esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Image Processing
### Thresholding  
The objective of this stage is to get a first mask of detections with all the pixels that have a similar color to what we want to detect. This goal is accomplished with two conditions based on the color of the pixels, assuming that good leaves have a green color and bad leaves have a color similar to brown, but covering many more color shades.  
A first condition detects good leaves in order to discard them. Therefore, it is compound of all the pixels which highest RGB component is the green. Figure 10(a) shows an example image, where there are several bad leaves, both in the tree and on the ground, which are the target to be detected. Also, is important to note that the ground can be easily confused with the bad leaves. The good leaves are easily detected with this \"green condition\", as shown in Figure 10(b).  
The second condition is applied on the remaining pixels after discarding the previously detected. It uses a previous transformation of the original image from RGB values to HSV (Hue Saturation Value). This is a non-linear transformation that changes the RGB values in a way that corresponds more naturally to human perception. This allows setting thresholds to hue and saturation values of the pixels in order to detect the target colors. More precisely, a maximum threshold on the hue component have been fixed to TH = 0.2, and a minimum threshold of TS = 0.3 for the saturation, which have been adjusted to be appropriate for the simulations done. Figure 11 shows the colors included by these thresholds, which covers a great variety of hues.  
Figure 10: **(a)** Example of a typical image to be processed. **(b)** Pixels detected as good leaves, that is, applying the green condition.  
Figure 12(b) shows the result of applying this second condition to the example in Figure 10. In Figure 12(a) it is shown the remaining pixels of the example image after discarding the pixels with the green condition, which will be the pixels on which this condition will be applied. We can see how, besides the green pixels, others pixels of the ground and the back of the image have been discarded, keeping the pixels of the bad leaves. However, the image has a lot of noise that should be removed.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 4 FOXS-GSC Performance Evaluation
### Scenario Coverage Evaluation  
#### 4.2.1 Methodology  
To assess the impact of the number of RSUs on the proposed protocol, these experiments were performed while varying both the number of RSUs and the density of the scenario. The scenario we used was the same as in the previous assessment (Manhattan 1 km\({}^{2}\) with densities of 50, 150, 500 vehicles per km\({}^{2}\)). We varied the number of RSUs from  
Figure 7: Results of comparative evaluation between FOXS-GSC and other solutions from the literature: (**a**) total of packets re-transmitted; (**b**) number of collisions per vehicle; (**c**) receive coverage (%); (**d**) percentage of messages returned.  
one to four and then eight, with the scenario with eight RSUs providing full coverage of the scenario. The metrics evaluated were total packets re-transmitted, number of collisions per vehicle, receive coverage, and percentage of messages answered. These experiments worked similarly to the previous network evaluation experiment, with all vehicles periodically sending messages to the nearest cloudlet and the cloudlet responding to the vehicles. The simulation parameters and values used in this evaluation are presented in Table 3.
## 2 Materials and Methods
### Experimental Setup
#### 2.2.1 Measurement of the Magnetic Field  
The presence of a static magnetic field was measured in the wells before the insertion of the cells using a Gaussmeter with a Hall effect probe: Hall-effect transducer--UGN3501U--Allegro Microsystem Inc. The UGN3501U integrated device uses the Hall effect to measure the magnetic field and provides an output voltage that is linearly proportional to the intensity of the sensed flux. The device can also measure slight variations in the magnetic field, which can be applied to an amplifier to achieve higher voltages.  
The transducer includes in the same package a hall sensor, linear amplifier, emitter-tracker output, and a voltage regulator.  
The operating temperature is between 0 and 70 \({}^{\circ}\)C, and the working voltage is between 8 and 10 V.  
Specifications of Gaussmeter S.M.F.  
- Transducer: Hall effect UGN3501 Allegro Microsystems;  
- Conditioning circuit;  
- Sensitivity: 1 mV/Gauss;  
- Min. and max. range 0-1500 G;  
- Error: \(+/-\)5% in range \(+/-\)1500 mVdc;  
- High impedance voltmeter (>10 kOhm);  
- Payload \(+/-\)1999 mVdc;  
- Positive polarity under the specified measurement conditions.  
Three series of the SMF field strength measurements were made at the same ambient temperature and under a sterile hood and in contact with the magnetic dam and 1 mm from the surface of the magnetic dam. In each series of measurements, care was taken to control the positioning of the probe with extreme precision.
