esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Non-Linear Diffusion Method  
The proposed method is to restore the speckled SAR image using an adaptive filter and thresholding method that helps post-process the SAR images in object detection. The despeckling ability of the proposed method is to give exact results and preserve the fine details, such as edge preservation along with the edge [45; 46] directions, texture, etc., of SAR images. Figure 2 represents the working model of the proposed framework. The efficiency of the proposed framework is estimated by its perceptible characteristics and by employing additional analyzing parameters like SNR, PSNR, SSIM, and UIQI [47]. The objective of the proposed method is to achieve maximum precision in removing speckle noise from SAR images, addressing a key limitation of conventional methods that primarily focus on edge smoothing while neglecting fine details. The approach integrates three main techniques: anisotropic diffusion, discrete wavelet transforms, and method-noise thresholding. Non-linear diffusion and discrete wavelet transforms are used in the pre-processing stage to suppress speckle noise while preserving edges, and method-noise thresholding is applied in the post-processing stage to further refine noise reduction. This combined approach optimizes noise suppression by selectively targeting noisy areas, ensuring that important image features, such as edges and textures, are retained. The fusion-based thresholding technique distinguishes noise from key image details, enhancing both clarity and feature preservation. By merging fusion-based thresholding with non-linear diffusion, the proposed method effectively mitigates speckle noise while overcoming the shortcomings of traditional techniques that often sacrifice fine details. The integration of these methods offers a more comprehensive solution, improving both noise reduction and detail preservation compared to single-method approaches. This enhanced despeckling not only improves image quality but also supports post-processing tasks like object detection by maintaining critical features such as edges, textures, and boundaries.  
Figure 2: Flowchart of proposed diffusion method.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.2 Price  
Another project that dealt with energy management and grid monitoring has been the \"PRICE\" project led by the Spanish distribution system operator (DSO) Iberdrola. Among others, automation and monitoring of the grid have been issues of investigation as well as grid energy management. In addition, a monitoring system has been implemented that enables end-users to better manage their consumptions. Therefore, smart appliances communicated with the so-called Energy Box (E-box), which played the role of the household energy manager. The technologies used for this are ZigBee and PLC [99].
## 4 Results: An Experimental Analysis
### Performance of the ANN-Based Approximation of the Laplacian  
In the first series of experiments, we investigate the accuracy of the numerical approximation of the Laplacian. In concrete terms, we compare the standard five-point approximation in (3) with the approximation provided by the trained ANN.  
The computational domain is \(\Omega=(0,1)\times(0,1)\), and we apply a nonuniform grid, as shown in Figure 4. Here, we have used the number of \(21\times 21\) grid points.  
The approximation was tested in the case of the following model problem:  
\[\begin{cases}\Delta u(x,y)=(2-x^{2}y^{2}-x^{4})\cdot\cos xy-4 xy\sin xy\quad \text{in }\Omega\\ u(0,y)=0,\ u(1,y)=\cos y,\ u(x,0)=x^{2},\ u(x,1)=x^{2}\cdot\cos x,\end{cases} \tag{11}\]  
which has the analytic solution \(u(x,y)=x^{2}\cdot\cos xy\). Our aim here is to compare the pointwise approximation given by the ANN-based approximation with the \(\Delta\) operator. To quantify this, we compute the quantity  
\[\left(\sum_{\mathbf{x}_{j}}|\Delta u(\mathbf{x}_{j})-(D_{h,\text{FD}}\mathbf{ u})_{j}|^{2}\right)^{\frac{1}{2}}, \tag{12}\]  
which is the \(\ell_{2}\)-norm of the error of the approximation \(D_{h,\text{NN}}\approx\Delta\) on the grid. Here, \(\mathbf{x}_{j}\) in the summation applies to all of the grid points.  
We compare this quantity with the \(\ell_{2}\)-norm  
\[\left(\sum_{\mathbf{x}_{j}}|\Delta u(\mathbf{x}_{j})-\frac{1}{0.05^{2}}(-4u( \mathbf{x}_{j})+u(\mathbf{x}_{j+1})+u(\mathbf{x}_{j+2})+u(\mathbf{x}_{j+3})+u (\mathbf{x}_{j+4})\right)^{\frac{1}{2}}, \tag{13}\]  
given by the standard five-point approximation. Here, according to the geometric setup in Figure 1, the indices \(j+1,j+2,j+3\) and \(j+4\) denote the indices of the neighboring grid points of \(\mathbf{x}_{j}\).  
