esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Engineering Applications
### Application Result  
According to the geological data extracted from the field investigation and drilling, the stability of the slope was evaluated, and a two-dimensional model of the slope in the monitoring area was established using the Lizheng slope stability calculation software. The established model is shown in Figure 13.  
Figure 12: (**a**) Geographical location of the monitoring area; (**b**) status of the site of the monitoring area.  
Figure 13: Stratigraphic model of the slope in the monitoring area.  
This work adopted the technical code for building slope engineering and did not consider earthquakes. The method for the calculations was to automatically search for the most dangerous sliding surface, and using a center step of 1 m, the actual safety factor of the monitoring area was calculated. The results are shown in Table 3.  
Table 4 shows the input parameters of the slope in the monitoring area and the slope safety factors predicted by the three machine learning methods. Compared with the actual safety factors in Table 3, the ISSA-BP's results were very close to the actual results. The error was only 0.02, and the result of the ISSA-BP model remained the best, which was the same as the above experimental results, as verified by multiple slope examples.  
In addition, due to the structural characteristics of the artificial neural network, the results of the model operations were closely related to the dataset, and a high-quality dataset can improve the prediction accuracy of the model. Therefore, in the following research, more strategies can be considered to achieve an accurate prediction of the slope safety factor.
## 5 Simulation and Experimental Verification
### Experimental Verification  
In order to further verify the feasibility of joint localization method proposed in this paper, the broken-lead experiment is performed on the steel plate, as shown in Figure 8. It includes four AE sensors connected to the preamplifier to obtain the signal, and the data acquisition instrument connected to the PC to analyze and process the signal. The sensors are arranged in a row with coordinates (150 mm, 0 mm), (270 mm, 0 mm), (390 mm, 0 mm), and (510 mm, 0 mm), respectively. The sensor spacing is 120 mm. The broken-lead experiments are performed at the location of AE sources shown in Table 6, and the signal received by the sensors are extracted. According to the method and operation steps introduced in Section 2.2, the localization results are shown in Table 6. N.A. indicates that the localization result has an imaginary number.  
Figure 8: Experimental setup of acoustic emission (AE) detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Actual Location**} & \multicolumn{1}{c}{**TDOA Localization**} & \multicolumn{1}{c}{**Beamforming**} & \multicolumn{1}{c}{**Beamforming**} \\ \hline
**x (mm)** & **y (mm)** & **Result (mm)** & **Scanning Region** & **Localization Result (mm)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: Simulation localization results.  
It can be seen from Table 6 that when the experimental data are used for TDOA localization, the x-coordinates location are accurate, but the y-coordinates error are relatively large. However, the beamforming localization results are very accurate. Table 7 shows the comparison of the average calculation time between the beamforming method and the joint localization method proposed in this paper under different localization accuracy. Since beamforming scans only a quarter of the steel plate, the localization time of joint localization method is about a quarter of that of beamforming method. For large structures or high-precision localization, the benefits of using the joint localization method are obvious. At the same time, the memory requirements of the computer are reduced.
## 3 Results and Discussion
### Sensitive Analysis of Parameters  
#### 3.2.1 Effect of Dimension Ratio  
The pressure versus displacement curves when the value of \(DR\) is changed are shown in Figure 7. With the dimension ratio increase, the liner's critical buckling pressure decreases and the displacement increases. Comparing the buckling equilibrium paths in Figure 7a,b, it can be found that the pressure increase rate of the liner is significantly greater than the other groups when \(DR\) = 100, and the pressure reaches 15 kPa when the displacement of the buckling lobe reaches 70 mm in Group C-1, while the pressure reaches 4.76 kPa in Group C-2. The pressure does not increase significantly while the displacement of the buckling lobe increases in the pre-buckling equilibrium path when \(DR\) = 150, 200, 250 and 300. The critical buckling pressure of Group D-1 compared to Group C-1 decreased by 2.31 kPa, while the displacement increased 15.25 mm. Under the same conditions, decreasing the value of \(a/R\) will decrease the critical buckling pressure and increase the deformation of the buckling lobe.  
