esa-sceva/satcom-chunk-collection huggingface dataset:
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 3 Description of the Intent Recognition Problem in AUV Collaborative Search Mission
### Dataset Construction for Intent Recognition in Multi-AUV Collaborative Search Mission
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline \multicolumn{1}{c}{**Characteristic**} & \multicolumn{1}{c}{**Description**} \\ \hline Relative direction to the PNCT & Similarly to the relative direction to the positioning point, this is used to measure the bearing relationship between the AUV and the PNCT, to determine whether the AUV needs to adjust its heading to improve communication quality. \\ \hline Number of companions & Represents the number of potential communication nodes available for the AUV during the mission. The greater the number of companions, the more complete and optimized the subsequent communication topology can be. As a result, the AUV may be more inclined to prioritize the completion of the positioning mission, as there are fewer concerns regarding communication. \\ \hline Average detection error & This can be used to determine the current stage of the mission: During the positioning phase, since the AUV is still at a relatively large distance from the target, the detection error generally remains at a high level. Once the tracking phase begins, the AUV’s trajectory gradually optimizes, and the detection error decreases and stabilizes, indicating that the tracking process is improving. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._annotated based on the actual performance of the AUV's state features in the mission. For example, during the positioning phase, the AUV's intent label can be either \"positioning priority\" or \"communication priority\", depending on the AUV's mission requirements and communication status. During the tracking phase, the AUV's intent label may be \"detection priority\" or \"positioning priority\", depending on the current detection accuracy and mission requirements. To improve the model's generalization ability and avoid overfitting, we applied data augmentation techniques to enhance the dataset. By injecting noise and performing data normalization, the dataset is better suited to the diversity and uncertainty of real-world environments. Once the dataset is ready, we divide it into training, validation, and test sets for model training and evaluation. The training set is used to train the model, the validation set is used for parameter tuning and preventing overfitting, and the test set is used to evaluate the model's performance and generalization ability.
Most of the existing data are limited to a holdup below 35 vol.-%. In that respect [PERSON] and [PERSON] measured droplet diameters manually up to 40 vol.-% in a sample cell with a microscope [3] and [PERSON] and [PERSON] also measured their emulsion photographically in a sample tube at a holdup up to 50 vol.-% [16]. [PERSON] et al. used a laser granulometer. up to 60 vol.-% disperse phase fraction but dilution was necessary as the laser granulometer is limited to 1 vol.-% holdup [17]. [PERSON] et al. [18] used FBRM and a Particle Vision Measurement (PVM) probe from Mettler Toledo, to detect phase inversion in regions above 50 vol.-% disperse phase fraction [18]. [PERSON] et al. developed an entocentric light-based endoscopic image-based probe and measured the DSD at disperse phase fractions up to 50 vol.-% [19, 20]. Since all these measurement methods have limitations in terms of accuracy and disperse phase fraction concentration, they are difficult to compare. Therefore, the significance of any models to predict the DSD is depends highly on the type of measurement method used in the specific environment (e.g., phase fraction). It can be summarized that offline methods (sampling) provide a defective DSD. inline probes, as if laser backscattering, as a measurement principle, is controversially discussed, so minimal invasive, optical inline probes are probably the best choice. In this work, a novel image-based telecentric shadowgraphic probe is utilized to acquire images of droplets over a large range of phase fraction concentrations in a DN300 stirred batch vessel in a water/paraffin oil system. The images are analyzed with a modern neural network image processing tool to determine statistically significant, high resolution DSD database, which is rarely found in the literature. With these data and Computational Fluid Dynamics (CFD) methods, a deeper understanding of the hydrodynamic phenomena in liquid-liquid systems can be achieved, which was already shown in our previous work of [PERSON] et al. [21]. In that respect, a telecentric image-based optical probe suitable to measure at industrial relevant phase fractions above 35 vol.-% is described in more detail below.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 2 Materials and Methods
### Regression Models
#### 2.4.2 Multivariate Regression Models  
PLSR, SVR and RFR were used as multivariate regression models, with pixelwise individual spectral bands and calculated VIs (Table 4) as the input variables. This approach enables all of the spectral information to be utilized [38; 39; 40].  
