esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Milling CFRP with Staggered PCD Cutter
## 3 Results and Discussion
### Burr Formation on the Workpiece Surface  
Figure 8 shows burr formation on the machined workpiece surface when milling CFRP with the staggered PCD cutter. In the initial stage of tool wear, the machining surface quality was better, and no obvious burrs were found. With the increase in milling length, only minimal shorter burrs appeared on the upper edge of workpiece surface when the cutting-edge radius reached 61 \(\upmu\)m. Afterwards, with the accumulation of cutting edge wear, the section of the cutting edge in contact with the upper edge of the workpiece surface became obviously blunt; thus, even inclination-angle cutting could not effectively cut off the burrs on the workpiece surface edge. Moreover, the length and density of burrs  
Figure 7: Surface SEM image of CFRP in various wear states: (**a**) \(L\): 3 m, \(R_{\text{max}}\): 23 \(\upmu\)m; (**b**) \(L\): 51 m, \(R_{\text{max}}\): 61 \(\upmu\)m; (**c**) \(L\): 81 m, \(R_{\text{max}}\): 76 \(\upmu\)m.  
increased gradually. When the cutting-edge radius reached 76 \(\upmu\)m, a large range of burrs appeared on the upper and lower surfaces of the machined workpiece, and almost covering the entire edges of the upper and lower surfaces.
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 2 Related Work
Although it prevents the source data from being forwarded repeatedly, it cannot exploit the benefits of the early data aggregation within a cluster since the source data are directly sent to the sink instead of the cluster head. The main differences between the MIMO-LEACH and the CMIMO are as follows: First, the MIMO-LEACH invokes the LEACH that does not take into account MIMO operations in the clustering process, whereas the CMIMO has its own clustering protocol that exploits MIMO operations in its selecting criteria. Second, three nodes (one cluster head and two cooperative nodes) are needed to aggregate and forward the data in the MIMO-LEACH, whereas only the two cluster heads are the ones that perform these functions in CMIMO. Finally, the four transmission modes are available in the CMIMO for each link between any two clusters. However, in the MIMO-LEACH, the complete transmission mode (i.e., MIMO) does not exist among the network, since one cluster head in a cluster is responsible for aggregating data and broadcasting it to two other cooperative nodes in the same cluster, and then these cooperative nodes are responsible for forwarding the data to the cluster head of a receiving cluster. The main difference between our scheme (i.e., ICV-MIMO) and the MIMO-LEACH is that the four transmission modes are available in our scheme for each link between any two clusters. Also, the main difference between the ICV-MIMO and the CMIMO is that the ICV-MIMO adds a data fusion cluster head to amortize the cooperation overhead of a master cluster head. Although the ICV-MIMO has one cluster head per cluster more than the CMIMO, it does not involves more clustering overhead than the CMIMO since this cluster head (i.e., a data fusion cluster head) selection is integrated into the confirmation process of cluster membership.
## 4 Results
### Monitor and Analysis Drought from 2001 to 2020  
The determination of drought thresholds has consistently posed challenging research; this study employed the quartile method for drought classification. Upon calculation, an SIF anomaly \(>\) 0.087 indicated no drought, normal precipitation, moist soil, and regular growth of vegetation crops without significant drought conditions. When 0.007 \(<\) SIF anomaly \(<\) 0.087, this suggested mild drought with slightly reduced precipitation and soil moisture levels, exerting a minor impact on vegetation and crops. In the range of \(-\)0.081 \(<\) SIF anomaly \(<\) 0.007, moderate drought is indicated by decreased precipitation and dry soil with insufficient moisture content, leading to notable drought conditions significantly affecting vegetation and crop growth. An SIF anomaly \(<-\)0.087 signified severe drought characterized by a substantial reduction in precipitation and severe deficiency in soil moisture, resulting in wilting and a diminished yield of vegetation crops. Figure 10 shows the spatial distribution of drought in Henan Province from 2001 to 2020.  
To monitor and analysis drought, the pixel values of Henan Province were averaged over the period from 2001 to 2020. In this study, a lower SIF anomaly index value indicates more severe drought conditions, and vice versa. As illustrated in Figure 11, the drought situation in Henan Province had been fluctuating up and down over the past 20 years, with a general trend of weakening over time.  
