esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Finite Element Model Updating
### Analysis of the Influence of the Boundary Conditions--Model 1  
To determine the influence of the boundary conditions on the change in the value of the natural frequencies of the structure, a model with fixed and clamped boundary conditions was developed. In the numerical model, the values of the entered masses of partition walls, roofs, and staircases were retained. A graphical comparison of the experimentally obtained natural frequency values with the frequencies obtained for both clamped \(\left(f_{1}^{num,C}\right)\) and hinge \(\left(f_{1}^{num,H}\right)\) boundary conditions is given in the Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode, i** & \(\begin{array}{c}f_{i}^{exp}\\ \textbf{[Hz]}\end{array}\) & \(\begin{array}{c}f_{1}^{num}\\ \textbf{[Hz]}\end{array}\) & \(\begin{array}{c}\textbf{\Delta}f_{i}^{exp,num}\\ \textbf{[\%]}\end{array}\) \\ \hline
1 & 2.98 & 4.22 & \(-41.6\) \\
2 & 3.96 & 5.75 & \(-45.2\) \\
3 & 4.45 & 6.42 & \(-44.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the experimentally \(\left(f_{i}^{exp}\right)\) and numerically \(\left(f_{i}^{num}\right)\) obtained natural frequency values in form of deviation \(\left(\Delta f_{i}^{exp,num}\right)\).  
Figure 10: Graphical comparison of the experimentally obtained natural frequency values \(\left(f_{1}^{exp}\right)\) with the frequencies obtained for hinge \(\left(f_{1}^{num,C}\right)\) and clamped \(\left(f_{1}^{num,H}\right)\) boundary conditions for **(a)** Model 1 and **(b)** Model 2.  
Based on the analysis of the influence of the boundary conditions on the first natural frequency values, it can be seen and concluded from the graphical representation (Figure 10a) that there are no differences between the natural frequency values for the hinge \(\left(f_{1}^{num,H}\right)\) and clamped \(\left(f_{1}^{num,C}\right)\) boundary conditions, i.e., boundary conditions do not affect the natural frequency values. A clamped boundary condition, also known as fixed or fully restrained boundary condition, means that a structure is completely fixed or immobilized at a particular boundary. A hinge boundary condition, also referred to as a pinned or simply supported boundary condition, allows rotation at a specific boundary while preventing translation. Based on this observation and conclusion, for the further analysis and updating steps, the numerical model with hinge boundary conditions is considered. For the sake of simplicity, the selected model is referred to as M1 in the following text.
## 4 Conclusions and Future Work  
### Conclusions  
(1) In this study, the YOLOv8n algorithm was improved and redesigned by enhancing feature extraction and reconstructing the feature fusion network. In the feature extraction stage, large-kernel convolutions are introduced to expand the receptive field and enhance object perception, while a lightweight ELA mechanism is integrated to effectively reduce the number of parameters and floating-point operations. In the feature fusion stage, the network is redesigned to improve detection accuracy without significantly increasing computational cost, thus reducing both false positives and false negatives. Ablation experiments show that compared to the original YOLOv8n, the improved YOLOv8-URE reduces the number of parameters by 27.3% and increases precision by 1.1%, recall by 5.1%, and [EMAIL_ADDRESS] by 1.8%, while reducing FLOPs by 2.7 GFLOPs and improving inference speed by 25 FPS. In generalization comparison experiments, YOLOv8-URE achieved 0.7% higher recall than YOLOv8n, with a nearly identical AP value, and the overall model size was reduced by 31.1%. These results demonstrate that YOLOv8-URE achieves improved accuracy while maintaining a lightweight design and strong generalization, making it a practical and versatile solution.  
(2) In the point cloud processing module, this paper proposes a point cloud segmentation algorithm that integrates 3D coordinate transformation and KD-Tree search. By transforming coordinates to provide initial target points for KD-Tree, the method effectively enables fast localization and segmentation of the target point cloud, reduces the number of points involved in computation, and accelerates subsequent alignment. At the alignment stage, combining sample consensus with the NDT algorithm achieves higher alignment accuracy and robustness in complex environments compared to traditional methods, providing a more reliable positional basis for subsequent workpiece grasping.  