PLSR links the predictor and response variables by decomposing data matrices so that only the most important linear combinations are utilized in the regression, optimizing the covariance between predictor and response variables. The \"_pls_\" package [41] was used to run PLSR. The proper number of components was determined by minimizing the root mean square error (RMSE) for the K-fold cross-validated predictions, with K = 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Locations** & **2019 (_n_)** & **2020 (_n_)** \\ \hline As & 2 & 1 \\ Lannas & 5 & 6 \\ Öebyn & 9 & 0 \\ Röbácksdalen & 9 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of samples available after data preprocessing for each site and year.  
In the basic form, SVM is a binary classifier (linear or nonlinear) which identifies the boundary between two different classes, assuming that the multidimensional data are distinguishable. In practice, SVMs define an optimal separation hyperplane to divide the datasets into several discrete predefined classes within the training data [42]. SVM regression analysis is expected to produce a continuous prediction output [26]. The package _\"e1071\"_ was used to conduct SVR. A grid search was implemented with a radial kernel to determine the hyperparameters \(\varepsilon\), C and \(\gamma\) to an optimal manner as they measure and define the model prediction errors so that they affect the accuracy and generality capabilities of the SVR. The details of \(\varepsilon\), C and \(\gamma\) were presented in [PERSON] and [PERSON]'s paper [43].  
RFR utilized two-thirds of the samples (_in-bag_ samples) to train several regression trees, and the remaining one-third (_out-of-bag_ (OOB) samples) was used for internal cross-validation [27]. Each tree split was defined using a random subsection of the predictor variables at each node. The average of the results from all the trees is the final result [44]. The \"_RandomForest_\" package was used to build the models, which were tuned using the _\"tuneRF\"_ function, and a default \(n_{\text{tree}}\) was set to 500. The detailed procedures of RFR algorithms running can be found in [PERSON] et al.'s work [19]. Two measures (%IncMSE and IncNodePurity) were calculated to show the importance of predictor variables [45]. The mean square error, %IncMSE, is measured as the mean decrease in accuracy of predictions of the OOB samples when a given variable is not included in the model. The training residual sum of squares, IncNodePurity, describes the total decrease in node impurity derived from splits over that variable.
# Middle Rock Pillar Stability Criteria for a Bifurcated Small Clear-Distance Tunnel
## 5 Stability Criteria of the MRP During Construction
### Plasticity Criteria of the MRP  
#### 5.4.1 Distribution of the Plastic Zone  
The change process of the plastic zone during construction at case 1 is depicted in Figure 17, providing the basis for optimizing the key excavation nodes.  
Figure 16: Distribution of actual stress value in the center of the MRP.  
Figure 17: The change in the plastic zone of the MRP during the construction process. Mu is the length of the upper step excavation distance from the entrance of the main tunnel. Ru is the length of the upper step excavation distance from the entrance of the ramp tunnel.  
Upon entering the tunnel, the plastic zone initially appeared in the middle part of the left wall, 10 m from the upper bench excavation. No plastic zone was observed in the middle area of the middle rock or the ramp side. As shown in Figure 6a, as the upper step face of the main line tunnel progressed, the plastic zone extended along the left wall and increased in size toward the lower right of the intercalated rock area, whereas the ramp tunnel had not yet entered the tunnel.  
Figure 17b, c illustrate that once the ramp tunnel entered the tunnel and the upper step face was excavated for 10 m, the plastic zone of the MRP was primarily distributed in the center, with both tunnels exhibiting a plastic distribution near the sidewall of the MRP. As shown in Figure 17d, as both tunnel faces continued to advance, the plastic zone in the central area increased. Additionally, the plastic zone in the sidewall of the ramp was found to be larger than that in the sidewall of the main tunnel, mainly because of the ramp's late entrance and the close distance between the tunnel face and the opening.  
In Figure 17e,f, the plastic zone in the sidewall of the main line tunnel extended from the entrance to the exit, corresponding to the upper steps of the main line tunnel. Therefore, after the main tunnel and ramp were excavated, the plastic zone of the MRP was mainly distributed in the inner wall and center of the tunnel during the construction process. It is crucial to carefully consider the construction speed and implement appropriate support measures to ensure construction safety and the stability of the rock mass.  
