esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Ablation Experiment  
We used the average accuracy, the detection speed of a single picture in Jetson nano, and the recall as the evaluation indexes. Formulas (7)-(9) is the calculation method of the average accuracy. Among them, average accuracy is a popular evaluation method of the target detection model, which is used to evaluate whether the model detects objects in the image accurately.  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{7}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{8}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{9}\]  
where \(P\) is the correct rate (%), \(R\) is the recall rate (%), \(TP\) is the real-positive sample, \(FP\) is the false-positive sample, and \(FN\) is the false-negative sample.  
To understand the model performance and prove the effectiveness of the optimized model in this paper, we compared each improved part and conducted the ablation experiment of the model on all citrus test set, as shown in Table 1.  
Figure 8: Model pruning process, to ensure accuracy, the model used in this paper is optimized twice.  
It can be seen from the table that after adding the CBAM module, the detection accuracy of the model is improved by 2.39%, but the detection time is increased by 40 ms. After adding ASFF, the detection accuracy is improved by 0.44%. To recover and speed up the reasoning speed of the model, the detection speed of citrus by the pruned model is 180 ms/frame.  
In this paper, to ensure the accuracy of the model, we use twice pruning to optimize the model, compared the results of pruning processes, as shown in the Table 2. After the first pruning, the memory occupied by the model is reduced by 6 M when a small amount of accuracy is lost. After the second pruning, the memory volume of the model is only 21 M, while ensuring accuracy, we reduced the amount of calculation and parameters of the model, which provides convenience for deployment to edge computing devices.
###### Abstract  
A prerequisite for refined load management, crucial for intelligent energy management, is the precise classification of electric loads. However, the high dimensionality of electric load samples and poor identification accuracy of industrial scenarios make it difficult to be used in actual production. As such, this research presents a selection approach equilibrium optimizer-based joint time-frequency entropy feature selection method for electric loads in industrial scenarios to address these issues. The method first introduces entropy value features based on extracting time-frequency domain features and then uses an equilibrium optimizer (EO) to screen the joint feature set. A Chinese cement plant was chosen as the acquisition site for the experiments, and the low-frequency data from power equipment were gathered to form an original dataset for power analysis. The features screened by the EO were used as model inputs to verify the effectiveness of the EO on the joint feature set under K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and discriminant analysis (DA) models. Experimental results show that introducing entropy value features for the joint feature set can significantly improve the classification performance. The average accuracy of the features screened by the EO was as high as 95.58% on SVM, while the computation time was 0.75 s. Therefore, for industrial electricity scenarios, the approach suggested in this research can enhance the identification accuracy of electric loads and significantly reduce the computation time of the model to a great extent. This has essential research significance for intelligent energy management in real industrial scenarios.  
feature selection; equilibrium optimizer; entropy; industrial scenario; low-frequency data +
Footnote †: journal: Applied Sciences
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 5 Experimental Results  
### Prototype  
We implemented our prototype using discrete components and off-the-shelf chips to prove the feasibility of the related IC design, as shown in Figure 10. The circuit parameters in the prototype are the same as the parameters optimized in Section 4. The out-body digital circuits (including the downlink modulator, uplink demodulator and the out-body controller) were implemented in an MCU (STM32F207, STMicroelectronics, Geneva, Switzerland). The in-body digital circuits (including the uplink modulator and the downlink demodulator) were implemented in an FPGA (Cyclone IV, Altera, San Jose, CA, USA).  
The overall power consumption of the prototype (including out-body and in-body circuits) is 63.8 mW when the coil separation is 20 mm, and when the system is on standby, the overall power is about 1 mW. The average period of a complete transaction including IOP sensing and communication is about 0.05 s. If the monitoring frequency is once every 5 s and the out-body battery is a small lithium-ion cell with the capacity of 0.5 Wh, the estimated battery life would be about 12.8 days, which is acceptable to the patient to recharge the glasses every 12 days.  
Figure 9: Downlink waves recorded from PSpice simulation, when the BPSK is modulated on the power link (**a**) without using the shaping filter and (**b**) with using the shaping filter.  
The uplink PA circuit of the in-body transmitter and the uplink filter circuit of the out-body receiver are shown in Figure 11. They have been presented in our conference paper [18]. The uplink PA is merged in an H-bridge modulator to achieve low power and high efficiency as seen in Figure 11a. The uplink filter consists of a low noise amplifier (LNA), two Butterworth active filters, two high-pass passive filters and a driver of the analog to digital converter (ADC). The two Butterworth filters are set to be the two-order low-pass filter to block the high frequency power carrier interference. The high-pass passive filters are used to cancel the DC offset of amplifiers and to reject pink noise. The LNA of the uplink filter circuit uses an operational amplifier (OPA656, Texas Instruments, Dallas, TX, USA) with low noise, high input impedance and wide bandwidth.  
