esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Role of Matrix-Weights Elements in Consensus Algorithms for Multi-Agent Systems
## 2 Problem Formulation
### Non-Diagonal Matrix-Weights
#### 3.2.2 Control Law Depends on Other States in Different Clusters  
In this case, the evolution of all states follows  
\[\dot{x}(t)=u(t)\]  
where \(u(t)\) is given in Equation (11). By solving the above differential equation, we have  
\[x(t)=e^{-t\mathcal{L}_{\mathcal{W}}}x(0). \tag{13}\]  
The choice of the weight matrix \(\mathcal{W}\) will affect the rate of convergence of the consensus algorithm of the states of all agents. Since the Laplacian matrix \(\mathcal{L}_{\mathcal{W}}\) is positive semi-definite, all states will converge to a constant value eventually.  
**Theorem 2**.: _Assume that the k-state graph has incoming links from \(y\) different connected components in the l-state graph. The following statements hold:_  
1. _There will at least be_ \(y\) _clusters for the corresponding agent states in the graph causing a k-cluster consensus (KCC)._
2. _In the k-state graph, if_ \(x_{j}^{[k]}\) _and_ \(x_{i}^{[k]}\) _belong to the same connected component, which has only one incoming link from the other state graphs, then_ \(x_{j}^{[k]},x_{i}^{[k]}\in C_{p}^{k}\)_._
3. _In the k-state graph, assume that_ \(x_{j}^{[k]},\,x_{i}^{[k]}\)_, and_ \(x_{p}^{[k]}\) _belong to the same connected components. If_ \(x_{i}^{[k]}\) _and_ \(x_{p}^{[k]}\) _have incoming links from different connected components in the l-state graph, and_ \(x_{j}^{[k]}\) _has no incoming links from other state graphs, then_ \(x_{j}^{[k]}\) _will be in a different cluster of_ \(x_{i}^{[k]}\) _and_ \(x_{p}^{[k]}\)_._  
Proof of Theorem 2.: The \(y\) different connected components in the \(l\)-state graph will at least form \(y\) different clusters. The \(y\) different clusters will be \(y\) virtual leaders to states in the \(k\)-state graph. Therefore, there will be KCC with a minimum of \(y\) clusters.  
When there is only one leader for a connected component in the \(k\)-state graph, the connected component will form a cluster.  
When the connected component including \(x_{j}^{[k]},x_{i}^{[k]}\), and \(x_{p}^{[k]}\) has incoming links from different connected components in other state graphs, it is equivalent to the case that all three states have two different leaders. Then all the three states will be in different clusters.
# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 3 Pose Regression Methods
### Data Acquisition  
In traditional pose regression studies, researchers use images as inputs to train the neural networks and pose estimation is considered a classification problem. In contrast, our method diverges from traditional approaches by utilizing numerical 3D data to train our pose regression model. We acquire the numerical 3D data by attaching optical motion capture sensors to specific body joints, a standard method of obtaining human motion capture data. We focus more on VR interaction poses, considering that VR users stay in a limited space and perform actions such as twisting their bodies, talking with moving arms, sitting, standing, and selecting and manipulating objects using their arms. Public human datasets, such as CMU [37], PFN [38], and MHAD [39], have walking, running, stepping, and sports motions. These public datasets are deficient regarding the selection and manipulation motions utilizing arms.  
Our motion-capturing strategy is one way to achieve various human poses within a limited time frame. To focus more on VR interaction poses, we hire four choreographers to design moves to meet our requirements. We use an OptiTrack motion capture device while the subjects pose the moves according to the music. Our datasets expand further by capturing more human poses while a human performs activities in different environments and incorporating the public human pose datasets. The resulting data serves as a testbed for our VR pose regression model, providing a comprehensive and diverse representation of VR interaction poses.  
From a total duration of approximately 2838 s, we select 60 fps out of 200 fps by filtering out similar poses. Consequently, our pose dataset includes 170,280 pose data samples divided into 80 to 20 between train and test data. We use nine upper body joints for our pose regression model with 54 parameters. For every joint, the dataset includes six values, local positions in the \(x\)-, \(y\)-, and \(z\)-coordinates, and local orientation angles about the \(x\)-, \(y\)-, and \(z\)-axes.  
