esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 3 Proposed Approach
### Computer Vision Model
#### 3.1.1 Dataset  
To train the computer vision models, we used a public dataset [27] that contains 284 images (5184 \(\times\) 3456) of yellow sticky traps with WF and other insects (Figure 5). The dataset contains 4940 annotated WF instances and, as described by the dataset authors [27], some unannotated or improperly annotated instances. This affects the precision of CV models, as the detection of an unannotated instance is classified as a False Positive detection. To improve the dataset, we contacted technicians and, with their help, we annotated the missing WF instances. From that process, the total annotations passed from 4940 to 5863 WF instances. Finally, we divided the whole dataset into a training (80%) and a testing (20%) dataset.
## 4 Simulation Study on Foam Acoustic Parameters
### Effect of Foam tortuosity Factor on Acoustic Radiation Performance  
The tortuosity factor is a parameter that quantifies the degree of curvature of internal cavities in porous materials. Its essence is the ratio of the shortest path between one end and the other end of the porous material to the material's thickness. Therefore, it is a dimensionless number with a minimum value of 1 and no theoretical upper limit [23]. To examine how the tortuosity factor of the filled foam affects the reinforced laminated plate's structural sound radiation performance, other parameters were kept constant as different foam tortuosity factor values were tested: the original value, double the original value, and half the original value, which equate to 2.52, 5.04, and 1.26, respectively. Using the provided values in the calculations, Figure 11 illustrates the sound radiation power for different tortuosity factors.  
According to Table 4, at frequencies near 854 Hz, the values are 39.22 dB, 40.44 dB, and 41.68 dB. At frequencies near 1467 Hz, the values are 17.86 dB, 17.78 dB, and 16.06 dB. Finally, at frequencies near 1588 Hz, the corresponding values are 43.17 dB, 42.42 dB, and 39.51 dB. This suggests that, within the mid-low frequency range, there is a positive correlation between the tortuosity factor and the radiated sound power. As the frequency increases, the tortuosity factor increasingly inhibits the radiated sound power, with the magnitude of this effect rising with the tortuosity factor.  
In conclusion, it can be seen that different peak frequencies exhibit varying effects on the sound radiation of the plate with respect to the tortuosity factor. At approximately 854 Hz, the radiated sound power increases as the tortuosity factor increases. However, around 1467 Hz and 1588 Hz, the radiated sound power decreases as the tortuosity factor increases. In addition, it should be noted that at any peak frequency, an increase in the tortuosity factor results in a shift of the sound power peak towards lower frequencies.
## 1 Introduction
The use of artificial physics (virtual forces) makes the guidance law founded on solid physical theory and computationally simple. The physical meanings of the parameters are definite, and the VFGL has a large parameter adaptation. These factors make the guidance law easy to tune in application. To demonstrate the performance of the proposed guidance law, different parameters were evaluated in the simulation. The results show that the VFGL is easy to tune in application, and has a large parameter adaptation. Several methods were implemented in the simulation as comparison. The obtained simulation results demonstrated the superiority of the proposed guidance law. The VFGL performed over the NLGL, PLOS, and PID in curve following, and performed over GVF and VFF in obstacle avoidance during straight-line following. To further evaluate the VFGL, a highly realistic hardware-in-the-loop (HIL) simulation system was developed by combining an autopilot with a heavily developed and commercially available flight simulator, X-Plane. Then, the VFGL was applied to the problem of path following and obstacle avoidance for a fixed-wing UAV. The results demonstrated the effectiveness of the VFGL. This paper is organized as follows. In Section 2, we formulate the problem. In Section 3, the idea and the procedure of the VFGL is presented for path following and obstacle avoidance. In Section 4, both numerical simulation and the HIL experiment are described to demonstrate the effectiveness of the proposed guidance law. Finally, we conclude the paper in Section 5.
## 4 Experimental Results and Analysis
### Model Robustness
#### 4.2.1 Robustness of the Designed System to Image Resolution  
In this study, the whole test set of the IP102 dataset is adopted as a test bench. Five image datasets with different resolutions are generated to evaluate the influence of the resolution factor on the method performance. The five datasets are composed of the same images (all the images of the IP102 test set) but they differ in image resolution value. Denoting with res the resolution of the images of the IP102 test set, the five datasets are composed of images with a resolution equal to 90% res, 80% res, 65% res, 50% res, and 25% res, respectively.  