Since the prototype was implemented by the discrete components, it could consume much more power than that to be implemented in the integrated circuit (IC). Because the low power IC is not yet implemented, the delivered power from the WPT was not sufficient to supply the in-body circuits of the prototype. However, in order to ensure the power supply condition to be well consistent with that of the practical situation, the in-body uplink PA and the in-body downlink comparator were powered by the WPT.  
Figure 11: (**a**) The circuit diagram of the in-body uplink PA; and (**b**) the circuit diagram of the out-body uplink filter [18].  
Figure 10: The prototype system was implemented to setup the measurements of power and bi-directional data transmissions with beef tissue.
## 3 Results
### The Sines Massif
#### 3.1.3 Petrography, Mineral Chemistry, and Geochemistry
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Cumulate Gabbro**} & \multicolumn{2}{c}{**Gabbro-Diorite**} \\ \cline{2-5}  & **wt \%** & **Stat Error** & **wt \%** & **Stat Error** \\ \hline
**SiO\({}_{2}\)** & 42.30 & \(\pm\)0.0344 & 49.00 & \(\pm\)0.0356 \\
**TiO\({}_{2}\)** & 3.34 & \(\pm\)0.0175 & 3.26 & \(\pm\)0.0176 \\
**Al\({}_{2}\)O\({}_{3}\)** & 9.40 & \(\pm\)0.0295 & 16.20 & \(\pm\)0.0368 \\
**Fe\({}_{2}\)O\({}_{3}\)** & 15.50 & \(\pm\)0.0133 & 11.20 & \(\pm\)0.0115 \\
**P\({}_{2}\)O\({}_{5}\)** & 0.28 & \(\pm\)0.00427 & 0.85 & \(\pm\)0.00506 \\
**MnO** & 0.40 & \(\pm\)0.005 & 0.32 & \(\pm\)0.005 \\
**MgO** & 12.90 & \(\pm\)0.0506 & 4.48 & \(\pm\)0.0349 \\
**CaO** & 12.70 & \(\pm\)0.0428 & 8.33 & \(\pm\)0.0375 \\
**BaO** & 0.23 & \(\pm\)0.013 & 0.28 & \(\pm\)0.013 \\
**Na\({}_{2}\)O** & 0.84 & \(\pm\)0.0519 & 3.46 & \(\pm\)0.0607 \\
**K\({}_{2}\)O** & 0.19 & \(\pm\)0.0345 & 1.42 & \(\pm\)0.0395 \\ LOI & 0.89 & - & 0.03 & - \\
**Total** & 98.97 & - & 98.83 & - \\ \hline  & **ppm** & & ppm & \\  
l** & 43 & \(\pm\)0.364 & 53 & \(\pm\)0.359 \\ \hline \end{tabular}
\end{table} 
Table 5: Geochemical data in weight percent (wt %) and parts per million (ppm). alternated with ferromagnesian-enriched layers. Despite some fractures with chlorite and incipient sericitization, the sample had no significant alteration. The mineral chemistry data (Table 4) indicated a more evolved composition with ferrous olivines (Fo = 0.40-0.46), diopside-augite compositions for clinopyroxenes, slightly fewer calcium plagioclase (An = 0.61-0.72), ferromagnesium micas, and Fe-rich ilmenite. The XRD (Figure 9) and mineral chemistry (Table 4) data support the previous observed and analyzed mineralogy. The geochemical data revealed (Table 5) a basic composition (SiO\({}_{2}\) = 49.00 wt %) and displayed lower MgO (4.48 wt %), CaO (8.33 wt %), and Fe\({}_{2}\)O\({}_{3}\) (11.20 wt %), enhanced by low Cr (20 ppm) and Ni (7 ppm) and high Sr (748 ppm) values, which were directly related to the modal abundance of plagioclase, clinopyroxene, and olivine. The established basic composition and the TiO\({}_{2}\) value (3.26 wt %) obtained for the gabbro-diorite agree with the values reported by Canilho [38].
