esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
## 2 System Model  
In this paper, we consider the uplink cooperative system in the LPWAN with multiple GWs. Considering a real situation and a simplification of analysis, the system consists of \(N\) GWs, which are equipped with \(K_{i}\) (\(i\in\{1,2,\ldots,N\}\)) multiple antennas in each and an IoT device (ID) with a single antenna, as shown in Figure 1. Then, the received signal at \(i\)-th GW, \(\mathbf{y}_{i}\in\mathbb{C}^{K_{i}\times 1}\) (\(i\in\{1,2,\ldots,N\}\)), is given by  
\[\mathbf{y}_{i}=\mathbf{h}_{i}\sqrt{Pd_{i}^{-\alpha}}x+\mathbf{n}_{i}, \tag{1}\]  
where \(P\) denotes the transmission power of the ID and \(d_{i}\) denotes the distance between the ID and the \(i\)-th GW. The term \(\alpha\) denotes the path-loss exponent and \(x\) is a signal of the ID, which has a unit power; i.e., \(\mathbb{E}\left[|x|^{2}\right]=1\). In this paper, it is assumed that the small-scale fading channel between the ID and each GW follows a Rayleigh fading model, such as [16]. Thus, the fading channel vector from the ID to \(i\)-th GW is represented as \(\mathbf{h}_{i}=\left[h_{i,1}\ h_{i,2}\ \ldots\ h_{i,K_{i}}\right]^{T}\) and follows the identically and independently distributed (i.i.d.) complex Gaussian distribution with zero mean and unit variance; i.e., \(\mathbf{h}_{i}\sim\mathcal{CN}\left(0,\mathbf{I}_{K_{i}}\right)\). The additive white Gaussian noise (AWGN) at \(i\)-th GW is represented as \(\mathbf{n}_{i}\in\mathbb{C}^{K_{i}\times 1}\), each element of which is identically and independently distributed complex Gaussian random variable with zero mean and variable of \(N_{0}\). It is assumed that each ID knows the local channel state information by a pilot signal from the ID. At each GW, the maximum ratio combining (MRC) is operated in order to maximize the SNR, and the minimal essential information, a real-valued combining signal, is derived to the cloud server.  
\[\tilde{y}_{i}=\sqrt{d_{i}^{-\alpha}}\mathbf{h}_{i}^{H}\mathbf{y}_{i}=\sqrt{P}d _{i}^{-\alpha}\|\mathbf{h}_{i}\|^{2}x+\tilde{n}_{i}, \tag{2}\]  
where \(\tilde{n}_{i}\sim\mathcal{CN}(0,d_{i}^{-\alpha}\|\mathbf{h}_{i}\|^{2})\) is related to the additive white Gaussian noise term. The cloud server receives the essential information from multiple GWs and then combines them as follows:  
\[\tilde{y}=\frac{1}{\sqrt{\sum_{i=1}^{N}d_{i}^{-\alpha}\|\mathbf{h}_{i}\|^{2}} }\sum_{i=1}^{N}\tilde{y}_{i}=\sqrt{P\ \sum_{i=1}^{N}d_{i}^{-\alpha}\|\mathbf{h}_{i}\|^{2}}\ x+\tilde{n}, \tag{3}\]  
where \(\tilde{n}\sim\mathcal{CN}\left(0,N_{0}\right)\).  
Figure 1: Uplink cooperative technique in a low-power wide-area network (LPWAN) with multiple antenna gateways (GWs).
## 1 Introduction
### Related Works  
Unlike the active wake-up radios, which has been in the limelight for the past few years and has been reviewed substantially, passive wake-up has not been largely reviewed. General hardware survey of wake-up radios is presented in [16; 17], whereby their hardware layer characteristics are studied. Similar studies consider the advantages of wake-up radios in reducing network latency and increasing reliability [18; 19] while others highlight their benefits for improving the energy efficiency of individual nodes [17; 20; 21]. Few studies [22; 23] considers the passive (energy harvesting) wake-upradio hardware. Another study [24] presented a review on the state-of-the-art in energy harvesting from ambient energy for environmental monitoring applications while [25] having passive hardware design as a section of their study. The authors in [26] presented a study on power management techniques for wake-up radios.  
On the other hand, many other authors have reviewed the MAC protocols for the wake-up radios [27; 28; 29; 30], with only a few considering its combination with the energy harvesting system [31; 32; 33]. Some surveys consider the receiver only [17; 34], while others [35], made a comparison of some of the wake-up radio schemes. Another study [36] explains the benefits, challenges, and applications of the wake-up radio while surveying some of the available MAC protocols used. The analytical models for the energy efficiency of wake-up radio are described in [37], while the mathematical models for the power consumption are derived in [38]. The work in [39] analyzed three wake-up radio MAC protocols and [40] analyzed the duty cycled MAC protocols. Some other studies highlighted the routing protocols [41; 42; 43; 44] in WSN independently and in the multilayer application in [45; 46].  
