esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results on Metro-Access Network Convergence
Figure 6: Design of the metro path and access path for @\(BER=10^{-2}\) obtained for coherent transmission and offline processing (using test-bed of Figure 2). (**Left**): Results shown as access optical power budget (\(OPB_{Access}\)) vs. metro optical power budget (\(OPB_{Metro}\)). (**Right**): Metro–access network convergence performance in terms of access optical power budget (\(OPB_{Access}\)) vs. OSNR at the metro segment output (\(OSNR_{METRO}\)). These results seem to suggest that the coherent transmission depicted by the blue curve in Figure 7 and implemented with the set-up shown in Figure 2 outperforms the other two pre-commercial transceivers. However, it is important to note that the transmitted power levels differ, as the blue curve is obtained with a transmitted optical power of 11 dBm, while the red and green curves are achieved with 4 dBm, so the performance comparison is performed in different conditions. Therefore, to have a correct comparison between all transceivers, we present the optical sensitivity power at the BER target of \(10^{-2}\) versus metro OSNR, resulting in the graph shown on the right in Figure 7. From this graph, it is evident that transceiver number 2 exhibits the best sensitivity, being 1 dB higher than transceiver number 1. This latter instead (green curve) has a similar sensitivity to the receiver used in the offline processing (blue curve). Because the sensitivity is almost the same, this explains why the blue curve is approximately 7 dB higher than the green in the left of Figure 7. Indeed, it is due to the difference in the transmitted power from 11 dBm to 4 dBm in the two test-beds. This implies that if a higher power budget is required, it is also achievable with pre-commercial transceivers by simply increasing their transmitted optical power.
## 3 Results and Discussion
### Analysis of the Laser Driven Proton Tracks in CR-39 Detectors by Atomic Force Microscopy (AFM)
The correlation between the proton track diameter and energy was performed by using the fitting curve. The analytical function y = 12 \(\times\) exp(\(-\)0.29 \(\times\) E) proposed in Section 3.1 to establish the proton track diameter dependence on proton energy, was used as a calibration  
The evolution of the proton track diameter on the incident proton energy for both CR-39 detectors, within the 1-9 MeV range, is shown in Figure 8 together with the fitting curve previously presented in Figure 4b. As the dependence of the pits identified on the front side of the second CR-39 detector is also an exponential one with respect to energy, result that the measured track diameters can be attributed to protons. Figure 7: AFM images of first (**a**,**c**,**e**,**g**) and second (**b**,**d**,**f**,**h**) post etched CR-39 detectors when high-power laser pulse irradiates: 5 \(\upmu\)m thick Ag target; 5 \(\upmu\)m thick Al target; 10 \(\upmu\)m thick Ag target; respectively, 10 \(\upmu\)m thick Al target.
## 4 System Architecture
### Three-Dimensional Visualization Application  
The 3D Visualization Application serves as the user interface of the SCADA system, providing operators with an immersive and intuitive platform to monitor and control the water supply network of the Lerma Basin. Built using Unity, a powerful cross-platform game engine, the application integrates real-time data from the central server, enabling operators to visualize the entire system in a dynamic 3D environment. It is designed to include not only the 160 operational wells but also the 200 wells currently under construction, offering a comprehensive view of the system's current and future states [16] (see Figure 6). The minimum characteristics of the computing platform required for executing the 3D Visualization Application are a quad-core processor I7 or equivalent, with a 6 GB VRAM video card, 16 GB RAM, and Windows 11 operating system.  
The application features a user-friendly interface designed to minimize the learning curve for operators while maximizing efficiency. Operators can navigate the 3D environment to monitor key parameters such as water flow rates, pressure levels, and pump status in real time, (see Figure 7). The interactivity of the application allows users to select specific components, such as pumps or valves, to view detailed operational data or make adjustments remotely. This capability enhances situational awareness and enables quick decision-making, particularly in emergency scenarios [15].  
Figure 6: Full System Map.  
One of the most innovative aspects of the 3D visualization is its ability to simulate future scenarios. By incorporating data from wells under construction, the application provides a forward-looking perspective of the Lerma Basin's water supply network. This feature is invaluable for planning and contingency management, as it allows operators to anticipate system behavior and optimize resource allocation [18].  
