esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### In-Orbit Results  
The first in-orbit measurement was performed in December 2020. Three more measurement campaigns were carried out in the first half of 2021 during the months of February, May, and June. Each measurement set consisted of 10 individual calls to the measurement software as previously explained. In total, 300 individual measurements were executed. The main areas that the measurements covered were the South Pacific, South Atlantic, North America, Europe, and the Arctic.
## 3 Materials and Methods
### Data Exchange Based on Implicit Feature Expression  
Real-time and efficient collaborative data exchange is the key to realizing the collaboration of multiple XR terminals. The data exchange method based on implicit feature expression has the characteristics of small and real-time transmission data. The implementation process is shown in Figure 3. The specific steps are as follows:  
1. Client 1 calls the API function to extract implicit expression information of all model-building features as transmission data;
2. Data encapsulation of implicit feature expression information and serialization;
3. Serialized data is distributed through the server;
4. Client 2 receives the data and deserializes the network encapsulation message and parses it to identify and extract the implicit expression parameters of the features;  
Figure 2: (**a**) Circular template; (**b**) USAN area schematic. In (**b**), circles a–e represent the circular template of (**a**), and “e” is illustrative diagram of circual template, the dark area is target, the overlaps between circls a–e and dark area is USAN area.  
After conversion, the API function is called for feature reconstruction and displayed in Client 2.  
In response to the characteristics of multi-terminal, MR equipment replication-based collaboration, object references are divided into direct references and indirect references. Directly referenced objects refer to the operational objects when editing parts or features in collaboration. They are referenced by object identification numbers, a system which is simple to execute, and spatial mapping is used to realize the direct referencing of models. The indirect reference object refers to the topological object of the auxiliary reference and operational object. The set matching algorithm is used to realize the indirect object reference between models.  
Geometric matching algorithm framework: when determining a referenced object according to the network message, the pickup type of the shape unit, the geometric feature value of the referenced object, and the set feature point are given first; then, an AABB hierarchical envelope box, i.e., a feature envelope box, is constructed with the feature point as the center, and the edge length can be set according to the drawing accuracy of the current scene, usually taking 5 units. Finally, the feature envelope box is used to intersect with the object-oriented dynamic. The local identification number of the referenced object is determined by the collision intersection of the nodes of the octree. Since the algorithm does not depend on the naming of topological entities, the operation order, and the initial state of the design, it is also applicable to \"late joining\" clients.
## 2 Positioning in the Forest  
### Global Navigation Satellite Systems (GNSS)  
Satellite-based positioning includes deriving the location of a user's receiver based on radio frequency signals transmitted by satellite systems. Current fully-operational GNSS applications include the US Navstar GPS (Global Positioning System) and the Russian GLONASS (GLObalnaja Navigatsionnaja Sputnikovaja Sistema). Satellite-based positioning is based on accurate time determination: the time difference between the transmitted and the received radio signal denotes the signal travel time, which reveals the distance between the satellite and the user's antenna. Utilizing these measurements of the distance between the user antenna and a minimum of four different satellites, the receiver can obtain three-dimensional receiver coordinates in a global reference frame and the time difference between the receiver and the satellite's clocks.  
Satellite-based positioning is undergoing a period of rapid change. There is a need to reform the GPS and GLONASS systems due to the increasing number of applications, more demanding requirements from users, and the need to mitigate interference and disturbances to the radio signals used by these systems. Both of these systems are being modernized to better serve the challenging present-day applications in obstructed signal conditions. These modernizations include an increasing the number of transmission frequencies and changes to signal components. In addition, the European Galileo and the Chinese BeiDou systems are under development. There is a determined intention to design the future, modernized systems to be resistant to interference, as well as to be more accurate and available over a wider range of conditions. Also, the use of multiple systems for positioning increases the accuracy and reliability of positioning even further [20].
