esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[26] modeled and conducted research in the form of adding Gaussian noise while simulating the light intensity curve of charge-coupled device (CCD)-based light microscopy. In addition, previous studies for noise reduction [27; 28; 29] have investigated that the degrading process of conventional microscopy system using photomultiplier, CCD, and complementary metal-oxide-semiconductor sensors can be described as Equation (1), and this study performed based on this assumption. The presence of noise in the image leads to challenges in the accurate estimation of PSF and noise amplification owing to similar high-pass filtering. In addition, noise reduction introduces additional blurring by low-pass filtering, which affects the intrinsic PSF of the light microscopy imaging system. Thus, it is different from the intrinsic PSF. Proposed method improves the resolution preventing the noise amplification based on the multi-resolution analysis (MRA) and noise level prediction for each subband image in light microscopy image. In addition, we also restored the blur components generated by the system and noise reduction algorithms with a blind deconvolution method to improve the resolution. This approach can derive improvement in sharpness while preventing image deterioration due to noise amplification shown in other state-of-the-art methods, thereby improving overall image quality. Blind deconvolution method is the most reasonable approach to predicting the final PSF. Blind deconvolution attempts to estimate the final PSF, without relying on auxiliary measures. It is to resort to iteratively until minimization of fidelity term in object function. This is a very challenging nonlinear problem [14]. Among image restoration techniques, MRA has been representatively used because it can effectively separate several frequency components while considering the spatial domain [30; 31]. Recently, convolution neural network denoiser and regularized term-based iterative method was presented to restore the noise and sharpness [32]. Moreover, deep learning module based on U-Net and half instance normalization block was introduced and this showed the outstanding results in terms of visual perception and evaluation metrics [33]. We investigated \(l_{1}\)-norm-based blind deconvolution after noise reduction with a non-subsampled contourlet transform (NSCT) [34] in light microscopic images and evaluated the image performance. First, we describe the proposed restoration scheme using light microscope images, materials and conditions for the simulation and experiment, and factors for a quantitative evaluation. Then, we present and analyze the results and demonstrate the effectiveness of the proposed framework on both the simulated and experimentally acquired data. Finally, based on the results and subsequent discussion, the main conclusions of this study are summarized.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 2 Group Communication
### CoAP-Based Group Communication
#### 2.2.2 Multicast Group Communication  
The IETF CoRE working group has recognized the need to support a non-reliable multicast message to be sent to a group of devices to manipulate a resource on all of the devices in the group. Therefore, they have developed the \"Group Communication for CoAP-RFC 7390\" [8], which provides guidance for how the CoAP protocol should be used in a group communication context. Group communication refers to sending a single CoAP non-confirmable message to all members of a specific group by utilizing UDP/IP multicast for the requests, and unicast UDP/IP for the responses (if any). This implies that all of the group members (the destination nodes) receive the exact same message.  
The use of multicast is efficient in sending the requests, but does not affect the number of responses sent by the members, since these are sent as unicasts. However, the use of multicasts has its limitations and challenges:  
* The most prominent limitation is the lack of reliability, which makes it not suitable for all use cases.
* Another important limitation is the cache-unfriendly nature of multicasts, preventing the possible reduction of requests and replies by utilizing caches. Depending on the use case and network topology, the reduction of packets as a result of using a cache can be better than the reduction obtained from using multicasts.  
Figure 2: Example of CoAP Confirmable Message (CON) exchange. If the client does not receive an ACK for its CON within a certain time, it retransmits the same CON again until it gets acknowledged or until the client runs out of retransmission attempts.  
* Furthermore, multicasts are not useful when a single user action needs to trigger different sensor requests, since one multicast request delivers the same message to all group members.
* Secure communication with the group members is not possible, since all communication based on this RFC operates in CoAP NoSec (No Security) mode.
* Finally, the use of multicast requires multicast support in the network. Typically, IP multicast relies on topology maintenance mechanisms to discover and maintain routes to all subscribers of a multicast group. However, maintaining such topologies in LLNs may not be feasible given the available resources. As a result, special multicast protocols have been proposed for the use inside LLNs. For example, the \"Multicast Protocol for Low power and Lossy Networks (MPL)\" Internet draft uses the Trickle Multicast (TM) algorithm to manage message transmissions for both control and data-plane messages and avoids the need to construct or maintain any multicast forwarding topology [14]. An alternative is the Stateless Multicast RPL Forwarding (SMRF) algorithm, which according to [15] achieves significant delay and energy efficiency improvements at the cost of a small increase in packet loss. Regardless of the used multicast protocol, all nodes on the path between the senders and receivers must be extended to support the protocol.
