esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 2 System Model and Problem Statement
### System Model
The connection between two nodes occurs when and only when they are within the transmission radius. One of the crucial research concerns is the energy problem, which includes things such as patterns of energy consumption and battery dynamics. We direct readers to [29] and any references therein because it is outside the scope of this article. For ease of description, we designate the UAV assigned to reconnaissance mission \(m\) as monitoring UAV (MU) and denote it as \(\varepsilon_{m}\). Therefore, the set of MUs can be expressed as \(M=\left\{\varepsilon_{1},\ldots,\varepsilon_{m}\right\}\). The remaining drones that are not assigned to any reconnaissance mission are used as communication relay nodes in the network, which are in charge of transmitting data between MUs and the ACP in the upper air. Relay UAVs (RUs) is the term we use to identify them, i.e., \(R=U\backslash M\). Figure 1 describes the hierarchical structure of our UAV persistent surveillance team. Because of the quadcopter's limited communication range, the high-altitude UAV undertakes information exchange with the remote ground end. Some necessary control instructions for drone swarms are also sent from the high-altitude UAV since its bigger role is the ACP of the network. Inside the FANET, a buffer layer of UAV swarm between the ACP and ground objects extends this system's ability on connection service and real-time tracking. With many agents within the UAV swarm layer, an adaptive formation policy can be developed to fit various requests, including avoiding dangerous areas such as Anti-UAV Defensive System (AUDS), thunderstorm areas, strong communication interference areas, etc. Next, it is challenging to guarantee direct communication between each UAV node and the ACP to maximize the drone swarm's search and surveillance range. Therefore,  
Figure 1: Hierarchical structure of proposed FANET persistent surveillance system. multi-hop communication is usually adopted, which requires establishing a routing path for packet transmission. As a result, the choice of routing path significantly impacts the performance of the FANET. When coming up with a solution for the UAV locations, the routing protocol should be considered. Different UAVs may operate in different ideal locations depending on the routing protocol. Therefore, in this paper, we focus on routing protocols that offer a selection of routes between MUs and the ACP based on the positions of the nodes and presumptively employ a routing protocol that is known in advance. Accordingly, we define a routing function,  
\[\rho:\{x_{\varepsilon_{m}},x_{A},X_{R}\}\Leftrightarrow\rho(m), \tag{1}\]  
where \(\rho(m)\) is the series of wireless links from the MU \(\varepsilon_{m}\) to the ACP \(A\) through the relay UAVs in \(R\).
# Design of a Wideband Antenna for Wireless Network-On-Chip in Multimedia Applications
## 2 Design of the On-Chip High-Bandwidth Antenna

Table 1: Values of the antenna parameters in Figure 1.  
Figure 1: Bow-tie wideband antenna design, 65 nm CMOS SOI. a dielectric layer with a thickness of about 3 \(\upmu\)m and a relative dielectric permittivity \(\varepsilon\tau\) of about 3.5;
2. a buried oxide layer with a thickness of 150 nm (a value typical for SOI manufacturing technology, see p. 11 of [72]) and a relative dielectric permittivity \(\varepsilon\tau\) of about 4;
3. a high-resistivity (HR) layer with a thickness of 350 \(\upmu\)m (a value typical for wafers used in RF applications, see [73]) and the following electrical parameters: \(\rho=5\) k\(\Omega\cdot\)cm and \(\varepsilon\tau=11.7\). Figure 2 shows a side view of the multi-layer structure in the example case of the CPS implementation. For the sake of space, the figure is not to scale, since the height of the high resistivity layer is two orders of magnitude higher than the height of the conductive and dielectric layers, and three orders of magnitude higher than the height of the buried oxide layer. Moreover, on top of the layers in Figure 2, a passivation layer may be needed due to packaging issues. The effect of a passivation layer and of the packaging on the proposed on-chip antenna is out of the scope of this paper. The antenna-feeding network is also included in Figure 1. It is represented by the CPS circuit of length L4 and total width W4, whose values are reported in Table 1. The gap between the two strip-lines of the CPS is about 1 \(\upmu\)m. To design the antenna, and to properly size all parameters reported in Table 1, electromagnetic simulations have been carried out using the CAD (Computer Aided Design) environment CST (Computer Simulation Technology) Microwave Studio. It is to be noted that there is no equation that is able to size the antenna, but the antenna sizing is the result of an iterative process, supported by simulations in the CAD environment. After several simulations and design trials in the CST Microwave Studio tool, the values in Table 1 have been achieved. The area of the rectangle including the antenna is about 0.6 mm\({}^{2}\), which is much lower than the area of some cm\({}^{2}\) for the antennas proposed by [67; 70]. This sizing is not valid for only a specific frequency, but the same antenna has been characterized in a wide frequency range.