###### Abstract  
Rapid growth in the volume of transmitted information has lead to the emergence of new wireless networking technologies with variable heterogeneous topologies. With limited radio frequency resources, optimal routing problems arise, both at the network design stage and during its operation. We propose an algorithm based on a minimum loss intensity (greedy-gradient algorithm) to search for optimal routes of information transmission in telecommunication networks. The relevance of the developed algorithm is determined by its practical use in data-transmitting modeling systems. The proposed algorithm satisfies several requirements, such as the speed of the calculations performed, the fulfillment of the conditions for its convergence, and its independence on the selected loss probability function, as well as on the network topology. The idea of the algorithm is a step-by-step recalculation of metrics based on derivatives of the loss intensity function with simultaneous redistribution of information flows along the routes determined by the Floyd algorithm. The comparative efficiency of the proposed algorithm is demonstrated by computational experiments on various network topologies (up to 100 nodes) with various traffic intensities.  
Keywords: telecommunication networks; traffic distribution; greedy algorithms; Floyd-Warshall algorithm +
Footnote †: journal:
# Rapid Planning of an Assembly Path by Reusing the Prior Path
## 2 Establishment of Prior Path Space
### Transformation from Assembly Space to Configuration Space  
A large number of parts, assembly tools, workbenches, and various auxiliary equipment in the assembly space are obstacles that need to be bypassed in APP, which leads to a high complexity of collision detection and a low efficiency of path planning.  
Therefore, the configuration space method is usually used to model the assembly space [53], which maps the assembly space into a two-dimensional configuration space containing the obstacle space and the free space. The assembly path can be solved by using a two-dimensional path planning algorithm in the configuration space, which is helpful to reduce the difficulty and improve the efficiency of path planning.  
In this paper, in addition to the obstacle space and the free space, the configuration space also contains the prior path space constructed by the planned assembly paths. The paths in the prior space constitute a prior path set, which is described by a tree structure, as shown in Figure 2. Each prior path is composed of the nodes in sequence from the start node (the leaf node) \(x_{start\_j}\) (\(i\) = 1,..., \(m\)) to the end node (the root node) \(x_{end}\) with some nodes \(p_{ij}\) (\(i\) = 1,..., \(m\); \(j\) = 1,..., \(n\)) between them. Therefore, the path planning for the logistics phase in the assembly space is converted into a non-interference path search from the start node to the end node in the configuration space.  
A path planning algorithm based on RRT expands the path by generating random points in the configuration space and detects collision interference by connecting the points  
Figure 1: Segmentation of an assembly path.  
Figure 2: Tree structure of a prior path set.  
with lines. If a prior path is represented only by some nodes of the tree, the probability that the sampling points happen to be generated on these nodes is extremely small. Therefore, a prior path tree is inflated into a prior path space [54] which is helpful for improving the reusability of a prior path.  
There are two steps to inflate a path tree into a path space. First, each node of the tree is expanded into a circle, where the radius of the circle is positively related to the number of parts that appear at the node and the size of their bounding volume. In the process of moving parts from the start node to the end note, the size of parts consumes some space along the assembly path which cannot be ignored. Therefore, this virtual space is applied to simulate the real space which is consumed by the size of each part, and the radius of each circle can be changed to adjust the envelope of the path space in a real application. Second, the outer common tangents of each two adjacent circles are connected in sequence to construct the prior path space. The prior path tree shown in Figure 2 is inflated into the prior path space shown in Figure 3.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 4 Evaluation
### Accuracy Measurement
#### 4.3.2 Accuracy of Node Positioning  
In the platform, RSSI is used to measure the distance from the node, and spatial spectrum direction finding technology is used to get the node direction. In order to reduce the error, we adopt the idea of multiple measurements to get the intersection. In the simulation experiment, the platform uses irregular curve track motion to locate the nodes from the near end area, the middle area and the far end area of the node communication range. For each region, the platform has carried out 30 localization experiments on the target node independently. In each experiment, the initial position of the platform, the motion route of the platform, the sampling times, the position of the target node, the RSSI measurement error of the platform and the direction finding accuracy of the spatial spectrum are all random changes. In the motion of the platform, the electromagnetic wave of the signal source  
Figure 12: Part (**a**) is the original topology of the target network of GEAR routing protocol, and Part (**b**) is the topology rebuilt by the platform.  
Figure 13: The red lines represent the topology detection accuracy rate of the target network using GEM routing protocol in 30 experiments. The blue lines represent the topology detection accuracy rate of the target network using GEAR routing protocol in 30 experiments.  
is received in equal time interval. When the platform is closer to the node, the more accurate the RSSI ranging is, the more accurate the spatial spectrum direction finding is. Otherwise, the accuracy decreases. The closer the platform is to the node, the more times it receives electromagnetic wave, and vice versa. Based on the simulation results, even if the platform is in the remote area, the ratio of the positioning error to the communication range is within 10% (the illustration of detection is shown in Figures 14 and 15; the accuracy of detection is shown in Figure 16).  
