esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 2 Materials and Methods
### YOLOv7
Firstly, it can save memory when dealing with large-size images. Secondly, it enables the network to adapt to input images of varying sizes, thereby enhancing the model's generalization capability. Finally, adaptive scaling contributes to improved detection accuracy by ensuring that small objects occupy a more significant portion of the image, leading to better object localization. The adaptive anchor box is responsible for automatically selecting the number and size of prior boxes. By adjusting to different object scales and aspect ratios, the adaptive anchor box enhances the accuracy of object detection during testing. Moreover, it offers flexibility in accommodating diverse scenarios and tasks through the utilization of the K-means algorithm. This adaptability allows the model to adjust to different object scales and aspect ratios, improving its overall performance in various detection scenarios. Figure 3: Model structure of YOLOv7. The backbone component of YOLOv7 is responsible for feature extraction and consists of three modules: CBS (Conv-BN-SiLU), ELAN (Extended Latent Attention Network), and MP (Max-Pooling). The CBS module comprises a sequence of layers, including Convolutional (Conv) layers, Batch Normalization (BN) layers, and SiLU (Sigmoid Linear Unit) layers. It employs three different convolutional kernel sizes and step sizes, allowing it to capture features at various scales. The CBS module plays a crucial role in extracting informative features from the input data. The ELAN module is an efficient network structure designed to control the shortest and longest gradient paths within the network. By doing so, it encourages the network to learn more diverse and discriminative features, resulting in improved robustness. The ELAN module enhances the model's capability to extract meaningful representations from the input data. The MP module consists of two branches for down-sampling. It utilizes max-pooling operations to reduce the spatial dimension of the feature maps, effectively capturing essential information at different scales. Together, the CBS, ELAN, and MP modules within the backbone component of YOLOv7 work harmoniously to extract relevant features from the input data, enabling accurate and efficient object detection. The head component is responsible for further processing the extracted features and performing object detection. It consists of several modules: Spatial Pyramid Pooling (SPPCSPC), Feature Pyramid Network (FPN), Path Aggregation Network (PANet), and the detection heads. The SPPCSPC module adapts to images of different resolutions by obtaining different receptive fields with maximum pooling. The FPN and PANet enhanced the ability of network to integrate different feature layers. RepVGG is introduced to the head for training and to achieve recognition and classification of images. YOLOv7 has three detection heads, which are used to detect different sizes.
## 1 Introduction  
The electrical grid is being revolutionarily transformed as Smart grid. Smart Grid is an automated and broadly distributed energy generation, transmission and distribution network. It is characterized by full duplex network with bidirectional flow of electricity and information. It is a close loop system for monitoring and response [1; 2; 3; 4]. Smart Grid is being conceptualized and developed by various organizations around the world such as National Institute of Standards and Technology (NIST), Institute of Electrical and Electronics Engineers (IEEE), European Technology Platform (ETP), International Electrotechnical Commission (IEC), Electric Power Research Institute (EPRI), etc. Diverse set of standards and harmonization between various standards are also being rigorously researched by these organizations [4]. It can be defined in various ways as per its functional, technological or beneficial aspects. As per the definition given by U.S. department of energy, \"A smart grid uses digital technology to improve reliability, security, and efficiency (both economic and energy) of the electric system from large generation, through the delivery systems to electricity consumers and a growing number of distributed-generation and storage resources\" [5]. Smart Grid is an integration of electrical as well as information and communication technologies to make the power grid more reliable, flexible, efficient and robust. It is an intelligent power grid with assimilation of various alternative and renewable energy resources. Automated monitoring, data acquisition, control and emerging communication technologies are the most prominent features of smart grid deployment. Use of diverse set of communication standards requires analysis and optimization depending upon constraints and requirements [6; 7; 8; 9]. These requirements can be decided on the basis of area of coverage, type of application, bandwidth requirement, etc [10]. Smart grid hierarchical communication network can be categorized as Home Area Network (HAN), Neighborhood Area Network (NAN) and Wide Area Network (WAN) as per the applications of communication technologies at various levels of deployment of smart grid [10].