The comparison of the critical buckling pressure between the numerical result and calculation results when the value of \(DR\) is changed under different values of \(a/R\) are shown in Figure 8. The critical buckling pressure calculated by Equation (1) reduces by 7.5 kPa when DR changed from 100 to 150, while the critical buckling pressure of the simulation result reduced by 10.25 kPa and 10.15 kPa when the \(a/R\) = 0.99 and \(a/R\) = 0.98. The increase in annular gaps causes an increase in the amount of reduction in critical buckling pressure. The critical buckling pressure in Group C-2 decreases by 68.18% compared to Group C-1 and Group D-2 decreases by 79.74% compared to Group D-1. The reduction of the value of \(a/R\) enhances the decrease caused by the changing value of \(DR\) on the critical buckling  
Figure 7: The pressure–displacement curves of FIPP liner with different \(DR\) under (**a**) \(a/R\) = 0.99; (**b**) \(a/R\) = 0.98.  
pressure. When \(DR\) = 100, the critical buckling pressure of Group D-1 decreased by 15.36% compared to Group C-1; when \(DR\) = 200, the critical buckling pressure of Group D-3 decreased by 42.95% compared to Group C-3 and the effect caused by changing the value of \(a/R\) on critical buckling pressure reduced as the value of \(DR\) increased.
## 5 Implementation of Multimedia Independent Multipath Routing Algorithms
### Algorithmic Implementation Details
#### 5.2.3 Selection of the Sending Angle Range at the Source End  
For the source, because the nodes are not located in the middle of the allocated angle range, the transmission angle range of the path should be considered. In order to keep the sending routes between source nodes from crossing, the range of the source sending angle needs to be determined according to the receiving angle allocated by the sink terminal. The formula is as follows:  
\[\left\{\begin{array}{c}Angle_{Source}=Angle_{\text{sink}}+180\\ Angle_{Source\_begin}=Angle_{Source}-(Angle_{\text{sink\_begin}}-Angle_ {\text{sink\_begin}})\)\(\end{array}\)\(\end{array}\)\(\end{array}\right. \tag{33}\]  
As shown in Figures 4-8, for the sink, Src1 is located at 90 degree angle of the sink, and the range of angle allocated is (\(-\)45\({}^{\circ}\) to 45\({}^{\circ}\)), while the relative angle offset is (\(-\)135\({}^{\circ}\) to 45\({}^{\circ}\)). According to Equation (33), that is, the sink is at a 270 degree angle of SRC 1. According to Equation (33), \(Angle_{Source}=Angle_{\text{sink}}+180=270^{\circ}\), that is, the sink is at 270 degrees of Src1. The starting angle of Src1 is 270\({}^{\circ}-\)(\(-\)135\({}^{\circ}\)) = 405\({}^{\circ}\). The end angle is 270\({}^{\circ}-\)(\(-\)45\({}^{\circ}\)) = 315\({}^{\circ}\). The transmission curve is shown in Figure 8.
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 2 Materials and Methods
### Stem Detection and Fruit Body Subsidiary Positioning
#### 2.3.1 FasterNet Feature Extraction Network  
For the task of stem detection, there is a problem of high redundancy due to the similarity between feature maps. Moreover, in the apple grading pipeline, terminal devices have limited resources. To reduce the computational load and complexity of the model, this paper redesigns the feature extraction network using the efficient FasterNet model. The FasterNet network has three lightweight architectures: t0, t1, and t2. This study opted for the smallest model size, FasterNet-t0, with the specific network structure shown in the Backbone section of Figure 3. It includes four stages and consists of three modules: Embedding, Merging, and FasterBlock. These modules extract visual feature information of stems at the network's shallow layers and semantic feature information at deeper layers. The Embedding and Merging modules are used to transform the dimensions and sizes of feature maps. The core FasterBlock module was constructed based on the Partial Convolution (PConv) operator, as illustrated in Figure 4. PConv performs feature extraction only on local feature maps, while the remaining channels retain the original feature information. This reduces computational load and memory access while decreasing redundancy in feature information. The Pointwise Convolution (PWConv) operator is used to fully integrate cross-channel feature information, enhancing diversity, and a residual connection was introduced to prevent gradient vanishing.