(3) Grasping experiments were conducted on the target workpiece under various poses. The results demonstrate that the system maintains a high success rate across different orientations, indicating strong robustness and reliable performance in completing the grasping tasks.
## 3 System Model  
### Network Model  
A heterogeneous square area is considered as the network model of our presented schema, and plenty of sensors are deployed using a random way. We initially set the sink at the center of the sensor field. In every round, each node needs to transmit its sensor data to the sink by single or multi-hop transmission. A sensor can choose a CH within its communication range otherwise it will transmit the data to a relay node for forwarding. CHs are usually nodes with high energy and distribute evenly by the PSO algorithm mentioned in Section 4, and they take the responsibility for data fusing and transmit the fused data to the sink. CHs apply the TDMA mechanism to control their member nodes' transmission time, and once a node joins a cluster, the corresponding CH will allocate a TDMA time slot to it. With the purpose of facilitating the experiments, we make the following assumptions:  
* Sensors have the knowledge of their own location according to an equipped GPS and they know their neighbors' location during the initial phase by information exchange.
* The sink has the geography information of all the sensor and in every round, each sensor will report its residual energy to sink in transmitted data.
* We assume that the network is in a favorable transmission environment and don't consider the collision during the transmission. The radio channel is symmetric [44].
* The clocks of sensors are synchronized using a GPS module or a time synchronization method such as Flooding Time Synchronization Protocol (FTSP) or Glossy [45].
## 2 Laboratory Investigation of the Effect of a Landslide on Stabilizing Piles  
### Material Parameters  
The Three Gorges reservoir area starts from Zigui County in Yichang City and ends in the vicinity of Jiangjin District, Chongqing. The terrain within the reservoir area is mainly hilly, the mountains are undulating, the water system is vertical and horizontal, the length of the mainstream is approximately 1300 km, the total length of the tributaries is over 3600 km, and the water body is enriched. A typical slippery sandstone-mudstone interbedded formation is common in the reservoir area, which creates conditions for the breeding of landslides and dangerous rock masses. After the Three Gorges dam was impounded, the rising water level accelerated the evolution of geological disasters in the area. According to statistics, there are more than 550 landslides and 120 collapses with a total volume of over 3 billion cubic meters on both sides of the main tributaries. Soft and hard interbedded formations are prone to landslides, and even if the dip angle is small or the terrain is slightly slow, the instabilities of the bedding landslide and the tangential landslide, which are extremely harmful, will also have occurred.  
The bedrock in the Three Gorges Reservoir is mainly composed of sandstone and mudstone belonging to the Suining Formation of the Upper Jurassic. The Majiagou landslide is situated on the left bank of the Zhaxi River, a tributary of the Yangtze River. The landslide is approximately 550 m long, 180 m wide and 24 m high. The main lithology of Majiagou landslide is the interbedded layers of feldspar-quartz fine sandstone and silty mudstone belonging to the Suining Formation of the Upper Jurassic which are very typical soft and hard interlayers of the Jurassic in the Three Gorges reservoir area. The material parameters of the bedrock and the shear strength parameters of the discontinuities are presented in Tables 1 and 2, respectively [41; 42; 43]. In the tables, \(\rho\) is the density, E is the elastic modulus, \(\mu\) is the Poisson's ratio, c is the cohesion, and \(\varphi\) is the internal friction angle.  
Most of the discontinuities of mudstone are smooth, undulating and unfilled, while those of sandstone are rough, flat and unfilled. In the shear test of mudstone, when the normal stress is small, the climbing effect would be appeared; when the normal stress is large, the gnawing effect would be appeared, thus the shear strength of the mudstone discontinuities will be increased [41].