## 2 Methodology
### Signal Preprocess and Determination of State Boundaries
#### 2.3.1 EEMD Denoise Algorithm for System Signal
It can be clearly seen from the figure that the denoised signal recovers good smoothness and periodic structure, and is significantly closer to the original state of the top figure than the noisy signal, which verifies the effectiveness of the proposed method in retaining the principal components of the signal and removing random disturbances. Figure 3: Signal processing procedure. By selecting the IMF components 4, 5 and 6 in Table 2, which have correlation coefficients greater than 0.6, the signal is reconstructed. Comparing the reconstructed signal with the original signal reveals that the two are highly consistent. This indicates that the EEMD method, combined with the correlation coefficient approach, effectively denoises the noisy original signal. The results demonstrate that the EEMD method is capable of successfully reducing noise in the original signal, ensuring a more accurate representation of the system's operational state.
## 4 Separation Methodology
### The Method and Case of Separating Abnormal Data after Identifying Data  
After identifying the normal data and their corresponding hours or days, the work of separating abnormal data begins.  
Abnormal data appear on abnormal days or hours, which need to be separated. The normal power consumption for the nearest days or hours of the abnormal days or hours are selected for interpolation. If the nearest data are also abnormal, the second-nearest data need to be selected for interpolation. By analogy, the normal power consumption data of lighting sockets without air-conditioning can finally be obtained.  
Equation (4) is used to calculate the relative error between the lighting socket power consumption of the separation method and the actual situation, as follows:  
\[\delta=\frac{\Delta}{p_{i,j}}\times 100\%=\frac{\left|e_{i,j}-p_{i,j}\right|}{p_{ i,j}}\times 100\% \tag{4}\]  
where \(\delta\) is the relative error when separating mixed data (%), \(e_{i,j}\) is the lighting socket power consumption of the separation method (kWh), and \(p_{i,j}\) is the lighting socket power consumption of the actual situation (kWh). The data of Building A from April to August 2018 are used for verifying the results of the separation. The error of 355 data points is shown in Figure 14, and some parts of the details are shown in Figure 15.  
It is seen from Figure 15 that the relative error when separating mixed data is mostly within 10%. The numbers of samples for which the relative error is more than 10% accounts for 3.94%. Therefore, this method is feasible to a certain extent.  
Figure 14: Comparison of separating the mixed data of the separation method and actual data.  
Figure 16: Comparison of identifying and separating abnormal data by the proposed method.  
Figure 17: Distribution of the relative error between the separation data and the actual values.  
At present, there is no accurate specification for energy consumption data processing, but there are clear specifications in the related field of energy consumption prediction, as shown in Table 7[35; 36]. From the strictest point of view, the error of this method is within the acceptable range.  
For future research, on the one hand, the results of the study show that the effect is obvious in cold regions and severely cold regions. As this method is purely data-driven, its applicability to other climatic zones is of value for further study. On the other hand, the kind of methodology that is driven by historical data with nearest-neighbor clustering algorithm is also widely cited in other fields about building energy efficiency--for example, the research about the effects of human behavior or meteorology on energy consumption. So, it also has reference value for the research in other fields.
## 1 Introduction  
As electronic devices become increasingly lighter and smaller, the electronic elements in the electronic devices need to be highly integrated. The high level of integration results in various problems caused by the heat they discharge, including reduced performance, malfunctioning, and substrate deterioration. For this reason, various heat sink materials, including carbon, ceramic, and metallic materials, have been studied and used to solve the problem [1; 2; 3; 4; 5; 6]. In particular, carbon-based materials possess excellent properties, such as low density, high thermal conductivity, and high processability [7].  
