esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Discussions
### Ablation Experiment
The results showed that the separate introduction of the module traded off a 10.9% and 18.5% improvement in the mAP@.5 (%) vs. mAP@.5:95 (%) metrics, respectively, at a cost of a 5.5% increase in the parametric count of the model and an 8.49 G rise in arithmetic complexity. Although the introduction of the small target detection layer increased the depth of the network increasing the number of network parameters and computational complexity, the method passed more shallow features to deeper features, and improved the ability of the network to learn multi-level feature information of the target, so that the network could detect the smallest target with a resolution of 160 \(\times\) 160, which could re-detect a large number of the small targets that were originally missed, and effectively improve the target missing detection situation. Finally, the \(L_{asp}\) in EIOU_loss directly minimized the difference between the width and height of the predicted frame and the true frame, resulting in faster convergence, greatly alleviating the problem of positive and negative sample imbalance in the CIOU, and, thus, without altering the model parameters and arithmetic complexity, led to a 3.8% increase in mAP@.5 (%) and a 7.4% increase in mAP@.5:95 (%), while also enhancing the precision of regression. By comparing the experimental results of this method and baseline ablation shown in Table 2, it is evident that the incorporation of EfficientNetv2, LPAN, CBAM module, L2 detection head, and EIOU_loss enhanced the precision of the network, with most improvement achieved in the small target detection layer module. Compared with YOLOv5-S, mAP@.5 (%) and mAP@.5:95 (%) increased by 19.2% and 32.4%, respectively, and the parameters and computational complexity reduced by 3.74 M and 2.49 G. When fusing multiple feature extraction methods, adjustments were necessary in the LPAN feature fusion module to match the output of EfficientNetV2. Specifically, some features with 512 channels were converted to 160 channels, which substantially diminished the parameter count and arithmetic complexity of fusing all methods. The experimental results verified the reliability of the enhanced algorithm in this paper, indicating that the modified model can efficiently and precisely identify coal and gangue.
# A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
## 3 Results and Analysis  
### Fuel Center of Gravity Variation  
Figure 4 illustrates the changes in the fuel center of gravity in the heading, pitch, and roll directions during the maneuver process. Initially, as the maneuver commences, the acceleration increases abruptly, causing the fuel inside the tank to accumulate in the opposite direction of flight. Consequently, the heading fuel center of gravity undergoes a rapid aft movement. However, after 1.5 s, although the acceleration and pitch angle continue to change, most of the fuel has already filled the rear section of the tank due to its limited volume. As a result, the variation in the heading fuel center of gravity becomes less significant.  
Figure 4: Fuel center of gravity in the heading, pitch, and roll directions during the maneuver process.  
Figure 3: Curves of heading acceleration and pitch angle variation during the maneuver process.  
Simultaneously, as the acceleration increases rapidly and the fuel flow is obstructed by the baffle within the tank, the fuel gathers and flows through the flow holes on the baffle towards the wingtip direction. This results in a gradual increase in the roll fuel center of gravity. Nonetheless, the amplitude of variation in the roll fuel center of gravity remains relatively small.  