## 3 Feature Extraction and Recognition Based on IPSO-VMD
### Probabilistic Neural Networks  
PNN is an artificial neural network model utilizing Bayes law and Palzen window. The PNN model has the advantages of simple structure, fast calculation speed, and high classification accuracy. Furthermore, the PNN learning algorithm is based on sample distribution, it does not require extensive parameter tuning like other machine learning algorithms, and it can automatically learn and classify based on the provided data without complex parameter tuning. At the model architecture level, the PNN model consists of four parts: input, mode, sum, and output, and its network model structure is shown in Figure 7. For pattern recognition, the input layer is used to receive input samples, and the number of nodes depends on the dimension of the input feature matrix. The function of the mode layer is to calculate the matching relationship between input samples and each node of the mode layer, and the number of nodes depends on the number of training samples. The function of the summation layer is to calculate the probability belonging to the same category in the model layer, whose number of nodes equals the number of categories of input samples, while the output layer outputs the probability density estimates of different categories through a competitive method and determines that the corresponding type of the maximum value is the judgement result [36].  
The spread parameter is one of the most important parameters in PNN, which controls the smoothness of the radial basis function. The smaller the spread parameter, the more complex is the decision boundary, which may lead to overfitting. The larger the spread parameter, the smoother is the model, which may lead to underfitting.  
At the same time the size of the data volume and the data dimension have a significant impact on the PNN network, which is directly related to the training effect and generalization ability of the model. Larger data volume can provide richer information to help the PNN model better learn and understand the distribution of data, thus improving the training effect and prediction accuracy of the model. A larger data volume also helps to improve the generalization ability of the model and reduce the risk of overfitting. When the data volume is small, the model may overlearn the noise in the training data, leading to performance degradation on new data. However, with too large data dimensions, it is difficult for the network to maintain sparse invariance, and it is difficult to train the PNN network under high-dimensional data, and some irrelevant or redundant features may interfere with the learning process of the PNN model, leading to performance degradation.
## 3 Algorithm for Multi-Station Satellite Orbit Optimization in Common-View  
### A Description of the Problem  
The trajectory of satellites in their orbits determines the duration of common-view between ground SLR stations. Calculating the minimum observable satellite orbit height, which corresponds to the distance between different stations, involves using the coordinates  
Figure 2: Process of Converting Satellite Coordinate Systems.  
between these stations. The visibility of satellites with varied trajectories is determined by the simultaneous observation of the same satellite at different elevation angles from each ground station. During a complete satellite orbit, if two or more ground stations can observe it simultaneously without obstruction from their lowest elevation angle, the duration is known as the common-view duration. This common-view duration is contingent on both the satellite's orbital geometry and the distance between ground stations.  
The following elements, as shown by the problem's description above, have an impact on the multi-station satellite orbit optimization problem under the common-view mode:  
* The initial precise instant in time as defined by the UTC Time system;
* The ground station coordinates, including the separation for each station;
* The Keplerian orbit of the satellite is determined by six parameters: \(a\), \(e\), \(i\), \(\Omega\), \(\omega\), and \(M\). These parameters define the satellite's current location coordinates at any given time. For the sake of simplicity, this paper only discusses the case of circular orbits, the \(e=0\), \(\omega=0\), and \(M=E\) were selected. The mean anomaly of the circular orbits is equal to the eccentric anomaly. This type of orbit is also the choice of most satellites for scientific experiments.
## 6 Numerical Simulation Analysis for the Optimization of Roadway Support
### Analysis of Numerical Simulation Results  
The root cause of the hidden danger of roof collapse in a mining-disturbed roadway is the failure of the weak interlayer in the roof during the mining. According to the roof caving control method for roofs containing weak interlayers, the support system for the gateway is optimized with the aim to limit failure propagation in the weak interlayer of the roadway roof. Considering the location of the weak interlayer, cables are increased from 4500 mm to 8500 m and installed at the row and column spacing of 2100 \(\times\) 3300 mm to ensure that they are anchored in the hard, stable strata above the weak interlayer to control overall stability of the weak interlayer. Bolts are increased from 2000 mm to 3000 mm and installed at the row and column spacing of 900 \(\times\) 1000 mm so that they are anchored in the hard strata as far as practically possible to further control the development of fracture  
Figure 13: Plastic zone failure process of roadway surrounding rock.  
zones in the shallow surrounding rock. In order to effectively control the stability of the surrounding rock, the most unfavorable thickness is selected, that is, the thickness of the soft sandwich is 1 m, and the thickness of the underlying hard rock is 1m, to verify the rationality of the supporting parameters. The plastic zone of the roadway and the roof subsidence are selected as evaluation indexes. The roadway plastic zone of the original support scheme is shown in Figure 14, and the optimized roadway sinkage is shown in Figure 15.  
