esa-sceva/satcom-chunk-collection huggingface dataset:
# R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption
## 4 Experiment and Analysis
### Experiment Process  
The server hardware configuration for all network training is as follows: Ubuntu 20.04 system, 2-core 12-thread Intel Xeon Platinum 8255C processor, 1 NVIDIA RTX 2080 Ti graphics card, Pytorch 1.4.0.  
In order to exclude the discrepancy of a training environment among compared networks, we repeated the rest of the networks under the mentioned conditions. During the training period, the augmentation strategy is as follows: random anisotropic scaling in the range [\(-\)0.67, 1.5]; translation in the range [\(-\)0.2, 0.2]; and random dropout 20% points. For all network models, the batch size is 16, the training epoch is 200, and the initial learning rate is 0.01.  
R-PointNet is tested on two datasets, including ModelNet40, a synthetic dataset commonly used as a point cloud recognition benchmark, and ModelNet40-C, the latest corruption dataset used for robustness benchmark, in order to simulate real-world applications.  
ModelNet40 is a point cloud synthesis dataset that is widely used for point cloud recognition benchmark tests. It contains 40 classes with 12,311 point cloud models. For a fair comparison with other networks, we used the official division with 9843 objects for training and 2468 objects for validation. The same sampling strategy as in PointNet++ was used, sampling each object uniformly to 1024 points. ModelNet40-C is the first systematic corruption robustness benchmark for 3D point cloud recognition. It contains 185,000 models  
Figure 5: Architecture of our R-PointNet. R-PointNet iteratively extracts the features from local regions of the point cloud and integrates local features across different regions and levels into the local-global feature by set abstraction levels, while the Offset-Adjustment module minimizes the effect of anomalies in the raw point cloud. Then, the muti-level features are integrated into local–global feature. Finally, R-PointNet obtains the recognition results by several fully connected layers.  
of point clouds with three common categories of distortion: density distortion, noise distortion and transformation distortion. Details are shown in Figure 2.
## 2 Materials and Methods
### Measurement Method and Experimental Setup  
There are various methods for determining the breakdown strength of solid insulating materials. The following methods are suggested for different voltage types by the IEC-60243 and IEC-60060 standards [26; 27; 36; 37; 38]: if the breakdown strength of the insulating materials to be tested shows relatively low voltage, there is no harm in applying the test methods mentioned in this section in air. However, if voltage levels where surface discharge will be severe are to be reached, the relevant breakdown strength tests should be performed in environments with stronger insulation properties such as mineral oil, silicone fluid, or ester fluids [39; 40; 41].  
In the method called \"Short-time (rapid-rise) test\" in section 10 of IEC 60243-1, the test voltage is increased from zero to the insulating material at speeds of 100 V/s, 200 V/s, 500 V/s, 1000 V/s, 2000 V/s, 5000 V/s, etc. The 500 V/s speed among these rising speeds is the most widely preferred rising speed in determining the breakdown strength of different types of materials.  
Breakdown tests were carried out in a polymethyl methacrylate (plexiglass) test container with dimensions of 200 \(\times\) 200 \(\times\) 200 mm. The test container is shown in Figure 2. The cylinder-cylinder electrode arrangement in the test electrodes is a system consisting of identical cylinder electrodes with rounded corners of a 3 mm radius, 25 mm diameter, and 25 mm height, as mentioned in the IEC 60243-1 standard.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Qidi Tech Q1 Pro 3D** \\ \hline Print Size (W\(\times\)D\(\times\)H) & 245 \(\times\) 245 \(\times\) 240 mm \\ \hline XY Structure & CoreXY \\ \hline Print Head Temperature & \(\leq\)350 \({}^{\circ}\)C \\ \hline Hot Bed Temperature & \(\leq\)120 \({}^{\circ}\)C \\ \hline Max Speed of Tool Head & 600 mm/s \\ \hline Chamber Temperature & 60 \({}^{\circ}\)C Independent Chamber Heating \\ \hline Recommended Filament & PLA, ABS, ASA, PETG \\ \hline Compatible Filament & TPU, PA, PC, Carbon/Glass Fiber-Reinforced Polymer \\ \hline Automatic Leveling & Hands-free Automatic Leveling \\ \hline \hline \end{tabular}
\end{table}
Table 2: Qidi Tech Q1 Pro 3D printer technical specifications [35].  
Figure 1: Qidi Tech Q1 Pro 3D printer.  
An AC voltage source with a capacity of 90 kV was used to apply voltage to the insulating materials, with rise rates set at 500 V/s. To prevent surface discharges, the electrodes and test sample were submerged in Nytro Lyra X mineral oil within the test container. The connection diagram used for the tests is presented in Figure 3.
