esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Implementation of NIST P-256 Elliptic Curve Cryptography on 8-Bit AVR Processor
## 1 Introduction  
Wireless sensor networks (WSNs) that consist of a numerous number of resource-constrained sensor devices have attracted substantial attraction due to the rapid advancement of Internet of Things (IoT). In various IoT applications, such as monitoring physical and environmental conditions (temperature, sound, and pollution levels), battlefield reconnaissance, home automation, etc., many constrained devices are employed as wireless sensor nodes for their low cost and energy efficiency. When compared with traditional cable networks, it is difficult to ensure secure and reliable communications in WSNs, since those wireless sensor nodes are often deployed in unattended environments. Hence, it can be easily accessed and manipulated by malicious adversaries. Thus, the cryptographic mechanism is required in order to provide sufficient security in WSNs. However, it is hard to deploy cryptographic schemes (especially of public key cryptography) on wireless sensor nodes due to their construction in resources, such as computation power, memory, energy, and even storage space, since they usually assumed to be operated with battery-power. For example, MICAz more, which is recognized as one of most widely used constrained 8-bit devices, is equipped with an AVR ATmega128 processor that has 128 KB of programmable flash memory and 4 KB of RAM with a clock frequency of 7.3728 MHz.  
In early days, it is considered that Public-Key Cryptosystems (PKCs) are infeasible in resource-constrained devices due to its significant amount of computation. In order to overcomethe restrictions of resource-constrained device, a lot of cryptography techniques have been proposed to apply PKCs for securing communication in WSNs [1; 2; 3; 4].  
When compared to conventional PKCs, Elliptic curve cryptography (ECC) has a smaller key size providing equivalent security. Hence, ECC is regarded as a better choice for WSNs than other PKCs, such as Rivest-Shamir-Adleman (RSA) and Digital Signature Algorithm (DSA). For example, the RSA scheme with 1024-bit key provides the same level of security in ECC with the 160-bit key. In addition, it is considered that a scalar multiplication, whic is the most expensive operation in ECC, only requires 5% of the execution time of main operation in RSA [1; 3; 5].  
A scalar multiplication requires elliptic curve point arithmetic operations, such as point addition and doubling. These point arithmetic operations require field operations, including multiplication, squaring, reduction, addition, and inversion. In particular, it is considered that the most time-consuming operation in field operation is multiplication accounting for almost 80% of execution time of scalar multiplication. Hence, many researches have naturally focused on optimizing the multiplication technique in order to improve the performance of scalar multiplication.
## 4 Engineering Example
### Results and Analysis  
#### 4.3.1 Prediction Results and Analysis Under Different Combinations of Input Features  
In order to better illustrate the superiority of adaptive MTGNN in dealing with multi-feature non-smooth time series, this section first conducts a comparative experiment with three combinations of input features to determine the optimal input for the nodes. Taking the corrosion rate prediction results of monitoring point M1 as an example, the predictive performance of adaptive MTGNN under different input features is shown in Figure 10.  
Figure 10: _Cont._  
As shown in Figure 8, when the corrosion rate is relatively stable, the prediction results of the single feature input and the strongly correlated features input are not much different. When the corrosion rate fluctuates greatly, the strongly correlated features input performs better than the single feature input in capturing the peak of corrosion rate fluctuation, and it is more sensitive to the trend of data change. However, all features input will instead introduce redundant signals, making the information transfer between nodes less efficient and leading to a decrease in model prediction accuracy.  
Table 2 further quantitatively analyzes the performance of the model under different combinations of input features. From Table 2, it can be seen that the MAE of the data is reduced by 22.64% and 16.32%, the RMSE is reduced by 18.35% and 5.44%, and the MAPE achieves the minimum value when the prediction is made by using the strongly correlated features, as compared with the single feature input and the all features input, respectively. The experimental results show that the predictions of the model are closest to the actual values when the node inputs are strongly correlated features.  
