\rangle\rangle^{*} \tag{5}\]  
is given by the Wigner-Eckart theorem, \(\langle\langle n_{-},\ell_{-}|T^{(n,\ell)}|n_{+},\ell_{+}\rangle\rangle\) being the reduced matrix element. The structure of an elementary open component in terms of the closed irreducible subspaces is controlled by the reduced matrix element. The more detailed structure within the irreducible spaces is given by the Clebsch-Gordan coefficient. The importance of the tensor operator subspaces, \(\mathcal{A}_{n,l,\ell}\), is that they remain closed during the time evolution of a rotationally invariant open dynamics.  
The basis transformation from the open to the closed elementary components describes the dressing of a state by the environment. Consider for the sake of example a polaron, a point charge inserted into a polarizable medium and dressed by the induced polarization cloud in the medium, its environment. The density matrix of the elementary polaron states is the tensor operators, \(T_{m}^{(n,l)}\). The decomposition of the polaron into the elementary closed mixed state components, \(|n_{+},l_{+},m_{+}\rangle\langle n_{-},l_{-},m_{-}|\), is given by (5). When the point charge-medium interaction is suddenly switched off, then each elementary closed component is conserved one-by-one. The basis transformation from the closed to the open elementary components appears in the opposite thought experiment, where we start with a closed component, switch on the point charge-medium interactions, and find in the resulting mixed state component a linear sum of different elementary open components.
## 1 _Applied_
### _Acticle_
The vibration of the UAV frame [36, 37] and the motion of a robot manipulator mounted on the UAV [38] might affect the system dynamics and motion planning, and this will be investigated in future works. In particular, a reactionless motion of the manipulator [39, 40, 41, 42] will be beneficial because it will not significantly affect the system dynamics. Most of the search algorithms present in the literature have only been investigated for ground robotic applications, and therefore, their implementation is limited only to two-dimensional cases. However, this paper augments a heuristic-based A* search algorithm from a two-dimensional scenario to three-dimensional space, and this work specifically involves a quadrotor UAV traversing the generated shortest path. The major contribution of this paper is the development of modified A* formalism in order to plan a motion for a quadrotor UAV in a 3D space. This includes the generation of a dynamically feasible trajectory to reduce the control effort as well. In 3D space, a conventional A* approach may expand extra nodes if its expansion is kept to six neighboring sides. Therefore, in many scenarios, the searched 3D path may not be the shortest one and can involve extra nodes. In this work, we have modified the conventional approach of an A* algorithm by developing a truncation algorithm which takes the path nodes computed using A* as input. The modified approach guarantees a collision free shortest possible path by truncating the extra nodes. These nodes of the determined shortest path are then used to generate a dynamically feasible trajectory using the differential flatness theorem. Various constraints have been considered in order to generate an optimal trajectory using the Euler-Lagrange formalism. This optimal trajectory reduces control effort and enhances the flight endurance of the UAV. Subsequently, the generated trajectory is followed autonomously by a micro quadrotor UAV in order to validate the developments of this work. The rest of the paper is organized as follows: Section 2 presents a dynamical model of micro quadrotor UAV and controller structure which are subsequently used to traverse the generated trajectory. Section 3 explains the modified A* algorithm and trajectory generation. Section 4 illustrates the results of numerical simulations performed in MATLAB. Finally, Section 5 concludes the contribution and developments of this study.