## Materials and Methods
### Determination of Total Phenolic Content  
Total phenolic content of Portuguese honeys was determined in aqueous amounts of samples using the Folin-Ciocalteu colorimetric method [14]. In a 5 mL volumetric flask, 0.2 mL of the aforementioned aqueous honey solution (0.12 g/mL), followed by 2.5 mL of distilled water and 0.25 mL Folin-Ciocalteu reagent were properly added. Then, 0.5 mL of saturated Na\({}_{2}\)CO\({}_{3}\) (30%, \(w/v\)) were also added after 3 min. Finally, the obtained solution was filled to the mark with distilled water (final volume 5 mL). This solution was left for 2 h in the dark (time starts when Folin-Ciocalteu reagent is added to the medium) at room temperature and the absorbance was measured at 760 nm in a UV/VIS Spectrophotometer (SHIMADJU, UV-1280, Japan). Prior absorbance measurements all solutions were filtered using Whatman filters (UK) with a pore size of 0.45 um. For quantification purposes, a calibration curve was constructed of standard gallic acid using a range of concentrations between 195-6240 mg/L:  
\[y=0.0004x+0.2041,\,R^{2}=0.9954 \tag{6}\]  
Total phenolic content was expressed as mg of gallic acid equivalents per kg (mg GAE/kg) of honey, considering the specific weight of each honey sample. Reported results are the average \(\pm\) standard deviation values of three replicates (\(n=3\)).  
Headspace Solid Phase Microextraction Coupled to Gas Chromatography/Mass Spectrometry (HS-SPME/GC-MS)
# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 4 Methodology  
### Problem Definition  
Traditional meta-learning methods randomly select tasks from \(p(T)\), often leading to inefficient training due to redundant and uninformative tasks. This work introduces Uncertainty-Aware Active Meta-Learning (UA-AML), a framework that actively selects high-uncertainty tasks to improve generalization. The problem is defined in this sub-section.  
Given a dataset \(D=(x_{i},\ y_{i})_{i=1}^{|D|}\), where \(x_{i}\) is a text instance \(y_{i}\) is its class label, the few-shot learning objective is to train a classifier \(f_{\theta}\) that generalizes to new tasks using only a small number of labeled examples. Each task \(T\) consists of a support set \(S=(x_{i},\ y_{i})_{i=1}^{N\times k}\) which is used for training, and a query set \(Q=(x_{i},\ y_{i})_{i=1}^{M}\) used for evaluation. The goal of meta-learning is to learn a function \(M\) that maps a set of training tasks to an optimal initialization \(\theta^{*}\) for a base-learner \(f_{\theta}\), enabling fast adaptation to new tasks. Given a distribution of tasks \(p(T)\) and a classification loss \(L\), we define:  
\[\theta^{*}=\underset{\theta}{\text{argmin}}E_{T\sim p(T)}[L(f_{\theta}(S),\ Q)] \tag{2}\]  
Standard meta-learning samples tasks randomly, which can be suboptimal as many tasks contribute little to improving generalization. We aim to address this problem in this work.
## 2 Materials and Methods
### Temperature Monitoring  
Temperature plays a critical role in the growth and reproduction of yeast. Yeasts are highly sensitive to temperature variations, and such fluctuations can significantly impact their growth rate. The optimal temperature range for yeast growth and multiplication varies depending on the yeast strain and the specific conditions of the growth medium. However, generally, a temperature range of 30 \({}^{\circ}\)C to 35 \({}^{\circ}\)C is considered ideal for the initial rapid development of yeasts [45]. Therefore, it is essential to meticulously monitor and control temperature during yeast-related experiments to ensure precise and reproducible outcomes.  
To address this need, a device was applied for monitoring temperature at the separate exposure and control sample points during irradiation experiments, recording the results at 10-minute intervals onto a memory medium. The DS18B20 temperature sensor, manufactured by Dallas Semiconductor in the USA, was used for this purpose. It was chosen for its capacity to power multiple sensors simultaneously using only three wires, two for power and one for data transfer.  
