esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar
## 1 Introduction
Lite-PointNet achieved high accuracy with a lightweight network using 79.7 K parameters, and the HAR system was ported to Raspberry Pi to implement the edge device. To effectively utilize HAR technology in indoor environments, edge devices capable of performing HAR must be deployed to infer the activity of people, wherever they are. Multiple edge devices are required to build such an environment, whose cost is closely related to the cost of the edge devices. To reduce the cost of edge devices, a lightweight HAR system aimed at low memory usage is essential, and a low-power design should be used to reduce maintenance costs. Although high HAR accuracy is important, it is also important to design an appropriate HAR model that considers network complexity and therefore memory usage. In this study, a pillar feature encoder (PFE) was used as an encoder for 3D point-cloud data for the purpose of lightweighting the network [19]. The PFE clusters 3D point clouds and converts them into 2D images. The advantage of using PFE is that 2D convolution can be applied on 2D images instead of 3D images in the classification network, thereby reducing the network complexity. The classification network was optimized based on depth-wise separable convolution and has the advantage of low complexity. Depth-wise separable convolution consists of depth-wise convolution and point-wise convolution, in which fewer parameters are required for the operation compared to a general convolution [20]. Existing radar point-cloud HAR studies have achieved high accuracy but are limited by the lack of discussion on lightweight HAR systems and low-power designs. Considering the practicality of HAR technology, it is crucial to develop a lightweight HAR system that operates efficiently at low power on edge devices. Therefore, in this study we used Xilin's FINN [21] to design a hardware-software implementation of an HAR system on an edge device, aiming to achieve a low-power, lightweight solution while maintaining an inference accuracy comparable to existing studies. The remainder of this paper is organized as follows: In Section 2, we introduce the dataset collection. In Section 3, we describe the structure of the proposed HAR system in relation to data pre-processing, the encoding of point-cloud data, and a classification network. In Section 4, we evaluate the performance of the hardware-software implementation of the HAR system using FINN. Finally, in Section 5, we conclude the study.
# Silk Protein-Based Membrane for Guided Bone Regeneration
## 3 Silk Fibroin Membrane
### Silk Fibroin Membrane with Other Components
#### 3.2.3 Chitosan, Hydroxyapatite, and Growth Factors  
Chitosan is a natural polysaccharide derived from chitin that is known for its excellent biologic properties, including biodegradability, biocompatibility, and antimicrobial activity [84]. Chitosan has been used in biomedical and pharmaceutical applications, such as in wound healing, drug carriers, and tissue engineering scaffolding [85]. Chitosan is blended with SF and widely used in the tissue engineering scaffolding of bone and cartilage and skin healing [86]. Various forms of chitosan-blended SF scaffolds can be manufactured for bone tissue engineering, such as film, nanofiber, and membranes, and it shows good osteogenic properties by increasing osteogenic cell proliferation and gene expression [84; 87; 88].  
HA is blended with chitosan-SF membrane to strengthen its mechanical properties [87]. HA is the main inorganic component of natural bone and has biocompatible, biodegradable, and osteoconductive properties [89; 90]. It provides higher mechanic strength and stability in the membrane [91]. Chitosan-fibroin-HA membranes have shown comparable new bone regeneration with commercial collagen membranes in a rat calvarial defect [92; 93]. They have not shown any obvious inflammatory reaction, and they have the potential to be used as a GBR membrane [93].  
Growth factors play an important role in bone tissue repair and remodeling [94; 95]. Growth factors, such as bone morphogenetic protein (BMP) and vascular endothelial growth factor (VEGF), are combined with the chitosan-fibroin scaffold to promote bone regeneration [96; 97], Chitosan-fibroin-HA membranes have biocompatibility and mechanical properties, and BMP has osteoinductive activity [98]. BMP has been successfully incorporated in the core of chitosan-fibroin-HA nanofiber membranes, and these membranes have increased osteogenic cell differentiation and gene expression [96]. VEGF is a major regulator of angiogenesis and supports osteogenesis [99; 100]. VEGF combined with a chitosan-SF scaffold has enhanced osteoblast cell proliferation and has shown its potential as a scaffold of bone tissue engineering [97]. BMP and VEGF provide osteogenic benefits to the scaffolding, and chitosan-fibroin is expected to be an ideal bone tissue engineering scaffold.
