esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 7 Conclusions  
In this paper, we proposed a transfer-learning-based object detection and tracking method for small sample datasets. The YOLOv5 network is improved by introducing the Shuffle Attention mechanism and smaller anchors. Using a transfer learning strategy, the UA-DETRAC high-speed dataset is then introduced to train a pre-trained model. A small amount of self-collected Yangtze River vessel dataset is used to fine-tune the model, combined with the tracking algorithm DeepSORT for vessel detection and tracking in the case of limited data. Finally, the feasibility of our method is demonstrated through extensive experiments. The main contributions of this article are as follows: (1) improvements and innovations have been made to the YOLOv5 network, specifically for ship detection; (2) implementing a tracking monitoring method that only requires a small amount of ship image data; and (3) proposing an improved YOLOv5 + DeepSORT object detection and tracking network and introducing a transfer learning strategy to enhance detection and tracking accuracy. The proposed method is characterized by fast training speed and high accuracy with small datasets. Compared with existing methods, the proposed method achieved 84.9% ([EMAIL_ADDRESS]) with only 585 training images.  
Future research directions are considered to improve the method. In terms of ship detection, the accuracy of the ship detection model may be significantly reduced due to extreme weather conditions. In the future, fusion with LiDAR can be added, as LiDAR can provide more geometric and visual information to improve detection accuracy. Regarding ship tracking, rapid ID switching may occur after ship crossing.  
Conceptualization, J.W.; methodology, J.W. and M.G.; investigation, J.W. and M.L.; writing--original draft preparation, J.W.; writing--review and editing, J.W. and M.L.; supervision, J.M. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Key R&D Program of China under grant No. 2023 YFE0203700, the National Natural Science Foundation of China (NSFC) under grant No. 52271367, and the Fundamental Research Funds for the Central Universities under grant WUT: 2023 IVA100.  
Not applicable.  
Data available on request due to restrictions of privacy.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of the Concentration and pH of the Buffer Solutions  
Resolution and selectivity are strongly affected by the pH and concentration of the running buffer. To investigate the ionic strength of the phosphate buffer, a constant SDS concentration (20 mM) was added to different concentrations of phosphate buffer (1-15 mM, pH 3.5). Results revealed that with increasing phosphate concentration, the analysis time increases (Figure 1). Each peak was identified individually by spiking known concentration of standard in the sample solution and the increased intensity of peak was used to confirm the standard of the peak. The increase in the ionic strength of the phosphate buffer leads to an increase in the EOF. With the application of a high voltage acrossthe capillary, EOF corresponded to the movement of the buffer solution. However, at high phosphate concentrations (\(\geq\)15 mM), high Joule heating was observed, leading to band broadening (Figure 1D). DA and Trp were not separated using 10 mM phosphate (Figure 1C). Furthermore, the sharpest peaks were observed using 5 mM phosphate. Hence, the optimum phosphate concentration is selected as 5 mM.  
In addition, pH of the buffer is an important parameter for regulating EOF and mobilities of ionic analytes. Thus, it is vital to select the most suitable pH for the phosphate buffer. The p\(K_{\text{a}}\) value of T, 5-HT, TA, DA, Trp, E, 5-HTP, NE, and Tyr were 10.20, 9.97, 9.77, 8.93, 9.39, 8.59, 9.18, 8.58, and 10.46, respectively. The pH was optimized by increasing it from 3.0 to 4.0, all analytes were protonated at this pH interval. With increasing pH, the migration time of the analytes obtained using a 5 mM phosphate buffer containing 20 mM SDS increased (Figure 2). 5-HTP, NE, and Tyr co-migrated with a long migration time (>11 min) at pH 4.0 (Figure 2D). At pH 3.7, the best separation was achieved. Therefore, the optimum pH is selected at 3.7 and the RSDs of the migration time of the analytes were less than 10%.
## 2 Statistical Analysis of Tunnel Floor Heave
### Statistical Analysis of Tunnel Floor Heave during Operation  
An in-depth investigation of 14 tunnels across eight projects led to the following conclusions:  
1. Among the 14 tunnels surveyed, a total of 39 instances of tunnel floor heave were observed, spanning 5.1 km, which represents 5.2% of the total tunnel length. The factors influencing these deformations are illustrated in Figure 1.