## 3 Results
### Bunch Structure Measures  
Bunch structure parameters of second position bunches along a shoot are shown in Table 3. Riesling showed an increase in bunch length over years for the eCO\({}_{2}\) treatment compared to aCO\({}_{2}\). The highest percentage increase in relation to aCO\({}_{2}\) treatment was found for all bunch parameters of Riesling in 2016 with increases \(>\)80% for bunch weight and total berry weight (TBW). The percentage increase of Cabernet Sauvignon bunch length and width under eCO\({}_{2}\) treatment was higher in 2016 compared to 2015, when bunch weight, number of berries, and total berry weight were higher under eCO\({}_{2}\) conditions (Table 3). Posterior predictions of differences between elevated and ambient CO\({}_{2}\) conditions for yearly measurements of five different bunch parameters (Figure 10) support inter-annual results (Figures 8 and 9) from a slightly different angle. For 2015 and 2016 data of Cabernet Sauvignon, the maximum probabilities found for an effect were associated with bunch weight at 89% and TBW at 90% in 2015 (Figure 10). In general, there is a trend for higher bunch weight, TBW, length, width, and number of berries, both in Cabernet Sauvignon and Riesling, with probabilities in the range of 57% to 92%. Estimates indicate the Riesling bunch length, weight, TBW, and number of berries in 2016 to be significantly affected with probabilities \(>\)90%-92%. The distribution of berry sizes within second positioned bunches showed a seasonal pattern for both cultivars. Whereas Riesling berry size group 5 (12.5-14 mm) was most frequent in 2014 and 2015, the highest number of berries was detected for size group 6 (14-16 mm) in 2016 (Figure 11). This was already shown by higher single berry weights of Riesling in 2016 compared to the two previous years (Table 3). Between the treatments, no differences occurred in 2014 and 2015 for single berry size categories. In 2016, the eCO\({}_{2}\) treatment showed an increase of 10% for the three biggest berry size classes (7 (16-18 mm), 6 (14-16 mm), and 5 (12.5-14 mm)). All smaller berry size classes were less frequent under eCO\({}_{2}\) compared to aCO\({}_{2}\) treatment in 2016. Within Riesling, smaller berries were found in all years, whereas bigger berries of category 7 were only present in 2016 with more than 1%.
# Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System
## 1 Introduction
The model, the analysis software, the IMU, and the relative setups and protocols of our system are designed for gait analysis. The preliminary validation [23; 24] was carried out on a population of healthy subjects with different genders, Body Mass Index (BMI) values, and anthropometric values; the subject walks over a linear path with a fixed step length. These constraints were introduced to have a controlled experimental protocol, leaving the subject with the freedom to adopt a self-selected speed and walking time. The use of the system is not limited to these constraints. More protocol setups and experiments have been carried out to test it when functional tests are submitted to subjects that are healthy and pathological. The proposed system can be used stand-alone or as a modular component of a sensorized WHS t-shirt. Every new system for measurements in motion analysis parameters needs a comparison with a reference gold standard. In this study, we enforce the validation approach of our system in stand-alone modality, using a comparison with optoelectronic GA which represents the gold standard system reference. This requirement is a milestone. No specific standard protocols were found in the literature, specifically dedicated to single trunk IMU systems for GA gold standard reference validation. Several studieshave been carried out to assess the gait analysis reliability with wearable sensors [8; 9; 10; 11; 12; 15; 16; 17; 18; 19; 20; 21; 22], but the evaluation protocols have not been homogeneous; they are mostly based on recording gait trials in a controlled setup to produce some reference datasets. These datasets have then been used to validate the methods. One study has presented the validation of temporal gait metrics from three IMU locations to the GA using Bland-Altman diagrams [25; 26]. We have chosen to use a structured methodology that adopts a robust approach to complete the relative task for the validation protocol, as required by the general methodology defined in [24], to validate the use of the new system. To obtain the validation of the system with respect to the true values of the GA gold standard reference, we first had to define the protocol guidelines. Therefore, the goal of this paper is threefold. The first goal is to present and define structured Protocol Design Guidelines, where the related setup is implemented for the validation of wearable IMU systems particularly dedicated to GA and gait monitoring. The second goal is to apply these Protocol Design Guidelines to a case study in order to verify their feasibility, user-friendliness, and efficacy. The third goal is the validation of our biomechanical kinematic model with the gold standard reference. We do not consider this validation usable only in the laboratory with the protocol setup setting, but it means that the biomechanical kinematic model is robust and new validation protocols can be designed and carried out for accuracy measurements to prove that our system works when it is applied to the walking functional tests and balance tests.