Figures 11 and 12 show the robustness against resolution degradation of model A and model B, respectively.  
Figure 11: Model A performance in pest localization and classification versus image resolution.  
By analyzing the obtained results, the ability of both model A and model B to maintain adequate performance even under extreme conditions due to the low quality images adopted as the test bench is highlighted.
# Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
## 3 Our Methodology
### Loss Functions  
For efficient network training, we employ a composite loss function \(\mathcal{L}_{joint}\), incorporating global shape consistency loss \(\mathcal{L}_{gsc}\), local geometric structure consistency loss \(\mathcal{L}_{gsc}\), and uniform loss \(\mathcal{L}_{ui}\)[17].  
\[\mathcal{L}_{joint}=\alpha\mathcal{L}_{gsc}+\beta\mathcal{L}_{gsc}+\gamma \mathcal{L}_{ui}+\eta||\theta||^{2} \tag{6}\]  
where \(\alpha,\beta,\gamma\) are hyperparameters to balance the various loss weights, \(\eta\) is the weight decay hyperparameter, and \(\theta\) is the hyperparameter in SSPU-FENet.  
**Global shape consistency loss.** The input sparse point cloud \(S\) can generally be considered as a downsampled version of the output dense point cloud \(D\), which shares consistent global shapes. By applying a downsampling function \(g(\cdot)\), we obtain the downsampled point cloud \(\hat{D}\) from \(D\). The covariance matrices \(C_{\hat{D}}\) and \(C_{S}\) are computed for \(\hat{D}\) and \(S\), respectively, effectively capturing the point cloud structure for applications like 3D detection, similarity analysis, and 2D image matching. The similarity measure between these two covariance matrices is then computed as follows:  
\[\rho\big{(}C_{\hat{D}},C_{S}\big{)}=\sqrt{\sum_{i=1}^{3}{ln^{2}\lambda_{i}(C_{ \hat{D}},C_{S})}} \tag{7}\]  
where \(\lambda_{i}(C_{\hat{D}},C_{S})\) denotes the generalized eigenvalues of the covariance matrix \(C_{\hat{D}}\) and \(C_{S}\), i.e., computing \(\big{|}\lambda C_{\hat{D}}-C_{S}\big{|}=0\).  
Consequently, the input-output geometric feature consistency loss function is expressed as follows:  
\[\mathcal{L}_{gsc}=\min_{g}\rho\big{(}C_{\hat{D}},C_{S}\big{)} \tag{8}\]  
where \(\hat{D}=g(D)\).  
**Local geometric structure consistency loss.** We introduce the local geometric structure consistency loss for point cloud upsampling tasks, motivated by the observation that 3D models usually share consistent (similar) local geometric structures. An illustration can be seen in Figure 4, where two regions highlighted in red and blue exhibit similar local geometric details. The measurement of such similarity can be computed by their Gram matrices [20; 21]. Without loss of generality, assuming that the output dense point cloud is denoted by \(D\), we construct the Gram matrix \(G(x_{i})\) (\(G(x_{i})\)\(\in\mathbb{R}^{k\times k}\)) for any point \(x_{i}\in D\) with the assistance of the KNN algorithm. The local geometric structure consistency loss is thus defined as follows.  
Figure 4: Schematic diagram of local geometric structures’ consistency.  
\[\mathcal{L}_{lgsc}=\frac{1}{m}\sum_{j=1,j\
eq i}^{m}{||G(x_{i})-G(x_{j})||}_{F} \tag{9}\]  
where \(m\) denotes the number of regions sharing similar local geometric structures with the region of point \(x_{i}\). \(1\leq m\leq N-1\) (\(N\) is the number of points).
## 5 Discussion
### Advantage and Limitation  
Large-scale crop mapping is restricted by the difficulty in obtaining sample data and the lack of generalization ability of models [38; 39]. Transfer learning provides a promising solution to this problem by transferring samples or models across regions or years [34; 42; 43; 44]. However, existing research on the size of the sample dataset required for model transfer is lacking, and research on the difference between the transferred model and the model trained with a large number of local samples is also insufficient, which restricts large-scale crop mapping based on the transfer learning method. In response to this situation, this study evaluated the impact of different sample sizes on model transfer accuracy, analyzed the spatiotemporal generalization ability of the transferred model and possible influencing factors that affect this ability. It was found that the transferred model can achieve accuracy close to those trained with a large number of local samples, providing a solid practical foundation for crop mapping based on transferred models. In addition, only time-series SAR images were used in this study, making it easier to combine transfer learning methods for applications of large-scale crop mapping.  