The prediction framework proposed in this paper first screens out strongly correlated features as graph node inputs and then integrates the dynamic patterns among covariates through the graph convolution module and the temporal convolution module, which can extract complementary information for reflecting the volatility of the target variable and overcome the limitations of single-feature modeling.
## 5 Conclusions and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Method** & **Sensor** & **NO. Images** & **Reported Metrics** \\ \hline [PERSON] et al., 2020 [31] & Mask R-CNN with ResNet-101-FPN & RGB camera & 900 images with data augmentation & Detection accuracy of cherry tomato is 98\% \\ [PERSON] et al., 2020 [32] & Mask R-CNN with ResNet-101 & 4 RealSense cameras mounted on a pipe rail tofoley & 123 images without data augmentation & F1-Score of red tomato is 0.93, and green tomato is 0.94 \\ [PERSON] et al., 2021 [33] & MobileNetV1 CNN for detection \& color space detection \& color space a pipe rail tofoley & 254 images with data augmentation & Detection accuracy of cherry tomato cluster is 95.98\% \\ [PERSON] et al., 2020 [34] & Sobel operator for detection, color-based segmentation and size-based segmentation & RGB camera located perpendicular to the soil surface & 175 images & Detection accuracy of beef tomato 90\%, and cluster tomato is 97.7\% \\ Proposed & Mask R-CNN with ResNet-50-FPN & RGB camera mounted on a mobile greenhouse robot & 3180 images without data augmentation & F1-Score of mask for green tomato is 0.9284 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Scientific studies in tomato detection and segmentation based on image analysis. and provide technical support for the development of green fruit and vegetable automatic picking. Conceptualization, P.L. and F.S. ; methodology, F.S. ; software, Y.Z. ([PERSON]); validation, L.Z. and Y.Z. ([PERSON]); formal analysis, L.Z. ; resources, J.L. ; writing--original draft preparation, Y.Z. ([PERSON]) and J.L. ; writing--review and editing, L.Z., F.S. and Y.Z. ([PERSON]); visualization, P.L. ; supervision, F.S. ; project administration, P.L. ; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Key Research and Development Project of Shandong Province (2019 GNC106103), the Major Agricultural Applied Technology Innovation Project of Shandong Province (SD2019 ZZ019) and the Science and Technology Commissioner Project of Shandong Province (2020 KJTPY078). Not applicable. We are very thankful to [PERSON] for allowing us to capture data from the greenhouse. The authors declare no conflict of interest.
## 2 Connectivity in Colombia
Colombian government initiatives to bridge the digital divide in rural areas have concentrated efforts on offering broadband connectivity through conventional telecommunications infrastructure with high maintenance costs using technology not specifically designed for rural topology. WiBACK technology is a wireless backhaul solution that connects rural areas and municipalities to the Internet. WiBACK extends existing networks such as an extension cable over long distances and connects hard-to-reach areas to the network [12]. These public and private initiatives have encouraged the building of a better country and have been a key factor for the digital divide closing. However, the needs and respective solutions regarding last-mile devices and power supplies must be considered.
###### Abstract  
The ear models and shells of hearing devices are generally designed using ear impressions. Computed tomography (CT) can provide information concerning ear structure, which can be utilized in computer-aided design to create hearing devices. This study aimed to compare the differences in hearing devices designed using either ear impressions or CT images. The ear chip of a cartilage conduction (CC) hearing device was designed based on the ear impression and CT images of patients with aural atresia. Three patients with bilateral (\(n=1\)) or unilateral (\(n=2\)) aural atresia participated in this study. The aided thresholds and subjective evaluations were compared. There were no serious problems with the use of either device, and no remarkable differences were observed regarding their respective audiological and subjective benefits. Regarding the subjective patient preferences, the hearing aid with the device designed via CT imaging was better in three ears of two patients, whereas the wearing comfort of the transducer was better for the device designed via the ear impression. The subjective evaluation of appearance varied among the cases. Finally, a device that was designed using CT findings was purchased for one patient. CT images can be utilized in computer-aided design to create CC hearing devices.  