As depicted in Figure 2, we name each joint in order: head, left hand, right hand, left elbow, right elbow, left shoulder, right shoulder, spine, and pelvis, referred to as \(j_{1},j_{2}\), \(j_{3}\), \(j_{4}\), \(j_{5}\), \(j_{6}\), \(j_{7}\), \(j_{8}\), and \(j_{9}\), respectively. Let \(\mathbf{p_{i}}=(\text{px}_{i},\text{py}_{i},\text{pz}_{i})\) and \(\mathbf{r_{i}}=(\text{rx}_{i},\text{ry}_{i},\text{rz}_{i})\) be the position and orientation of a joint \(j_{i}\) in three dimensions. As depicted in Figure 2, our global coordinate system is defined as follows: the \(x\)-coordinate is from the left shoulder to the right shoulder, the \(y\)-coordinate is upward, and the \(z\)-coordinate is forward. Other than the head and the root joint, the other joints' orientations are relative to the corresponding parent joints. This approach correlates highly with Ben-Ari [40] and further supports the concept of IK [9] for orientation. We normalize our dataset before training.
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 3 System Model, Problem Formulation and Proposed Scheme
### Problem Formulation  
Based on our system model, we can formulate the optimization problem to find the optimal set of FBSs to be activated. In the formulated problem, the objective is to minimize the total power consumption of MCS. However, the data rate of cooperative transmission can be severely deteriorated because more FBSs are likely to be turned off to save more power. Accordingly, we also take into account an additional constraint that specifies the maximum allowable degradation of throughput. To be more specific, we assume that the expected throughput should be higher than \(R_{thr}\).  
Then, the optimization problem to find an optimal set of turned off FBSs can be formulated as follows:  
minimize \[P_{active}|\mathbb{A}|+P_{inactive}|\mathbb{I}|\] s.t. \[\mathbb{E}\left[\sum\limits_{i=1}^{K}R_{i,t}(\mathbb{A})\right] \geq R_{thr}\] (3) \[R_{i,t}\cdot R_{i2,t}=0,\ \ \text{for}\ \ \ i_{1}\
eq i_{2}\] \[\mathbb{A}\subset\mathbb{N}\] \[\mathbb{A}\cup\mathbb{I}=\mathbb{N}\] \[\mathbb{A}\cap\mathbb{I}=\mathcal{O}.\]  
In problem (3), the second constraint, \(R_{i_{1},t}\cdot R_{i_{2},t}=0\), comes from the fact that only one MS can receive data at the same time.  
Figure 2: System model which depicts femto-cell base stations (FBSs) with cooperative transmission, MS: mobile station.  
Note that the problem (3) is non-convex integer programming such that the computational complexity needed to find the solution grows exponentially as \(|\mathbb{N}|\) increases [35], as we have shown in the performance evaluation. Therefore, it is hard to find the optimal solution, in practice, especially when the number of FBSs is large, i.e., \(|\mathbb{N}|\gg 0\).  
It should be noted that when the conventional on-off power control scheme is considered, in which FBSs to shut down are selected based on the traffic load, the optimization problem to find an optimal set of active FBSs can be revised as follows:  
\[\begin{array}{ll}\text{minimize}&P_{active}|A|+P_{mactive}\left(|\mathbb{N}| -|\mathbb{A}|\right)\\ \text{s.t.}&\mathbb{E}\left[\sum\limits_{i=1}^{K}R_{i,t}(\mathbb{A})\right] \geq R_{thr}\\ &R_{i,t}\cdot R_{i,t}=0,\ \ \text{for}\ \ i_{1}\
eq i_{2}\\ &L_{a}(\mathbb{N})\geq L_{b}(\mathbb{N}),\forall a\in\mathbb{A},b\in\mathbb{I }\\ &\mathbb{A}\subset\mathbb{N}\\ &\mathbb{A}\cup\mathbb{I}=\mathbb{N}\\ &\mathbb{A}\cap\mathbb{I}=\emptyset.\end{array} \tag{4}\]  
In problem (4), the fourth constraint, \(L_{a}(\mathbb{N})\geq L_{b}(\mathbb{N}),\forall a\in\mathbb{A},b\in\mathbb{I}\), is added in order to guarantee the FBSs with low traffic load, i.e., the FBSs which do not participate in many cooperative transmission, to be turned off.