The previous sections analyzed the stability of the MRP via displacement and stress. While these were key factors, the size and extent of the plastic zone in the rock also played important roles in determining rock stability. In practical engineering, the plastic zone cannot be measured with standard methods, but it reflects the yield state and range of the rock mass. Therefore, it is essential to establish criteria for the plastic zone and enhance the overall stability criteria.
## 2 Experiment
### Experimental Apparatus  
The isotherm of the selected adsorbent/adsorbate pair was measured using a constant-volume-variable-pressure (CVVP) apparatus to analyze the adsorption isotherm. The apparatus was designed and set up in the Thermal Energy Conversion Systems (TECS) laboratory of Kyushu University. Figure 3 shows the schematic diagram of the experimental setup. The CVVP method by general knowledge, is a differential manometric method of establishing the adsorption amount by estimating the pressure difference between the beginning and the end of the adsorption steps. The experimental device consists of the adsorption cell (V-Tech, 150 \(\pm\) 2 cm\({}^{3}\)), the load cell (304L-HDF4-1000, Swagelok, Solon, OH, USA, 1011 \(\pm\) 8 cm\({}^{3}\)), the liquid cell (304L-05 SF4-150, Swagelok), and tube assembly (SUS304 BA \(\varphi\)6.35 mm (1/4 in), Mecc Technia, Nishinoyama, Japan). The adsorption and the load cells' temperatures were maintained by a constant temperature water bath (UB50, Lauda). The vacuum pump (VPC-050A, ULVAC Kiko, Saito, Japan) was used for the evacuation of the experimental system, while the vacuum levels were determined by a vacuum gauge (PG-D5A, Sato vac. Inc, Tokyo, Japan). Two absolute pressure transducers (2 MPa, Kyowa, Tokyo, Japan, PA-20 KB; 100 kPa, Swagelok PTI-S-MA.1-22 AQ-T) were used to acquire the equilibrium pressure during the adsorption isotherm measurements. K-type thermocouples were installed to measure the temperature in all cases. The data from temperature and pressure measurements were logged using a data acquisition system (MX100, Yokogawa Electric, Tokyo, Japan). Before starting the experiment, all thermocouples were calibrated against a master temperature sensor (Standard platinum resistance thermometer, Chino R800-2).
# Modelling Regime Changes of Dunes to Upper-Stage Plane Bed in Flumes and in Rivers
## 2 Dune Model
### Bed Load Sediment Transport Model  
The pick-up and deposition model of [PERSON] and [PERSON] (1980) [23] uses the following formulae to determine bed load transport. Pick-up of sediment (probability of a particle being picked up in s\({}^{-1}\)) is determined by:  
\[p_{s}(x)=F_{0}\sqrt{\frac{\Delta g}{D_{50}}}\theta(x)\left[1-\frac{\theta_{c}} {\theta(x)}\right]^{3} \tag{7}\]  
where \(F_{0}\) = 0.03, \(\theta\) is the Shields parameter, \(\theta_{c}\) is the critical Shields parameter, and \(\Delta\) = \(\rho_{s}\)/\(\rho\)\(-\)1. The grain density \(\rho_{s}\) is set to 2650 kg/m\({}^{3}\), and the density of the water \(\rho\) is set to 1000 kg/m\({}^{3}\)  
The local, critical volumetric bed shear stress \(\tau_{c}(x)\), corrected for bed slope effects, is given by the following equation:  
\[\tau_{c}(x)=\tau_{c0}\frac{1+\eta\frac{\partial\tau_{b}}{\partial x}}{\sqrt{1 +\left(\frac{\partial\tau_{b}}{\partial x}\right)^{2}}} \tag{8}\]  
with \(\tau_{c0}\) the critical volumetric bed shear stress for flat bed, defined by Equation (9) and \(\eta\) = tan(\(\varphi\))\({}^{-1}\), in which the angle of repose \(\varphi\) = 30\({}^{\circ}\) for sand. In this equation, \(\theta_{c0}\) is the critical Shields parameter for flat bed, and \(D_{50}\) is the median grain size.  