While the first one is approximately 0.5, the second one is \(\approx\)6. This was the average of a number of experiments. Here, the random positions in Figure 4 can effect the above error terms.  
This shows a real advance of our approach versus the conventional approximation. At the same time, we need to solve the problem in (11) numerically.
# Research on an Evaluation Method of Snowdrift Hazard for Railway Subgrades
###### Abstract
[PERSON] [14] evaluated the risk of railway snowdrift based on an entropy-cloud coupling model, and the snowdrift risk evaluation model established by [PERSON] [15] for the Xinjiang Jingxin Expressway, all of which provide theoretical support and practical guidance for snow disaster prevention and control. These research outcomes have provided theoretical guidance and practical references for the prevention and control of snowdrift disasters. However, current research has predominantly focused on the distribution and grading of snow disasters within provincial boundaries. This focus limits the applicability of  
Figure 1: Map of days of snow cover on railways. existing evaluation methods to the widespread and extensive nature of snowdrift disasters along transportation infrastructure, such as railways and highways. Furthermore, the development and evolution of snowdrift disasters are influenced by various factors, including local climate conditions like topography, wind speed and direction, and snowfall amounts, as well as the structural form of the subgrade. These influences impart a strong regional characteristic to these events. Therefore, for the evaluation of snowdrift disasters in specific areas, in-depth analysis based on actual engineering conditions is required to ensure the accuracy and applicability of the evaluation results [16; 17; 18]. This study focuses on the Altay-Fuyun (Afu) Railway, situated in the snowdrift-prone area on the northern slopes of the Eastern Tianshan in Xinjiang. By employing a combination of the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive evaluation (FCE), an integrated evaluation system was constructed to assess the risk of snowdrift to railway subgrades. Through analyzing the weights of contributing factors and conducting a fuzzy comprehensive evaluation, the study determined the risk levels of snowdrift disasters along the railway line and proposed corresponding disaster prevention and mitigation recommendations. The research results not only align with on-site observations, demonstrating the accuracy and practicality of the evaluation method, but also achieve a \"qualitative + quantitative\" comprehensive and synergistic risk assessment of snowdrift disasters on railway subgrades. This provides a scientific basis for the risk assessment of snowdrift disasters on railways and offers significant guidance for the design of railway subgrades and the formulation of disaster prevention and mitigation measures.
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 4 Discussion
### Implications  
In accordance with our findings, an increase in both atmospheric CO\({}_{2}\) concentration and precipitation contributes to an augmentation of wheat PY. Conversely, a warmer climate poses challenges for wheat growth while concurrently providing an opportunity to select an earlier sowing date, which enhances PY. These observations underscore two principal strategies aimed at boosting wheat PY or reducing the yield gap between actual yield and PY through enhanced resource utilization. Firstly, ensuring an adequate water supply emerges as a pivotal step to augment wheat PY in rainfed agricultural areas and to mitigate the yield gap in irrigated regions. In the future, there is a notable likelihood of heightened radiation [61] and warmer climatic conditions [62], resulting in elevated VPD and consequently increasing ET. This trend necessitates an increased water allocation to counterbalance the growing demand [24]. Adequate water supplementation becomes indispensable, especially within irrigation areas, while simultaneously enhancing farm-lands' water conservation capabilities [22] and improving irrigation efficiency, leading toenhanced water use efficiency [63]. Secondly, optimizing the sowing date emerges as a valuable strategy, as it fosters better synchronization of crop growth with radiative cycles, thereby augmenting wheat PY. Furthermore, enhancing light interception efficiency through the optimization of vertical leaf profiles via genetic engineering has also demonstrated effectiveness in bolstering wheat PY [64].
