esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multidimensional Data Collection and Edge Computing Analysis Method
## 3 Overall System Architecture and Improved Target Detection Model
### Object Detection and Sensing Data Correction  
The improved target detection model proposed in this paper is divided into three main parts: first, due to the consideration of image clarity, U-Net convolutional neural network is introduced to enhance the clarity of the image. Our own dataset with higher definition is then used to train a lightweight YOLOv4-tiny target detection model. An algorithmic model is trained for observing public space. Lastly, with this improved target detection model, temperature and GPS sensors are introduced to successfully detect the age, location, height, coordinates, number of people, distance between image sensors and people, and temperature parameters of people in public spaces through a combination of algorithms and sensors. The extraction of these parameters is used to effectively study the use of public space.
# Dynamic Optimization of Buckling Problems for Panel Structures with Stiffening Characteristics
## 3 Optimization of the Panel with Ribs and Frames
### Method of the MMCs
#### 3.2.3 Finite Element Method Analysis  
Due to the complexity and variability of the model, X-FEM will be used for analysis here [25]. Map the structural topology to the Eulerian mesh, determine the material parameters of each element, and calculate the structural response through finite element analysis of the Eulerian mesh model.  
Firstly, calculate the structural topology description function value \(\mathcal{K}^{s}\) of the element nodes according to Equations (16)-(20), and then calculate the regularized \(H(\mathcal{K}^{s})\) according to Equation (30). When the element is filled with substance, the corresponding node \(H(\mathcal{K}^{s})=1\), and when the element is empty, \(H(\mathcal{K}^{s})=\tau\).  
The Young's modulus and density of the element are constructed by the following equations:  
\[E^{\varepsilon}=\frac{E\left(\sum_{i=1}^{n}\left(H\left(\mathcal{K}^{s}_{i} \right)\right)^{q}\right)}{n} \tag{31}\]  
\[\rho^{\varepsilon}=\frac{\rho\left(\sum_{i=1}^{n}\left(H\left(\mathcal{K}^{s} _{i}\right)\right)^{q}\right)}{n} \tag{32}\]  
Here, \(q\) is a constant, usually taken as \(q=2\). \(n\) is the number of element nodes. For two-dimensional problems, bilinear four-node elements are used; that is, \(n=4\).  
After determining the material properties, the stiffness matrix of the element can be calculated as  
\[\mathbf{K}^{\varepsilon}=\int_{\Omega}\mathbf{B}^{T}\mathbf{D}^{\varepsilon}Bd\Omega \tag{33}\]  
\(\mathbf{B}\) is the strain displacement matrix. \(\mathbf{D}^{\varepsilon}\) is the elasticity matrix. The calculation formula for \(\mathbf{D}\) includes \(E^{\varepsilon}\).  
The mass matrix of the element can be calculated by the following equation:  
\[\mathbf{M}^{\varepsilon}=\int_{\Omega}\rho^{\varepsilon}\mathbf{N}^{T}\mathbf{N}d\Omega \tag{34}\]  
\(\mathbf{N}\) is the shape function of the element.
# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 2 Materials and Methods  
### Data Acquisition, Augmentation, and Annotation  
In this research paper, a dataset of maize seedling images was collected in the northeastern region of China, specifically using the Xianyu 335 maize variety. The images were acquired using a Dajiang drone (4 RTK) and had an original size of 5472 \(\times\) 3648 pixels. The image acquisition period spanned from the emergence stage to the jointing stage of maize growth. During the data collection process, the drone followed a predetermined flight path and captured images from four different flight heights: 1.6 m, 2 m, 3 m, and 5 m, respectively. The camera was positioned to capture top-view images, and approximately 250 images were collected at each height. As a result, the total number of original images in the dataset amounted to 1000.  