cartilage conduction; hearing device; hearing aid; ear mold; shell; ear impression; computer-aided design; computed tomography 2023  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], T. Comparison of Cartilage Conduction Hearing Devices Designed by Ear Impression and Computed Tomography. _Appl. Sci._**2023**, _13_, 6152. [[https://doi.org/10.3090/app1306152](https://doi.org/10.3090/app1306152)]([https://doi.org/10.3090/app1306152](https://doi.org/10.3090/app1306152))
## 1 Introduction
* Hypergraph neural networks are incorporated into the forecasting stage to model spatial dependencies among meteorological data, thereby more effectively capturing higher-order clustering of meteorological phenomena. * The proposed method is validated on large-scale reanalysis benchmarks and achieved substantial improvements in accuracy compared to multiple models.
## 6 Simulation Results  
### Simulation Environment  
We used ns-2.34 for performance evaluation of the proposed CCBE protocol. In our simulation environment, a network of 1800 nodes is deployed in an area of 400 m \(\times\) 400 m with the sink in the center (200, 200) and the sink not in the center (350, 200). We set the initial energy of each node to 2 J. The area of one hexagonal cluster based on the optimum hexagonal side of 25.06 m is 1631 m\({}^{2}\). The number of clusters created in the given area is 90 with the same number of CHs. There are 20 CMs in each cluster. The association of CMs with a particular cluster remains unchanged in the entire simulation. Only CHs change in the entire simulation. In this protocol, we consider periodic message transmission to the sink node. In the case of LEACH and HEED, 802.15.4 MAC is used to compare with the CCBE protocol. In this paper, we consider each node's energy consumption as the summation of energy consumed in the transmission and reception of data packets per round. The simulation parameters are given in Table 2, in which the parameters of the radio model are the same as those in [3]. We first obtain the optimal side length of the hexagon (\(r\)) from Equation (11) (\(r\) = 25.06 m for \(n\) = 2). We will examine the energy consumption and the maximum time delay with various numbers of nodes for CCBE, LEACH and HEED. Each CH is allocated a transmission slot in each frame of the sink node. According to the hexagonal structure, there are six neighboring CHs for the sink. These six CH nodes require a frame length of \(\omega\times 6\) s, where \(\omega\) is the slot duration of CH. At the beginning of each slot, nodes that are not transmitting go to sleep. The CHs aggregate the received data based on their residual energy and forward to the next CH or the sink node.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 3 Experiment and Analysis
### Model Training  
The experiment was performed on an Inspurserver with operating system UBUNTU18.04 and two NVIDIA Tesla T4 16G GPUs. The deep learning framework was TensorFlow1.15.0. The COCO dataset contains complex scenes of common objects in the natural environment, which is often used for object detection and instance segmentation [38]. The improved Mask R-CNN is transfer learning based on the pre-trained weights for the COCO dataset. The image resolution is adjusted to 1024 \(\times\) 1024 (pixels). Stochastic gradient descent was performed with the stochastic gradient descent with momentum (SGDM) optimizer, of which, a batch size is 2, an initial learning rate is 0.001, and a momentum factor is 0.9. The head network is trained for 20 epochs, and all network layers are trained for 40 epochs. ResNet50-FPN, ResNet101-FPN, and ResNet152-FPN backbone networks were used for testing, respectively [39]. From the training results in Figure 14, it is found that the loss curve of ResNet101-FPN has the best convergence effect, and the loss is reduced to less than 0.2. Therefore, ResNet101-FPN is selected as the backbone network of the model.  