During the middle stage of the maneuver, the fuel rapidly accumulates towards the tail of the aircraft, leading to a rapid increase in the vertical fuel center of gravity. Due to the relatively narrow dimensions of the flat fuel tank and the inertia of fuel motion, a portion of the accumulated fuel will move towards the front of the aircraft along the top of the tank. Under the influence of gravity, this portion of fuel gradually descends to the bottom of the fuel tank. Throughout the maneuver process, the fuel inside the tank repeatedly undergoes movement and detachment, resulting in fluctuations in the vertical fuel center of gravity.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Environment
#### 5.1.3 Convergence Time  
Convergence time is the time required by the proposed hybrid algorithm to compute the number of iterations and decide the best coordinates to move to. Therefore, the convergence time from one stage to another is calculated and compared with the literature's widely discussed algorithms. In our proposed algorithm, several iterations are involvedfor both PRM and ABC algorithms. The roadmap is generated beforehand using the PRM algorithm, and thus the convergence time of PRM does not impact the flight time of the UAV. Because the proposed UWSN model's performance depends on the convergence time of the ABC algorithm, the convergence time of ABC is analyzed and compared with other path planning algorithms in the following section.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 4 Experiments and Results
### Test Result from Open Space into Narrow Alley  
In this scenario, a test was performed in an open space with good GNSS conditions and LTE environment as shown in Figure 13. To verify the performance of the proposed fingerprinting method, we compared its result with the GPS, the conventional fingerprinting method (kNN), and a Particle Filter (PF) as shown in Figure 16. We set the k as 5 to estimate the location from the kNN fingerprinting method. We utilized the heading and stride information from PDR on a resampling process of the PF for the particle propagation. Additionally, 150 particles were used to compute the location. As shown in Figure 16a-d, the kNN-based fingerprinting and PF show a relatively stable performance in a region with good LTE environment. However, in a region with poor LTE signal environment, the performance is very unstable due to increased ambiguity. The GPS showed a relatively stable performance when entering a narrow alley after convergence of performance in open space. However, it can be seen that a multipath effect on the satellite signal in the alley deviates it substantially from the actual route. The SC method shows a very stable performance on the overall test route. However, at the initial stage of the test, we confirmed that the location was matched to a different link than the actual link, just like the kNN as in Figure 17a. As shown in Figure 17b, this is because the RSS pattern for the two reference trajectories is very similar near the starting point and sufficient RSS pattern is not accumulated for ambiguity reduction. It can be seen that the localization result is very stable after 35 m point where the RSS pattern is accumulated sufficiently and ambiguity decreases. Except for the initial phase, we can confirm that the SC-based fingerprinting method is very stable and accurate in most areas as demonstrated in Figure 18a,b. The Root Mean Square Error (RMSE) of the GPS is 10.86 m and of the conventional fingerprinting method is 41.22 m, respectively. The RMSE of the PF is 28.59 m while the surface correlation method has an RMSE of 4.67 m except for the initial error.  
Figure 15: The CDF of localization error according to the length of the _Surface_.  
Figure 16: Scenario 1 test results: (**a**) GPS; (**b**) Conventional fingerprinting (kNN); (**c**) Particle Filter with PDR; (**d**) SC.  
Figure 17: Initial localization error: (**a**) Result of initial localization; (**b**) RSS pattern of reference trajectories from six Base Stations (BSs).
## 6 Open Challenges, Future Prospects and Research Directions
### Integrating Machine Learning Algorithms  
The integration of ML algorithms into aperture averaging techniques is another exciting avenue for future research. ML can be employed to develop predictive models that anticipate the effects of AT. Also, ML can also facilitate more intelligent decision-making within FSO systems [400].  
Predictive Modeling of Turbulence Effects: The integration of ML algorithms into aperture averaging techniques can revolutionize how FSO systems handle atmospheric disturbances. ML models can analyze historical and real-time data to predict turbulence patterns and their effects on optical signals. By employing predictive modeling, FSO systems can proactively adjust their aperture configurations or signal processing parameters, enhancing resilience against turbulence-related challenges.  
Smart Decision-Making: ML can also enable smarter decision-making within FSO systems. For example, algorithms could assess the current atmospheric conditions and determine the optimal aperture configuration or modulation technique to employ, ensuring that the system adapts to real-time changes. This capability will improve link reliability and enhance overall system performance, particularly in dynamic environments where conditions fluctuate rapidly.
# Research on Image Mosaic Method Based on Fracture Edge Contour of Bone Tag
## 3 The Proposed Algorithm  
In the process of computational vision image processing, it is easily affected by the external environment, such as light intensity, humidity, and shooting angle. Smoothing, removing noise, extracting feature points, confirming similar pixels on the edge of the image, and finally performing image stitching processing, etc., improve the image processing process, reduce errors from external environmental factors, and ensure that image processing is practical and real-time (Figure 1).  