## 2 System Model and Problem Formulation
### Markov Game  
When users are under the influence of co-channel interference, the anti-jamming channel selection problem can be formulated as a Markov game; each user's strategy is related to other users' strategies. Thus, all users in the group take joint actions to fight against the malicious jammer and avoid co-channel interference as much as possible. Inspired by [27], we define the Markov game as:  
**Definition 2**: _When users are influenced by co-channel interference, the anti-jamming channel selection problem can be formulated as a Markov game, which can be expressed as \(\mathcal{G}\)\(=\left\{\mathcal{S},\mathcal{A},\mathcal{T},\mathcal{R}_{1},...,\mathcal{R}_{N}\right\}\). The details are shown as follows:_  
* \(\mathcal{S}\) _is the discrete state set. In the cooperative anti-jamming issue,_ \(s\left(t\right)=\left(f_{1}\left(t\right),...,f_{N}\left(t\right),f_{j}\left(t \right)\right),s\left(t\right)\in\mathcal{S}\) _represents all users' states and the jammer's state. Users' states are correlative._
* _Denote_ \(A_{n}\) _as the channel selection set of user_ \(n\)_, and_ \(\mathcal{A}\) _is the joint action set of all users in the UAV group. The action space is_ \(\mathcal{A}=A_{1}\times\cdots\times A_{N}\)_._
* \(\mathcal{T}\) _is the state transition function, and the state space is_ \(\mathcal{S}\times\mathcal{A}\times\mathcal{S}\)_, which satisfies_ \(\sum\limits_{s\in\mathcal{S}}\mathcal{T}\left(s,\mathbf{a},s^{\prime}\right)=1\)_._ _Specifically,_ \(\mathbf{a}\) _is the joint channel selection strategy, and_ \(s\) _is the current state._ \(s^{\prime}\) _is the coming state after all users take joint action_ \(\mathbf{a}\) _under state_ \(s\)_. The state transition function_ \(\mathcal{T}\) _satisfies the Markov property, as well._
* \(\mathcal{R}_{1},...,\mathcal{R}_{N}\) _are the reward functions of each user, and they satisfy_ \(\mathcal{S}\times\mathcal{A}\rightarrow\mathcal{R}_{n},n\in\mathcal{N}\)_. For UAVs in the group, no matter what joint actions are being taken, each one can obtain an immediate reward._  
Aiming at two different states in the UAV group, the anti-jamming channel selection problems are formulated as a Markov decision process and a Markov game, respectively. For the Markov decision process, the single Q-learning approach is used to obtain each user's optimal channel selection strategies; whereas for the Markov game, the multi-agent learning method is adopted for the purpose of acquiring the joint channel selection strategies for all users.
Sensors_**2009**, \(9\), 8684-8708; doi:10.3390/s91108684  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Topology Reorganization Scheme for Reliable Communication in Underwater Wireless Sensor Networks**  
**Affected by Shadow Zones**  
**[PERSON]**  
Department of Telematics Engineering, Barcelona Tech University, Esteve Terradas, 7 - 08860 Castelldefels (Barcelona), Spain; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-93-413-70-51; Fax: +34-93-413-70-07  
_Received: 31 August 2009; in revised form: 8 October 2009 / Accepted: 19 October 2009 / Published: 29 October 2009_  
**Abstract:** Effective solutions should be devised to handle the effects of shadow zones in Underwater Wireless Sensor Networks (UWSNs). An adaptive topology reorganization scheme that maintains connectivity in multi-hop UWSNs affected by shadow zones has been developed in the context of two Spanish-funded research projects. A mathematical model has been proposed to find the optimal location for sensors with two objectives: the minimization of the transmission loss and the maintenance of network connectivity. The theoretical analysis and the numerical evaluations reveal that our scheme reduces the transmission loss under all propagation phenomena scenarios for all water depths in UWSNs and improves the signal-to-noise ratio.  