# Closed Form Solutions for Thermal Buckling of Functionally Graded Rectangular Thin Plates
## 1 Introduction
[PERSON] and [PERSON] [17] studied the critical buckling temperature of FGM plates with various boundary conditions under uniform, linear and sinusoidal temperature fields by establishing the finite element model via three-dimensional linear elasticity theory. Subsequently, [PERSON] and [PERSON] [18] improved their previous work and studied the thermal buckling of FGM composite structures. [PERSON] [19] investigated the thermal buckling of FGM arbitrary straight-sided quadrilateral plates via the differential quadrature method (DQM) based on three-dimensional linear elasticity theory. [PERSON] et al. [20] investigated buckling of functionally graded plates under biaxial compression, shear, tension-compression, and shear-compression load conditions by establishing a full compatible three-dimensional elasticity element, but they didn't consider the thermal environment in their work. A review paper of various modeling techniques and solution methods in thermal analysis of FGM plates was also published [21]. From the above reviews, it can be seen that closed form solutions for thermal buckling of FGM plates are only available for a few boundary conditions such as fully simply supported parallel edges. Furthermore, there is currently no literature on the closed form solution for the critical buckling temperature of FGM thin plates for other boundary conditions. Furthermore, closed form solutions can play a reference role to study the convergence and precision of various numerical methods and serve the purpose of parametric designs of structures. In the context of closed form solutions for the critical thermal parameter of simply supported FGM rectangular thin plates, guide and clamp boundary conditions are obtained via the separation-of-variable method. A novel method is presented to determine the critical buckling temperature from the critical thermal parameter. It is assumed that the FGM properties follow the power law distribution in the thickness direction. Two types of FGM thin plates and uniform, linear and nonlinear temperature fields are taken into account. The influences of the length-thickness ratio, length-width ratio, power law index and initial temperature on the critical buckling temperature are investigated in detail.
# Event-Based Probabilistic Embedding for POI Recommendation
## 2 Topic And Region Embedding
### Model Description  
The POI recommendation is mainly used to predict user behavior. In order to handle POI recommendations better, we analyze which factors influence user behavior and how these factors affect user behavior. Figure 1 shows the decision-making process of users. An intuitive factor that affects user behavior is time. Furthermore, the time affects the activity type of a user which progressively affects the user's geographic preferences and POI category preferences. For example, users are inclined to decide which place to eat between 12:00 p.m. and 1:00 p.m. Meanwhile, geographic information and POI category are also affecting the decision. Users may choose an appropriate POI geographic location and POI category according to personal preference and current location. Finally, users select a POI to visit based on activity type, POI location and POI category.  
According to the above process, this paper proposes a new embedding method called Topic And Region Embedding (TARE), which embeds events according to user preferences, event time, event location and POI by simulating the user decision process. TARE divides the factors that influence user behavior into four categories: geographic information, time factor, POI category, and personal preference. User behavior has a similar periodic pattern, and this is manifested in the fact that users always visit similar POIs at specific  
Figure 1: The decision-making process of a user to visit a place.  
times [18]. In other words, time factors affect user behavior all the time. To this end, TARE uses two hidden variables (\(r\), \(z\)) to discover the relationship between time, POI category and geographic information. Table 1 lists the notations of our model. Table 2 lists the abbreviation and full name of different concepts in this paper. Figure 2 shows the graphical representation of TARE. TARE consists of two parts: embedding of events and prediction of event occurrence, which will be described below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(N,V,R,Z\) & The number of users, POI, region, topic \\ \hline \(D_{u}\) & The profile of user \(u\) \\ \hline \(v_{u,i}\) & The POI of \(i\)-th record in \(D_{u}\) \\ \hline \(l_{u,i}\) & The location of POI \(v_{u,i}\) \\ \hline \(\theta_{z|u^{\prime}},\theta_{r|u}\) & The interest of user \(u\), distribution over topic, region \\ \hline \(\theta_{w|z}\) & The topic \(z\)’s distribution over POI categories \\ \hline \(\mu_{r},\Sigma_{r}\) & The mean location, location convariance of region \(r\) \\ \hline \(\theta_{v|r,z},\theta_{t|r,z}\) & The region \(r\) and topic \(z\) distribution over POI and time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations of parameters.  
Figure 2: The Graphical Representation of TARE.