The integration of real-time data with 3D modeling ensures that the visualization is both accurate and up to date. Operators can observe changes in the system as they occur, such as fluctuations in water levels or pressure drops, and respond proactively. The application also includes tools for generating reports and analytics, which can be used to identify trends, evaluate performance, and support long-term planning [8]. By combining advanced visualization techniques with robust data processing capabilities, the 3D interface transforms complex information into actionable insights, making it an indispensable tool for managing the Lerma Basin's water resources, as shown in Figure 8. The QR code shown on the pump control screen indicates the exact geographic location of the well.  
Figure 8: Real-Time Data and Control Interfaces.  
Figure 7: GIS Navigation and Well Information.
## 5 V-HOE Based Solar Concentrators
### Cylindrical Holographic Lenses  
Cylindrical holographic lenses, that allow obtaining a compact and wide-angle structure, were also considered as a solar concentrator. With the aim to take into account a specific set of designed parameter, such as bandwidth, angular selectivity, PV cell size, optical polarization and so on, a proper simulation tool has been developed [10; 13]. The possibility to realize a high-efficient system that only requires one-axis tracking was demonstrated.  
An interesting application of cylindrical holographic lenses is reported in Figure 5a where the conceptual recording set-up is illustrated [56]. Here, the reference and object beams are an edge-lit and a cylindrical converging beam, respectively, allowing to simultaneously record a combination of a lens and a mirror. An array of V-HOEs is then recorded simply by shifting the medium and by repeating the recording process; this method allows having a large angular acceptance. In fact, the diffracted wave of the incident sunlight coming from different incident angles is guided to the edge of the recording medium where a PV cell is positioned (Figure 5b). When a 2 mm thick holographic recording material (phenanthrnequinone doped PMMA photopolymer) was used to record the proposed architecture, the collection angle from increases 0.01\({}^{\circ}\) to 6\({}^{\circ}\).  
Recently, [PERSON] et al. [57] theoretically and experimentally explored a novel approach to overcoming the problem related to the V-HOEs chromatic selectivity by using HOEs operating in the transition regime, which led to a lower chromatic selectivity while maintaining rather high efficiencies. In particular, they developed a model that takes into account the recording material's response to evaluate the index modulation reached for different spatial frequency and exposure dosage. Three cylindrical holographic lenses with different spatial frequency ranges were recorded in Bayfol HX  
Figure 4: **(a)** Holographic solar concentrator structure realized by [PERSON] et al. [9]. In the dashed box the unit cell is highlighted. **(b)** Holographic design to reduce the optical crosstalk.  
photopolymer to experimentally validate the method. Promising results were obtained when a system composed of two cylindrical holographic lenses with lower spatial frequencies and a mono-c Si PV cell is implemented (see Figure 6). Indeed, a total current intensity of 3.72 times respect that would be reached without the concentrator was achieved.
Sensors_**2014**, _14_, 15163-15181; doi:10.3390/s140815163  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
Article  
An Energy Efficient Distance-Aware Routing Algorithm with Multiple Mobile Sinks for Wireless Sensor Networks  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 4,***  
1 College of Information Engineering, Yangzhou University, Yangzhou 225009, China; E-Mails: [EMAIL_ADDRESS] (J.W.); [EMAIL_ADDRESS] (B.L.)  
2 School of Software, Dalian University of Technology, Dalian 116620, China; E-Mail: [EMAIL_ADDRESS]  
3 Department of Energy IT, Gachon University, Seongnam 461-701, Korea; E-Mail: [EMAIL_ADDRESS]  
4 Department of Energy Grid, Sangmyung University, Seoul 110-743, Korea  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-222-875-327.  
Received: 20 June 2014; in revised form: 7 August 2014 / Accepted: 13 August 2014 /  
_Published: 18 August 2014_  
**Abstract:** Traffic patterns in wireless sensor networks (WSNs) usually follow a many-to-one model. Sensor nodes close to static sinks will deplete their limited energy more rapidly than other sensors, since they will have more data to forward during multihop transmission. This will cause network partition, isolated nodes and much shortened network lifetime. Thus, how to balance energy consumption for sensor nodes is an important research issue. In recent years, exploiting sink mobility technology in WSNs has attracted much research attention because it can not only improve energy efficiency, but prolong network lifetime. In this paper, we propose an energy efficient distance-aware routing algorithm with multiple mobile sink for WSNs, where sink nodes will move with a certain speed along the network boundary to collect monitored data. We study the influence of multiple mobile sink nodes on energy consumption and network lifetime, and we mainly focus on the selection of mobile sink node number and the selection of parking positions, as well as their impact on performance metrics above. We can see that both mobile sink node number and the selection of parking position have important influence on network performance. Simulation results show that our proposed routing algorithm has better performance than traditional routing ones in terms of energy consumption.  
