esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 4 Analysis of Experimental Results
### Results Analysis  
The recognition performance of different object detection models on sugarcane is illustrated in Figure 7.  
The results analysis indicates that the improved model outperforms other object detection models in recognizing various varieties of sugarcane and their different layout configurations, demonstrating high accuracy and confidence. It ensures that the detection boxes precisely encapsulate the sugarcane without any instances of missed detections. In comparisons, the SSD model shows lower confidence and fails to identify all top-layer sugarcane in the image, resulting in omissions; although the YOLOv3 model displays higher confidence in identifying all top-layer sugarcane, its slower detection speed makes it unsuitable for real-time applications on mobile devices. The Faster-RCNN model, while exhibiting high confidence, may interfere with the recognition and retrieval of top-layer sugarcane due to its detection of obscured lower layers, affecting subsequent operational efficiency; the YOLOv5n and YOLOv7-tiny models perform less effectively on this dataset compared to the improved YOLOv8-based model, as the YOLOv5 and v7 series primarily optimize detection performance for multiple targets, whereas this dataset contains only a single category.  
The enhanced YOLOv8n model, with its reduced parameter count, excels in handling stacked sugarcane configurations, achieving the highest mean Average Precision (mAP) and accurately recognizing top-layer sugarcane without missing any detections. Thus, the improved model effectively meets the needs of stacked sugarcane detection, with a smaller parameter count making it more suitable for deployment on mobile devices.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Detection** & **Precision** & **Recall** & **mAP0.5** & **mAP0.5:0.9** & **F1 Score** & **Parameter/10\({}^{6}\)** \\
**Network** & **\%** & **\%** & **\%** & **\%** & **\%** & **Parameter/10\({}^{6}\)** \\ \hline YOLOv3 & 95.7 & 91.0 & 96.9 & 78.0 & 93.3 & 61.5 \\ YOLOv5n & 90.4 & 76.8 & 85.1 & 54.0 & 83.0 & 1.8 \\ Faster-RCNN & 55.88 & 94.35 & 88.83 & 45.7 & 70.1 & 28.28 \\ SSD & 91.48 & 72.53 & 88.75 & 46.9 & 81.0 & 23.61 \\ YOLOv7\_tiny & 89.9 & 81.2 & 89.6 & 58.7 & 85.3 & 6.2 \\ YOLOv8n & 89.0 & 82.7 & 90.9 & 65.1 & 85.7 & 3.0 \\ VIEM-YOLOv8n & 90.1 & 93.3 & 96.0 & 71.5 & 91.7 & 3.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative Test Results of Different Models.
## 4 Conclusions  
This paper develops a new travel time estimation method under low-frequency probe data based on the Bayesian approach. Due to the variability of travel times during peak periods, this paper adopts a mixture distribution of travel times in the Bayesian approach instead of the conventional single distribution. According to the characteristics of travel time regularity, the proposed estimation method adopts a historical data-based data-driven method to estimate the prior distribution in the Bayesian approach. The Gibbs sampling method, which includes the burn-in period, is used to generate a series of sample draws from an unknown joint posterior distribution and to estimate the posterior distribution of the parameters.  
The individual probe data on the selected freeway sections connecting Giheung IC to Dongtan IC are used to study the performance of different estimation methods. To evaluate different estimation methods, the performance criteria are used to specify good estimations using unbiasedness and efficiency. The MAPE and RMSE of estimation results for the unbiasedness show that the proposed method produces the smallest deviation from the ground truth travel times, compared to simple mean and moving average methods. Furthermore, the proposed Bayesian mixture method yields the smallest standard deviation of the MAPE of efficiency compared to the different estimation methods for all test days. Besides, the proposed approach provides good accuracy in estimating travel time reliability according to the credible intervals.  
This method can not only estimate the travel time, but also estimate the travel time distribution under the low-frequency sounding data. The method essentially proposed in  
Figure 4: The credible intervals of the estimated travel time on: (**a**) Case I, 29 April 2017, (**b**) Case II, 29 April 2017, (**c**) Case I, 5 May 2017, (**d**) Case II, 5 May 2017.  
the Bayesian approach does not require a specific type of data source and can be applied to low-frequency data problems in other application fields. The proposed approach is also flexible in addressing data estimation problems in other application fields and can potentially yield a relatively high accuracy if sufficient historical data are provided during peak periods. Implementation of the proposed estimator into arterial travel time estimation will be considered in the future. Moreover, future research will consider the use of the other observable factors (e.g., weather conditions) to accurately estimate prior distributions of parameters in data-driven methods.  