**Keywords:** underwater sensor networks; shadow zones; transmission loss; optimal location
## 5 Conclusions  
This article proposes an integrated longitudinal and lateral control method for intelligent vehicles during emergency braking on curved roads. The purpose of this method is to effectively reduce the lateral offset of the vehicle and enhance its active safety. The proposed control method is validated through joint simulation using Matlab/Simulink, PreScan, and CarSim to simulate typical curved road scenarios. During emergency braking on curves, the existing AEB system can effectively prevent collisions with obstacles in front, but there is a risk of colliding with obstacles on the sides due to excessive lateral deviation. To address this issue, the proposed control method combines the longitudinal control by the AEB system with the lateral control by the LKA system. Three different control systems are designed for comparative simulation analysis. The results of the simulations show the following observations:  
(1) In the control system without LKA system involvement, i.e., the AEB control system alone, there is a noticeable lateral offset of the vehicle during emergency braking.  
Figure 11: Simulation results (\(V=60\) km/h, \(R=120\) m).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Curvature** & **Speed of Ego** & & **Collision** & **Final Gap** & **Lateral** \\
**Radius \(R\)** & **Vehicle** & **System Type** & **Avoidance** & **(m)** & **Offset (m)** \\
**(m)** & **(km/h)** & & **Successfully** & **(m)** & **Offset (m)** \\ \hline \multirow{4}{*}{120} & 60 & \�(2) In the control system with LKA system participation, rear-end collisions are effectively avoided and the lateral offsets during emergency braking are reduced.  
(3) The performance of the integrated control system, which triggers the LKA system immediately when the AEB system comes into action, is superior to the independent control system of AEB and LKA. In the scenario with a curve radius of 60 m and an initial vehicle speed of 60 km/h, the lateral offsets of the integrated control system, independent control system, and only the AEB control system are 0.21 m, 0.29 m, and 1.72 m, respectively.  
(4) Based on these findings, it can be concluded that integrating the AEB and LKA systems leads to improved collision avoidance and reduced lateral offset during emergency braking on curved roads.  
This study is limited in its focus on fixed-curvature-radius curves as the testing scenario. Future research should aim to investigate working conditions involving variable-curvature-radius curves. Furthermore, it is important to conduct additional investigation regarding passenger comfort during the collision avoidance process. Of course, experimental verification is a crucial research task in the future.  
Conceptualization, F.L.; methodology, formal analysis, F.L. and H.Y.; software, validation, H.Y.; writing, F.L. and H.Y.; supervision, project administration, F.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Research Program of Chongqing Education Commission of China (KJQN202101105).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Formation Methods and Simulation
### 11 \(\times\) 11 Matlab Simulations of UAVs Magic Square Formation
#### 3.3.3 Simulations of Scale UAV Grid Formations  
Considering different scales of nested magic squares, this study will not illustrate them one by one. However, they share the same formation rule and topological structure, so the 11 \(\times\) 11-scale UAV grid formation (121 UAVs) was used as an example. Its simulation results are as shown in Figures 18-20.  
According to the UAV flying trajectory in the simulation results, it could be concluded that the UAV cluster initially moved from the unformatted sector to the formatted one. In addition, the initial flying orientation was along the x axis. From the curve graph, the cluster converged to 200 m in height within 5 s and soon entered the formatted sector. Based on the speed graph, all UAVs achieved uniform convergence in axis x, y, and z at 15 s, when the curve graph of controller output, controller input, and executive output achieved convergence. Thus, it can be seen that the 121 UAVs in that formation generally realized convergence in speed and completed the formation in 15 s. This formation is large in scale, stable in plane, and swift in convergence compared with other formations.
## 3 Trajectory Tracking Controller Design Based on LADRC
### LADRC Design Process for Trajectory Tracking
#### 3.2.2 Altitude Controller  
Altitude control is comparatively more straightforward than horizontal trajectory tracking since it does not require the implementation of guidance laws. Nevertheless, the design process for the altitude controller follows a similar approach to that of the horizontal controller, as both require knowledge of the system order.  