## 4 Result and Discussion
### PLS-DA Model Result
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Pre-Processing Methods**} & \multicolumn{3}{c}{**Calibration Data Set (310)**} & \multicolumn{3}{c}{**Validation Data Set (138)**} \\ \cline{2-9}  & **Almond** & **Apricot** & \
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & **Almond** & **Apricot** &
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} & **Latent** \\ \hline \multirow{7}{*}{Normalization} & Mean & 99.4 & 96.8 & 98.1 & 100 & 88.4 & 94.2 & 7 \\ \cline{2-9}  & Max & 99.4 & 97.4 & 98.4 & 98.6 & 94.2 & 96.4 & 7 \\ \cline{2-9}  & Range & 99.4 & 98.7 & 99.0 & 100 & 89.9 & 94.9 & 7 \\ \cline{2-9}  & MSC & 99.4 & 98.1 & 98.7 & 100 & 92.8 & 96.4 & 7 \\ \cline{2-9}  & SNV & 98.7 & 98.1 & 98.4 & 100 & 91.3 & 95.7 & 7 \\ \hline \multirow{3}{*}{Derivatives} & SG 1 st & 99.4 & 96.8 & 98.1 & 100 & 79.7 & 89.9 & 7 \\ \cline{2-9}  & SG 2 nd & 100 & 97.4 & 98.7 & 100 & 82.6 & 91.3 & 7 \\ \hline \multicolumn{9}{c}{Raw} & 99.4 & 96.1 & 97.7 & 100 & 94.2 & 97.1 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The partial least square discrimination analysis (PLS-DA) analysis results were obtained for the classification of adulterated almonds. Figure 6: The partial least square discrimination analysis (PLS-DA) classification plots for almonds and apricot seeds based on the raw preprocessing. Figure 7: The resultant images of the PLS-DA model from raw data in the first and second variety with 50% detection (red indicates apricot seeds and green indicates almonds), presenting a total image-based classification accuracy of 98%.
## 2 Detection of Tunnel Base Voids  
At the time of this research, all of the 20 single-hole and double-line tunnels with the detected prominent base damage problems are from a special heavy-haul railway for coal transport that has been in service for more than 30 years. The tunnels are mainly of short-medium length. The cumulative length of the measured tunnel is 41,102 m. The tunnel length accounts for the largest proportion in the grade V surrounding rock environment, approximately 48.0%.  
Geological radar is used to detect the voids and loose areas of a tunnel basement, and this radar mainly detects the scope, position, and scale. A typical base void radar effect diagram is shown in Figure 1. The statistical plots of the tunnel base voids  
From Figure 2, the average longitudinal length of the base voids below the tunnel's heavy-haul line is approximately 2.63 times that below the empty cabin line. The continuous base void length below the heavy-haul line is approximately 65.1% within the range of 0-8 m, and the ultra-long distance voids above 12 m also account for a considerable proportion.  
Figure 3 shows the cumulative length statistical diagram of different widths corresponding to different surrounding rock levels. D is the effective width of the tunnel invert, that is, the widest transverse distance of the invert. From the general trend, the transverse void width of the base is mainly concentrated in the range of 0-1/2 D. The worse the tunnel damage interval is with the surrounding rock mass, the larger the base void width is. In grade IV surrounding rock, the base void widths of 1/2 D-3/4 D and 3/4 D-D account for 21.9% and 11.4%, respectively, while in grade V surrounding rock, the base void widths of 1/2 D-3/4 D and 3/4 D-D account for 22.9% and 12.1%, respectively.  
In summary, the base voids of heavy-haul railway tunnels mostly occur in heavy-haul lines. The base void length is mostly concentrated in the range of 0-8 m, and the width is mostly concentrated in the range of 0-1/2 D. From grade II to grade V surrounding rocks, the cumulative value of the average base voids width increases by approximately 31.1%, 47.7%, and 52.3%.
## 2 Related Work
### Graph-Based Recommendation Methods  
In recent years, graph neural networks (GNNs) have emerged as a groundbreaking paradigm in recommendation systems, demonstrating remarkable effectiveness in addressing collaborative filtering challenges. Compared to traditional approaches, GNNs excel at capturing higher-order connectivity patterns within user-item interactions, thereby enabling more precise recommendations. [PERSON] et al. [12] introduced the graph convolutional matrix completion (GC-MC) framework, which innovatively reformulates the matrix completion problem as a link prediction task on graphs. Through the application of graph convolution operations on user-item bipartite graphs, this method partially mitigates the data sparsity problem and improves recommendation accuracy, but due to its lack of dedicated mechanisms for modeling geographical features and limited consideration of event dynamics, it exhibits notable deficiencies when addressing highly seasonal and region-specific minor agricultural product recommendation problems. Nevertheless, its approach of utilizing graph convolutional networks to integrate multi-source heterogeneous user-item features has provided valuable inspiration for the research presented in this paper.  