At present, there is no comprehensive study dedicated to the passive wake-up radios for IoT applications in the literature; from the classification related to the wake-up radio techniques based on previous works, the cross-layer design considerations for potential IoT applications, the opportunities, design challenges, and possible solutions. This survey highlights the major advances in the passive wake-up radio hardware and MAC protocols design. We establish a framework for the design and development of the passive wake-up radio over a cross-layer approach. Our paper also seeks to analyze the opportunities and challenges of the technique, as well as identifying the potential application areas of the passive wake-up radios for the Internet of Things utilization.
## 2 Mathematical Model and Controller Design
### LQR Controller Design  
For the purpose of engaging the linear quadratic regulator (LQR) control algorithm, the motion equations were linearized. Generally, the clockwise swing angle is set as a positive value. In this paper, the swing angle \(\theta\) was supposed to be very small. It was supposed that \(\sin\theta\approx\theta\), \(\cos\theta\approx 1\). Thus, the original nonlinear motion equations can be linearized and expressed into matrix form as:  
\[\left[\begin{array}{cc}(m+m_{a})l^{2}&0\\ 0&J_{a}\\ \end{array}\right]+\left[\begin{array}{cc}c&0\\ 0&0\\ \end{array}\right]+\left[\begin{array}{cc}(m+m_{a})gl&0\\ 0&0\\ \end{array}\right]+\left[\begin{array}{cc}(m+m_{a})gl&0\\ 0&0\\ \end{array}\right]=\left[\begin{array}{cc}-1\\ 1\\ \end{array}\right]M_{a}(t)-\left[\begin{array}{cc}(m+m_{a})l\\ 0\\ \end{array}\right]_{ax0} \tag{5}\]  
Figure 1: Simplified analysis model of the suspended structure with the ARID system.  
\[\begin{array}{c}\text{Set }\mathbf{M}=\left[\begin{array}{cc}(m+m_{a})l^{2}&0\\ 0&J_{a}\end{array}\right]\mathbf{C}=\left[\begin{array}{cc}c&0\\ 0&0\end{array}\right]\mathbf{K}=\left[\begin{array}{cc}(m+m_{a})gl&0\\ 0&0\end{array}\right]\mathbf{A}=\left[\begin{array}{cc}0_{2\times 2}&\mathbf{I}_{2}\\ -\mathbf{M}^{-1}\mathbf{K}&-\mathbf{M}^{-1}\mathbf{C}\end{array}\right]\\ \mathbf{B}=\left[\begin{array}{cc}0_{2\times 1}\\ \mathbf{M}^{-1}\mathbf{B}_{\mathbf{s}}\end{array}\right]\mathbf{D}_{\mathbf{g} }=\left[\begin{array}{cc}0_{2\times 1}\\ \mathbf{M}^{-1}\mathbf{D}_{\mathbf{s}}\end{array}\right]\mathbf{B}_{\mathbf{s} }=\left[\begin{array}{cc}-1\\ 1\end{array}\right]\mathbf{D}_{\mathbf{s}}=\left[\begin{array}{cc}(m+m_{a})l \\ 0\end{array}\right]\\ \\ Z(t)=(\theta(t),\varnothing(t),\dot{\theta}(t),\dot{\varnothing}(t))^{\top}\end{array} \tag{6}\]  
Thus, the motion equation can be written as follows:  
\[\dot{Z}(t)=\mathbf{A}Z(t)+\mathbf{B}M_{a}(t)+\mathbf{D}_{\mathbf{g}}a_{\times 0 }(t) \tag{7}\]  
When:  
\[\mathbf{Q}=\alpha\left[\begin{array}{cc}\mathbf{K}&\mathbf{0}_{2\times 2}\\ \mathbf{0}_{2\times 2}&\mathbf{M}\end{array}\right]\mathbf{R}=\beta\mathbf{I}, \mathbf{G}=lqr(\mathbf{A},\mathbf{B},\mathbf{Q},\ \mathbf{R})\]  
Therefore, based on the LQR algorithm, the active control torque is defined as follows:  
\[M_{a}(t)=-\mathbf{G}Z(t) \tag{8}\]  
Furthermore, the influence of LQR control algorithm parameters to control effectiveness curves are shown in Figure 2. When \(\beta\) is constant, the control effectiveness becomes better as \(\alpha\) increases. The value of the weighting matrix \(\mathbf{Q}\) increases as \(\alpha\) increases. In the reasonable value range of \(\alpha\), the larger the \(\mathbf{Q}\), the better the control effect will be. However, when \(\alpha\) is constant, the control effectiveness becomes worse as \(\beta\) increases.