Carbon-based radiating products can be broadly divided into sheets and components. The components are mainly manufactured by preparing carbon molded bodies. The thermal conductivity of carbon molded bodies is dependent upon the molding process, polymer base filler characteristics, and treatment temperature. Factors important to thermal conductivity, including polymer filler orientation and the porosity of the carbon molded body, can be controlled in the molding process [8; 9]. The carbon molding process is divided into isotropic molding and anisotropic (uniaxial) molding types, depending on the application method. In isotropic molding, a molded body is manufactured by applying equal pressure from all directions. Anisotropic (uniaxial) molding is a method in which pressure is applied in a single direction and it enables one to control the polymer filler orientation of a carbon molded body [10; 11; 12]. The most important parameter in anisotropic molding is the molding pressure. Consequently, it is useful to investigate the properties of a carbon molded body, such as thermal conductivity and porosity, which are dependent upon pressure conditions.  
In the present study, petroleum pitch polymer-based carbon molded bodies were manufactured by applying anisotropic (uniaxial) molding, and the properties of the carbon molded bodies were evaluated to determine the effects of molding pressure. The filler used in the manufacturing of the carbon molded bodies was needle coke, which has a directionality, and a petroleum-based pitch polymer (softening point: 150 \({}^{\circ}\)C) was used as a binder. The petroleum pitch-based carbon molded bodies were molded using a hot-press that allows for anisotropic (uniaxial) molding under various pressure conditions from about 1.25 to 25.0 MPa and then graphitizes the molded body at 2800 \({}^{\circ}\)C. The properties of the graphitized carbon molded bodies, including cross-sectional shape, thermal conductivity, and density, were investigated in relation to the pressure conditions.
# Design of A Parallel Decoding Method for LDPC Code Generated via Primitive Polynomial
## 2 Motivation  
For an m-sequence generated by an LFSR which employs a primitive polynomial as connected polynomial, the segment of the m-sequence can be an LDPC codeword. This LDPC code is called m-sequence code [1]. In this section, we briefly introduce the basis of the code and refer readers to [1] for more details. Then, we discuss the problem of designing parallel decoding method for m-sequence codes.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Diurnal Variation of Rainfall
#### 5.2.1 Diurnal Variation of Precipitation  
Many statistical analyses showed that the precipitation over land had peaks at noon to late afternoon while the precipitation over oceans had peaks at midnight to early morning (Figure 7) [120; 121; 122; 123; 124; 125; 126; 127; 128; 129], and some showed that the peaks over land had larger amplitudes than over oceans [120; 122; 126; 128; 129]. Over land, the peak in the afternoon for convective precipitation was more significant and about two hours earlier than that for stratiform precipitation [106; 121; 128; 131]. Moreover, there was another peak in the evening for stratiform precipitation in addition to the one in the afternoon [106; 125].  
Figure 6: Cumulative frequency of rainfall fraction categorized by (**a**) storm size; (**b**) echo top; (**c**) intense echo top; and (**d**) lightning. Red curves represent continental regimes (Congo (CONG), West Africa (WAFR), Argentina (ARGE), and Southeast US (SEUS)), green curves represent monsoon regimes (East Asian Summer Monsoon (EASM), Indian Summer Monsoon (ISM), Australia Summer Monsoon (ASM), and South American Summer Monsoon (SASM)), and blue curves represent oceanic regimes (Western Pacific warm pool (WPAC), Indian Ocean (INOC), GATE ocean (GATE), and East Pacific (EPAC)). Filled squares or circles represent a set of active and break monsoon periods [112].