The open-source SUN-RGBD dataset is a large-scale 3D dataset with dense annotations, often used for scene understanding [40]. When training PointNet, the SUN-RGBD dataset label is first modified to retain only the foreground and background, and then it is used to pre-train PointNet to enable the network to perform feature extraction. On this basis, the manually annotated frustum point cloud is trained, and gradient descent using adaptive moments estimation (Adam) optimizer with batch size 8 is used. An exponential decay learning rate is adopted, the initial learning rate is 0.001, the decay period is 300,000 steps,  
Figure 13: The fitting result of the central axis of the pole. (**a**) Point cloud intercepted; (**b**) RANSAC fitting cylinder; (**c**) Central axis fitted.  
and the decay rate is 0.5. A total of 50 epochs were trained. In Figure 15, the Loss curve and Accuracy curve of the training process show that the loss value decreases rapidly at the beginning of training, the accuracy rate increases rapidly, and then gradually stabilizes. Finally, the loss value decreases to less than 0.2, and the accuracy rate gradually increases to about 94%.
## 1 Introduction
One of the key challenges in EF modeling is achieving an optimal balance between stiffness and flexibility. Increased stiffness improves stability, but may hinder bone healing by restricting IFMs. Factors like pin diameter, placement, and number also play a crucial role. [PERSON] et al. proved that larger pin diameters improve rigidity [23], but cause complications such as excessive stress at the pin-bone interface, resulting in loosening or infection. [PERSON] et al. optimized the pin diameter selection and found that a value of 5.5 mm provides the best compromise between stiffness and stress distribution [24]. Computational methods like genetic algorithms [25] and Taguchi-ANOVA [26] have been used for geometrical optimization, reducing simulations by over 500 times. These studies identified rod elevation (i.e., the distance between the fixator rod and the bone) as the most influential factor on stability--lowering it reduces IFM strain and accelerates recovery. While conventional EF designs are well-studied, only one work [27] proposed a cost-effective alternative to Illizarov rings, using a glass-fiber-reinforced composite ring validated through FEM and standard testing. Clinically, hybrid EFs have shown advantages such as earlier weight-bearing and an improved range of motion, but they are not studied by numerical means [28]. Moreover, just a few numerical studies incorporate callus formation as a time-dependent variable. [PERSON] et al. monitored strain evolution in bone and fixators in different healing stages [29], while [PERSON] et al. identified three stiffness evolution phases [30]: an early stage (0-5 weeks) where the fixator bears most of the load, an intermediate stage (5-12 weeks) where increasing the callus stiffness gradually transfers load to the bone, and a late stage (12-15 weeks) where the bone fully supports the load, reducing reliance on the fixator. A literature review showed that hybrid fixators with 3D-printed polymeric composite rings are largely unexplored. This study addresses this gap by investigating the feasibility of printing short carbon-fiber-reinforced polylactic acid (PLA-CF) rings, integrating pre-stressed fixation wires, and analyzing their mechanical behavior across healing stages. A novel contribution is the inclusion of pre-stressing in the wires, rarely considered in simulations, which improves biomechanical assessment accuracy. Mechanical performance is evaluated at two important orthopedic milestones: the initial fracture gap and early weight-bearing (day 1 to week 4), with a focus on tibial fractures, one of the most commonly injured long bones [31].
## 3 Comparative Analysis of Numerical Forward Simulation and Soil Trench Modeling Experiments
### Analysis of Experimental Results
#### 4.1.1 Numerical Simulation of Intermediate Gradient Detection Device Detection
As shown in Figure 10b, when the terrain is undulating, the overall trend of the apparent amplitude frequency curve change rule is similar to when the terrain is flat. However, the smoothness of the curve is worse than that when the terrain is flat. Thus, the judgment of the main body of the contamination will be affected by the undulation of the ground. It can also be seen from the figure that on the 10 th and 100 th days, the pollution field was closer to the ground. The peaks on the top of the polluted area were just above it, and at this time, the pollution field showed a trend of longitudinal migration with no obvious horizontal diffusion. At this time, the apparent amplitude frequency curve still showed a \"peak\" for a heavily polluted area. Which indicated that the effect of the raised mountain peaks on the shape of the apparent amplitude frequency curve was not obvious. It only weakened the value of the apparent amplitude frequency monitoring. On the 200 th, 300 th, and 400 th days, the valley locations (point numbers 20, 30, and 40) caused significant fluctuations in the apparent amplitude frequency curves, which resulted in a \"multi-peak\" effect in the apparent amplitude frequency curves. Avoiding the valley locations during the monitoring is a better way to analyze the number and location of heavily polluted areas from the curve patterns. From Figure 11a, it can be seen that the valleys of the apparent resistivity curves at the 100 th, 200 th, 300 th, and 400 th days correspond to the measurement point numbers 15, 25, 30, and 36, respectively. In contrast to Figure 10a, the valleys of the apparent resistivity curves in Figure 11a correspond to the peaks of the apparent amplitude frequency curves, almost the same as the orientations of the heavily polluted areas. In Figure 11b, the apparent resistivity curves are affected by the undulation of the ground, and the phenomenon of \"multiple valleys\" occurs in a heavily polluted area. This shows that it is not possible to accurately determine the orientation of the heavily polluted area only based on the apparent resistivity map. The numerical values of the measurement points where valleysoccur increase with the migration of the pollution source to the right for both topographies. All the valleys of the apparent resistivity curves are near 30-60 \(\Omega\cdot\)m, which indicates that the downward migration of the pollution source does not have a significant effect on the valleys of the apparent resistivity curves.