Figure 10: _Cont._  
Figure 9: The correlation of values of the SIF anomaly index and areas affected by drought (black line is fitting line).  
Figure 10: _Cont._
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 3 Materials and Methods
### Overall Structure of the Network
#### 3.2.1 Overview  
Figure 1 shows the overall structure of the network, which mainly consists of four parts as follows:  
* Semantic Segmentation Module: This module uses the semantic segmentation network proposed in PoseCNN. It segments the target object's bounding box from the RGB image, crops the corresponding mask, and combines the camera parameters to convert the depth map into the corresponding 3D point cloud, laying the foundation for the subsequent extraction of image and point cloud features.
* Multiscale Point Cloud Feature Extraction Module: This module primarily extracts features from the segmented point cloud data. In this module, the point cloud information passes through multiple graph convolution layers, each extracting local information from the point cloud at different scales based on neighborhoods of varying sizes. The local features are then fused into more expressive multiscale point cloud features using a self-attention mechanism.
* Lightweight Image Feature Extraction Module: This module introduces an inverted residual structure to improve the original CNN network. To ensure that the extracted features can be fused with the point cloud features at the pixel level, the features are upsampled into texture feature maps of the original size.  
Feature Fusion and Pose Estimation: The multiscale point cloud features and texture feature maps are concatenated at the pixel level and then encoded to construct global features. These pixel-level features are fused with global features and then input into the pose estimation network, which consists of multiple consecutive convolutional layers that directly regress the translation and rotation matrices.
## 5 Simulation Study
### Steady State Analysis with Fuzzy-PI Controller  
To evaluate the steady state performance of the proposed system, responses for wind speed data shown in Figure 28 are evaluated for the cases of FSWT-IG and the Fuzzy-PI controlled VSWT-PMSG. Figure 29 shows the active power output of both wind generators. It can be seen that the active power of the wind generators can be delivered to the grid effectively. Figure 30 shows the rotor speed response of the PMSG. The blade pitch angle responses are shown in Figure 31.  
Figure 32 shows the reactive power output of both wind generators. It is seen that the reactive power from the grid side converter of VSWT-PMSG provides reactive power compensation for FSWT-IG for voltage regulation. Therefore the voltage at the PCC is controlled almost constant as shown in Figure 33.  
Figure 28: Wind speed data.  
Figure 27: DC link voltage of PMSG. (**a**) case 1; (**b**) Case 2.  
Figure 29: Active power output of wind generators.
# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 2 Materials and Methods
### Datasets Description and Preparation
#### 2.2.2 Additional Test Datasets  
Furthermore, three additional test datasets were used to evaluate different model detection and to assess the potential use of the trained models for weed detection in other contexts.  
The Home Lawn dataset comprises 180 images featuring 473 annotations of weeds proliferating in a mature stand of bermudagrass (_Cymodon dactylon_ (L.) Pers.) during its early green-up phase (indicative of low-quality turf) as well as weeds emerging on hard surfaces such as streets, curbs, and brick floors. These intricate background settings, commonly found in residential and urban areas, could potentially influence the efficacy of weed detection. The images were captured in various locations, including residential lawns and parks, in Seville, Spain (37\({}^{\circ}\)389 N, 5\({}^{\circ}\)985 W; Datum: WGS84).  
The Baseball Field dataset comprised 180 images and 285 annotations of weeds developing in a bermudagrass turf overseeded with ryegrass (_Lolium perenne_ L.) actively growing. In this dataset, image backgrounds are considered uniform (high-quality turf) and most weeds are small in size and partially growing within the turf or with altered shapes due to the intense management. Images of this dataset were collected at the Opelika High School baseball field (Opelika, AL, USA; 32\({}^{\circ}\)645 N, 85\({}^{\circ}\)378 W; Datum: WGS84).  
The Manila grass dataset consisted of 180 digital images and 242 annotations of weeds developing in a mature stand of manila grass (_Zoysia matrella_ ([PERSON]) [PERSON] cv 'Diamond') actively growing. In this dataset, most weeds are large in size and with full growth shape. Images were taken at the experimental farm of the Department of Agriculture, Food and Environment of the University of Pisa (San Piero a Grado, Pisa, Italy; 43\({}^{\circ}\)400 N, 10\({}^{\circ}\)190 E; Datum: WGS84).  