# Time Series Irradiance Synthesizer for Optical GEO Satellite Downlinks in 5G Networks
## 4 Validation and Numerical Results
### Long-term Received Power  
In this section the long-term received power statistics, the first order statistics, are presented using the validated synthesizer. The simulation of the turbulent optical channel is performed under various weather conditions in terms of different \(C_{n}^{2}(h)\) values, that is, different \(V_{g},\ A_{0}\) leading to various Fried parameters and scintillation index values.  
The model hypothetical inputs are given below:  
(a) Communication wavelength is \(\lambda=1040\) nm; (b) transmitting power is \(P_{T}=5\) W; (c) transmitter's efficiency is \(\eta_{T}=0.7\); (d) receiver's efficiency is \(\eta_{R}=0.3\) including the quantum efficiency; (e) the atmospheric transmittance is \(\eta_{atm}=0.8\); (f) receiver's aperture diameter is \(D_{r}=260\) mm; (g) beam diameter is \(2W_{0}=125\) mm; (h) ground station's height is \(H_{GS}=1500\) m with elevation angle \(37^{\circ}\).  
In Figure 10 the simulated CDFs are demonstrated.  
Figure 9: Second order irradiance statistics validation for session on 10/09/2003 20:10. PSD of measured data vs. PSD of generated data.  
In Table 4 the resulting Fried parameters and SIs from the numerical data are also given. It is concluded that the worse the turbulence effects the smaller the Fried parameter and the larger the SI, leading to reduced received power as it is easily observed by the simulated CDFs.
# Well-Separated Pair Decompositions for High-Dimensional Datasets
## 2 Preliminaries  
Let \(S\) be a set of \(n\) points in \(R^{d}\). For any \(A\subseteq S\), let \(R(A)\) denote the minimum enclosing axis-aligned box of \(A\). Let \(C_{A}\) be the minimum enclosing ball of \(R(A)\), and let \(r(A)\) denote the radius of \(C_{A}\). Let \(C^{r}_{A}\) be the ball with the same center as \(C_{A}\) but with radius \(r\). Furthermore, for two sets \(A\), \(B\subseteq S\), let \(r=\max(r(A),r(B))\), and let \(d(A,B)\) denote the minimum distance between \(C^{r}_{A}\) and \(C^{r}_{B}\). For example, if the \(C_{A}\) intersects \(C_{B}\), then \(d(A,B)=0\) (Figure 1).  
**Definition 1**.: _A pair of sets \(A\) and \(B\) are said to be well separated (a dumbbell) if \(d(A,B)>s\cdot r\), for any given separation constant \(s>0\) and \(r=\max\{r(A),r(B)\}\)._  
**Definition 2** (WSPD).: _A well-separated pair decomposition of \(S\subset\mathbb{R}^{d}\), for a given \(s>0\), is a sequence \((A_{1},B_{1}),\ldots,(A_{k},B_{k})\), where \(A_{i},B_{i}\subseteq S\), such that the following applies:_  
1. \(A_{i},B_{i}\) _are well separated with respect to separation constant_ \(s\)_, for all_ \(i=1,\ldots,k\)_;_
2. _For all_ \(p\
eq q\in S\)_, there exists a unique pair_ \((A_{i},B_{i})\) _such that_ \(p\in A_{i},q\in B_{i}\) _or_ \(q\in\ A_{i},p\in\ B_{i}\)_._  
Note that a WSPD always exists since one could use all singleton pairs \((\{p\}\{q\})\), for all pairs \(p,q\in S\). However, this would yield a sequence of dumbbells of size \(k=\Theta(n^{2})\). The question is whether one could do better than that. The answer to that question was given by the following theorem.  
**Theorem 1** ([1]).: _Given a set \(S\) of \(n\) points in \(\mathbb{R}^{d}\) and a separation constant \(s>0\), a WSPD of \(S\) with \(O(s^{d}d^{d/2}n)\) amount of dumbbells can be computed in \(O(dn\log n+s^{d}d^{d/2}n)\) time._  
While Theorem 1 states that it is possible to compute only \(O(n)\) dumbbells, for fixed dimension \(d\), it is still not clear how to efficiently determine the appropriate dumbbell for a given query pair \((p,q)\). In [3], it has been shown that retrieving the corresponding dumbbell can be performed in \(O(1)\) time for a fixed dimension \(d\).  