Figure 14: (**a,b**) is the positioning simulation of the platform node when the communication range is near and middle. (\(\lx@sectionsign\)\(\chi\) is the positioning position of the platform to the node, and \(\bigcirc\) is the actual position of the node).  
Figure 15: (**a**) is the positioning simulation of the platform node when the communication range is far; (**b**) is the simulation precision display. (\(\lx@sectionsign\)\(\chi\) is the positioning position of the platform to the node, and \(\bigcirc\) is the actual position of the node).
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 2 Materials and Methods
### Apparatus-HPLC Determination of Bisphenols  
Apparatus for sample preparation was a tube rotator RL-2002 from J.W. Electronic (Warsaw, Poland) and an MPW-55 centrifuge from MPW Medical Instruments (Warsaw, Poland). HPLC analysis was performed on Merck/Hitachi HPLC System (Darmstadt, Germany) equipped with a vacuum degasser, a gradient pump, an autosampler and a fluorescence detector. The data were collected and evaluated by using Merck chromato  
Figure 1: Structures of the analyzed bisphenols.  
graphic software D-7000. Chromatographic separation was performed on a reversed-phase SunFireTM C18 column (2.5 \(\upmu\)m \(\times\) 4.6 mm \(\times\) 75 mm).  
Column temperature was kept at 30 \({}^{\circ}\)C, and injection volume was 5 \(\upmu\)L. For all HPLC analyses, an isocratic program (70% A, 30% B) was used at a flow rate of 1.5 mL\(\cdot\)min\({}^{-1}\) by combining solvent A (water with formic acid, 2 mmol) and solvent B (acetonitrile). Fluorescence detection was carried out at 230 nm excitation wavelength and 316 nm emission wavelength.  
The method performance was assessed in terms of linearity, limits of detection and analytical instrumental repeatability. Six-point calibration curves were constructed using standard mixtures of the analytes. Linearity testing indicated a linear response for the method for the concentration range from 0.001 to 1 mg\(\cdot\)L\({}^{-1}\) with an R\({}^{2}\) value 0.9996 for BPF, R\({}^{2}\) value 0.9992 for BPA and R\({}^{2}\) value 0.9969 for BPB, respectively. The limits of detection achieved for the LC-FLD method were determined to be 5 \(\upmu\)g\(\cdot\)L\({}^{-1}\) for BPF, 12 \(\upmu\)g\(\cdot\)L\({}^{-1}\) for BPF, 4 \(\upmu\)g\(\cdot\)L\({}^{-1}\) BPA and 15 \(\upmu\)g\(\cdot\)L\({}^{-1}\) for BPB, respectively. Repeatability was determined as the RSD of three consecutive injections of the same sample.
## 4 Link Budget
### Link Budget under Realistic Atmospheric Conditions  
Spaceborne observations are always performed in presence of clouds and aerosol plumes, which increase the total atmospheric optical thickness \(\tau\) and decrease the SNR. In this section the effect of cloud and aerosol covers are taken into account to complete the previous link budget performed under ideal atmospheric conditions. We first present the assumptions of the study and finish by the results and discussions.