Building upon these theoretical foundations, [PERSON] et al. [13] pioneered the PinSage algorithm, which effectively implements graph neural networks (GNNs) on large-scale graph structures comprising billions of nodes and tens of billions of edges through the integration of efficient random walks and graph convolution operations. The successful deployment of PinSage in Pinterest's recommendation system demonstrated substantial enhancements in both recommendation accuracy and user engagement metrics, thereby further validating the practical efficacy of GNNs in industrial-scale recommendation applications. Although [PERSON] et al. [14] neural collaborative filtering (NCF) framework does not explicitly utilize graph structures, their innovative approach of employing multi-layer neural networks to capture non-linear user-item interaction patterns has provided valuable experience that can be leveraged for the design of the multi-layer graph convolutional network presented in this paper.  
Unlike existing graph-based recommendation methods that predominantly focus on static graph structures, this study presents APGCN-CF, a novel graph convolutional network model that integrates dynamic node feature evolution, specifically tailored for minor agricultural product recommendations. The proposed model extends beyond conventional approaches by not only capturing the graph structural information of user-item interactions but also effectively modeling the regional and temporal characteristics inherent to minor agricultural products. Through the incorporation of location-aware and time-sensitive embedding layers, APGCN-CF dynamically adapts node representations to precisely reflect the characteristic variations of minor agricultural products, thereby enhancing both recommendation accuracy and temporal relevance.
## 2 Materials and Methods
### VEMP Testing  
cVEMP and oVEMP recordings were performed using the validated \(\upmu\)VEMP device, [12]. The device was connected via USB 2.0 to an Apple Macintosh laptop (macOS 10.14) running a custom programme for the continuous acquisition of data. Standard EMG electrodes (Clearface, Conned Corp, Utica, NY, USA) were connected via shielded coaxial cables and alligator clips. The surface electrode montage for cVEMP recording involved an active electrode placed on alcohol-cleaned skin on the upper third of the anterior portion of the sternocleidomastoid muscle and reference electrodes placed on the bony part of the ipsilateral medial clavicles. The ground electrode was placed on the forehead. The sternocleidomastoid muscles were tonically activated by asking the participant to lift and turn their head contralaterally to the stimulated ear for the ACS or lift their head in midline from a semi-reclined position for BCV. The oVEMP responses were taken from active electrodes placed beneath the midpoint of the inferior eye lines with reference electrodes spaced approximately 2 cm below. Participants looked up at a target to tonically activate the inferior oblique muscle during both mono-aural ACS and BCV. The ACS were approximately 100 clicks (0.1 ms, 105 dB LAeq, 5.1 Hz) delivered mono-aurally by acoustically shielded headphones (DD52, 10 \(\Omega\)\(\pm\) 1.0 \(\Omega\), Rated Power: 1000 mW, Frequency response: 100 Hz-8 kHz, Sensitivity: 108 \(\pm\) 3 dB SPL@1000 Hz @1 mW, Harmonic distortion below 1% @ 120 dB SPL, f = 1 kHz) to the left then the right ear. Bone-conducted vibration to the forehead (Fz) was delivered via 50 repetitions of tendon hammer taps (with an integrated 3-axis accelerometer to generate trigger signals). cVEMPresponses were considered present if the early first positive wave, p13, and second negative wave, n23, were visible. oVEMP responses were considered present if the first negative wave, n10, and second positive wave, p15, were visible. Reflex peak-to-peak amplitudes (\(\upmu\)V) and latencies (ms) of the peaks were calculated.
## 3 Measurement, Control, and Communication Architecture
### Software and Communication  
Following the description of the hardware components, this section details the software structure and communication protocols that govern the real-time monitoring and control functionalities of the developed SWT platform. The system is organized into three functional layers that manage data acquisition, communication, and supervisory control tasks, ensuring robust and low-latency performance.  
The software architecture is distributed across the three main hardware components: the Arduino(r) Mega microcontroller (measurement and actuation), the Raspberry Pi(r) 3 Model B+ (communication and data management), and the PC-based MATLAB(r) application (user interface and supervisory control).