# A Review on Upper Limb Rehabilitation Robots
## 4 Examples of Rehabilitation Robots
### Mime  
Mirror image movement enabler (MIME) is a CPM robotic system that consists of a wheelchair and height-adjustable table [38], where the patient sits on the wheelchair and puts his/her affected limb on the adjustable table. The affected limb is trapped into the forearm splint that restricts the movements of the wrist and the hand [38]. A Puma 560 (Programmable Universal Machine for Assembly) robot was used as the manipulator, where the robot attached to the splint and applied force to the affected limb during its movements [39]. The Puma 560 robot provided six degrees of freedom, which provided a large range of movement positions in a 3D space [40]. MIME, provides the three treatments modes that are the passive, active-assistive/active-resistive and bilateral modes [20]. In addition, MIME is commonly used in the passive and active-resistive modes [22]. In the passive mode, the robot moves the limb in a specific trajectory towards the target. The active-resistive mode involves prohibiting the limb's movements in the identified trajectory by providing a viscous resistance, and the patient has to provide maximal effort to reach the target [4]. MIME is used to strengthen the muscles and improve the limb's motions [28].
## 4 Flow Chart of the Proposed Method  
To handle the difficult problem of early weak fault feature extraction of rolling bearing, a corresponding two-stage method is proposed by combining the AMF and FBS strategy. The scale and shape of SE could be determined by AMF adaptively based on the intrinsic characteristics of the signal to be analyzed itself, which could reduce the interferences to a maximum extent. To further reduce the influence of noise interference caused by full frequency band ES analysis, the proposed FBS is used to select the fault information's most sensitive frequency band. Finally, ES analysis is implemented on the selected fault information sensitive frequency band and a satisfactory feature extraction result could be obtained. Figure 5 shows the overall flow of the proposed method, whose specific details are as follows:  
Step 1: Collect the vibration signal of the faulty rolling bearing using accelerated sensors.  
Step 2: The scale and shape of SEs are determined according to the processes in Section 2.2.  
Step 3: Apply the MF shown in Equation (13) using the obtained SEs in step 2 on the original vibration signal.  
Step 4: Select the fault information sensitive frequency band based on the proposed FBS in Section 3 to further eliminate the noise pollution caused by full frequency band ES analysis.  
Step 5: Apply ES analysis on the signal contained in the selected frequency band.  
Figure 4: Five signals with their four indexes. (**a**) Five signals. (**b**) Four indexes corresponding to the five signals as shown in (**a**).
## 1 Introduction
[PERSON] and [PERSON] [28] conducted experiments to investigate the hydrological, functional, and structural characteristics of crumb rubber-modified pervious concrete with four dosages of crumb rubber. It was used to replace the coarse aggregate. Permeability, abrasion resistance, and compressive strength were measured, and the results showed that the addition of rubber enhanced the abrasion resistance, but decreased compressive strength and permeability compared to the unmodified control mixtures. [PERSON] et al. [29] studied the mechanical properties, ductility, and damping capacity of rubber-modified pervious concrete using tire chips, crumb rubber, and fine crumb rubber. The replacement levels of the coarse aggregate were 10% and 20% with the equivalent volume method. The results of the tests indicate that the addition of rubber decreases the compressive strength, flexural strength, and elastic modulus, but significantly increase ductility and damping capacity. In another research conducted by [PERSON] et al. [30], abrasion and freeze-thaw resistance of pervious concretes were investigated with the same rubber particle size and volume content. The test results revealed that the addition of rubber significantly enhanced the abrasion and freeze-thaw resistance of pervious concrete, and fine rubber was superior to coarse rubber. There are few studies concentrating on rubber-modified pervious concrete at present, and the properties of rubber-modified pervious concrete still need systematic and comprehensive research. The influence of aggregate size, porosity, and water to binder ratio on strength, permeability, and freeze-thaw durability of pervious concrete were investigated by our group [31]. Based on our previous study and existing research by other scholars, this paper investigates the effect of rubber particle size (fine and coarse) and dosage (2%, 4%, 6%, and 8%) on the properties of pervious concrete. Permeability, compressive strength, flexural strength, flexural strain, and freeze-thaw resistance tests were carried out on rubber-modified pervious concrete.
