esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Design of Imaging System for Online Detection of Large-Sized Step-Shaft Runout Errors
## 2 Principle and Simulation Analysis
### Imaging Optical System Design
#### 2.2.1 Parameter Calculation of Imaging Optical System
This is calculated from the displacement \(X\) of the light-energy centroid on the PSD, which is caused by the object plane tilt. \[\Delta X=\frac{R^{2}}{f^{2}}X\left(1+2\frac{X}{f}\cos\theta\right)\left[\tan \theta-\tan(\theta-\alpha)\right] \tag{13}\]  
In this design, in order to better correct the tilt error to improve the measurement ac-curacy of the runout error detection, in the imaging optical system between the lens group and the position detector to add a set of plane mirrors (M1, M2) to change the direction of the image plane, so that the system in the actual surface of the object to be  
Figure 4: [PERSON]’s law diagram. Figure 5: Light plane reception diagram. measured when the small tilt can also be clearly imaged, and still meet the \"Scheimpflug\"conditions, that is, the equivalent optical path diagram in the laser beam of the laser transmitter, imaging optical system of the main optical axis and the position detector main plane of the exten-sion line intersects at a point O, equivalent optical path diagram shown in Figure 6[10; 12; 14]. The planar mirrors shown in Figure 6 only change the direction of the optical path without changing the imaging characteristics. Therefore, after adding a pair of planar mirrors, the system can still perform perfect imaging at the light-energy centroid of the PSD position detector. Considering the overall size of the imaging system, a planar mirror with a radius of 5 mm is used as M1 and M2. According to the requirements of the runout error detection of the large-sized step shaft, the design parameters of the imaging optical system are given in Table 1.  
Since the overall length of the system should not exceed 50 mm, in order to make the structure of the imaging system simpler and smaller, a four-piece lens group with a half-object height of 1 mm and a half-image height of 2 mm was selected as the initial structure of the imaging system based on the optical design manual. The lens parameters of the initial structure are shown in \[k=\frac{dy}{dy^{\prime}}=\frac{f(L-f)\sin\beta\sin\alpha}{\left[f\sin\alpha\mp y ^{\prime}\Big{(}1-\frac{f}{L}\Big{)}\sin(\alpha+\beta)\right]^{2}} \tag{9}\]0 The system had the best imaging effect at \(\alpha=40^{\circ}\) and \(\beta=26^{\circ}\) \begin{table}
\begin{tabular}{c c} \hline \hline
**System Parameters** & **Value** \\ \hline PSD position detector effective area/mm\({}^{2}\) & \(1.3\times 15\) \\ Detection range/mm & \(\pm 1.5\) \\ Operating band/nm & 658 \\ Integral length/mm & \(<\)50 \\ Dip correction/\({}^{\circ}\) & 5 \\ Resolution & \(>\)30\%@42 lp/mm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Design parameters of imaging optical system. Figure 6: Diagram of the “Scheimpflug” conditional equivalent optical path.
# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 4 Proposed Hybrid PSOGA
### Trust-Based Cluster Head Selection
The sum of \(w_{1}\) and \(w_{2}\) should be equal to 1. \(e\) is the energy and \(d\) is the distance. After measuring the fitness function, the node with the highest fitness value is selected as the cluster head. One sensor node in each cluster must act as the CH. The CH depletes its energy faster compared to the other nodes. Hence, the role of the CH must be periodically rotated among different sensor nodes to achieve a longer lifetime of the WSN. The CH node aggregates the results from nodes in the cluster to recognize events in the region of interest. Finally, the CH reports such events to the sink node. Further, the sink node determines the average node energy for all the nodes in a network. Now, the nodes with less energy than a threshold will not be selected as the CH during subsequent rounds. This will enhance the network lifetime by distributing the CH role uniformly among all the networks. See the flowchart in Figure 2 for the cluster head selection process.
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 2 Materials and Methods
### Deep Learning Models
#### 2.3.2 SegNet Model  
[PERSON] et al. [19] introduced Fully Convolutional Networks (FCNs) for semantic segmentation, a deep learning framework designed for pixel-level image annotation. SegNet, an FCN-based model, employs an encoder-decoder structure wherein the encoder extracts hierarchical features, and the decoder reconstructs these features to match the original image resolution for precise pixel-level classification. The encoder is derived from the VGG16 architecture proposed by [45], incorporating its first 13 convolutional layers while omitting fully connected layers. This modification enhances the efficiency of semantic segmentation by reducing computational complexity.  
A distinguishing feature of SegNet is its use of max-pooling indices from the encoder to guide non-linear upsampling in the decoder. This eliminates the need for learning additional upsampling techniques, thereby improving memory efficiency, reducing model size, and lowering the number of parameters from 134 million to 14.7 million while accelerating convergence. By leveraging broader contextual information, SegNet enhances accuracy and optimizes memory utilization and computational efficiency. Figure 2b illustrates the architecture of the SegNet model.  