## 4 Performance and Simulation Results
### The Modified BA Protocol, MBA
#### 4.3.4 The Impact of Length and Distributions of Tags IDs  
In this subsection, a simulation of 300 tags that existed in the entire zone of Figure 3 moving at any velocity for GQT1 and GQT2 protocols was conducted with varying tag ID lengths from 9 to 11 bits and applying three different types of non-uniform distributions (one type for each length of tag ID). Figure 9a shows the number of success cycles for any length of tag ID. Figure 9b and c show that GQT1 and GQT2 with uniform distributions consumed more of collision and idle cycles to identify the same number of tags when tag IDs were lengthened. This happens because they take longer path to reach success prefixes (one more collision cycle and one more idle cycle for each bit appended), whereas the number of collision and idle cycles for non-uniform distributions is nearly neglected affected by the lengthening of the IDs when compared with the influence of the manner in which the non-uniform distributed tags are spread (the type of the non-uniform distribution). As a result, Figure 9d shows that GQT1 and GQT2 with uniform distributions consume more total cycles when tag IDs are lengthened (two more cycles for each one bit appended). However, for the non-uniform distributions, the length of tag IDs is not noticeably affected especially for few appended bits. When the number of appended bits is one (changing tag IDs bit length from 9 to 10) and another type of non-uniform distribution is used, the total number of cycles of GQT1 and GQT2 increases significantly from 420 and 337 to 463 and 367, respectively. Furthermore, when changing tag IDs bits length from 10 to 11 (with one appended bit too) and third type of non-uniform distribution is used, the number of total cycles for GQT1 and GQT2 increases slightly from 463 and 367 to 471 and 370, respectively. As a result, the type of the non-uniform distribution has much greater influence than tag IDs' length. Thus, MBA outperforms the former protocols, since it does not use IDs to identify tags. Therefore, MBA consumes a constant total number of cycles for specific interference probability, C.E probability, and total number of tags.  
Figure 8: The impact of total number of tags: (a) success cycles, (b) collision cycles, (c) idle cycles, (d) total cycles.  
Figure 9: _Cont._
# Space Debris In-Orbit Detection with Commercial LiDAR Sensors
## 3 Discussion
This approach enables efficient handling of the sensor onboard a small satellite, ensuring minimal power consumption and simplified data management processes. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Sensor ID** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Range (m) & 450 & 200 & 350 & 200 & 70 & 40 & 12 & 8 & 60 & 40 & 40 & 30 \\ \hline Satellites & 5 & 10 & 6 & 10 & 27 & 47 & 155 & 233 & 31 & 47 & 47 & 62 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Configuration of the LCL for each sensor in Figure 1 at an altitude of 840 km and scanning time of 6 months. \begin{table}
\begin{tabular}{|l|c c|} \hline \hline
**Color** & **Number of Satellites** & **Description** \\ \hline Green & \(<\)5 & Low constellation \\ \hline Orange & 5–10 & Moderate constellation \\ \hline Purple & 10–50 & High constellation \\ \hline Red & \(>\)50 & Very high constellation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Color code classification for the LCL system by number of satellites.
# Trajectory Classification and Recognition of Planar Mechanisms Based on ResNet18 Network
## 5 Conclusions  
The experiments on the four-bar mechanism samples indicate that, under similar conditions, the ResNet18 network outperforms AlexNet and VGG16 networks in terms of accuracy for classifying the trajectories of regular and inverted configurations of the planar four-bar mechanism. ResNet18 also demonstrates lower time consumption and memory usage. When applied to the classification of trajectories generated by two classes of planar five-bar mechanisms and three classes of planar six-bar mechanisms, the trained models achieve a minimum classification accuracy of 96.40% on the test set. This suggests that the ResNet18 network is suitable for the classification of trajectories for individual mechanism categories and performs exceptionally well.  
Using the model for trajectory recognition in a single installation method effectively addresses the challenge of determining the category of a trajectory belonging to different mechanism types. The trained model exhibits excellent performance on the test set, with accuracy ranging from 94% to 100%. Overall, the ResNet18 network employed in this study demonstrates robust recognition capabilities for mechanism trajectories, offering a new perspective for trajectory synthesis.  
The method proposed in this paper can classify and recognize trajectory images with high accuracy. Before conducting trajectory synthesis, this method can be used to determine the trajectory image, thereby improving the accuracy of subsequent trajectory synthesis. Therefore, the method has a wide range of applications. It can be used whenever the given trajectory can be expressed in the form of an image, including but not limited to mechanisms with prismatic joints, spatial mechanisms, and others.  
Conceptualization, J.W.; Software, Y.W.; Writing--original draft, B.C.; Visualization, X.J.; Validation, D.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 51275408.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to [In order to adapt to our study, we processed the dataset].  
The authors declare no conflict of interest.