# High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects
## 3 High Performance MRAM Writing
### Energy Efficiency  
VCMA-MTJ switching performance is dependent on transistor sizing (W/L), VCMA pulse duration and pulse amplitude. Compared to traditional STT-MTJ magnetization switching, an electric field is used in the VCMA effect to replace electric current, which significantly lowers the dynamic power consumption. Leakage power consumption in VCMA-MTJ is negligible because much thicker tunneling barrier is used. A VCMA pulse is normally-off (with 0 V pulse amplitude) during idle and MTJ-sensing operation.  
Access transistor sizing strategy, VCMA pulse duration and pulse amplitude scaling are investigated. The energy-delay efficiency of VCMA switching is studied within the 1T-1 MTJ circuit. Figure 5 illustrates STT-MTJ and VCMA-MTJ switching energy-delay efficient front and design space. In STT-MTJ, minimum 0.95 V writing amplitude is required to have a successful writing operation. In Figure 5b, minimum energy (3.18 fJ/bit) is realized with access transistor W = 400 nm, \(V_{\text{b}}\) = 1.1 V and \(t_{\text{b}}\) = 0.44 ns. Minimum delay is achieved with W = 400 nm, \(V_{\text{b}}\) = 1.3 V and \(t_{\text{b}}\) = 0.38 ns.  
Figure 4: (a) STT-MTJ switching: low bias voltage may cause switching failure (e.g., \(V_{\text{H}}\) = 0.9 V). (b) Precessional VCMA is implemented with a different \(V_{\text{b}}\) amplitude and 0.5 ns pulse duration. A minimum 1 V \(V_{\text{b}}\) is required to successful switch the MTJ. A decreased \(V_{\text{b}}\) amplitude could lead to longer switching latency. (c) Based on the setup of Figure 3a, at least 0.3 V \(V_{\text{b2}}\) is required to complete STT-assisted precessional VCMA switching [25].  
Table 3 compares the achieved energy point with different CMOS technology nodes. Comparing with the VCMA-MTJ reported in 32 nm PTM and 40 nm bulk-CMOS, the minimum energy per 1-bit of non-volatile data writing are improved by 68.5% and 48.2%, respectively, with 28 nm FD-SOI CMOS implementation. Although STT-MRAM is an outstanding alternative memory of conventional SRAM, the memory density and writing efficiency still need to be improved [39].
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 6 Three-Dimensional Spatial Representation and Annotation in Mixed Reality
### Spatial Mapping and Depth Estimation  
For the system to properly place vial annotations in MR, it must understand the depth and spatial relationships of the objects in the user's surroundings. HoloLens 2, which serves as the primary MR platform for this work, continuously generates a spatial mesh of the environment using its built-in depth sensors. This real-time mesh acts as a virtual reconstruction of the physical world, allowing for the precise alignment of the detected vials with the actual surfaces.  
A key step in this process is to convert the detected vials from their original image coordinates, \((x_{i},y_{i})\), to real-world coordinates, \([X_{i},Y_{i},Z_{i}]^{T}\). This transformation (9) is performed using the well-known pinhole camera model:  
\[\begin{bmatrix}X_{i}\\ Y_{i}\\ Z_{i}\end{bmatrix}=d_{i}\cdot K^{-1}\cdot\begin{bmatrix}x_{i}\\ y_{i}\\ 1\end{bmatrix} \tag{9}\]  
where \(d_{i}\) is the depth value retrieved from the spatial mapping system and (10) represents the intrinsic camera matrix, \(K\):  
\[K=\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix} \tag{10}\]  
Here, \(f_{x}\) and \(f_{y}\) denote the focal lengths of the camera in the x and y directions, while \(c_{x},c_{y}\) indicate the optical center of the image sensor. This calculation ensures that the detected vials are positioned accurately in the user's field of view, even as they move through the environment.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 4 Discussion
### Future Directions
#### 4.3.3 Cloud and Edge Computing  
Most of the mobile positioning systems nowadays rely on cloud and edge computing services, and this is something that is expected to grow in the future.  