According to Equation (4),  
\[\dot{z}=-u_{s}\sin\theta+v_{s}\sin\phi\cos\theta+w_{s}\cos\phi\cos\theta \tag{22}\]  
In fact, according to ref. [5], there exists a relationship between the parafoil canopy's speed and the thrust \(u_{2}\), which can be expressed as follows,  
\[\begin{cases}\dot{v}_{s}=\frac{\cos\theta_{r}\sin\psi_{r}}{m_{s}+m_{a,11}}u_{ 2}+...\\ \dot{u}_{s}=\frac{\cos\theta_{r}\cos\psi_{r}}{m_{s}+m_{a,22}}u_{ 2}+...\\ \dot{w}_{s}=\frac{-\sin\theta_{r}}{m_{s}+m_{a,33}}u_{ 2}+...\end{cases} \tag{23}\]  
where the ellipsis represents items not related to \(u_{2}\).  
Then, further deriving Equation (22), it can be deduced that  
\[\dot{z} = -\dot{u}_{s}\text{sin}\theta+\dot{v}_{s}\text{sin}\phi\text{cos} \theta+\dot{w}_{s}\text{cos}\phi\text{cos}\theta-(u_{s}cos\theta+v_{s}sin \theta\text{sin}\theta+w_{s}cos\phi sin\theta)\theta \tag{24}\] \[-(w_{s}cos\theta sin\phi-v_{s}cos\theta cos\phi)\tilde{\phi}\] \[= \left(-\frac{\cos\theta_{r}\cos\psi_{r}\sin\theta}{m_{s}+m_{a,22} }+\frac{\sin\phi\cos\theta\cos\theta_{r}\sin\psi_{r}}{m_{s}+m_{a,11}}-\frac{ \sin\theta_{r}\cos\phi\cos\theta}{m_{s}+m_{a,33}}\right)u_{2}+\ldots\]  
As a result, a second-order system can also be obtained:  
\[\dot{z}=f_{a}+b_{02}u_{2} \tag{25}\]  
where \(f_{a}\) represents the total unknown disturbance in this system.  
Next, we can proceed with the sequential design of the LESO and PD control law based on Equations (19)-(21) mentioned earlier. However, the detailed description of this process is beyond the scope of this discussion.  
In summary, the trajectory tracking control of a powered parafoil in this paper depends on two LADRC controllers, each requiring the adjustment of four parameters. Hence, the parameters that necessitate adjustment in the control system are as follows: \(\omega_{01}\), \(k_{p1}\), \(k_{d1}\), \(b_{01}\) for the horizontal controller, and \(\omega_{02}\), \(k_{p12}\), \(k_{d2}\), \(b_{02}\) for the height controller. These parameters will undoubtedly affect the trajectory tracking accuracy. To explore the parameter adjustment rules within the LADRC controller, interested readers are encouraged to consult ref. [36].
# Dispersion and Pressure Sensitivity of Carbon Nanofiber-Reinforced Polyurethane Cement
## Results and Discussions
### Determination of the Optimal Mixture Ratio and Sensitivity of CNF  
When the CNF content is the same, a more uniform dispersion of CNF will correspond to a high conductivity utilization ratio. Moreover, the conductive network of CNFPUC is more perfect and thus, the strain sensing ability of the composite material is better. Because of the large length and diameter of the CNF and owing to the low resistance to dispersion of CNF in the matrix, a larger ratio of polymer to cement means that the CNF find it easier to accumulate in a ball form during stirring. When the cement ratio is excessively small and the fluidity of the mixture is low, the friction resistance owing to the fiber movement increases. Therefore, the fiber cannot disperse evenly but it is more prone to clustering and breaking. Therefore, the dispersion of CNF has an optimal ratio of aggregates to ash, as shown in Table 8.  
As shown in the SEM image and Figures 8 and 9, when the CNF content is 4% and the coefficient of variation is less than 0.35, the dispersion effect of the CNF is better and the sensitivity is more than 500. In this work, a sensitivity of 500 is used as the reference index of the varistor characteristics. When the sensitivity is greater than 500, the pressure sensitivity exerts a reasonable effect. Using the  
Figure 8: SEM images of different parts of CP1.6 CNFPUC hexahedron.  
