esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
### Performance Accuracy Analysis  
Prior to confirming the effectiveness of the algorithm by means of actual inventory data, we verify the performance of the LSTM algorithm by means of our own test data. The accuracy of the prediction model according to the noise level of the test data is confirmed.  
Table 2 shows the prediction performance of the LSTM prediction model for the random period signal. An experiment was conducted based on random signal data five times, and each RMSE and MAPE value was derived. A total of five experimental averages were obtained so that the more severe the noise, the more significant the difference was. As can be seen from the experiment, the greater the random variation because of the increase in noise, the less accurate the prediction. That is, the variability of the data greatly affects the performance of the prediction algorithm. For SMEs, inventory data is difficult to be regularized because of the nature of the enterprise; the system of inventory management is inadequate, and there are many factors that greatly affect the performance of the company. Therefore, inventory data of SMEs has a large random variation. This leads to a decline in performance in predicting inventory data by means of predictive models. The following is the result of forecasting performance from the D company's warehouse data. As explained in Experimental design, a total of four LSTM models were tested from 2016 to 2019 September data as training sets and from the remaining 2020 September data as test datasets. The purpose of this experiment is to find out how volatile the warehousing data of SMEs is and to find out the similarity with random signal data. Table 3 shows the predictive performance of the LSTM model for warehousing out data. The prediction error is higher than that for random data of the same condition. This implies that the warehousing out data is very fluctuative. The data used in the experiments are data from actual SMEs, which showed much more growth in 2019 than in the previous year. This irregularity lowers the reliability of the results of the prediction model and makes it difficult to apply it to the field. Therefore, for the inventory-forecasting system for SMEs, it is necessary to design an appro  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{LSTM} & units & 100 \\ \cline{2-3}  & return\_sequence & True \\ \hline \multirow{3}{*}{compile} & \multirow{3}{*}{loss function} & root\_mean\_squared\_error \\ \cline{3-3}  & & Mean\_absolute\_percentage\_error \\ \cline{2-3}  & optimizer & adam \\ \hline \multirow{3}{*}{fit} & nb\_epoch & 1000 \\ \cline{2-3}  & batch\_size & 4 \\ \cline{1-1} \cline{2-3}  & verbose & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: LSTM model parameter.  
Figure 5: Box plot and seasonality of the real inventory data.  
priate forecasting model that takes into consideration the cost of inventory management, while expressing the reliability of the forecasting results in view of such volatility.
## 5 Conclusions  
This research presents the development of an innovative robotic system employing machine vision to automate the sorting of oysters on a conveyor belt within a cold-chain production setting. The system integrates the YOLOv8-OBB deep learning algorithm for object detection, effectively addressing challenges associated with oyster localization and gripper angle determination. To enhance the performance of the vision module, YOLOv8-OBB was optimized with a lightweight MobileNetV4 network, significantly reducing the number of parameters by approximately 50% and lowering GFLOPS by 32.4%. KF, combined with a Low-Pass filter, was also integrated into the system to enable the robot to accurately grasp dynamic oysters. The control framework, built on the ROS platform, successfully facilitated real-time communication between the vision system, motion planning, and hardware control, achieving a static grasping success rate of 95.54%. However, the dynamic grasping success rate was found to vary with the speed of the conveyor belt, with an 84% success rate at the lowest speed that diminished as the belt speed increased. Despite the success, several areas for improvement remain. Future research should focus on enhancing the dynamic grasping capabilities by improving the prediction accuracy of the KF and addressing system limitations related to the physical setup of the robotic arm and vision module. In addition, it is recommended to expand the training dataset to include a wider range of object orientations and illumination conditions to enhance the adaptability and robustness of the detection module. Optimizing the reaction time and gripper mechanics of the robot for faster moving objects is also critical to improving efficiency in real-world applications. Overall, this study underscores the potential of robotic systems in advancing the automation of food processing by reducing direct human interaction, enhancing operational productivity, and improving food safety. Future research will prioritize optimizing the performance of the system in high-speed scenarios, addressing hardware limitations, and scaling for large-scale industrial applications.  