SegNet demonstrates high efficiency in processing images and generating accurate, coherent predictions across various scene depths and contexts. Through its encoder-decoder structure, it reconstructs compressed feature-space representations to match the original image resolution, enabling precise pixel-level annotations. This approach effectively addresses the limitations of object detection models, which were not originally designed for semantic segmentation, a challenge frequently encountered in traditional deep learning methods. Comparative evaluations with models such as FCN, DeepLab, and other FCN variants highlight SegNet's robustness, efficient inference times, and optimized memory usage, establishing it as a viable solution for semantic segmentation tasks.
## 3 System Architecture and Design
### Overall System  
As described in Figure 1, the PES consists of a Telematics Sensor Network (TSN) and a T-Mon host PC. Wireless Sensor Network (WSN) is still more general terminology, but we call the network architecture TSN due to the unique characteristics of the proposed application.  
Figure 1: (**a**) Overall system description (**b**) System description with network connections.  
A TSN is a network of T-Sensor nodes, a T-Sink node, and a T-BS-com node with a video camera. The PES is deployed in the place where the VDS under evaluation is installed. A pair of T-Sensor nodes is taped to the surface of the road with strong stickers. Whenever each node detects any vehicle event (APPROACH or DEPARTURE), it reports the event to the T-Sink node through the pre-assigned channel. When the T-Sink node receives the event packets, it takes the timestamp for each event packet, appends the timestamp to each event packet, and forwards it to the T-BS-com node. All of the packets including event packets are delivered to the T-Mon Host via the T-BS-com node, and the T-Mon host estimates traffic information such as speed, volume, and occupancy for each lane based on the received event packets. The T-Mon host shows the current communication and battery status and traffic information measured and estimated by PES in real-time and saves the information for comparison with the performance of the evaluated VDS. We present a detailed explanation in the following sub-sections.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 6 Proficiency Evaluation  
Public auditing is a very demanding resource in terms of communication costs, computational resources, and memory space. This paper assesses the suggested system's proficiency, durability, and validity by evaluating the system based on the intended goals. Data integrity checking for limited source devices is a resource-demanding service in terms of computational and communication costs. This study compared the computation and communication costs of the proposed system and the existing audit system proposed by [PERSON] and [PERSON] [7].  
The system's initial abilities were quantified by calculating the uploading duration and integrity checking stages. The discussion expenses were quantified based on the overall extent of the integrity checking appeal in contrast with the bilinear map-founded system [7].
## 3 Dynamic CH Selection and Energy-Efficient Routing Design
### Proposed Methodology
#### 3.4.4 Cluster Maintenance  
For balancing the load amongst clusters, the management of clusters is one of the most critical phases in this study. For inter-cluster traffic, clusters closer to the BS consume too much energy. As a result, the management of the cluster phase is necessary to prevent the failure of the node. As a consequence, the lifetime of data transmission from the source node to the BS increases. The ICOA is re-set to cluster the network if the CH's residual energy exceeds the threshold level. The CHs are then chosen using the clustering process, and for calculating the routing path between the BS and CHs, the IFLFA is used.  
In this proposed methodology the ICOA algorithm is used to achieve an effective CH selection. The CHs are chosen based on five criteria: residual energy, distance from neighbors, node degree, node centrality, and distance from the BS. Among the nodes, these factors are utilized to choose the best CH. To avoid node failure during the transmission of data, BS constantly monitors the nodes' residual energy. From the source to BS through CH, the IJLFA algorithm is used to find the best transmission path. To minimize the nodes' energy consumption, it finds the shortest path. This IJLFA and ICOA-based route generation and optimal CH selection resulted in the development of an energy-efficient WSN. During the transmission of data, an energy efficient WSN is utilized to increase the overall packets transferred to BS, extending the network lifetime.
## 1 Introduction
However, it prevents the resources located in the hybrid distribution grid downstream the ST from providing power-frequency regulation services directly to the upstream AC power system, which constitutes a major drawback regarding current grid code developments for AC power grids [13]. As a result, the ST is required to incorporate advanced control solutions to coordinate all the resources available in the hybrid distribution grid downstream the ST in order to overcome such a limitation. In the literature, it is possible to identify some approaches addressing the possibility of using the ST to control the frequency in the AC grid connected to it, aiming to enable the power-frequency response of the resources existing in the same grid [27; 28; 29]. This operation principle envisioned for the ST has been exploited within the scope of limiting the reverse power flow to the upstream grid in case of high generation in the grid connected to it [29], as well as to mitigate the risk of overloading conditions in an ST [24; 28]. However, they are not designed for the provision of power-frequency regulation services to the upstream AC system. The regulation of active power demand in the LV AC network fed by an ST as a function of frequency deviations in the main power system can also be achieved through the estimation and exploitation of load voltage sensitivity in LV AC networks, as demonstrated in [24; 28; 30; 31]. However, the presented approaches are not designed to be applied to the DC network sections integrating a hybrid AC/DC distribution network fed by a ST.  