Conceptualization, H.K. and L.Y.; methodology, H.K. and L.Y.; software, H.K.; validation, L.Y.; formal analysis, H.K. and L.Y.; writing--original draft preparation, H.K.; writing--review and editing, L.Y.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1C1C1003296). The authors declare no conflict of interest.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 3 Results
### Specific Absorption Rate (SAR) Analysis of Conventional Dual Band Antenna
Hence, there is no need for EBG for the purpose of SAR. There are several reasons for the achieved lower SAR values. First, the proposed dual-band antenna has a full finite ground plane, which works as a reflector to protect the human body from radiation. Secondly, the radiation pattern of the patch antenna is perpendicular to the antenna plane. This is the reason that patch antenna is the best candidate among all types of antennas for wearable applications. Furthermore, as shown in Figures 20 and 21, when the designed antenna is mounted on the body, it radiates the minimum amount of side and back lobes towards the body. Based on these results, we consider that the proposed antenna can serve as an extremely useful component of many wearable applications, which cannot compromise on safety precautions due to their utilization in health critical systems.
## 3 Orbital Considerations  
Given the objective function described in Section 2, this section aims to characterize the selected parameters used in the configuration for the numerical search algorithm. These variables are summarized in Table 2.  
Figure 7: Temporal grid cells at a given spatial cell for the period of one day.  
In the analysis of geophysical signals [7], monthly solutions are of high interest. For this reason, we chose to optimize the constellation design for a propagation time T of 29 days. Altitude is the most critical design variable for a satellite gravity mission. The closer the satellite constellation orbits around Earth, the higher the strength of geophysical signals detected. However, the choice of altitude drives the satellite design and targeted lifetime. At lower altitudes, the orbital period of the satellites is faster, allowing them to observe a higher percentage of Earth in a shorter time. At the same time, flying at low altitudes would require the addition of drag compensation to extend the mission lifetime to reach GRACE-like levels. If the GA had a free choice in altitude, it would tend to select constellations at low altitudes. Due to this, the user must choose an altitude range as a fixed parameter rather than allow it to be a free parameter. To guarantee consistency in the constellation coverage, all pairs' orbits have been constrained to repeat orbits of 29 days at an altitude of \(500\pm 5\) km. Realistically, the constellation should incorporate some orbit maintenance capability to maintain the orbits at their repeat period. To guarantee the same levels of geophysical signal strength experienced by each pair throughout their orbit, we chose an eccentricity equal to zero. Additional values that have been fixed are the argument of perigee and the intersatellite distance at 90\({}^{\circ}\) and 100 km, respectively. To verify the viability of this tool, we chose six GRACE-like pairs for the forthcoming simulations. As previously discussed in Section 2, the spatial and temporal grid have been selected to be 16 and 4551, respectively. These values correspond to a temporal sampling requirement of 1.5 h and a spatial 3\({}^{\circ}\) equal area. The parameters that we vary are inclination (i), right ascension of the ascending node (RAAN), and the mean anomaly (M). These can vary freely between prograde and retrograde values and, in the case of RAAN and M, across 360\({}^{\circ}\).
## 2 Proposed Circuit Configuration and Operation Analysis
### Operation Analysis  
For simplifying the operation analysis, the following assumptions are made.  
* Ignoring the conduction voltage drop for all switching devices, such as diodes and active switches.
* The switching frequency is much greater than the input-line frequency.
* Ignoring voltage ripples on all capacitors because the Cbus and Cb1 are large enough.
* The output inductor Lo is large enough, and its current is assumed to be a constant ILo.  