keywords: wireless sensor networks; mobile sink; distance aware; energy; lifetime +
Footnote †: journal: Journal of the American Statistical Association
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
###### Abstract  
The rapid growth of the automotive industry has exacerbated the conflict between the complex traffic environment, increasing communication demands, and limited resources. Given the imperative to mitigate traffic and network congestion, analyzing the performance of Internet of Vehicles (IoV) mesh networks is of great practical significance. Most studies focus solely on individual performance metrics and influencing factors, and the adopted simulation tools, such as OPNET, cannot achieve the dynamic link generation of IoV mesh networks. To address these problems, a network performance analysis model based on actual switches is proposed. First, a typical IoV mesh network architecture is constructed and abstracted into a mathematical model that describes how the link and topology changes over time. Then, the task generation model and the task forwarding model based on actual switches are proposed to obtain the real traffic distribution of the network. Finally, a scientific network performance indicator system is constructed. Simulation results demonstrate that, with rising task traffic and decreasing node caching capacity, the packet loss rate increases, and the task arrival rate decreases in the network. The proposed model can effectively evaluate the network performance across various traffic states and provide valuable insights for network construction and enhancement.  
internet of vehicles; performance analysis; vehicle-to-vehicle communications; capacity +
Footnote †: journal: Electronics Electronics
# Dual Threshold Self-Corrected Minimum Sum Algorithm for 5G LDPC Decoders
## 3 Proposed DT-SCMS Algorithm
### DT-SCMS Algorithm  
Based on the SCMS algorithm, the proposed DT-SCMS algorithm adds two thresholds when judging the reliability of the variable node messages, and dynamically adjusts the range of the erasure messages so that it can better adapt to different environments. In addition, the new algorithm uses the NMS algorithm in the check node update process to ensure the performance of the decoding.  
In the variable node processing, if the variable node message changes between two set thresholds in the opposite sign direction, it will be set to zero in the present update. This can be understood by means of Figure 1.  
In Figure 1, \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\) is the variable node message in the previous iteration. \(th1\) and \(th2\) are the set thresholds. If \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\) is a negative value, then \(L^{(l)}\Big{(}q_{ij}\Big{)}\) will be erased when it is between \(th1\) and \(th2\) in Figure 1a. If \(L^{(l-1)}\Big{(}q_{ij}\Big{)}\) is a positive value, then \(L^{(l)}\Big{(}q_{ij}\Big{)}\) will be erased if it falls between \(th1\) and \(th2\) in Figure 1b.
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 2 Literature Review
### Image-Based Detection Networks  
The success of 2D detection networks [9; 11; 23] using RGB images gave rise to the development of image-based 3D detection techniques to localize and classify objects in a 3D frame. The main idea is to generate 3D feature maps from the images to estimate 3D bounding boxes, 3D locations, and orientations of an object in 3D space. Image-based methods can be further classified into monocular image and multi-view image methods. As the name suggests, multi-view image methods infer images from different views to create a depth map [32]. Monocular image methods utilize sliding windows to predict object category, bounding box coordinates, and orientation angle of 2D bounding boxes before estimating 3D losses [33]. Since this method only utilizes a monocular RGB camera, it is economical and cheap compared to multi-views or point cloud-based methods. Two primary techniques, feature lifting, and data lifting methods, extract required 3D features from an image. The camera calibration parameters are utilized by feature lifting methods to transform 2D image features to 3D voxel features [34; 35]. The data lifting method takes an alternate route by transforming 2D image data to 3D point cloud information, popularly known as pseudo-LiDAR pipeline [36]. However, this method needs to be improved due to limited depth perception, light sensitivity, overlap of multi-objects on crowded scenes, and limited detection range [37].