2. Tunnel floor heave predominantly occurs due to the gently inclined surrounding rock and the water pressure at the tunnel's base. It is notably absent in regions characterized by large deformations, rock bursts, active fractures, or high ground temperatures in soft rock under high ground stress. While arching was observed in some gas sections, the gas itself did not contribute to tunnel floor heave.
3. There are seven sections where water pressure at the tunnel base is the primary factor (50%) and seven sections where the gently inclined surrounding rock is the main cause (50%).
4. In sections where floor heave is primarily caused by water pressure at the tunnel base, remedial measures have prevented further deformation. Conversely, in sections where gently inclined surrounding rock is the dominant factor, five tunnels were successfully stabilized following intervention, though two tunnels continue to experience arching despite the measures taken.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 3 Methodology
### Dynamic Detection Module  
In the odometry of vSLAM, the conventional tracking module determines the inter-frame poses based on the matching relationship pairs of feature points in the previous frame \(k_{n-1}\) and the current frame \(k_{n}\). When objects are moving within the camera field of view, inter-frame matching is easily affected. The specific reason is that feature points located on dynamic objects usually produce incorrect matching relationships. Additionally, when the camera is in motion, the positions of stationary objects, background and dynamic objects in the image all change, which makes it more difficult to determine dynamic regions. Considering the necessity of real-time inter-frame pose estimation for odometry, we propose a method for inter-frame pose estimation based on the homography and dense optical flow method, which can solve the above problems efficiently and effectively. As shown in Figure 2, the homography is used to align \(k_{n}\) to the moment where \(k_{n-1}\) is located. Then, the dynamic region in \(k_{n}\) is obtained using the dense optical flow method, and the feature points located in the dynamic region in \(k_{n}\) are eliminated, as shown in Figure 3. Finally, the inter-frame pose is re-estimated based on the static feature points in \(k_{n-1}\) and \(k_{n}\).  
Figure 2: Schematic of dynamic area detection.
## 2 Materials and Methods  
### Materials  
Sodium montmorillonite (Mt), with a cationic exchange capacity (CEC) of 1 mmol/1 g of clay, was provided by the Huai An Saibei Technology Co. Ltd., Zhangjiakou, China. Hydrochloric acid (HCl, 37% \(w\)/\(w\)) was purchased from Beijing Chemical Reagents Company, Beijing, China. The tributyl tetradecyl phosphonium chloride (TTPC, 97%) and \(\gamma\)-methacrylopropyltrimethoxysilane (\(\gamma\)-MPS, 97%) were purchased from Aladdin Industrial Corporation, Shanghai, China. Styrene (St, \(>\)98%) monomer and divinylbenzene (DVB, \(>\)80%) were supplied by the Tianjin Guang fu Fine Chemicals Research Institute, Tianjing, China. All the other chemicals were of analytical grade and were used without further purification. Deionized water was used in all experiments.
## 3 Results and Discussion
### Sensory Profiles of Fermented Samples  
The sensory analysis results of the samples were evaluated in terms of appearance, smell, and taste characteristics (Figure 5). There was no difference between samples in terms of color and serum separation from appearance parameters except from the LB-p sample. This sample received a significantly lower score, meaning that the _L. bulgaricus_ strain showed poor foaming performance. Foam formation is generally associated with CO\({}_{2}\). The low value of this strain in this category is due to factors such as the strain being a homofermative bacterium and the strain's slow reproduction rate. At fluency property from the consistency parameter, the LC-p received the highest score, while the LB-p received the lowest score. These results are consistent with the ecological results of the LB-p product with the _L. bulgaricus_ strain that has the low viscosity value and the LC-p product with the _L. casei_ strain that has the high viscosity value.  
When aroma attributes were evaluated, the LC-p had the highest fermented odor score, while the LB-p had a lower score, indicating that the _L. casei_ strain produced a more intense fermented aroma. Regarding fruits aroma, the LC-p had the highest score, followed by the LH-p (_p_ < 0.05). The BB-p and the ST-p had the lowest scores. These results are consistent with the aroma profiles of the samples. This suggests that the _L. casei_ and _L. helveticus_ strains are associated with fruits aroma. In terms of the desired acidic taste attribute, the LA-p, LC-p, and LH-p samples stood out, while in terms of the desired sweetness attribute, the same samples and, additionally, the ST-p sample stood out. In the refreshing taste  
Figure 5: Sensory evaluation of fermented samples.  