## 3 Modeling and Data Sources
### Data Source
#### 3.3.2 Data Source  
The data on the number and area of forest fires in Yunnan Province from 1991 to 2021 were obtained from the \"Yunnan Disaster Reduction Yearbook\" and the official website of the Yunnan Provincial Forestry Bureau [41] ([[http://lkj.yn.gov.cn/](http://lkj.yn.gov.cn/)]([http://lkj.yn.gov.cn/](http://lkj.yn.gov.cn/)), accessed on 1 November 2023). The related meteorological data, including annual average temperature, relative humidity, annual average rainfall, and average wind speed, were sourced from the National Meteorological Science Data Center, encompassing 17 meteorological stations across Yunnan Province. For missing parts in the dataset, long-term average values from these meteorological stations were used as substitutes. These long-term averages, derived from multi-year observational data, effectively reflect the typical climatic conditions of the region and are considered reliable representations of the climatic characteristics.  
The forest fire and meteorological data collected for this study, spanning from 1991 to 2021 in Yunnan Province, form the foundation for our analysis. The collection and processing of these data, including the treatment of missing data, ensured comprehensive and accurate analysis. These datasets not only supported the construction and validation of the stepwise regression and ARIMA models but also provided valuable insights into the historical patterns and trends of forest fires in Yunnan Province.
## 3 Results and Discussion
### Ni\({}^{2+}\) Transport in the Saturated Porous Media
#### 3.3.2 Effect of Ion Strength  
The effect of IS on Ni\({}^{2+}\) transport in the sand column is shown in Figure 6. In the absence of soil colloids (Figure 6b), as compared to the breakthrough curve of the Ni\({}^{2+}\) transport at the IS of 0 mM, the retention of Ni\({}^{2+}\) in the sand column increased at the IS of 3 mM. Moreover, the migration rate of Ni\({}^{2+}\) at the IS of 3 mM in the initial phase was faster than that of Ni\({}^{2+}\) at the IS of 0 mM. However, in the presence of soil colloids (Figure 6a), although the migration rate of Ni\({}^{2+}\) at the IS of 3 mM in the initial phase was also faster than that of Ni\({}^{2+}\) at the IS of 0 mM, there was a small variation in the overall transport efficiency of Ni\({}^{2+}\) in the sand column at IS of 0 mM (71.30%) and 3 mM (72.92%), which is shown in Table 3.  
It is obvious that the transport of Ni\({}^{2+}\) at the IS of 3 mM in the initial phase was faster than the IS of 0 mM, whether the soil colloids existed or not. The possible reason is that the increasing Na\({}^{+}\) presented competitive adsorption with the targeted Ni\({}^{2+}\) for the active site on the surface of the quartz sand at the initial phase when the Ni\({}^{2+}\) transported in the sand column. In the case of the presence of soil colloids (Figure 6a), the final transport efficiency of Ni\({}^{2+}\) in the effluent slightly increased from 71.3% to 72.92% with the increase of ionic strength from 0 to 3 mmol\(\cdot\)L\({}^{-1}\) (Table 3), indicating that less colloid-associated Ni\({}^{2+}\) may accumulate in the sand column at higher IS conditions. It is reasonable to conclude that the ionic strength will affect the thickness of the double layer of the colloid surface [45, 49, 50]. Compared with monovalent cations, divalent cations in solutions were more favorable for the colloid deposition by increasing electrostatic attraction between the colloids and sand surfaces, causing the cation bridge [46, 47, 51]. Thus, the transport of reduced Ni\({}^{2+}\) may be because it involved the interaction between soil colloids and sand.