To mitigate the risk of overfitting during the training process [33], we increase training samples through data augmentation. General data-augmentation methods include Coutoutout [34], Random Erasing [35], Mixup [36], Mosaic [37], salt and pepper noise, etc. Considering the factors such as environment complexity, plant morphology, and planting density, Coutout and Random Erasing will result in a decrease in the number of positive samples that is not suitable for small objects. Therefore, we use augmentation methods with random brightness, cropping, and salt and pepper noise to reduce the loss of positive sample. The effect of data augmentation is shown in Figure 1. The total number of images after data augmentation is 2000; we divide all images into 8:1:1 rate and obtain 1600 training images, 200 validation images, and 200 test images, respectively. The distributions of the dataset are shown in Table 1.  
During the data annotation process, the open-source tool LabelImg was utilized, as depicted in Figure 2. Each image was labeled using a single-category bounding-box format. In the case of top-view maize seedlings, each seedling was assigned a corresponding bounding box, ensuring that all the pixels of the seedling were fully encompassed within the rectangular area.
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 1 Introduction
### Motivations and Contributions  
From the aforementioned studies, it is evident that relatively few investigations have focused on RIS-empowered UAV communication systems aimed at enhancing the performance of FWNs. Much of the existing research has concentrated on maximizing network throughput, secrecy, and sum rate while reducing energy consumption. However, none of these studies have addressed GU mobility management and HO performance in FWNs through the joint utilization of UAVs and RIS. Deploying low-cost RIS on UAVs provides multiple transmission paths for GUs, improving communication quality and overcoming obstacles and limitations in terrestrial wireless networks. Motivated by the research works in [18; 21; 22], which focus on UAV-only, RIS-only, and UAV-RIS deployments, respectively, this paper proposes an RIS-assisted UAV system that employs DDPG and DQN algorithms to manage GU mobility and improve HO/mobility performance in dense urban environments. Furthermore, the proposed framework enhances communication by adjusting RIS phase shifts and utilizing UAV mobility to optimize its trajectory based on the GU's location and requirements, ensuring stable link quality and improved connectivity. The main contributions of this paper are as follows:  
* A novel UAV-RIS framework is proposed to enhance signal strength and improve LoS connectivity between UAVs and GUs in dense urban environments by addressing communication disruptions caused by ground obstacles.
* A modified K-means clustering algorithm is introduced for efficient user partitioning, alongside a DDPG algorithm to intelligently optimize UAV trajectories and RIS configurations simultaneously in a continuous action space for managing GUs mobility.
* The proposed framework, utilizing the DDPG algorithm, significantly improves key network performance metrics, including HOF, OP, EE, throughput, and LoS probability, compared to state-of-the-art schemes.  
The rest of this paper is organized as follows: Section 2 presents the system model for the RIS-assisted UAV communication system. Section 3 describes the problem formulation and discusses the proposed iterative solution. Performance results are then presented and analyzed in Section 4. Finally, the conclusions and future work are outlined in Section 5.
## 4 Conclusions  
In summary, we employed a novel method to quantitatively study the exciton characteristics of WS\({}_{2}\)/MoS\({}_{2}\) vdW heterostructures through tip contact PL spectra. Upon Si probe contact, the emissions of the A-excitons and trions can be observed in the PL spectra at the heterostructure region, specifically highlighting the contribution of MoS\({}_{2}\) A-excitons. The Si probe not only acts as an electrode to remove electrons but also exerts out-of-plane pressure on the micro-region of the heterostructure to quantitatively control the electronic structure and interlayer coupling. As pressure on the heterostructure increases, the PL intensity corresponding to WS\({}_{2}\) trions significantly decreases while the PL energy exhibits an obvious blue-shift, revealing that the trions are more sensitive to pressure than A-excitons. This work provides a valuable approach for understanding the relationship between interlayer interactions and electronic/optical properties in 2D TMDC vdW heterostructures. In future studies, more work should be conducted to further combine the local pressure induced by an AFM probe with novel physics in the TMDC heterostructures, such as interlayer excitons, moire superlattices, and exciton condensations.  