At steady state, the operation of the proposed circuit can be divided into seven modes according to the conducting status of the semiconductor devices in one high-frequency switching cycle. These operation modes and the theoretical voltage and current waveforms of the key components are shown in Figures 2 and 3, respectively. For simplifying the circuit analysis, the low-pass filter and the diode rectifier at the input side are represented by the rectified voltage \(v_{rec}\), and the LED string is represented by its equivalent resistance \(R  
winding \(i_{T_{B}}\) flows through \(D_{2}\), \(D_{3}\) and \(L_{o}\). During this interval, \(i_{T_{B}}\) is equal to the output inductor current \(I_{Lo}\). The \(i_{Lm}\) and the primary current \(i_{Tp}\) can be written as Equations (1) and (2), respectively.  
\[i_{Lm}(t)=i_{Lm}(t_{0})-\frac{V_{Cb1}}{L_{m}}(t-t_{0}), \tag{1}\]  
\[i_{Tp}(t)=i_{Lm}(t)-\frac{N_{S}}{N_{P}}I_{Lo}, \tag{2}\]  
where \(N_{S}\) and \(N_{P}\) represent the turns of the primary winding and the secondary winding, respectively.  
Since \(S_{2}\) is clamped at zero volts, the voltage across the PFC inductor is equal to the rectified input voltage. The PFC current increases linearly and is expressed as:  
\[i_{LPFC}(t)=\frac{v_{rec}(t)}{L_{PFC}}(t-t_{0})=\frac{V_{m}\Big{|}\sin(2\pi f_{ L}t)\Big{|}}{L_{PFC}}(t-t_{0}), \tag{3}\]  
Figure 2: Operation modes. (**a**) Mode I, (**b**) Mode II, (**c**) Mode III, (**d**) Mode VI, (**e**) Mode V, (**f**) Mode VI, (**g**) Mode VII.  
where \(V_{m}\) and \(f_{L}\) represent the amplitude and frequency of the input line voltage, respectively. After a very short deadtime, the gate signal \(v_{CS2}\) turns from a low level to a high level in this mode. Based on Equations (1)-(3), \(i_{Tp}\) decreases. On the contrary, \(i_{LPFC}\) increases. The mode ends at the time when \(i_{LPFC}\) becomes higher than \(i_{Tp}\).
## 3 Proposed Method
### Head  
In YOLOv5, feature maps are generated from the input image at three different levels. These feature maps are fed to the detection head of YOLOv5 for object detection and classification. While the P3 feature map has the highest resolution and the most input image detail, it covers smaller areas. The P4 feature map has a lower resolution and contains more depth channels than P3. The P5 feature map has the lowest spatial resolution, has less input image detail, covers larger areas, and contains the most depth channels. However, since the P5 feature map is large in size, the computational cost is high. When the dataset was analysed, it was observed that there are no very large defects in the defect size distribution. Therefore, the prediction head of the model was updated to detect small and medium-sized defects.  
Because the P5 processes feature maps with a larger receptive field and more depth channels, it requires a significant amount of memory and FLOPs. In the ablation experiments (Section 4.5), removing P5 from the detection head reduced the computational load from 11.4 GFLOP to 8.4 GFLOP (\(-26.3\%\)) and increased the inference speed from 138.8 FPS to 163.9 FPS (+18.1%). Meanwhile, the number of parameters dropped from 4.83 M to 3.96 M, which means that computational complexity was reduced for the model.  
Despite this reduction in the head structure, small and medium-sized defect detection has been improved, where the sensitivity increased from 0.799 to 0.822 (+2.9%) and [EMAIL_ADDRESS] from 0.777 to 0.806 (+3.7%). These values confirm that removing P5 not only reduces the computational load but also improves performance due to the specific defect size distribution in the dataset.  
Considering the dataset characteristics, resource limitations, and real-time efficiency requirements together, and as a result of ablation experiments, this architectural change is justified. A two-level head structure (P3 and P4) is proposed in the architecture.
## 4 Experiment and Results  
### Experimental Systems and Specimens  
An MPPT system is designed to accommodate a wide range of steel pipe diameter specifications, as shown in Figure 11. The experimental setup consists of the following parts: a circumferential magnetizer, a DC power supply, sample pipes, a probe, and a signal acquisition and signal processing system. The magnetizer provides a magnetic field perpendicular to the direction of the defect. The probe sweeps through the sample pipe to output a signal, which is processed by a low-pass filter and signal amplifier to reduce background noise and amplify the output signal, and finally received by the signal acquisition module. In all experiments, the raw output voltages are processed using a tlc2262-based amplifier with a designed amplifier gain of 1000.  