In all datasets, _T. officinale, Plantago lanccolata_ L., and _Sonchus_ spp. were identified as significant weed species and examples of images are depicted in Figure 1.  
Figure 1: Weeds images and annotations randomly retrieved from the ‘Weeds’ public dataset and the three additional test datasets (Home Lawn, Baseball Field, and Manila grass).  
The Robotflow API ([[https://app.roboflow.com](https://app.roboflow.com)]([https://app.roboflow.com](https://app.roboflow.com)); accessed on 2 July 2023) was used to annotate additional test dataset images and convert them into the respective YOLO version format and dataset split for the five-fold cross-validation.
## 6 Conclusions and Future Works  
This paper aims to address the optimal allocation and sizing of parking lots within a distribution system to efficiently supply loads. A particle swarm optimization-based approach is proposed. The model focuses on maximizing total benefits, considering network data and market prices. Results from the study showed that installing parking lots could be economically profitable for the distribution company and could improve the voltage profile. The simulation results indicate that changes in the battery capacity of EVs in parking lots and the power rate at which the EVs are charged (Pv) result in variations in the outcomes. To achieve more accurate results, it is essential to precisely determine the size of the batteries and the power rate at which the EVs are charged. This would involve careful consideration of factors such as the expected usage patterns of the EVs, the charging infrastructure available, and the overall objectives of the optimization process. Fine-tuning these parameters can lead to more precise and effective outcomes in the optimization of parking lot allocations for EV charging. In addition to the benefits already mentioned, this approach offers several other advantages such as improvements in the voltage profile, reductions in the power flow, and enhanced equipment lifespan. Several avenues for future research and development can be pursued based on the findings and limitations of this work. These directions include the following:  
1. A simple system with nine buses was considered in this paper. The proposed method can be used for a complex system like one with 33 buses or 69 buses.
2. The weighting coefficient can lead to variations in the results. In this study, the weighting coefficient was equal and all arrays in matrix A were 1. The same approach can be used to examine the effects of various weighting coefficients.
3. It was assumed that all EV owners respected the contract, and the availability of vehicles was 100 percent. If the availability of vehicles is 80 percent or less, it can have an effect on the result. Reliability improvement has not been investigated in this work, and can be part of future works.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
In the modeling of parking lots, it is assumed that the initial state of charge (SOC) of EVs has three levels. The proposed model can be used for other SOC levels.  
Several operational constraints that can affect the efficiency of the proposed parking system for BEVs/PHEVs, such as ambient temperature, technical issues, or the need for a vehicle to leave the parking lot earlier than planned, can also be integrated in future studies.  
Methodology, M.S.A.; Writing--original draft, M.S.A.; Writing--review & editing, A.A.; Supervision, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies
### Packet Error Rate  
The packet error rate (PER) in transmission represents the percentage of data packets transmitted between vehicles that contain errors during transmission. Evaluating the PER in a V2V communication is fundamental to guaranteeing reliable, safe, and effective communication between vehicles. Therefore, lower PER values denote the greater reliability of a communication system [39].
## 3 Coverage Prediction Tool
### Simulation Settings  
Similar to the measurement setup, both antennas are vertically polarized, and the access point is placed at a height of 2 m, whereas the receiver is at a height of 1 m. The parameters used for testing the implementation are summarized in Table 2. To compare the simulation results to the measurements, we perform simulations with the same access point locations as in the experiments. These simulations yield a coverage map with a received power or maximum throughput for every grid point. For the grid points corresponding to the RX antenna locations of the measurements, we store all propagation paths. The maximum PL threshold is set to 140 dB. The maximum number of reflections is set to 5, but it should be noted that the number of paths consisting of 5 reflections and still having a PL less than 140 dB is limited.