From the analysis of (a) and (b) diagrams in Figure 14, it can be seen that after the original support scheme is adopted in the roadway, the sinkage of the roadway roof slab reaches 106 mm, and the roadway anchor rods and anchor cables do not effectively pass through the plastic zone, resulting in anchorage failure, thus causing the roadway roof slab to bulge outwards. From Figure 15a,b, it can be seen that after adopting the optimized solution, the amount of sinking of the roadway roof slab is 69.3 mm, and the anchor rods and anchor cables effectively pass through the plastic zone of the roadway roof slab and anchor into the overlying key rock layer. Due to the increase in anchor cable length, the damaged area of the lower hard rock layer and the plastic zone of the weak interlayer in the roadway roof slab is reduced and the bearing capacity is increased. As a result, the sinkage of the roof slab is reduced from 106 mm to 69.3 mm, a reduction of 35%.
## 7 Future Research Directions and Projects
### Market- and Application-Driven Research Trends  
The development of WSN solutions require significant effort in terms of tailoring of the available HW/SW platforms to the specific needs of applications. Therefore, the development/deployment costs can be very high, if the market size is small. As a consequence, the most successful applications of WSN technology will be those oriented to applications including large number of nodes.  
Large number of nodes require the presence of many contexts of similar nature where the same technology/application can be deployed. As examples, for this discussion, we consider buildings, humans, and vehicles; all these \"contexts\" exist in the current world in large numbers and as such they represent huge potential markets.  
- Buildings; monitoring and control of building energy efficiency is, as also described formerly, one of the most relevant applications for WSNs in the short term. The sensing of energy consumption in residential buildings require the installation of sensor nodes in each electric appliance, counting up to tens of devices per residential unit. In a large building, hundreds or even thousand of nodes might be deployed, and interference and network management issues might be based on complex approaches. Scale factors might be introduced in the market because of these large numbers, bringing to significant cost decreases. In this scenario, part of the nodes might be networked through wired connections using the electric grid, and part by means of the technology of WSNs. The application of cognitive radio networking concepts, mentioned above, might be very useful in such dynamic and unpredictable interference contexts.  
- Humans; Body Area Networks for health applications are one of the emerging markets for WSNs. However, other applications might be considered dealing with humans: inter-body communication networks might be of interest in several fields of applications, ranging from mood-based services for human networking, or in specific professional contexts. This type of application requires the exchange of personal body-generated information among humans that can interact for few seconds while walking or moving. Therefore, many challenges raise from the viewpoint of the ability of the WSN to fast react to new requests of association, etc.  
- Vehicles; in this context, two types of applications might be considered. First, those were vehicles download data from fixed sensors which need to be used on-board for transportation purposes. Second, those were fixed sensors upload the sensed data on vehicles passing by, with the opportunistic goal of having such information carried to the final destination exploiting the partially predictable movements of vehicles. The latter concept is sometimes mentioned as _opportunistic networking_ and is one of the emerging paradigms of future wireless networks; only delay tolerant applications can be run, with the advantage of the efficient exploitation of all types of resources available in the environment where the WSN is deployed.
## 3 Test Results and Discussion
### Unconfined Compression Tests  
The unconfined compression test was conducted to assess the effectiveness of a magnesium chloride (MgCl\({}_{2}\)) solution in improving the soil's compressive strength. Figure 4 displays the stress-strain behavior of both untreated and MgCl\({}_{2}\)-treated soil samples, with curing periods of 7 and 28 days, as well as those without any curing. The results suggest that adding a MgCl\({}_{2}\) solution generally leads to an increase in soil strength across all curing durations, and this enhancement is directly proportional to the concentration of the MgCl\({}_{2}\) solution.  
Figure 4: Stress-Strain diagrams for untreated and treated samples over various curing periods: (**a**) uncured, (**b**) 7 days cured, and (**c**) 28 days cured.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**MgCl\({}_{2}\) Solution**} & \multicolumn{1}{c}{**Optimum Water Content (\%)**} & \multicolumn{1}{c}{**Maximum Dry Density**} \\
**Concentration (\%)** & **Optimum Water Content (\%)** & **(kN/m3)** \\ \hline
0\% & 19 & 17.4 \\
4\% & 18.9 & 17.52 \\
9\% & 18.3 & 17.68 \\
14\% & 17.71 & 17.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: OWC and MDD of soil with different MgCl\({}_{2}\) solution concentrations.  