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
## 2 Antenna Design
### Eight-Element, Slotted Rectangular-Shaped MIMO Antenna Structure  
To obtain a high data rate and channel capacity, the four-element antenna was further modified to an eight-element antenna, with the radiator footprint kept the same as the four-element antenna produced in Figure 9, with a final size of 70 \(\times\) 44 \(\times\) 0.8 mm\({}^{3}\). The proposed eight-element antenna consisted of eight slotted rectangular-shaped radiators with chamfered lower edges and was fed by 50 \(\Omega\) microstrip lines. The radiator shared a common ground plane with four half-circles slots. Figure 13 shows both front and back views of the suggested eight-element MIMO antenna. The eight-port antenna was divided into four blocks of dual ports with a neutralization line between them for better isolation results.  
Figure 14 illustrates the current distribution of the eight-port antenna for further analysis. When port 1 was excited at 30 GHz, the current distribution revealed that the highest current was concentrated at the edges of radiator 1, strip line 1 and the neutralization block between port 1 and port 2, as shown in Figure 14a, resulting in an enhanced isolation of 30 dB. Therefore, a much-enhanced isolation between ports was achieved. To further analyze the antenna, the current distribution of the antenna is given in Figure 14b while exciting port 7, and it is evident from Figure 14b that the other component was not affected, and enhanced isolation is observed without any extra decoupling structure.  
Figure 12: ECC and DG of the quad-port antenna.  
Figure 13: Antenna’s (**a**) front and (**b**) back views.
## 3 Results and Discussion
### Convective and Infrared Drying
#### 3.1.1 Kinetics and Drying Time of Terebinth
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Source of Variation** & **Time** & \(D_{df}\) & **SEC** & \(\eta_{e}\) & \(\Delta\)E & **RR** & **TPC** & **TFC** & **pH** \\ \hline Pretreatments & 266.63 ** & 2.65 \(\times\) 10\({}^{-20}\) ** & 7351.68 ** & 152.36 ** & 167.04 ** & 0.396 ** & 27.78 ** & 353.03 ** & 0.014 ** \\ Condition & 1326.76 ** & 4.362 \(\times\) 10\({}^{-17}\) ** & 9716.76 ** & 126.80 ** & 26.63 ** & 1.73 & 4.35 ** & 140.49 ** & 0.004 ** \\ \(\text{P}\times\text{C}\) & 11.28 ** & 4.36 \(\times\) 10\({}^{-17}\) ** & 182.948 ** & 1.2488 ** & 1.400 ** & 0.024 ** & 0.08 ** & 5.05 ** & 0.005 ** \\ Error & 79.17 & 2.65 \(\times\) 10\({}^{-20}\) & 142.84 & 0.07 & 0.739 & 0.005 & 17.52 & 0.89 & 0.00 \\ \hline CV & 5.62 & 6.08 & 4.33 & 4.60 & 04.30 & 3.044 & 3.22 & 2.94 & 0.17 \\ \hline \hline \end{tabular}  
* **Significant at 1% probability level. P—Pretreatments; C—Condition, d\(\bar{\text{d}}\)—Degree of freedom.  
\end{table} 
Table 2: Analysis of variance (ANOVA) for the effect of pretreatments and different condition of two drying methods on time, effective moisture diffusivity coefficient (\(D_{\text{eff}}\)), specific energy consumption _SEC_, energy efficiency (\(\bar{\eta}_{e}\)), the total color difference (\(\Delta\)E), RR, TPC, TFC, and pH of treebinth. results were reported by other researchers for example [PERSON] et al. [6], [PERSON] et al. [35], and [PERSON] et al. [31] in drying white cabbage and apple slices and black mulberry using different pretreatments (US and BL), respectively. Figure 1: Changes in the moisture ratio of treebinth drying using ultrasound and blanching pretreatments in (**a**) convective dryer; (**b**) infrared dryer; (**c**) using microwave pretreatment in convective and infrared dryers.