## 3 Eigenvalue Fusion and other Fusion Methods for Detecting General Wideband Signals
### Sample Fusion to Detect General Wideband Signals  
Traditional data fusion cooperative spectrum sensing combines the samples collected by each CR at the FC, as described in Section 2. In order to form a matrix with received samples in the frequency domain, consider that each CR collects \(N\) samples from the \(k\)-th sub-band--we can also view \(N\) as the number of snapshots of the received signal samples in the frequency domain given in Equation (9).  
The \(k\)-th matrix of received samples in the frequency domain is made up by grouping the \(N\) samples taken from the \(k\)-th sub-band by each of the \(m\) sensors (antennas or CRs). In this case, the order of the resulting matrix will be \(m\times N\). The matrices with received samples in this sample fusion scenario for general wideband signals are then  
\[\mathbf{A}_{k}=\begin{bmatrix}Y_{k}^{(1)}(1)&\ldots&Y_{k}^{(1)}(N)\\ \vdots&\ddots&\vdots\\ Y_{k}^{(m)}(1)&\cdots&Y_{k}^{(m)}(N)\end{bmatrix} \tag{10}\]  
where \(Y_{k}^{(i)}(j)\) is the \(j\)-th sample collected by the \(i\)-th CR in the \(k\)-th sub-band. The total number of samples received by the FC is then \(mKN\). The sample covariance matrix in this case is computed at the FC and is given by  
\[\mathbf{R}_{k}=\frac{1}{N}\mathbf{A}_{k}\mathbf{A}_{k}^{\dagger} \tag{11}\]  
The eigenvalues of \(\mathbf{R}_{k}\) are also computed at the FC and the test statistic for the \(k\)-th sub-band of a general wideband signal with sample fusion is formed via Equations (3)-(6).
## 3 Numerical Simulation
### Influence of Disturbance Force on Dynamic Rotational Error  
The spindle is rotated by the drive system. The mechanical spindle often uses a wedge belt or a timing belt drive, while the electric spindle is driven by built-in motor. During the rotation of the spindle, the transmission system and the electromagnetic force will generate a disturbance force on the SRB system. The dynamic response of the SRB system to disturbance forces is also reflected in the dynamic rotational error. Based on the established model, the influence of disturbance force on dynamic rotational error can be analyzed numerically. Assuming that the frequency of the disturbance force is an integer multiple of the fundamental frequency, the frequency order ranges from 2 to 10 UPR. Polar plots of the dynamic rotational error, which are caused by the disturbance force with the amplitude of 200 N, are shown in Figure 14.  
Figure 12: Trends of impact of dynamic unbalance force on TIR, synchronous/asynchronous errors.  
Figure 13: Frequency analysis of response displacement at 10,000 rpm.  
As shown in the polar plots, the influence of external disturbance force, such as the drive system, on the dynamic rotational error is mainly forced vibration, and the frequency of the radial synchronous error is the same as the frequency of the disturbance force. By increasing the amplitude of the disturbance force, the comparison results of the dynamic rotational errors of different orders and amplitudes can be obtained, as shown in Figure 15.  
The simulation result shows that both synchronous and asynchronous error motion values increase slowly with the increasing of disturbance force order. When increasing the amplitude of the disturbance force from 200 to 400 N, the range of synchronous error motion values increase from 2.13-2.99 to 4.26-6.03 \(\upmu\)m, and the range of asynchronous error motion values increase from 0.28-1.63 to 0.47-3.26 \(\upmu\)m. Both the synchronous error and the asynchronous error increase as the amplitude of the disturbance force increases.