Conceptualization, W.-H.S. and J.W.; methodology, W.-H.S. and J.W.; software, J.W.; validation, J.W., H.-R.Q., and W.-H.S.; formal analysis, J.W. and L.-R.L.; investigation, J.W.; resources, W.-H.S.; data curation, J.W.; writing--original draft preparation, J.W. and H.-R.Q.; writing--review and editing, H.-R.Q., W.-H.S., and L.-R.L.; visualization, H.-R.Q., W.-H.S., and L.-R.L.; supervision, W.-H.S.; project administration, W.-H.S.; funding acquisition, W.-H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 32371991.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are available on request due to privacy.  
The authors declare no conflicts of interest.
# Regional Traffic Event Detection Using Data Crowdsourcing
## 4 Performance Evaluation
The average accuracy for the five extraction results was 80.4%. Two factors reduced administrative district extraction accuracy. Firstly, it was difficult to extract administrative districts if a precise location name such as \"National Highway 46\" was not mentioned. Secondly, when sections of a single large road were described based on bridges, as in \"construction on lane 4 of Seoul's Gangbyeonbuk Expressway in the Guri direction from the Hannam Bridge to the Dongho Bridge\", the administrative district switched to the bridge location, which reduced accuracy for non-autonomous regions.
## 3 Results and Investigations  
### Performance Evaluation under Cooling Operation  
The experimental set-up stabilized after 30 min of test operation, and data were recorded once per day for five days, with each recording session lasting a continuous 6 h. Through this experiment, the initial underground temperature, temperatures at the entrance and exit of the geothermal circulation water, circulation flow rate, and the electricity used were recorded by the data logger. The results are presented in Figures 3 and 4. The COP was calculated by using the data recorded and by applying Equation (1). The measured values of the power and COP of the two systems are listed in Tables 3 and 4.  
Figure 2: Cross operation of the balancing well geothermal heat exchanger system.  
Figure 3: Temperature variation characteristics of the SCW and BW cross-mixing ground heat exchanger system during cooling operation.  
Figure 4: COP characteristics of the SCW and BW cross-mixing ground heat exchanger system during cooling operation.  
The plots of Figure 3a showed that the temperatures of the geothermal side shown in Figure 3a were significantly higher than those in Figure 3b when compared with the results of Figure 3b. The method of the conventional SCW ground heat exchangers shows that the temperature of the geothermal side of Figure 3a was significantly higher than that of Figure 3b. This phenomenon was caused by the accumulation of heat in the underground water due to the increase in heat load. However, Figure 3b demonstrates the characteristic that the cyclical water temperature of the geothermal heat remained constant without increasing. This phenomenon is assumed to be the result of the cross-mixing of the balancing well, which activates underground water in the aquifer to improve the heat transfer characteristics.  
Figure 4 illustrates the results for the overall power and COP characteristics. To compare the performance of SCW and balancing well underground heat exchangers, the total power consumption, system power, and COP characteristics were compared and analyzed. The cross-mixed balancing well underground heat exchanger exhibited an improved heat transfer effect owing to underground water utilization in the ground and groundwater flow because of the difference in water level between the two wells. It is observed that an overall improvement of the COP of the balancing well SCW system was reported over that of the SCW system, as indicated by the plots of Figure 4a,b. As mentioned previously, the cross mixing of the balancing well resulted in an approximately 23% improvement of the COP.
## 4 Simulations
### Numerical Simulations  
In order to test the performance of the proposed observers, two numerical simulations were carried out. Considering the actuator and process models given by Equations (44) and (46), Observer 1 in Equation (47) and Observer 2 in Equation (48) were designed for estimating unmeasured inlet flows\(\rm F_{p},F_{u}\) and monitoring the performance of the final products \(\rm T_{p},T_{u}\). Available measurements involve the inlet-outlet temperatures of the hex reactor and the pneumatic pressure \(\rm p_{c1},P_{c2}\) of the actuators.  