Figure 5a shows the unconfined compressive strength (UCS) enhancements as the MgCl\({}_{2}\) solution concentration increases, highlighting the effect of curing time on treated samples with various concentrations. For instance, uncured samples showed UCS improvements of 9%, 26%, and 38% with 4%, 9%, and 14% MgCl\({}_{2}\) solution concentrations, respectively, leading to UCS values increasing from 223.5 kPa to 244, 281, and 308 kPa.  
Additionally, the data indicates a slight enhancement in UCS values associated with extended curing periods. For example, a 28-day curing of soil treated with a 14% MgCl\({}_{2}\) solution resulted in a 5% improvement in UCS values. Additionally, Figure 5b demonstrates that an increase in the MgCl\({}_{2}\) concentration reduces the failure strain for treated soils. Consequently, soils with higher MgCl\({}_{2}\) levels become less deformable.
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 2 Related Work
### Image Feature Point
[24] designed a simpler convolutional neural network to detect keypoints and calculate descriptors, which reduced the time-consuming process of feature extraction. It can run on the graphics processor (GPU) in real time to meet the needs of SLAM's real-time performance, but when it is applied to ORB-SLAM2, its time consumption can reach 5.2 times that of the original system. [PERSON], [PERSON] et al. [25] designed a lightweight network for feature point detection and descriptor extraction that can run images with a size of 640 \(\times\) 480 at a speed of 95 frames per second on a commercial GPU. We applied it to ORB-SLAM2 and tested it in the experimental environment of this paper. The speed is faster than the abovemethods, but when considered comprehensively, its performance in SLAM tasks is still not as good as ORB feature points. We also enhance the ORB approach in this paper. We suggest a global adaptive threshold calculation method that computes the threshold for various images based on their gray information. We adaptively construct an image pyramid to provide scale invariance. Different from the predefined threshold in ORB-SLAM2 and the FAST threshold calculated for each image block after meshing the image pyramid in [22], we use the gray information of the entire image to calculate a global adaptive threshold, which reduces the amount of calculation before feature detection and takes into account the adaptability of the threshold. Additionally, we design an adaptive meshing technique to segment the pyramid image, allowing for the use of various detection thresholds in the same image. Different from ORB [18] using the FAST algorithm, we use the AGAST approach to detect feature points, which has better adaptability and extraction speed [20]. We apply the NMS method for filtering the detected feature points and eliminating the local dense feature points. The quad-tree algorithm is then applied to achieve equal division of feature points, similar to the feature extraction part of ORB-SLAM2. Then the direction of the feature points is calculated like the ORB algorithm.
# A Novel Evolution Strategy of Level Set Method for the Segmentation of Overlapping Cervical Cells
###### Abstract
Our method is unsupervised, which provides the possibility for cervical cell segmentation with limited data, and is potentially more valuable in guiding automated slide analysis for cervical pathology. The main goal of the proposed method is to alleviate nonconvergence of the level set function (LSF) in delineating the overlapping regions of cells caused by poor initialization of the cellular contours in [33]. Our main contributions lie in the following aspects:  
1. We designed a morphological scaling-based topology filter (MSTF) to filter out the false positive fragments caused by improper allocation of the initial contour points of touching cells (i.e., misallocation). In MSTF, we constructed the signed distance function (SDF) as the LSF of the initialized cytoplasm contour based on the linear time Euclidean distance transform (LTEDT) algorithm [62], which is denoted by LTEDT-SDF. 2. We theoretically derived a new mathematical toolbox about vector calculus for evolution of the LSF as the supplementary for the codimension two-level set method (CTLSM) [63], aiming to keep the initialized contours of the nonoverlapping region fixed. Our proposed evolution method of partially fixed contour is called the 2D codimension two-object level set method (DCTLSM), which can alleviate the accuracy loss of a MSTF. 3. We proposed a novel evolution strategy of LSF inspired by the watershed method [64]. In this strategy, we provided an effective guidance mechanism for attracting and repelling the LSF to converge towards its actual cell boundary. 4. We used the dataset published by the First Overlapping Cervical Cytology Image Segmentation Challenge held in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI-2014 challenge) to evaluate our proposed method. The experimental results showed that cellular clumps consisting of two to 10 cells under an overlap ratio less than 0.2 can be accurately segmented. Furthermore, the segmentation of cellular clumps consisting of two to four cells can be effectively segmented with an overlap ratio less than 0.5. By qualitive and quantitative comparisons, our method outperformed the other segmentation methods.