## 3 First Step of Identification Algorithm
When only measurement noise error exists, and only three BSs are LOS BS in all BSs, if we choose two standard deviations as identification threshold value of \(\Delta r_{i}\) only resulting from measurement noise errors, the probability of LOS BS is misjudged as NLOS BS, which equals to the probability of LOS BS combination is misjudged as NLOS BS combination, is  
\[P_{mis}=1-P(\Delta r_{1}<2\sigma\ \&\ \Delta r_{2}<2\sigma\ \&\ \Delta r_{3}<2\sigma)\cong 1 -P(\Delta r_{i}<2\sigma\ )\cong 4.5\% \tag{24}\]  
The probability of LOS BS is misjudged is small enough to be neglected, so we can choose two standard deviations (\(2\sigma\)) as the identification threshold value for the case of only three BSs are LOS BS in all BSs. If there are multiple BS combinations satisfying Inequality (23), adopting the same method in [39], the BS combination, which has the least overlapping area, is identified as the sole LOS BS combination. The overlapping area is the area of triangle whose three vertices are intersection points of the three localization circles. Accordingly, the first step of algorithm procedure can be summarized as follows. 1. All BSs receiving signal from the same MS are divided into multiple BS combinations with every three BSs. 2.1. If there is not any BS combination satisfying inequality (20), the BS combination which satisfy inequality (23) and has the least overlapping area, is identified as LOS BS combination, and the whole identification procedure is completed. If there is not any BS combination satisfying inequality (23), we conclude that the number of LOS BS less than three, and the whole identification procedure is completed. 2.2. If there is only one BS combination satisfying inequality (20), this BS combination is identified as LOS BS combination, and the whole identification procedure is completed. 2.3. If there are more than one BS combination satisfying inequality (20), corresponding BS combinations are roughly identified as LOS BS combination and enter the second step of identification algorithm, the other BS combinations are discarded as NLOS BS combination.
## 2 OCC System Overview
### Data Decoding Principle  
An image sensor is built with pixel arrays and a built-in read-out circuit. Each pixel of an image sensor acts as a photodetector. The transmitted bits are decoded through several image-processing techniques. At the beginning of the image processing, the LED is tracked and detected using a camera that utilizes computer vision algorithms. In general, the image frames are examined when the pixels receive optical pulses. Furthermore, each image is converted to grayscale with each pixel having a certain value called the pixel value. Thereafter, a threshold is set to binarize the images. Finally, the data is extracted as binary bits.  
There are two techniques that can be used to receive optical pulses sent from an LED: global shutter and rolling shutter. All the pixels of the image sensor are exposed to light at the same time for cameras with the global-shutter technique. However, this technique suffers from a high level of pixel noise [15]. In addition, it is not useful for CMOS image sensors. With the rolling-shutter technique, the image pixels are scanned on a sequential basis. This results in a sequential read-out of pixels that leads to dark and bright strips on the image sensor. The data can be decoded by measuring the strip configurations, which will be discussed further in Section 3.2.  
Figure 1: The schematic block diagram of an OCC system.
# Systematic Review and Meta-Analysis of Melatonin Quantification in Wine
## 3 Results and Discussion
### The Methodology of Melatonin Quantification in Wines
It should be noted that the lowest mean level of melatonin concentration in wines was 0.05 ng mL\({}^{-1}\)[29], confirming once again the capabilities of this technique. The highest mean level of melatonin concentration was 56.86 ng mL\({}^{-1}\)[26]. Significant heterogeneity was detected (\(p<0.01\) and I\({}^{2}=100\%\)). In five studies, for the analysis of 25 wine samples, the analytical method used was HPLC-FL. The smallest total number of samples used in a study was one [28]. The MRAW obtained