The overall algorithm implementation steps are as follows:  
Input: ordered fracture edge bone tag images, divided into upper and lower parts, the number is equal, upper part \(I_{top}(I_{1},I_{2},\cdots,I_{N})\), the lower part \(I_{bottom}(I_{1},I_{2},\cdots,I_{N})\)  
Output: stitching complete image \(I\)  
Step 1: Binarize all images \(I_{B\_top/bottom,i}(i\in[1,N])\) according to Contour edge calculation, process the horizontal and vertical directions of image pixels and perform pixel box filtering to calculate the filtering difference and normalization of image pixels, get \(I_{top/bottom,filter_{j}}(i\in[1,N])\), highlight the image edge, and facilitate image edge detection.  
Step 2: Use the Canny operator to extract the edge of the binarized image \(I_{top/bottom,Camy,i}(i\in[1,N])\), perform Gaussian smoothing on the image row and column pixels, calculate the row and column pixel gradient, distinguish weak and strong pixels, and clarify the texture information and non-texture information of the image edge.  
Step 3: ORB builds an image pyramid, collects image features of different resolutions, obtains the key points of each layer of the pyramid image, summarizes the keys \(S_{Feature}\) of all levels, assigns the direction of the key points, creates the feature vector of the key points, calculates the feature description, and facilitates the extraction of feature points.  
Step 4: Use the SAD algorithm to calculate the average similarity value \(I_{avg\_RGB}\) of the R, G, and B channels of all image edge pixels, and set the high and low thresholds; the ORB feature description point extracts the corresponding pixel position, calculates the average pixel similarity value, and judges that the pixel average similarity is within the threshold value. The range is used as a feature descriptor pair \(S_{Feature\_T\_in}\) for image edge pixel splicing points.  
Figure 1: Algorithm flow.  
Step 5: Extract the image edge feature points in Step 4, calculate the minimum line of pixel color and structural strength difference, and then combine the horizontal and vertical gradient operators to calculate the line-by-line, pixel-by-pixel color of the image edge and the minimum line difference of structural strength. Calculate, obtain the minimum difference, and update it as the position of the image edge breaking point. When calculating the minimum difference intensity pixel by pixel, similar pixels can be controlled and corrected downward, and finally, filled in accordance with the edge position of similar pixels, a Panoramic Mosaic image \(I\) is obtained.
## 3 Results and Discussion  
### The Preparation of DNA Building Units  
As illustrated in Figure 1, the X-DNA served as a building unit, which was assembled from four single-stranded DNAs (ssDNAs), and each strand had a \"sticky end\" portion (marked in black). The L-DNA was a linear duplex assembled through two ssDNA that contained two \"sticky ends\", which were complementary to those of X-DNA. The S-DNA was an ssDNA, of which one side was a \"sticky end\" (marked in black) and another side was a thrombin aptamer (marked in green). The \"sticky ends\" of the S-DNA were also complementary to those of the X-DNA.  
Figure 1: Principle diagram of aptamer-functionalized DNA hydrogel formation. The X-shaped DNA (X-DNA), L-shaped DNA (L-DNA), and single-stranded DNA aptamer (S-DNA) were cross-linked by hybridizing their “sticky ends” (marked in black). The “sticky ends” of the X-DNA were complementary to those of the L-DNA and S-DNA.  
Agarose gel electrophoresis was used to testify the formation of the X-DNA and L-DNA structure. In Figure S3, X-DNA (lane 5) did not move as far as X1, X2, X3, and X4 (lanes 1, 2, 3, and 4, respectively). Similarly, the L-DNA (lane 8) did not move as far as L1 and L2 (lanes 6 and 7, respectively). The results indicated that X-DNA and L-DNA indeed formed as designed, and the pure single band that was shown in lanes 5 and 8 demonstrated that the assembly processes were highly efficiinct.
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 3 Design of a Vision-Based Deep Learning Loading-Arm Positioning System  
### Description of the Positioning System  
The positioning system consists of a monocular camera and an ultrasonic sensor, as shown in Figure 7, which work together to accurately position the loading arm at different docking stages.