attribute, the LC-p and ST-p samples had the highest scores. According to these results, the _L. casei_ and _S. thermophilus_ strains stood out for both sweetness and freshness, while the _L. casei_, _L. helveticus_, and _L. acidophilus_ strains stood out for suitable fermented and acidic taste. On the other hand, in terms of yeasts and animalic aroma, the BB-p produced with the _B. bifidum_ strain had a significantly higher score than the other samples (_p_ < 0.05). This can be explained by the higher amount of 3-methyl-1-butanol detected in the aroma component profile. The burnt aroma is somewhat related to the _L. acidophilus_, _L. helveticus_, and _L. casei_ strains due to 2-methylbutanal. This does not give a negative aroma to the product; on the contrary, it gives a slightly sweet caramel note.  
According to the general sensory evaluation, the strains can be ranked as the _L. acidophilus_, followed by _L. casei_, _L. helveticus_, and _B. bifidum_. The _L. bulgaricus_ and _S. thermophilus_ strains were found to be the least liked.
# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
## 2 Preliminaries
### The Entropy-Based Privacy Measurement Method  
Entropy was first used to measure the degree of confusion of an anonymous set in [11]. In this paper, entropy is also used to measure the degree of privacy preservation for an anonymous set. It can be regarded as the uncertainty of recognizing the user's location from the dummy location set. The greater the entropy, the harder it is to recognize the user's location from the anonymous set. The entropy is calculated based on the historical query probability (denoted as \(p_{i}\)) for each location in the anonymous set, which can be summarized by Equation (2):  
\[\mathrm{H}=-\sum_{i=1}^{k}q_{i}\log_{2}q_{i} \tag{2}\]  
where \(q_{i}=p_{i}/\sum_{i=1}^{k}p_{i}\) The sum of all \(q_{i}\,(i=1,2,3,...,k)\) is \(1\). This ensures that the historical query probability of all locations in the anonymous set is in the same range.
## 2 Materials and Methods
### CFD Simulation
#### 2.4.2 Turbulence Model  
According to studies by [PERSON] et al. (2016), and by [PERSON] et al. (2017), the k--\(\omega\) SST turbulence model is preferred when analyzing the Darrieus H-type turbine, including the effects of airflow around the airfoil and the flow separation. Since similar phenomena occur when analyzing the VAWT with a carousel wind rotor, analogous assumptions can be adopted. The k--\(\omega\) SST turbulence model uses blending functions to switch models between flow zones. The k--\(\epsilon\) turbulence model is used in the main stream, whereas thek--\(\omega\) turbulence model is used in the boundary layer near the wall. In addition, the SST formulation is calibrated for a smooth wall. The k--\(\omega\) SST is a two-equation model, including the turbulent kinetic energy Equation (15) and turbulent dissipation rate Equation (16).  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial k}{\partial x_{i}}(\rho ku_ {i})\ =\ \frac{\partial}{\partial x_{j}}\left(\Gamma_{k}\frac{\partial k}{\partial x_{j} }\right)+G_{k}-Y_{k}+S_{k}+G_{b} \tag{15}\]  
\[\frac{\partial}{\partial t}(\rho\omega)+\frac{\partial k}{\partial x_{i}}(\rho \omega u_{i})\ =\ \frac{\partial}{\partial x_{j}}\left(\Gamma_{\omega}\frac{\partial k}{ \partial x_{j}}\right)+G_{\omega}-Y_{\omega}+D_{\omega}+S_{\omega}+G_{\omega b} \tag{16}\]  
where \(k\)--the turbulent kinetic energy; \(\omega\)'the specific dissipation rate; \(G_{k}\)'the production of \(k\); \(G_{\omega}\)'the production of \(\omega\); \(\Gamma_{k}\), \(\Gamma_{\omega}\)--the effective diffusivity of \(k\) and \(\omega\); \(D_{\omega}\)--cross diffusion term; \(S_{k}\), \(S_{\omega}\)--user-defined source terms; \(G_{b}\), \(G_{\omega b}\)--buoyancy terms.