# Design of Low-Power ECG Sampling and Compression Circuit
## 2 Sampling and Compression Circuit Based on Compressed Sensing
### Sampling and Compression Circuit  
Figure 1 illustrates the sensor node structure, mainly comprising a sensor module, data processing module, wireless communication module, and energy supply module. Specifically, the sensor samples, filters, and amplifies the sensing signal, and the ADC converts the analog signal into a digital signal. Once the data are processed, they are transmitted wirelessly. The sampling and compression circuit designed in this paper mainly involves the ADC and data processing module enclosed in the dotted box of Figure 1.  
Given that ECG is a weak signal of low frequency and small amplitude, the sampled data are prone to distortion due to the influence of non-ideal factors in the circuit. Therefore, the performance of ADC directly affects the subsequent data processing and signal reconstruction. In this paper, we customize a SAR ADC with high accuracy and low power consumption, which can achieve a great adaptation with the data processing module.  
The data processing module relies on the CS algorithm to compress the ECG signal. The compression and storage structure is depicted in Figure 2, where \(N\) original signals are added after being selected by the measurement matrix, and \(M\) adders work simultaneously. The compression register group stores the sum after each addition and feeds the stored value into the adder as an addend for the subsequent addition calculation. After each compression round, the storage register group stores and outputs the compressed results, and the stored data are not updated until the next compression round is completed. It should be noted that adders and registers are the main components of the compression and storage structure, so optimizing them can significantly reduce hardware consumption.  
Figure 1: The sensor node structure.
## 4 Results and Discussion
### Results of NACA0012 Models
#### 4.1.1 Blunt Trailing Edge  
Figure 6 shows the error ratios of \(C_{d}\) and \(C_{l}\) between simulation data and experimental data, where the \(X\)-axis is the angle of attack and the \(Y\)-axis is the error ratio. The corresponding mean error ratios under different AOA ranges are shown in Table 8. SST+16 m, SST+12 m, SST+20 m, SST+20 m, SST+20 m, and SA+20 m afforded the optimal results and the related mean error ratios were (0.21%, 0.00%), (1.02%, 1.85%), (3.43%, 1.75%), (5.63%, 1.34%), (6.52%, 1.61%), and (7.72%, 1.65%), respectively. At the range of (0\({}^{\circ}\), 2\({}^{\circ}\)), although the \(C_{d}\) of SST+16 m is the smallest, \(C_{l}\) is relatively large. Considering the \(C_{d}\) and \(C_{l}\) comprehensively, the result of SST+12 m is the best. At the range of (0\({}^{\circ}\), 10\({}^{\circ}\)), the \(C_{l}\) of SA+20 m is not the best, while the \(C_{d}\) is the minimum. Because the \(C_{d}\) is a magnitude greater than the \(C_{l}\), the optimal result is achieved by SA+20 m.
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 2 Materials and Methods
### Lightning
#### 2.4.3 Iss Lis  
The International Space Station Lightning Imaging Sensor (ISS LIS) is the modified flight spare of the original Tropical Rainfall Measuring Mission (TRMM) LIS [33; 34]. This instrument has been in orbit on the ISS since early 2017, and provides routine monitoring of lightning between \(\pm\)55\({}^{\circ}\) latitude. ISS LIS has a detection efficiency of \(\sim\)60%, and a location accuracy better than its 4-km IFOV [33]. All months of version 1 ISS LIS data from May 2017 through December 2019 were analyzed in this study (32 total months) [35].  
Because ISS LIS is a LEO instrument, it does not provide a continuous FOV, and thus instrument viewing time needs to be considered. Lightning matchups were considered differently compared to WWLLN and GLM-16, for which viewing time was not a constraint. Similar to the ASCAT matchup approach, only CYGNSS tracks that intersected the ISS LIS swath during a particular orbit were considered. The ISS LIS swath was defined as the portion of the FOV where viewing time was at least 60 s. ISS LIS viewing time for a given location on the ground is nominally close to 90 s [36], but can be reduced near swath edge, during ISS orbital maneuvers, or when the ISS solar panels pass through the FOV. This viewing time filter thus removes the most obscured portions of the orbit from consideration, although it may potentially cause low-flash-rate storms to appear as having no lightning. Once the properly observed swath was defined, matchups with CYGNSS tracks proceeded similarly to GLM-16, which has a very similar data structure. Only flash centroids, and not groups or events, were considered.  