Two cases were considered. In Case 1, constant inlet flows \(\rm F_{p},F_{u}\) were considered in both fluids. By contrast, in Case 2, \(\rm F_{p},F_{u}\) were considered to be time varying. Observers 1 and 2 were simulated with respect to the actuator and process subsystems, using the values corresponding to an IHEX system from [17]. The parameters in the process subsystem were as follows: \(\rm hA=214.8\,W\cdot K^{-1},V_{p}=2.685\times 10^{-5}\) m\({}^{3}\), \(\rm V_{u}=1.141\times 10^{-4}\) m\({}^{3}\), and the inlet temperatures \(\rm T_{pi}\) and \(\rm T_{ui}\) were 76 \({}^{\circ}\)C and 15 \({}^{\circ}\)C, respectively. The parameters in the actuator subsystem were as follows: \(\rm m=2\) kg, \(\rm A_{a}=0.029\) m\({}^{2}\), \(\rm\mu=1500\) Ns/m, \(\rm k=6089\) Ns/m, \(\rm Pc\) for the utility fluid was 1 MPa (1.2 Mpa for the process fluid), and the pressure drop \(\rm\Delta P\) in the utility fluid was 0.6 MPa (60 KPa in the process fluid).  
In order to illustrate the robustness, external disturbances and measurement noise were considered. Suppose the output measurement is corrupted by a colored noise. In addition to noise, no error is assumed in measuring \(\rm T_{p}\) and \(\rm T_{u}\). Moreover, to compare the effectiveness of the proposed method with other existing ones, we employed a typical unknown input observer (UIO) in [31] to show the differences.
## 2 Materials and Methods
### Measurement Method
#### 2.1.3 Visual Textures  
It is desirable for the video used for the experiment be a photographic image. However, in the case of an image that is close to reality, the apparent quality of the texture and the immersion in the experience may change depending on the rendering quality of the image and the arrangement and brightness of the lighting in the image. Therefore, in this study, experiments were performed using simple two-dimensional images with black and white stripes.  
The images were drawn using the Processing environment [19]. Specifically, the drawing application created by Processing was installed on the smartphone and communicated with using the Unity program on a PC using OSC (OpenSound Control, a UDP-based communication method). Communication between the PC and smartphone was mediated via wireless LAN in our laboratory.  
The texture is represented by white and black stripes, as shown in Figure 1b. The texture was adjusted for display on the smartphone screen in a 40 mm square. In the image, one white stripe represents a ridge, and one black stripe represents a groove. The minimum size at which the black and white stripes could be observed with the naked eye without moire was two pixels (px). The smartphone used in the experiment required 1276 px to draw a 40 mm \(\times\) 40 mm texture. Hence, the size of 1 px was 40/1276 = 0.0313 mm, and that of 2 px was 0.0626 mm. In the experiment, the participants were informed in advance that the width of the stripe corresponded to the ridge and groove of the real texture.
## 3 Materials and Methods
### First-Frame Localization Model Based on Improved RFBNet
#### 3.2.2 Re-Localization Method for Punching Robotic Arm Based on Target Detection  
By combining the tracking ability of SiamRPN and the prediction ability of Kalman filtering, useful dependencies can be established between different frames to improve the overall tracking performance of the perforating robot arm. However, during the whole tracking process of the perforating robot arm, due to the long tracking time and the large rotation of the robot arm, the target loss of the perforating robot arm will occur, resulting in the tracking interruption. This study proposes a re-localization method for perforating robot arms based on target detection to improve continuous tracking stability. Firstly, the tracking is based on the SiamRPN algorithm and Kalman filtering; secondly, the current perforating robot arm target is lost based on the loss detection module before the end of each frame--if the perforating robot arm is not lost, the perforating robot arm position is output to continue the tracking, and if it is determined that the perforating robot arm target is lost, the target detection module is based on the target detection module to re-detect and localize the perforating robot arm position in the global image.  
In the context of target detection, this study employs a first-frame localization model based on an enhanced RFBNet approach. Regarding loss determination, this study incorporates a target loss determination mechanism. If no discernible peak is present in the response map, the tracker assigns a score of 0 to the current frame, indicating a perforated robot arm target tracking failure. Consequently, the current frame is designated as being in a perforated robot arm target loss state. The judgment strategy is as follows:  
\[F=\begin{cases}\max(\,f(z,s)),&f(z,s)>\alpha\\ 0,&\text{otherwise}\end{cases} \tag{23}\]  
\[f(z,s)=\varphi(z)*\varphi(s)+b_{1} \tag{24}\]  
where \(F\) denotes the maximum response value, \(f(z,s)\) denotes the feature comparison formula, \(z\) denotes the perforated robotic arm target template, \(s\) denotes the current frame, \(b_{1}\) denotes the bias term for the values taken in the score map, and \(\varphi\) denotes the feature extraction method.