## 4 Scheduling Method
### Scheduling a System with Three Distillers
#### 4.2.2 Case 2: Five Charging Tanks
The difference is that \(CTK_{4}\) is used to feed \(DS_{1}\) under the normal mode for some time due to that \(CTK_{4}\) has idle time for the schedule obtained by Proposition 5. Thus, by using the scheduling method given in Proposition 6, \(CTK_{4}\) is fully utilized, but \(CTK_{2}\) is empty and idle during \([\tau_{0}+\Psi,\)\(\tau_{0}+(\mu\times\alpha_{1})/f_{\text{pmax}}]\), i.e., the utilization ratio for \(CTK_{2}\) and \(CTK_{4}\) is \(\rho_{2}=1-(\mu\times f_{\text{d81}}-f_{\text{pmax}})/(\mu\times f_{\text{pmax}})\) and \(\rho_{4}=1\), respectively. In this case, we cannot make \(\rho=1\) for the entire system, since both \(CTK_{2}\) and \(CTK_{4}\) are emptied at time \(\tau_{0}+\Psi\) but only one of them can be charged at a time. Moreover, if we charge \(CTK_{2}\) with oil type 3 instead of oil type 2 at time \(\tau_{0}+(\mu\times\alpha_{1})/f_{\text{pmax}}\), the system can be scheduled similarly and the time taken for \(CTK_{1}\) to feed \(DS_{1}\) under the SCF mode is reduced to \(\varpi^{\prime}\times\Psi=(\mu\times f_{\text{d81}}+\mu\times f_{\text{d83}}-2 \times f_{\text{pmax}})/(f_{\text{d81}}+f_{\text{pmax}})\times\Psi\leq\varpi \times\Psi\) as \(f_{\text{d82}}\geq f_{\text{d83}}\) holds. Note that the charging of \(CTK_{1}\) is completed at time \(\tau_{0}+(\mu\times\alpha_{1})/f_{\text{pmax}}\) and does not feed \(DS_{1}\) during \([\tau_{2}-\varpi\times\Psi,\)\(\tau_{2}]\). If \(\tau_{2}-\max\{\tau_{0}\)\(+(\mu\times\alpha_{1})/f_{\text{pmax}}\), \(\tau_{2}-\varpi\times\Psi\}\geq\Psi\) holds, \(CTK_{1}\) can feed \(DS_{1}\) under the normal mode during \([\tau_{2},\)\(\tau_{3}]\), which implies \(\delta=(\mu-\varpi)/3\mu-f_{\text{d83}}/3f_{\text{pmax}}\). Moreover, we can further reduce \(\delta\) by increasing the utilization of both \(CTK_{2}\) and \(CTK_{4}\) rather than just one of them. By doing so, a more complex schedule with a large number of charging tank switches may be obtained. However, \(\delta\) is reduced slightly such that the obtained schedule is not meaningful in practice. Nevertheless, it is applicable for a system with more than three distillers, which is discussed in the next section.
## 6 Conclusions  
The approach used has shown improved performance in predicting dog sounds in various contexts. The reinforcement learning method was used for predicting and classifying the characteristics of dog sounds during various scenarios. An 85.19% prediction accuracy was achieved based on the training sounds of the two dog species, Rajapalayam and Kombai Hounds. The classification and predictions were made on the basis of the sounds recorded in different surroundings. The proposed prediction approach was efficient, and it could be implemented in other types of dogs to investigate its performance in the overall prediction of dog sounds. The Q-learning algorithm with k-nearest neighbor learns each state of action the dogs perform during particular scenarios. The proposed algorithm outputs an accurate prediction by estimating the optimal Q-function with high probability using the number of observations. Hence, dog sounds have contextual information that can be trained and predicted through machine learning approaches. The limitations of the work include the lack of analysis of the behavior of the dogs during different scenarios, such as different environments, climate factors, etc., which still need to be analyzed. Different breeds of dog could be chosen for study to identify the nature of their behavior.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, writing--original draft preparation--P.M., J.S.A., K.H., D.V., T.M., V.R., M.M.A. and R.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Ethical review and approval were waived for this study as no animal was harmed during the study.  
Not applicable.  
The authors declare no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.  
Figure 8: Comparison of prediction results between trained and untrained dog sounds.
# Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
## 3 Results
### Analysis of False Results  
Falsely predicted images using to Md5 are presented in Figure 4. One of the FN regions exhibited relatively small tumor clusters and neural bundles (Figure 4b). In another region, the surrounding inflammatory cells and nerve cells were misclassified as tumor cells (Figure 4d). An FP region included thick blood vessels around a tumor, which were misclassified as PNI. The model falsely identified the smooth muscle cells of the wall of vessels as nerve bundle Schwann cells because of their similar spindle shapes. All the predicted results regarding Md1 to Md6 can be accessed through the web page ([[http://pni.ssus.work/](http://pni.ssus.work/)]([http://pni.ssus.work/](http://pni.ssus.work/)), accessed on 13 September 2022).  
In the current pipeline, falsely predicted results can be classified into six subgroups according to the types of tissue in error. FN prediction occurred in tumor, nerve, or both tissues (Figure 52a,c,e). FP originated from errors in tumor or nerve tissues (Figure 52b,d). The FP result was not observed in either the tumor or nerve tissues of our model. This classification allowed us to intuitively distinguish the tasks the model performed incorrectly, providing the interpretability of false results in the current model.  
Figure 3: ROC curves of pipelines used for detecting PNI across various segmentation models. The results revealed that Md5, using a multi-class segmentation network, achieved the highest AUC of 0.92.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Module a** & **Accuracy\({}_{\text{R}}\)** & **Sensitivity\({}_{\text{R}}\)** & **Specificity\({}_{\text{R}}\)** & **NPV\({}_{\text{R}}\) b** & **Precision\({}_{\text{R}}\)** & **F1-Score\({}_{\text{R}}\)** & **AUC (95\% CD)** \\ \hline Md1 & 0.85 & 0.85 & 0.85 & 0.85 & 0.85 & 0.85 & 0.85 \(\pm\) 0111 \\ Md2 & 0.80 & 0.85 & 0.75 & 0.83 & 0.77 & 0.81 & 0.80 \(\pm\) 0.124 \\ Md3 & 0.80 & 0.75 & 0.85 & 0.77 & 0.83 & 0.79 & 0.80 \(\pm\) 0.124 \\ Md4 & 0.72 & 0.75 & 0.70 & 0.74 & 0.71 & 0.73 & 0.72 \(\pm\) 0.138 \\ Md5 & 0.92 & 0.90 & 0.95 & 0.90 & 0.95 & 0.92 & 0.92 \(\pm\) 0.078 \\ Md6 & 0.88 & 0.80 & 0.95 & 0.83 & 0.94 & 0.87 & 0.88 \(\pm\) 0.102 \\ \hline \hline \end{tabular}  
* Architectures used for nerve and tumor segmentation in each sequential binary model are as follows: Md1: U-Net + U-Net; Md2: U-Net + DeepLabv3+; Md3: DeepLabv3+ + U-Net; Md4: DeepLabv3+ + DeepLabv3+. In Md5, U-Net is adopted for simple multiple segmentation; Md6: SegFormer is adopted for simple multiple segmentation. b Negative Predictive Value.  
\end{table}
Table 3: Performance of PNI classifier according to the various combinations of segmentation models.
# Investigating a Toolchain from Trajectory Recording to Resimulation
## 3 Results
### Quantitative Comparison  
A quantitative comparison of velocities between recorded and simulated trajectories was performed using the Root Mean Square Error (RMSE) presented in Table 4. The spatial deviation of trajectories has been defined by the RMSE of the Distance of the Nearest Neighbors (DNN) between locations along the trajectories.  
These values underline the visual interpretation of the comparison between recorded and simulated trajectories for each scenario. Significant deviations are most prominent for the non-linear scenarios, which are due to systematic errors as described above and the existing behavioral models that are not suitable for non-linear scenarios. The large deviations in velocity and spatial distance in scenario 2 result from the lack of traffic rules in the simulation. For scenario 3, the deviation stems from the simulated car that does not consider a lateral safety distance, i.e., to wait until the pedestrian has left the street entirely before continuing to drive.  
Figure 9: Results for Scenario 3: (**a**) Recorded (blue) versus simulated (green) vehicle trajectories; (**b**) Recorded (blue) versus simulated (green) velocities of the pedestrian; (**c**) Recorded (blue) versus simulated (green) velocities of the vehicle.
