esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In response to the need for precision and intelligence in the assessment of transplanting machine operation quality, this study addresses challenges such as low accuracy and efficiency associated with manual observation and random field sampling for the evaluation of rice seedling planting conditions. Therefore, in order to build a seedling insertion condition detection system, this study proposes an approach based on the combination of image processing and deep learning. The image processing stage is primarily applied to seedling absence detection, utilizing the centroid detection method to obtain precise coordinates of missing seedlings with an accuracy of 93.7%. In the target recognition stage, an improved YOLOv8 Nano network model is introduced, leveraging deep learning algorithms to detect qualified and misplaced seedlings. This model incorporates ASPP (atrous spatial pyramid pooling) to enhance the network's multiscale feature extraction capabilities, integrates SimAM (Simple, Parameter-free Attention Module) to improve the model's ability to extract detailed seedling features, and introduces AFPN (Asymptotic Feature Pyramid Network) to facilitate direct interaction between non-adjacent hierarchical levels, thereby enhancing feature fusion efficiency. Experimental results demonstrate that the enhanced YOLOv8n model achieves precision (P), recall (R), and mean average precision (mAP) of 95.5%, 92.7%, and 95.2%, respectively. Compared to the original YOLOv8n model, the enhanced model shows improvements of 3.6%, 0.9%, and 1.7% in P, R, and mAP, respectively. This research provides data support for the efficiency and quality of transplanting machine operations, contributing to the further development and application of unmanned field management in subsequent rice seedling cultivation.  
YOLOv8n; rice seeding planting conditions; deep learning; image processing +
Footnote †: journal: Applied sciences  
Article  
Detection Method for Rice Seedling Planting Conditions on Image Processing and an Improved YOLOv8n Model
## 4 Fatigue Performance Estimation  
When designing the fatigue life of steel bridge deck asphalt concrete pavement, the stress states can be calculated directly based on the nominal stress for typical pavement structures [31]. In the design analysis, the two most critical steps are to calculate the fatigue strength value under a given number of cycles and the actual stress variation range under the fatigue load. According to these two calculated values, strength checking, stress ratio calculation, and stress state grade evaluation can be carried out further. With the above derivation, the specific steps for fatigue life design based on the nonlinear damage accumulation theory are as follows.  
1. Calculate the fatigue strength value \(\Delta\sigma_{\text{ref}}\) under the specified number of cycles [32]. The fatigue grade of steel bridge deck asphalt pavement structure is confirmed according to the road standard, and the S-N curve data are obtained for the fatigue strength value \(\Delta\sigma_{\text{eq}}\) of the specified number of cycles [33].
2. Calculate the actual stress variation range under fatigue load, namely, the equivalent stress variation range \(\Delta\sigma_{\text{eq}}\)
3. Check the strength based on the fatigue strength value \(\Delta\sigma_{\text{ref}}\) and the equivalent stress range \(\Delta\sigma_{\text{eq}}\)
4. Calculate the stress ratio \(s=\Delta\sigma_{n}/\Delta\sigma_{\text{ref}}\) of asphalt pavement structure from the fatigue strength value \(\Delta\sigma_{\text{ref}}\) and the equivalent stress variation range \(\Delta\sigma_{n}\)
5. Determine the grade of stress states according to the stress ratio.
## 5 Influence of Date Synchronization Errors on Protection
### Error Analysis of Fault Date Self-Synchronization Algorithm  
After a fault occurs, the additional electromotive force at the fault point uses the it as the starting point. It propagates electromagnetic waves to both sides of the line, thus causing a sudden change in the measured current of the relays on both sides. The protection initiating element at both ends detects the fault after a certain time, as shown in Figure 14. Because the feeder is shorter in the distribution network, generally less than 20 km, the propagation time of electromagnetic waves on the feeder can be neglected. It can be considered that the fault time detected by the relays at both ends is consistent. FDSS takes advantage of this characteristic. The first time the relay detects a fault is used as a reference time to calculate the relevant protection information to achieve data synchronization.  
Figure 13: Flowchart of the proposed protection scheme.  