Cloud computing includes the delivery of computing services over the Internet (Cloud) for achieving faster, flexible resource utilization and economies of scale. These resources include tools and applications, such as data storage, servers, databases, networking, software, analytics, and intelligence. In [136], a cloud-based computing system was proposed for Wi-Fi indoor positioning and navigation through a single-hop wireless network. The system consisted of a self-driving card, a core cloud, and a moving edge cloud composed of a small-box center (cloudlet) available in a wireless AP. The core cloud was responsible for storing all information in the corridors, including the wireless access point map and forwarding the destination positioning packet.The moving edge cloud, on the other hand, was used for calculating the route path, taking movement decisions, and making positioning estimations. Combined, the cloud network could provide the destination position and wireless access point map and effectively help navigate the cart towards its final destination.  
Edge computing, on the other hand, is the range of networks and devices provided near the data source. In this way, the response time is improved, less bandwidth is required, and latency is reduced, which has a positive impact on the applications' performance and economic cost. Edge computing is useful for IoT and real-time applications with minimum latency, such as autonomous vehicles and multi-camera video analytics. In [137], a visible light positioning (VLP) system was developed, including a transmitter, a receiver, and an edge computing platform. The transmitter included LEDs, a microcontroller, and driving circuits, while the receiver was a smartphone that captured the LED images though its embedded front camera and used a series of imaging processing methods for obtaining the LED centroid coordinates. Both offline and online positioning were used for enhancing the flexibility and scalability of the system. It was concluded that, by placing more modules on the edge computing platform, both the CPU usage and the memory consumption of the smartphone decreased and the data traffic consumption of the smartphone increased. The memory consumption and CPU usage were found proportional to the data traffic consumption.
## 2 Methodology
### Meteorological Data Collection  
The data used in this work was collected from hourly weather data pertaining to Seoul (capital of South Korea), collected over nine years (from 2008 to 2016) by the Korea Metrological Administration. The main elements of the weather conditions are temperature, humidity ratio, global horizontal irradiance (GHI), which is the total amount of shortwave radiation received by a surface horizontal to the ground including direct normal irradiance and diffused horizontal irradiance, cloud data (the amount of total cloud cover, the amount of middle and low-layer cloud cover), air pressure, and wind velocity and direction. Plots of each weather parameter for nine years are shown in Figures 3 and 4.  
Figures 3-5 present historical climatic factors for nine years: temperature (\({}^{\circ}\)C), humidity ratio (g/kg), air pressure (mbar), wind velocity (m/s), wind direction (0-360\({}^{\circ}\)), amount of total cloud cover (0-10), amount of middle and low-layer cover (0-10), and global horizontal irradiance (GHI, MJ/m\({}^{2}\)). Over nine years, 78,840 hourly measurements were collected. As shown in Figure 3, temperature, humidity, and air pressure values had shown periodic patterns regularly with time series analysis every year, and these have similar patterns with solar irradiance variations in Figure 5. However, wind speed, direction, middle layer cloud cover rate and total cloud cover rate have shown a non-periodic pattern. In the training process, ANNs combine periodic and non-periodic pattern parameters. With good structure for learning temporal patterns, it makes it useful for various tasks related to time-series analysis. Therefore, this estimated that ANNs could have more advantages compared with conventional linear or non-linear modeling as they can much more efficiently learn long-term time series data [60; 61; 62; 63].
## 3 Results
### Residual Radioactivity Studies  
Secondary particles generated during the irradiation lead to the activation of various components of the target system and the shielding concrete.  
The USRBIN card in FLUKA was used to score the residual dose rate distribution inside the terminal immediately post-EOB and up to 30 days afterward. As illustrated in Figure 14, the ambient dose equivalent rate is the highest near the target, greater than 0.1 Sv/h at 0 s post-EOB. The dose rate level gradually declines through time. The average dose equivalent rate in a region with a size 30 cm \(\times\) 60 cm \(\times\) 170 cm at a distance of 1 m from the center of the target surface was scored. The value drops to \(\sim\)96 \(\mu\)Sv/h at 64 h after EOB, which is lower than the limit.  