From the literature review, it is possible to conclude that the exploitation of the resources available in both AC and DC sections of a hybrid AC/DC network fed by an ST, regarding the provision of power-frequency regulation services to the upstream system, remains comprehensibly unexplored. The main contribution of this paper is to present a set of advanced control functionalities, to be embedded in the ST, capable of enabling the ST to actively coordinate the fleet of resources available in both AC and DC sections of a hybrid AC/DC distribution grid supplied by the ST in order to enable active power-frequency regulation services to the upstream AC grid. The proposed control functionalities are based on a coordinated action of droop controllers and are extensively demonstrated through computational simulations developed in a MATLAB(r)/Simulink(r) environment. The presented control solutions are expected to take an active role in future power systems regarding the expected reduction of the power system capability to perform frequency regulation, as a result of the progressive decommissioning of large synchronous generators that should take place in the face of increasing penetration of RES.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 4 Analysis of the Influence of Vital System Parameters on System Stability
### Influence of the Control Parameter on System Stability  
In this subsection, the influences of the current loop bandwidth and the DSOGI-PLL parameters on the system stability are investigated. Similar to Section 4.1, the extra inductance of 1.5 mH is added to the \(a\)-phase grid impedance to simulate the unbalanced grid condition, and the other parameters are listed in Table 1.  
The Nyquist curves of the inverter with different current loop bandwidths are shown in Figure 14. As the bandwidth of the current loop increases, the Nyquist curves no longer encircle the point \((-1,\mathrm{j0})\), which means that as the bandwidth of the current loop increases, the grid-connected system's stability is continuously strengthened. Therefore, when designing parameters of the current loop, the current loop bandwidth can be appropriately designed to be relatively larger within design principles.  
Figure 14: Nyquist curves of the DSOGI-PLL-based inverter with different current loop bandwidths.  
Figure 13: Nyquist curves of the inverter system with different amounts of extra \(a\)-phase grid impedance.  
For the SRF-PLL, the only factor that affects its characteristics is the PI controller parameter. However, the DSOGI-PLL is composed of two SOGIs and the subsequent PLL, so the parameters that affect its characteristics include the damping coefficient in SOGI and the control bandwidth of the PLL. Therefore, the influences of the damping coefficient and control bandwidth of the PLL (\(B_{\text{PLL}}\)) on system stability should be analyzed separately.  
The Nyquist curves for different damping coefficients are shown in Figure 15a. It can be seen that as the damping coefficient increases, the Nyquist curves are closer to the point \((-1,\text{j0})\), which means that the larger the damping coefficient, the higher the risk of system instability. The Nyquist curves for different \(B_{\text{PLLS}}\) are shown in Figure 15b. It can be observed that as the \(B_{\text{PLL}}\) increases, the Nyquist curves move closer to the point \((-1,\text{j0})\), which means that as the \(B_{\text{PLL}}\) increases, the stability margin of the system reduces.
## 3 Simulation and Results  
### Experimental Setting  
A series of experiments were performed to verify the effectiveness of the proposed algorithm. The noise removal function was objectively evaluated using 512 \(\times\) 512 8-bit gray images in the simulation, as shown in Figure 4. The standard deviation of the AWGN, ranging from 5-30, was prepared to evaluate and analyze the denoising performance based on the noise level of the proposed algorithm. Figure 5 shows the four types of test images used in the simulation and the noise image corrupted by the AWGN with \(\sigma=30\).  
The parameters used in the proposed algorithm were simulated using multiple test images. After the resulting images were analyzed according to the changes in the parameter values, the parameters were selected such that excellent PSNR and SSIM [15; 16; 17; 18] characteristics could be achieved in images other than the test images. Table 1 shows the optimal values of the parameters used in the proposed algorithm.  
The higher the value of the window size parameter, the higher the size of the center and matching windows, thus leading to an enhanced AWGN-removal function. However, exceedingly high values lead to more intense smoothing effects, and the edge component becomes vague. The filter weight threshold is a constant that determines the number of matching windows that has been used to calculate weights through window matching. The larger the filter weight threshold, the more the matching windows are used to calculate weights; however, a result image obtained with more matching windows can be blurred as it may include values lacking relevance. The smoothing parameter and matching area size were set by referring to the values in [6] and [3], respectively. The smoothing parameter is a constant that determines the shape of the steering kernel, and the smaller its value, the higher the weight set in the center area of the kernel. A high smoothing parameter can distribute the weight throughout the kernel. The matching area size is a constant that  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Variable** & **Value** \\ \hline Window size parameter & \(\alpha\) & 0.1 \\ Smoothing parameter & \(h\) & 1.5 \\ Matching area size & \(g\) & 10 \\ Filter weight threshold & \(\hat{d}\) & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter set of proposed modified steering kernel filter.  