**Theorem 2** ([3]).: _Given a well-separated pair decomposition of a point set \(S\) with separation constant \(s>2\) and fixed dimension \(d\), \(I\) can construct a data structure in space \(O(n\cdot s^{2})\) and construction time \(O(n\cdot s^{2})\) such that for any pair of points \((p,q)\) in \(S\) I can determine the unique pair of clusters \((A,B)\) that is part of the well-separated pair decomposition with \(p\in A,q\in B\) in constant time._
# Fracture Prediction in Weldox 700E Steel Subjected to High Velocity Impact Using LS-DYNA
## 4 Conclusions  
The fracture behaviour of Weldox 700E steel plates is investigated using the LS-DYNA/explicit. The study is conducted on flat target plates using varying critical fracture parameters and fracture strain criteria impacted by blunt-nosed cylindrical projectiles. This work also analyzes the fracture behaviour of the target with varying plate thicknesses (2-6 mm) impacted using varying projectile masses (6.4 g and 12.8 g) and impact velocities of 257.5 m/s, 300 m/s, and 400 m/s.  
It is concluded that a higher value of the critical fracture parameter results in a smoother plug formation, whereas a lower value of the critical fracture parameter leads to a less smooth plug. Regarding the fracture strain criteria, the plug formed using ef-JC fracture strain criteria is smoother compared to those with ef-DJC and ef-XJC fracture strain criteria. For varying plate thicknesses, the 6 mm plate exhibits more deformation than the 2 mm and 4 mm plates. In the case of varying velocity, a smoother plug is observed at lower projectile velocities (257.5 m/s and 300 m/s). Regarding the effect of projectile mass, a smoother plug with higher deformation is obtained using lower projectile mass. However, no plug formation is observed at 257.5 m/s and 300 m/s for the 6 mm thick plate with a lower projectile mass (6.4 g), whereas a plug is formed at 400 m/s. Plug formation occurs for all thicknesses (2-6 mm) when impacted by a higher projectile mass (12.8 g).  
The effect of temperature and equivalent plastic strain is more noticeable for the mid element compared to the top and bottom elements. Overall, the formation of the plug initiates at the top, then progresses to the bottom, and finally to the mid element in the case of a velocity of 275.7 m/s. For higher projectile velocities, the trend follows the order of top-mid-bottom. Further, it is observed that for higher-thickness plates, both plastic strain rate and temperature play significant roles, whereas for lower-thickness plates, the plastic strain rate primarily influences element deletion.  
N.K.O.: data curation, writing--original draft; R.K.S.: writing--review, editing and supervision; G.V.: writing--review and editing, methodology; S.C.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
Not applicable.  
Data Availability Statement: Data can be made available upon reasonable request.  
Authors acknowledge the Ministry of Education, Government of India for providing a scholarship to the first author for carrying out the presented research work.  
The authors declare no conflicts of interest.
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 4 Discussion  
### DEM Differences  
A GDEM validation study found the standard deviation of elevation differences for the GDEM2 in forested mountainous terrain to be 12.7 m [45], which compared to 12.3 m observed in the Pisang landslides AOI in the study (Table 3). [PERSON] and [PERSON] [46] used >300,000 differential GPS measurements over a 4000 m elevation range to evaluate the performance of the AW3D30, GDEM2, and SRTM and similarly observed the highest uncertainty in the GDEM2. The effective horizontal resolution of the GDEM2 is reported to be 72 m [45], which is ineffective for capturing rapidly varying or steeply sloping topography, similar to the initial release of the SRTM at 90 m resolution [47]. By contrast, the UAV, HMA, and AW3D30 products were derived from finer resolution imagery (all <3 m resolution).  
The similarity between the UAV and HMA DEMs for the Pisang landslides AOI (Table 3) shows the benefit of conducting UAV surveys even if ground control is unavailable. In this case, the high initial model RMSE (6.54 m) due to poor native UAV image georeferencing was reduced to 3.54 m (Table 2) through coregistration to the HMA DEM. The increasing availability of real-time kinematic (RTK) positioning-enabled UAV will further reduce the requirements for extensive ground control point surveys (e.g., [48; 49]). It is clear that HMA DEMs represent an important new baseline topographic dataset for the Himalaya but still lack the spatial completeness of other open-access global DEMs.