The advantage of ISS LIS is that it provides global coverage (Figure 1) of total lightning with high detection efficiency. The disadvantage is that it does not provide continuous coverage of a particular region. Thus, the number of samples is greatly reduced compared to WWLLN and even GLM-16, despite a significantly longer dataset. The reason ISS LIS was used was to enable a longer sampling period that included additional seasons (namely, January-July), and to confirm whether trends observed using the regional total lightning provided by GLM-16 also manifested globally.
## 3 Theoretical Formulations
### Solution Procedure
By substitution of Equations (44) and (45) into Equation (41), expanding and equating coefficients of \(\epsilon\), \(\epsilon^{2}\), and \(\epsilon^{3}\) to zero, we get the following relations:  
\[\text{Order }\epsilon\text{: }D_{0}^{2}W_{1}+\omega_{L}^{2}W_{1}=0 \tag{46}\]  
\[\text{Order }\epsilon^{2}\text{: }D_{0}^{2}W_{2}+\omega_{L}^{2}W_{2}=-2D_{0}D_{1 }W_{1} \tag{47}\]  
\[\text{Order }\epsilon^{3}\text{: }D_{0}^{3}W_{3}+\omega_{L}^{2}W_{3}=-2D_{0}D_{ 2}W_{2}-D_{1}^{2}W_{1}-2D_{0}D_{2}W_{1}-\alpha_{2}W_{1}^{3} \tag{48}\]  
The solution of Equation (46) takes the following form:  
\[W_{1}=A(T_{1},T_{2})\exp(i\omega_{L}T_{0})+\overline{A}\exp(-i\omega_{L}T_{0}) \tag{49}\]  
where \(A\) is an unknown complex function and \(\overline{A}\) is its complex conjugate. By substitution of Equation (49) into Equation (46) we obtain the following relation:  
\[D_{0}^{2}W_{2}+\omega_{L}^{2}W_{2}=-2i\omega_{L}D_{1}A\exp(i\omega_{L}T_{0})+c \tag{50}\]  
\(cc\) being the complex conjugate of the previous term. Any particular solution of Equation (50) has a secular term containing the factor \(T_{0}\exp(i\omega_{L}T_{0})\) unless \(D_{1}A=0\). This means that \(A\) is independent of \(T_{1}\), whereby the solution of Equation (50) is verified to be identically null. By substitution of \(W_{2}=0\), together with Equation (49), into Equation (48) we get the following expression  
\[D_{0}^{2}W_{3}+\omega_{L}^{2}W_{3}=-\Big{[}2i\omega_{L}D_{2}A-3\alpha_{2}A^{2 }\overline{A}\Big{]}\exp(i\omega_{L}T_{0})-\alpha_{2}A^{3}\exp(i\omega_{L}T_{0}) \tag{51}\]  
In this last relation the secular terms containing \(\exp(i\omega_{L}T_{0})\) must be equal to zero to have a periodic solution, which corresponds to enforce the following relation:  
\[2i\omega_{L}D_{2}A-3\alpha_{2}A^{2}\overline{A}=0 \tag{52}\]  
whose solution can be found by defining \(A\) as  
\[A=\frac{1}{2}a\exp(i\beta) \tag{53}\]  
where \(a\) and \(\beta\) are real functions of \(T_{2}\). By substituting Equation (53) into Equation (52) and by equating the real and imaginary parts to zero, we obtain  
\[\omega_{L}a\prime=0\text{ and }\omega_{L}a\beta^{\prime}-3/8\alpha_{2}a^{3}=0 \tag{54}\]  
where the prime denotes the derivative with respect to \(T_{2}\). Solving both relations in Equation (54), it follows that \(a\) is a constant and  
\[\beta=3/8\frac{\alpha_{2}}{\omega_{L}}a^{2}T_{2}+\beta_{0} \tag{55}\]  
where \(\beta_{0}\) is a constant. By combination of Equations (53) and (55) with Equation (49), we obtain the following closed-form solution for the nonlinear frequency of the transverse vibration of GPL-reinforced sandwich beams based on third-order shear deformation theory:  
\[\text{Order }\epsilon^{2}\text{: }D_{0}^{2}W_{2}+\omega_{L}^{2}W_{2}=-2D_{0}D_{1 }W_{1} \tag{47}\]0  
where \(a\) is the amplitude of the vibration.