Figure 4: Original image of test images (Lena, Baboon, Barbara, and Boat).  
Figure 5: Corresponding noisy images corrupted by AWGN (\(\sigma=30\)).  
The resulting images of conventional noise removal methods and the proposed algorithm were compared to observe the visual effects.
## 2 Design and Methods
### Materials and Fabrication  
Metallic mesh films can be fabricated using various methods, including photolithography [26; 33], printing [34; 35], nano-imprinting [36], and laser direct-writing technology [29]. To obtain meshes with fine lines, a mature process of ultraviolet (UV) photolithography  
Figure 1: Design of the IR transparent EMI shielding window. (**a**) Schematical illustration of the structure design and function of IR transparency and microwave reflection. (**b**) Mesh pattern of irregular polygons.  
was adopted in this study to prepare the samples. The specific fabricating process is illustrated in Figure 2a. First, positive photoresist (AZ 5214E) was spin-coated onto the cleaned sapphire substrate. Then, photoresist film (~2 \(\upmu\)m in thickness) with mesh patterns was obtained after exposure and the development process. Afterwards, metal films (Cr/Au) were routinely deposited by a magnetron sputtering system (PVD75, [PERSON]) in the presence of argon with a base pressure of 3.0 \(\times\) 10\({}^{-6}\) Torr. Cr and Au deposition rates were 0.66 A/s and 1.00 A/s, respectively. The thin Cr mesh layer is provided for better adhesion of the grid layer to the sapphire substrate. The photoresist was then dissolved to reveal the metal meshes in accordance with the designed mesh pattern. Figure 2b shows the micrograph of metallic meshes under a magnification of 50\(\times\). It can be observed that the metallic wires are continuous and have no fractures, which ensures that the mesh coating has good electrical conductivity. According to Figure 2c, the average linewidth of the mesh is measured to be ~2.5 \(\upmu\)m. The photolithography technology adopted in this work has high precision and can obtain narrow linewidths, resulting in minimal loss of transparency. Additionally, the process is stable, highly reproducible, and capable of batch production.
# Energy Sources of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency
## Energy Sources of Mobile Robots
### Energy Storage and Battery Technologies
#### 3.1.1 Types of Rechargeable Batteries  
To date, the lowest cost (per Wh) can be achieved by using lead-acid batteries, but unfortunately, they also provide the lowest energy density per unit mass (35-40 Wh/kg) [50]. On the other hand, the highest unit cost is for LiFePO4 batteries, which offer the highest energy density per unit mass (90-160 Wh/kg) [51]. Several years ago, new rechargeable battery technologies were developed that dominated the market. These were based on nickel; first Ni-Cd, but later replaced with Ni-MH, and both were characterized by a cell voltage of 1.2 V. They have now been replaced, mostly by lithium-based batteries, which provide higher energy density (100-265 Wh/kg) and higher cell voltage--3.6 V. In addition, they do not suffer from the so-called memory effect and are characterized by a slight self-discharge effect (1.5-2% per month) [52]. As a cost trade-off, a problem that occurs in working with Li-ion batteries is the risk of fire. Contemporary lithium-ion batteries for walking robots (1.1 kWh) manufactured by CEO [53] use four fans to maintain their safe operation conditions, by cooling 98 cells that produce 302.4 V in total. A detailed review of battery technology is given in [54]. All batteries, including Li-ion ones, however, have two main limitations--low energy capacity as compared to combustible fuels (petrol for example provides 13 kWh/kg, natural gas provides 15 kWh/kg, and hydrogen provides 34 kWh/kg), and relatively long charging time as compared to refueling time. Therefore, in applications where a long active work time is required, either combustion engine generators are used or a battery swapping mechanism is adopted for fast replacement of batteries. While the battery operation range for all major battery types is broad enough to accommodate use in all environments and seasons, the charging temperature limits in some cases require at least 0 \({}^{\circ}\)C, which must be considered while selecting a charging place or considering an energy regeneration system. A summary of typical rechargeable battery parameters, which depend on the battery's material composition, is presented in Table 2. The comparison shown in Table 2 shows that NiMH, LiPo, and LiFePO4 batteries have the highest specific energy, specific power, cycle durability, charge current, and discharge current, but at the same time these batteries are the most expensive. There is no single battery type that satisfies all criteria at the same time, hence the need to look for a temporary local optimum and to develop technologies towards better solutions than the current ones.