## 3 Theoretical Background of the Predictive Torque Control
### Predictive PWM-DITC Algorithm  
To minimize the torque ripple, a predictive PWM-DITC algorithm has been developed. This method allocates the phase reference torque within the reachable range and has a remarkable effect on suppressing the torque ripple. When commutation starts, the incoming phase is excited and provides as much output torque as possible. Afterward, the remaining reference torque is tracked by the outgoing phase until its value decreases to zero. Sometimes the outgoing phase cannot reduce its torque as required. In this case, the phase reference torque is reallocated to ensure that the reference torque for both phases is within their reachable range.  
To predict the range of phase output torque, it is assumed that the duty cycle is \(1\) and \(-1\), respectively, in a control period \(\Delta t\), then the maximum and minimum values of the flux linkage can be calculated as  
\[\left\{\begin{array}{l}\varphi_{p,\max}(k+1)=\varphi_{p}(k)+V_{p,1}\Delta t \\ \varphi_{p,\min}(k+1)=\varphi_{p}(k)+V_{p,-1}\Delta t\end{array}\right. \tag{12}\]  
where \(V_{p,1}\) and \(V_{p,-1}\) are the two cases in (4), and \(\varphi_{p,\min}\geq 0\). Further, using the LUT \(i_{p}(\psi_{p},\theta_{p})\) and LUT \(T_{p}(i_{p},\theta_{p})\), the phase reference torque of the next period is expressed as  
\[T_{p,\min}(\theta,\varphi_{p,\min})\leq T_{p,ref}\leq T_{p,\max}(\theta,\varphi _{p,\max}) \tag{13}\]  
After determining the capacity of each phase to deliver torque, the total reference torque needs to be allocated. In commutation, the incoming phase always has the priority of delivering torque. Subsequently, in order to ensure the tracking effect of the outgoing phase on the remaining reference torque, the following inequality constraint is constructed:  
\[T_{ref}-T_{in,\max}(k+1)\geq T_{ou,\min}(k+1) \tag{14}\]  
where \(T_{ou,\min}\) is the minimum output torque of the outgoing phase, and \(T_{in,\max}\) is the maximum output torque of the incoming phase. When relationship (14) is satisfied, the phase reference torque is directly expressed as  
\[\left\{\begin{array}{l}T_{in,ref}(k+1)=T_{in,\max}(k+1)\\ T_{ou,ref}(k+1)=T_{ref}-T_{in,ref}(k+1)\end{array}\right. \tag{15}\]  
However, the outgoing phase sometimes fails to reduce its torque immediately and the inequality (14) cannot be satisfied. In this case, the reference torque of the incoming phase is corrected as  
\[T_{in,ref}(k+1)=T_{ref}-T_{ou,\min}(k+1) \tag{16}\]  
The reference torque of the outgoing phase is expressed as  
\[T_{ou,ref}(k+1)=T_{ref}-T_{in,ref}(k+1)=T_{ou,\min}(k+1) \tag{17}\]  
Figure 3: Sector definitions of the electric cycle for three-phase 12/8-pole SRM.  
Therefore, according to the above torque allocation rules, the corresponding duty cycles are calculated by (10), so that the suppression of torque ripple is realized.
###### Abstract  
Laser direct joining enables non-destructive and lightweight joining of carbon fiber reinforced thermoplastic (CFRTP) composites and aluminum alloys. The interfacial bonding process determines the joint performance and is influenced by the time-varying temperature distribution. However, the interfacial bonding process occurs inside the joint, making it difficult to study the effect of temperature distribution. To resolve this issue, a novel online observation device for the interfacial bonding process between CFRTP composites and aluminum alloys is design, and the polymer melting, flowing, and bonding with metal during laser direct joining are observed. Further, temperature field simulation models for laser direct joining are established, and temperature distribution and gradient are calculated. The results show that the temperature distribution determines the melting of CFRTP composites, and bubbles generated by the thermal decomposition of the polymer hinder the melting. The temperature gradient is related to the movement of the molten matrix and fibers, and the movement towards the aluminum alloy induces cracking and delamination. Once the interface is filled with polymer, the motion changes to along the laser scanning direction and the joining defects are reduced. The study can provide a foundation for promoting interfacial bonding and reducing the defects of laser direct joining.  