## 2 Materials and Methods
### Characterization of the Zeolites  
Rigaku D/Max-2500 X-ray diffractometer was used to get the X-ray Powder Diffraction (XRD) peaks of the zeolite materials. It was equipped with Cu K\(\alpha\) and a Ni filter from 5-50 in 2 \(\theta\) range. The scanning speed was at 5\({}^{\circ}\)/min. A scanning electron microscope (SEM, Hitachi S-4800, Tokyo, Japan) was used to observe the zeolite's morphology. Energy-dispersive X-ray spectroscopy (EDS, EDX S-10, Oxford, UK) was fitted to it to detect the specific X-ray radiation). The measurement of electron paramagnetic resonance (EPR) spectra was done by JOEL JES-FE1C X-band spectrometer. FT-IR spectra were obtained by analyzing it in the FT-IR spectrometer (Bruker VERTEX 80V, Massachusetts, United States) in a spectral range of 400-4000 cm\({}^{-1}\) and 1 cm\({}^{-1}\) resolution. The X-ray photoelectron spectroscopy (XPS) peaks were obtained under Al K\(\alpha\) X-ray source (Kratos Axis Ultra, Manchester, UK). The test chamber's background pressure was kept under \(1.33\times 10^{-7}\) Pa. A single-beam spectrometer having 60 added scans with a resolution of 0.02 m\({}^{-1}\) was used. The above characterization techniques were used in order to assure proper synthesis and observe the modifications in the parameters of the zeolite samples. A Raman microspectrometer (XploRA, Horiba Jobin Yvon, Horiba-Scientific, Kyoto, Japan) having a 100\(\times\) and numerical aperture objective (Olympus, Tokyo, Japan) was used to record the Raman spectra. A CCD detector and a \(\lambda\) -785 nm diode laser were used to detect the signals and do the measurement, respectively.
## 4 Experimental Results
### Influence of Arcing Time on Breaking Capacity in Type A Contact  
In order to analyze the influence of arcing time on the breaking capacity, experiments were conducted on the type A contact for arcing times of 1, 2, 4, and 6 ms. The experimental results of the breaking performance are shown in Figure 12. The symbols '\(\blacktriangledown\)' and '\(\times\)' denote interruption success and failure, respectively, at the first current zero.  
It can be concluded that, as the arcing time increases, the breaking capacity of the VI is greatly reduced. When the arcing time is 1 ms, the intensive arc mode is not developed and the breaking performance is much better. If the arcing time is longer than 1 ms, the breaking performance deteriorates, and the maximum breaking ability becomes proportional to the arcing time. When the arcing time is 2 ms, the maximum breaking ability is about 2.9 kA, but re-ignition happens at  
Figure 11: Arc appearance of re-ignition under a 2 ms arcing time with the type A contact at a 2380 A main circuit current.  
Figure 10: Experimental waveforms of interruption failure at first current zero and success at second current zero with a 2 ms arcing time for type A.  
2380 A. A similar phenomenon occurs for an arcing time of 4 ms at an interruption current of 2.2 kA. From the analysis above, this is directly related to the occurrence of the intensive arc mode. From the obtained arc images, it is found that the intensive arc mode is not observed for arcing times less than 1 ms. On the contrary, when the arcing time is 2 ms or longer, some cathode spots develop in the intensive arc. Therefore, it can be inferred that the intensive arc mode increases the probability of re-ignition and reduces the breaking capacity of the VIs.
# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 4 Conclusions  
The crucial part for the detection of skin cancer is to segment the correct region of the lesion site because it is the stage in which the targeted region should be as close as possible to the actual skin lesion. This study showed a method of a fully automatic detection technique of skin lesions, which does not require any training phase.  
In this study, the ABC algorithm was proposed and applied in the skin lesion images from four public datasets, and a threshold is calculated based on the results of the image histogram, and then the binarized (segmented) image is obtained using the threshold value which is taken from the ABC  
Figure 11: Various examples of segmentation failure: Row (1–4) PH2, ISBI 2016, ISBI 2017, and DermIS datasets; Columns **(a)** original image; **(b)** manual segmentation image (ground truth); **(c)** proposed method segmentation image; and **(d)** result of proposed method (blue) and ground truth (red). (Scale bar: 150 μm).  
method. This method was compared with the other previous methods in order to determine the best method to segment these images. The ABC algorithm was applied to the image with the objective of segmenting it, that is, to detect the edge of the lesion.  
The experimental results suggest that the proposed method accomplished higher performance comparatively to the ground truth images supported by a skin cancer lesions doctor. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95.24-97.61%, and 83.56-85.25% in these four databases. The results are compared with the results from the existing algorithm of melanoma detection. The ABC algorithm is effective and improves early detection with high accuracy for skin lesions leading to a decrease in death rates. The high values for estimation performance confirmation that the proposed melanoma detection is best from the other algorithms, which demonstrates the high difference power of the newly introduced features.  
[PERSON], [PERSON], [PERSON], and [PERSON] worked together to complete this work.  
The authors declare no conflict of interest.