## 3 Theoretical Model
### Rayleigh Approximation Theory  
Given the relatively small radius of fog droplets, the Rayleigh approximation model is suitable for calculating fog attenuation. In accordance with this model, the fog attenuation of terahertz waves can be expressed as [33]:  
\[\alpha_{\rm c}=K_{f}\cdot M\ (\rm dB/km), \tag{8}\]  
where \(M\) is the density of liquid water in fog (\(\rm g/m^{3}\)); \(K_{f}\) is the attenuation coefficient in (\(\rm(dB/km)/(g/m^{3})\)), which is calculated by:  
\[\left\{\begin{array}{c}K_{f}=\frac{0.819f}{\varepsilon^{\prime\prime}(1+ \eta^{2})},\\ \eta=\frac{2+\varepsilon^{\prime}}{\varepsilon^{\prime\prime}},\end{array}\right. \tag{9}\]  
where \(f\) is the signal frequency (GHz); \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) are the real and imaginary parts of the complex permittivity, respectively, which are determined by:  
\[\left\{\begin{array}{c}\varepsilon^{\prime}(f)=\frac{\varepsilon_{0}- \varepsilon_{1}}{\left[1+\left(f/f_{p}\right)^{2}\right]}+\frac{\varepsilon_{1 }-\varepsilon_{2}}{\left[1+\left(f/f_{s}\right)^{2}\right]}+\varepsilon_{2}, \\ \varepsilon^{\prime\prime}(f)=\frac{(\varepsilon_{0}-\varepsilon_{1})f}{f_{p} \left[1+\left(f/f_{p}\right)^{2}\right]}+\frac{(\varepsilon_{1}-\varepsilon_ {2})f}{f_{s}\left[1+\left(f/f_{s}\right)^{2}\right]},\end{array}\right. \tag{10}\]  
where \(\varepsilon_{0}=77.66+103.3(\theta-1)\), \(\varepsilon_{1}=0.0671\varepsilon_{0}\), \(\varepsilon_{2}=3.52\), \(\theta=300/(273.15+T)\), and \(T\) is the temperature of rain and fog (\({}^{\rm C}\)).  
The main relaxation frequency \(f_{p}\) and the secondary relaxation frequency \(f_{s}\) are:  
\[\left\{\begin{array}{c}f_{p}=20.20-146(\theta-1)+316(\theta-1)^{2}\ \ (\rm GHz ),\\ f_{s}=39.8f_{p}\ \ (\rm GHz).\end{array}\right. \tag{11}\]
## 3 Results and Discussion
### Build Orientation Effect
#### 3.1.2 Case 2: Adjusting the Loading Condition  
The loading condition of the L-beam was changed from the lower right corner of the leg to the right face, while all other factors remained unchanged, as shown in Figure 9a. The result of the topology optimization shows that there is no significant improvement in the structure of the beam despite a substantial increase in the objective function values. Moreover, the optimized design has limited practical applicability and would require more post-processing efforts as seen in Figure 9b,c.  
Using various computational experiments with different build orientations, it has been observed that even when changing constraint combinations or loading conditions, there remains a potential for challenges in manufacturability within the optimized design. It is possible that the design may require extensive post-processing efforts after topology optimization. Therefore, the selection of the appropriate build orientation becomes crucial in topology optimization, especially when incorporating overhang constraints.
# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
## 4 Experiments and Results
### Results  
Stacking model parameter optimisation aims to determine the optimal configuration of base models (SVM and LSTM), the appropriate number of base models and the most effective combination method. Several combinations and ratios are systematically tested and evaluated using the validation set. Subsequently, the most appropriate meta-model is  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Rank** & **Factor** & **Importance Index** & **Impact Level** & **Detailed Description** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Number of fracturing Sections} & \multirow{4}{*}{0.3462} & \multirow{4}{*}{High Impact} & \multicolumn{3}{c}{Increasing the number of fracturing sections can enhance shale gas production but leads to higher costs in drilling, fracturing, and equipment. Greater well depth results in higher exploration and development costs, requiring longer drilling time, larger drilling equipment, and more investment. Increasing platform wells improves extraction efficiency and reduces production costs by centralizing multiple wells in one location, saving on drilling and production equipment. Longer fracturing lengths can increase shale gas production but require more fracturing materials and fluids, resulting in higher construction costs. Higher pump pressure can increase well production but results in increased energy consumption and equipment costs. Higher and intensity can improve fracturing results and increase production but increases the amount and cost of sand and fracturing fluid used. Other listed factors have relatively low impact on shale gas costs. While they have some influence in specific situations, their impact needs to be considered from a comprehensive perspective. \\ \hline \end{tabular}
\end{table}
Table 7: Analysis of Factors Affecting Shale Gas Costs.  
selected for the final prediction based on the outputs of the base models. The grid search methodology is used to evaluate different parameter combinations, with selection based on quantitative performance metrics. To ensure model efficiency and parameter optimisation, the data is systematically partitioned into training, validation and test sets, followed by evaluation of the model's predictive ability on the test set. This methodological approach effectively addresses potential overfitting issues and provides a more robust assessment of the model's ability to generalise.  