The test platform mainly contains three specifications of steel pipe specimens with diameters of \(\upphi\)50 mm, \(\upphi\)54 mm, and \(\upphi\)60 mm. The magnetic pole shoe in the testing device consists of a left pole shoe, a right pole shoe, and adjustable screws. In the experiment, in order to avoid the influence of the change in the distance between the pole shoe and the external wall of different pipes, the distance between the left and right pole shoes can be adjusted during the experiment to ensure that the minimum distance between the left and right pole shoes and the external wall of different specifications of the sample pipes is  
Figure 11: The MPPT system for the small-diameter thick-walled steel pipes. (**a**) is a diagram of the experimental platform model. (**b**) shows the experimental platform.  
Figure 10: Detection signals at different wall thicknesses and external diameters.  
5 mm, the width of the pole shoe is 25 mm, and the axial length along the sample pipe is 225 mm.  
Six experimental sample pipes were used in the experiments, as shown in Figure 12. The axially scored grooves were scored using EDM. The crack length and width dimensions are 20 mm \(\times\) 1 mm, and the depths are 0.5 mm, 1 mm, and 1.5 mm respectively.
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 5 Conclusions  
Most of the existing SST prediction methods fail to fully mine and utilize the spatial correlation of SST, and most of the graph neural networks which model the variable relationship rely heavily on the predefined graph structure (i.e., use prior knowledge to construct the spatial point dependence). To solve the above problems, this paper specially designed an end-to-end model AGLNM for SST prediction without explicit graph structure, which can automatically learn the relationship between variables and accurately capture the fine-grained spatial correlations hidden in sequence data.  
The experimental results of the performance test on the Bohai sea and South China Sea SST data sets show that: Firstly, AGLNM can effectively capture the dependence relationship between ocean spatial points. Secondly, the overall performance of the AGLNM is significantly better than that of the CGMP, FC-LSTM, CFCC-LSTM, GED, and SVR models in different sea areas and at different prediction scales. Finally, under the same prediction scale, the SST of the South China Sea is easier to predict than that of the Bohai Sea.  
The AGLNM proposed in this paper has a better portability and can self-mine the hidden spatial association relationship contained in the data set that is the most consistentwith the characteristics of the real data and can be better applied to large and complex data sets in the future. Based on the advantages and disadvantages of the AGLNM, the model can be better applied to data sets with more complex environments, large feature fluctuations, and stronger time-space correlations, such as the data set of the first island chain affected by monsoons, ocean currents, and man-made operations simultaneously, which can make full use to the advantages of the model and have stronger military significance.  
Conceptualization, T.W. and Z.L.; methodology, Z.L.; software, T.W.; validation, Z.L., B.J. and L.X.; formal analysis, L.X.; investigation, B.J. and L.X.; resources, B.J. and L.X.; data curation, T.W.; writing--original draft preparation, T.W.; writing--review and editing, X.G.; visualization, T.W.; supervision, B.J. and L.X.; project administration, B.J. and L.X.; funding acquisition, B.J. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Program on Key Research Project of China, grant number 2016 YFC1401900.  
Not applicable.
# Effect of Heat Input on LMHMW Joint of Carbon Steel
## 3 Experimental Results and Discussions
### Analysis of Joints Microhardness  
Micro-Vickers hardness testing of each layer was conducted with an interval of 0.6 mm between test points, as shown in Figure 6.  
The microstructure of the BM is equiaxed ferrite and pearlite, whose microhardness is only approximately 158 HV. Due to the formation of potent phases in the normalized and superheated zone, known for arresting the dislocations more effectively as compared to annealed base metal microstructure, the overall trend of higher hardness was observed in the HAZ lying in the range of 160-240 HV as compared to the base metal. The microhardness gradient appears to be very steep in HAZ owing to its narrow width. In addition, different microhardness values were observed at correspondingly silimar locations on each side of the fusion zone because of little variations in microstructure. The microstructure of WS is predominantly plate and needle-like pro-eutectoid ferrite which is relatively uniform; hence there are few variations, but the microhardness of the weld center fluctuates between 260-310 HV because of different heat inputs.  