Conceptualization, Y.F. and J.M.; methodology, W.K. and Z.R.; software, Z.R. and P.L.; validation, W.K. and J.C.; formal analysis, W.K. and Y.L.; investigation, W.K. and P.C.; data curation, J.W. and W.L.; writing--original draft preparation, W.K. and Y.C.; writing--review and editing, Y.F. and J.M.; visualization, W.K.; supervision, Y.F. and J.M.; project administration, Y.F. and J.M.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2017 YFA0304203); the National Natural Science Foundation of China (62020106014, 62175140, 61901249, 92165106, and 12104276); the PCSIRT (IRT-17R70); the 111 project (D18001); the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi (OII); the Applied Basic Research Project of Shanxi Province, China (201901D211191 and 201901D211188); and the collaborative grant by the Russian Foundation for Basic Research and NSF of China (62011530047 and 20-53-53025 in the RFBR classification).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Joint Deployment of Sensors and Chargers in Wireless
## 3 Model and Problem Statement
### Problem Formulation  
In summary, our goal is to minimize the deployment costs of sensors and chargers with the constraints of continuous monitoring of all PoIs. The deployment cost includes sensor deployment cost and charger deployment cost. Therefore, we divide the problem into two parts: P1 and P2. P1 represents the deployment cost of the sensor:  
\[\begin{split} P1:\;min\;M\\ s.t.\;0\leq\big{|}|s_{i}|\big{|}_{1}\leq\tau_{max},s_{it}\in S_{T}\\ w.r.t\;s_{1},s_{2},\ldots,s_{M}\end{split} \tag{6}\]  
where \(M\) is the number of sensors deployed.  
P2 represents the deployment cost of the charger:  
\[\begin{split} P2:\;min\;K\\ s.t.\;P_{h}^{s_{j},C}\geq P_{min}^{s_{j}}\forall s_{j}\in S\\ w.r.t\;c_{1},c_{2},\ldots,c_{K}\end{split} \tag{7}\]  
where \(K\) is the number of chargers deployed.  
To facilitate the calculation, we introduce an evaluation function to reconstruct P1 and P2:  
\[Q=\frac{1}{M}{\sum_{n=1}^{N}min\big{[}\frac{P(s_{j})}{c_{th}},1\big{]}} \tag{8}\]  
\[W=\frac{1}{K}{\sum_{m=1}^{M}min\big{[}\frac{P_{h}^{s_{j},C}}{P_{min}},1\big{]}} \tag{9}\]  
Then, the formulation of P1 and P2 can be rewritten as:  
\[\begin{split}\text{P1}^{\prime}\;\;\;Min\;Q\\ s.t.\;\;\;Q=1\\ w.r.t\;s_{1},s_{2},\ldots,s_{M}\end{split} \tag{10}\]  
\[\begin{split}\text{P2}^{\prime}\;\;\;Min\;W\\ s.t.\;\;\;W=1\\ w.r.t\;c_{1},c_{2},\ldots,c_{K}\end{split} \tag{11}\]
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 1 Introduction  
A critical design challenge in all spark ignition (SI) engines, whether it is for a Model T or a modern hybrid vehicle, is preventing engine knock. Engine knock, an occurrence stemming from the rapid autoignition of the end-gas preceding a flame front, leads to an irritating noise, and severe knocking can result in engine damage. This autoignition process is governed by the chemical reactions within the cylinder, typically happening under elevated pressures and temperatures. Since the peak temperatures and pressures in the cylinder establish the thermal efficiency of an engine, avoiding knock is the limiting factor for the maximum engine efficiency. Modern engines employ knock sensors to detect when knock is occurring and then adjust the spark timing of the engine to lower the pressures and temperatures, thus avoiding knock, albeit at a lower engine performance.  
This paper aims to comprehensively examine the different signals that are typically measured from an engine to forecast the onset of knock. Both conventional and novel methods for measuring knock are discussed in this paper, including the in-cylinder pressure, engine block vibrations, and acoustic noise. While the in-cylinder pressure is the most precise detector of knock, it necessitates intrusive access to the combustion chamber. Meanwhile, vibration and acoustic measurements might not adequately capture knock incontemporary engines. The paper also reviews a number of new methods for detecting and predicting knock. The present paper critically evaluates advancements in measuring these signals, discussing their strengths and limitations.