Temperature monitoring was conducted in all experimental sets. The temperature was measured from the outside of the Erlenmeyer flask at each position of the biological sample with the intention not to affect the cultured cells in other than a magnetic way. However, during our previous study [44], the temperature measurements were also applied directly from the medium with no differences between the measurements performed from the outside of the flask. Figure 4 represents an example of temperature monitoring at position C within the coils situated in the incubator, which was set to 30 \({}^{\circ}\)C. Temperature data were collected for two different experiments: one involving EMF exposure with nonspecific conditions and the second involving exposure with artificially set IPR conditions. In both groups, the temperature difference between the coils remained less than 0.3 \({}^{\circ}\)C. This variation can be deemed negligible with respect to works [46; 47], implying that temperature had no discernible impact on the growth and proliferation of _Saccharomyces cerevisiae_ yeast during the exposure experiments. This was confirmed also by our preliminary work presented in [48], where the control vs. control experiments were performed with both coils unexcited by the electromagnetic signal, but with similar temperature differences measured as in the case described herein. The experiments resulted in statistically no significant effect.  
Figure 4: Temperature difference at position C of the exposure and control coil during 7-h EMF exposure with diverse settings.
## 3 Result and Discussions
### Current Model  
The current, wave, and sediment models were coupled to obtain patterns of currents, waves, and abrasion areas at Pulau Baai Port, particularly at a point of severe coastal abrasion where an installed pipeline is located. In this modeling, previously analyzed wave data, the National Digital Elevation Model (DEMNAS), and field sediment sample data were utilized. Each sediment sample underwent laboratory testing, certified to meet national standards (accredited by KAN). Tests were conducted in the laboratory for each sediment sample. The laboratory results indicate that sediment in the study area is sand with a specific gravity value of 2.645 and grain size of 0.1050. One current verification point and one tidal verification point were conducted to obtain the most accurate current model. Tidal verification results are presented in a diagram in Figure 10. From the data, it can be observed that the amplitude of the tidal model is slightly smaller relative to the observational amplitude, while the tidal phase in the field and model are the same. The graph shows that the tidal model values closely approximate the field tidal values. The RMSE value for a tidal point is 0.07 m.  
Figure 8: Wind speed graph from 2012 to 2022.  
Figure 7: Wave height (Hs) and wave period (Ts) graphs from 2012 to 2022.  
Shifting to the validation process for a single point of the current, the verification was conducted by considering both the direction and speed of the current. The simulated results for both components of the velocity exhibit patterns that align remarkably well with the observed data. This alignment is consistent with the expectations derived from the simulation outcomes. The overall patterns of the current speed and direction in the model closely resemble the observed values, showcasing a high degree of fidelity with an RMSE value of 0.05 m/s. The graphical representation of the velocity, comparing observational and modelled current speeds, is depicted in Figure 11. This graphical representation provides visual insight into the degree of agreement between the observed and simulated current velocities at the specified point. The primary reason for the greater dominant current survey velocity compared to the model's is the larger amplitude of the dominant tide survey in contrast to the tide model's amplitude. Moreover, differences in the wind parameters employed in the model input, as opposed to the actual wind parameters at the current survey location, could also contribute to these variations.  
Figure 10: Field and model tidal comparison graphs.  
Figure 9: Wave rose model ADCP 2021 (**left**) and wave rose model ADCP 2022 (**right**).
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 3 Methods
### Ensemble Learning
#### 3.2.1 Random Forest  
Random forest (RF) improves the accuracy and stability of predictions by constructing multiple decision trees and using average prediction values. It has advantages in processing non-linear data and automatic feature selection and performs well in handling high-dimensional data and preventing overfitting [93]. It is particularly suitable for processing datasets with complex structures in this study.  
First, RF performs self-sampling by randomly selecting multiple samples from the original dataset to construct multiple sub-datasets, with duplicate sampling allowed. The number of samples extracted is a hyperparameter, usually set to the square root of the size of the original dataset. Next, it constructs a decision tree and trains one decision tree for each sub-dataset. During the node splitting process, a subset of features is randomly selected to determine the optimal split point. The number of features selected is also a hyperparameter, typically set to the logarithm base 2 of the total number of features. Finally, the prediction results are obtained using a voting method, with the specific formula as follows:  
\[Y=\frac{1}{N}{\sum_{i=1}^{N}{y_{i}}},\]  
where \(N\) is the number of decision trees, and \(y_{i}\) represents the result of the \(i\)th tree.