However, there are still certain shortcomings in this study. Firstly, although SAR data has a strong recognition ability for rice, its recognition ability for other crops is relatively low. Therefore, optical images are required to improve classification accuracy. Secondly, the crop mapping model is still limited in its recognition ability under complex situations, such as different crop phenology and planting structures. Thirdly, only CDL data were used as label data in this study, which limited feature extraction ability of pre-trained models in other regions. With the increasing abundance of label data, a more powerful pre-trained model based on more and more widely distributed crop label data will help improve crop mapping ability based on transfer learning. Finally, it should be noted that the validation samples and reference rice maps used in Northeast China in this paper, as well as the CDL data from the United States used for model pre-training, are not entirely accurate label data. Due to the need for a large number of samples to support crop mapping based on deep learning, current research on large-scale deep learning-based crop mapping mainly relied on existing high-accuracy crop classification results such as CDL [21; 22; 34]. Assessing theimpact of inaccurate label data on the reliability of deep learning crop mapping models will help improve the credibility of crop mapping results based on deep learning.
# A Study on the Vertical Bearing Characteristics of Screw Piles in Permafrost Regions
## 2 Model Test
### Test Results and Analysis  
The load-settlement curves obtained from the two types of piles under model tests are indicated in Figure 4. As depicted in the figure, the load-settlement curve of the ordinary pile indicates a typical failure mode of the friction pile, with a settlement of 4.12 mm under the load of 1.58 kN. For the screw pile, the initial load stage is 1.4 kN, while the failure load reaches 4.69 kN, with a corresponding settlement of 4.99 mm. The ultimate bearing load of the screw pile is 2.97 times greater than that of the ordinary pile.  
The lateral friction resistance of screw piles is simplified and calculated based on the equivalent lateral friction resistance of the pile's body, and the calculation formula is as follows [10; 20]:  
\[\tau_{i}=\frac{Q_{i+1}-Q_{i}}{\pi Dh_{i}} \tag{1}\]  
Figure 4: Load-settlement curves of two types of piles under model tests.  
Figure 3: Test equipment system: (**a**) Model pile, low-temperature test chamber, and loading system. (**b**) Loading displacement and pressure acquisition system.  
where \(\tau_{i}\) represents the equivalent friction resistance of the pile's side (kPa). \(Q_{i+1}\) and \(Q_{i}\) are the axial forces at both ends of the pile's body's stress section, respectively (kN). \(D\) denotes the diameter of the pile's body, and the screw pile takes the outer diameter (m). \(h_{i}\) represents the length of the bearing section of the pile's body.  
Figure 5 indicates the axial force and the equivalent frictional resistance of the model piles. It can be observed from the figure that the axial force on the ordinary pile's body gradually decreases along the depth, and the lateral friction resistance of the pile begins from the top of the pile, with the increase of the load, and then gradually reaches the maximum shear strength of 35 kPa. The axial force on the screw pile changes sharply, and the equivalent friction resistance of the pile's side indicates non-uniformity, and the friction resistance of the pile's top develops first with the increase of the load. Generally, the equivalent friction resistance on the side of a screw pile is significantly greater than that of the ordinary pile.  
Figure 5: Axial force and equivalent friction resistance of model pile: (**a**) Ordinary pile; (**b**) screw pile.