The relays generally adopt the starting algorithm based on the mutation of phase currents in the practical distribution network, as shown in Equation (19).  
\[||i(k)-i(k-N)|-|i(k-N)-i(k-2N)||\geqslant K_{\mathrm{S}}I_{\mathrm{N}} \tag{19}\]  
where \(i(k)\) is the sampling value of the phase current at time \(k\); \(N\) is the number of sampling points per cycle; \(K_{\mathrm{s}}\) is the starting coefficient, whose value is 0.1-0.3; and \(I_{\mathrm{N}}\) is the rated current of the protected feeder.  
From Equation (19), it takes some time for the phase current to increase to the threshold value at fault, and the protection startup delay is unavoidable for the relay. However, the FDSS algorithm ignores the time difference of the startup delay of the two-end protection.
## 2 Control Formulation and Design  
### Droop Control for Active Power Curtailment  
Overvoltage prevention using APC technique is more effective in comparison to reactive power compensation in LV distribution feeders [36]. Droop-based algorithms are well known for coordinating power curtailment in PV inverters to prevent overvoltage without any form of communication between those PV inverters [21]. Figure 1 illustrates how a droop-based controller works. The main objective of the controller in this work is to limit the voltage below the upper limit of Range B voltage denoted as \(V_{cri1}\) (1.058 p.u. of nominal voltage) and to curtail the power after the voltage exceeds the upper limit of Range A voltage, denoted as \(V_{cri1}\) (1.05 p.u. of nominal voltage). The power injected by the smart PV inverter \(P_{inv}\) into the grid with droop-based control is calculated using  
\[P_{inv}=P_{MPPT}-m\times max\{0,V(t)-V_{cri1}\} \tag{1}\]  
where \(P_{MPPT}\) is maximum PV power output for a particular solar irradiance (kW), \(m\) is represented as droop coefficient (kW/V), and \(V(t)\) is measured voltage at point of connection (POC) of the PV with the LV feeder at time \(t\). When the voltage at POC, \(V(t)\) operates in normal limits (Range A), i.e., \(V(t)<V_{cri1}\), at that time the total PV generation is injected into the LV network, i.e., \(P_{inv}=P_{MPPT}\). When \(V(t)\) operates beyond the normal limits, i.e., \(V(t)>V_{cri1}\), then PV power injected to LV feeder is reduced by a droop factor based on (1). During this condition, the power curtailment \(P_{c}\) is calculated using  
\[P_{c}=P_{MPPT}-P_{inv}. \tag{2}\]  
The droop coefficient obtained from (1) is a constant value, and the power curtailment occurs in a linear fashion with the droop coefficient as the slope of the function, as shown in Figure 2. As the voltage at POC exceeds \(V_{cri1}\) (1.050 p.u.), the droop-based APC begins to curtail PV power injection, until the voltage reaches \(V_{cri2}\) (1.058 p.u.), where maximum curtailment occurs. The same logic can be summarized using values in per unit form. When the voltage difference at the POC (\(\Delta V\)) is 0, there is no power curtailment. Similarly, when the voltage difference at the POC (\(\Delta V\)) is 0.008 p.u., the power curtailment is 1 p.u. Similarly, a set of \(\Delta V\) and power curtailment values were created and used to obtain different functions by analytically solving the quadratic and expon  
Figure 1: Droop-based active power curtailment (APC) method for LV distribution networks with PV systems.  
minimum and maximum curtailment at \(V_{cri1}\) and \(V_{cri2}\), respectively. The quadratic droop and exponential droop function are given by (3) and (4), respectively.  
\[P_{c,quad}=1.52\times 10^{4}(\Delta V)^{2} \tag{3}\]  
\[P_{c,exp}=0.00516e^{650(\Delta V)} \tag{4}\]
## 5 Methodology  
This section outlines the architectural and algorithmic details of the proposed Edge-FLGuard framework. The proposed approach integrates lightweight edge-deployable models with a privacy-preserving federated learning (FL) process for distributed, real-time anomaly detection in 5G-enabled IoT networks. The methodology is structured into three key components: (i) edge machine learning models, (ii) the federated learning workflow, and (iii) the anomaly detection pipeline.