Figure 9: SEM images of different parts of CP1.4-5 CNFPUC hexahedron.  
method of calculating the sensitivity and the coefficient of variation when the CNF dosage is 4%, the critical value of the coefficient of variation can be calculated for the case in which the CNF is introduced with other additives. At the same time, the curve of quadratic polynomial is synthesized as shown in Figure 10. The initial rising speed of the curve is fast and the curve then tends to be flat. It can be determined that when the amount of CNF is low, a better dispersion effect (smaller coefficient of variation) may satisfy the sensitivity requirement. When the content of CNF is greater than 4%, the conductive network has been essentially formed; however, because of the large specific surface area and aspect ratio of the CNF, dispersion does not occur easily and the curve tends to be smooth. Therefore, according to the fitted quadratic curve, in the range of percolation curves II and III, the dispersion effect of CNF in the PU matrix can be verified according to the average value of the coefficient of variation of the measured hexahedron and subsequently, the sensitivity of the CNFPUC can be judged to satisfy the engineering needs.
# Numerical Study on the Bending Performance of Steel-Ribbed Composite Slabs for Substations
## 3 Finite Element Modeling  
### Basic Assumptions  
The integrity of composite slabs is critical under loading, especially at the interfaces of the laminated (precast and post-cast) concrete layers. According to the experimental results [14], the existence of the inverted T-shaped ribs can increase the contact surface, making the laminated concrete layers work compositely. As a result, the following basic assumptions are made for the numerical simulation and analysis for simplicity:  
1. The materials are assumed to be isotropic and homogeneous;
2. The relative slip between the laminated concrete layers is negligible, and the two layers work together.
3. Perfect bonding is assumed between the concrete and steel (rebars, prestressing steels, and steel ribs) without any relative slip.
# A Dense Small Object Detection Algorithm Based on a Global Normalization Attention Mechanism
## 3 Experiments
### Ablation Studies  
To validate the effectiveness of the GNAM and the high-resolution feature enhancement network, an object detection network ablation experiment was conducted based on the YOLOv8l model.  
Figure 6: Training set object size feature plot.  
Under the same conditions, YOLOv8l was selected as the baseline network for the comparative experiments to verify the impact of the GNAM and the high-resolution feature enhancement network with a large-scale detection head. Additionally, the SE, CBAM, GAM, CA, NAM and GNAM were added to the backbone network based on YOLOv8l, and the performance of different attention mechanisms was compared. The experimental results are shown in Table 2.  
Using ablation experiments, it was concluded that compared to the baseline network YOLOv8l, GNYL achieved a 7.2% improvement in mAP at 0.5 and a 5.0% improvement in mAP at 0.95, while increasing the parameters by 37.3% and the floating-point computations by 34.9%.  
When adding the SE, CA, CBAM, and GAM attention mechanisms to YOLOv8l, there was a slight increase in the parameters and floating-point computations, but there was almost no change in mAP\({}_{0.5}\) and mAP\({}_{0.95}\). The addition of the SE, CA, CBAM, and GAM to the YOLOv8l network failed to effectively exploit the channel and spatial information of the input features. When adding NAM attention mechanisms to YOLOv8l, there was achieved a 0.5% improvement in mAP\({}_{0.5}\) and mAP\({}_{0.95}\). Based on YOLOv8l_rec, GNYL incorporates the GNAM and uses batch normalization's scale factors to suppress irrelevant channels or pixels. It introduces a three-dimensional arrangement with a multi-layer perceptron for the spatial attention sub-module, reducing information loss and amplifying global interaction representation. Finally, the global normalization attention weights are weighted to increase the utilization of effective channel and spatial information in the input features.  
The experimental results show that:  
1. The scheme combining feature enhancement networks has significant accuracy advantages in detecting dense small objects in aerial photography;
2. The GNAM can fully mine the channel and spatial information of input features, effectively improving the utilization rate of input feature information.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 2 Sensor Node Energy Model
### Energy Model Parameters  
In Table 1, all the parameters that have an impact on the overall energy consumption of the studied SN, based on the CC1310 system-on-chip (SoC) [49], are listed. It should be noted that only the parameters linked to the sensor considered (i.e., Bosch Sensortec BME280 [50]) are specific to this SN. Otherwise, the list of parameters can be used and adjusted to estimate the energy consumption of approximately any wireless sensing system.