# Revisiting Text Guide, a Truncation Method for Long Text Classification
## 3 Results and Discussion
### Can Text Guide Be Used Successfully for Other Data Sets?  
To demonstrate that Text Guide can be successfully deployed on another data set, for the IMDB FULL and IMDB 510-1000 data sets, we repeated the final experiments carried out earlier for DMOZ data sets with various models. The experiments on DMOZ data sets were conducted without any data set-specific Text Guide optimization. Instead, we adopted parameter values that performed well for the AlBERT model and DMOZ data sets in previously described experiments. The results presented in Table 7 allow us to conclude that: (1) For three out of four tested models, the utility of the Text Guide was evident when considering the IMDB 510-1000 data set in which all data instances could be transformed with Text Guide. Specifically, for the RoBERTa model, the positive impact of the Text Guide was highly significant (\(p\) = 0.0001). However, there was no significant (\(p\) = 0.7118) improvement provided by the Text Guide for BERT. Text Guide used with DistilBERT significantly (\(p\) = 0.01) improved performance over naive truncation. The application of the Text Guide for the SqueezeBERT model was significant to a minor extent (\(p\) = 0.0135). (2) In the case of IMDB FULL data set, given the great dominance of text instances shorter than 510 tokens, the obtained differences in results were mostly inconclusive. The RoBERTa model trained with the Text Guide was the only one with weak significance (\(p\) = 0.0873) in improvement over naive truncation. Finally, there were no significant (\(p\) = 0.9078) improvements for the BERT model, as was also the case for the DistilBERT (\(p\) = 0.6238) and SqueezeBERT (\(p\) = 0.1106) models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Truncation** & **DMOZ** & **DMOZ 1500+** & **DMOZ 4090+** \\  & **Method** & **510–1500** & **DMOZ 1500+** & **DMOZ 4090+** \\ \hline \multirow{2}{*}{RoBERTa} & Naive & 0.8350 & 0.7466 & 0.6793 \\  & Text Guide & 0.8358 & 0.7767 & 0.7158 \\ \hline \multirow{2}{*}{BERT} & Naive & 0.7952 & 0.7168 & 0.6427 \\  & Text Guide & 0.7673 & 0.7047 & 0.6353 \\ \hline \multirow{2}{*}{DistilBERT} & Naive & 0.7997 & 0.7188 & 0.6440 \\  & Text Guide & 0.7973 & 0.7412 & 0.6794 \\ \hline \multirow{2}{*}{SqueezeBERT} & Naive & 0.8107 & 0.7273 & 0.6485 \\  & Text Guide & 0.8235 & 0.7720 & 0.7295 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experiments performed with selected pre-trained transformer models. Text Guide parameters were adopted (1) from the optimization procedure for the AlBERT model from the original Text Guide study and (2) according to suggestions regarding other parameter values, as presented in this study.
# Optimal Design and Hybrid Control for the Electro-Hydraulic Dual-Shaking Table System
## 2 System Design and Optimization
### Hydraulic Cylinder Design  
Hydraulic cylinders are the actuators of the hydraulic system, as is shown in Figure 1. Figure 2 shows the hydraulic principle diagram of the horizontal and vertical valve-controlled hydraulic cylinders, respectively. The motion of shaking tables has the characteristics of high frequency and small displacement amplitude. So, to reduce the friction and wear and prolong the lifespan of hydraulic cylinder, the clearance seal technique [34; 35] is adopted. Note that the horizontal hydraulic cylinders are designed as asymmetric to save space.  
For each horizontal hydraulic cylinder, with the maximum acceleration 2 g shown in Table 1, the maximum dynamic force is derived as  
\[F_{x,y}=\left(\frac{m_{0}}{2}+\frac{m}{2}\right)\times a=1.47\times 10^{5}\mathrm{N} \tag{1}\]  
where, \(m_{0}\) is the mass of the table, \(m_{0}\) = 5 t; \(m\) is the mass of maximum load, \(m\) = 10 t; \(a\) is the maximum acceleration, \(a\) = 2 g.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values Units** \\ \hline Size of the platform & 2.5 m \(\times\) 2.5 m \\ Maximum mass of load & 10 t \\ Maximum displacement & \(\pm\)0.125(\(X/Y\)), \(\pm\)0.1(\(Z\)) m \\ Maximum velocity & \(\pm\)0.5 m/s \\ Maximum acceleration & 2.0 g \\ Frequency width of system & 0.1–50 Hz \\ D1/D2/D3 in Figure 1b & 2.547/1.55/2.7 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of dual-shaking table system.  
Figure 2: The hydraulic principle diagram of the designed valve-controlled hydraulic cylinders.  
To ensure the system performance, we define 80% of the maximum static force of hydraulic cylinder as the maximum dynamic force, so the effective area of the hydraulic cylinder is  
\[A_{x,y}=\frac{5F_{x,y}}{4P_{s}}=73.5\;\mathrm{cm}^{2} \tag{2}\]  
where, \(P_{s}\) is the hydraulic power supply pressure, \(P_{s}=25\;\mathrm{MPa}\).  