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 3 Results and Discussion
### Prediction and Analysis
#### 3.2.3 Experiment III: The Impact of BiLSTM Layers  
As shown in Figure 10, it is evident that the prediction curve is most closely aligned with the actual value curve when the number of stacking layers is three. Analyzing the data in Table 5, it can be found that when stacking three layers of BiLSTM, all error metrics are optimal. Compared to a single layer of BiLSTM, the _MAE_, _MSE_, _RMSE_, and \(R^{2}\) values of the three-layer BiLSTM increased by 17.95%, 32.19%, 17.65%, and 1.35%, respectively. Similarly, compared to the two-layer BiLSTM, the four error metrics increased by 5.97%, 2.42%, 1.21%, and 0.10%, respectively. This indicates that stacking three layers of a BiLSTM network can learn more advanced and richer feature representations, better capture the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline CNN & 7.984 & 102.825 & 10.140 & 0.938 \\ MCNN & 6.550 & 68.181 & 8.257 & 0.959 \\ CNN-LSTM & 4.895 & 41.091 & 6.410 & 0.975 \\ MCNN-LSTM & **4.794** & **32.162** & **5.671** & **0.981** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error metrics for prediction models.  
Figure 9: Comparison of predictive performances of various models.  
temporal relationships between data, and improve prediction accuracy. However, when the number of layers is four, the prediction accuracy decreases. Through analysis, it is found that too many stacked layers will increase the complexity of the model, reduce its interpretability, and thus affect the prediction performance.
## 3 Results
### Sequential Extractions
As extracted in the fifth step of sequential extractions was on average 28.4% of total extracted As and ranged between 8.4% (core S53/1D, depth 1.0-1.5 m) and 60.4% (core S53/1D, depth 3.0-4.0 m) (Table 4). Metal and metalloid species extracted at this step are not easily available, in particular considering extraction conditions (HNO\({}_{3}\) 8 M for 3 h at 85\({}^{\circ}\)C). The fifth fraction was the most abundant in 7 out of 30 soil samples (23.3%) and represented by far the most abundant As share at the level between 4.0 and 5.0 m (Table 4). After soil sample analysis, sequential extraction procedures were performed also for coal samples (Table 5). There was a good match between total As concentration, determined in a unique mineralization, and the sum of the fractions derived from sequential extractions. At the first and second steps extracted As was not detectable, whereas the highest concentrations were obtained at the fifth step (27.0%) and in the final residue (61.3%). In Figure 3 the profiles of arsenic released in the soil of the examined area are compared with the release profiles found in the coal samples provided by the ENEL power company, thus highlighting the similarities in the distribution of the fractions. Figure 2: Average values of the six fractions of As resulting from sequential extraction, in the six levels of the five cores of point S53 and its close surroundings. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **I Step** & **II Step** & **III Step** & **IV Step** & **V Step** & **Resolute** & **Sum of Fractions** & **Total** \\ \hline
**[As] (ppb)** & n. d. & n. d. & 48 & 51 & 231 & 524 & 855 & 825 \\
**[As] (\%)** & n. d. & n. d. & 5.6 & 6.0 & 27.0 & 61.3 & 103.7 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for the sequential extractions of the As for the coal samples of the Federico II power plant. The values are reported in ppb (\(\mu\)g/kg dry weight) and as percentage of the total extracted As.
# K-Means++ Clustering Algorithm in Categorization of Glass Cultural Relics
## Estimate Std. Error t value Pr(>|t|)
### Analysis of Classification Laws Based on Sample Data  
It is necessary to explore the classification pattern of high-potassium and lead-barium glass, thus enabling the further classification of subcategories in terms of chemical composition content. In order to explore the relationship between the proportion of chemical components and the division of main categories (high potassium, lead and barium), we used the supervised learning algorithm KNN to construct a K-nearest neighbor graph between sample points, which proved well that K\({}_{2}\)O content and (PbO + BaO) content were the key indicators for distinguishing the main categories.  