# Numerical Simulation on Thermoelectric Cooling of Core Power Devices in Air Conditioning
## 4 Conclusions  
This work explored the active heat dissipation of thermoelectric cooling (TEC) for power devices in air conditioners through COMSOL finite element simulations. The simulation results revealed that in the single air-cooling mode, the maximum temperature of the power device with 173.8 W power consumption reached 394.4 K, and the average temperature reached 373.9 K, which exceeded its rated operating temperature of 368.1 K. However, after incorporating the TEC, the maximum temperature of the power device decreased to 331.8 K at an operating current of 7.5 A, and the average temperature of the device was reduced to 326.5 K. This demonstrated that TEC active cooling had a significant effect on the temperature control of the power device, reducing the maximum and average temperatures by 62.6 K and 47.4 K, respectively, compared to single air-cooling. Furthermore, we examined the effect of the TEC operating current on the temperature control effect of the power device, revealing a minimum TEC operating current of 5 A. In short, the research results showed that thermoelectric cooling technology can be well-used in the power devices in the air conditioner control circuit to prevent the reduction of the air-conditioning cooling efficiency caused by overheating of the power devices theoretically.  
Figure 10: Influence of the TEC working current on the average temperature of: (**a**) the high-power-consumption power devices, (**b**) the low-power-consumption power devices, and (**c**) the heat-sink fins.  
It can not only precisely adjust the temperature of the power devices on the circuit board to ensure that the air conditioner always operates in a high-efficiency mode but can also minimize the energy consumption and reduce emissions.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13127274/s1](https://www.mdpi.com/article/10.3390/app13127274/s1)]([https://www.mdpi.com/article/10.3390/app13127274/s1](https://www.mdpi.com/article/10.3390/app13127274/s1)), Figure S1: The optical photo of air conditioner circuit board; Figure S2: The CAD model of cooling system; Figure S3: Temperature distribution of the system without TEC cooling at different times; Figure S4: Temperature distribution of the system with TEC cooing at different times.  
[PERSON] and [PERSON] conceived the project; [PERSON], [PERSON] and [PERSON]. ([PERSON]) carried out the finite element simulation; [PERSON], [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] co-wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (52202289), the International Postdoctoral Exchange Fellowship Program (PC2022044), and the National Key Research and Development Program of China (2019 YFA0704900).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Performance Analysis and Simulations
### System Efficiency  
This section describes the system efficiency analysis of the proposed CPV system with the Si solar cell for capturing diffuse solar radiation compared to the conventional CPV system without using the additional solar cell.  
A CPV system consisting of an CPV unit and an Si low-cost solar cell was considered. The design of the CPV system is shown in Figure 4, and its ray-tracing layout is shown in Figure 5.
The Difference of Volatile Compounds in Female and Male Buds of _Herpetosperm pedunculosum_ Based on HS-SPME-GC-MS and Multivariate Statistical Analysis  
[PERSON]  
1 Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (C.W.J.); [EMAIL_ADDRESS] (B.X.);  
[EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (P.S.); [EMAIL_ADDRESS] (H.W.)  
[PERSON]  
Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +86-135-2270-516  
raceme; pedicels with 2.0-6.0 cm length and sparse villous hairs; calyx tube with 2.0-2.5 cm length, enlarged similarly to a funnel, tubular lower part, and lanceolate lobes; yellow corolla with 2.0-2.2 cm in length and 1.2-1.3 cm in width, and with elliptic lobes and sharp tips. The female flower is solitary with the same perianth as male flowers and with three staminodes or none, and oblong ovary with three rooms. The mature fruit is 7-8 cm in length and 3-4 cm in width with long pubescent hairs.  
There were many studies of _H. pedunculosum_. Its seeds had some pharmacological activities of hepatoprotection, anti-hepatitis B virus, and anti-liver fibrosis [6; 7], contained chemical constitutes of fatty acid, polysaccharides, coumarin, and spinasterol glycoside [6; 7; 8; 9], and had 156,531 bp length of the whole genome [10]. Because the seeds have medicinal, economic, and sowing values, the female plant is more popular with farmers [11; 12]. Fifty metabolites including carbohydrates, amino acids, organic acids, lipids, and polyamine were identified in its leaves [13]. Twelve compounds including _n_-benzyltytramine, 1H-indol-3-carboxylic acid, rhodiocyanide B, and matteflavoside A were isolated from its stems [14]. The moisture, total ash, acid-insoluble ash, and alcohol-soluble extract were determined as quality indexes of its flower [15; 16; 17]. However, there were a few studies on the gender difference in the dioecious plant of _H. pedunculosum_.  