laser direct joining; carbon fiber reinforced thermoplastic composite; aluminum alloy; interfacial bonding process +
Footnote †: journal: Applied sciences
###### Abstract
Additionally, introducing III-V compounds such as gallium nitride (GaN), gallium arsenide (GaAs), and indium antimonide (InSb) can enhance the functionality and reliability of MMICs. For example, see Ref. [12], where the insertion loss is 0.95 dB and the physical size is 0.0369 \(\times\) 0.0428 \(\lambda_{0}\)\({}^{2}\). However, they exhibit low power capability and are incapable of making design changes during manufacturing, which restricts the development of MMICs to a certain extent [13; 14]. Integrated passive device (IPD) techniques have rapidly developed, benefiting from the advantages of integration ability, compact size, and small parasitic effects compared to standard discrete systems [15; 16]. Additionally, IPDs can be packaged with active integrated circuits or other IPDs in electronic system components, or stacked in the third dimension (3D), which further improves their integration. The quality factor (Q-factor) is given in this study because it is the most important indicator to evaluate the quality of BPF suffering from ohmic loss, eddy current, and electromagnetic (EM) interference when spiral inductors appear in the design [17]. Based on our previous research, the IPD technology can provide a better Q-factor to a certain extent [18; 19; 20]. Additionally, this study explores an IPD-based BPF using a GaAs substrate, which is a very important semiconductor material in the medical, communications, and military fields. Electrons move 5-10 times faster in GaAs than in silicon. Furthermore, it has a higher breakdown voltage compared to silicon and glass. Therefore, a semiconductor made of GaAs has the characteristics of high energy band, high cut-off frequency and high power [21; 22]. The wide application was profit from its low field mobility, low parasitic tendency, and good isolation between devices [23; 24; 25; 26; 27]. The proposed IPD-based BPF comprises an approximately circular (36-gon) spiral intertwined inductor with five air-bridges on the outside and a non-crossing circinate capacitor at the center. Section 2 describes the design, optimization and micro-nano fabrication of the BPF, in which Section 2.1 introduces the design, optimization, equivalent circuit modeling and analysis, and Section 2.2 presents the micro-nano fabrication process to clarify the realization of the complex physical structure of the device in detail. Section 3 demonstrates the measured result of the proposed BPF using a vector network analyzer (VNA), and satisfactory agreement of the measured and simulated results is achieved. Lastly, we demonstrate the advantages of this research by comparing it with the published BPFs.
## 3 Optimization Agent Model and Sensitivity Analysis
### Sensitivity Analysis
#### 3.3.2 Withdrawal Speed  
During unloader withdrawal, the reset spring was the main power source, and the return pressure was the main resistance source. Figure 7a shows that the withdrawal speed increased with an increase in spring stiffness when the intake pressure was constant, and the withdrawal speed when the intake pressure was high was slower than that at a low intake pressure. This was because the increase in the inlet pressure and the slight decrease in the return flow increased the pressure in the cylinder (Figure 9). Additionally, the change rates of the high and low intake pressures were different, taking into account  
Figure 6: Effect of design variables on the unloader ejection speed under different loads: (**a**) Effect of the stiffness of the reset spring and oil inlet pressure on the ejection speed; (**b**) effect of the stiffness of the reset spring and return pressure on the ejection speed; (**c**) effect of inlet and return pressures on the ejection speed.  
the spring stiffness, and the two curves were not parallel, which indicated that there was an interaction between the two design variables; the interaction decreased with a decrease in the load. As the main resistance source, the increase in oil return pressure decreased along with the withdrawal speed. As shown in Figure 7b, the withdrawal speed at a high oil return pressure level was lower than that at a low level. Furthermore, with the decrease in load, the interaction effect of the spring's stiffness and return pressure on withdrawal speed increased, which indicated that the withdrawal speed can be significantly reduced by increasing the backpressure, while decreasing the stiffness of the spring. The increase in return pressure increased the effect of spring stiffness reduction on the withdrawal speed. Figure 7c shows the interaction between the return pressure and inlet pressure, whereby the degree of interaction decreased with a decrease in load.