Semantically, \"high-potassium glass\" should be glass with a high potassium oxide content (noted as K\({}_{2}\)O below), while \"lead-barium glass\" is glass with a high content of lead oxide (PbO) and barium oxide (BaO). It can be assumed that if the K\({}_{2}\)O content and the (PbO + BaO) content of the glass cultural relics are taken as two dimensions, a clear demarcation line can be drawn under the plane right-angle coordinate system. We validated this idea based on 67 data samples by the KNN (K-Nearest Neighbor) algorithm of supervised learning.  
The KNN algorithm is a method to classify each record in a dataset, which is a typical supervised learning algorithm. The process of a KNN algorithm classifying one new point is as follows: the distances between this point and all marked points are calculated, from which n_neighbors points with the closest distance are selected. The category with the largest proportion of these n_neighbors points is the classification of the new point [9; 10; 11; 12].  
First, the values of the two independent input variables K\({}_{2}\)O and (PbO + BaO) were calculated for the 67 samples. Among the 67 samples, 70% were randomly selected as the training set and 30% as the test set, taking n_neighbors = 5 to obtain a 100% correct classification rate, as shown in Figure 6 (both training and testing data are plotted in the figure).  
Figure 5: Model diagnostics graphs.  
Figure 6: KNN classification results.  
To further determine the correctness of this classification criterion, the proportion of the training set was reduced to 50% and the other 50% selected as the test set, taking n_neighbors = 5, and we still obtained a 100% correct classification rate.  
From this, we can infer statistically that the classification of high-potassium glass and lead-barium glass is precisely binary clustering according to the K\({}_{2}\)O, (PbO + BaO) content.
## 2 Materials and Methods
### Methods for Qualitative Analysis of Laser-Modified Samples  
The laser-induced morphologies on the surface of the pellets were investigated by confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX) analysis. The chemical composition of treated and untreated surfaces was examined by Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analyses. Comparison between laser-treated and untreated zones of the samples was done in all analyses.  
Confocal laser scanning microscope (Leica DCM 3D) with a standard resolution of \(1024\times 768\) was used for evaluating the surface topography of the ceramic pellets, after fs-laser treatment. Three-dimensional topography images and cross sections of 3D reconstructed images of laser patterned surfaces were obtained.  
For analysis of the morphology of the \(\beta\)-TCP pellets, SEM-TESCAN/LYRA/XMU (TESCAN ORSAY HOLDING, a.s., Brno, Czech Republic) equipped with a dual beam scanning electron/focused ion beam system was used. The system allows operation at high, medium, and low vacuum modes up to 500 Pa. The SEM has an energy-dispersive X-ray spectroscopy (EDX) module (Quantax 200, Bruker). The scanning electron micrographs were acquired at an operational voltage of 10 kV.  
All the samples were carbon sputtered with 10 nm thin film via a carbon sputtering system (Quorum Technologies) and images were taken at non-treated and laser treated zones of each sample. In order to analyse the elemental composition of samples' surfaces, EDX was performed before and after laser irradiation with an operational voltage of 20 kV. The samples were examined at different zones in the processed area.  
IR spectra of the \(\beta\)-TCP discs before and after laser treatment were obtained using FTIR spectrophotometer (IR Affinity-1, Shimadzu, Kyoto, Japan) with resolution of 4 cm\({}^{-1}\), in order to gain information about chemical bond changes and the phase transformations in the samples. The measurements were taken in transmittance mode in the range 4000-500 cm\({}^{-1}\). XRD analysis was performed using a PANalytical-Empyrean system (Malvern Panalytical, Malvern, UK) equipped with a Cu tube and a PIXcel 3D detector. Software High Score Plus (Malvern Panalytical, Malvern, UK) was used to analyse the obtained X-ray diffraction data of the \(\beta\)-TCP discs' surface after laser treatment. The experimental conditions were as follows: 40 kV, 40 mA, exposition 100 s, and step 0.03 degree.