## 3 Results and Discussion  
To evaluate the effect and robustness of the proposed model, in Section 3.1, we discuss three parts: Time-frequency features extraction, network structure, and network parameter optimization. In Section 3.2, we compare the proposed method with the best methods we know so far. Specifically, in Section 3.1, we discuss window functions of different types and sizes when applying STFT to convert samples to time-frequency features. In addition, we discuss the structure of CNN. Then, we optimize the network parameters containing regularization, batch size, and loss function types in the network. All experiment results in the section are based on a tea database collected by an e-tongue system we designed. The code of proposed model is posted in the following link. [[https://github.com/Shunzhange/auto-feature-extraction-method-for-e-tongue](https://github.com/Shunzhange/auto-feature-extraction-method-for-e-tongue)]([https://github.com/Shunzhange/auto-feature-extraction-method-for-e-tongue](https://github.com/Shunzhange/auto-feature-extraction-method-for-e-tongue)).
## 3 Semantic Service Description and Context-Based Orchestration
### Construction of Ontologies
#### 3.2.4 Equipment Ontology  
The equipment ontology contains a collection of device categories (types of devices) in the domain of production automation and manufacturing technology. The device categories are represented in a taxonomic structure. Names as well as the classification of devices in this taxonomy are strongly influenced by the eCl@ss classification and product description, which itself resembles the United Nations Standard Products and Services Code. Despite the heavy influence of eCl@ss on the equipment module it is not just a mere excerpt of it. Instead, we performed several changes in the taxonomic structure itself due to information transferred to the parameter ontology as well as the addition of device categories that are not clearly defined in eCl@ss. Figure 2 shows a segment of the equipment ontology.
## 4 Control System Design
### Control System Software Design  
The software design of the control system mainly includes the design of the upper computer control software and the writing of the lower computer control program.  
The upper computer software of the control box is developed to integrate the various functional modules of the control box. On the one hand, it can allow for human-computer interaction between the operator and the ROV, and the various operations on the control panel can be used to accurately send instructions to the lower computer of the ROV. On the other hand, various feedback information from the ROV can be monitored through the visual interface.  
We used C # language to write the code, mainly running on the Windows system. We created a C # WinForm application with an object-oriented design [29].  
The host computer software mainly includes the following modules:  
1. Control box communication module;
2. Pid parameter debugging module;
3. Control panel command display module;
4. Underwater image and sensor display module.  
The workflow of the host computer software is shown in Figure 11.  
Figure 11: Work flowchart of the control box’s PC software.  
Figure 10: Power carrier module.  
The underwater control program is developed based on STM32 CubeMX and Keil. The Free RTOS real-time operating system is introduced in programming. The process of using the operating system to program the lower computer is as follows [30; 31; 32]:  
1. System initialization: * Initialize hardware peripherals (such as sensors, motors, communication modules, etc.); * Initialize the FreeRTOS kernel and create tasks, queues, semaphores, and other resources.
2. Create tasks: * Sensor acquisition task: periodically read sensor data (such as depth, attitude, temperature, etc.) and send the data to the queue; * Motor control task: read control instructions from the queue to control motor motion; * Communication tasks: communication with the host computer, receiving control instructions and sending sensor data; * Path planning task: according to the sensor data and task requirements, calculate the motion path and send control instructions; * Fault detection task: monitor system status, detect faults, and deal with them.
3. Start scheduler: * Start the FreeRTOS scheduler and start task scheduling.
4. Task operation: * Sensor acquisition task: read sensor data and send them to the queue; * Communication task: receiving instructions from the host computer and sending sensor data; * Motor control task: read data from the queue and control the motor; * Path planning task: calculate the path and send control instructions; * Fault detection task: monitor system status and deal with faults.
5. System operation monitoring: * Monitor the task status, record the log, and ensure the normal operation of the system.