The specific activity of residual nuclides in different components of the target system and the inner shielding concrete after the irradiation was tallified with the RESNUCLEi estimators. It was found that the vacuum chamber (SS316L) features the largest inventory and the highest specific activity, even after a 30 day cooling time; hence, it is the main radiation source at EOB. The residual nuclides found to be critical in the vacuum chamber at 30 days post-EOB are \({}^{58}\)Co (T\({}_{1/2}\) = 70.82 d) 434.49 Bq/g, \({}^{51}\)Cr (T\({}_{1/2}\) = 27.702 d) 176.01 Bq/g, \({}^{54}\)Mn (T\({}_{1/2}\) = 312.12 d) 37.47 Bq/g, \({}^{56}\)Co (T\({}_{1/2}\) = 77.27 d) 30.64 Bq/g, and \({}^{55}\)Fe (T\({}_{1/2}\) = 2.73 y) 18.69 Bq/g.
## 4 The Trust Evaluation Process
### Trust Update  
In order to timely and accurately update trust values, a new trust value \(T_{new}\) is calculated based on the historical trust \(T_{old}\) and the calculated trust in current time period \(T_{now}\):  
\[T_{new}=\omega_{old}T_{old}+\omega_{now}T_{now} \tag{17}\]  
where \(\omega_{old}\) and \(\omega_{now}\) are the weight values of \(T_{old}\) and \(T_{now}\), \(\omega_{old}+\omega_{now}=1\). The two weight values are decided by the number of neighbor node's cooperative behaviors. If the number of neighbor node's cooperative behaviors in current time period \(n_{1 new}\) is higher than that in the last time period \(n_{1 old}\), we obtain \(0.5\leq\omega_{old}<1,0\leq\omega_{new}<0.5\). The historical trust is mainly used as reference for trust update in order to prevent malicious nodes from intentionally and rapidly increasing their own trust values by camouflage or lying. Otherwise, \(0\leq\omega_{old}<0.5,0.5\leq\omega_{new}<1\). The current calculated trust is mainly used as reference to punish the behaviors of malicious nodes.  
Figure 5: Trust calculation for two-hop neighbor nodes.
# Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles
## 3 Preliminaries
### Gazebo Simulator  
Unlike typical software, which is limited to the virtual world, the software for robots and UAVs takes action in the physical world. Hence, visualizing the steps taken and the decisions made by the robots is part of the experiment. For this, we employed the Gazebo Simulator, which is an open-source 3D robotics simulator, to see the simulation and action [9].  
In addition to being open-source, there are several advantages to using Gazebo. It has a range of robots that are highly accurate, efficient, and have great visualization. Furthermore, the integration with ROS is simple, and testing as well as training it with AI algorithms in realistic scenarios is possible. A comparison of robotic simulation tools is available in [36].  
_Drone model_: In this work, a drone model, which has been developed by the Technical University of Munich (TUM), is used, which represents most off-the-shelf quadrotors on the market. This package is based on TU Darmstadt ROS PKG and the simulator Ardrone. The simulator can simulate AR.Drone 1.0 and 2.0. This simulator can connect to sticks and other devices, and Figure 2 from [37] shows how a joystic or a mouse can be used to control the drone in the simulator.  
The TUM drone simulator has been forked by Shanghai Jiao Tong University for development to test the SLAM algorithm with different sensors such as inertial measurement unit (IMU), range finder, and laser range. This simulator will work on Ubuntu 16.04 and 18.4 and Gazebo 7.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Round Trip Time (RTT)  
The RTT requires bidirectional communication between the nodes and the timestamp to be included in the messages exchanged, as shown in Figure 2c. It has been used in some Wi-Fi amendments, such as the IEEE 802.11 mc with Wi-Fi fine timing measurement (FTM) [34]. The distance between the two nodes is estimated by:  
\[d=\frac{(t_{4}-t_{1})-(t_{3}-t_{2})}{2}*v, \tag{3}\]  
where \(t_{1}\) and \(t_{4}\) are the timestamps of the signal sent and received at the transmitting node, \(t_{2}\) and \(t_{3}\) are the timestamps of the signal received and sent at the receiving node, and \(v\) is the velocity of the signal.  
Figure 2: Time-based estimations: (**a**) Time of Arrival (ToA), (**b**) Time Difference of Arrival (TDoA) and (**c**) Round Trip Time (RTT).