To evaluate the effectiveness of the SVM_LSTM stacked model, a comparative analysis is performed against the individual SVM and LSTM models. To visually demonstrate the advantages of the SVM_LSTM methodology, the predicted values from the test data are compared across all three methods, as shown in Figure 4. The analysis shows that, while both the traditional SVM and LSTM models demonstrate satisfactory drilling cost prediction capabilities after training and generally meet the required prediction requirements, the SVM_LSTM stacked model exhibits improved prediction accuracy.
## 2 Exosome Features
### Mechanisms of Exosome-Target Tissue Interaction and Exosome Transport
#### 2.3.2 Endocytosis and Macropinocytosis  
Exosomes can be internalized by recipient cells through clathrin-dependent or caveolin-dependent endocytosis and macropinocytosis (Figure 1). These pathways allow exosomes to enter the endosomal system, where their cargo can be sorted, released, or degraded.  
Figure 1: Central mechanisms for exosome–cell interactions. After exosomes are released from the donor cell through exocytosis, the target cell can interact with exosomes via various mechanisms, such as receptor–ligand interaction, receptor–receptor interaction, endocytosis, direct membrane fusion, and phagocytosis. Additionally, these interactions depend on factors such as the specific protein and lipid composition of the exosomal membrane and the surface receptors in the cellular context of the target tissue [9]. Adapted from Xu. et al., 2022 [34]. Zoom image: Exosomes carry bioactive molecules like miRNAs, proteins, and lipids that influence inflammation, insulin sensitivity, cell proliferation, and apoptosis.  
There is an equilibrium between endocytosis and exocytosis, as it was recently shown that endocytosis leads to the inhibition of the secretion of exosome marker tetrasapanin proteins such as CD81, CD9, and CD63 [35]. Studies suggest that exosomal uptake often depends on lipid rafts, facilitating vesicle docking and internalization [36]. Although several components of the cellular machinery required for exosome uptake are currently known, the protein interactome of this process is not complete. Recently developed, Turbo-ID-EV offers a proteomic-scale tool to shed light on the required protein interactions between EV cargo and receptor cells [37].  
This process occurs through the interaction of receptors with the target cell, inducing the endocytosis of the exosome. Once internalized, the exosome is coated with proteins to form vesicles that are transported to cellular endosomes, where they either fuse with the endosomal membrane or undergo disassembly.
###### Abstract  
Nickel oxide and Sc-doped ZrO\({}_{2}\) electrode material with a 1:1 wt % composition of NiO and Zr\({}_{0.84}\)Sc\({}_{0.16}\)O\({}_{1.92}\) was synthesized via a single-step glycine-nitrate combustion method. Different glycine to nitrate (g/n) molar ratios of 0.27, 0.54, and 1.1 were used to investigate its effect on the structural, morphological, and electrical properties of the heat-treated samples. X-ray diffraction (XRD) patterns of the as-sintered samples for all the g/n ratios were indexed to cubic phases of NiO and ScSZ. Upon reduction at 700 \({}^{\circ}\)C, NiO was fully reduced to Ni. In-situ XRD patterns showed that the composite Ni/Zr\({}_{0.84}\)Sc\({}_{0.16}\)O\({}_{1.92}\) electrode material retains its cubic structure at intermediate temperatures from 500 \({}^{\circ}\)C to 800 \({}^{\circ}\)C. High magnification scanning electron microscopy (SEM) images revealed that nanoparticles of Ni are also formed and situated at the surfaces of ScSZ grains, apart from agglomerated submicron particles of Ni. SEM and electron-dispersive spectroscopy mapping revealed interconnected grains of ScSZ oxide-ion conducting phase. From the calculated conductivity based on electrochemical impedance spectroscopy results, the 0.27 g/n ratio showed an order of magnitude-higher total conductivity among the other prepared samples.  