\[\tau_{c0}=\theta_{c0}g\Delta D_{50} \tag{9}\]  
Deposition at a location is determined by adding all the sediment that arrives at that specific location. Therefore, in order to determine the deposition at a certain location \(x\), the distribution of picked-up sediment from all upstream locations is needed. The determination of deposition is done by applying the following formula:  
\[p_{d}(x)=\int\limits_{0}^{\infty}p_{x}(x-s)f(s)ds \tag{10}\]where the distribution \(f(s)\) determines the fraction of sediment that is deposited a distance s away from the pick-up point (\(x-s\)). The distribution function is defined as follows:  
\[f(s)=\frac{1}{\Lambda}\exp\left(\frac{-s}{\Lambda}\right) \tag{11}\]  
Herein \(\Lambda\) is defined as the mean step length, which is further discussed in Section 2.4. By using this function, 99.3% of the sediment that has been picked up at certain location is deposited between that location and 5 times the step length in the downstream direction. The remaining 0.7% is deposited at \(x=5\Lambda\). Finally, the transport gradient is determined as follows:  
\[\frac{\partial q_{b}}{\partial x}=D_{50}[p_{s}(x)-p_{d}(x)] \tag{12}\]  
To summarize, the entire sediment transport calculation process is as follows. First, the dimensionless bed shear stress is determined from flow characteristics. Then the pick-up of sediment along the dunes follows from bed shear stress. With an exponential decay function, the deposition of sediment away from each pick-up point is determined. The difference between sediment deposition and pick-up determines the net transport gradient along the dune.
# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
###### Abstract  
High-resolution remote sensing image building target detection has wide application value in the fields of land planning, geographic monitoring, smart cities and other fields. However, due to the complex background of remote sensing imagery, some detailed features of building targets are less distinguishable from the background. When carrying out the detection task, it is prone to problems such as distortion and the missing of the building outline. To address this challenge, we developed a novel building target detection method. First, a building detection method based on rectangular approximation and region growth was proposed, and a saliency detection model based on the foreground compactness and local contrast of manifold ranking is used to obtain the saliency map of the building region. Then, the boundary prior saliency detection method based on the improved manifold ranking algorithm was proposed for the target area of buildings with low contrast with the background in remote sensing imagery. Finally, fusing the results of the rectangular approximation-based and saliency-based detection, the proposed fusion method improved the Recall and F1 value of building detection, indicating that this paper provides an effective and efficient high-resolution remote sensing image building target detection method.  
optical remote sensing images; building target detection; saliency model; data fusion +
Footnote †: journal: Medical Image Analysis  
1
###### Abstract  
To examine the efficacy of wild ginseng extract (WGE) on psychomotor and neuromuscular performance recovery following acute eccentric exercise. This study was a double-blind, crossover, and placebo-controlled design with a 14-day washout period. Ten male adults, aged 27.1 \(\pm\) 4.33 years old, voluntarily participated in the study. Subjects were assigned to one of two parallel conditions (WGE or placebo) in a counterbalanced manner. Subjects consumed two packs of WGE (350 mg/package) or placebo drink immediately after acute eccentric exercise and the following four days. The eccentric exercise consisted of 20 min of downhill running at 60% of VO2 peak and five sets (of 20) of drop jump exercise. Computer-based cognitive function test and neuromuscular performance tests, including straight leg raise, vertical jump, isometric leg strength, and anaerobic power test were administered four times, at baseline, 2 h, 48 h, and 96 h after acute exercise. The interleukin-6 (IL-6), myoglobin, cortisol, total antioxidant capacity (TAC), and perceived muscle soreness were also assessed at each time point. A significance level was set at 0.05. No significant differences between the WGE and the placebo groups were observed in psychomotor and neuromuscular performance variables. Blood markers, including IL-6 (p = 0.013), myoglobin (p \(<\) 0.001), and cortisol level (p = 0.047) were changed significantly across the time. A post-hoc test revealed that a significant increase in IL-6 was observed only in the placebo group (p = 0.014), while no significant changes found in the WGE condition. The perceived muscle soreness was not different between the WGE and the placebo conditions. The administration of WGE immediately after acute eccentric exercise and the following four days have no benefits on psychomotor and neuromuscular performance recovery in healthy adults. However, the acute WGE supplementation may attenuate the eccentric exercise-induced inflammatory process, such as IL-6, but future study with a large sample size is required to clarify the anti-inflammation process in response to acute eccentric exercise.  