## 5 Informative Path Planning for Mobile Network Mapping
### IPP Simulation Setup  
The performance of the proposed IPP approach is evaluated for the physics-informed mean functions presented in Section 3.1. The 3 GPP-based mean functions (\(m_{\text{3 GPP}}\) and \(m_{\text{3 GPP}-\text{UMi}}\)) are disregarded due to their inferior performance in the previous evaluation (cf., Section 4.1). Fixed travel budgets of 250 m and 500 m are compared. For each, a maximum of 25 iterations is set. In each iteration, \(20\times 20\times 5=2000\) candidate points\(\mathbf{p}_{i}\) in the latitude, longitude, and altitude directions, respectively, are drawn at random. Different configurations for the tuning parameters (\(\kappa\) and \(\alpha\)) are used for each mean function. Each simulated run uses the same start location, with no training points influentially in the middle of the cluster shown in Figure 5d. The UAV simulated by AirSim travels at a fixed speed of \(v=10\) m/s and collects points at 1 Hz, similar to the hardware setup. An ROS node that publishes the linear interpolated measurement based on the simulated UAV location is used to mimic the 5G measurement unit. Once either the travel budget or the maximum number of iterations is reached, again, the RMSE between the predicted value of the utilized GP and the ground-truth measurements is calculated. The search space is defined by the area where ground-truth measurements (see Section 2) are available. Due to the lack of measurement data between the two cuboids (see Figure 3), only the left cuboid is used as the ground truth and respective search space.  
The IPP algorithm is also implemented in Python using the GPFlow package [16] in combination with TensorFlow [17].
###### Abstract  
Offshore wind energy has attracted worldwide attention and investments in the last decade due to the stability and abundance of wind resources. As one of the main components of this, internal array cables have a great impact on the levelised cost of energy of offshore wind farms, and thus their connection layout is a matter of concern. In this paper, a classical mathematical problem--the traveling salesman problem, which belongs to the field of graph theory--is applied to solve the offshore wind farm cable connection layout optimization problem. Both the capital investment on cables, cable laying, and the cost of power losses associated with array cables are considered in the proposed model. A modified bat algorithm is presented to resolve the problem. Furthermore, a cable crossing detection method is also adopted to avoid obtaining crossed cable connection layouts. The effectiveness was verified through a case study.  
offshore wind farm; cable connection layout; power losses; traveling salesman problem; bat algorithm +
Footnote †: journal: Applied Sciences
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
## 5 Validation of the Present Study  
In this study, swarm intelligence method has been implemented to improve the network lifetime. Further, the obtained experimental results of the swarm intelligence were validated by the machine learning approach artificial neural network (ANN). The three-layer architecture of the ANN model depicted in Figure 5 was employed for this study. The first layer consists of three neurons-like nodes. For the location of the nodes, data transfer rate, and output layer, only a single neuron was employed, i.e., the remaining energy of nodes was selected. The neural network complexity was evaluated by the presence of hidden nodes in the layer. Although, the presence of several neurons may lead to over fitting for the model [26]. After several trials and training, the best model was generated. The greatest number of epochs was found by trial and error method. The performance goal and minimum performance gradient were usually for the affirmation of the good performance. The total dataset was separated into three subsets--50% data for training, a further 25% for validation, and the remaining 25% of the data was employed as an investigation set. The dataset was normalized (0,1) [27] by applying the equation  
\[x_{i}^{\prime}=0.8(\frac{X_{i}-X_{min}}{X_{max}-X_{min}})+0.1 \tag{18}\]  
Here, \(X_{i}\) represents input or output variable of \(X\), \(X_{min}\), and \(X_{max}\) denote the minimum and maximum value of \(X\). The artificial neural network (ANN) is performed by applying the Toolbox of MATLAB Version 2012a in this present study.  
The evaluation affirms that 10 neurons generate at fewest errors in the training and endorsement sets. The remaining energy in the nodes was selected as an output and the best coefficient correlation (R) among the model prediction and obtained experimental results showed close to 1 recognized to be an ideal model for this present study. The maximum ANN architecture is based on feed forward networks and it has been trained via a backpropagation algorithm. The linear transfer function \"PURELIN\" was selected for the input to the hidden layer further \"Tansig\" functions were selected for the hidden layer to the output layer. The obtained data of correlation coefficient (R) calculated the best match among 20 consecutive runs. The regression plot is shown in Figure 6. After completion of the ANN training, the network provides the correlation coefficient value of 0.992. In the present study, conjugate gradient backpropagation with Polak-Ribiere (Traingcp), resilient back propagation (Trainrp), gradient descent with momentum, adaptive learning rate backpropagation (Traingdx), scaled conjugate gradient (Trainscg), and Levenberg-Marquardt backpropagation (Trainlm) were evaluated for the best output result. It suggests that Resilient Backpropagation fits well with the obtained data as demonstrated in Table 3. After training the ANN, the resilient backpropagation (Trainrp) fit well compared to other algorithms applied in this present study.  