## 5 Simulation Results
### Network Lifetime Comparison  
In this section, we compare the network lifetime between HCR and HCR-1 with different network scenarios. At first, the simulations are implemented to study the network lifetime with \(R_{c}\) in the range \([20\) m, \(70\) m\(]\). The simulation is run in the network with 400 nodes randomly deployed in an area of 200 \(\times\) 200 m\({}^{2}\). As shown in Figure 9, in both HCR and HCR-1, the network lifetime gets longer with the growth of \(R_{c}\), and the network lifetime reaches the maximum when \(R_{c}=R_{t}=70\) m. Compared to HCR, the improvement of the network lifetime of HCR-1 is over \(30\%\) when \(R_{c}=70\) m. The main reason is that the energy consumption in \(\mathcal{R}(1)\) is effectively balanced by adding relays for the CHs with low residual energy.  
Then, we study the network lifetime in different network areas. The simulations are carried out with the side length of network area varying from \(100\) m to \(300\) m. The node density is fixed at \(0.01\) node/m\({}^{2}\), and the \(R_{c}\) is set to \(70\) m. The results are given in Figure 10.  
Generally, HCR-1 performs better than HCR when the network area is larger than 100 \(\times\) 100 m\({}^{2}\), since the imbalance of energy consumption in \(\mathcal{R}(1)\) is alleviated by the relay candidate scheme in HCR-1. Nevertheless, HCR-1 does not have better performance in a small area, such as 100 \(\times\) 100 m\({}^{2}\). This is because the traffic load accumulated by CHs is not severe in a small-scale network. On the other hand, the advantage of HCR-1 decreases when the network area grows larger than 250 \(\times\) 250 m\({}^{2}\). The reason is that HCR-1 focuses on balancing the energy consumption in \(\mathcal{R}(1)\), but can hardly solve the energy imbalance in other gradients. The energy imbalance among different gradients gets worse with the growth of the network area. Therefore, it reduces the benefits of HCR-1.  
Figure 9: Network lifetime comparison with different \(R_{c}\).  
At last, we study the impacts of node density on network lifetime. Considering a fixed network area of 200 \(\times\) 200 m\({}^{2}\), the number of nodes is set as 200, 400, 600 and 800. As shown in Figure 11, the network with higher node density has longer network lifetime due to more energy resources. Moreover, in HCR-1, the improvements of network lifetime become larger when the node density increases. This is because when the number of nodes grows, there are more CHs that can serve as relays for the CH with low energy. In addition, the relay selection can be more reasonable with more candidates. Thus, HCR-1 performs better in a dense network.
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols  
Network management protocols play a crucial role in healthcare systems, enabling the efficient monitoring, configuration, and maintenance of vast device networks. These protocols form the backbone of healthcare system management, ensuring reliable operation and seamless communication between devices and management systems. This section examines the primary protocols that facilitate IoT network management and their specific applications.