# Code Smell Detection Using Ensemble Machine Learning Algorithms
## 4 The Outcome of Proposed Algorithms
### Effect of Subset of Features Selected by Chi-Square FSA on Model Accuracy  
Chi-square FSAs were used to answer RQ2. This experiment was done to see the effect of Chi-square FSA to identify the software features which are important in recognizing the code smells. Table 13 shows how the performance accuracy of the ensemble and deep learning approaches is affected when the number of selected metrics isincreased by one in each step. The comparison indicates that the feature extraction is helpful to improve the accuracy of nearly all ensemble methods for all datasets, and that it has slightly different effects on each model and each dataset. However, in some models, such as Bagging, Gradient boosting, XGBoost algorithms, ANN, and CNN, the FSA increases accuracy greatly. In Adaboosting and the Max voting algorithm, feature extraction had no significant effect.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**MLT**} & **Number of Selected Features** & **Accuracy for DC Dataset (\%)** & **Accuracy for GC Dataset (\%)** & **Accuracy for FE Dataset (\%)** & **Accuracy for LM Dataset (\%)** \\ \hline \multirow{6}{*}{Adaboost algorithm} & 8 & 96.43 & 95.23 & 100 & 100 \\  & 9 & 97.61 & 97.62 & 100 & 100 \\  & 10 & 98.80 & 97.62 & 100 & 100 \\  & 11 & 98.80 & 97.62 & 97.87 & 97.91 \\  & 12 & 97.61 & 97.62 & 100 & 100 \\  & All features & 98.80 & 97.62 & 100 & 100 \\ \hline \multirow{6}{*}{Bagging algorithm} & 8 & 99.92 & 98.80 & 97.87 & 97.92 \\  & 9 & 97.62 & 97.62 & 97.87 & 97.92 \\  & 10 & 98.80 & 97.62 & 100 & 99.94 \\  & 11 & 97.62 & 98.80 & 97.87 & 100 \\  & 12 & 97.62 & 99.24 & 100 & 100 \\  & All Features & 98.80 & 98.80 & 97.87 & 100 \\ \hline \multirow{6}{*}{Max voting algorithm} & 8 & 98.81 & 95.24 & 97.87 & 97.92 \\  & 9 & 100 & 97.61 & 97.87 & 100 \\  & 10 & 98.81 & 97.62 & 97.87 & 97.92 \\  & 11 & 98.80 & 97.62 & 100 & 97.92 \\  & 12 & 98.80 & 96.42 & 91.45 & 97.92 \\  & All Features & 97.62 & 95.23 & 100 & 100 \\ \hline \multirow{6}{*}{Gradient boosting algorithm} & 8 & 99.96 & 98.80 & 100 & 100 \\  & 9 & 98.80 & 96.43 & 100 & 100 \\ \cline{1-1}  & 10 & 98.80 & 97.62 & 95.74 & 100 \\ \cline{1-1}  & 11 & 99.28 & 98.80 & 95.74 & 100 \\ \cline{1-1}  & 12 & 98.80 & 97.62 & 97.87 & 100 \\ \cline{1-1}  & All features & 98.80 & 98.80 & 97.87 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Outcomes of Ensemble algorithms for different sets of selected features.
## 3 Results and Discussion  
### Agronomic Performance Under Semi-Organic and Conventional Systems  
Soybeans cultivated under the P1 system consistently demonstrated higher plant height and pod number, likely due to enhanced vegetative development supported by mechanical weeding and optimal sowing timing (Table 3). These findings align with [PERSON] et al. [8], who emphasized the need for aligning sowing date and cultivar maturity with regional photothermal conditions. In our study, the mechanical disturbance associated with rotary weeding may have promoted compensatory pod development, consistent with reports by [PERSON] et al. [16]. Although P2 plots initially showed higher post-emergence plant density in certain years (2021, 2024), P1 maintained superior pre-harvest density and yield across most seasons. In 2022 and 2024, low rainfall in May (36.5 mm and 8.0 mm, respectively) and higher temperatures disrupted early development. This likely shortened the R1-R7 phase, limiting assimilate accumulation and pod filling. Conversely, cooler, wetter conditions in 2021 and 2023 supported prolonged phenological stages, aligning with higher yields, especially under the P1 cultivation system. The lowest yields were observed in 2022, coinciding with delayed sowing and drought during flowering. Drought and elevated temperatures negatively impact modulation and biological nitrogen fixation [17; 18]. Our results corroborate this, showing yield suppression during critical phenophases. Otherwise, year 2023 and 2024 showed yield recovery due to earlier sowing and better rainfall distribution [19; 20]. Yield variability from 2021 to 2024 reflects strong climatic influences on soybean development. [PERSON] et al. [20] emphasized temperature and daylength as key drivers of phenology, with elevated temperatures accelerating development and reducing yield potential if critical phases like flowering or seed filling are shortened. [PERSON] et al. [21] observed similar outcomes in temperate zones under fluctuating moisture regimes. In 2021 and 2024, the yield gap between P1 and P2 reached 1.5 to 2.0 dt/ha (Table 3). The use of mechanical weed control also contributed to higher thousand-seed weight (TSW), especially in dry years like 2024. The P1 system, combining mechanical weeding and increased sowing precision, effectively suppressed weed infestation without post-emergence herbicide use. This is in line with the findings of [PERSON] et al., [10] and [PERSON] et al., [11] who supported mechanical solutions as glyphosate _alternatives_ under EU Green Deal constraints. Advances in AI-powered weed detection offer future opportunities for automation of mechanical weeding [22].