Figure 5: Artificial neural network (ANN) architecture.
## 2 Materials and Methods
### Numerical Model and Methods
#### 2.2.2 Numerical Methods  
For the numerical model, the unsteady incompressible flow equations were solved using the software package STAR-CCM+ (Siemens PLM Software, Plano, TX, USA) with a finite-volume cell-centered non-staggered grid. LES with a WALE (wall-adapting local eddy-viscosity) [61] subgrid scale model was performed to model the turbulent flow in the larynx. A central difference scheme with second-order accuracy was used to discretize the convective and diffusive terms of the Navier-Stokes equations. These pressure-velocity linked equations were solved non-iteratively within a PISO (Pressure-Implicit with Splitting of Operators) algorithm [62]. An algebraic multigrid (AMG) method with a Gauss-Seidel relaxation scheme and biconjugate gradient accelerator was used to solve the final linear system of equations. The density and kinematic viscosity of air were considered to be \(\rho=1.18\,\mathrm{kg/m^{3}}\) and \(\
u=1.57\times 10^{-5}\,\mathrm{m^{2}/s}\), respectively.
# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
## 5 Conclusions
In general, the detection skills and stability of GK2A_FDA were observed to be slightly increased and decreased in the validation cases, respectively, which were mainly related to the fog area (variations in the number of fog pixels), as shown in Figure 11, and were found to be slightly dependent on geographic location and time. In the training cases, the detection skill of GK2A_FDA was observed to be better on land (POD: 0.82, FAR: 0.37, and ETS: 0.54) than (POD: 0.72, FAR: 0.40, and ETS: 0.46) over the coastal area. The detection levels during nighttime (POD: 0.80, FAR: 0.34, and ETS: 0.54) and dawn (POD: 0.85, FAR: 0.36, and ETS: 0.53) were better than that (POD: 0.77, FAR: 0.47, and ETS: 0.44) during daytime. In addition, the detection skill of GK2A_FDA was found to be the most stable at night in terms of the standard deviation of POD (0.16), FAR (0.10), Bias (0.28), and ETS (0.11). Further, the detection skill of GK2A_FDA for the validation cases was found to be better at land (POD: 0.83, FAR: 0.28, Bias: 1.15, and ETS: 0.60) than at the coastal area (POD: 0.72, FAR: 0.43, Bias: 1.25, and ETS: 0.46), and better at night (POD: 0.83, FAR: 0.28, Bias: 1.16, and ETS: 0.60) than during the day (POD: 0.78, FAR: 0.30, Bias: 1.12, and ETS: 0.57), similar to the training cases. In order for the fog detection algorithm developed in this study to be used in a forecast office (e.g., NMSC/KMA), it is necessary to evaluate not only fog cases but also non fog cases (e.g., precipitation cases, clear cases). So, we will continue to evaluate the level of this fog detection algorithm using various fog and non-fog cases. **Author Contributions:** Conceptualization, M.-S.S.; methodology, M.-S.S.; software, J.-H.H. and H.-Y.Y. ; validation, J.-H.H. ; formal analysis, J.-H.H., M.-S.S., and H.-Y.Y. ; investigation, N.-Y.R. ; resources, M.-S.S.; data curation, J.-H.H., N.-Y.R., and H.-Y.Y. ; writing--original draft preparation, J.-H.H. ; writing--review and editing, M.-S.S.; visualization, J.-H.H. and H.-Y.Y. ; supervision, M.-S.S.; project administration, M.-S.S.; funding acquisition, M.-S.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2018-06510. This work was also supported by \"Development of Scene Analysis & Surface Algorithms\" project, funded by ETRI, which is a subproject of \"Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2019-01)\" program funded by NMSC (National Meteorological Satellite Center) of KMA (Korea Meteorological Administration). **Acknowledgments:** The authors would like to thank the anonymous reviewers for their very competent comments and helpful suggestions. We are also grateful to Korean Meteorological Administration's National Meteorological Satellite Center for providing vast amounts of satellite and numerical data. **Conflicts of Interest:** The authors declare no conflict of interest.