## 3 Basic System Operation
### Metric-Based Searching
#### 3.3.1 Pivot Point Generation  
The domain of the one dimensional derived hash key \(H_{D}\) of an aggregated _l_-dimensional sensed event can be defined by \(\alpha\) (\(\alpha_{\text{min}}\), \(\alpha_{\text{max}}\)) as illustrated in Figure 3. In Equations (8)-(11), A\({}_{i(\text{min})}\), A\({}_{i(\text{max})}\), A\({}_{i(\text{avg})}\) and A\({}_{i(\theta)}\) denote the minimum, maximum, average and threshold value of _i_th attribute. The center of mass (COM), denoted by \(\beta\), is derived in Equation (10) to find the normalized center point of the domain of the hash key \(H_{D}\) whereas \(\delta\) is the separating factor between two pivot point. However, in order to balance the load among sectors, it is important to find the range where the concentration of the data points is high. Hence, \(\beta\) and \(\delta\) can be used to find this COM range, denoted by \(\beta\) (\(\beta_{range\text{-min}}\), \(\beta_{range\text{-max}}\)) as shown in Equation (12):  
\[\alpha_{\text{min}}=\sum_{i=1}^{l}\Biggl{[}\Biggl{(}\begin{array}{c}\text{A} _{(\text{min})}\\ \text{A}_{i(\text{max})}\end{array}\Biggr{)}\times w_{i}\Biggr{)} \tag{8}\]  
\[\alpha_{\text{max}}=\sum_{i=1}^{l}\Biggl{[}\Biggl{(}\begin{array}{c}\text{A} _{(\text{max})}\\ \text{A}_{i(\text{max})}\end{array}\Biggr{)}\times w_{i}\Biggr{)} \tag{9}\]  
\[\beta=\sum_{i=1}^{l}\Biggl{[}\Biggl{(}\begin{array}{c}\text{A}_{i(\text{avg} )}\\ \text{A}_{i(\text{max})}\end{array}\Biggr{)}\times w_{i}\Biggr{)} \tag{10}\]  
\[\delta=\sum_{i=1}^{l}\Biggl{[}\Biggl{(}\begin{array}{c}\text{A}_{i(\theta)} \\ \text{A}_{i(\text{max})}\end{array}\Biggr{)}\times w_{i}\Biggr{)} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Attribute** & **Weight** \\ \hline \(A_{1}\) & \(W_{1}\) \\ \(A_{2}\) & \(W_{2}\) \\.... &.... \\ \(A_{l}\) & \(w_{l}\) \\ \hline \end{tabular}
\end{table}
Table 3: Weight settings.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Member Node** & **First Attribute** & **Second Attribute** & **Third Attribute** \\ \hline
1 & \(v_{11}\) & \(v_{12}\) & \(v_{13}\) \\
2 & \(v_{21}\) & \(v_{22}\) & \(v_{23}\) \\
3 & \(v_{31}\) & \(v_{32}\) & \(v_{33}\) \\ \hline \multicolumn{4}{c}{_After applying Equations (6) and (7)_} \\ \hline  & _max_ (\(v_{11}\), \(v_{21}\), \(v_{31}\)) & _max_ (\(v_{12}\), \(v_{22}\), \(v_{32}\)) & _max_ (\(v_{13}\), \(v_{23}\), \(v_{33}\)) \\  & _min_ (\(v_{11}\), \(v_{21}\), \(v_{31}\)) & _min_ (\(v_{12}\), \(v_{22}\), \(v_{32}\)) & _min_ (\(v_{13}\), \(v_{23}\), \(v_{33}\)) \\  & _avg_ (\(v_{11}\), \(v_{21}\), \(v_{31}\)) & _avg_ (\(v_{12}\), \(v_{22}\), \(v_{32}\)) & _avg_ (\(v_{13}\), \(v_{23}\), \(v_{33}\)) \\ \hline \end{tabular}
\end{table}
Table 2: Illustration of Equations (6) and (7).
## 2 Development of an Advanced SR-JOINT (SR-II)  
### Finite Element Analysis  
#### 2.1.1 Design of a New SR-Joint (II) and Material Properties  
An SR-joint structure is composed of an inner pipe, an SR-joint, a metal ring, and a rubber ring, similar to the conventional SR-I and the proposed SR-II, as shown in Figure 2. SUS304 is generally used for pipes, SR-joints, and metal rings, whereas ethylene propylene diene monomer rubber (EPDM) is used for rubber rings [18]. Material tests of SUS304 and EDPM were conducted to obtain material properties to be used in finite element analysis. EDPM was tested using compressed specimens with diameters and heights of 29 mm and 12.7 mm, respectively, because the rubber ring deforms under compression force. The specimen shape of SUS304 complied with the KS B 0801 standard [19], and tensile and compression tests were performed at a displacement-controlled load speed  
Figure 2: Schematic diagram of SR-joint.  
of 4 mm/min using the universal testing machine SHIMADZU AG-X. The mechanical properties obtained through tensile and compression tests are shown in Table 1. The flow stress curve of the pipe material is expressed as shown in Equation (1) using the Swift equation based on the results of tensile and compression tests [20].  
\[\overline{\sigma}=k(\overline{\varepsilon}+\varepsilon_{0})^{n} \tag{1}\]  
In the equation, \(\overline{\sigma}\) denotes the effective stress, \(k\) denotes the strength coefficient, \(\overline{\varepsilon}\) denotes the effective strain, \(n\) denotes the strain-hardening exponent, and \(\varepsilon_{0}\) denotes the initial strain. Figure 3 shows the specimen shape and true stress-true strain curve of each material.