## 2 Evaluation Study and Design
### Fluid-Dynamic Methods and Simulations
#### 2.3.2 Eulerian Method  
In the Eulerian approach, the diffusion and distribution of inert gas are simulated by tracking individual particles. The software calculates the steady-state mass balance of the inert gas within the discretized volumes of space. In this method, the observation point for the balances is a control volume through which traverse flows. This approach allows for the simulation and calculation of long-term dynamics under steady-state conditions, i.e., at the end of the transient phase. However, it does not permit the resolution of individual particle trajectories. The behavior of a reference gas molecule is assumed to be unaffected by gravity or aerodynamic forces, diffusing instead by Brownian motion and being transported by advection through the air.  
To model the bio-aerosol emission from an infected individual, carbon dioxide (CO\({}_{2}\)) gas was utilized, constituting 4% of the total exhaled volume [29; 38; 39]. A stationary approach was employed to simulate a long-term scenario, maintaining a constant emission velocity of air of 3 m/s from the individual's mouth.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Diameter & \(<\)1 & 1–3.8 & 3.8–6.6 & 6.6–14.7 & 14.7–19.2 & 90–120 & 120–800 \\ \hline Emission & & & & & & & \\ rate & 217.6 & 20.3 & 3.1 & 5.7 & 1.8 & 1.6 & 4.6 \\ (particle/s) & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 4: Dimensional spectrum of bio-aerosol emission from the infected individual.
# Design and Research of Photonic Reservoir Computing for Optical Channel Equalization
## 4 Conclusions  
In this paper, we designed four PhRC structures for channel equalization in IMDD systems. These structures adopt an outermost-node input-output scheme, reducing the use of crossing devices and minimizing power loss and noise. The optimization of parameters for the DC splitting ratio and the length of the waveguide delay line is conducted based on four reservoir layouts with varying numbers of nodes. In a 25 Gb/s OOK transmission link over 20 km, the 36-node layout reduces the BER of the equalized signal by three orders of magnitude, the optimal length coefficient being 0.1 for all layouts. Studies on the number of input-output ports reveal that the input port count of a reservoir layer has minimal impact on equalization results, while equalization performance improved with increasing output count. We demonstrated that these four layouts possess equalization capabilities for both dispersion noise and ASE noise. Furthermore, we have verified the existence of a directly proportional relationship between the number of nodes and the equalization performance in terms of ASE noise. At a transmission distance of 30 km and a signal speed of 50 Gb/s, all layouts demonstrated equalization capability. The proposed structures can be readily integrated with an optical readout layer for all-optical equalization, offering broader prospects for high-speed, high-capacity communication systems.  
Conceptualization, X.Z., Q.L. and R.Y.; methodology, X.Z., L.P. and B.B. ([PERSON]); software, X.Z. and B.B. ([PERSON]); validation, X.Z. and J.W.; formal analysis, X.Z.; investigation, X.Z.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, L.P.; visualization, X.Z.; supervision, L.P.; project administration, J.W.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 62235003 and 62401038; This research was funded by the Talent Fund of Beijing Jiaotong University, grant number 2024 XKRC079.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company Photocounts Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract
Compared to these existing works about the BDS PPP-b2b-based PPP, our contributions in this paper can be summarized as (1) introducing the BDS PPP-B2b service-based PPP into a smartphone, and (2) a hybrid weight function based on the C/N\({}_{0}\)-dependent model and the satellite elevation-dependent model being adopted by such a PPP-B2b service-based PPP model. To validate the performance of this method, two sets of tests were conducted to collect the smartphone's GNSS measurements and PPP-B2b orbit/clock corrections. This paper is arranged as follows. After the Introduction Section, the methods of smartphone GNSS observation generation, PPP-B2b-based orbit and clock correction recovery, the mixed-frequency PPP model, and the hybrid weight function are presented in the Methods Section. Then, the GNSS data quality of a smartphone is evaluated in terms of the C/N\({}_{0}\), multipath error, satellite availability, and Position Dilution of Precision (PDOP), respectively. After that, the performance of the presented smartphone PPP is presented based on a static test and a dynamic experimental test. Finally, the Discussion Section and the Conclusion Section are presented.