In order to round the diameters, according to the hydraulic design handbook [36], the diameters of piston rod and piston are chosen as 100 mm/140 mm, which has a close effective area to the computed value in Equation (2). Consequently, the maximum static thrust/drawing forces of horizontal hydraulic cylinder designed are 384 kN/188.5 kN. The maximum dynamic thrust/drawing forces are 307 kN/150.8 kN.  
Similarly, for the vertical hydraulic cylinder, the maximum force is  
\[F_{z}=\left(\frac{m_{0}}{4}+\frac{m}{4}\right)\times a+\frac{M}{4D_{2}}=1.22 \times 10^{5}\mathrm{N} \tag{3}\]  
where, \(M\) is the maximum overturning moment, \(M=150\;\mathrm{kN}\cdot\mathrm{m}\).  
The diameters of the piston rod and piston are 100 mm/140 mm. The maximum static/dynamic forces are 188.5 kN/150.8 kN.
## 4 Experiment Details and Results
### Final Solution
The cases where the algorithm fails to identify the optimal solution stem from the fact that when the diversity constraint is fulfilled for a certain category, the recommendations of this category can only change on the condition that the diversity and coverage constraints for this category are not violated with a certain swap. Thus, it is possible to find a different recommendation plan in which the total predicted rating would be slightly higher than the total rating of the heuristic solution. For example, the approach at hand, fails to identify cases where the most recommended item's rating of a category is not as good as the second ones, while by recommending the second most recommended item of the category fulfills the diversity constraint. Moreover, if in a certain swap a category's diversity constraint is violated, the heuristic solution will not revisit the latter category to fix its diversity, even if there are options to do that. However, at least for the specific dataset the results are very close, not only to the optimal solution but also to the baseline solution, which is the upper bound for the total rating.
## 3 Results and Discussion
The improvements in wear resistance as well as reduction in friction coefficient after addition of NDs has also been found for acrylonitrile butadiene rubber, fluororubber and epoxy [25; 45; 47]  
Dynamic properties play an important role in several rubber applications, such as tires or dampers. Figure 5 presents the storage and loss moduli of the compounds as a function of temperature. The storage modulus curve can be divided into three stages: the glassy region, glass transition and the rubbery region. In the rubbery plateau region, the storage modulus is the highest when 0.5 phr CB is replaced by NDs, possibly due to the improved filler dispersion. When the ND loading increases, the storage modulus decreases. At the same time, a reduction in loss modulus is observed for higher ND loadings due to the plasticizing effect [25] and decreased polymer-filler interaction. Figure 3: The effect of NDs on the mechanical properties (**a**) tensile strength, (**b**) elongation at break, (**c**) 300% modulus, and (**d**) tear resistance. Figure 4: Wear resistance of ND-CB filled rubber compound. Figure 5: The effect of partial replacement of CB by NDs on (**a**) storage modulus, (**b**) loss modulus. The loss factor of the compounds is presented in Figure 6. The gradual replacement of CB by NDs has only minor influence on the peak intensity. This behavior differs from the behavior of CNTs. The height of the loss factor peak decreases when CB is partially replaced by them due to the restricted mobility of the polymer chains [14]. In this case, only the compound containing 0.5 phr NDs show this effect, and still the effect is minimal. The addition of the higher amount of NDs results in the higher peak, thus NDs improve the mobility of the polymer chains. However, they do not affect the glass transition temperature. The loss factor curve is used to predict the tire properties, such as wet grip and rolling resistance. At \(-20\)\({}^{\circ}\)C, the loss factor is improved when 1 or 2.5 phr CB is replaced by NDs, which indicates better grip on ice. At \(0\)\({}^{\circ}\)C, the temperature used to predict the wet grip, the compound containing 1 phr NDs shows the highest loss factor and thus the best wet grip in tire applications. The loss factor values at \(60\)\({}^{\circ}\)C are used as an indicator for the rolling resistance. At this temperature the loss factor is increasing when 0.5 or 1 phr CB is replaced by NDs, indicating the poorer polymer-filler interaction of NDs and the chain slipping of polymers. However, at a 2.5 phr ND concentration, the loss factor is remarkably lower than without NDs. Hence, the ND-CB filler combination has potential to reduce the rolling resistance in tires with proper concentrations.