# A Novel Method for Spectrometry Based on Imaging Systems
###### Abstract  
\(\gamma\)-ray spectrometry is a well-known technique in environmental radioactivity measurements where easily handled systems are needed. Scintillators coupled to a photomultiplier tube (PMT), are typically favoured over solid-state detectors as mobile spectrometers. Replacing PMT with position sensitive devices represents an innovative solution that provides the evaluation of the interaction point of the incident radiation. The knowledge of spectrometry as a function of the depth of interaction (DoI) assures a better understanding of the spectrum and a more reliable identification of the source. In this paper, the efficiency of a simple DoI estimator has been studied using a CRY018 monolithic crystal coupled to a multi-anode photomultiplier tube. The DoI estimator has been evaluated studying charge distributions and the dependency of spectrometric properties on the DoI has been qualitatively analyzed. The estimator has shown to be highly sensitive to the DoI, enabling a better understanding of the internal interaction processes of light and an efficient rejection of the background component on the spectra. The novelty of this work lies in the application of the DoI selection in spectrometry made available by the use of MAPMT. The proposed method is practical since it does not require complicated hardware solutions or complex computational procedures.  
eurmental radioactivity measurements; DoI determination; \(\gamma\) spectrometry; imaging system; multiple sources identification
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 3 Proposed Framework
### BlendMask  
BlendMask is a model capable of performing semantic-segmentation and instance-segmentation tasks simultaneously. It features two branches: an instance-mask branch (IMB) and a semantic branch (SMB) [24]. It delivers high accuracy and speed, owing particularly to the IMB's use of a RoI transformer and soft proposal generator to extract precise object boundaries. It achieves state-of-the-art performance on the COCO dataset, outperforming other models in terms of accuracy and speed. BlendMask's pipeline is shown in Figure 3.  
Although Mask R-CNN demonstrates high performance, its high computational demands and GPU memory requirements pose challenges for real-time tasks. YOLO offers fast processing but is inferior in segmentation accuracy to BlendMask or Mask R  
Figure 3: BlendMask pipeline.  
Figure 2: Mask R-CNN architecture.  
CNN. BlendMask is utilized in various fields because it ensures high accuracy and speed. Compared to other instance-segmentation models, BlendMask has several advantages:  
* High Accuracy: BlendMask's superior performance on the COCO dataset is because of its two-branch structure (IMB and SMB) and the integration of a RoI transformer and soft proposal generator, which together enhance the accuracy of segmentation boundaries.
* High Speed: The use of a RoI transformer, instead of traditional RoI pooling, contributes to BlendMask's fast processing capabilities. Additionally, its ability to maintain accuracy with fewer parameters enables high performance in memory-constrained environments.
* Scale Invariance: BlendMask's scale invariance ensures consistent performance across multiple image sizes, a feature influenced by the pre-trained models; this allows BlendMask to maintain high accuracy at various image sizes.
* Multi-Object Segmentation: BlendMask's capability to segment multiple objects within a single image is a huge advantage in both commercial processes and computer vision.
* Small Training-Data Requirement: Typically, instance-segmentation models necessitate extensive labeling of training data, particularly in specific domains. BlendMask can achieve high performance with a limited dataset by using techniques that reduce the demand for a large amount of labeled data.  
For these reasons, BlendMask is an outstanding instance-segmentation model.
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 4 Mathematical Model  
The steady mathematical model is based on the set of partial differential equations and the Butler-Volmer electrochemical model, with the inclusion of the averaged microstructure parameters (see Figure 3). The computational domain is shown on Figure 8. In this model, it is assumed that the computational domain is divided into five subdomains: the cathode and anode channels, the cathode, the electrolyte and the anode. The dimensions of the subdomains are the same as for the cells investigated experimentally (see Table 1). The porous anode is fabricated from the Ni/YSZ and the solid electrolyte is made from the YSZ. The layered structure of the cathode is neglected and it is assumed that the cathode is made from the pure LSCF. Because the GDC is not introduced in the model, the microstructure was recalculated for the pure LSCF cathode (for the recalculation, it was assumed that the analyzed cathode, shown in Figure 7b, is composed of the single LSCF phase). Such an assumption leads to an unavoidable error in the numerical results. Having said that, the cathodic voltage losses are usually lower than the anodic polarization losses [36]. Moreover, at the elevated temperature (>650 \({}^{\circ}\)C), the LSCF/GDC behavior is similar to the pure LSCF performance [50]. The results are presented in Table 8.  