6. The end of the system: * Stop all tasks, close the hardware, and end the system's operation.
## 7 Quadratic Adaptive Replication (QAR)
### Analytical Model
Thus, the overall production traffic is  
\[T_{p}(\lambda_{r})=\beta\lambda_{p}A\frac{\delta}{\sqrt{\lambda_{r}}}+\beta \lambda_{p}A(\lambda_{r}A-1)\frac{1}{\sqrt{\lambda_{r}}}\ \ \mbox{bits-m/sec},\]  
where \(\beta\) plays the same role for production cost than \(\alpha\) in the consumption cost, that is, the average size in bits of the production event messages. Thus, the overall network traffic is computed as \(T_{t}(\lambda_{r})=T_{c}(\lambda_{r})+T_{p}(\lambda_{r})\). By optimizing this expression we are able to find the optimal number of replicas (\(N_{r}^{*}\)) in order to minimize the overall network traffic,  
\[N_{r}^{*}=\lambda_{r}^{*}A=\delta\frac{\alpha\lambda_{c}}{\beta\lambda_{p}}-( 1-\delta)\]  
Figure 7 shows the model validation via simulation. The results demonstrate that the model is accurate enough and can be used to find the optimal number of replicas to apply QAR. **Production dominates Consumption (\(\lambda_{p}>\lambda_{c}\))**  
In this case the producers just push the data to the closest replication node, whereas a consumption query first reaches the closest replica, which then routes the query to the rest of replication nodes (using the combing routing) that finally reply back with the requested information if any is available. Therefore, we can use the same model than in the previous case, but interchanging consumption and production costs we assume that the distance traveled by the query to all replicas is the same as the distance of all replies. We already mentioned when introducing ToW (see Section 5.3.) that this is only possible if aggregation is assumed. Therefore, in order to fairly compare our proposal with ToW we follow the same assumptions considered by ToW..  
Then the consumption and production costs and \(N_{r}^{*}\) are in this case:  
\[T_{c}(\lambda_{r})=\alpha\lambda_{c}A\frac{\delta}{\sqrt{\lambda_{r}}}+\alpha \lambda_{c}A(\lambda_{r}A-1)\frac{1}{\lambda_{r}}\ \ \mbox{bits-m/sec},\]  
\[T_{p}(\lambda_{r})=\beta\lambda_{p}A\frac{\delta}{\sqrt{\lambda_{r}}}\ \ \mbox{bits-m/sec},\]  
\[N_{r}^{*}=\lambda_{r}^{*}A=\delta\frac{\beta\lambda_{p}}{\alpha\lambda_{c}}-( 1-\delta)\]  
This model (\(\lambda_{p}>\lambda_{c}\)) is symmetric to the previous one (\(\lambda_{c}>\lambda_{p}\)) thus, it could be also validated by Figure 7.
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 5 Conclusions  
We have presented an active WSN congestion-aware routing algorithm (CARA) for network congestion control. The algorithm considers both spatial relationship and traffic load and puts forward four route evaluation parameters: forward rate, node load factor, cache remaining rate, and forward average cache remaining rate. The multiparameter fusion method is used to make routing selections. As a result, the CARA algorithm realizes the congestion perception of the sensor network node itself and the nearby area and optimizes network transmission performance. Our simulation results indicate that the CARA algorithm performs better than the TER and CCOR algorithms in terms of congestion control, which significantly reduces the network packet loss rate and results in lower average network energy consumption and delay.  
In future research, we will mainly focus on the application of WSNs in the power and energy sectors. For integrated energy management and microgrid management in industrial parks, we plan to build a WSN test validation environment based on the IEEE  
Figure 4: The average hops with the number of nodes. (**a**) Cache queue lengths = 20; (**b**) cache queue lengths = 30.  
Figure 5: The average energy consumption (mJ) with the number of nodes. (**a**) Cache queue lengths = 20; (**b**) cache queue lengths = 30.  
802.15.4 protocol. The control packet is an important part of the routing protocol. We will comprehensively analyze the congestion caused by control packets and data packets based on real-world scenarios and physical layer protocols. We also will test and validate our algorithm in real-world scenarios to further analyze and improve the algorithm based on practical data.  
Conceptualization, B.Q. and J.Y.; methodology, B.Q. and J.Y.; software, J.Y.; validation, J.Y.; formal analysis, J.Y.; investigation, J.Y.; resources, B.Q.; data curation, J.Y.; writing original draft preparation, J.Y.; writing--review and editing B.Q.; visualization, J.Y. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51677065).  
The authors declare no conflict of interest.