## Materials and Methods
### Satellite Imagery  
Satellite images upon which the visual interpretation was based were PlanetScope, RapidEye, and Sentinel-2, which were all in their true color image (red, blue, and green).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Site** & **Biome Type** & **Condition of Forest in the Area \({}^{*}\)** \\ \hline A & _Acacia-Commiphora_ and partly dry Afromontane & Both tree gain and loss of similar magnitude \\ B & Moist Afromontane & Characterized by tree loss \\ C & Partly moist Afromontane and partly _Acacia-Commiphora_ & Characterized by tree loss and very little gain \\ D & Moist Afromontane & Characterized by tree loss \\ E & Moist Afromontane & Both tree gain and loss of similar magnitude \\ F & Dry Afromontane & Characterized by tree gain \\ G & _Combretum-Terminalia_ & Characterized by tree loss \\ \hline \hline \end{tabular} \({}^{*}\) Source: Global Forest Watch (www.globalforestwatch.org).  
\end{table}
Table 1: Description of the study sites.  
Figure 1: The major biomes in Ethiopia and the seven sites (A, B,..., G) selected for the study.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 3 Experiments and Results  
### Datasets  
In this study, we used two datasets to train and pre-train the model: the Tibetan Lhasa dialect dataset and the LJSpeech dataset [24].  
We utilized the Tibetan multi-dialect speech dataset from the Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE at Minzu University of China to train the model. In this experiment, we employed the 16 kHz, 16-bit mono Tibetan Lhasa dialect dataset. This dataset, which includes recordings from four different speakers, has a total duration of approximately 7.47 h and contains 6452 speech samples, with an average duration of approximately 4 s per sentence.  
To further improve the model's performance, we also used the LJSpeech dataset for pre-training. LJSpeech is a widely used, open, single-speaker speech dataset, featuring recordings of a female speaker. This dataset has a total duration of approximately 24 h, containing 13,100 speech samples, with an average duration of 6.57 s per sentence. The audio specifications are 22.05 kHz, 16-bit mono.  
For consistency, we downsampled the LJSpeech audio to 16 kHz to match the sampling rate of the Tibetan Lhasa dialect dataset. This ensures uniformity in audio processing across both pre-training and training stages.  
In the training process, we used two GPUs for parallel processing, with the batch size set to 32. The output of the acoustic model is an eighty-dimensional Mel spectrogram, which is converted into audio samples using the pre-trained HiFi-GAN vocoder. The generated audio signals have a sampling frequency of 16 kHz.
## 4 Discussion and Conclusions  
This section discusses the findings of this study with the focus on analysing the future applicability of this numerical methodology in a broader context, as introduced in Section 1. First, the accuracy of the simulation results was compared with the accuracy reached by the container experimental measurement system as additional benchmark. [PERSON] et al. [22], as measurement system, employed an optical tracking algorithm introduced in [PERSON] et al. [57] where it is investigated its accuracy using the parameters introduced in Section 2.5. The mean values of the parameters calculated by [PERSON] et al. [57] for the experimental validation tests with a single container are summarised in Table 6. \(|\)ME\(|\) and MDE values for the simulations of the present study are additionally calculated for both OR1 and OR2. This helps to highlight the very similar accuracy of the present numerical model and the experimental tracking system in the description of the container trajectory giving further confidence on the employed numerical method.  
Figure 8: Qualitative comparison of the flow debris interaction between (**a**–**c**) OR1 and (**d**–**f**) OR2.  
In conclusion, the coupled models DualSPHysics and CHRONO can accurately simulate the behaviour of a debris transported by a dam break flow. Note that the modelling accuracy is referred to the mean trajectory among the ones resulting from repetitions of the experiment. This is because the numerical model was calibrated using the mean flow and container kinematic conditions, which was necessary given the highly variable characteristics of this phenomenon. Additionally, the calibration process of the model has shown that the details of the container internal structure and, above all, mass distribution is essential to achieve accuracy due to the importance of the container rotation in its kinematics. This rotation is, in fact, responsible for the container drift from the flume axis as in its absence the container would otherwise follow an almost one-dimensional trajectory. The difference in drift from the flume axis between simulations and experiments during the initial stage of the container motion, in turn affecting the velocity, suggests a lower accuracy in the forces if an impact would occur during this stage. However, as the trajectory stabilises the accuracy significantly improves especially for OR1. This has important consequences in modelling the impact of these debris on structures, which is the natural next step of this investigation. It is expected that impact forces are more accurately predicted if the impact occurs at later stages of the transport.  
The authors declare no conflict of interest.