## 4 Simulation
### Environment Setup  
This paper considers a scenario with a flat surface. There is a mobile anchor and multiple unknown nodes. The mobile anchor is equipped with a precise GPS component to accurately obtain its location information when traveling within the deployment area. The unknown nodes are randomly deployed in the deployment area, waiting for positioning. They are stationary after being deployed. The mobile anchor knows the number of unknown nodes at the beginning. Each unknown node has a unique identity and has the same communication range as the mobile anchor.  
The deployment area is considered to be a 600 m \(\times\) 600 m square, with no obstacles or sources of radio interference. The numbers of unknown nodes evaluated are [20, 25, 30, 35, 40, 45, 50, 55, 60, 65]. The evaluated communication ranges are R = [50 m, 75 m, 100 m, 125 m, 150 m]. The movement step of the mobile anchor is set to 1 m. In the non-ideal signal case, the evaluated signal-to-distance errors in RSSI are \(\varepsilon=\) [0-3%, 0-4%, 0-5%]. This will influence the distance evaluation when the ISP algorithm is used to locate the unknown nodes. All simulation results are the average of 100 random scenarios.
# Fracture Assessment of Weld Joints of High-Strength Steel in Pre-Strained Condition
## 3 Pre-Strain Effect on Fracture Toughness  
### Critical CTOD of 3.0% Pre-strained HAZ Specim  
In order to evaluate the fracture resistance of the 3.0% pre-stained ETCP, toughness deterioration due to the pre-strain should first be estimated. Generally, fracture toughness is required by the CTOD value in the line pipe and the offshore structural steel plate [23]. Therefore, in this study, fracture toughness was evaluated using CTOD. The CTOD fracture toughness of the pre-strained HAZ (fusion line + 1 mm) was measured by a three-point bend (3 PB) test. The 3 PB tests were conducted to measure the CTOD fracture toughness of 3.0% pre-strained specimens, with full thickness. The thickness of the 3 PB specimen, \(B\), was 22 mm, because the specimens should be manufactured by surface finishing in welded joints, which have angular distortion. The 3 PB specimen had a deep through-thickness crack; \(a/W=0.5\), where \(a\) and \(W\) are the crack depth and the specimen width, respectively. The 3 PB test temperatures were \(-60\)\({}^{\circ}\)C, \(-40\)\({}^{\circ}\)C, \(-20\)\({}^{\circ}\)C, and \(0\)\({}^{\circ}\)C. The specimen was cooled with liquid nitrogen in a cooling bath. The yield strength of HSB600 steel at the test temperature was used for the calculation of the elastic component of CTOD. The critical CTOD at the fracture was calculated in accordance with ISO12135 [24] and ISO15653 [25]. The fracture toughness results of the pre-strained HAZ are shown in Figure 3. The specific temperature at which the critical CTOD was 0.1 mm, tended to increase with increasing pre-strain. The 3.0% pre-strain shifted the specific transition temperature by approximately 40 \({}^{\circ}\)C. These results indicate that the deterioration of the fracture toughness due to pre-strain should be taken into account, during the safety assessment against fracture from the weld HAZ, in the pre-strained condition. Table 5 summarizes the critical CTODs of the 3.0% pre-strained HAZ used in the assessment [19].  
Figure 3: Effect of pre-strain on ductile-to-brittle transition temperature [19].  
Figure 2: Double-edged through-thickness crack panel with a crack in the heat-affected zone (HAZ).  
The critical CTOD of the ETCP would be larger than that of the 3 PB standard fracture toughness specimen that was subjected to tension, owing to the plastic constraint loss around the crack tip in the ETCP. ISO 27306 [15] provides an engineering method to correct the CTOD toughness for the constraint loss in structural components. The toughness correction ratio, defined as the equivalent CTOD ratio \(\beta\), was standardized in ISO 27306. Figure 4 shows a monograph of \(\beta_{0}\) for an ETCP, including a reference size of the crack. \(\beta_{0}\) is a function of \(YR\) and \(m\), where \(YR\) and \(m\) are the yield-to-tensile ratio and the Weibull shape parameter, respectively.