was 1.21, with a 95% confidence interval of [0.64; 1.78] and the heterogeneity was considerable (\(p<0.01\); I\({}^{2}=95\%\)). The significant heterogeneities detected were not due to the analysis techniques but were influenced by grape type, winemaking practice, and country. For example, in the study carried out by [PERSON] [27], the determination of melatonin in wine samples was performed using HPLC-DAD after an DLIME ultrasound procedure and, also, with a comparative method, i.e., HPLC-MS/MS. The \(t\)-test showed that there was no significant difference between the results found using the HPLC-DAD method and the HPLC-MS/MS method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Experimental**} & \multicolumn{1}{c}{**Mean**} \\
**Study** & **Mean** & **SD** & **Total** & **Weight** & **IV** & **Random** & **95\% CI** \\ \hline
2020 & 7.61 & 3.5182 & 35 & 13.8\% & 7.61 [ 6.44; 8.77] \\
2011 & 56.86 & 51.7587 & 5 & 0.1\% & 56.86 [11.49; 102.23] \\
2013 & 0.24 & 0.1455 & 13 & 14.8\% & 0.24 [ 0.16; 0.32] \\
2024 & 2.26 & 3.7026 & 4 & 8.9\% & 2.26 [-1.37; 5.89] \\
2011 & 5.93 & 0.1273 & 2 & 14.8\% & 5.93 [ 5.75; 6.11] \\ MS & 2019 & 0.05 & 0.0089 & 8 & 14.8\% & 0.05 [ 0.05; 0.06] \\
2024 & 5.93 & 0.1273 & 2 & 14.8\% & 5.93 [ 5.75; 6.11] \\
2020 & 17.93 & 20.2771 & 24 & 3.4\% & 17.93 [ 9.82; 26.05] \\
2020 & 3.82 & 0.7522 & _?_ & 14.6\% & 3.82 [ 3.26; 4.38] \\
**Total (95\% CI)** & & **100** & **100.0\%** & **4.30 [ 2.59; 6.00]** \\
**Prediction interval** & & & & & \\  & & & & & \\These meta-analyses results indicate that SPE and DLLME are highly efficient sample pretreatment and sensitive detectors, and like MS or FL, are considered suitable for the accurate detection of melatonin in wines.
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 4 WEC Control Using Estimated Instantaneous Frequency  
### WEC Control Overview  
There are many WEC control approaches [17]. At the most basic level, WEC control can be considered a mechanical impedance matching problem. The WEC hydrodynamics present an intrinsic frequency-dependent combination of mass, damping, and spring behavior. Linear optimal power transfer to a PTO system is achieved when the apparent spring, mass, and damping behavior of the PTO is matched to the equivalent intrinsic mechanical impedance of the WEC. In regular sea conditions, the reactive elements of the intrinsic mechanical impedance--the mass and spring--can be combined into a single reactive element. Therefore, under regular sea conditions, optimal impedance matching can be achieved by a PTO presenting a damping and spring element, such that the damping of the PTO matches the intrinsic damping of the WEC, and the spring effect of the PTO cancels out the combined mass and spring effect of the WEC.  
If the PTO is controlled to present damping (i.e., force proportional to velocity) and a spring term (i.e., force proportional to position), then it can be considered to be a form of proportional-integral (PI) control.  
\[F_{pto}(t)=K_{p}v_{pto}(t)+K_{i}\int v_{pto}(t)\ dt=K_{p}v_{pto}(t)+K_{i}z_{pto} (t) \tag{12}\]  
where \(v_{pto}\) is the PTO velocity, \(z_{pto}\) is the PTO position, the proportional term \(K_{p}\) corresponds to a damping constant, and the integral term \(K_{i}\) corresponds to a spring constant.  
In the frequency domain, the mechanical impedance of this control is expressed as  
\[Z_{pto}(\omega)=K_{p}-jK_{i}/\omega \tag{13}\]  
Consider the WEC intrinsic mechanical impedance expressed as a frequency-dependent complex impedance \(Z_{i}(\omega)\). Optimal impedance matching for some frequency \(\hat{\omega}\) is achieved when  
\[K_{p}=\text{Re}[Z_{i}(\hat{\omega})] \tag{14}\]  
\[K_{i}=\hat{\omega}\ \text{Im}[Z_{i}(\hat{\omega})] \tag{15}\]  
In some cases it is desirable for the PTO to avoid reactive power. In that case, the suboptimal control policy is  
\[K_{p}=\sqrt{\text{Re}[Z_{i}(\hat{\omega})]^{2}+\text{Im}[Z_{i}(\hat{\omega})] ^{2}} \tag{16}\]  
\[K_{i}=0 \tag{17}\]In this paper, the former case, (14) and (15), which utilizes both \(K_{p}\) and \(K_{i}\) for full reactive control, will be referred to at \(PI\) control. The second case, (16) and (17), which utilizes only \(K_{p}\), will be referred to as \(P\) control.