The gases in the system are treated as the Newtonian ideal gases, and the radiation heat transfer, viscous dissipation, gravity and other body forces are neglected. It is also assumed that, at the anode inlet, only hydrogen, water vapor and nitrogen are present, whereas, in the cathode inlet, air containing 21 % of the oxygen and 79 % of the nitrogen is provided. Please note that the model presented here is based on our previous isothermal model of the PEN [36].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Phase** & **Tortuosity Factor \(\tau\)** \\ \hline  & **anode** \\ \hline Ni & 3.647 \\ YSZ & 2.240 \\ pore & 24.534 \\ \hline  & **cathode** \\ \hline LSCF & 3.953 \\ GDC & 10.324 \\ pore & 2.419 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Tortuosity factors of each phase.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**anodic TPB length density**\(\rho_{\text{TPB,ano}}^{L}\) & \([\text{$\mu$m}\,\text{$\mu$m$}^{-3}]\) & 4.44 \\
**cathodic TPB length density**\(\rho_{\text{TPB,cath}}^{L}\) & \([\text{$\mu$m}\,\text{$\mu$m$}^{-3}]\) & 2.11 \\
**cathodic DPB area density**\(\rho_{\text{DPB,cath}}^{A}\) & \([\text{$\mu$m}^{2}\,\text{$\mu$m$}^{-3}]\) & 1.63 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effective phase boundaries’ densities.
# A Portable Optical Sensor for Microplastic Detection: Development and Calibration
## 3 Results and Discussion
### Operation in a Controlled Environment  
The second stage of the experiment transitions into a controlled environment, where we manipulate variables to observe the sensor's response to specific conditions. We conducted meticulously controlled tests to eliminate extraneous variables that could affect the data, ensuring accurate and reliable measurements. Our method incorporates a nocturnal testing phase to ensure the stability of parameters and to mitigate the impact of extraneous light sources on sensor readings. It should be noted that this approach is not the sole option, and alternative methods may also prove effective. We optimize the conditions by using a black fabric tarp to obstruct external light sources and schedule tests after 11:00 PM, ensuring that the green light beam remains the only variable influencing the sensor readings.  
The prototype underwent testing and calibration in a controlled setting using wavelengths in the green and blue spectral ranges. This process generated a spectrum based on the attenuation value obtained by the sensor. It is noteworthy that a dataset comprising 2000 values was collected. Analysis of the prototype's graph revealed that the sensor's response plateaued at approximately 500 values, after which it stabilized. This observation persisted, prompting the collection of an extensive dataset to determine if any potential disturbances occurred beyond this point. Additionally, measurements were conducted in the presence of noise to evaluate its impact on the signal, ensuring a comprehensive understanding of the sensor's behavior.  
The high volume of data points collected per measurement can facilitate a detailed statistical analysis, enabling the identification of any anomalies or patterns within the data. With such a dataset, one could employ various analytical techniques, such as regression analysis, standard deviation calculations, and hypothesis testing, to draw meaningful conclusions about the green light's properties and effects.
## 4 Data Processing Scheme
### Involvement of MEOs  
According to the development schedule of BeiDou, GEOs and IGSOs are now the base of the current regional system and will still play a significant role for the region in the future global system. Nowadays, there are already four MEO satellites in operation and more and more will come into service. As MEOs can be tracked globally and their PODs can easily reach an accuracy of few cm from the IGS expertise, it is obviously an interesting question whether orbits of the GEO and IGSO satellites can be improved if MEOs are involved in POD processing.  
In order to have a preliminary impression of such possible improvement, we process the global network with and without the two MEOs C11 and C12, respectively. The estimated GEO and IGSO orbits are assessed to show the effect of the involvement of MEOs. The result is presented in sub-Section 5.4.