# Real-Time Phase Bias Estimation for BeiDou Satellites Based on Consideration of Orbit Errors
## 2 Methods
### FCB and Orbit Error Estimation Algorithm  
Assume that there are \(n\) stations that observe \(m\) satellites in a network; then, based on consideration of Equation (5), we obtain the observation equation in the form of Equation (7) for all NL ambiguities in the network as follows:  
\[\left[\begin{array}{c}B_{1}\\ B_{2}\\ \vdots\\ B_{n}\end{array}\right]=\left[\begin{array}{cccccccc}I&&&&\begin{array}{c}U _{1}^{r}&U_{1}^{s}&O_{1}^{x}&O_{1}^{y}&O_{1}^{z}\\ I&&&&U_{2}^{r}&U_{2}^{s}&O_{2}^{x}&O_{2}^{y}&O_{2}^{z}\\ &&\ddots&&\vdots&\vdots&\vdots&\vdots&\vdots\\ &&&&I&U_{n}^{r}&U_{n}^{s}&O_{n}^{x}&O_{n}^{y}&O_{n}^{z}\\ &&&&&&&&&&&\\ &&&&&&&&&&&\\ &&&&&&&&&&&\\ \end{array}\right]\left[\begin{array}{c}N_{1}\\ N_{2}\\ \vdots\\ N_{n}\\ \Phi_{r}\\ \Phi^{s}\\ X^{s}\\ Z^{s}\end{array}\right] \tag{7}\]  
where \(B_{i}\) represents the float ambiguities for receiver \(i\) and \(N_{i}\) stands for the corresponding integer ambiguities; \(\Phi_{r}\) and \(\Phi^{s}\) are the receiver and satellite FCBs, while \(U_{i}^{r}\) and \(U_{i}^{s}\) represent the corresponding design matrices, respectively; \(X^{s}\), \(Y^{s}\), and \(Z^{s}\) are the orbit errors, and \(O_{i}^{x}\), \(O_{i}^{y}\), and \(O_{i}^{z}\) are the corresponding design matrices, respectively. If we can fix all integer ambiguities in Equation (7), then the NL FCBs and the satellite orbit errors can be estimated together using the least mean squares (LSQ) adjustment method.  
The orbit error may be very large, particularly for the GEO satellites (see Table 3), and we have no a priori values, which means that it is impossible to fix all the ambiguities in Equation (7) for all stations in a straightforward manner. In this study, we propose an improved strategy to fix the ambiguities in Equation (7), which is detailed as follows:  
1. Add a loose zero-value constraint to all the orbit error and FCB parameters to eliminate the singular values in Equation (7). The variance of this constraint is set at 10 m in this study.
2. Generate independent baselines among all the reference stations. For each baseline, assuming the initial satellite orbit error to be zero, form double-differenced (DD) NL ambiguities and then attempt to fix these DD ambiguities by rounding.
3. Solve Equation (7) to obtain the updated satellite orbit error. Take this updated orbit error into account and then repeat procedure (b) until no more of the DD ambiguities can be fixed.
4. Now that the satellite orbit error has been recovered, the undifferenced NL ambiguities can be fixed by following the procedures that were proposed by [PERSON] et al. [8]. The satellite orbit error and the satellite and receiver FCBs can then be updated using the LSQ to obtain the final estimates.  
The orbit can be refined using the estimated orbit error and broadcast together with the satellite clock and FCB products to the users to fix the undifferenced ambiguities using a single receiver.