## 5 Fault Location in Direct Current (DC) Grid Having Cable and Overhead Line Segments
### Three-Line Segment Model
#### 5.1.3 Case 3  
Suppose that a fault \(F_{3}\) occurs in the third OH line segment of length \(L_{3}\) as shown in Figure 4. If this fault occurs at \(t\) = 0, then the arrival times of the traveling waves at terminal \(T_{1}\) and \(T_{2}\) are:  
\[t_{1-F3}=\frac{x_{F3}}{v_{3}}+\frac{L_{2}}{v_{2}}+\frac{L_{1}}{v_{1}} \tag{17}\]  
\[t_{2-F3}=\frac{L_{3}-x_{F3}}{v_{3}} \tag{18}\]  
The difference between arrival times is given by:  
\[\Delta t_{12-F3}=\frac{2}{v_{3}}+\frac{L_{1}}{v_{1}}+\frac{L_{2}}{v_{2}}-\frac {L_{3}}{v_{3}} \tag{19}\]  
The distance of the FL from start of segment \(L_{3}\) is given by:  
\[x_{F3}=\left(\Delta t_{12-F3}-\frac{L_{1}}{v_{1}}-\frac{L_{2}}{v_{2}}+\frac{L _{3}}{v_{3}}\right)\times\frac{v_{3}}{2} \tag{20}\]  
Based on the distance of fault location from the beginning/start of respective segment, particular faulty segment is identified via algorithm given in subsequent section.
## 3 The Model Validation
### Load Sharing Ratio Contrast  
The load sharing ratio can be defined as  
\[Lr=F_{\text{t1}}/F_{\text{t}} \tag{31}\]  
where \(F_{\text{t1}}\) denotes the load undertaken by one gear tooth, and \(F_{\text{t}}\) denotes the transverse mesh force.  
The load sharing ratio obtained by the analytical model is consistent with that of the finite element model. This result further verifies that the analytical method in this paper is accurate. The maximum load sharing ratio of gear pair 1 equals one; in contrast, that of gear pair 2 is less than 1. This is because the contact ratio of gear pair 1 equals 1.8379, which indicates that the number of teeth in the engagement region switches from 1 to 2 (Figure 11), and there exists a situation where a single tooth bears the load alone. However, there are at least two teeth bearing the load simultaneously for gear  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**1\#**} & \multicolumn{3}{c}{**2\#**} \\ \cline{2-7}  & **Max Value** & **Min Value** & **Mean Value** & **Max Value** & **Min Value** & **Mean Value** \\ \hline Analytical method in this paper & 8.0745 & 5.2916 & 6.9367 & 3.7393 & 3.3846 & 3.4810 \\ Finite element method & 8.1798 & 5.2314 & 6.9553 & 3.6043 & 3.3564 & 3.4357 \\ Error (\%) & 1.29 & 1.15 & 0.27 & 3.75 & 0.84 & 1.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mesh stiffness obtained by different models (\(\times 10^{8}\) N/m).  
Figure 10: The mesh stiffness of different parameters (**a**) gear pair 1 and (**b**) gear pair 2.  
when the helical angle increases, which leads to a deformation caused by the decrease in axial mesh force, and to a decrease in mesh stiffness. That is, one factor will enhance the mesh stiffness, while the other will reduce it. Consequently, the mesh stiffness remains unchanged when the two factors are coupled. As a result, the influence of the axial component of mesh force on mesh stiffness does not exceed 10% if the helical angle is less than 20\({}^{\circ}\), while the influence increases significantly when the helical angle is more than 20\({}^{\circ}\), as shown in Figure 14b.