## 2 Introduction to 3-RDE Cutter  
As shown in Figure 2b, the 3-RDE cutter has a similar cylindrical tungsten carbide base (Figure 2a); however, the diamond table is a non-planar structure represented as three convex ridges, three ramps and a central plane, which is different from the conventional plane cutter. Each ridge is formed by the interaction of two ramps (or working surfaces), which connect with the central plane by a curved transition.  
The 3-RDE cutter-rock interaction is quietly different from the conventional cutter owing to its unique structure. There are mainly three aspects: (1) The convex edge of 3-RDE cutter, act as a \"sharp point\" to fracture the rock, which is similar to the crush action of a traditional roller cone bit; (2) the 3-RDE cutter consist of two working faces at the both sides of the convex edge, which can performs the bevel cutting and make the debris automatically formed into two branches that extruded  
Figure 1: Illustration of non-planar cutters: (**a**) Conical cutter; (**b**) Axe cutter; (**c**) 3-RDE cutter.  
across the working surfaces; and (3) the thickness of 3-RDE cutters' PDC layer is greater than the conventional cutter, which indicates the 3-RDE cutter has higher wear and impact resistance.  
Due to the flat structure of conventional cutters' PDC layer (Figure 2a), the actual working angle of each point on the cutting face is consistent with the positioning angle of the cutter, such as the normal angle and back-rake angle. Although the 3-RDE cutter can be oriented by the orientation angle of conventional cutter, the actual working angle is more complicated due to the specific structure. For instance, the actual back-rake angle of two working faces, named after 3 in Figure 2b, is the combined effect of the back-rake angle of the cutter and the inclination angle of the working faces relative to the cutter's axis. However, the cutting geometry of the 3-RDE cutter is not the content of this paper.  
The profile of 3-RDE cutter is more complex than that of the conventional cutter. The main structural parameters are the diameter \(\varphi\), the total height H, the height of Tungsten carbide alloy H\({}_{1}\), the length of convex edge L\({}_{1}\), the slope of the convex edge \(\zeta\), the angle of the and the angle \(\eta\) between the two working faces (Figure 3). The parameter \(\eta\) not only determines the working performance of the cutter, but also relates to the cutter's life and its manufacturing cost. In this paper, the structural parameters are set as \(\varphi=16\) mm, H = 13.6 mm, H\({}_{1}\) = 10.6 mm, L\({}_{1}\) = 3 mm, \(\eta=157^{\circ}\), and \(\zeta=8^{\circ}\). The convex edges are evenly distributed in the circumferential direction.
# Nano-HPLC-HRMS Analysis to Evaluate Leptin Level in Milk Samples: A Pilot Study
###### Abstract  
Leptin is a 16 kDa lipophilic protein hormone secreted by adipocytes and its most significant function is to inform the brain with negative feedback that regulates food intake. Recently the protein found in human breast milk was related to breast feeding and onset of obesity, and the evidence of a low probability to develop pediatric obesity in children fed with breast milk was also confirmed. Since leptin could have a critical role, its quantitation both in human breast, bovine milk and in infant formula products is interesting. For this reason, we developed an analytical method based on immunoaffinity purification followed by an analysis with nano-High Pressure Liquid Chromatography coupled with High Resolution Mass Spectrometry analyzer (nano-HPLC-HRMS) to identify and quantify leptin in milk samples and performed a pilot study using samples of human breast milk, bovine milk and infant formulas. With an obtained lower limit of quantitation (LLOQ) of 100 ng mL\({}^{-1}\) we quantified leptin in human breast milk finding an average of 6.70 ng mL\({}^{-1}\). Our results show that leptin was under LLOQ both in bovine milk and in infant formula products. In conclusion, the developed analytical method here described was suitable to quantify leptin in milk samples with a good sensitivity and selectivity, and without the use of radioactive reagents.  
Keywords:leptin; human breast milk; infant formulas; immunoaffinity; nano-High Pressure Liquid Chromatography; High Resolution Mass Spectrometry +
Footnote †: journal: Applied Physics Letters