## 2 Experimental  
Si wafers with (111) orientation were soaked in acetone and ultrasonically cleaned for 15 min. After, acetone-treated Si wafers were immediately put into methanol and cleaned for 10 min to blow off the surface acetone components. The final washing and drying were performed using DI pure water and N2 gas. Then, MWCNT dispersed in water (US Research Nanomaterials, Inc., Houston, TX, USA, 4 science) having an outside diameter of 5-15 nm and length of 50 \(\upmu\)m was used to form a MWCNT-based buffer layer on the prepared Si wafer, and detailed specifications of MWCNT are shown in Table 1. The amount of MWCNTs used for the formation of the buffer layer was adjusted to 30, 50, and 70 \(\upmu\)L, and dropped on a Si wafer, followed by drying at 50 \({}^{\circ}\)C for 1 h using a hot plate. The CNW was grown on a MWCNT-based buffer layer using microwave PECVD. The growth conditions of CNW are 1300 W (microwave power), growth time (10 min), 700 \({}^{\circ}\)C (substrate temperature), gas ratio (H\({}_{2}\): 25 sccm and CH\({}_{4}\): 50 sccm), \(2.5\times 10^{-2}\) torr (working pressure). The various analyses were operated to investigate the characteristics of CNW grown on a MWCNT-based buffer layer. FE-SEM (field emission scanning electron microscopy, Hitachi-S4800) was operated for surface and cross section analysis under acceleration voltage (10-15 kV), emission current (8600 nA), and lens mode (high). Raman spectra (FEX MRA10, NOST) analysis was performed to examine the successful growth of CNW and structural changes caused by the MWCNT-based buffer layer under conditions such as excitation laser wavelength (\(\sim\)531 nm), excitation laser power (\(\sim\)0.30 mW), objective lens (50\(\times\)) and spectral resolution (1.87-2.89 cm\({}^{-1}\)). The hall measurement system (HMS-3000, Ecopia) analysis was conducted to observe the electrical characteristics such as carrier concentration, carrier mobility, resistivity, I-V characteristics, and I-R characteristics. Then, the applied current was 1 mA under magnetic field (0.550 T), and measurement number and temperature were set to 1000 times and room temperature, respectively. The overall experimental procedure is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Purity** & **Diameter** & **Length** & **Ash** & **Electrical Conductivity** & **Density** \\ \hline
95 wt\% (CNT—carbon nanotube) & Out: 5–15 nm & 50 \(\upmu\)m & 1.5 \(\upmu\)s & 100 s/cm & Tap: 0.27 g \\
95 wt\% (Carbon) & In: 3–5 nm & 50 \(\upmu\)m & 1.5 \(\upmu\)s & 100 s/cm & True: \(\sim\)2.1 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of MWCNT (multi-walled carbon nanotube).
## 3 Research Methods
### NISDL Algorithm
#### 3.3.2 NISDL Method I  
As shown in Figure 1, SSI values are used as input data and imported into the deep learning network at the very beginning. The ROI images were also combined with SSI values in the first step. According to the size of ROI images, the SSI value of each ROI image was expanded into a matrix. The matrix is considered as a channel and combined with the 3 channels of ROI images.  
The merged data between ROI images and SSI values above is inputted into four convolution layers, which are used for dimensionality reduction. In NISDL method I, the DenseNet201 [33] is adopted for features extraction. The last two layers of DenseNet201 are not suitable for skin temperature measurement, hence the two layers are removed. The reason for this is that the activation function of the last layers is softmax. Instead of these two layers, an average pooling layer and a fully connected layer are added behind denseNet201. Based on the deep learning networks designed above, n ROI images and n SSI values were inputted into NISDL method I. Therefore, n skin temperatures can be obtained.
## 3 Numerical Modeling of the Filling Phase
### Rheological Models
#### 3.2.3 Herschel-Bulkley  
The Herschel-Bulkley model is a widely utilized non-Newtonian fluid model, particularly effective in characterizing fluids with a yield stress, such as Bingham fluids, which also display shear thinning behavior. This mathematical representation of the Herschel-Bulkley model is as follows [27; 28]:  
\[\tau=\tau_{0}+m\dot{\gamma}^{n} \tag{12}\] \[\eta=\frac{\tau_{0}}{\dot{\gamma}}+m\dot{\gamma}^{n-1}\qquad\quad \text{if}\qquad\tau>\tau_{0} \tag{13}\]  
The Herschel-Bulkley model incorporates three key parameters: yield stress, consistency index, and flow index. The model is contingent upon the presence of a specific threshold stress \(\tau_{0}\). Below this threshold, the material behaves like a solid and can sustain stress without flow. Above the threshold, the material shows the characteristics of a Power-Law fluid. The value of \(n\) determines the fluid's behavior: \(n<1\) indicates shear thinning, \(n>1\) indicates shear thickening and \(n=1\) reduces the model to a Newtonian fluid behavior above the critical yield stress [28; 29; 30; 31; 32].