## 3 Proposed Method
### SPT Determination  
The bearing vibration signal before SPT rarely contains information about bearing degradation; it is very important to determine SPT, which affects the accuracy of RUL estimation [24]. The root mean square (RMS) is the most appropriate indicator of bearing health because it shows the trends of the different health statuses of the bearing: in the normal stage, the RMS remains stable; in the initial degradation stage, the RMS starts linear growth; in the severe degradation stage, the RMS starts nonlinear growth, but there are some outliers caused by random noises in the RMS.  
This paper adopts a simple and effective method to determine the SPT. At the initial stage of bearing operation, the RMS of the bearing vibration signal and its mean value \(\mu\) and variance \(\sigma\) were calculated. Then the moving average (MA) method is used to compute the RMS mean value \(\mu_{i}\) of the next five consecutive samples; when \(|\mu_{i}-\mu|>3\sigma\), the time corresponding to the maximum sample of the five samples is selected as SPT. Experiments show that the method effectively filters out RMS abnormal points.
## 4 Results
### Prediction Results of the Time Series Models  
The results of the time series models are verified in the Sichuan-Yunnan region, which are shown in Table 2. The data in the table represent the number of corresponding stations.  
Overall, the three time series prediction models all has great prediction results. The two indicators of the LSTM model, AUC, and distance_average, are better than those of other models. In the next section, the paper compares the prediction results of the above eight models in detail.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AUC**\(\geq\) 0.65 & **RP**\(\geq\) 0.70 & **Distance\_Average**\(\leq\) 100 **km** & **Mag\_Mae**\(\leq\) 0.25 \\ \hline LightGBM & 68 & 43 & 52 & 36 \\ NN & 57 & 49 & 47 & 39 \\ SVM & 51 & 39 & 43 & 41 \\ GBDT & 36 & 42 & 39 & 43 \\ RF & 45 & 37 & 41 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of non-time series models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AUC**\(\geq\) 0.65 & **RP**\(\geq\) 0.70 & **Distance\_Average**\(\leq\) 100 **km** & **Mag\_Mae**\(\leq\) 0.25 \\ \hline LSTM & 84 & 55 & 64 & 47 \\ GRU & 82 & 58 & 61 & 48 \\ CNN+GRU & 79 & 49 & 56 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of time series models.
## 5 Discussion
### What Decision Maps Really Are  
A key observation running through all our experiments is that decision maps are imperfect instruments that map a _part_ of the high-dimensional space of a classification model to a 2D surface or map. Although our various metrics have demonstrated differences between the three evaluated decision-map methods, it is still unclear how these 2D maps are created. To gain more insights into this, we consider a simple experiment. First, we create a three-dimensional dataset with six concentrated blobs (data-point clusters), each of a separate class. Next, we use the three studied techniques to construct the respective decision maps for a given classifier (note that the classifier choice is not important). Finally, we backproject the map pixels onto the data space, which is three-dimensional in our case, and directly visualize the obtained set of 3D points.  
Figure 14 shows these 'backprojected' decision maps for the DBM, the SDBM, and DV, with colors mapping the six classes. Strikingly, in all three cases, the backprojected shapes are actually _surfaces_ embedded in 3D, which connect the six data-point clusters. The SDBM and DBM create smoother surfaces, whereas DV creates a tighter, locally less  
smooth surface that connects the six clusters (this fully matches the earlier observations concerning the line-like gradient maxima of the DV method). The fact that decision maps are actually sampling a _surface_ from the high-dimensional data space is, to the best of our knowledge, an insight that has not been described in any earlier works on decision maps. Note that this is not a trivial finding--the fact that the backprojection of a 2D decision map is itself a surface may not be immediately apparent based on the construction of current decision-map techniques. Also note that the actual decision boundaries in the data space (which are themselves surfaces) are not visualized by the decision maps as such--rather, what the decision maps do show are the _intersections_ of these decision boundaries with the aforementioned surface. As such, the way this surface is constructed by a given decision-map method will strongly influence which parts of the actual decision surfaces will be shown in the final decision map.  
Figure 14: Decision maps for a synthetic blob dataset with 3 dimensions and 6 classes (see Section 5.3).  
Figure 13: Workflow for choosing the most suitable decision map in a given user application context.