**Keywords:** ginseng; performance; recovery; eccentric exercise; inflammation +
Footnote †: journal: Applied Soft. 2020, 10, 5839; doi:10.3390/app10175839
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 2 Related Works  
### Text Analysis of Tourism Reviews  
Current research primarily focuses on two analytical dimensions: (i) Textual theme classification--leveraging topic models or clustering techniques to uncover dominant tourists' interests [13, 14]. [PERSON] et al. applied the LDA to evaluate themes from multilingual reviews of the Chengdu Panda Base [15], while [PERSON] et al. used K-means clustering on TripAdvisor data to reveal satisfaction patterns across destinations [16]. (ii) Sentiment polarity analysis--quantifying overall or attribute-specific sentiment using lexicon-based or deep learning models (DL) [17, 18, 19]. [PERSON], [PERSON] et al. enhanced LSTM architecture for sentiment feature extraction [20], and [PERSON], [PERSON] et al. integrated attention mechanisms with bidirectional RNNs to classify sentiment in Ctrip reviews [21].  
Despite methodological advances, key limitations remain: (i) Coarse analytical resolution. Individual reviews often encompass multi-aspect evaluations (e.g., facilities, services, environment), yet prevailing approaches target sentence-level classification [22, 23], overlooking fine-grained aspect-level insights (e.g., sentiments toward ticket prices or guided tours). (ii) Decoupled topic-sentiment modeling. Topic distributions and associated sentiments are frequently treated independently, lacking unified frameworks that jointly model thematic salience and affective orientation [24, 25]. Consequently, the interplay between topic prominence and its sentiment valence is insufficiently captured. These shortcomings limit the availability of fine-grained data necessary for evaluating thematic-sentiment continuity across attractions, thereby constraining the development of semantic networks capable of informing optimized travel routes.
# A New Monocular Pose Estimation Method for the Coplanar P4P Problem
## 3 Experimental Results  
In the experiments, a parallelogram object is used. The object's vertices' coordinates in the object coordinate system are \(P_{1}\) (\(-\)70 mm, 120 mm, 0), \(P_{2}\) (\(-\)70 mm, \(-\)60 mm, 0), \(P_{3}\) (70 mm, 60 mm, 0) and \(P_{4}\) (70 mm, \(-\)120 mm, 0), as shown in Figure 4.  
A camera with known intrinsic parameters is used to capture images of the object. The proposed method is used to estimate the parallelogram object's pose parameters relative to the camera and the coordinates of the four vertices. As a comparison, a traditional iterative algorithm is used to estimate the object's pose parameters. The \"iterative algorithm\" is actually the bundle adjustment method, a kind of iterative correction and optimization algorithm from initial values, which is famous in photogrammetry and computer vision for its performance of high accuracy. For the traditional method, the parallelogram vertices' coordinates are taken as known, only the parallelogram's pose parameters are to be solved. One of the experimental images is shown in Figure 5, where the white crosses show the extracting positions of the parallelogram vertices. Figure 4: The parallelogram object used in the experiments. Figure 3: Camera system, object system and the four vertices’ coordinates in the two systems. The estimation results in experiments are shown in Table 1 which consists of:  
1. The translation vectors (T\({}_{\text{BX}}\), T\({}_{\text{BY}}\), T\({}_{\text{BZ}}\)) and rotation angles (A\({}_{\text{BX}}\), A\({}_{\text{BY}}\), A\({}_{\text{BZ}}\)) between the object and the camera estimated by the new method (noted by New P4P in the table). 2. The translation vectors and rotation angles estimated by the traditional method (noted by Old P4P in the table) taking all of the vertices as known. 3. The difference between the pose estimation results by the two methods, respectively. 4. The mean errors of the four vertices' coordinates in the object system estimated by the new method in each set of experiment. In the experiments, one of the side lengths of the parallelogram is taken as known and all of the vertex coordinates are taken as unknown for the new method. While, for the traditional method, the parallelogram vertices' coordinates are known. Relatively, the new method solves the pose parameters linearly from fewer conditions, so its accuracy is undoubtedly inferior to bundle adjustment. The new method's advantage is achieving pose estimation under less known conditions than traditional methods require. Comparing the pose estimation results by the new method with that by the traditional method shows the correctness and effectiveness of the presented new method.