In this study, the female and male buds of _H. pedunculosum_ were collected. Headspace solid-phase microextraction (HS-SPME) was used to extract their volatile components. Gas chromatography-mass spectrometry (GC-MS) was used to separate and identify these volatile compounds. The multivariate statistical analysis was chosen to screen the differential compounds, aiming to explore the difference in volatile compounds between female and male buds of this dioecious plant.
# Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
## 3 Bidirectional Cached Rapidly Exploring Random Tree Star
### Random Tree Search Strategy of Bidirectionally Extended RRT* Algorithm  
During path planning of autonomous underwater vehicles in the ocean, optimization of the planning process can ameliorate both the efficiency and caliber of the resultant path. However, because of the uniform sampling in the basic RRT* algorithm, numerous redundant branches will be generated on the search tree, which are typically planned. Consequently, the paths are suboptimal and comprise many noisy nodes, leading to insufficient smoothness. To tackle this issue, redundant path nodes are smoothed by filtering. The smoothing operation starts from the second node and checks backwardly to determine whether the connection between the starting node and the current node interferes with obstacles. If interference happens, the node is removed; otherwise, it is retained. Following the anomaly detection process, the superfluous nodes along the path can be expeditiously eliminated. Therefore, this study proposes an improved RRT*-based approach for completing underwater path planning. By introducing the Dijkstra algorithm for subsampling the extended random tree and searching for the set matrix of nodes, the exploratory bidirectional tree search methodology is employed to establish an initial pathway. If the cost of a path exceeds a critical value, the pathway is refined and selected as the optimal solution, leading to an update of the initial path to its optimized counterpart. This approach not only improves the search efficiency but also guarantees the quality of the planned path.  
The comprehensive workflow of subaquatic trajectory planning utilizing the refined RRT* algorithm is depicted in Figure 2.  
This study aims to address the issue of underwater robot path planning by efficiently connecting the start and target points through a short path. Unlike the basic RRT* algorithm, which relies on random sampling from the initial node to expand search space, this paper proposes the simultaneous growth of fast-expanding random trees from both the start and target points to improve search efficiency and quickly find an initial cable planning path. The parallel search enhances solution efficiency while maintaining a shorter initial path through alternating attraction between the two trees. By employing the dual-aspect enhanced random tree exploration methodology suggested in this study, prompt acquisition of an initial trajectory linking the origin and destination is achievable, to which lies further node-level fine-tuning for optimal path inference.
# A Novel Interactive Fusion Method with Images and Point Clouds for 3D Object Detection
## 3 Experiments  
We elevated our fusion algorithm on published AVOD [16] where the metrics are 3D AP (average precision) and AHS (average heading similarity). The descriptions of the metrics are in the formulas below,  
Figure 1: Visual structure of the fusion algorithm with single level feature maps: first, image feature maps and point cloud feature maps fuse by element-wise mean. Then, the fused part concatenates with the preceding two layer feature maps. Next, each of the two branches is fed through a convolution layer to reorganize the feature maps, finally the two branches fuse again by element-wise mean to the region proposal.  
Figure 2: Visual structure of the fusion algorithm with multilevel feature maps: first, image feature maps and point cloud feature maps in each group are fused by element-wise mean separately (im1 and pc1 are of one group, and im2 and pc2 are of another group). Then, the fused part concatenates with another corresponding level feature maps. Next, each of the four branches is fed through the convolution layer to reorganize the feature maps. Finally, the four branches fuse again by element-wise mean to the region proposal.  
\[AP=\frac{1}{\Pi}\sum\limits_{r\in\{0,0.1,\ldots,1\}}\frac{\max P(\hat{r})}{ \hat{r}:\hat{r}\geq r}\] \[AHP=\frac{1}{\Pi}\sum\limits_{r\in\{0,0.1,\ldots,1\}}\frac{\max S( \hat{r})}{\hat{r}:\hat{r}\geq r}\]  
In the two formulas, \(r\) means recall, \(P(r)\) means precision at the recall equal to \(r\) and \(S(r)\) means orientation similarity at the recall equal to orientation similarity. To get the AP we calculate the IoU between the ground truth and test results in 3D space. Because of not considering the direction in AP, we use AHP. It is used to measure the similarity between the test results and the ground truth in terms of direction.  
To test our proposed fusion algorithms, we replace AVOD [16] data fusion parts by our proposed fusion algorithms and use the default hyper-parameter, training set and validation set, the same with AVOD, to eliminate other influences and the performances on KITTI val set are shown in Table 1, Table 2, Figures 3 and  
Figure 4: _Cont_.