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
## 1 Introduction
In addition, as the axles do not make direct contact with FAD sensors, their reliability and durability are greatly improved. Although the FAD sensors have many advantages in comparison to conventional axle detectors, field tests have demonstrated that the collected FAD signals sometimes fail to identify axles with either missed axles or failed whole-vehicle recognition [28; 29]. A missed axle means that the BWIM system is trying to fit an (\(n\)-1)-axle theoretical model to measurements from an \(n\)-axle vehicle which, understandably, results in significant errors. To address the axle-identification problem, a wavelet-based approach has been applied by some authors to extract information from those FAD signals where axle presence is not immediately obvious [28; 30; 31]. It is shown that the wavelet transform is an efficient signal processing tool that can improve the accuracy of the axle identification process in such cases. However, it appears that selecting a suitable scale of the wavelet transform for accurate results is a challenge and results are sensitive to the scale chosen. In addition, there are many base wavelet functions available which give different results and the selection of the base wavelet function is also a challenge in the process. To date, there are no objective criteria for selecting the scale and the base wavelet function. This paper presents a wavelet-based approach with Shannon entropy used to achieve optimum axle identification from bridge strain signals. Shannon entropy guides the selection of the wavelet scales while a correlation factor is used to guide the selection of the base wavelet function. The feasibility of the proposed approach is first verified through numerical simulation. Then two field tests are carried out to further demonstrate the validity of the proposed method.
## 2 Materials and Methods
### Instrumental Color Measurement  
The surface color of the vacuum-packed venison samples was determined using a Chroma Meter CR-400 (Konica Minolta, Tokyo, Japan) with a 4 mm diameter aperture, an illuminant D65, and a 10\({}^{\circ}\) standard observer. The instrument was routinely calibrated with a white tile before each measurement session. For each sample, 20 replicate measurements were performed across the entire surface of the meat within the vacuum packaging. The measurements recorded the values for lightness (\(L^{*}\)), redness (\(a^{*}\)), and yellowness (\(b^{*}\)). Additionally, chroma (\(C^{*}\)), indicating color vividness, was calculated using the following Formula (1):  
\[C^{*}=\sqrt{\left(a^{*}\right)^{2}+\left(b^{*}\right)^{2}} \tag{1}\]  
The hue angle (\(h\)), which describes the hue of the color [20], was calculated as Formula (2):  
\[h=\arctan\left(\frac{b^{*}}{a^{*}}\right) \tag{2}\]  
The color difference (\(\Delta E\)) was calculated using Formula (3):  
\[E^{*}_{ab}=\sqrt{\left(\Delta L^{*}\right)^{2}+\left(\Delta a^{*}\right)^{2}+ \left(\Delta b^{*}\right)^{2}} \tag{3}\]  
To interpret the \(\Delta E\) values, the observer criteria outlined by [PERSON] and [PERSON] [29] were applied, which categorize color differences as follows:  
* When 0 \(<\Delta E<\) 1, the observer does not notice the difference;
* When 1 \(<\Delta E<\) 2, only an experienced observer may notice the difference;
* When 2 \(<\Delta E<\) 3.5, an inexperienced observer also notices the difference;
* When 3.5 \(<\Delta E<\) 5, a clear difference in color is noticed;
* When \(\Delta E>\) 5, the observer notices two different colors.
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Plug and Play
#### 2.1.1 Wired Communication Technologies  
There are some benefits to using wired communication technologies. They are more immune to interferences and their operational dependency on batteries are less than with wireless technologies. However, they present some drawbacks such as high implementation costs and signal quality degradation in power line communication (PLC) technology [35]. In fact this technology is the only wired technology with an implementation cost comparable to wireless methods, as it uses power cables for transferring data [37; 42]. In addition, this technology has wide coverage, easy to implement and long range [43].  
PLC technology has been divided into two categories. Narrow band (NB) PLCs, which operate in transmission frequencies bellow 500 kHz and broadband (BB) PLCs which use higher frequencies of up to 30 MHz. NB-PLC technology provides lower data rate but a much higher range. In contrast, BB-PLCs characteristics are exactly the opposite with also lower reliability. Regarding these facts, for smart inverters or smart grids the NB-PLC is a more suitable option, whereas BB-PLC can be considered for home applications [37; 44; 45]. In addition to PLC technologies, there are several other supports and protocols available in this category such as optical fiber based communication, Digital Subscriber Lines (DSL), coaxial cable and Ethernet [37; 38]. A brief comparison of characteristics of wired communication technologies is summarized in Table 1.