It is evident that the microhardness of the No. 3 sample is the highest, and that of sample 2 is the lowest. As mentioned earlier in the text, from a macro morphological point of view, sample 3 had the most optimum parameters because of the highest weld joint soundness. To investigate this, it is more prudent to analyze the parameters of the cosmetic layer of each sample, because, in other layers, each succeeding layer remelts the preceding layer and causes slight variations in the microstructure as observed in microhardness graphs of filling and backing layers in Figure 6b,c, respectively. However, a relatively more uniform and stable microstructure exists in the cosmetic layer, as can be observed from the trend in microhardness data shown in Figure 6a. This can be attributed to the difference in  
Figure 5: Microstructure morphology of HAZ: (**a**) position of HAZ; (**b**) localized region marked in (**a**); (**c**) localized region marked in (**a**); (**d**) localized region marked in (**a**).  
laser to arc power ratio when the heat input is similar. The filling and cosmetic layers of samples No. 1-3 are laid down with the same laser power of 2000 W, but the amperage used is different, as can be noticed from Table 3. Under the given conditions used in this research, when the MIG amperage in the cosmetic layer is higher or lower than 234 A, the microhardness increases. When the current increases, the heat input increases and the microhardness value is enlarged, whose maximum value can approach 315.9 HV. Generally speaking, the heat input at each position of the No. 3 sample is highest amongst the group; similar results were obtained for microhardness.
# An Asymptotic Cyclicity Analysis of Live Autonomous Timed Event Graphs
## 5 Conclusions  
Some discrete event systems with time window constraints require a method to analyze asymptotic stability and cyclicity. The system designers want to check whether the same time pattern repeats eventually after a long time. There is a traditional way to analyze the stability and the cyclicity of a discrete event system: model it using a timed event graph, convert it into a standardized matrix form of (max,+) algebra, and analyze the cyclicity using the derived precedence graph from the matrix.  
However, the traditional method uses different formal methods for modeling and analysis, so improving the system to make it stable is complex and unintuitive. The main contribution of this paper is to propose a method to calculate the cyclicity directly from a timed event graph. That is, we can determine whether a given system has asymptotical stability with the initial tokens, the cycle time of each cycle, and its connectivity of a TEG. Additionally, the article suggests how to measure the cyclicity of a system that is not stable. That is, we can measure after how many cycles the time pattern repeats. In other words, to analyze asymptotic cyclicity and stability, we should know the TEG's critical cycle, the sum of the tokens in each critical cycle, and the connectivity of critical cycles. Our results directly interpret the system's model to view its asymptotic behavior, so it is easy to improve the system to make the system stable.  
The result of this paper can improve a time-constrained system design. For example, we expect to use the suggested method to analyze time-constrained discrete event systems such as CVD or a host system and to simulate the effect to validate our theory practically. Finally, we can extend the investigated relationship between the properties of a Petri net and (max,+) matrix to apply other analyses of (max,+) algebra to a Petri net.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Critical Cycle (Cyclicity)** & \(\pi_{1}\) & \(\pi_{2}\) & \(\pi_{3}\) & \(\pi_{4}\) \\ \hline \(\pi_{1}\) & \(\pi_{1}(1)\) & \(\pi_{1}(1)\) & \(\pi_{1},\pi_{2}(1)\) & \(\pi_{1},\pi_{3},\pi_{4}(1)\) \\ \(\pi_{2}\) & \(\pi_{1}(1)\) & \(\pi_{1}(1)\) & \(\pi_{1},\pi_{2}(1)\) & \(\pi_{1},\pi_{3},\pi_{4}(1)\) \\ \(\pi_{3}\) & \(\pi_{1},\pi_{2}(1)\) & \(\pi_{1},\pi_{2}(1)\) & \(\pi_{2}(3)\) & \(\pi_{2},\pi_{3}(6)\) \\ \(\pi_{4}\) & \(\pi_{1},\pi_{3},\pi_{4}(1)\) & \(\pi_{1},\pi_{3},\pi_{4}(1)\) & \(\pi_{2},\pi_{3}(6)\) & \(\pi_{3}(2)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The cyclicity and corresponding critical cycles of each pair of critical cycles of Figure 1.