porous electrode; solid oxide fuel cell (SOFC); solid oxide electrolysis cell (SOEC); composite electrode; NiO/ScSZ; combustion method +
Footnote †: journal: Applied Physics Letters B
## 4 Routing Protocols Using Ant-Like Mobile Agents
### Probabilistic Emergent Routing Algorithm (PERA)  
Probabilistic Emergent Routing Algorithm (PERA) uses three types of ant-like mobile agents [44]. They are the _forward ant_, the _uniform ant_, and the _backward ant_. Each node periodically creates _forward ants_, and sends them to randomly selected destinations in the network. Each intermediate node transmits the forward ants to the next node towards the destination based on the probability of reaching the destination. Since the next node is selected from the entries in the routing table in the intermediate node, this method fails to find new routes. In order to promote the adaptability of the algorithm, the participating nodes create the _uniform ants_ and send them to explore the network. The _uniform ants_ are transmitted to the neighbor nodes with uniform probabilities. The number of _uniform ants_ is much smaller than the _forward ants_.  
When the _forward ant_ or the _uniform ant_ reaches the destination, the _backward ant_ is created and transmitted to the source node. The _forward ant_ or the _uniform ant_ hands the history list to the _backward ant_ so that the _backward ant_ can trace the route the _forward ant_ or the _uniform ant_ came along. While following the route, the _backward ant_ updates the routing tables of the intermediate nodes based on the information in the history list.
## 4 Review of Existing Methods and Capabilities
### Safety Controls
#### 4.2.3 Boundary Control  
Geofence is a general concept of providing boundary control of where a vehicle can operate. For UASs this can include ceilings and floors as well as walls of operation. Geofencing is seen as one means of aiding in the sense and avoid issue of UASs, especially small UASs. [PERSON] _et al._[141] proposed a geofence system that was platform independent. Many of the small UAS flight controls systems, such as Arduino [142], include a geofence capability inherent in its system that can be utilized for safe operations. [PERSON] _et al._[143] propose that standards be developed for assured containment that provides an independent capability separate from the geofence algorithms that are generally resident internal to system software.
## 3 Materials and Methods
### Experimental Setup
#### 3.2.2 Data Augmentation  
Data augmentation is a technique for increasing the diversity of training data and reducing the risk of overfitting. It involves applying transformations to existing data to help improve a model's generalization.  
The transformations employed in this work include vertical and horizontal flip, rotation at 90\({}^{\circ}\) in a random direction, gamma correction with a random gamma parameter, Contrast Limited Adaptive Histogram Equalization (CLAHE), image transposition, and the addition of Gaussian noise with random variance. Each training image is subjected to each one of these transformations with a probability of 50% of occurrence in each epoch. Moreover, the images were resized to 256 \(\times\) 256 pixels.  
The data augmentation was implemented in Python using the Albumentations library [37].
# Object-Tracking Algorithm Combining Motion Direction and Time Series
###### Abstract  
Object tracking using deep learning is a crucial research direction within intelligent vision processing. One of the key challenges in object tracking is accurately predicting the object's motion direction in consecutive frames while accounting for the reliability of the tracking results during template updates. In this work, we propose an innovative object-tracking algorithm that leverages both motion direction and time series information. We propose a loss function that guides the tracking model to learn the direction of object motion between consecutive frames, resulting in improved object localization accuracy. Furthermore, to enhance the algorithm's ability to discriminate the reliability of tracking results and improve the quality of template updates, the proposed approach includes an attention mechanism-based tracking result reliability scoring module, which takes into account the time series of tracking results. Compressive experiment evaluation on four datasets shows our algorithm effectively improves the performances of object tracking. The ablation experiments and qualitative analysis prove the effectiveness of the proposed module and loss function.  
object tracking; deep learning; motion direction; attention mechanism; time series Article
## 2 Background
### Tree-Based Ensembles
* **XGBoost (XGB)**[16], or extreme gradient boosting, is similar to the GB algorithm, but instead of gradients, it fits a new base classifier using second-order derivatives of the loss function. XGB is thought to be more precise and effective than GB. * **CatBoost (CAT)**[17], or categorical boosting, is a boosting ensemble with two key characteristics: (1) it handles categorical features with one-hot encoding, and (2) it produces oblivious decision trees as base classifiers with the same features used as a splitting criterion for all nodes within the same tree level. Oblivious trees are symmetrical; thus, overfitting is minimized and training time is reduced.
## 6 Experimental Results and Discussion
### Comparison with Baselines
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**WN18 RR**} & \multicolumn{3}{c}{**FB15k**} & \multicolumn{3}{c}{**FB15k-237**} \\  & & **MRR** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** \\ \hline
1 & TransE & 0.243 & 0.043 & 0.532 & 0.391 & 0.031 & 0.624 & 0.279 & 0.198 & 0.441 \\
2 & RotatE & 0.476 & 0.428 & 0.571 & 0.791 & 0.742 & **0.881** & 0.338 & 0.241 & 0.533 \\ \hline
3 & AMIE & 0.357 & 0.287 & 0.356 & 0.797 & 0.617 & **0.881** & 0.308 & 0.174 & 0.477 \\
4 & AnyBURL & 0.480 & 0.445 & 0.549 & **0.830** & **0.808** & 0.876 & 0.260 & 0.196 & 0.410 \\ \hline
5 & KG-BERT & 0.219 & 0.095 & 0.497 & – & – & – & 0.237 & 0.144 & 0.427 \\
6 & KGT5 & 0.508 & 0.487 & 0.544 & – & – & – & 0.276 & 0.210 & 0.414 \\
7 & GPT\({}_{one-shot}\) & 0.511 & 0.482 & 0.556 & 0.815 & 0.731 & 0.867 & 0.323 & 0.255 & 0.551 \\ \hline
8 & RuleLLM & **0.518** & **0.493** & **0.573** & 0.826 & 0.737 & 0.879 & **0.341** & **0.260** & **0.557** \\ \hline \hline \end{tabular} The bolded data indicates the best performance.  
\end{table} 
Table 2: Performance of baselines and proposed model on WN18 RR, FB15k, and FB15k-237. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**YAGO3-10-dr**} & \multicolumn{3}{c}{**Wikidata5m**} \\  & & **MRR** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** \\ \hline
1 & TransE & 0.190 & 0.136 & 0.323 & 0.253 & 0.170 & 0.392 \\
2 & RotatE & **0.214** & 0.153 & 0.332 & 0.290 & 0.234 & 0.390 \\ \hline
3 & AMIE & – & – & – & – & – & – \\
4 & AnyBURL & 0.211 & **0.154** & 0.331 & – & – & – \\ \hline
5 & KG-BERT & – & – & – & – & – & – \\
6 & KGT5 & 0.211 & 0.151 & 0.327 & 0.300 & 0.267 & 0.365 \\
7 & GPT\({}_{one-shot}\) & 0.213 & 0.153 & 0.333 & 0.326 & 0.298 & 0.397 \\ \hline
8 & RuleLLM & 0.212 & 0.152 & **0.337** & **0.357** & **0.321** & **0.448** \\ \hline \hline \end{tabular} The bolded data indicates the best performance.  
\end{table} 
Table 3: Performance of baselines and proposed model on YAGO3-10-dr and Wikidata5m.
## 4 Simulation Results
### Performance Measures  
As mentioned earlier, the efficacy of the SCEHO-IPSO algorithm is analyzed by using performance measures like cost, execution time, makespan, latency, and memory storage. At first, the cost represents the total cost (dollars) required for task scheduling in the cloud environments and it is mathematically depicted in Equation (15). Then, makespan is the completion time of the last task to leave the system, and it is mathematically stated in Equation (16). The term \(F_{t_{i}}\) is stated as the finishing time of task \(t_{i}\), \(EC_{t_{i}\tau_{n}}\) is denoted as the execution cost of task \(t_{i}\) on resource \(r_{n}\), and \(n\) is indicated as the number of tasks.  
\[Cost=\sum_{i=1}^{n}EC_{t_{i}\tau_{n}} \tag{15}\]  
\[Makespan=\sum_{i=1}^{n}F_{t_{i}} \tag{16}\]  
The execution time of the task is defined as the time consumed by the system for executing a specific task. The mathematical formula for execution time \(E_{t_{i}}\) is given in Equation (17). The term \(P_{P_{i}}\) is represented as the processing power of the VMs, \(S_{t_{i}}\) is specified as the task size, and \(E_{t_{i}}\) is indicated as the execution time of task \(t_{i}\) on the VMs.  
\[E_{t_{i}}=\frac{S_{t_{i}}}{P_{P_{i}}} \tag{17}\]  
The latency is defined as the time consumed for balancing the data load. The effectiveness of the system is improved by decreasing the latency. The mathematical formula to compute latency is specified in Equation (18). The term \(\beta_{i}\) is represented as the instances generated from the source code, \(k_{j}\) is stated as the number of instances per unit, and \(u(t)\) is indicated as the total count of the workload. In addition to this, the memory storage is defined as the space required by the proposed optimization algorithm in order to execute a specific task. The execution time is minimized by reducing the memory storage.  
\[Latency=\Bigg{(}\frac{\beta_{i}}{k_{j}}\Bigg{)}(u(t)+1) \tag{18}\]